\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\"\",\n \"yield # Remove dependencies from the graph del graph.dependencies[-len(dependencies):] #pylint:\",\n \"= self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal value\",\n \"is_ = nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le\",\n \"= None self.op_name = op_name # Get the stack context\",\n \"other): return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"isinstance(succ, Node): self._succesors.append(succ.gname) else: self._succesors.append(succ) def set_edges(self): for e in\",\n \"in graph.nodes: graph.update_graph_key(self._name, name) else: graph.nodes[name] = self self._name =\",\n \"graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod, other, self, graph=self.graph) def\",\n \"1: res = res[0] self.domain.set_computed(out_shape, indices) return res def __add__(self,\",\n \"of integers or parameter node names. \\\"\\\"\\\" return self._shape @property\",\n \"np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2) return value\",\n \"\\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class for nodes.\",\n \"__floordiv__(self, other): return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"= nodeop(builtins.setattr) all_ = nodeop(builtins.all) dir_ = nodeop(builtins.dir) hex_ =\",\n \": dict Normalised context in which to evaluate the node.\",\n \"getattr_(self, name, graph=self.graph) def __getitem__(self, key): if self.__class__.__name__ != \\\"Node\\\":\",\n \"fname.startswith('<'): break # pragma: no cover interactive = fname.startswith('<') stack.append(frame)\",\n \"list(map(lambda k: (new_key, self.nodes[k]) if k == old_key else (k,\",\n \": Node The multi-dimensional variable used for indexing into. idx\",\n \"**kwargs): \\\"\\\"\\\" Call `func` with positional arguments `args` and keyword\",\n \"neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos\",\n \"f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if self._name is\",\n \"sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies,\",\n \"value=value, **kwargs) return n def __bool__(self): return True def __hash__(self):\",\n \"var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else: return\",\n \"= frame.filename if 'polymath' in fname: continue # Stop tracing\",\n \"else other.__lshift__(self) def __rshift__(self, other): return rshift(self, other, graph=self.graph) if\",\n \"raise ValueError(f\\\"'{node}' is not an `Node` instance or node name\\\")\",\n \"return self def __exit__(self, *args): assert self == Node._graph_stack.pop() def\",\n \"Get a list of all dependencies relevant to this node\",\n \"no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message\",\n \"max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_\",\n \"for s in self.shape: if not isinstance(s, Integral): return False\",\n \"single = False else: raise ValueError(\\\"`fetches` must be an `Node`\",\n \"if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args =\",\n \"\\\"index\\\")) else other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph)\",\n \"str : Operation name which describes the node functionality. \\\"\\\"\\\"\",\n \"__pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context,\",\n \"execution, it can be set using `value`. kwargs : dict\",\n \"= [] if dependencies is None else dependencies if self.graph:\",\n \"__repr__(self): return \\\"\\\" % self.name def add_attribute(self, key, value):\",\n \"ipow = nodeop(operator.ipow) irshift = nodeop(operator.irshift) is_ = nodeop(operator.is_) is_not\",\n \"= name or uuid.uuid4().hex # TODO: Need a way to\",\n \"\\\"\\\"\\\" Base class for nodes. Parameters ---------- args : tuple\",\n \"self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape]) elif\",\n \"elif isinstance(fetches, Sequence): single = False else: raise ValueError(\\\"`fetches` must\",\n \"slice_op(operator.add, self, other, graph=self.graph) def __radd__(self, other): return slice_op(operator.add, other,\",\n \"__len__(self): #TODO: Update this to check for finalzied shape if\",\n \"context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for element in\",\n \"{self.var.shape}\\\\n\\\" f\\\"\\\\tIndex Upper bounds: {indices[-1]}\\\") indices = list(map(lambda x: x.tolist()\",\n \"nodeop(operator.attrgetter) concat = nodeop(operator.concat) contains = nodeop(operator.contains) countOf = nodeop(operator.countOf)\",\n \"used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args,\",\n \"Parameters ---------- context : dict Normalised context in which to\",\n \"the node name. Parameters ---------- node : Node or str\",\n \"but does not belong to this graph. \\\"\\\"\\\" if isinstance(node,\",\n \"index, making sure to replicate the new name with a\",\n \"pylint:disable=W0621 value = context.pop(node) node = self.instantiate_node(node) if node in\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self)\",\n \"or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif _is_node_type_instance(slice2_var, \\\"index\\\"): slice2_idx\",\n \"name which describes the node functionality. value : Any or\",\n \"self.graph: self.dependencies.extend(self.graph.dependencies) # Choose a name for the node and\",\n \"graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return\",\n \"node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name): g = self.graph\",\n \"= nodeop(builtins.sorted) ascii_ = nodeop(builtins.ascii) enumerate_ = nodeop(builtins.enumerate) input_ =\",\n \"np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise\",\n \"iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_\",\n \"\\\"index\\\")) else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph)\",\n \": callable function to evaluate the node args : tuple\",\n \"__pow__(self, other): return slice_op(builtins.pow, self, other, graph=self.graph) def __rpow__(self, other):\",\n \"return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies\",\n \"nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass)\",\n \"and len(self.domain) == np.product(shape) and len(shape) > 0: self._shape =\",\n \"args : tuple positional arguments passed to the target kwargs\",\n \"= nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_ =\",\n \"This function modifies the context in place. Use :code:`context=context.copy()` to\",\n \"def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance by\",\n \"------ ValueError If `fetches` is not an `Node` instance, node\",\n \"def __sub__(self, other): return slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self,\",\n \"if isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain\",\n \"= target @property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key):\",\n \"context : dict or None Context in which to evaluate\",\n \"for nodes. Parameters ---------- args : tuple Positional arguments passed\",\n \"self self._name = name return self def __getitem__(self, key): if\",\n \"tuple) else tuple(shape) else: for idx, d in enumerate(self.domain.dom_set): if\",\n \"= self.graph while g is not None and name not\",\n \"with node instances. .. note:: This function modifies the context\",\n \"return context def run(self, fetches, context=None, *, callback=None, **kwargs): \\\"\\\"\\\"\",\n \"ret else: if isinstance(key, (list)): ret = var_index(self.var, tuple(key), graph=self)\",\n \"@property def op_name(self): \\\"\\\"\\\" str : Operation name which describes\",\n \"list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self, *args,\",\n \"def __rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self,\",\n \"as ex: # pragma: no cover messages = [] interactive\",\n \"return slice_op(operator.mod, self, other, graph=self.graph) def __rmod__(self, other): return slice_op(operator.mod,\",\n \"tuple positional arguments passed to the target kwargs : dict\",\n \"{name} in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update\",\n \"kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain, op_name=f\\\"slice_{target.__name__}\\\",\",\n \"f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys():\",\n \"to evaluate an node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223\",\n \"and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\"\",\n \"node callback = callback or _noop_callback with callback(self, context): if\",\n \"else np.asarray([var[idx] for idx in indices]).reshape(out_shape) if out_shape == (1,)\",\n \"in list(kwargs.keys()): if aa in self.added_attrs: kwargs.pop(aa) return self.target(*args, **kwargs)\",\n \"i in range(len(out_shape)): if out_shape[i] == 1: var = np.expand_dims(var,\",\n \"graph=self) else: return var_index(self, [key], graph=self) else: return self.nodes[key] def\",\n \"__rlshift__(self, other): return slice_op(operator.lshift, other, self, graph=self.graph) def __rshift__(self, other):\",\n \"node was defined self._stack = traceback.extract_stack(limit=1) @property def graph(self): \\\"\\\"\\\"\",\n \"reversed(n._stack): # pylint: disable=protected-access # Do not capture any internal\",\n \"= nodeop(operator.matmul) methodcaller = nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul =\",\n \"= {} elif not isinstance(context, Mapping): raise ValueError(\\\"`context` must be\",\n \"representing values of a variable corresponding to input index values.\",\n \"__ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self): return\",\n \"\\\"\\\" % (self.target.__name__, self.name) class func_op(Node): # pylint: disable=C0103,R0903\",\n \"to this node self.dependencies = [] if dependencies is None\",\n \"Callback to be evaluated when an node is evaluated. kwargs\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__floordiv__(self) def __mod__(self, other): return\",\n \"f\\\"dimensions for slice {self.args[0].name} with {out_shape}.\\\\n\\\" f\\\"Domain: {self.domain}\\\\n\\\" f\\\"Eval Stack:\",\n \"__lt__(self, other): return lt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var, slice1_idx,\",\n \"import operator from collections import OrderedDict, Mapping, Sequence, deque import\",\n \"def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph) def __repr__(self):\",\n \"call when the node is executed. args : list Sequence\",\n \"value self.dependencies = [] self._args = [] self._predeecessors = []\",\n \"== 0: return self elif self.is_shape_finalized() and len(self.nodes) > 0:\",\n \"self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self,\",\n \"unique identifier. shape : tuple or None Shape of the\",\n \"else tuple(values) def __getstate__(self): return self.__dict__ def __setstate__(self, data): self.__dict__.update(data)\",\n \"{var} with type {type(var)} is not a list or numpy\",\n \"raise ValueError(\\\"`fetches` must be an `Node` instance, node name, or\",\n \"len(key) != len(self.shape): raise KeyError(f\\\"Invalid key shape for {self.name}:\\\\n\\\" f\\\"Shape:\",\n \"nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth = nodeop(operator.truth)\",\n \"for dim in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif\",\n \"n in node: stack = [] if isinstance(n, Node): for\",\n \"self.instantiate_graph(context, **kwargs) for c in context: if c in fetches\",\n \"= nodeop(builtins.bin) eval_ = nodeop(builtins.eval) int_ = nodeop(builtins.int) open_ =\",\n \"dependencies=dependencies, value=value, **kwargs) return n def __bool__(self): return True def\",\n \"and_(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"None and name not in g.nodes: g = g.graph if\",\n \"return slice_op(operator.truediv, self, other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv,\",\n \"if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") else: domain = Domain(idx) super(var_index,\",\n \"__rshift__(self, other): return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def\",\n \"def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if aa\",\n \"can be a tuple of integers or parameter node names.\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmul__(self) def __rmul__(self,\",\n \"node in context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node]\",\n \"for i in range(len(out_shape)): if out_shape[i] == 1: var =\",\n \"kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"], list) else kwargs.pop(\\\"domain\\\") elif\",\n \"for n in node: stack = [] if isinstance(n, Node):\",\n \"self._preds def succs(self): return self._preds def add_predecessor(self, pred): if isinstance(pred,\",\n \"in fetches] context = self.instantiate_graph(context, **kwargs) for c in context:\",\n \"must be a mapping.\\\") nodes = list(context) # Add the\",\n \"return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return slice_op(builtins.pow,\",\n \"graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self, graph=self.graph) def\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self,\",\n \"isinstance(val, np.ndarray) or (len(val.shape) == 1 and val.shape[0] == 1)):\",\n \"on a node. Parameters ---------- target : cal The multi-dimensional\",\n \"k in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else:\",\n \"return floordiv(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"self, graph=self.graph) def __rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph)\",\n \"to evaluate the node args : tuple positional arguments passed\",\n \"isinstance(node, str): return self.nodes[node] if isinstance(node, Node): if node.name not\",\n \"def instantiate_graph(self, context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing\",\n \"all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var, slice2_idx = self.get_index_nodes(all_args[0], all_args[1])\",\n \"self.args] kwargs = {key: partial(value) for key, value in self.kwargs.items()\",\n \"slice1_var is None and slice2_var is None: slice1_var, slice2_var =\",\n \"name or uuid.uuid4().hex self._op_name = None self.op_name = op_name #\",\n \"\\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return matmul(self, other, graph=self.graph)\",\n \"is `None`, this is the top-level graph. op_name : str\",\n \"float): s.append(int(shape[idx])) elif isinstance(d, float): s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain)\",\n \"f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if isinstance(kwargs[\\\"domain\\\"],\",\n \"pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless functions. Parameters ----------\",\n \"context: raise ValueError(f\\\"duplicate value for node '{node}'\\\") context[node] = value\",\n \"str, object] Context whose keys are node instances or names.\",\n \"slice2_idx def _evaluate(self, *args, **kwargs): for aa in list(kwargs.keys()): if\",\n \"\\\"\\\"\\\" # Add dependencies to the graph graph = Node.get_active_graph(graph)\",\n \"to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return node\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def\",\n \"graph=self.graph) def __neg__(self): return neg(self, graph=self.graph) def __abs__(self): return abs_(self,\",\n \"other): return slice_op(operator.lt, self, other, graph=self.graph) def __le__(self, other): return\",\n \"self._name = name return self def __getitem__(self, key): if self.is_shape_finalized()\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other): return\",\n \"__hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns the functional\",\n \"of the node or `None` to use a random, unique\",\n \"names. graph : Node or None Parent graph of this\",\n \"arguments passed to `func`. \\\"\\\"\\\" return func(*args, **kwargs) @contextlib.contextmanager def\",\n \"*args): assert self == Node._graph_stack.pop() def __repr__(self): return \\\"\\\"\",\n \"decorator is used without arguments @functools.wraps(target) def _wrapper(*args, **kwargs_inner): return\",\n \"instance but does not belong to this graph. \\\"\\\"\\\" if\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self, other): return truediv(other,\",\n \"no `Graph` instance can be obtained. \\\"\\\"\\\" graph = graph\",\n \"to use the default graph. Raises ------ ValueError If no\",\n \"= Domain(tuple([])) self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs)\",\n \"init=True) @graph.setter def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def\",\n \"in shape]) and len(self.domain) == np.product(shape) and len(shape) > 0:\",\n \"__matmul__(self, other): return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return\",\n \"slice_op(operator.xor, self, other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other,\",\n \"Instantiate a graph by replacing all node names with node\",\n \"dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value\",\n \"other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self, other): return\",\n \"nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray)\",\n \"mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"**kwargs) def __repr__(self): return \\\">\\\" %\",\n \"nodeop(builtins.any) divmod_ = nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object)\",\n \"defined at:\\\\n\\\\n%s\\\" % (n, stack) messages.append(message) raise ex from EvaluationError(\\\"\\\".join(messages))\",\n \"the node given a context. Parameters ---------- context : dict\",\n \"= nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne) neg =\",\n \"graph self._shape = OrderedDict() self.shape = shape or tuple([]) #\",\n \"**kwargs): kwargs[\\\"op_name\\\"] = kwargs[\\\"op_name\\\"] if \\\"op_name\\\" in kwargs \\\\ else\",\n \"elif isinstance(key, tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return\",\n \"for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context, callback=None):\",\n \"the parents partial = functools.partial(self.evaluate_node, context=context, callback=callback) args = [partial(arg)\",\n \"passed to the target \\\"\\\"\\\" def __init__(self, target, *args, **kwargs):\",\n \"cover messages = [] interactive = False if isinstance(node, Node)\",\n \"graph=self.graph) def __rfloordiv__(self, other): return slice_op(operator.floordiv, other, self, graph=self.graph) def\",\n \"ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret =\",\n \"all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\") target_name\",\n \"and (len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape\",\n \"xor(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"= f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise KeyError(f\\\"{name} not\",\n \"0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs)\",\n \"Integral): self._shape = tuple([shape]) elif isinstance(shape, Node): self._shape = tuple([shape])\",\n \"found in the associated graph. \\\"\\\"\\\" # TODO: Need a\",\n \"s.append(int(d)) elif isinstance(d, var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s)\",\n \"pylint: disable=W0235 if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if\",\n \"not node.is_shape_finalized(): context[node] = node.evaluate(context) for name, value in kwargs.items():\",\n \"self._args = tuple(new_args) @shape.setter def shape(self, shape): self.set_shape(shape, init=True) @graph.setter\",\n \"args): new_args = [] for arg in args: if isinstance(arg,\",\n \"multi-dimensional variable used for indexing into. idx : tuple Tuple\",\n \"= nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ =\",\n \"value : str Unique name of the node. Returns -------\",\n \"graph = graph or Node._graph_stack[-1] return graph def instantiate_node(self, node):\",\n \"\\\"\\\"\\\" return self(*args, context, **kwargs) @classmethod def evaluate_node(cls, node, context,\",\n \"`None` to use the default graph. Raises ------ ValueError If\",\n \"node = self.instantiate_node(node) if node in context: raise ValueError(f\\\"duplicate unequal\",\n \"or names to evaluate. context : dict or None Context\",\n \"the `_evaluate` method. name : str or None Name of\",\n \"with their values. .. note:: This function modifies the context\",\n \"def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph) def __rmul__(self,\",\n \"associated graph. \\\"\\\"\\\" name = name or uuid.uuid4().hex # TODO:\",\n \"either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, target,\",\n \"= nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ =\",\n \"in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]: write_name = \\\"/\\\".join([f\\\"{i}{c.write_count-1}\\\" for i in\",\n \"_flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable class Node(object): \\\"\\\"\\\" Base class\",\n \"equal to the current ndoe as ewll if self.graph and\",\n \"in shape: if isinstance(dim, (Node, Integral)): shapes.append(dim) elif isinstance(dim, float):\",\n \"return values[0] if single else tuple(values) def __getstate__(self): return self.__dict__\",\n \"shape or tuple([]) # Get a list of all dependencies\",\n \"break # pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack\",\n \"self.shape[0] def __iter__(self): num = len(self) for i in range(num):\",\n \"nodeop(builtins.repr) zip_ = nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals)\",\n \"f\\\"{fnc.__module__}.{fnc.__name__}\\\" def __getitem__(self, key): return self @property def domain(self): return\",\n \"graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield # Remove dependencies from\",\n \"else other.__rand__(self) def __rand__(self, other): return and_(other, self, graph=self.graph) if\",\n \"\\\"index\\\")) else other.__and__(self) def __or__(self, other): return or_(self, other, graph=self.graph)\",\n \"Node): self._predecessors.append(pred.gname) else: self._predecessors.append(pred) def add_successor(self, succ): if isinstance(succ, Node):\",\n \"nodeop(builtins.divmod) id_ = nodeop(builtins.id) object_ = nodeop(builtins.object) sorted_ = nodeop(builtins.sorted)\",\n \"\\\"temp\\\"] and not node.is_shape_finalized(): context[node] = node.evaluate(context) return context def\",\n \"graph=self.graph) def __or__(self, other): return slice_op(operator.or_, self, other, graph=self.graph) def\",\n \"return self.nodes[node] if isinstance(node, Node): if node.name not in self.nodes\",\n \"evaluated when an node is evaluated. kwargs : dict Additional\",\n \"with positional arguments `args` and keyword arguments `kwargs`. Parameters ----------\",\n \"passed to the `_evaluate` method. name : str or None\",\n \"other.__add__(self) def __sub__(self, other): return sub(self, other, graph=self.graph) if not\",\n \"and name not in g.nodes: g = g.graph if name\",\n \"kwargs.pop(aa) return self.target(*args, **kwargs) def __call__(self, *args, **kwargs): return call(self,\",\n \"the variable, index combination. Parameters ---------- value : str Unique\",\n \"import importlib from .graph import Graph from .domain import Domain\",\n \"of either integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self,\",\n \"nodes to be evaluted before evaluating any nodes defined in\",\n \"= nodeop(builtins.super) bytes_ = nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ =\",\n \"other): return or_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find {name} in graph nodes.\",\n \"% self.name def add_attribute(self, key, value): self.added_attrs.append(key) self.kwargs[key] = value\",\n \"var index {self} with variable shape {self.var.shape}\\\") return True else:\",\n \"op_name elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name\",\n \"ret = self.nodes.item_by_index(idx) return ret else: if isinstance(key, (list)): ret\",\n \"not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)}\",\n \"# Evaluate the node callback = callback or _noop_callback with\",\n \"= np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var}\",\n \"0: return self elif self.is_shape_finalized() and len(self.nodes) > 0: if\",\n \"constant given a context. \\\"\\\"\\\" Node.evaluated_nodes += 1 try: if\",\n \"nodeop(builtins.bytes) float_ = nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print)\",\n \"name in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self,\",\n \"a node. This can be a tuple of integers or\",\n \"self._shape = tuple(shapes) @staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently\",\n \"def __and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self,\",\n \"*args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes =\",\n \"len(out_shape) and np.prod(var.shape) != np.prod(out_shape): raise ValueError(f\\\"Index list does not\",\n \"in self.args: self.add_predecessor(e) if isinstance(e, Node): e.add_successor(self) @property def domain(self):\",\n \"1: var = np.expand_dims(var, axis=i) else: var = np.squeeze(var) if\",\n \": list Sequence of nodes to be evaluted before evaluating\",\n \"slice_op(operator.sub, self, other, graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other,\",\n \"node names. \\\"\\\"\\\" return self._shape @property def var(self): return self\",\n \"this node. \\\"\\\"\\\" return tuple(self._args) @property def argnames(self): return [a.name\",\n \"= Node.get_active_graph(graph) @property def gname(self): scope_names = [self.name] cgraph =\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rshift__(self) def __and__(self, other):\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__or__(self) def __xor__(self, other): return xor(self,\",\n \"= nodeop(builtins.range) vars_ = nodeop(builtins.vars) classmethod_ = nodeop(builtins.classmethod) getattr_ =\",\n \"None and (not isinstance(val, np.ndarray) or (len(val.shape) == 1 and\",\n \"(len(val.shape) == 1 and val.shape[0] == 1) def _evaluate(self, op1,\",\n \"graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def\",\n \"if val is not None and (not isinstance(val, np.ndarray) or\",\n \": list Sequence of positional arguments passed to `func`. kwargs\",\n \"nodeop(operator.delitem) eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge)\",\n \"__rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def __and__(self, other):\",\n \"{key}\\\") name = f\\\"{self.name}{key}\\\" if name not in self.nodes.keys(): raise\",\n \"in context: if c in fetches and c.op_name in [\\\"output\\\",\",\n \"@functools.wraps(target) def _wrapper(*args, **kwargs_inner): return func_op(target, *args, **kwargs_inner, **kwargs) return\",\n \"Evaluate one or more nodes given a dictionary of node\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__lshift__(self) def __rshift__(self, other): return rshift(self,\",\n \": str Operation name which describes the node functionality. value\",\n \": list[str or Node] or str or Node One or\",\n \"nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map) reversed_ = nodeop(builtins.reversed)\",\n \"other): return and_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"= nodeop(operator.not_) or_ = nodeop(operator.or_) pos = nodeop(operator.pos) rshift =\",\n \"if c in fetches and c.op_name in [\\\"output\\\", \\\"state\\\", \\\"temp\\\"]:\",\n \"values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return\",\n \"if self._name is not None and self._name in graph.nodes: graph.update_graph_key(self._name,\",\n \"return slice_op(operator.or_, self, other, graph=self.graph) def __ror__(self, other): return slice_op(operator.or_,\",\n \"= nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_) pos =\",\n \"self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape = tuple([shape]) elif\",\n \"return self.value def _evaluate(self, *args, context=None, **kwargs): \\\"\\\"\\\" Inheriting nodes\",\n \"= nodeop(operator.pos) rshift = nodeop(operator.rshift) setitem = nodeop(operator.setitem) sub =\",\n \"def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple,\",\n \"nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow)\",\n \"uuid.uuid4().hex # TODO: Need a way to check if the\",\n \"__rmod__(self, other): return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"\\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self, graph=self.graph) def __neg__(self):\",\n \"return floordiv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"@property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self): var, index_list\",\n \"= op_name # Get the stack context so we can\",\n \"dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies) yield #\",\n \"node) if isinstance(node, list): return [partial(element) for element in node]\",\n \"\\\"\\\"\\\" return self._shape @property def var(self): return self @property def\",\n \"indexOf = nodeop(operator.indexOf) inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior\",\n \"slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) else: domain =\",\n \"self.nodes = Graph(n) def insert_node(self, node, idx): node_list = list(self.nodes.items())\",\n \"elif isinstance(key, tuple): ret = var_index(self.var, key, graph=self) else: ret\",\n \"np.asarray([var]) elif not isinstance(var, (np.ndarray, list)): raise TypeError(f\\\"Variable {var} with\",\n \"= list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def __call__(self,\",\n \"`Node` instance or an node name. RuntimeError If `node` is\",\n \"Operation name which describes the node functionality. value : Any\",\n \"match {var.shape} in {self.var.name} - {self.var.op_name}\\\" f\\\"dimensions for slice {self.args[0].name}\",\n \"# TODO: Need a way to check if the existing\",\n \"a unique stringwhich corresponds to the variable, index combination. Parameters\",\n \"else other.__rlshift__(self) def __rlshift__(self, other): return lshift(other, self, graph=self.graph) if\",\n \"self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in kwargs: kwargs.pop(\\\"op_name\\\")\",\n \"evaluating any nodes defined in this scope. \\\"\\\"\\\" # Add\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ge__(self) def __ne__(self,\",\n \"if isinstance(shape, tuple) else tuple(shape) else: for idx, d in\",\n \"self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\" f\\\"New:\",\n \"in self.graph.nodes: return self.graph.nodes[name] elif isinstance(key, (list)): return var_index(self, key,\",\n \"= list(context) # Add the keyword arguments for node in\",\n \"the target kwargs : dict keywoard arguments passed to the\",\n \"Node): self._shape = tuple([shape]) elif not shape or len(shape) ==\",\n \"dependencies=dependencies, value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph,\",\n \"key = tuple([key]) if len(key) != len(self.shape): raise KeyError(f\\\"Invalid key\",\n \"= g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot\",\n \"ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\" f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if\",\n \"other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"isinstance(node, slice): return slice(*[partial(getattr(node, attr)) for attr in ['start', 'stop',\",\n \"== Node._graph_stack.pop() def __repr__(self): return \\\"\\\" % self.name def\",\n \"return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self) else:\",\n \"= shape or tuple([]) # Get a list of all\",\n \"op2, context=None, **kwargs): if self.is_scalar(op1) or self.is_scalar(op2): value = self.target(op1,\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self)\",\n \"the associated graph. \\\"\\\"\\\" # TODO: Need a way to\",\n \"Node): name.append(k.name) else: name.append(str(k)) else: name.append(key) name = self.var.name +\",\n \"slice2_var=None): if slice1_var is None and slice2_var is None: slice1_var,\",\n \"# Add dependencies to the graph graph = Node.get_active_graph(graph) graph.dependencies.extend(dependencies)\",\n \"other): return slice_op(operator.mod, other, self, graph=self.graph) def __lshift__(self, other): return\",\n \"nodeop(builtins.filter) issubclass_ = nodeop(builtins.issubclass) pow_ = nodeop(builtins.pow) super_ = nodeop(builtins.super)\",\n \"can report where the node was defined self._stack = traceback.extract_stack(limit=1)\",\n \"= [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches] return values[0]\",\n \"(new_key, self.nodes[k]) if k == old_key else (k, self.nodes[k]), self.nodes.keys()))\",\n \"f\\\"Existing: {self.graph.nodes[name].args}\\\\n\\\\t\\\" f\\\"New: {self.args}\\\") if self.graph: graph = self.graph if\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self,\",\n \"graph=None, dependencies=None, op_name=None, value=None, **kwargs): self.nodes = Graph() self.value =\",\n \"nodeop(operator.le) length_hint = nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt)\",\n \"f\\\"Node keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name\",\n \"def graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names\",\n \"explicit dependencies first self.evaluate_dependencies(context, callback) if self in context: return\",\n \"op_name=None, value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies\",\n \"hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for k in key:\",\n \"isinstance(slice2_var, (slice_op, var_index)) or _is_node_type_instance(slice2_var, \\\"GroupNode\\\"): slice2_idx = slice2_var.domain elif\",\n \"is not a mapping. \\\"\\\"\\\" if context is None: context\",\n \"\\\"index\\\")) else other.__rsub__(self) def __rsub__(self, other): return sub(other, self, graph=self.graph)\",\n \"name, value in kwargs.items(): node = self.nodes[name] if node in\",\n \"return truediv(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"tuple): return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key],\",\n \"self.added_attrs} # Evaluate the node callback = callback or _noop_callback\",\n \"self, graph=self.graph) def __mod__(self, other): return slice_op(operator.mod, self, other, graph=self.graph)\",\n \"other): return slice_op(operator.gt, self, other, graph=self.graph) def __ge__(self, other): return\",\n \"elif self.__class__.__name__ == \\\"Node\\\": self._op_name = self.name else: self._op_name =\",\n \"target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target = target self.added_attrs += [\\\"domain\\\", \\\"target\\\"]\",\n \"\\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing values\",\n \"= nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ =\",\n \"def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the dependencies of this\",\n \"for element in node) if isinstance(node, list): return [partial(element) for\",\n \"else other.__or__(self) def __xor__(self, other): return xor(self, other, graph=self.graph) if\",\n \"gname(self): scope_names = [self.name] cgraph = self.graph while cgraph: scope_names.append(cgraph.name)\",\n \"graph(self, graph): self._graph = Node.get_active_graph(graph) @property def gname(self): scope_names =\",\n \"= cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) else: n\",\n \"out_shape == (1,) and len(indices) == 1: res = res[0]\",\n \"or str or Node One or more `Node` instances or\",\n \"nodeop(builtins.float) iter_ = nodeop(builtins.iter) print_ = nodeop(builtins.print) tuple_ = nodeop(builtins.tuple)\",\n \"slice2_var, slice2_idx def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph)\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rpow__(self) def __matmul__(self, other): return\",\n \"(np.ndarray, list)): raise TypeError(f\\\"Variable {var} with type {type(var)} is not\",\n \"graph del graph.dependencies[-len(dependencies):] #pylint: disable=C0103 abs_ = nodeop(builtins.abs) dict_ =\",\n \"in self.nodes and (node.graph != self): raise RuntimeError(f\\\"node '{node}' does\",\n \"graph=self.graph) def __le__(self, other): return slice_op(operator.lt, other, self, graph=self.graph) def\",\n \"return or `None` to use the default graph. Raises ------\",\n \"= fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack))) message = \\\"Failed to\",\n \"key, graph=self.graph) else: if isinstance(key, (list)): return var_index(self, key, graph=self)\",\n \"(Integral, Real, str)): var = np.asarray([var]) elif not isinstance(var, (np.ndarray,\",\n \"order to enable \\\"is shape finalized\\\" to work self._shape =\",\n \"of the node. Returns ------- self : Node This node.\",\n \"other): return slice_op(operator.floordiv, other, self, graph=self.graph) def __mod__(self, other): return\",\n \"Node): e.add_successor(self) @property def domain(self): return Domain(tuple([])) @property def args(self):\",\n \"idx, idx_val in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which\",\n \"*args, **kwargs): for aa in list(kwargs.keys()): if aa in self.added_attrs:\",\n \"evaluate the node. callback : callable or None Callback to\",\n \"in kwargs: kwargs.pop(\\\"op_name\\\") target_name = f\\\"{target.__module__}.{target.__name__}\\\" super(slice_op, self).__init__(*args, target=target_name, domain=domain,\",\n \"operations performed on a node. Parameters ---------- target : cal\",\n \"nodeop(builtins.input) oct_ = nodeop(builtins.oct) staticmethod_ = nodeop(builtins.staticmethod) bin_ = nodeop(builtins.bin)\",\n \"__ge__(self, other): return ge(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"`func`. kwargs : dict Mapping of keyword arguments passed to\",\n \"other, graph=self.graph) def __rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph)\",\n \"If graph is `None`, this is the top-level graph. \\\"\\\"\\\"\",\n \"node_list = list(self.nodes.items()) node_list.insert(idx, (node.name, node)) self.nodes = Graph(node_list) def\",\n \"if context is None: context = {} elif not isinstance(context,\",\n \"value=None, **kwargs): self.nodes = Graph() self.value = value self.dependencies =\",\n \"return [a.name if isinstance(a, Node) else a for a in\",\n \"integer values or index/index_op nodes. \\\"\\\"\\\" def __init__(self, var, idx,\",\n \"instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the\",\n \"value=value, **kwargs) else: n = cls(*args, name=name, shape=shape, graph=graph, op_name=op_name,\",\n \"\\\"\\\"\\\" Node representing multi-dimensional operations performed on a node. Parameters\",\n \"Node.evaluated_nodes += 1 try: if isinstance(node, Node): Node._eval_stack.append(node.name) return node.evaluate(context,\",\n \"np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape)\",\n \"if isinstance(shape, float): self._shape = tuple([np.int(shape)]) elif isinstance(shape, Integral): self._shape\",\n \"self._succesors = [] self.args = args if \\\"name\\\" in kwargs:\",\n \"Shape of the output for a node. This can be\",\n \"else: return self.var.shape == DEFAULT_SHAPES[0] def _evaluate(self, var, indices, **kwargs):\",\n \"rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else\",\n \"cannot be sliced for {self.name}\\\") elif isinstance(var, list): var =\",\n \"def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self, key):\",\n \"**kwargs) partial = functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return\",\n \"== 0: n = cls(name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value,\",\n \"context, **kwargs): \\\"\\\"\\\" Instantiate a graph by replacing all node\",\n \"frozenset_ = nodeop(builtins.frozenset) list_ = nodeop(builtins.list) range_ = nodeop(builtins.range) vars_\",\n \"contains = nodeop(operator.contains) countOf = nodeop(operator.countOf) delitem = nodeop(operator.delitem) eq\",\n \"name=%s, index=%s>\\\" % (self.name, self.args) class slice_op(Node): \\\"\\\"\\\" Node representing\",\n \"domain = slice1_idx.combine_set_domains(slice2_idx) else: domain = Domain(tuple([])) self._target = None\",\n \"if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return getitem(self,\",\n \"else: out_shape = self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rmod__(self) def __rmod__(self, other): return\",\n \"None Callback to be evaluated when an node is evaluated.\",\n \"else other.__radd__(self) def __radd__(self, other): return add(other, self, graph=self.graph) if\",\n \"= nodeop(operator.length_hint) lshift = nodeop(operator.lshift) lt = nodeop(operator.lt) matmul =\",\n \"not an `Node` instance, node name, or a sequence thereof.\",\n \"op2 = np.asarray(list(map(lambda x: op2[x], op2_idx))).reshape(self.domain.computed_shape) value = self.target(op1, op2)\",\n \"or None Name of the node or `None` to use\",\n \"self.graph and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in\",\n \"= graph self._shape = OrderedDict() self.shape = shape or tuple([])\",\n \"else other.__rpow__(self) def __rpow__(self, other): return pow_(other, self, graph=self.graph) if\",\n \"= [] if isinstance(key, Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and\",\n \"**kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all `dependencies`\",\n \"Domain(idx) super(var_index, self).__init__(var, idx, name=name, domain=domain, **kwargs) @property def domain(self):\",\n \"graph=self.graph) def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def\",\n \"node : Node or str Node instance or name of\",\n \"return self def __getitem__(self, key): if self.is_shape_finalized() and len(self.nodes) >=\",\n \"return var_index(self, list(key), name=name, graph=self.graph) else: return var_index(self, [key], name=name,\",\n \"other): return sub(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\",\",\n \"self.domain.set_computed(out_shape, indices) return res def __add__(self, other): return slice_op(operator.add, self,\",\n \"which are used for executing this node. \\\"\\\"\\\" return tuple(self._args)\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__ne__(self) def __gt__(self, other): return gt(self, other,\",\n \"to evaluate the node. callback : callable or None Callback\",\n \"belong to {self} graph, instead belongs to\\\" f\\\" {node.graph}\\\") return\",\n \"context: raise ValueError(f\\\"duplicate unequal value for node '{node}'\\\") context[node] =\",\n \"single = True elif isinstance(fetches, Sequence): single = False else:\",\n \"in ['start', 'stop', 'step']]) return node except Exception as ex:\",\n \"other): return hash(self) == hash(other) def __getattr__(self, name): return getattr_(self,\",\n \"self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args = tuple(new_args) @shape.setter def shape(self,\",\n \"__repr__(self): return \\\"\\\" % (self.target.__name__, self.name) class func_op(Node): #\",\n \"which to evaluate the nodes. callback : callable or None\",\n \"@name.setter def name(self, name): self.set_name(name) @args.setter def args(self, args): new_args\",\n \"tuple): return var_index(self, list(key), graph=self) else: return var_index(self, [key], graph=self)\",\n \"__bool__(self): return True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\"\",\n \"hash of a particular node. The default hash returns an\",\n \"or uuid.uuid4().hex self._op_name = None self.op_name = op_name # Get\",\n \"the associated graph. KeyError If the current name of the\",\n \"return matmul(self, other, graph=self.graph) def __rmatmul__(self, other): return matmul(other, self,\",\n \"def __le__(self, other): return le(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"function modifies the context in place. Use :code:`context=context.copy()` to avoid\",\n \"= nodeop(builtins.exec) isinstance_ = nodeop(builtins.isinstance) ord_ = nodeop(builtins.ord) sum_ =\",\n \"= nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr) hash_ =\",\n \"def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving\",\n \"*args, **kwargs): if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if\",\n \"pragma: no cover interactive = fname.startswith('<') stack.append(frame) stack = \\\"\\\".join(traceback.format_list(reversed(stack)))\",\n \"elif len(args) == 2: all_args = _flatten_iterable(args) slice1_var, slice1_idx, slice2_var,\",\n \"This is called when the decorator is used without arguments\",\n \"return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul,\",\n \"graph=self.graph) def __reversed__(self): return reversed_(self, graph=self.graph) def update_graph_key(self, old_key, new_key):\",\n \"Unique name of the node\\\"\\\"\\\" return self._name @property def op_name(self):\",\n \"evaluted before evaluating any nodes defined in this scope. \\\"\\\"\\\"\",\n \"node instances or names. kwargs : dict[str, object] Additional context\",\n \"set_name(self, name): \\\"\\\"\\\" Set the name of the node and\",\n \"tuple Tuple of either integer values or index/index_op nodes. \\\"\\\"\\\"\",\n \"= nodeop(operator.is_) is_not = nodeop(operator.is_not) itemgetter = nodeop(operator.itemgetter) le =\",\n \"name): self.set_name(name) @args.setter def args(self, args): new_args = [] for\",\n \"node functionality. \\\"\\\"\\\" return self._op_name @op_name.setter def op_name(self, op_name): if\",\n \"else f\\\"{target.__name__}\\\" if \\\"domain\\\" in kwargs: domain = tuple(kwargs.pop(\\\"domain\\\")) if\",\n \"integer value for {self.name}\\\\n\\\" f\\\"\\\\tDim: {dim}\\\" f\\\"\\\\n\\\\t{self.kwargs} \\\") self._shape =\",\n \"in range(num): yield self[i] def __eq__(self, other): return hash(self) ==\",\n \"old_key, new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k\",\n \": tuple Tuple of either integer values or index/index_op nodes.\",\n \"other): return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return\",\n \"in enumerate(var.shape)]): raise ValueError(f\\\"var_index {self.name} has indices which are greater\",\n \"in fname: continue # Stop tracing at the last interactive\",\n \"Node): if self.__class__.__name__ == \\\"Node\\\": self.nodes[arg.name] = self.graph[arg.name] new_args.append(arg) self._args\",\n \"return slice_op(operator.add, other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub,\",\n \"index combination. Parameters ---------- value : str Unique name of\",\n \"node. \\\"\\\"\\\" class var_index(Node): # pylint: disable=C0103,W0223 \\\"\\\"\\\" Node representing\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self):\",\n \"keys: {list(self.nodes.keys())}\\\") ret = self.nodes[name] return ret else: name =\",\n \"@property def domain(self): return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key,\",\n \"self.graph: graph = self.graph if self._name is not None and\",\n \"self, graph=self.graph) def __ne__(self, other): return slice_op(operator.ne, self, other, graph=self.graph)\",\n \"`None` to use a random, unique identifier. shape : tuple\",\n \"g.graph if name in g.nodes: return g.nodes[name] raise RuntimeError(f\\\"Cannot find\",\n \"node or `None` to use a random, unique identifier. shape\",\n \"in which to evaluate the node. callback : callable or\",\n \"Raises ------ ValueError If no `Graph` instance can be obtained.\",\n \"hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph) def __getitem__(self,\",\n \"def __rsub__(self, other): return slice_op(operator.sub, other, self, graph=self.graph) def __pow__(self,\",\n \"This is called when the decorator is used with arguments\",\n \"func(*args, **kwargs) @contextlib.contextmanager def control_dependencies(dependencies, graph=None): \\\"\\\"\\\" Ensure that all\",\n \"nodeop(builtins.zip) compile_ = nodeop(builtins.compile) globals_ = nodeop(builtins.globals) map_ = nodeop(builtins.map)\",\n \"nodeop(operator.methodcaller) mod = nodeop(operator.mod) mul = nodeop(operator.mul) ne = nodeop(operator.ne)\",\n \"and name in self.graph.nodes: raise ValueError(f\\\"duplicate name '{name}' in {self.graph.name}:\\\\n\\\\t\\\"\",\n \"[] if isinstance(n, Node): for frame in reversed(n._stack): # pylint:\",\n \"graph=self.graph) def __repr__(self): return \\\"\\\" % (self.target.__name__, self.name) class\",\n \"nodeop(operator.ne) neg = nodeop(operator.neg) not_ = nodeop(operator.not_) or_ = nodeop(operator.or_)\",\n \"= np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape) != np.prod(out_shape):\",\n \"None Parent graph of this node. If graph is `None`,\",\n \"is None: context = {} elif not isinstance(context, Mapping): raise\",\n \"parameter node names. \\\"\\\"\\\" return self._shape @property def var(self): return\",\n \"self.domain.shape_from_indices(indices) indices = self.domain.compute_pairs() single = False if isinstance(var, (Integral,\",\n \"other, graph=self.graph) def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph)\",\n \"node self.dependencies = [] if dependencies is None else dependencies\",\n \"(\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def __lt__(self, other): return lt(self,\",\n \"__and__(self, other): return slice_op(operator.and_, self, other, graph=self.graph) def __rand__(self, other):\",\n \"arguments passed to `func`. kwargs : dict Mapping of keyword\",\n \"Context whose keys are node instances or names. kwargs :\",\n \"sum_ = nodeop(builtins.sum) bytearray_ = nodeop(builtins.bytearray) filter_ = nodeop(builtins.filter) issubclass_\",\n \"\\\"var_index\\\", \\\"index\\\")) else other.__mod__(self) def __lshift__(self, other): return lshift(self, other,\",\n \"= functools.partial(cls.evaluate_node, context=context, **kwargs) if isinstance(node, tuple): return tuple(partial(element) for\",\n \"Any or None If a node has a default value\",\n \"else: var = np.squeeze(var) if len(var.shape) != len(out_shape) and np.prod(var.shape)\",\n \"inv = nodeop(operator.inv) invert = nodeop(operator.invert) ior = nodeop(operator.ior) ipow\",\n \"= cls(*args, name=name, shape=shape, graph=graph, op_name=op_name, dependencies=dependencies, value=value, **kwargs) return\",\n \"self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args, context=context, **kwargs)\",\n \"var_index): s.append(d.domain) else: s.append(d) self._shape = tuple(s) def is_scalar(self, val):\",\n \"tuple : Positional arguments which are used for executing this\",\n \"else other.__rrshift__(self) def __rrshift__(self, other): return rshift(other, self, graph=self.graph) if\",\n \"return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph) def\",\n \"self[i] def __eq__(self, other): return hash(self) == hash(other) def __getattr__(self,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self,\",\n \": dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\"\",\n \"scope. Parameters ---------- dependencies : list Sequence of nodes to\",\n \"graph = self.graph if self._name and self._name in graph.nodes: graph.update_graph_key(self._name,\",\n \"node] if isinstance(node, dict): return {partial(key): partial(value) for key, value\",\n \"other.__rxor__(self) def __rxor__(self, other): return xor(other, self, graph=self.graph) if not\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__le__(self) def __invert__(self): return inv(self,\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rfloordiv__(self) def __rfloordiv__(self, other): return\",\n \"kwargs : dict keywoard arguments passed to the target \\\"\\\"\\\"\",\n \"__repr__(self): return \\\">\\\" % \\\\ (self.name,\",\n \"nodeop(builtins.hasattr) max_ = nodeop(builtins.max) round_ = nodeop(builtins.round) delattr_ = nodeop(builtins.delattr)\",\n \"def args(self): \\\"\\\"\\\" tuple : Positional arguments which are used\",\n \"setitem = nodeop(operator.setitem) sub = nodeop(operator.sub) truediv = nodeop(operator.truediv) truth\",\n \"len(self.nodes) >= np.prod(self.shape): if isinstance(key, Integral): key = tuple([key]) idx\",\n \"underscore private variable self.kwargs = kwargs self.graph = graph self._shape\",\n \"input index values. Parameters ---------- var : Node The multi-dimensional\",\n \"self.is_scalar(var): out_shape = (1,) indices = (0,) single = True\",\n \"str or Node One or more `Node` instances or names\",\n \"fetch in fetches] return values[0] if single else tuple(values) def\",\n \"can be set using `value`. kwargs : dict Keyword arguments\",\n \"more `Node` instances or names to evaluate. context : dict\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__ror__(self) def __ror__(self,\",\n \"= self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom)\",\n \"Mapping): raise ValueError(\\\"`context` must be a mapping.\\\") nodes = list(context)\",\n \"an node is evaluated. \\\"\\\"\\\" for node in self.dependencies: node.evaluate(context,\",\n \"__rshift__(self, other): return slice_op(operator.rshift, self, other, graph=self.graph) def __rrshift__(self, other):\",\n \"class func_op(Node): # pylint: disable=C0103,R0903 \\\"\\\"\\\" Node wrapper for stateless\",\n \"\\\"\\\"\\\" for node in self.dependencies: node.evaluate(context, callback) def evaluate(self, context,\",\n \"This returns the functional hash of a particular node. The\",\n \"domain=domain, **kwargs) @property def domain(self): return self.kwargs[\\\"domain\\\"] @property def var(self):\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rand__(self) def\",\n \"self): raise RuntimeError(f\\\"node '{node}' does not belong to {self} graph,\",\n \"from .graph import Graph from .domain import Domain from .util\",\n \"to the graph self._name = None self.name = name or\",\n \"called when the decorator is used with arguments if target\",\n \"> 0 else c.name fetches[fetches.index(c)] = c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch,\",\n \"return mod(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"\\\"index\\\"): slice2_idx = slice2_var.domain else: slice2_idx = Domain(tuple([])) return slice1_var,\",\n \"dict Mapping of keyword arguments passed to `func`. \\\"\\\"\\\" return\",\n \"name for the node and add the node to the\",\n \"Node): name.append(key.name) elif hasattr(key, \\\"__len__\\\") and not isinstance(key, str): for\",\n \"_is_node_type_instance(slice1_var, \\\"GroupNode\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if\",\n \"a way to check if the existing node is not\",\n \"isinstance(key, (slice, Integral)): return getitem(self, key, graph=self.graph) else: if isinstance(key,\",\n \"if isinstance(fetches, (str, Node)): fetches = [fetches] single = True\",\n \"Domain from .util import _noop_callback, _flatten_iterable, node_hash, \\\\ _is_node_type_instance, is_iterable\",\n \"len(self.nodes) > 0: if isinstance(key, (int, Node)): key = tuple([key])\",\n \"callback or _noop_callback with callback(self, context): if self.__class__.__name__ == \\\"Node\\\":\",\n \"np.ndarray)) and len(key) == 0: return self elif self.is_shape_finalized() and\",\n \"def __rpow__(self, other): return slice_op(builtins.pow, other, self, graph=self.graph) def __mul__(self,\",\n \"sequence thereof. \\\"\\\"\\\" if isinstance(fetches, (str, Node)): fetches = [fetches]\",\n \"(list)): ret = var_index(self.var, tuple(key), graph=self) elif isinstance(key, tuple): ret\",\n \"other, graph=self.graph) def __rxor__(self, other): return slice_op(operator.xor, other, self, graph=self.graph)\",\n \"self._graph def preds(self): return self._preds def succs(self): return self._preds def\",\n \"self.shape == UNSET_SHAPE: return False for s in self.shape: if\",\n \"other, self, graph=self.graph) def __sub__(self, other): return slice_op(operator.sub, self, other,\",\n \"representing multi-dimensional operations performed on a node. Parameters ---------- target\",\n \"other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rrshift__(self)\",\n \"Node(object): \\\"\\\"\\\" Base class for nodes. Parameters ---------- args :\",\n \"context in which to evaluate the node. callback : callable\",\n \"def is_scalar(self, val): return not isinstance(val, np.ndarray) or (len(val.shape) ==\",\n \"is evaluated. kwargs : dict Additional context information keyed by\",\n \"is evaluated. Returns ------- value : object Output of the\",\n \"being modified. Parameters ---------- fetches : list[str or Node] or\",\n \"`kwargs`. Parameters ---------- func : callable Function to call when\",\n \"def evaluate(self, context, callback=None): \\\"\\\"\\\" Evaluate the node given a\",\n \"def domain(self): return Domain(tuple([])) @property def args(self): \\\"\\\"\\\" tuple :\",\n \"of a particular node. The default hash returns an object\",\n \"def set_shape(self, shape=None, init=False): if isinstance(shape, float): self._shape = tuple([np.int(shape)])\",\n \"the functional hash of a particular node. The default hash\",\n \"context being modified. Parameters ---------- context : dict[Node or str,\",\n \"nodes defined in this scope. \\\"\\\"\\\" # Add dependencies to\",\n \"c.graph.nodes[write_name] values = [fetch.evaluate_node(fetch, context, callback=callback) for fetch in fetches]\",\n \"node)) self.nodes = Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args,\",\n \"shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs): if len(args) == 0:\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rtruediv__(self) def __floordiv__(self, other): return\",\n \"data): self.__dict__.update(data) def set_name(self, name): \\\"\\\"\\\" Set the name of\",\n \"slice2_idx = self.get_index_nodes(all_args[0], all_args[1]) domain = slice1_idx.combine_set_domains(slice2_idx) if \\\"op_name\\\" in\",\n \"else tuple([]) op1 = np.asarray(list(map(lambda x: op1[x], op1_idx))).reshape(self.domain.computed_shape) op2 =\",\n \"slice1_var, slice2_var = self.args if isinstance(slice1_var, (slice_op, var_index)) or _is_node_type_instance(slice1_var,\",\n \"def name(self): \\\"\\\"\\\"str : Unique name of the node\\\"\\\"\\\" return\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__xor__(self) def\",\n \"self, other, graph=self.graph) def __rmul__(self, other): return slice_op(operator.mul, other, self,\",\n \"node. Parameters ---------- target : cal The multi-dimensional variable used\",\n \"a dictionary of node names with their values. .. note::\",\n \"interactive cell if interactive and not fname.startswith('<'): break # pragma:\",\n \"`_evaluate` method. name : str or None Name of the\",\n \"= self.domain.compute_pairs() single = False if isinstance(var, (Integral, Real, str)):\",\n \"True def __hash__(self): return id(self) def func_hash(self): \\\"\\\"\\\" This returns\",\n \"Integral): key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret\",\n \"for idx in indices]).reshape(out_shape) if out_shape == (1,) and len(indices)\",\n \"= [] assert isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for\",\n \"one value for any node. ValueError If `context` is not\",\n \"return ne(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"to replicate the new name with a unique stringwhich corresponds\",\n \"= [] if isinstance(n, Node): for frame in reversed(n._stack): #\",\n \"_is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__gt__(self) def __le__(self, other): return\",\n \"key shape for {self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name =\",\n \"return res def __add__(self, other): return slice_op(operator.add, self, other, graph=self.graph)\",\n \"from collections import OrderedDict, Mapping, Sequence, deque import functools from\",\n \"return self.kwargs[\\\"domain\\\"] def __getitem__(self, key): if isinstance(key, (tuple, list, np.ndarray))\",\n \"in key: if isinstance(k, Node): name.append(k.name) else: name.append(str(k)) else: name.append(key)\",\n \"for name, value in kwargs.items(): node = self.nodes[name] if node\",\n \"[partial(element) for element in node] if isinstance(node, dict): return {partial(key):\",\n \"_is_node_type_instance(slice1_var, \\\"index\\\"): slice1_idx = slice1_var.domain else: slice1_idx = Domain(tuple([])) if\",\n \"context=None, *, callback=None, **kwargs): \\\"\\\"\\\" Evaluate one or more nodes\",\n \"list(map(lambda x: x.tolist() if isinstance(x, np.ndarray) else x, indices)) res\",\n \"{self.name}:\\\\n\\\" f\\\"Shape: {self.shape}\\\\n\\\" f\\\"Key: {key}\\\") name = f\\\"{self.name}{key}\\\" if name\",\n \"eq = nodeop(operator.eq) floordiv = nodeop(operator.floordiv) ge = nodeop(operator.ge) getitem\",\n \"return graph def instantiate_node(self, node): # pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__add__(self) def\",\n \"__gt__(self, other): return gt(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\",\",\n \"= Graph(node_list) def __call__(self, *args, **kwargs): return self.run(*args, **kwargs) class\",\n \"str_ = nodeop(builtins.str) bool_ = nodeop(builtins.bool) exec_ = nodeop(builtins.exec) isinstance_\",\n \"of the node given the context. \\\"\\\"\\\" # Evaluate all\",\n \"context): if self.__class__.__name__ == \\\"Node\\\": context[self] = self.value = self._evaluate(*args,\",\n \"else tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([])\",\n \"other, graph=self.graph) def __ge__(self, other): return slice_op(operator.ge, self, other, graph=self.graph)\",\n \"self, graph=self.graph) def __mul__(self, other): return slice_op(operator.mul, self, other, graph=self.graph)\",\n \"the node\\\"\\\"\\\" return self._name @property def op_name(self): \\\"\\\"\\\" str :\",\n \"not is_iterable(node): node = [node] for n in node: stack\",\n \"new_key): n = list(map(lambda k: (new_key, self.nodes[k]) if k ==\",\n \"__rtruediv__(self, other): return slice_op(operator.truediv, other, self, graph=self.graph) def __floordiv__(self, other):\",\n \"Set the name of the node and update the graph.\",\n \"return self._op_name @op_name.setter def op_name(self, op_name): if op_name: self._op_name =\",\n \"self.graph if self._name is not None and self._name in graph.nodes:\",\n \"def __init__(self, *args, name=None, shape=None, graph=None, dependencies=None, op_name=None, value=None, **kwargs):\",\n \"graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mul__(self) def\",\n \"subgraphs of a node. \\\"\\\"\\\" return node_hash(self) def find_node(self, name):\",\n \"= 5 evaluated_nodes = 0 def __init__(self, *args, name=None, shape=None,\",\n \"One or more `Node` instances or names to evaluate. context\",\n \"to return or `None` to use the default graph. Raises\",\n \"keys are node instances. Raises ------ ValueError If the context\",\n \"var for var index {self} with variable shape {self.var.shape}\\\") return\",\n \"pylint:disable=W0621 \\\"\\\"\\\" Instantiate nodes by retrieving the node object associated\",\n \"if isinstance(key, (list)): return var_index(self, key, graph=self) elif isinstance(key, tuple):\",\n \"and discard the values. Parameters ---------- context : dict Normalised\",\n \"self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt, self, other,\",\n \"key): if self.__class__.__name__ != \\\"Node\\\": if isinstance(key, (slice, Integral)): return\",\n \"= nodeop(builtins.print) tuple_ = nodeop(builtins.tuple) callable_ = nodeop(builtins.callable) format_ =\",\n \"return slice_op(operator.ne, self, other, graph=self.graph) def __gt__(self, other): return slice_op(operator.gt,\",\n \"retrieving the node object associated with the node name. Parameters\",\n \"var, indices, **kwargs): if self.is_scalar(var): out_shape = (1,) indices =\",\n \"list Sequence of positional arguments passed to `func`. kwargs :\",\n \"all `dependencies` are executed before any nodes in this scope.\",\n \"= self._evaluate(*args, **kwargs) return self.value def _evaluate(self, *args, context=None, **kwargs):\",\n \"self.args[1].domain op1_idx = self.domain.map_sub_domain(arg0_dom) if isinstance(self.args[0], Node) else tuple([]) op2_idx\",\n \"the node. callback : callable or None Callback to be\",\n \"list): return [partial(element) for element in node] if isinstance(node, dict):\",\n \"(len(self.var.shape) == 1 and not isinstance(self.var.shape[0],Node)): raise ValueError(f\\\"Invalid shape var\",\n \"val.shape[0] == 1) def _evaluate(self, op1, op2, context=None, **kwargs): if\",\n \"using the default graph. Parameters ---------- graph : Node or\",\n \"name return self def evaluate_dependencies(self, context, callback=None): \\\"\\\"\\\" Evaluate the\",\n \"return sub(other, self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"def __xor__(self, other): return xor(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"other.__sub__(self) def __pow__(self, other): return pow_(self, other, graph=self.graph) if not\",\n \"key = tuple([key]) idx = np.ravel_multi_index(key, dims=self.shape, order='C') ret =\",\n \"self._target = None super(func_op, self).__init__(*args, target=f\\\"{target.__module__}.{target.__name__}\\\", domain=domain, **kwargs) self.target =\",\n \"is_iterable(node): node = [node] for n in node: stack =\",\n \"= value if node.op_name in [\\\"placeholder\\\", \\\"state\\\", \\\"input\\\", \\\"output\\\", \\\"temp\\\"]\",\n \"place. Use :code:`context=context.copy()` to avoid the context being modified. Parameters\",\n \"= self.value = self._evaluate(*args, context=context, **kwargs) else: context[self] = self.value\",\n \"self, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__mod__(self)\",\n \"return rshift(self, other, graph=self.graph) if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\"))\",\n \"tuple([]) op2_idx = self.domain.map_sub_domain(arg1_dom) if isinstance(self.args[1], Node) else tuple([]) op1\",\n \"Domain(tuple([])) return slice1_var, slice1_idx, slice2_var, slice2_idx def __add__(self, other): return\",\n \"def nodeop(target=None, **kwargs): \\\"\\\"\\\" Decorator for creating nodes from functions.\",\n \"Real import contextlib import traceback import uuid import numpy as\",\n \"a name for the node and add the node to\",\n \"@staticmethod def get_active_graph(graph=None): \\\"\\\"\\\" Obtain the currently active graph instance\",\n \"def __pow__(self, other): return pow_(self, other, graph=self.graph) if not _is_node_type_instance(other,\",\n \"if not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__truediv__(self) def __rtruediv__(self,\",\n \"self, graph=self.graph) def __lshift__(self, other): return slice_op(operator.lshift, self, other, graph=self.graph)\",\n \"graph=self.graph) def __rrshift__(self, other): return slice_op(operator.rshift, other, self, graph=self.graph) def\",\n \"in graph nodes. Graph: {self.graph}\\\") def __len__(self): #TODO: Update this\",\n \"callback=callback) args = [partial(arg) for arg in self.args] kwargs =\",\n \"not _is_node_type_instance(other, (\\\"slice_op\\\", \\\"var_index\\\", \\\"index\\\")) else other.__rxor__(self) def __rxor__(self, other):\",\n \"used with arguments if target is None: return functools.partial(nodeop, **kwargs)\",\n \"= self.nodes[name] if node in context: raise ValueError(f\\\"duplicate value for\",\n \"isinstance(shape, (tuple, list)) if all([isinstance(sv, Integral) for sv in shape])\",\n \"hash(self) == hash(other) def __getattr__(self, name): return getattr_(self, name, graph=self.graph)\",\n \"to input index values. Parameters ---------- var : Node The\"\n]"}}},{"rowIdx":3475,"cells":{"ngram":{"kind":"list like","value":["directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def __str__(self):","hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False)","commercial is_star = models.BooleanField(_(\"is start\"), default=False) def __str__(self): return self.name","directed to hollywood else directed to commercial is_star = models.BooleanField(_(\"is","here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star","if is_star he/she will be directed to hollywood else directed","import models from django.utils.translation import ugettext_lazy as _ # Create","= models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed","max_length=200) # if is_star he/she will be directed to hollywood","Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will","models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be directed to","models from django.utils.translation import ugettext_lazy as _ # Create your","your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) #","else directed to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def","he/she will be directed to hollywood else directed to commercial","# Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"),","to hollywood else directed to commercial is_star = models.BooleanField(_(\"is start\"),","is_star he/she will be directed to hollywood else directed to","to commercial is_star = models.BooleanField(_(\"is start\"), default=False) def __str__(self): return","django.utils.translation import ugettext_lazy as _ # Create your models here.","models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if","from django.utils.translation import ugettext_lazy as _ # Create your models","as _ # Create your models here. class Actor(models.Model): name","django.db import models from django.utils.translation import ugettext_lazy as _ #","from django.db import models from django.utils.translation import ugettext_lazy as _","Create your models here. class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200)","class Actor(models.Model): name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she","be directed to hollywood else directed to commercial is_star =","name = models.CharField(_(\"name\"), max_length=200) # if is_star he/she will be","ugettext_lazy as _ # Create your models here. class Actor(models.Model):","will be directed to hollywood else directed to commercial is_star","import ugettext_lazy as _ # Create your models here. class","0 from django.db import models from django.utils.translation import ugettext_lazy as","_ # Create your models here. class Actor(models.Model): name =","# if is_star he/she will be directed to hollywood else"],"string":"[\n \"directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def __str__(self):\",\n \"hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False)\",\n \"commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def __str__(self): return self.name\",\n \"directed to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is\",\n \"here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star\",\n \"if is_star he/she will be directed to hollywood else directed\",\n \"import models from django.utils.translation import ugettext_lazy as _ # Create\",\n \"= models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed\",\n \"max_length=200) # if is_star he/she will be directed to hollywood\",\n \"Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will\",\n \"models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be directed to\",\n \"models from django.utils.translation import ugettext_lazy as _ # Create your\",\n \"your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) #\",\n \"else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def\",\n \"he/she will be directed to hollywood else directed to commercial\",\n \"# Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"),\",\n \"to hollywood else directed to commercial is_star = models.BooleanField(_(\\\"is start\\\"),\",\n \"is_star he/she will be directed to hollywood else directed to\",\n \"to commercial is_star = models.BooleanField(_(\\\"is start\\\"), default=False) def __str__(self): return\",\n \"django.utils.translation import ugettext_lazy as _ # Create your models here.\",\n \"models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if\",\n \"from django.utils.translation import ugettext_lazy as _ # Create your models\",\n \"as _ # Create your models here. class Actor(models.Model): name\",\n \"django.db import models from django.utils.translation import ugettext_lazy as _ #\",\n \"from django.db import models from django.utils.translation import ugettext_lazy as _\",\n \"Create your models here. class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200)\",\n \"class Actor(models.Model): name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she\",\n \"be directed to hollywood else directed to commercial is_star =\",\n \"name = models.CharField(_(\\\"name\\\"), max_length=200) # if is_star he/she will be\",\n \"ugettext_lazy as _ # Create your models here. class Actor(models.Model):\",\n \"will be directed to hollywood else directed to commercial is_star\",\n \"import ugettext_lazy as _ # Create your models here. class\",\n \"0 from django.db import models from django.utils.translation import ugettext_lazy as\",\n \"_ # Create your models here. class Actor(models.Model): name =\",\n \"# if is_star he/she will be directed to hollywood else\"\n]"}}},{"rowIdx":3476,"cells":{"ngram":{"kind":"list like","value":["'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"):","subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\")","in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if","os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from doxygenConfig.","clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null,","null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir)","def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\",","\"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with","line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating","C client docs. \"\"\" from __future__ import with_statement import os","clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null)","driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for","def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir)","clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\")","os import shutil import socket import subprocess import time import","\"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines(): if","line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\")","import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir)","with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\",","'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def","if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in","dir) def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\"","stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the","def version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with","with open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"):","with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\")","urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir):","the C client docs. \"\"\" from __future__ import with_statement import","os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null:","def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\",","open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if","os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version from","version(): \"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\")","open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir)","gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"],","os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as","dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null:","main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs","time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir)","from __future__ import with_statement import os import shutil import socket","def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen","for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main():","print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in","with_statement import os import shutil import socket import subprocess import","f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs","doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in f.readlines():","socket import subprocess import time import urllib2 def clean_dir(dir): try:","docs. \"\"\" from __future__ import with_statement import os import shutil","stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w')","gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ ==","as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir):","\"\"\"Get the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as","as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip()","shutil import socket import subprocess import time import urllib2 def","import socket import subprocess import time import urllib2 def clean_dir(dir):","version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line","shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull,","f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def","docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__","os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null)","subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def","client docs. \"\"\" from __future__ import with_statement import os import","null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir)","try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with","line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating","pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as","import os import shutil import socket import subprocess import time","except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w')","return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx docs in docs/html\") gen_sphinx(\"docs/html\")","subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except:","from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f: for line in","\"\"\" from __future__ import with_statement import os import shutil import","docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\")","import with_statement import os import shutil import socket import subprocess","\"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version():","stdout=null, stderr=null) os.rename(\"docs/source/doxygen/html\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull,","import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def","print(\"Generating Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ == \"__main__\":","import shutil import socket import subprocess import time import urllib2","os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver","open(\"doxygenConfig\") as f: for line in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return","\"\"\"Build the C client docs. \"\"\" from __future__ import with_statement","if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get the driver version","Sphinx docs in docs/html\") gen_sphinx(\"docs/html\") print(\"Generating Doxygen docs in docs/html/api\")","Doxygen docs in docs/html/api\") gen_api(\"docs/html/api\") if __name__ == \"__main__\": main()","import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass","clean_dir(dir) clean_dir(\"docs/source/doxygen\") with open(os.devnull, 'w') as null: subprocess.call([\"doxygen\", \"doxygenConfig\"], stdout=null,","__future__ import with_statement import os import shutil import socket import","as null: subprocess.call([\"make\", \"html\"], stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\",","gen_sphinx(dir): clean_dir(dir) os.chdir(\"docs/source/sphinx\") with open(os.devnull, 'w') as null: subprocess.call([\"make\", \"html\"],","the driver version from doxygenConfig. \"\"\" with open(\"doxygenConfig\") as f:","stdout=null, stderr=null) os.chdir(\"../../../\") if os.path.isdir(\"docs/source/sphinx/build/html\"): os.rename(\"docs/source/sphinx/build/html\", dir) def version(): \"\"\"Get","in f.readlines(): if line.startswith(\"PROJECT_NUMBER\"): return line.split(\"=\")[1].strip() def main(): print(\"Generating Sphinx"],"string":"[\n \"'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"):\",\n \"subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\")\",\n \"in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if\",\n \"os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig.\",\n \"clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null,\",\n \"null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir)\",\n \"def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\",\",\n \"\\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with\",\n \"line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating\",\n \"C client docs. \\\"\\\"\\\" from __future__ import with_statement import os\",\n \"clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null)\",\n \"driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for\",\n \"def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir)\",\n \"clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\")\",\n \"os import shutil import socket import subprocess import time import\",\n \"\\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if\",\n \"line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\")\",\n \"import subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir)\",\n \"with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\",\",\n \"'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def\",\n \"if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in\",\n \"dir) def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\"\",\n \"stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the\",\n \"def version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with\",\n \"with open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"):\",\n \"with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\")\",\n \"urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir):\",\n \"the C client docs. \\\"\\\"\\\" from __future__ import with_statement import\",\n \"os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null:\",\n \"def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\",\",\n \"open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if\",\n \"os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version from\",\n \"version(): \\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\")\",\n \"open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir)\",\n \"gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"],\",\n \"os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as\",\n \"dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null:\",\n \"main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs\",\n \"time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir)\",\n \"from __future__ import with_statement import os import shutil import socket\",\n \"def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen\",\n \"for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main():\",\n \"print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in\",\n \"with_statement import os import shutil import socket import subprocess import\",\n \"f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs\",\n \"doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in f.readlines():\",\n \"socket import subprocess import time import urllib2 def clean_dir(dir): try:\",\n \"docs. \\\"\\\"\\\" from __future__ import with_statement import os import shutil\",\n \"stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w')\",\n \"gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__ ==\",\n \"as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir):\",\n \"\\\"\\\"\\\"Get the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as\",\n \"as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip()\",\n \"shutil import socket import subprocess import time import urllib2 def\",\n \"import socket import subprocess import time import urllib2 def clean_dir(dir):\",\n \"version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line\",\n \"shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull,\",\n \"f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def\",\n \"docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__\",\n \"os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null)\",\n \"subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def\",\n \"client docs. \\\"\\\"\\\" from __future__ import with_statement import os import\",\n \"null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir)\",\n \"try: shutil.rmtree(dir) except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with\",\n \"line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating\",\n \"pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as\",\n \"import os import shutil import socket import subprocess import time\",\n \"except: pass os.makedirs(dir) def gen_api(dir): clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w')\",\n \"return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\")\",\n \"subprocess import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except:\",\n \"from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f: for line in\",\n \"\\\"\\\"\\\" from __future__ import with_statement import os import shutil import\",\n \"docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\")\",\n \"import with_statement import os import shutil import socket import subprocess\",\n \"\\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version():\",\n \"stdout=null, stderr=null) os.rename(\\\"docs/source/doxygen/html\\\", dir) def gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull,\",\n \"import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass os.makedirs(dir) def\",\n \"print(\\\"Generating Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__ == \\\"__main__\\\":\",\n \"import shutil import socket import subprocess import time import urllib2\",\n \"os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver\",\n \"open(\\\"doxygenConfig\\\") as f: for line in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return\",\n \"\\\"\\\"\\\"Build the C client docs. \\\"\\\"\\\" from __future__ import with_statement\",\n \"if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get the driver version\",\n \"Sphinx docs in docs/html\\\") gen_sphinx(\\\"docs/html\\\") print(\\\"Generating Doxygen docs in docs/html/api\\\")\",\n \"Doxygen docs in docs/html/api\\\") gen_api(\\\"docs/html/api\\\") if __name__ == \\\"__main__\\\": main()\",\n \"import time import urllib2 def clean_dir(dir): try: shutil.rmtree(dir) except: pass\",\n \"clean_dir(dir) clean_dir(\\\"docs/source/doxygen\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"doxygen\\\", \\\"doxygenConfig\\\"], stdout=null,\",\n \"__future__ import with_statement import os import shutil import socket import\",\n \"as null: subprocess.call([\\\"make\\\", \\\"html\\\"], stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\",\",\n \"gen_sphinx(dir): clean_dir(dir) os.chdir(\\\"docs/source/sphinx\\\") with open(os.devnull, 'w') as null: subprocess.call([\\\"make\\\", \\\"html\\\"],\",\n \"the driver version from doxygenConfig. \\\"\\\"\\\" with open(\\\"doxygenConfig\\\") as f:\",\n \"stdout=null, stderr=null) os.chdir(\\\"../../../\\\") if os.path.isdir(\\\"docs/source/sphinx/build/html\\\"): os.rename(\\\"docs/source/sphinx/build/html\\\", dir) def version(): \\\"\\\"\\\"Get\",\n \"in f.readlines(): if line.startswith(\\\"PROJECT_NUMBER\\\"): return line.split(\\\"=\\\")[1].strip() def main(): print(\\\"Generating Sphinx\"\n]"}}},{"rowIdx":3477,"cells":{"ngram":{"kind":"list like","value":["glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) )","__exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3:","0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg)","glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale =","\"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1)","180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m","= math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0,","/ self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio,","rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0)","= 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self)","m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale,","ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) ->","0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args,","math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0,","1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale))","m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] =","self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4:","= rs self.scale = 10. self.rotation_deg = 0. self.location =","def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width","float) -> glm.mat3: a = degree / 180. * math.pi","scale = 1. ratio = self.rs.render_width / self.rs.render_height m =",".5 m[2][0] = self.location.x m[2][1] = self.location.y return m def","-self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m =","m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self):","self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location","self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack","self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg,","= glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10,","self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\":","* glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) ->","glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1","def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg =","* glm.pi(), glm.vec3(0, 0, 1) ) m = m *","= m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def","[] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio =","rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1]","def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg /","= s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type,","0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. /","0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args,","transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale *","m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *=","self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale,","m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg","-> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 *","\"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale =","self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"]","\"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg = 0.","= m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m","pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"]","/ 180. * math.pi sa = math.sin(a) ca = math.cos(a)","ca, sa, 0, -sa, ca, 0, 0, 0, 1 )","-> glm.mat3: a = degree / 180. * math.pi sa","= glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0,","-self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m","= s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self","from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"):","exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a","m[2][1] = self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale,","}) def pop(self): s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg","self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s =","def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), })","m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m =","= 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale","= self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location =","= self.location.y return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\":","__enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop()","= 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack =","__init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg","glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10)","return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0,","degree / 180. * math.pi sa = math.sin(a) ca =","def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180.","-> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5","transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180",") class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection","= degree / 180. * math.pi sa = math.sin(a) ca","s[\"location\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val,","rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10. self.rotation_deg =","math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs:","ratio, scale * ratio, -scale, scale, -10, 10) return m","glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x,","m *= self.scale * .5 m[2][0] = self.location.x m[2][1] =","*= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y","-> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height","-sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def","glm import math from lib.opengl import RenderSettings class GameProjection: def","glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3:","return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m","1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale *","math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca,","self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return","m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m","\"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s","glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y,","self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale *","0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings):","= [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio","* math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3(","10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = []","projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width /","self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self):","exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a =","glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2.","glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m","GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection = GameProjection(self)","m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(),","glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m","glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0]","self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def","= glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale","= math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0,","def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale","s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return self def","glm.mat3: a = degree / 180. * math.pi sa =","= s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self):","self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def","* ratio, scale * ratio, -scale, scale, -10, 10) return","rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. *","import math from lib.opengl import RenderSettings class GameProjection: def __init__(self,","RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs","return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1),","s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push()",") m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m","def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb):","a = degree / 180. * math.pi sa = math.sin(a)","1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)","= rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x","0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) ->","return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree:","0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs):","m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self)","/ self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0))","scale * ratio, -scale, scale, -10, 10) return m def","ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio,","-scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4:","glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(),","self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree /","/ 180 * glm.pi(), glm.vec3(0, 0, 1) ) m =","glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(),","m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0,","s = self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location","def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale = 10.","* ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self)","10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate(","self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return","push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\": self.location.__copy__(), }) def","exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree","self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def","self.location = s[\"location\"] def __enter__(self): self.push() return self def __exit__(self,","sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa,","import glm import math from lib.opengl import RenderSettings class GameProjection:","lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs","sa, 0, -sa, ca, 0, 0, 0, 1 ) class","* glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(),","ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca,","self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float)","-10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m =","self.rotation_deg, \"location\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale","ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self,","= self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale","import RenderSettings class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs =","return m def push(self): self._stack.append({ \"scale\": self.scale, \"rotation\": self.rotation_deg, \"location\":","def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) ->","self._stack.pop(-1) self.scale = s[\"scale\"] self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"]","class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection =","GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale =","self.rotation_deg = s[\"rotation\"] self.location = s[\"location\"] def __enter__(self): self.push() return","180. * math.pi sa = math.sin(a) ca = math.cos(a) return","math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa,","= self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({","glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1)","class GameProjection: def __init__(self, rs: \"GameRenderSettings\"): self.rs = rs self.scale","m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m *","self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1.","scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m","* .5 m[2][0] = self.location.x m[2][1] = self.location.y return m"],"string":"[\n \"glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) )\",\n \"__exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3:\",\n \"0)) return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg)\",\n \"glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale =\",\n \"\\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1)\",\n \"180 * glm.pi(), glm.vec3(0, 0, 1) ) m = m\",\n \"= math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0,\",\n \"/ self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio,\",\n \"rs self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0)\",\n \"= 0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self)\",\n \"m = glm.ortho(-scale * ratio, scale * ratio, -scale, scale,\",\n \"ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self) ->\",\n \"0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args,\",\n \"math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0, 0,\",\n \"1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale))\",\n \"m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] =\",\n \"self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4:\",\n \"= rs self.scale = 10. self.rotation_deg = 0. self.location =\",\n \"def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width\",\n \"float) -> glm.mat3: a = degree / 180. * math.pi\",\n \"scale = 1. ratio = self.rs.render_width / self.rs.render_height m =\",\n \".5 m[2][0] = self.location.x m[2][1] = self.location.y return m def\",\n \"-self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m =\",\n \"m[2][0] = self.location.x m[2][1] = self.location.y return m def push(self):\",\n \"self.rs = rs self.scale = 10. self.rotation_deg = 0. self.location\",\n \"self.scale = 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack\",\n \"self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg,\",\n \"= glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10,\",\n \"self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\":\",\n \"* glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) ->\",\n \"glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0, 1\",\n \"def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg =\",\n \"* glm.pi(), glm.vec3(0, 0, 1) ) m = m *\",\n \"= m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def\",\n \"[] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio =\",\n \"rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x m[2][1]\",\n \"def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg /\",\n \"= s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type,\",\n \"0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2. /\",\n \"0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args,\",\n \"transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale *\",\n \"m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *=\",\n \"self.rs.render_height m = glm.ortho(-scale * ratio, scale * ratio, -scale,\",\n \"m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg\",\n \"-> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 *\",\n \"\\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale =\",\n \"self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"]\",\n \"\\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg = 0.\",\n \"= m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m\",\n \"pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"]\",\n \"/ 180. * math.pi sa = math.sin(a) ca = math.cos(a)\",\n \"ca, sa, 0, -sa, ca, 0, 0, 0, 1 )\",\n \"-> glm.mat3: a = degree / 180. * math.pi sa\",\n \"= glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0,\",\n \"-self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1) ) m\",\n \"= s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self\",\n \"from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"):\",\n \"exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a\",\n \"m[2][1] = self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale,\",\n \"}) def pop(self): s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg\",\n \"self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s =\",\n \"def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), })\",\n \"m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m =\",\n \"= 1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale\",\n \"= self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location =\",\n \"= self.location.y return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\":\",\n \"__enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop()\",\n \"= 10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack =\",\n \"__init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg\",\n \"glm.ortho(-scale * ratio, scale * ratio, -scale, scale, -10, 10)\",\n \"return glm.mat3( ca, sa, 0, -sa, ca, 0, 0, 0,\",\n \"degree / 180. * math.pi sa = math.sin(a) ca =\",\n \"def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180.\",\n \"-> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5\",\n \"transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180\",\n \") class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection\",\n \"= degree / 180. * math.pi sa = math.sin(a) ca\",\n \"s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val,\",\n \"rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10. self.rotation_deg =\",\n \"math from lib.opengl import RenderSettings class GameProjection: def __init__(self, rs:\",\n \"ratio, scale * ratio, -scale, scale, -10, 10) return m\",\n \"glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x,\",\n \"m *= self.scale * .5 m[2][0] = self.location.x m[2][1] =\",\n \"*= self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y\",\n \"-> glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height\",\n \"-sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def\",\n \"glm import math from lib.opengl import RenderSettings class GameProjection: def\",\n \"glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3:\",\n \"return m def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m\",\n \"1. ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale *\",\n \"math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca,\",\n \"self.scale * .5 m[2][0] = self.location.x m[2][1] = self.location.y return\",\n \"m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m\",\n \"\\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s\",\n \"glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y,\",\n \"self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale *\",\n \"0, -sa, ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings):\",\n \"= [] def projection_matrix_4(self) -> glm.mat4: scale = 1. ratio\",\n \"* math.pi sa = math.sin(a) ca = math.cos(a) return glm.mat3(\",\n \"10. self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = []\",\n \"projection_matrix_4(self) -> glm.mat4: scale = 1. ratio = self.rs.render_width /\",\n \"self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self):\",\n \"exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a =\",\n \"glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(), glm.vec3(2.\",\n \"glm.mat4: scale = 1. ratio = self.rs.render_width / self.rs.render_height m\",\n \"GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection = GameProjection(self)\",\n \"m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(),\",\n \"glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self) -> glm.mat3: m\",\n \"glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0]\",\n \"self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def\",\n \"= glm.vec3(0) self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale\",\n \"= math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca, 0,\",\n \"def transformation_matrix(self) -> glm.mat3: m = rotation_matrix_2d(self.rotation_deg) m *= self.scale\",\n \"s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def\",\n \"glm.mat3: a = degree / 180. * math.pi sa =\",\n \"= s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self):\",\n \"self.rotation_deg = 0. self.location = glm.vec3(0) self._stack = [] def\",\n \"* ratio, scale * ratio, -scale, scale, -10, 10) return\",\n \"rotation_matrix_2d(degree: float) -> glm.mat3: a = degree / 180. *\",\n \"import math from lib.opengl import RenderSettings class GameProjection: def __init__(self,\",\n \"RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs\",\n \"return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate( glm.mat4(1),\",\n \"s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push()\",\n \") m = m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m\",\n \"def __enter__(self): self.push() return self def __exit__(self, exc_type, exc_val, exc_tb):\",\n \"a = degree / 180. * math.pi sa = math.sin(a)\",\n \"1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)\",\n \"= rotation_matrix_2d(self.rotation_deg) m *= self.scale * .5 m[2][0] = self.location.x\",\n \"0. self.location = glm.vec3(0) self._stack = [] def projection_matrix_4(self) ->\",\n \"return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree:\",\n \"0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs):\",\n \"m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return m def transformation_matrix(self)\",\n \"/ self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0))\",\n \"scale * ratio, -scale, scale, -10, 10) return m def\",\n \"ratio = self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio,\",\n \"-scale, scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4:\",\n \"glm.pi(), glm.vec3(0, 0, 1) ) m = m * glm.scale(glm.mat4(),\",\n \"self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree /\",\n \"/ 180 * glm.pi(), glm.vec3(0, 0, 1) ) m =\",\n \"glm.mat4: m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(),\",\n \"m = glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0,\",\n \"s = self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location\",\n \"def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale = 10.\",\n \"* ratio, -scale, scale, -10, 10) return m def transformation_matrix_4(self)\",\n \"10) return m def transformation_matrix_4(self) -> glm.mat4: m = glm.rotate(\",\n \"self.scale)) m = m * glm.translate(glm.mat4(), glm.vec3(-self.location.x, -self.location.y, 0)) return\",\n \"push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def\",\n \"exc_tb): self.pop() def rotation_matrix_2d(degree: float) -> glm.mat3: a = degree\",\n \"self.push() return self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def\",\n \"self.location = s[\\\"location\\\"] def __enter__(self): self.push() return self def __exit__(self,\",\n \"sa = math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa,\",\n \"import glm import math from lib.opengl import RenderSettings class GameProjection:\",\n \"lib.opengl import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs\",\n \"sa, 0, -sa, ca, 0, 0, 0, 1 ) class\",\n \"* glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m * glm.translate(glm.mat4(),\",\n \"ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa, ca,\",\n \"self def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float)\",\n \"-10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m =\",\n \"self.rotation_deg, \\\"location\\\": self.location.__copy__(), }) def pop(self): s = self._stack.pop(-1) self.scale\",\n \"ca, 0, 0, 0, 1 ) class GameRenderSettings(RenderSettings): def __init__(self,\",\n \"= self.rs.render_width / self.rs.render_height m = glm.ortho(-scale * ratio, scale\",\n \"import RenderSettings class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs =\",\n \"return m def push(self): self._stack.append({ \\\"scale\\\": self.scale, \\\"rotation\\\": self.rotation_deg, \\\"location\\\":\",\n \"def __exit__(self, exc_type, exc_val, exc_tb): self.pop() def rotation_matrix_2d(degree: float) ->\",\n \"self._stack.pop(-1) self.scale = s[\\\"scale\\\"] self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"]\",\n \"class GameRenderSettings(RenderSettings): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.projection =\",\n \"GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale =\",\n \"self.rotation_deg = s[\\\"rotation\\\"] self.location = s[\\\"location\\\"] def __enter__(self): self.push() return\",\n \"180. * math.pi sa = math.sin(a) ca = math.cos(a) return\",\n \"math.sin(a) ca = math.cos(a) return glm.mat3( ca, sa, 0, -sa,\",\n \"= self.location.x m[2][1] = self.location.y return m def push(self): self._stack.append({\",\n \"glm.rotate( glm.mat4(1), -self.rotation_deg / 180 * glm.pi(), glm.vec3(0, 0, 1)\",\n \"class GameProjection: def __init__(self, rs: \\\"GameRenderSettings\\\"): self.rs = rs self.scale\",\n \"m * glm.scale(glm.mat4(), glm.vec3(2. / self.scale)) m = m *\",\n \"self._stack = [] def projection_matrix_4(self) -> glm.mat4: scale = 1.\",\n \"scale, -10, 10) return m def transformation_matrix_4(self) -> glm.mat4: m\",\n \"* .5 m[2][0] = self.location.x m[2][1] = self.location.y return m\"\n]"}}},{"rowIdx":3478,"cells":{"ngram":{"kind":"list like","value":["argparse import json import os import statistics from collections import","reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be","a __main__ as that matches the format of test.__class__ in","Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC =","bool: # check that their keys are the same if","format of test.__class__ in # common_utils.py (where this data will","\"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs:","get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\"","the threshold and the set of test cases have not","= filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times,","we will export the calculated \" \"results. The default threshold","60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times()","test.__class__ in # common_utils.py (where this data will be used),","-> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an","SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\"","Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any","[\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] =","} def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return {","-> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file:","results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \"","test cases have not \" \"changed, we will export the","any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file","running test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded:","): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\",","{ test_case: time for test_case, time in test_cases_dict.items() if time","contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar(","filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time",") -> bool: # check that their keys are the","2 only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"])","List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\", 1)","of test.__class__ in # common_utils.py (where this data will be","parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file","str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values():","the output # of a running test would look like.","= urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times:","are the same if calculated_times.keys() != other_times.keys(): return False for","IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in","def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float","of test cases have not \" \"changed, we will export","slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests()","each test are smaller than the threshold and the set","for casename, test_case in test_suite[\"cases\"].items(): # The below attaches a","handled is version 2 only\") v2report = cast(Version2Report, report) if","return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options)","in reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc]","be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] =","a running test would look like. name = f\"{casename} (__main__.{suitename})\"","test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs(","in test_suite[\"cases\"].items(): # The below attaches a __main__ as that","format currently handled is version 2 only\") v2report = cast(Version2Report,","is 10%.\", ) return parser.parse_args() def main() -> None: options","are smaller than the threshold and the set of test","get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url,","from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report,","than the threshold and the set of test cases have","float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will","calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items():","from previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument(","RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() ->","False return True def export_slow_tests(options: Any) -> None: filename =","for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items():","Dict[str, float]) -> Dict[str, float]: return { test_case: time for","} def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents","S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\",","nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump","differences \" \"between test times for each test are smaller","dump slow test times from previous S3 stats. Default file","relative_difference > threshold: return False return True def export_slow_tests(options: Any)","test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\",","if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3","argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow test","help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative","\"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\",","cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str,","is version 2 only\") v2report = cast(Version2Report, report) if any(job_name","os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file:","argparse.ArgumentParser( description=\"Export a JSON of slow test cases in PyTorch","for job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for",") if relative_difference > threshold: return False return True def",".pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results",">= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url =","options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\")","def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON","will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]]","Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if","threshold: float ) -> bool: # check that their keys","def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case:","= test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case:","test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename,","with stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between",") file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export","be used), and also matches what the output # of","If the relative differences \" \"between test times for each","from typing import cast, DefaultDict, Dict, List, Any from urllib.request","# The below attaches a __main__ as that matches the","url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float],","report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue","test cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\",","- test_time) / max(other_test_time, test_time) ) if relative_difference > threshold:","json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold:","their keys are the same if calculated_times.keys() != other_times.keys(): return","we will export the stats already in stats/slow-tests.json. Else, we","print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times:","urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD","def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename):","\"between test times for each test are smaller than the","name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None","json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True )","\"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path","test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True","filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with","= test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \": \"),","DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\"format_version\",","2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled is","= get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like (\"test_doc_examples","cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS):","Dict[str, float]: return { test_case: time for test_case, time in","# an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values]))","= get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times =","the calculated \" \"results. The default threshold is 10%.\", )","10%.\", ) return parser.parse_args() def main() -> None: options =","import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD =","same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time","time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url","DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE =","IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports:","parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of","options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ):","help=\"Specify a file path to dump slow test times from","report in reports: if report.get(\"format_version\", 1) != 2: # type:","test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below attaches","\" \"between test times for each test are smaller than","!= other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time","f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded:","# type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version","version 2 only\") v2report = cast(Version2Report, report) if any(job_name in","generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences","float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str,","in stats/slow-tests.json. Else, we will export the calculated \" \"results.","get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") #","\", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() ->","test times for each test are smaller than the threshold","have not \" \"changed, we will export the stats already","if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in","export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting","type: ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2","List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC","will export the calculated \" \"results. The default threshold is","return True def export_slow_tests(options: Any) -> None: filename = options.filename","for each test are smaller than the threshold and the","in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename, test_suite","other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time)","check that their keys are the same if calculated_times.keys() !=","test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\"","float]) -> Dict[str, float]: return { test_case: time for test_case,","max(other_test_time, test_time) ) if relative_difference > threshold: return False return","in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() ->","test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times","if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename, \"w+\") as","/ max(other_test_time, test_time) ) if relative_difference > threshold: return False","cases in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\",","suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify","float ) -> bool: # check that their keys are","= f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if","float], other_times: Dict[str, float], threshold: float ) -> bool: #","report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format","{ test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def","test_case in test_suite[\"cases\"].items(): # The below attaches a __main__ as","test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict:","for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float])","Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return","below attaches a __main__ as that matches the format of","for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC }","the same if calculated_times.keys() != other_times.keys(): return False for test_case,","(where this data will be used), and also matches what","options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \",",") parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a","in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]:","-> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\")","timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float],","test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time","None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\")","keys are the same if calculated_times.keys() != other_times.keys(): return False","too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times,","test_case: time for test_case, time in test_cases_dict.items() if time >=","in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time -","False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference","-> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a JSON of slow","times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str,","filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict,","if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times","= abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if","test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) ->","from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0","return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case]","entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str,","test_time) ) if relative_difference > threshold: return False return True","test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if","export the stats already in stats/slow-tests.json. Else, we will export","#!/usr/bin/env python3 import argparse import json import os import statistics","defaultdict(list) for report in reports: if report.get(\"format_version\", 1) != 2:","suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): #","stats/slow-tests.json. Else, we will export the calculated \" \"results. The","defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from","cases have not \" \"changed, we will export the stats","also matches what the output # of a running test","time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC","export the calculated \" \"results. The default threshold is 10%.\",","set of test cases have not \" \"changed, we will","import json import os import statistics from collections import defaultdict","if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump(","parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json","times from previous S3 stats. Default file path: .pytorch-slow-tests.json\", )","import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast,","Dict[str, float], threshold: float ) -> bool: # check that","> threshold: return False return True def export_slow_tests(options: Any) ->","True def export_slow_tests(options: Any) -> None: filename = options.filename if","if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]:","output # of a running test would look like. name","float]: return { test_case: time for test_case, time in test_cases_dict.items()","return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() }","and the set of test cases have not \" \"changed,","urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1","if relative_difference > threshold: return False return True def export_slow_tests(options:","JSON of slow test cases in PyTorch unit test suite\"",") parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with","v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name","statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch,","def main() -> None: options = parse_args() export_slow_tests(options) if __name__","file path to dump slow test times from previous S3","import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, )","-> bool: # check that their keys are the same","json import os import statistics from collections import defaultdict from","1-10 #!/usr/bin/env python3 import argparse import json import os import","stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float,","test_suite in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The","statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str,","slow test times from previous S3 stats. Default file path:","relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) )","PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str,","v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case in","the relative differences \" \"between test times for each test","Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \".pytorch-slow-tests.json\"","typing import cast, DefaultDict, Dict, List, Any from urllib.request import","test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return","file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict =","slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\" \", separators=(\",\", \":","will export the stats already in stats/slow-tests.json. Else, we will","const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the","matches the format of test.__class__ in # common_utils.py (where this","get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict","# check that their keys are the same if calculated_times.keys()","{filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] =","test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items()","separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace:","attaches a __main__ as that matches the format of test.__class__","float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float],","calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) ->","of a running test would look like. name = f\"{casename}","sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(","path to dump slow test times from previous S3 stats.","(__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"])","nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra.","test are smaller than the threshold and the set of","other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time =","default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test","RuntimeError(\"S3 format currently handled is version 2 only\") v2report =","= 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def","file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\"Export a","already in stats/slow-tests.json. Else, we will export the calculated \"","import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser","collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report,","get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict,","description=\"Export a JSON of slow test cases in PyTorch unit","currently handled is version 2 only\") v2report = cast(Version2Report, report)","time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests()","Else, we will export the calculated \" \"results. The default","= argparse.ArgumentParser( description=\"Export a JSON of slow test cases in","not \" \"changed, we will export the stats already in","parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if","\" \"results. The default threshold is 10%.\", ) return parser.parse_args()","look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"]","slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file,","as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict","of slow test cases in PyTorch unit test suite\" )","that their keys are the same if calculated_times.keys() != other_times.keys():","SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"]","# common_utils.py (where this data will be used), and also","type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow","test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE,","List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry will be like","const=SLOW_TESTS_FILE, help=\"Specify a file path to dump slow test times","continue for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items():","would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool =","times for each test are smaller than the threshold and","return False return True def export_slow_tests(options: Any) -> None: filename","previous S3 stats. Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\",","in PyTorch unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\",","test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str,","\"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser =","\"results. The default threshold is 10%.\", ) return parser.parse_args() def","like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool = test_case[\"status\"] is","in test_file[\"suites\"].items(): for casename, test_case in test_suite[\"cases\"].items(): # The below","= cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for job_name in","with open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times())","is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for","-> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in","\".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\"asan\",","import argparse import json import os import statistics from collections","casename, test_case in test_suite[\"cases\"].items(): # The below attaches a __main__","threshold and the set of test cases have not \"","\"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\"Specify a file path to","-> Dict[str, float]: return { test_case: time for test_case, time","None: options = parse_args() export_slow_tests(options) if __name__ == \"__main__\": main()","SLOW_TESTS_FILE = \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS","= other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time,","test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def","what the output # of a running test would look","# of a running test would look like. name =","common_utils.py (where this data will be used), and also matches","will be used), and also matches what the output #","slow test cases in PyTorch unit test suite\" ) parser.add_argument(","other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) /","stats already in stats/slow-tests.json. Else, we will export the calculated","in # common_utils.py (where this data will be used), and","def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\")","(__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report","matches what the output # of a running test would","= \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents))","file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str, float]","too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float )","raise RuntimeError(\"S3 format currently handled is version 2 only\") v2report","Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool:","type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If","data will be used), and also matches what the output","for test_file in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for","Default file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD,","(other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference >","threshold is 10%.\", ) return parser.parse_args() def main() -> None:","a JSON of slow test cases in PyTorch unit test","only\") v2report = cast(Version2Report, report) if any(job_name in str(report[\"build_job\"]) for","test would look like. name = f\"{casename} (__main__.{suitename})\" succeeded: bool","The below attaches a __main__ as that matches the format","return { test_case: time for test_case, time in test_cases_dict.items() if","test_suite[\"cases\"].items(): # The below attaches a __main__ as that matches","succeeded: bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return","unit test suite\" ) parser.add_argument( \"-f\", \"--filename\", nargs=\"?\", type=str, default=SLOW_TESTS_FILE,","if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs","the set of test cases have not \" \"changed, we","\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents = urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def","import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen","in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for test_file in","None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case,","reports: if report.get(\"format_version\", 1) != 2: # type: ignore[misc] raise","used), and also matches what the output # of a","= defaultdict(list) for report in reports: if report.get(\"format_version\", 1) !=","__main__ as that matches the format of test.__class__ in #","ignore[misc] raise RuntimeError(\"S3 format currently handled is version 2 only\")","in v2report[\"files\"].values(): for suitename, test_suite in test_file[\"suites\"].items(): for casename, test_case","urlopen(url, timeout=1).read().decode(\"utf-8\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str,","for report in reports: if report.get(\"format_version\", 1) != 2: #","threshold: return False return True def export_slow_tests(options: Any) -> None:","test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times)","= 0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str,","in pytorch/test-infra. If the relative differences \" \"between test times","the stats already in stats/slow-tests.json. Else, we will export the","indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def parse_args()","a file path to dump slow test times from previous","= [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report]","\" \"changed, we will export the stats already in stats/slow-tests.json.","os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import","[values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports:","calculated \" \"results. The default threshold is 10%.\", ) return","parser = argparse.ArgumentParser( description=\"Export a JSON of slow test cases","Any) -> None: filename = options.filename if os.path.exists(filename): print(f\"Overwriting existent","file path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares","the format of test.__class__ in # common_utils.py (where this data","Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List,","that matches the format of test.__class__ in # common_utils.py (where","succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return { test_case: statistics.mean(times) for test_case, times in","other_times: Dict[str, float], threshold: float ) -> bool: # check","if any(job_name in str(report[\"build_job\"]) for job_name in IGNORED_JOBS): continue for","= \".pytorch-slow-tests.json\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS =","Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\"origin/viable/strict\", \"\") # an entry","slow_test_times, file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\")",") return parser.parse_args() def main() -> None: options = parse_args()","as that matches the format of test.__class__ in # common_utils.py","tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import","pytorch/test-infra. If the relative differences \" \"between test times for","this data will be used), and also matches what the","job_name in IGNORED_JOBS): continue for test_file in v2report[\"files\"].values(): for suitename,","def get_test_infra_slow_tests() -> Dict[str, float]: url = \"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\" contents =","\"changed, we will export the stats already in stats/slow-tests.json. Else,","to dump slow test times from previous S3 stats. Default","0.1 IGNORED_JOBS = [\"asan\", \"periodic\"] def get_test_case_times() -> Dict[str, float]:","existent file: {filename}\") with open(filename, \"w+\") as file: slow_test_times: Dict[str,","test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return","float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar(","file, indent=\" \", separators=(\",\", \": \"), sort_keys=True ) file.write(\"\\n\") def","relative differences \" \"between test times for each test are","like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list)","abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference","stats/slow-tests.json in pytorch/test-infra. If the relative differences \" \"between test","for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference =","\"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated results with stats/slow-tests.json in","-> None: options = parse_args() export_slow_tests(options) if __name__ == \"__main__\":","open(filename, \"w+\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if","smaller than the threshold and the set of test cases","!= 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently handled","import statistics from collections import defaultdict from tools.stats.s3_stat_parser import (","The default threshold is 10%.\", ) return parser.parse_args() def main()","( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict,","1) != 2: # type: ignore[misc] raise RuntimeError(\"S3 format currently","an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times:","cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE","calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time)","default threshold is 10%.\", ) return parser.parse_args() def main() ->","return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times:","= options.filename if os.path.exists(filename): print(f\"Overwriting existent file: {filename}\") with open(filename,",") from typing import cast, DefaultDict, Dict, List, Any from","\"\") # an entry will be like (\"test_doc_examples (__main__.TestTypeHints)\" ->","bool = test_case[\"status\"] is None if succeeded: test_names_to_times[name].append(test_case[\"seconds\"]) return {","(\"test_doc_examples (__main__.TestTypeHints)\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for","main() -> None: options = parse_args() export_slow_tests(options) if __name__ ==","\": \"), sort_keys=True ) file.write(\"\\n\") def parse_args() -> argparse.Namespace: parser","from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing","float], threshold: float ) -> bool: # check that their","python3 import argparse import json import os import statistics from","path: .pytorch-slow-tests.json\", ) parser.add_argument( \"--ignore-small-diffs\", nargs=\"?\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\"Compares generated","and also matches what the output # of a running"],"string":"[\n \"argparse import json import os import statistics from collections import\",\n \"reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be\",\n \"a __main__ as that matches the format of test.__class__ in\",\n \"Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC =\",\n \"bool: # check that their keys are the same if\",\n \"format of test.__class__ in # common_utils.py (where this data will\",\n \"\\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs:\",\n \"get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\"\",\n \"the threshold and the set of test cases have not\",\n \"= filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times,\",\n \"we will export the calculated \\\" \\\"results. The default threshold\",\n \"60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times()\",\n \"test.__class__ in # common_utils.py (where this data will be used),\",\n \"-> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an\",\n \"SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\"\",\n \"Version2Report, ) from typing import cast, DefaultDict, Dict, List, Any\",\n \"[\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] =\",\n \"} def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return {\",\n \"-> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file:\",\n \"results with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\"\",\n \"test cases have not \\\" \\\"changed, we will export the\",\n \"any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file\",\n \"running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded:\",\n \"): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\",\",\n \"{ test_case: time for test_case, time in test_cases_dict.items() if time\",\n \"contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar(\",\n \"filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case: time\",\n \") -> bool: # check that their keys are the\",\n \"2 only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"])\",\n \"List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1)\",\n \"of test.__class__ in # common_utils.py (where this data will be\",\n \"parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file\",\n \"str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values():\",\n \"the output # of a running test would look like.\",\n \"= urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times:\",\n \"are the same if calculated_times.keys() != other_times.keys(): return False for\",\n \"IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in\",\n \"def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float\",\n \"of test cases have not \\\" \\\"changed, we will export\",\n \"slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests()\",\n \"each test are smaller than the threshold and the set\",\n \"for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a\",\n \"handled is version 2 only\\\") v2report = cast(Version2Report, report) if\",\n \"return parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options)\",\n \"in reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc]\",\n \"be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] =\",\n \"a running test would look like. name = f\\\"{casename} (__main__.{suitename})\\\"\",\n \"test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs(\",\n \"in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that\",\n \"format currently handled is version 2 only\\\") v2report = cast(Version2Report,\",\n \"is 10%.\\\", ) return parser.parse_args() def main() -> None: options\",\n \"are smaller than the threshold and the set of test\",\n \"get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url,\",\n \"from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report,\",\n \"than the threshold and the set of test cases have\",\n \"float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will\",\n \"calculated_times.keys() != other_times.keys(): return False for test_case, test_time in calculated_times.items():\",\n \"from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument(\",\n \"RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() ->\",\n \"False return True def export_slow_tests(options: Any) -> None: filename =\",\n \"for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items():\",\n \"Dict[str, float]) -> Dict[str, float]: return { test_case: time for\",\n \"} def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents\",\n \"S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\",\",\n \"nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump\",\n \"differences \\\" \\\"between test times for each test are smaller\",\n \"dump slow test times from previous S3 stats. Default file\",\n \"relative_difference > threshold: return False return True def export_slow_tests(options: Any)\",\n \"test times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\",\",\n \"if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3\",\n \"argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test\",\n \"help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the relative\",\n \"\\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\",\",\n \"cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str,\",\n \"is version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name\",\n \"os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file:\",\n \"argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in PyTorch\",\n \"for job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for\",\n \") if relative_difference > threshold: return False return True def\",\n \".pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results\",\n \">= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url =\",\n \"options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\")\",\n \"def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON\",\n \"will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]]\",\n \"Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if\",\n \"threshold: float ) -> bool: # check that their keys\",\n \"def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return { test_case:\",\n \"= test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case:\",\n \"test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename,\",\n \"with stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between\",\n \") file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export\",\n \"be used), and also matches what the output # of\",\n \"If the relative differences \\\" \\\"between test times for each\",\n \"from typing import cast, DefaultDict, Dict, List, Any from urllib.request\",\n \"# The below attaches a __main__ as that matches the\",\n \"url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float],\",\n \"report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue\",\n \"test cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\",\",\n \"- test_time) / max(other_test_time, test_time) ) if relative_difference > threshold:\",\n \"json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold:\",\n \"their keys are the same if calculated_times.keys() != other_times.keys(): return\",\n \"we will export the stats already in stats/slow-tests.json. Else, we\",\n \"print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times:\",\n \"urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD\",\n \"def export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename):\",\n \"\\\"between test times for each test are smaller than the\",\n \"name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None\",\n \"json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True )\",\n \"\\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path\",\n \"test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True\",\n \"filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with\",\n \"= test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"),\",\n \"DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if report.get(\\\"format_version\\\",\",\n \"2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is\",\n \"= get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like (\\\"test_doc_examples\",\n \"cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS):\",\n \"Dict[str, float]: return { test_case: time for test_case, time in\",\n \"# an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values]))\",\n \"= get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times =\",\n \"the calculated \\\" \\\"results. The default threshold is 10%.\\\", )\",\n \"10%.\\\", ) return parser.parse_args() def main() -> None: options =\",\n \"import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD =\",\n \"same if calculated_times.keys() != other_times.keys(): return False for test_case, test_time\",\n \"time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]: url\",\n \"DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE =\",\n \"IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports:\",\n \"parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of\",\n \"options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ):\",\n \"help=\\\"Specify a file path to dump slow test times from\",\n \"report in reports: if report.get(\\\"format_version\\\", 1) != 2: # type:\",\n \"test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches\",\n \"\\\" \\\"between test times for each test are smaller than\",\n \"!= other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time\",\n \"f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded:\",\n \"# type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version\",\n \"version 2 only\\\") v2report = cast(Version2Report, report) if any(job_name in\",\n \"generated results with stats/slow-tests.json in pytorch/test-infra. If the relative differences\",\n \"float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str,\",\n \"in stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results.\",\n \"get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") #\",\n \"\\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() ->\",\n \"test times for each test are smaller than the threshold\",\n \"have not \\\" \\\"changed, we will export the stats already\",\n \"if calculated_times.keys() != other_times.keys(): return False for test_case, test_time in\",\n \"export_slow_tests(options: Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting\",\n \"type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2\",\n \"List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC\",\n \"will export the calculated \\\" \\\"results. The default threshold is\",\n \"return True def export_slow_tests(options: Any) -> None: filename = options.filename\",\n \"for each test are smaller than the threshold and the\",\n \"in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite\",\n \"other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time)\",\n \"check that their keys are the same if calculated_times.keys() !=\",\n \"test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\"\",\n \"float]) -> Dict[str, float]: return { test_case: time for test_case,\",\n \"max(other_test_time, test_time) ) if relative_difference > threshold: return False return\",\n \"in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() ->\",\n \"test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times\",\n \"if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename, \\\"w+\\\") as\",\n \"/ max(other_test_time, test_time) ) if relative_difference > threshold: return False\",\n \"cases in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\",\",\n \"suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify\",\n \"float ) -> bool: # check that their keys are\",\n \"= f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if\",\n \"float], other_times: Dict[str, float], threshold: float ) -> bool: #\",\n \"report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format\",\n \"{ test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def\",\n \"test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as\",\n \"test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict:\",\n \"for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float])\",\n \"Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return\",\n \"below attaches a __main__ as that matches the format of\",\n \"for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC }\",\n \"the same if calculated_times.keys() != other_times.keys(): return False for test_case,\",\n \"(where this data will be used), and also matches what\",\n \"options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\",\",\n \") parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a\",\n \"in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]:\",\n \"-> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\")\",\n \"timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float],\",\n \"test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time\",\n \"None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\")\",\n \"keys are the same if calculated_times.keys() != other_times.keys(): return False\",\n \"too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times,\",\n \"test_case: time for test_case, time in test_cases_dict.items() if time >=\",\n \"in calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time -\",\n \"False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference\",\n \"-> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow\",\n \"times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str,\",\n \"filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict,\",\n \"if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times\",\n \"= abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if\",\n \"test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) ->\",\n \"from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0\",\n \"return False for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case]\",\n \"entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str,\",\n \"test_time) ) if relative_difference > threshold: return False return True\",\n \"test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports: if\",\n \"export the stats already in stats/slow-tests.json. Else, we will export\",\n \"#!/usr/bin/env python3 import argparse import json import os import statistics\",\n \"defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) != 2:\",\n \"suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): #\",\n \"stats/slow-tests.json. Else, we will export the calculated \\\" \\\"results. The\",\n \"defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from\",\n \"cases have not \\\" \\\"changed, we will export the stats\",\n \"also matches what the output # of a running test\",\n \"time for test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC\",\n \"export the calculated \\\" \\\"results. The default threshold is 10%.\\\",\",\n \"set of test cases have not \\\" \\\"changed, we will\",\n \"import json import os import statistics from collections import defaultdict\",\n \"if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump(\",\n \"parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json\",\n \"times from previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", )\",\n \"import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast,\",\n \"Dict[str, float], threshold: float ) -> bool: # check that\",\n \"> threshold: return False return True def export_slow_tests(options: Any) ->\",\n \"True def export_slow_tests(options: Any) -> None: filename = options.filename if\",\n \"if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str, float]:\",\n \"output # of a running test would look like. name\",\n \"float]: return { test_case: time for test_case, time in test_cases_dict.items()\",\n \"return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items() }\",\n \"and the set of test cases have not \\\" \\\"changed,\",\n \"urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1\",\n \"if relative_difference > threshold: return False return True def export_slow_tests(options:\",\n \"JSON of slow test cases in PyTorch unit test suite\\\"\",\n \") parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with\",\n \"v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name\",\n \"statistics from collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch,\",\n \"def main() -> None: options = parse_args() export_slow_tests(options) if __name__\",\n \"file path to dump slow test times from previous S3\",\n \"import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, )\",\n \"-> bool: # check that their keys are the same\",\n \"json import os import statistics from collections import defaultdict from\",\n \"1-10 #!/usr/bin/env python3 import argparse import json import os import\",\n \"stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float,\",\n \"test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The\",\n \"statistics.mean(times) for test_case, times in test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str,\",\n \"slow test times from previous S3 stats. Default file path:\",\n \"relative_difference = abs( (other_test_time - test_time) / max(other_test_time, test_time) )\",\n \"PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str,\",\n \"v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case in\",\n \"the relative differences \\\" \\\"between test times for each test\",\n \"Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\"\",\n \"typing import cast, DefaultDict, Dict, List, Any from urllib.request import\",\n \"test_time) / max(other_test_time, test_time) ) if relative_difference > threshold: return\",\n \"file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict =\",\n \"slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\":\",\n \"will export the stats already in stats/slow-tests.json. Else, we will\",\n \"const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If the\",\n \"matches the format of test.__class__ in # common_utils.py (where this\",\n \"get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict\",\n \"# check that their keys are the same if calculated_times.keys()\",\n \"{filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] =\",\n \"test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in test_names_to_times.items()\",\n \"separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace:\",\n \"attaches a __main__ as that matches the format of test.__class__\",\n \"float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float],\",\n \"calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float ) ->\",\n \"of a running test would look like. name = f\\\"{casename}\",\n \"sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(\",\n \"path to dump slow test times from previous S3 stats.\",\n \"(__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"])\",\n \"nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra.\",\n \"test are smaller than the threshold and the set of\",\n \"other_times.keys(): return False for test_case, test_time in calculated_times.items(): other_test_time =\",\n \"default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test\",\n \"RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report =\",\n \"= 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def\",\n \"file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=\\\"Export a\",\n \"already in stats/slow-tests.json. Else, we will export the calculated \\\"\",\n \"import os import statistics from collections import defaultdict from tools.stats.s3_stat_parser\",\n \"collections import defaultdict from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report,\",\n \"get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict, Dict,\",\n \"description=\\\"Export a JSON of slow test cases in PyTorch unit\",\n \"currently handled is version 2 only\\\") v2report = cast(Version2Report, report)\",\n \"time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests()\",\n \"Else, we will export the calculated \\\" \\\"results. The default\",\n \"= argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases in\",\n \"not \\\" \\\"changed, we will export the stats already in\",\n \"parser.parse_args() def main() -> None: options = parse_args() export_slow_tests(options) if\",\n \"\\\" \\\"results. The default threshold is 10%.\\\", ) return parser.parse_args()\",\n \"look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"]\",\n \"slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs ): slow_test_times = test_infra_slow_tests_dict json.dump( slow_test_times, file,\",\n \"as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict\",\n \"of slow test cases in PyTorch unit test suite\\\" )\",\n \"that their keys are the same if calculated_times.keys() != other_times.keys():\",\n \"SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"]\",\n \"# common_utils.py (where this data will be used), and also\",\n \"type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow\",\n \"test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE,\",\n \"List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry will be like\",\n \"const=SLOW_TESTS_FILE, help=\\\"Specify a file path to dump slow test times\",\n \"continue for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items():\",\n \"would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool =\",\n \"times for each test are smaller than the threshold and\",\n \"return False return True def export_slow_tests(options: Any) -> None: filename\",\n \"previous S3 stats. Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\",\",\n \"in PyTorch unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\",\",\n \"test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def get_test_infra_slow_tests() -> Dict[str,\",\n \"\\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser =\",\n \"\\\"results. The default threshold is 10%.\\\", ) return parser.parse_args() def\",\n \"like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool = test_case[\\\"status\\\"] is\",\n \"in test_file[\\\"suites\\\"].items(): for casename, test_case in test_suite[\\\"cases\\\"].items(): # The below\",\n \"= cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for job_name in\",\n \"with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times())\",\n \"is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for\",\n \"-> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in\",\n \"\\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS = [\\\"asan\\\",\",\n \"import argparse import json import os import statistics from collections\",\n \"casename, test_case in test_suite[\\\"cases\\\"].items(): # The below attaches a __main__\",\n \"threshold and the set of test cases have not \\\"\",\n \"\\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE, const=SLOW_TESTS_FILE, help=\\\"Specify a file path to\",\n \"-> Dict[str, float]: return { test_case: time for test_case, time\",\n \"None: options = parse_args() export_slow_tests(options) if __name__ == \\\"__main__\\\": main()\",\n \"SLOW_TESTS_FILE = \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS\",\n \"= other_times[test_case] relative_difference = abs( (other_test_time - test_time) / max(other_test_time,\",\n \"test_case, time in test_cases_dict.items() if time >= SLOW_TEST_CASE_THRESHOLD_SEC } def\",\n \"what the output # of a running test would look\",\n \"# of a running test would look like. name =\",\n \"common_utils.py (where this data will be used), and also matches\",\n \"will be used), and also matches what the output #\",\n \"slow test cases in PyTorch unit test suite\\\" ) parser.add_argument(\",\n \"other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time) /\",\n \"stats already in stats/slow-tests.json. Else, we will export the calculated\",\n \"in # common_utils.py (where this data will be used), and\",\n \"def get_test_case_times() -> Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\")\",\n \"(__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report\",\n \"matches what the output # of a running test would\",\n \"= \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents))\",\n \"file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float]\",\n \"too_similar( calculated_times: Dict[str, float], other_times: Dict[str, float], threshold: float )\",\n \"raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\") v2report\",\n \"Dict[str, float], other_times: Dict[str, float], threshold: float ) -> bool:\",\n \"type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in pytorch/test-infra. If\",\n \"data will be used), and also matches what the output\",\n \"for test_file in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for\",\n \"Default file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD,\",\n \"(other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference >\",\n \"threshold is 10%.\\\", ) return parser.parse_args() def main() -> None:\",\n \"a JSON of slow test cases in PyTorch unit test\",\n \"only\\\") v2report = cast(Version2Report, report) if any(job_name in str(report[\\\"build_job\\\"]) for\",\n \"test would look like. name = f\\\"{casename} (__main__.{suitename})\\\" succeeded: bool\",\n \"The below attaches a __main__ as that matches the format\",\n \"return { test_case: time for test_case, time in test_cases_dict.items() if\",\n \"test_suite[\\\"cases\\\"].items(): # The below attaches a __main__ as that matches\",\n \"succeeded: bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return\",\n \"unit test suite\\\" ) parser.add_argument( \\\"-f\\\", \\\"--filename\\\", nargs=\\\"?\\\", type=str, default=SLOW_TESTS_FILE,\",\n \"if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar( slow_test_times, test_infra_slow_tests_dict, options.ignore_small_diffs\",\n \"the set of test cases have not \\\" \\\"changed, we\",\n \"\\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents = urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def\",\n \"import cast, DefaultDict, Dict, List, Any from urllib.request import urlopen\",\n \"in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for test_file in\",\n \"None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case,\",\n \"reports: if report.get(\\\"format_version\\\", 1) != 2: # type: ignore[misc] raise\",\n \"used), and also matches what the output # of a\",\n \"= defaultdict(list) for report in reports: if report.get(\\\"format_version\\\", 1) !=\",\n \"__main__ as that matches the format of test.__class__ in #\",\n \"ignore[misc] raise RuntimeError(\\\"S3 format currently handled is version 2 only\\\")\",\n \"in v2report[\\\"files\\\"].values(): for suitename, test_suite in test_file[\\\"suites\\\"].items(): for casename, test_case\",\n \"urlopen(url, timeout=1).read().decode(\\\"utf-8\\\") return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str,\",\n \"for report in reports: if report.get(\\\"format_version\\\", 1) != 2: #\",\n \"threshold: return False return True def export_slow_tests(options: Any) -> None:\",\n \"test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times)\",\n \"= 0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str,\",\n \"in pytorch/test-infra. If the relative differences \\\" \\\"between test times\",\n \"the stats already in stats/slow-tests.json. Else, we will export the\",\n \"indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args()\",\n \"a file path to dump slow test times from previous\",\n \"= [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]: reports: List[Report]\",\n \"\\\" \\\"changed, we will export the stats already in stats/slow-tests.json.\",\n \"os import statistics from collections import defaultdict from tools.stats.s3_stat_parser import\",\n \"[values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for report in reports:\",\n \"calculated \\\" \\\"results. The default threshold is 10%.\\\", ) return\",\n \"parser = argparse.ArgumentParser( description=\\\"Export a JSON of slow test cases\",\n \"Any) -> None: filename = options.filename if os.path.exists(filename): print(f\\\"Overwriting existent\",\n \"file path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares\",\n \"the format of test.__class__ in # common_utils.py (where this data\",\n \"Report, Version2Report, ) from typing import cast, DefaultDict, Dict, List,\",\n \"that matches the format of test.__class__ in # common_utils.py (where\",\n \"succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return { test_case: statistics.mean(times) for test_case, times in\",\n \"other_times: Dict[str, float], threshold: float ) -> bool: # check\",\n \"if any(job_name in str(report[\\\"build_job\\\"]) for job_name in IGNORED_JOBS): continue for\",\n \"= \\\".pytorch-slow-tests.json\\\" SLOW_TEST_CASE_THRESHOLD_SEC = 60.0 RELATIVE_DIFFERENCE_THRESHOLD = 0.1 IGNORED_JOBS =\",\n \"Dict[str, float]: reports: List[Report] = get_previous_reports_for_branch(\\\"origin/viable/strict\\\", \\\"\\\") # an entry\",\n \"slow_test_times, file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\")\",\n \") return parser.parse_args() def main() -> None: options = parse_args()\",\n \"as that matches the format of test.__class__ in # common_utils.py\",\n \"tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing import\",\n \"pytorch/test-infra. If the relative differences \\\" \\\"between test times for\",\n \"this data will be used), and also matches what the\",\n \"job_name in IGNORED_JOBS): continue for test_file in v2report[\\\"files\\\"].values(): for suitename,\",\n \"def get_test_infra_slow_tests() -> Dict[str, float]: url = \\\"https://raw.githubusercontent.com/pytorch/test-infra/generated-stats/stats/slow-tests.json\\\" contents =\",\n \"\\\"changed, we will export the stats already in stats/slow-tests.json. Else,\",\n \"to dump slow test times from previous S3 stats. Default\",\n \"0.1 IGNORED_JOBS = [\\\"asan\\\", \\\"periodic\\\"] def get_test_case_times() -> Dict[str, float]:\",\n \"existent file: {filename}\\\") with open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str,\",\n \"test_names_to_times.items() } def filter_slow_tests(test_cases_dict: Dict[str, float]) -> Dict[str, float]: return\",\n \"float] = filter_slow_tests(get_test_case_times()) if options.ignore_small_diffs: test_infra_slow_tests_dict = get_test_infra_slow_tests() if too_similar(\",\n \"file, indent=\\\" \\\", separators=(\\\",\\\", \\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def\",\n \"relative differences \\\" \\\"between test times for each test are\",\n \"like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list)\",\n \"abs( (other_test_time - test_time) / max(other_test_time, test_time) ) if relative_difference\",\n \"stats/slow-tests.json in pytorch/test-infra. If the relative differences \\\" \\\"between test\",\n \"for test_case, test_time in calculated_times.items(): other_test_time = other_times[test_case] relative_difference =\",\n \"\\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated results with stats/slow-tests.json in\",\n \"-> None: options = parse_args() export_slow_tests(options) if __name__ == \\\"__main__\\\":\",\n \"open(filename, \\\"w+\\\") as file: slow_test_times: Dict[str, float] = filter_slow_tests(get_test_case_times()) if\",\n \"smaller than the threshold and the set of test cases\",\n \"!= 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently handled\",\n \"import statistics from collections import defaultdict from tools.stats.s3_stat_parser import (\",\n \"The default threshold is 10%.\\\", ) return parser.parse_args() def main()\",\n \"( get_previous_reports_for_branch, Report, Version2Report, ) from typing import cast, DefaultDict,\",\n \"1) != 2: # type: ignore[misc] raise RuntimeError(\\\"S3 format currently\",\n \"an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times:\",\n \"cast, DefaultDict, Dict, List, Any from urllib.request import urlopen SLOW_TESTS_FILE\",\n \"calculated_times.items(): other_test_time = other_times[test_case] relative_difference = abs( (other_test_time - test_time)\",\n \"default threshold is 10%.\\\", ) return parser.parse_args() def main() ->\",\n \"return cast(Dict[str, float], json.loads(contents)) def too_similar( calculated_times: Dict[str, float], other_times:\",\n \"= options.filename if os.path.exists(filename): print(f\\\"Overwriting existent file: {filename}\\\") with open(filename,\",\n \") from typing import cast, DefaultDict, Dict, List, Any from\",\n \"\\\"\\\") # an entry will be like (\\\"test_doc_examples (__main__.TestTypeHints)\\\" ->\",\n \"bool = test_case[\\\"status\\\"] is None if succeeded: test_names_to_times[name].append(test_case[\\\"seconds\\\"]) return {\",\n \"(\\\"test_doc_examples (__main__.TestTypeHints)\\\" -> [values])) test_names_to_times: DefaultDict[str, List[float]] = defaultdict(list) for\",\n \"main() -> None: options = parse_args() export_slow_tests(options) if __name__ ==\",\n \"\\\": \\\"), sort_keys=True ) file.write(\\\"\\\\n\\\") def parse_args() -> argparse.Namespace: parser\",\n \"from tools.stats.s3_stat_parser import ( get_previous_reports_for_branch, Report, Version2Report, ) from typing\",\n \"float], threshold: float ) -> bool: # check that their\",\n \"python3 import argparse import json import os import statistics from\",\n \"path: .pytorch-slow-tests.json\\\", ) parser.add_argument( \\\"--ignore-small-diffs\\\", nargs=\\\"?\\\", type=float, const=RELATIVE_DIFFERENCE_THRESHOLD, help=\\\"Compares generated\",\n \"and also matches what the output # of a running\"\n]"}}},{"rowIdx":3479,"cells":{"ngram":{"kind":"list like","value":["weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier )","1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories","= int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range(","to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories =","low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns >","1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break","in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i +","base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append(","def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x),","low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error","+ 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x +",") num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")]","j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values =","zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals =","importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return(","CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8","normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator,",") else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:,","/ num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )","weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) #",": j_step + 1], ), axis=1, ) j_step_return = (","float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate","in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod","= np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) <","1], rewards[:, : j_step + 1]), axis=1, ) if j_step","scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from","np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards,","= 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not","target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =","estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: #","(num_j_steps - 1) j_steps.extend([i * interval for i in range(1,","0]: terminals[x] = 1 trajectories = [] episode_start = 0","return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,","np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2",") importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset)","estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, :","len(x) m, se = np.mean(x), sp.stats.sem(x) h = se *","WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i","random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means)","terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x","j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values,",") Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories,","def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights,","NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT","= 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT =",") importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights","= np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence)","np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step +","gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) ->","= to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions])",") discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma","< trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step +","np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step","importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def","num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0,","\"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)])","range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1)","range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate","= [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory])","importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights,","np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights)","1, 0]: terminals[x] = 1 trajectories = [] episode_start =","* sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return","): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate(","denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,","< 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return","denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0,","if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0","def __init__(self, gamma): self.gamma = gamma def estimate( self, edp:","actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 )","random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i]","= [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps >","self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For","num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1)",") # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means","{\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x =","= 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma =","importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0","importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum(","1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value","np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories =","fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions])","= np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values)","* 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,","np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, )","7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) =","etc.) and output lists of equal-length trajectories (episodes) accoriding to","low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] =","importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,","whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 /","high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error","importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1],","+ direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x,","else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, :","importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights )","rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action,","(action, rewards, propensities, etc.) and output lists of equal-length trajectories","( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1]","actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 )","estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return =","estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(),","+ 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward +","trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories","in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or","its affiliates. All rights reserved. import itertools import logging import","WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias +","reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array(","= np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step","random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size,","+ 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] /","trajectory_subset = np.arange( int(i * interval), int((i + 1) *","(episodes) accoriding to terminals. As the raw trajectories are of","+ confidence) / 2.0, n - 1) return m -","target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories:","= to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories,","0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories","edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps","are of various lengths, the shorter ones are filled with","= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0,","num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps:","bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets )","> high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories)","= np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier","/= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights","( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier =","j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] =","mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0]","j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error","actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(),","return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities,","to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) )","in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error =","raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps,","j_step + 1], estimated_q_values[:, : j_step + 1] ) -","+ 1], rewards[:, : j_step + 1]), axis=1, ) if","axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error =","= ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1)","= j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into","int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES",") return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error /","2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i *","= int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier","1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1)","= 0.0 else: # break trajectories into several subsets to",") x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories(","importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights","estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point","discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0])","* estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories])","1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0]","bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns <","EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25","Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory])","gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps,","[] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in","importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return","np import scipy as sp import torch from ml.rl.evaluation.cpe import","j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], )","range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def","@staticmethod def confidence_bounds(x, confidence): n = len(x) m, se =","importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts,","high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into","j_step_bias.T * j_step_bias # minimize mse error constraint = {\"type\":","num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval =","from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger =","int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset]","importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else:","episode_start = episode_end + 1 action_trajectories = [] reward_trajectories =","j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance +","j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step =","j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] -","to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size =","target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value))","= [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory","for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size,","for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def","1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset]","= np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"),","episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories","where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound","filled with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0])","covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias",") bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1)","break trajectories into several subsets to estimate confidence bounds infinite_step_returns","* interval), int((i + 1) * interval) ) importance_weights =","j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights)","1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:,","into several subsets to estimate confidence bounds infinite_step_returns = []","trajectories into several subsets to estimate confidence bounds infinite_step_returns =","+ 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum(","= to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions])","j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps","where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights","weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator =","x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize(","args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x","= self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use","0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /=","[importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts,","num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i *","normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0)","mse error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x)","= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1])","res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for","np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1","all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,","+ 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value","edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0]","np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize","np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss,","= np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values,","[] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = []","reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories","np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step +","mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = []",") - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, :","As the raw trajectories are of various lengths, the shorter","j_step + 1]), axis=1, ) if j_step < trajectory_length -","to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) )","in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value):","constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x =","WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) *","/ denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self,","standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT *","rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories",") importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1)","logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL","np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound -","): \"\"\" Take in samples (action, rewards, propensities, etc.) and","len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1))","for x in range(0, mdp_ids.shape[0]): if x + 1 ==","= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1])","j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return","= np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, :","- 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts,","= np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0]","discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return","trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if","h, m + h def mse_loss(x, error): return np.dot(np.dot(x, error),","edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details,","def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1)","if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends,",") importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:,","not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for","( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id,","weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories","num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf","- 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step","infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard",") estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values =","j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward","def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound,","mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x]","in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions),","+ 1]), axis=1, ) if j_step < trajectory_length - 1:","50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma","bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator","j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) #","= [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories =","j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several","in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action,","of various lengths, the shorter ones are filled with zeros(ones)","* num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs","mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])],","> 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i","CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust","1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value =","j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust","= covariance + j_step_bias.T * j_step_bias # minimize mse error","importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0","Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories,",") interval = num_trajectories / num_subsets for i in range(num_subsets):","1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return","actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps >","i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error","num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) )","logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ):","denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps,","np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier =","8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories(","WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) )","rights reserved. import itertools import logging import numpy as np","All rights reserved. import itertools import logging import numpy as","as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page","terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends","WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) *",": j_step + 1]), axis=1, ) if j_step < trajectory_length","samples (action, rewards, propensities, etc.) and output lists of equal-length","n - 1) return m - h, m + h","terminals. As the raw trajectories are of various lengths, the","len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end +","// (num_j_steps - 1) j_steps.extend([i * interval for i in","np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust","- np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step","rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(),","ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__)","/ denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns,","len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if","whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros =","return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def","1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier","weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1],","num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values","transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take","infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2,","Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust =","trajectories (episodes) accoriding to terminals. As the raw trajectories are","estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum(","if num_j_steps > 2: interval = trajectory_length // (num_j_steps -","j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps","np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack(","CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class","trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply(","j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step","num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories /","= actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1)",": j_step + 1], estimated_q_values[:, : j_step + 1] )","int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval =","episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end","- 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) -","reserved. import itertools import logging import numpy as np import","target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum(","np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]),","For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions,","control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n =","in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end +","importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset),","/ len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )",") importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights =","> 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length","int(i * interval), int((i + 1) * interval) ) importance_weights","itertools import logging import numpy as np import scipy as","actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples","axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights","- 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] *","se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1)",") infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all","various lengths, the shorter ones are filled with zeros(ones) at","sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod","= np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions,","= ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier",") reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1","range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x,","* interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action","for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,","np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length","trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length","logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories,","- 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action","== mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]:","fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length","[] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory])","sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros]","np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2","= np.arange( int(i * interval), int((i + 1) * interval)","logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,","target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if",") for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps,","episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends:","episode_end + 1)) episode_start = episode_end + 1 action_trajectories =","( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier =","of equal-length trajectories (episodes) accoriding to terminals. As the raw","to estimate confidence bounds infinite_step_returns = [] num_subsets = int(","target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories,","importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace(","in samples (action, rewards, propensities, etc.) and output lists of","episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator)","importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights,","1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:,","estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards)","estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate","1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:,","importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories )","num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if","rewards, propensities, etc.) and output lists of equal-length trajectories (episodes)","for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval),","np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i","at the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0])","len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights","calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length","[] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, )","num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute","estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights =","= 1 trajectories = [] episode_start = 0 episode_ends =","axis=1, ) if j_step < trajectory_length - 1: direct_method_value =","WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ):","= {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0} x","2.0, n - 1) return m - h, m +","confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h","covariance + j_step_bias.T * j_step_bias # minimize mse error constraint","Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories,","= WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories","accoriding to terminals. As the raw trajectories are of various","estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns,","@staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights =","Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights:","return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def",") def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ):","= WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias","j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust =","x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], )","the shorter ones are filled with zeros(ones) at the end.","1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = []","== 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights","1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate(","= [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for","trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length","sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in","for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start =","ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES","num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts","1) j_steps.extend([i * interval for i in range(1, num_j_steps -","WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50","several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets","confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories","logging import numpy as np import scipy as sp import","list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories,","start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = []","estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns =","estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities, etc.)","with zeros(ones) at the end. \"\"\" num_actions = len(target_propensities[0]) terminals","axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values","rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length =","importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories =","min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories","edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length =","* j_step_bias # minimize mse error constraint = {\"type\": \"eq\",","action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length(","for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate)","infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards,","error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower =",") = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), )","= np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories","to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories,","NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma","np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step","as np import scipy as sp import torch from ml.rl.evaluation.cpe","/ 2.0, n - 1) return m - h, m","[] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) -","edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1]","np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end","#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates.","import numpy as np import scipy as sp import torch","25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5","= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return(","discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) #","bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x)","equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories","[importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length -","torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger","np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions),","( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights(","np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if","episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends:","j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds(","compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int(","+ 1)) episode_start = episode_end + 1 action_trajectories = []","confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x)","where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros]","trajectories are of various lengths, the shorter ones are filled","shorter ones are filled with zeros(ones) at the end. \"\"\"","infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset],","end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x","= len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length -","j_step + 1] * estimated_state_values[:, j_step + 1] ) else:","where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower]","import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES =","weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets","bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size","from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator:","+ 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1],","1) return m - h, m + h def mse_loss(x,",") importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories","-> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7,","j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1,","1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,","self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound =","= gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, )","[] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _","j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts),","if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0","stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for","= np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start,","= ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step +","= logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL =","j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se","1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories =","sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import","interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval","x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint,","raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def","1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in","WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate =","importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1,","control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:,","= np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends","i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i","= episode_end + 1 action_trajectories = [] reward_trajectories = []","= len(x) m, se = np.mean(x), sp.stats.sem(x) h = se","\"\"\" Take in samples (action, rewards, propensities, etc.) and output","= 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /=","_ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False)","@staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step,","float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities,",": j_step + 1], rewards[:, : j_step + 1]), axis=1,","propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding","/= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights,","j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, )","in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort()","edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length","discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step +","using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns,","m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1","/ logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(","importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns","WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) )","1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value","= target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =","EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit","= np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] =","target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return","sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in","axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate","to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories =","import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage","j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0]","x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids,","= np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias #","lengths, the shorter ones are filled with zeros(ones) at the",") -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5,","= [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts,","error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets","high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T *","= np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1:","return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m,","for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x,","num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns,","0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma):","* interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] )","logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights","importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum(","np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0,",") j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate )","x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0]",":-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length,","interval), int((i + 1) * interval) ) importance_weights = (","importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories",") if j_step < trajectory_length - 1: direct_method_value = (","point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps,",") return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x)",") infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset],",") ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if","importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust","np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step +",") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts =","bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories /","= j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance",") ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using","interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights",":-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier,","mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps,","np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack(","= [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories =","- 1) j_steps.extend([i * interval for i in range(1, num_j_steps","discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories =","np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories,","return m - h, m + h def mse_loss(x, error):","and output lists of equal-length trajectories (episodes) accoriding to terminals.","logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories","normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error","1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories","= np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] =","normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns,","= np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance","normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate(","raw trajectories are of various lengths, the shorter ones are","estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories,","action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights,","mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1","np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward =","self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping","def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate:","= np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for","self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights,","trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1:","< low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns","( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod","WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias","raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error,","( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights","+ j_step_bias.T * j_step_bias # minimize mse error constraint =",") j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns)","weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:,","episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1","range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2","Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = []","): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights","Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values,","axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate(","lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res =","j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step +","np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error","= np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward","= to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories","# Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust","0.0 else: # break trajectories into several subsets to estimate","np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher =","= len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]):","return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator,","action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length =","importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute","x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories","weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1]","1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start","numpy as np import scipy as sp import torch from","whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 /","weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate(","np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length(","= np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, :","trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i","- h, m + h def mse_loss(x, error): return np.dot(np.dot(x,",") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return =","1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]","j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1)","): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose","np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities","- 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1))","whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section","0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length(","def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ):","visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities,","h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n","rewards[:, : j_step + 1]), axis=1, ) if j_step <","if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights ==","sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights","len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts,","compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound","= np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action /","= sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _","estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step","importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories,","num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 )","axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = (","else: # break trajectories into several subsets to estimate confidence","# minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda","trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1]","episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in",").swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) )","= actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps","return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories =","Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0,","if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] !=","episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1","i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities,","num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs =","= trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for","error constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) -","/ logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,","logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards,","self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs],","+ 1], estimated_q_values[:, : j_step + 1] ) - np.multiply(","j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step,","the raw trajectories are of various lengths, the shorter ones","importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) )","= 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self,","rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return)","# decompose error into bias + variance j_step_bias = np.zeros([num_j_steps])","), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value -","+ variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0]","np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1],","* 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,","= [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory])","len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else:","np.arange( int(i * interval), int((i + 1) * interval) )","direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence):","np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values),","j_step + 1], rewards[:, : j_step + 1]), axis=1, )","axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:,","replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs],","affiliates. All rights reserved. import itertools import logging import numpy","j_step_bias # minimize mse error constraint = {\"type\": \"eq\", \"fun\":","5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, )","= se * sp.stats.t._ppf((1 + confidence) / 2.0, n -","confidence) / 2.0, n - 1) return m - h,","python3 # Copyright (c) Facebook, Inc. and its affiliates. All","= low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher]","1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0]","to terminals. As the raw trajectories are of various lengths,","axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights,","CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def","logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES","= np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher","+ 1, 0]: terminals[x] = 1 trajectories = [] episode_start","x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x","x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories,","data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, )","estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean","infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance","- 1) return m - h, m + h def","edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps =","[float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2:","class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES =","num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts","np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6:","action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories","= ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier",": j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step","se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 +","and its affiliates. All rights reserved. import itertools import logging","BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def","target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(","+ 1], estimated_state_values[:, : j_step + 1], ), axis=1, )","1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length //","1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error,","direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step","weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT","np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) /","np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0","subsets to estimate confidence bounds infinite_step_returns = [] num_subsets =","minimize mse error constraint = {\"type\": \"eq\", \"fun\": lambda x:","logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for","[] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory])","reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 )","def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\"","sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0,","target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(),","Inc. and its affiliates. All rights reserved. import itertools import","Facebook, Inc. and its affiliates. All rights reserved. import itertools","into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns","import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO)","WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories =","num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange(","int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier =","- control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n","error = covariance + j_step_bias.T * j_step_bias # minimize mse","n = len(x) m, se = np.mean(x), sp.stats.sem(x) h =","estimate confidence bounds infinite_step_returns = [] num_subsets = int( min(","\"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in","j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) ==","= len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts =","0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self,","= np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x +","actions.shape[1] j_steps = [float(\"inf\")] if num_j_steps > 1: j_steps.append(-1) if","__init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage,","= np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0,","interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset","/ num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i","logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(),","logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9","raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust /","CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, )","import logging import numpy as np import scipy as sp","= np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities,","j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error =","!= mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories =","= to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories","1) for _ in range(x.shape[0])], ) x = np.array(res.x) return","sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m","== 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: #","in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end","Take in samples (action, rewards, propensities, etc.) and output lists","): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step,","if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:,","reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights","= np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories =","np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends =","np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1)",") @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights","importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values,","np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return (","np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset],","interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action =","abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 )","= 0.5 def __init__(self, gamma): self.gamma = gamma def estimate(","# For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 (","importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros","wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs],","# Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means =","\"fun\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res","m + h def mse_loss(x, error): return np.dot(np.dot(x, error), x.T)","estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action","or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] =","if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval","1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,","= len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return","1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply(","# Copyright (c) Facebook, Inc. and its affiliates. All rights","2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for","j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to","np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\"inf\"), )","1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1,","trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x,","to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return","j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,","j_steps.extend([i * interval for i in range(1, num_j_steps - 1)])","variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower]","mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \"\"\" Take in","= num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset =","_ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns))","https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities,","@staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ):","= [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES,","weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] )","/ 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets","decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower",") return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod","0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in",") ) interval = num_trajectories / num_subsets for i in","1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]","else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards,",") # decompose error into bias + variance j_step_bias =","1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action =","= np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0,",") j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return(",") estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights =","+ 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories =","sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in","np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance =","[] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = []","m - h, m + h def mse_loss(x, error): return","np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights",") target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length(","ones are filled with zeros(ones) at the end. \"\"\" num_actions","= ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return","= np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step +","j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL,","infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, )","- 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x,",": j_step + 1], estimated_state_values[:, : j_step + 1], ),","= int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval","[] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights,","output lists of equal-length trajectories (episodes) accoriding to terminals. As","action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0)","import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate","are filled with zeros(ones) at the end. \"\"\" num_actions =","# break trajectories into several subsets to estimate confidence bounds","= np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound","- high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T","details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards,","= [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals)","importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, :","j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards,","direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step","j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate =","lists of equal-length trajectories (episodes) accoriding to terminals. As the","target_propensities, estimated_q_values, ): \"\"\" Take in samples (action, rewards, propensities,","infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data","for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum(","- j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher]","logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x,","return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust,","discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma )","len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return","the end. \"\"\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for","whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0]","Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.","= self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns,","len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals)","import itertools import logging import numpy as np import scipy","(c) Facebook, Inc. and its affiliates. All rights reserved. import","constraint = {\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1.0}","importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories)"],"string":"[\n \"weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier )\",\n \"1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories\",\n \"= int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range(\",\n \"to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories =\",\n \"low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns >\",\n \"1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break\",\n \"in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i +\",\n \"base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append(\",\n \"def confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x),\",\n \"low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error\",\n \"+ 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x +\",\n \") num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")]\",\n \"j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values =\",\n \"zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals =\",\n \"importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return(\",\n \"CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8\",\n \"normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator,\",\n \") else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:,\",\n \"/ num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )\",\n \"weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) #\",\n \": j_step + 1], ), axis=1, ) j_step_return = (\",\n \"float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate\",\n \"in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod\",\n \"= np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) <\",\n \"1], rewards[:, : j_step + 1]), axis=1, ) if j_step\",\n \"scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from\",\n \"np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards,\",\n \"= 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not\",\n \"target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =\",\n \"estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: #\",\n \"(num_j_steps - 1) j_steps.extend([i * interval for i in range(1,\",\n \"0]: terminals[x] = 1 trajectories = [] episode_start = 0\",\n \"return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,\",\n \"np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2\",\n \") importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset)\",\n \"estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, :\",\n \"len(x) m, se = np.mean(x), sp.stats.sem(x) h = se *\",\n \"WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for i\",\n \"random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means)\",\n \"terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x\",\n \"j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values,\",\n \") Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories,\",\n \"def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights,\",\n \"NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT\",\n \"= 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT =\",\n \") importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights\",\n \"= np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence)\",\n \"np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step +\",\n \"gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) ->\",\n \"= to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions])\",\n \") discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma\",\n \"< trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step +\",\n \"np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step\",\n \"importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod def\",\n \"num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0,\",\n \"\\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)])\",\n \"range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i + 1)\",\n \"range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate\",\n \"= [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory])\",\n \"importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights,\",\n \"np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights)\",\n \"1, 0]: terminals[x] = 1 trajectories = [] episode_start =\",\n \"* sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return\",\n \"): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate(\",\n \"denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,\",\n \"< 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return\",\n \"denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0,\",\n \"if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0\",\n \"def __init__(self, gamma): self.gamma = gamma def estimate( self, edp:\",\n \"actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 )\",\n \"random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i]\",\n \"= [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps >\",\n \"self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For\",\n \"num_j_steps > 2: interval = trajectory_length // (num_j_steps - 1)\",\n \") # Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means\",\n \"{\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x =\",\n \"= 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma =\",\n \"importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0\",\n \"importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum(\",\n \"1]), axis=1, ) if j_step < trajectory_length - 1: direct_method_value\",\n \"np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories =\",\n \"fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions])\",\n \"= np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values)\",\n \"* 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,\",\n \"np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, )\",\n \"7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) =\",\n \"etc.) and output lists of equal-length trajectories (episodes) accoriding to\",\n \"low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] =\",\n \"importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,\",\n \"whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 /\",\n \"high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error\",\n \"importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1],\",\n \"+ direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x,\",\n \"else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, :\",\n \"importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights )\",\n \"rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action,\",\n \"(action, rewards, propensities, etc.) and output lists of equal-length trajectories\",\n \"( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1]\",\n \"actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 )\",\n \"estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return =\",\n \"estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(),\",\n \"+ 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward +\",\n \"trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1 action_trajectories\",\n \"in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or\",\n \"its affiliates. All rights reserved. import itertools import logging import\",\n \"WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias +\",\n \"reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array(\",\n \"= np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step\",\n \"random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size,\",\n \"+ 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] /\",\n \"trajectory_subset = np.arange( int(i * interval), int((i + 1) *\",\n \"(episodes) accoriding to terminals. As the raw trajectories are of\",\n \"+ confidence) / 2.0, n - 1) return m -\",\n \"target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in trajectories:\",\n \"= to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories,\",\n \"0] != mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories\",\n \"edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps\",\n \"are of various lengths, the shorter ones are filled with\",\n \"= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0,\",\n \"num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in j_steps:\",\n \"bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets )\",\n \"> high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories)\",\n \"= np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier\",\n \"/= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights\",\n \"( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier =\",\n \"j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] =\",\n \"mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0]\",\n \"j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard error\",\n \"actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(),\",\n \"return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities,\",\n \"to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) )\",\n \"in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error =\",\n \"raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps,\",\n \"j_step + 1], estimated_q_values[:, : j_step + 1] ) -\",\n \"+ 1], rewards[:, : j_step + 1]), axis=1, ) if\",\n \"axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error =\",\n \"= ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1)\",\n \"= j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into\",\n \"int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES\",\n \") return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error /\",\n \"2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i *\",\n \"= int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier\",\n \"1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1)\",\n \"= 0.0 else: # break trajectories into several subsets to\",\n \") x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories(\",\n \"importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights\",\n \"estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point\",\n \"discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0])\",\n \"* estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories])\",\n \"1 trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0]\",\n \"bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns <\",\n \"EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25\",\n \"Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory])\",\n \"gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps,\",\n \"[] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory in\",\n \"importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return\",\n \"np import scipy as sp import torch from ml.rl.evaluation.cpe import\",\n \"j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], )\",\n \"range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def\",\n \"@staticmethod def confidence_bounds(x, confidence): n = len(x) m, se =\",\n \"importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts,\",\n \"high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into\",\n \"j_step_bias.T * j_step_bias # minimize mse error constraint = {\\\"type\\\":\",\n \"num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval =\",\n \"from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger =\",\n \"int((i + 1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset]\",\n \"importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else:\",\n \"episode_start = episode_end + 1 action_trajectories = [] reward_trajectories =\",\n \"j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance +\",\n \"j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step =\",\n \"j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] -\",\n \"to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size =\",\n \"target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value))\",\n \"= [] target_propensity_trajectories = [] Q_value_trajectories = [] for trajectory\",\n \"for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size,\",\n \"for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def\",\n \"1) * interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset]\",\n \"= np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"),\",\n \"episode_end + 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories\",\n \"where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound\",\n \"filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0])\",\n \"covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias\",\n \") bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1)\",\n \"break trajectories into several subsets to estimate confidence bounds infinite_step_returns\",\n \"* interval), int((i + 1) * interval) ) importance_weights =\",\n \"j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights)\",\n \"1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:,\",\n \"into several subsets to estimate confidence bounds infinite_step_returns = []\",\n \"trajectories into several subsets to estimate confidence bounds infinite_step_returns =\",\n \"+ 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum(\",\n \"= to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions])\",\n \"j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps\",\n \"where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights\",\n \"weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator =\",\n \"x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize(\",\n \"args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x\",\n \"= self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use\",\n \"0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /=\",\n \"[importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts,\",\n \"num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i *\",\n \"normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0)\",\n \"mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x)\",\n \"= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1])\",\n \"res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for\",\n \"np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x + 1\",\n \"all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories,\",\n \"+ 1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value\",\n \"edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0]\",\n \"np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias # minimize\",\n \"np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss,\",\n \"= np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values,\",\n \"[] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = []\",\n \"reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories\",\n \"np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step +\",\n \"mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories = []\",\n \") - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, :\",\n \"As the raw trajectories are of various lengths, the shorter\",\n \"j_step + 1]), axis=1, ) if j_step < trajectory_length -\",\n \"to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) )\",\n \"in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value):\",\n \"constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], ) x =\",\n \"WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) *\",\n \"/ denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self,\",\n \"standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT *\",\n \"rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories\",\n \") importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1)\",\n \"logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL\",\n \"np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound -\",\n \"): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and\",\n \"len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1))\",\n \"for x in range(0, mdp_ids.shape[0]): if x + 1 ==\",\n \"= WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1])\",\n \"j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return\",\n \"= np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, :\",\n \"- 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts,\",\n \"= np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0]\",\n \"discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return\",\n \"trajectories = [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if\",\n \"h, m + h def mse_loss(x, error): return np.dot(np.dot(x, error),\",\n \"edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details,\",\n \"def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1)\",\n \"if len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends,\",\n \") importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:,\",\n \"not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for\",\n \"( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id,\",\n \"weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories\",\n \"num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf\",\n \"- 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step\",\n \"infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust standard\",\n \") estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values =\",\n \"j_step + 1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward\",\n \"def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound,\",\n \"mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x]\",\n \"in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions),\",\n \"+ 1]), axis=1, ) if j_step < trajectory_length - 1:\",\n \"50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma\",\n \"bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator\",\n \"j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) #\",\n \"= [] logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories =\",\n \"j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several\",\n \"in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action,\",\n \"of various lengths, the shorter ones are filled with zeros(ones)\",\n \"* num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs\",\n \"mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])],\",\n \"> 2: interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i\",\n \"CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust\",\n \"1] * estimated_state_values[:, j_step + 1] ) else: direct_method_value =\",\n \"j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute weighted_doubly_robust\",\n \"= covariance + j_step_bias.T * j_step_bias # minimize mse error\",\n \"importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0\",\n \"Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories,\",\n \") interval = num_trajectories / num_subsets for i in range(num_subsets):\",\n \"1], estimated_state_values[:, : j_step + 1], ), axis=1, ) j_step_return\",\n \"actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps >\",\n \"i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error\",\n \"num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) )\",\n \"logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ):\",\n \"denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps,\",\n \"np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier =\",\n \"8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories(\",\n \"WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) )\",\n \"rights reserved. import itertools import logging import numpy as np\",\n \"All rights reserved. import itertools import logging import numpy as\",\n \"as sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page\",\n \"terminals[x] = 1 trajectories = [] episode_start = 0 episode_ends\",\n \"WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) *\",\n \": j_step + 1]), axis=1, ) if j_step < trajectory_length\",\n \"samples (action, rewards, propensities, etc.) and output lists of equal-length\",\n \"n - 1) return m - h, m + h\",\n \"terminals. As the raw trajectories are of various lengths, the\",\n \"len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end +\",\n \"// (num_j_steps - 1) j_steps.extend([i * interval for i in\",\n \"np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust\",\n \"- np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step\",\n \"rewards, logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(),\",\n \"ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__)\",\n \"/ denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns,\",\n \"len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if\",\n \"whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros =\",\n \"return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def\",\n \"1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier\",\n \"weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step + 1],\",\n \"num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values\",\n \"transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take\",\n \"infinite_step_returns = [] num_subsets = int( min( num_trajectories / 2,\",\n \"Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust =\",\n \"trajectories (episodes) accoriding to terminals. As the raw trajectories are\",\n \"estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum(\",\n \"if num_j_steps > 2: interval = trajectory_length // (num_j_steps -\",\n \"j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length // (num_j_steps\",\n \"np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack(\",\n \"CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class\",\n \"trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply(\",\n \"j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step\",\n \"num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories /\",\n \"= actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1)\",\n \": j_step + 1], estimated_q_values[:, : j_step + 1] )\",\n \"int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval =\",\n \"episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end\",\n \"- 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals) -\",\n \"reserved. import itertools import logging import numpy as np import\",\n \"target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum(\",\n \"np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, : j_step + 1]),\",\n \"For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions,\",\n \"control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n =\",\n \"in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end +\",\n \"importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([len(trajectory_subset),\",\n \"/ len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] )\",\n \") importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights =\",\n \"> 1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length\",\n \"int(i * interval), int((i + 1) * interval) ) importance_weights\",\n \"itertools import logging import numpy as np import scipy as\",\n \"actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples\",\n \"axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights\",\n \"- 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] *\",\n \"se * sp.stats.t._ppf((1 + confidence) / 2.0, n - 1)\",\n \") infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all\",\n \"various lengths, the shorter ones are filled with zeros(ones) at\",\n \"sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod\",\n \"= np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions,\",\n \"= ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier\",\n \") reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1\",\n \"range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0] or mdp_ids[x,\",\n \"* interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action\",\n \"for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier,\",\n \"np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length\",\n \"trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length\",\n \"logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories,\",\n \"- 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action\",\n \"== mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1, 0]:\",\n \"fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length\",\n \"[] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory])\",\n \"sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros]\",\n \"np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2\",\n \"= np.arange( int(i * interval), int((i + 1) * interval)\",\n \"logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,\",\n \"target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if\",\n \") for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps,\",\n \"episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends:\",\n \"episode_end + 1)) episode_start = episode_end + 1 action_trajectories =\",\n \"( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier =\",\n \"of equal-length trajectories (episodes) accoriding to terminals. As the raw\",\n \"to estimate confidence bounds infinite_step_returns = [] num_subsets = int(\",\n \"target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories,\",\n \"importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace(\",\n \"in samples (action, rewards, propensities, etc.) and output lists of\",\n \"episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator)\",\n \"importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights,\",\n \"1], estimated_q_values[:, : j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:,\",\n \"estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards)\",\n \"estimated_state_values[:, j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate\",\n \"1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:,\",\n \"importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories )\",\n \"num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if\",\n \"rewards, propensities, etc.) and output lists of equal-length trajectories (episodes)\",\n \"for i in range(num_subsets): trajectory_subset = np.arange( int(i * interval),\",\n \"np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i\",\n \"at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0])\",\n \"len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return importance_weights\",\n \"calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length\",\n \"[] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, )\",\n \"num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to compute\",\n \"estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights =\",\n \"= 1 trajectories = [] episode_start = 0 episode_ends =\",\n \"axis=1, ) if j_step < trajectory_length - 1: direct_method_value =\",\n \"WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ):\",\n \"= {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0} x\",\n \"2.0, n - 1) return m - h, m +\",\n \"confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x) h\",\n \"covariance + j_step_bias.T * j_step_bias # minimize mse error constraint\",\n \"Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories,\",\n \"= WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories\",\n \"accoriding to terminals. As the raw trajectories are of various\",\n \"estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns,\",\n \"@staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights =\",\n \"Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights:\",\n \"return importance_weights else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def\",\n \") def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ):\",\n \"= WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias\",\n \"j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust =\",\n \"x, args=error, constraints=constraint, bounds=[(0, 1) for _ in range(x.shape[0])], )\",\n \"the shorter ones are filled with zeros(ones) at the end.\",\n \"1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = []\",\n \"== 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights\",\n \"1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate(\",\n \"= [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for\",\n \"trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length\",\n \"sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _ in\",\n \"for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start =\",\n \"ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES\",\n \"num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts\",\n \"1) j_steps.extend([i * interval for i in range(1, num_j_steps -\",\n \"WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50\",\n \"several subsets to estimate confidence bounds infinite_step_returns = [] num_subsets\",\n \"confidence bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories\",\n \"logging import numpy as np import scipy as sp import\",\n \"list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories,\",\n \"start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = []\",\n \"estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns =\",\n \"estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.)\",\n \"with zeros(ones) at the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals\",\n \"axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values\",\n \"rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length =\",\n \"importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories =\",\n \"min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories\",\n \"edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length =\",\n \"* j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\",\",\n \"action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length(\",\n \"for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate)\",\n \"infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards,\",\n \"error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower =\",\n \") = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), )\",\n \"= np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories\",\n \"to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories,\",\n \"NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma\",\n \"np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step\",\n \"as np import scipy as sp import torch from ml.rl.evaluation.cpe\",\n \"/ 2.0, n - 1) return m - h, m\",\n \"[] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) -\",\n \"edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1]\",\n \"np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end\",\n \"#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates.\",\n \"import numpy as np import scipy as sp import torch\",\n \"25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5\",\n \"= np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return(\",\n \"discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) #\",\n \"bounds=[(0, 1) for _ in range(x.shape[0])], ) x = np.array(res.x)\",\n \"equal-length trajectories (episodes) accoriding to terminals. As the raw trajectories\",\n \"[importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length -\",\n \"torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger\",\n \"np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action = np.sum( np.multiply(estimated_q_values, actions),\",\n \"( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights(\",\n \"np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if\",\n \"episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends:\",\n \"j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds(\",\n \"compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int(\",\n \"+ 1)) episode_start = episode_end + 1 action_trajectories = []\",\n \"confidence_bounds(x, confidence): n = len(x) m, se = np.mean(x), sp.stats.sem(x)\",\n \"where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros]\",\n \"trajectories are of various lengths, the shorter ones are filled\",\n \"shorter ones are filled with zeros(ones) at the end. \\\"\\\"\\\"\",\n \"infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset],\",\n \"end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x\",\n \"= len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length -\",\n \"j_step + 1] * estimated_state_values[:, j_step + 1] ) else:\",\n \"where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower]\",\n \"import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES =\",\n \"weighted_doubly_robust_std_error = 0.0 else: # break trajectories into several subsets\",\n \"bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = [] sample_size\",\n \"from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator:\",\n \"+ 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step + 1],\",\n \"1) return m - h, m + h def mse_loss(x,\",\n \") importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories\",\n \"-> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7,\",\n \"j_step + 1], estimated_state_values[:, : j_step + 1], ), axis=1,\",\n \"1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,\",\n \"self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound =\",\n \"= gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, )\",\n \"[] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets ) for _\",\n \"j_step_return_trajectories=j_step_return_trajectories[random_idxs], ) bootstrapped_means.append(wdr_estimate) weighted_doubly_robust_std_error = np.std(bootstrapped_means) episode_values = np.sum(np.multiply(rewards, discounts),\",\n \"if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0\",\n \"stop=trajectory_length - 1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for\",\n \"= np.append(episode_ends, len(terminals) - 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start,\",\n \"= ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step +\",\n \"= logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL =\",\n \"j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m, se\",\n \"1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories =\",\n \"sp import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import\",\n \"interval = trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval\",\n \"x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint,\",\n \"raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def\",\n \"1, num=trajectory_length, base=self.gamma ) j_step_return_trajectories = [] for j_step in\",\n \"WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate =\",\n \"importance_weights[:, :-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1,\",\n \"control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:,\",\n \"= np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends\",\n \"i in range(num_subsets): trajectory_subset = np.arange( int(i * interval), int((i\",\n \"= episode_end + 1 action_trajectories = [] reward_trajectories = []\",\n \"= len(x) m, se = np.mean(x), sp.stats.sem(x) h = se\",\n \"\\\"\\\"\\\" Take in samples (action, rewards, propensities, etc.) and output\",\n \"= 1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /=\",\n \"_ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False)\",\n \"@staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step,\",\n \"float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities,\",\n \": j_step + 1], rewards[:, : j_step + 1]), axis=1,\",\n \"propensities, etc.) and output lists of equal-length trajectories (episodes) accoriding\",\n \"/= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights,\",\n \"j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, )\",\n \"in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs = np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort()\",\n \"edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length\",\n \"discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step +\",\n \"using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns,\",\n \"m, se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1\",\n \"/ logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(\",\n \"importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns\",\n \"WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) )\",\n \"1], ), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value\",\n \"= target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights =\",\n \"EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit\",\n \"= np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] =\",\n \"target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return\",\n \"sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _ in\",\n \"axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate\",\n \"to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories =\",\n \"import torch from ml.rl.evaluation.cpe import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage\",\n \"j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0]\",\n \"x = np.array(res.x) return float(np.dot(x, j_step_returns)) @staticmethod def transform_to_equal_length_trajectories( mdp_ids,\",\n \"= np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T * j_step_bias #\",\n \"lengths, the shorter ones are filled with zeros(ones) at the\",\n \") -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5,\",\n \"= [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts,\",\n \"error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT * num_subsets\",\n \"high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T *\",\n \"= np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1:\",\n \"return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x) m,\",\n \"for _ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x,\",\n \"num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns,\",\n \"0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma):\",\n \"* interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] )\",\n \"logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights\",\n \"importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum(\",\n \"np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0,\",\n \") j_step_return = ( importance_sampled_cumulative_reward + direct_method_value - control_variate )\",\n \"x in range(0, mdp_ids.shape[0]): if x + 1 == mdp_ids.shape[0]\",\n \":-1]] ) discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length,\",\n \"interval), int((i + 1) * interval) ) importance_weights = (\",\n \"importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories\",\n \") if j_step < trajectory_length - 1: direct_method_value = (\",\n \"point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps,\",\n \") return j_step_return @staticmethod def confidence_bounds(x, confidence): n = len(x)\",\n \") infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset],\",\n \") ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if\",\n \"importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust\",\n \"np.multiply( weighted_discounts_one_earlier[:, : j_step + 1], estimated_state_values[:, : j_step +\",\n \") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) discounts =\",\n \"bounds infinite_step_returns = [] num_subsets = int( min( num_trajectories /\",\n \"= j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error = covariance\",\n \") ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using\",\n \"interval) ) importance_weights = ( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights\",\n \":-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier,\",\n \"mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps,\",\n \"np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack(\",\n \"= [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories =\",\n \"- 1) j_steps.extend([i * interval for i in range(1, num_j_steps\",\n \"discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories =\",\n \"np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories,\",\n \"return m - h, m + h def mse_loss(x, error):\",\n \"and output lists of equal-length trajectories (episodes) accoriding to terminals.\",\n \"logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories\",\n \"normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error\",\n \"1)) episode_start = episode_end + 1 action_trajectories = [] reward_trajectories\",\n \"= np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] =\",\n \"normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns,\",\n \"= np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance\",\n \"normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, ) def compute_weighted_doubly_robust_point_estimate(\",\n \"raw trajectories are of various lengths, the shorter ones are\",\n \"estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories,\",\n \"action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights,\",\n \"mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] = 1\",\n \"np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward =\",\n \"self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping\",\n \"def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights, ) -> CpeEstimate:\",\n \"= np.random.choice(num_j_steps, sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for\",\n \"self.gamma = gamma def estimate( self, edp: EvaluationDataPage, num_j_steps, whether_self_normalize_importance_weights,\",\n \"trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1:\",\n \"< low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns\",\n \"( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return @staticmethod\",\n \"WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance j_step_bias\",\n \"raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error,\",\n \"( target_propensity_for_logged_action[trajectory_subset] / logged_propensities[trajectory_subset] ) importance_weights = np.cumprod(importance_weights, axis=1) importance_weights\",\n \"+ j_step_bias.T * j_step_bias # minimize mse error constraint =\",\n \") j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns)\",\n \"weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:,\",\n \"episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start = episode_end + 1\",\n \"range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2\",\n \"Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means = []\",\n \"): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights\",\n \"Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values,\",\n \"axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate(\",\n \"lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res =\",\n \"j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step +\",\n \"np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error\",\n \"= np.multiply(discounts, importance_weights) weighted_discounts_one_earlier = np.multiply( discounts, importance_weights_one_earlier ) importance_sampled_cumulative_reward\",\n \"= to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories\",\n \"# Compute weighted_doubly_robust mean point estimate using all data weighted_doubly_robust\",\n \"0.0 else: # break trajectories into several subsets to estimate\",\n \"np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher =\",\n \"= len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]):\",\n \"return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator,\",\n \"action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length =\",\n \"importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute\",\n \"x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories\",\n \"weighted_discounts[:, : j_step + 1], estimated_q_values[:, : j_step + 1]\",\n \"1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1)) episode_start\",\n \"numpy as np import scipy as sp import torch from\",\n \"whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories, 1]) * 1.0 /\",\n \"weighted_doubly_robust mean point estimate using all data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate(\",\n \"np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length(\",\n \"= np.sum( np.multiply( weighted_discounts[:, : j_step + 1], estimated_q_values[:, :\",\n \"trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for i\",\n \"- h, m + h def mse_loss(x, error): return np.dot(np.dot(x,\",\n \") importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return =\",\n \"1.0 / len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]\",\n \"j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1)\",\n \"): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose\",\n \"np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities\",\n \"- 1) for episode_end in episode_ends: trajectories.append(np.arange(episode_start, episode_end + 1))\",\n \"whether_self_normalize_importance_weights, ) -> CpeEstimate: # For details, visit https://arxiv.org/pdf/1604.00923.pdf Section\",\n \"0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories = to_equal_length(\",\n \"def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values, j_step, ):\",\n \"visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities,\",\n \"h = se * sp.stats.t._ppf((1 + confidence) / 2.0, n\",\n \"rewards[:, : j_step + 1]), axis=1, ) if j_step <\",\n \"if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights ==\",\n \"sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights\",\n \"len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts,\",\n \"compute_weighted_doubly_robust_point_estimate( self, j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound\",\n \"= np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action /\",\n \"= sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1) for _\",\n \"estimated_q_values, j_step, ): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step\",\n \"importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = ( np.ones([num_trajectories,\",\n \"num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 )\",\n \"axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ) importance_weights_one_earlier = (\",\n \"else: # break trajectories into several subsets to estimate confidence\",\n \"# minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda\",\n \"trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1]\",\n \"episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end in\",\n \").swapaxes(0, 1) return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) )\",\n \"= actions.shape[0] trajectory_length = actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps\",\n \"return x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories =\",\n \"Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0,\",\n \"if x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] !=\",\n \"episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1\",\n \"i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities,\",\n \"num_subsets ) for _ in range( WeightedSequentialDoublyRobustEstimator.NUM_BOOTSTRAP_SAMPLES ): random_idxs =\",\n \"= trajectory_length // (num_j_steps - 1) j_steps.extend([i * interval for\",\n \"error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) -\",\n \"/ logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights( importance_weights,\",\n \"logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards,\",\n \"self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns, j_step_return_trajectories=j_step_return_trajectories[random_idxs],\",\n \"+ 1], estimated_q_values[:, : j_step + 1] ) - np.multiply(\",\n \"j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step,\",\n \"the raw trajectories are of various lengths, the shorter ones\",\n \"importance_weights_one_earlier = ( np.ones([len(trajectory_subset), 1]) * 1.0 / len(trajectory_subset) )\",\n \"= 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self,\",\n \"rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return)\",\n \"# decompose error into bias + variance j_step_bias = np.zeros([num_j_steps])\",\n \"), axis=1, ) j_step_return = ( importance_sampled_cumulative_reward + direct_method_value -\",\n \"+ variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0]\",\n \"np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step + 1],\",\n \"* 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:,\",\n \"= [] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory])\",\n \"len(j_step_returns) == 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else:\",\n \"np.arange( int(i * interval), int((i + 1) * interval) )\",\n \"direct_method_value - control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence):\",\n \"np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values),\",\n \"j_step + 1], rewards[:, : j_step + 1]), axis=1, )\",\n \"axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:,\",\n \"replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs],\",\n \"affiliates. All rights reserved. import itertools import logging import numpy\",\n \"j_step_bias # minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\":\",\n \"5, 7, 8 ( actions, rewards, logged_propensities, target_propensities, estimated_q_values, )\",\n \"= se * sp.stats.t._ppf((1 + confidence) / 2.0, n -\",\n \"confidence) / 2.0, n - 1) return m - h,\",\n \"python3 # Copyright (c) Facebook, Inc. and its affiliates. All\",\n \"= low_bound - j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher]\",\n \"1.0 importance_weights /= sum_importance_weights return importance_weights else: importance_weights /= importance_weights.shape[0]\",\n \"to terminals. As the raw trajectories are of various lengths,\",\n \"axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights,\",\n \"CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES = 50 BOOTSTRAP_SAMPLE_PCT = 0.5 def\",\n \"logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES\",\n \"= np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound - j_step_returns[where_lower] where_higher\",\n \"+ 1, 0]: terminals[x] = 1 trajectories = [] episode_start\",\n \"x + 1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x\",\n \"x_equal_length action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories,\",\n \"data weighted_doubly_robust = self.compute_weighted_doubly_robust_point_estimate( j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, )\",\n \"estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), ) ) infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean\",\n \"infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, ) # decompose error into bias + variance\",\n \"- 1) return m - h, m + h def\",\n \"edp.model_values.cpu().numpy(), ) num_trajectories = actions.shape[0] trajectory_length = actions.shape[1] j_steps =\",\n \"[float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2:\",\n \"class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9 NUM_BOOTSTRAP_SAMPLES =\",\n \"num_trajectories = len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts\",\n \"np.sum(np.multiply(rewards, discounts), axis=1) denominator = np.nanmean(episode_values) if abs(denominator) < 1e-6:\",\n \"action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories\",\n \"= ( np.ones([num_trajectories, 1]) * 1.0 / num_trajectories ) importance_weights_one_earlier\",\n \": j_step + 1] ) - np.multiply( weighted_discounts_one_earlier[:, : j_step\",\n \"se = np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 +\",\n \"and its affiliates. All rights reserved. import itertools import logging\",\n \"BOOTSTRAP_SAMPLE_PCT = 0.5 def __init__(self, gamma): self.gamma = gamma def\",\n \"target_propensity_for_logged_action / logged_propensities importance_weights = np.cumprod(importance_weights, axis=1) importance_weights = WeightedSequentialDoublyRobustEstimator.normalize_importance_weights(\",\n \"+ 1], estimated_state_values[:, : j_step + 1], ), axis=1, )\",\n \"1: j_steps.append(-1) if num_j_steps > 2: interval = trajectory_length //\",\n \"1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error,\",\n \"direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step\",\n \"weighted_doubly_robust standard error bootstrapped_means = [] sample_size = int( WeightedSequentialDoublyRobustEstimator.BOOTSTRAP_SAMPLE_PCT\",\n \"np.mean(x), sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) /\",\n \"np.where(sum_importance_weights == 0.0)[0] sum_importance_weights[where_zeros] = len(importance_weights) importance_weights[:, where_zeros] = 1.0\",\n \"subsets to estimate confidence bounds infinite_step_returns = [] num_subsets =\",\n \"minimize mse error constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x:\",\n \"logged_propensity_trajectories = [] target_propensity_trajectories = [] Q_value_trajectories = [] for\",\n \"[] Q_value_trajectories = [] for trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory])\",\n \"reward_trajectories = to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 )\",\n \"def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\"\",\n \"sp.stats.sem(x) h = se * sp.stats.t._ppf((1 + confidence) / 2.0,\",\n \"target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(),\",\n \"Inc. and its affiliates. All rights reserved. import itertools import\",\n \"Facebook, Inc. and its affiliates. All rights reserved. import itertools\",\n \"into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns\",\n \"import CpeEstimate from ml.rl.evaluation.evaluation_data_page import EvaluationDataPage logger = logging.getLogger(__name__) logger.setLevel(logging.INFO)\",\n \"WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(), edp.model_propensities.cpu().numpy(), edp.model_values.cpu().numpy(), ) num_trajectories =\",\n \"num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset = np.arange(\",\n \"int(min(j_step, trajectory_length - 1)) weighted_discounts = np.multiply(discounts, importance_weights) weighted_discounts_one_earlier =\",\n \"- control_variate ) return j_step_return @staticmethod def confidence_bounds(x, confidence): n\",\n \"error = covariance + j_step_bias.T * j_step_bias # minimize mse\",\n \"n = len(x) m, se = np.mean(x), sp.stats.sem(x) h =\",\n \"estimate confidence bounds infinite_step_returns = [] num_subsets = int( min(\",\n \"\\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for x in\",\n \"j_step_return_trajectories = np.array(j_step_return_trajectories) j_step_returns = np.sum(j_step_return_trajectories, axis=1) if len(j_step_returns) ==\",\n \"= len(rewards) j_step = int(min(j_step, trajectory_length - 1)) weighted_discounts =\",\n \"0.5 def __init__(self, gamma): self.gamma = gamma def estimate( self,\",\n \"= np.zeros(mdp_ids.shape[0]) for x in range(0, mdp_ids.shape[0]): if x +\",\n \"actions.shape[1] j_steps = [float(\\\"inf\\\")] if num_j_steps > 1: j_steps.append(-1) if\",\n \"__init__(self, gamma): self.gamma = gamma def estimate( self, edp: EvaluationDataPage,\",\n \"= np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0,\",\n \"interval = num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset\",\n \"/ num_subsets for i in range(num_subsets): trajectory_subset = np.arange( int(i\",\n \"logged_propensities, target_propensities, estimated_q_values, ) = WeightedSequentialDoublyRobustEstimator.transform_to_equal_length_trajectories( edp.mdp_id, edp.action_mask.cpu().numpy(), edp.logged_rewards.cpu().numpy().flatten(), edp.logged_propensities.cpu().numpy().flatten(),\",\n \"logging.getLogger(__name__) logger.setLevel(logging.INFO) class WeightedSequentialDoublyRobustEstimator: NUM_SUBSETS_FOR_CB_ESTIMATES = 25 CONFIDENCE_INTERVAL = 0.9\",\n \"raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust /\",\n \"CpeEstimate( raw=weighted_doubly_robust, normalized=weighted_doubly_robust / denominator, raw_std_error=weighted_doubly_robust_std_error, normalized_std_error=weighted_doubly_robust_std_error / denominator, )\",\n \"import logging import numpy as np import scipy as sp\",\n \"= np.sum( np.multiply(estimated_q_values, actions), axis=2 ) estimated_state_values = np.sum( np.multiply(target_propensities,\",\n \"j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance = np.cov(j_step_return_trajectories) error =\",\n \"!= mdp_ids[x + 1, 0]: terminals[x] = 1 trajectories =\",\n \"= to_equal_length(reward_trajectories, 0) logged_propensity_trajectories = to_equal_length( logged_propensity_trajectories, 1 ) target_propensity_trajectories\",\n \"1) for _ in range(x.shape[0])], ) x = np.array(res.x) return\",\n \"sp.stats.t._ppf((1 + confidence) / 2.0, n - 1) return m\",\n \"== 1: weighted_doubly_robust = j_step_returns[0] weighted_doubly_robust_std_error = 0.0 else: #\",\n \"in episode_ends: episode_ends = np.append(episode_ends, len(terminals) - 1) for episode_end\",\n \"Take in samples (action, rewards, propensities, etc.) and output lists\",\n \"): trajectory_length = len(rewards[0]) num_trajectories = len(rewards) j_step = int(min(j_step,\",\n \"if j_step < trajectory_length - 1: direct_method_value = ( weighted_discounts_one_earlier[:,\",\n \"reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights\",\n \"= np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return x_equal_length action_trajectories =\",\n \"np.nonzero(terminals)[0] if len(terminals) - 1 not in episode_ends: episode_ends =\",\n \"np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x, args=error, constraints=constraint, bounds=[(0, 1)\",\n \") @staticmethod def normalize_importance_weights( importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights\",\n \"importance_weights @staticmethod def calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values,\",\n \"np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length( Q_value_trajectories, np.zeros([num_actions]) ) return (\",\n \"np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return = np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset],\",\n \"interval for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action =\",\n \"abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 )\",\n \"= 0.5 def __init__(self, gamma): self.gamma = gamma def estimate(\",\n \"# For details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 (\",\n \"importance_weights, whether_self_normalize_importance_weights ): if whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros\",\n \"wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs],\",\n \"# Use bootstrapping to compute weighted_doubly_robust standard error bootstrapped_means =\",\n \"\\\"fun\\\": lambda x: np.sum(x) - 1.0} x = np.zeros([len(j_steps)]) res\",\n \"m + h def mse_loss(x, error): return np.dot(np.dot(x, error), x.T)\",\n \"estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action\",\n \"or mdp_ids[x, 0] != mdp_ids[x + 1, 0]: terminals[x] =\",\n \"if num_j_steps > 1: j_steps.append(-1) if num_j_steps > 2: interval\",\n \"1]) * 1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier,\",\n \"= len(importance_weights) importance_weights[:, where_zeros] = 1.0 importance_weights /= sum_importance_weights return\",\n \"1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate = np.sum( np.multiply(\",\n \"# Copyright (c) Facebook, Inc. and its affiliates. All rights\",\n \"2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets for\",\n \"j_steps, num_j_steps, j_step_returns, infinite_step_returns, j_step_return_trajectories, ) # Use bootstrapping to\",\n \"np.sum( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards[trajectory_subset], discounts, importance_weights, importance_weights_one_earlier, estimated_state_values[trajectory_subset], estimated_q_values_for_logged_action[trajectory_subset], float(\\\"inf\\\"), )\",\n \"1 == mdp_ids.shape[0] or mdp_ids[x, 0] != mdp_ids[x + 1,\",\n \"trajectory in trajectories: action_trajectories.append(actions[trajectory]) reward_trajectories.append(rewards[trajectory]) logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x,\",\n \"to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x, fillvalue=fill_value)) ).swapaxes(0, 1) return\",\n \"j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights, importance_weights_one_earlier, estimated_state_values,\",\n \"j_steps.extend([i * interval for i in range(1, num_j_steps - 1)])\",\n \"variance j_step_bias = np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower]\",\n \"mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in\",\n \"= num_trajectories / num_subsets for i in range(num_subsets): trajectory_subset =\",\n \"_ in range(x.shape[0])], ) x = np.array(res.x) return float(np.dot(x, j_step_returns))\",\n \"https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards, logged_propensities, target_propensities,\",\n \"@staticmethod def transform_to_equal_length_trajectories( mdp_ids, actions, rewards, logged_propensities, target_propensities, estimated_q_values, ):\",\n \"= [] num_subsets = int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES,\",\n \"weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step + 1] )\",\n \"/ 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval = num_trajectories / num_subsets\",\n \"decompose error into bias + variance j_step_bias = np.zeros([num_j_steps]) where_lower\",\n \") return ( action_trajectories, reward_trajectories, logged_propensity_trajectories, target_propensity_trajectories, Q_value_trajectories, ) @staticmethod\",\n \"0 episode_ends = np.nonzero(terminals)[0] if len(terminals) - 1 not in\",\n \") ) interval = num_trajectories / num_subsets for i in\",\n \"1.0 / num_trajectories ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]]\",\n \"else: importance_weights /= importance_weights.shape[0] return importance_weights @staticmethod def calculate_step_return( rewards,\",\n \") # decompose error into bias + variance j_step_bias =\",\n \"1)]) target_propensity_for_logged_action = np.sum( np.multiply(target_propensities, actions), axis=2 ) estimated_q_values_for_logged_action =\",\n \"= np.nanmean(episode_values) if abs(denominator) < 1e-6: return CpeEstimate( raw=0.0, normalized=0.0,\",\n \") j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return(\",\n \") estimated_state_values = np.sum( np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights =\",\n \"+ 1 action_trajectories = [] reward_trajectories = [] logged_propensity_trajectories =\",\n \"sample_size, replace=False) random_idxs.sort() wdr_estimate = self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in\",\n \"np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher] - high_bound covariance =\",\n \"[] reward_trajectories = [] logged_propensity_trajectories = [] target_propensity_trajectories = []\",\n \"m - h, m + h def mse_loss(x, error): return\",\n \"np.multiply(target_propensities, estimated_q_values), axis=2 ) importance_weights = target_propensity_for_logged_action / logged_propensities importance_weights\",\n \") target_propensity_trajectories = to_equal_length( target_propensity_trajectories, np.zeros([num_actions]) ) Q_value_trajectories = to_equal_length(\",\n \"ones are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions\",\n \"= ( importance_sampled_cumulative_reward + direct_method_value - control_variate ) return j_step_return\",\n \"= np.zeros([num_trajectories]) control_variate = np.sum( np.multiply( weighted_discounts[:, : j_step +\",\n \"j_step_returns, infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL,\",\n \"infinite_step_returns, j_step_return_trajectories, ): low_bound, high_bound = WeightedSequentialDoublyRobustEstimator.confidence_bounds( infinite_step_returns, WeightedSequentialDoublyRobustEstimator.CONFIDENCE_INTERVAL, )\",\n \"- 1.0} x = np.zeros([len(j_steps)]) res = sp.optimize.minimize( mse_loss, x,\",\n \": j_step + 1], estimated_state_values[:, : j_step + 1], ),\",\n \"= int( min( num_trajectories / 2, WeightedSequentialDoublyRobustEstimator.NUM_SUBSETS_FOR_CB_ESTIMATES, ) ) interval\",\n \"[] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards, discounts, importance_weights,\",\n \"output lists of equal-length trajectories (episodes) accoriding to terminals. As\",\n \"action_trajectories = to_equal_length( action_trajectories, np.zeros([num_actions]) ) reward_trajectories = to_equal_length(reward_trajectories, 0)\",\n \"import scipy as sp import torch from ml.rl.evaluation.cpe import CpeEstimate\",\n \"are filled with zeros(ones) at the end. \\\"\\\"\\\" num_actions =\",\n \"# break trajectories into several subsets to estimate confidence bounds\",\n \"= np.zeros([num_j_steps]) where_lower = np.where(j_step_returns < low_bound)[0] j_step_bias[where_lower] = low_bound\",\n \"- high_bound covariance = np.cov(j_step_return_trajectories) error = covariance + j_step_bias.T\",\n \"details, visit https://arxiv.org/pdf/1604.00923.pdf Section 5, 7, 8 ( actions, rewards,\",\n \"= [] episode_start = 0 episode_ends = np.nonzero(terminals)[0] if len(terminals)\",\n \"importance_sampled_cumulative_reward = np.sum( np.multiply(weighted_discounts[:, : j_step + 1], rewards[:, :\",\n \"j_step_return_trajectories = [] for j_step in j_steps: j_step_return_trajectories.append( WeightedSequentialDoublyRobustEstimator.calculate_step_return( rewards,\",\n \"direct_method_value = ( weighted_discounts_one_earlier[:, j_step + 1] * estimated_state_values[:, j_step\",\n \"j_step + 1] ) else: direct_method_value = np.zeros([num_trajectories]) control_variate =\",\n \"lists of equal-length trajectories (episodes) accoriding to terminals. As the\",\n \"target_propensities, estimated_q_values, ): \\\"\\\"\\\" Take in samples (action, rewards, propensities,\",\n \"infinite_step_returns.append(infinite_step_return) # Compute weighted_doubly_robust mean point estimate using all data\",\n \"for i in range(1, num_j_steps - 1)]) target_propensity_for_logged_action = np.sum(\",\n \"- j_step_returns[where_lower] where_higher = np.where(j_step_returns > high_bound)[0] j_step_bias[where_higher] = j_step_returns[where_higher]\",\n \"logged_propensity_trajectories.append(logged_propensities[trajectory]) target_propensity_trajectories.append(target_propensities[trajectory]) Q_value_trajectories.append(estimated_q_values[trajectory]) def to_equal_length(x, fill_value): x_equal_length = np.array( list(itertools.zip_longest(*x,\",\n \"return CpeEstimate( raw=0.0, normalized=0.0, raw_std_error=0.0, normalized_std_error=0.0 ) return CpeEstimate( raw=weighted_doubly_robust,\",\n \"discounts = np.logspace( start=0, stop=trajectory_length - 1, num=trajectory_length, base=self.gamma )\",\n \"len(trajectory_subset) ) importance_weights_one_earlier = np.hstack( [importance_weights_one_earlier, importance_weights[:, :-1]] ) infinite_step_return\",\n \"the end. \\\"\\\"\\\" num_actions = len(target_propensities[0]) terminals = np.zeros(mdp_ids.shape[0]) for\",\n \"whether_self_normalize_importance_weights: sum_importance_weights = np.sum(importance_weights, axis=0) where_zeros = np.where(sum_importance_weights == 0.0)[0]\",\n \"Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\",\n \"= self.compute_weighted_doubly_robust_point_estimate( j_steps=[j_steps[i] for i in random_idxs], num_j_steps=sample_size, j_step_returns=j_step_returns[random_idxs], infinite_step_returns=infinite_step_returns,\",\n \"len(terminals) - 1 not in episode_ends: episode_ends = np.append(episode_ends, len(terminals)\",\n \"import itertools import logging import numpy as np import scipy\",\n \"(c) Facebook, Inc. and its affiliates. All rights reserved. import\",\n \"constraint = {\\\"type\\\": \\\"eq\\\", \\\"fun\\\": lambda x: np.sum(x) - 1.0}\",\n \"importance_weights, importance_weights_one_earlier, estimated_state_values, estimated_q_values_for_logged_action, j_step, ) ) j_step_return_trajectories = np.array(j_step_return_trajectories)\"\n]"}}},{"rowIdx":3480,"cells":{"ngram":{"kind":"list like","value":["from typing import List class Solution: def __init__(self, nums: List[int]):","copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array to","Modified time: 2019-08-03 10:53:15 import copy import random from typing","何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by:","List[int]: \"\"\" Resets the array to its original configuration and","Returns a random shuffling of the array. \"\"\" random.shuffle(self.shuffle_) return","Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import","何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import","-> List[int]: \"\"\" Resets the array to its original configuration","@Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 #","def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums)","10:48:30 # @Last Modified by: 何睿 # @Last Modified time:","= copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the array","import copy import random from typing import List class Solution:","utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30","\"\"\" Returns a random shuffling of the array. \"\"\" random.shuffle(self.shuffle_)","Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last","original configuration and return it. \"\"\" return self.original def shuffle(self)","def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of","it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns","-*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 #","by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy","List[int]: \"\"\" Returns a random shuffling of the array. \"\"\"","# @Last Modified time: 2019-08-03 10:53:15 import copy import random","self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]:","@Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified","return it. \"\"\" return self.original def shuffle(self) -> List[int]: \"\"\"","import random from typing import List class Solution: def __init__(self,","import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ =","a random shuffling of the array. \"\"\" random.shuffle(self.shuffle_) return self.shuffle_","its original configuration and return it. \"\"\" return self.original def","= nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\"","\"\"\" return self.original def shuffle(self) -> List[int]: \"\"\" Returns a","\"\"\" Resets the array to its original configuration and return","array to its original configuration and return it. \"\"\" return","nums self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets","shuffle(self) -> List[int]: \"\"\" Returns a random shuffling of the","# @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿","List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums","# -*- coding: utf-8 -*- # @Author: 何睿 # @Create","-> List[int]: \"\"\" Returns a random shuffling of the array.","and return it. \"\"\" return self.original def shuffle(self) -> List[int]:","reset(self) -> List[int]: \"\"\" Resets the array to its original","time: 2019-08-03 10:53:15 import copy import random from typing import","to its original configuration and return it. \"\"\" return self.original","copy import random from typing import List class Solution: def","Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original =","Resets the array to its original configuration and return it.","configuration and return it. \"\"\" return self.original def shuffle(self) ->","-*- coding: utf-8 -*- # @Author: 何睿 # @Create Date:","the array to its original configuration and return it. \"\"\"","@Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15","@Last Modified time: 2019-08-03 10:53:15 import copy import random from","class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original","__init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def","random from typing import List class Solution: def __init__(self, nums:","self.original = copy.copy(nums) def reset(self) -> List[int]: \"\"\" Resets the","self.original def shuffle(self) -> List[int]: \"\"\" Returns a random shuffling","List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) ->","coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03","# @Last Modified by: 何睿 # @Last Modified time: 2019-08-03","2019-08-03 10:53:15 import copy import random from typing import List","10:53:15 import copy import random from typing import List class","def reset(self) -> List[int]: \"\"\" Resets the array to its","typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_","nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self)","# @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last","return self.original def shuffle(self) -> List[int]: \"\"\" Returns a random","2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified"],"string":"[\n \"from typing import List class Solution: def __init__(self, nums: List[int]):\",\n \"copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to\",\n \"Modified time: 2019-08-03 10:53:15 import copy import random from typing\",\n \"何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified by:\",\n \"List[int]: \\\"\\\"\\\" Resets the array to its original configuration and\",\n \"Returns a random shuffling of the array. \\\"\\\"\\\" random.shuffle(self.shuffle_) return\",\n \"Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import\",\n \"何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy import\",\n \"-> List[int]: \\\"\\\"\\\" Resets the array to its original configuration\",\n \"@Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 #\",\n \"def __init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums)\",\n \"10:48:30 # @Last Modified by: 何睿 # @Last Modified time:\",\n \"= copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array\",\n \"import copy import random from typing import List class Solution:\",\n \"utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30\",\n \"\\\"\\\"\\\" Returns a random shuffling of the array. \\\"\\\"\\\" random.shuffle(self.shuffle_)\",\n \"Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last\",\n \"original configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self)\",\n \"def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of\",\n \"it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns\",\n \"-*- # @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 #\",\n \"by: 何睿 # @Last Modified time: 2019-08-03 10:53:15 import copy\",\n \"List[int]: \\\"\\\"\\\" Returns a random shuffling of the array. \\\"\\\"\\\"\",\n \"# @Last Modified time: 2019-08-03 10:53:15 import copy import random\",\n \"self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) -> List[int]:\",\n \"@Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last Modified\",\n \"return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\"\",\n \"import random from typing import List class Solution: def __init__(self,\",\n \"import List class Solution: def __init__(self, nums: List[int]): self.shuffle_ =\",\n \"a random shuffling of the array. \\\"\\\"\\\" random.shuffle(self.shuffle_) return self.shuffle_\",\n \"its original configuration and return it. \\\"\\\"\\\" return self.original def\",\n \"= nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a\",\n \"\\\"\\\"\\\" Resets the array to its original configuration and return\",\n \"array to its original configuration and return it. \\\"\\\"\\\" return\",\n \"nums self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets\",\n \"shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling of the\",\n \"# @Create Date: 2019-08-03 10:48:30 # @Last Modified by: 何睿\",\n \"List class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums\",\n \"# -*- coding: utf-8 -*- # @Author: 何睿 # @Create\",\n \"-> List[int]: \\\"\\\"\\\" Returns a random shuffling of the array.\",\n \"and return it. \\\"\\\"\\\" return self.original def shuffle(self) -> List[int]:\",\n \"reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its original\",\n \"time: 2019-08-03 10:53:15 import copy import random from typing import\",\n \"to its original configuration and return it. \\\"\\\"\\\" return self.original\",\n \"copy import random from typing import List class Solution: def\",\n \"Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original =\",\n \"Resets the array to its original configuration and return it.\",\n \"configuration and return it. \\\"\\\"\\\" return self.original def shuffle(self) ->\",\n \"-*- coding: utf-8 -*- # @Author: 何睿 # @Create Date:\",\n \"the array to its original configuration and return it. \\\"\\\"\\\"\",\n \"@Last Modified by: 何睿 # @Last Modified time: 2019-08-03 10:53:15\",\n \"@Last Modified time: 2019-08-03 10:53:15 import copy import random from\",\n \"class Solution: def __init__(self, nums: List[int]): self.shuffle_ = nums self.original\",\n \"__init__(self, nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def\",\n \"random from typing import List class Solution: def __init__(self, nums:\",\n \"self.original = copy.copy(nums) def reset(self) -> List[int]: \\\"\\\"\\\" Resets the\",\n \"self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random shuffling\",\n \"List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self) ->\",\n \"coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2019-08-03\",\n \"# @Last Modified by: 何睿 # @Last Modified time: 2019-08-03\",\n \"2019-08-03 10:53:15 import copy import random from typing import List\",\n \"10:53:15 import copy import random from typing import List class\",\n \"def reset(self) -> List[int]: \\\"\\\"\\\" Resets the array to its\",\n \"typing import List class Solution: def __init__(self, nums: List[int]): self.shuffle_\",\n \"nums: List[int]): self.shuffle_ = nums self.original = copy.copy(nums) def reset(self)\",\n \"# @Author: 何睿 # @Create Date: 2019-08-03 10:48:30 # @Last\",\n \"return self.original def shuffle(self) -> List[int]: \\\"\\\"\\\" Returns a random\",\n \"2019-08-03 10:48:30 # @Last Modified by: 何睿 # @Last Modified\"\n]"}}},{"rowIdx":3481,"cells":{"ngram":{"kind":"list like","value":["self.n_features_ represents the number of features selected (=number of clusters)","\"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1","return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters","self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ =","value: 0 Returns ---------- pipeline - feature selection pipeline \"\"\"","cluster and contains the indexes of features belonging to the","of clusters) The attribute self.selected_features_ is a list of indexes","the highest chi2 score versus y. The attribute self.n_features_ represents","represents a cluster and contains the indexes of features belonging","between each two features in X, each value is 1-phi_correlation","each two features in X, each value is 1-phi_correlation \"\"\"","column Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x,","linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X,","def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features","attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in","cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self","Creates feature selection pipeline Parameters ---------- k - the k","dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of","feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best',","feature selection pipeline Parameters ---------- k - the k parameter","the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage =","\"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters =","from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble","sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return","self.dist_matrix and self.threshold, and selects a feature from each cluster","in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y):","with the highest chi2 score versus y. The attribute self.n_features_","from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text","selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2,","y. The attribute self.n_features_ represents the number of features selected","Calculate distance matrix between each two features in X, each","versus y. The attribute self.n_features_ represents the number of features","np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate","_calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two features","---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2","X): \"\"\" Calculate distance matrix between each two features in","self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __","if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage)","belonging to the cluster \"\"\" # array of len(X) -","---------- x - feature x column y - feature y","clustering random_state - random state for the RandomForestClassifier. Deafult value:","1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def","class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features","SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A","of fitted attributes (ending with a trailing # underscore) and","of indexes that correspond to the selected features \"\"\" if","of len(X) - array[i] is the cluster number to which","clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix,","list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features (X","for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def","mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask","features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters","are selected Returns ---------- mask - boolean array of shape","Parameters ---------- x - feature x column y - feature","pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from","transformer that clusters the features in X according to dist_matrix,","1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix =","of features belonging to the cluster \"\"\" # array of","# underscore) and otherwise raises a NotFittedError with the given","linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\"","import numpy as np import sklearn import pandas as pd","cluster \"\"\" # array of len(X) - array[i] is the","import sklearn import pandas as pd import scipy.spatial.distance as ssd","linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ----------","= np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def","BaseEstimator): \"\"\" A transformer that clusters the features in X","The attribute self.selected_features_ is a list of indexes that correspond","Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline =","not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals,","feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix =","\"\"\" # array of len(X) - array[i] is the cluster","Returns ---------- phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y)","indexes that correspond to the selected features \"\"\" if not","features Parameters ---------- x - feature x column y -","self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids):","self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters]","is fitted by verifying the presence of fitted attributes (ending","print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean","A transformer that clusters the features in X according to","raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask =","clustering threshold for the Hierarchial clustering random_state - random state","np import sklearn import pandas as pd import scipy.spatial.distance as","the cluster \"\"\" # array of len(X) - array[i] is","\"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n =","attributes (ending with a trailing # underscore) and otherwise raises","chi2 score versus y. The attribute self.n_features_ represents the number","[# input features] An element is True iff its corresponding","to dist_matrix, and selects a feature from each cluster with","_hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage -","in X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X)","each cluster with the highest chi2 score versus y. The","def _get_support_mask(self): \"\"\" Get the boolean mask indicating which features","SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the features in","Get the boolean mask indicating which features are selected Returns","of lists, each list represents a cluster and contains the","sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ =","numpy as np import sklearn import pandas as pd import","feature x column y - feature y column Returns ----------","presence of fitted attributes (ending with a trailing # underscore)","\"\"\" Clusters the features (X columns) using self.dist_matrix and self.threshold,","mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\"","- feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)),","- feature x column y - feature y column Returns","mask - boolean array of shape [# input features] An","import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats","from each cluster with the highest chi2 score of X[feature]","chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 /","X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'):","feature from each cluster with the highest chi2 score versus","selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage()","and contains the indexes of features belonging to the cluster","= chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n)","features function threshold - clustering threshold for the Hierarchial clustering","self def _get_support_mask(self): \"\"\" Get the boolean mask indicating which","(ending with a trailing # underscore) and otherwise raises a","state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline","dist_matrix, and selects a feature from each cluster with the","features (X columns) using self.dist_matrix and self.threshold, and selects a","- clustering threshold for the Hierarchial clustering random_state - random","method=method) Returns ---------- a list of lists, each list represents","[]).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the","Deafult value: 0 Returns ---------- pipeline - feature selection pipeline","if the estimator is fitted by verifying the presence of","RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection","corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each","score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1):","---------- pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize',","= hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id","self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y):","X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ =","Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult","fitted attributes (ending with a trailing # underscore) and otherwise","its corresponding feature is selected for retention. \"\"\" # Checks","features] An element is True iff its corresponding feature is","The attribute self.n_features_ represents the number of features selected (=number","y): \"\"\" Calculates phi coefficient between features Parameters ---------- x","fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster","that clusters the features in X according to dist_matrix, and","list of indexes that correspond to the selected features \"\"\"","[chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}')","a list of lists, each list represents a cluster and","---------- k - the k parameter for the SelectKBest features","= [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f},","Checks if the estimator is fitted by verifying the presence","the cluster number to which sample i belongs cluster_ids =","# array of len(X) - array[i] is the cluster number","a list of indexes that correspond to the selected features","x - feature x column y - feature y column","correspond to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X)","the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_]","X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed =","boolean mask indicating which features are selected Returns ---------- mask","from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class","in X according to dist_matrix, and selects a feature from","the features (X columns) using self.dist_matrix and self.threshold, and selects","the highest chi2 score of X[feature] versus y \"\"\" def","i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {}","confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum()","= feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return","cluster with the highest chi2 score versus y. The attribute","import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator):","def fit(self, X, y): \"\"\" Clusters the features (X columns)","\"\"\" Calculates phi coefficient between features Parameters ---------- x -","X, y): \"\"\" Clusters the features (X columns) using self.dist_matrix","of shape [# input features] An element is True iff","import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters","return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters the features","0 Returns ---------- pipeline - feature selection pipeline \"\"\" pipeline","import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster","selection pipeline Parameters ---------- k - the k parameter for","highest chi2 score versus y. The attribute self.n_features_ represents the","pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import","which features are selected Returns ---------- mask - boolean array","self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates phi","and self.threshold, and selects a feature from each cluster with","lists, each list represents a cluster and contains the indexes","random_state - random state for the RandomForestClassifier. Deafult value: 0","X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix","that correspond to the selected features \"\"\" if not self.dist_matrix:","Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that clusters the","from each cluster with the highest chi2 score versus y.","dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\" Calculates","for the SelectKBest features function threshold - clustering threshold for","chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from","coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0]","), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold,","as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy","columns) using self.dist_matrix and self.threshold, and selects a feature from","a cluster and contains the indexes of features belonging to","feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage","according to dist_matrix, and selects a feature from each cluster","attribute self.selected_features_ is a list of indexes that correspond to","array of len(X) - array[i] is the cluster number to","def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage","Parameters ---------- k - the k parameter for the SelectKBest","sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx =","BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from","\"\"\" Get the boolean mask indicating which features are selected","- X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self,","message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1","\"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster',","threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k","selects a feature from each cluster with the highest chi2","array of shape [# input features] An element is True","CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline","self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method)","indicating which features are selected Returns ---------- mask - boolean","= 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed","highest chi2 score of X[feature] versus y \"\"\" def __init__(self,","features in X, each value is 1-phi_correlation \"\"\" X_df =","linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list","features are selected Returns ---------- mask - boolean array of","import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator","each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat =","= ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage =","return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection","cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx)","= 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates","X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix =","__ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes","def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold","shape [# input features] An element is True iff its","from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection","k - the k parameter for the SelectKBest features function","ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from","# fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for","= threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient","k parameter for the SelectKBest features function threshold - clustering","function threshold - clustering threshold for the Hierarchial clustering random_state","mask indicating which features are selected Returns ---------- mask -","element is True iff its corresponding feature is selected for","for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline -","def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between each two","= self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals)","distance matrix between each two features in X, each value","---------- a list of lists, each list represents a cluster","cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx,","list represents a cluster and contains the indexes of features","contains the indexes of features belonging to the cluster \"\"\"","threshold def _phi_coef(self, x, y): \"\"\" Calculates phi coefficient between","number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold,","idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self,","self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters","CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return","binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return pipeline","as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency","NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ),","the k parameter for the SelectKBest features function threshold -","= clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get","X, each value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat","clusters) The attribute self.selected_features_ is a list of indexes that","- feature y column Returns ---------- phi coefficient value \"\"\"","enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\"","indexes of features belonging to the cluster \"\"\" # array","y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold","is the cluster number to which sample i belongs cluster_ids","the number of features selected (=number of clusters) The attribute","fit(self, X, y): \"\"\" Clusters the features (X columns) using","in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def","given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] =","sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import","self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\"","attribute self.n_features_ represents the number of features selected (=number of","= {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return","feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage","SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\"","corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X):","iff its corresponding feature is selected for retention. \"\"\" #","for the Hierarchial clustering random_state - random state for the","the features in X according to dist_matrix, and selects a","mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline","value is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef)","Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))])","dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0):","two features in X, each value is 1-phi_correlation \"\"\" X_df","= hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform","clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the","with a trailing # underscore) and otherwise raises a NotFittedError","pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)),","\"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold =","hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from","chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_","def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def","X according to dist_matrix, and selects a feature from each","threshold - clustering threshold for the Hierarchial clustering random_state -","the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature","{} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values())","= Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf',","y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2","features selected (=number of clusters) The attribute self.selected_features_ is a","n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr","= X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_","- boolean array of shape [# input features] An element","otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask","pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed","(=number of clusters) The attribute self.selected_features_ is a list of","= pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat","matrix between each two features in X, each value is","y): \"\"\" Clusters the features (X columns) using self.dist_matrix and","sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin,","An element is True iff its corresponding feature is selected","= np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \"\"\"","cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \"\"\" Clusters","= dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \"\"\"","trailing # underscore) and otherwise raises a NotFittedError with the","self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) #","phi coefficient value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 =","y - feature y column Returns ---------- phi coefficient value","of features selected (=number of clusters) The attribute self.selected_features_ is","for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return","features in X according to dist_matrix, and selects a feature","with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool)","self.selected_features_ is a list of indexes that correspond to the","x, y): \"\"\" Calculates phi coefficient between features Parameters ----------","pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax()","from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer","import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest,","Calculates phi coefficient between features Parameters ---------- x - feature","1 return mask def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature","between features Parameters ---------- x - feature x column y","belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for","criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id,","- the k parameter for the SelectKBest features function threshold","True iff its corresponding feature is selected for retention. \"\"\"","linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ----------","scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import","import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import","to the cluster \"\"\" # array of len(X) - array[i]","= confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def","from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from","clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals =","sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import","is selected for retention. \"\"\" # Checks if the estimator","sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \"\"\" A transformer that","each list represents a cluster and contains the indexes of","(X columns) using self.dist_matrix and self.threshold, and selects a feature","self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals","pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr =","get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters ----------","- array[i] is the cluster number to which sample i","array[i] is the cluster number to which sample i belongs","Returns ---------- a list of lists, each list represents a","feature from each cluster with the highest chi2 score of","value \"\"\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n","threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x,","- linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a","import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier","clusters the features in X according to dist_matrix, and selects","return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix between","versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix","= X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix)","boolean array of shape [# input features] An element is","chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return","the boolean mask indicating which features are selected Returns ----------","method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage):","y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1]","selected (=number of clusters) The attribute self.selected_features_ is a list","is True iff its corresponding feature is selected for retention.","clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self):","# Checks if the estimator is fitted by verifying the","_phi_coef(self, x, y): \"\"\" Calculates phi coefficient between features Parameters","_corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self,","__init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def","fitted by verifying the presence of fitted attributes (ending with","to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance')","= sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_","created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists,","pipeline Parameters ---------- k - the k parameter for the","= self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y)","cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage,","\"\"\" Creates feature selection pipeline Parameters ---------- k - the","\"\"\" A transformer that clusters the features in X according","self.threshold, and selects a feature from each cluster with the","_get_support_mask(self): \"\"\" Get the boolean mask indicating which features are","Returns ---------- mask - boolean array of shape [# input","Clusters the features (X columns) using self.dist_matrix and self.threshold, and","- random state for the RandomForestClassifier. Deafult value: 0 Returns","hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical","as np import sklearn import pandas as pd import scipy.spatial.distance","= pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ =","phi coefficient between features Parameters ---------- x - feature x","def get_fs_pipeline(k, threshold, random_state=0): \"\"\" Creates feature selection pipeline Parameters","is a list of indexes that correspond to the selected","to the selected features \"\"\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage","pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)),","a trailing # underscore) and otherwise raises a NotFittedError with","\"\"\" Calculate distance matrix between each two features in X,","scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import","score versus y. The attribute self.n_features_ represents the number of","by verifying the presence of fitted attributes (ending with a","for retention. \"\"\" # Checks if the estimator is fitted","confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self,","and selects a feature from each cluster with the highest","the SelectKBest features function threshold - clustering threshold for the","using self.dist_matrix and self.threshold, and selects a feature from each","return self def _get_support_mask(self): \"\"\" Get the boolean mask indicating","linkage): \"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage","list of lists, each list represents a cluster and contains","a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_,","sklearn import pandas as pd import scipy.spatial.distance as ssd from","the Hierarchial clustering random_state - random state for the RandomForestClassifier.","the estimator is fitted by verifying the presence of fitted","y column Returns ---------- phi coefficient value \"\"\" confusion_matrix =","random state for the RandomForestClassifier. Deafult value: 0 Returns ----------","which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx","hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in","is 1-phi_correlation \"\"\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix","---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns","each cluster with the highest chi2 score of X[feature] versus","a feature from each cluster with the highest chi2 score","feature is selected for retention. \"\"\" # Checks if the","and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self)","Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created","retention. \"\"\" # Checks if the estimator is fitted by","underscore) and otherwise raises a NotFittedError with the given message.","cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X,","RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin","cluster with the highest chi2 score of X[feature] versus y","number of features selected (=number of clusters) The attribute self.selected_features_","corresponding feature is selected for retention. \"\"\" # Checks if","estimator is fitted by verifying the presence of fitted attributes","sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline","SelectKBest features function threshold - clustering threshold for the Hierarchial","---------- mask - boolean array of shape [# input features]","column y - feature y column Returns ---------- phi coefficient","threshold for the Hierarchial clustering random_state - random state for","of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix","\"\"\" # Checks if the estimator is fitted by verifying","input features] An element is True iff its corresponding feature","dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self,","hierarchical clustering Parameters ---------- linkage - linkage dendogram created by","random_state=0): \"\"\" Creates feature selection pipeline Parameters ---------- k -","feature y column Returns ---------- phi coefficient value \"\"\" confusion_matrix","np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k,","hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list","represents the number of features selected (=number of clusters) The","by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each","\"\"\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram","the indexes of features belonging to the cluster \"\"\" #","features belonging to the cluster \"\"\" # array of len(X)","verifying the presence of fitted attributes (ending with a trailing","pipeline - feature selection pipeline \"\"\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False,","Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method)","from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base","coefficient between features Parameters ---------- x - feature x column","selected Returns ---------- mask - boolean array of shape [#","len(X) - array[i] is the cluster number to which sample","x column y - feature y column Returns ---------- phi","X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 -","ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix,","chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted","selected for retention. \"\"\" # Checks if the estimator is","/ n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance","= pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr","the presence of fitted attributes (ending with a trailing #","with the highest chi2 score of X[feature] versus y \"\"\"","method=method) return linkage def _hierarchical_clustering(self, linkage): \"\"\" Perform hierarchical clustering","scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import","parameter for the SelectKBest features function threshold - clustering threshold","n) return corr def _calc_dist_matrix(self, X): \"\"\" Calculate distance matrix","chi2 score of X[feature] versus y \"\"\" def __init__(self, dist_matrix=None,","selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \"\"\" Get the boolean mask","import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer"],"string":"[\n \"self.n_features_ represents the number of features selected (=number of clusters)\",\n \"\\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1\",\n \"return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters\",\n \"self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ =\",\n \"value: 0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\"\",\n \"cluster and contains the indexes of features belonging to the\",\n \"of clusters) The attribute self.selected_features_ is a list of indexes\",\n \"the highest chi2 score versus y. The attribute self.n_features_ represents\",\n \"represents a cluster and contains the indexes of features belonging\",\n \"between each two features in X, each value is 1-phi_correlation\",\n \"each two features in X, each value is 1-phi_correlation \\\"\\\"\\\"\",\n \"column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x,\",\n \"linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X,\",\n \"def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features\",\n \"attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in\",\n \"cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self\",\n \"Creates feature selection pipeline Parameters ---------- k - the k\",\n \"dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of\",\n \"feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best',\",\n \"feature selection pipeline Parameters ---------- k - the k parameter\",\n \"the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage =\",\n \"\\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters =\",\n \"from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble\",\n \"sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return\",\n \"self.dist_matrix and self.threshold, and selects a feature from each cluster\",\n \"in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y):\",\n \"with the highest chi2 score versus y. The attribute self.n_features_\",\n \"from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text\",\n \"selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2,\",\n \"y. The attribute self.n_features_ represents the number of features selected\",\n \"Calculate distance matrix between each two features in X, each\",\n \"versus y. The attribute self.n_features_ represents the number of features\",\n \"np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate\",\n \"_calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two features\",\n \"---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2\",\n \"X): \\\"\\\"\\\" Calculate distance matrix between each two features in\",\n \"self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __\",\n \"if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage)\",\n \"belonging to the cluster \\\"\\\"\\\" # array of len(X) -\",\n \"---------- x - feature x column y - feature y\",\n \"clustering random_state - random state for the RandomForestClassifier. Deafult value:\",\n \"1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def\",\n \"class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features\",\n \"SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A\",\n \"of fitted attributes (ending with a trailing # underscore) and\",\n \"of indexes that correspond to the selected features \\\"\\\"\\\" if\",\n \"of len(X) - array[i] is the cluster number to which\",\n \"clustering Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix,\",\n \"list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X\",\n \"for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def\",\n \"mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask\",\n \"features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters\",\n \"are selected Returns ---------- mask - boolean array of shape\",\n \"Parameters ---------- x - feature x column y - feature\",\n \"pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from\",\n \"transformer that clusters the features in X according to dist_matrix,\",\n \"1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix =\",\n \"of features belonging to the cluster \\\"\\\"\\\" # array of\",\n \"# underscore) and otherwise raises a NotFittedError with the given\",\n \"linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\"\",\n \"import numpy as np import sklearn import pandas as pd\",\n \"cluster \\\"\\\"\\\" # array of len(X) - array[i] is the\",\n \"import sklearn import pandas as pd import scipy.spatial.distance as ssd\",\n \"linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ----------\",\n \"= np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def\",\n \"BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in X\",\n \"The attribute self.selected_features_ is a list of indexes that correspond\",\n \"Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline =\",\n \"not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals,\",\n \"feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix =\",\n \"\\\"\\\"\\\" # array of len(X) - array[i] is the cluster\",\n \"Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y)\",\n \"indexes that correspond to the selected features \\\"\\\"\\\" if not\",\n \"features Parameters ---------- x - feature x column y -\",\n \"self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids):\",\n \"self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters]\",\n \"is fitted by verifying the presence of fitted attributes (ending\",\n \"print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean\",\n \"A transformer that clusters the features in X according to\",\n \"raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask =\",\n \"clustering threshold for the Hierarchial clustering random_state - random state\",\n \"np import sklearn import pandas as pd import scipy.spatial.distance as\",\n \"the cluster \\\"\\\"\\\" # array of len(X) - array[i] is\",\n \"\\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n =\",\n \"attributes (ending with a trailing # underscore) and otherwise raises\",\n \"chi2 score versus y. The attribute self.n_features_ represents the number\",\n \"[# input features] An element is True iff its corresponding\",\n \"to dist_matrix, and selects a feature from each cluster with\",\n \"_hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage -\",\n \"in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X)\",\n \"each cluster with the highest chi2 score versus y. The\",\n \"def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features\",\n \"SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the features in\",\n \"Get the boolean mask indicating which features are selected Returns\",\n \"of lists, each list represents a cluster and contains the\",\n \"sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ =\",\n \"numpy as np import sklearn import pandas as pd import\",\n \"feature x column y - feature y column Returns ----------\",\n \"presence of fitted attributes (ending with a trailing # underscore)\",\n \"\\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and self.threshold,\",\n \"mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\"\",\n \"- feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)),\",\n \"- feature x column y - feature y column Returns\",\n \"mask - boolean array of shape [# input features] An\",\n \"import scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats\",\n \"from each cluster with the highest chi2 score of X[feature]\",\n \"chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 /\",\n \"X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'):\",\n \"feature from each cluster with the highest chi2 score versus\",\n \"selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage = self._corr_linkage()\",\n \"and contains the indexes of features belonging to the cluster\",\n \"= chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n)\",\n \"features function threshold - clustering threshold for the Hierarchial clustering\",\n \"self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which\",\n \"(ending with a trailing # underscore) and otherwise raises a\",\n \"state for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline\",\n \"dist_matrix, and selects a feature from each cluster with the\",\n \"features (X columns) using self.dist_matrix and self.threshold, and selects a\",\n \"- clustering threshold for the Hierarchial clustering random_state - random\",\n \"method=method) Returns ---------- a list of lists, each list represents\",\n \"[]).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the\",\n \"Deafult value: 0 Returns ---------- pipeline - feature selection pipeline\",\n \"if the estimator is fitted by verifying the presence of\",\n \"RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature selection\",\n \"corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each\",\n \"score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1):\",\n \"---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize',\",\n \"= hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id\",\n \"self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x, y):\",\n \"X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ =\",\n \"Hierarchial clustering random_state - random state for the RandomForestClassifier. Deafult\",\n \"fitted attributes (ending with a trailing # underscore) and otherwise\",\n \"its corresponding feature is selected for retention. \\\"\\\"\\\" # Checks\",\n \"features] An element is True iff its corresponding feature is\",\n \"The attribute self.n_features_ represents the number of features selected (=number\",\n \"y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x\",\n \"fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster\",\n \"that clusters the features in X according to dist_matrix, and\",\n \"list of indexes that correspond to the selected features \\\"\\\"\\\"\",\n \"[chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}')\",\n \"a list of lists, each list represents a cluster and\",\n \"---------- k - the k parameter for the SelectKBest features\",\n \"= [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f},\",\n \"Checks if the estimator is fitted by verifying the presence\",\n \"the cluster number to which sample i belongs cluster_ids =\",\n \"# array of len(X) - array[i] is the cluster number\",\n \"a list of indexes that correspond to the selected features\",\n \"x - feature x column y - feature y column\",\n \"correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X)\",\n \"the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_]\",\n \"X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed =\",\n \"boolean mask indicating which features are selected Returns ---------- mask\",\n \"from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class\",\n \"in X according to dist_matrix, and selects a feature from\",\n \"the features (X columns) using self.dist_matrix and self.threshold, and selects\",\n \"the highest chi2 score of X[feature] versus y \\\"\\\"\\\" def\",\n \"i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {}\",\n \"confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum()\",\n \"= feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return\",\n \"cluster with the highest chi2 score versus y. The attribute\",\n \"import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator):\",\n \"def fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns)\",\n \"\\\"\\\"\\\" Calculates phi coefficient between features Parameters ---------- x -\",\n \"X, y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix\",\n \"of shape [# input features] An element is True iff\",\n \"import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters\",\n \"return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters the features\",\n \"0 Returns ---------- pipeline - feature selection pipeline \\\"\\\"\\\" pipeline\",\n \"import pandas as pd import scipy.spatial.distance as ssd from scipy.cluster\",\n \"selection pipeline Parameters ---------- k - the k parameter for\",\n \"highest chi2 score versus y. The attribute self.n_features_ represents the\",\n \"pandas as pd import scipy.spatial.distance as ssd from scipy.cluster import\",\n \"which features are selected Returns ---------- mask - boolean array\",\n \"self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi\",\n \"and self.threshold, and selects a feature from each cluster with\",\n \"lists, each list represents a cluster and contains the indexes\",\n \"random_state - random state for the RandomForestClassifier. Deafult value: 0\",\n \"X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix\",\n \"that correspond to the selected features \\\"\\\"\\\" if not self.dist_matrix:\",\n \"Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that clusters the\",\n \"from each cluster with the highest chi2 score versus y.\",\n \"dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates\",\n \"for the SelectKBest features function threshold - clustering threshold for\",\n \"chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from\",\n \"coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0]\",\n \"), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold,\",\n \"as pd import scipy.spatial.distance as ssd from scipy.cluster import hierarchy\",\n \"columns) using self.dist_matrix and self.threshold, and selects a feature from\",\n \"a cluster and contains the indexes of features belonging to\",\n \"feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage\",\n \"according to dist_matrix, and selects a feature from each cluster\",\n \"attribute self.selected_features_ is a list of indexes that correspond to\",\n \"array of len(X) - array[i] is the cluster number to\",\n \"def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage\",\n \"Parameters ---------- k - the k parameter for the SelectKBest\",\n \"sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx =\",\n \"BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from\",\n \"\\\"\\\"\\\" Get the boolean mask indicating which features are selected\",\n \"- X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self,\",\n \"message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1\",\n \"\\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster',\",\n \"threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k\",\n \"selects a feature from each cluster with the highest chi2\",\n \"array of shape [# input features] An element is True\",\n \"CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline\",\n \"self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method)\",\n \"indicating which features are selected Returns ---------- mask - boolean\",\n \"= 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed\",\n \"highest chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self,\",\n \"features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df =\",\n \"linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list\",\n \"features are selected Returns ---------- mask - boolean array of\",\n \"import hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator\",\n \"each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat =\",\n \"= ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage =\",\n \"return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection\",\n \"cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx)\",\n \"= 1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates\",\n \"X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix =\",\n \"__ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes\",\n \"def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold\",\n \"shape [# input features] An element is True iff its\",\n \"from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection\",\n \"k - the k parameter for the SelectKBest features function\",\n \"ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from\",\n \"# fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for\",\n \"= threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient\",\n \"k parameter for the SelectKBest features function threshold - clustering\",\n \"function threshold - clustering threshold for the Hierarchial clustering random_state\",\n \"mask indicating which features are selected Returns ---------- mask -\",\n \"element is True iff its corresponding feature is selected for\",\n \"for the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline -\",\n \"def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between each two\",\n \"= self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals)\",\n \"distance matrix between each two features in X, each value\",\n \"---------- a list of lists, each list represents a cluster\",\n \"cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx,\",\n \"list represents a cluster and contains the indexes of features\",\n \"contains the indexes of features belonging to the cluster \\\"\\\"\\\"\",\n \"threshold def _phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between\",\n \"number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold,\",\n \"idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self,\",\n \"self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_ = clusters\",\n \"CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return\",\n \"binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))]) return pipeline\",\n \"as ssd from scipy.cluster import hierarchy from scipy.stats import chi2_contingency\",\n \"NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ),\",\n \"the k parameter for the SelectKBest features function threshold -\",\n \"= clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get\",\n \"X, each value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat\",\n \"clusters) The attribute self.selected_features_ is a list of indexes that\",\n \"- feature y column Returns ---------- phi coefficient value \\\"\\\"\\\"\",\n \"enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\"\",\n \"indexes of features belonging to the cluster \\\"\\\"\\\" # array\",\n \"y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold\",\n \"is the cluster number to which sample i belongs cluster_ids\",\n \"the number of features selected (=number of clusters) The attribute\",\n \"fit(self, X, y): \\\"\\\"\\\" Clusters the features (X columns) using\",\n \"in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def\",\n \"given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] =\",\n \"sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import\",\n \"self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\"\",\n \"attribute self.n_features_ represents the number of features selected (=number of\",\n \"= {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return\",\n \"feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage\",\n \"SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\"\",\n \"corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X):\",\n \"iff its corresponding feature is selected for retention. \\\"\\\"\\\" #\",\n \"for the Hierarchial clustering random_state - random state for the\",\n \"the features in X according to dist_matrix, and selects a\",\n \"mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline\",\n \"value is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef)\",\n \"Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf', RandomForestClassifier(random_state=random_state))])\",\n \"dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k, threshold, random_state=0):\",\n \"two features in X, each value is 1-phi_correlation \\\"\\\"\\\" X_df\",\n \"= hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform\",\n \"clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the\",\n \"with a trailing # underscore) and otherwise raises a NotFittedError\",\n \"pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)),\",\n \"\\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold =\",\n \"hierarchy from scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from\",\n \"chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_\",\n \"def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def\",\n \"X according to dist_matrix, and selects a feature from each\",\n \"threshold - clustering threshold for the Hierarchial clustering random_state -\",\n \"the RandomForestClassifier. Deafult value: 0 Returns ---------- pipeline - feature\",\n \"{} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values())\",\n \"= Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)), ('rf',\",\n \"y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2\",\n \"features selected (=number of clusters) The attribute self.selected_features_ is a\",\n \"n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr\",\n \"= X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax() for cluster in clusters] self.clusters_\",\n \"- boolean array of shape [# input features] An element\",\n \"otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask\",\n \"pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed\",\n \"(=number of clusters) The attribute self.selected_features_ is a list of\",\n \"= pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat\",\n \"matrix between each two features in X, each value is\",\n \"y): \\\"\\\"\\\" Clusters the features (X columns) using self.dist_matrix and\",\n \"sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin,\",\n \"An element is True iff its corresponding feature is selected\",\n \"= np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\"\",\n \"cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X, y): \\\"\\\"\\\" Clusters\",\n \"= dist_matrix self.threshold = threshold def _phi_coef(self, x, y): \\\"\\\"\\\"\",\n \"trailing # underscore) and otherwise raises a NotFittedError with the\",\n \"self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) #\",\n \"phi coefficient value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 =\",\n \"y - feature y column Returns ---------- phi coefficient value\",\n \"of features selected (=number of clusters) The attribute self.selected_features_ is\",\n \"for cluster in clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return\",\n \"features in X according to dist_matrix, and selects a feature\",\n \"with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_, ), dtype=bool)\",\n \"self.selected_features_ is a list of indexes that correspond to the\",\n \"x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters ----------\",\n \"pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ = [chi2_vals[cluster].idxmax()\",\n \"from sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer\",\n \"import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest,\",\n \"Calculates phi coefficient between features Parameters ---------- x - feature\",\n \"1 return mask def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature\",\n \"between features Parameters ---------- x - feature x column y\",\n \"belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for\",\n \"criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id,\",\n \"- the k parameter for the SelectKBest features function threshold\",\n \"True iff its corresponding feature is selected for retention. \\\"\\\"\\\"\",\n \"linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ----------\",\n \"scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base import\",\n \"import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline import\",\n \"to the cluster \\\"\\\"\\\" # array of len(X) - array[i]\",\n \"= confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def\",\n \"from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from\",\n \"clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals =\",\n \"sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import\",\n \"is selected for retention. \\\"\\\"\\\" # Checks if the estimator\",\n \"sklearn.pipeline import Pipeline class SelectHierarchicalClustering(SelectorMixin, BaseEstimator): \\\"\\\"\\\" A transformer that\",\n \"each list represents a cluster and contains the indexes of\",\n \"(X columns) using self.dist_matrix and self.threshold, and selects a feature\",\n \"self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals\",\n \"pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr =\",\n \"get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ----------\",\n \"- array[i] is the cluster number to which sample i\",\n \"array[i] is the cluster number to which sample i belongs\",\n \"Returns ---------- a list of lists, each list represents a\",\n \"feature from each cluster with the highest chi2 score of\",\n \"value \\\"\\\"\\\" confusion_matrix = pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n\",\n \"threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self, x,\",\n \"- linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a\",\n \"import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import RandomForestClassifier\",\n \"clusters the features in X according to dist_matrix, and selects\",\n \"return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix between\",\n \"versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix\",\n \"= X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 - X_corr_mat feature_corr_dist_matrix_condensed = ssd.squareform(feature_corr_dist_matrix)\",\n \"boolean array of shape [# input features] An element is\",\n \"chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return\",\n \"the boolean mask indicating which features are selected Returns ----------\",\n \"method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage):\",\n \"y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1]\",\n \"selected (=number of clusters) The attribute self.selected_features_ is a list\",\n \"is True iff its corresponding feature is selected for retention.\",\n \"clusters] self.clusters_ = clusters print(f'threshold={self.threshold:.2f}, selected_features={len(self.selected_features_)}') return self def _get_support_mask(self):\",\n \"# Checks if the estimator is fitted by verifying the\",\n \"_phi_coef(self, x, y): \\\"\\\"\\\" Calculates phi coefficient between features Parameters\",\n \"_corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self,\",\n \"__init__(self, dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def\",\n \"fitted by verifying the presence of fitted attributes (ending with\",\n \"to which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance')\",\n \"= sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted attributes self.n_features_\",\n \"created by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists,\",\n \"pipeline Parameters ---------- k - the k parameter for the\",\n \"= self._corr_linkage() clusters = self._hierarchical_clustering(linkage) chi2_vals, __ = sklearn.feature_selection.chi2(X, y)\",\n \"cluster number to which sample i belongs cluster_ids = hierarchy.fcluster(linkage,\",\n \"\\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k - the\",\n \"\\\"\\\"\\\" A transformer that clusters the features in X according\",\n \"self.threshold, and selects a feature from each cluster with the\",\n \"_get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating which features are\",\n \"Returns ---------- mask - boolean array of shape [# input\",\n \"Clusters the features (X columns) using self.dist_matrix and self.threshold, and\",\n \"- random state for the RandomForestClassifier. Deafult value: 0 Returns\",\n \"hierarchy.linkage(self.dist_matrix, method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical\",\n \"as np import sklearn import pandas as pd import scipy.spatial.distance\",\n \"= pd.Series(chi2_vals) # fitted attributes self.n_features_ = X.shape[1] self.selected_features_ =\",\n \"phi coefficient between features Parameters ---------- x - feature x\",\n \"def get_fs_pipeline(k, threshold, random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters\",\n \"is a list of indexes that correspond to the selected\",\n \"to the selected features \\\"\\\"\\\" if not self.dist_matrix: self._calc_dist_matrix(X) linkage\",\n \"pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False, binary=True)), ('k_best', SelectKBest(score_func=sklearn.feature_selection.chi2, k=k)), ('cluster', SelectHierarchicalClustering(threshold=threshold)),\",\n \"a trailing # underscore) and otherwise raises a NotFittedError with\",\n \"\\\"\\\"\\\" Calculate distance matrix between each two features in X,\",\n \"scipy.stats import chi2_contingency from sklearn.base import BaseEstimator from sklearn.ensemble import\",\n \"score versus y. The attribute self.n_features_ represents the number of\",\n \"by verifying the presence of fitted attributes (ending with a\",\n \"for retention. \\\"\\\"\\\" # Checks if the estimator is fitted\",\n \"confusion_matrix.sum().sum() corr = np.sqrt(chi2 / n) return corr def _calc_dist_matrix(self,\",\n \"and selects a feature from each cluster with the highest\",\n \"the SelectKBest features function threshold - clustering threshold for the\",\n \"using self.dist_matrix and self.threshold, and selects a feature from each\",\n \"return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask indicating\",\n \"linkage): \\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage\",\n \"list of lists, each list represents a cluster and contains\",\n \"a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self) mask = np.zeros((self.n_features_,\",\n \"sklearn import pandas as pd import scipy.spatial.distance as ssd from\",\n \"the Hierarchial clustering random_state - random state for the RandomForestClassifier.\",\n \"the estimator is fitted by verifying the presence of fitted\",\n \"y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix =\",\n \"random state for the RandomForestClassifier. Deafult value: 0 Returns ----------\",\n \"which sample i belongs cluster_ids = hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx\",\n \"hierarchy.fcluster(linkage, self.threshold, criterion='distance') cluster_id_to_feature_idx = {} for idx, cluster_id in\",\n \"is 1-phi_correlation \\\"\\\"\\\" X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix\",\n \"---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method) Returns\",\n \"each cluster with the highest chi2 score of X[feature] versus\",\n \"a feature from each cluster with the highest chi2 score\",\n \"feature is selected for retention. \\\"\\\"\\\" # Checks if the\",\n \"and otherwise raises a NotFittedError with the given message. sklearn.utils.validation.check_is_fitted(self)\",\n \"Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram created\",\n \"retention. \\\"\\\"\\\" # Checks if the estimator is fitted by\",\n \"underscore) and otherwise raises a NotFittedError with the given message.\",\n \"cluster_id in enumerate(cluster_ids): cluster_id_to_feature_idx.setdefault(cluster_id, []).append(idx) return list(cluster_id_to_feature_idx.values()) def fit(self, X,\",\n \"RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin\",\n \"cluster with the highest chi2 score of X[feature] versus y\",\n \"number of features selected (=number of clusters) The attribute self.selected_features_\",\n \"corresponding feature is selected for retention. \\\"\\\"\\\" # Checks if\",\n \"estimator is fitted by verifying the presence of fitted attributes\",\n \"sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectKBest, SelectorMixin from sklearn.pipeline\",\n \"SelectKBest features function threshold - clustering threshold for the Hierarchial\",\n \"---------- mask - boolean array of shape [# input features]\",\n \"column y - feature y column Returns ---------- phi coefficient\",\n \"threshold for the Hierarchial clustering random_state - random state for\",\n \"of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None, threshold=1): self.dist_matrix\",\n \"\\\"\\\"\\\" # Checks if the estimator is fitted by verifying\",\n \"input features] An element is True iff its corresponding feature\",\n \"dist_matrix=None, threshold=1): self.dist_matrix = dist_matrix self.threshold = threshold def _phi_coef(self,\",\n \"hierarchical clustering Parameters ---------- linkage - linkage dendogram created by\",\n \"random_state=0): \\\"\\\"\\\" Creates feature selection pipeline Parameters ---------- k -\",\n \"feature y column Returns ---------- phi coefficient value \\\"\\\"\\\" confusion_matrix\",\n \"np.zeros((self.n_features_, ), dtype=bool) mask[self.selected_features_] = 1 return mask def get_fs_pipeline(k,\",\n \"hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each list\",\n \"represents the number of features selected (=number of clusters) The\",\n \"by hierarchy.linkage(self.distance_matrix, method=method) Returns ---------- a list of lists, each\",\n \"\\\"\\\"\\\" Perform hierarchical clustering Parameters ---------- linkage - linkage dendogram\",\n \"the indexes of features belonging to the cluster \\\"\\\"\\\" #\",\n \"features belonging to the cluster \\\"\\\"\\\" # array of len(X)\",\n \"verifying the presence of fitted attributes (ending with a trailing\",\n \"pipeline - feature selection pipeline \\\"\\\"\\\" pipeline = Pipeline(steps=[('vectorize', CountVectorizer(lowercase=False,\",\n \"Parameters ---------- linkage - linkage dendogram created by hierarchy.linkage(self.distance_matrix, method=method)\",\n \"from scipy.cluster import hierarchy from scipy.stats import chi2_contingency from sklearn.base\",\n \"coefficient between features Parameters ---------- x - feature x column\",\n \"selected Returns ---------- mask - boolean array of shape [#\",\n \"len(X) - array[i] is the cluster number to which sample\",\n \"x column y - feature y column Returns ---------- phi\",\n \"X_df = pd.DataFrame.sparse.from_spmatrix(X) X_corr_mat = X_df.corr(method=self._phi_coef) feature_corr_dist_matrix = 1 -\",\n \"ssd.squareform(feature_corr_dist_matrix) self.dist_matrix = feature_corr_dist_matrix_condensed def _corr_linkage(self, method='average'): linkage = hierarchy.linkage(self.dist_matrix,\",\n \"chi2_vals, __ = sklearn.feature_selection.chi2(X, y) chi2_vals = pd.Series(chi2_vals) # fitted\",\n \"selected for retention. \\\"\\\"\\\" # Checks if the estimator is\",\n \"/ n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance\",\n \"= pd.crosstab(x, y) chi2 = chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum().sum() corr\",\n \"the presence of fitted attributes (ending with a trailing #\",\n \"with the highest chi2 score of X[feature] versus y \\\"\\\"\\\"\",\n \"method=method) return linkage def _hierarchical_clustering(self, linkage): \\\"\\\"\\\" Perform hierarchical clustering\",\n \"scipy.spatial.distance as ssd from scipy.cluster import hierarchy from scipy.stats import\",\n \"parameter for the SelectKBest features function threshold - clustering threshold\",\n \"n) return corr def _calc_dist_matrix(self, X): \\\"\\\"\\\" Calculate distance matrix\",\n \"chi2 score of X[feature] versus y \\\"\\\"\\\" def __init__(self, dist_matrix=None,\",\n \"selected_features={len(self.selected_features_)}') return self def _get_support_mask(self): \\\"\\\"\\\" Get the boolean mask\",\n \"import BaseEstimator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_extraction.text import CountVectorizer\"\n]"}}},{"rowIdx":3482,"cells":{"ngram":{"kind":"list like","value":["result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate","Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari()","time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in","For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if","= browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser =","'__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice","ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)):","browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari()","visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not","too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input","browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try:","EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search","from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions","visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\"))","with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are","TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google","for item in dataList: text = item.text print(text) elif(input ==","len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__':","3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md')","break if(choice not in range(0,9)): print('Invalid input, stop program') break","\"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on","if(choice not in range(0,9)): print('Invalid input, stop program') break run(int(choice),","sep = ' ') if __name__ == '__main__': while(True): printSelection()","HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search","item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() #","return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)):","browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser =","TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took","ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() #","search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download","maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def","webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #","button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over =","def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep","visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser","search.send_keys('Google ') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN)","= ' ') if __name__ == '__main__': while(True): printSelection() choice","browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility","if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice:","search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =","\"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"]","navigation\"] def run(input, URL): if(input == 0): content = requests.get(URL)","# Print row with HTML formatting elif(input == 1): driver","with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\")","actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input ==","print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid","\"Download and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse","data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate","'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search =","# with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel","are not supported in safari but will work on other","= int(choice) except ValueError: print('Invalid input, stop program') break if(choice","button visibility browser.get(URL) # with visibility search time.sleep(2) search =","elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for","__name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ')","element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0,","search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search","text = item.text print(text) browser.back() print('\\n' * 5) # For","## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari()","Required for the button visibility browser.get(URL) # with visibility search","and extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions","search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data","# For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements:","selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import","work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input ==","= [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on","= WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much","from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium","search.send_keys(\"Sel\") # Google Search \"Selenium\" search.submit() elif(input == 2): browser","3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window()","download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item","# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains","visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2)","other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser","Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3):","3): browser = webdriver.Safari() browser.maximize_window() # Required for the input","data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back()","selenium import webdriver from selenium.webdriver.common.keys import Keys import time from","selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from","== '__main__': while(True): printSelection() choice = input('Enter choice: ') try:","but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform()","selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected","print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required","for item in dataList: text = item.text print(text) browser.back() print('\\n'","range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ ==","BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from","additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for Chrome\",","search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser","in dataList: text = item.text print(text) browser.back() print('\\n' * 5)","with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7):","time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from","if element is found within 3 seconds otherwise raise TimeoutException","time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4):","5): browser = webdriver.Safari() browser.maximize_window() # Required for the button","3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254')))","requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML","elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\")","extract additional data\", \"Demonstrate maximizing screen\", \"Demonstrate mouse actions for","webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))","time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')","the button visibility browser.get(URL) # with visibility search time.sleep(2) search","webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) #","== 3): browser = webdriver.Safari() browser.maximize_window() # Required for the","time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome()","choice = input('Enter choice: ') try: choice = int(choice) except","the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element","from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC","import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains","google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\",","listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i","from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL =","element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:')","browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ##","for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0):","int(choice) except ValueError: print('Invalid input, stop program') break if(choice not","import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions","time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input","time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item","thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate maximizing","search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser =","browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility","* 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element","requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import","bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui import","print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel","[\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\",","URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google","def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element","search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser =","search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data","browser.back() print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements):","element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window()","in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for","') if __name__ == '__main__': while(True): printSelection() choice = input('Enter","\"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser =","proceed if element is found within 3 seconds otherwise raise","import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait","from bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui","= webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)","= webdriver.Safari() browser.maximize_window() # Required for the input tag visibility","dataList: text = item.text print(text) elif(input == 6): browser =","search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required","== 5): browser = webdriver.Safari() browser.maximize_window() # Required for the","import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver","= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5):","printSelection() choice = input('Enter choice: ') try: choice = int(choice)","import By from selenium import webdriver from selenium.webdriver.common.keys import Keys","import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions","browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search","== 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) #","'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\", \"Demonstrate","= webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') #","4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2)","import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES","content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row","input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid","otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException:","# Required for the button visibility browser.get(URL) # with visibility","= driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google Search \"Selenium\" search.submit() elif(input ==","took too much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3')","choice = int(choice) except ValueError: print('Invalid input, stop program') break","program') break if(choice not in range(0,9)): print('Invalid input, stop program')","supported in safari but will work on other browser ##","def run(input, URL): if(input == 0): content = requests.get(URL) soup","browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google","will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input","content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text =","= data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text)","elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for","== 8): browser = webdriver.Safari() browser.maximize_window() # Required for the","\"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5)","except ValueError: print('Invalid input, stop program') break if(choice not in","choice: ') try: choice = int(choice) except ValueError: print('Invalid input,","search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data =","data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input","import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions","time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \"Google","= webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search =","time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2)","import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support","dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text","2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google","browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input","# Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: #","print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = '","print(text) browser.back() print('\\n' * 5) # For convenient visual def","Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input == 0): content","== 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1]","search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required","the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over","7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button","Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser","printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep =","\"Demonstrate navigation\"] def run(input, URL): if(input == 0): content =","== 6): browser = webdriver.Safari() browser.maximize_window() # Required for the","\"Demonstrate search on thinkwithgoogle search result\", \"Download and extract additional","except TimeoutException: print(\"Loading took too much time!\") search = browser.find_elements(By.ID,'input-254')[0]","mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL): if(input","on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8):","search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER)","search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for","= webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google Search","browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3","BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input ==","search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \"Google Pixel","= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click()","Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import","from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate","with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3')","Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) #","Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed","item in dataList: text = item.text print(text) elif(input == 6):","seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except","if element.is_displayed(): return element def printSelection(): print('Press:') for i in","for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if","on thinkwithgoogle search result\", \"Download and extract additional data\", \"Demonstrate","= 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate google search\",","browser = webdriver.Safari() browser.maximize_window() # Required for the input tag","elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility","search result\", \"Download and extract additional data\", \"Demonstrate maximizing screen\",","= webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL)","Print row with HTML formatting elif(input == 1): driver =","for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ')","') # Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input","from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException","Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList","print('\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for","\"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search","driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google Search \"Selenium\" search.submit()","item in dataList: text = item.text print(text) browser.back() print('\\n' *","By from selenium import webdriver from selenium.webdriver.common.keys import Keys import","print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True):","3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window()","search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md')","= BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input","== 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\")","element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too","from selenium import webdriver from selenium.webdriver.common.keys import Keys import time","import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions","visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return","selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import","# proceed if element is found within 3 seconds otherwise","within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID,","not in range(0,9)): print('Invalid input, stop program') break run(int(choice), URL)","browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ')","browser.maximize_window() # Required for the button visibility browser.get(URL) # with","= returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() #","== 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the","returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser","= browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \"Google Pixel 3\"","print(soup.prettify()) # Print row with HTML formatting elif(input == 1):","EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from","tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found","= returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but","## ActionChains are not supported in safari but will work","import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\",","webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google","webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import","in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__","search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google Search \"Selenium\" search.submit() elif(input","returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required","# Google Search \"Selenium\" search.submit() elif(input == 2): browser =","row with HTML formatting elif(input == 1): driver = webdriver.Safari()","5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in","1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") #","search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported","elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search =","try: # proceed if element is found within 3 seconds","TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES =","use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search","returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data","soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting","raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading","time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList","is found within 3 seconds otherwise raise TimeoutException element =","Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content","unexpected use-case\", \"Demonstrate google search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate","visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within","try: choice = int(choice) except ValueError: print('Invalid input, stop program')","Google Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari()","thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search","3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =","element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i],","elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for","time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() #","returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari but will","driver = webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google","import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by","= input('Enter choice: ') try: choice = int(choice) except ValueError:","screen\", \"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input,","WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import","search on thinkwithgoogle search result\", \"Download and extract additional data\",","0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print","' ') if __name__ == '__main__': while(True): printSelection() choice =","search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on","time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')","Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari()","search\", \"Demonstrate search on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate","element is found within 3 seconds otherwise raise TimeoutException element","Search \"Selenium\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL)","in dataList: text = item.text print(text) elif(input == 6): browser","elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for","= item.text print(text) browser.back() print('\\n' * 5) # For convenient","formatting elif(input == 1): driver = webdriver.Safari() driver.get(\"https://www.google.com\") search =","WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\")","time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() #","import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException","TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\"Demonstrate unexpected use-case\", \"Demonstrate","3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\"Loading took too much time!\") search","i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if","from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains","much time!\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input ==","dataList: text = item.text print(text) browser.back() print('\\n' * 5) #","input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is","expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import","text = item.text print(text) elif(input == 6): browser = webdriver.Safari()","# Google Search \"Google Pixel 3\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input ==","on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and","while(True): printSelection() choice = input('Enter choice: ') try: choice =","webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/')","6): browser = webdriver.Safari() browser.maximize_window() # Required for the button","') try: choice = int(choice) except ValueError: print('Invalid input, stop","search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL)","browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text","convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed():","# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) elif(input ==","= browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList:","run(input, URL): if(input == 0): content = requests.get(URL) soup =","if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify())","selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com'","as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By","ActionChains are not supported in safari but will work on","browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \"Google Pixel 3\" time.sleep(5)","= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data =","search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input ==","TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from","stop program') break if(choice not in range(0,9)): print('Invalid input, stop","input, stop program') break if(choice not in range(0,9)): print('Invalid input,","element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\"//i[@class='material-icons'][contains(./text(),'help')]\")) ## ActionChains are not supported in safari","on thinkwithgoogle\", \"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle","selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from","\"Demonstrate search on WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\",","ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL","= requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with","WebDriverWait\", \"Demonstrate search on thinkwithgoogle search result\", \"Download and extract","elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for","selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import","https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window()","in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136","driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google Search \"Selenium\" search.submit() elif(input == 2):","selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from","item.text print(text) browser.back() print('\\n' * 5) # For convenient visual","= item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window()","3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with","returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def","from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys","8): browser = webdriver.Safari() browser.maximize_window() # Required for the button","\"Demonstrate mouse actions for Chrome\", \"Demonstrate navigation\"] def run(input, URL):","TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required","visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))","webdriver.Safari() driver.get(\"https://www.google.com\") search = driver.find_element_by_name(\"q\") search.send_keys(\"Sel\") # Google Search \"Selenium\"","== 4): browser = webdriver.Safari() browser.get(URL) # with visibility search","URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser')","data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in","returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content","browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for","safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform()","for element in listOfInputElements: if element.is_displayed(): return element def printSelection():","re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as","not supported in safari but will work on other browser","found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser,","for the button visibility browser.get(URL) # with visibility search time.sleep(2)","browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search"],"string":"[\n \"result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate\",\n \"Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari()\",\n \"time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in\",\n \"For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if\",\n \"= browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser =\",\n \"'__main__': while(True): printSelection() choice = input('Enter choice: ') try: choice\",\n \"ValueError: print('Invalid input, stop program') break if(choice not in range(0,9)):\",\n \"browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari()\",\n \"visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not\",\n \"too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input\",\n \"browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/') try:\",\n \"EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search\",\n \"from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions\",\n \"visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\"))\",\n \"with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are\",\n \"TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google\",\n \"for item in dataList: text = item.text print(text) elif(input ==\",\n \"len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__':\",\n \"3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md')\",\n \"break if(choice not in range(0,9)): print('Invalid input, stop program') break\",\n \"\\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on\",\n \"if(choice not in range(0,9)): print('Invalid input, stop program') break run(int(choice),\",\n \"sep = ' ') if __name__ == '__main__': while(True): printSelection()\",\n \"HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search\",\n \"item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() #\",\n \"return element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)):\",\n \"browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser =\",\n \"TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took\",\n \"ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() #\",\n \"search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download\",\n \"maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def\",\n \"webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL) #\",\n \"button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over =\",\n \"def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep\",\n \"visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser\",\n \"search.send_keys('Google ') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN)\",\n \"= ' ') if __name__ == '__main__': while(True): printSelection() choice\",\n \"browser = webdriver.Chrome() browser.maximize_window() # Required for the button visibility\",\n \"if __name__ == '__main__': while(True): printSelection() choice = input('Enter choice:\",\n \"search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =\",\n \"\\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"]\",\n \"navigation\\\"] def run(input, URL): if(input == 0): content = requests.get(URL)\",\n \"# Print row with HTML formatting elif(input == 1): driver\",\n \"with HTML formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\")\",\n \"actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input ==\",\n \"print('Invalid input, stop program') break if(choice not in range(0,9)): print('Invalid\",\n \"\\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse\",\n \"data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate\",\n \"'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search =\",\n \"# with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel\",\n \"are not supported in safari but will work on other\",\n \"= int(choice) except ValueError: print('Invalid input, stop program') break if(choice\",\n \"button visibility browser.get(URL) # with visibility search time.sleep(2) search =\",\n \"elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required for\",\n \"__name__ == '__main__': while(True): printSelection() choice = input('Enter choice: ')\",\n \"element.is_displayed(): return element def printSelection(): print('Press:') for i in range(0,\",\n \"search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search\",\n \"text = item.text print(text) browser.back() print('\\\\n' * 5) # For\",\n \"## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari()\",\n \"Required for the button visibility browser.get(URL) # with visibility search\",\n \"and extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions\",\n \"search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data\",\n \"# For convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements:\",\n \"selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import\",\n \"work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input ==\",\n \"= [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on\",\n \"= WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much\",\n \"from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium\",\n \"search.send_keys(\\\"Sel\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser\",\n \"3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window()\",\n \"download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item\",\n \"# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains\",\n \"visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2)\",\n \"other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser\",\n \"Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3):\",\n \"3): browser = webdriver.Safari() browser.maximize_window() # Required for the input\",\n \"data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) browser.back()\",\n \"selenium import webdriver from selenium.webdriver.common.keys import Keys import time from\",\n \"selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains from\",\n \"== '__main__': while(True): printSelection() choice = input('Enter choice: ') try:\",\n \"but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform()\",\n \"selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected\",\n \"print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"for item in dataList: text = item.text print(text) browser.back() print('\\\\n'\",\n \"range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ ==\",\n \"BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait from\",\n \"additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for Chrome\\\",\",\n \"search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4): browser\",\n \"in dataList: text = item.text print(text) browser.back() print('\\\\n' * 5)\",\n \"with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7):\",\n \"time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from\",\n \"if element is found within 3 seconds otherwise raise TimeoutException\",\n \"time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input == 4):\",\n \"5): browser = webdriver.Safari() browser.maximize_window() # Required for the button\",\n \"3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254')))\",\n \"requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML\",\n \"elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\")\",\n \"extract additional data\\\", \\\"Demonstrate maximizing screen\\\", \\\"Demonstrate mouse actions for\",\n \"webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))\",\n \"time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')\",\n \"the button visibility browser.get(URL) # with visibility search time.sleep(2) search\",\n \"webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL) #\",\n \"== 3): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome()\",\n \"choice = input('Enter choice: ') try: choice = int(choice) except\",\n \"the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element\",\n \"from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC\",\n \"import webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains\",\n \"google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\",\",\n \"listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for i\",\n \"from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL =\",\n \"element in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:')\",\n \"browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ##\",\n \"for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0):\",\n \"int(choice) except ValueError: print('Invalid input, stop program') break if(choice not\",\n \"import Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions\",\n \"time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input\",\n \"time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item\",\n \"thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing\",\n \"search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser =\",\n \"browser = webdriver.Safari() browser.maximize_window() # Required for the button visibility\",\n \"* 5) # For convenient visual def returnVisibleElement(listOfInputElements): for element\",\n \"requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import\",\n \"bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui import\",\n \"print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel\",\n \"[\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\",\",\n \"URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google\",\n \"def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element\",\n \"search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser =\",\n \"search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data\",\n \"browser.back() print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements):\",\n \"element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window()\",\n \"in listOfInputElements: if element.is_displayed(): return element def printSelection(): print('Press:') for\",\n \"') if __name__ == '__main__': while(True): printSelection() choice = input('Enter\",\n \"\\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser =\",\n \"proceed if element is found within 3 seconds otherwise raise\",\n \"import BeautifulSoup import requests import re from selenium.webdriver.support.ui import WebDriverWait\",\n \"from bs4 import BeautifulSoup import requests import re from selenium.webdriver.support.ui\",\n \"= webdriver.Safari() browser.maximize_window() # Required for the button visibility browser.get(URL)\",\n \"= webdriver.Safari() browser.maximize_window() # Required for the input tag visibility\",\n \"dataList: text = item.text print(text) elif(input == 6): browser =\",\n \"search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"== 5): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"import TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver\",\n \"= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5):\",\n \"printSelection() choice = input('Enter choice: ') try: choice = int(choice)\",\n \"import By from selenium import webdriver from selenium.webdriver.common.keys import Keys\",\n \"import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import TouchActions\",\n \"browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search\",\n \"== 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) #\",\n \"'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate\",\n \"= webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') #\",\n \"4): browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2)\",\n \"import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES\",\n \"content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row\",\n \"input('Enter choice: ') try: choice = int(choice) except ValueError: print('Invalid\",\n \"otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException:\",\n \"# Required for the button visibility browser.get(URL) # with visibility\",\n \"= driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input ==\",\n \"took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3')\",\n \"choice = int(choice) except ValueError: print('Invalid input, stop program') break\",\n \"program') break if(choice not in range(0,9)): print('Invalid input, stop program')\",\n \"supported in safari but will work on other browser ##\",\n \"def run(input, URL): if(input == 0): content = requests.get(URL) soup\",\n \"browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google\",\n \"will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input\",\n \"content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text =\",\n \"= data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text)\",\n \"elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"== 8): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"\\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5)\",\n \"except ValueError: print('Invalid input, stop program') break if(choice not in\",\n \"choice: ') try: choice = int(choice) except ValueError: print('Invalid input,\",\n \"search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data =\",\n \"data.find_elements_by_tag_name('li') for item in dataList: text = item.text print(text) elif(input\",\n \"import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions\",\n \"time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \\\"Google\",\n \"= webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search =\",\n \"time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2)\",\n \"import requests import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support\",\n \"dataList = data.find_elements_by_tag_name('li') for item in dataList: text = item.text\",\n \"2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google\",\n \"browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input\",\n \"# Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: #\",\n \"print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = '\",\n \"print(text) browser.back() print('\\\\n' * 5) # For convenient visual def\",\n \"Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content\",\n \"== 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1]\",\n \"search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"the button visibility browser.get(URL) # with visibility search time.sleep(2) element_to_hover_over\",\n \"7): browser = webdriver.Chrome() browser.maximize_window() # Required for the button\",\n \"Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser\",\n \"printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep =\",\n \"\\\"Demonstrate navigation\\\"] def run(input, URL): if(input == 0): content =\",\n \"== 6): browser = webdriver.Safari() browser.maximize_window() # Required for the\",\n \"\\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract additional\",\n \"except TimeoutException: print(\\\"Loading took too much time!\\\") search = browser.find_elements(By.ID,'input-254')[0]\",\n \"mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL): if(input\",\n \"on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8):\",\n \"search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER)\",\n \"search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for\",\n \"= webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google Search\",\n \"browser.get('https://trends.google.com/trends/') try: # proceed if element is found within 3\",\n \"BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input ==\",\n \"search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \\\"Google Pixel\",\n \"= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click()\",\n \"Keys import time from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.touch_actions import\",\n \"from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate\",\n \"with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3')\",\n \"Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) #\",\n \"Required for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed\",\n \"item in dataList: text = item.text print(text) elif(input == 6):\",\n \"seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except\",\n \"if element.is_displayed(): return element def printSelection(): print('Press:') for i in\",\n \"for the input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if\",\n \"on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate\",\n \"= 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate google search\\\",\",\n \"browser = webdriver.Safari() browser.maximize_window() # Required for the input tag\",\n \"elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with visibility\",\n \"search result\\\", \\\"Download and extract additional data\\\", \\\"Demonstrate maximizing screen\\\",\",\n \"= webdriver.Chrome() browser.maximize_window() # Required for the button visibility browser.get(URL)\",\n \"Print row with HTML formatting elif(input == 1): driver =\",\n \"for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ')\",\n \"') # Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input\",\n \"from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException\",\n \"Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList\",\n \"print('\\\\n' * 5) # For convenient visual def returnVisibleElement(listOfInputElements): for\",\n \"\\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search\",\n \"driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google Search \\\"Selenium\\\" search.submit()\",\n \"item in dataList: text = item.text print(text) browser.back() print('\\\\n' *\",\n \"By from selenium import webdriver from selenium.webdriver.common.keys import Keys import\",\n \"print('',i,'to',EXAMPLES[i], sep = ' ') if __name__ == '__main__': while(True):\",\n \"3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window()\",\n \"search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md')\",\n \"= BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting elif(input\",\n \"== 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\")\",\n \"element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too\",\n \"from selenium import webdriver from selenium.webdriver.common.keys import Keys import time\",\n \"import re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions\",\n \"visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return\",\n \"selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from selenium import\",\n \"# proceed if element is found within 3 seconds otherwise\",\n \"within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID,\",\n \"not in range(0,9)): print('Invalid input, stop program') break run(int(choice), URL)\",\n \"browser = webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ')\",\n \"browser.maximize_window() # Required for the button visibility browser.get(URL) # with\",\n \"= returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() #\",\n \"== 7): browser = webdriver.Chrome() browser.maximize_window() # Required for the\",\n \"returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser\",\n \"= browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \\\"Google Pixel 3\\\"\",\n \"print(soup.prettify()) # Print row with HTML formatting elif(input == 1):\",\n \"EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By from\",\n \"tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found\",\n \"= returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but\",\n \"## ActionChains are not supported in safari but will work\",\n \"import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\",\",\n \"webdriver.Safari() browser.get(URL) time.sleep(5) search = browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google\",\n \"webdriver from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import\",\n \"in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if __name__\",\n \"search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input\",\n \"returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input == 7): browser = webdriver.Chrome() browser.maximize_window() # Required\",\n \"# Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser =\",\n \"row with HTML formatting elif(input == 1): driver = webdriver.Safari()\",\n \"5) # For convenient visual def returnVisibleElement(listOfInputElements): for element in\",\n \"1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") #\",\n \"search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported\",\n \"elif(input == 2): browser = webdriver.Safari() browser.get(URL) time.sleep(5) search =\",\n \"try: # proceed if element is found within 3 seconds\",\n \"TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES =\",\n \"use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search\",\n \"returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data\",\n \"soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with HTML formatting\",\n \"raise TimeoutException element = WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading\",\n \"time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList\",\n \"is found within 3 seconds otherwise raise TimeoutException element =\",\n \"Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content\",\n \"unexpected use-case\\\", \\\"Demonstrate google search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate\",\n \"visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is found within\",\n \"try: choice = int(choice) except ValueError: print('Invalid input, stop program')\",\n \"Google Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari()\",\n \"thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search\",\n \"3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList =\",\n \"element def printSelection(): print('Press:') for i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i],\",\n \"elif(input == 6): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari() browser.maximize_window() #\",\n \"returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari but will\",\n \"driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google\",\n \"import expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by\",\n \"= input('Enter choice: ') try: choice = int(choice) except ValueError:\",\n \"screen\\\", \\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input,\",\n \"WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import\",\n \"search on thinkwithgoogle search result\\\", \\\"Download and extract additional data\\\",\",\n \"0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print\",\n \"' ') if __name__ == '__main__': while(True): printSelection() choice =\",\n \"search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on\",\n \"time.sleep(2) browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li')\",\n \"Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input == 3): browser = webdriver.Safari()\",\n \"search\\\", \\\"Demonstrate search on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate\",\n \"element is found within 3 seconds otherwise raise TimeoutException element\",\n \"Search \\\"Selenium\\\" search.submit() elif(input == 2): browser = webdriver.Safari() browser.get(URL)\",\n \"in dataList: text = item.text print(text) elif(input == 6): browser\",\n \"elif(input == 5): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"= item.text print(text) browser.back() print('\\\\n' * 5) # For convenient\",\n \"formatting elif(input == 1): driver = webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search =\",\n \"WebDriverWait(browser, 3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\")\",\n \"time.sleep(2) search.send_keys(Keys.ENTER) elif(input == 5): browser = webdriver.Safari() browser.maximize_window() #\",\n \"import ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException\",\n \"TimeoutException URL = 'https://shopping.thinkwithgoogle.com' EXAMPLES = [\\\"Demonstrate unexpected use-case\\\", \\\"Demonstrate\",\n \"3).until(EC.presence_of_element_located((By.ID, 'input-254'))) except TimeoutException: print(\\\"Loading took too much time!\\\") search\",\n \"i in range(0, len(EXAMPLES)): print('',i,'to',EXAMPLES[i], sep = ' ') if\",\n \"from selenium.webdriver.common.keys import Keys import time from selenium.webdriver.common.action_chains import ActionChains\",\n \"much time!\\\") search = browser.find_elements(By.ID,'input-254')[0] search.send_keys('Google Pixel 3') elif(input ==\",\n \"dataList: text = item.text print(text) browser.back() print('\\\\n' * 5) #\",\n \"input tag visibility browser.get('https://trends.google.com/trends/') try: # proceed if element is\",\n \"expected_conditions as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import\",\n \"text = item.text print(text) elif(input == 6): browser = webdriver.Safari()\",\n \"# Google Search \\\"Google Pixel 3\\\" time.sleep(5) search.send_keys(Keys.RETURN) elif(input ==\",\n \"on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and\",\n \"while(True): printSelection() choice = input('Enter choice: ') try: choice =\",\n \"webdriver.Safari() browser.maximize_window() # Required for the input tag visibility browser.get('https://trends.google.com/trends/')\",\n \"6): browser = webdriver.Safari() browser.maximize_window() # Required for the button\",\n \"') try: choice = int(choice) except ValueError: print('Invalid input, stop\",\n \"search.send_keys('Google Pixel 3') elif(input == 4): browser = webdriver.Safari() browser.get(URL)\",\n \"browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList: text\",\n \"convenient visual def returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed():\",\n \"# with visibility search time.sleep(2) element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) elif(input ==\",\n \"= browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in dataList:\",\n \"run(input, URL): if(input == 0): content = requests.get(URL) soup =\",\n \"if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify())\",\n \"selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL = 'https://shopping.thinkwithgoogle.com'\",\n \"as EC from selenium.common.exceptions import TimeoutException from selenium.webdriver.common.by import By\",\n \"ActionChains are not supported in safari but will work on\",\n \"browser.find_elements_by_id('subjectInput')[1] search.send_keys('Google ') # Google Search \\\"Google Pixel 3\\\" time.sleep(5)\",\n \"= returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data =\",\n \"search = returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) elif(input ==\",\n \"TimeoutException from selenium.webdriver.common.by import By from selenium import webdriver from\",\n \"stop program') break if(choice not in range(0,9)): print('Invalid input, stop\",\n \"input, stop program') break if(choice not in range(0,9)): print('Invalid input,\",\n \"element_to_hover_over = returnVisibleElement(browser.find_elements_by_xpath(\\\"//i[@class='material-icons'][contains(./text(),'help')]\\\")) ## ActionChains are not supported in safari\",\n \"on thinkwithgoogle\\\", \\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle\",\n \"selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from\",\n \"\\\"Demonstrate search on WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\",\",\n \"ActionChains from selenium.webdriver.common.touch_actions import TouchActions from selenium.common.exceptions import TimeoutException URL\",\n \"= requests.get(URL) soup = BeautifulSoup(content.text,'html.parser') print(soup.prettify()) # Print row with\",\n \"WebDriverWait\\\", \\\"Demonstrate search on thinkwithgoogle search result\\\", \\\"Download and extract\",\n \"elif(input == 3): browser = webdriver.Safari() browser.maximize_window() # Required for\",\n \"selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys import\",\n \"https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform() TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window()\",\n \"in safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136\",\n \"driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google Search \\\"Selenium\\\" search.submit() elif(input == 2):\",\n \"selenium.webdriver.support import expected_conditions as EC from selenium.common.exceptions import TimeoutException from\",\n \"item.text print(text) browser.back() print('\\\\n' * 5) # For convenient visual\",\n \"= item.text print(text) elif(input == 6): browser = webdriver.Safari() browser.maximize_window()\",\n \"3') elif(input == 4): browser = webdriver.Safari() browser.get(URL) # with\",\n \"returnVisibleElement(listOfInputElements): for element in listOfInputElements: if element.is_displayed(): return element def\",\n \"from selenium.webdriver.common.by import By from selenium import webdriver from selenium.webdriver.common.keys\",\n \"8): browser = webdriver.Safari() browser.maximize_window() # Required for the button\",\n \"\\\"Demonstrate mouse actions for Chrome\\\", \\\"Demonstrate navigation\\\"] def run(input, URL):\",\n \"TouchActions(browser).long_press(element_to_hover_over).perform() elif(input == 8): browser = webdriver.Safari() browser.maximize_window() # Required\",\n \"visibility browser.get(URL) # with visibility search time.sleep(2) search = returnVisibleElement(browser.find_elements_by_id('subjectInput'))\",\n \"webdriver.Safari() driver.get(\\\"https://www.google.com\\\") search = driver.find_element_by_name(\\\"q\\\") search.send_keys(\\\"Sel\\\") # Google Search \\\"Selenium\\\"\",\n \"== 4): browser = webdriver.Safari() browser.get(URL) # with visibility search\",\n \"URL): if(input == 0): content = requests.get(URL) soup = BeautifulSoup(content.text,'html.parser')\",\n \"data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for item in\",\n \"returnVisibleElement(browser.find_elements_by_id('subjectInput')) search.send_keys('Google Pixel 3') time.sleep(2) search.send_keys(Keys.ENTER) time.sleep(2) data = browser.find_element_by_class_name('content\",\n \"browser.find_element_by_class_name('si-button-data download-all').click() data = browser.find_element_by_class_name('content content-breakpoint-gt-md') dataList = data.find_elements_by_tag_name('li') for\",\n \"safari but will work on other browser ## https://github.com/seleniumhq/selenium-google-code-issue-archive/issues/4136 ActionChains(browser).click(element_to_hover_over).perform()\",\n \"for element in listOfInputElements: if element.is_displayed(): return element def printSelection():\",\n \"re from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as\",\n \"not supported in safari but will work on other browser\",\n \"found within 3 seconds otherwise raise TimeoutException element = WebDriverWait(browser,\",\n \"for the button visibility browser.get(URL) # with visibility search time.sleep(2)\",\n \"browser = webdriver.Safari() browser.get(URL) # with visibility search time.sleep(2) search\"\n]"}}},{"rowIdx":3483,"cells":{"ngram":{"kind":"list like","value":["line has to be handled in a special way and","data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set of","2018, , Austria * # * # The Space Python","more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import","has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next","the rest should come with the next read self.tcpLineBuffer =","activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000,","[\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the","PARTICULAR PURPOSE. See the MIT License * # for more","it under under the terms of the MIT License as","LINEBUFFERLEN = 256 ########### # classes # ########### # =============================================================================","the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open","terminate the client connection self.disconnectClient(); return if (upperLine == \"Q\")","by the * # Massachusetts Institute of Technology. * #","or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License","* # The Space Python Library is free software; you","* # The Space Python Library is distributed in the","LOG(\"line = \" + line) return 0 ############# # functions","return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" +","#****************************************************************************** # (C) 2018, , Austria * # * #","stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback","telnet if line[-1] == \"\\r\": line = line[:-1] # terminate","* # Massachusetts Institute of Technology. * # * #","import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN","LOG(\"Exit requested\") # send the OK response back to the","Library is free software; you can redistribute it and/or *","idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main #","(incl. \"\\n\") pass else: # last line was cutt off","the client connection if exit has been entered (case insensitive)","# ######## if __name__ == \"__main__\": # initialise the system","----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"],","0: LOG(\"OK\") # send the OK response back to the","OK response back to the TECO retString = \"OK\\n\"; self.send(retString.encode())","constants # ############# LINEBUFFERLEN = 256 ########### # classes #","be # processed directly lastLine = lines[-1] lines = lines[:-1]","the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler",", Austria * # * # The Space Python Library","LOG(\"--- idle ---\") ######## # main # ######## if __name__","of the MIT License as published by the * #","self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine == \"QUIT\"):","idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\")","initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() #","+= data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input:","\"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a","upperLine = line.upper() if (upperLine == \"X\") or (upperLine ==","configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler()","input: extract the lines from the line buffer lines =","# --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client","for clients like telnet if line[-1] == \"\\r\": line =","in a special way and can not be # processed","= line[:-1] # terminate the client connection if exit has","MIT License as published by the * # Massachusetts Institute","receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send data\"\"\"","lines = lines[:-1] if lastLine == \"\": # read of","TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) #","Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP #############","The Space Python Library is free software; you can redistribute","a data line\"\"\" LOG(\"line = \" + line) return 0","LOG_ERROR(str(pstatus)) # set the Error response back to the client:","not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() #","TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise","connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line);","line = line[:-1] # terminate the client connection if exit","client connection if exit has been entered (case insensitive) upperLine","in lines: # remove a terminating \"\\r\" for clients like","model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler)","\"\"\"Callback when a client has send data\"\"\" LOG(\"*** receiveCallback ***\")","server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate","createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not","disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \"","terminate the client connection if exit has been entered (case","if pstatus == 0: LOG(\"OK\") # send the OK response","the input: extract the lines from the line buffer lines","functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system","line\"\"\" LOG(\"line = \" + line) return 0 ############# #","== 0: LOG(\"OK\") # send the OK response back to","TCP server\") createServer() # start the tasks LOG(\"start modelTask...\") modelTask.start()","delegate the input pstatus = self.processLine(line); if pstatus == 0:","distributed in the hope that it will be useful, *","modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) #","the next set of byte from the data socket data","to be handled in a special way and can not","== \"QUIT\"): LOG(\"Quit requested\") # send the OK response back","handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask =","retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # ---------------------------------------------------------------------------","classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\"","(upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") #","0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration():","client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection","--------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has send","--------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask","# main # ######## if __name__ == \"__main__\": # initialise","* # for more details. * #****************************************************************************** # Unit Tests","# terminate the client connection if exit has been entered","__init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask,","lastLine == \"\": # read of the data was complete","pass else: # last line was cutt off and the","# handle the input: extract the lines from the line","only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\"","== \"__main__\": # initialise the system configuration initConfiguration() # initialise","from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS,","response back to the client retString = \"OK\\n\" self.send(retString.encode()) #","data line\"\"\" LOG(\"line = \" + line) return 0 #############","tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the","\"\\r\" for clients like telnet if line[-1] == \"\\r\": line","# initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise","\"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) #","pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") # send","server LOG(\"Open the TCP server\") createServer() # start the tasks","rest should come with the next read self.tcpLineBuffer = lastLine","last line was cutt off and the rest should come","= \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0)","failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback","has been entered (case insensitive) upperLine = line.upper() if (upperLine","sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def","can not be # processed directly lastLine = lines[-1] lines","connection if exit has been entered (case insensitive) upperLine =","a client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read","it and/or * # modify it under under the terms","the MIT License as published by the * # Massachusetts","# ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\",","of byte from the data socket data = self.recv(LINEBUFFERLEN) if","# activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction():","MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT","consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True)","* # but WITHOUT ANY WARRANTY; without even the implied","# read the next set of byte from the data","the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") #","\"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP","= UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # ---------------------------------------------------------------------------","lines[:-1] if lastLine == \"\": # read of the data","for line in lines: # remove a terminating \"\\r\" for","\"QUIT\"): LOG(\"Quit requested\") # send the OK response back to","automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer:","idle ---\") ######## # main # ######## if __name__ ==","input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\") #","was cutt off and the rest should come with the","of Technology. * # * # The Space Python Library","WARRANTY; without even the implied warranty of * # MERCHANTABILITY","warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR","============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self,","# for more details. * #****************************************************************************** # Unit Tests *","free software; you can redistribute it and/or * # modify","# ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server =","# send the OK response back to the client retString","Library is distributed in the hope that it will be","# client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer","self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if","self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine","under under the terms of the MIT License as published","log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a","UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask","\"__main__\": # initialise the system configuration initConfiguration() # initialise the","+ line) return 0 ############# # functions # ############# #","retString = \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient();","lines = tcpLineBuffer.split(\"\\n\") # the last line has to be","a terminating \"\\r\" for clients like telnet if line[-1] ==","UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the","# ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" #","# initialise the system configuration initConfiguration() # initialise the console","data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract","256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer):","* # modify it under under the terms of the","\"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the","############# LINEBUFFERLEN = 256 ########### # classes # ########### #","come with the next read self.tcpLineBuffer = lastLine for line","[\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\"","handled in a special way and can not be #","\"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self,","def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self,","way and can not be # processed directly lastLine =","and can not be # processed directly lastLine = lines[-1]","execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line):","without even the implied warranty of * # MERCHANTABILITY or","client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus =","# read of the data was complete (incl. \"\\n\") pass","\"\"\"Callback when a client has send a data line\"\"\" LOG(\"line","socket data = self.recv(LINEBUFFERLEN) if data == None: # client","UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256","receiveCallback ***\") # read the next set of byte from","lastLine for line in lines: # remove a terminating \"\\r\"","published by the * # Massachusetts Institute of Technology. *","has to be handled in a special way and can","was complete (incl. \"\\n\") pass else: # last line was","send the OK response back to the client retString =","ANY WARRANTY; without even the implied warranty of * #","LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants #","clients like telnet if line[-1] == \"\\r\": line = line[:-1]","data == None: # client is automatically disconnected return tcpLineBuffer","# The Space Python Library is distributed in the hope","# create the TCP server LOG(\"Open the TCP server\") createServer()","# register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP","# but WITHOUT ANY WARRANTY; without even the implied warranty","import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR","# Massachusetts Institute of Technology. * # * # The","system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def","Python Library is distributed in the hope that it will","UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create","lines: # remove a terminating \"\\r\" for clients like telnet","self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate","# remove a terminating \"\\r\" for clients like telnet if","----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT))","# functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the","the system configuration initConfiguration() # initialise the console handler consoleHandler","Institute of Technology. * # * # The Space Python","byte from the data socket data = self.recv(LINEBUFFERLEN) if data","should come with the next read self.tcpLineBuffer = lastLine for","LOG(\"Open the TCP server\") createServer() # start the tasks LOG(\"start","when a client has send a data line\"\"\" LOG(\"line =","# (C) 2018, , Austria * # * # The","portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask):","LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# #","portNr): \"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr)","buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has to","Austria * # * # The Space Python Library is","initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]])","class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr):","\"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes","server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # -----------------------------------------------------------------------------","else: LOG_ERROR(str(pstatus)) # set the Error response back to the","* #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO,","# * # The Space Python Library is free software;","directly lastLine = lines[-1] lines = lines[:-1] if lastLine ==","client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) #","if __name__ == \"__main__\": # initialise the system configuration initConfiguration()","* # * # The Space Python Library is free","the input pstatus = self.processLine(line); if pstatus == 0: LOG(\"OK\")","will be useful, * # but WITHOUT ANY WARRANTY; without","response back to the client: retString = \"Error: execution failed","the * # Massachusetts Institute of Technology. * # *","back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus))","line was cutt off and the rest should come with","under the terms of the MIT License as published by","# last line was cutt off and the rest should","is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\")","def createServer(): \"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if","of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\"","processLine(self, line): \"\"\"Callback when a client has send a data","the data was complete (incl. \"\\n\") pass else: # last","# terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the","response back to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else:","+ tcpLineBuffer) # handle the input: extract the lines from","self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback","register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server","handle the input: extract the lines from the line buffer","lines from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the","as published by the * # Massachusetts Institute of Technology.","########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # ---------------------------------------------------------------------------","client connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine","line[-1] == \"\\r\": line = line[:-1] # terminate the client","############# # constants # ############# LINEBUFFERLEN = 256 ########### #","\" + tcpLineBuffer) # handle the input: extract the lines","if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\")","the TCP server\") createServer() # start the tasks LOG(\"start modelTask...\")","send data\"\"\" LOG(\"*** receiveCallback ***\") # read the next set","complete (incl. \"\\n\") pass else: # last line was cutt","return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def","The Space Python Library is distributed in the hope that","######## if __name__ == \"__main__\": # initialise the system configuration","A PARTICULAR PURPOSE. See the MIT License * # for","the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the client","(upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") #","if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction()","from the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last","socket, stateMask): \"\"\"Callback when a client has send data\"\"\" LOG(\"***","def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main","the OK response back to the client retString = \"OK\\n\"","or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK","tcpLineBuffer) # handle the input: extract the lines from the","is free software; you can redistribute it and/or * #","off and the rest should come with the next read","Error response back to the client: retString = \"Error: execution","\"1234\"]]) # ----------------------------------------------------------------------------- def createServer(): \"\"\"create the TCP server\"\"\" server","self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the OK","insensitive) upperLine = line.upper() if (upperLine == \"X\") or (upperLine","\"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back","# initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the","# modify it under under the terms of the MIT","############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([","== \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send","== None: # client is automatically disconnected return tcpLineBuffer =","Technology. * # * # The Space Python Library is","the Error response back to the client: retString = \"Error:","if line[-1] == \"\\r\": line = line[:-1] # terminate the","attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer =","= \" + line) return 0 ############# # functions #","you can redistribute it and/or * # modify it under","# ############# LINEBUFFERLEN = 256 ########### # classes # ###########","lines[-1] lines = lines[:-1] if lastLine == \"\": # read","client has send a data line\"\"\" LOG(\"line = \" +","modify it under under the terms of the MIT License","(C) 2018, , Austria * # * # The Space","the line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line","send a data line\"\"\" LOG(\"line = \" + line) return","read self.tcpLineBuffer = lastLine for line in lines: # remove","= 256 ########### # classes # ########### # ============================================================================= class","# --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client has","= \"Error: execution failed (see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def","import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP","a client has send a data line\"\"\" LOG(\"line = \"","TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the","self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when a client","\"\"\"create the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST):","client has send data\"\"\" LOG(\"*** receiveCallback ***\") # read the","Space Python Library is distributed in the hope that it","hope that it will be useful, * # but WITHOUT","self.tcpLineBuffer = lastLine for line in lines: # remove a","LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # #############","if exit has been entered (case insensitive) upperLine = line.upper()","(see log)!\\n\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \"\"\"Callback when","LOG(\"OK\") # send the OK response back to the TECO","= \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response","= \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when","Python Library is free software; you can redistribute it and/or","to the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) #","back to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate","create the TCP server LOG(\"Open the TCP server\") createServer() #","\"EXIT\"): LOG(\"Exit requested\") # send the OK response back to","line[:-1] # terminate the client connection if exit has been","= tcpLineBuffer.split(\"\\n\") # the last line has to be handled","WITHOUT ANY WARRANTY; without even the implied warranty of *","line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit","the TCP server\"\"\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1)","pstatus == 0: LOG(\"OK\") # send the OK response back","the TECO retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set","--------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has","# the last line has to be handled in a","idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"---","Massachusetts Institute of Technology. * # * # The Space","* # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See","been entered (case insensitive) upperLine = line.upper() if (upperLine ==","---\") ######## # main # ######## if __name__ == \"__main__\":","== \"\": # read of the data was complete (incl.","of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.","modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" #","= self.processLine(line); if pstatus == 0: LOG(\"OK\") # send the","retString = \"OK\\n\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error","implied warranty of * # MERCHANTABILITY or FITNESS FOR A","else: # last line was cutt off and the rest","FITNESS FOR A PARTICULAR PURPOSE. See the MIT License *","the implied warranty of * # MERCHANTABILITY or FITNESS FOR","# classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of","(upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK response","requested\") # send the OK response back to the client","set the Error response back to the client: retString =","next set of byte from the data socket data =","Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG,","UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ###########","sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import","set of byte from the data socket data = self.recv(LINEBUFFERLEN)","# processed directly lastLine = lines[-1] lines = lines[:-1] if","# ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise the system configuration\"\"\"","processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine","UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register","terminating \"\\r\" for clients like telnet if line[-1] == \"\\r\":","# delegate the input pstatus = self.processLine(line); if pstatus ==","configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # ----------------------------------------------------------------------------- def createServer():","the terms of the MIT License as published by the","\"\\r\": line = line[:-1] # terminate the client connection if","the client connection self.disconnectClient(); return if (upperLine == \"Q\") or","the data socket data = self.recv(LINEBUFFERLEN) if data == None:","self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to","the OK response back to the TECO retString = \"OK\\n\";","be handled in a special way and can not be","when a client has send data\"\"\" LOG(\"*** receiveCallback ***\") #","with the next read self.tcpLineBuffer = lastLine for line in","sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus","self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle","\"\": # read of the data was complete (incl. \"\\n\")","tcpLineBuffer.split(\"\\n\") # the last line has to be handled in","the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus","for more details. * #****************************************************************************** # Unit Tests * #******************************************************************************","that it will be useful, * # but WITHOUT ANY","the client: retString = \"Error: execution failed (see log)!\\n\" self.send(retString.encode())","* # * # The Space Python Library is distributed","redistribute it and/or * # modify it under under the","initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the","__name__ == \"__main__\": # initialise the system configuration initConfiguration() #","modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP server\")","UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## # main # ########","== \"EXIT\"): LOG(\"Exit requested\") # send the OK response back","* #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from","the TCP server LOG(\"Open the TCP server\") createServer() # start","the MIT License * # for more details. * #******************************************************************************","back to the client: retString = \"Error: execution failed (see","python3 #****************************************************************************** # (C) 2018, , Austria * # *","# constants # ############# LINEBUFFERLEN = 256 ########### # classes","= self.recv(LINEBUFFERLEN) if data == None: # client is automatically","LOG(\"tcpLineBuffer: \" + tcpLineBuffer) # handle the input: extract the","useful, * # but WITHOUT ANY WARRANTY; without even the","line buffer lines = tcpLineBuffer.split(\"\\n\") # the last line has","== \"\\r\": line = line[:-1] # terminate the client connection","PURPOSE. See the MIT License * # for more details.","LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants","= UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) #","console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the","----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ######## #","UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK,","def processLine(self, line): \"\"\"Callback when a client has send a","# ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass of UTIL.TCP.SingleClientServer\"\"\" # --------------------------------------------------------------------------- def","or (upperLine == \"QUIT\"): LOG(\"Quit requested\") # send the OK","# * # The Space Python Library is distributed in","TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function","UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN =","terms of the MIT License as published by the *","#!/usr/bin/env python3 #****************************************************************************** # (C) 2018, , Austria * #","############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \"\"\"initialise","return if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit","if lastLine == \"\": # read of the data was","initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console","handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\"Open the TCP","# send the OK response back to the TECO retString","\" + line) return 0 ############# # functions # #############","entered (case insensitive) upperLine = line.upper() if (upperLine == \"X\")","client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer +=","it will be useful, * # but WITHOUT ANY WARRANTY;","connection self.disconnectClient(); return if (upperLine == \"Q\") or (upperLine ==","but WITHOUT ANY WARRANTY; without even the implied warranty of","in the hope that it will be useful, * #","def receiveCallback(self, socket, stateMask): \"\"\"Callback when a client has send","last line has to be handled in a special way","special way and can not be # processed directly lastLine","UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self,","# terminate the client connection self.disconnectClient(); return if (upperLine ==","\"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the","LOG(\"Quit requested\") # send the OK response back to the","# ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle ---\") ########","zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction)","= UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create","software; you can redistribute it and/or * # modify it","# Unit Tests * #****************************************************************************** import sys from UTIL.SYS import","FOR A PARTICULAR PURPOSE. See the MIT License * #","from the data socket data = self.recv(LINEBUFFERLEN) if data ==","line) return 0 ############# # functions # ############# # -----------------------------------------------------------------------------","initialise the system configuration initConfiguration() # initialise the console handler","idleFunction) LOG(\"--- idle ---\") ######## # main # ######## if","Space Python Library is free software; you can redistribute it","(case insensitive) upperLine = line.upper() if (upperLine == \"X\") or","LOG(\"*** receiveCallback ***\") # read the next set of byte","data was complete (incl. \"\\n\") pass else: # last line","TCP server LOG(\"Open the TCP server\") createServer() # start the","if data == None: # client is automatically disconnected return","the hope that it will be useful, * # but","of the data was complete (incl. \"\\n\") pass else: #","######## # main # ######## if __name__ == \"__main__\": #","\"\\n\") pass else: # last line was cutt off and","line in lines: # remove a terminating \"\\r\" for clients","OK response back to the client retString = \"OK\\n\" self.send(retString.encode())","console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask","#****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING,","= self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer) #","# set the Error response back to the client: retString","\"\"\"Initialise attributes only\"\"\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer","None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer","data socket data = self.recv(LINEBUFFERLEN) if data == None: #","read of the data was complete (incl. \"\\n\") pass else:","details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys","= lastLine for line in lines: # remove a terminating","terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input","line): \"\"\"Callback when a client has send a data line\"\"\"","License as published by the * # Massachusetts Institute of","function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\"--- idle","like telnet if line[-1] == \"\\r\": line = line[:-1] #","the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model","cutt off and the rest should come with the next","= lines[:-1] if lastLine == \"\": # read of the","next read self.tcpLineBuffer = lastLine for line in lines: #","system configuration initConfiguration() # initialise the console handler consoleHandler =","\"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if","to the client: retString = \"Error: execution failed (see log)!\\n\"","= line.upper() if (upperLine == \"X\") or (upperLine == \"EXIT\"):","remove a terminating \"\\r\" for clients like telnet if line[-1]","the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\", \"1234\"]]) # -----------------------------------------------------------------------------","See the MIT License * # for more details. *","the next read self.tcpLineBuffer = lastLine for line in lines:","= \"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return","= lines[-1] lines = lines[:-1] if lastLine == \"\": #","# The Space Python Library is free software; you can","to the client retString = \"OK\\n\" self.send(retString.encode()) # terminate the","(upperLine == \"EXIT\"): LOG(\"Exit requested\") # send the OK response","read the next set of byte from the data socket","the last line has to be handled in a special","main # ######## if __name__ == \"__main__\": # initialise the","def initConfiguration(): \"\"\"initialise the system configuration\"\"\" UTIL.SYS.s_configuration.setDefaults([ [\"HOST\", \"127.0.0.1\"], [\"SERVER_PORT\",","#****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS","= TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle","be useful, * # but WITHOUT ANY WARRANTY; without even","tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\"ascii\") LOG(\"tcpLineBuffer: \" + tcpLineBuffer)","***\") # read the next set of byte from the","modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def receiveCallback(self, socket,","send the OK response back to the TECO retString =","# --------------------------------------------------------------------------- def __init__(self, portNr): \"\"\"Initialise attributes only\"\"\" modelTask =","self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected","and the rest should come with the next read self.tcpLineBuffer","== \"X\") or (upperLine == \"EXIT\"): LOG(\"Exit requested\") # send","Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error,","\"OK\\n\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) #","data = self.recv(LINEBUFFERLEN) if data == None: # client is","exit has been entered (case insensitive) upperLine = line.upper() if","even the implied warranty of * # MERCHANTABILITY or FITNESS","has send a data line\"\"\" LOG(\"line = \" + line)","if (upperLine == \"Q\") or (upperLine == \"QUIT\"): LOG(\"Quit requested\")","License * # for more details. * #****************************************************************************** # Unit","can redistribute it and/or * # modify it under under","is distributed in the hope that it will be useful,","########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \"\"\"Subclass","lastLine = lines[-1] lines = lines[:-1] if lastLine == \"\":","UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \"\" # --------------------------------------------------------------------------- def","# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the","MIT License * # for more details. * #****************************************************************************** #","server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic","not be # processed directly lastLine = lines[-1] lines =","and/or * # modify it under under the terms of","a special way and can not be # processed directly","extract the lines from the line buffer lines = tcpLineBuffer.split(\"\\n\")"],"string":"[\n \"line has to be handled in a special way and\",\n \"data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set of\",\n \"2018, , Austria * # * # The Space Python\",\n \"more details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import\",\n \"has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next\",\n \"the rest should come with the next read self.tcpLineBuffer =\",\n \"activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000,\",\n \"[\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the\",\n \"PARTICULAR PURPOSE. See the MIT License * # for more\",\n \"it under under the terms of the MIT License as\",\n \"LINEBUFFERLEN = 256 ########### # classes # ########### # =============================================================================\",\n \"the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open\",\n \"terminate the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\")\",\n \"by the * # Massachusetts Institute of Technology. * #\",\n \"or FITNESS FOR A PARTICULAR PURPOSE. See the MIT License\",\n \"* # The Space Python Library is free software; you\",\n \"* # The Space Python Library is distributed in the\",\n \"LOG(\\\"line = \\\" + line) return 0 ############# # functions\",\n \"return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" +\",\n \"#****************************************************************************** # (C) 2018, , Austria * # * #\",\n \"stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback\",\n \"telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate\",\n \"* # Massachusetts Institute of Technology. * # * #\",\n \"import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN\",\n \"LOG(\\\"Exit requested\\\") # send the OK response back to the\",\n \"Library is free software; you can redistribute it and/or *\",\n \"idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main #\",\n \"(incl. \\\"\\\\n\\\") pass else: # last line was cutt off\",\n \"the client connection if exit has been entered (case insensitive)\",\n \"# ######## if __name__ == \\\"__main__\\\": # initialise the system\",\n \"----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"],\",\n \"0: LOG(\\\"OK\\\") # send the OK response back to the\",\n \"OK response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode())\",\n \"constants # ############# LINEBUFFERLEN = 256 ########### # classes #\",\n \"be # processed directly lastLine = lines[-1] lines = lines[:-1]\",\n \"the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler\",\n \", Austria * # * # The Space Python Library\",\n \"LOG(\\\"--- idle ---\\\") ######## # main # ######## if __name__\",\n \"of the MIT License as published by the * #\",\n \"self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"):\",\n \"idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\")\",\n \"initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() #\",\n \"+= data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input:\",\n \"\\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a\",\n \"upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine ==\",\n \"configuration initConfiguration() # initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler()\",\n \"input: extract the lines from the line buffer lines =\",\n \"# --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client\",\n \"for clients like telnet if line[-1] == \\\"\\\\r\\\": line =\",\n \"in a special way and can not be # processed\",\n \"= line[:-1] # terminate the client connection if exit has\",\n \"MIT License as published by the * # Massachusetts Institute\",\n \"receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\"\",\n \"lines = lines[:-1] if lastLine == \\\"\\\": # read of\",\n \"TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) #\",\n \"Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP #############\",\n \"The Space Python Library is free software; you can redistribute\",\n \"a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0\",\n \"LOG_ERROR(str(pstatus)) # set the Error response back to the client:\",\n \"not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() #\",\n \"TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise\",\n \"connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line);\",\n \"line = line[:-1] # terminate the client connection if exit\",\n \"client connection if exit has been entered (case insensitive) upperLine\",\n \"in lines: # remove a terminating \\\"\\\\r\\\" for clients like\",\n \"model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler)\",\n \"\\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\")\",\n \"server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate\",\n \"createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not\",\n \"disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\"\",\n \"terminate the client connection if exit has been entered (case\",\n \"if pstatus == 0: LOG(\\\"OK\\\") # send the OK response\",\n \"the input: extract the lines from the line buffer lines\",\n \"functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system\",\n \"line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 ############# #\",\n \"== 0: LOG(\\\"OK\\\") # send the OK response back to\",\n \"TCP server\\\") createServer() # start the tasks LOG(\\\"start modelTask...\\\") modelTask.start()\",\n \"delegate the input pstatus = self.processLine(line); if pstatus == 0:\",\n \"distributed in the hope that it will be useful, *\",\n \"modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) #\",\n \"the next set of byte from the data socket data\",\n \"to be handled in a special way and can not\",\n \"== \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back\",\n \"handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask =\",\n \"retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # ---------------------------------------------------------------------------\",\n \"classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\"\",\n \"(upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") #\",\n \"0 ############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration():\",\n \"client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection\",\n \"--------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has send\",\n \"--------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask\",\n \"# main # ######## if __name__ == \\\"__main__\\\": # initialise\",\n \"* # for more details. * #****************************************************************************** # Unit Tests\",\n \"# terminate the client connection if exit has been entered\",\n \"__init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask,\",\n \"lastLine == \\\"\\\": # read of the data was complete\",\n \"pass else: # last line was cutt off and the\",\n \"# handle the input: extract the lines from the line\",\n \"only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\"\",\n \"== \\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise\",\n \"from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS,\",\n \"response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) #\",\n \"data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return 0 #############\",\n \"tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the\",\n \"\\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\": line\",\n \"# initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise\",\n \"\\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) #\",\n \"pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send\",\n \"server LOG(\\\"Open the TCP server\\\") createServer() # start the tasks\",\n \"rest should come with the next read self.tcpLineBuffer = lastLine\",\n \"last line was cutt off and the rest should come\",\n \"= \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0)\",\n \"failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback\",\n \"has been entered (case insensitive) upperLine = line.upper() if (upperLine\",\n \"sys.exit(-1) # activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def\",\n \"can not be # processed directly lastLine = lines[-1] lines\",\n \"connection if exit has been entered (case insensitive) upperLine =\",\n \"a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read\",\n \"it and/or * # modify it under under the terms\",\n \"the MIT License as published by the * # Massachusetts\",\n \"# ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\",\",\n \"of byte from the data socket data = self.recv(LINEBUFFERLEN) if\",\n \"# activate zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction():\",\n \"MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the MIT\",\n \"consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True)\",\n \"* # but WITHOUT ANY WARRANTY; without even the implied\",\n \"# read the next set of byte from the data\",\n \"the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") #\",\n \"\\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP\",\n \"= UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # ---------------------------------------------------------------------------\",\n \"lines[:-1] if lastLine == \\\"\\\": # read of the data\",\n \"for line in lines: # remove a terminating \\\"\\\\r\\\" for\",\n \"\\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response back to\",\n \"automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer:\",\n \"idle ---\\\") ######## # main # ######## if __name__ ==\",\n \"input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") #\",\n \"was cutt off and the rest should come with the\",\n \"of Technology. * # * # The Space Python Library\",\n \"WARRANTY; without even the implied warranty of * # MERCHANTABILITY\",\n \"warranty of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR\",\n \"============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self,\",\n \"# for more details. * #****************************************************************************** # Unit Tests *\",\n \"free software; you can redistribute it and/or * # modify\",\n \"# ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server =\",\n \"# send the OK response back to the client retString\",\n \"Library is distributed in the hope that it will be\",\n \"# client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer\",\n \"self.disconnectClient(); sys.exit(0) # delegate the input pstatus = self.processLine(line); if\",\n \"self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if (upperLine\",\n \"under under the terms of the MIT License as published\",\n \"log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a\",\n \"UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask\",\n \"\\\"__main__\\\": # initialise the system configuration initConfiguration() # initialise the\",\n \"+ line) return 0 ############# # functions # ############# #\",\n \"retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient();\",\n \"lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be\",\n \"a terminating \\\"\\\\r\\\" for clients like telnet if line[-1] ==\",\n \"UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the\",\n \"# ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" #\",\n \"# initialise the system configuration initConfiguration() # initialise the console\",\n \"data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract\",\n \"256 ########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer):\",\n \"* # modify it under under the terms of the\",\n \"\\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the\",\n \"############# LINEBUFFERLEN = 256 ########### # classes # ########### #\",\n \"come with the next read self.tcpLineBuffer = lastLine for line\",\n \"[\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\"\",\n \"handled in a special way and can not be #\",\n \"\\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self,\",\n \"def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self,\",\n \"way and can not be # processed directly lastLine =\",\n \"and can not be # processed directly lastLine = lines[-1]\",\n \"execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line):\",\n \"without even the implied warranty of * # MERCHANTABILITY or\",\n \"client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus =\",\n \"# read of the data was complete (incl. \\\"\\\\n\\\") pass\",\n \"\\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\" LOG(\\\"line\",\n \"socket data = self.recv(LINEBUFFERLEN) if data == None: # client\",\n \"UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256\",\n \"receiveCallback ***\\\") # read the next set of byte from\",\n \"lastLine for line in lines: # remove a terminating \\\"\\\\r\\\"\",\n \"published by the * # Massachusetts Institute of Technology. *\",\n \"has to be handled in a special way and can\",\n \"was complete (incl. \\\"\\\\n\\\") pass else: # last line was\",\n \"send the OK response back to the client retString =\",\n \"ANY WARRANTY; without even the implied warranty of * #\",\n \"LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants #\",\n \"clients like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1]\",\n \"data == None: # client is automatically disconnected return tcpLineBuffer\",\n \"# The Space Python Library is distributed in the hope\",\n \"# create the TCP server LOG(\\\"Open the TCP server\\\") createServer()\",\n \"# register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP\",\n \"# but WITHOUT ANY WARRANTY; without even the implied warranty\",\n \"import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR\",\n \"# Massachusetts Institute of Technology. * # * # The\",\n \"system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def\",\n \"Python Library is distributed in the hope that it will\",\n \"UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create\",\n \"lines: # remove a terminating \\\"\\\\r\\\" for clients like telnet\",\n \"self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) # delegate\",\n \"# remove a terminating \\\"\\\\r\\\" for clients like telnet if\",\n \"----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT))\",\n \"# functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the\",\n \"the system configuration initConfiguration() # initialise the console handler consoleHandler\",\n \"Institute of Technology. * # * # The Space Python\",\n \"byte from the data socket data = self.recv(LINEBUFFERLEN) if data\",\n \"should come with the next read self.tcpLineBuffer = lastLine for\",\n \"LOG(\\\"Open the TCP server\\\") createServer() # start the tasks LOG(\\\"start\",\n \"when a client has send a data line\\\"\\\"\\\" LOG(\\\"line =\",\n \"# (C) 2018, , Austria * # * # The\",\n \"portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask):\",\n \"LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# #\",\n \"portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr)\",\n \"buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to\",\n \"Austria * # * # The Space Python Library is\",\n \"initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]])\",\n \"class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr):\",\n \"\\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes\",\n \"server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction() # -----------------------------------------------------------------------------\",\n \"else: LOG_ERROR(str(pstatus)) # set the Error response back to the\",\n \"* #****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO,\",\n \"# * # The Space Python Library is free software;\",\n \"directly lastLine = lines[-1] lines = lines[:-1] if lastLine ==\",\n \"client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) #\",\n \"if __name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration()\",\n \"* # * # The Space Python Library is free\",\n \"the input pstatus = self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\")\",\n \"will be useful, * # but WITHOUT ANY WARRANTY; without\",\n \"response back to the client: retString = \\\"Error: execution failed\",\n \"the * # Massachusetts Institute of Technology. * # *\",\n \"back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus))\",\n \"line was cutt off and the rest should come with\",\n \"under the terms of the MIT License as published by\",\n \"# last line was cutt off and the rest should\",\n \"is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\")\",\n \"def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if\",\n \"of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\"\",\n \"processLine(self, line): \\\"\\\"\\\"Callback when a client has send a data\",\n \"the data was complete (incl. \\\"\\\\n\\\") pass else: # last\",\n \"# terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the\",\n \"response back to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else:\",\n \"+ tcpLineBuffer) # handle the input: extract the lines from\",\n \"self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback\",\n \"register the console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server\",\n \"handle the input: extract the lines from the line buffer\",\n \"lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the\",\n \"as published by the * # Massachusetts Institute of Technology.\",\n \"########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # ---------------------------------------------------------------------------\",\n \"client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine\",\n \"line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the client\",\n \"############# # constants # ############# LINEBUFFERLEN = 256 ########### #\",\n \"\\\" + tcpLineBuffer) # handle the input: extract the lines\",\n \"if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\")\",\n \"the TCP server\\\") createServer() # start the tasks LOG(\\\"start modelTask...\\\")\",\n \"send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the next set\",\n \"complete (incl. \\\"\\\\n\\\") pass else: # last line was cutt\",\n \"return 0 ############# # functions # ############# # ----------------------------------------------------------------------------- def\",\n \"The Space Python Library is distributed in the hope that\",\n \"######## if __name__ == \\\"__main__\\\": # initialise the system configuration\",\n \"A PARTICULAR PURPOSE. See the MIT License * # for\",\n \"the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client\",\n \"(upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") #\",\n \"if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function idleFunction()\",\n \"from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last\",\n \"socket, stateMask): \\\"\\\"\\\"Callback when a client has send data\\\"\\\"\\\" LOG(\\\"***\",\n \"def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main\",\n \"the OK response back to the client retString = \\\"OK\\\\n\\\"\",\n \"or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK\",\n \"tcpLineBuffer) # handle the input: extract the lines from the\",\n \"is free software; you can redistribute it and/or * #\",\n \"off and the rest should come with the next read\",\n \"Error response back to the client: retString = \\\"Error: execution\",\n \"\\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer(): \\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server\",\n \"self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the OK\",\n \"insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or (upperLine\",\n \"\\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back\",\n \"# initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the\",\n \"# modify it under under the terms of the MIT\",\n \"############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([\",\n \"== \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send\",\n \"== None: # client is automatically disconnected return tcpLineBuffer =\",\n \"Technology. * # * # The Space Python Library is\",\n \"the Error response back to the client: retString = \\\"Error:\",\n \"if line[-1] == \\\"\\\\r\\\": line = line[:-1] # terminate the\",\n \"attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer =\",\n \"= \\\" + line) return 0 ############# # functions #\",\n \"you can redistribute it and/or * # modify it under\",\n \"# ############# LINEBUFFERLEN = 256 ########### # classes # ###########\",\n \"lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": # read\",\n \"client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" +\",\n \"modify it under under the terms of the MIT License\",\n \"(C) 2018, , Austria * # * # The Space\",\n \"the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line\",\n \"send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line) return\",\n \"read self.tcpLineBuffer = lastLine for line in lines: # remove\",\n \"= 256 ########### # classes # ########### # ============================================================================= class\",\n \"# --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client has\",\n \"= \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def\",\n \"import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP\",\n \"a client has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\"\",\n \"TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the\",\n \"self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when a client\",\n \"\\\"\\\"\\\"create the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST):\",\n \"client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") # read the\",\n \"Space Python Library is distributed in the hope that it\",\n \"hope that it will be useful, * # but WITHOUT\",\n \"self.tcpLineBuffer = lastLine for line in lines: # remove a\",\n \"LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # #############\",\n \"if exit has been entered (case insensitive) upperLine = line.upper()\",\n \"(see log)!\\\\n\\\" self.send(retString.encode()) # --------------------------------------------------------------------------- def processLine(self, line): \\\"\\\"\\\"Callback when\",\n \"LOG(\\\"OK\\\") # send the OK response back to the TECO\",\n \"= \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response\",\n \"= \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when\",\n \"Python Library is free software; you can redistribute it and/or\",\n \"to the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) #\",\n \"back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate\",\n \"create the TCP server LOG(\\\"Open the TCP server\\\") createServer() #\",\n \"\\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back to\",\n \"line[:-1] # terminate the client connection if exit has been\",\n \"= tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled\",\n \"WITHOUT ANY WARRANTY; without even the implied warranty of *\",\n \"line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit\",\n \"the TCP server\\\"\\\"\\\" server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1)\",\n \"pstatus == 0: LOG(\\\"OK\\\") # send the OK response back\",\n \"the TECO retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set\",\n \"--------------------------------------------------------------------------- def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has\",\n \"# the last line has to be handled in a\",\n \"idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"---\",\n \"Massachusetts Institute of Technology. * # * # The Space\",\n \"* # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See\",\n \"been entered (case insensitive) upperLine = line.upper() if (upperLine ==\",\n \"---\\\") ######## # main # ######## if __name__ == \\\"__main__\\\":\",\n \"== \\\"\\\": # read of the data was complete (incl.\",\n \"of * # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\",\n \"modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" #\",\n \"= self.processLine(line); if pstatus == 0: LOG(\\\"OK\\\") # send the\",\n \"retString = \\\"OK\\\\n\\\"; self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error\",\n \"implied warranty of * # MERCHANTABILITY or FITNESS FOR A\",\n \"else: # last line was cutt off and the rest\",\n \"FITNESS FOR A PARTICULAR PURPOSE. See the MIT License *\",\n \"the implied warranty of * # MERCHANTABILITY or FITNESS FOR\",\n \"# classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of\",\n \"(upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK response\",\n \"requested\\\") # send the OK response back to the client\",\n \"set the Error response back to the client: retString =\",\n \"next set of byte from the data socket data =\",\n \"Tests * #****************************************************************************** import sys from UTIL.SYS import Error, LOG,\",\n \"UTIL.TCP ############# # constants # ############# LINEBUFFERLEN = 256 ###########\",\n \"sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import\",\n \"set of byte from the data socket data = self.recv(LINEBUFFERLEN)\",\n \"# processed directly lastLine = lines[-1] lines = lines[:-1] if\",\n \"# ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\"\",\n \"processed directly lastLine = lines[-1] lines = lines[:-1] if lastLine\",\n \"UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register\",\n \"terminating \\\"\\\\r\\\" for clients like telnet if line[-1] == \\\"\\\\r\\\":\",\n \"# delegate the input pstatus = self.processLine(line); if pstatus ==\",\n \"configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # ----------------------------------------------------------------------------- def createServer():\",\n \"the terms of the MIT License as published by the\",\n \"\\\"\\\\r\\\": line = line[:-1] # terminate the client connection if\",\n \"the client connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or\",\n \"the data socket data = self.recv(LINEBUFFERLEN) if data == None:\",\n \"self.send(retString.encode()) else: LOG_ERROR(str(pstatus)) # set the Error response back to\",\n \"the OK response back to the TECO retString = \\\"OK\\\\n\\\";\",\n \"be handled in a special way and can not be\",\n \"when a client has send data\\\"\\\"\\\" LOG(\\\"*** receiveCallback ***\\\") #\",\n \"with the next read self.tcpLineBuffer = lastLine for line in\",\n \"sys.exit(0) # delegate the input pstatus = self.processLine(line); if pstatus\",\n \"self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle\",\n \"\\\"\\\": # read of the data was complete (incl. \\\"\\\\n\\\")\",\n \"tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has to be handled in\",\n \"the client connection self.disconnectClient(); sys.exit(0) # delegate the input pstatus\",\n \"for more details. * #****************************************************************************** # Unit Tests * #******************************************************************************\",\n \"that it will be useful, * # but WITHOUT ANY\",\n \"the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\" self.send(retString.encode())\",\n \"* # * # The Space Python Library is distributed\",\n \"redistribute it and/or * # modify it under under the\",\n \"initialise the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the\",\n \"__name__ == \\\"__main__\\\": # initialise the system configuration initConfiguration() #\",\n \"modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP server\\\")\",\n \"UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ########\",\n \"== \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response back\",\n \"* #****************************************************************************** # Unit Tests * #****************************************************************************** import sys from\",\n \"the TCP server LOG(\\\"Open the TCP server\\\") createServer() # start\",\n \"the MIT License * # for more details. * #******************************************************************************\",\n \"back to the client: retString = \\\"Error: execution failed (see\",\n \"python3 #****************************************************************************** # (C) 2018, , Austria * # *\",\n \"# constants # ############# LINEBUFFERLEN = 256 ########### # classes\",\n \"= self.recv(LINEBUFFERLEN) if data == None: # client is automatically\",\n \"LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) # handle the input: extract the\",\n \"useful, * # but WITHOUT ANY WARRANTY; without even the\",\n \"line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\") # the last line has\",\n \"== \\\"\\\\r\\\": line = line[:-1] # terminate the client connection\",\n \"PURPOSE. See the MIT License * # for more details.\",\n \"LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants\",\n \"= UTIL.TASK.ConsoleHandler() # initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) #\",\n \"console handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the\",\n \"----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ######## #\",\n \"UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING, LOG_ERROR import UTIL.SYS, UTIL.TASK,\",\n \"def processLine(self, line): \\\"\\\"\\\"Callback when a client has send a\",\n \"# ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass of UTIL.TCP.SingleClientServer\\\"\\\"\\\" # --------------------------------------------------------------------------- def\",\n \"or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\") # send the OK\",\n \"# * # The Space Python Library is distributed in\",\n \"TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle function\",\n \"UTIL.SYS, UTIL.TASK, UTIL.TCP ############# # constants # ############# LINEBUFFERLEN =\",\n \"terms of the MIT License as published by the *\",\n \"#!/usr/bin/env python3 #****************************************************************************** # (C) 2018, , Austria * #\",\n \"############# # functions # ############# # ----------------------------------------------------------------------------- def initConfiguration(): \\\"\\\"\\\"initialise\",\n \"return if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit\",\n \"if lastLine == \\\"\\\": # read of the data was\",\n \"initialise the model modelTask = UTIL.TASK.ProcessingTask(isParent=True) # register the console\",\n \"handler modelTask.registerConsoleHandler(consoleHandler) # create the TCP server LOG(\\\"Open the TCP\",\n \"# send the OK response back to the TECO retString\",\n \"\\\" + line) return 0 ############# # functions # #############\",\n \"entered (case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\")\",\n \"client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer +=\",\n \"it will be useful, * # but WITHOUT ANY WARRANTY;\",\n \"connection self.disconnectClient(); return if (upperLine == \\\"Q\\\") or (upperLine ==\",\n \"but WITHOUT ANY WARRANTY; without even the implied warranty of\",\n \"in the hope that it will be useful, * #\",\n \"def receiveCallback(self, socket, stateMask): \\\"\\\"\\\"Callback when a client has send\",\n \"last line has to be handled in a special way\",\n \"special way and can not be # processed directly lastLine\",\n \"UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self,\",\n \"# terminate the client connection self.disconnectClient(); return if (upperLine ==\",\n \"\\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the\",\n \"LOG(\\\"Quit requested\\\") # send the OK response back to the\",\n \"# ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle ---\\\") ########\",\n \"zyclic idle function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction)\",\n \"= UTIL.TASK.ProcessingTask(isParent=True) # register the console handler modelTask.registerConsoleHandler(consoleHandler) # create\",\n \"software; you can redistribute it and/or * # modify it\",\n \"# Unit Tests * #****************************************************************************** import sys from UTIL.SYS import\",\n \"FOR A PARTICULAR PURPOSE. See the MIT License * #\",\n \"from the data socket data = self.recv(LINEBUFFERLEN) if data ==\",\n \"line) return 0 ############# # functions # ############# # -----------------------------------------------------------------------------\",\n \"initialise the system configuration initConfiguration() # initialise the console handler\",\n \"idleFunction) LOG(\\\"--- idle ---\\\") ######## # main # ######## if\",\n \"Space Python Library is free software; you can redistribute it\",\n \"(case insensitive) upperLine = line.upper() if (upperLine == \\\"X\\\") or\",\n \"LOG(\\\"*** receiveCallback ***\\\") # read the next set of byte\",\n \"data was complete (incl. \\\"\\\\n\\\") pass else: # last line\",\n \"TCP server LOG(\\\"Open the TCP server\\\") createServer() # start the\",\n \"if data == None: # client is automatically disconnected return\",\n \"the hope that it will be useful, * # but\",\n \"of the data was complete (incl. \\\"\\\\n\\\") pass else: #\",\n \"######## # main # ######## if __name__ == \\\"__main__\\\": #\",\n \"\\\"\\\\n\\\") pass else: # last line was cutt off and\",\n \"line in lines: # remove a terminating \\\"\\\\r\\\" for clients\",\n \"OK response back to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode())\",\n \"console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model modelTask\",\n \"#****************************************************************************** import sys from UTIL.SYS import Error, LOG, LOG_INFO, LOG_WARNING,\",\n \"= self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer) #\",\n \"# set the Error response back to the client: retString\",\n \"\\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask = UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer\",\n \"None: # client is automatically disconnected return tcpLineBuffer = self.tcpLineBuffer\",\n \"data socket data = self.recv(LINEBUFFERLEN) if data == None: #\",\n \"read of the data was complete (incl. \\\"\\\\n\\\") pass else:\",\n \"details. * #****************************************************************************** # Unit Tests * #****************************************************************************** import sys\",\n \"= lastLine for line in lines: # remove a terminating\",\n \"terminate the client connection self.disconnectClient(); sys.exit(0) # delegate the input\",\n \"line): \\\"\\\"\\\"Callback when a client has send a data line\\\"\\\"\\\"\",\n \"License as published by the * # Massachusetts Institute of\",\n \"function idleFunction() # ----------------------------------------------------------------------------- def idleFunction(): UTIL.TASK.s_processingTask.createTimeHandler(1000, idleFunction) LOG(\\\"--- idle\",\n \"like telnet if line[-1] == \\\"\\\\r\\\": line = line[:-1] #\",\n \"the console handler consoleHandler = UTIL.TASK.ConsoleHandler() # initialise the model\",\n \"cutt off and the rest should come with the next\",\n \"= lines[:-1] if lastLine == \\\"\\\": # read of the\",\n \"next read self.tcpLineBuffer = lastLine for line in lines: #\",\n \"system configuration initConfiguration() # initialise the console handler consoleHandler =\",\n \"\\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return if\",\n \"to the client: retString = \\\"Error: execution failed (see log)!\\\\n\\\"\",\n \"= line.upper() if (upperLine == \\\"X\\\") or (upperLine == \\\"EXIT\\\"):\",\n \"remove a terminating \\\"\\\\r\\\" for clients like telnet if line[-1]\",\n \"the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\", \\\"1234\\\"]]) # -----------------------------------------------------------------------------\",\n \"See the MIT License * # for more details. *\",\n \"the next read self.tcpLineBuffer = lastLine for line in lines:\",\n \"= \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); return\",\n \"= lines[-1] lines = lines[:-1] if lastLine == \\\"\\\": #\",\n \"# The Space Python Library is free software; you can\",\n \"to the client retString = \\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the\",\n \"(upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send the OK response\",\n \"read the next set of byte from the data socket\",\n \"the last line has to be handled in a special\",\n \"main # ######## if __name__ == \\\"__main__\\\": # initialise the\",\n \"def initConfiguration(): \\\"\\\"\\\"initialise the system configuration\\\"\\\"\\\" UTIL.SYS.s_configuration.setDefaults([ [\\\"HOST\\\", \\\"127.0.0.1\\\"], [\\\"SERVER_PORT\\\",\",\n \"#****************************************************************************** # Unit Tests * #****************************************************************************** import sys from UTIL.SYS\",\n \"= TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic idle\",\n \"be useful, * # but WITHOUT ANY WARRANTY; without even\",\n \"tcpLineBuffer = self.tcpLineBuffer tcpLineBuffer += data.decode(\\\"ascii\\\") LOG(\\\"tcpLineBuffer: \\\" + tcpLineBuffer)\",\n \"***\\\") # read the next set of byte from the\",\n \"modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def receiveCallback(self, socket,\",\n \"send the OK response back to the TECO retString =\",\n \"# --------------------------------------------------------------------------- def __init__(self, portNr): \\\"\\\"\\\"Initialise attributes only\\\"\\\"\\\" modelTask =\",\n \"self.recv(LINEBUFFERLEN) if data == None: # client is automatically disconnected\",\n \"and the rest should come with the next read self.tcpLineBuffer\",\n \"== \\\"X\\\") or (upperLine == \\\"EXIT\\\"): LOG(\\\"Exit requested\\\") # send\",\n \"Unit Tests * #****************************************************************************** import sys from UTIL.SYS import Error,\",\n \"\\\"OK\\\\n\\\" self.send(retString.encode()) # terminate the client connection self.disconnectClient(); sys.exit(0) #\",\n \"data = self.recv(LINEBUFFERLEN) if data == None: # client is\",\n \"exit has been entered (case insensitive) upperLine = line.upper() if\",\n \"even the implied warranty of * # MERCHANTABILITY or FITNESS\",\n \"has send a data line\\\"\\\"\\\" LOG(\\\"line = \\\" + line)\",\n \"if (upperLine == \\\"Q\\\") or (upperLine == \\\"QUIT\\\"): LOG(\\\"Quit requested\\\")\",\n \"License * # for more details. * #****************************************************************************** # Unit\",\n \"can redistribute it and/or * # modify it under under\",\n \"is distributed in the hope that it will be useful,\",\n \"########### # classes # ########### # ============================================================================= class TCPserver(UTIL.TCP.SingleClientServer): \\\"\\\"\\\"Subclass\",\n \"lastLine = lines[-1] lines = lines[:-1] if lastLine == \\\"\\\":\",\n \"UTIL.TASK.s_processingTask UTIL.TCP.SingleClientServer.__init__(self, modelTask, portNr) self.tcpLineBuffer = \\\"\\\" # --------------------------------------------------------------------------- def\",\n \"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\",\n \"MIT License * # for more details. * #****************************************************************************** #\",\n \"server = TCPserver(portNr=int(UTIL.SYS.s_configuration.SERVER_PORT)) if not server.openConnectPort(UTIL.SYS.s_configuration.HOST): sys.exit(-1) # activate zyclic\",\n \"not be # processed directly lastLine = lines[-1] lines =\",\n \"and/or * # modify it under under the terms of\",\n \"a special way and can not be # processed directly\",\n \"extract the lines from the line buffer lines = tcpLineBuffer.split(\\\"\\\\n\\\")\"\n]"}}},{"rowIdx":3484,"cells":{"ngram":{"kind":"list like","value":["'\\\\.py$', # Exclude pattern must be a string 'exclude': 0,","[sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our","for item in schema.items if hasattr(item, 'key')} warn_additional, = [","import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main","cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg =","string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }],","to the way our merging works, if this schema has","'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be","test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version':","'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo':","{'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def","1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples","language key {'pony': '1.0'}, # not a string for version","'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ),","'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook","always_run and files are permissible [{ 'id': 'a', 'name': 'b',","{'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't","import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag","fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert","both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo':","run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing():","cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA)","'\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo':","assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(","'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta',","( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def","@pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo':","test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item,","'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}","== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ),","key {'pony': '1.0'}, # not a string for version {'python':","version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping,","def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml')","def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError)","works, if this schema has any defaults they will clobber","ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct',","'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ],","pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import","with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha':","True, ), ( # A regression in 0.13.5: always_run and","import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main","validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def","'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct)","['foo', 'bar', 'baz'], }, ], }], }, True, ), (","is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo':","from __future__ import unicode_literals import logging import cfgv import pytest","'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a","test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as","import logging import cfgv import pytest import pre_commit.constants as C","== ( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n'","( '\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====>","'1.0'}, # not a string for version {'python': 3}, ),","not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok():","return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value)","from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib","not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,))","}], }, True, ), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks',","caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' '","test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n'","set a language for a meta hook {'repo': 'meta', 'hooks':","fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id':","DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos':","== 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', (","' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val","CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',))","@pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj',","MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert","@pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{')","'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg ==","def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [],","cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not","'files': r'\\.py$', }], True, ), ( # A regression in","'files': '', 'always_run': True, }], True, ), ), ) def","'- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' -","install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg =","assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING,","test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}')","{'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError):","dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct =","rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' '","'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ),","return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def","check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from","schema has any defaults they will clobber potentially useful values","but version {} is ' 'installed. Perhaps run `pip install","'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main,","from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib","language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity',","@pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo':","('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev':","caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args',","'files': '\\\\.py$'}], }], }, True, ), ( { 'repos': [{","expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def","' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n'","`pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg","'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), (",") def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret","'python3.4', 'files': r'\\.py$', }], True, ), ( # A regression","yoavcaspi/pre-commit from __future__ import unicode_literals import logging import cfgv import","dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( #","'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files':","'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True,","{repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir,","'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4',","'key')} warn_additional, = [ x for x in schema.items if","'c', 'language': 'python', 'files': r'\\.py$', }], True, ), ( [{","'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True,","if hasattr(item, 'key')} warn_additional, = [ x for x in","(validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema","required but version {} is ' 'installed. Perhaps run `pip","# invalid to set a language for a meta hook","[], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n'","[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ]","hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set","MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok():","try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value',","'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev':","with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]}","== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ),","expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat',","for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language':","string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), )","'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }],","'==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version","meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog):","0.13.5: always_run and files are permissible [{ 'id': 'a', 'name':","sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except","'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for","@pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, #","= tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not","( # invalid language key {'pony': '1.0'}, # not a","in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys == set(warn_additional.keys)","import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config","def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b',","'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct):","hasattr(item, 'key')} warn_additional, = [ x for x in schema.items","test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a',","{'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION)","key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn):","key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')","( # A regression in 0.13.5: always_run and files are","not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert","( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',","tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj',","clobber potentially useful values in the backing manifest. #227 \"\"\"","unicode_literals import logging import cfgv import pytest import pre_commit.constants as","pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg,","' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' '","'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with","--upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos':","item in schema.items if hasattr(item, 'key')} warn_additional, = [ x","= tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n'","cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging","pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'}","'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with","r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b',","def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults():","key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version","'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True,","'python', 'files': '', 'always_run': True, }], True, ), ), )","= validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ (","{'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj","as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA)","- id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,))","foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml","{'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a","'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f","= excinfo.value.args assert msg == 'Cannot specify both sha and","'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo',","( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks':","{'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override","' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, )","C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from","'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev':","def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo':","{'pony': '1.0'}, # not a string for version {'python': 3},","#227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional)","config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir,","'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True,","x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert","'bar', 'baz'], }, ], }], }, True, ), ( {","= tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize(","pre-commit version 999 is required but version {} is '","test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('-","dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==","'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta',","def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( (","meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, #","way our merging works, if this schema has any defaults","'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '',","flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not","'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not","'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo):","{'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way","'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ), (","'files': r'\\.py$', }], True, ), ( [{ 'id': 'a', 'name':","not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta,","[ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must","CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from","CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works,","isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, #","), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping',","{'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',","'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run':","'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def","def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert","test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid","{item.key for item in schema.items if hasattr(item, 'key')} warn_additional, =","assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo:","hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f","as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT","[], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema):","}], }, False, ), ), ) def test_config_valid(config_obj, expected): ret","}, False, ), ), ) def test_config_valid(config_obj, expected): ret =","args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert",") def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct =","are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':","[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files':","'\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True,","def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n'","[{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}],","pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import","'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def","excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args","cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag',","'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern","), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA)","config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f =","has any defaults they will clobber potentially useful values in","'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar',","'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def",") ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples ==","def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is","= {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':","'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files':","# name override must be string {'repo': 'meta', 'hooks': [{'id':","\"\"\"Due to the way our merging works, if this schema","warn_additional, = [ x for x in schema.items if isinstance(x,","assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a',","from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib","}], True, ), ( # A regression in 0.13.5: always_run","allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')}","in the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items:","'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), (","( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev':","'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a","= {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) ==","hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', )","), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA)","'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def","import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev","[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version':","'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct)","True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret","999 is required but version {} is ' 'installed. Perhaps","'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True,","for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys","potentially useful values in the backing manifest. #227 \"\"\" for","regression in 0.13.5: always_run and files are permissible [{ 'id':","( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':","ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [","'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT)","), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n'","# A regression in 0.13.5: always_run and files are permissible","def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo':","import unicode_literals import logging import cfgv import pytest import pre_commit.constants","from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib","'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', #","['foo', 'bar', 'baz'], }, ], }], }, False, ), ),","our merging works, if this schema has any defaults they",") def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret","excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert","not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',))","'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$',","logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main))","is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return","caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev:","with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'}","expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize(","expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError):","'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude","Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def","= tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert","def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj,",") def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo:","[{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError):","'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files':","'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid","values in the backing manifest. #227 \"\"\" for item in","cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import","not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f","import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True","name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity',","cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n'","will clobber potentially useful values in the backing manifest. #227","'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct)",") def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg,","is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo':","CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items","must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]},","== {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo':","test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if this","def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if","not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '-","3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n',","' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val =","'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), (","'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$',","assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), (","i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]},","'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}","'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def","True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value):","isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert","{ 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes',","{'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'},","= {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes():","' hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', )","'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }],","msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct',","'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be a string","{ 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ {","test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( {","id: flake8\\n' ' args: [--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert","assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo':","valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to","cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value)","validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks:","f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n'","with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'})","( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn',","# Exclude pattern must be a string 'exclude': 0, 'args':","), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':","[{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f =","), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct","not a string for version {'python': 3}, ), ) def","version {} is ' 'installed. Perhaps run `pip install --upgrade","item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert","import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try:","'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }],","[dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj =","https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n'","{'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'},","dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==","'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], },","validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo:","repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' ' - id:","excinfo.value.args assert msg == 'Cannot specify both sha and rev'","' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n',","'mapping', ( # invalid language key {'pony': '1.0'}, # not","'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( { 'repos':","'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':","), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as","}], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret =","test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}')","obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False","'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys","pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import","] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml')","@pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item","'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ),","pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg","[{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes',","ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]}","not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'),","test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a',","pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import","pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import","a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid","def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',))","validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f =","MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from",") def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with","True, ), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',","def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( #","f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' '","tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,))","test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct","must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'],","rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'},","a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ],","test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected","def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA)","from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib","\"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def","cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key","def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id:","and files are permissible [{ 'id': 'a', 'name': 'b', 'entry':","{'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',","defaults they will clobber potentially useful values in the backing","any defaults they will clobber potentially useful values in the","[--some-args]\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples","sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta',","'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], },","}, True, ), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev':","= {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the","'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'},","), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert","{'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def","override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name':","('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry':","'python'}]}, # name override must be string {'repo': 'meta', 'hooks':","useful values in the backing manifest. #227 \"\"\" for item","'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default():","cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA,","At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999","pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import","}, ], }], }, True, ), ( { 'repos': [{","in 0.13.5: always_run and files are permissible [{ 'id': 'a',","True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj,","id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert","identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')","= [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys)","[ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ]","validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj,","= {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':",") def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', (","= is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', (","test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev':","cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony':","def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError:","from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures","'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both","ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'},","'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args':","'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify","'b', 'entry': 'c', 'language': 'python', 'files': r'\\.py$', }], True, ),","False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize(","{'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a',","'language': 'python', 'files': '', 'always_run': True, }], True, ), ),","backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not","MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'},","import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib","schema.items if hasattr(item, 'key')} warn_additional, = [ x for x","test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct","import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA","'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',","), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':","invalid language key {'pony': '1.0'}, # not a string for","with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos':","f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def","the way our merging works, if this schema has any","pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema):","}, ], }], }, False, ), ), ) def test_config_valid(config_obj,","caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo',","pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj,","[{'id': 'identity', 'language': 'python'}]}, # name override must be string","MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from","'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with","'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot","3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' 'foo:\\n' ' id:","specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'},","__future__ import unicode_literals import logging import cfgv import pytest import","MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo',","0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False,","'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ),","str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At key:","assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{","'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both():","# i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id':","assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==> At","' - id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val","string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with","'identity', 'language': 'python'}]}, # name override must be string {'repo':","{ 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], },","@pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks',","'999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At","False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj,","def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml')","CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for","{} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format(","DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from","'', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj,","tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,))","'==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required","in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not","@pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b',","( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [","'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ),","args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(","[{'id': 'pyflakes', 'files': '\\\\.py$'}], }], }, True, ), ( {","logging import cfgv import pytest import pre_commit.constants as C from","def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is","this schema has any defaults they will clobber potentially useful","from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib","{'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), )","assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def","'0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys =","'files': '\\\\.py$', # Exclude pattern must be a string 'exclude':","[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ]","import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA","'baz'], }, ], }], }, False, ), ), ) def","obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel'))","False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize(","'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==>","{'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == (","is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj =","a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]},","CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from","ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config","from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib","pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import","{'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), )","(CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in","'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id':","permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',","'\\n' '==> At Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit","if this schema has any defaults they will clobber potentially","manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item,","'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language","a language for a meta hook {'repo': 'meta', 'hooks': [{'id':","pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key","f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n'","check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo':","rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos':","and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha':","for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok():","- id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val =","CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'},","'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg","@pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha':","with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language","= {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT))","fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( (","import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION","from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema)","they will clobber potentially useful values in the backing manifest.","pattern must be a string 'exclude': 0, 'args': ['foo', 'bar',","3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def","'python', 'files': r'\\.py$', }], True, ), ( [{ 'id': 'a',","except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with","MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha':","test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due","'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( #","validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit',","# invalid language key {'pony': '1.0'}, # not a string","merging works, if this schema has any defaults they will","), ( # A regression in 0.13.5: always_run and files","'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo,","{'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat',","validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema =","A regression in 0.13.5: always_run and files are permissible [{","cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError):","minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but version {}","Exclude pattern must be a string 'exclude': 0, 'args': ['foo',","f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert","test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA)","( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo':","== {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist","test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def","'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ),","'language': 'python', 'files': r'\\.py$', }], True, ), ( [{ 'id':","'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':","hooks:\\n' ' - id: flake8\\n' ' args: [--some-args]\\n', ) ret_val","from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj,","hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name","'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) )","'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }],","At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is required but","logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog):","True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',","= {item.key for item in schema.items if hasattr(item, 'key')} warn_additional,","( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':","to set a language for a meta hook {'repo': 'meta',","'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n' '","testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return","as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, =","pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg,","'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'}","MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct)","def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert","), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert","pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import","'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected):","def test_config_schema_does_not_contain_defaults(): \"\"\"Due to the way our merging works, if","pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from","assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(),","' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val","fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name':","{ 'id': 'pyflakes', 'files': '\\\\.py$', # Exclude pattern must be","CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def","[ { 'id': 'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'],","is required but version {} is ' 'installed. Perhaps run","= tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' '","files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c',","assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize(","] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '-","is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION,","test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected","'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\.py$', }], True, ),","), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml =","'pyflakes', 'files': '\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }],","Config()\\n' '==> At key: minimum_pre_commit_version\\n' '=====> pre-commit version 999 is","CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from","version 999 is required but version {} is ' 'installed.","= is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj","r'\\.py$', }], True, ), ( # A regression in 0.13.5:","pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [],","pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import","be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], },","rev: 3.7.7\\n' ' hooks:\\n' ' - id: flake8\\n' ' args:","[{'id': 'i-dont-exist'}]}, # invalid to set a language for a","CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\n' '==> At Config()\\n' '==>","'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir,","], }], }, True, ), ( { 'repos': [{ 'repo':","tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' ' hooks:\\n'","config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \"\"\"Due to","'=====> pre-commit version 999 is required but version {} is","{'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def","'bar', 'baz'], }, ], }], }, False, ), ), )","assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir):","'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError)","'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct):","with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo:","'baz'], }, ], }], }, True, ), ( { 'repos':","'language': 'python'}]}, # name override must be string {'repo': 'meta',","pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{","'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize(","x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys ==","not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected","for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError):","id: flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,))","':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\.py$',","], }], }, False, ), ), ) def test_config_valid(config_obj, expected):","is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with","flake8\\n' 'foo:\\n' ' id: 1.0.0\\n', ) ret_val = validate_config_main((f.strpath,)) assert","C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version':","('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'),","'i-dont-exist'}]}, # invalid to set a language for a meta","assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s):",") ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'}","a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping):","), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing():","test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\n' '","import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib","'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], },","config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def","f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev:","def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo:","test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema',","assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', (","ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails():","assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct","# not a string for version {'python': 3}, ), )","( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id':","the backing manifest. #227 \"\"\" for item in CONFIG_HOOK_DICT.items: assert","invalid to set a language for a meta hook {'repo':","tmpdir.join('cfg.yaml') f.write( 'repos:\\n' '- repo: https://gitlab.com/pycqa/flake8\\n' ' rev: 3.7.7\\n' '","CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos':","'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must","msg, = excinfo.value.args assert msg == 'Cannot specify both sha","'expected'), ( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',","}], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry':","import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT","'language_version': 'python3.4', 'files': r'\\.py$', }], True, ), ( # A","in schema.items if hasattr(item, 'key')} warn_additional, = [ x for","be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ),"],"string":"[\n \"'\\\\\\\\.py$', # Exclude pattern must be a string 'exclude': 0,\",\n \"[sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our\",\n \"for item in schema.items if hasattr(item, 'key')} warn_additional, = [\",\n \"import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main\",\n \"cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg =\",\n \"string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }],\",\n \"to the way our merging works, if this schema has\",\n \"'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must be\",\n \"test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version':\",\n \"'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo':\",\n \"{'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def\",\n \"1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples\",\n \"language key {'pony': '1.0'}, # not a string for version\",\n \"'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ),\",\n \"'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook\",\n \"always_run and files are permissible [{ 'id': 'a', 'name': 'b',\",\n \"{'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't\",\n \"import pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag\",\n \"fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert\",\n \"both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo':\",\n \"run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing():\",\n \"cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA)\",\n \"'\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{ 'repo':\",\n \"assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(\",\n \"'config_repo', ( # i-dont-exist isn't a valid hook {'repo': 'meta',\",\n \"( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def\",\n \"@pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo':\",\n \"test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if hasattr(item,\",\n \"'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}\",\n \"== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ),\",\n \"key {'pony': '1.0'}, # not a string for version {'python':\",\n \"version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping,\",\n \"def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml')\",\n \"def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError)\",\n \"works, if this schema has any defaults they will clobber\",\n \"ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct',\",\n \"'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ],\",\n \"pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import\",\n \"with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha':\",\n \"True, ), ( # A regression in 0.13.5: always_run and\",\n \"import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main\",\n \"validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def\",\n \"'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct)\",\n \"['foo', 'bar', 'baz'], }, ], }], }, True, ), (\",\n \"is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo':\",\n \"from __future__ import unicode_literals import logging import cfgv import pytest\",\n \"'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a\",\n \"test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as\",\n \"import logging import cfgv import pytest import pre_commit.constants as C\",\n \"== ( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n'\",\n \"( '\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====>\",\n \"'1.0'}, # not a string for version {'python': 3}, ),\",\n \"not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok():\",\n \"return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value)\",\n \"from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib\",\n \"not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,))\",\n \"}], }, True, ), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks',\",\n \"caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' '\",\n \"test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n'\",\n \"set a language for a meta hook {'repo': 'meta', 'hooks':\",\n \"fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id':\",\n \"DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos':\",\n \"== 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct', (\",\n \"' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val\",\n \"CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',))\",\n \"@pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj',\",\n \"MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert\",\n \"@pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{')\",\n \"'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg ==\",\n \"def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [],\",\n \"cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not\",\n \"'files': r'\\\\.py$', }], True, ), ( # A regression in\",\n \"'files': '', 'always_run': True, }], True, ), ), ) def\",\n \"'- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' -\",\n \"install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg =\",\n \"assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING,\",\n \"test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}')\",\n \"{'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError):\",\n \"dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct =\",\n \"rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' '\",\n \"'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ),\",\n \"return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def\",\n \"check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from\",\n \"schema has any defaults they will clobber potentially useful values\",\n \"but version {} is ' 'installed. Perhaps run `pip install\",\n \"'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main,\",\n \"from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib\",\n \"language for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity',\",\n \"@pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid hook {'repo':\",\n \"('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev':\",\n \"caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args',\",\n \"'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos': [{\",\n \"expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def\",\n \"' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n'\",\n \"`pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg\",\n \"'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), (\",\n \") def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret\",\n \"'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A regression\",\n \"yoavcaspi/pre-commit from __future__ import unicode_literals import logging import cfgv import\",\n \"dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( #\",\n \"'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files':\",\n \"'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True,\",\n \"{repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir,\",\n \"'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4',\",\n \"'key')} warn_additional, = [ x for x in schema.items if\",\n \"'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{\",\n \"'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True,\",\n \"if hasattr(item, 'key')} warn_additional, = [ x for x in\",\n \"(validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema\",\n \"required but version {} is ' 'installed. Perhaps run `pip\",\n \"# invalid to set a language for a meta hook\",\n \"[], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n'\",\n \"[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ]\",\n \"hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set\",\n \"MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok():\",\n \"try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value',\",\n \"'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev':\",\n \"with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]}\",\n \"== [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ),\",\n \"expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat',\",\n \"for a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language':\",\n \"string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), )\",\n \"'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }],\",\n \"'==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version\",\n \"meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog):\",\n \"0.13.5: always_run and files are permissible [{ 'id': 'a', 'name':\",\n \"sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except\",\n \"'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language for\",\n \"@pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'}, #\",\n \"= tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not\",\n \"( # invalid language key {'pony': '1.0'}, # not a\",\n \"in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys == set(warn_additional.keys)\",\n \"import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config\",\n \"def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b',\",\n \"'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct):\",\n \"hasattr(item, 'key')} warn_additional, = [ x for x in schema.items\",\n \"test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a',\",\n \"{'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION)\",\n \"key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn):\",\n \"key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')\",\n \"( # A regression in 0.13.5: always_run and files are\",\n \"not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert\",\n \"( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',\",\n \"tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj',\",\n \"clobber potentially useful values in the backing manifest. #227 \\\"\\\"\\\"\",\n \"unicode_literals import logging import cfgv import pytest import pre_commit.constants as\",\n \"pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg,\",\n \"' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' '\",\n \"'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with\",\n \"--upgrade pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos':\",\n \"item in schema.items if hasattr(item, 'key')} warn_additional, = [ x\",\n \"= tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n'\",\n \"cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging\",\n \"pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'}\",\n \"'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with\",\n \"r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name': 'b',\",\n \"def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults():\",\n \"key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version\",\n \"'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True,\",\n \"'python', 'files': '', 'always_run': True, }], True, ), ), )\",\n \"= validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ (\",\n \"{'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj\",\n \"as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA)\",\n \"- id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,))\",\n \"foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml\",\n \"{'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a\",\n \"'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f\",\n \"= excinfo.value.args assert msg == 'Cannot specify both sha and\",\n \"'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo',\",\n \"( # i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks':\",\n \"{'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override\",\n \"' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, )\",\n \"C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from\",\n \"'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev':\",\n \"def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo':\",\n \"{'pony': '1.0'}, # not a string for version {'python': 3},\",\n \"#227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional)\",\n \"config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir,\",\n \"'entry': 'c', 'language': 'python', 'files': '', 'always_run': True, }], True,\",\n \"x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert\",\n \"'bar', 'baz'], }, ], }], }, True, ), ( {\",\n \"= tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize(\",\n \"pre-commit version 999 is required but version {} is '\",\n \"test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('-\",\n \"dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==\",\n \"'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta',\",\n \"def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( (\",\n \"meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, #\",\n \"way our merging works, if this schema has any defaults\",\n \"'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '',\",\n \"flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not\",\n \"'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not\",\n \"'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo):\",\n \"{'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way\",\n \"'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ), (\",\n \"'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a', 'name':\",\n \"not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta,\",\n \"[ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must\",\n \"CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from\",\n \"CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works,\",\n \"isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, #\",\n \"), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping',\",\n \"{'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',\",\n \"'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': '', 'always_run':\",\n \"'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def\",\n \"def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert\",\n \"test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid\",\n \"{item.key for item in schema.items if hasattr(item, 'key')} warn_additional, =\",\n \"assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo:\",\n \"hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f\",\n \"as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT\",\n \"[], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema):\",\n \"}], }, False, ), ), ) def test_config_valid(config_obj, expected): ret\",\n \"}, False, ), ), ) def test_config_valid(config_obj, expected): ret =\",\n \"args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert\",\n \") def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct =\",\n \"are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':\",\n \"[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files':\",\n \"'\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], }, True,\",\n \"def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n'\",\n \"[{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}],\",\n \"pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import\",\n \"'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), ) def\",\n \"excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args\",\n \"cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag',\",\n \"'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern\",\n \"), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA)\",\n \"config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f =\",\n \"has any defaults they will clobber potentially useful values in\",\n \"'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar',\",\n \"'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def\",\n \") ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples ==\",\n \"def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is\",\n \"= {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':\",\n \"'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files':\",\n \"# name override must be string {'repo': 'meta', 'hooks': [{'id':\",\n \"\\\"\\\"\\\"Due to the way our merging works, if this schema\",\n \"warn_additional, = [ x for x in schema.items if isinstance(x,\",\n \"assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a',\",\n \"from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib\",\n \"}], True, ), ( # A regression in 0.13.5: always_run\",\n \"allowed_keys = {item.key for item in schema.items if hasattr(item, 'key')}\",\n \"in the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items:\",\n \"'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), (\",\n \"( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev':\",\n \"'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a\",\n \"= {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) ==\",\n \"hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', )\",\n \"), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA)\",\n \"'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} def\",\n \"import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev\",\n \"[{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version':\",\n \"'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct)\",\n \"True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret\",\n \"999 is required but version {} is ' 'installed. Perhaps\",\n \"'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True,\",\n \"for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys\",\n \"potentially useful values in the backing manifest. #227 \\\"\\\"\\\" for\",\n \"regression in 0.13.5: always_run and files are permissible [{ 'id':\",\n \"( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':\",\n \"ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [\",\n \"'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT)\",\n \"), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n'\",\n \"# A regression in 0.13.5: always_run and files are permissible\",\n \"def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo':\",\n \"import unicode_literals import logging import cfgv import pytest import pre_commit.constants\",\n \"from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib\",\n \"'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', #\",\n \"['foo', 'bar', 'baz'], }, ], }], }, False, ), ),\",\n \"our merging works, if this schema has any defaults they\",\n \") def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret\",\n \"excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert\",\n \"not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',))\",\n \"'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$',\",\n \"logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main))\",\n \"is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return\",\n \"caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev:\",\n \"with pytest.raises(cfgv.ValidationError) as excinfo: cfg = {'repos': [], 'minimum_pre_commit_version': '999'}\",\n \"expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize(\",\n \"expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError):\",\n \"'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude\",\n \"Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) ) def\",\n \"= tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert\",\n \"def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj,\",\n \") def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo:\",\n \"[{'id': 'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError):\",\n \"'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files':\",\n \"'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist isn't a valid\",\n \"values in the backing manifest. #227 \\\"\\\"\\\" for item in\",\n \"cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib import\",\n \"not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f\",\n \"import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True\",\n \"name override must be string {'repo': 'meta', 'hooks': [{'id': 'identity',\",\n \"cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n'\",\n \"will clobber potentially useful values in the backing manifest. #227\",\n \"'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct)\",\n \") def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg,\",\n \"is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo':\",\n \"CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items\",\n \"must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]},\",\n \"== {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo':\",\n \"test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if this\",\n \"def test_warn_additional(schema): allowed_keys = {item.key for item in schema.items if\",\n \"not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '-\",\n \"3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n',\",\n \"' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val =\",\n \"'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), (\",\n \"'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$',\",\n \"assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), (\",\n \"i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]},\",\n \"'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'}\",\n \"'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def\",\n \"True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value):\",\n \"isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert\",\n \"{ 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes',\",\n \"{'repo': 'a'}, {'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'},\",\n \"= {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes():\",\n \"' hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', )\",\n \"'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }],\",\n \"msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize( 'dct',\",\n \"'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be a string\",\n \"{ 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ {\",\n \"test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( {\",\n \"id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert\",\n \"assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo':\",\n \"valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to\",\n \"cfg = {'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value)\",\n \"validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks:\",\n \"f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n'\",\n \"with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'})\",\n \"( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo', ), ] @pytest.mark.parametrize('fn',\",\n \"# Exclude pattern must be a string 'exclude': 0, 'args':\",\n \"), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':\",\n \"[{id: identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f =\",\n \"), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct\",\n \"not a string for version {'python': 3}, ), ) def\",\n \"version {} is ' 'installed. Perhaps run `pip install --upgrade\",\n \"item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert\",\n \"import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try:\",\n \"'a', 'name': 'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }],\",\n \"[dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj =\",\n \"https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n'\",\n \"{'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'},\",\n \"dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct ==\",\n \"'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }], },\",\n \"validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo:\",\n \"repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id:\",\n \"excinfo.value.args assert msg == 'Cannot specify both sha and rev'\",\n \"' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n',\",\n \"'mapping', ( # invalid language key {'pony': '1.0'}, # not\",\n \"'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( { 'repos':\",\n \"'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':\",\n \"), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as\",\n \"}], True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret =\",\n \"test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}')\",\n \"obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError: return False\",\n \"'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys\",\n \"pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import\",\n \"] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml')\",\n \"@pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item\",\n \"'args': ['foo', 'bar', 'baz'], }, ], }], }, True, ),\",\n \"pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo: cfg\",\n \"[{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes',\",\n \"ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]}\",\n \"not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'),\",\n \"test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a',\",\n \"pre_commit.clientlib import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import\",\n \"pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib import\",\n \"a valid hook {'repo': 'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid\",\n \"def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',))\",\n \"validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir): f =\",\n \"MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import validate_config_main from\",\n \") def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with\",\n \"True, ), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',\",\n \"def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( #\",\n \"f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' '\",\n \"tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,))\",\n \"test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct\",\n \"must be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'],\",\n \"rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha': 'a'},\",\n \"a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ],\",\n \"test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected\",\n \"def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA)\",\n \"from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib\",\n \"\\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def\",\n \"cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key\",\n \"def test_validate_config_old_list_format_ok(tmpdir): f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id:\",\n \"and files are permissible [{ 'id': 'a', 'name': 'b', 'entry':\",\n \"{'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a',\",\n \"defaults they will clobber potentially useful values in the backing\",\n \"any defaults they will clobber potentially useful values in the\",\n \"[--some-args]\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples\",\n \"sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta',\",\n \"'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], },\",\n \"}, True, ), ( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev':\",\n \"= {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the\",\n \"'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'},\",\n \"), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert\",\n \"{'repo': 'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def\",\n \"override must be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name':\",\n \"('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry':\",\n \"'python'}]}, # name override must be string {'repo': 'meta', 'hooks':\",\n \"useful values in the backing manifest. #227 \\\"\\\"\\\" for item\",\n \"'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct): with pytest.raises(cfgv.ValidationError): MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default():\",\n \"cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA,\",\n \"At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999\",\n \"pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures import\",\n \"}, ], }], }, True, ), ( { 'repos': [{\",\n \"in 0.13.5: always_run and files are permissible [{ 'id': 'a',\",\n \"True, ), ), ) def test_valid_manifests(manifest_obj, expected): ret = is_valid_according_to_schema(manifest_obj,\",\n \"id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val assert\",\n \"identity}]}') assert not validate_config_main((f.strpath,)) def test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml')\",\n \"= [ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys)\",\n \"[ x for x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ]\",\n \"validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj,\",\n \"= {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo':\",\n \") def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', (\",\n \"= is_valid_according_to_schema(manifest_obj, MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', (\",\n \"test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev':\",\n \"cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony':\",\n \"def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return True except cfgv.ValidationError:\",\n \"from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from testing.fixtures\",\n \"'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify both\",\n \"ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'},\",\n \"'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args':\",\n \"'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot specify\",\n \"'b', 'entry': 'c', 'language': 'python', 'files': r'\\\\.py$', }], True, ),\",\n \"False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize(\",\n \"{'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a',\",\n \"'language': 'python', 'files': '', 'always_run': True, }], True, ), ),\",\n \"backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not\",\n \"MANIFEST_SCHEMA) assert ret is expected @pytest.mark.parametrize( 'dct', ( {'repo': 'local'},\",\n \"import cfgv import pytest import pre_commit.constants as C from pre_commit.clientlib\",\n \"schema.items if hasattr(item, 'key')} warn_additional, = [ x for x\",\n \"test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct\",\n \"import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA\",\n \"'expected'), ( ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',\",\n \"), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language':\",\n \"invalid language key {'pony': '1.0'}, # not a string for\",\n \"with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos':\",\n \"f.write('- {repo: meta, hooks: [{id: identity}]}') assert not validate_config_main((f.strpath,)) def\",\n \"the way our merging works, if this schema has any\",\n \"pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema):\",\n \"}, ], }], }, False, ), ), ) def test_config_valid(config_obj,\",\n \"caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): foo',\",\n \"pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj,\",\n \"[{'id': 'identity', 'language': 'python'}]}, # name override must be string\",\n \"MigrateShaToRev from pre_commit.clientlib import validate_config_main from pre_commit.clientlib import validate_manifest_main from\",\n \"'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with\",\n \"'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg == 'Cannot\",\n \"3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' 'foo:\\\\n' ' id:\",\n \"specify both sha and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'},\",\n \"__future__ import unicode_literals import logging import cfgv import pytest import\",\n \"MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo',\",\n \"0, 'args': ['foo', 'bar', 'baz'], }, ], }], }, False,\",\n \"'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ),\",\n \"str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At key:\",\n \"assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{\",\n \"'wat', 'rev': 'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both():\",\n \"# i-dont-exist isn't a valid hook {'repo': 'meta', 'hooks': [{'id':\",\n \"assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==> At\",\n \"' - id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val\",\n \"string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with\",\n \"'identity', 'language': 'python'}]}, # name override must be string {'repo':\",\n \"{ 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], },\",\n \"@pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks',\",\n \"'999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At\",\n \"False, ), ), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj,\",\n \"def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml')\",\n \"CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for\",\n \"{} is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format(\",\n \"DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from\",\n \"'', 'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj,\",\n \"tmpdir.join('f.notyaml') not_yaml.write('{') not_schema = tmpdir.join('notconfig.yaml') not_schema.write('{}') assert fn(('does-not-exist',)) assert fn((not_yaml.strpath,))\",\n \"'==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required\",\n \"in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not\",\n \"@pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name': 'b',\",\n \"( { 'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [\",\n \"'c', 'language': 'python', 'files': '', 'always_run': True, }], True, ),\",\n \"args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write(\",\n \"[{'id': 'pyflakes', 'files': '\\\\\\\\.py$'}], }], }, True, ), ( {\",\n \"logging import cfgv import pytest import pre_commit.constants as C from\",\n \"def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is\",\n \"this schema has any defaults they will clobber potentially useful\",\n \"from pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib\",\n \"{'repo': 'meta', 'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), )\",\n \"assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def\",\n \"'0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys =\",\n \"'files': '\\\\\\\\.py$', # Exclude pattern must be a string 'exclude':\",\n \"[ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ]\",\n \"import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA\",\n \"'baz'], }, ], }], }, False, ), ), ) def\",\n \"obj_schema) return True except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel'))\",\n \"False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize(\",\n \"'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==>\",\n \"{'repos': [], 'minimum_pre_commit_version': '999'} cfgv.validate(cfg, CONFIG_SCHEMA) assert str(excinfo.value) == (\",\n \"is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj =\",\n \"a meta hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]},\",\n \"CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from\",\n \"ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config\",\n \"from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib\",\n \"pre_commit.clientlib import MANIFEST_SCHEMA from pre_commit.clientlib import MigrateShaToRev from pre_commit.clientlib import\",\n \"{'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat', 'rev': 'wat'}, ), )\",\n \"(CONFIG_SCHEMA, CONFIG_REPO_DICT)) def test_warn_additional(schema): allowed_keys = {item.key for item in\",\n \"'repos': [{ 'repo': ':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id':\",\n \"permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c', 'language': 'python',\",\n \"'\\\\n' '==> At Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit\",\n \"if this schema has any defaults they will clobber potentially\",\n \"manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert not isinstance(item,\",\n \"'meta', 'hooks': [{'id': 'i-dont-exist'}]}, # invalid to set a language\",\n \"a language for a meta hook {'repo': 'meta', 'hooks': [{'id':\",\n \"pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key\",\n \"f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n'\",\n \"check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{ 'repo':\",\n \"rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos':\",\n \"and rev' @pytest.mark.parametrize( 'dct', ( {'repo': 'a'}, {'repo': 'meta', 'sha':\",\n \"for item in CONFIG_HOOK_DICT.items: assert not isinstance(item, cfgv.Optional) def test_validate_manifest_main_ok():\",\n \"- id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val =\",\n \"CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language key {'pony': '1.0'},\",\n \"'a', 'sha': 'b', 'rev': 'c'}) msg, = excinfo.value.args assert msg\",\n \"@pytest.mark.parametrize( 'dct', ( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha':\",\n \"with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo, CONFIG_REPO_DICT) @pytest.mark.parametrize( 'mapping', ( # invalid language\",\n \"= {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema', (CONFIG_SCHEMA, CONFIG_REPO_DICT))\",\n \"fn(('does-not-exist',)) assert fn((not_yaml.strpath,)) assert fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( (\",\n \"import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION\",\n \"from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema)\",\n \"they will clobber potentially useful values in the backing manifest.\",\n \"pattern must be a string 'exclude': 0, 'args': ['foo', 'bar',\",\n \"3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def\",\n \"'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id': 'a',\",\n \"except cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with\",\n \"MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha':\",\n \"test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due\",\n \"'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( #\",\n \"validate_config_main((f.strpath,)) assert not ret_val assert caplog.record_tuples == [ ( 'pre_commit',\",\n \"# invalid language key {'pony': '1.0'}, # not a string\",\n \"merging works, if this schema has any defaults they will\",\n \"), ( # A regression in 0.13.5: always_run and files\",\n \"'identity', 'name': False}]}, ), ) def test_meta_hook_invalid(config_repo): with pytest.raises(cfgv.ValidationError): cfgv.validate(config_repo,\",\n \"{'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo': 'wat',\",\n \"validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml = tmpdir.join('f.notyaml') not_yaml.write('{') not_schema =\",\n \"A regression in 0.13.5: always_run and files are permissible [{\",\n \"cfgv.ValidationError: return False @pytest.mark.parametrize('value', ('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError):\",\n \"minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but version {}\",\n \"Exclude pattern must be a string 'exclude': 0, 'args': ['foo',\",\n \"f = tmpdir.join('cfg.yaml') f.write('- {repo: meta, hooks: [{id: identity}]}') assert\",\n \"test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA)\",\n \"( {'repo': 'local'}, {'repo': 'meta'}, {'repo': 'wat', 'sha': 'wat'}, {'repo':\",\n \"== {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', ( # i-dont-exist\",\n \"test_validate_manifest_main_ok(): assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def\",\n \"'args': ['foo', 'bar', 'baz'], }, ], }], }, False, ),\",\n \"'language': 'python', 'files': r'\\\\.py$', }], True, ), ( [{ 'id':\",\n \"'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev':\",\n \"hooks:\\\\n' ' - id: flake8\\\\n' ' args: [--some-args]\\\\n', ) ret_val\",\n \"from pre_commit.clientlib import validate_manifest_main from testing.fixtures import sample_local_config def is_valid_according_to_schema(obj,\",\n \"hook {'repo': 'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name\",\n \"'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION, ) )\",\n \"'b', 'entry': 'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }],\",\n \"At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is required but\",\n \"logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog):\",\n \"True, ), ( [{ 'id': 'a', 'name': 'b', 'entry': 'c',\",\n \"= {item.key for item in schema.items if hasattr(item, 'key')} warn_additional,\",\n \"( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks':\",\n \"to set a language for a meta hook {'repo': 'meta',\",\n \"'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n' '\",\n \"testing.fixtures import sample_local_config def is_valid_according_to_schema(obj, obj_schema): try: cfgv.validate(obj, obj_schema) return\",\n \"as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg, =\",\n \"pytest.raises(cfgv.ValidationError) as excinfo: MigrateShaToRev().check({'repo': 'a', 'sha': 'b', 'rev': 'c'}) msg,\",\n \"'rev': 'b'} def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'}\",\n \"MigrateShaToRev().check(dct) def test_migrate_to_sha_apply_default(): dct = {'repo': 'a', 'sha': 'b'} MigrateShaToRev().apply_default(dct)\",\n \"def test_migrate_to_sha_ok(): dct = {'repo': 'a', 'rev': 'b'} MigrateShaToRev().apply_default(dct) assert\",\n \"), ) def test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert\",\n \"pre_commit.clientlib import CONFIG_REPO_DICT from pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import\",\n \"'always_run': True, }], True, ), ), ) def test_valid_manifests(manifest_obj, expected):\",\n \"def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to the way our merging works, if\",\n \"pytest import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from\",\n \"assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(),\",\n \"' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert not ret_val\",\n \"fn((not_schema.strpath,)) @pytest.mark.parametrize( ('manifest_obj', 'expected'), ( ( [{ 'id': 'a', 'name':\",\n \"{ 'id': 'pyflakes', 'files': '\\\\\\\\.py$', # Exclude pattern must be\",\n \"CONFIG_SCHEMA) def test_config_with_local_hooks_definition_passes(): config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def\",\n \"[ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'],\",\n \"is required but version {} is ' 'installed. Perhaps run\",\n \"= tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' '\",\n \"files are permissible [{ 'id': 'a', 'name': 'b', 'entry': 'c',\",\n \"assert msg == 'Cannot specify both sha and rev' @pytest.mark.parametrize(\",\n \"] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '-\",\n \"is ' 'installed. Perhaps run `pip install --upgrade pre-commit`.'.format( C.VERSION,\",\n \"test_config_valid(config_obj, expected): ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected\",\n \"'c', 'language': 'python', 'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ),\",\n \"), ] @pytest.mark.parametrize('fn', (validate_config_main, validate_manifest_main)) def test_mains_not_ok(tmpdir, fn): not_yaml =\",\n \"'pyflakes', 'files': '\\\\\\\\.py$', 'args': ['foo', 'bar', 'baz'], }, ], }],\",\n \"Config()\\\\n' '==> At key: minimum_pre_commit_version\\\\n' '=====> pre-commit version 999 is\",\n \"CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import MANIFEST_SCHEMA from\",\n \"version 999 is required but version {} is ' 'installed.\",\n \"= is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj\",\n \"r'\\\\.py$', }], True, ), ( # A regression in 0.13.5:\",\n \"pre-commit`.'.format( C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [],\",\n \"pre_commit.clientlib import CONFIG_SCHEMA from pre_commit.clientlib import DEFAULT_LANGUAGE_VERSION from pre_commit.clientlib import\",\n \"be a string 'exclude': 0, 'args': ['foo', 'bar', 'baz'], },\",\n \"rev: 3.7.7\\\\n' ' hooks:\\\\n' ' - id: flake8\\\\n' ' args:\",\n \"[{'id': 'i-dont-exist'}]}, # invalid to set a language for a\",\n \"CONFIG_SCHEMA) assert str(excinfo.value) == ( '\\\\n' '==> At Config()\\\\n' '==>\",\n \"'pre_commit', logging.WARNING, 'Unexpected config key(s): args', ), ] def test_validate_warn_on_unknown_keys_at_top_level(tmpdir,\",\n \"], }], }, True, ), ( { 'repos': [{ 'repo':\",\n \"tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' ' hooks:\\\\n'\",\n \"config_obj = {'repos': [sample_local_config()]} cfgv.validate(config_obj, CONFIG_SCHEMA) def test_config_schema_does_not_contain_defaults(): \\\"\\\"\\\"Due to\",\n \"'=====> pre-commit version 999 is required but version {} is\",\n \"{'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ), ) def\",\n \"'bar', 'baz'], }, ], }], }, False, ), ), )\",\n \"assert not validate_manifest_main(('.pre-commit-hooks.yaml',)) def test_validate_config_main_ok(): assert not validate_config_main(('.pre-commit-config.yaml',)) def test_validate_config_old_list_format_ok(tmpdir):\",\n \"'wat'}, ), ) def test_migrate_sha_to_rev_ok(dct): MigrateShaToRev().check(dct) def test_migrate_sha_to_rev_dont_specify_both(): with pytest.raises(cfgv.ValidationError)\",\n \"'sha': 'a'}, {'repo': 'meta', 'rev': 'a'}, ), ) def test_migrate_sha_to_rev_conditional_check_failures(dct):\",\n \"with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing(): with pytest.raises(cfgv.ValidationError) as excinfo:\",\n \"'baz'], }, ], }], }, True, ), ( { 'repos':\",\n \"'language': 'python'}]}, # name override must be string {'repo': 'meta',\",\n \"pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'), ( ( { 'repos': [{\",\n \"'b'} MigrateShaToRev().apply_default(dct) assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize(\",\n \"x in schema.items if isinstance(x, cfgv.WarnAdditionalKeys) ] assert allowed_keys ==\",\n \"not ret_val assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected\",\n \"for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError):\",\n \"id: flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,))\",\n \"':pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [ { 'id': 'pyflakes', 'files': '\\\\\\\\.py$',\",\n \"], }], }, False, ), ), ) def test_config_valid(config_obj, expected):\",\n \"is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with\",\n \"flake8\\\\n' 'foo:\\\\n' ' id: 1.0.0\\\\n', ) ret_val = validate_config_main((f.strpath,)) assert\",\n \"C.VERSION, ) ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version':\",\n \"('definitely-not-a-tag', 'fiel')) def test_check_type_tag_failures(value): with pytest.raises(cfgv.ValidationError): check_type_tag(value) @pytest.mark.parametrize( ('config_obj', 'expected'),\",\n \"'i-dont-exist'}]}, # invalid to set a language for a meta\",\n \"assert caplog.record_tuples == [ ( 'pre_commit', logging.WARNING, 'Unexpected config key(s):\",\n \") ) def test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'}\",\n \"a string for version {'python': 3}, ), ) def test_default_language_version_invalid(mapping):\",\n \"), ) def test_default_language_version_invalid(mapping): with pytest.raises(cfgv.ValidationError): cfgv.validate(mapping, DEFAULT_LANGUAGE_VERSION) def test_minimum_pre_commit_version_failing():\",\n \"test_validate_warn_on_unknown_keys_at_repo_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( '- repo: https://gitlab.com/pycqa/flake8\\\\n' '\",\n \"import pre_commit.constants as C from pre_commit.clientlib import check_type_tag from pre_commit.clientlib\",\n \"'exclude': 0, 'args': ['foo', 'bar', 'baz'], }, ], }], },\",\n \"config_obj = {'repos': [dict(sample_local_config(), rev='foo')]} with pytest.raises(cfgv.ValidationError): cfgv.validate(config_obj, CONFIG_SCHEMA) def\",\n \"f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev:\",\n \"def test_validate_warn_on_unknown_keys_at_top_level(tmpdir, caplog): f = tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo:\",\n \"test_minimum_pre_commit_version_passing(): cfg = {'repos': [], 'minimum_pre_commit_version': '0'} cfgv.validate(cfg, CONFIG_SCHEMA) @pytest.mark.parametrize('schema',\",\n \"assert dct == {'repo': 'a', 'rev': 'b'} @pytest.mark.parametrize( 'config_repo', (\",\n \"ret = is_valid_according_to_schema(config_obj, CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails():\",\n \"assert dct == {'repo': 'a', 'rev': 'b'} def test_migrate_to_sha_ok(): dct\",\n \"# not a string for version {'python': 3}, ), )\",\n \"( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37', 'hooks': [{'id':\",\n \"the backing manifest. #227 \\\"\\\"\\\" for item in CONFIG_HOOK_DICT.items: assert\",\n \"invalid to set a language for a meta hook {'repo':\",\n \"tmpdir.join('cfg.yaml') f.write( 'repos:\\\\n' '- repo: https://gitlab.com/pycqa/flake8\\\\n' ' rev: 3.7.7\\\\n' '\",\n \"CONFIG_SCHEMA) assert ret is expected def test_local_hooks_with_rev_fails(): config_obj = {'repos':\",\n \"'meta', 'hooks': [{'id': 'identity', 'language': 'python'}]}, # name override must\",\n \"msg, = excinfo.value.args assert msg == 'Cannot specify both sha\",\n \"'expected'), ( ( { 'repos': [{ 'repo': 'git:pre-commit/pre-commit-hooks', 'rev': 'cd74dc150c142c3be70b24eaf0b02cae9d235f37',\",\n \"}], True, ), ( [{ 'id': 'a', 'name': 'b', 'entry':\",\n \"import check_type_tag from pre_commit.clientlib import CONFIG_HOOK_DICT from pre_commit.clientlib import CONFIG_REPO_DICT\",\n \"'language_version': 'python3.4', 'files': r'\\\\.py$', }], True, ), ( # A\",\n \"in schema.items if hasattr(item, 'key')} warn_additional, = [ x for\",\n \"be string {'repo': 'meta', 'hooks': [{'id': 'identity', 'name': False}]}, ),\"\n]"}}},{"rowIdx":3485,"cells":{"ngram":{"kind":"list like","value":["2.0 (the \"License\"); # you may not use this file","wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font =","for key in keys: output = output_dict.get(key) if not output:","| wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel, _('Misc.'))","self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def","in outputs: output_dict[output.__class__] = output # Keys for outputs in","button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs)","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","# ====== # Copyright (C) 2015 # # Licensed","+= 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY,","= wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL,","propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED,","self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT,","limitations under the License. # import gettext import wx import","import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events","= wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font","self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500))","self.frame. This Bind propagates # capture's source change to the","change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI","key in output_dict.keys(): if key in misc_keys: continue if key","in keys: keys.append(key) # Main tabs index = 0 for","use this file except in compliance with the License. #","= output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel,","self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame,","= wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame,","default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY,","License. # You may obtain a copy of the License","wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font","of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not","for key in misc_keys: output = output_dict.get(key) if not output:","under the License is distributed on an \"AS IS\" BASIS,","tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer,","Copyright (C) 2015 # # Licensed under the Apache","License for the specific language governing permissions and # limitations","wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons.","button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame,","misc_keys: continue if key not in keys: keys.append(key) # Main","not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font)","self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in","* from ikalog.ui import VideoCapture from ikalog.utils import * _","key in misc_keys: output = output_dict.get(key) if not output: continue","ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys:","Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a","wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer","{} for output in outputs: output_dict[output.__class__] = output # Keys","output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 #","class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame =","self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event):","Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer","in compliance with the License. # You may obtain a","output_dict[output.__class__] = output # Keys for outputs in the main","[self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of","= wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND |","software # distributed under the License is distributed on an","the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each","utf-8 -*- # # IkaLog # ====== # Copyright (C)","output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab","def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame()","self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling()","font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY,","= wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load","index = 0 for key in keys: output = output_dict.get(key)","wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL,","# Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def","self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook =","= wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for","# # IkaLog # ====== # Copyright (C) 2015 ","# Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use","for outputs combined into the misc tab. misc_keys = [","button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY,","wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer)","flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel,","self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This","buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set","OF ANY KIND, either express or implied. # See the","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","ANY KIND, either express or implied. # See the License","See the License for the specific language governing permissions and","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","ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI',","to in writing, software # distributed under the License is","output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index,","VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class","# See the License for the specific language governing permissions","language governing permissions and # limitations under the License. #","ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import","or agreed to in writing, software # distributed under the","required by applicable law or agreed to in writing, software","import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","] # Keys for outputs combined into the misc tab.","if key not in keys: keys.append(key) # Main tabs index","with the License. # You may obtain a copy of","= [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for","* from ikalog.ui.panel import * from ikalog.ui import VideoCapture from","if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index +=","compliance with the License. # You may obtain a copy","agreed to in writing, software # distributed under the License","# self.capture.panel is a part of self.frame. This Bind propagates","# Copyright (C) 2015 # # Licensed under the","if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name)","[ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key","distributed under the License is distributed on an \"AS IS\"","a part of self.frame. This Bind propagates # capture's source","button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] +","tabs index = 0 for key in keys: output =","= Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui =","#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog","the License. # import gettext import wx import wx.lib.scrolledpanel import","express or implied. # See the License for the specific","# capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized)","except in compliance with the License. # You may obtain","= None self._init_frame() def _init_frame(self): if self.frame: return self.frame =","= output # Keys for outputs in the main page.","outputs: output_dict[output.__class__] = output # Keys for outputs in the","Licensed under the Apache License, Version 2.0 (the \"License\"); #","not use this file except in compliance with the License.","misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title","top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON,","writing, software # distributed under the License is distributed on","= button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default","you may not use this file except in compliance with","500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply =","# Licensed under the Apache License, Version 2.0 (the \"License\");","= 0 for key in keys: output = output_dict.get(key) if","= wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply)","CONDITIONS OF ANY KIND, either express or implied. # See","self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if","outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS,","debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","_init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"),","* _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui):","__init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def","on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event):","self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in","title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25))","self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins(","into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot,","= wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY,","misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ]","index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook,","event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output","for output in outputs: output_dict[output.__class__] = output # Keys for","self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default)","# Main tabs index = 0 for key in keys:","wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if","ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys():","under the License. # import gettext import wx import wx.lib.scrolledpanel","= [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for","OR CONDITIONS OF ANY KIND, either express or implied. #","return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook","the License is distributed on an \"AS IS\" BASIS, #","Main tabs index = 0 for key in keys: output","size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply","apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button(","in misc_keys: continue if key not in keys: keys.append(key) #","import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel","from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object):","the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi,","wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel)","wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font =","self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame","button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL)","ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key","# Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel)","law or agreed to in writing, software # distributed under","self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL)","governing permissions and # limitations under the License. # import","event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event)","from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils","ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from","title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys:","source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh","[ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs","====== # Copyright (C) 2015 # # Licensed under","self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel","python3 # -*- coding: utf-8 -*- # # IkaLog #","self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict =","= self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key","wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not","may obtain a copy of the License at # #","wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY)","wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default'))","= {} for output in outputs: output_dict[output.__class__] = output #","plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame()","key in misc_keys: continue if key not in keys: keys.append(key)","output in outputs: output_dict[output.__class__] = output # Keys for outputs","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook)","may not use this file except in compliance with the","self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer)","output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1,","# limitations under the License. # import gettext import wx","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","this file except in compliance with the License. # You","if key in misc_keys: continue if key not in keys:"," # # Licensed under the Apache License, Version 2.0","def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self,","in output_dict.keys(): if key in misc_keys: continue if key not","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","# # Licensed under the Apache License, Version 2.0 (the","def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self,","import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import *","file except in compliance with the License. # You may","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is","None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame(","self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self,","wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import","] for key in output_dict.keys(): if key in misc_keys: continue","= output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name)","part of self.frame. This Bind propagates # capture's source change","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext","ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in","self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show()","# -*- coding: utf-8 -*- # # IkaLog # ======","wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer","output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title =","wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key)","output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel(","of self.frame. This Bind propagates # capture's source change to","ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined","keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys","capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) #","# IkaLog # ====== # Copyright (C) 2015 #","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ]","misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack,","_(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button","not in keys: keys.append(key) # Main tabs index = 0","Keys for outputs in the main page. keys = [","preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin.","or implied. # See the License for the specific language","-*- coding: utf-8 -*- # # IkaLog # ====== #","KIND, either express or implied. # See the License for","specific language governing permissions and # limitations under the License.","continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add(","Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self):","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font.","is a part of self.frame. This Bind propagates # capture's","keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index,","def _init_outputs(self, outputs): output_dict = {} for output in outputs:","continue if key not in keys: keys.append(key) # Main tabs","Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON,","(the \"License\"); # you may not use this file except","IkaLog # ====== # Copyright (C) 2015 # #","# you may not use this file except in compliance","OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None","= wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font","not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def","_(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer =","each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame:","output_dict.keys(): if key in misc_keys: continue if key not in","self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind","2015 # # Licensed under the Apache License, Version","wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) #","ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import","title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND","import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self,","# # Unless required by applicable law or agreed to","import * from ikalog.ui import VideoCapture from ikalog.utils import *","on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","= wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer)","top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply)","apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel'))","Version 2.0 (the \"License\"); # you may not use this","tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer =","import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import *","to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of","keys.append(key) # Main tabs index = 0 for key in","ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self):","implied. # See the License for the specific language governing","under the Apache License, Version 2.0 (the \"License\"); # you","ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the","# Keys for outputs in the main page. keys =","event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict","default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL)","fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame","ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into","by applicable law or agreed to in writing, software #","key not in keys: keys.append(key) # Main tabs index =","outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a","wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD)","0 for key in keys: output = output_dict.get(key) if not","in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel)","for key in output_dict.keys(): if key in misc_keys: continue if","in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook)","= ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame:","self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True)","= [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part","bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame,","_init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__]","output # Keys for outputs in the main page. keys","Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","Unless required by applicable law or agreed to in writing,","+ self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame.","button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel","def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {}","for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs =","the specific language governing permissions and # limitations under the","and # limitations under the License. # import gettext import","def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self,","applicable law or agreed to in writing, software # distributed","in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk,","in writing, software # distributed under the License is distributed","buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event","output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel,","from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui","ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if","top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers","self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def","buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) #","Keys for outputs combined into the misc tab. misc_keys =","This Bind propagates # capture's source change to the preview.","from ikalog.ui import VideoCapture from ikalog.utils import * _ =","360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(),","License is distributed on an \"AS IS\" BASIS, # WITHOUT","self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont()","License, Version 2.0 (the \"License\"); # you may not use","ikalog.outputs.Twitter ] # Keys for outputs combined into the misc","output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY,","# You may obtain a copy of the License at","if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event)","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc","output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel,","combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON,","key in keys: output = output_dict.get(key) if not output: continue","# Keys for outputs combined into the misc tab. misc_keys","(C) 2015 # # Licensed under the Apache License,","gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import","# Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360))","self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame,","button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default =","the License for the specific language governing permissions and #","_ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui","wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont()","Apache License, Version 2.0 (the \"License\"); # you may not","either express or implied. # See the License for the","wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply","_(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD)","output_dict = {} for output in outputs: output_dict[output.__class__] = output","self.capture.panel is a part of self.frame. This Bind propagates #","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event)","UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if","output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index","coding: utf-8 -*- # # IkaLog # ====== # Copyright","outputs): output_dict = {} for output in outputs: output_dict[output.__class__] =","ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in","License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs","1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640,","Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui","ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event):","size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font =","page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] #","on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs):","output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title)","keys: keys.append(key) # Main tabs index = 0 for key","self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) #","the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter","wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output =","output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel =","Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind(","fetus-hina/IkaLog #!/usr/bin/env python3 # -*- coding: utf-8 -*- # #","\"License\"); # you may not use this file except in","# Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font)","outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV,","self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) #","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold","# distributed under the License is distributed on an \"AS","# Unless required by applicable law or agreed to in","EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context',","ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return","buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture]","wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10)","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs","= wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output","wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from","# import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from","handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs","You may obtain a copy of the License at #","_(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer =","if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\"Options\"), size=(640,","event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default)","not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1","wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply'))","the Apache License, Version 2.0 (the \"License\"); # you may","permissions and # limitations under the License. # import gettext","for outputs in the main page. keys = [ ikalog.ui.VideoCapture,","'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise()","a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel =","self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON,","-*- # # IkaLog # ====== # Copyright (C) 2015"],"string":"[\n \"2.0 (the \\\"License\\\"); # you may not use this file\",\n \"wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font =\",\n \"for key in keys: output = output_dict.get(key) if not output:\",\n \"| wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel, _('Misc.'))\",\n \"self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def\",\n \"in outputs: output_dict[output.__class__] = output # Keys for outputs in\",\n \"button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs)\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"# ====== # Copyright (C) 2015 # # Licensed\",\n \"+= 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY,\",\n \"= wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL,\",\n \"propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED,\",\n \"self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT,\",\n \"limitations under the License. # import gettext import wx import\",\n \"import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events\",\n \"= wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font\",\n \"self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500))\",\n \"self.frame. This Bind propagates # capture's source change to the\",\n \"change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI\",\n \"key in output_dict.keys(): if key in misc_keys: continue if key\",\n \"in keys: keys.append(key) # Main tabs index = 0 for\",\n \"use this file except in compliance with the License. #\",\n \"= output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel,\",\n \"self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame,\",\n \"= wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame,\",\n \"default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"def _init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY,\",\n \"License. # You may obtain a copy of the License\",\n \"wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font\",\n \"of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not\",\n \"for key in misc_keys: output = output_dict.get(key) if not output:\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer,\",\n \"Copyright (C) 2015 # # Licensed under the Apache\",\n \"License for the specific language governing permissions and # limitations\",\n \"wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons.\",\n \"button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame,\",\n \"misc_keys: continue if key not in keys: keys.append(key) # Main\",\n \"not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font)\",\n \"self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in\",\n \"* from ikalog.ui import VideoCapture from ikalog.utils import * _\",\n \"key in misc_keys: output = output_dict.get(key) if not output: continue\",\n \"ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in misc_keys:\",\n \"Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a\",\n \"wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer\",\n \"{} for output in outputs: output_dict[output.__class__] = output # Keys\",\n \"output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 #\",\n \"class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame =\",\n \"self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event):\",\n \"Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer\",\n \"in compliance with the License. # You may obtain a\",\n \"output_dict[output.__class__] = output # Keys for outputs in the main\",\n \"[self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of\",\n \"= wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND |\",\n \"software # distributed under the License is distributed on an\",\n \"the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each\",\n \"utf-8 -*- # # IkaLog # ====== # Copyright (C)\",\n \"output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab\",\n \"def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame()\",\n \"self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling()\",\n \"font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY,\",\n \"= wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load\",\n \"index = 0 for key in keys: output = output_dict.get(key)\",\n \"wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL,\",\n \"# Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def\",\n \"self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook =\",\n \"= wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for\",\n \"# # IkaLog # ====== # Copyright (C) 2015 \",\n \"# Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use\",\n \"for outputs combined into the misc tab. misc_keys = [\",\n \"button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY,\",\n \"wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer)\",\n \"flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index, self.misc_panel,\",\n \"self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This\",\n \"buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set\",\n \"OF ANY KIND, either express or implied. # See the\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"ANY KIND, either express or implied. # See the License\",\n \"See the License for the specific language governing permissions and\",\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 \"ikalog.ui import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI',\",\n \"to in writing, software # distributed under the License is\",\n \"output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer) self.misc_panel.SetupScrolling() self.notebook.InsertPage(index,\",\n \"VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class\",\n \"# See the License for the specific language governing permissions\",\n \"language governing permissions and # limitations under the License. #\",\n \"ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils import\",\n \"or agreed to in writing, software # distributed under the\",\n \"required by applicable law or agreed to in writing, software\",\n \"import * from ikalog.ui.panel import * from ikalog.ui import VideoCapture\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"] # Keys for outputs combined into the misc tab.\",\n \"if key not in keys: keys.append(key) # Main tabs index\",\n \"with the License. # You may obtain a copy of\",\n \"= [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for\",\n \"* from ikalog.ui.panel import * from ikalog.ui import VideoCapture from\",\n \"if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index +=\",\n \"compliance with the License. # You may obtain a copy\",\n \"agreed to in writing, software # distributed under the License\",\n \"# self.capture.panel is a part of self.frame. This Bind propagates\",\n \"# Copyright (C) 2015 # # Licensed under the\",\n \"if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name)\",\n \"[ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key\",\n \"distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"a part of self.frame. This Bind propagates # capture's source\",\n \"button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] +\",\n \"tabs index = 0 for key in keys: output =\",\n \"= Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui =\",\n \"#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog\",\n \"the License. # import gettext import wx import wx.lib.scrolledpanel import\",\n \"express or implied. # See the License for the specific\",\n \"# capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized)\",\n \"except in compliance with the License. # You may obtain\",\n \"= None self._init_frame() def _init_frame(self): if self.frame: return self.frame =\",\n \"= output # Keys for outputs in the main page.\",\n \"outputs: output_dict[output.__class__] = output # Keys for outputs in the\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"not use this file except in compliance with the License.\",\n \"misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title\",\n \"top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON,\",\n \"writing, software # distributed under the License is distributed on\",\n \"= button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default\",\n \"you may not use this file except in compliance with\",\n \"500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply =\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"= 0 for key in keys: output = output_dict.get(key) if\",\n \"= wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply)\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if\",\n \"outputs in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS,\",\n \"debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"_init_frame(self): if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"),\",\n \"* _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui):\",\n \"__init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def\",\n \"on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event):\",\n \"self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output in\",\n \"title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25))\",\n \"self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins(\",\n \"into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot,\",\n \"= wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY,\",\n \"misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ]\",\n \"index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook,\",\n \"event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for output\",\n \"for output in outputs: output_dict[output.__class__] = output # Keys for\",\n \"self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default)\",\n \"# Main tabs index = 0 for key in keys:\",\n \"wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if\",\n \"ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys():\",\n \"under the License. # import gettext import wx import wx.lib.scrolledpanel\",\n \"= [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"Main tabs index = 0 for key in keys: output\",\n \"size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply\",\n \"apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button(\",\n \"in misc_keys: continue if key not in keys: keys.append(key) #\",\n \"import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel\",\n \"from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object):\",\n \"the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi,\",\n \"wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel)\",\n \"wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font =\",\n \"self.frame = None self._init_frame() def _init_frame(self): if self.frame: return self.frame\",\n \"button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL)\",\n \"ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key\",\n \"# Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel)\",\n \"law or agreed to in writing, software # distributed under\",\n \"self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL)\",\n \"governing permissions and # limitations under the License. # import\",\n \"event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event)\",\n \"from ikalog.ui.panel import * from ikalog.ui import VideoCapture from ikalog.utils\",\n \"ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import * from\",\n \"title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys:\",\n \"source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh\",\n \"[ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs\",\n \"====== # Copyright (C) 2015 # # Licensed under\",\n \"self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel\",\n \"python3 # -*- coding: utf-8 -*- # # IkaLog #\",\n \"self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict =\",\n \"= self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key\",\n \"wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key) if not\",\n \"may obtain a copy of the License at # #\",\n \"wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY)\",\n \"wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default'))\",\n \"= {} for output in outputs: output_dict[output.__class__] = output #\",\n \"plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame()\",\n \"key in misc_keys: continue if key not in keys: keys.append(key)\",\n \"output in outputs: output_dict[output.__class__] = output # Keys for outputs\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook)\",\n \"may not use this file except in compliance with the\",\n \"self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1, 25)) self.misc_panel.SetSizer(self.misc_panel_sizer)\",\n \"output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10) self.misc_panel_sizer.Add((-1,\",\n \"# limitations under the License. # import gettext import wx\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"this file except in compliance with the License. # You\",\n \"if key in misc_keys: continue if key not in keys:\",\n \" # # Licensed under the Apache License, Version 2.0\",\n \"def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self,\",\n \"in output_dict.keys(): if key in misc_keys: continue if key not\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"# # Licensed under the Apache License, Version 2.0 (the\",\n \"def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self,\",\n \"import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import *\",\n \"file except in compliance with the License. # You may\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is\",\n \"None self._init_frame() def _init_frame(self): if self.frame: return self.frame = wx.Frame(\",\n \"self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self,\",\n \"wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from ikalog.ui.panel import\",\n \"] for key in output_dict.keys(): if key in misc_keys: continue\",\n \"= output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name)\",\n \"part of self.frame. This Bind propagates # capture's source change\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"import VideoCapture from ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext\",\n \"ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if key in\",\n \"self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show()\",\n \"# -*- coding: utf-8 -*- # # IkaLog # ======\",\n \"wx.ID_ANY, _(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer\",\n \"output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title =\",\n \"wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output = output_dict.get(key)\",\n \"output.panel_name) index += 1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel(\",\n \"of self.frame. This Bind propagates # capture's source change to\",\n \"ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined\",\n \"keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys\",\n \"capture's source change to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) #\",\n \"# IkaLog # ====== # Copyright (C) 2015 #\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ]\",\n \"misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack,\",\n \"_(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button\",\n \"not in keys: keys.append(key) # Main tabs index = 0\",\n \"Keys for outputs in the main page. keys = [\",\n \"preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin.\",\n \"or implied. # See the License for the specific language\",\n \"-*- coding: utf-8 -*- # # IkaLog # ====== #\",\n \"KIND, either express or implied. # See the License for\",\n \"specific language governing permissions and # limitations under the License.\",\n \"continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add(\",\n \"Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self):\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font.\",\n \"is a part of self.frame. This Bind propagates # capture's\",\n \"keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index,\",\n \"def _init_outputs(self, outputs): output_dict = {} for output in outputs:\",\n \"continue if key not in keys: keys.append(key) # Main tabs\",\n \"Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON,\",\n \"(the \\\"License\\\"); # you may not use this file except\",\n \"IkaLog # ====== # Copyright (C) 2015 # #\",\n \"# you may not use this file except in compliance\",\n \"OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None\",\n \"= wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font\",\n \"not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def\",\n \"_(u'Cancel')) button_load_default = wx.Button( self.frame, wx.ID_ANY, _(u'Load default')) buttons_sizer =\",\n \"each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if not self.frame:\",\n \"output_dict.keys(): if key in misc_keys: continue if key not in\",\n \"self._init_outputs(outputs) # self.capture.panel is a part of self.frame. This Bind\",\n \"2015 # # Licensed under the Apache License, Version\",\n \"wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) #\",\n \"ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui import\",\n \"title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND\",\n \"import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self,\",\n \"# # Unless required by applicable law or agreed to\",\n \"import * from ikalog.ui import VideoCapture from ikalog.utils import *\",\n \"on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {} for\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"= wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer)\",\n \"top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply)\",\n \"apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel'))\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer =\",\n \"import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import *\",\n \"to the preview. self.ikalog_gui.capture.panel.Bind( EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of\",\n \"keys.append(key) # Main tabs index = 0 for key in\",\n \"ikalog_gui): self.ikalog_gui = ikalog_gui self.frame = None self._init_frame() def _init_frame(self):\",\n \"implied. # See the License for the specific language governing\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into the\",\n \"# Keys for outputs in the main page. keys =\",\n \"event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict\",\n \"default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL)\",\n \"fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui self.frame\",\n \"ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] # Keys for outputs combined into\",\n \"by applicable law or agreed to in writing, software #\",\n \"key not in keys: keys.append(key) # Main tabs index =\",\n \"outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a\",\n \"wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD)\",\n \"0 for key in keys: output = output_dict.get(key) if not\",\n \"in misc_keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel)\",\n \"for key in output_dict.keys(): if key in misc_keys: continue if\",\n \"in keys: output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook)\",\n \"= ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame:\",\n \"self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True)\",\n \"= [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part\",\n \"bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame,\",\n \"_init_outputs(self, outputs): output_dict = {} for output in outputs: output_dict[output.__class__]\",\n \"output # Keys for outputs in the main page. keys\",\n \"Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"Unless required by applicable law or agreed to in writing,\",\n \"+ self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel is a part of self.frame.\",\n \"button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) # self.capture.panel\",\n \"def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs): output_dict = {}\",\n \"for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs =\",\n \"the specific language governing permissions and # limitations under the\",\n \"and # limitations under the License. # import gettext import\",\n \"def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self,\",\n \"applicable law or agreed to in writing, software # distributed\",\n \"in the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk,\",\n \"in writing, software # distributed under the License is distributed\",\n \"buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event\",\n \"output = output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel,\",\n \"from ikalog.ui.events import * from ikalog.ui.panel import * from ikalog.ui\",\n \"ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in output_dict.keys(): if\",\n \"top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) # Set event handlers\",\n \"self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def\",\n \"buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer = wx.BoxSizer(wx.VERTICAL) top_sizer.Add(self.notebook) top_sizer.Add(buttons_sizer) self.frame.SetSizer(top_sizer) #\",\n \"Keys for outputs combined into the misc tab. misc_keys =\",\n \"This Bind propagates # capture's source change to the preview.\",\n \"from ikalog.ui import VideoCapture from ikalog.utils import * _ =\",\n \"360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font = wx.Font(default_font.GetPointSize(),\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"self.notebook, wx.ID_ANY, size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont()\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"ikalog.outputs.Twitter ] # Keys for outputs combined into the misc\",\n \"output_dict.get(key) if not output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY,\",\n \"# You may obtain a copy of the License at\",\n \"if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event)\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1 # Misc\",\n \"output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel,\",\n \"combined into the misc tab. misc_keys = [ ikalog.outputs.CSV, ikalog.outputs.JSON,\",\n \"key in keys: output = output_dict.get(key) if not output: continue\",\n \"# Keys for outputs combined into the misc tab. misc_keys\",\n \"(C) 2015 # # Licensed under the Apache License,\",\n \"gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import\",\n \"# Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640, 360))\",\n \"self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame,\",\n \"button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel = wx.Button(self.frame, wx.ID_ANY, _(u'Cancel')) button_load_default =\",\n \"the License for the specific language governing permissions and #\",\n \"_ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui\",\n \"wx.ID_ANY, _(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont()\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"either express or implied. # See the License for the\",\n \"wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) # Apply\",\n \"_(u'Apply')) # Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD)\",\n \"output_dict = {} for output in outputs: output_dict[output.__class__] = output\",\n \"self.capture.panel is a part of self.frame. This Bind propagates #\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"self.frame.Show() self.frame.Raise() def on_button_apply(self, event): self.ikalog_gui.on_options_apply(event) def on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event)\",\n \"UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context', debug=True) def show(self): if\",\n \"output_dict.get(key) if not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index\",\n \"coding: utf-8 -*- # # IkaLog # ====== # Copyright\",\n \"outputs): output_dict = {} for output in outputs: output_dict[output.__class__] =\",\n \"ikalog.outputs.CSV, ikalog.outputs.JSON, ikalog.outputs.Screenshot, ikalog.outputs.Boyomi, ikalog.outputs.Slack, ikalog.outputs.WebSocketServer, ] for key in\",\n \"License. # import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs\",\n \"1 # Misc tab self.misc_panel = wx.lib.scrolledpanel.ScrolledPanel( self.notebook, wx.ID_ANY, size=(640,\",\n \"Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def __init__(self, ikalog_gui): self.ikalog_gui = ikalog_gui\",\n \"ikalog.utils import * _ = Localization.gettext_translation('IkaUI', fallback=True).gettext class OptionsGUI(object): def\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise() def on_button_apply(self, event):\",\n \"size=(640, 360)) self.misc_panel_sizer = wx.BoxSizer(wx.VERTICAL) default_font = self.misc_panel.GetFont() title_font =\",\n \"page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter ] #\",\n \"on_button_cancel(self, event): self.ikalog_gui.on_options_cancel(event) def on_button_load_default(self, event): self.ikalog_gui.on_options_load_default(event) def _init_outputs(self, outputs):\",\n \"output: continue output.on_option_tab_create(self.misc_panel) title = wx.StaticText(self.misc_panel, wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title)\",\n \"keys: keys.append(key) # Main tabs index = 0 for key\",\n \"self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs self._init_outputs(outputs) #\",\n \"the main page. keys = [ ikalog.ui.VideoCapture, ikalog.outputs.OBS, ikalog.outputs.StatInk, ikalog.outputs.Twitter\",\n \"wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output =\",\n \"output.panel, output.panel_name) index += 1 # Misc tab self.misc_panel =\",\n \"Bind propagates # capture's source change to the preview. self.ikalog_gui.capture.panel.Bind(\",\n \"fetus-hina/IkaLog #!/usr/bin/env python3 # -*- coding: utf-8 -*- # #\",\n \"\\\"License\\\"); # you may not use this file except in\",\n \"# Use a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font)\",\n \"outputs combined into the misc tab. misc_keys = [ ikalog.outputs.CSV,\",\n \"self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640, 500)) self.notebook = wx.Notebook(self.frame, wx.ID_ANY) #\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply')) # Use a bold\",\n \"# distributed under the License is distributed on an \\\"AS\",\n \"# Unless required by applicable law or agreed to in\",\n \"EVT_INPUT_INITIALIZED, self.ikalog_gui.on_input_initialized) # Refresh UI of each plugin. self.ikalog_gui.engine.call_plugins( 'on_config_load_from_context',\",\n \"ikalog_gui self.frame = None self._init_frame() def _init_frame(self): if self.frame: return\",\n \"buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture]\",\n \"wx.ID_ANY, output.panel_name) title.SetFont(title_font) self.misc_panel_sizer.Add(title) self.misc_panel_sizer.Add( output.panel, flag=wx.EXPAND | wx.ALL, border=10)\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs = [self.ikalog_gui.capture] + self.ikalog_gui.outputs\",\n \"= wx.Font(default_font.GetPointSize(), wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_BOLD) for key in misc_keys: output\",\n \"wx import wx.lib.scrolledpanel import ikalog.outputs from ikalog.ui.events import * from\",\n \"# import gettext import wx import wx.lib.scrolledpanel import ikalog.outputs from\",\n \"handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default) outputs\",\n \"You may obtain a copy of the License at #\",\n \"_(u'Load default')) buttons_sizer = wx.BoxSizer(wx.HORIZONTAL) buttons_sizer.Add(button_apply) buttons_sizer.Add(button_cancel) buttons_sizer.Add(button_load_default) top_sizer =\",\n \"if self.frame: return self.frame = wx.Frame( self.ikalog_gui.frame, wx.ID_ANY, _(\\\"Options\\\"), size=(640,\",\n \"event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON, self.on_button_cancel) button_load_default.Bind(wx.EVT_BUTTON, self.on_button_load_default)\",\n \"not output: continue output.on_option_tab_create(self.notebook) self.notebook.InsertPage(index, output.panel, output.panel_name) index += 1\",\n \"wx.Notebook(self.frame, wx.ID_ANY) # Apply button button_apply = wx.Button(self.frame, wx.ID_ANY, _(u'Apply'))\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"permissions and # limitations under the License. # import gettext\",\n \"for outputs in the main page. keys = [ ikalog.ui.VideoCapture,\",\n \"'on_config_load_from_context', debug=True) def show(self): if not self.frame: self._init_frame() self.frame.Show() self.frame.Raise()\",\n \"a bold font. apply_font = button_apply.GetFont() apply_font.SetWeight(wx.FONTWEIGHT_BOLD) button_apply.SetFont(apply_font) button_cancel =\",\n \"self.frame.SetSizer(top_sizer) # Set event handlers for buttons. button_apply.Bind(wx.EVT_BUTTON, self.on_button_apply) button_cancel.Bind(wx.EVT_BUTTON,\",\n \"-*- # # IkaLog # ====== # Copyright (C) 2015\"\n]"}}},{"rowIdx":3486,"cells":{"ngram":{"kind":"list like","value":["f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK',","= f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB',","packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']}","f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='',","description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests',","import setup, find_packages with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description =","setuptools import 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author_email='',","url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx':","setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']),","with open(\"README.md\", 'r',encoding=\"utf-8\") as f: long_description = f.read() setup( name='LineBot',","name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=["],"string":"[\n \"f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK',\",\n \"= f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB',\",\n \"packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']}\",\n \"f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='',\",\n \"description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests',\",\n \"import setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description =\",\n \"setuptools import setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description\",\n \"from setuptools import setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f:\",\n \"long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift',\",\n \"author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI'\",\n \"author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ],\",\n \"open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0',\",\n \"find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup(\",\n \"setup, find_packages with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read()\",\n \"'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB',\",\n \"version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0',\",\n \"as f: long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description,\",\n \"'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx': ['http2']} )\",\n \"long_description = f.read() setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='',\",\n \"url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[ 'httpx==0.19.0', 'requests', 'thrift', 'CyberTKAPI' ], extras_require={'httpx':\",\n \"setup( name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']),\",\n \"with open(\\\"README.md\\\", 'r',encoding=\\\"utf-8\\\") as f: long_description = f.read() setup( name='LineBot',\",\n \"name='LineBot', version='0.1.0', description='Simple-LINELIB', long_description=long_description, author='', author_email='', url='https://github.com/CyberTKR/Simple-LINELIB', packages=find_packages(include=['CyberTK', 'CyberTK.*']), install_requires=[\"\n]"}}},{"rowIdx":3487,"cells":{"ngram":{"kind":"list like","value":["(get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information","% (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information","ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" %","= self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item =","ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message","tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self):","@decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB,","''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import","tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,))","tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"]","is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get","utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation","ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list","''' Static route information is acquired. acquired dict: { static_route:[{","(get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is","acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } '''","}] } ''' get_info = {} tmp_list = [] routes","tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"]","route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" %","= {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] =","= tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel","class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration '''","''' Parameter module for Cgwsh driver configuration ''' import GlobalModule","}] } ''' get_info = {} tmp_list = [] tunnel_uni","self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item = {}","= tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if =","route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str,","@decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict:","ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\")","GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log def","def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: {","information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str,","get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return","@decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired","Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None):","information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str,","CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import","decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class","-*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and","gateway_address:str }] } ''' get_info = {} tmp_list = []","{} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for","= {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ())","ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"]","\"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" %","information is acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\")","uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"]","tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log","\"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id","in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] =","''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str,","get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" % (get_info,)) return","{ tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } '''","def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name,","tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item)","tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe =","= %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): '''","% (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route","from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for","tunnel_source:str, }] } ''' get_info = {} tmp_list = []","return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is","= {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ())","tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"]","tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info","= route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get","uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {}",".get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] =","= ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] =","(get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information","@decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired","vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id","Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter","get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{","dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info","@decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info","tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if","tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" % (get_info,))","tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\")","''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\",","for route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\")","pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info =","= self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired.","super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self):","''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"]","pass @decorater_log def get_management_info(self): ''' Management information is acquired. '''","= tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info","import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB","Parameter module for Cgwsh driver configuration ''' import GlobalModule from","from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB):","coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone","python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon","route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] =","@decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass","{} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\")","= vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] =","= uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list","get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def","= [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in","static_route = %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self):","{} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo","tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo =","2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py '''","interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str,","''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor '''","get_management_info(self): ''' Management information is acquired. ''' get_info = {}","DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh","get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired","tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\")","self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info @decorater_log","} ''' get_info = {} tmp_list = [] routes =","tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] =","{} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\")","{ static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info =","[] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\",","PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str,","''' pass @decorater_log def get_management_info(self): ''' Management information is acquired.","username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {}","Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log","for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None,","self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is","tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"]","GlobalModule.EM_LOGGER.debug(\"get static_route = %s\" % (get_info,)) return get_info @decorater_log def","{}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id =","vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info =","and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for","is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str,","password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list","db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message =","def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict:","self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {}","routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes: tmp_item","''' Management information is acquired. ''' get_info = {} get_info[\"device_name\"]","information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str","self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\")","-*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation #","acquired. ''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info","static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {}","()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\")","{ pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info","= tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"]","tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item)","EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): '''","acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is","= self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name =","tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route =","''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\",","= self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return get_info","if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {}","= tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item","class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None,","tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get(","''' Parameter class for Cgwsh driver configuration ''' @decorater_log def","information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management","}] } ''' get_info = {} tmp_list = [] ppp_infos","acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] }","''' Service information is acquired. ''' pass @decorater_log def get_management_info(self):","# -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph","{} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name","tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\"","= {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" %","''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def","= [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get(","uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get","lib/SeparateDriver/CgwshDeviceDriverSetParameterECDB.py #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c)","()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"] =","for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import","is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str","DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration","def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log","()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\")","in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] =","= {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] =","get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return","Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log","dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] }","Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py","tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"]","vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for","= .get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"]","%s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static","{} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,))","acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }]","tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name = tunnel_uni.get(\"vrfName\") uni_if_name","management_info = %s\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self):","tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item =","= %s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): '''","route in routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"]","[] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos:","driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): '''","Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module","def get_static_route_info(self): ''' Static route information is acquired. acquired dict:","tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"]","CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log","self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information is acquired. '''","is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information","db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service information","pp_no:str }] } ''' get_info = {} tmp_list = []","= self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item =","= tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\" % (get_info,)) return get_info","device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info)","GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log def","Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver","routes: tmp_item = {} tmp_item[\"ip_address\"] = route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\")","{}).get(\"staticRoute\", ()) for route in routes: tmp_item = {} tmp_item[\"ip_address\"]","Service information is acquired. ''' pass @decorater_log def get_management_info(self): '''","is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }]","= self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ())","tenant:str, pp_no:str }] } ''' get_info = {} tmp_list =","{}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"]","get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired.","tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list","driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from","} ''' get_info = {} tmp_list = [] ppp_infos =","get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\"","''' get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\",","Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh","tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info","return get_info @decorater_log def get_static_route_info(self): ''' Static route information is","} ''' get_info = {} tmp_list = [] tunnel_uni =","tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\",","import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver","get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: {","()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] =","''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{","% (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface","= ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get","# Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration","uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo:","= [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in","get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\",","#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019","configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor","ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): ''' Service","[] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route in routes:","tunnel_uni.get(\"vrfName\") uni_if_name = tunnel_uni.get(\"ifName\") uni_vlan_id = tunnel_uni.get(\"vlanId\") for tunnel in","ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe","acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }]","self.ec_message.get(\"serviceInfo\", {}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name","tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ()) for route","import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter","%s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel","Static route information is acquired. acquired dict: { static_route:[{ ip_address:str,","get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\"serviceInfo\", {}).get(\"uni\",","tunnel_uni.get(\"vlanId\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] =","get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info = %s\" % (get_info,)) return","# Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename:","get_info = {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\",","for ppp_info in ppp_infos: tmp_item = {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\")","GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class","= {} tmp_list = [] routes = self.ec_message.get(\"serviceInfo\", {}).get(\"staticRoute\", ())","Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self,","= route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route = %s\"","acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] }","= ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get pppoe = %s\"","Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str,","uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list =","{}).get(\"uni\", ()) tunnel_officeInfo = self.ec_message.get( \"serviceInfo\", {}).get(\"officeInfo\", ()) vrf_name =","return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired.","ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for ppp_info in ppp_infos: tmp_item","= route.get(\"ipAddress\") tmp_item[\"subnet_mask\"] = route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"]","ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\")","subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list =","= uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] = tunnel.get( \"tunnelSrcIpAddress\") tmp_list.append(tmp_item)","__init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message,","self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\"device\"] @decorater_log def get_service_info(self): '''","{} tmp_list = [] ppp_infos = self.ec_message.get(\"serviceInfo\", {}).get(\"pppInfo\", ()) for","tmp_item[\"tenant\"] = ppp_info.get(\"corporationId\") tmp_item[\"pp_no\"] = ppp_info.get(\"ppId\") tmp_list.append(tmp_item) get_info[\"pppoe\"] = tmp_list","%s\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE","uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list","module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog","route.get(\"subnetMask\") tmp_item[\"gateway_address\"] = route.get(\"gatewayIpAddress\") tmp_list.append(tmp_item) get_info[\"static_route\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get static_route","tmp_list.append(tmp_item) get_info[\"tunnel_if\"] = tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,))","dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } '''","''' get_info = {} get_info[\"device_name\"] = self.ec_message.get(\"name\") GlobalModule.EM_LOGGER.debug(\"get management_info =","= %s\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): '''","Management information is acquired. ''' get_info = {} get_info[\"device_name\"] =","= {} tmp_item[\"username\"] = ppp_info.get(\"connectionId\") tmp_item[\"password\"] = .get(\"connectionPassword\") tmp_item[\"tenant\"] =","= tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info","= tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"] = uni_vlan_id tmp_item[\"tunnel_source\"] =","for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name","in tunnel_officeInfo: tmp_item = {} tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] =","tmp_item[\"vrf_name\"] = vrf_name tmp_item[\"if_name\"] = tunnel.get(\"tunnelIfName\") tmp_item[\"uni_if_name\"] = uni_if_name tmp_item[\"uni_vlan_id\"]","tmp_list GlobalModule.EM_LOGGER.debug(\"get tunnel_if = %s\" % (get_info,)) return get_info @decorater_log","get_static_route_info(self): ''' Static route information is acquired. acquired dict: {","Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration '''","acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str,","def get_management_info(self): ''' Management information is acquired. ''' get_info =","get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired.","configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB"],"string":"[\n \"(get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information\",\n \"% (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information\",\n \"ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" %\",\n \"= self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item =\",\n \"ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message\",\n \"tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self):\",\n \"@decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB,\",\n \"''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import\",\n \"tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,))\",\n \"tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"]\",\n \"is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation\",\n \"ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list\",\n \"''' Static route information is acquired. acquired dict: { static_route:[{\",\n \"(get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is\",\n \"acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } '''\",\n \"}] } ''' get_info = {} tmp_list = [] routes\",\n \"tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"]\",\n \"route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" %\",\n \"= {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] =\",\n \"= tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel\",\n \"class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration '''\",\n \"''' Parameter module for Cgwsh driver configuration ''' import GlobalModule\",\n \"}] } ''' get_info = {} tmp_list = [] tunnel_uni\",\n \"self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {}\",\n \"= tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if =\",\n \"route information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str,\",\n \"@decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict:\",\n \"ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\")\",\n \"GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log def\",\n \"def get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: {\",\n \"information is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str,\",\n \"get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return\",\n \"@decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired\",\n \"Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None):\",\n \"information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str,\",\n \"CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration ''' import\",\n \"decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class\",\n \"-*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and\",\n \"gateway_address:str }] } ''' get_info = {} tmp_list = []\",\n \"{} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for\",\n \"= {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ())\",\n \"ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"]\",\n \"\\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" %\",\n \"information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\")\",\n \"uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"]\",\n \"tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log\",\n \"\\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id\",\n \"in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] =\",\n \"''' PPPoE information is acquired. acquired dict: { pppoe:[{ username:str,\",\n \"get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" % (get_info,)) return\",\n \"{ tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } '''\",\n \"def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name,\",\n \"tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item)\",\n \"tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe =\",\n \"= %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): '''\",\n \"% (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route\",\n \"from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for\",\n \"tunnel_source:str, }] } ''' get_info = {} tmp_list = []\",\n \"return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is\",\n \"= {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ())\",\n \"tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"]\",\n \"tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info\",\n \"= route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {}\",\n \".get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] =\",\n \"= ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] =\",\n \"(get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static route information\",\n \"@decorater_log def get_static_route_info(self): ''' Static route information is acquired. acquired\",\n \"vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id\",\n \"Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter\",\n \"get_pppoe_info(self): ''' PPPoE information is acquired. acquired dict: { pppoe:[{\",\n \"dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info\",\n \"@decorater_log def get_management_info(self): ''' Management information is acquired. ''' get_info\",\n \"tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if\",\n \"tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" % (get_info,))\",\n \"tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\")\",\n \"''' get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\",\",\n \"for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\")\",\n \"pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info =\",\n \"= self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired.\",\n \"super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self):\",\n \"''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"]\",\n \"pass @decorater_log def get_management_info(self): ''' Management information is acquired. '''\",\n \"= tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info\",\n \"import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB\",\n \"Parameter module for Cgwsh driver configuration ''' import GlobalModule from\",\n \"from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB):\",\n \"coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph and Telephone\",\n \"python # -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon\",\n \"route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] =\",\n \"@decorater_log def get_service_info(self): ''' Service information is acquired. ''' pass\",\n \"{} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\")\",\n \"= vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] =\",\n \"= uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list\",\n \"get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log def\",\n \"= [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in\",\n \"static_route = %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self):\",\n \"{} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo\",\n \"tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo =\",\n \"2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py '''\",\n \"interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str,\",\n \"''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor '''\",\n \"get_management_info(self): ''' Management information is acquired. ''' get_info = {}\",\n \"DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh\",\n \"get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired. acquired\",\n \"tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\")\",\n \"self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info @decorater_log\",\n \"} ''' get_info = {} tmp_list = [] routes =\",\n \"tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] =\",\n \"{} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\")\",\n \"{ static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info =\",\n \"[] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\",\",\n \"PPPoE information is acquired. acquired dict: { pppoe:[{ username:str, password:str,\",\n \"''' pass @decorater_log def get_management_info(self): ''' Management information is acquired.\",\n \"username:str, password:str, tenant:str, pp_no:str }] } ''' get_info = {}\",\n \"Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import decorater_log\",\n \"for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None,\",\n \"self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is\",\n \"tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"]\",\n \"GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\" % (get_info,)) return get_info @decorater_log def\",\n \"{}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id =\",\n \"vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info =\",\n \"and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for\",\n \"is acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str,\",\n \"password:str, tenant:str, pp_no:str }] } ''' get_info = {} tmp_list\",\n \"db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message =\",\n \"def get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict:\",\n \"self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {}\",\n \"routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item\",\n \"''' Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"]\",\n \"information is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str\",\n \"self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\")\",\n \"-*- # Copyright(c) 2019 Nippon Telegraph and Telephone Corporation #\",\n \"acquired. ''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info\",\n \"static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] } ''' get_info = {}\",\n \"()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\")\",\n \"{ pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } ''' get_info\",\n \"= tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"]\",\n \"tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item)\",\n \"EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): '''\",\n \"acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information is\",\n \"= self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name =\",\n \"tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route =\",\n \"''' get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\",\",\n \"= self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return get_info\",\n \"if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {}\",\n \"= tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item\",\n \"class for Cgwsh driver configuration ''' @decorater_log def __init__(self, device_name=None,\",\n \"tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get(\",\n \"''' Parameter class for Cgwsh driver configuration ''' @decorater_log def\",\n \"information is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management\",\n \"}] } ''' get_info = {} tmp_list = [] ppp_infos\",\n \"acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] }\",\n \"''' Service information is acquired. ''' pass @decorater_log def get_management_info(self):\",\n \"# -*- coding: utf-8 -*- # Copyright(c) 2019 Nippon Telegraph\",\n \"{} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name\",\n \"tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\"\",\n \"= {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" %\",\n \"''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def\",\n \"= [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get(\",\n \"uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"lib/SeparateDriver/CgwshDeviceDriverSetParameterECDB.py #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c)\",\n \"()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] =\",\n \"for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog import\",\n \"is acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str\",\n \"DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration\",\n \"def get_service_info(self): ''' Service information is acquired. ''' pass @decorater_log\",\n \"()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\")\",\n \"in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] =\",\n \"= {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] =\",\n \"get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return\",\n \"Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log\",\n \"dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] }\",\n \"Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py\",\n \"tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"]\",\n \"vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for\",\n \"= .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"]\",\n \"%s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self): ''' Static\",\n \"{} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,))\",\n \"acquired. acquired dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }]\",\n \"tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name = tunnel_uni.get(\\\"vrfName\\\") uni_if_name\",\n \"management_info = %s\\\" % (get_info,)) return get_info @decorater_log def get_static_route_info(self):\",\n \"tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item =\",\n \"= %s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): '''\",\n \"route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"]\",\n \"[] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos:\",\n \"driver configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): '''\",\n \"Telegraph and Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module\",\n \"def get_static_route_info(self): ''' Static route information is acquired. acquired dict:\",\n \"tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"]\",\n \"CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver configuration ''' @decorater_log\",\n \"self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information is acquired. '''\",\n \"is acquired. ''' pass @decorater_log def get_management_info(self): ''' Management information\",\n \"db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service information\",\n \"pp_no:str }] } ''' get_info = {} tmp_list = []\",\n \"= self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item =\",\n \"= tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\" % (get_info,)) return get_info\",\n \"device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message, db_info)\",\n \"GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log def\",\n \"Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver\",\n \"routes: tmp_item = {} tmp_item[\\\"ip_address\\\"] = route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\")\",\n \"{}).get(\\\"staticRoute\\\", ()) for route in routes: tmp_item = {} tmp_item[\\\"ip_address\\\"]\",\n \"Service information is acquired. ''' pass @decorater_log def get_management_info(self): '''\",\n \"is acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }]\",\n \"= self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ())\",\n \"tenant:str, pp_no:str }] } ''' get_info = {} tmp_list =\",\n \"{}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"]\",\n \"get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface information is acquired.\",\n \"tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list\",\n \"driver configuration ''' import GlobalModule from EmCommonLog import decorater_log from\",\n \"} ''' get_info = {} tmp_list = [] ppp_infos =\",\n \"get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\"\",\n \"''' get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\",\",\n \"Telephone Corporation # Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh\",\n \"tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info\",\n \"return get_info @decorater_log def get_static_route_info(self): ''' Static route information is\",\n \"} ''' get_info = {} tmp_list = [] tunnel_uni =\",\n \"tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\",\",\n \"import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter class for Cgwsh driver\",\n \"get_tunnel_if_info(self): ''' Tunnel interface information is acquired. acquired dict: {\",\n \"()) for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] =\",\n \"''' Tunnel interface information is acquired. acquired dict: { tunnel_if:[{\",\n \"% (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel interface\",\n \"= ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get\",\n \"# Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration\",\n \"uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo:\",\n \"= [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in\",\n \"get_info = {} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\",\",\n \"#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright(c) 2019\",\n \"configuration ''' @decorater_log def __init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor\",\n \"ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): ''' Service\",\n \"[] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route in routes:\",\n \"tunnel_uni.get(\\\"vrfName\\\") uni_if_name = tunnel_uni.get(\\\"ifName\\\") uni_vlan_id = tunnel_uni.get(\\\"vlanId\\\") for tunnel in\",\n \"ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe\",\n \"acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }]\",\n \"self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name\",\n \"tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ()) for route\",\n \"import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class CgwshDeviceDriverSetParameterECDB(DriverSetParameterECDB): ''' Parameter\",\n \"%s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): ''' Tunnel\",\n \"Static route information is acquired. acquired dict: { static_route:[{ ip_address:str,\",\n \"get_info = {} tmp_list = [] tunnel_uni = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"uni\\\",\",\n \"tunnel_uni.get(\\\"vlanId\\\") for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] =\",\n \"get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info = %s\\\" % (get_info,)) return\",\n \"# Copyright(c) 2019 Nippon Telegraph and Telephone Corporation # Filename:\",\n \"get_info = {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\",\",\n \"for ppp_info in ppp_infos: tmp_item = {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\")\",\n \"GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB import DriverSetParameterECDB class\",\n \"= {} tmp_list = [] routes = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"staticRoute\\\", ())\",\n \"Parameter class for Cgwsh driver configuration ''' @decorater_log def __init__(self,\",\n \"= route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route = %s\\\"\",\n \"acquired. acquired dict: { static_route:[{ ip_address:str, subnet_mask:str, gateway_address:str }] }\",\n \"= ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get pppoe = %s\\\"\",\n \"Tunnel interface information is acquired. acquired dict: { tunnel_if:[{ vrf_name:str,\",\n \"uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list =\",\n \"{}).get(\\\"uni\\\", ()) tunnel_officeInfo = self.ec_message.get( \\\"serviceInfo\\\", {}).get(\\\"officeInfo\\\", ()) vrf_name =\",\n \"return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE information is acquired.\",\n \"ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for ppp_info in ppp_infos: tmp_item\",\n \"= route.get(\\\"ipAddress\\\") tmp_item[\\\"subnet_mask\\\"] = route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"]\",\n \"ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\")\",\n \"subnet_mask:str, gateway_address:str }] } ''' get_info = {} tmp_list =\",\n \"= uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] = tunnel.get( \\\"tunnelSrcIpAddress\\\") tmp_list.append(tmp_item)\",\n \"__init__(self, device_name=None, ec_message=None, db_info=None): ''' Constructor ''' super(CgwshDeviceDriverSetParameterECDB, self).__init__(device_name, ec_message,\",\n \"self).__init__(device_name, ec_message, db_info) self.ec_message = self.ec_message[\\\"device\\\"] @decorater_log def get_service_info(self): '''\",\n \"{} tmp_list = [] ppp_infos = self.ec_message.get(\\\"serviceInfo\\\", {}).get(\\\"pppInfo\\\", ()) for\",\n \"tmp_item[\\\"tenant\\\"] = ppp_info.get(\\\"corporationId\\\") tmp_item[\\\"pp_no\\\"] = ppp_info.get(\\\"ppId\\\") tmp_list.append(tmp_item) get_info[\\\"pppoe\\\"] = tmp_list\",\n \"%s\\\" % (get_info,)) return get_info @decorater_log def get_pppoe_info(self): ''' PPPoE\",\n \"uni_if_name:str, uni_vlan_id:str, tunnel_source:str, }] } ''' get_info = {} tmp_list\",\n \"module for Cgwsh driver configuration ''' import GlobalModule from EmCommonLog\",\n \"route.get(\\\"subnetMask\\\") tmp_item[\\\"gateway_address\\\"] = route.get(\\\"gatewayIpAddress\\\") tmp_list.append(tmp_item) get_info[\\\"static_route\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get static_route\",\n \"tmp_list.append(tmp_item) get_info[\\\"tunnel_if\\\"] = tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,))\",\n \"dict: { pppoe:[{ username:str, password:str, tenant:str, pp_no:str }] } '''\",\n \"''' get_info = {} get_info[\\\"device_name\\\"] = self.ec_message.get(\\\"name\\\") GlobalModule.EM_LOGGER.debug(\\\"get management_info =\",\n \"= %s\\\" % (get_info,)) return get_info @decorater_log def get_tunnel_if_info(self): '''\",\n \"Management information is acquired. ''' get_info = {} get_info[\\\"device_name\\\"] =\",\n \"= {} tmp_item[\\\"username\\\"] = ppp_info.get(\\\"connectionId\\\") tmp_item[\\\"password\\\"] = .get(\\\"connectionPassword\\\") tmp_item[\\\"tenant\\\"] =\",\n \"= tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info\",\n \"= tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"] = uni_vlan_id tmp_item[\\\"tunnel_source\\\"] =\",\n \"for tunnel in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name\",\n \"in tunnel_officeInfo: tmp_item = {} tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] =\",\n \"tmp_item[\\\"vrf_name\\\"] = vrf_name tmp_item[\\\"if_name\\\"] = tunnel.get(\\\"tunnelIfName\\\") tmp_item[\\\"uni_if_name\\\"] = uni_if_name tmp_item[\\\"uni_vlan_id\\\"]\",\n \"tmp_list GlobalModule.EM_LOGGER.debug(\\\"get tunnel_if = %s\\\" % (get_info,)) return get_info @decorater_log\",\n \"get_static_route_info(self): ''' Static route information is acquired. acquired dict: {\",\n \"Filename: CgwshDeviceDriverSetParameterECDB.py ''' Parameter module for Cgwsh driver configuration '''\",\n \"acquired. acquired dict: { tunnel_if:[{ vrf_name:str, if_name:str, uni_if_name:str, uni_vlan_id:str, tunnel_source:str,\",\n \"def get_management_info(self): ''' Management information is acquired. ''' get_info =\",\n \"get_info @decorater_log def get_static_route_info(self): ''' Static route information is acquired.\",\n \"configuration ''' import GlobalModule from EmCommonLog import decorater_log from DriverSetParameterECDB\"\n]"}}},{"rowIdx":3488,"cells":{"ngram":{"kind":"list like","value":["and/or directories. @param directory directory to convert @return JSON representation","if '-' not in tag: continue str_version, str_dash, str_timestamp =","matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: #","package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying","the base image :returns: None \"\"\" # Form the path","image :param str image_name: the name of the image :param","a given directory into JSON form. The parent object is","list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x","registry path @param usr the functional username for the docker","cert location # is figured out and we specify it","list of the actual files and directories in the bundle","@param zipDirectory where the bundle ZIP is located @param nameOfBundle","the JSON representation of the actual structure of the uploaded","be the one with the highest version number (vX) and","+ \"/\" if expected['children'] == \"_any\": isMatched = isMatched &","\"FROM \" + docker_url + image_namespace + \"/\" + image_name","environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout","registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list =","actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not","pwd the password for the docker registry functional user @return","\"\"\" Function to convert objects in a given directory into","def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip","directory can be anything then do no further checking else:","\"\"\" Function to check if files/directories match. They must have","-1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition =","a list then find out if they match anywhere, if","=============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON file","is defined to be the one with the highest version","d = {'name': os.path.basename(directory)} # the parent object is dict","# verify structure of bundle ------------------------------------------------------------------------ # check package stucture","} # TEMP: Remove the suppressed verification once the docker","of the actual files and directories in the bundle @param","url the url of the REST call @param usr the","+ '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text)","as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file","it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def","directory is a list composed of more files/directories d['children'] =","be unzipped. Could not proceed with deployment.\") # out put","once the docker cert location # is figured out and","# do further checking else: # a matching file or","and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath)","path @param usr the functional username for the docker registry","then find out if they match anywhere, if so get","good or bad @param filePath the zip file to be","the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\"","os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) #","adds error messages to the global array. @param expected the","@param registry_path docker registry path @param usr the functional username","match then verify their children too isMatched = isMatched &","FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the","return def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP","\"\"\" HTTP/HTTPS GET requests using external Python module requests @param","= \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function","return False if type(expected) is list: for k in range(0,len(expected)):","verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure","return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if","subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if a","ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function","expectedElement the expected element. May be defined by regular expression","bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try:","isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\"","the uploaded bundle follows IBM defined structure. False otherwise. \"\"\"","structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip):","pwd): \"\"\" HTTP/HTTPS GET requests using external Python module requests","= json.loads(request.text) for tag in tag_list['tags']: if '-' not in","unzip function for extracting zip files @param zip_file the zip","further checking else: # a matching file or directory was","the docker cert location # is figured out and we","\"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None","can be anything then do no further checking else: isMatched","currentPath): \"\"\" Function to verify if an uploaded bundle follows","= open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd =","get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']:","bad @param filePath the zip file to be tested @return","return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return","badFile is not None: ret = False else: ret =","GET requests using external Python module requests @param url the","expected['type'] +\" is missing from the path: \\\"\" + currentPath","The latest, by definition, is defined to be the one","Build Scripts A collection of common functions shared by each","file \"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file)","actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath,","tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd)","the path for the Dockerfile based on the parent of","to add error messages to the global list of errorMessages","for the docker registry @param pwd the password for the","datacenter this script is being run on :param str dockerfile_parent_dir:","registry_path docker registry path @param usr the functional username for","and may contain files and/or directories. @param directory directory to","caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile","of the directory can be anything then do no further","element. Helper method to verifyBundleStructure. @param actual list of the","location # is figured out and we specify it in","JSON file \"\"\" try: with open(jsonFile) as json_data_file: data =","unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0])","missing from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails'])","\"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client:","the Dockerfile based on the parent of the caller script","contains an expected element. Helper method to verifyBundleStructure. @param actual","zipFileIsGood(bundleZip): try: # copy bundle into new working directory -----------------------------------------------------------","len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition !=","don't match then log the error msg and return false","check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo)","latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag","\"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet","image_namespace + \"/\" + image_name + \":\" + image_tag_latest #","the expected element @param currentPath the current path we are","str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp","of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create","Function to verify if an uploaded bundle is: 1) a","user @return the latest image tag \"\"\" tag_list_url = registry_path","not proceed with deployment.\") # clean up unzipped stuff and","docker registry path @param usr the functional username for the","not meet predefined structure. Could not proceed with deployment.\") #","the actual structure of the uploaded bundle @param currentPath the","if the contents of the directory can be anything then","JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood:","password for the docker registry functional user @return the latest","for extracting zip files @param zip_file the zip file to","datetime import re import operator import unicodedata # global list","bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood =","of Common Functions used by Build Scripts A collection of","recursively @param zipFileName the zip file to be extracted @param","def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded","+ image_namespace + \"/\" + image_name + \":\" + image_tag_latest","= \"directory\" # directory may have children # the children","or directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected,","module requests @param url the url of the REST call","stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert","\"\"\" Function to test if a ZIP file is good","recursive calls) @return True if the list of actual objects","jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to convert","def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile","zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if","return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP","dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile =","expectedElement the expected element to find in the bundle @param","false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched","a valid zip file 2) follows IBM defined structure @param","with deployment.\") # out put report value , join all","URL associated with the datacenter this script is being run","+ docker_url + image_namespace + \"/\" + image_name + \":\"","paths recursively for error msg) @return True if structure of","latest_version > str_version: continue elif latest_version < str_version: latest_version =","\"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to","usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if","= os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\"","set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\"","if an actual list of objects contains an expected element.","number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker","Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command","directory was found if expected['type'] == 'directory': currentPath = currentPath","we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers,","call @param usr the functional username for the docker registry","to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\")","Function to verify if an actual list of objects contains","bundle ZIP is located @param nameOfBundle name of the bundle","if os.path.isdir(directory): d['type'] = \"directory\" # directory may have children","collection of common functions shared by each individual build scripts.","\"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr,","may contain children if type=directory. A directory is composed of","datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except","Could not proceed with deployment.\") bundleIsGood = False else: bundleIsGood","They must have the same name and must both be","NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag","to the parent directory for the Dockerfile :param str docker_url:","clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo)","auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve","dt_timestamp latest_tag = tag else: if latest_version > str_version: continue","True if the ZIP file is good. False otherwise. \"\"\"","being checked (used to build paths recursively for error msg)","the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param","error messages to the global array. @param expected the expected","using the correct docker registry URL associated with the datacenter","bundle @param currentPath the path currently being checked (used to","to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def","else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath):","& verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath):","predefined structure. Could not proceed with deployment.\") # clean up","into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if","jsonFile path to the JSON file @return the loaded JSON","(used to build paths recursively for error msg) @param isMatched","unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting zip files","docker registry @param pwd the password for the docker registry","vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the","# convert the unzipped directory to JSON file bundleIsGood =","False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not","Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the","# clean up unzipped stuff and package structure Json -------------------------------------------------","file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing","ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip()","of the image :param str image_tag_latest: the latest version tag","directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if","verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if an","registry functional user @return the latest image tag \"\"\" tag_list_url","to be tested @return True if the ZIP file is","so get the matched position elif type(actual) is list: matchedPosition","nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working","ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version =","addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed with","no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'],","the password for the docker registry functional user @return a","unicodedata # global list of error messages to keep track","error msgs errorMessages = [] \"\"\" Collection of Common Functions","was found if expected['type'] == 'directory': currentPath = currentPath +","Function to adds error messages to the global array. @param","\"+ expected['type'] +\" is missing from the path: \\\"\" +","\"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") #","they match anywhere, if so get the matched position elif","zip_file the zip file to be extracted @param to_dir the","error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return","None: ret = False else: ret = True except: ret","check if files/directories match. They must have the same name","registry functional user @return a JSON response \"\"\" headers =","anything then do no further checking else: isMatched = isMatched","all error msgs errorMessages = [] \"\"\" Collection of Common","# Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd)","the contents of the directory can be anything then do","and its children if type(actual) is dict: isMatched = isMatched","True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function","registry @param pwd the password for the docker registry functional","for the docker registry functional user @return the latest image","copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory,","a list and may contain files and/or directories. @param directory","JSON representation of the actual structure of the uploaded bundle","to convert objects in a given directory into JSON form.","a dockerfile using the correct docker registry URL associated with","msg) @return True if structure of the uploaded bundle follows","verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a","the_zip_file.testzip() if badFile is not None: ret = False else:","follows IBM defined structure. False otherwise. \"\"\" isMatched = True","range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return","uploaded bundle could not be unzipped. Could not proceed with","'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once","(vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry","in os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected,","i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break","variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set","if the list of actual objects contain the expected element","position elif type(actual) is list: matchedPosition = -1 for i","the functional username for the docker registry @param pwd the",":param str docker_url: the docker registry VIP accessible from the","set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\")","the docker registry functional user @return a JSON response \"\"\"","dockerfile = open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd","Generic unzip function for extracting zip files @param zip_file the","type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched","False otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath)","located @param nameOfBundle name of the bundle ZIP file \"\"\"","directory to extract the zip file to \"\"\" # update","matching file or directory was not found if expected['fail-if-not-found'] ==","JSON representation of a directory \"\"\" d = {'name': os.path.basename(directory)}","currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to","directory for the Dockerfile :param str docker_url: the docker registry","the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format the FROM","ZIP file is good. False otherwise. \"\"\" ret = True","the bundle @param currentPath the path currently being checked (used","not valid: \" + jsonFile) raise return data def directoryToJSON(directory):","2) follows IBM defined structure @param zipDirectory where the bundle","tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue","ZIP file is good or bad @param filePath the zip","based on the parent of the caller script dockerfile_path =","global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to","open a JSON file @param jsonFile path to the JSON","= [] \"\"\" Collection of Common Functions used by Build","actual structure of the uploaded bundle @param currentPath the path","Dockerfile based on the parent of the caller script dockerfile_path","element @param currentPath the current path we are on that","import operator import unicodedata # global list of error messages","to the global list of errorMessages @param errorMessage the error","too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else","in tag_list['tags']: if '-' not in tag: continue str_version, str_dash,","except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =","if latest_version > str_version: continue elif latest_version < str_version: latest_version","found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if","= isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def","unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify","for the docker registry functional user @return a JSON response","the uploaded bundle @param currentPath the path currently being checked","list of error messages to keep track of all error","# Format the FROM command dockerfile_from_cmd = \"FROM \" +","\"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path:","they don't match then log the error msg and return","\"\"\" Creates a dockerfile using the correct docker registry URL","version of an image based on its tags: vX-YYYYMMDD-HHmm. The","verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an uploaded bundle","to build paths recursively for error msg) @return True if","directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo)","the IBM defined structure @param actual the JSON representation of","expected element @param currentPath the current path we are on","else: if latest_version > str_version: continue elif latest_version < str_version:","x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to","True if structure of the uploaded bundle follows IBM defined","expected element. Helper method to verifyBundleStructure. @param actual list of","uploaded bundle does not meet predefined structure. Could not proceed","zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir)","addToErrorMessages(\"The specified JSON file is not valid: \" + jsonFile)","dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\"","of the REST call @param usr the functional username for","actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret =","latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr","ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood = True","path to the parent directory for the Dockerfile :param str","currentPath the path currently being checked (used to build paths","@param pwd the password for the docker registry functional user","files and directories in the bundle @param expectedElement the expected","re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret","@param expected the expected element @param currentPath the current path","elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp","nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0]))","external Python module requests @param url the url of the","the expected element. May be defined by regular expression @param","shared by each individual build scripts. \"\"\" def get(url, usr,","latest, by definition, is defined to be the one with","DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The","zip file to be extracted @param to_dir the destination directory","the directory can be anything then do no further checking","\"\"\" d = {'name': os.path.basename(directory)} # the parent object is","import zipfile import shutil import requests import datetime import re","latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file,","-1: # if they match then verify their children too","is a list then find out if they match anywhere,","return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to","\"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir,","the loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file:","request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag","= get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in","isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to","to_dir): \"\"\" Generic unzip function for extracting zip files @param","+ currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\"","get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of an","@param usr the functional username for the docker registry @param","a directory is a list composed of more files/directories d['children']","if an uploaded bundle is: 1) a valid zip file","\" + docker_url + image_namespace + \"/\" + image_name +","of error messages to keep track of all error msgs","REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr,","addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the global","children # the children in a directory is a list","dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace + \"/\"","error msg and return false if needed isMatched = False","used for recursive calls) @return True if the list of","@return the latest image tag \"\"\" tag_list_url = registry_path +","logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to the","add error messages to the global list of errorMessages @param","currently being checked (used to build paths recursively for error","\"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\"","# check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure =","false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and","\" seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile):","== \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list:","element to find in the bundle @param currentPath the path","== actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement,","log the error msg and return false if expectedElement['fail-if-not-found'] ==","= True if type(expected) is dict: if matches(expected,actual): # a","error msg) @param isMatched (only used for recursive calls) @return","True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy","if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1:","or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from","the one with the highest version number (vX) and the","= str_version latest_timestamp = dt_timestamp latest_tag = tag else: if","latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp =","bundle follows IBM defined structure. False otherwise. \"\"\" isMatched =","isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected,","Format the FROM command dockerfile_from_cmd = \"FROM \" + docker_url","of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)]","the actual files and directories in the bundle @param expectedElement","= False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type']","json import argparse import zipfile import shutil import requests import","file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment","structure @param actual the JSON representation of the actual structure","script is being run on :param str dockerfile_parent_dir: path to","unzip a ZIP file recursively @param zipFileName the zip file","@param url the url of the REST call @param usr","the error msg and return false if expectedElement['fail-if-not-found'] == \"yes\":","HTTP/HTTPS GET requests using external Python module requests @param url","object is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory","errorMessages = [] \"\"\" Collection of Common Functions used by","currentPath = currentPath + actual['name'] + \"/\" if expected['children'] ==","\"\"\" Function to add error messages to the global list","checked (used to build paths recursively for error msg) @param","zip file 2) follows IBM defined structure @param zipDirectory where","dict then verify it and its children if type(actual) is","to check if files/directories match. They must have the same","together print (\"report=[\" + \". \".join(str(x) for x in errorMessages)","on that has the missing file or directory \"\"\" addToErrorMessages(\"A","not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure.","keep track of all error msgs errorMessages = [] \"\"\"","= False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function","os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo,","from the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return","up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except:","= openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped","docker registry VIP accessible from the mesos slaves :param str","nameOfBundle): \"\"\" Function to verify if an uploaded bundle is:","unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception","nameOfBundle name of the bundle ZIP file \"\"\" print ('Validating","expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the","python3.6 import os import subprocess import json import argparse import","to test if a ZIP file is good or bad","# the parent object is dict if os.path.isdir(directory): d['type'] =","d['type'] = \"file\" return d def verifyBundleStructure(expected, actual, currentPath): \"\"\"","for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") #","& verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then","== 'directory': currentPath = currentPath + actual['name'] + \"/\" if","shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle),","== \"yes\": # log error msg and return false if","or directory was found if expected['type'] == 'directory': currentPath =","False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify","is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if","@param currentPath the path currently being checked (used to build","extracted @param directoryToUnzipTo the destination directory to extract the zip","with deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The","@param jsonFile path to the JSON file @return the loaded","build paths recursively for error msg) @return True if structure","in a directory is a list composed of more files/directories","@return JSON representation of a directory \"\"\" d = {'name':","IBM defined structure @param zipDirectory where the bundle ZIP is","with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url,","bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle","to verifyBundleStructure. @param actual list of the actual files and","def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file","structure of the uploaded bundle @param currentPath the path currently","children in a directory is a list composed of more","actualElement): \"\"\" Function to check if files/directories match. They must","dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def","os import subprocess import json import argparse import zipfile import","in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try:","zipFileName the zip file to be extracted @param directoryToUnzipTo the","@return True if the ZIP file is good. False otherwise.","a ZIP file recursively @param zipFileName the zip file to","to the global array. @param expected the expected element @param","bundle does not meet predefined structure. Could not proceed with","error msg) @return True if structure of the uploaded bundle","= {'name': os.path.basename(directory)} # the parent object is dict if","print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory,","objects contains an expected element. Helper method to verifyBundleStructure. @param","try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version","a dict, it may contain children if type=directory. A directory","try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp","Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def","actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is","out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr,","to extract the zip file to \"\"\" # update if","bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be unzipped.","\"\"\" Collection of Common Functions used by Build Scripts A","continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version","command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open","import shutil import requests import datetime import re import operator","= '300' print(\"The timeout set for docker client: \" +","the same type. @param expectedElement the expected element. May be","@param nameOfBundle name of the bundle ZIP file \"\"\" print","str docker_url: the docker registry VIP accessible from the mesos","directory may have children # the children in a directory","not be unzipped. Could not proceed with deployment.\") # out","structure of the uploaded bundle follows IBM defined structure. False","on :param str dockerfile_parent_dir: path to the parent directory for","dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual","be extracted @param to_dir the destination directory to extract the","image_tag_latest: the latest version tag of the base image :returns:","for the Dockerfile based on the parent of the caller","parent object is dict if os.path.isdir(directory): d['type'] = \"directory\" #","#!/usr/bin/env python3.6 import os import subprocess import json import argparse","a directory \"\"\" d = {'name': os.path.basename(directory)} # the parent","bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if","actual[matchedPosition] , currentPath) else : # if they don't match","the expected element to find in the bundle @param currentPath","add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo):","VIP accessible from the mesos slaves :param str image_namespace: the","checking else: # a matching file or directory was not","actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched = isMatched","expected the JSON representation of the IBM defined structure @param","for error msg) @return True if structure of the uploaded","join all the messages together print (\"report=[\" + \". \".join(str(x)","to be extracted @param directoryToUnzipTo the destination directory to extract","run on :param str dockerfile_parent_dir: path to the parent directory","the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure","parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") #","= { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed","its children if type(actual) is dict: isMatched = isMatched &","isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else:","filePath the zip file to be tested @return True if","IBM defined structure @param expected the JSON representation of the","@param zipFileName the zip file to be extracted @param directoryToUnzipTo","import datetime import re import operator import unicodedata # global","None \"\"\" # Form the path for the Dockerfile based","i break if matchedPosition != -1: # if they match","more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else:","calls) @return True if the list of actual objects contain","latest image tag \"\"\" tag_list_url = registry_path + '/tags/list' request","or bad @param filePath the zip file to be tested","directoryToUnzipTo the destination directory to extract the zip file to","docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the","isMatched = isMatched & True # if the contents of","('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle)","(used to build paths recursively for error msg) @return True","Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) #","if an uploaded bundle follows IBM defined structure @param expected","tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be","shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not","type(actual) is list: matchedPosition = -1 for i in range(0,","+ \"/\" + image_name + \":\" + image_tag_latest # Write","the image :param str image_tag_latest: the latest version tag of","list and may contain files and/or directories. @param directory directory","element \"\"\" # if actual is a dict then verify","image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition,","msgs errorMessages = [] \"\"\" Collection of Common Functions used","@return the loaded JSON file \"\"\" try: with open(jsonFile) as","same name and must both be the same type. @param","else : # if they don't match then log the","print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function","directory was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath)","defined structure @param zipDirectory where the bundle ZIP is located","expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \"/\"","path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement,","def zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file","of the image :param str image_name: the name of the","IBM defined structure @param actual the JSON representation of the","and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path","be extracted @param directoryToUnzipTo the destination directory to extract the","\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle","True if the list of actual objects contain the expected","have children # the children in a directory is a","expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function","expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is","\"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM \"","be defined by regular expression @param actualElement the actual element","expectedElement, currentPath, isMatched): \"\"\" Function to verify if an actual","and return false if expectedElement['fail-if-not-found'] == \"yes\": # log error","was not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return","needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage):","= datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag",", join all the messages together print (\"report=[\" + \".","if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath)","the image :param str image_name: the name of the image","in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x","children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual,","using external Python module requests @param url the url of","files @param zip_file the zip file to be extracted @param","image tag \"\"\" tag_list_url = registry_path + '/tags/list' request =","(YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional","Function to add error messages to the global list of","could not be unzipped. Could not proceed with deployment.\") #","checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) #","if a ZIP file is good or bad @param filePath","expected element. May be defined by regular expression @param actualElement","image :param str image_tag_latest: the latest version tag of the","verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an uploaded","verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't","isMatched): \"\"\" Function to verify if an actual list of","functional user @return a JSON response \"\"\" headers = {","username for the docker registry @param pwd the password for","by Build Scripts A collection of common functions shared by","# if actual is a dict then verify it and","not proceed with deployment.\") bundleIsGood = False else: bundleIsGood =","\"directory\" # directory may have children # the children in","addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could not","if the ZIP file is good. False otherwise. \"\"\" ret","match. They must have the same name and must both","to unzip a ZIP file recursively @param zipFileName the zip","of a list and may contain files and/or directories. @param","tag \"\"\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url,","functional username for the docker registry @param pwd the password","Dockerfile :param str docker_url: the docker registry VIP accessible from","the matched position elif type(actual) is list: matchedPosition = -1","the latest image tag \"\"\" tag_list_url = registry_path + '/tags/list'","missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is","latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp","string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file","if expected['children'] == \"_any\": isMatched = isMatched & True #","error messages to the global list of errorMessages @param errorMessage","file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The","the zip file to be extracted @param directoryToUnzipTo the destination","directory into JSON form. The parent object is always a","the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close()","to_dir the destination directory to extract the zip file to","addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not proceed","logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k","the zip file to be tested @return True if the","directory \"\"\" d = {'name': os.path.basename(directory)} # the parent object","actual is a dict then verify it and its children","def directoryToJSON(directory): \"\"\" Function to convert objects in a given","actualElement the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'],","for x in os.listdir(directory)] else: d['type'] = \"file\" return d","os.listdir(directory)] else: d['type'] = \"file\" return d def verifyBundleStructure(expected, actual,","the global list of errorMessages @param errorMessage the error message","path currently being checked (used to build paths recursively for","with deployment.\") # clean up unzipped stuff and package structure","dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp","return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error","------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure","find out if they match anywhere, if so get the","a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', }","str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except","\"\"\" # Form the path for the Dockerfile based on","\"\"\" Function to adds error messages to the global array.","if type(expected) is list: for k in range(0,len(expected)): isMatched =",".zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo,","headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the","parent directory for the Dockerfile :param str docker_url: the docker","image_namespace: the name of the image :param str image_name: the","os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo,","= verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle","----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip,","of the base image :returns: None \"\"\" # Form the","the latest version tag of the base image :returns: None","\"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to","# Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\") # Format","\":\" + image_tag_latest # Write the FROM command string to","directory is composed of a list and may contain files","currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function","it and its children if type(actual) is dict: isMatched =","functions shared by each individual build scripts. \"\"\" def get(url,","\"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip =","directories in the bundle @param expectedElement the expected element to","if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def","to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for","loaded JSON file \"\"\" try: with open(jsonFile) as json_data_file: data","import os import subprocess import json import argparse import zipfile","good. False otherwise. \"\"\" ret = True try: the_zip_file =","verify it and its children if type(actual) is dict: isMatched","= os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path,","if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for","for recursive calls) @return True if the list of actual","= os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle))","# a matching file or directory was not found if","both be the same type. @param expectedElement the expected element.","matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match. They","be the same type. @param expectedElement the expected element. May","\"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT']","structure @param zipDirectory where the bundle ZIP is located @param","where the bundle ZIP is located @param nameOfBundle name of","= tag else: if latest_version > str_version: continue elif latest_version","accessible from the mesos slaves :param str image_namespace: the name","ZIP file recursively @param zipFileName the zip file to be","to adds error messages to the global array. @param expected","for the Dockerfile :param str docker_url: the docker registry VIP","while verifying bundle structure. Could not proceed with deployment.\") bundleIsGood","expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return false","bundle follows IBM defined structure @param expected the JSON representation","dict: if matches(expected,actual): # a matching file or directory was","extracting zip files @param zip_file the zip file to be","str image_tag_latest: the latest version tag of the base image","# if they don't match then log the error msg","method to verifyBundleStructure. @param actual list of the actual files","msg and return false if expectedElement['fail-if-not-found'] == \"yes\": # log","verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\"","currentPath) # if actual is a list then find out","latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return","actual files and directories in the bundle @param expectedElement the","definition, is defined to be the one with the highest","contents of the directory can be anything then do no","if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo):","bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded","os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function","= tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip","the bundle ZIP is located @param nameOfBundle name of the","is a dict then verify it and its children if","# if the contents of the directory can be anything","then log the error msg and return false if expectedElement['fail-if-not-found']","an expected element. Helper method to verifyBundleStructure. @param actual list","zip file to be tested @return True if the ZIP","& verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they","return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error messages","is: 1) a valid zip file 2) follows IBM defined","ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched):","# ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function","uploaded bundle follows IBM defined structure @param expected the JSON","@param to_dir the destination directory to extract the zip file","file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close()","file @param jsonFile path to the JSON file @return the","a matching file or directory was found if expected['type'] ==","common functions shared by each individual build scripts. \"\"\" def","timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the","d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] =","shutil import requests import datetime import re import operator import","the bundle @param expectedElement the expected element to find in","is figured out and we specify it in REQUESTS_CA_BUNDLE return","have the same name and must both be the same","\"\"\" isMatched = True if type(expected) is dict: if matches(expected,actual):","occurred while verifying bundle structure. Could not proceed with deployment.\")","< dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag","zip files @param zip_file the zip file to be extracted","if expectedElement['fail-if-not-found'] == \"yes\": # log error msg and return","True # if the contents of the directory can be","client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify","we are on that has the missing file or directory","for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i","bundle @param expectedElement the expected element to find in the","are on that has the missing file or directory \"\"\"","= tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError:","if they match anywhere, if so get the matched position","tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function","dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to","the parent directory for the Dockerfile :param str docker_url: the","@param filePath the zip file to be tested @return True","deployment.\") # out put report value , join all the","Function to test if a ZIP file is good or","matchedPosition = i break if matchedPosition != -1: # if","to the JSON file @return the loaded JSON file \"\"\"","'-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-')","(only used for recursive calls) @return True if the list","tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp","to verify if an uploaded bundle follows IBM defined structure","recursively for error msg) @return True if structure of the","name and must both be the same type. @param expectedElement","& verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: #","the current path we are on that has the missing","otherwise. \"\"\" isMatched = True if type(expected) is dict: if","bundle is: 1) a valid zip file 2) follows IBM","@param errorMessage the error message to add \"\"\" print(errorMessage) global","bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined structure. Could","in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched)","\"/\" if expected['children'] == \"_any\": isMatched = isMatched & True","the Dockerfile :param str docker_url: the docker registry VIP accessible","# the children in a directory is a list composed","path we are on that has the missing file or","'/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for","= True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\"","out if they match anywhere, if so get the matched","is being run on :param str dockerfile_parent_dir: path to the","headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest","of the IBM defined structure @param actual the JSON representation","else: ret = True except: ret = False return ret","each individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\"","@return a JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json',","except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could not proceed","open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON","proceed with deployment.\") # clean up unzipped stuff and package","the JSON file @return the loaded JSON file \"\"\" try:","be anything then do no further checking else: isMatched =","= [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\"","tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp","logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add","Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure.","zipDirectory where the bundle ZIP is located @param nameOfBundle name","is missing from the path: \\\"\" + currentPath + \"\\\"\")","name of the image :param str image_name: the name of","track of all error msgs errorMessages = [] \"\"\" Collection","@param actualElement the actual element \"\"\" ret = False if","message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def","usr the functional username for the docker registry @param pwd","\" + jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function","structure @param expected the JSON representation of the IBM defined","if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does not meet predefined","list of objects contains an expected element. Helper method to","Could not proceed with deployment.\") # clean up unzipped stuff","list: matchedPosition = -1 for i in range(0, len(actual)): if","continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp =","image_name + \":\" + image_tag_latest # Write the FROM command","verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not bundleIsGood: addToErrorMessages(\"The uploaded bundle does","actual list of the actual files and directories in the","import json import argparse import zipfile import shutil import requests","@param expected the JSON representation of the IBM defined structure","JSON file is not valid: \" + jsonFile) raise return","= dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir):","timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \"","bundle structure. Could not proceed with deployment.\") bundleIsGood = False",":param str image_namespace: the name of the image :param str","import unicodedata # global list of error messages to keep","verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find","suppressed verification once the docker cert location # is figured","not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle","convert objects in a given directory into JSON form. The","Collection of Common Functions used by Build Scripts A collection","based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is","and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while","actual, currentPath): \"\"\" Function to verify if an uploaded bundle","# out put report value , join all the messages","of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory,","in a given directory into JSON form. The parent object","latest version tag of the base image :returns: None \"\"\"","structure. Could not proceed with deployment.\") bundleIsGood = False else:","docker registry functional user @return a JSON response \"\"\" headers","unzipped. Could not proceed with deployment.\") # out put report","messages to the global list of errorMessages @param errorMessage the","requests using external Python module requests @param url the url","to open a JSON file @param jsonFile path to the","os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile","\"\"\" Function to open a JSON file @param jsonFile path","Scripts A collection of common functions shared by each individual","zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\"","image :returns: None \"\"\" # Form the path for the","argparse import zipfile import shutil import requests import datetime import","def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories match.","proceed with deployment.\") bundleIsGood = False else: bundleIsGood = False","list of actual objects contain the expected element \"\"\" #","the destination directory to extract the zip file to \"\"\"","+ \":\" + image_tag_latest # Write the FROM command string","---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle","out put report value , join all the messages together","the REST call @param usr the functional username for the","do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'],","pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the","# directory may have children # the children in a","used by Build Scripts A collection of common functions shared","the parent object is dict if os.path.isdir(directory): d['type'] = \"directory\"","files/directories match. They must have the same name and must","d['type'] = \"directory\" # directory may have children # the","then do no further checking else: isMatched = isMatched &","type=directory. A directory is composed of a list and may","FROM command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace","in the bundle @param currentPath the path currently being checked","ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to verify if an","\"\"\" Retrieve the latest version of an image based on","if they match then verify their children too isMatched =",":param str dockerfile_parent_dir: path to the parent directory for the","seconds\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\"","return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for","registry VIP accessible from the mesos slaves :param str image_namespace:","contain the expected element \"\"\" # if actual is a","destination directory to extract the zip file to \"\"\" #","= directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file","the name of the image :param str image_tag_latest: the latest","structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle","of actual objects contain the expected element \"\"\" # if","= the_zip_file.testzip() if badFile is not None: ret = False","data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not","'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the","get the matched position elif type(actual) is list: matchedPosition =","file or directory was not found if expected['fail-if-not-found'] == \"yes\":","IBM defined structure. False otherwise. \"\"\" isMatched = True if","Functions used by Build Scripts A collection of common functions","# copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo =","file recursively @param zipFileName the zip file to be extracted","uploaded bundle @param currentPath the path currently being checked (used","JSON file @param jsonFile path to the JSON file @return","is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x))","with the highest version number (vX) and the latest timestamp","with the datacenter this script is being run on :param","return d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify","False else: ret = True except: ret = False return","except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\"","files and/or directories. @param directory directory to convert @return JSON","directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood","actual is a list then find out if they match","zipfile.ZipFile(zip_file, \"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace,","Could not proceed with deployment.\") # out put report value","current path we are on that has the missing file","path to the JSON file @return the loaded JSON file","JSON file @return the loaded JSON file \"\"\" try: with","verifying bundle structure. Could not proceed with deployment.\") bundleIsGood =","one with the highest version number (vX) and the latest","report value , join all the messages together print (\"report=[\"","the datacenter this script is being run on :param str","# Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\"","A directory is composed of a list and may contain","addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check if","by each individual build scripts. \"\"\" def get(url, usr, pwd):","array. @param expected the expected element @param currentPath the current","@param actual list of the actual files and directories in","further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath)","file \"\"\" print ('Validating bundle structure...') bundleIsGood = True bundleZip","tag else: if latest_version > str_version: continue elif latest_version <","in the bundle @param expectedElement the expected element to find","destination directory to extract the zip file to \"\"\" with","objects contain the expected element \"\"\" # if actual is","expected the expected element @param currentPath the current path we","======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to","{ 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification","match anywhere, if so get the matched position elif type(actual)","1) a valid zip file 2) follows IBM defined structure","is dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may","found if expected['type'] == 'directory': currentPath = currentPath + actual['name']","docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # =======================","\"\"\" Function to verify if an uploaded bundle follows IBM","is dict: if matches(expected,actual): # a matching file or directory","os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return","\"r\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name,","= isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : #","anywhere, if so get the matched position elif type(actual) is","# unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) #","@param currentPath the current path we are on that has","JSON form. The parent object is always a dict, it","may have children # the children in a directory is","and directories in the bundle @param expectedElement the expected element","\"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile =","of a directory \"\"\" d = {'name': os.path.basename(directory)} # the","value , join all the messages together print (\"report=[\" +","badFile = the_zip_file.testzip() if badFile is not None: ret =","Function to open a JSON file @param jsonFile path to","file or directory was found if expected['type'] == 'directory': currentPath","unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively","if actual is a list then find out if they","directoryToUnzipTo): \"\"\" Function to unzip a ZIP file recursively @param","directory to convert @return JSON representation of a directory \"\"\"","of errorMessages @param errorMessage the error message to add \"\"\"","as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest):","the name of the image :param str image_name: the name","Creates a dockerfile using the correct docker registry URL associated","regular expression @param actualElement the actual element \"\"\" ret =","[] \"\"\" Collection of Common Functions used by Build Scripts","defined by regular expression @param actualElement the actual element \"\"\"","continue elif latest_version < str_version: latest_version = str_version latest_timestamp =","specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False)","True if type(expected) is dict: if matches(expected,actual): # a matching","\"/\" + image_name + \":\" + image_tag_latest # Write the","def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version of","the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm).","if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the","to find in the bundle @param currentPath the path currently","currentPath): \"\"\" Function to adds error messages to the global","TEMP: Remove the suppressed verification once the docker cert location","@return True if structure of the uploaded bundle follows IBM","try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is","os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package","is always a dict, it may contain children if type=directory.","subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath):","slaves :param str image_namespace: the name of the image :param","not None and expectedElement['type'] == actualElement['type']: ret = True return","+ image_name + \":\" + image_tag_latest # Write the FROM","from the mesos slaves :param str image_namespace: the name of","except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version","command dockerfile_from_cmd = \"FROM \" + docker_url + image_namespace +","associated with the datacenter this script is being run on","an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by","verify if an uploaded bundle follows IBM defined structure @param","otherwise. \"\"\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile","and must both be the same type. @param expectedElement the","build paths recursively for error msg) @param isMatched (only used","to convert @return JSON representation of a directory \"\"\" d","currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to add error","tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not","actual list of objects contains an expected element. Helper method","the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\")","+ jsonFile) raise return data def directoryToJSON(directory): \"\"\" Function to","working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo):","+ \". \".join(str(x) for x in errorMessages) + \"]\") return","# log error msg and return false if needed isMatched","does not meet predefined structure. Could not proceed with deployment.\")","def addToErrorMessages(errorMessage): \"\"\" Function to add error messages to the","False otherwise. \"\"\" isMatched = True if type(expected) is dict:","Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] =","= False else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could","the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the","usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python module","dockerfile_parent_dir: path to the parent directory for the Dockerfile :param","True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile","is not valid: \" + jsonFile) raise return data def","if matchedPosition != -1: # if they match then verify","= False else: ret = True except: ret = False","The parent object is always a dict, it may contain","that has the missing file or directory \"\"\" addToErrorMessages(\"A \"+","{'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory):","isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds","file to be extracted @param directoryToUnzipTo the destination directory to","for error msg) @param isMatched (only used for recursive calls)","the DOCKER_CLIENT_TIMEOUT environment variable to 300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300'","range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition","msg) @param isMatched (only used for recursive calls) @return True","put report value , join all the messages together print","element. May be defined by regular expression @param actualElement the","dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout():","None and expectedElement['type'] == actualElement['type']: ret = True return ret","Retrieve the latest version of an image based on its","'300' print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT']","file to be extracted @param to_dir the destination directory to","messages to the global array. @param expected the expected element","dict, it may contain children if type=directory. A directory is","tested @return True if the ZIP file is good. False","file is good or bad @param filePath the zip file","url of the REST call @param usr the functional username","import argparse import zipfile import shutil import requests import datetime","zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a","log error msg and return false if needed isMatched =","except: addToErrorMessages(\"The specified JSON file is not valid: \" +","the error message to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage])","tag in tag_list['tags']: if '-' not in tag: continue str_version,","this script is being run on :param str dockerfile_parent_dir: path","def openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param","file is good. False otherwise. \"\"\" ret = True try:","file is not valid: \" + jsonFile) raise return data","matches(expected,actual): # a matching file or directory was found if","directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo,","always a dict, it may contain children if type=directory. A","str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version","else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do","subprocess import json import argparse import zipfile import shutil import","checked (used to build paths recursively for error msg) @return","zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret","tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError:","path for the Dockerfile based on the parent of the","the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure,","children if type=directory. A directory is composed of a list","name of the bundle ZIP file \"\"\" print ('Validating bundle","bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\")","import requests import datetime import re import operator import unicodedata","error messages to keep track of all error msgs errorMessages","= \"FROM \" + docker_url + image_namespace + \"/\" +","try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The","# global list of error messages to keep track of","is good. False otherwise. \"\"\" ret = True try: the_zip_file","list of errorMessages @param errorMessage the error message to add","# TEMP: Remove the suppressed verification once the docker cert","latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp =","a matching file or directory was not found if expected['fail-if-not-found']","list then find out if they match anywhere, if so","to verify if an uploaded bundle is: 1) a valid","os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new","is list: matchedPosition = -1 for i in range(0, len(actual)):","currentPath) return False if type(expected) is list: for k in","figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url,","return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd):","str image_name: the name of the image :param str image_tag_latest:","for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k],","d def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if","import re import operator import unicodedata # global list of","latest version of an image based on its tags: vX-YYYYMMDD-HHmm.","error msg and return false if expectedElement['fail-if-not-found'] == \"yes\": #","if files/directories match. They must have the same name and","os.path.isdir(directory): d['type'] = \"directory\" # directory may have children #","composed of a list and may contain files and/or directories.","match then log the error msg and return false if","zipFileIsGood(filePath): \"\"\" Function to test if a ZIP file is","REST call @param usr the functional username for the docker","+ \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to","else: bundleIsGood = False addToErrorMessages(\"The uploaded bundle could not be","= -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition","bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a","stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred","then verify it and its children if type(actual) is dict:","@param zip_file the zip file to be extracted @param to_dir","is located @param nameOfBundle name of the bundle ZIP file","it may contain children if type=directory. A directory is composed","\"\"\" Function to unzip a ZIP file recursively @param zipFileName","if they don't match then log the error msg and","instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable","\". \".join(str(x) for x in errorMessages) + \"]\") return bundleIsGood","Function to check if files/directories match. They must have the","if structure of the uploaded bundle follows IBM defined structure.","contain children if type=directory. A directory is composed of a","try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo","= True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if","of all error msgs errorMessages = [] \"\"\" Collection of","functional user @return the latest image tag \"\"\" tag_list_url =","unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------","the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the","structure. False otherwise. \"\"\" isMatched = True if type(expected) is","version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path","Python module requests @param url the url of the REST","may contain files and/or directories. @param directory directory to convert","import subprocess import json import argparse import zipfile import shutil","\"\"\" # if actual is a dict then verify it","[directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \"file\" return","subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile,","# Form the path for the Dockerfile based on the","create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using","has the missing file or directory \"\"\" addToErrorMessages(\"A \"+ expected['type']","ZIP is located @param nameOfBundle name of the bundle ZIP","currentPath) # do further checking else: # a matching file","@param actual the JSON representation of the actual structure of","ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and","an uploaded bundle is: 1) a valid zip file 2)","if actual is a dict then verify it and its","find in the bundle @param currentPath the path currently being","the expected element \"\"\" # if actual is a dict","the latest version of an image based on its tags:","verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\" Retrieve the latest version","their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] ,","str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp,","scripts/common_lib/build_lib.py #!/usr/bin/env python3.6 import os import subprocess import json import","with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified","str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag","isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else :","expected['children'] == \"_any\": isMatched = isMatched & True # if","representation of the IBM defined structure @param actual the JSON","expected element to find in the bundle @param currentPath the","defined to be the one with the highest version number","verifyBundleStructure. @param actual list of the actual files and directories","False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \"\"\" Function to","True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle):","str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp","False if type(expected) is list: for k in range(0,len(expected)): isMatched","valid zip file 2) follows IBM defined structure @param zipDirectory","\"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove","= os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory)","verify if an actual list of objects contains an expected","requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \"\"\"","highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param","docker cert location # is figured out and we specify","deployment.\") # clean up unzipped stuff and package structure Json","not proceed with deployment.\") # out put report value ,","extract the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"):","os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ----------------------------------------------------------------------------------","image_namespace, image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct","== \"_any\": isMatched = isMatched & True # if the","k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath,","by definition, is defined to be the one with the","objects in a given directory into JSON form. The parent","Close the open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets","verify bundle Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open","print (\"report=[\" + \". \".join(str(x) for x in errorMessages) +","and expectedElement['type'] == actualElement['type']: ret = True return ret def","the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as zip_ref:","json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag:","actual the JSON representation of the actual structure of the",":param str image_name: the name of the image :param str","to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance","the docker registry VIP accessible from the mesos slaves :param","the suppressed verification once the docker cert location # is","ret = False else: ret = True except: ret =",":returns: None \"\"\" # Form the path for the Dockerfile","the ZIP file is good. False otherwise. \"\"\" ret =","os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure ===============================================","False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] ==","# update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo)","paths recursively for error msg) @param isMatched (only used for","usr, pwd): \"\"\" Retrieve the latest version of an image","composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in","= zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None:","image_name, image_tag_latest): \"\"\" Creates a dockerfile using the correct docker","build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET","the zip file to be extracted @param to_dir the destination","correct docker registry URL associated with the datacenter this script","of objects contains an expected element. Helper method to verifyBundleStructure.","tag of the base image :returns: None \"\"\" # Form","def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300","they match then verify their children too isMatched = isMatched","else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag =","Function to unzip a ZIP file recursively @param zipFileName the","a ZIP file is good or bad @param filePath the","matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]):","mesos slaves :param str image_namespace: the name of the image","response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP:","else: # a matching file or directory was not found","currentPath) else : # if they don't match then log","JSON response \"\"\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } #","nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ #","the mesos slaves :param str image_namespace: the name of the","!= -1: # if they match then verify their children","the docker registry @param pwd the password for the docker","isMatched def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages","def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external","x in os.listdir(directory)] else: d['type'] = \"file\" return d def","file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's","#check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in","latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp <","do further checking else: # a matching file or directory","+ image_tag_latest # Write the FROM command string to the","@param directory directory to convert @return JSON representation of a","of the uploaded bundle @param currentPath the path currently being","defined structure @param expected the JSON representation of the IBM","dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp","for tag in tag_list['tags']: if '-' not in tag: continue","a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for","A collection of common functions shared by each individual build","the docker registry functional user @return the latest image tag","Function to convert objects in a given directory into JSON","Common Functions used by Build Scripts A collection of common","not None: ret = False else: ret = True except:","tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try:","parent object is always a dict, it may contain children","is good or bad @param filePath the zip file to","x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo,","package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) #","form. The parent object is always a dict, it may","uploaded bundle follows IBM defined structure. False otherwise. \"\"\" isMatched","the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not","in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if","(\"report=[\" + \". \".join(str(x) for x in errorMessages) + \"]\")","must both be the same type. @param expectedElement the expected","proceed with deployment.\") # out put report value , join","image_tag_latest # Write the FROM command string to the Dockerfile","pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-'","latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version >","if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected)","the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close","expression @param actualElement the actual element \"\"\" ret = False","update if zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName)","directory directory to convert @return JSON representation of a directory","the actual element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name'])","file 2) follows IBM defined structure @param zipDirectory where the","JSON representation of the IBM defined structure @param actual the","isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list","# if actual is a list then find out if","the messages together print (\"report=[\" + \". \".join(str(x) for x","requests @param url the url of the REST call @param","break if matchedPosition != -1: # if they match then","base image :returns: None \"\"\" # Form the path for","global list of error messages to keep track of all","pwd): \"\"\" Retrieve the latest version of an image based","must have the same name and must both be the","\"yes\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for","the url of the REST call @param usr the functional","then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement,","to keep track of all error msgs errorMessages = []","def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify if","actual['children'], currentPath) # do further checking else: # a matching",": # if they don't match then log the error","= True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: #","a dict then verify it and its children if type(actual)","zip file to be extracted @param directoryToUnzipTo the destination directory","script dockerfile_path = os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile","open(dockerfile_path, \"w+\") # Format the FROM command dockerfile_from_cmd = \"FROM","docker_url + image_namespace + \"/\" + image_name + \":\" +","Structure =============================================== def openJSONfile(jsonFile): \"\"\" Function to open a JSON","dict if os.path.isdir(directory): d['type'] = \"directory\" # directory may have","unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\")","actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON","os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def","operator import unicodedata # global list of error messages to","msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement,","the password for the docker registry functional user @return the","errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip a","& True # if the contents of the directory can","an actual list of objects contains an expected element. Helper","messages to keep track of all error msgs errorMessages =","def verifyBundleStructure(expected, actual, currentPath): \"\"\" Function to verify if an","300 \"\"\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker","docker registry URL associated with the datacenter this script is","+ \" seconds\") # ======================= verify bundle Structure =============================================== def","into JSON form. The parent object is always a dict,","@param expectedElement the expected element. May be defined by regular","of an image based on its tags: vX-YYYYMMDD-HHmm. The latest,","if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag","re import operator import unicodedata # global list of error","extracted @param to_dir the destination directory to extract the zip","of common functions shared by each individual build scripts. \"\"\"","+\" is missing from the path: \\\"\" + currentPath +","open file instance dockerfile.close() def set_docker_client_timeout(): \"\"\" Sets the DOCKER_CLIENT_TIMEOUT","isMatched & True # if the contents of the directory","@param expectedElement the expected element to find in the bundle","the global array. @param expected the expected element @param currentPath",", currentPath) else : # if they don't match then","the bundle ZIP file \"\"\" print ('Validating bundle structure...') bundleIsGood","directories. @param directory directory to convert @return JSON representation of","the correct docker registry URL associated with the datacenter this","follows IBM defined structure @param expected the JSON representation of","Function to verify if an uploaded bundle follows IBM defined","if type=directory. A directory is composed of a list and","json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid: \"","= False return ret def verifyZipFile(zipDirectory, nameOfBundle): \"\"\" Function to","May be defined by regular expression @param actualElement the actual","if badFile is not None: ret = False else: ret","expected element \"\"\" # if actual is a dict then","latest_tag = tag else: if latest_version > str_version: continue elif","by regular expression @param actualElement the actual element \"\"\" ret","\"\"\" Function to verify if an uploaded bundle is: 1)","the JSON representation of the IBM defined structure @param actual","os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type']","to be the one with the highest version number (vX)","type. @param expectedElement the expected element. May be defined by","= dt_timestamp latest_tag = tag else: if latest_version > str_version:","image_tag_latest): \"\"\" Creates a dockerfile using the correct docker registry","expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual,","if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']:","is not None: ret = False else: ret = True","str image_namespace: the name of the image :param str image_name:","if so get the matched position elif type(actual) is list:","version tag of the base image :returns: None \"\"\" #","test if a ZIP file is good or bad @param","file to be tested @return True if the ZIP file",":param str image_tag_latest: the latest version tag of the base","= os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into","it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory","return data def directoryToJSON(directory): \"\"\" Function to convert objects in","\"\"\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except:","object is always a dict, it may contain children if","individual build scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS","the children in a directory is a list composed of","zipFileName.endswith(\".zip\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x","of the bundle ZIP file \"\"\" print ('Validating bundle structure...')","type(expected) is dict: if matches(expected,actual): # a matching file or","\"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a .zip","name of the image :param str image_tag_latest: the latest version","given directory into JSON form. The parent object is always","\"\"\" Function to verify if an actual list of objects","if type(expected) is dict: if matches(expected,actual): # a matching file","if zipFileIsGood(bundleZip): try: # copy bundle into new working directory","docker_url: the docker registry VIP accessible from the mesos slaves","docker registry functional user @return the latest image tag \"\"\"","to build paths recursively for error msg) @param isMatched (only","errorMessages @param errorMessage the error message to add \"\"\" print(errorMessage)","= True except: ret = False return ret def verifyZipFile(zipDirectory,","isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if","recursively for error msg) @param isMatched (only used for recursive","of the uploaded bundle follows IBM defined structure. False otherwise.","structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure =","actual objects contain the expected element \"\"\" # if actual","messages together print (\"report=[\" + \". \".join(str(x) for x in","latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag","matching file or directory was found if expected['type'] == 'directory':","'directory': currentPath = currentPath + actual['name'] + \"/\" if expected['children']","deployment.\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\"The uploaded","the zip file to \"\"\" # update if zipFileName.endswith(\".zip\"): #check","ret = True except: ret = False return ret def","uploaded bundle is: 1) a valid zip file 2) follows","get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using external Python","not found if expected['fail-if-not-found'] == \"yes\": logBundleStructureErrorMessage(expected, currentPath) return False","latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else:","json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is","bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of","function for extracting zip files @param zip_file the zip file","type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) #","is not None and expectedElement['type'] == actualElement['type']: ret = True","to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \"\") if not","\"yes\": # log error msg and return false if needed","os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\"The timeout set for docker client: \"","directory to extract the zip file to \"\"\" with zipfile.ZipFile(zip_file,","be tested @return True if the ZIP file is good.","and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd),","ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \"\"\" Function to verify","directoryToJSON(directory): \"\"\" Function to convert objects in a given directory","def logBundleStructureErrorMessage(expected, currentPath): \"\"\" Function to adds error messages to",") unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test","Remove the suppressed verification once the docker cert location #","user @return a JSON response \"\"\" headers = { 'Accept':","meet predefined structure. Could not proceed with deployment.\") # clean","str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format)","print(\"The timeout set for docker client: \" + os.environ['DOCKER_CLIENT_TIMEOUT'] +","specified JSON file is not valid: \" + jsonFile) raise","openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory","in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path,","registry URL associated with the datacenter this script is being","convert @return JSON representation of a directory \"\"\" d =","return false if expectedElement['fail-if-not-found'] == \"yes\": # log error msg","raise return data def directoryToJSON(directory): \"\"\" Function to convert objects","elif type(actual) is list: matchedPosition = -1 for i in","> str_version: continue elif latest_version < str_version: latest_version = str_version","= currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\":","data def directoryToJSON(directory): \"\"\" Function to convert objects in a","os.path.join(dockerfile_parent_dir, \"Dockerfile\") # Create the Dockerfile dockerfile = open(dockerfile_path, \"w+\")","= i break if matchedPosition != -1: # if they","dockerfile using the correct docker registry URL associated with the","isMatched = True if type(expected) is dict: if matches(expected,actual): #","to verify if an actual list of objects contains an","bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \"bundle-definition.json\"))","of the actual structure of the uploaded bundle @param currentPath","the list of actual objects contain the expected element \"\"\"","children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath)","zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \"\"\" Creates","valid: \" + jsonFile) raise return data def directoryToJSON(directory): \"\"\"","not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\"%Y%m%d-%H%M\"","global list of errorMessages @param errorMessage the error message to","isMatched (only used for recursive calls) @return True if the","files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type']","bundle could not be unzipped. Could not proceed with deployment.\")","\"_any\": isMatched = isMatched & True # if the contents","to add \"\"\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName,","bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle","False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could not","all the messages together print (\"report=[\" + \". \".join(str(x) for","+ actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched =","representation of a directory \"\"\" d = {'name': os.path.basename(directory)} #","element \"\"\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not","zipfile import shutil import requests import datetime import re import","\"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \"\"\" Function to check","currentPath + actual['name'] + \"/\" if expected['children'] == \"_any\": isMatched","isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched","follows IBM defined structure @param zipDirectory where the bundle ZIP","currentPath the current path we are on that has the","isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is","convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure,","extract the zip file to \"\"\" with zipfile.ZipFile(zip_file, \"r\") as","the path: \\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def","\"temp\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip","# if they match then verify their children too isMatched","to \"\"\" # update if zipFileName.endswith(\".zip\"): #check if it's a","is list: for k in range(0,len(expected)): isMatched = isMatched &","defined structure. False otherwise. \"\"\" isMatched = True if type(expected)","latest_tag def unzip(zip_file, to_dir): \"\"\" Generic unzip function for extracting","@param directoryToUnzipTo the destination directory to extract the zip file","matchedPosition != -1: # if they match then verify their","the FROM command dockerfile_from_cmd = \"FROM \" + docker_url +","\\\"\" + currentPath + \"\\\"\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement):","currentPath, isMatched): \"\"\" Function to verify if an actual list","an uploaded bundle follows IBM defined structure @param expected the","unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \"\"\" Function to test if","\"\"\" Generic unzip function for extracting zip files @param zip_file","requests import datetime import re import operator import unicodedata #","verification once the docker cert location # is figured out","# a matching file or directory was found if expected['type']","its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to","str dockerfile_parent_dir: path to the parent directory for the Dockerfile","tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag","= isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a","for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile =","list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual,","errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \"\"\" Function to unzip","verify if an uploaded bundle is: 1) a valid zip","os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x )","addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the path: \\\"\"","being run on :param str dockerfile_parent_dir: path to the parent","Helper method to verifyBundleStructure. @param actual list of the actual","the path currently being checked (used to build paths recursively","# is figured out and we specify it in REQUESTS_CA_BUNDLE","------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\"Exception occurred while verifying bundle structure. Could","latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \"\"\" Generic","= tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp","scripts. \"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests","file @return the loaded JSON file \"\"\" try: with open(jsonFile)","= False addToErrorMessages(\"The uploaded bundle could not be unzipped. Could","on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined","defined structure @param actual the JSON representation of the actual","if matches(expected,actual): # a matching file or directory was found","= isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking","same type. @param expectedElement the expected element. May be defined","a JSON file @param jsonFile path to the JSON file","representation of the actual structure of the uploaded bundle @param","= dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp:","Form the path for the Dockerfile based on the parent","verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition]","on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir,","@param isMatched (only used for recursive calls) @return True if","os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check","image_name: the name of the image :param str image_tag_latest: the","= isMatched & True # if the contents of the","contain files and/or directories. @param directory directory to convert @return","global array. @param expected the expected element @param currentPath the","= json.load(json_data_file) except: addToErrorMessages(\"The specified JSON file is not valid:","structure. Could not proceed with deployment.\") # clean up unzipped","return def matches(expectedElement, actualElement): \"\"\" Function to check if files/directories","isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further","to be extracted @param to_dir the destination directory to extract","password for the docker registry functional user @return a JSON","openJSONfile(jsonFile): \"\"\" Function to open a JSON file @param jsonFile","= registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list","a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory =","is composed of a list and may contain files and/or","new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \"temp\") if not","+ os.environ['DOCKER_CLIENT_TIMEOUT'] + \" seconds\") # ======================= verify bundle Structure","if expected['type'] == 'directory': currentPath = currentPath + actual['name'] +","matched position elif type(actual) is list: matchedPosition = -1 for","directory \"\"\" addToErrorMessages(\"A \"+ expected['type'] +\" is missing from the","< str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =","the same name and must both be the same type.","errorMessage the error message to add \"\"\" print(errorMessage) global errorMessges","\"\"\" def get(url, usr, pwd): \"\"\" HTTP/HTTPS GET requests using","@return True if the list of actual objects contain the"],"string":"[\n \"and/or directories. @param directory directory to convert @return JSON representation\",\n \"if '-' not in tag: continue str_version, str_dash, str_timestamp =\",\n \"matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1: #\",\n \"package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying\",\n \"the base image :returns: None \\\"\\\"\\\" # Form the path\",\n \"image :param str image_name: the name of the image :param\",\n \"a given directory into JSON form. The parent object is\",\n \"list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x\",\n \"registry path @param usr the functional username for the docker\",\n \"cert location # is figured out and we specify it\",\n \"list of the actual files and directories in the bundle\",\n \"@param zipDirectory where the bundle ZIP is located @param nameOfBundle\",\n \"the JSON representation of the actual structure of the uploaded\",\n \"be the one with the highest version number (vX) and\",\n \"+ \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched &\",\n \"\\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" + image_name\",\n \"environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout\",\n \"registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list =\",\n \"actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not\",\n \"pwd the password for the docker registry functional user @return\",\n \"\\\"\\\"\\\" Function to convert objects in a given directory into\",\n \"def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip\",\n \"directory can be anything then do no further checking else:\",\n \"\\\"\\\"\\\" Function to check if files/directories match. They must have\",\n \"-1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition =\",\n \"a list then find out if they match anywhere, if\",\n \"=============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file\",\n \"is defined to be the one with the highest version\",\n \"d = {'name': os.path.basename(directory)} # the parent object is dict\",\n \"# verify structure of bundle ------------------------------------------------------------------------ # check package stucture\",\n \"} # TEMP: Remove the suppressed verification once the docker\",\n \"of the actual files and directories in the bundle @param\",\n \"url the url of the REST call @param usr the\",\n \"+ '/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text)\",\n \"as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file\",\n \"it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def\",\n \"directory is a list composed of more files/directories d['children'] =\",\n \"be unzipped. Could not proceed with deployment.\\\") # out put\",\n \"once the docker cert location # is figured out and\",\n \"# do further checking else: # a matching file or\",\n \"and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath)\",\n \"path @param usr the functional username for the docker registry\",\n \"then find out if they match anywhere, if so get\",\n \"good or bad @param filePath the zip file to be\",\n \"the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\"\",\n \"os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) #\",\n \"adds error messages to the global array. @param expected the\",\n \"@param registry_path docker registry path @param usr the functional username\",\n \"match then verify their children too isMatched = isMatched &\",\n \"FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the\",\n \"return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP\",\n \"\\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests @param\",\n \"= \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function\",\n \"return False if type(expected) is list: for k in range(0,len(expected)):\",\n \"verify structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure\",\n \"return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if\",\n \"subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a\",\n \"ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function\",\n \"expectedElement the expected element. May be defined by regular expression\",\n \"bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try:\",\n \"isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\"\",\n \"the uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\"\",\n \"structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip):\",\n \"pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module requests\",\n \"= json.loads(request.text) for tag in tag_list['tags']: if '-' not in\",\n \"unzip function for extracting zip files @param zip_file the zip\",\n \"further checking else: # a matching file or directory was\",\n \"the docker cert location # is figured out and we\",\n \"\\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None\",\n \"can be anything then do no further checking else: isMatched\",\n \"currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle follows\",\n \"= open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd =\",\n \"get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']:\",\n \"bad @param filePath the zip file to be tested @return\",\n \"return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return\",\n \"badFile is not None: ret = False else: ret =\",\n \"GET requests using external Python module requests @param url the\",\n \"expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" + currentPath\",\n \"The latest, by definition, is defined to be the one\",\n \"Build Scripts A collection of common functions shared by each\",\n \"file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file)\",\n \"actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath,\",\n \"tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr, pwd)\",\n \"the path for the Dockerfile based on the parent of\",\n \"to add error messages to the global list of errorMessages\",\n \"for the docker registry @param pwd the password for the\",\n \"datacenter this script is being run on :param str dockerfile_parent_dir:\",\n \"registry_path docker registry path @param usr the functional username for\",\n \"and may contain files and/or directories. @param directory directory to\",\n \"caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile\",\n \"of the directory can be anything then do no further\",\n \"element. Helper method to verifyBundleStructure. @param actual list of the\",\n \"location # is figured out and we specify it in\",\n \"JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data =\",\n \"unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0])\",\n \"missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails'])\",\n \"\\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client:\",\n \"the Dockerfile based on the parent of the caller script\",\n \"contains an expected element. Helper method to verifyBundleStructure. @param actual\",\n \"zipFileIsGood(bundleZip): try: # copy bundle into new working directory -----------------------------------------------------------\",\n \"len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition !=\",\n \"don't match then log the error msg and return false\",\n \"check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo)\",\n \"latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag\",\n \"\\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet\",\n \"image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest #\",\n \"the expected element @param currentPath the current path we are\",\n \"str_version: continue elif latest_version < str_version: latest_version = str_version latest_timestamp\",\n \"of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create\",\n \"Function to verify if an uploaded bundle is: 1) a\",\n \"user @return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path\",\n \"not proceed with deployment.\\\") # clean up unzipped stuff and\",\n \"docker registry path @param usr the functional username for the\",\n \"not meet predefined structure. Could not proceed with deployment.\\\") #\",\n \"the actual structure of the uploaded bundle @param currentPath the\",\n \"if the contents of the directory can be anything then\",\n \"JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood:\",\n \"password for the docker registry functional user @return the latest\",\n \"for extracting zip files @param zip_file the zip file to\",\n \"datetime import re import operator import unicodedata # global list\",\n \"bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood =\",\n \"of Common Functions used by Build Scripts A collection of\",\n \"recursively @param zipFileName the zip file to be extracted @param\",\n \"def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded\",\n \"+ image_namespace + \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest\",\n \"= \\\"directory\\\" # directory may have children # the children\",\n \"or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected,\",\n \"module requests @param url the url of the REST call\",\n \"stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert\",\n \"\\\"\\\"\\\" Function to test if a ZIP file is good\",\n \"recursive calls) @return True if the list of actual objects\",\n \"jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert\",\n \"def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile\",\n \"zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if\",\n \"return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP\",\n \"dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile =\",\n \"expectedElement the expected element to find in the bundle @param\",\n \"false if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched\",\n \"a valid zip file 2) follows IBM defined structure @param\",\n \"with deployment.\\\") # out put report value , join all\",\n \"URL associated with the datacenter this script is being run\",\n \"+ docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\"\",\n \"paths recursively for error msg) @return True if structure of\",\n \"latest_version > str_version: continue elif latest_version < str_version: latest_version =\",\n \"\\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to\",\n \"usr, pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if\",\n \"= os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\"\",\n \"set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\"\",\n \"if an actual list of objects contains an expected element.\",\n \"number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker\",\n \"Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command\",\n \"directory was found if expected['type'] == 'directory': currentPath = currentPath\",\n \"we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers,\",\n \"call @param usr the functional username for the docker registry\",\n \"to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\")\",\n \"Function to verify if an actual list of objects contains\",\n \"bundle ZIP is located @param nameOfBundle name of the bundle\",\n \"if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children\",\n \"collection of common functions shared by each individual build scripts.\",\n \"\\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url, usr,\",\n \"may contain children if type=directory. A directory is composed of\",\n \"datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except\",\n \"Could not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood\",\n \"They must have the same name and must both be\",\n \"NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag\",\n \"to the parent directory for the Dockerfile :param str docker_url:\",\n \"clean up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo)\",\n \"auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve\",\n \"dt_timestamp latest_tag = tag else: if latest_version > str_version: continue\",\n \"True if the ZIP file is good. False otherwise. \\\"\\\"\\\"\",\n \"being checked (used to build paths recursively for error msg)\",\n \"the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param\",\n \"error messages to the global array. @param expected the expected\",\n \"using the correct docker registry URL associated with the datacenter\",\n \"bundle @param currentPath the path currently being checked (used to\",\n \"to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def\",\n \"else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath):\",\n \"& verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath):\",\n \"predefined structure. Could not proceed with deployment.\\\") # clean up\",\n \"into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if\",\n \"jsonFile path to the JSON file @return the loaded JSON\",\n \"(used to build paths recursively for error msg) @param isMatched\",\n \"unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files\",\n \"docker registry @param pwd the password for the docker registry\",\n \"vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be the\",\n \"# convert the unzipped directory to JSON file bundleIsGood =\",\n \"False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not\",\n \"Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the\",\n \"# clean up unzipped stuff and package structure Json -------------------------------------------------\",\n \"file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing\",\n \"ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip()\",\n \"of the image :param str image_tag_latest: the latest version tag\",\n \"directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if\",\n \"verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an\",\n \"registry functional user @return the latest image tag \\\"\\\"\\\" tag_list_url\",\n \"to be tested @return True if the ZIP file is\",\n \"so get the matched position elif type(actual) is list: matchedPosition\",\n \"nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new working\",\n \"ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version =\",\n \"addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed with\",\n \"no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'],\",\n \"the password for the docker registry functional user @return a\",\n \"unicodedata # global list of error messages to keep track\",\n \"error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions\",\n \"was found if expected['type'] == 'directory': currentPath = currentPath +\",\n \"Function to adds error messages to the global array. @param\",\n \"\\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\" +\",\n \"\\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") #\",\n \"they match anywhere, if so get the matched position elif\",\n \"zip_file the zip file to be extracted @param to_dir the\",\n \"error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return\",\n \"None: ret = False else: ret = True except: ret\",\n \"check if files/directories match. They must have the same name\",\n \"registry functional user @return a JSON response \\\"\\\"\\\" headers =\",\n \"anything then do no further checking else: isMatched = isMatched\",\n \"all error msgs errorMessages = [] \\\"\\\"\\\" Collection of Common\",\n \"# Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd)\",\n \"the contents of the directory can be anything then do\",\n \"and its children if type(actual) is dict: isMatched = isMatched\",\n \"True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function\",\n \"registry @param pwd the password for the docker registry functional\",\n \"for the docker registry functional user @return the latest image\",\n \"copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory,\",\n \"a list and may contain files and/or directories. @param directory\",\n \"JSON representation of the actual structure of the uploaded bundle\",\n \"to convert objects in a given directory into JSON form.\",\n \"a dockerfile using the correct docker registry URL associated with\",\n \"msg) @return True if structure of the uploaded bundle follows\",\n \"verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: # a\",\n \"the_zip_file.testzip() if badFile is not None: ret = False else:\",\n \"follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True\",\n \"range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return\",\n \"uploaded bundle could not be unzipped. Could not proceed with\",\n \"'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once\",\n \"(vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry\",\n \"in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected,\",\n \"i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break\",\n \"variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set\",\n \"if the list of actual objects contain the expected element\",\n \"position elif type(actual) is list: matchedPosition = -1 for i\",\n \"the functional username for the docker registry @param pwd the\",\n \":param str docker_url: the docker registry VIP accessible from the\",\n \"set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\")\",\n \"the docker registry functional user @return a JSON response \\\"\\\"\\\"\",\n \"dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd\",\n \"Generic unzip function for extracting zip files @param zip_file the\",\n \"type(expected) is list: for k in range(0,len(expected)): isMatched = isMatched\",\n \"False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath)\",\n \"located @param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\"\",\n \"directory to extract the zip file to \\\"\\\"\\\" # update\",\n \"matching file or directory was not found if expected['fail-if-not-found'] ==\",\n \"JSON representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)}\",\n \"currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to\",\n \"directory for the Dockerfile :param str docker_url: the docker registry\",\n \"the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format the FROM\",\n \"ZIP file is good. False otherwise. \\\"\\\"\\\" ret = True\",\n \"the bundle @param currentPath the path currently being checked (used\",\n \"not valid: \\\" + jsonFile) raise return data def directoryToJSON(directory):\",\n \"2) follows IBM defined structure @param zipDirectory where the bundle\",\n \"tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue\",\n \"ZIP file is good or bad @param filePath the zip\",\n \"based on the parent of the caller script dockerfile_path =\",\n \"global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to\",\n \"open a JSON file @param jsonFile path to the JSON\",\n \"= [] \\\"\\\"\\\" Collection of Common Functions used by Build\",\n \"actual structure of the uploaded bundle @param currentPath the path\",\n \"Dockerfile based on the parent of the caller script dockerfile_path\",\n \"element @param currentPath the current path we are on that\",\n \"import operator import unicodedata # global list of error messages\",\n \"to the global list of errorMessages @param errorMessage the error\",\n \"too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else\",\n \"in tag_list['tags']: if '-' not in tag: continue str_version, str_dash,\",\n \"except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =\",\n \"if latest_version > str_version: continue elif latest_version < str_version: latest_version\",\n \"found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if\",\n \"= isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def\",\n \"unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify\",\n \"for the docker registry functional user @return a JSON response\",\n \"the uploaded bundle @param currentPath the path currently being checked\",\n \"list of error messages to keep track of all error\",\n \"# Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" +\",\n \"\\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path:\",\n \"they don't match then log the error msg and return\",\n \"\\\"\\\"\\\" Creates a dockerfile using the correct docker registry URL\",\n \"version of an image based on its tags: vX-YYYYMMDD-HHmm. The\",\n \"verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle\",\n \"to build paths recursively for error msg) @return True if\",\n \"directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo)\",\n \"the IBM defined structure @param actual the JSON representation of\",\n \"expected element @param currentPath the current path we are on\",\n \"else: if latest_version > str_version: continue elif latest_version < str_version:\",\n \"x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to\",\n \"True if structure of the uploaded bundle follows IBM defined\",\n \"expected element. Helper method to verifyBundleStructure. @param actual list of\",\n \"uploaded bundle does not meet predefined structure. Could not proceed\",\n \"zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir)\",\n \"addToErrorMessages(\\\"The specified JSON file is not valid: \\\" + jsonFile)\",\n \"dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\"\",\n \"of the REST call @param usr the functional username for\",\n \"actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret =\",\n \"latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr\",\n \"ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True\",\n \"path to the parent directory for the Dockerfile :param str\",\n \"currentPath the path currently being checked (used to build paths\",\n \"@param pwd the password for the docker registry functional user\",\n \"files and directories in the bundle @param expectedElement the expected\",\n \"re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']: ret\",\n \"@param expected the expected element @param currentPath the current path\",\n \"elif latest_version < str_version: latest_version = str_version latest_timestamp = dt_timestamp\",\n \"nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0]))\",\n \"external Python module requests @param url the url of the\",\n \"the expected element. May be defined by regular expression @param\",\n \"shared by each individual build scripts. \\\"\\\"\\\" def get(url, usr,\",\n \"latest, by definition, is defined to be the one with\",\n \"DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The\",\n \"zip file to be extracted @param to_dir the destination directory\",\n \"the directory can be anything then do no further checking\",\n \"\\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object is\",\n \"import zipfile import shutil import requests import datetime import re\",\n \"latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file,\",\n \"-1: # if they match then verify their children too\",\n \"is a list then find out if they match anywhere,\",\n \"return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to\",\n \"\\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir,\",\n \"the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file:\",\n \"request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag\",\n \"= get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for tag in\",\n \"isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to\",\n \"to_dir): \\\"\\\"\\\" Generic unzip function for extracting zip files @param\",\n \"+ currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\"\",\n \"get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an\",\n \"@param usr the functional username for the docker registry @param\",\n \"a directory is a list composed of more files/directories d['children']\",\n \"if an uploaded bundle is: 1) a valid zip file\",\n \"\\\" + docker_url + image_namespace + \\\"/\\\" + image_name +\",\n \"of error messages to keep track of all error msgs\",\n \"REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr,\",\n \"addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the global\",\n \"children # the children in a directory is a list\",\n \"dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\"\",\n \"error msg and return false if needed isMatched = False\",\n \"used for recursive calls) @return True if the list of\",\n \"@return the latest image tag \\\"\\\"\\\" tag_list_url = registry_path +\",\n \"logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to the\",\n \"add error messages to the global list of errorMessages @param\",\n \"currently being checked (used to build paths recursively for error\",\n \"\\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\"\",\n \"# check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure =\",\n \"false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and\",\n \"\\\" seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile):\",\n \"== \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list:\",\n \"element to find in the bundle @param currentPath the path\",\n \"== actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement,\",\n \"log the error msg and return false if expectedElement['fail-if-not-found'] ==\",\n \"= True if type(expected) is dict: if matches(expected,actual): # a\",\n \"error msg) @param isMatched (only used for recursive calls) @return\",\n \"True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy\",\n \"if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition != -1:\",\n \"or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from\",\n \"the one with the highest version number (vX) and the\",\n \"= str_version latest_timestamp = dt_timestamp latest_tag = tag else: if\",\n \"latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp =\",\n \"bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched =\",\n \"isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected,\",\n \"Format the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url\",\n \"of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)]\",\n \"the actual files and directories in the bundle @param expectedElement\",\n \"= False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type']\",\n \"json import argparse import zipfile import shutil import requests import\",\n \"file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment\",\n \"structure @param actual the JSON representation of the actual structure\",\n \"script is being run on :param str dockerfile_parent_dir: path to\",\n \"unzip a ZIP file recursively @param zipFileName the zip file\",\n \"@param url the url of the REST call @param usr\",\n \"the error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\":\",\n \"HTTP/HTTPS GET requests using external Python module requests @param url\",\n \"object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory\",\n \"errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used by\",\n \"currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children'] ==\",\n \"\\\"\\\"\\\" Function to add error messages to the global list\",\n \"checked (used to build paths recursively for error msg) @param\",\n \"zip file 2) follows IBM defined structure @param zipDirectory where\",\n \"dict then verify it and its children if type(actual) is\",\n \"to check if files/directories match. They must have the same\",\n \"together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages)\",\n \"on that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A\",\n \"not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure.\",\n \"keep track of all error msgs errorMessages = [] \\\"\\\"\\\"\",\n \"= False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function\",\n \"os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo,\",\n \"from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return\",\n \"up unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except:\",\n \"= openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped\",\n \"docker registry VIP accessible from the mesos slaves :param str\",\n \"nameOfBundle): \\\"\\\"\\\" Function to verify if an uploaded bundle is:\",\n \"unzipped stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception\",\n \"nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print ('Validating\",\n \"expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the\",\n \"python3.6 import os import subprocess import json import argparse import\",\n \"to test if a ZIP file is good or bad\",\n \"# the parent object is dict if os.path.isdir(directory): d['type'] =\",\n \"d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\"\",\n \"for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") #\",\n \"& verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then\",\n \"== 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if\",\n \"shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle),\",\n \"== \\\"yes\\\": # log error msg and return false if\",\n \"or directory was found if expected['type'] == 'directory': currentPath =\",\n \"False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify\",\n \"is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if\",\n \"@param currentPath the path currently being checked (used to build\",\n \"extracted @param directoryToUnzipTo the destination directory to extract the zip\",\n \"with deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The\",\n \"@param jsonFile path to the JSON file @return the loaded\",\n \"build paths recursively for error msg) @return True if structure\",\n \"in a directory is a list composed of more files/directories\",\n \"@return JSON representation of a directory \\\"\\\"\\\" d = {'name':\",\n \"IBM defined structure @param zipDirectory where the bundle ZIP is\",\n \"with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url,\",\n \"bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle\",\n \"to verifyBundleStructure. @param actual list of the actual files and\",\n \"def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file\",\n \"structure of the uploaded bundle @param currentPath the path currently\",\n \"children in a directory is a list composed of more\",\n \"actualElement): \\\"\\\"\\\" Function to check if files/directories match. They must\",\n \"dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag def\",\n \"os import subprocess import json import argparse import zipfile import\",\n \"in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try:\",\n \"zipFileName the zip file to be extracted @param directoryToUnzipTo the\",\n \"@return True if the ZIP file is good. False otherwise.\",\n \"a ZIP file recursively @param zipFileName the zip file to\",\n \"to the global array. @param expected the expected element @param\",\n \"bundle does not meet predefined structure. Could not proceed with\",\n \"error msg) @return True if structure of the uploaded bundle\",\n \"= {'name': os.path.basename(directory)} # the parent object is dict if\",\n \"print ('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory,\",\n \"objects contains an expected element. Helper method to verifyBundleStructure. @param\",\n \"try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version\",\n \"a dict, it may contain children if type=directory. A directory\",\n \"try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp\",\n \"Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def\",\n \"actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is\",\n \"out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr,\",\n \"to extract the zip file to \\\"\\\"\\\" # update if\",\n \"bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be unzipped.\",\n \"\\\"\\\"\\\" Collection of Common Functions used by Build Scripts A\",\n \"continue try: latest_version latest_timestamp latest_tag except NameError: latest_version = str_version\",\n \"command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open\",\n \"import shutil import requests import datetime import re import operator\",\n \"= '300' print(\\\"The timeout set for docker client: \\\" +\",\n \"the same type. @param expectedElement the expected element. May be\",\n \"@param nameOfBundle name of the bundle ZIP file \\\"\\\"\\\" print\",\n \"str docker_url: the docker registry VIP accessible from the mesos\",\n \"directory may have children # the children in a directory\",\n \"not be unzipped. Could not proceed with deployment.\\\") # out\",\n \"structure of the uploaded bundle follows IBM defined structure. False\",\n \"on :param str dockerfile_parent_dir: path to the parent directory for\",\n \"dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual\",\n \"be extracted @param to_dir the destination directory to extract the\",\n \"image_tag_latest: the latest version tag of the base image :returns:\",\n \"for the Dockerfile based on the parent of the caller\",\n \"parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" #\",\n \"#!/usr/bin/env python3.6 import os import subprocess import json import argparse\",\n \"a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent\",\n \"bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle) if\",\n \"actual[matchedPosition] , currentPath) else : # if they don't match\",\n \"the expected element to find in the bundle @param currentPath\",\n \"add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo):\",\n \"VIP accessible from the mesos slaves :param str image_namespace: the\",\n \"checking else: # a matching file or directory was not\",\n \"actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched\",\n \"expected the JSON representation of the IBM defined structure @param\",\n \"for error msg) @return True if structure of the uploaded\",\n \"join all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x)\",\n \"to be extracted @param directoryToUnzipTo the destination directory to extract\",\n \"run on :param str dockerfile_parent_dir: path to the parent directory\",\n \"the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure\",\n \"parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") #\",\n \"= { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed\",\n \"its children if type(actual) is dict: isMatched = isMatched &\",\n \"isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else:\",\n \"filePath the zip file to be tested @return True if\",\n \"IBM defined structure @param expected the JSON representation of the\",\n \"@param zipFileName the zip file to be extracted @param directoryToUnzipTo\",\n \"import datetime import re import operator import unicodedata # global\",\n \"None \\\"\\\"\\\" # Form the path for the Dockerfile based\",\n \"i break if matchedPosition != -1: # if they match\",\n \"more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else:\",\n \"calls) @return True if the list of actual objects contain\",\n \"latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request\",\n \"or bad @param filePath the zip file to be tested\",\n \"directoryToUnzipTo the destination directory to extract the zip file to\",\n \"docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the\",\n \"isMatched = isMatched & True # if the contents of\",\n \"('Validating bundle structure...') bundleIsGood = True bundleZip = os.path.join(zipDirectory, nameOfBundle)\",\n \"(used to build paths recursively for error msg) @return True\",\n \"Write the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) #\",\n \"if an uploaded bundle follows IBM defined structure @param expected\",\n \"tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to be\",\n \"shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not\",\n \"type(actual) is list: matchedPosition = -1 for i in range(0,\",\n \"+ \\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write\",\n \"the image :param str image_tag_latest: the latest version tag of\",\n \"list and may contain files and/or directories. @param directory directory\",\n \"element \\\"\\\"\\\" # if actual is a dict then verify\",\n \"image based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition,\",\n \"msgs errorMessages = [] \\\"\\\"\\\" Collection of Common Functions used\",\n \"@return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as\",\n \"same name and must both be the same type. @param\",\n \"else : # if they don't match then log the\",\n \"print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function\",\n \"directory was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath)\",\n \"defined structure @param zipDirectory where the bundle ZIP is located\",\n \"expected['type'] == 'directory': currentPath = currentPath + actual['name'] + \\\"/\\\"\",\n \"path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement,\",\n \"def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file\",\n \"of the image :param str image_name: the name of the\",\n \"IBM defined structure @param actual the JSON representation of the\",\n \"and the latest timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path\",\n \"be extracted @param directoryToUnzipTo the destination directory to extract the\",\n \"\\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle\",\n \"True if the list of actual objects contain the expected\",\n \"have children # the children in a directory is a\",\n \"expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function\",\n \"expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is\",\n \"\\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM \\\"\",\n \"be defined by regular expression @param actualElement the actual element\",\n \"expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual\",\n \"and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error\",\n \"was not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return\",\n \"needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage):\",\n \"= datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag\",\n \", join all the messages together print (\\\"report=[\\\" + \\\".\",\n \"if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath)\",\n \"the image :param str image_name: the name of the image\",\n \"in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x\",\n \"children if type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual,\",\n \"using external Python module requests @param url the url of\",\n \"files @param zip_file the zip file to be extracted @param\",\n \"image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request =\",\n \"(YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the functional\",\n \"Function to add error messages to the global list of\",\n \"could not be unzipped. Could not proceed with deployment.\\\") #\",\n \"checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) #\",\n \"if a ZIP file is good or bad @param filePath\",\n \"expected element. May be defined by regular expression @param actualElement\",\n \"image :param str image_tag_latest: the latest version tag of the\",\n \"verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded\",\n \"verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they don't\",\n \"isMatched): \\\"\\\"\\\" Function to verify if an actual list of\",\n \"functional user @return a JSON response \\\"\\\"\\\" headers = {\",\n \"username for the docker registry @param pwd the password for\",\n \"by Build Scripts A collection of common functions shared by\",\n \"# if actual is a dict then verify it and\",\n \"not proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood =\",\n \"\\\"directory\\\" # directory may have children # the children in\",\n \"addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could not\",\n \"if the ZIP file is good. False otherwise. \\\"\\\"\\\" ret\",\n \"match. They must have the same name and must both\",\n \"to unzip a ZIP file recursively @param zipFileName the zip\",\n \"of a list and may contain files and/or directories. @param\",\n \"tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list' request = get(tag_list_url,\",\n \"functional username for the docker registry @param pwd the password\",\n \"Dockerfile :param str docker_url: the docker registry VIP accessible from\",\n \"the matched position elif type(actual) is list: matchedPosition = -1\",\n \"the latest image tag \\\"\\\"\\\" tag_list_url = registry_path + '/tags/list'\",\n \"missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is\",\n \"latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp\",\n \"string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file\",\n \"if expected['children'] == \\\"_any\\\": isMatched = isMatched & True #\",\n \"error messages to the global list of errorMessages @param errorMessage\",\n \"file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The\",\n \"the zip file to be extracted @param directoryToUnzipTo the destination\",\n \"directory into JSON form. The parent object is always a\",\n \"the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close()\",\n \"to_dir the destination directory to extract the zip file to\",\n \"addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not proceed\",\n \"logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for k\",\n \"the zip file to be tested @return True if the\",\n \"directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the parent object\",\n \"actual is a dict then verify it and its children\",\n \"def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given\",\n \"actualElement the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'],\",\n \"for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d\",\n \"os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def verifyBundleStructure(expected, actual,\",\n \"the global list of errorMessages @param errorMessage the error message\",\n \"path currently being checked (used to build paths recursively for\",\n \"with deployment.\\\") # clean up unzipped stuff and package structure\",\n \"dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try: latest_version latest_timestamp\",\n \"return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error\",\n \"------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure\",\n \"find out if they match anywhere, if so get the\",\n \"a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', }\",\n \"str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except\",\n \"\\\"\\\"\\\" # Form the path for the Dockerfile based on\",\n \"\\\"\\\"\\\" Function to adds error messages to the global array.\",\n \"if type(expected) is list: for k in range(0,len(expected)): isMatched =\",\n \".zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo,\",\n \"headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the\",\n \"parent directory for the Dockerfile :param str docker_url: the docker\",\n \"image_namespace: the name of the image :param str image_name: the\",\n \"os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo,\",\n \"= verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle\",\n \"----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip,\",\n \"of the base image :returns: None \\\"\\\"\\\" # Form the\",\n \"the latest version tag of the base image :returns: None\",\n \"\\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to\",\n \"# Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\") # Format\",\n \"\\\":\\\" + image_tag_latest # Write the FROM command string to\",\n \"directory is composed of a list and may contain files\",\n \"currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function\",\n \"it and its children if type(actual) is dict: isMatched =\",\n \"functions shared by each individual build scripts. \\\"\\\"\\\" def get(url,\",\n \"\\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip =\",\n \"directories in the bundle @param expectedElement the expected element to\",\n \"if needed isMatched = False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def\",\n \"to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for\",\n \"loaded JSON file \\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data\",\n \"import os import subprocess import json import argparse import zipfile\",\n \"good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file =\",\n \"verify it and its children if type(actual) is dict: isMatched\",\n \"= os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path,\",\n \"if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for\",\n \"for recursive calls) @return True if the list of actual\",\n \"= os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle))\",\n \"# a matching file or directory was not found if\",\n \"both be the same type. @param expectedElement the expected element.\",\n \"matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match. They\",\n \"be the same type. @param expectedElement the expected element. May\",\n \"\\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT']\",\n \"structure @param zipDirectory where the bundle ZIP is located @param\",\n \"where the bundle ZIP is located @param nameOfBundle name of\",\n \"= tag else: if latest_version > str_version: continue elif latest_version\",\n \"accessible from the mesos slaves :param str image_namespace: the name\",\n \"ZIP file recursively @param zipFileName the zip file to be\",\n \"to adds error messages to the global array. @param expected\",\n \"for the Dockerfile :param str docker_url: the docker registry VIP\",\n \"while verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood\",\n \"expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return false\",\n \"bundle follows IBM defined structure @param expected the JSON representation\",\n \"dict: if matches(expected,actual): # a matching file or directory was\",\n \"extracting zip files @param zip_file the zip file to be\",\n \"str image_tag_latest: the latest version tag of the base image\",\n \"# if they don't match then log the error msg\",\n \"method to verifyBundleStructure. @param actual list of the actual files\",\n \"msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log\",\n \"verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\"\",\n \"currentPath) # if actual is a list then find out\",\n \"latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return\",\n \"actual files and directories in the bundle @param expectedElement the\",\n \"definition, is defined to be the one with the highest\",\n \"contents of the directory can be anything then do no\",\n \"if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo):\",\n \"bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded\",\n \"os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function\",\n \"= tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip\",\n \"the bundle ZIP is located @param nameOfBundle name of the\",\n \"is a dict then verify it and its children if\",\n \"# if the contents of the directory can be anything\",\n \"then log the error msg and return false if expectedElement['fail-if-not-found']\",\n \"an expected element. Helper method to verifyBundleStructure. @param actual list\",\n \"zip file to be tested @return True if the ZIP\",\n \"& verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if they\",\n \"return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages\",\n \"is: 1) a valid zip file 2) follows IBM defined\",\n \"ret = True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched):\",\n \"# ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function\",\n \"uploaded bundle follows IBM defined structure @param expected the JSON\",\n \"@param to_dir the destination directory to extract the zip file\",\n \"file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close()\",\n \"file @param jsonFile path to the JSON file @return the\",\n \"a matching file or directory was found if expected['type'] ==\",\n \"common functions shared by each individual build scripts. \\\"\\\"\\\" def\",\n \"timestamp (YYYYMMDD-HHmm). @param registry_path docker registry path @param usr the\",\n \"d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] =\",\n \"shutil import requests import datetime import re import operator import\",\n \"the bundle @param expectedElement the expected element to find in\",\n \"is figured out and we specify it in REQUESTS_CA_BUNDLE return\",\n \"have the same name and must both be the same\",\n \"\\\"\\\"\\\" isMatched = True if type(expected) is dict: if matches(expected,actual):\",\n \"occurred while verifying bundle structure. Could not proceed with deployment.\\\")\",\n \"< dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag return latest_tag\",\n \"zip files @param zip_file the zip file to be extracted\",\n \"if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg and return\",\n \"True # if the contents of the directory can be\",\n \"client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify\",\n \"we are on that has the missing file or directory\",\n \"for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i\",\n \"bundle @param expectedElement the expected element to find in the\",\n \"are on that has the missing file or directory \\\"\\\"\\\"\",\n \"= tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError:\",\n \"if they match anywhere, if so get the matched position\",\n \"tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function\",\n \"dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to\",\n \"the parent directory for the Dockerfile :param str docker_url: the\",\n \"@param filePath the zip file to be tested @return True\",\n \"deployment.\\\") # out put report value , join all the\",\n \"Function to test if a ZIP file is good or\",\n \"matchedPosition = i break if matchedPosition != -1: # if\",\n \"to the JSON file @return the loaded JSON file \\\"\\\"\\\"\",\n \"'-' not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-')\",\n \"(only used for recursive calls) @return True if the list\",\n \"tag_list['tags']: if '-' not in tag: continue str_version, str_dash, str_timestamp\",\n \"to verify if an uploaded bundle follows IBM defined structure\",\n \"recursively for error msg) @return True if structure of the\",\n \"name and must both be the same type. @param expectedElement\",\n \"& verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking else: #\",\n \"the current path we are on that has the missing\",\n \"otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict: if\",\n \"bundle is: 1) a valid zip file 2) follows IBM\",\n \"@param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global\",\n \"bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined structure. Could\",\n \"in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched)\",\n \"\\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched = isMatched & True\",\n \"the Dockerfile :param str docker_url: the docker registry VIP accessible\",\n \"# the children in a directory is a list composed\",\n \"path we are on that has the missing file or\",\n \"'/tags/list' request = get(tag_list_url, usr, pwd) tag_list = json.loads(request.text) for\",\n \"= True return ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\"\",\n \"out if they match anywhere, if so get the matched\",\n \"is being run on :param str dockerfile_parent_dir: path to the\",\n \"headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest\",\n \"of the IBM defined structure @param actual the JSON representation\",\n \"else: ret = True except: ret = False return ret\",\n \"each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\"\",\n \"@return a JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json',\",\n \"except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could not proceed\",\n \"open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON\",\n \"proceed with deployment.\\\") # clean up unzipped stuff and package\",\n \"the JSON file @return the loaded JSON file \\\"\\\"\\\" try:\",\n \"be anything then do no further checking else: isMatched =\",\n \"= [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\"\",\n \"tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp\",\n \"logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add\",\n \"Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure.\",\n \"zipDirectory where the bundle ZIP is located @param nameOfBundle name\",\n \"is missing from the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\")\",\n \"name of the image :param str image_name: the name of\",\n \"track of all error msgs errorMessages = [] \\\"\\\"\\\" Collection\",\n \"@param actualElement the actual element \\\"\\\"\\\" ret = False if\",\n \"message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def\",\n \"usr the functional username for the docker registry @param pwd\",\n \"\\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function\",\n \"structure @param expected the JSON representation of the IBM defined\",\n \"if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does not meet predefined\",\n \"list of objects contains an expected element. Helper method to\",\n \"Could not proceed with deployment.\\\") # clean up unzipped stuff\",\n \"list: matchedPosition = -1 for i in range(0, len(actual)): if\",\n \"continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp =\",\n \"image_name + \\\":\\\" + image_tag_latest # Write the FROM command\",\n \"verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not bundleIsGood: addToErrorMessages(\\\"The uploaded bundle does\",\n \"actual list of the actual files and directories in the\",\n \"import json import argparse import zipfile import shutil import requests\",\n \"@param expected the JSON representation of the IBM defined structure\",\n \"JSON file is not valid: \\\" + jsonFile) raise return\",\n \"= dt_timestamp latest_tag = tag return latest_tag def unzip(zip_file, to_dir):\",\n \"timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\"\",\n \"bundle structure. Could not proceed with deployment.\\\") bundleIsGood = False\",\n \":param str image_namespace: the name of the image :param str\",\n \"import unicodedata # global list of error messages to keep\",\n \"verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list then find\",\n \"suppressed verification once the docker cert location # is figured\",\n \"not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle\",\n \"convert objects in a given directory into JSON form. The\",\n \"Collection of Common Functions used by Build Scripts A collection\",\n \"based on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is\",\n \"and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while\",\n \"actual, currentPath): \\\"\\\"\\\" Function to verify if an uploaded bundle\",\n \"# out put report value , join all the messages\",\n \"of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory,\",\n \"in a given directory into JSON form. The parent object\",\n \"latest version tag of the base image :returns: None \\\"\\\"\\\"\",\n \"structure. Could not proceed with deployment.\\\") bundleIsGood = False else:\",\n \"docker registry functional user @return a JSON response \\\"\\\"\\\" headers\",\n \"unzipped. Could not proceed with deployment.\\\") # out put report\",\n \"messages to the global list of errorMessages @param errorMessage the\",\n \"requests using external Python module requests @param url the url\",\n \"to open a JSON file @param jsonFile path to the\",\n \"os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile\",\n \"\\\"\\\"\\\" Function to open a JSON file @param jsonFile path\",\n \"Scripts A collection of common functions shared by each individual\",\n \"zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\"\",\n \"image :returns: None \\\"\\\"\\\" # Form the path for the\",\n \"argparse import zipfile import shutil import requests import datetime import\",\n \"def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories match.\",\n \"proceed with deployment.\\\") bundleIsGood = False else: bundleIsGood = False\",\n \"list of actual objects contain the expected element \\\"\\\"\\\" #\",\n \"the destination directory to extract the zip file to \\\"\\\"\\\"\",\n \"+ \\\":\\\" + image_tag_latest # Write the FROM command string\",\n \"---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle\",\n \"out put report value , join all the messages together\",\n \"the REST call @param usr the functional username for the\",\n \"do no further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'],\",\n \"pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the\",\n \"# directory may have children # the children in a\",\n \"used by Build Scripts A collection of common functions shared\",\n \"the parent object is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\"\",\n \"files/directories match. They must have the same name and must\",\n \"d['type'] = \\\"directory\\\" # directory may have children # the\",\n \"then do no further checking else: isMatched = isMatched &\",\n \"type=directory. A directory is composed of a list and may\",\n \"FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace\",\n \"in the bundle @param currentPath the path currently being checked\",\n \"ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to verify if an\",\n \"\\\"\\\"\\\" Retrieve the latest version of an image based on\",\n \"if they match then verify their children too isMatched =\",\n \":param str dockerfile_parent_dir: path to the parent directory for the\",\n \"seconds\\\") # ======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\"\",\n \"return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for\",\n \"registry VIP accessible from the mesos slaves :param str image_namespace:\",\n \"contain the expected element \\\"\\\"\\\" # if actual is a\",\n \"destination directory to extract the zip file to \\\"\\\"\\\" #\",\n \"= directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file\",\n \"the name of the image :param str image_tag_latest: the latest\",\n \"structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle\",\n \"of actual objects contain the expected element \\\"\\\"\\\" # if\",\n \"= the_zip_file.testzip() if badFile is not None: ret = False\",\n \"data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not\",\n \"'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification once the\",\n \"get the matched position elif type(actual) is list: matchedPosition =\",\n \"file or directory was not found if expected['fail-if-not-found'] == \\\"yes\\\":\",\n \"IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if\",\n \"Functions used by Build Scripts A collection of common functions\",\n \"# copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo =\",\n \"file recursively @param zipFileName the zip file to be extracted\",\n \"uploaded bundle @param currentPath the path currently being checked (used\",\n \"JSON file @param jsonFile path to the JSON file @return\",\n \"is a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x))\",\n \"with the highest version number (vX) and the latest timestamp\",\n \"with the datacenter this script is being run on :param\",\n \"return d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify\",\n \"False else: ret = True except: ret = False return\",\n \"except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\"\",\n \"files and/or directories. @param directory directory to convert @return JSON\",\n \"directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON file bundleIsGood\",\n \"actual is a list then find out if they match\",\n \"zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace,\",\n \"Could not proceed with deployment.\\\") # out put report value\",\n \"current path we are on that has the missing file\",\n \"path to the JSON file @return the loaded JSON file\",\n \"JSON file @return the loaded JSON file \\\"\\\"\\\" try: with\",\n \"verifying bundle structure. Could not proceed with deployment.\\\") bundleIsGood =\",\n \"one with the highest version number (vX) and the latest\",\n \"report value , join all the messages together print (\\\"report=[\\\"\",\n \"the datacenter this script is being run on :param str\",\n \"# Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\"\",\n \"A directory is composed of a list and may contain\",\n \"addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if\",\n \"by each individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd):\",\n \"array. @param expected the expected element @param currentPath the current\",\n \"@param actual list of the actual files and directories in\",\n \"further checking else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath)\",\n \"file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood = True bundleZip\",\n \"tag else: if latest_version > str_version: continue elif latest_version <\",\n \"in the bundle @param expectedElement the expected element to find\",\n \"destination directory to extract the zip file to \\\"\\\"\\\" with\",\n \"objects contain the expected element \\\"\\\"\\\" # if actual is\",\n \"expected the expected element @param currentPath the current path we\",\n \"======================= verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to\",\n \"{ 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove the suppressed verification\",\n \"match anywhere, if so get the matched position elif type(actual)\",\n \"1) a valid zip file 2) follows IBM defined structure\",\n \"is dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may\",\n \"found if expected['type'] == 'directory': currentPath = currentPath + actual['name']\",\n \"docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # =======================\",\n \"\\\"\\\"\\\" Function to verify if an uploaded bundle follows IBM\",\n \"is dict: if matches(expected,actual): # a matching file or directory\",\n \"os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return\",\n \"\\\"r\\\") as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name,\",\n \"= isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : #\",\n \"anywhere, if so get the matched position elif type(actual) is\",\n \"# unzip the bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) #\",\n \"@param currentPath the current path we are on that has\",\n \"JSON form. The parent object is always a dict, it\",\n \"may have children # the children in a directory is\",\n \"and directories in the bundle @param expectedElement the expected element\",\n \"\\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile =\",\n \"of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} # the\",\n \"value , join all the messages together print (\\\"report=[\\\" +\",\n \"badFile = the_zip_file.testzip() if badFile is not None: ret =\",\n \"Function to open a JSON file @param jsonFile path to\",\n \"file or directory was found if expected['type'] == 'directory': currentPath\",\n \"unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively\",\n \"if actual is a list then find out if they\",\n \"directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a ZIP file recursively @param\",\n \"directory to convert @return JSON representation of a directory \\\"\\\"\\\"\",\n \"of errorMessages @param errorMessage the error message to add \\\"\\\"\\\"\",\n \"as zip_ref: zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest):\",\n \"the name of the image :param str image_name: the name\",\n \"Creates a dockerfile using the correct docker registry URL associated\",\n \"regular expression @param actualElement the actual element \\\"\\\"\\\" ret =\",\n \"[] \\\"\\\"\\\" Collection of Common Functions used by Build Scripts\",\n \"defined by regular expression @param actualElement the actual element \\\"\\\"\\\"\",\n \"continue elif latest_version < str_version: latest_version = str_version latest_timestamp =\",\n \"specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False)\",\n \"True if type(expected) is dict: if matches(expected,actual): # a matching\",\n \"\\\"/\\\" + image_name + \\\":\\\" + image_tag_latest # Write the\",\n \"def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version of\",\n \"the highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm).\",\n \"if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the\",\n \"to find in the bundle @param currentPath the path currently\",\n \"currentPath): \\\"\\\"\\\" Function to adds error messages to the global\",\n \"TEMP: Remove the suppressed verification once the docker cert location\",\n \"@return True if structure of the uploaded bundle follows IBM\",\n \"try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is\",\n \"os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check package\",\n \"is always a dict, it may contain children if type=directory.\",\n \"subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath):\",\n \"slaves :param str image_namespace: the name of the image :param\",\n \"not None and expectedElement['type'] == actualElement['type']: ret = True return\",\n \"+ image_name + \\\":\\\" + image_tag_latest # Write the FROM\",\n \"from the mesos slaves :param str image_namespace: the name of\",\n \"except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError: latest_version\",\n \"command dockerfile_from_cmd = \\\"FROM \\\" + docker_url + image_namespace +\",\n \"associated with the datacenter this script is being run on\",\n \"an image based on its tags: vX-YYYYMMDD-HHmm. The latest, by\",\n \"verify if an uploaded bundle follows IBM defined structure @param\",\n \"otherwise. \\\"\\\"\\\" ret = True try: the_zip_file = zipfile.ZipFile(filePath) badFile\",\n \"and must both be the same type. @param expectedElement the\",\n \"build paths recursively for error msg) @param isMatched (only used\",\n \"to convert @return JSON representation of a directory \\\"\\\"\\\" d\",\n \"currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error\",\n \"tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-' not\",\n \"actual list of objects contains an expected element. Helper method\",\n \"the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\")\",\n \"+ jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to\",\n \"working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo):\",\n \"+ \\\". \\\".join(str(x) for x in errorMessages) + \\\"]\\\") return\",\n \"# log error msg and return false if needed isMatched\",\n \"does not meet predefined structure. Could not proceed with deployment.\\\")\",\n \"def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to add error messages to the\",\n \"False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is dict:\",\n \"Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] =\",\n \"= False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could\",\n \"the caller script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the\",\n \"usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python module\",\n \"dockerfile_parent_dir: path to the parent directory for the Dockerfile :param\",\n \"True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile\",\n \"is not valid: \\\" + jsonFile) raise return data def\",\n \"if matchedPosition != -1: # if they match then verify\",\n \"= False else: ret = True except: ret = False\",\n \"The parent object is always a dict, it may contain\",\n \"that has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+\",\n \"{'name': os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory):\",\n \"isMatched) return isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds\",\n \"file to be extracted @param directoryToUnzipTo the destination directory to\",\n \"for error msg) @param isMatched (only used for recursive calls)\",\n \"the DOCKER_CLIENT_TIMEOUT environment variable to 300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300'\",\n \"range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if matchedPosition\",\n \"msg) @param isMatched (only used for recursive calls) @return True\",\n \"put report value , join all the messages together print\",\n \"element. May be defined by regular expression @param actualElement the\",\n \"dockerfile.write(dockerfile_from_cmd) # Close the open file instance dockerfile.close() def set_docker_client_timeout():\",\n \"None and expectedElement['type'] == actualElement['type']: ret = True return ret\",\n \"Retrieve the latest version of an image based on its\",\n \"'300' print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT']\",\n \"file to be extracted @param to_dir the destination directory to\",\n \"messages to the global array. @param expected the expected element\",\n \"dict, it may contain children if type=directory. A directory is\",\n \"tested @return True if the ZIP file is good. False\",\n \"file is good or bad @param filePath the zip file\",\n \"url of the REST call @param usr the functional username\",\n \"import argparse import zipfile import shutil import requests import datetime\",\n \"zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a\",\n \"log error msg and return false if needed isMatched =\",\n \"except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\" +\",\n \"the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage])\",\n \"tag in tag_list['tags']: if '-' not in tag: continue str_version,\",\n \"this script is being run on :param str dockerfile_parent_dir: path\",\n \"def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param\",\n \"file is good. False otherwise. \\\"\\\"\\\" ret = True try:\",\n \"file is not valid: \\\" + jsonFile) raise return data\",\n \"matches(expected,actual): # a matching file or directory was found if\",\n \"directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo,\",\n \"always a dict, it may contain children if type=directory. A\",\n \"str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version\",\n \"else: isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do\",\n \"subprocess import json import argparse import zipfile import shutil import\",\n \"checked (used to build paths recursively for error msg) @return\",\n \"zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None: ret\",\n \"tag_format) except ValueError: continue try: latest_version latest_timestamp latest_tag except NameError:\",\n \"path for the Dockerfile based on the parent of the\",\n \"the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure,\",\n \"children if type=directory. A directory is composed of a list\",\n \"name of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle\",\n \"bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\")\",\n \"import requests import datetime import re import operator import unicodedata\",\n \"error messages to keep track of all error msgs errorMessages\",\n \"= \\\"FROM \\\" + docker_url + image_namespace + \\\"/\\\" +\",\n \"try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The\",\n \"# global list of error messages to keep track of\",\n \"is good. False otherwise. \\\"\\\"\\\" ret = True try: the_zip_file\",\n \"list of errorMessages @param errorMessage the error message to add\",\n \"# TEMP: Remove the suppressed verification once the docker cert\",\n \"latest_version latest_timestamp latest_tag except NameError: latest_version = str_version latest_timestamp =\",\n \"a matching file or directory was not found if expected['fail-if-not-found']\",\n \"list then find out if they match anywhere, if so\",\n \"to verify if an uploaded bundle is: 1) a valid\",\n \"os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into new\",\n \"is list: matchedPosition = -1 for i in range(0, len(actual)):\",\n \"currentPath) return False if type(expected) is list: for k in\",\n \"figured out and we specify it in REQUESTS_CA_BUNDLE return requests.get(url,\",\n \"return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd):\",\n \"str image_name: the name of the image :param str image_tag_latest:\",\n \"for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k],\",\n \"d def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if\",\n \"import re import operator import unicodedata # global list of\",\n \"latest version of an image based on its tags: vX-YYYYMMDD-HHmm.\",\n \"error msg and return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": #\",\n \"if files/directories match. They must have the same name and\",\n \"os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have children #\",\n \"composed of a list and may contain files and/or directories.\",\n \"match then log the error msg and return false if\",\n \"zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if a ZIP file is\",\n \"REST call @param usr the functional username for the docker\",\n \"+ \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to\",\n \"else: bundleIsGood = False addToErrorMessages(\\\"The uploaded bundle could not be\",\n \"= -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition\",\n \"bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a\",\n \"stuff and package structure Json ------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred\",\n \"then verify it and its children if type(actual) is dict:\",\n \"@param zip_file the zip file to be extracted @param to_dir\",\n \"is located @param nameOfBundle name of the bundle ZIP file\",\n \"it may contain children if type=directory. A directory is composed\",\n \"\\\"\\\"\\\" Function to unzip a ZIP file recursively @param zipFileName\",\n \"if they don't match then log the error msg and\",\n \"instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable\",\n \"\\\". \\\".join(str(x) for x in errorMessages) + \\\"]\\\") return bundleIsGood\",\n \"Function to check if files/directories match. They must have the\",\n \"if structure of the uploaded bundle follows IBM defined structure.\",\n \"contain children if type=directory. A directory is composed of a\",\n \"try: # copy bundle into new working directory ----------------------------------------------------------- directoryToUnzipTo\",\n \"= True try: the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if\",\n \"of all error msgs errorMessages = [] \\\"\\\"\\\" Collection of\",\n \"functional user @return the latest image tag \\\"\\\"\\\" tag_list_url =\",\n \"unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------\",\n \"the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the\",\n \"structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected) is\",\n \"version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param registry_path\",\n \"Python module requests @param url the url of the REST\",\n \"may contain files and/or directories. @param directory directory to convert\",\n \"import subprocess import json import argparse import zipfile import shutil\",\n \"\\\"\\\"\\\" # if actual is a dict then verify it\",\n \"[directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return\",\n \"subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile,\",\n \"# Form the path for the Dockerfile based on the\",\n \"create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using\",\n \"has the missing file or directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type']\",\n \"ZIP is located @param nameOfBundle name of the bundle ZIP\",\n \"currentPath) # do further checking else: # a matching file\",\n \"@param actual the JSON representation of the actual structure of\",\n \"ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and\",\n \"an uploaded bundle is: 1) a valid zip file 2)\",\n \"if actual is a dict then verify it and its\",\n \"find in the bundle @param currentPath the path currently being\",\n \"the expected element \\\"\\\"\\\" # if actual is a dict\",\n \"the latest version of an image based on its tags:\",\n \"verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\" Retrieve the latest version\",\n \"their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] ,\",\n \"str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp,\",\n \"scripts/common_lib/build_lib.py #!/usr/bin/env python3.6 import os import subprocess import json import\",\n \"with open(jsonFile) as json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified\",\n \"str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag\",\n \"isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else :\",\n \"expected['children'] == \\\"_any\\\": isMatched = isMatched & True # if\",\n \"representation of the IBM defined structure @param actual the JSON\",\n \"expected element to find in the bundle @param currentPath the\",\n \"defined to be the one with the highest version number\",\n \"verifyBundleStructure. @param actual list of the actual files and directories\",\n \"False logBundleStructureErrorMessage(expectedElement, currentPath) return isMatched def addToErrorMessages(errorMessage): \\\"\\\"\\\" Function to\",\n \"True except: ret = False return ret def verifyZipFile(zipDirectory, nameOfBundle):\",\n \"str_version latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp\",\n \"False if type(expected) is list: for k in range(0,len(expected)): isMatched\",\n \"valid zip file 2) follows IBM defined structure @param zipDirectory\",\n \"\\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP: Remove\",\n \"= os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory)\",\n \"verify if an actual list of objects contains an expected\",\n \"requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path, usr, pwd): \\\"\\\"\\\"\",\n \"highest version number (vX) and the latest timestamp (YYYYMMDD-HHmm). @param\",\n \"docker cert location # is figured out and we specify\",\n \"deployment.\\\") # clean up unzipped stuff and package structure Json\",\n \"not proceed with deployment.\\\") # out put report value ,\",\n \"extract the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"):\",\n \"os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip the bundle ----------------------------------------------------------------------------------\",\n \"image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct\",\n \"== \\\"_any\\\": isMatched = isMatched & True # if the\",\n \"k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath,\",\n \"by definition, is defined to be the one with the\",\n \"objects in a given directory into JSON form. The parent\",\n \"Close the open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets\",\n \"verify bundle Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open\",\n \"print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages) +\",\n \"and expectedElement['type'] == actualElement['type']: ret = True return ret def\",\n \"the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as zip_ref:\",\n \"json.loads(request.text) for tag in tag_list['tags']: if '-' not in tag:\",\n \"actual the JSON representation of the actual structure of the\",\n \":param str image_name: the name of the image :param str\",\n \"to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close the open file instance\",\n \"the docker registry VIP accessible from the mesos slaves :param\",\n \"the suppressed verification once the docker cert location # is\",\n \"ret = False else: ret = True except: ret =\",\n \":returns: None \\\"\\\"\\\" # Form the path for the Dockerfile\",\n \"the ZIP file is good. False otherwise. \\\"\\\"\\\" ret =\",\n \"os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure ===============================================\",\n \"False if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] ==\",\n \"# update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo)\",\n \"paths recursively for error msg) @param isMatched (only used for\",\n \"usr, pwd): \\\"\\\"\\\" Retrieve the latest version of an image\",\n \"composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in\",\n \"= zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not None:\",\n \"image_name, image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker\",\n \"build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET\",\n \"the zip file to be extracted @param to_dir the destination\",\n \"correct docker registry URL associated with the datacenter this script\",\n \"of objects contains an expected element. Helper method to verifyBundleStructure.\",\n \"tag of the base image :returns: None \\\"\\\"\\\" # Form\",\n \"def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT environment variable to 300\",\n \"they match then verify their children too isMatched = isMatched\",\n \"else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag =\",\n \"Function to unzip a ZIP file recursively @param zipFileName the\",\n \"a ZIP file is good or bad @param filePath the\",\n \"matchedPosition = -1 for i in range(0, len(actual)): if matches(expectedElement,actual[i]):\",\n \"mesos slaves :param str image_namespace: the name of the image\",\n \"response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } # TEMP:\",\n \"else: # a matching file or directory was not found\",\n \"currentPath) else : # if they don't match then log\",\n \"JSON response \\\"\\\"\\\" headers = { 'Accept': 'application/vnd.docker.distribution.manifest.v1+json', } #\",\n \"nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ #\",\n \"the mesos slaves :param str image_namespace: the name of the\",\n \"!= -1: # if they match then verify their children\",\n \"the docker registry @param pwd the password for the docker\",\n \"isMatched def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages\",\n \"def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external\",\n \"x in os.listdir(directory)] else: d['type'] = \\\"file\\\" return d def\",\n \"file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's\",\n \"#check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in\",\n \"latest_timestamp = dt_timestamp latest_tag = tag else: if latest_timestamp <\",\n \"do further checking else: # a matching file or directory\",\n \"+ image_tag_latest # Write the FROM command string to the\",\n \"@param directory directory to convert @return JSON representation of a\",\n \"of the uploaded bundle @param currentPath the path currently being\",\n \"defined structure @param expected the JSON representation of the IBM\",\n \"dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp: latest_timestamp\",\n \"for tag in tag_list['tags']: if '-' not in tag: continue\",\n \"a list composed of more files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for\",\n \"A collection of common functions shared by each individual build\",\n \"the docker registry functional user @return the latest image tag\",\n \"Function to convert objects in a given directory into JSON\",\n \"Common Functions used by Build Scripts A collection of common\",\n \"not None: ret = False else: ret = True except:\",\n \"tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format) except ValueError: continue try:\",\n \"parent object is always a dict, it may contain children\",\n \"is good or bad @param filePath the zip file to\",\n \"x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo,\",\n \"package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) #\",\n \"form. The parent object is always a dict, it may\",\n \"uploaded bundle follows IBM defined structure. False otherwise. \\\"\\\"\\\" isMatched\",\n \"the_zip_file = zipfile.ZipFile(filePath) badFile = the_zip_file.testzip() if badFile is not\",\n \"in range(0, len(actual)): if matches(expectedElement,actual[i]): matchedPosition = i break if\",\n \"(\\\"report=[\\\" + \\\". \\\".join(str(x) for x in errorMessages) + \\\"]\\\")\",\n \"must both be the same type. @param expectedElement the expected\",\n \"proceed with deployment.\\\") # out put report value , join\",\n \"image_tag_latest # Write the FROM command string to the Dockerfile\",\n \"pwd) tag_list = json.loads(request.text) for tag in tag_list['tags']: if '-'\",\n \"latest_timestamp = dt_timestamp latest_tag = tag else: if latest_version >\",\n \"if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected)\",\n \"the FROM command string to the Dockerfile dockerfile.write(dockerfile_from_cmd) # Close\",\n \"expression @param actualElement the actual element \\\"\\\"\\\" ret = False\",\n \"update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName)\",\n \"directory directory to convert @return JSON representation of a directory\",\n \"the actual element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name'])\",\n \"file 2) follows IBM defined structure @param zipDirectory where the\",\n \"JSON representation of the IBM defined structure @param actual the\",\n \"isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a list\",\n \"# if actual is a list then find out if\",\n \"the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x\",\n \"requests @param url the url of the REST call @param\",\n \"break if matchedPosition != -1: # if they match then\",\n \"base image :returns: None \\\"\\\"\\\" # Form the path for\",\n \"global list of error messages to keep track of all\",\n \"pwd): \\\"\\\"\\\" Retrieve the latest version of an image based\",\n \"must have the same name and must both be the\",\n \"\\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False if type(expected) is list: for\",\n \"the url of the REST call @param usr the functional\",\n \"then verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement,\",\n \"to keep track of all error msgs errorMessages = []\",\n \"def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify if\",\n \"actual['children'], currentPath) # do further checking else: # a matching\",\n \": # if they don't match then log the error\",\n \"= True bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: #\",\n \"a dict then verify it and its children if type(actual)\",\n \"zip file to be extracted @param directoryToUnzipTo the destination directory\",\n \"script dockerfile_path = os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile\",\n \"open(dockerfile_path, \\\"w+\\\") # Format the FROM command dockerfile_from_cmd = \\\"FROM\",\n \"docker_url + image_namespace + \\\"/\\\" + image_name + \\\":\\\" +\",\n \"Structure =============================================== def openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON\",\n \"dict if os.path.isdir(directory): d['type'] = \\\"directory\\\" # directory may have\",\n \"unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\")\",\n \"actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory to JSON\",\n \"os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x ) unzipRecursively(subfile, subdirectory) return def\",\n \"operator import unicodedata # global list of error messages to\",\n \"msg and return false if needed isMatched = False logBundleStructureErrorMessage(expectedElement,\",\n \"the password for the docker registry functional user @return the\",\n \"errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip a\",\n \"& True # if the contents of the directory can\",\n \"an actual list of objects contains an expected element. Helper\",\n \"messages to keep track of all error msgs errorMessages =\",\n \"def verifyBundleStructure(expected, actual, currentPath): \\\"\\\"\\\" Function to verify if an\",\n \"300 \\\"\\\"\\\" os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker\",\n \"docker registry URL associated with the datacenter this script is\",\n \"+ \\\" seconds\\\") # ======================= verify bundle Structure =============================================== def\",\n \"into JSON form. The parent object is always a dict,\",\n \"@param expectedElement the expected element. May be defined by regular\",\n \"of an image based on its tags: vX-YYYYMMDD-HHmm. The latest,\",\n \"if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag = tag\",\n \"re import operator import unicodedata # global list of error\",\n \"extracted @param to_dir the destination directory to extract the zip\",\n \"of common functions shared by each individual build scripts. \\\"\\\"\\\"\",\n \"+\\\" is missing from the path: \\\\\\\"\\\" + currentPath +\",\n \"open file instance dockerfile.close() def set_docker_client_timeout(): \\\"\\\"\\\" Sets the DOCKER_CLIENT_TIMEOUT\",\n \"isMatched & True # if the contents of the directory\",\n \"@param expectedElement the expected element to find in the bundle\",\n \"the global array. @param expected the expected element @param currentPath\",\n \", currentPath) else : # if they don't match then\",\n \"the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...') bundleIsGood\",\n \"directories. @param directory directory to convert @return JSON representation of\",\n \"the correct docker registry URL associated with the datacenter this\",\n \"follows IBM defined structure @param expected the JSON representation of\",\n \"Function to verify if an uploaded bundle follows IBM defined\",\n \"if type=directory. A directory is composed of a list and\",\n \"json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid: \\\"\",\n \"= False return ret def verifyZipFile(zipDirectory, nameOfBundle): \\\"\\\"\\\" Function to\",\n \"May be defined by regular expression @param actualElement the actual\",\n \"if badFile is not None: ret = False else: ret\",\n \"expected element \\\"\\\"\\\" # if actual is a dict then\",\n \"latest_tag = tag else: if latest_version > str_version: continue elif\",\n \"by regular expression @param actualElement the actual element \\\"\\\"\\\" ret\",\n \"\\\"\\\"\\\" Function to verify if an uploaded bundle is: 1)\",\n \"the JSON representation of the IBM defined structure @param actual\",\n \"os.path.basename(directory)} # the parent object is dict if os.path.isdir(directory): d['type']\",\n \"to be the one with the highest version number (vX)\",\n \"type. @param expectedElement the expected element. May be defined by\",\n \"= dt_timestamp latest_tag = tag else: if latest_version > str_version:\",\n \"image_tag_latest): \\\"\\\"\\\" Creates a dockerfile using the correct docker registry\",\n \"expectedElement['type'] == actualElement['type']: ret = True return ret def verifyActualContainsExpectedElement(actual,\",\n \"if re.fullmatch(expectedElement['name'], actualElement['name']) is not None and expectedElement['type'] == actualElement['type']:\",\n \"is not None: ret = False else: ret = True\",\n \"str image_namespace: the name of the image :param str image_name:\",\n \"if so get the matched position elif type(actual) is list:\",\n \"version tag of the base image :returns: None \\\"\\\"\\\" #\",\n \"test if a ZIP file is good or bad @param\",\n \"file to be tested @return True if the ZIP file\",\n \":param str image_tag_latest: the latest version tag of the base\",\n \"= os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle into\",\n \"it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory\",\n \"return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in\",\n \"\\\"\\\"\\\" try: with open(jsonFile) as json_data_file: data = json.load(json_data_file) except:\",\n \"object is always a dict, it may contain children if\",\n \"individual build scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS\",\n \"the children in a directory is a list composed of\",\n \"zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x\",\n \"of the bundle ZIP file \\\"\\\"\\\" print ('Validating bundle structure...')\",\n \"type(expected) is dict: if matches(expected,actual): # a matching file or\",\n \"\\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a .zip\",\n \"name of the image :param str image_tag_latest: the latest version\",\n \"given directory into JSON form. The parent object is always\",\n \"\\\"\\\"\\\" Function to verify if an actual list of objects\",\n \"if type(expected) is dict: if matches(expected,actual): # a matching file\",\n \"if zipFileIsGood(bundleZip): try: # copy bundle into new working directory\",\n \"docker_url: the docker registry VIP accessible from the mesos slaves\",\n \"docker registry functional user @return the latest image tag \\\"\\\"\\\"\",\n \"to build paths recursively for error msg) @param isMatched (only\",\n \"errorMessages @param errorMessage the error message to add \\\"\\\"\\\" print(errorMessage)\",\n \"= True except: ret = False return ret def verifyZipFile(zipDirectory,\",\n \"isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath) else : # if\",\n \"recursively for error msg) @param isMatched (only used for recursive\",\n \"of the uploaded bundle follows IBM defined structure. False otherwise.\",\n \"structure of bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure =\",\n \"actual objects contain the expected element \\\"\\\"\\\" # if actual\",\n \"messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for x in\",\n \"latest_tag except NameError: latest_version = str_version latest_timestamp = dt_timestamp latest_tag\",\n \"matching file or directory was found if expected['type'] == 'directory':\",\n \"'directory': currentPath = currentPath + actual['name'] + \\\"/\\\" if expected['children']\",\n \"deployment.\\\") bundleIsGood = False else: bundleIsGood = False addToErrorMessages(\\\"The uploaded\",\n \"the zip file to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check\",\n \"ret = True except: ret = False return ret def\",\n \"uploaded bundle is: 1) a valid zip file 2) follows\",\n \"get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using external Python\",\n \"not found if expected['fail-if-not-found'] == \\\"yes\\\": logBundleStructureErrorMessage(expected, currentPath) return False\",\n \"latest_version = str_version latest_timestamp = dt_timestamp latest_tag = tag else:\",\n \"json_data_file: data = json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is\",\n \"bundle ---------------------------------------------------------------------------------- unzipRecursively(os.path.join(directoryToUnzipTo, nameOfBundle), os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of\",\n \"function for extracting zip files @param zip_file the zip file\",\n \"type(actual) is dict: isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) #\",\n \"is not None and expectedElement['type'] == actualElement['type']: ret = True\",\n \"to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure, actualBundleStructure, \\\"\\\") if not\",\n \"\\\"yes\\\": # log error msg and return false if needed\",\n \"os.environ['DOCKER_CLIENT_TIMEOUT'] = '300' print(\\\"The timeout set for docker client: \\\"\",\n \"directory to extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file,\",\n \"be tested @return True if the ZIP file is good.\",\n \"and we specify it in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd),\",\n \"ret def verifyActualContainsExpectedElement(actual, expectedElement, currentPath, isMatched): \\\"\\\"\\\" Function to verify\",\n \"directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a given directory\",\n \"def logBundleStructureErrorMessage(expected, currentPath): \\\"\\\"\\\" Function to adds error messages to\",\n \") unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test\",\n \"Remove the suppressed verification once the docker cert location #\",\n \"user @return a JSON response \\\"\\\"\\\" headers = { 'Accept':\",\n \"meet predefined structure. Could not proceed with deployment.\\\") # clean\",\n \"str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\" try: dt_timestamp = datetime.datetime.strptime(str_timestamp, tag_format)\",\n \"print(\\\"The timeout set for docker client: \\\" + os.environ['DOCKER_CLIENT_TIMEOUT'] +\",\n \"specified JSON file is not valid: \\\" + jsonFile) raise\",\n \"openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\")) actualBundleStructure = directoryToJSON(directoryToUnzipTo) # convert the unzipped directory\",\n \"in REQUESTS_CA_BUNDLE return requests.get(url, auth=(usr, pwd), headers=headers, verify=False) def get_latest_tag(registry_path,\",\n \"registry URL associated with the datacenter this script is being\",\n \"convert @return JSON representation of a directory \\\"\\\"\\\" d =\",\n \"return false if expectedElement['fail-if-not-found'] == \\\"yes\\\": # log error msg\",\n \"raise return data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects\",\n \"elif type(actual) is list: matchedPosition = -1 for i in\",\n \"> str_version: continue elif latest_version < str_version: latest_version = str_version\",\n \"= currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\":\",\n \"data def directoryToJSON(directory): \\\"\\\"\\\" Function to convert objects in a\",\n \"os.path.join(dockerfile_parent_dir, \\\"Dockerfile\\\") # Create the Dockerfile dockerfile = open(dockerfile_path, \\\"w+\\\")\",\n \"= i break if matchedPosition != -1: # if they\",\n \"dockerfile using the correct docker registry URL associated with the\",\n \"isMatched = True if type(expected) is dict: if matches(expected,actual): #\",\n \"to verify if an actual list of objects contains an\",\n \"bundle ------------------------------------------------------------------------ # check package stucture expectedPackageStructure = openJSONfile(os.path.join(zipDirectory, \\\"bundle-definition.json\\\"))\",\n \"of the actual structure of the uploaded bundle @param currentPath\",\n \"the list of actual objects contain the expected element \\\"\\\"\\\"\",\n \"children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition] , currentPath)\",\n \"zip_ref.extractall(to_dir) zip_ref.close() def create_dockerfile(dockerfile_parent_dir, docker_url, image_namespace, image_name, image_tag_latest): \\\"\\\"\\\" Creates\",\n \"valid: \\\" + jsonFile) raise return data def directoryToJSON(directory): \\\"\\\"\\\"\",\n \"not in tag: continue str_version, str_dash, str_timestamp = tag.partition('-') tag_format=\\\"%Y%m%d-%H%M\\\"\",\n \"global list of errorMessages @param errorMessage the error message to\",\n \"isMatched (only used for recursive calls) @return True if the\",\n \"files/directories d['children'] = [directoryToJSON(os.path.join(directory,x)) for x in os.listdir(directory)] else: d['type']\",\n \"bundle could not be unzipped. Could not proceed with deployment.\\\")\",\n \"\\\"_any\\\": isMatched = isMatched & True # if the contents\",\n \"to add \\\"\\\"\\\" print(errorMessage) global errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName,\",\n \"bundleZip = os.path.join(zipDirectory, nameOfBundle) if zipFileIsGood(bundleZip): try: # copy bundle\",\n \"False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could not\",\n \"all the messages together print (\\\"report=[\\\" + \\\". \\\".join(str(x) for\",\n \"+ actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched =\",\n \"representation of a directory \\\"\\\"\\\" d = {'name': os.path.basename(directory)} #\",\n \"element \\\"\\\"\\\" ret = False if re.fullmatch(expectedElement['name'], actualElement['name']) is not\",\n \"zipfile import shutil import requests import datetime import re import\",\n \"\\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check\",\n \"currentPath + actual['name'] + \\\"/\\\" if expected['children'] == \\\"_any\\\": isMatched\",\n \"isMatched = isMatched & verifyActualContainsExpectedElement(actual, expected[k], currentPath, isMatched) return isMatched\",\n \"follows IBM defined structure @param zipDirectory where the bundle ZIP\",\n \"currentPath the current path we are on that has the\",\n \"isMatched = isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is\",\n \"convert the unzipped directory to JSON file bundleIsGood = verifyBundleStructure(expectedPackageStructure,\",\n \"extract the zip file to \\\"\\\"\\\" with zipfile.ZipFile(zip_file, \\\"r\\\") as\",\n \"the path: \\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def\",\n \"\\\"temp\\\") if not os.path.exists(directoryToUnzipTo): os.makedirs(directoryToUnzipTo) shutil.copy(bundleZip, os.path.join(directoryToUnzipTo, nameOfBundle)) # unzip\",\n \"# if they match then verify their children too isMatched\",\n \"to \\\"\\\"\\\" # update if zipFileName.endswith(\\\".zip\\\"): #check if it's a\",\n \"is list: for k in range(0,len(expected)): isMatched = isMatched &\",\n \"defined structure. False otherwise. \\\"\\\"\\\" isMatched = True if type(expected)\",\n \"latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic unzip function for extracting\",\n \"@param directoryToUnzipTo the destination directory to extract the zip file\",\n \"matchedPosition != -1: # if they match then verify their\",\n \"the FROM command dockerfile_from_cmd = \\\"FROM \\\" + docker_url +\",\n \"\\\\\\\"\\\" + currentPath + \\\"\\\\\\\"\\\") addToErrorMessages(expected['error-message-if-fails']) return def matches(expectedElement, actualElement):\",\n \"currentPath, isMatched): \\\"\\\"\\\" Function to verify if an actual list\",\n \"an uploaded bundle follows IBM defined structure @param expected the\",\n \"unzipRecursively(subfile, subdirectory) return def zipFileIsGood(filePath): \\\"\\\"\\\" Function to test if\",\n \"\\\"\\\"\\\" Generic unzip function for extracting zip files @param zip_file\",\n \"requests import datetime import re import operator import unicodedata #\",\n \"verification once the docker cert location # is figured out\",\n \"# a matching file or directory was found if expected['type']\",\n \"its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined to\",\n \"str dockerfile_parent_dir: path to the parent directory for the Dockerfile\",\n \"tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp latest_tag\",\n \"= isMatched & verifyBundleStructure(expectedElement,actual, currentPath) # if actual is a\",\n \"for x in os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile =\",\n \"list: for k in range(0,len(expected)): isMatched = isMatched & verifyActualContainsExpectedElement(actual,\",\n \"errorMessges errorMessages.extend([errorMessage]) return def unzipRecursively(zipFileName, directoryToUnzipTo): \\\"\\\"\\\" Function to unzip\",\n \"verify if an uploaded bundle is: 1) a valid zip\",\n \"os.listdir(directoryToUnzipTo): subdirectory = os.path.join(directoryToUnzipTo, os.path.splitext(x)[0]) subfile = os.path.join(directoryToUnzipTo, x )\",\n \"addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the path: \\\\\\\"\\\"\",\n \"being run on :param str dockerfile_parent_dir: path to the parent\",\n \"Helper method to verifyBundleStructure. @param actual list of the actual\",\n \"the path currently being checked (used to build paths recursively\",\n \"# is figured out and we specify it in REQUESTS_CA_BUNDLE\",\n \"------------------------------------------------- shutil.rmtree(directoryToUnzipTo) except: addToErrorMessages(\\\"Exception occurred while verifying bundle structure. Could\",\n \"latest_tag = tag return latest_tag def unzip(zip_file, to_dir): \\\"\\\"\\\" Generic\",\n \"= tag else: if latest_timestamp < dt_timestamp: latest_timestamp = dt_timestamp\",\n \"scripts. \\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests\",\n \"file @return the loaded JSON file \\\"\\\"\\\" try: with open(jsonFile)\",\n \"= False addToErrorMessages(\\\"The uploaded bundle could not be unzipped. Could\",\n \"on its tags: vX-YYYYMMDD-HHmm. The latest, by definition, is defined\",\n \"defined structure @param actual the JSON representation of the actual\",\n \"if matches(expected,actual): # a matching file or directory was found\",\n \"= isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further checking\",\n \"same type. @param expectedElement the expected element. May be defined\",\n \"a JSON file @param jsonFile path to the JSON file\",\n \"representation of the actual structure of the uploaded bundle @param\",\n \"= dt_timestamp latest_tag = tag else: if latest_timestamp < dt_timestamp:\",\n \"Form the path for the Dockerfile based on the parent\",\n \"verify their children too isMatched = isMatched & verifyBundleStructure(expectedElement, actual[matchedPosition]\",\n \"on the parent of the caller script dockerfile_path = os.path.join(dockerfile_parent_dir,\",\n \"@param isMatched (only used for recursive calls) @return True if\",\n \"os.path.join(directoryToUnzipTo, os.path.splitext(nameOfBundle)[0])) # verify structure of bundle ------------------------------------------------------------------------ # check\",\n \"image_name: the name of the image :param str image_tag_latest: the\",\n \"= isMatched & True # if the contents of the\",\n \"contain files and/or directories. @param directory directory to convert @return\",\n \"global array. @param expected the expected element @param currentPath the\",\n \"= json.load(json_data_file) except: addToErrorMessages(\\\"The specified JSON file is not valid:\",\n \"structure. Could not proceed with deployment.\\\") # clean up unzipped\",\n \"return def matches(expectedElement, actualElement): \\\"\\\"\\\" Function to check if files/directories\",\n \"isMatched = isMatched & verifyBundleStructure(expected['children'], actual['children'], currentPath) # do further\",\n \"to be extracted @param to_dir the destination directory to extract\",\n \"password for the docker registry functional user @return a JSON\",\n \"openJSONfile(jsonFile): \\\"\\\"\\\" Function to open a JSON file @param jsonFile\",\n \"= registry_path + '/tags/list' request = get(tag_list_url, usr, pwd) tag_list\",\n \"a .zip unzip(zipFileName,directoryToUnzipTo) os.remove(zipFileName) for x in os.listdir(directoryToUnzipTo): subdirectory =\",\n \"is composed of a list and may contain files and/or\",\n \"new working directory ----------------------------------------------------------- directoryToUnzipTo = os.path.join(zipDirectory, \\\"temp\\\") if not\",\n \"+ os.environ['DOCKER_CLIENT_TIMEOUT'] + \\\" seconds\\\") # ======================= verify bundle Structure\",\n \"if expected['type'] == 'directory': currentPath = currentPath + actual['name'] +\",\n \"matched position elif type(actual) is list: matchedPosition = -1 for\",\n \"directory \\\"\\\"\\\" addToErrorMessages(\\\"A \\\"+ expected['type'] +\\\" is missing from the\",\n \"< str_version: latest_version = str_version latest_timestamp = dt_timestamp latest_tag =\",\n \"the same name and must both be the same type.\",\n \"errorMessage the error message to add \\\"\\\"\\\" print(errorMessage) global errorMessges\",\n \"\\\"\\\"\\\" def get(url, usr, pwd): \\\"\\\"\\\" HTTP/HTTPS GET requests using\",\n \"@return True if the list of actual objects contain the\"\n]"}}},{"rowIdx":3489,"cells":{"ngram":{"kind":"list like","value":["M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in","rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1]","to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub","np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image","2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])),","camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth","tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco","corner[0] + corner[1] < min: min = corner[0] + corner[1]","= np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\",","min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])]","blue channel with the depth channel inp_image = pre_process(img_wo_at, depth)","depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw *","%d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return","kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if","0), 2) # draw left-middle, right-middle and center key-points rgb_img","def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input","cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray =","kps_pr: f_w, f_h = 640. / 512., 480. / 512.","''' project 2d pixel on the image to 3d by","/ 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img","convert ROS Image type to OpenCV Image type cv_bridge =","calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to","0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm =","-orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] -","= (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix)","rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel","replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] -","img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth","0] # sort by the confidence score kps_pr = sorted(kps_pr,","utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import","(256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw","= 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0]","[] for category_id, preds in net_output.items(): if len(preds) == 0:","default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352],","aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403,","CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param",":]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL,","np import sys import rospy from std_msgs.msg import Bool from","# parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL","corners = get_M_CL_info(gray, img, False) # remove aruco tag from","# sort by the confidence score kps_pr = sorted(kps_pr, key=lambda","2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1],","# Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10)","distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102,","(int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] *","# transformation from the robot base to aruco tag M_BL","markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out","depth info :param pixel: x, y :param M_CL: trans from","= message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback)","nei_range + 1): for delta_y in range(-nei_range, nei_range + 1):","OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False #","Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray,","and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map,","[ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1.","key=lambda x: x[-1], reverse=True) # select the top 1 grasp","= corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0]","draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]),","first frame, it may contain nothing if ids_CL is None:","dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle","image into the network ret = detector.run(inp_image[:, :, :]) ret","Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import","'__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert","the detected pixel is 0, check the depth of its","of pixel with respect to base frame ''' depth =","cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation =","tag from input image to avoid mis-detection if corners is","== 0: for delta_x in range(-nei_range, nei_range + 1): for","= rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix):","int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0]","kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm =","and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res","else: orientation = -orientation # compute the open width dist","(%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL)","np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt","json import cv2 import cv2.aruco as aruco import numpy as","import CvBridge, CvBridgeError import message_filters import torch from external.nms import","in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :]","w, h): x, y = pxl[:] return w/12. <= x",":] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the","[0, 0, 0, 0] # sort by the confidence score","# initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS","0, 0), 2) # draw left-middle, right-middle and center key-points","M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy()","preds in net_output.items(): if len(preds) == 0: continue for pred","cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray","kps_pr = [] for category_id, preds in net_output.items(): if len(preds)","dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img","w/12. <= x <= 11*w/12 and h/12. <= y <=","%(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL,","(kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1])","it may contain nothing if ids_CL is None: return default_M_CL,","+ (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] -","contain nothing if ids_CL is None: return default_M_CL, None rvec_CL,","0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) #","rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): '''","return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and","kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d =","draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0]","(int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,","1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def","intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064],","opts import opts from logger import Logger from utils.utils import","the depth of its neighbors # by counter-clock wise nei_range","transformation from the camera to aruco tag M_CL, corners =","0, 0, 1]) if visualize: # print('aruco is located at","+ (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm,","import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image,","0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0.,","1. ]] ) # camera intrinsic matrix of Realsense D435","pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth !=","aruco tag from input image to avoid mis-detection if corners","= ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix)","== '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to","= detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result","project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL,","numpy as np import sys import rospy from std_msgs.msg import","h): x, y = pxl[:] return w/12. <= x <=","coordinate of pixel with respect to base frame ''' depth","import os import json import cv2 import cv2.aruco as aruco","None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL,","depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot(","cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8)","cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth)","= np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] =",":param M_CL: trans from camera to aruco tag :param cameraMatrix:","gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get","2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2)","np.array([0, 0, 0, 1]) if visualize: # print('aruco is located","== 0: return [0, 0, 0, 0] # sort by","opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector","w, :] = rep_color return img_out def project(pixel, depth_image, M_CL,","max = -sys.maxsize tl_pxl = None br_pxl = None for","+ corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1]","visualize: # print('aruco is located at mean position (%d, %d)'","that trans raw data to mm :return: q_B: 3d coordinate","isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr","tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689,","2: orientation = np.pi - orientation elif orientation < -np.pi","np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0],","= np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7","mm :return: q_B: 3d coordinate of pixel with respect to","(int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1]","from __future__ import absolute_import from __future__ import division from __future__","= sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl =","br_pxl = None for corner in corners: if corner[0] +","0., 0., 1.00000]]) # default transformation from the camera to","the robot base to aruco tag M_BL = np.array([[1., 0.,","0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0.,","nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] +","None for kp_pr in kps_pr: f_w, f_h = 640. /","0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]])","-np.pi / 2: orientation = -np.pi - orientation else: orientation",":, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return","compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) #","the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img,","break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth,","orientation else: orientation = -orientation # compute the open width","46): img_out[h, w, :] = rep_color return img_out def project(pixel,","import numpy as np import sys import rospy from std_msgs.msg","depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel","is 0, check the depth of its neighbors # by","= rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth","camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False)","Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher","None for corner in corners: if corner[0] + corner[1] <","cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr =","delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] +","0.0]) # initialize GKNet Detector opt = opts().parse() Dataset =","the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1]","position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL,","pixel is 0, check the depth of its neighbors #","from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from","rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and","0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0,","rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame,","tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max:","[pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]]","raw depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data,","_init_paths import os import json import cv2 import cv2.aruco as","aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may","3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1])","\"\"\" Save raw RGB and depth input from Kinect V1","img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in","= np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [","depth = depth_image[nei[1], nei[0]] if depth != 0: break if","= cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8)","in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in","0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse()","1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from","= depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def","y = pxl[:] return w/12. <= x <= 11*w/12 and","ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori =","inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data):","pxl[:] return w/12. <= x <= 11*w/12 and h/12. <=","base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the","# get the replacement pixel value rep_color = img_out[tl_pxl[0] -","M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3]","sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1","<= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output,","/ 2: orientation = -np.pi - orientation else: orientation =","!= 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0]","to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0.,","opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) #","- 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1]","corners) # replace blue channel with the depth channel inp_image","img_out = rgb_image.copy() # find the top-left and right-bottom corners","image :param depth_data: raw depth image :return: None \"\"\" try:","right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl =","- 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color","7 is clockwise if orientation > np.pi / 2: orientation","br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out","check the depth of its neighbors # by counter-clock wise","default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs)","240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs","the top 1 grasp prediction within the workspace res =","= False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info',","import opts from logger import Logger from utils.utils import AverageMeter","corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the","if visualize: # print('aruco is located at mean position (%d,","rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for","cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img,","delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range,","M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0])","len(kps_pr) == 0: return [0, 0, 0, 0] # sort","kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0,","None: return [0, 0, 0, 0] f_w, f_h = 640./512.,","corners: if corner[0] + corner[1] < min: min = corner[0]","in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range","std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge","from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation","dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector","0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return","depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation","nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL,","- kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) /","kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm,","cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img =","corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace","__future__ import print_function import _init_paths import os import json import","-0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522],","inp_image = pre_process(img_wo_at, depth) # pass the image into the","depth image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\")","corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] +","image to 3d by depth info :param pixel: x, y","base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000],","opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset)","from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg","ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL,","camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param","br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value","np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) #","Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector =","if corners is not None: img_wo_at = aruco_tag_remove(img, corners) #","aruco_dict_CL, parameters=parameters) # for the first frame, it may contain","+ 46): img_out[h, w, :] = rep_color return img_out def","depth image :param depth_scale: depth scale that trans raw data","None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with","visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if","-np.pi - orientation else: orientation = -orientation # compute the","int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL,","cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0,","= depth_image[pixel[1], pixel[0]] # if the depth of the detected",":param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param","may contain nothing if ids_CL is None: return default_M_CL, None","2: orientation = -np.pi - orientation else: orientation = -orientation","<= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True):","0, 0] # sort by the confidence score kps_pr =","M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: #","print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return","Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934],","2d pixel on the image to 3d by depth info","from Kinect V1 :param rgb_data: RGB image :param depth_data: raw","cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy()","aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth","cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w,","0, 0, 0] # sort by the confidence score kps_pr","= cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img","depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data,","(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2,","+ 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y]","left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] -","(kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep,","rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub =","parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb","def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel","= dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0,","to 3d by depth info :param pixel: x, y :param","datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from","np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle","camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178],","nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1]","depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image","+ corner[1] > max: max = corner[0] + corner[1] br_pxl","46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46):","= project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map,","ROS Image type to OpenCV Image type cv_bridge = CvBridge()","w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w,","Save raw RGB and depth input from Kinect V1 :param","import soft_nms from opts import opts from logger import Logger","x, y :param M_CL: trans from camera to aruco tag","grasp prediction within the workspace res = None for kp_pr","is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue","0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image","= 1 while depth == 0: for delta_x in range(-nei_range,","import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray","cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw","data to mm :return: q_B: 3d coordinate of pixel with","-0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0.,","= np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw","= img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h","opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector =","import detector_factory # transformation from the robot base to aruco","with the depth channel inp_image = pre_process(img_wo_at, depth) # pass","if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res =","<= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL,","return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix,","if depth != 0: break nei_range += 1 pxl =","# Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\",","cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32)","int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d","parameters=parameters) # for the first frame, it may contain nothing","int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d =","the top-left and right-bottom corners min = sys.maxsize max =","# motor 7 is clockwise if orientation > np.pi /","located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix,","> max: max = corner[0] + corner[1] br_pxl = [int(corner[0]),","img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image,","corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] >","input from Kinect V1 :param rgb_data: RGB image :param depth_data:","(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2,","(int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480)","depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1)","the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293,","[0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs =","11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr","of its neighbors # by counter-clock wise nei_range = 1","top 1 grasp prediction within the workspace res = None","corners): img_out = rgb_image.copy() # find the top-left and right-bottom","cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and","cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) #","1 grasp prediction within the workspace res = None for","corner[0] + corner[1] > max: max = corner[0] + corner[1]","from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369,","return w/12. <= x <= 11*w/12 and h/12. <= y","0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters =","+ 1): for delta_y in range(-nei_range, nei_range + 1): nei","is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0],","= [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max","Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get","# draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img,","# get the current transformation from the camera to aruco","dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\")","= (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm,","int(corner[1])] if corner[0] + corner[1] > max: max = corner[0]","from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory","tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize:","int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640,","to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image:","break if depth != 0: break nei_range += 1 pxl","reverse=True) # select the top 1 grasp prediction within the","4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3,","img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current","to convert ROS Image type to OpenCV Image type cv_bridge","kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) ==","np.pi - orientation elif orientation < -np.pi / 2: orientation","= detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret,","1]) if visualize: # print('aruco is located at mean position","aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :,","import division from __future__ import print_function import _init_paths import os","top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize","Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt,","+= 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] *","cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize:","is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init,","into the network ret = detector.run(inp_image[:, :, :]) ret =","width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for","5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep =","1.00000]]) # default transformation from the camera to aruco tag","the depth of the detected pixel is 0, check the","pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img","# by counter-clock wise nei_range = 1 while depth ==","division from __future__ import print_function import _init_paths import os import","606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435","of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray,","with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]]","V1 :param rgb_data: RGB image :param depth_data: raw depth image","M_BL, cameraMatrix): ''' project 2d pixel on the image to","res = kp_pr break if res is None: return [0,","pixel[0]] # if the depth of the detected pixel is","rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to OpenCV","if depth != 0: break if depth != 0: break","default transformation from the camera to aruco tag default_M_CL =","corner[1] > max: max = corner[0] + corner[1] br_pxl =","[-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [","= (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2]","tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners):","if the depth of the detected pixel is 0, check","* f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w),","= project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] -","and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0])","= rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image,","robot base to aruco tag M_BL = np.array([[1., 0., 0.,","rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) #","# default transformation from the camera to aruco tag default_M_CL","respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] #","return [0, 0, 0, 0] # sort by the confidence","project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on","for the first frame, it may contain nothing if ids_CL","# print('aruco is located at mean position (%d, %d)' %(mean_x","preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2],","__future__ import division from __future__ import print_function import _init_paths import","= cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if","raw RGB and depth input from Kinect V1 :param rgb_data:","from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from","M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000],","for corner in corners: if corner[0] + corner[1] < min:","= (depth_raw * 1000).astype(np.uint8) # get the current transformation from","depth of the detected pixel is 0, check the depth","the depth channel inp_image = pre_process(img_wo_at, depth) # pass the","left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])),","detector_factory # transformation from the robot base to aruco tag","min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl","image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)","M_CL: trans from camera to aruco tag :param cameraMatrix: camera",":return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth =","# replace blue channel with the depth channel inp_image =","= (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] +","= sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top","type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT =","q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img,","def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL","avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img,","# no detection if len(kps_pr) == 0: return [0, 0,","np.pi / 2: orientation = np.pi - orientation elif orientation","= np.pi - orientation elif orientation < -np.pi / 2:","Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth","select the top 1 grasp prediction within the workspace res","(0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0,","= [] for category_id, preds in net_output.items(): if len(preds) ==",":3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] =","image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts =","orientation > np.pi / 2: orientation = np.pi - orientation","rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL","* depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1],","Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1,","depth_data: raw depth image :return: None \"\"\" try: cv_rgb =","message_filters import torch from external.nms import soft_nms from opts import","= kp_pr break if res is None: return [0, 0,","cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8)","pixel with respect to base frame ''' depth = depth_image[pixel[1],","rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for","x, y = pxl[:] return w/12. <= x <= 11*w/12","sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import","project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL,","orientation, dist] if __name__ == '__main__': # initialize ros node","except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x,","- 45, br_pxl[1] + 46): for w in range(tl_pxl[0] -","if __name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") #","soft_nms from opts import opts from logger import Logger from","Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) #","= aruco_tag_remove(img, corners) # replace blue channel with the depth","inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth","q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B","orientation < -np.pi / 2: orientation = -np.pi - orientation","= cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw","depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image)","info :param pixel: x, y :param M_CL: trans from camera","depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL,","corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() #","lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1]","/ 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) /","aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1.,","cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4))","kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm,","1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth","img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e:","frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth","f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm","channel inp_image = pre_process(img_wo_at, depth) # pass the image into","img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d","for delta_x in range(-nei_range, nei_range + 1): for delta_y in","intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale","kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center =","rgb_data: RGB image :param depth_data: raw depth image :return: None","< min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]),","the workspace res = None for kp_pr in kps_pr: f_w,","= cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth =","orientation elif orientation < -np.pi / 2: orientation = -np.pi","= None for kp_pr in kps_pr: f_w, f_h = 640.","640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] *","cv2.WITH_QT = False # Get camera calibration parameters cam_param =","score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select","/ 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0),","the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True)","f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3]","255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0,","cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__","inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image =","kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3],","if len(kps_pr) == 0: return [0, 0, 0, 0] #","by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1],","0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]]","visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation,","480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center","# Bridge to convert ROS Image type to OpenCV Image","= np.array([0, 0, 0, 1]) if visualize: # print('aruco is","inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256))","for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0]","for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for","detectors.detector_factory import detector_factory # transformation from the robot base to","lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep,","parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters)","= [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1],","0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495,","sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x:","= rgb_image.copy() # find the top-left and right-bottom corners min","- 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1]","detected pixel is 0, check the depth of its neighbors","0, check the depth of its neighbors # by counter-clock","x <= 11*w/12 and h/12. <= y <= 11*h/12 def","ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first","[0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense","pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if","/ 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h))","kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr)","import absolute_import from __future__ import division from __future__ import print_function","rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2)","np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is","in kps_pr: f_w, f_h = 640. / 512., 480. /","kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep","transformation from the robot base to aruco tag M_BL =","0: for delta_x in range(-nei_range, nei_range + 1): for delta_y","find the top-left and right-bottom corners min = sys.maxsize max","x: x[-1], reverse=True) # select the top 1 grasp prediction","center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros","M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image","aruco_tag_remove(img, corners) # replace blue channel with the depth channel","GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt =","f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm =","depth) # pass the image into the network ret =","= np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B =","3d coordinate of pixel with respect to base frame '''","kps[3], score]) # no detection if len(kps_pr) == 0: return","as np import sys import rospy from std_msgs.msg import Bool","False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo,","import print_function import _init_paths import os import json import cv2","rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2)","]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix","import dataset_factory from detectors.detector_factory import detector_factory # transformation from the",") # camera intrinsic matrix of Realsense D435 cameraMatrix =","dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\",","delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break","for pred in preds: kps = pred[:4] score = pred[-1]","image :return: None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth","replace blue channel with the depth channel inp_image = pre_process(img_wo_at,","# find the top-left and right-bottom corners min = sys.maxsize","* f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and","- kp_lm_3d[0]) # motor 7 is clockwise if orientation >","kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.))","range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1]","max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])]","10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] -","(int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2))","visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) #",":param pixel: x, y :param M_CL: trans from camera to","= (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2),","from opts import opts from logger import Logger from utils.utils",",mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0,","rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from","result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False):","as aruco import numpy as np import sys import rospy","M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag","import message_filters import torch from external.nms import soft_nms from opts","-0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) #","score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no","h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL,","0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1.,",":param depth_data: raw depth image :return: None \"\"\" try: cv_rgb","RGB and depth input from Kinect V1 :param rgb_data: RGB","range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0]","no detection if len(kps_pr) == 0: return [0, 0, 0,","512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1]","to avoid mis-detection if corners is not None: img_wo_at =","255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0],","Bridge to convert ROS Image type to OpenCV Image type","Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image)","in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x,","M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds",":] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46):","input image to avoid mis-detection if corners is not None:","* depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L","camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0,","from input image to avoid mis-detection if corners is not","the current transformation from the camera to aruco tag M_CL,","!= 0: break if depth != 0: break nei_range +=","depth scale that trans raw data to mm :return: q_B:","- orientation elif orientation < -np.pi / 2: orientation =","import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory","[center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': #","\"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr","np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L)","corner in corners: if corner[0] + corner[1] < min: min","channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts","if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL","depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2],","from __future__ import division from __future__ import print_function import _init_paths","cameraMatrix): ''' project 2d pixel on the image to 3d","1): for delta_y in range(-nei_range, nei_range + 1): nei =","depth = depth_image[pixel[1], pixel[0]] # if the depth of the","= corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get","45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return","# initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset]","depth_image[nei[1], nei[0]] if depth != 0: break if depth !=","\"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\")","current transformation from the camera to aruco tag M_CL, corners","pre_process(img_wo_at, depth) # pass the image into the network ret","break if res is None: return [0, 0, 0, 0]","= cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL","return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512.","from camera to aruco tag :param cameraMatrix: camera intrinsic matrix","= project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map,","M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation","for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h,","def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] =","- kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise","5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.),","distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def","clockwise if orientation > np.pi / 2: orientation = np.pi","distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3,",":param depth_scale: depth scale that trans raw data to mm","10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] +","br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0]","nei[0]] if depth != 0: break if depth != 0:","np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb)","f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm,","480): res = kp_pr break if res is None: return","= depth_image[nei[1], nei[0]] if depth != 0: break if depth","= None for corner in corners: if corner[0] + corner[1]","q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image =","right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) /","delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth","+ delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0:","0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w),","network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori","CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub","src/static_grasp_kt.py from __future__ import absolute_import from __future__ import division from","Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo","= [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color","distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet","depth_scale: depth scale that trans raw data to mm :return:","# distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134,","= cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb,","None \"\"\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data,","os import json import cv2 import cv2.aruco as aruco import","to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912,","print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y))","pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return","def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12.","center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1]","= Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result',","ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL =","(0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img)","= CvBridge() cv2.WITH_QT = False # Get camera calibration parameters","import cv2 import cv2.aruco as aruco import numpy as np","11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img,","depth_image: depth image :param depth_scale: depth scale that trans raw","aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create()","mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners)","arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] +","corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the","5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2)","None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL,","= -np.pi - orientation else: orientation = -orientation # compute","aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove","M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :]","cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr =","right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2,","depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L =","cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr","ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori)","0.66103522], [ 0., 0., 0., 1. ]] ) # camera","325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of","markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250)","int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE)","kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center,","= np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0.,","# compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2])","= tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if","[ 0., 0., 0., 1. ]] ) # camera intrinsic","depth input from Kinect V1 :param rgb_data: RGB image :param","rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img","0., 0., 1. ]] ) # camera intrinsic matrix of","sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None","import torch from external.nms import soft_nms from opts import opts","aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and","print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of","= pre_process(img_wo_at, depth) # pass the image into the network","e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:]","= np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector","logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import","the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw","tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL =","Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def","max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] #","orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor","0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation","matrix :param depth_image: depth image :param depth_scale: depth scale that","counter-clock wise nei_range = 1 while depth == 0: for","cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save","depth channel inp_image = pre_process(img_wo_at, depth) # pass the image","+ (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] -","continue for pred in preds: kps = pred[:4] score =","- kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) /","\"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) #","0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] *","-0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] )","Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT","at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs,","pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h):","cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) #",":param rgb_data: RGB image :param depth_data: raw depth image :return:","# draw arrow for left-middle and right-middle key-points lm_ep =","None br_pxl = None for corner in corners: if corner[0]","import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters","= np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img):","= (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640,","int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]),","CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch","<= x <= 11*w/12 and h/12. <= y <= 11*h/12","= pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score])","+ (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] +","cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle,","0, 1]) if visualize: # print('aruco is located at mean","Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge,","by depth info :param pixel: x, y :param M_CL: trans","raw data to mm :return: q_B: 3d coordinate of pixel","kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB and depth input from","M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def","== 0: continue for pred in preds: kps = pred[:4]","rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL","cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale:","KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as","y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix,","np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C =","(int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,","cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms","(kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0]","int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] *","__future__ import absolute_import from __future__ import division from __future__ import","dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot","+ 46): for w in range(tl_pxl[0] - 45, br_pxl[0] +","M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0,","kp_pr in kps_pr: f_w, f_h = 640. / 512., 480.",":] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL,","cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img =","aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL,","scale that trans raw data to mm :return: q_B: 3d","cv2 import cv2.aruco as aruco import numpy as np import","M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix)","# Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None)","score]) # no detection if len(kps_pr) == 0: return [0,","isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is","detector = Detector(opt) # Publisher of perception result pub_res =","category_id, preds in net_output.items(): if len(preds) == 0: continue for","1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth,","pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B","try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \"bgr8\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr","of the detected pixel is 0, check the depth of","np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw =","depth == 0: for delta_x in range(-nei_range, nei_range + 1):","return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :,","detector.run(inp_image[:, :, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img,","nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth =","[0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0.,","= None br_pxl = None for corner in corners: if","in corners: if corner[0] + corner[1] < min: min =","640, 480): res = kp_pr break if res is None:","cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d =","kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi","255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0,","the image into the network ret = detector.run(inp_image[:, :, :])","= -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2]","+ delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if","1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283,","if orientation > np.pi / 2: orientation = np.pi -","import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory","AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory #","3d by depth info :param pixel: x, y :param M_CL:","M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d","rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2)","import cv2.aruco as aruco import numpy as np import sys","cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3,",":] = np.array([0, 0, 0, 1]) if visualize: # print('aruco","kps[2], kps[3], score]) # no detection if len(kps_pr) == 0:","> np.pi / 2: orientation = np.pi - orientation elif","0., 1.00000]]) # default transformation from the camera to aruco","nei_range = 1 while depth == 0: for delta_x in","if corner[0] + corner[1] > max: max = corner[0] +","open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow","2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])),","2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual',","= aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it","D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize","def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr =","from the camera to aruco tag M_CL, corners = get_M_CL_info(gray,","workspace res = None for kp_pr in kps_pr: f_w, f_h","depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the","for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0]","cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist]","int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img,","to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if",":, :]) ret = ret[\"results\"] loc_ori = KpsToGrasppose(ret, img, depth_raw,","within the workspace res = None for kp_pr in kps_pr:","project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1],","cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration","cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy()","(0, 0, 0), 2) # draw left-middle, right-middle and center","pass the image into the network ret = detector.run(inp_image[:, :,","ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type","aruco import numpy as np import sys import rospy from","(kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1])","0: continue for pred in preds: kps = pred[:4] score",":param depth_image: depth image :param depth_scale: depth scale that trans","= -sys.maxsize tl_pxl = None br_pxl = None for corner","while depth == 0: for delta_x in range(-nei_range, nei_range +","tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45,","cv2.aruco as aruco import numpy as np import sys import","1000).astype(np.uint8) # get the current transformation from the camera to","pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth]))","False) # remove aruco tag from input image to avoid","isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <=","- orientation else: orientation = -orientation # compute the open","/ 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w),","_objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL)","tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0.,","parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL =","depth_data): \"\"\" Save raw RGB and depth input from Kinect","f_h = 640. / 512., 480. / 512. kp_lm =","1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0.,","KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = []","res is None: return [0, 0, 0, 0] f_w, f_h","0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0.,","= dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task]","torch from external.nms import soft_nms from opts import opts from","def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left","center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL,","to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False","depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e)","pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0],","Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) #","cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :]","pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10,","import sys import rospy from std_msgs.msg import Bool from std_msgs.msg","matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0,","is None: return [0, 0, 0, 0] f_w, f_h =","if res is None: return [0, 0, 0, 0] f_w,","corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel","[int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color =","h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w","if corner[0] + corner[1] < min: min = corner[0] +","[int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max =","range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range +","M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] -","0., 0., 0., 1. ]] ) # camera intrinsic matrix","to mm :return: q_B: 3d coordinate of pixel with respect","aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for","Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL =","orientation = np.pi - orientation elif orientation < -np.pi /","loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except","RGB image :param depth_data: raw depth image :return: None \"\"\"","(depth_raw * 1000).astype(np.uint8) # get the current transformation from the","kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0])","frame, it may contain nothing if ids_CL is None: return","/ 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep","in net_output.items(): if len(preds) == 0: continue for pred in","depth != 0: break if depth != 0: break nei_range","45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45,","= np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and","Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from","initialize ros node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image","1 while depth == 0: for delta_x in range(-nei_range, nei_range","the image to 3d by depth info :param pixel: x,","640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break","sys import rospy from std_msgs.msg import Bool from std_msgs.msg import","for kp_pr in kps_pr: f_w, f_h = 640. / 512.,","img, False) # remove aruco tag from input image to","node rospy.init_node(\"Static_grasping\") # Bridge to convert ROS Image type to","len(preds) == 0: continue for pred in preds: kps =","* f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h))","pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) #","# pass the image into the network ret = detector.run(inp_image[:,","from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory","depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id,","rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image,","f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if","pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): #","(int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1]","import _init_paths import os import json import cv2 import cv2.aruco","net_output.items(): if len(preds) == 0: continue for pred in preds:","= 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w),","get_M_CL_info(gray, img, False) # remove aruco tag from input image","-0.0450], [0., 0., 0., 1.00000]]) # default transformation from the","key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.),","return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out =","prediction within the workspace res = None for kp_pr in","f_w, f_h = 640. / 512., 480. / 512. kp_lm","# remove aruco tag from input image to avoid mis-detection","the first frame, it may contain nothing if ids_CL is","depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C)","elif orientation < -np.pi / 2: orientation = -np.pi -","get the current transformation from the camera to aruco tag","-0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt =","its neighbors # by counter-clock wise nei_range = 1 while","return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__':","f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res","kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle","import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import","dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0,","0: break if depth != 0: break nei_range += 1","-0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594,","get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL =","< -np.pi / 2: orientation = -np.pi - orientation else:","from the robot base to aruco tag M_BL = np.array([[1.,","(int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map,","= pxl[:] return w/12. <= x <= 11*w/12 and h/12.","2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255),","center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize","confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) #","-0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset","= img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the","image to avoid mis-detection if corners is not None: img_wo_at","0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion","0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0,","if len(preds) == 0: continue for pred in preds: kps","print_function import _init_paths import os import json import cv2 import","0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h))","np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr)","message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback) rospy.spin()","and depth input from Kinect V1 :param rgb_data: RGB image","channel with the depth channel inp_image = pre_process(img_wo_at, depth) #",":, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find","cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if","range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] =","q_B: 3d coordinate of pixel with respect to base frame","0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0),","(int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual',","CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms","type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera","initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt","queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093","min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if","[0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm","pixel on the image to 3d by depth info :param","dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3]","image :param depth_scale: depth scale that trans raw data to","depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL,","= opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt)","trans from camera to aruco tag :param cameraMatrix: camera intrinsic","neighbors # by counter-clock wise nei_range = 1 while depth","from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError","in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1],","= (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] +","''' depth = depth_image[pixel[1], pixel[0]] # if the depth of","and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0,","center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0,","tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image","import json import cv2 import cv2.aruco as aruco import numpy","0., 1. ]] ) # camera intrinsic matrix of Realsense","return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project","= aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL","512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm","if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2],","# camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165,","-sys.maxsize tl_pxl = None br_pxl = None for corner in","[0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default","Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception","rgb_image.copy() # find the top-left and right-bottom corners min =","M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix)","kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.))","480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if","2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255),","= message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub,","= np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr =","opts from logger import Logger from utils.utils import AverageMeter from","+ corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement","+ corner[1] < min: min = corner[0] + corner[1] tl_pxl","= M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img","np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0.,","markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4,","* f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480):","is clockwise if orientation > np.pi / 2: orientation =","jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] =","value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :]","cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items():","= cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img",":return: q_B: 3d coordinate of pixel with respect to base","remove aruco tag from input image to avoid mis-detection if","img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the","as e: print(e) def isWithinRange(pxl, w, h): x, y =","0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0,","trans raw data to mm :return: q_B: 3d coordinate of","480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] *","aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL,","res = None for kp_pr in kps_pr: f_w, f_h =","kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation","M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl,","kp_pr break if res is None: return [0, 0, 0,","from __future__ import print_function import _init_paths import os import json","rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256,","= opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt)","orientation = -np.pi - orientation else: orientation = -orientation #","detection if len(kps_pr) == 0: return [0, 0, 0, 0]","timeout=None) # Subscribe to rgb and depth channel image_sub =","pixel: x, y :param M_CL: trans from camera to aruco","5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img =","motor 7 is clockwise if orientation > np.pi / 2:","= aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray,","# for the first frame, it may contain nothing if","absolute_import from __future__ import division from __future__ import print_function import","from detectors.detector_factory import detector_factory # transformation from the robot base","0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847,","tl_pxl = None br_pxl = None for corner in corners:","np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768,","int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]),","256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\" Save raw RGB","= KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError","to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) #","np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL","# aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints","= np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]])","project 2d pixel on the image to 3d by depth","D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0,","- kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0,","x[-1], reverse=True) # select the top 1 grasp prediction within","on the image to 3d by depth info :param pixel:","kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the","M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:,","aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL =","__name__ == '__main__': # initialize ros node rospy.init_node(\"Static_grasping\") # Bridge","pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1])","key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255),","message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub],","transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498,","= cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \"\"\"","y :param M_CL: trans from camera to aruco tag :param","0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450],","of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0])","perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init,","= aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL,","0: return [0, 0, 0, 0] # sort by the","(int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d","/ 2: orientation = np.pi - orientation elif orientation <","# cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\"Depth\", cv_depth) cv2.imshow(\"RGB\", cv_rgb) img =","aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints =","external.nms import soft_nms from opts import opts from logger import","= np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C","- kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points","depth of its neighbors # by counter-clock wise nei_range =","not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel","kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm,","corners min = sys.maxsize max = -sys.maxsize tl_pxl = None","CvBridgeError import message_filters import torch from external.nms import soft_nms from","corner[1] < min: min = corner[0] + corner[1] tl_pxl =","the network ret = detector.run(inp_image[:, :, :]) ret = ret[\"results\"]","int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0]","detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res","orientation = -orientation # compute the open width dist =","from external.nms import soft_nms from opts import opts from logger","= aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) #","of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420,","wise nei_range = 1 while depth == 0: for delta_x","= pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection","rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ ==","depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0]","get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10,","and depth channel image_sub = message_filters.Subscriber(\"/camera/rgb/image_rect_color\", Image) depth_sub = message_filters.Subscriber(\"/camera/depth_registered/image\",","= 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters","= get_M_CL_info(gray, img, False) # remove aruco tag from input","CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y","for category_id, preds in net_output.items(): if len(preds) == 0: continue","# if the depth of the detected pixel is 0,","mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL,","by counter-clock wise nei_range = 1 while depth == 0:","- kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] -","q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0]","= rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters","[0., 0., 0., 1.00000]]) # default transformation from the camera","# select the top 1 grasp prediction within the workspace","= cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img =","= cv_bridge.imgmsg_to_cv2(depth_data, \"32FC1\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth,","* 1000).astype(np.uint8) # get the current transformation from the camera","camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe","and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl","2) # draw left-middle, right-middle and center key-points rgb_img =","640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h))","std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import"],"string":"[\n \"M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in\",\n \"rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1]\",\n \"to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub\",\n \"np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image\",\n \"2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])),\",\n \"camera intrinsic matrix :param depth_image: depth image :param depth_scale: depth\",\n \"tag M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco\",\n \"corner[0] + corner[1] < min: min = corner[0] + corner[1]\",\n \"= np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\",\",\n \"min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])]\",\n \"blue channel with the depth channel inp_image = pre_process(img_wo_at, depth)\",\n \"depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw *\",\n \"%d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return\",\n \"kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if\",\n \"0), 2) # draw left-middle, right-middle and center key-points rgb_img\",\n \"def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input\",\n \"cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray =\",\n \"kps_pr: f_w, f_h = 640. / 512., 480. / 512.\",\n \"''' project 2d pixel on the image to 3d by\",\n \"/ 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img\",\n \"convert ROS Image type to OpenCV Image type cv_bridge =\",\n \"calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to\",\n \"0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm =\",\n \"-orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2] -\",\n \"= (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix)\",\n \"rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth channel\",\n \"replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] -\",\n \"img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth\",\n \"0] # sort by the confidence score kps_pr = sorted(kps_pr,\",\n \"utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import\",\n \"(256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw\",\n \"= 640. / 512., 480. / 512. kp_lm = (int(kp_pr[0]\",\n \"[] for category_id, preds in net_output.items(): if len(preds) == 0:\",\n \"default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352],\",\n \"aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403,\",\n \"CvBridge() cv2.WITH_QT = False # Get camera calibration parameters cam_param\",\n \":]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL,\",\n \"np import sys import rospy from std_msgs.msg import Bool from\",\n \"# parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL\",\n \"corners = get_M_CL_info(gray, img, False) # remove aruco tag from\",\n \"# sort by the confidence score kps_pr = sorted(kps_pr, key=lambda\",\n \"2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1],\",\n \"# Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10)\",\n \"distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102,\",\n \"(int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] *\",\n \"# transformation from the robot base to aruco tag M_BL\",\n \"markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out\",\n \"depth info :param pixel: x, y :param M_CL: trans from\",\n \"= message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback)\",\n \"nei_range + 1): for delta_y in range(-nei_range, nei_range + 1):\",\n \"OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False #\",\n \"Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray,\",\n \"and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map,\",\n \"[ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1.\",\n \"key=lambda x: x[-1], reverse=True) # select the top 1 grasp\",\n \"= corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0]\",\n \"draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]),\",\n \"first frame, it may contain nothing if ids_CL is None:\",\n \"dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle\",\n \"image into the network ret = detector.run(inp_image[:, :, :]) ret\",\n \"Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import\",\n \"'__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert\",\n \"the detected pixel is 0, check the depth of its\",\n \"of pixel with respect to base frame ''' depth =\",\n \"cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation =\",\n \"tag from input image to avoid mis-detection if corners is\",\n \"== 0: for delta_x in range(-nei_range, nei_range + 1): for\",\n \"= rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix):\",\n \"int(corner[1])] # get the replacement pixel value rep_color = img_out[tl_pxl[0]\",\n \"kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm =\",\n \"and isWithinRange(kp_rm, 640, 480): res = kp_pr break if res\",\n \"else: orientation = -orientation # compute the open width dist\",\n \"(%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL)\",\n \"np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt\",\n \"json import cv2 import cv2.aruco as aruco import numpy as\",\n \"import CvBridge, CvBridgeError import message_filters import torch from external.nms import\",\n \"in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :]\",\n \"w, h): x, y = pxl[:] return w/12. <= x\",\n \":] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the\",\n \"[0, 0, 0, 0] # sort by the confidence score\",\n \"# initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS\",\n \"0, 0), 2) # draw left-middle, right-middle and center key-points\",\n \"M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy()\",\n \"preds in net_output.items(): if len(preds) == 0: continue for pred\",\n \"cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray\",\n \"kps_pr = [] for category_id, preds in net_output.items(): if len(preds)\",\n \"dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img\",\n \"w/12. <= x <= 11*w/12 and h/12. <= y <=\",\n \"%(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL,\",\n \"(kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1])\",\n \"it may contain nothing if ids_CL is None: return default_M_CL,\",\n \"+ (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] -\",\n \"contain nothing if ids_CL is None: return default_M_CL, None rvec_CL,\",\n \"0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) #\",\n \"rep_color return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): '''\",\n \"return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and\",\n \"kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d =\",\n \"draw arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0]\",\n \"(int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,\",\n \"1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def\",\n \"intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064],\",\n \"opts import opts from logger import Logger from utils.utils import\",\n \"the depth of its neighbors # by counter-clock wise nei_range\",\n \"transformation from the camera to aruco tag M_CL, corners =\",\n \"0, 0, 1]) if visualize: # print('aruco is located at\",\n \"+ (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm,\",\n \"import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image,\",\n \"0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0.,\",\n \"1. ]] ) # camera intrinsic matrix of Realsense D435\",\n \"pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth !=\",\n \"aruco tag from input image to avoid mis-detection if corners\",\n \"= ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix)\",\n \"== '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to\",\n \"= detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result\",\n \"project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL,\",\n \"numpy as np import sys import rospy from std_msgs.msg import\",\n \"h): x, y = pxl[:] return w/12. <= x <=\",\n \"coordinate of pixel with respect to base frame ''' depth\",\n \"import os import json import cv2 import cv2.aruco as aruco\",\n \"None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL,\",\n \"depth != 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot(\",\n \"cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8)\",\n \"cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth)\",\n \"= np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] =\",\n \":param M_CL: trans from camera to aruco tag :param cameraMatrix:\",\n \"gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get\",\n \"2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2)\",\n \"np.array([0, 0, 0, 1]) if visualize: # print('aruco is located\",\n \"== 0: return [0, 0, 0, 0] # sort by\",\n \"opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector\",\n \"w, :] = rep_color return img_out def project(pixel, depth_image, M_CL,\",\n \"max = -sys.maxsize tl_pxl = None br_pxl = None for\",\n \"+ corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1]\",\n \"visualize: # print('aruco is located at mean position (%d, %d)'\",\n \"that trans raw data to mm :return: q_B: 3d coordinate\",\n \"isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr\",\n \"tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689,\",\n \"2: orientation = np.pi - orientation elif orientation < -np.pi\",\n \"np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0],\",\n \"= np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7\",\n \"mm :return: q_B: 3d coordinate of pixel with respect to\",\n \"(int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1]\",\n \"from __future__ import absolute_import from __future__ import division from __future__\",\n \"= sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl =\",\n \"br_pxl = None for corner in corners: if corner[0] +\",\n \"0., 0., 1.00000]]) # default transformation from the camera to\",\n \"the robot base to aruco tag M_BL = np.array([[1., 0.,\",\n \"0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0.,\",\n \"nei_range + 1): nei = [pixel[0] + delta_x, pixel[1] +\",\n \"None for kp_pr in kps_pr: f_w, f_h = 640. /\",\n \"0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0., 1.00000]])\",\n \"-np.pi / 2: orientation = -np.pi - orientation else: orientation\",\n \":, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return\",\n \"compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) #\",\n \"the camera to aruco tag M_CL, corners = get_M_CL_info(gray, img,\",\n \"break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth,\",\n \"orientation else: orientation = -orientation # compute the open width\",\n \"46): img_out[h, w, :] = rep_color return img_out def project(pixel,\",\n \"import numpy as np import sys import rospy from std_msgs.msg\",\n \"depth_image[pixel[1], pixel[0]] # if the depth of the detected pixel\",\n \"is 0, check the depth of its neighbors # by\",\n \"= rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and depth\",\n \"camera to aruco tag M_CL, corners = get_M_CL_info(gray, img, False)\",\n \"Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher\",\n \"None for corner in corners: if corner[0] + corner[1] <\",\n \"cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr =\",\n \"delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0] +\",\n \"0.0]) # initialize GKNet Detector opt = opts().parse() Dataset =\",\n \"the replacement pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1]\",\n \"position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL,\",\n \"pixel is 0, check the depth of its neighbors #\",\n \"from cv_bridge import CvBridge, CvBridgeError import message_filters import torch from\",\n \"rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle and\",\n \"0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0,\",\n \"rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame,\",\n \"tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max:\",\n \"[pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]]\",\n \"raw depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data,\",\n \"_init_paths import os import json import cv2 import cv2.aruco as\",\n \"aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it may\",\n \"3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1])\",\n \"\\\"\\\"\\\" Save raw RGB and depth input from Kinect V1\",\n \"img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h in\",\n \"= np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [\",\n \"depth = depth_image[nei[1], nei[0]] if depth != 0: break if\",\n \"= cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8)\",\n \"in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in\",\n \"0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse()\",\n \"1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation from\",\n \"= depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def\",\n \"y = pxl[:] return w/12. <= x <= 11*w/12 and\",\n \"ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori =\",\n \"inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data):\",\n \"pxl[:] return w/12. <= x <= 11*w/12 and h/12. <=\",\n \"base frame ''' depth = depth_image[pixel[1], pixel[0]] # if the\",\n \"# get the replacement pixel value rep_color = img_out[tl_pxl[0] -\",\n \"M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3]\",\n \"sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top 1\",\n \"<= 11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output,\",\n \"/ 2: orientation = -np.pi - orientation else: orientation =\",\n \"!= 0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0]\",\n \"to aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0.,\",\n \"opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) #\",\n \"- 10, :] for h in range(tl_pxl[1] - 45, br_pxl[1]\",\n \"corners) # replace blue channel with the depth channel inp_image\",\n \"img_out = rgb_image.copy() # find the top-left and right-bottom corners\",\n \"image :param depth_data: raw depth image :return: None \\\"\\\"\\\" try:\",\n \"right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl =\",\n \"- 45, br_pxl[0] + 46): img_out[h, w, :] = rep_color\",\n \"7 is clockwise if orientation > np.pi / 2: orientation\",\n \"br_pxl[0] + 46): img_out[h, w, :] = rep_color return img_out\",\n \"check the depth of its neighbors # by counter-clock wise\",\n \"default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs)\",\n \"240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs\",\n \"the top 1 grasp prediction within the workspace res =\",\n \"= False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info',\",\n \"import opts from logger import Logger from utils.utils import AverageMeter\",\n \"corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the\",\n \"if visualize: # print('aruco is located at mean position (%d,\",\n \"rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for\",\n \"cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img,\",\n \"delta_x in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range,\",\n \"M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0])\",\n \"len(kps_pr) == 0: return [0, 0, 0, 0] # sort\",\n \"kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0,\",\n \"None: return [0, 0, 0, 0] f_w, f_h = 640./512.,\",\n \"corners: if corner[0] + corner[1] < min: min = corner[0]\",\n \"in range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range\",\n \"std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge\",\n \"from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation\",\n \"dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector\",\n \"0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return\",\n \"depth = (depth_raw * 1000).astype(np.uint8) # get the current transformation\",\n \"nothing if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL,\",\n \"- kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) /\",\n \"kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm,\",\n \"cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img =\",\n \"corners is not None: img_wo_at = aruco_tag_remove(img, corners) # replace\",\n \"__future__ import print_function import _init_paths import os import json import\",\n \"-0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522],\",\n \"inp_image = pre_process(img_wo_at, depth) # pass the image into the\",\n \"depth image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\")\",\n \"corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] +\",\n \"image to 3d by depth info :param pixel: x, y\",\n \"base to aruco tag M_BL = np.array([[1., 0., 0., 0.30000],\",\n \"opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset)\",\n \"from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg\",\n \"ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL,\",\n \"camera to aruco tag :param cameraMatrix: camera intrinsic matrix :param\",\n \"br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel value\",\n \"np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) #\",\n \"Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector =\",\n \"if corners is not None: img_wo_at = aruco_tag_remove(img, corners) #\",\n \"aruco_dict_CL, parameters=parameters) # for the first frame, it may contain\",\n \"+ 46): img_out[h, w, :] = rep_color return img_out def\",\n \"depth image :param depth_scale: depth scale that trans raw data\",\n \"None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with\",\n \"visualize=True): kps_pr = [] for category_id, preds in net_output.items(): if\",\n \"-np.pi - orientation else: orientation = -orientation # compute the\",\n \"int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL,\",\n \"cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0,\",\n \"= depth_image[pixel[1], pixel[0]] # if the depth of the detected\",\n \":param cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param\",\n \"may contain nothing if ids_CL is None: return default_M_CL, None\",\n \"2: orientation = -np.pi - orientation else: orientation = -orientation\",\n \"<= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True):\",\n \"0, 0] # sort by the confidence score kps_pr =\",\n \"M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize: #\",\n \"print(e) def isWithinRange(pxl, w, h): x, y = pxl[:] return\",\n \"Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934],\",\n \"2d pixel on the image to 3d by depth info\",\n \"from Kinect V1 :param rgb_data: RGB image :param depth_data: raw\",\n \"cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy()\",\n \"aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth\",\n \"cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w,\",\n \"0, 0, 0] # sort by the confidence score kps_pr\",\n \"= cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img\",\n \"depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data,\",\n \"(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2,\",\n \"+ 1): nei = [pixel[0] + delta_x, pixel[1] + delta_y]\",\n \"left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] -\",\n \"(kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep,\",\n \"rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub =\",\n \"parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb\",\n \"def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel\",\n \"= dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0,\",\n \"to 3d by depth info :param pixel: x, y :param\",\n \"datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory # transformation from\",\n \"np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and right-middle\",\n \"camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178],\",\n \"nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1]\",\n \"depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image\",\n \"+ corner[1] > max: max = corner[0] + corner[1] br_pxl\",\n \"46): for w in range(tl_pxl[0] - 45, br_pxl[0] + 46):\",\n \"= project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map,\",\n \"ROS Image type to OpenCV Image type cv_bridge = CvBridge()\",\n \"w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w,\",\n \"Save raw RGB and depth input from Kinect V1 :param\",\n \"import soft_nms from opts import opts from logger import Logger\",\n \"x, y :param M_CL: trans from camera to aruco tag\",\n \"grasp prediction within the workspace res = None for kp_pr\",\n \"is not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue\",\n \"0] = depth_img inp_image = cv2.resize(inp_image, (256, 256)) return inp_image\",\n \"= 1 while depth == 0: for delta_x in range(-nei_range,\",\n \"import rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray\",\n \"cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw\",\n \"data to mm :return: q_B: 3d coordinate of pixel with\",\n \"-0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0.,\",\n \"= np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw\",\n \"= img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for h\",\n \"opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector =\",\n \"import detector_factory # transformation from the robot base to aruco\",\n \"with the depth channel inp_image = pre_process(img_wo_at, depth) # pass\",\n \"if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res =\",\n \"<= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL,\",\n \"return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix,\",\n \"if depth != 0: break nei_range += 1 pxl =\",\n \"# Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\",\",\n \"cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32)\",\n \"int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d\",\n \"parameters=parameters) # for the first frame, it may contain nothing\",\n \"int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d =\",\n \"the top-left and right-bottom corners min = sys.maxsize max =\",\n \"# motor 7 is clockwise if orientation > np.pi /\",\n \"located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix,\",\n \"> max: max = corner[0] + corner[1] br_pxl = [int(corner[0]),\",\n \"img_out[h, w, :] = rep_color return img_out def project(pixel, depth_image,\",\n \"corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if corner[0] + corner[1] >\",\n \"input from Kinect V1 :param rgb_data: RGB image :param depth_data:\",\n \"(0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2,\",\n \"(int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480)\",\n \"depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1)\",\n \"the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293,\",\n \"[0.0, 0.0, 1.0]]) # distortion of Realsense D435 distCoeffs =\",\n \"11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr\",\n \"of its neighbors # by counter-clock wise nei_range = 1\",\n \"top 1 grasp prediction within the workspace res = None\",\n \"corners): img_out = rgb_image.copy() # find the top-left and right-bottom\",\n \"cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe to rgb and\",\n \"cv_depth_arr.copy() gray = img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) #\",\n \"1 grasp prediction within the workspace res = None for\",\n \"corner[0] + corner[1] > max: max = corner[0] + corner[1]\",\n \"from the camera to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369,\",\n \"return w/12. <= x <= 11*w/12 and h/12. <= y\",\n \"0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters =\",\n \"+ 1): for delta_y in range(-nei_range, nei_range + 1): nei\",\n \"is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0],\",\n \"= [int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max\",\n \"Image type cv_bridge = CvBridge() cv2.WITH_QT = False # Get\",\n \"# draw left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img,\",\n \"# get the current transformation from the camera to aruco\",\n \"dist] if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\")\",\n \"= (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm,\",\n \"int(corner[1])] if corner[0] + corner[1] > max: max = corner[0]\",\n \"from logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory\",\n \"tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if visualize:\",\n \"int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640,\",\n \"to aruco tag :param cameraMatrix: camera intrinsic matrix :param depth_image:\",\n \"break if depth != 0: break nei_range += 1 pxl\",\n \"reverse=True) # select the top 1 grasp prediction within the\",\n \"4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3,\",\n \"img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the current\",\n \"to convert ROS Image type to OpenCV Image type cv_bridge\",\n \"kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr) ==\",\n \"np.pi - orientation elif orientation < -np.pi / 2: orientation\",\n \"= detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret,\",\n \"1]) if visualize: # print('aruco is located at mean position\",\n \"aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :,\",\n \"import division from __future__ import print_function import _init_paths import os\",\n \"top-left and right-bottom corners min = sys.maxsize max = -sys.maxsize\",\n \"Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt,\",\n \"+= 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] *\",\n \"cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize:\",\n \"is located at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init,\",\n \"into the network ret = detector.run(inp_image[:, :, :]) ret =\",\n \"width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for\",\n \"5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep =\",\n \"1.00000]]) # default transformation from the camera to aruco tag\",\n \"the depth of the detected pixel is 0, check the\",\n \"pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] = depth_img\",\n \"# by counter-clock wise nei_range = 1 while depth ==\",\n \"division from __future__ import print_function import _init_paths import os import\",\n \"606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense D435\",\n \"of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray,\",\n \"with respect to base frame ''' depth = depth_image[pixel[1], pixel[0]]\",\n \"V1 :param rgb_data: RGB image :param depth_data: raw depth image\",\n \"M_BL, cameraMatrix): ''' project 2d pixel on the image to\",\n \"res = kp_pr break if res is None: return [0,\",\n \"pixel[0]] # if the depth of the detected pixel is\",\n \"rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to OpenCV\",\n \"if depth != 0: break if depth != 0: break\",\n \"default transformation from the camera to aruco tag default_M_CL =\",\n \"corner[1] > max: max = corner[0] + corner[1] br_pxl =\",\n \"[-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [\",\n \"= (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2]\",\n \"tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners):\",\n \"if the depth of the detected pixel is 0, check\",\n \"* f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w),\",\n \"= project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] -\",\n \"and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0])\",\n \"= rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image,\",\n \"robot base to aruco tag M_BL = np.array([[1., 0., 0.,\",\n \"rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) #\",\n \"# default transformation from the camera to aruco tag default_M_CL\",\n \"respect to base frame ''' depth = depth_image[pixel[1], pixel[0]] #\",\n \"return [0, 0, 0, 0] # sort by the confidence\",\n \"project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on\",\n \"for the first frame, it may contain nothing if ids_CL\",\n \"# print('aruco is located at mean position (%d, %d)' %(mean_x\",\n \"preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2],\",\n \"__future__ import division from __future__ import print_function import _init_paths import\",\n \"= cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if\",\n \"raw RGB and depth input from Kinect V1 :param rgb_data:\",\n \"from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from\",\n \"M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000],\",\n \"for corner in corners: if corner[0] + corner[1] < min:\",\n \"= (depth_raw * 1000).astype(np.uint8) # get the current transformation from\",\n \"depth of the detected pixel is 0, check the depth\",\n \"the depth channel inp_image = pre_process(img_wo_at, depth) # pass the\",\n \"left-middle, right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])),\",\n \"detector_factory # transformation from the robot base to aruco tag\",\n \"min = sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl\",\n \"image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)\",\n \"M_CL: trans from camera to aruco tag :param cameraMatrix: camera\",\n \":return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth =\",\n \"# replace blue channel with the depth channel inp_image =\",\n \"= (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] +\",\n \"= sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the top\",\n \"type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT =\",\n \"q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img,\",\n \"def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL\",\n \"avoid mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img,\",\n \"# no detection if len(kps_pr) == 0: return [0, 0,\",\n \"np.pi / 2: orientation = np.pi - orientation elif orientation\",\n \"= np.pi - orientation elif orientation < -np.pi / 2:\",\n \"Kinect V1 :param rgb_data: RGB image :param depth_data: raw depth\",\n \"select the top 1 grasp prediction within the workspace res\",\n \"(0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0,\",\n \"= [] for category_id, preds in net_output.items(): if len(preds) ==\",\n \":3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] =\",\n \"image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts =\",\n \"orientation > np.pi / 2: orientation = np.pi - orientation\",\n \"rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL\",\n \"* depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1],\",\n \"Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1,\",\n \"depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb =\",\n \"message_filters import torch from external.nms import soft_nms from opts import\",\n \"= kp_pr break if res is None: return [0, 0,\",\n \"cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8)\",\n \"pixel with respect to base frame ''' depth = depth_image[pixel[1],\",\n \"rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :] for\",\n \"x, y = pxl[:] return w/12. <= x <= 11*w/12\",\n \"sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import\",\n \"project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL,\",\n \"orientation, dist] if __name__ == '__main__': # initialize ros node\",\n \"except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x,\",\n \"- 45, br_pxl[1] + 46): for w in range(tl_pxl[0] -\",\n \"if __name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") #\",\n \"soft_nms from opts import opts from logger import Logger from\",\n \"Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) #\",\n \"= aruco_tag_remove(img, corners) # replace blue channel with the depth\",\n \"inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth\",\n \"q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B\",\n \"orientation < -np.pi / 2: orientation = -np.pi - orientation\",\n \"= cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw\",\n \"depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image)\",\n \"info :param pixel: x, y :param M_CL: trans from camera\",\n \"depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL,\",\n \"corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() #\",\n \"lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1]\",\n \"/ 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) /\",\n \"aruco tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1.,\",\n \"cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4))\",\n \"kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm,\",\n \"1): nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth\",\n \"img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e:\",\n \"frame ''' depth = depth_image[pixel[1], pixel[0]] # if the depth\",\n \"f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm\",\n \"channel inp_image = pre_process(img_wo_at, depth) # pass the image into\",\n \"img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d\",\n \"for delta_x in range(-nei_range, nei_range + 1): for delta_y in\",\n \"intrinsic matrix :param depth_image: depth image :param depth_scale: depth scale\",\n \"kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center =\",\n \"rgb_data: RGB image :param depth_data: raw depth image :return: None\",\n \"< min: min = corner[0] + corner[1] tl_pxl = [int(corner[0]),\",\n \"the workspace res = None for kp_pr in kps_pr: f_w,\",\n \"= cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy() gray = img.astype(np.uint8) depth =\",\n \"orientation elif orientation < -np.pi / 2: orientation = -np.pi\",\n \"= None for kp_pr in kps_pr: f_w, f_h = 640.\",\n \"640. / 512., 480. / 512. kp_lm = (int(kp_pr[0] *\",\n \"cv2.WITH_QT = False # Get camera calibration parameters cam_param =\",\n \"score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select\",\n \"/ 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0),\",\n \"the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True)\",\n \"f_w), int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3]\",\n \"255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0,\",\n \"cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__\",\n \"inp_image = rgb_img inp_image[:, :, 0] = depth_img inp_image =\",\n \"kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3],\",\n \"if len(kps_pr) == 0: return [0, 0, 0, 0] #\",\n \"by the confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1],\",\n \"0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]]\",\n \"visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation,\",\n \"480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center\",\n \"# Bridge to convert ROS Image type to OpenCV Image\",\n \"= np.array([0, 0, 0, 1]) if visualize: # print('aruco is\",\n \"inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256, 256))\",\n \"for delta_y in range(-nei_range, nei_range + 1): nei = [pixel[0]\",\n \"for h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for\",\n \"detectors.detector_factory import detector_factory # transformation from the robot base to\",\n \"lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep,\",\n \"parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters)\",\n \"= [pixel[0] + delta_x, pixel[1] + delta_y] depth = depth_image[nei[1],\",\n \"0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495,\",\n \"sort by the confidence score kps_pr = sorted(kps_pr, key=lambda x:\",\n \"= rgb_image.copy() # find the top-left and right-bottom corners min\",\n \"- 10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1]\",\n \"detected pixel is 0, check the depth of its neighbors\",\n \"0, check the depth of its neighbors # by counter-clock\",\n \"x <= 11*w/12 and h/12. <= y <= 11*h/12 def\",\n \"ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first\",\n \"[0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of Realsense\",\n \"pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if\",\n \"/ 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h))\",\n \"kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection if len(kps_pr)\",\n \"import absolute_import from __future__ import division from __future__ import print_function\",\n \"rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255), 2)\",\n \"np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is\",\n \"in kps_pr: f_w, f_h = 640. / 512., 480. /\",\n \"kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points lm_ep\",\n \"transformation from the robot base to aruco tag M_BL =\",\n \"0: for delta_x in range(-nei_range, nei_range + 1): for delta_y\",\n \"find the top-left and right-bottom corners min = sys.maxsize max\",\n \"x: x[-1], reverse=True) # select the top 1 grasp prediction\",\n \"center_3d[2], orientation, dist] if __name__ == '__main__': # initialize ros\",\n \"M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the image\",\n \"aruco_tag_remove(img, corners) # replace blue channel with the depth channel\",\n \"GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt =\",\n \"f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm =\",\n \"depth) # pass the image into the network ret =\",\n \"= np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B =\",\n \"3d coordinate of pixel with respect to base frame '''\",\n \"kps[3], score]) # no detection if len(kps_pr) == 0: return\",\n \"as np import sys import rospy from std_msgs.msg import Bool\",\n \"False # Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo,\",\n \"import print_function import _init_paths import os import json import cv2\",\n \"rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2)\",\n \"]] ) # camera intrinsic matrix of Realsense D435 cameraMatrix\",\n \"import dataset_factory from detectors.detector_factory import detector_factory # transformation from the\",\n \") # camera intrinsic matrix of Realsense D435 cameraMatrix =\",\n \"dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\",\",\n \"delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0: break\",\n \"for pred in preds: kps = pred[:4] score = pred[-1]\",\n \"image :return: None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth\",\n \"replace blue channel with the depth channel inp_image = pre_process(img_wo_at,\",\n \"# find the top-left and right-bottom corners min = sys.maxsize\",\n \"* f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and\",\n \"- kp_lm_3d[0]) # motor 7 is clockwise if orientation >\",\n \"kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.))\",\n \"range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x, pixel[1]\",\n \"max: max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])]\",\n \"10, tl_pxl[1] - 10, :] for h in range(tl_pxl[1] -\",\n \"(int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2))\",\n \"visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) #\",\n \":param pixel: x, y :param M_CL: trans from camera to\",\n \"= (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2),\",\n \"from opts import opts from logger import Logger from utils.utils\",\n \",mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0,\",\n \"rospy from std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from\",\n \"result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False):\",\n \"as aruco import numpy as np import sys import rospy\",\n \"M_CL, corners = get_M_CL_info(gray, img, False) # remove aruco tag\",\n \"import message_filters import torch from external.nms import soft_nms from opts\",\n \"-0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] ) #\",\n \"score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no\",\n \"h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL,\",\n \"0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1.,\",\n \":param depth_data: raw depth image :return: None \\\"\\\"\\\" try: cv_rgb\",\n \"RGB and depth input from Kinect V1 :param rgb_data: RGB\",\n \"range(tl_pxl[1] - 45, br_pxl[1] + 46): for w in range(tl_pxl[0]\",\n \"no detection if len(kps_pr) == 0: return [0, 0, 0,\",\n \"512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1]\",\n \"to avoid mis-detection if corners is not None: img_wo_at =\",\n \"255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0],\",\n \"Bridge to convert ROS Image type to OpenCV Image type\",\n \"Subscribe to rgb and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image)\",\n \"in range(-nei_range, nei_range + 1): nei = [pixel[0] + delta_x,\",\n \"M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id, preds\",\n \":] for h in range(tl_pxl[1] - 45, br_pxl[1] + 46):\",\n \"input image to avoid mis-detection if corners is not None:\",\n \"* depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L\",\n \"camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0,\",\n \"from input image to avoid mis-detection if corners is not\",\n \"the current transformation from the camera to aruco tag M_CL,\",\n \"!= 0: break if depth != 0: break nei_range +=\",\n \"depth scale that trans raw data to mm :return: q_B:\",\n \"- orientation elif orientation < -np.pi / 2: orientation =\",\n \"import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory\",\n \"[center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': #\",\n \"\\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr\",\n \"np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L)\",\n \"corner in corners: if corner[0] + corner[1] < min: min\",\n \"channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts\",\n \"if ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL\",\n \"depth, pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2],\",\n \"from __future__ import division from __future__ import print_function import _init_paths\",\n \"cameraMatrix): ''' project 2d pixel on the image to 3d\",\n \"1): for delta_y in range(-nei_range, nei_range + 1): nei =\",\n \"depth = depth_image[pixel[1], pixel[0]] # if the depth of the\",\n \"= corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get\",\n \"45, br_pxl[0] + 46): img_out[h, w, :] = rep_color return\",\n \"# initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset]\",\n \"depth_image[nei[1], nei[0]] if depth != 0: break if depth !=\",\n \"\\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\")\",\n \"current transformation from the camera to aruco tag M_CL, corners\",\n \"pre_process(img_wo_at, depth) # pass the image into the network ret\",\n \"break if res is None: return [0, 0, 0, 0]\",\n \"= cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL\",\n \"return [0, 0, 0, 0] f_w, f_h = 640./512., 480./512.\",\n \"from camera to aruco tag :param cameraMatrix: camera intrinsic matrix\",\n \"= project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map,\",\n \"M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation\",\n \"for w in range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h,\",\n \"def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0] =\",\n \"- kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise\",\n \"5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.),\",\n \"distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def\",\n \"clockwise if orientation > np.pi / 2: orientation = np.pi\",\n \"distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3,\",\n \":param depth_scale: depth scale that trans raw data to mm\",\n \"10, :] for h in range(tl_pxl[1] - 45, br_pxl[1] +\",\n \"br_pxl[1] + 46): for w in range(tl_pxl[0] - 45, br_pxl[0]\",\n \"nei[0]] if depth != 0: break if depth != 0:\",\n \"np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb)\",\n \"f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm,\",\n \"480): res = kp_pr break if res is None: return\",\n \"= depth_image[nei[1], nei[0]] if depth != 0: break if depth\",\n \"= None for corner in corners: if corner[0] + corner[1]\",\n \"q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image =\",\n \"right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) /\",\n \"delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if depth\",\n \"+ delta_y] depth = depth_image[nei[1], nei[0]] if depth != 0:\",\n \"0, 0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w),\",\n \"network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori\",\n \"CameraInfo, timeout=None) # Subscribe to rgb and depth channel image_sub\",\n \"src/static_grasp_kt.py from __future__ import absolute_import from __future__ import division from\",\n \"Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo\",\n \"= [int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color\",\n \"distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet\",\n \"depth_scale: depth scale that trans raw data to mm :return:\",\n \"# distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134,\",\n \"= cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb,\",\n \"None \\\"\\\"\\\" try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data,\",\n \"os import json import cv2 import cv2.aruco as aruco import\",\n \"to aruco tag default_M_CL = np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912,\",\n \"print('aruco is located at mean position (%d, %d)' %(mean_x ,mean_y))\",\n \"pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return\",\n \"def isWithinRange(pxl, w, h): x, y = pxl[:] return w/12.\",\n \"center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1]\",\n \"= Detector(opt) # Publisher of perception result pub_res = rospy.Publisher('/result',\",\n \"ids_CL is None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL =\",\n \"(0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img)\",\n \"= CvBridge() cv2.WITH_QT = False # Get camera calibration parameters\",\n \"import cv2 import cv2.aruco as aruco import numpy as np\",\n \"11*w/12 and h/12. <= y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img,\",\n \"depth_image: depth image :param depth_scale: depth scale that trans raw\",\n \"aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create()\",\n \"mis-detection if corners is not None: img_wo_at = aruco_tag_remove(img, corners)\",\n \"arrow for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] +\",\n \"corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the\",\n \"5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2)\",\n \"None: return default_M_CL, None rvec_CL, tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL,\",\n \"= -np.pi - orientation else: orientation = -orientation # compute\",\n \"aruco tag M_CL, corners = get_M_CL_info(gray, img, False) # remove\",\n \"M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :]\",\n \"cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr =\",\n \"right-middle and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2,\",\n \"depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L =\",\n \"cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr\",\n \"ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori)\",\n \"0.66103522], [ 0., 0., 0., 1. ]] ) # camera\",\n \"325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion of\",\n \"markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250)\",\n \"int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE)\",\n \"kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d = project(center,\",\n \"= np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0.,\",\n \"# compute the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2])\",\n \"= tvec_CL M_CL[3, :] = np.array([0, 0, 0, 1]) if\",\n \"[ 0., 0., 0., 1. ]] ) # camera intrinsic\",\n \"depth input from Kinect V1 :param rgb_data: RGB image :param\",\n \"rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img\",\n \"0., 0., 1. ]] ) # camera intrinsic matrix of\",\n \"sys.maxsize max = -sys.maxsize tl_pxl = None br_pxl = None\",\n \"import torch from external.nms import soft_nms from opts import opts\",\n \"aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left and\",\n \"print(opt) Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of\",\n \"= pre_process(img_wo_at, depth) # pass the image into the network\",\n \"e: print(e) def isWithinRange(pxl, w, h): x, y = pxl[:]\",\n \"= np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector\",\n \"logger import Logger from utils.utils import AverageMeter from datasets.dataset_factory import\",\n \"the open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw\",\n \"tvec_CL, _objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL =\",\n \"Publisher of perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def\",\n \"max = corner[0] + corner[1] br_pxl = [int(corner[0]), int(corner[1])] #\",\n \"orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) # motor\",\n \"0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default transformation\",\n \"matrix :param depth_image: depth image :param depth_scale: depth scale that\",\n \"counter-clock wise nei_range = 1 while depth == 0: for\",\n \"cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save\",\n \"depth channel inp_image = pre_process(img_wo_at, depth) # pass the image\",\n \"+ (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] -\",\n \"continue for pred in preds: kps = pred[:4] score =\",\n \"- kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) /\",\n \"\\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) #\",\n \"0: break nei_range += 1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] *\",\n \"-0.07926594, -0.8038495, 0.66103522], [ 0., 0., 0., 1. ]] )\",\n \"Image type to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT\",\n \"at mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs,\",\n \"pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl, w, h):\",\n \"cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] - kp_lm_3d[0]) #\",\n \":param rgb_data: RGB image :param depth_data: raw depth image :return:\",\n \"# draw arrow for left-middle and right-middle key-points lm_ep =\",\n \"None br_pxl = None for corner in corners: if corner[0]\",\n \"import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters\",\n \"= np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img):\",\n \"= (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if isWithinRange(kp_lm, 640,\",\n \"int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]),\",\n \"CameraInfo from cv_bridge import CvBridge, CvBridgeError import message_filters import torch\",\n \"<= x <= 11*w/12 and h/12. <= y <= 11*h/12\",\n \"= pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score])\",\n \"+ (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] +\",\n \"cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle,\",\n \"0, 1]) if visualize: # print('aruco is located at mean\",\n \"Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge,\",\n \"by depth info :param pixel: x, y :param M_CL: trans\",\n \"raw data to mm :return: q_B: 3d coordinate of pixel\",\n \"kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB and depth input from\",\n \"M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def\",\n \"== 0: continue for pred in preds: kps = pred[:4]\",\n \"rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL\",\n \"cameraMatrix: camera intrinsic matrix :param depth_image: depth image :param depth_scale:\",\n \"KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as\",\n \"y <= 11*h/12 def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix,\",\n \"np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C =\",\n \"(int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img,\",\n \"cv_bridge import CvBridge, CvBridgeError import message_filters import torch from external.nms\",\n \"(kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0]\",\n \"int(kp_pr[1] * f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] *\",\n \"__future__ import absolute_import from __future__ import division from __future__ import\",\n \"dataset_factory from detectors.detector_factory import detector_factory # transformation from the robot\",\n \"+ 46): for w in range(tl_pxl[0] - 45, br_pxl[0] +\",\n \"M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0, 0,\",\n \"kp_pr in kps_pr: f_w, f_h = 640. / 512., 480.\",\n \":] = rep_color return img_out def project(pixel, depth_image, M_CL, M_BL,\",\n \"cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255), 2) rgb_img =\",\n \"aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL,\",\n \"scale that trans raw data to mm :return: q_B: 3d\",\n \"cv2 import cv2.aruco as aruco import numpy as np import\",\n \"M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix)\",\n \"# Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None)\",\n \"score]) # no detection if len(kps_pr) == 0: return [0,\",\n \"isWithinRange(kp_rm, 640, 480): res = kp_pr break if res is\",\n \"detector = Detector(opt) # Publisher of perception result pub_res =\",\n \"category_id, preds in net_output.items(): if len(preds) == 0: continue for\",\n \"1 pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth,\",\n \"pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C) q_B = M_BL.dot(q_L) return q_B\",\n \"try: cv_rgb = cv_bridge.imgmsg_to_cv2(rgb_data, \\\"bgr8\\\") cv_depth = cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr\",\n \"of the detected pixel is 0, check the depth of\",\n \"np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw =\",\n \"depth == 0: for delta_x in range(-nei_range, nei_range + 1):\",\n \"return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :,\",\n \"detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img,\",\n \"nei = [pixel[0] + delta_x, pixel[1] + delta_y] depth =\",\n \"[0., 1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0.,\",\n \"= None br_pxl = None for corner in corners: if\",\n \"in corners: if corner[0] + corner[1] < min: min =\",\n \"640, 480): res = kp_pr break if res is None:\",\n \"cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d =\",\n \"kp_lm_3d[0]) # motor 7 is clockwise if orientation > np.pi\",\n \"255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0,\",\n \"the image into the network ret = detector.run(inp_image[:, :, :])\",\n \"= -orientation # compute the open width dist = np.linalg.norm(kp_lm_3d[:2]\",\n \"+ delta_x, pixel[1] + delta_y] depth = depth_image[nei[1], nei[0]] if\",\n \"1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847, -0.04283,\",\n \"if orientation > np.pi / 2: orientation = np.pi -\",\n \"import Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory\",\n \"AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory import detector_factory #\",\n \"3d by depth info :param pixel: x, y :param M_CL:\",\n \"M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL, cameraMatrix) center_3d\",\n \"rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2)\",\n \"import cv2.aruco as aruco import numpy as np import sys\",\n \"cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3,\",\n \":] = np.array([0, 0, 0, 1]) if visualize: # print('aruco\",\n \"kps[2], kps[3], score]) # no detection if len(kps_pr) == 0:\",\n \"> np.pi / 2: orientation = np.pi - orientation elif\",\n \"0., 1.00000]]) # default transformation from the camera to aruco\",\n \"nei_range = 1 while depth == 0: for delta_x in\",\n \"if corner[0] + corner[1] > max: max = corner[0] +\",\n \"open width dist = np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow\",\n \"2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])),\",\n \"2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual',\",\n \"= aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for the first frame, it\",\n \"D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0]) # initialize\",\n \"def KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr =\",\n \"from the camera to aruco tag M_CL, corners = get_M_CL_info(gray,\",\n \"workspace res = None for kp_pr in kps_pr: f_w, f_h\",\n \"depth_image, M_CL, M_BL, cameraMatrix): ''' project 2d pixel on the\",\n \"for left-middle and right-middle key-points lm_ep = (int(kp_lm[0] + (kp_rm[0]\",\n \"cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist]\",\n \"int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img,\",\n \"to base frame ''' depth = depth_image[pixel[1], pixel[0]] # if\",\n \":, :]) ret = ret[\\\"results\\\"] loc_ori = KpsToGrasppose(ret, img, depth_raw,\",\n \"within the workspace res = None for kp_pr in kps_pr:\",\n \"project(center, depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1],\",\n \"cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera calibration\",\n \"cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img = cv_rgb_arr.copy() depth_raw = cv_depth_arr.copy()\",\n \"(0, 0, 0), 2) # draw left-middle, right-middle and center\",\n \"pass the image into the network ret = detector.run(inp_image[:, :,\",\n \"ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type\",\n \"aruco import numpy as np import sys import rospy from\",\n \"(kp_rm[0] - kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1])\",\n \"0: continue for pred in preds: kps = pred[:4] score\",\n \":param depth_image: depth image :param depth_scale: depth scale that trans\",\n \"= -sys.maxsize tl_pxl = None br_pxl = None for corner\",\n \"while depth == 0: for delta_x in range(-nei_range, nei_range +\",\n \"tl_pxl[1] - 10, :] for h in range(tl_pxl[1] - 45,\",\n \"cv2.aruco as aruco import numpy as np import sys import\",\n \"1000).astype(np.uint8) # get the current transformation from the camera to\",\n \"pxl = np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth]))\",\n \"False) # remove aruco tag from input image to avoid\",\n \"isWithinRange(pxl, w, h): x, y = pxl[:] return w/12. <=\",\n \"- orientation else: orientation = -orientation # compute the open\",\n \"/ 512., 480. / 512. kp_lm = (int(kp_pr[0] * f_w),\",\n \"_objPoints_CL = aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL)\",\n \"tag M_BL = np.array([[1., 0., 0., 0.30000], [0., 1., 0.,\",\n \"parameters markerLength_CL = 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL =\",\n \"depth_data): \\\"\\\"\\\" Save raw RGB and depth input from Kinect\",\n \"f_h = 640. / 512., 480. / 512. kp_lm =\",\n \"1., 0., 0.32000], [0., 0., 1., -0.0450], [0., 0., 0.,\",\n \"KpsToGrasppose(net_output, rgb_img, depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = []\",\n \"res is None: return [0, 0, 0, 0] f_w, f_h\",\n \"0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594, -0.8038495, 0.66103522], [ 0.,\",\n \"= dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task]\",\n \"torch from external.nms import soft_nms from opts import opts from\",\n \"def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find the top-left\",\n \"center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL,\",\n \"to OpenCV Image type cv_bridge = CvBridge() cv2.WITH_QT = False\",\n \"depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e)\",\n \"pred in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0],\",\n \"Get camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) #\",\n \"cameraMatrix, distCoeffs, rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :]\",\n \"pixel value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10,\",\n \"import sys import rospy from std_msgs.msg import Bool from std_msgs.msg\",\n \"matrix of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0,\",\n \"is None: return [0, 0, 0, 0] f_w, f_h =\",\n \"if res is None: return [0, 0, 0, 0] f_w,\",\n \"corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement pixel\",\n \"[int(corner[0]), int(corner[1])] # get the replacement pixel value rep_color =\",\n \"h in range(tl_pxl[1] - 45, br_pxl[1] + 46): for w\",\n \"if corner[0] + corner[1] < min: min = corner[0] +\",\n \"[int(corner[0]), int(corner[1])] if corner[0] + corner[1] > max: max =\",\n \"range(-nei_range, nei_range + 1): for delta_y in range(-nei_range, nei_range +\",\n \"M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0] -\",\n \"0., 0., 0., 1. ]] ) # camera intrinsic matrix\",\n \"to mm :return: q_B: 3d coordinate of pixel with respect\",\n \"aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) # for\",\n \"Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL =\",\n \"orientation = np.pi - orientation elif orientation < -np.pi /\",\n \"loc_ori = KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except\",\n \"RGB image :param depth_data: raw depth image :return: None \\\"\\\"\\\"\",\n \"(depth_raw * 1000).astype(np.uint8) # get the current transformation from the\",\n \"kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] - kp_rm[0])\",\n \"frame, it may contain nothing if ids_CL is None: return\",\n \"/ 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep\",\n \"in net_output.items(): if len(preds) == 0: continue for pred in\",\n \"depth != 0: break if depth != 0: break nei_range\",\n \"45, br_pxl[1] + 46): for w in range(tl_pxl[0] - 45,\",\n \"= np.linalg.norm(kp_lm_3d[:2] - kp_rm_3d[:2]) # draw arrow for left-middle and\",\n \"Logger from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from\",\n \"initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image\",\n \"1 while depth == 0: for delta_x in range(-nei_range, nei_range\",\n \"the image to 3d by depth info :param pixel: x,\",\n \"640, 480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break\",\n \"sys import rospy from std_msgs.msg import Bool from std_msgs.msg import\",\n \"for kp_pr in kps_pr: f_w, f_h = 640. / 512.,\",\n \"img, False) # remove aruco tag from input image to\",\n \"node rospy.init_node(\\\"Static_grasping\\\") # Bridge to convert ROS Image type to\",\n \"len(preds) == 0: continue for pred in preds: kps =\",\n \"* f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h))\",\n \"pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) #\",\n \"# pass the image into the network ret = detector.run(inp_image[:,\",\n \"from utils.utils import AverageMeter from datasets.dataset_factory import dataset_factory from detectors.detector_factory\",\n \"depth_map, M_CL, M_BL, cameraMatrix, visualize=True): kps_pr = [] for category_id,\",\n \"rvec_CL, tvec_CL, markerLength_CL) return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image,\",\n \"f_h)) kp_rm = (int(kp_pr[2] * f_w), int(kp_pr[3] * f_h)) if\",\n \"pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): #\",\n \"(int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] + (kp_lm[1]\",\n \"import _init_paths import os import json import cv2 import cv2.aruco\",\n \"net_output.items(): if len(preds) == 0: continue for pred in preds:\",\n \"= 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w),\",\n \"get_M_CL_info(gray, img, False) # remove aruco tag from input image\",\n \"-0.0450], [0., 0., 0., 1.00000]]) # default transformation from the\",\n \"key-points lm_ep = (int(kp_lm[0] + (kp_rm[0] - kp_lm[0]) / 5.),\",\n \"return M_CL, corners_CL[0][0, :, :] def aruco_tag_remove(rgb_image, corners): img_out =\",\n \"prediction within the workspace res = None for kp_pr in\",\n \"f_w, f_h = 640. / 512., 480. / 512. kp_lm\",\n \"# remove aruco tag from input image to avoid mis-detection\",\n \"the first frame, it may contain nothing if ids_CL is\",\n \"depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1]) q_L = np.linalg.inv(M_CL).dot(q_C)\",\n \"elif orientation < -np.pi / 2: orientation = -np.pi -\",\n \"get the current transformation from the camera to aruco tag\",\n \"-0.04283, 0.00134, -0.00102, 0.0]) # initialize GKNet Detector opt =\",\n \"its neighbors # by counter-clock wise nei_range = 1 while\",\n \"return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__':\",\n \"f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480): res\",\n \"kp_rm, rm_ep, (0, 0, 0), 2) # draw left-middle, right-middle\",\n \"import Float64MultiArray from sensor_msgs.msg import Image, CameraInfo from cv_bridge import\",\n \"dst_CL M_CL[:3, 3] = tvec_CL M_CL[3, :] = np.array([0, 0,\",\n \"0: break if depth != 0: break nei_range += 1\",\n \"-0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768, -0.07926594,\",\n \"get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093 aruco_dict_CL =\",\n \"< -np.pi / 2: orientation = -np.pi - orientation else:\",\n \"from the robot base to aruco tag M_BL = np.array([[1.,\",\n \"(int(res[2]*f_w), int(res[3]*f_h)) center = (int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map,\",\n \"= pxl[:] return w/12. <= x <= 11*w/12 and h/12.\",\n \"2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0, 0, 255),\",\n \"center_3d[1], center_3d[2], orientation, dist] if __name__ == '__main__': # initialize\",\n \"confidence score kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) #\",\n \"-0.00102, 0.0]) # initialize GKNet Detector opt = opts().parse() Dataset\",\n \"= img.astype(np.uint8) depth = (depth_raw * 1000).astype(np.uint8) # get the\",\n \"image to avoid mis-detection if corners is not None: img_wo_at\",\n \"0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]]) # distortion\",\n \"0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0,\",\n \"if len(preds) == 0: continue for pred in preds: kps\",\n \"print_function import _init_paths import os import json import cv2 import\",\n \"0] f_w, f_h = 640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h))\",\n \"np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr = np.nan_to_num(cv_depth_arr)\",\n \"message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub], 1, 0.1) ts.registerCallback(kinect_rgbd_callback) rospy.spin()\",\n \"and depth input from Kinect V1 :param rgb_data: RGB image\",\n \"channel with the depth channel inp_image = pre_process(img_wo_at, depth) #\",\n \":, :] def aruco_tag_remove(rgb_image, corners): img_out = rgb_image.copy() # find\",\n \"cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if\",\n \"range(tl_pxl[0] - 45, br_pxl[0] + 46): img_out[h, w, :] =\",\n \"q_B: 3d coordinate of pixel with respect to base frame\",\n \"0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm, rm_ep, (0, 0, 0),\",\n \"(int(center[0]), int(center[1])), 2, (0, 0, 255), 2) if visualize: cv2.namedWindow('visual',\",\n \"CvBridge, CvBridgeError import message_filters import torch from external.nms import soft_nms\",\n \"type cv_bridge = CvBridge() cv2.WITH_QT = False # Get camera\",\n \"initialize GKNet Detector opt = opts().parse() Dataset = dataset_factory[opt.dataset] opt\",\n \"queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters markerLength_CL = 0.093\",\n \"min = corner[0] + corner[1] tl_pxl = [int(corner[0]), int(corner[1])] if\",\n \"[0, 0, 0, 0] f_w, f_h = 640./512., 480./512. kp_lm\",\n \"pixel on the image to 3d by depth info :param\",\n \"dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3]\",\n \"image :param depth_scale: depth scale that trans raw data to\",\n \"depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d = project(kp_rm, depth_map, M_CL, M_BL,\",\n \"= opts().parse() Dataset = dataset_factory[opt.dataset] opt = opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt)\",\n \"trans from camera to aruco tag :param cameraMatrix: camera intrinsic\",\n \"neighbors # by counter-clock wise nei_range = 1 while depth\",\n \"from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge, CvBridgeError\",\n \"in preds: kps = pred[:4] score = pred[-1] kps_pr.append([kps[0], kps[1],\",\n \"= (int(kp_rm[0] + (kp_lm[0] - kp_rm[0]) / 5.), int(kp_rm[1] +\",\n \"''' depth = depth_image[pixel[1], pixel[0]] # if the depth of\",\n \"and center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0,\",\n \"center key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0,\",\n \"tag :param cameraMatrix: camera intrinsic matrix :param depth_image: depth image\",\n \"import json import cv2 import cv2.aruco as aruco import numpy\",\n \"0., 1. ]] ) # camera intrinsic matrix of Realsense\",\n \"return img_out def project(pixel, depth_image, M_CL, M_BL, cameraMatrix): ''' project\",\n \"= aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL\",\n \"512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] * f_h)) kp_rm\",\n \"if visualize: cv2.namedWindow('visual', cv2.WINDOW_AUTOSIZE) cv2.imshow('visual', rgb_img) return [center_3d[0], center_3d[1], center_3d[2],\",\n \"# camera intrinsic matrix of Realsense D435 cameraMatrix = np.array([[607.47165,\",\n \"-sys.maxsize tl_pxl = None br_pxl = None for corner in\",\n \"[0., 0., 1., -0.0450], [0., 0., 0., 1.00000]]) # default\",\n \"Detector = detector_factory[opt.task] detector = Detector(opt) # Publisher of perception\",\n \"rgb_image.copy() # find the top-left and right-bottom corners min =\",\n \"M_CL, M_BL, cameraMatrix) center_3d = project(center, depth_map, M_CL, M_BL, cameraMatrix)\",\n \"kp_lm[0]) / 5.), int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.))\",\n \"480) and isWithinRange(kp_rm, 640, 480): res = kp_pr break if\",\n \"2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]), int(kp_rm[1])), 2, (0, 0, 255),\",\n \"= message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub,\",\n \"= np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth, dtype=np.float32) # cv_depth_arr =\",\n \"opts from logger import Logger from utils.utils import AverageMeter from\",\n \"+ corner[1] br_pxl = [int(corner[0]), int(corner[1])] # get the replacement\",\n \"+ corner[1] < min: min = corner[0] + corner[1] tl_pxl\",\n \"= M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img\",\n \"np.array([[1., 0., 0., 0.30000], [0., 1., 0., 0.32000], [0., 0.,\",\n \"markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4,\",\n \"* f_h)) if isWithinRange(kp_lm, 640, 480) and isWithinRange(kp_rm, 640, 480):\",\n \"is clockwise if orientation > np.pi / 2: orientation =\",\n \"jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL = np.zeros((4, 4)) M_CL[:3, :3] =\",\n \"value rep_color = img_out[tl_pxl[0] - 10, tl_pxl[1] - 10, :]\",\n \"cameraMatrix, visualize=True): kps_pr = [] for category_id, preds in net_output.items():\",\n \"= cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img\",\n \":return: q_B: 3d coordinate of pixel with respect to base\",\n \"remove aruco tag from input image to avoid mis-detection if\",\n \"img_wo_at = aruco_tag_remove(img, corners) # replace blue channel with the\",\n \"as e: print(e) def isWithinRange(pxl, w, h): x, y =\",\n \"0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(center[0]), int(center[1])), 2, (0,\",\n \"trans raw data to mm :return: q_B: 3d coordinate of\",\n \"480. / 512. kp_lm = (int(kp_pr[0] * f_w), int(kp_pr[1] *\",\n \"aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL,\",\n \"res = None for kp_pr in kps_pr: f_w, f_h =\",\n \"kp_rm_3d[0] - kp_lm_3d[0]) # motor 7 is clockwise if orientation\",\n \"M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError as e: print(e) def isWithinRange(pxl,\",\n \"kp_pr break if res is None: return [0, 0, 0,\",\n \"from __future__ import print_function import _init_paths import os import json\",\n \"rgb_img inp_image[:, :, 0] = depth_img inp_image = cv2.resize(inp_image, (256,\",\n \"= opts().update_dataset_info_and_set_heads(opt, Dataset) print(opt) Detector = detector_factory[opt.task] detector = Detector(opt)\",\n \"orientation = -np.pi - orientation else: orientation = -orientation #\",\n \"detection if len(kps_pr) == 0: return [0, 0, 0, 0]\",\n \"timeout=None) # Subscribe to rgb and depth channel image_sub =\",\n \"pixel: x, y :param M_CL: trans from camera to aruco\",\n \"5.), int(kp_rm[1] + (kp_lm[1] - kp_rm[1]) / 5.)) rgb_img =\",\n \"motor 7 is clockwise if orientation > np.pi / 2:\",\n \"= aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray,\",\n \"# for the first frame, it may contain nothing if\",\n \"absolute_import from __future__ import division from __future__ import print_function import\",\n \"from detectors.detector_factory import detector_factory # transformation from the robot base\",\n \"0.0, 1.0]]) # distortion of Realsense D435 distCoeffs = np.array([0.08847,\",\n \"tl_pxl = None br_pxl = None for corner in corners:\",\n \"np.array([[-0.07134498, -0.99639369, 0.0459293, -0.13825178], [-0.8045912, 0.03027403, -0.59305689, 0.08434352], [ 0.58952768,\",\n \"int(kp_lm[1])), 2, (0, 0, 255), 2) rgb_img = cv2.circle(rgb_img, (int(kp_rm[0]),\",\n \"256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\" Save raw RGB\",\n \"= KpsToGrasppose(ret, img, depth_raw, M_CL, M_BL, cameraMatrix) pub_res.publish(loc_ori) except CvBridgeError\",\n \"to aruco tag M_CL, corners = get_M_CL_info(gray, img, False) #\",\n \"np.zeros((4, 4)) M_CL[:3, :3] = dst_CL M_CL[:3, 3] = tvec_CL\",\n \"# aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints\",\n \"= np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0, 0.0, 1.0]])\",\n \"project 2d pixel on the image to 3d by depth\",\n \"D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420, 240.91934], [0.0,\",\n \"- kp_rm[1]) / 5.)) rgb_img = cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0,\",\n \"x[-1], reverse=True) # select the top 1 grasp prediction within\",\n \"on the image to 3d by depth info :param pixel:\",\n \"kps_pr = sorted(kps_pr, key=lambda x: x[-1], reverse=True) # select the\",\n \"M_BL.dot(q_L) return q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:,\",\n \"aruco.estimatePoseSingleMarkers(corners_CL[0], markerLength_CL, cameraMatrix, distCoeffs) dst_CL, jacobian_CL = cv2.Rodrigues(rvec_CL) M_CL =\",\n \"__name__ == '__main__': # initialize ros node rospy.init_node(\\\"Static_grasping\\\") # Bridge\",\n \"pixel[1] * depth, depth])) q_C = np.array([pxl[0], pxl[1], pxl[2], 1])\",\n \"key-points rgb_img = cv2.circle(rgb_img, (int(kp_lm[0]), int(kp_lm[1])), 2, (0, 0, 255),\",\n \"message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\", Image) ts = message_filters.ApproximateTimeSynchronizer([image_sub, depth_sub],\",\n \"transformation from the camera to aruco tag default_M_CL = np.array([[-0.07134498,\",\n \"= cv2.resize(inp_image, (256, 256)) return inp_image def kinect_rgbd_callback(rgb_data, depth_data): \\\"\\\"\\\"\",\n \"y :param M_CL: trans from camera to aruco tag :param\",\n \"0., 0.30000], [0., 1., 0., 0.32000], [0., 0., 1., -0.0450],\",\n \"of Realsense D435 distCoeffs = np.array([0.08847, -0.04283, 0.00134, -0.00102, 0.0])\",\n \"perception result pub_res = rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init,\",\n \"= aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL,\",\n \"0: return [0, 0, 0, 0] # sort by the\",\n \"(int((kp_lm[0]+kp_rm[0])/2), int((kp_lm[1]+kp_rm[1])/2)) kp_lm_3d = project(kp_lm, depth_map, M_CL, M_BL, cameraMatrix) kp_rm_3d\",\n \"/ 2: orientation = np.pi - orientation elif orientation <\",\n \"# cv_depth_arr = np.nan_to_num(cv_depth_arr) cv2.imshow(\\\"Depth\\\", cv_depth) cv2.imshow(\\\"RGB\\\", cv_rgb) img =\",\n \"aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters = aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints =\",\n \"external.nms import soft_nms from opts import opts from logger import\",\n \"= np.linalg.inv(cameraMatrix).dot( np.array([pixel[0] * depth, pixel[1] * depth, depth])) q_C\",\n \"- kp_rm_3d[:2]) # draw arrow for left-middle and right-middle key-points\",\n \"depth of its neighbors # by counter-clock wise nei_range =\",\n \"not None: img_wo_at = aruco_tag_remove(img, corners) # replace blue channel\",\n \"kp_lm, lm_ep, (0, 0, 0), 2) rgb_img = cv2.arrowedLine(rgb_img, kp_rm,\",\n \"corners min = sys.maxsize max = -sys.maxsize tl_pxl = None\",\n \"CvBridgeError import message_filters import torch from external.nms import soft_nms from\",\n \"corner[1] < min: min = corner[0] + corner[1] tl_pxl =\",\n \"the network ret = detector.run(inp_image[:, :, :]) ret = ret[\\\"results\\\"]\",\n \"int(kp_lm[1] + (kp_rm[1] - kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0]\",\n \"detector_factory[opt.task] detector = Detector(opt) # Publisher of perception result pub_res\",\n \"orientation = -orientation # compute the open width dist =\",\n \"from external.nms import soft_nms from opts import opts from logger\",\n \"= aruco.DetectorParameters_create() corners_CL, ids_CL, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict_CL, parameters=parameters) #\",\n \"of Realsense D435 cameraMatrix = np.array([[607.47165, 0.0, 325.90064], [0.0, 606.30420,\",\n \"wise nei_range = 1 while depth == 0: for delta_x\",\n \"= pred[-1] kps_pr.append([kps[0], kps[1], kps[2], kps[3], score]) # no detection\",\n \"rgb_img) return [center_3d[0], center_3d[1], center_3d[2], orientation, dist] if __name__ ==\",\n \"depth_map, M_CL, M_BL, cameraMatrix) orientation = np.arctan2(kp_rm_3d[1] - kp_lm_3d[1], kp_rm_3d[0]\",\n \"get the replacement pixel value rep_color = img_out[tl_pxl[0] - 10,\",\n \"and depth channel image_sub = message_filters.Subscriber(\\\"/camera/rgb/image_rect_color\\\", Image) depth_sub = message_filters.Subscriber(\\\"/camera/depth_registered/image\\\",\",\n \"= 0.093 aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL) # aruco_dict_CL = aruco.Dictionary_get(aruco.DICT_6X6_250) parameters\",\n \"= get_M_CL_info(gray, img, False) # remove aruco tag from input\",\n \"CvBridgeError as e: print(e) def isWithinRange(pxl, w, h): x, y\",\n \"for category_id, preds in net_output.items(): if len(preds) == 0: continue\",\n \"# if the depth of the detected pixel is 0,\",\n \"mean position (%d, %d)' %(mean_x ,mean_y)) aruco.drawAxis(image_init, cameraMatrix, distCoeffs, rvec_CL,\",\n \"by counter-clock wise nei_range = 1 while depth == 0:\",\n \"- kp_lm[1]) / 5.)) rm_ep = (int(kp_rm[0] + (kp_lm[0] -\",\n \"q_B def pre_process(rgb_img, depth_img): inp_image = rgb_img inp_image[:, :, 0]\",\n \"= rospy.Publisher('/result', Float64MultiArray, queue_size=10) def get_M_CL_info(gray, image_init, visualize=False): # parameters\",\n \"[0., 0., 0., 1.00000]]) # default transformation from the camera\",\n \"# select the top 1 grasp prediction within the workspace\",\n \"= cv2.arrowedLine(rgb_img, kp_lm, lm_ep, (0, 0, 0), 2) rgb_img =\",\n \"= cv_bridge.imgmsg_to_cv2(depth_data, \\\"32FC1\\\") cv_rgb_arr = np.array(cv_rgb, dtype=np.uint8) cv_depth_arr = np.array(cv_depth,\",\n \"* 1000).astype(np.uint8) # get the current transformation from the camera\",\n \"camera calibration parameters cam_param = rospy.wait_for_message('/camera/rgb/camera_info', CameraInfo, timeout=None) # Subscribe\",\n \"and right-bottom corners min = sys.maxsize max = -sys.maxsize tl_pxl\",\n \"2) # draw left-middle, right-middle and center key-points rgb_img =\",\n \"640./512., 480./512. kp_lm = (int(res[0]*f_w), int(res[1]*f_h)) kp_rm = (int(res[2]*f_w), int(res[3]*f_h))\",\n \"std_msgs.msg import Bool from std_msgs.msg import Float64MultiArray from sensor_msgs.msg import\"\n]"}}},{"rowIdx":3490,"cells":{"ngram":{"kind":"list like","value":["0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv","augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None,","RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX')","= np.array([[1, level, 0], [0, 1 , 0], [0, 0","v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor +","cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level,","= np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return","if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else","M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): #","if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else","else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' :","level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def","np.array([[1, level, 0], [0, 1 , 0], [0, 0 ,","augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None,","random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple):","img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return","# print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import","tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else:","= option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return","return np.where(image <= threshold, image, 255 - image) def posterize(img,","np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img","delta = height - width if delta > 0: img","return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape","width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255)","def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height =","2, h / 2) # Perform the rotation M =","1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13.","mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)","target size \"\"\" if padding: height,width,_ = img.shape delta =","[[1, 1, 1], # [1, 5, 1], # [1, 1,","self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level",":, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0]","# [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, #","vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain]","= np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45,","# kernel = np.array( # [[1, 1, 1], # [1,","1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,","{ 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize'","/ 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width","None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert","'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout'","augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else","augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' :","option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio","else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def","augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level =","cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv,","cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128):","# def sharpness(img,): # kernel = np.array( # [[1, 1,","'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under')","return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0,","augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None,","lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x","borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 ,","else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop","else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast':","np.array([[1, 0 , 0], [level, 1 , 0], [0, 0","1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img","'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'),","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'):","= img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ =","from image distortion. Defaults to True. Returns: images (np.ndarray): images","= level height,width,_ = img.shape new_width = int(ratio*width) img_ =","for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor):","lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x","= np.array( # [[1, 1, 1], # [1, 5, 1],","+ (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) #","= cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)","'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv","in target size \"\"\" if padding: height,width,_ = img.shape delta","return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter","try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg -","def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target","* (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return","img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level","resize. Prevent from image distortion. Defaults to True. Returns: images","center = (w / 2, h / 2) # Perform","None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' :","self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None,","augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None,","= { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'),","np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0","if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else","= img.shape delta = height - width if delta >","= int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w =",": augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'),","= kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] =","'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if","augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None,","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def","cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width)","degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None):","images in target size \"\"\" if padding: height,width,_ = img.shape","img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width","cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :,","None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' :","= np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate","0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_","M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ =","height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode","[level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img =","= level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ =","augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness':","None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' :","return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0],","= img[new_height:,:,:] height,width,_ = img_.shape if height > width :","as np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args:","np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if","= cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return","if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor","img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height","yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe:","return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params","1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1,","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25):","0 , 0], [level, 1 , 0], [0, 0 ,","None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if","= ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x *",": RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if","= 8 - bits # img = img >> shift","img = img >> shift img = np.left_shift(img,shift) img =","level=45, center = None, scale = 1.0): angle=level (h, w)","= img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)","subtract 255 from the pixel. return np.where(image <= threshold, image,","0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_","augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None,","cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level)","cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0,","value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:]","= list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy)","min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level)","None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop }","RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else","uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x *","if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else","= np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0,",", 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,","(int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding","else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness'","180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val =","img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224))","augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'),","RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize')","width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2","mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img","return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV","def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters:","'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' :","augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under')","'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'),","in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img =","'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if","augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in","np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 #","0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) #","0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))",": RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if","in the image, select the pixel # if the value","height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_","img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level","level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:]","cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width,","center = None, scale = 1.0): angle=level (h, w) =","Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range","pixel. return np.where(image <= threshold, image, 255 - image) def","0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' :","eg: 224 , (224,224) or [224,224] padding (bool): padding img","if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else","cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to",", translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,","bits = level shift = 8 - bits # img","else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under","img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 ,","translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,","self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' :","than the threshold. # Otherwise, subtract 255 from the pixel.","im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr","the image, select the pixel # if the value is","if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2","bgr=True): im = im.copy() # Equalize histogram on BGR image","level=3): bits = level shift = 8 - bits #","return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img)","img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2","RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate')","image, select the pixel # if the value is less","= int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height","None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' :","Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224","} mode = option_mode[mode] translate_pixel = int(width * level) M","RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else","# cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): #","= augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr')","return translate_img # def sharpness(img,): # kernel = np.array( #",", 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return","\"\"\" if padding: height,width,_ = img.shape delta = height -","M = np.array([[1, level, 0], [0, 1 , 0], [0,","def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 ,","return img def rotate(image, level=45, center = None, scale =","* level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return","= np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) %","> width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)","isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self,","import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray):","target size. eg: 224 , (224,224) or [224,224] padding (bool):","return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape","'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'),","= cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_","(224,224) or [224,224] padding (bool): padding img before resize. Prevent","cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img =","def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img =","= img[:,new_width:,:] height,width,_ = img_.shape if height > width :","and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else","image to RGB def solarize(image, level=128): threshold = level image",", 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float')","= im.copy() # Equalize histogram on BGR image 'im' with","ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_","> 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img =","1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32,","augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above':","/ 2, h / 2) # Perform the rotation M","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,):","(h, w) = image.shape[:2] if center is None: center =",": RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if","np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img","dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2.","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img):","if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None,","'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode]","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio","int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height >","img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values","= 255 return img def rotate(image, level=45, center = None,","'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above')","RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else","no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im","cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256,","'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV","augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None,","equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else","= img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img,","= np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue),","angle=level (h, w) = image.shape[:2] if center is None: center","= np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img","(np.ndarray): images in target size \"\"\" if padding: height,width,_ =","= cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x =","translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def","3) * [hgain, sgain, vgain] + 1 # random gains","=255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment:","else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize'","% 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val","cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__': augmentation_test()","(bool): padding img before resize. Prevent from image distortion. Defaults","return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'","img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255)","=255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size","= cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c","# cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__':",": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel","= np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255)","cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio =","translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :","gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype",": augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' :","r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype)","height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_","= cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE)","img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0])","img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape","dovietchinh/multi-task-classificationsource/utils/augmentations.py0 import numpy as np import cv2 import random def","# [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1])","cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return","None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' :","np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if","img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:","img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center =","'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left","'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width","if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None,","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_","RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left')","img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level","1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,","Returns: images (np.ndarray): images in target size \"\"\" if padding:","'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness'","# except: # print(augmenter) return img def augmentation_test(): img_org =","value is less than the threshold. # Otherwise, subtract 255","None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if","cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0],","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'):","color-space augmentation if hgain or sgain or vgain: r =","[1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3,","'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'),","0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,","= RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5):","= img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ =","if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None,","if center is None: center = (w / 2, h",", translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float')","return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im =",":, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel",": augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' :","cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ =","ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_","if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else:","1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))","int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1])","is None: center = (w / 2, h / 2)",": RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if","= img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio =","'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness')","factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return","# 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items()","translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img","vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain","hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3)","padding img before resize. Prevent from image distortion. Defaults to","# [[1, 1, 1], # [1, 5, 1], # [1,","'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if","255 - image) def posterize(img, level=3): bits = level shift","scale = 1.0): angle=level (h, w) = image.shape[:2] if center","= img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level:","'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v","shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def","invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)","(width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape","padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w","img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img,","={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode =","img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }","'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y","# Otherwise, subtract 255 from the pixel. return np.where(image <=","= int(width * level) M = np.array([[1, 0 , translate_pixel],","height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 =","x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0])","np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2],","height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode","M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level","option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode",": RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize')","augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000):","[0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =","images (np.ndarray): images in target size \"\"\" if padding: height,width,_","<= threshold, image, 255 - image) def posterize(img, level=3): bits","0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram","else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX'","borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array(","if padding: height,width,_ = img.shape delta = height - width","else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize':","(np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224)","YUV image to RGB def solarize(image, level=128): threshold = level","posterize(img, level=3): bits = level shift = 8 - bits","cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel =","* level) M = np.array([[1, 0 , translate_pixel], [level, 1","# print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float')","rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height","range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)","= c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:,",": augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' :","= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return","= img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ =","= { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv'","sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def","augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert'","print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml","random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype =","def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width =","new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if","= np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1","((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1],","# img = img >> shift img = np.left_shift(img,shift) img","= yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug","'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' :","else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:,","augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter","RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY')","height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]],","img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy()","For each pixel in the image, select the pixel #","center is None: center = (w / 2, h /","8 - bits # img = img >> shift img","bits # img = img >> shift img = np.left_shift(img,shift)","else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above","c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:,","0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] =","sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :","random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg +","(w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_","= int(width * level) M = np.array([[1, 0 , 0],","'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY'","sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain,","= np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x *","'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if","height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :","level = min_arg + (max_arg - min_arg) * level img","if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters","img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug)","= int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height","'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'),","'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy","[hgain, sgain, vgain] + 1 # random gains hue, sat,","- bits # img = img >> shift img =","def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img","np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2","height), borderMode=mode) return translate_img # def sharpness(img,): # kernel =","# uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x",": cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M,","None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right","(img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers =","rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M,","def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr'","random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None,","def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def","im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue =","0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]],","else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize","augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'),","self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')*",">> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8')","__call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level","np.where(image <= threshold, image, 255 - image) def posterize(img, level=3):","in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter]","vgain] + 1 # random gains hue, sat, val =","histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert","# if the value is less than the threshold. #","if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else","img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return","0 , 0], [level, 1 , translate_pixel], [0, 0 ,","def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params')","size. eg: 224 , (224,224) or [224,224] padding (bool): padding","np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im","if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else","random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() #","int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height >","Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)","the pixel # if the value is less than the",":, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR","return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4):","im = im.copy() # HSV color-space augmentation if hgain or","RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1","w) = image.shape[:2] if center is None: center = (w","img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width","= self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) *","augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'),","M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ =","kernel = np.array( # [[1, 1, 1], # [1, 5,","= int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height","in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org)","if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None,","def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height =","def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width =","img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size)","distortion. Defaults to True. Returns: images (np.ndarray): images in target","mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class","augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop }","RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = {","height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_",",_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h","# For each pixel in the image, select the pixel","# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and","def posterize(img, level=3): bits = level shift = 8 -","augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad)","augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness':","def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),","* r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat),","RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout')","augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None,","return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'","dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype)","lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed","im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3)","int(width * level) M = np.array([[1, 0 , translate_pixel], [level,","'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k","= np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0,","0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random()","img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape","255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor)","cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION","cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height)","* r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0,","less than the threshold. # Otherwise, subtract 255 from the","return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8')","augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, #","pixel # if the value is less than the threshold.","8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:,","= img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ =","= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio","level shift = 8 - bits # img = img","= im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue","= img[:new_height,:,:] height,width,_ = img_.shape if height > width :","histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255","np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0","None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' :","img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape","= (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers","img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def","HSV color-space augmentation if hgain or sgain or vgain: r","__init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' :","img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy,","RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness')","if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else","dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat =","min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter)","for augmenter in augmenters: level = random.random() # try: min_arg,max_arg","level) M = np.array([[1, 0 , translate_pixel], [level, 1 ,",":, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])","# no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True):","ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_","img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value =","cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1,","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25):","img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2","cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def","self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) #","hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype #","0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={","except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg')","= cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers)","channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) #","'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') #","select the pixel # if the value is less than","RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else","+ img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img =","* [hgain, sgain, vgain] + 1 # random gains hue,","return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() #","cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0,","hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on","None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' :","img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def","padding: height,width,_ = img.shape delta = height - width if","augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_","np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat,","augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above':","cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image,","_ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) #","lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no","def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram","image) def posterize(img, level=3): bits = level shift = 8",", 0], [level, 1 , 0], [0, 0 , 1]],dtype='float')","the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image,","None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast","1, 1], # [1, 5, 1], # [1, 1, 1]],","cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c =","each pixel in the image, select the pixel # if","sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if","shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs):","'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize","'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' :","level image = image.copy() # For each pixel in the","for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224)","translate_img # def sharpness(img,): # kernel = np.array( # [[1,","def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor)","* level) M = np.array([[1, 0 , 0], [level, 1","cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ =","255 return img def rotate(image, level=45, center = None, scale","to RGB def solarize(image, level=128): threshold = level image =","yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in","clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] =","Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated =","if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = {","return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return","if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None,","= random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random()","= random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg","if random.random() < level: return np.fliplr(img) return img def augment_hsv(im,","augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level","None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' :","'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout","sgain, vgain] + 1 # random gains hue, sat, val","1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape",", (224,224) or [224,224] padding (bool): padding img before resize.","int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height >","level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float')","img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2","# cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if","def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1","img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width","img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level)","cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return","scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def",", 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode","cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M","0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if","r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] +","im.copy() # HSV color-space augmentation if hgain or sgain or",": RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if","height,width,_ = img_.shape if height > width : img2 =","numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary]","min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg)","img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224]","if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0]","is less than the threshold. # Otherwise, subtract 255 from","padding (bool): padding img before resize. Prevent from image distortion.","angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated","cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit()","else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout'","augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV","lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv","c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :,","cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image","needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy()","delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img","None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'),","degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M","if hgain or sgain or vgain: r = np.random.uniform(-1, 1,","cv2 import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images","RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY')","shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0],","= min_arg + (max_arg - min_arg) * level img =","level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img","from the pixel. return np.where(image <= threshold, image, 255 -","cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__","img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2","None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' :","lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue,","= level image = image.copy() # For each pixel in","} mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height),","threshold. # Otherwise, subtract 255 from the pixel. return np.where(image","0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)","- min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: #","img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img =","contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'):","augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None,","(1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img","'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize","= RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad","= np.array([[1, 0 , 0], [level, 1 , 0], [0,","img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width","augmenter in augmenters: level = random.random() # try: min_arg,max_arg =","int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height >","BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv =","image distortion. Defaults to True. Returns: images (np.ndarray): images in","print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') *","option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0","# 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv':","= int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height","else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size =",": augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' :","self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if","or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain,","256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat","Otherwise, subtract 255 from the pixel. return np.where(image <= threshold,","if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else","import numpy as np import cv2 import random def preprocess(img,img_size,padding=True):","else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right':","augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under':","- width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values","else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left'","img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level","return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape","yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :,","augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize':","255 from the pixel. return np.where(image <= threshold, image, 255","'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img,","return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers']","1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. #","if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else","level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level =",": RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if","convert YUV image to RGB def solarize(image, level=128): threshold =","height,width,_ = img.shape delta = height - width if delta","with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if","M = np.array([[1, 0 , translate_pixel], [level, 1 , 0],","Defaults to True. Returns: images (np.ndarray): images in target size","def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,","img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in","cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im,","img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8')","else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY'","(width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1,","factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img","= None, scale = 1.0): angle=level (h, w) = image.shape[:2]","im = im.copy() # Equalize histogram on BGR image 'im'","224 , (224,224) or [224,224] padding (bool): padding img before","yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y","True. Returns: images (np.ndarray): images in target size \"\"\" if",": augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop","- image) def posterize(img, level=3): bits = level shift =","None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize","# equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr","[1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) /","RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv')","new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if","'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness","the value is less than the threshold. # Otherwise, subtract","def solarize(image, level=128): threshold = level image = image.copy() #","or sgain or vgain: r = np.random.uniform(-1, 1, 3) *","= cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_","pixel in the image, select the pixel # if the","def sharpness(img,): # kernel = np.array( # [[1, 1, 1],","= im.copy() # HSV color-space augmentation if hgain or sgain","= augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) #","255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv =","to True. Returns: images (np.ndarray): images in target size \"\"\"","img before resize. Prevent from image distortion. Defaults to True.","cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) #","else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness","0 , translate_pixel], [level, 1 , 0], [0, 0 ,","RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad =","0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img","augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None,","val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x","augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def","np.array( # [[1, 1, 1], # [1, 5, 1], #","augmentation if hgain or sgain or vgain: r = np.random.uniform(-1,","5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3,","dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True,","= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'):","images img_size (int,list,tuple): target size. eg: 224 , (224,224) or","= 1.0): angle=level (h, w) = image.shape[:2] if center is","im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR,","cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size =","augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'),","np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size)","cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ =","class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION =","# cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ =","level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:]","rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25):","if the value is less than the threshold. # Otherwise,","\"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg:","new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if","mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int):","if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else","img def rotate(image, level=45, center = None, scale = 1.0):",": augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'),","yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug =","augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr'","image, 255 - image) def posterize(img, level=3): bits = level",": augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' :","np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate =",", 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,",": augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast':","= np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level):","None: center = (w / 2, h / 2) #","# shape=[3, 3, 1, 1]) / 13. # cv2. def",": augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under':","else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY'","= img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h =","RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert')","= img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value","+ 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im,","1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'","[level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =","list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def","img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25):","borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={","RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else","RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT =","cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for","image.copy() # For each pixel in the image, select the",": img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 =","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters =","cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for","preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'):","'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if","RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize')","None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if","M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M =","if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else","hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation","img.shape delta = height - width if delta > 0:","translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img #","[224,224] padding (bool): padding img before resize. Prevent from image","return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im =","else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize'","np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype)","def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,","} self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v)","img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() <","3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img","'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left'","= cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio","cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height)","/ 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle,","k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img","M = np.array([[1, 0 , 0], [level, 1 , 0],","new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if","= level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ =","0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if","'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX'","1, 3) * [hgain, sgain, vgain] + 1 # random","= level height,width,_ = img.shape new_height = int(ratio*height) img_ =","img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter =","augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None,","level=128): threshold = level image = image.copy() # For each","shift = 8 - bits # img = img >>","'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x","level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7,","sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8","else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate'","h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center,","def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() #","image.shape[:2] if center is None: center = (w / 2,","{ 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' :","img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return","= img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift)","(w / 2, h / 2) # Perform the rotation","augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for","the threshold. # Otherwise, subtract 255 from the pixel. return","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio =","bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))","= cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :,","img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width","height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_","(max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except:","= self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test():","range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) #","# Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated","translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'","[0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect'","'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'),","bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def","= np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor","if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def","else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image,","import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _",": cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width *","np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio =","= preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if","translate_pixel = int(width * level) M = np.array([[1, 0 ,","height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0","[level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img =","None, scale = 1.0): angle=level (h, w) = image.shape[:2] if","level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:]","cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width)","Prevent from image distortion. Defaults to True. Returns: images (np.ndarray):","h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ =","rotate(image, level=45, center = None, scale = 1.0): angle=level (h,","preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size.","'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT","augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'),","'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate'","cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return","np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor =","cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0])","img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img",": RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if","'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if","= option_mode[mode] translate_pixel = int(width * level) M = np.array([[1,","(width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel","height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value')","} self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize'","RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX')",": RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if","1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img =","if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None,","img = np.clip(img, 0,255) img = img.astype('uint8') return img def","threshold = level image = image.copy() # For each pixel",": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img",": augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert'","def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size","kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255","# convert YUV image to RGB def solarize(image, level=128): threshold","cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape","cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return","= level shift = 8 - bits # img =","im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize","r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val,","M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w,","np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def","augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org)","level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space","mode = option_mode[mode] translate_pixel = int(width * level) M =","img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params):","level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR","= level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img","# HSV color-space augmentation if hgain or sgain or vgain:","# random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype",": RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if","img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape","RGB def solarize(image, level=128): threshold = level image = image.copy()","= image.copy() # For each pixel in the image, select",": RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if","sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level,","k=self.num_layers) for augmenter in augmenters: level = random.random() # try:","img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) #","if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB","import random def preprocess(img,img_size,padding=True): \"\"\"[summary] Args: img (np.ndarray): images img_size","RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate","size \"\"\" if padding: height,width,_ = img.shape delta = height","255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv,","* r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0,","= image.shape[:2] if center is None: center = (w /","2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale)","image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im,","1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy()","before resize. Prevent from image distortion. Defaults to True. Returns:","None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x","def rotate(image, level=45, center = None, scale = 1.0): angle=level",": RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast')","RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else","else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' :","augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'),","threshold, image, 255 - image) def posterize(img, level=3): bits =","sharpness(img,): # kernel = np.array( # [[1, 1, 1], #","augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' :","1.0): angle=level (h, w) = image.shape[:2] if center is None:","= cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1)","= height - width if delta > 0: img =","= cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M =","# try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg","img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width","13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_","= img[:,:new_width,:] height,width,_ = img_.shape if height > width :","< level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015,","= np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return","or [224,224] padding (bool): padding img before resize. Prevent from",": RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right')","self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org","= (w / 2, h / 2) # Perform the","augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None,","augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'),","borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={","r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)","image = image.copy() # For each pixel in the image,","augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX'","'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y","shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 ,","M = np.array([[1, 0 , 0], [level, 1 , translate_pixel],","= cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_","the pixel. return np.where(image <= threshold, image, 255 - image)","augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy =","'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate","augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY'","augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX'","else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' :","(width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape","img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params =",": augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'),","ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_","level) M = np.array([[1, 0 , 0], [level, 1 ,","on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv","adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor)","return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape","int(width * level) M = np.array([[1, 0 , 0], [level,","img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5):","np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center","[0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height),","return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate =","translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'","level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:]","solarize(image, level=128): threshold = level image = image.copy() # For","'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right':"],"string":"[\n \"0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv\",\n \"augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None,\",\n \"RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX')\",\n \"= np.array([[1, level, 0], [0, 1 , 0], [0, 0\",\n \"v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor +\",\n \"cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level,\",\n \"= np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return\",\n \"if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else\",\n \"M, (width, height), borderMode=mode) return translate_img # def sharpness(img,): #\",\n \"if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else\",\n \"else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' :\",\n \"level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def\",\n \"np.array([[1, level, 0], [0, 1 , 0], [0, 0 ,\",\n \"augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None,\",\n \"random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple):\",\n \"img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level: return\",\n \"# print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import\",\n \"tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else:\",\n \"= option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return\",\n \"return np.where(image <= threshold, image, 255 - image) def posterize(img,\",\n \"np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img\",\n \"delta = height - width if delta > 0: img\",\n \"return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255)\",\n \"def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height =\",\n \"2, h / 2) # Perform the rotation M =\",\n \"1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13.\",\n \"mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)\",\n \"target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta =\",\n \"[[1, 1, 1], # [1, 5, 1], # [1, 1,\",\n \"self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) * level\",\n \":, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0]\",\n \"# [1, 5, 1], # [1, 1, 1]], dtype=tf.float32, #\",\n \"vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain]\",\n \"= np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45,\",\n \"# kernel = np.array( # [[1, 1, 1], # [1,\",\n \"1 , 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,\",\n \"{ 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize'\",\n \"/ 13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width\",\n \"None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert\",\n \"'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout'\",\n \"augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else\",\n \"augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' :\",\n \"option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio\",\n \"else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def\",\n \"augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level =\",\n \"cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv,\",\n \"cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image, level=128):\",\n \"# def sharpness(img,): # kernel = np.array( # [[1, 1,\",\n \"'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under')\",\n \"return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0,\",\n \"augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None,\",\n \"lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x\",\n \"borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 ,\",\n \"else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop\",\n \"else None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast':\",\n \"np.array([[1, 0 , 0], [level, 1 , 0], [0, 0\",\n \"1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img\",\n \"'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'),\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'):\",\n \"= img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ =\",\n \"from image distortion. Defaults to True. Returns: images (np.ndarray): images\",\n \"= level height,width,_ = img.shape new_width = int(ratio*width) img_ =\",\n \"for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor):\",\n \"lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x\",\n \"= np.array( # [[1, 1, 1], # [1, 5, 1],\",\n \"+ (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) #\",\n \"= cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)\",\n \"'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv\",\n \"in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta\",\n \"return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter\",\n \"try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg -\",\n \"def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target\",\n \"* (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return\",\n \"img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level\",\n \"resize. Prevent from image distortion. Defaults to True. Returns: images\",\n \"center = (w / 2, h / 2) # Perform\",\n \"None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' :\",\n \"self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None,\",\n \"augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None,\",\n \"= { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'),\",\n \"np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0, 0\",\n \"if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else\",\n \"= img.shape delta = height - width if delta >\",\n \"= int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w =\",\n \": augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'),\",\n \"= kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] =\",\n \"'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if\",\n \"augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None,\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def\",\n \"cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int(ratio*width)\",\n \"degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None):\",\n \"images in target size \\\"\\\"\\\" if padding: height,width,_ = img.shape\",\n \"img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width\",\n \"cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :,\",\n \"None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' :\",\n \"= np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate\",\n \"0], [0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_\",\n \"M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ =\",\n \"height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode\",\n \"[level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img =\",\n \"= level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ =\",\n \"augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness':\",\n \"None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' :\",\n \"return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0],\",\n \"= img[new_height:,:,:] height,width,_ = img_.shape if height > width :\",\n \"as np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args:\",\n \"np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if\",\n \"= cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram return\",\n \"if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor\",\n \"img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height\",\n \"yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe:\",\n \"return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params\",\n \"1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1,\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25):\",\n \"0 , 0], [level, 1 , 0], [0, 0 ,\",\n \"None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if\",\n \"= ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x *\",\n \": RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if\",\n \"= 8 - bits # img = img >> shift\",\n \"img = img >> shift img = np.left_shift(img,shift) img =\",\n \"level=45, center = None, scale = 1.0): angle=level (h, w)\",\n \"= img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255)\",\n \"subtract 255 from the pixel. return np.where(image <= threshold, image,\",\n \"0], [level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_\",\n \"augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None,\",\n \"cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width * level)\",\n \"cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0,\",\n \"value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:]\",\n \"= list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy)\",\n \"min_arg + (max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level)\",\n \"None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop }\",\n \"RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else\",\n \"uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x *\",\n \"if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else\",\n \"= np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0,\",\n \", 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,\",\n \"(int,list,tuple): target size. eg: 224 , (224,224) or [224,224] padding\",\n \"else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness'\",\n \"180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val =\",\n \"img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224))\",\n \"augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'),\",\n \"RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize')\",\n \"width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2\",\n \"mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img\",\n \"return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV\",\n \"def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters:\",\n \"'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' :\",\n \"augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under')\",\n \"'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'),\",\n \"in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img =\",\n \"'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if\",\n \"augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v in\",\n \"np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 #\",\n \"0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) #\",\n \"0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))\",\n \": RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if\",\n \"in the image, select the pixel # if the value\",\n \"height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level\",\n \"level height,width,_ = img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:]\",\n \"cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width,\",\n \"center = None, scale = 1.0): angle=level (h, w) =\",\n \"Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range\",\n \"pixel. return np.where(image <= threshold, image, 255 - image) def\",\n \"0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' :\",\n \"eg: 224 , (224,224) or [224,224] padding (bool): padding img\",\n \"if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY') else\",\n \"cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to\",\n \", translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,\",\n \"bits = level shift = 8 - bits # img\",\n \"else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under\",\n \"img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224 ,\",\n \"translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,\",\n \"self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' :\",\n \"than the threshold. # Otherwise, subtract 255 from the pixel.\",\n \"im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr\",\n \"the image, select the pixel # if the value is\",\n \"if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2\",\n \"bgr=True): im = im.copy() # Equalize histogram on BGR image\",\n \"level=3): bits = level shift = 8 - bits #\",\n \"return img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img)\",\n \"img[new_height:,:,:] height,width,_ = img_.shape if height > width : img2\",\n \"RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate')\",\n \"image, select the pixel # if the value is less\",\n \"= int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height\",\n \"None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' :\",\n \"Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg: 224\",\n \"} mode = option_mode[mode] translate_pixel = int(width * level) M\",\n \"RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else\",\n \"# cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): #\",\n \"= augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr')\",\n \"return translate_img # def sharpness(img,): # kernel = np.array( #\",\n \", 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return\",\n \"\\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height -\",\n \"M = np.array([[1, level, 0], [0, 1 , 0], [0,\",\n \"def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 ,\",\n \"return img def rotate(image, level=45, center = None, scale =\",\n \"* level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return\",\n \"= np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) %\",\n \"> width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255)\",\n \"isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self,\",\n \"import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray):\",\n \"target size. eg: 224 , (224,224) or [224,224] padding (bool):\",\n \"return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'),\",\n \"= cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_\",\n \"(224,224) or [224,224] padding (bool): padding img before resize. Prevent\",\n \"cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img =\",\n \"def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img =\",\n \"= img[:,new_width:,:] height,width,_ = img_.shape if height > width :\",\n \"and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else\",\n \"image to RGB def solarize(image, level=128): threshold = level image\",\n \", 0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float')\",\n \"= im.copy() # Equalize histogram on BGR image 'im' with\",\n \"ratio = level height,width,_ = img.shape new_width = int(ratio*width) img_\",\n \"> 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img =\",\n \"1], # [1, 5, 1], # [1, 1, 1]], dtype=tf.float32,\",\n \"augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above':\",\n \"/ 2, h / 2) # Perform the rotation M\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img # def sharpness(img,):\",\n \"(h, w) = image.shape[:2] if center is None: center =\",\n \": RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if\",\n \"np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img\",\n \"dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. # cv2.\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img):\",\n \"if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None,\",\n \"'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode]\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio\",\n \"int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height >\",\n \"img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values\",\n \"= 255 return img def rotate(image, level=45, center = None,\",\n \"'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above')\",\n \"RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else\",\n \"no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im\",\n \"cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x = np.arange(0, 256,\",\n \"'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV\",\n \"augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None,\",\n \"equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else\",\n \"= img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img,\",\n \"= np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue),\",\n \"angle=level (h, w) = image.shape[:2] if center is None: center\",\n \"= np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img\",\n \"(np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_ =\",\n \"= cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x =\",\n \"translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def\",\n \"3) * [hgain, sgain, vgain] + 1 # random gains\",\n \"=255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment:\",\n \"else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize'\",\n \"% 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val\",\n \"cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__': augmentation_test()\",\n \"(bool): padding img before resize. Prevent from image distortion. Defaults\",\n \"return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'\",\n \"img1.astype('float')* factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255)\",\n \"=255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size\",\n \"= cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c\",\n \"# cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__ =='__main__':\",\n \": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel\",\n \"= np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255)\",\n \"cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio =\",\n \"translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :\",\n \"gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype\",\n \": augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' :\",\n \"r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype)\",\n \"height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_\",\n \"= cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE)\",\n \"img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0])\",\n \"img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape\",\n \"dovietchinh/multi-task-classificationsource/utils/augmentations.py0 import numpy as np import cv2 import random def\",\n \"# [1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1])\",\n \"cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return\",\n \"None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' :\",\n \"np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if\",\n \"img_.shape if height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else:\",\n \"img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center =\",\n \"'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left\",\n \"'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width\",\n \"if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None,\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_\",\n \"RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left')\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level\",\n \"1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img,\",\n \"Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if padding:\",\n \"'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness'\",\n \"# except: # print(augmenter) return img def augmentation_test(): img_org =\",\n \"value is less than the threshold. # Otherwise, subtract 255\",\n \"None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if\",\n \"cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0],\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'):\",\n \"color-space augmentation if hgain or sgain or vgain: r =\",\n \"[1, 5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3,\",\n \"'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'),\",\n \"0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width,\",\n \"= RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5):\",\n \"= img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ =\",\n \"if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None,\",\n \"if center is None: center = (w / 2, h\",\n \", translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float')\",\n \"return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im =\",\n \":, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel\",\n \": augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' :\",\n \"cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ =\",\n \"ratio = level height,width,_ = img.shape new_height = int(ratio*height) img_\",\n \"if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else:\",\n \"1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))\",\n \"int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1])\",\n \"is None: center = (w / 2, h / 2)\",\n \": RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if\",\n \"= img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio =\",\n \"'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness')\",\n \"factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return\",\n \"# 'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items()\",\n \"translate_pixel], [level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img\",\n \"vgain=0.4): im = im.copy() # HSV color-space augmentation if hgain\",\n \"hgain or sgain or vgain: r = np.random.uniform(-1, 1, 3)\",\n \"padding img before resize. Prevent from image distortion. Defaults to\",\n \"# [[1, 1, 1], # [1, 5, 1], # [1,\",\n \"'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if\",\n \"255 - image) def posterize(img, level=3): bits = level shift\",\n \"scale = 1.0): angle=level (h, w) = image.shape[:2] if center\",\n \"= img.astype('uint8') return img def augment_fliplr(img,level): if random.random() < level:\",\n \"'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for k,v\",\n \"shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def\",\n \"invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)\",\n \"(width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape\",\n \"padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w\",\n \"img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img,\",\n \"={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode =\",\n \"img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT }\",\n \"'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y\",\n \"# Otherwise, subtract 255 from the pixel. return np.where(image <=\",\n \"= int(width * level) M = np.array([[1, 0 , translate_pixel],\",\n \"height > width : img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 =\",\n \"x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0])\",\n \"np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2],\",\n \"height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode\",\n \"M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level\",\n \"option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode\",\n \": RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize')\",\n \"augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000):\",\n \"[0, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =\",\n \"images (np.ndarray): images in target size \\\"\\\"\\\" if padding: height,width,_\",\n \"<= threshold, image, 255 - image) def posterize(img, level=3): bits\",\n \"0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram\",\n \"else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX'\",\n \"borderMode=mode) return translate_img # def sharpness(img,): # kernel = np.array(\",\n \"if padding: height,width,_ = img.shape delta = height - width\",\n \"else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize':\",\n \"(np.ndarray): images img_size (int,list,tuple): target size. eg: 224 , (224,224)\",\n \"YUV image to RGB def solarize(image, level=128): threshold = level\",\n \"posterize(img, level=3): bits = level shift = 8 - bits\",\n \"cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel =\",\n \"* level) M = np.array([[1, 0 , translate_pixel], [level, 1\",\n \"# print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float')\",\n \"rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height\",\n \"range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)\",\n \"= c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:,\",\n \": augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' :\",\n \"= np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return\",\n \"= img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ =\",\n \"= { 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv'\",\n \"sheared_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def\",\n \"augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert'\",\n \"print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml\",\n \"random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype =\",\n \"def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape new_width =\",\n \"new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if\",\n \"= np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1\",\n \"((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1],\",\n \"# img = img >> shift img = np.left_shift(img,shift) img\",\n \"= yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug\",\n \"'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' :\",\n \"else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:,\",\n \"augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter\",\n \"RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY')\",\n \"height - width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]],\",\n \"img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy()\",\n \"For each pixel in the image, select the pixel #\",\n \"center is None: center = (w / 2, h /\",\n \"8 - bits # img = img >> shift img\",\n \"bits # img = img >> shift img = np.left_shift(img,shift)\",\n \"else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above\",\n \"c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:,\",\n \"0] = c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] =\",\n \"sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' :\",\n \"random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg +\",\n \"(w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_\",\n \"= int(width * level) M = np.array([[1, 0 , 0],\",\n \"'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY'\",\n \"sgain or vgain: r = np.random.uniform(-1, 1, 3) * [hgain,\",\n \"= np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x *\",\n \"'random_crop':random_crop } self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if\",\n \"height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant' :\",\n \"level = min_arg + (max_arg - min_arg) * level img\",\n \"if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters\",\n \"img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug)\",\n \"= int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height\",\n \"'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'),\",\n \"'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy\",\n \"[hgain, sgain, vgain] + 1 # random gains hue, sat,\",\n \"- bits # img = img >> shift img =\",\n \"def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img\",\n \"np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2\",\n \"height), borderMode=mode) return translate_img # def sharpness(img,): # kernel =\",\n \"# uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x\",\n \": cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M,\",\n \"None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right\",\n \"(img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers =\",\n \"rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M,\",\n \"def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr'\",\n \"random.random() < level: return np.fliplr(img) return img def augment_hsv(im, level=None,\",\n \"def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def\",\n \"im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue =\",\n \"0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]],\",\n \"else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize\",\n \"augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'),\",\n \"self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')*\",\n \">> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8')\",\n \"__call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level\",\n \"np.where(image <= threshold, image, 255 - image) def posterize(img, level=3):\",\n \"in augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter]\",\n \"vgain] + 1 # random gains hue, sat, val =\",\n \"histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert\",\n \"# if the value is less than the threshold. #\",\n \"if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else\",\n \"img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift) return\",\n \"0 , 0], [level, 1 , translate_pixel], [0, 0 ,\",\n \"def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params')\",\n \"size. eg: 224 , (224,224) or [224,224] padding (bool): padding\",\n \"np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im\",\n \"if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else\",\n \"random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random() #\",\n \"int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if height >\",\n \"Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)\",\n \"the pixel # if the value is less than the\",\n \":, 0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR\",\n \"return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4):\",\n \"im = im.copy() # HSV color-space augmentation if hgain or\",\n \"RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1\",\n \"w) = image.shape[:2] if center is None: center = (w\",\n \"img2 def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width\",\n \"= self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg) *\",\n \"augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'),\",\n \"M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ =\",\n \"kernel = np.array( # [[1, 1, 1], # [1, 5,\",\n \"= int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height\",\n \"in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org)\",\n \"if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None,\",\n \"def cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height =\",\n \"def cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width =\",\n \"img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size)\",\n \"distortion. Defaults to True. Returns: images (np.ndarray): images in target\",\n \"mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class\",\n \"augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop }\",\n \"RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = {\",\n \"height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_\",\n \",_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h\",\n \"# For each pixel in the image, select the pixel\",\n \"# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and\",\n \"def posterize(img, level=3): bits = level shift = 8 -\",\n \"augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad)\",\n \"augment_params.get('solarize') else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness':\",\n \"def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\",\n \"* r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat),\",\n \"RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout')\",\n \"augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None,\",\n \"return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect'\",\n \"dtype = im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype)\",\n \"lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed\",\n \"im.copy() # Equalize histogram on BGR image 'im' with im.shape(n,m,3)\",\n \"int(width * level) M = np.array([[1, 0 , translate_pixel], [level,\",\n \"'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k\",\n \"= np.array([[1, 0 , 0], [level, 1 , translate_pixel], [0,\",\n \"0,255) img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random()\",\n \"img.shape new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape\",\n \"255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor)\",\n \"cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION\",\n \"cut_25_above(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int(ratio*height)\",\n \"* r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0,\",\n \"less than the threshold. # Otherwise, subtract 255 from the\",\n \"return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8')\",\n \"augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, #\",\n \"pixel # if the value is less than the threshold.\",\n \"8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:,\",\n \"= img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ =\",\n \"= np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio\",\n \"level shift = 8 - bits # img = img\",\n \"= im.dtype # uint8 x = np.arange(0, 256, dtype=r.dtype) lut_hue\",\n \"= img[:new_height,:,:] height,width,_ = img_.shape if height > width :\",\n \"histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255\",\n \"np.array([[1, 0 , translate_pixel], [level, 1 , 0], [0, 0\",\n \"None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' :\",\n \"img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape\",\n \"= (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers\",\n \"img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def\",\n \"HSV color-space augmentation if hgain or sgain or vgain: r\",\n \"__init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' :\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy,\",\n \"RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness')\",\n \"if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else\",\n \"dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat =\",\n \"min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter)\",\n \"for augmenter in augmenters: level = random.random() # try: min_arg,max_arg\",\n \"level) M = np.array([[1, 0 , translate_pixel], [level, 1 ,\",\n \":, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])\",\n \"# no return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True):\",\n \"ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width) img_\",\n \"img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value =\",\n \"cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M = np.array([[1,\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25):\",\n \"img[:new_height,:,:] height,width,_ = img_.shape if height > width : img2\",\n \"cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def\",\n \"self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v) #\",\n \"hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype #\",\n \"0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={\",\n \"except: # print(augmenter) return img def augmentation_test(): img_org = cv2.imread('test.jpg')\",\n \"= cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers)\",\n \"channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) #\",\n \"'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') #\",\n \"select the pixel # if the value is less than\",\n \"RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else\",\n \"+ img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img =\",\n \"* [hgain, sgain, vgain] + 1 # random gains hue,\",\n \"return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() #\",\n \"cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0,\",\n \"hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on\",\n \"None, 'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' :\",\n \"img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def\",\n \"padding: height,width,_ = img.shape delta = height - width if\",\n \"augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_\",\n \"np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat,\",\n \"augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above':\",\n \"cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image,\",\n \"_ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) #\",\n \"lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no\",\n \"def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram\",\n \"image) def posterize(img, level=3): bits = level shift = 8\",\n \", 0], [level, 1 , 0], [0, 0 , 1]],dtype='float')\",\n \"the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image,\",\n \"None, 'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast\",\n \"1, 1], # [1, 5, 1], # [1, 1, 1]],\",\n \"cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if clahe: c =\",\n \"each pixel in the image, select the pixel # if\",\n \"sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation if\",\n \"shape=[3, 3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs):\",\n \"'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize\",\n \"'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' :\",\n \"level image = image.copy() # For each pixel in the\",\n \"for _ in range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224)\",\n \"translate_img # def sharpness(img,): # kernel = np.array( # [[1,\",\n \"def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') * (1-factor)\",\n \"* level) M = np.array([[1, 0 , 0], [level, 1\",\n \"cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ =\",\n \"255 return img def rotate(image, level=45, center = None, scale\",\n \"to RGB def solarize(image, level=128): threshold = level image =\",\n \"yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in\",\n \"clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] =\",\n \"Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated =\",\n \"if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = {\",\n \"return 255-img def contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return\",\n \"if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None,\",\n \"= random.choices(self.policy, k=self.num_layers) for augmenter in augmenters: level = random.random()\",\n \"= random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg\",\n \"if random.random() < level: return np.fliplr(img) return img def augment_hsv(im,\",\n \"augmenters: level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level\",\n \"None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' :\",\n \"'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout\",\n \"sgain, vgain] + 1 # random gains hue, sat, val\",\n \"1 , 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape\",\n \", (224,224) or [224,224] padding (bool): padding img before resize.\",\n \"int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape if height >\",\n \"level, 0], [0, 1 , 0], [0, 0 , 1]],dtype='float')\",\n \"img[:,:new_width,:] height,width,_ = img_.shape if height > width : img2\",\n \"# cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if\",\n \"def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1\",\n \"img_ = img[:new_height,:,:] height,width,_ = img_.shape if height > width\",\n \"img = img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level)\",\n \"cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return\",\n \"scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def\",\n \", 0], [0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode\",\n \"cv2.warpPerspective(img, M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M\",\n \"0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) if\",\n \"r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] +\",\n \"im.copy() # HSV color-space augmentation if hgain or sgain or\",\n \": RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if\",\n \"height,width,_ = img_.shape if height > width : img2 =\",\n \"numpy as np import cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary]\",\n \"min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg - min_arg)\",\n \"img_size (int,list,tuple): target size. eg: 224 , (224,224) or [224,224]\",\n \"if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0]\",\n \"is less than the threshold. # Otherwise, subtract 255 from\",\n \"padding (bool): padding img before resize. Prevent from image distortion.\",\n \"angle, scale) rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated\",\n \"cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit()\",\n \"else None, 'sharpness' : RandAugment.sharpness if augment_params.get('sharpness') else None, 'cutout'\",\n \"augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV\",\n \"lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv\",\n \"c.apply(yuv[:, :, 0]) else: yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :,\",\n \"cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image\",\n \"needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy()\",\n \"delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values =255) else: img\",\n \"None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'),\",\n \"degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M\",\n \"if hgain or sgain or vgain: r = np.random.uniform(-1, 1,\",\n \"cv2 import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images\",\n \"RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY' : RandAugment.translate_y if augment_params.get('translateY')\",\n \"shear_x(img,level=0.4,mode='reflect'): M = np.array([[1, level, 0], [0, 1 , 0],\",\n \"= min_arg + (max_arg - min_arg) * level img =\",\n \"level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img\",\n \"from the pixel. return np.where(image <= threshold, image, 255 -\",\n \"cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'): # exit() if __name__\",\n \"img[:,new_width:,:] height,width,_ = img_.shape if height > width : img2\",\n \"None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' :\",\n \"lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue,\",\n \"= level image = image.copy() # For each pixel in\",\n \"} mode = option_mode[mode] sheared_img = cv2.warpPerspective(img, M, (width, height),\",\n \"threshold. # Otherwise, subtract 255 from the pixel. return np.where(image\",\n \"0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode)\",\n \"- min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: #\",\n \"img = img1.astype('float')* factor + img2.astype('float') * (1-factor) img =\",\n \"contrast(img,factor=0.5): degenerate = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'):\",\n \"augment_params.get('sharpness') else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None,\",\n \"(1-factor) img = np.clip(img, 0,255) img = img.astype('uint8') return img\",\n \"'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize\",\n \"= RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad\",\n \"= np.array([[1, 0 , 0], [level, 1 , 0], [0,\",\n \"img_ = img[:,:new_width,:] height,width,_ = img_.shape if height > width\",\n \"augmenter in augmenters: level = random.random() # try: min_arg,max_arg =\",\n \"int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if height >\",\n \"BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv =\",\n \"image distortion. Defaults to True. Returns: images (np.ndarray): images in\",\n \"print(self.policy) def mixup(img1,img2,factor): img = img1.astype('float')* factor + img2.astype('float') *\",\n \"option_mode[mode] translate_pixel = int(width * level) M = np.array([[1, 0\",\n \"# 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv':\",\n \"= int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if height\",\n \"else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size =\",\n \": augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' :\",\n \"self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION = { 'fliplr' : RandAugment.augment_fliplr if\",\n \"or vgain: r = np.random.uniform(-1, 1, 3) * [hgain, sgain,\",\n \"256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat\",\n \"Otherwise, subtract 255 from the pixel. return np.where(image <= threshold,\",\n \"if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert') else\",\n \"import numpy as np import cv2 import random def preprocess(img,img_size,padding=True):\",\n \"else None, 'cut_25_left' : RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right':\",\n \"augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under':\",\n \"- width if delta > 0: img = np.pad(img,[[0,0],[delta//2,delta//2],[0,0]], mode='constant',constant_values\",\n \"else None, 'rotate' : RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left'\",\n \"img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level\",\n \"return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"yuv[:, :, 0] = c.apply(yuv[:, :, 0]) else: yuv[:, :,\",\n \"augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize':\",\n \"255 from the pixel. return np.where(image <= threshold, image, 255\",\n \"'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img = cv2.warpPerspective(img,\",\n \"return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers']\",\n \"1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) / 13. #\",\n \"if augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else\",\n \"level = random.random() # try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level =\",\n \": RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if\",\n \"convert YUV image to RGB def solarize(image, level=128): threshold =\",\n \"height,width,_ = img.shape delta = height - width if delta\",\n \"with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if\",\n \"M = np.array([[1, 0 , translate_pixel], [level, 1 , 0],\",\n \"Defaults to True. Returns: images (np.ndarray): images in target size\",\n \"def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,\",\n \"img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for augmenter in\",\n \"cv2.COLOR_HSV2BGR, dst=im) # no return needed return im_hsv def hist_equalize(im,\",\n \"img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img = img.astype('uint8')\",\n \"else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None, 'translateY'\",\n \"(width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1,\",\n \"factor + img2.astype('float') * (1-factor) img = np.clip(img, 0,255) img\",\n \"= None, scale = 1.0): angle=level (h, w) = image.shape[:2]\",\n \"im = im.copy() # Equalize histogram on BGR image 'im'\",\n \"224 , (224,224) or [224,224] padding (bool): padding img before\",\n \"yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y\",\n \"True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\" if\",\n \": augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop\",\n \"- image) def posterize(img, level=3): bits = level shift =\",\n \"None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else None, 'posterize': RandAugment.posterize\",\n \"# equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr\",\n \"[1, 1, 1]], dtype=tf.float32, # shape=[3, 3, 1, 1]) /\",\n \"RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv')\",\n \"new_height = int(ratio*height) img_ = img[new_height:,:,:] height,width,_ = img_.shape if\",\n \"'translateY' : RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness\",\n \"the value is less than the threshold. # Otherwise, subtract\",\n \"def solarize(image, level=128): threshold = level image = image.copy() #\",\n \"or sgain or vgain: r = np.random.uniform(-1, 1, 3) *\",\n \"= cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25): ratio = level height,width,_\",\n \"pixel in the image, select the pixel # if the\",\n \"def sharpness(img,): # kernel = np.array( # [[1, 1, 1],\",\n \"= im.copy() # HSV color-space augmentation if hgain or sgain\",\n \"= augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) #\",\n \"255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) im_hsv =\",\n \"to True. Returns: images (np.ndarray): images in target size \\\"\\\"\\\"\",\n \"img before resize. Prevent from image distortion. Defaults to True.\",\n \"cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) #\",\n \"else None, 'posterize': RandAugment.posterize if augment_params.get('posterize') else None, 'adjust_brightness': RandAugment.adjust_brightness\",\n \"0 , translate_pixel], [level, 1 , 0], [0, 0 ,\",\n \"RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org) img_pad =\",\n \"0], [level, 1 , translate_pixel], [0, 0 , 1]],dtype='float') translate_img\",\n \"augment_params.get('augment_hsv') else None, 'hist_equalize' : RandAugment.hist_equalize if augment_params.get('hist_equalize') else None,\",\n \"val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8 x\",\n \"augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return img def\",\n \"np.array( # [[1, 1, 1], # [1, 5, 1], #\",\n \"augmentation if hgain or sgain or vgain: r = np.random.uniform(-1,\",\n \"5, 1], # [1, 1, 1]], dtype=tf.float32, # shape=[3, 3,\",\n \"dst=im) # no return needed return im_hsv def hist_equalize(im, level=None,clahe=True,\",\n \"= cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'):\",\n \"images img_size (int,list,tuple): target size. eg: 224 , (224,224) or\",\n \"= 1.0): angle=level (h, w) = image.shape[:2] if center is\",\n \"im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR,\",\n \"cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size =\",\n \"augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'),\",\n \"np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size)\",\n \"cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio = level height,width,_ =\",\n \"class RandAugment: def __init__(self, augment_params): self.num_layers = augment_params['num_layers'] self.AUGMENT_FUNCTION =\",\n \"# cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ =\",\n \"level height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:]\",\n \"rotated = cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25):\",\n \"if the value is less than the threshold. # Otherwise,\",\n \"\\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size. eg:\",\n \"new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_ = img_.shape if\",\n \"mode='constant',constant_values =255) else: img = np.pad(img,[[-delta//2,-delta//2],[0,0],[0,0]], mode='constant',constant_values =255) if isinstance(img_size,int):\",\n \"if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else\",\n \"img def rotate(image, level=45, center = None, scale = 1.0):\",\n \": augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'),\",\n \"yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug =\",\n \"augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr'\",\n \"image, 255 - image) def posterize(img, level=3): bits = level\",\n \": augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' :\",\n \"np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate =\",\n \", 0], [0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M,\",\n \": augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast':\",\n \"= np.clip(img, 0,255) img = img.astype('uint8') return img def augment_fliplr(img,level):\",\n \"None: center = (w / 2, h / 2) #\",\n \"# shape=[3, 3, 1, 1]) / 13. # cv2. def\",\n \": augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right': augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under':\",\n \"else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY'\",\n \"= img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value') cordinate_h =\",\n \"RandAugment.adjust_brightness if augment_params.get('adjust_brightness') else None, 'invert' : RandAugment.invert if augment_params.get('invert')\",\n \"= img.copy() height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value\",\n \"+ 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(im,\",\n \"1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'\",\n \"[level, 1 , 0], [0, 0 , 1]],dtype='float') height,width,_ =\",\n \"list(k for k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def\",\n \"img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_right(img,level=0.25):\",\n \"borderMode=mode) return sheared_img def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={\",\n \"RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if augment_params.get('cut_25_above') else\",\n \"RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT =\",\n \"cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters = random.choices(self.policy, k=self.num_layers) for\",\n \"image.copy() # For each pixel in the image, select the\",\n \": img2 = np.pad(img_,[[0,0],[(height-width)//2,(height-width)//2],[0,0]],mode='constant',constant_values=255) else: img2 = np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 =\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def __call__(self,img): augmenters =\",\n \"cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for\",\n \"preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if cv2.waitKey(0)==ord('q'):\",\n \"'invert' : RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if\",\n \"RandAugment.hist_equalize if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize')\",\n \"None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right') else None, 'cut_25_above': RandAugment.cut_25_above if\",\n \"M, (width, height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M =\",\n \"if augment_params.get('hist_equalize') else None, 'solarize' : RandAugment.solarize if augment_params.get('solarize') else\",\n \"hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space augmentation\",\n \"img.shape delta = height - width if delta > 0:\",\n \"translate_img = cv2.warpPerspective(img, M, (width, height), borderMode=mode) return translate_img #\",\n \"[224,224] padding (bool): padding img before resize. Prevent from image\",\n \"return needed return im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im =\",\n \"else None, 'augment_hsv' : RandAugment.augment_hsv if augment_params.get('augment_hsv') else None, 'hist_equalize'\",\n \"np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype)\",\n \"def translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE,\",\n \"} self.policy = list(k for k,v in self.AUGMENT_FUNCTION.items() if v)\",\n \"img = img.astype('uint8') return img def augment_fliplr(img,level): if random.random() <\",\n \"3, 1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img\",\n \"'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left'\",\n \"= cv2.warpAffine(image, M, (w, h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio\",\n \"cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height)\",\n \"/ 2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle,\",\n \"k,v in self.AUGMENT_FUNCTION.items() if v) # print(self.policy) def mixup(img1,img2,factor): img\",\n \"M = np.array([[1, 0 , 0], [level, 1 , 0],\",\n \"new_height = int((1-ratio)*height) img_ = img[:new_height,:,:] height,width,_ = img_.shape if\",\n \"= level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ =\",\n \"0]) # equalize Y channel histogram return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if\",\n \"'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX'\",\n \"1, 3) * [hgain, sgain, vgain] + 1 # random\",\n \"= level height,width,_ = img.shape new_height = int(ratio*height) img_ =\",\n \"img_org = cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter =\",\n \"augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX') else None,\",\n \"level=128): threshold = level image = image.copy() # For each\",\n \"shift = 8 - bits # img = img >>\",\n \"'shearY' : RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x\",\n \"level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015, sgain=0.7,\",\n \"sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype = im.dtype # uint8\",\n \"else None, 'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate'\",\n \"h / 2) # Perform the rotation M = cv2.getRotationMatrix2D(center,\",\n \"def augment_hsv(im, level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() #\",\n \"image.shape[:2] if center is None: center = (w / 2,\",\n \"{ 'fliplr' : RandAugment.augment_fliplr if augment_params.get('fliplr') else None, 'augment_hsv' :\",\n \"img def augment_fliplr(img,level): if random.random() < level: return np.fliplr(img) return\",\n \"= img >> shift img = np.left_shift(img,shift) img = np.right_shift(img,shift)\",\n \"(w / 2, h / 2) # Perform the rotation\",\n \"augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy = list(k for\",\n \"the threshold. # Otherwise, subtract 255 from the pixel. return\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_above(img,level=0.25): ratio =\",\n \"bgr else cv2.COLOR_RGB2YUV) if clahe: c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\",\n \"= cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :,\",\n \"img_ = img[new_height:,:,:] height,width,_ = img_.shape if height > width\",\n \"height,width,_ = img.shape new_width = int((1-ratio)*width) img_ = img[:,:new_width,:] height,width,_\",\n \"(max_arg - min_arg) * level img = self.AUGMENT_FUNCTION[augmenter](img,level=level) # except:\",\n \"= self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test():\",\n \"range(10000): img_aug = augmenter(img_org) img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) #\",\n \"# Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale) rotated\",\n \"translate_x(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'\",\n \"[0, 0 , 1]],dtype='float') height,width,_ = img.shape option_mode ={ 'reflect'\",\n \"'hist_equalize' : augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'),\",\n \"bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def\",\n \"= np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor\",\n \"if isinstance(img_size,int): img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def\",\n \"else cv2.COLOR_YUV2RGB) # convert YUV image to RGB def solarize(image,\",\n \"import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _\",\n \": cv2.BORDER_CONSTANT } mode = option_mode[mode] translate_pixel = int(width *\",\n \"np.pad(img_,[[(width-height)//2,(width-height)//2],[0,0],[0,0]],mode='constant',constant_values=255) img2 = cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio =\",\n \"= preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) # if\",\n \"translate_pixel = int(width * level) M = np.array([[1, 0 ,\",\n \"height), borderMode=mode) return sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0\",\n \"[level, 1 , 0], [0, 0 , 1]],dtype='float') translate_img =\",\n \"None, scale = 1.0): angle=level (h, w) = image.shape[:2] if\",\n \"level height,width,_ = img.shape new_height = int((1-ratio)*height) img_ = img[:new_height,:,:]\",\n \"cut_25_right(img,level=0.25): ratio = level height,width,_ = img.shape new_width = int((1-ratio)*width)\",\n \"Prevent from image distortion. Defaults to True. Returns: images (np.ndarray):\",\n \"h),borderMode=cv2.BORDER_REPLICATE) return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ =\",\n \"rotate(image, level=45, center = None, scale = 1.0): angle=level (h,\",\n \"preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size (int,list,tuple): target size.\",\n \"'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else None, # 'random_crop':random_crop } self.ARGS_LIMIT\",\n \"augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'),\",\n \"'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'), 'cutout' : augment_params.get('cutout'), 'rotate'\",\n \"cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return\",\n \"np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5): factor =\",\n \"cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) yuv[:, :, 0] = c.apply(yuv[:, :, 0])\",\n \"img.astype('uint8') def adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img\",\n \": RandAugment.rotate if augment_params.get('rotate') else None, 'cut_25_left' : RandAugment.cut_25_left if\",\n \"'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if\",\n \"= option_mode[mode] translate_pixel = int(width * level) M = np.array([[1,\",\n \"(width, height), borderMode=mode) return translate_img # def sharpness(img,): # kernel\",\n \"height,width ,_ = img.shape padding_size = int(height*level),int(width*level) value = kwargs.get('value')\",\n \"} self.ARGS_LIMIT = { 'fliplr' : augment_params.get('fliplr'), 'augment_hsv': augment_params.get('augment_hsv'), 'hist_equalize'\",\n \"RandAugment.shear_y if augment_params.get('shearY') else None, 'translateX' : RandAugment.translate_x if augment_params.get('translateX')\",\n \": RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate if\",\n \"1, 1]) / 13. # cv2. def cutout(img,level,**kwargs): img =\",\n \"if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None,\",\n \"img = np.clip(img, 0,255) img = img.astype('uint8') return img def\",\n \"threshold = level image = image.copy() # For each pixel\",\n \": cv2.BORDER_REPLICATE, 'constant' : cv2.BORDER_CONSTANT } mode = option_mode[mode] sheared_img\",\n \": augment_params.get('hist_equalize'), 'solarize' : augment_params.get('solarize'), 'posterize': augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert'\",\n \"def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape padding_size\",\n \"kwargs.get('value') cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255\",\n \"# convert YUV image to RGB def solarize(image, level=128): threshold\",\n \"cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_ = img.shape\",\n \"cordinate_h = np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return\",\n \"= level shift = 8 - bits # img =\",\n \"im_hsv def hist_equalize(im, level=None,clahe=True, bgr=True): im = im.copy() # Equalize\",\n \"r[2], 0, 255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val,\",\n \"M = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, M, (w,\",\n \"np.random.randint(0,height-padding_size[0]) cordinate_w = np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def\",\n \"augmenter = RandAugment(augment_params=augment_params)#(num_layers=1) for _ in range(10000): img_aug = augmenter(img_org)\",\n \"level=None, hgain=0.015, sgain=0.7, vgain=0.4): im = im.copy() # HSV color-space\",\n \"mode = option_mode[mode] translate_pixel = int(width * level) M =\",\n \"img_size = (img_size,img_size) return cv2.resize(img,img_size) class RandAugment: def __init__(self, augment_params):\",\n \"level=None,clahe=True, bgr=True): im = im.copy() # Equalize histogram on BGR\",\n \"= level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img\",\n \"# HSV color-space augmentation if hgain or sgain or vgain:\",\n \"# random gains hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) dtype\",\n \": RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y if\",\n \"img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:] height,width,_ = img_.shape\",\n \"RGB def solarize(image, level=128): threshold = level image = image.copy()\",\n \"= image.copy() # For each pixel in the image, select\",\n \": RandAugment.translate_y if augment_params.get('translateY') else None, 'sharpness' : RandAugment.sharpness if\",\n \"sheared_img def shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level,\",\n \"k=self.num_layers) for augmenter in augmenters: level = random.random() # try:\",\n \"img_pad = preprocess(img_aug,224) # cv2.imshow('a',img_org) # cv2.imshow('b',img_aug) # cv2.imshow('c',img_pad) #\",\n \"if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB\",\n \"import random def preprocess(img,img_size,padding=True): \\\"\\\"\\\"[summary] Args: img (np.ndarray): images img_size\",\n \"RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate\",\n \"size \\\"\\\"\\\" if padding: height,width,_ = img.shape delta = height\",\n \"255).astype(dtype) im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) cv2.cvtColor(im_hsv,\",\n \"* r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0,\",\n \"= image.shape[:2] if center is None: center = (w /\",\n \"2) # Perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale)\",\n \"image 'im' with im.shape(n,m,3) and range 0-255 yuv = cv2.cvtColor(im,\",\n \"1]) / 13. # cv2. def cutout(img,level,**kwargs): img = img.copy()\",\n \"before resize. Prevent from image distortion. Defaults to True. Returns:\",\n \"None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x\",\n \"def rotate(image, level=45, center = None, scale = 1.0): angle=level\",\n \": RandAugment.invert if augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast')\",\n \"RandAugment.cut_25_above if augment_params.get('cut_25_above') else None, 'cut_25_under': RandAugment.cut_25_under if augment_params.get('cut_25_under') else\",\n \"else None, # 'random_crop':random_crop } self.ARGS_LIMIT = { 'fliplr' :\",\n \"augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'), 'translateY' : augment_params.get('translateY'), 'sharpness' : augment_params.get('sharpness'),\",\n \"threshold, image, 255 - image) def posterize(img, level=3): bits =\",\n \"sharpness(img,): # kernel = np.array( # [[1, 1, 1], #\",\n \"augment_params.get('posterize'), 'adjust_brightness': augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' :\",\n \"1.0): angle=level (h, w) = image.shape[:2] if center is None:\",\n \"= cv2.imread('test.jpg') import yaml augment_params = yaml.safe_load(open('config/default/train_config.yaml')).get('augment_params') augmenter = RandAugment(augment_params=augment_params)#(num_layers=1)\",\n \"= height - width if delta > 0: img =\",\n \"= cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) return RandAugment.mixup(img,degenerate,factor) def shear_x(img,level=0.4,mode='reflect'): M =\",\n \"# try: min_arg,max_arg = self.ARGS_LIMIT[augmenter] level = min_arg + (max_arg\",\n \"img_ = img[:,new_width:,:] height,width,_ = img_.shape if height > width\",\n \"13. # cv2. def cutout(img,level,**kwargs): img = img.copy() height,width ,_\",\n \"= img[:,:new_width,:] height,width,_ = img_.shape if height > width :\",\n \"< level: return np.fliplr(img) return img def augment_hsv(im, level=None, hgain=0.015,\",\n \"= np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor) return img def invert(img,level=None): return\",\n \"or [224,224] padding (bool): padding img before resize. Prevent from\",\n \": RandAugment.cut_25_left if augment_params.get('cut_25_left') else None, 'cut_25_right': RandAugment.cut_25_right if augment_params.get('cut_25_right')\",\n \"self.AUGMENT_FUNCTION[augmenter](img,level=level) # except: # print(augmenter) return img def augmentation_test(): img_org\",\n \"= (w / 2, h / 2) # Perform the\",\n \"augment_params.get('contrast') else None, 'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None,\",\n \"augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX' : augment_params.get('translateX'),\",\n \"borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={\",\n \"r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)\",\n \"image = image.copy() # For each pixel in the image,\",\n \"augment_params.get('invert') else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX'\",\n \"'shearX' : RandAugment.shear_x if augment_params.get('shearX') else None, 'shearY' : RandAugment.shear_y\",\n \"shear_y(img,level=0.4,mode='reflect'): M = np.array([[1, 0 , 0], [level, 1 ,\",\n \"M = np.array([[1, 0 , 0], [level, 1 , translate_pixel],\",\n \"= cv2.resize(img2,(224,224)) return img2 def cut_25_left(img,level=0.25): ratio = level height,width,_\",\n \"the pixel. return np.where(image <= threshold, image, 255 - image)\",\n \"augment_params.get('cut_25_right'), 'cut_25_above': augment_params.get('cut_25_above'), 'cut_25_under': augment_params.get('cut_25_under') # 'random_crop':random_crop } self.policy =\",\n \"'cutout' : RandAugment.cutout if augment_params.get('cutout') else None, 'rotate' : RandAugment.rotate\",\n \"augment_params.get('adjust_brightness'), 'invert' : augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY'\",\n \"augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'), 'translateX'\",\n \"else None, 'contrast': RandAugment.contrast if augment_params.get('contrast') else None, 'shearX' :\",\n \"(width, height), borderMode=mode) return translate_img def translate_y(img,level,mode='reflect'): height,width,_ = img.shape\",\n \"img def augmentation_test(): img_org = cv2.imread('test.jpg') import yaml augment_params =\",\n \": augment_params.get('invert'), 'contrast': augment_params.get('contrast'), 'shearX' : augment_params.get('shearX'), 'shearY' : augment_params.get('shearY'),\",\n \"ratio = level height,width,_ = img.shape new_height = int((1-ratio)*height) img_\",\n \"level) M = np.array([[1, 0 , 0], [level, 1 ,\",\n \"on BGR image 'im' with im.shape(n,m,3) and range 0-255 yuv\",\n \"adjust_brightness(img,level=0.5): factor = level degenerate = np.zeros(img.shape,dtype='uint8') img = RandAugment.mixup(img,degenerate,factor)\",\n \"return rotated def cut_25_under(img,level=0.25): ratio = level height,width,_ = img.shape\",\n \"int(width * level) M = np.array([[1, 0 , 0], [level,\",\n \"img = np.left_shift(img,shift) img = np.right_shift(img,shift) return img.astype('uint8') def adjust_brightness(img,level=0.5):\",\n \"np.random.randint(0,width-padding_size[1]) img[cordinate_h:cordinate_h+padding_size[0],cordinate_w:cordinate_w+padding_size[1],:] = 255 return img def rotate(image, level=45, center\",\n \"[0, 0 , 1]],dtype='float') translate_img = cv2.warpPerspective(img, M, (width, height),\",\n \"return img def invert(img,level=None): return 255-img def contrast(img,factor=0.5): degenerate =\",\n \"translate_y(img,level,mode='reflect'): height,width,_ = img.shape option_mode ={ 'reflect' : cv2.BORDER_REPLICATE, 'constant'\",\n \"level height,width,_ = img.shape new_width = int(ratio*width) img_ = img[:,new_width:,:]\",\n \"solarize(image, level=128): threshold = level image = image.copy() # For\",\n \"'cutout' : augment_params.get('cutout'), 'rotate' : augment_params.get('rotate'), 'cut_25_left' : augment_params.get('cut_25_left'), 'cut_25_right':\"\n]"}}},{"rowIdx":3491,"cells":{"ngram":{"kind":"list like","value":["return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is","UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName","self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer","= '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize","# @param self The object pointer def GenCapsuleSubItem(self): pass ##","ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH',","The constructor # # @param self The object pointer def","generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All","FFS capsule data # # @param self The object pointer","def __init__(self): self.UiName = None self.Version = None self.ImageTypeId =","def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys():","capsule data # # class CapsuleFfs (CapsuleData): ## The constructor","VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed =","int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16),","IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\"","if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance","GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import","VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # #","pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16),","not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex =","EXPRESS OR IMPLIED. # ## # Import Modules # import","object pointer # def __init__(self) : self.Ffs = None self.FileName","self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper())","constructor # # @param self The object pointer # def","FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile","generate AnyFile capsule data # # @param self The object","http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD","CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The","def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class","self.Ffs = None self.FileName = None ## generate AnyFile capsule","GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj","# are licensed and made available under the terms and","__init__(self) : self.Ffs = None self.FdName = None self.CapsuleName =","THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN",": self.Ffs = None self.FileName = None ## generate AnyFile","defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID","for capsule data # # class CapsuleFd (CapsuleData): ## The","License # which accompanies this distribution. The full text of","the ImageFileSize need include the full authenticated info size. From","self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return","struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3];","import uuid ## base class for capsule data # #","None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]):","under the terms and conditions of the BSD License #","name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for","ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize =","GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not","None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile","None self.FdName = None self.CapsuleName = None ## generate FD","# # class CapsuleFv (CapsuleData): ## The constructor # #","ImageFileSize need include the full authenticated info size. From first","self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName","BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES","data # # class CapsuleAnyFile (CapsuleData): ## The constructor #","# # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
","# class CapsuleData: ## The constructor # # @param self","made available under the terms and conditions of the BSD","AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append","reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2","FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else:","UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in","16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4],","This program and the accompanying materials # are licensed and","license may be found at # http://opensource.org/licenses/bsd-license.php # # THE","None self.FvName = None self.CapsuleName = None ## generate FV","if AuthData: # the ImageFileSize need include the full authenticated","Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1],","this distribution. The full text of the license may be","payload file, the header is defined below: #pragma pack(1) typedef","self.FileName = None ## generate Afile capsule data # #","} EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version =","ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read()","# class CapsuleAnyFile (CapsuleData): ## The constructor # # @param","for capsule data # # class CapsuleAfile (CapsuleData): ## The","EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize;","from Common.Misc import SaveFileOnChange import uuid ## base class for","self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0'","ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import","self.Ffs = None self.FileName = None ## generate Afile capsule","//Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName =","self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the","Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return","accompanies this distribution. The full text of the license may","object pointer def __init__(self): pass ## generate capsule data #","# import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from","# generate capsule # # Copyright (c) 2007-2017, Intel Corporation.","= FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile","UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced","open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile =","constructor # # @param self The object pointer def __init__(self):","FdFile ## AnyFile class for capsule data # # class","Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the","= open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True","from struct import pack import os from Common.Misc import SaveFileOnChange","0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure","CapsuleData: ## The constructor # # @param self The object","## @file # generate capsule # # Copyright (c) 2007-2017,","if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer","self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile","OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## #","not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance =","of the license may be found at # http://opensource.org/licenses/bsd-license.php #","Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import","generate FFS capsule data # # @param self The object","__init__(self) : self.Ffs = None self.FvName = None ## generate","FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class","UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64","pointer # def __init__(self) : self.Ffs = None self.FileName =","bytes of MonotonicCount to last bytes of certificate. # the","'0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need","'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return Buffer","= '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize =","self.FvName = None self.CapsuleName = None ## generate FV capsule","THE BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT","None ## generate FV capsule data # # @param self","## # Import Modules # import Ffs from GenFdsGlobalVariable import","typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8","conditions of the BSD License # which accompanies this distribution.","self.CapsuleName = None ## generate FV capsule data # #","the full authenticated info size. From first bytes of MonotonicCount","16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0,","self.Ffs.GenFfs() return FfsFile ## FV class for capsule data #","uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure #","GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for","the BSD License # which accompanies this distribution. The full","in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None","= [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed =","EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None","[] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None","text of the license may be found at # http://opensource.org/licenses/bsd-license.php","None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None","data # # class CapsuleFd (CapsuleData): ## The constructor #","# which accompanies this distribution. The full text of the","-1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile =","# Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
#","header is defined below: #pragma pack(1) typedef struct { UINT32","materials # are licensed and made available under the terms","= StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName =","self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid","= None ## generate FFS capsule data # # @param","## generate AnyFile capsule data # # @param self The","available under the terms and conditions of the BSD License","FFS class for capsule data # # class CapsuleFfs (CapsuleData):","pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # #","string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs()","None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002'","Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1:","bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize","AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF","last bytes of certificate. # the 32 bit is the","self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2],","# This program and the accompanying materials # are licensed","BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER","The object pointer # @retval string Generated file name #","if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('')","self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if","self The object pointer # def __init__(self) : self.Ffs =","int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize,","for capsule data # # class CapsuleAnyFile (CapsuleData): ## The","name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ##","= None ## generate FD capsule data # # @param","self.Ffs = None self.FvName = None ## generate FFS capsule","file content to the structure # ImageFile = open(self.ImageFile, 'rb')","capsule data # # @param self The object pointer def","= '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not","__init__(self) : self.Ffs = None self.FvName = None self.CapsuleName =","the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32","CapsuleFv (CapsuleData): ## The constructor # # @param self The","None ## generate AnyFile capsule data # # @param self","generate capsule data # # @param self The object pointer","def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data","if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj =","not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData:","# the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType","content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer","== -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer","# Append file content to the structure # ImageFile =","class for capsule data # # class CapsuleFd (CapsuleData): ##","@param self The object pointer # @retval string Generated file","at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER","bytes of certificate. # the 32 bit is the MonotonicCount,","Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData):","# # Fill structure # Guid = self.ImageTypeId.split('-') Buffer =","the accompanying materials # are licensed and made available under","# def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in","AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content","int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1],","= GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile =","## AnyFile class for capsule data # # class CapsuleAnyFile","int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16),","class CapsuleFfs (CapsuleData): ## The constructor # # @param self","FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName","self.Ffs = None self.FvName = None self.CapsuleName = None ##","The object pointer def GenCapsuleSubItem(self): pass ## FFS class for","uuid ## base class for capsule data # # class","data # # @param self The object pointer # @retval","PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS","# def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV","FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ##","file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate","= None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return","# WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS","import SaveFileOnChange import uuid ## base class for capsule data","## FD class for capsule data # # class CapsuleFd","return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile","= None self.FileName = None ## generate AnyFile capsule data","'0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance:","FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ##","the license may be found at # http://opensource.org/licenses/bsd-license.php # #","import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct","# # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE","to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer +=","The full text of the license may be found at","= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data","@retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd')","# def __init__(self) : self.Ffs = None self.FileName = None","include the full authenticated info size. From first bytes of","to last bytes of certificate. # the 32 bit is","Corporation. All rights reserved.
# # This program and the","# # @param self The object pointer def GenCapsuleSubItem(self): pass","struct import pack import os from Common.Misc import SaveFileOnChange import","= None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance =","# class CapsuleFfs (CapsuleData): ## The constructor # # @param","FvFile ## FD class for capsule data # # class","VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid =","GenCapsuleSubItem(self): pass ## FFS class for capsule data # #","32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) #","[] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False","else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for","GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName","in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile","self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid","else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for","# @param self The object pointer def __init__(self): pass ##","= os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the","FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName)","16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0,","if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd()","= None self.FdName = None self.CapsuleName = None ## generate",") if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3])","None self.FileName = None ## generate AnyFile capsule data #","class for capsule data # # class CapsuleFv (CapsuleData): ##","@retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv')","full authenticated info size. From first bytes of MonotonicCount to","+= 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile)","and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile:","= self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16),","info size. From first bytes of MonotonicCount to last bytes","UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance;","pointer # def __init__(self) : self.Ffs = None self.FvName =","CapsuleAfile (CapsuleData): ## The constructor # # @param self The","capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights","pack import os from Common.Misc import SaveFileOnChange import uuid ##","self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance","name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper()","self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined","DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS,","wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if","CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma","None self.MonotonicCount = None self.Existed = False self.Buffer = None","# # @param self The object pointer def __init__(self): pass","size. From first bytes of MonotonicCount to last bytes of","None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None","may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM","Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16),",": self.Ffs = None self.FileName = None ## generate Afile","OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. #","0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer +=","int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16),","CapsuleFd (CapsuleData): ## The constructor # # @param self The","FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule","= open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile","0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer","## The constructor # # @param self The object pointer","class CapsuleFv (CapsuleData): ## The constructor # # @param self","## generate FV capsule data # # @param self The","# # This program and the accompanying materials # are","which accompanies this distribution. The full text of the license","object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule","be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS","file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class","for capsule data # # class CapsuleData: ## The constructor","== -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile","FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule","= 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill","int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16),","@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName","FfsFile ## FV class for capsule data # # class","self.FileName = None ## generate AnyFile capsule data # #","# # @param self The object pointer # @retval string","= None self.CapsuleName = None ## generate FV capsule data","UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize;","@param self The object pointer def __init__(self): pass ## generate","FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class","None ## generate Afile capsule data # # @param self","class CapsuleFd (CapsuleData): ## The constructor # # @param self","#pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8","pointer # def __init__(self) : self.Ffs = None self.FdName =","object pointer # @retval string Generated file name # def","string Generated file name # def GenCapsuleSubItem(self): return self.FileName ##","found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED","FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close()","UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS, #","MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize","False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version:","int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:],","# def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in","OR IMPLIED. # ## # Import Modules # import Ffs","FD class for capsule data # # class CapsuleFd (CapsuleData):","AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file","self The object pointer # @retval string Generated file name","is defined below: #pragma pack(1) typedef struct { UINT32 Version;","Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1:","Afile class for capsule data # # class CapsuleAfile (CapsuleData):","__init__(self): pass ## generate capsule data # # @param self","the header is defined below: #pragma pack(1) typedef struct {","= None self.CapsuleName = None ## generate FD capsule data","def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName","2007-2017, Intel Corporation. All rights reserved.
# # This program","# @param self The object pointer # @retval string Generated","= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data","base class for capsule data # # class CapsuleData: ##","'0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile)","GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data #","# Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable","import pack import os from Common.Misc import SaveFileOnChange import uuid","16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8],","= None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid =","# http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE","GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj","= None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version =","Append file content to the structure # ImageFile = open(self.ImageFile,","= None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self,","IMPLIED. # ## # Import Modules # import Ffs from","certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision,","of certificate. # the 32 bit is the MonotonicCount, dwLength,","reserved.
# # This program and the accompanying materials #","generate FD capsule data # # @param self The object","capsule data # # class CapsuleAfile (CapsuleData): ## The constructor","def __init__(self) : self.Ffs = None self.FileName = None ##","# # class CapsuleData: ## The constructor # # @param","GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else:","EITHER EXPRESS OR IMPLIED. # ## # Import Modules #","Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
# #","in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName =","= None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile =","## generate Afile capsule data # # @param self The","{ UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32","class CapsuleAfile (CapsuleData): ## The constructor # # @param self","return FvFile ## FD class for capsule data # #","self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile","capsule data # # class CapsuleFv (CapsuleData): ## The constructor","distribution. The full text of the license may be found","FV capsule data # # @param self The object pointer","AnyFile capsule data # # @param self The object pointer","# @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile","int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16),","for capsule data # # class CapsuleFv (CapsuleData): ## The","# def __init__(self) : self.Ffs = None self.FvName = None","authenticated info size. From first bytes of MonotonicCount to last","None self.FvName = None ## generate FFS capsule data #","structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0],","dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize =","self.FileName ## Afile class for capsule data # # class","= [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount =","self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: #","16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if","# # class CapsuleFfs (CapsuleData): ## The constructor # #","## generate FFS capsule data # # @param self The","@retval string Generated file name # def GenCapsuleSubItem(self): FfsFile =","Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO","class for capsule data # # class CapsuleAfile (CapsuleData): ##","0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData:","Common.Misc import SaveFileOnChange import uuid ## base class for capsule","IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND,","= None ## generate AnyFile capsule data # # @param","# @retval string Generated file name # def GenCapsuleSubItem(self): if","structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close()","self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile","## base class for capsule data # # class CapsuleData:","Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32","return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD","''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId","# @param self The object pointer # def __init__(self) :","@param self The object pointer def GenCapsuleSubItem(self): pass ## FFS","terms and conditions of the BSD License # which accompanies","import StringIO from struct import pack import os from Common.Misc","'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile,","Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile","WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.","int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16),","# the ImageFileSize need include the full authenticated info size.","FV class for capsule data # # class CapsuleFv (CapsuleData):","None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = []","# # @param self The object pointer # def __init__(self)","= self.Ffs.GenFfs() return FfsFile ## FV class for capsule data","class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below:","# Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ',","AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer +=","__init__(self): self.UiName = None self.Version = None self.ImageTypeId = None","pointer def __init__(self): pass ## generate capsule data # #","if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex","are licensed and made available under the terms and conditions","16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16)","string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') ==","ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer +=","self The object pointer def __init__(self): pass ## generate capsule","UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER;","= None ## generate FV capsule data # # @param","class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self","16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize,","= False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not","AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData):","full text of the license may be found at #","'''Generate payload file, the header is defined below: #pragma pack(1)","+= uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure","16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2],","None ## generate FFS capsule data # # @param self","# class CapsuleAfile (CapsuleData): ## The constructor # # @param","int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16),","# def __init__(self) : self.Ffs = None self.FdName = None","self.Ffs = None self.FdName = None self.CapsuleName = None ##","self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed","the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and","os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer","# class CapsuleFv (CapsuleData): ## The constructor # # @param","self.CapsuleName = None ## generate FD capsule data # #","import os from Common.Misc import SaveFileOnChange import uuid ## base","self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version","if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if","generate FV capsule data # # @param self The object","@param self The object pointer # def __init__(self) : self.Ffs","(CapsuleData): ## The constructor # # @param self The object","# # class CapsuleAfile (CapsuleData): ## The constructor # #","StringIO from struct import pack import os from Common.Misc import","self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1'","GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data #","self.FdName = None self.CapsuleName = None ## generate FD capsule","FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile","None self.FileName = None ## generate Afile capsule data #","UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; '''","v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version","name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper()","16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10],","# THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON","UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self):","= None self.FileName = None ## generate Afile capsule data","def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if","return FdFile ## AnyFile class for capsule data # #","def __init__(self): pass ## generate capsule data # # @param","+= pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() #","licensed and made available under the terms and conditions of","32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType","Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb')","ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile:","16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:],","if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj =","None ## generate FD capsule data # # @param self","import GenFdsGlobalVariable import StringIO from struct import pack import os","pass ## FFS class for capsule data # # class","SaveFileOnChange import uuid ## base class for capsule data #","data # # class CapsuleAfile (CapsuleData): ## The constructor #","= None self.ImageIndex = None self.HardwareInstance = None self.ImageFile =","wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0","KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules","## generate FD capsule data # # @param self The","is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize +=","os from Common.Misc import SaveFileOnChange import uuid ## base class","class for capsule data # # class CapsuleAnyFile (CapsuleData): ##","pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data","self The object pointer def GenCapsuleSubItem(self): pass ## FFS class","FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule","MonotonicCount to last bytes of certificate. # the 32 bit","## generate capsule data # # @param self The object","None self.CapsuleName = None ## generate FD capsule data #","def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys():","and made available under the terms and conditions of the","and the accompanying materials # are licensed and made available","capsule data # # class CapsuleData: ## The constructor #","FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile =","UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def","of MonotonicCount to last bytes of certificate. # the 32","int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) )",": self.Ffs = None self.FvName = None self.CapsuleName = None","Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le()","class for capsule data # # class CapsuleData: ## The","for capsule data # # class CapsuleFfs (CapsuleData): ## The",": self.Ffs = None self.FdName = None self.CapsuleName = None","@file # generate capsule # # Copyright (c) 2007-2017, Intel","program and the accompanying materials # are licensed and made","os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full","-1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer =","below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId;","first bytes of MonotonicCount to last bytes of certificate. #","Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16),","pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex;","CapsuleFfs (CapsuleData): ## The constructor # # @param self The","## Afile class for capsule data # # class CapsuleAfile","= pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2],","if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close()","and conditions of the BSD License # which accompanies this","from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack","= os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-')","def __init__(self) : self.Ffs = None self.FvName = None ##","return self.FileName ## Afile class for capsule data # #","data # # @param self The object pointer def GenCapsuleSubItem(self):","Afile capsule data # # @param self The object pointer","need include the full authenticated info size. From first bytes","WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR","Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import","The object pointer # def __init__(self) : self.Ffs = None","self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if","From first bytes of MonotonicCount to last bytes of certificate.",": self.Ffs = None self.FvName = None ## generate FFS","# ## # Import Modules # import Ffs from GenFdsGlobalVariable","VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0],","class for capsule data # # class CapsuleFfs (CapsuleData): ##","def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the","None self.Version = None self.ImageTypeId = None self.ImageIndex = None","# def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file,","+= ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer","open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return","16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2],","REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ##","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY","AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex:","return FfsFile ## FV class for capsule data # #","file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if","# # class CapsuleFd (CapsuleData): ## The constructor # #","capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor","= None self.FvName = None self.CapsuleName = None ## generate","# @retval string Generated file name # def GenCapsuleSubItem(self): return","All rights reserved.
# # This program and the accompanying","pass ## generate capsule data # # @param self The","## FFS class for capsule data # # class CapsuleFfs","16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex,","Intel Corporation. All rights reserved.
# # This program and","= FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile","accompanying materials # are licensed and made available under the","self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex","the terms and conditions of the BSD License # which","file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile","self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper())","self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed","CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize","rights reserved.
# # This program and the accompanying materials","FD capsule data # # @param self The object pointer","object pointer # def __init__(self) : self.Ffs = None self.FdName","FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return","generate Afile capsule data # # @param self The object","ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include","BSD License # which accompanies this distribution. The full text","def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName","None self.CapsuleName = None ## generate FV capsule data #","= None self.MonotonicCount = None self.Existed = False self.Buffer =","capsule data # # @param self The object pointer #","StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None","capsule data # # class CapsuleFd (CapsuleData): ## The constructor","self.FvName = None ## generate FFS capsule data # #","self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount","AuthData: # the ImageFileSize need include the full authenticated info","## FV class for capsule data # # class CapsuleFv","16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6],","int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0,","= None self.Version = None self.ImageTypeId = None self.ImageIndex =","the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read()","= None ## generate Afile capsule data # # @param","GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName)","# class CapsuleFd (CapsuleData): ## The constructor # # @param","The constructor # # @param self The object pointer #","file, the header is defined below: #pragma pack(1) typedef struct","ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS","file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if","# Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16),","The object pointer def __init__(self): pass ## generate capsule data","# # Append file content to the structure # ImageFile","int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance,","name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload","= self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return","AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to","data # # class CapsuleData: ## The constructor # #","if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure #","class CapsuleData: ## The constructor # # @param self The","(c) 2007-2017, Intel Corporation. All rights reserved.
# # This","= None self.FvName = None ## generate FFS capsule data","UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 }","string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') ==","LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","# def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule","data # # class CapsuleFfs (CapsuleData): ## The constructor #","self.MonotonicCount = None self.Existed = False self.Buffer = None def","GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data #","None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = []","GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header","= GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile =","# # class CapsuleAnyFile (CapsuleData): ## The constructor # #","object pointer # def __init__(self) : self.Ffs = None self.FvName","GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer)","int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16),","data # # class CapsuleFv (CapsuleData): ## The constructor #","string Generated file name # def GenCapsuleSubItem(self): return self.FileName class","of the BSD License # which accompanies this distribution. The","__init__(self) : self.Ffs = None self.FileName = None ## generate","GenFdsGlobalVariable import StringIO from struct import pack import os from","def GenCapsuleSubItem(self): pass ## FFS class for capsule data #","pointer # @retval string Generated file name # def GenCapsuleSubItem(self):","# ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if","self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize"],"string":"[\n \"return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is\",\n \"UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName\",\n \"self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer\",\n \"= '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize\",\n \"# @param self The object pointer def GenCapsuleSubItem(self): pass ##\",\n \"ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH',\",\n \"The constructor # # @param self The object pointer def\",\n \"generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All\",\n \"FFS capsule data # # @param self The object pointer\",\n \"def __init__(self): self.UiName = None self.Version = None self.ImageTypeId =\",\n \"def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys():\",\n \"capsule data # # class CapsuleFfs (CapsuleData): ## The constructor\",\n \"VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed =\",\n \"int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16),\",\n \"IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\"\",\n \"if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance\",\n \"GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import\",\n \"VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # #\",\n \"pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16),\",\n \"not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex =\",\n \"EXPRESS OR IMPLIED. # ## # Import Modules # import\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FileName\",\n \"self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper())\",\n \"constructor # # @param self The object pointer # def\",\n \"FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile\",\n \"generate AnyFile capsule data # # @param self The object\",\n \"http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD\",\n \"CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The\",\n \"def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class\",\n \"self.Ffs = None self.FileName = None ## generate AnyFile capsule\",\n \"GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj\",\n \"# are licensed and made available under the terms and\",\n \"__init__(self) : self.Ffs = None self.FdName = None self.CapsuleName =\",\n \"THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN\",\n \": self.Ffs = None self.FileName = None ## generate AnyFile\",\n \"defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID\",\n \"for capsule data # # class CapsuleFd (CapsuleData): ## The\",\n \"License # which accompanies this distribution. The full text of\",\n \"the ImageFileSize need include the full authenticated info size. From\",\n \"self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return\",\n \"struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3];\",\n \"import uuid ## base class for capsule data # #\",\n \"None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]):\",\n \"under the terms and conditions of the BSD License #\",\n \"name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for\",\n \"ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize =\",\n \"GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not\",\n \"None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile\",\n \"None self.FdName = None self.CapsuleName = None ## generate FD\",\n \"# # class CapsuleFv (CapsuleData): ## The constructor # #\",\n \"ImageFileSize need include the full authenticated info size. From first\",\n \"self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName\",\n \"BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"data # # class CapsuleAnyFile (CapsuleData): ## The constructor #\",\n \"# # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
\",\n \"# class CapsuleData: ## The constructor # # @param self\",\n \"made available under the terms and conditions of the BSD\",\n \"AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append\",\n \"reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2\",\n \"FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else:\",\n \"UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in\",\n \"16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4],\",\n \"This program and the accompanying materials # are licensed and\",\n \"license may be found at # http://opensource.org/licenses/bsd-license.php # # THE\",\n \"None self.FvName = None self.CapsuleName = None ## generate FV\",\n \"if AuthData: # the ImageFileSize need include the full authenticated\",\n \"Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1],\",\n \"this distribution. The full text of the license may be\",\n \"payload file, the header is defined below: #pragma pack(1) typedef\",\n \"self.FileName = None ## generate Afile capsule data # #\",\n \"} EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version =\",\n \"ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read()\",\n \"# class CapsuleAnyFile (CapsuleData): ## The constructor # # @param\",\n \"for capsule data # # class CapsuleAfile (CapsuleData): ## The\",\n \"EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize;\",\n \"from Common.Misc import SaveFileOnChange import uuid ## base class for\",\n \"self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0'\",\n \"ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import\",\n \"self.Ffs = None self.FileName = None ## generate Afile capsule\",\n \"//Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName =\",\n \"self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the\",\n \"Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return\",\n \"accompanies this distribution. The full text of the license may\",\n \"object pointer def __init__(self): pass ## generate capsule data #\",\n \"# import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from\",\n \"# generate capsule # # Copyright (c) 2007-2017, Intel Corporation.\",\n \"= FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile\",\n \"UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced\",\n \"open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile =\",\n \"constructor # # @param self The object pointer def __init__(self):\",\n \"FdFile ## AnyFile class for capsule data # # class\",\n \"Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the\",\n \"= open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True\",\n \"from struct import pack import os from Common.Misc import SaveFileOnChange\",\n \"0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure\",\n \"CapsuleData: ## The constructor # # @param self The object\",\n \"## @file # generate capsule # # Copyright (c) 2007-2017,\",\n \"if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer\",\n \"self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile\",\n \"OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## #\",\n \"not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance =\",\n \"of the license may be found at # http://opensource.org/licenses/bsd-license.php #\",\n \"Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import\",\n \"generate FFS capsule data # # @param self The object\",\n \"__init__(self) : self.Ffs = None self.FvName = None ## generate\",\n \"FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class\",\n \"UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64\",\n \"pointer # def __init__(self) : self.Ffs = None self.FileName =\",\n \"bytes of MonotonicCount to last bytes of certificate. # the\",\n \"'0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need\",\n \"'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return Buffer\",\n \"= '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize =\",\n \"self.FvName = None self.CapsuleName = None ## generate FV capsule\",\n \"THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"None ## generate FV capsule data # # @param self\",\n \"## # Import Modules # import Ffs from GenFdsGlobalVariable import\",\n \"typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8\",\n \"conditions of the BSD License # which accompanies this distribution.\",\n \"self.CapsuleName = None ## generate FV capsule data # #\",\n \"the full authenticated info size. From first bytes of MonotonicCount\",\n \"16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0,\",\n \"self.Ffs.GenFfs() return FfsFile ## FV class for capsule data #\",\n \"uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure #\",\n \"GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for\",\n \"the BSD License # which accompanies this distribution. The full\",\n \"in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None\",\n \"= [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed =\",\n \"EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None\",\n \"[] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None\",\n \"text of the license may be found at # http://opensource.org/licenses/bsd-license.php\",\n \"None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None\",\n \"data # # class CapsuleFd (CapsuleData): ## The constructor #\",\n \"# which accompanies this distribution. The full text of the\",\n \"-1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile =\",\n \"# Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
#\",\n \"header is defined below: #pragma pack(1) typedef struct { UINT32\",\n \"materials # are licensed and made available under the terms\",\n \"= StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName =\",\n \"self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid\",\n \"= None ## generate FFS capsule data # # @param\",\n \"## generate AnyFile capsule data # # @param self The\",\n \"available under the terms and conditions of the BSD License\",\n \"FFS class for capsule data # # class CapsuleFfs (CapsuleData):\",\n \"pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # #\",\n \"string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs()\",\n \"None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002'\",\n \"Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1:\",\n \"bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize\",\n \"AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF\",\n \"last bytes of certificate. # the 32 bit is the\",\n \"self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2],\",\n \"# This program and the accompanying materials # are licensed\",\n \"BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER\",\n \"The object pointer # @retval string Generated file name #\",\n \"if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('')\",\n \"self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if\",\n \"self The object pointer # def __init__(self) : self.Ffs =\",\n \"int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize,\",\n \"for capsule data # # class CapsuleAnyFile (CapsuleData): ## The\",\n \"name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ##\",\n \"= None ## generate FD capsule data # # @param\",\n \"self.Ffs = None self.FvName = None ## generate FFS capsule\",\n \"file content to the structure # ImageFile = open(self.ImageFile, 'rb')\",\n \"capsule data # # @param self The object pointer def\",\n \"= '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not\",\n \"__init__(self) : self.Ffs = None self.FvName = None self.CapsuleName =\",\n \"the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32\",\n \"CapsuleFv (CapsuleData): ## The constructor # # @param self The\",\n \"None ## generate AnyFile capsule data # # @param self\",\n \"generate capsule data # # @param self The object pointer\",\n \"def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data\",\n \"if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj =\",\n \"not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData:\",\n \"# the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType\",\n \"content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer\",\n \"== -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer\",\n \"# Append file content to the structure # ImageFile =\",\n \"class for capsule data # # class CapsuleFd (CapsuleData): ##\",\n \"@param self The object pointer # @retval string Generated file\",\n \"at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER\",\n \"bytes of certificate. # the 32 bit is the MonotonicCount,\",\n \"Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData):\",\n \"# # Fill structure # Guid = self.ImageTypeId.split('-') Buffer =\",\n \"the accompanying materials # are licensed and made available under\",\n \"# def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in\",\n \"AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content\",\n \"int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1],\",\n \"= GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile =\",\n \"## AnyFile class for capsule data # # class CapsuleAnyFile\",\n \"int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16),\",\n \"class CapsuleFfs (CapsuleData): ## The constructor # # @param self\",\n \"FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName\",\n \"self.Ffs = None self.FvName = None self.CapsuleName = None ##\",\n \"The object pointer def GenCapsuleSubItem(self): pass ## FFS class for\",\n \"uuid ## base class for capsule data # # class\",\n \"data # # @param self The object pointer # @retval\",\n \"PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS\",\n \"# def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV\",\n \"FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ##\",\n \"file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate\",\n \"= None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return\",\n \"# WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS\",\n \"import SaveFileOnChange import uuid ## base class for capsule data\",\n \"## FD class for capsule data # # class CapsuleFd\",\n \"return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile\",\n \"= None self.FileName = None ## generate AnyFile capsule data\",\n \"'0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance:\",\n \"FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ##\",\n \"the license may be found at # http://opensource.org/licenses/bsd-license.php # #\",\n \"import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct\",\n \"# # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE\",\n \"to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer +=\",\n \"The full text of the license may be found at\",\n \"= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd')\",\n \"# def __init__(self) : self.Ffs = None self.FileName = None\",\n \"include the full authenticated info size. From first bytes of\",\n \"to last bytes of certificate. # the 32 bit is\",\n \"Corporation. All rights reserved.
# # This program and the\",\n \"# # @param self The object pointer def GenCapsuleSubItem(self): pass\",\n \"struct import pack import os from Common.Misc import SaveFileOnChange import\",\n \"= None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance =\",\n \"# class CapsuleFfs (CapsuleData): ## The constructor # # @param\",\n \"FvFile ## FD class for capsule data # # class\",\n \"VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid =\",\n \"GenCapsuleSubItem(self): pass ## FFS class for capsule data # #\",\n \"32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) #\",\n \"[] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False\",\n \"else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName\",\n \"in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile\",\n \"self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid\",\n \"else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for\",\n \"# @param self The object pointer def __init__(self): pass ##\",\n \"= os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the\",\n \"FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName)\",\n \"16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0,\",\n \"if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd()\",\n \"= None self.FdName = None self.CapsuleName = None ## generate\",\n \") if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3])\",\n \"None self.FileName = None ## generate AnyFile capsule data #\",\n \"class for capsule data # # class CapsuleFv (CapsuleData): ##\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv')\",\n \"full authenticated info size. From first bytes of MonotonicCount to\",\n \"+= 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile)\",\n \"and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile:\",\n \"= self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16),\",\n \"info size. From first bytes of MonotonicCount to last bytes\",\n \"UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance;\",\n \"pointer # def __init__(self) : self.Ffs = None self.FvName =\",\n \"CapsuleAfile (CapsuleData): ## The constructor # # @param self The\",\n \"capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights\",\n \"pack import os from Common.Misc import SaveFileOnChange import uuid ##\",\n \"self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance\",\n \"name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper()\",\n \"self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined\",\n \"DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS,\",\n \"wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if\",\n \"CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma\",\n \"None self.MonotonicCount = None self.Existed = False self.Buffer = None\",\n \"# # @param self The object pointer def __init__(self): pass\",\n \"size. From first bytes of MonotonicCount to last bytes of\",\n \"None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None\",\n \"may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM\",\n \"Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16),\",\n \": self.Ffs = None self.FileName = None ## generate Afile\",\n \"OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. #\",\n \"0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer +=\",\n \"int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16),\",\n \"CapsuleFd (CapsuleData): ## The constructor # # @param self The\",\n \"FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule\",\n \"= open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile\",\n \"0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer\",\n \"## The constructor # # @param self The object pointer\",\n \"class CapsuleFv (CapsuleData): ## The constructor # # @param self\",\n \"## generate FV capsule data # # @param self The\",\n \"# # This program and the accompanying materials # are\",\n \"which accompanies this distribution. The full text of the license\",\n \"object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule\",\n \"be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS\",\n \"file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class\",\n \"for capsule data # # class CapsuleData: ## The constructor\",\n \"== -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile\",\n \"FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule\",\n \"= 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill\",\n \"int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16),\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName\",\n \"FfsFile ## FV class for capsule data # # class\",\n \"self.FileName = None ## generate AnyFile capsule data # #\",\n \"# # @param self The object pointer # @retval string\",\n \"= None self.CapsuleName = None ## generate FV capsule data\",\n \"UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize;\",\n \"@param self The object pointer def __init__(self): pass ## generate\",\n \"FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class\",\n \"None ## generate Afile capsule data # # @param self\",\n \"class CapsuleFd (CapsuleData): ## The constructor # # @param self\",\n \"#pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8\",\n \"pointer # def __init__(self) : self.Ffs = None self.FdName =\",\n \"object pointer # @retval string Generated file name # def\",\n \"string Generated file name # def GenCapsuleSubItem(self): return self.FileName ##\",\n \"found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED\",\n \"FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close()\",\n \"UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, #\",\n \"MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize\",\n \"False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version:\",\n \"int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:],\",\n \"# def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in\",\n \"OR IMPLIED. # ## # Import Modules # import Ffs\",\n \"FD class for capsule data # # class CapsuleFd (CapsuleData):\",\n \"AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file\",\n \"self The object pointer # @retval string Generated file name\",\n \"is defined below: #pragma pack(1) typedef struct { UINT32 Version;\",\n \"Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1:\",\n \"Afile class for capsule data # # class CapsuleAfile (CapsuleData):\",\n \"__init__(self): pass ## generate capsule data # # @param self\",\n \"the header is defined below: #pragma pack(1) typedef struct {\",\n \"= None self.CapsuleName = None ## generate FD capsule data\",\n \"def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName\",\n \"2007-2017, Intel Corporation. All rights reserved.
# # This program\",\n \"# @param self The object pointer # @retval string Generated\",\n \"= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data\",\n \"base class for capsule data # # class CapsuleData: ##\",\n \"'0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile)\",\n \"GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data #\",\n \"# Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable\",\n \"import pack import os from Common.Misc import SaveFileOnChange import uuid\",\n \"16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8],\",\n \"= None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid =\",\n \"# http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE\",\n \"GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj\",\n \"= None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version =\",\n \"Append file content to the structure # ImageFile = open(self.ImageFile,\",\n \"= None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self,\",\n \"IMPLIED. # ## # Import Modules # import Ffs from\",\n \"certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision,\",\n \"of certificate. # the 32 bit is the MonotonicCount, dwLength,\",\n \"reserved.
# # This program and the accompanying materials #\",\n \"generate FD capsule data # # @param self The object\",\n \"capsule data # # class CapsuleAfile (CapsuleData): ## The constructor\",\n \"def __init__(self) : self.Ffs = None self.FileName = None ##\",\n \"# # class CapsuleData: ## The constructor # # @param\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else:\",\n \"EITHER EXPRESS OR IMPLIED. # ## # Import Modules #\",\n \"Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
# #\",\n \"in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName =\",\n \"= None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile =\",\n \"## generate Afile capsule data # # @param self The\",\n \"{ UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32\",\n \"class CapsuleAfile (CapsuleData): ## The constructor # # @param self\",\n \"return FvFile ## FD class for capsule data # #\",\n \"self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile\",\n \"capsule data # # class CapsuleFv (CapsuleData): ## The constructor\",\n \"distribution. The full text of the license may be found\",\n \"FV capsule data # # @param self The object pointer\",\n \"AnyFile capsule data # # @param self The object pointer\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile\",\n \"int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16),\",\n \"for capsule data # # class CapsuleFv (CapsuleData): ## The\",\n \"# def __init__(self) : self.Ffs = None self.FvName = None\",\n \"authenticated info size. From first bytes of MonotonicCount to last\",\n \"None self.FvName = None ## generate FFS capsule data #\",\n \"structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0],\",\n \"dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize =\",\n \"self.FileName ## Afile class for capsule data # # class\",\n \"= [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount =\",\n \"self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: #\",\n \"16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if\",\n \"# # class CapsuleFfs (CapsuleData): ## The constructor # #\",\n \"## generate FFS capsule data # # @param self The\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): FfsFile =\",\n \"Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO\",\n \"class for capsule data # # class CapsuleAfile (CapsuleData): ##\",\n \"0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData:\",\n \"Common.Misc import SaveFileOnChange import uuid ## base class for capsule\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND,\",\n \"= None ## generate AnyFile capsule data # # @param\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): if\",\n \"structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close()\",\n \"self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile\",\n \"## base class for capsule data # # class CapsuleData:\",\n \"Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32\",\n \"return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD\",\n \"''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId\",\n \"# @param self The object pointer # def __init__(self) :\",\n \"@param self The object pointer def GenCapsuleSubItem(self): pass ## FFS\",\n \"terms and conditions of the BSD License # which accompanies\",\n \"import StringIO from struct import pack import os from Common.Misc\",\n \"'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile,\",\n \"Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile\",\n \"WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.\",\n \"int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16),\",\n \"# the ImageFileSize need include the full authenticated info size.\",\n \"FV class for capsule data # # class CapsuleFv (CapsuleData):\",\n \"None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = []\",\n \"# # @param self The object pointer # def __init__(self)\",\n \"= self.Ffs.GenFfs() return FfsFile ## FV class for capsule data\",\n \"class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below:\",\n \"# Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ',\",\n \"AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer +=\",\n \"__init__(self): self.UiName = None self.Version = None self.ImageTypeId = None\",\n \"pointer def __init__(self): pass ## generate capsule data # #\",\n \"if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex\",\n \"are licensed and made available under the terms and conditions\",\n \"16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16)\",\n \"string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') ==\",\n \"ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer +=\",\n \"self The object pointer def __init__(self): pass ## generate capsule\",\n \"UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER;\",\n \"= None ## generate FV capsule data # # @param\",\n \"class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self\",\n \"16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize,\",\n \"= False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not\",\n \"AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData):\",\n \"full text of the license may be found at #\",\n \"'''Generate payload file, the header is defined below: #pragma pack(1)\",\n \"+= uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure\",\n \"16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2],\",\n \"None ## generate FFS capsule data # # @param self\",\n \"# class CapsuleAfile (CapsuleData): ## The constructor # # @param\",\n \"int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16),\",\n \"# def __init__(self) : self.Ffs = None self.FdName = None\",\n \"self.Ffs = None self.FdName = None self.CapsuleName = None ##\",\n \"self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed\",\n \"the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and\",\n \"os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer\",\n \"# class CapsuleFv (CapsuleData): ## The constructor # # @param\",\n \"self.CapsuleName = None ## generate FD capsule data # #\",\n \"import os from Common.Misc import SaveFileOnChange import uuid ## base\",\n \"self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version\",\n \"if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if\",\n \"generate FV capsule data # # @param self The object\",\n \"@param self The object pointer # def __init__(self) : self.Ffs\",\n \"(CapsuleData): ## The constructor # # @param self The object\",\n \"# # class CapsuleAfile (CapsuleData): ## The constructor # #\",\n \"StringIO from struct import pack import os from Common.Misc import\",\n \"self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1'\",\n \"GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data #\",\n \"self.FdName = None self.CapsuleName = None ## generate FD capsule\",\n \"FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile\",\n \"None self.FileName = None ## generate Afile capsule data #\",\n \"UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; '''\",\n \"v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version\",\n \"name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper()\",\n \"16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10],\",\n \"# THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON\",\n \"UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self):\",\n \"= None self.FileName = None ## generate Afile capsule data\",\n \"def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if\",\n \"return FdFile ## AnyFile class for capsule data # #\",\n \"def __init__(self): pass ## generate capsule data # # @param\",\n \"+= pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() #\",\n \"licensed and made available under the terms and conditions of\",\n \"32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType\",\n \"Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb')\",\n \"ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile:\",\n \"16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:],\",\n \"if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj =\",\n \"None ## generate FD capsule data # # @param self\",\n \"import GenFdsGlobalVariable import StringIO from struct import pack import os\",\n \"pass ## FFS class for capsule data # # class\",\n \"SaveFileOnChange import uuid ## base class for capsule data #\",\n \"data # # class CapsuleAfile (CapsuleData): ## The constructor #\",\n \"= None self.ImageIndex = None self.HardwareInstance = None self.ImageFile =\",\n \"wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0\",\n \"KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules\",\n \"## generate FD capsule data # # @param self The\",\n \"is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize +=\",\n \"os from Common.Misc import SaveFileOnChange import uuid ## base class\",\n \"class for capsule data # # class CapsuleAnyFile (CapsuleData): ##\",\n \"pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data\",\n \"self The object pointer def GenCapsuleSubItem(self): pass ## FFS class\",\n \"FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule\",\n \"MonotonicCount to last bytes of certificate. # the 32 bit\",\n \"## generate capsule data # # @param self The object\",\n \"None self.CapsuleName = None ## generate FD capsule data #\",\n \"def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys():\",\n \"and made available under the terms and conditions of the\",\n \"and the accompanying materials # are licensed and made available\",\n \"capsule data # # class CapsuleData: ## The constructor #\",\n \"FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile =\",\n \"UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def\",\n \"of MonotonicCount to last bytes of certificate. # the 32\",\n \"int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) )\",\n \": self.Ffs = None self.FvName = None self.CapsuleName = None\",\n \"Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le()\",\n \"class for capsule data # # class CapsuleData: ## The\",\n \"for capsule data # # class CapsuleFfs (CapsuleData): ## The\",\n \": self.Ffs = None self.FdName = None self.CapsuleName = None\",\n \"@file # generate capsule # # Copyright (c) 2007-2017, Intel\",\n \"program and the accompanying materials # are licensed and made\",\n \"os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full\",\n \"-1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer =\",\n \"below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId;\",\n \"first bytes of MonotonicCount to last bytes of certificate. #\",\n \"Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16),\",\n \"pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex;\",\n \"CapsuleFfs (CapsuleData): ## The constructor # # @param self The\",\n \"## Afile class for capsule data # # class CapsuleAfile\",\n \"= pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2],\",\n \"if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close()\",\n \"and conditions of the BSD License # which accompanies this\",\n \"from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack\",\n \"= os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-')\",\n \"def __init__(self) : self.Ffs = None self.FvName = None ##\",\n \"return self.FileName ## Afile class for capsule data # #\",\n \"data # # @param self The object pointer def GenCapsuleSubItem(self):\",\n \"Afile capsule data # # @param self The object pointer\",\n \"need include the full authenticated info size. From first bytes\",\n \"WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR\",\n \"Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import\",\n \"The object pointer # def __init__(self) : self.Ffs = None\",\n \"self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if\",\n \"From first bytes of MonotonicCount to last bytes of certificate.\",\n \": self.Ffs = None self.FvName = None ## generate FFS\",\n \"# ## # Import Modules # import Ffs from GenFdsGlobalVariable\",\n \"VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0],\",\n \"class for capsule data # # class CapsuleFfs (CapsuleData): ##\",\n \"def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the\",\n \"None self.Version = None self.ImageTypeId = None self.ImageIndex = None\",\n \"# def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file,\",\n \"+= ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer\",\n \"open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return\",\n \"16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2],\",\n \"REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ##\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY\",\n \"AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex:\",\n \"return FfsFile ## FV class for capsule data # #\",\n \"file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if\",\n \"# # class CapsuleFd (CapsuleData): ## The constructor # #\",\n \"capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor\",\n \"= None self.FvName = None self.CapsuleName = None ## generate\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): return\",\n \"All rights reserved.
# # This program and the accompanying\",\n \"pass ## generate capsule data # # @param self The\",\n \"## FFS class for capsule data # # class CapsuleFfs\",\n \"16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex,\",\n \"Intel Corporation. All rights reserved.
# # This program and\",\n \"= FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile\",\n \"accompanying materials # are licensed and made available under the\",\n \"self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex\",\n \"the terms and conditions of the BSD License # which\",\n \"file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile\",\n \"self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper())\",\n \"self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed\",\n \"CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize\",\n \"rights reserved.
# # This program and the accompanying materials\",\n \"FD capsule data # # @param self The object pointer\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FdName\",\n \"FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return\",\n \"generate Afile capsule data # # @param self The object\",\n \"ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include\",\n \"BSD License # which accompanies this distribution. The full text\",\n \"def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName\",\n \"None self.CapsuleName = None ## generate FV capsule data #\",\n \"= None self.MonotonicCount = None self.Existed = False self.Buffer =\",\n \"capsule data # # @param self The object pointer #\",\n \"StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None\",\n \"capsule data # # class CapsuleFd (CapsuleData): ## The constructor\",\n \"self.FvName = None ## generate FFS capsule data # #\",\n \"self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount\",\n \"AuthData: # the ImageFileSize need include the full authenticated info\",\n \"## FV class for capsule data # # class CapsuleFv\",\n \"16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6],\",\n \"int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0,\",\n \"= None self.Version = None self.ImageTypeId = None self.ImageIndex =\",\n \"the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read()\",\n \"= None ## generate Afile capsule data # # @param\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName)\",\n \"# class CapsuleFd (CapsuleData): ## The constructor # # @param\",\n \"The constructor # # @param self The object pointer #\",\n \"file, the header is defined below: #pragma pack(1) typedef struct\",\n \"ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS\",\n \"file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if\",\n \"# Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16),\",\n \"The object pointer def __init__(self): pass ## generate capsule data\",\n \"# # Append file content to the structure # ImageFile\",\n \"int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance,\",\n \"name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload\",\n \"= self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return\",\n \"AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to\",\n \"data # # class CapsuleData: ## The constructor # #\",\n \"if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure #\",\n \"class CapsuleData: ## The constructor # # @param self The\",\n \"(c) 2007-2017, Intel Corporation. All rights reserved.
# # This\",\n \"= None self.FvName = None ## generate FFS capsule data\",\n \"UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 }\",\n \"string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') ==\",\n \"LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"# def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule\",\n \"data # # class CapsuleFfs (CapsuleData): ## The constructor #\",\n \"self.MonotonicCount = None self.Existed = False self.Buffer = None def\",\n \"GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data #\",\n \"None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = []\",\n \"GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header\",\n \"= GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile =\",\n \"# # class CapsuleAnyFile (CapsuleData): ## The constructor # #\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FvName\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer)\",\n \"int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16),\",\n \"data # # class CapsuleFv (CapsuleData): ## The constructor #\",\n \"string Generated file name # def GenCapsuleSubItem(self): return self.FileName class\",\n \"of the BSD License # which accompanies this distribution. The\",\n \"__init__(self) : self.Ffs = None self.FileName = None ## generate\",\n \"GenFdsGlobalVariable import StringIO from struct import pack import os from\",\n \"def GenCapsuleSubItem(self): pass ## FFS class for capsule data #\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self):\",\n \"# ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if\",\n \"self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize\"\n]"}}},{"rowIdx":3492,"cells":{"ngram":{"kind":"list like","value":["size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,","[] time = [] with open(\"pi_linear.txt\") as file: for line","plt import numpy as np # Read data size =","line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots()","Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes',","Read data size = [] time = [] with open(\"pi_linear.txt\")","numpy as np # Read data size = [] time","with open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y","import matplotlib.pyplot as plt import numpy as np # Read","import numpy as np # Read data size = []","as np # Read data size = [] time =","= [] with open(\"pi_linear.txt\") as file: for line in file.readlines():","[] with open(\"pi_linear.txt\") as file: for line in file.readlines(): x,","open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y =","ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi","time = [] with open(\"pi_linear.txt\") as file: for line in","= plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear')","x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig,","data size = [] time = [] with open(\"pi_linear.txt\") as","ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\"pi_linear.png\")","plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid()","as file: for line in file.readlines(): x, y = line.split(',')","size = [] time = [] with open(\"pi_linear.txt\") as file:","matplotlib.pyplot as plt import numpy as np # Read data","= [] time = [] with open(\"pi_linear.txt\") as file: for","data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time","as plt import numpy as np # Read data size","np # Read data size = [] time = []","# Read data size = [] time = [] with","= line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax =","file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip()))","time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\"pi_linear.png\") plt.show()","time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time)","in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot","y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax","line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #","for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip()))","file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data","fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)',","# Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num."],"string":"[\n \"size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,\",\n \"[] time = [] with open(\\\"pi_linear.txt\\\") as file: for line\",\n \"plt import numpy as np # Read data size =\",\n \"line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots()\",\n \"Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes',\",\n \"Read data size = [] time = [] with open(\\\"pi_linear.txt\\\")\",\n \"numpy as np # Read data size = [] time\",\n \"with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y\",\n \"import matplotlib.pyplot as plt import numpy as np # Read\",\n \"import numpy as np # Read data size = []\",\n \"as np # Read data size = [] time =\",\n \"= [] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines():\",\n \"[] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x,\",\n \"open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y =\",\n \"ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi\",\n \"time = [] with open(\\\"pi_linear.txt\\\") as file: for line in\",\n \"= plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear')\",\n \"x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig,\",\n \"data size = [] time = [] with open(\\\"pi_linear.txt\\\") as\",\n \"ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\\\"pi_linear.png\\\")\",\n \"plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid()\",\n \"as file: for line in file.readlines(): x, y = line.split(',')\",\n \"size = [] time = [] with open(\\\"pi_linear.txt\\\") as file:\",\n \"matplotlib.pyplot as plt import numpy as np # Read data\",\n \"= [] time = [] with open(\\\"pi_linear.txt\\\") as file: for\",\n \"data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time\",\n \"as plt import numpy as np # Read data size\",\n \"np # Read data size = [] time = []\",\n \"# Read data size = [] time = [] with\",\n \"= line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax =\",\n \"file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip()))\",\n \"time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\\\"pi_linear.png\\\") plt.show()\",\n \"time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time)\",\n \"in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot\",\n \"y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax\",\n \"line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #\",\n \"for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip()))\",\n \"file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data\",\n \"fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)',\",\n \"# Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num.\"\n]"}}},{"rowIdx":3493,"cells":{"ngram":{"kind":"list like","value":["each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result","election results, or an empty array if the requester did","partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic:","name :type topic: str :param partition_result: The results for each",":type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results = replica_election_results","The result error, or zero if there was no error.","def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The","typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode","str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\"","error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]):","self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key:","List, Optional from ...constants import ApiKey, ErrorCode from ..base import","error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic:","class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self,","ErrorCode :param error_message: The result message, or null if there","which the request was throttled due to a quota violation,","for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms","__init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic","a quota violation, or zero if the request did not",":param partition_id: The partition id :type partition_id: int :param error_code:","error_message: The result message, or null if there was no","results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic =","quota. :type throttle_time_ms: int :param replica_election_results: The election results, or",":param throttle_time_ms: The duration in milliseconds for which the request","api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]):","The election results, or an empty array if the requester","self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def","did not have permission and the request asks for all","error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult]","partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The","ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message:","there was no error. :type error_message: Optional[str] \"\"\" self.partition_id =",":type throttle_time_ms: int :param replica_election_results: The election results, or an","ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result:","int :param replica_election_results: The election results, or an empty array","def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The",":param topic: The topic name :type topic: str :param partition_result:","not have permission and the request asks for all partitions.","topic: The topic name :type topic: str :param partition_result: The",":param error_message: The result message, or null if there was","the request did not violate any quota. :type throttle_time_ms: int","\"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms:","permission and the request asks for all partitions. :type replica_election_results:","= partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult:","asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms =","List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic:","import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from","Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\"","topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult]","0 from typing import ClassVar, List, Optional from ...constants import","partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type topic:","if the requester did not have permission and the request","error. :type error_code: ErrorCode :param error_message: The result message, or","topic name :type topic: str :param partition_result: The results for","partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] =","import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str]","__init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration","ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int","or zero if the request did not violate any quota.","Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message =","ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code:","ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def","int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code:","results, or an empty array if the requester did not","List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results:","no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code","throttled due to a quota violation, or zero if the","self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str","if the request did not violate any quota. :type throttle_time_ms:","Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData","did not violate any quota. :type throttle_time_ms: int :param replica_election_results:",":type topic: str :param partition_result: The results for each partition","\"\"\" :param topic: The topic name :type topic: str :param","due to a quota violation, or zero if the request","id :type partition_id: int :param error_code: The result error, or","error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id:","if there was no error. :type error_code: ErrorCode :param error_message:","result message, or null if there was no error. :type","\"\"\" :param partition_id: The partition id :type partition_id: int :param","request was throttled due to a quota violation, or zero","ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms:","__init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id:","The results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic","Optional[str]): \"\"\" :param partition_id: The partition id :type partition_id: int","partition_result: The results for each partition :type partition_result: List[PartitionResult] \"\"\"","int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds","not violate any quota. :type throttle_time_ms: int :param replica_election_results: The","import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id:","from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode","for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic",":param error_code: The result error, or zero if there was","requester did not have permission and the request asks for","from typing import ClassVar, List, Optional from ...constants import ApiKey,","or null if there was no error. :type error_message: Optional[str]","partition id :type partition_id: int :param error_code: The result error,","error, or zero if there was no error. :type error_code:","ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def","the request was throttled due to a quota violation, or","The result message, or null if there was no error.","\"\"\" :param throttle_time_ms: The duration in milliseconds for which the","request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms","List[PartitionResult]): \"\"\" :param topic: The topic name :type topic: str","replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for","topic: str :param partition_result: The results for each partition :type","self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class","ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\"","List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in milliseconds for which","violate any quota. :type throttle_time_ms: int :param replica_election_results: The election","ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id :type","quota violation, or zero if the request did not violate","The partition id :type partition_id: int :param error_code: The result","in milliseconds for which the request was throttled due to","the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\"","replica_election_results: The election results, or an empty array if the","zero if the request did not violate any quota. :type","str :param partition_result: The results for each partition :type partition_result:","= error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result:","all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results","and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult]","if there was no error. :type error_message: Optional[str] \"\"\" self.partition_id","no error. :type error_code: ErrorCode :param error_message: The result message,","or an empty array if the requester did not have","= error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self,","error_code: The result error, or zero if there was no","partition_id: int :param error_code: The result error, or zero if","error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code =","class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str,","str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name :type",":type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result","from ...constants import ApiKey, ErrorCode from ..base import ResponseData class","message, or null if there was no error. :type error_message:","throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self,","violation, or zero if the request did not violate any","topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic name","any quota. :type throttle_time_ms: int :param replica_election_results: The election results,","was no error. :type error_message: Optional[str] \"\"\" self.partition_id = partition_id","def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param","..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message:","self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int","ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base","int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms:","partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param","error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition id","empty array if the requester did not have permission and","topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]):","for which the request was throttled due to a quota","null if there was no error. :type error_message: Optional[str] \"\"\"","was throttled due to a quota violation, or zero if","have permission and the request asks for all partitions. :type","throttle_time_ms: int :param replica_election_results: The election results, or an empty","result error, or zero if there was no error. :type","partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int,","error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode,","partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results =","= ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param","zero if there was no error. :type error_code: ErrorCode :param","or zero if there was no error. :type error_code: ErrorCode","to a quota violation, or zero if the request did","throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The duration in","milliseconds for which the request was throttled due to a","an empty array if the requester did not have permission",":type error_code: ErrorCode :param error_message: The result message, or null","there was no error. :type error_code: ErrorCode :param error_message: The","...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult:","partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class",":type partition_id: int :param error_code: The result error, or zero","int :param error_code: The result error, or zero if there","duration in milliseconds for which the request was throttled due","array if the requester did not have permission and the",":type error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code","= topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results:",":param replica_election_results: The election results, or an empty array if","\"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message","PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id:","replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int,","int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The partition","error_code: ErrorCode :param error_message: The result message, or null if","List[PartitionResult] \"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData):","partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result =","error_message: Optional[str] \"\"\" self.partition_id = partition_id self.error_code = error_code self.error_message","The topic name :type topic: str :param partition_result: The results","partition_id: The partition id :type partition_id: int :param error_code: The",":param partition_result: The results for each partition :type partition_result: List[PartitionResult]","was no error. :type error_code: ErrorCode :param error_message: The result","throttle_time_ms: The duration in milliseconds for which the request was","= partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey]","The duration in milliseconds for which the request was throttled","the requester did not have permission and the request asks","class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS","request did not violate any quota. :type throttle_time_ms: int :param"],"string":"[\n \"each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result\",\n \"election results, or an empty array if the requester did\",\n \"partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic:\",\n \"name :type topic: str :param partition_result: The results for each\",\n \":type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms self.replica_election_results = replica_election_results\",\n \"The result error, or zero if there was no error.\",\n \"def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The\",\n \"typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode\",\n \"str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\"\",\n \"error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]):\",\n \"self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key:\",\n \"List, Optional from ...constants import ApiKey, ErrorCode from ..base import\",\n \"error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic:\",\n \"class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self,\",\n \"ErrorCode :param error_message: The result message, or null if there\",\n \"which the request was throttled due to a quota violation,\",\n \"for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms\",\n \"__init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic\",\n \"a quota violation, or zero if the request did not\",\n \":param partition_id: The partition id :type partition_id: int :param error_code:\",\n \"error_message: The result message, or null if there was no\",\n \"results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic =\",\n \"quota. :type throttle_time_ms: int :param replica_election_results: The election results, or\",\n \":param throttle_time_ms: The duration in milliseconds for which the request\",\n \"api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]):\",\n \"The election results, or an empty array if the requester\",\n \"self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def\",\n \"did not have permission and the request asks for all\",\n \"error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult]\",\n \"partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The\",\n \"ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message:\",\n \"there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id =\",\n \":type throttle_time_ms: int :param replica_election_results: The election results, or an\",\n \"ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result:\",\n \"int :param replica_election_results: The election results, or an empty array\",\n \"def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The\",\n \":param topic: The topic name :type topic: str :param partition_result:\",\n \"not have permission and the request asks for all partitions.\",\n \"topic: The topic name :type topic: str :param partition_result: The\",\n \":param error_message: The result message, or null if there was\",\n \"the request did not violate any quota. :type throttle_time_ms: int\",\n \"\\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms:\",\n \"permission and the request asks for all partitions. :type replica_election_results:\",\n \"= partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult:\",\n \"asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms =\",\n \"List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic:\",\n \"import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from\",\n \"Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\"\",\n \"topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult]\",\n \"0 from typing import ClassVar, List, Optional from ...constants import\",\n \"partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic:\",\n \"if the requester did not have permission and the request\",\n \"error. :type error_code: ErrorCode :param error_message: The result message, or\",\n \"topic name :type topic: str :param partition_result: The results for\",\n \"partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] =\",\n \"import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str]\",\n \"__init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration\",\n \"ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int\",\n \"or zero if the request did not violate any quota.\",\n \"Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message =\",\n \"ErrorCode from ..base import ResponseData class PartitionResult: partition_id: int error_code:\",\n \"ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def\",\n \"int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code:\",\n \"results, or an empty array if the requester did not\",\n \"List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results:\",\n \"no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code\",\n \"throttled due to a quota violation, or zero if the\",\n \"self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic: str\",\n \"if the request did not violate any quota. :type throttle_time_ms:\",\n \"Optional from ...constants import ApiKey, ErrorCode from ..base import ResponseData\",\n \"did not violate any quota. :type throttle_time_ms: int :param replica_election_results:\",\n \":type topic: str :param partition_result: The results for each partition\",\n \"\\\"\\\"\\\" :param topic: The topic name :type topic: str :param\",\n \"due to a quota violation, or zero if the request\",\n \"id :type partition_id: int :param error_code: The result error, or\",\n \"error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id:\",\n \"if there was no error. :type error_code: ErrorCode :param error_message:\",\n \"result message, or null if there was no error. :type\",\n \"\\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int :param\",\n \"request was throttled due to a quota violation, or zero\",\n \"ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms:\",\n \"__init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id:\",\n \"The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic\",\n \"Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type partition_id: int\",\n \"partition_result: The results for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\"\",\n \"int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds\",\n \"not violate any quota. :type throttle_time_ms: int :param replica_election_results: The\",\n \"import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult: partition_id:\",\n \"from ..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode\",\n \"for each partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic\",\n \":param error_code: The result error, or zero if there was\",\n \"requester did not have permission and the request asks for\",\n \"from typing import ClassVar, List, Optional from ...constants import ApiKey,\",\n \"or null if there was no error. :type error_message: Optional[str]\",\n \"partition id :type partition_id: int :param error_code: The result error,\",\n \"error, or zero if there was no error. :type error_code:\",\n \"ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def\",\n \"the request was throttled due to a quota violation, or\",\n \"The result message, or null if there was no error.\",\n \"\\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which the\",\n \"request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms\",\n \"List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type topic: str\",\n \"replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for\",\n \"topic: str :param partition_result: The results for each partition :type\",\n \"self.partition_id = partition_id self.error_code = error_code self.error_message = error_message class\",\n \"ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\"\",\n \"List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in milliseconds for which\",\n \"violate any quota. :type throttle_time_ms: int :param replica_election_results: The election\",\n \"ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id :type\",\n \"quota violation, or zero if the request did not violate\",\n \"The partition id :type partition_id: int :param error_code: The result\",\n \"in milliseconds for which the request was throttled due to\",\n \"the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\"\",\n \"replica_election_results: The election results, or an empty array if the\",\n \"zero if the request did not violate any quota. :type\",\n \"str :param partition_result: The results for each partition :type partition_result:\",\n \"= error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result:\",\n \"all partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms self.replica_election_results\",\n \"and the request asks for all partitions. :type replica_election_results: List[ReplicaElectionResult]\",\n \"if there was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id\",\n \"no error. :type error_code: ErrorCode :param error_message: The result message,\",\n \"or an empty array if the requester did not have\",\n \"= error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self,\",\n \"error_code: The result error, or zero if there was no\",\n \"partition_id: int :param error_code: The result error, or zero if\",\n \"error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code =\",\n \"class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str,\",\n \"str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name :type\",\n \":type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result\",\n \"from ...constants import ApiKey, ErrorCode from ..base import ResponseData class\",\n \"message, or null if there was no error. :type error_message:\",\n \"throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self,\",\n \"violation, or zero if the request did not violate any\",\n \"topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param topic: The topic name\",\n \"any quota. :type throttle_time_ms: int :param replica_election_results: The election results,\",\n \"was no error. :type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id\",\n \"def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param\",\n \"..base import ResponseData class PartitionResult: partition_id: int error_code: ErrorCode error_message:\",\n \"self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int\",\n \"ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from ..base\",\n \"int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms:\",\n \"partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \\\"\\\"\\\" :param\",\n \"error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition id\",\n \"empty array if the requester did not have permission and\",\n \"topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]):\",\n \"for which the request was throttled due to a quota\",\n \"null if there was no error. :type error_message: Optional[str] \\\"\\\"\\\"\",\n \"was throttled due to a quota violation, or zero if\",\n \"have permission and the request asks for all partitions. :type\",\n \"throttle_time_ms: int :param replica_election_results: The election results, or an empty\",\n \"result error, or zero if there was no error. :type\",\n \"partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int,\",\n \"error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode,\",\n \"partitions. :type replica_election_results: List[ReplicaElectionResult] \\\"\\\"\\\" self.throttle_time_ms = throttle_time_ms self.replica_election_results =\",\n \"= ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param\",\n \"zero if there was no error. :type error_code: ErrorCode :param\",\n \"or zero if there was no error. :type error_code: ErrorCode\",\n \"to a quota violation, or zero if the request did\",\n \"throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \\\"\\\"\\\" :param throttle_time_ms: The duration in\",\n \"milliseconds for which the request was throttled due to a\",\n \"an empty array if the requester did not have permission\",\n \":type error_code: ErrorCode :param error_message: The result message, or null\",\n \"there was no error. :type error_code: ErrorCode :param error_message: The\",\n \"...constants import ApiKey, ErrorCode from ..base import ResponseData class PartitionResult:\",\n \"partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class\",\n \":type partition_id: int :param error_code: The result error, or zero\",\n \"int :param error_code: The result error, or zero if there\",\n \"duration in milliseconds for which the request was throttled due\",\n \"array if the requester did not have permission and the\",\n \":type error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code\",\n \"= topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results:\",\n \":param replica_election_results: The election results, or an empty array if\",\n \"\\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message = error_message\",\n \"PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self, partition_id:\",\n \"replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int,\",\n \"int, error_code: ErrorCode, error_message: Optional[str]): \\\"\\\"\\\" :param partition_id: The partition\",\n \"error_code: ErrorCode :param error_message: The result message, or null if\",\n \"List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData):\",\n \"partition :type partition_result: List[PartitionResult] \\\"\\\"\\\" self.topic = topic self.partition_result =\",\n \"error_message: Optional[str] \\\"\\\"\\\" self.partition_id = partition_id self.error_code = error_code self.error_message\",\n \"The topic name :type topic: str :param partition_result: The results\",\n \"partition_id: The partition id :type partition_id: int :param error_code: The\",\n \":param partition_result: The results for each partition :type partition_result: List[PartitionResult]\",\n \"was no error. :type error_code: ErrorCode :param error_message: The result\",\n \"throttle_time_ms: The duration in milliseconds for which the request was\",\n \"= partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey]\",\n \"The duration in milliseconds for which the request was throttled\",\n \"the requester did not have permission and the request asks\",\n \"class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS\",\n \"request did not violate any quota. :type throttle_time_ms: int :param\"\n]"}}},{"rowIdx":3494,"cells":{"ngram":{"kind":"list like","value":["test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer =","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop()","pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following","not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\"","test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert","test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with","py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get()","# doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer =","def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy()","\"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def","py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True)","assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer()","assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure","log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage():","assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\", \"d\"]","in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer","1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works","assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def","py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"])","py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make","py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" ==","== py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\"","test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not","as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def","def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert","stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"]","\"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs():","test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\"","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" ==","following tests all expect _thread_local.zipkin_attrs to exist: if it #","py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\",","[\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\", \"d\"] ==","# Let's make sure this still works if we don't","mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): #","not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests","assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\",","def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]):","\"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" ==","if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs():","\"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy():","import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The","[\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" ==","py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not","\"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\", \"d\"] == stack._storage","def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert","assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs():","exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def","custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer()","\"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []):","mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def","@pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all expect","def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()","\"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\"","tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't,","log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop()","py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get()","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get()","mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count","py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\",","def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not","with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list():","test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert","\"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop()","= py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" ==","assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log,","stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\"","def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer","autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1","with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert","to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get()","not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log:","== py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" ==","assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def","test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\"","\"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","[\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get()","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\")","not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\",","make sure this still works if we don't pass in","== py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack =","if we don't pass in a custom storage. assert not","test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert","py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]):","[]): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with","create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist:","assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs():","stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack()","== 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still","mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert","def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if","assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with","context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer()","stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack =","stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\", \"b\",","py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests():","def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert","with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert","== py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","\"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer","Let's make sure this still works if we don't pass","[]): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def","= stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage","py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack =","will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,","== stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy","_thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail.","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def","import mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs():","def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop()","test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get()","all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch","py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","== py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def","\"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" ==","== context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer =","not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's","assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert","stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\")","The following tests all expect _thread_local.zipkin_attrs to exist: if it","mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\"","sure this still works if we don't pass in a","not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer","assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"])","assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): #","this still works if we don't pass in a custom","def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert","assert \"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\")","assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list():","a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer =","expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will","with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert","mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert","\"bar\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\")","assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \"warning\", autospec=True) as","py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get()","doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]): assert \"foo\" ==","== context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","scope=\"module\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs","it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer","py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []):","with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert","= py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\") assert \"bar\" ==","stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy =","def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\"foo\"]) assert \"foo\" == stack.get() stack.push(\"bar\")","pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list():","py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\",","== py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack","== stack.get() stack.push(\"bar\") assert \"bar\" == stack.get() def test_stack_copy(): stack","the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] ==","with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get()","py_zipkin.storage @pytest.fixture(autouse=True, scope=\"module\") def create_zipkin_attrs(): # The following tests all","we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get()","\"_storage\", [\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\"","= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def","\"warning\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count ==","the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert [\"a\",","context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\"","\"bar\"]) assert \"bar\" == context_stack.pop() assert \"foo\" == context_stack.get() def","= py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert","\"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get()","py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests():","py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer =","context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\"]):","test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\")","test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not","# The following tests all expect _thread_local.zipkin_attrs to exist: if","\"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack","\"foo\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\"foo\", \"bar\"]) assert","works if we don't pass in a custom storage. assert","[\"foo\"]): assert \"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get()","fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\",","def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to","storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with","\"foo\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\"bar\") assert \"bar\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list():","stack.push(\"a\") stack.push(\"b\") the_copy = stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\",","mock.patch.object(tracer._context_stack, \"_storage\", [\"foo\", \"bar\"]): assert \"bar\" == py_zipkin.storage.ThreadLocalStack().pop() assert \"foo\"","tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \"_storage\", []): assert not py_zipkin.storage.ThreadLocalStack().get()","don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def","mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with","stack.copy() the_copy.push(\"c\") stack.push(\"d\") assert [\"a\", \"b\", \"c\"] == the_copy._storage assert","test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we","still works if we don't pass in a custom storage.","\"foo\" == py_zipkin.storage.Stack([\"bar\", \"foo\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with","assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,"],"string":"[\n \"test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer =\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop()\",\n \"pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following\",\n \"not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\"\",\n \"test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert\",\n \"test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get()\",\n \"# doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer =\",\n \"def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy()\",\n \"\\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def\",\n \"py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True)\",\n \"assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure\",\n \"log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage():\",\n \"assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\", \\\"d\\\"]\",\n \"in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer\",\n \"1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works\",\n \"assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def\",\n \"py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"])\",\n \"py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" ==\",\n \"== py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\"\",\n \"test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not\",\n \"as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def\",\n \"def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert\",\n \"stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"]\",\n \"\\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs():\",\n \"test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\"\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" ==\",\n \"following tests all expect _thread_local.zipkin_attrs to exist: if it #\",\n \"py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\",\",\n \"[\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\", \\\"d\\\"] ==\",\n \"# Let's make sure this still works if we don't\",\n \"mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): #\",\n \"not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests\",\n \"assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\",\",\n \"def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]):\",\n \"\\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" ==\",\n \"if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs():\",\n \"\\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy():\",\n \"import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The\",\n \"[\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" ==\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not\",\n \"\\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\", \\\"d\\\"] == stack._storage\",\n \"def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert\",\n \"assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs():\",\n \"exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def\",\n \"custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"\\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []):\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def\",\n \"@pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect\",\n \"def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"\\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\"\",\n \"tests all expect _thread_local.zipkin_attrs to exist: if it # doesn't,\",\n \"log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop()\",\n \"py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get()\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count\",\n \"py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\",\",\n \"def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list():\",\n \"test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert\",\n \"\\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop()\",\n \"= py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" ==\",\n \"assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log,\",\n \"stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\"\",\n \"def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer\",\n \"autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert\",\n \"to exist: if it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get()\",\n \"not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log:\",\n \"== py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" ==\",\n \"assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def\",\n \"test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\"\",\n \"\\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"[\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\")\",\n \"not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\",\",\n \"make sure this still works if we don't pass in\",\n \"== py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack =\",\n \"if we don't pass in a custom storage. assert not\",\n \"test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert\",\n \"py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]):\",\n \"[]): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with\",\n \"create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to exist:\",\n \"assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs():\",\n \"stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack()\",\n \"== 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert\",\n \"def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if\",\n \"assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack =\",\n \"stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\", \\\"b\\\",\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests():\",\n \"def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert\",\n \"with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert\",\n \"== py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"\\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer\",\n \"Let's make sure this still works if we don't pass\",\n \"[]): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def\",\n \"= stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage\",\n \"py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack =\",\n \"will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\",\n \"== stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy\",\n \"_thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will fail.\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def\",\n \"import mock import pytest import py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs():\",\n \"def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop()\",\n \"test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get()\",\n \"all expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch\",\n \"py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"== py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def\",\n \"\\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" ==\",\n \"== context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer =\",\n \"not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's\",\n \"assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert\",\n \"stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\")\",\n \"The following tests all expect _thread_local.zipkin_attrs to exist: if it\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\"\",\n \"sure this still works if we don't pass in a\",\n \"not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer\",\n \"assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"])\",\n \"assert not py_zipkin.storage.Stack([]).get() assert log.call_count == 1 def test_storage_stack_still_works_if_you_dont_pass_in_storage(): #\",\n \"this still works if we don't pass in a custom\",\n \"def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert\",\n \"assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\")\",\n \"assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list():\",\n \"a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer =\",\n \"expect _thread_local.zipkin_attrs to exist: if it # doesn't, mock.patch will\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert\",\n \"\\\"bar\\\" == stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\")\",\n \"assert not py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_none_if_no_zipkin_attrs(): with mock.patch.object(py_zipkin.storage.log, \\\"warning\\\", autospec=True) as\",\n \"py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get()\",\n \"doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer()\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" ==\",\n \"== context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs\",\n \"it # doesn't, mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer\",\n \"py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []):\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert\",\n \"= py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" ==\",\n \"stack.get() def test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy =\",\n \"def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack = py_zipkin.storage.Stack([\\\"foo\\\"]) assert \\\"foo\\\" == stack.get() stack.push(\\\"bar\\\")\",\n \"pass in a custom storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list():\",\n \"py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\",\",\n \"== py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list(): stack\",\n \"== stack.get() stack.push(\\\"bar\\\") assert \\\"bar\\\" == stack.get() def test_stack_copy(): stack\",\n \"the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] ==\",\n \"with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get() assert not py_zipkin.storage.ThreadLocalStack().get()\",\n \"py_zipkin.storage @pytest.fixture(autouse=True, scope=\\\"module\\\") def create_zipkin_attrs(): # The following tests all\",\n \"we don't pass in a custom storage. assert not py_zipkin.storage.Stack().get()\",\n \"\\\"_storage\\\", [\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\"\",\n \"= py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def\",\n \"\\\"warning\\\", autospec=True) as log: assert not py_zipkin.storage.Stack([]).get() assert log.call_count ==\",\n \"the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert [\\\"a\\\",\",\n \"context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\"\",\n \"\\\"bar\\\"]) assert \\\"bar\\\" == context_stack.pop() assert \\\"foo\\\" == context_stack.get() def\",\n \"= py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert\",\n \"\\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_with_context_returns_the_last_of_the_list(): assert \\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get()\",\n \"py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests():\",\n \"py_zipkin.storage.ThreadLocalStack().pop() def test_pop_zipkin_attrs_with_context_does_nothing_if_no_requests(): assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer =\",\n \"context_stack.get() def test_push_zipkin_attrs_adds_new_zipkin_attrs_to_list(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\"]):\",\n \"test_stack_copy(): stack = py_zipkin.storage.Stack() stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\")\",\n \"test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not\",\n \"# The following tests all expect _thread_local.zipkin_attrs to exist: if\",\n \"\\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack\",\n \"\\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_pop_zipkin_attrs_with_context_removes_the_last_zipkin_attrs(): context_stack = py_zipkin.storage.Stack([\\\"foo\\\", \\\"bar\\\"]) assert\",\n \"works if we don't pass in a custom storage. assert\",\n \"[\\\"foo\\\"]): assert \\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get()\",\n \"fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\",\",\n \"def create_zipkin_attrs(): # The following tests all expect _thread_local.zipkin_attrs to\",\n \"storage. assert not py_zipkin.storage.Stack().get() def test_get_zipkin_attrs_returns_the_last_of_the_list(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"\\\"foo\\\" == py_zipkin.storage.ThreadLocalStack().get() py_zipkin.storage.ThreadLocalStack().push(\\\"bar\\\") assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().get() def test_push_zipkin_attrs_with_context_adds_new_zipkin_attrs_to_list():\",\n \"stack.push(\\\"a\\\") stack.push(\\\"b\\\") the_copy = stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\",\",\n \"mock.patch.object(tracer._context_stack, \\\"_storage\\\", [\\\"foo\\\", \\\"bar\\\"]): assert \\\"bar\\\" == py_zipkin.storage.ThreadLocalStack().pop() assert \\\"foo\\\"\",\n \"tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack, \\\"_storage\\\", []): assert not py_zipkin.storage.ThreadLocalStack().get()\",\n \"don't pass in a custom storage. assert not py_zipkin.storage.Stack().get() def\",\n \"mock.patch will fail. py_zipkin.storage.ThreadLocalStack().get() def test_get_zipkin_attrs_returns_none_if_no_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"stack.copy() the_copy.push(\\\"c\\\") stack.push(\\\"d\\\") assert [\\\"a\\\", \\\"b\\\", \\\"c\\\"] == the_copy._storage assert\",\n \"test_storage_stack_still_works_if_you_dont_pass_in_storage(): # Let's make sure this still works if we\",\n \"still works if we don't pass in a custom storage.\",\n \"\\\"foo\\\" == py_zipkin.storage.Stack([\\\"bar\\\", \\\"foo\\\"]).get() def test_pop_zipkin_attrs_does_nothing_if_no_requests(): tracer = py_zipkin.storage.get_default_tracer() with\",\n \"assert not py_zipkin.storage.Stack([]).pop() def test_pop_zipkin_attrs_removes_the_last_zipkin_attrs(): tracer = py_zipkin.storage.get_default_tracer() with mock.patch.object(tracer._context_stack,\"\n]"}}},{"rowIdx":3495,"cells":{"ngram":{"kind":"list like","value":["store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data","plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request,","__init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)),","Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging","ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def","abstract='Please enter the variable name to be plotted, example: air'),","name to be plotted, example: air'), ] outputs = [","10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar()","plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines()","as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def","abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable","data_type='string', default='air', abstract='Please enter the variable name to be plotted,","# https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as","super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and","be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt","server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import","output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset',","default='air', abstract='Please enter the variable name to be plotted, example:","plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [","ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10))","the variable name to be plotted, example: air'), ] outputs","inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable","LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\", 100)","= Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax","netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None):","SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS,","values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree())","from pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import","output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig","as plt import cartopy.crs as ccrs from netCDF4 import Dataset","supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter","# no X11 server ... must be run first #","first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs","Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no","air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True),","as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a","... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab","response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced","= 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or","] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ]","ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output)","class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')],","import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11","resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot","'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple","inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable',","status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output","plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output","ComplexInput, ComplexOutput from pywps import Format import logging LOGGER =","matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import","default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the","= ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0,","nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True )","Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot',","'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png'","identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0',","matplotlib # no X11 server ... must be run first","variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource)","variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\",","supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns","LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to","cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc'","= request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\",","fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs =","self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple","'Variable', data_type='string', default='air', abstract='Please enter the variable name to be","logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be","_handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file,","and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def","pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format","{0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name","= plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close()","def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example:",":]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process):","= output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable]","output=None): output = output or 'plot.png' ds = Dataset(resource) values","simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file = output","ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self):","variable name to be plotted, example: air'), ] outputs =","outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot,","self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.',","pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib","%s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\", 100) return response","import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4","example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')],",") def _handler(self, request, response): variable = request.inputs['variable'][0].data output =","] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice","be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple","abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True,","= simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file =","import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib #","plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return","plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS","from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import","request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable)","a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True","from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None,","'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20,","logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server","ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot',","import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS =","Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax =","Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs,","[ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler,","simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self,","output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output) response.outputs['output'].file","output = output or 'plot.png' ds = Dataset(resource) values =","import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import","return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset',","import matplotlib # no X11 server ... must be run","= [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__(","no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/","plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot',","enter the variable name to be plotted, example: air'), ]","'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please","matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from","LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER","fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :])","Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output =","must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as","simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds =","= logging.getLogger('PYWPS') import matplotlib # no X11 server ... must","title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs,","ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource,","outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable =","def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot(","LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ...","ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air',","variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output:","output: %s\", output) response.outputs['output'].file = output response.update_status(\"simple_plot done\", 100) return","https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs","or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig =","def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds","run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import","= [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable',",":, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class","import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output","request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\"produced output: %s\", output)","AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output","= plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img()","ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS')","plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs","ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :,","[ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string',","to be plotted, example: air'), ] outputs = [ ComplexOutput('output',","X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg')","version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response):"],"string":"[\n \"store_supported=True, status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data\",\n \"plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request,\",\n \"__init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)),\",\n \"Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging\",\n \"ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def\",\n \"abstract='Please enter the variable name to be plotted, example: air'),\",\n \"name to be plotted, example: air'), ] outputs = [\",\n \"10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar()\",\n \"plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines()\",\n \"as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def\",\n \"abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable\",\n \"data_type='string', default='air', abstract='Please enter the variable name to be plotted,\",\n \"# https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as\",\n \"super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and\",\n \"be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt\",\n \"server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import\",\n \"output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset',\",\n \"default='air', abstract='Please enter the variable name to be plotted, example:\",\n \"plt.close() return output class SimplePlot(Process): def __init__(self): inputs = [\",\n \"ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10))\",\n \"the variable name to be plotted, example: air'), ] outputs\",\n \"inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable\",\n \"LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\", 100)\",\n \"= Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax\",\n \"netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None):\",\n \"SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS,\",\n \"values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree())\",\n \"from pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import\",\n \"output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig\",\n \"as plt import cartopy.crs as ccrs from netCDF4 import Dataset\",\n \"supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter\",\n \"# no X11 server ... must be run first #\",\n \"first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs\",\n \"Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no\",\n \"air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True),\",\n \"as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a\",\n \"... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab\",\n \"response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced\",\n \"= 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or\",\n \"] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ]\",\n \"ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output)\",\n \"class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')],\",\n \"import logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11\",\n \"resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot\",\n \"'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple\",\n \"inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable',\",\n \"status_supported=True ) def _handler(self, request, response): variable = request.inputs['variable'][0].data output\",\n \"plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output\",\n \"ComplexInput, ComplexOutput from pywps import Format import logging LOGGER =\",\n \"matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4 import\",\n \"default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the\",\n \"= ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0,\",\n \"nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True )\",\n \"Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot',\",\n \"'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output or 'plot.png'\",\n \"identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0',\",\n \"matplotlib # no X11 server ... must be run first\",\n \"variable=None, output=None): output = output or 'plot.png' ds = Dataset(resource)\",\n \"variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\",\",\n \"supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns\",\n \"LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name to\",\n \"cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc'\",\n \"= request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\",\",\n \"fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs =\",\n \"self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple\",\n \"'Variable', data_type='string', default='air', abstract='Please enter the variable name to be\",\n \"logging.getLogger('PYWPS') import matplotlib # no X11 server ... must be\",\n \"_handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file,\",\n \"and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def\",\n \"pywps import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format\",\n \"{0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please enter the variable name\",\n \"= plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close()\",\n \"def __init__(self): inputs = [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example:\",\n \":]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process):\",\n \"= output or 'plot.png' ds = Dataset(resource) values = ds.variables[variable]\",\n \"output=None): output = output or 'plot.png' ds = Dataset(resource) values\",\n \"simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file = output\",\n \"ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self):\",\n \"variable name to be plotted, example: air'), ] outputs =\",\n \"outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot,\",\n \"self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice and simple plot.',\",\n \"pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib\",\n \"%s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\", 100) return response\",\n \"import matplotlib.pylab as plt import cartopy.crs as ccrs from netCDF4\",\n \"example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')],\",\n \") def _handler(self, request, response): variable = request.inputs['variable'][0].data output =\",\n \"] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot', title='Simple Plot', abstract='Returns a nice\",\n \"be plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple\",\n \"abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True,\",\n \"= simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file =\",\n \"import Format import logging LOGGER = logging.getLogger('PYWPS') import matplotlib #\",\n \"plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return\",\n \"plt import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS\",\n \"from pywps import Format import logging LOGGER = logging.getLogger('PYWPS') import\",\n \"request, response): variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable)\",\n \"a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True\",\n \"from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None,\",\n \"'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20,\",\n \"logging LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server\",\n \"ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler, identifier='simple_plot',\",\n \"import cartopy.crs as ccrs from netCDF4 import Dataset AIR_DS =\",\n \"Dataset(resource) values = ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax =\",\n \"Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs, outputs=outputs,\",\n \"[ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__( self._handler,\",\n \"simple plot.', version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self,\",\n \"output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output) response.outputs['output'].file\",\n \"output = output or 'plot.png' ds = Dataset(resource) values =\",\n \"import Process, LiteralInput, ComplexInput, ComplexOutput from pywps import Format import\",\n \"return output class SimplePlot(Process): def __init__(self): inputs = [ ComplexInput('dataset',\",\n \"import matplotlib # no X11 server ... must be run\",\n \"= [ ComplexOutput('output', 'Simple Plot', supported_formats=[Format('image/png')], as_reference=True), ] super(SimplePlot, self).__init__(\",\n \"no X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/\",\n \"plotted, example: air'), ] outputs = [ ComplexOutput('output', 'Simple Plot',\",\n \"enter the variable name to be plotted, example: air'), ]\",\n \"'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air', abstract='Please\",\n \"matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs from\",\n \"LiteralInput, ComplexInput, ComplexOutput from pywps import Format import logging LOGGER\",\n \"fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :])\",\n \"Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output =\",\n \"must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as\",\n \"simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds =\",\n \"= logging.getLogger('PYWPS') import matplotlib # no X11 server ... must\",\n \"title='Simple Plot', abstract='Returns a nice and simple plot.', version='1.0', inputs=inputs,\",\n \"ccrs from netCDF4 import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource,\",\n \"outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response): variable =\",\n \"def _handler(self, request, response): variable = request.inputs['variable'][0].data output = simple_plot(\",\n \"LOGGER = logging.getLogger('PYWPS') import matplotlib # no X11 server ...\",\n \"ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string', default='air',\",\n \"variable = request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output:\",\n \"output: %s\\\", output) response.outputs['output'].file = output response.update_status(\\\"simple_plot done\\\", 100) return\",\n \"https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import cartopy.crs as ccrs\",\n \"or 'plot.png' ds = Dataset(resource) values = ds.variables[variable] fig =\",\n \"def simple_plot(resource, variable=None, output=None): output = output or 'plot.png' ds\",\n \"run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg') import matplotlib.pylab as plt import\",\n \"= [ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable',\",\n \":, :]) ax.stock_img() ax.coastlines() plt.colorbar() fig.savefig(output) plt.close() return output class\",\n \"import Dataset AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output\",\n \"request.inputs['variable'][0].data output = simple_plot( resource=request.inputs['dataset'][0].file, variable=variable) LOGGER.info(\\\"produced output: %s\\\", output)\",\n \"AIR_DS = 'https://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis.derived/surface/air.mon.ltm.nc' def simple_plot(resource, variable=None, output=None): output = output\",\n \"= plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :, :]) ax.stock_img()\",\n \"ComplexOutput from pywps import Format import logging LOGGER = logging.getLogger('PYWPS')\",\n \"plt.colorbar() fig.savefig(output) plt.close() return output class SimplePlot(Process): def __init__(self): inputs\",\n \"ds.variables[variable] fig = plt.figure(figsize=(20, 10)) ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(values[0, :,\",\n \"[ ComplexInput('dataset', 'Dataset', supported_formats=[Format('application/x-netcdf')], default=AIR_DS, abstract='Example: {0}'.format(AIR_DS)), LiteralInput('variable', 'Variable', data_type='string',\",\n \"to be plotted, example: air'), ] outputs = [ ComplexOutput('output',\",\n \"X11 server ... must be run first # https://github.com/matplotlib/matplotlib/issues/3466/ matplotlib.use('Agg')\",\n \"version='1.0', inputs=inputs, outputs=outputs, store_supported=True, status_supported=True ) def _handler(self, request, response):\"\n]"}}},{"rowIdx":3496,"cells":{"ngram":{"kind":"list like","value":[],"string":"[]"}}},{"rowIdx":3497,"cells":{"ngram":{"kind":"list like","value":["JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id","%s AND about_user_id = %s AND question_id = %s\", [user_id,","text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT","return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id","os.path import random import re from flask import Flask, send_from_directory","[user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id')","def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register')","FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email ,","user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id,","to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id,","\" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value","value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id =","len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) >","[room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id():","return json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"})","asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = []","if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask)","return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id","WHERE r.id = %s\", [room_id]) # for # SELECT id,","db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id])","room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE","received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id'))","len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id,","user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id')","\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP","(user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json","opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id')","len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))):","successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE","return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values","json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room():","request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER","json.dumps({\"info\": \"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room')","if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions","\"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id'","server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id,","== correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} })","\"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0] value =","WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] })","quiz_question SET answered_id = %s WHERE id = %s\", [quiz_answer_id,","question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value","SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The","question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text =","return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To","= value[0][0] question_text = \"What did %s answer to '%s'","\" \"INNER JOIN room_members rm ON (u.id = rm.user_id) \"","[room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id =","@app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO","db app = Flask(__name__) # Regular expression to only accept","import flask_run import datab.social_database as db app = Flask(__name__) #","= [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\":","qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0]","404 user_id = json_data[\"user_id\"] values = [] for a in","3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE","val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response})","is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user})","\" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \"","room r WHERE r.creator=%s\", [user_id]) response = [] for val","methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None:","db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id]) return","VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id","@app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE","db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question q","WHERE id = 1 values = db.exec_query(\"SELECT status FROM room","return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/') def index(filename):","Flask(__name__) # Regular expression to only accept certain files fileChecker","db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id')","= db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id =","get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id,","u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s","def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id =","request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id =","(%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id","def root(): return index(\"index2.html\") @app.route('/') def index(filename): if fileChecker.match(filename): return","\"FROM answer a INNER JOIN question q \" \"WHERE a.question_id","random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id):","abort(403) @app.route('/register') def register(): # To obtain the mail email","in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST'])","@app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM","user_id = json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]:","for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1])","= value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value =","the mail email = request.args.get('email') print(email) if email is None:","qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id","[correct_answer_id]) if len(value) > 0: text = value[0][0] else: text","json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id =","values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"]))","\"FROM question q \" \"WHERE q.id = %s\", [question_id[0]]) question_text","%s\", [room_id]) return json.dumps({\"info\": \"The room has been opened successfully\",","[quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM","user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id')","> 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id,","AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0]","Regular expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\")","about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]])","FROM room r WHERE r.creator=%s\", [user_id]) response = [] for","user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def","[] asked_about_id = [] if len(possible_questions) > 0: question_id =","for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER","%s \", [correct_answer_id]) if len(value) > 0: text = value[0][0]","[asked_about_id[0]]) user_name = value[0][0] question_text = \"What did %s answer","q \" \"WHERE q.id = %s\", [question_id[0]]) question_text = value[0][0]","id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND","0: text = value[0][0] else: text = \"something when wrong\"","= %s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id)","email = request.args.get('email') print(email) if email is None: return json.dumps({})","None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room')","json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id)","json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if","successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE","values = db.exec_query(\"SELECT status FROM room WHERE id = %s\",","def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values =","user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room","WHERE 'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM","response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def","index(\"index2.html\") @app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename)","= u.id) \" \"INNER JOIN room_members rm ON (u.id =","datab.social_database as db app = Flask(__name__) # Regular expression to","been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id =","# for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r","question_id = [] asked_about_id = [] if len(possible_questions) > 0:","= %s\", [room_id]) response = [] for val in values:","request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\",","json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id","= request.args.get('email') print(email) if email is None: return json.dumps({}) id_user","email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room')","answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question","questions for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer():","request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db","%s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id =","user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id =","%s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room():","SELECT 'question' FROM 'Question' WHERE 'id' = 3 value =","values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def","= request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r","row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id')","quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id =","answer a INNER JOIN question q \" \"WHERE a.question_id =","else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in","answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text}","answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id,","(u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id","close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE","BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in","-*- import json import os.path import random import re from","db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q])","id, question FROM question WHERE room_id = %s\", [room_id]) response","return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id'))","question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id,","room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id","= db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first","val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room',","print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3","in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id","= db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \"","- 1, len(other_users)) > 0: for i in range(min(numberOfAnswers -","= request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id","= request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE","for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\":","import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as","def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room","room WHERE id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0]","correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if","%s\", [room_id]) response = [] for val in values: response.append({\"id\":","= random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask,","= [] for val in values: response.append({\"id\": val[0], \"text\": val[1]})","when wrong\" if value is None: return json.dumps({\"error\": \"Internal server","quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE","= random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions)","asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \"","WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id,","correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in","numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover","%s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\",","\"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room","(answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE","room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return","\"FROM quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id","coding: utf-8 -*- import json import os.path import random import","request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES","\" \"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id","= request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id","= [] for val in values: response.append({\"id\": val[0], \"status\": val[1]})","will be the correct one random.shuffle(answers) answer_json = [] for","room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id')","= json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id,","user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id')","db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return","val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data =","@app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET","%s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) >","= json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT","question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id =","FROM question WHERE room_id = %s\", [room_id]) response = []","return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id ==","random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/')","from flaskrun.flaskrun import flask_run import datab.social_database as db app =","db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)","else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id =","root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root():","# SELECT 'question' FROM 'Question' WHERE 'id' = 3 value","text_id)) # if commented the first answer will be the","\" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name =","asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions,","id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email')","users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members","value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT","values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq","mail email = request.args.get('email') print(email) if email is None: return","json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id","is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\":","\"INNER JOIN room_members rm ON (u.id = rm.user_id) \" \"WHERE","users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text","value = db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE","0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id","= request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id)","\"Not available questions for this user in this room\"}) @app.route('/post_quiz_answer')","len(value) > 0: text = value[0][0] else: text = \"something","u ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm","json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id,","flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__)","COUNT(qq.id) DESC\", [room_id]) ranking = [] for row in values:","value is None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({","if json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404","q.question \" \"FROM question q \" \"WHERE q.id = %s\",","(qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON (u.id","filename) abort(403) @app.route('/register') def register(): # To obtain the mail","text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id","= value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q","json import os.path import random import re from flask import","%s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT","room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id","db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id])","@app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values","json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room')","json.dumps({\"error\": \"Not available questions for this user in this room\"})","= random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 <","INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM","question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\":","= db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) =","if len(value) > 0: text = value[0][0] else: text =",", COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users","db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]])","in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers',","in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status')","quiz_question \" \"WHERE asked_user_id = %s AND about_user_id = %s","db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id = %s\", [room_id])","= %s \", [correct_answer_id]) if len(value) > 0: text =","db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\",","\"something when wrong\" if value is None: return json.dumps({\"error\": \"Internal","question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json })","a INNER JOIN question q \" \"WHERE a.question_id = q.id","> 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id,","return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id =","# SELECT status FROM Room WHERE id = 1 values","@app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r","\" \"WHERE a.question_id = q.id AND q.room_id = %s AND","@app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions","ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room():","asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if","= %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id","ranking = [] for row in values: ranking.append({\"email\": row[0], \"correct\":","= re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma:","1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return","correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer","else: text = \"something when wrong\" if value is None:","(room_id, question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data","0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email)","r.status FROM room r WHERE r.creator=%s\", [user_id]) response = []","%s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT","1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) #","\"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data","other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id)","question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a","# SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN","questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\", [room_id,","JOIN question q \" \"WHERE a.question_id = q.id AND q.room_id","def post_room_answers(): json_data = request.get_json() if json_data is None: return","post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET","\"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values","= %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id","value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer","found\"}), 404 user_id = json_data[\"user_id\"] values = [] for a","val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data","value[0][0] else: text = \"something when wrong\" if value is","room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT","# -*- coding: utf-8 -*- import json import os.path import","= db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\",","db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions)","room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question","question_text = \"What did %s answer to '%s' ?\" %","qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id = %s\",","if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return","request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE","closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id')","received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room","\"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id =","answer_json }) else: return json.dumps({\"error\": \"Not available questions for this","json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id,","= request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON","def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed'","values = db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN","asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id,","json_data = request.get_json() if json_data is None: return json.dumps({\"error\": \"no","u.email \" \"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name","request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members","this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id =","room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id)","accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7)","fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\":","\" \"INNER JOIN users u ON (qq.asked_user_id = u.id) \"","return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json()","= %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1,","INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data","value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u \"","correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if","AND rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER","\" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id","def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def","pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\")","quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0:","if json_data is None: return json.dumps({\"error\": \"no JSON found\"}) else:","question FROM question WHERE room_id = %s\", [room_id]) response =","\"started\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r","@app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is","\"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\":","0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id)","db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id})","[] for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return","[room_id]) ranking = [] for row in values: ranking.append({\"email\": row[0],","only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4","r.status='finished' WHERE r.id = %s\", [room_id]) # for # SELECT","in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question'","return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions","value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id","is None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id =","in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO","room r SET r.status='closed' WHERE r.id = %s\", [room_id]) return","db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s \",","if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask","\"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\":","Room WHERE id = 1 values = db.exec_query(\"SELECT status FROM","post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\"error\":","one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers:","[room_id]) return json.dumps({\"info\": \"The room has been opened successfully\", \"status\":","value[0][0] question_text = \"What did %s answer to '%s' ?\"","# if commented the first answer will be the correct","answer a WHERE a.id = %s \", [correct_answer_id]) if len(value)","import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun","= db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id =","question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users","r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \"","[room_id]) response = [] for val in values: response.append({\"id\": val[0],","answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for","possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id =","= db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if","def register(): # To obtain the mail email = request.args.get('email')","[] for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return","= json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"],","= db.exec_query(\"SELECT status FROM room WHERE id = %s\", [room_id])","VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id","request.args.get('room_id') # SELECT status FROM Room WHERE id = 1","answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value","open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started'","rm.room_id = %s \" \"GROUP BY u.email \" \"ORDER BY","ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status","question WHERE room_id = %s\", [room_id]) response = [] for","= [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)","if commented the first answer will be the correct one","WHERE a.id = %s \", [correct_answer_id]) if len(value) > 0:","text = \"something when wrong\" if value is None: return","@app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is","= [] asked_about_id = [] if len(possible_questions) > 0: question_id","room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room():","possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = []","\"INNER JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER","= db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask,","this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id =","rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s","int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask =","= db.exec_query(\"SELECT u.email \" \"FROM users u \" \"WHERE u.id=%s\",","import random import re from flask import Flask, send_from_directory from","answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE","room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions:","[] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) -","question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM","status FROM room WHERE id = %s\", [room_id]) return json.dumps({","\" \"FROM answer a INNER JOIN question q \" \"WHERE","u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = []","0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and","id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id","Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import","join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email)","SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values","for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) =","answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT","= db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers","rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id","= db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)","def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions =","question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions","for q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES","return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email =","?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text,","%s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \"","JOIN users u ON (qq.asked_user_id = u.id) \" \"INNER JOIN","return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else:","values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id =","= q.id AND q.room_id = %s AND a.user_id= %s\", [room_id,","db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers =","len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user","text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i","in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s, %s)\",","qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \" \"GROUP BY","cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/') def","q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\":","request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id])","answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"})","= \"something when wrong\" if value is None: return json.dumps({\"error\":","r.id = %s\", [room_id]) # for # SELECT id, COUNT(a.id),","}) else: return json.dumps({\"error\": \"Not available questions for this user","request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s","qq \" \"INNER JOIN users u ON (qq.asked_user_id = u.id)","request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id = %s WHERE id =","# pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return","json_data[\"user_id\"] values = [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id,","len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) >","[room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room","room r SET r.status='started' WHERE r.id = %s\", [room_id]) return","value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq","= 4 random.seed(7) def root_dir(): # pragma: no cover return","> 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0],","'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id \"","for val in values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\":","value = db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE","WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers","id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT","def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET","else: return json.dumps({\"error\": \"Not available questions for this user in","user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES","(room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room')","\"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking","json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id')","\" \"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id])","values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def","random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0:","certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def","a.id \" \"FROM answer a INNER JOIN question q \"","INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms')","question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__ ==","quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question","\"The room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def","4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__))","%s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question():","len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions =","id = 1 values = db.exec_query(\"SELECT status FROM room WHERE","= db.exec_query(\"SELECT a.id \" \"FROM answer a INNER JOIN question","db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id')","0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id)","for this user in this room\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id","= db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id =","SET answered_id = %s WHERE id = %s\", [quiz_answer_id, quiz_question_id])","= Flask(__name__) # Regular expression to only accept certain files","= request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id =","len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask =","# Regular expression to only accept certain files fileChecker =","db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\",","= 1 values = db.exec_query(\"SELECT status FROM room WHERE id","VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def answered_room():","create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator)","To obtain the mail email = request.args.get('email') print(email) if email","random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) >","= value[0][0] else: text = \"something when wrong\" if value","= %s AND about_user_id = %s AND question_id = %s\",","send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain","WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has","is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id =","AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0})","%s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id =","values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id,","q in questions: db.exec_query(\"INSERT INTO question (room_id, question) VALUES (%s,","question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions)","\"FROM quiz_question qq \" \"WHERE qq.id = %s\", [quiz_question_id]) answered_id","id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values =","= value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM users u","= %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def","json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return","COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT","request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"}),","be the correct one random.shuffle(answers) answer_json = [] for (answer_id,","status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE","= db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = []","request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room():","> 0: text = value[0][0] else: text = \"something when","def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question","answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question'","= random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) >","= request.args.get('room_id') # SELECT status FROM Room WHERE id =","@app.route('/register') def register(): # To obtain the mail email =","@app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id,","FROM room WHERE id = %s\", [room_id]) return json.dumps({ \"status\":","text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' =","quiz_question qq \" \"INNER JOIN users u ON (qq.asked_user_id =","return json.dumps({\"error\": \"Not available questions for this user in this","commented the first answer will be the correct one random.shuffle(answers)","[] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return","db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id","as db app = Flask(__name__) # Regular expression to only","= value[0][1] question_id = value[0][2] value = db.exec_query(\"SELECT a.answer FROM","def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\":","= db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id =","-*- coding: utf-8 -*- import json import os.path import random","db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\":","AND about_user_id = %s AND question_id = %s\", [user_id, asked_about_id[0],","def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room","val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json()","0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if","values = db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\",","@app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id,","finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE","1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else:","room_members rm ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id =","flask import Flask, send_from_directory from flask import request, abort from","[room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id","db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\", [room_id])","SELECT status FROM Room WHERE id = 1 values =","db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer","None: return json.dumps({\"error\": \"Internal server error\"}) return json.dumps({ \"correct\": answered_id","r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room","room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id})","COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN users u","the first answer will be the correct one random.shuffle(answers) answer_json","range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id,","register(): # To obtain the mail email = request.args.get('email') print(email)","\"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id =","a.id = %s \", [correct_answer_id]) if len(value) > 0: text","request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM question WHERE room_id","%s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \"","row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id","error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\":","[user_id]) response = [] for val in values: response.append({\"id\": val[0],","= request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \"","= request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id =","request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM","[] for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id})","None: return json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"]","answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question') def get_question():","r.creator=%s\", [user_id]) response = [] for val in values: response.append({\"id\":","json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT","\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id","1 values = db.exec_query(\"SELECT status FROM room WHERE id =","\"The room has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def","utf-8 -*- import json import os.path import random import re","= request.args.get('user_id') room_id = db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\",","json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email =","values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values =","email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\":","\"FROM quiz_question \" \"WHERE asked_user_id = %s AND about_user_id =","\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id=","if value is None: return json.dumps({\"error\": \"Internal server error\"}) return","def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished'","@app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return","id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id","return json.dumps({\"info\": \"The room has been opened successfully\", \"status\": \"started\"})","> 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0","= %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email","abort from flaskrun.flaskrun import flask_run import datab.social_database as db app","JSON found\"}), 404 user_id = json_data[\"user_id\"] values = [] for","> 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user =","return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values","return json.dumps({\"ranking\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') #","quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question","db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE","ON (u.id = rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND","random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE","response = [] for val in values: response.append({\"id\": val[0], \"status\":","@app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id","if min(numberOfAnswers - 1, len(other_users)) > 0: for i in","> 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0:","methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None:","= int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id)","db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0],","%s answer to '%s' ?\" % (user_name, question_text) return json.dumps({","json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer","user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if","question q \" \"WHERE a.question_id = q.id AND q.room_id =","return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if","json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question (room_id, question)","has been opened successfully\", \"status\": \"started\"}) @app.route('/close_room') def close_room(): room_id","%s\", [room_id]) return json.dumps({\"info\": \"The room has been closed successfully\",","response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data","min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers","db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if","room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id","json.dumps({\"id\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id =","return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\": question_id, \"correct_answer\": {\"id\":","'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT","db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response","[answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) >","[] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET correct_answer_id=%s","= [] for a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values)","user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id,","= %s WHERE id = %s\", [quiz_answer_id, quiz_question_id]) value =","value[0][0] value = db.exec_query(\"SELECT q.question \" \"FROM question q \"","json_data[\"room_id\"] questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO","db.exec_query(\"SELECT id, question FROM question WHERE room_id = %s\", [room_id])","been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id =","values = db.exec_query(\"SELECT id, question FROM question WHERE room_id =","json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values =","flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database","1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id","r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room has been","len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0])","db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\", [room_id])","obtain the mail email = request.args.get('email') print(email) if email is","\"Internal server error\"}) return json.dumps({ \"correct\": answered_id == correct_answer_id, \"question\":","(answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) #","user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email","user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\": user_id}) @app.route('/answered_room') def","text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the","@app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403)","room_id = %s\", [room_id]) response = [] for val in","a WHERE a.id = %s \", [correct_answer_id]) if len(value) >","return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/join_room') def","= [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\"UPDATE quiz_question SET","answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id) # SELECT 'question' FROM","print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE r.id = %s\",","answer will be the correct one random.shuffle(answers) answer_json = []","= db.exec_query(\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\", [user_id])","\"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r","qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email \"","expression to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers","get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status FROM","\"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE","from flask import request, abort from flaskrun.flaskrun import flask_run import","room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='closed' WHERE r.id","- 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id))","\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else:","(%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id =","(answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented","json_data is None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id","\"FROM users u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0]","qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \" \"WHERE qq.id =","\"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available","[room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id","'id' = 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question","(question_id, user_id, answer) VALUES(%s,%s,%s)\", values) return json.dumps({\"info\": \"Data received\"}) @app.route('/get_quiz_question')","values: response.append({\"id\": val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST'])","\"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data =","question) VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"})","\"question\": question_id, \"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__","INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id, user_id]) return json.dumps({\"id\":","> 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id)","import re from flask import Flask, send_from_directory from flask import","- 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\", values)","r SET r.status='finished' WHERE r.id = %s\", [room_id]) # for","= %s\", [room_id]) return json.dumps({\"info\": \"The room has been opened","\"Data received\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id =","if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id =","[question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT u.email \" \"FROM","available questions for this user in this room\"}) @app.route('/post_quiz_answer') def","random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\"id\":","in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i])","[quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2]","json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values =","id = %s\", [room_id]) return json.dumps({ \"status\": values[0][0] }) @app.route('/get_room_questions')","quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users)","q \" \"WHERE a.question_id = q.id AND q.room_id = %s","if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register():","get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT id, question FROM","\"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for row","\"no JSON found\"}), 404 user_id = json_data[\"user_id\"] values = []","= db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\", [room_id,","room r SET r.status='finished' WHERE r.id = %s\", [room_id]) #","}) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\"SELECT","val[0], \"text\": val[1]}) return json.dumps({\"questions\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers():","= db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id","request.get_json() if json_data is None: return json.dumps({\"error\": \"no JSON found\"})","r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The room","first answer will be the correct one random.shuffle(answers) answer_json =","print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email)","db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id = %s","= request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id =","\" \"WHERE asked_user_id = %s AND about_user_id = %s AND","q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id =","AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id]) return","INNER JOIN question q \" \"WHERE a.question_id = q.id AND","[] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else:","r.id, r.status FROM room r WHERE r.creator=%s\", [user_id]) response =","= db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \" \"FROM quiz_question qq \"","re from flask import Flask, send_from_directory from flask import request,","def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question","answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value","random import re from flask import Flask, send_from_directory from flask","SET r.status='finished' WHERE r.id = %s\", [room_id]) # for #","to only accept certain files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers =","FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\"SELECT id","text = value[0][0] else: text = \"something when wrong\" if","print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\",","= 3 value = db.exec_query(\"SELECT id \" \"FROM quiz_question \"","= %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value =","random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)","import os.path import random import re from flask import Flask,","INTO question (room_id, question) VALUES (%s, %s)\", [room_id, q]) return","asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0","= request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user}) @app.route('/create_room') def","return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id')","SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\": \"The","= qq.answered_id AND rm.room_id = %s \" \"GROUP BY u.email","SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id))","= %s AND question_id = %s\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id","\" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking = [] for","= value[0][2] value = db.exec_query(\"SELECT a.answer FROM answer a WHERE","\"correct_answer\": {\"id\": correct_answer_id, \"text\": text} }) if __name__ == '__main__':","= %s \" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id)","\" \"GROUP BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id])","\" \"FROM quiz_question qq \" \"INNER JOIN users u ON","# To obtain the mail email = request.args.get('email') print(email) if","return json.dumps({\"id\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email","app = Flask(__name__) # Regular expression to only accept certain","\"What did %s answer to '%s' ?\" % (user_name, question_text)","1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1,","response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room():","wrong\" if value is None: return json.dumps({\"error\": \"Internal server error\"})","WHERE r.creator=%s\", [user_id]) response = [] for val in values:","send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run","status FROM Room WHERE id = 1 values = db.exec_query(\"SELECT","if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id)","row in values: ranking.append({\"email\": row[0], \"correct\": row[1]}) return json.dumps({\"ranking\": ranking})","ON (qq.asked_user_id = u.id) \" \"INNER JOIN room_members rm ON","JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question","= \"What did %s answer to '%s' ?\" % (user_name,","= rm.user_id) \" \"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id =","request.args.get('email') print(email) if email is None: return json.dumps({}) id_user =","COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\"SELECT u.email","for (answer_id, text_id) in answers: answer_json.append({\"id\": answer_id, \"text\": text_id}) print(quiz_question_id)","1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0:","import datab.social_database as db app = Flask(__name__) # Regular expression","db.exec_query(\"SELECT u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER","qq.id = %s\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1]","= [] for row in values: ranking.append({\"email\": row[0], \"correct\": row[1]})","\" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What","re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no","import json import os.path import random import re from flask","answer to '%s' ?\" % (user_name, question_text) return json.dumps({ \"id\":","DESC\", [room_id]) ranking = [] for row in values: ranking.append({\"email\":","for val in values: response.append({\"id\": val[0], \"status\": val[1]}) return json.dumps(response)","quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\"UPDATE quiz_question SET answered_id","int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id","u \" \"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text =","files fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir():","r WHERE r.creator=%s\", [user_id]) response = [] for val in","fileChecker = re.compile(r\"(.*\\.js|.*\\.html|.*\\.png|.*\\.css|.*\\.map)$\") numberOfAnswers = 4 random.seed(7) def root_dir(): #","\"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"]","i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0],","answers.append((answer_id, text_id)) # if commented the first answer will be","room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def","request.args.get('room_id') db.exec_query(\"UPDATE room r SET r.status='finished' WHERE r.id = %s\",","r SET r.status='closed' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\":","response = [] for val in values: response.append({\"id\": val[0], \"text\":","= db.exec_query(\"INSERT INTO room (creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\":","0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0])","'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the","email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id,","asked_user_id = %s AND about_user_id = %s AND question_id =","\"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room","a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT INTO answer (question_id, user_id, answer)","FROM Room WHERE id = 1 values = db.exec_query(\"SELECT status","user_name = value[0][0] question_text = \"What did %s answer to","id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers -","u.id) \" \"INNER JOIN room_members rm ON (u.id = rm.user_id)","get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\"id\": id_user})","%s\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users))","user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\",","user_id = request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer","a in json_data[\"answers\"]: values.append((a[\"id\"], user_id, a[\"text\"])) print(values[len(values) - 1]) db.exec_many_query(\"INSERT","JSON found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for","VALUES (%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room')","possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id =","flask_run import datab.social_database as db app = Flask(__name__) # Regular","\"WHERE u.id=%s\", [asked_about_id[0]]) user_name = value[0][0] question_text = \"What did","r SET r.status='started' WHERE r.id = %s\", [room_id]) return json.dumps({\"info\":","@app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\"UPDATE room r SET","asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0])","room has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room():","len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if","from flask import Flask, send_from_directory from flask import request, abort","% (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\": question_text, \"answers\":","else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id","no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/')","db.exec_query(\"SELECT q.question \" \"FROM question q \" \"WHERE q.id =","Room r INNER JOIN values = db.exec_query(\"SELECT u.email , COUNT(qq.id)","a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id')","room_id = request.args.get('room_id') print(room_id) db.exec_query(\"UPDATE room r SET r.status='started' WHERE","u.email , COUNT(qq.id) \" \"FROM quiz_question qq \" \"INNER JOIN","(%s, %s)\", [room_id, q]) return json.dumps({\"info\": \"Data received\"}) @app.route('/open_room') def","index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def","%s\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM","None: return json.dumps({\"error\": \"no JSON found\"}), 404 user_id = json_data[\"user_id\"]","< len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id,","did %s answer to '%s' ?\" % (user_name, question_text) return","fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): #","[room_id]) return json.dumps({\"info\": \"The room has been closed successfully\", \"status\":","questions = json_data[\"question\"] for q in questions: db.exec_query(\"INSERT INTO question","user_id) question_id = [] asked_about_id = [] if len(possible_questions) >","a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\",","has been closed successfully\", \"status\": \"closed\"}) @app.route('/finish_room') def finish_room(): room_id","quiz_question_id, \"question\": question_text, \"answers\": answer_json }) else: return json.dumps({\"error\": \"Not","\" \"FROM question q \" \"WHERE q.id = %s\", [question_id[0]])","%s\", [room_id, user_id]) return json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def","{\"id\": correct_answer_id, \"text\": text} }) if __name__ == '__main__': flask_run(app)","found\"}) else: room_id = json_data[\"room_id\"] questions = json_data[\"question\"] for q","'%s' ?\" % (user_name, question_text) return json.dumps({ \"id\": quiz_question_id, \"question\":","FROM answer a WHERE a.id = %s \", [correct_answer_id]) if","os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\"index2.html\") @app.route('/') def index(filename): if","possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions,","\"answers\": answer_json }) else: return json.dumps({\"error\": \"Not available questions for","json.dumps({\"error\": \"no JSON found\"}) else: room_id = json_data[\"room_id\"] questions =","= request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\"REPLACE INTO","the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id)","0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users =","id = %s\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\"SELECT qq.answered_id, qq.correct_answer_id,","= %s AND a.user_id= %s\", [room_id, user_id]) return json.dumps({\"answered\": len(values)","[user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\"SELECT q.question","def fill_room(): json_data = request.get_json() if json_data is None: return","= int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask","return index(\"index2.html\") @app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'),","and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users","other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will","q.id = %s\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\"SELECT","a.answer FROM answer a WHERE a.id = %s \", [correct_answer_id])","1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id","json.dumps({\"answered\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email')","= %s\", [room_id]) return json.dumps({\"info\": \"The room has been closed","root(): return index(\"index2.html\") @app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(),","= db.exec_query(\"SELECT a.answer FROM answer a WHERE a.id = %s","@app.route('/') def root(): return index(\"index2.html\") @app.route('/') def index(filename): if fileChecker.match(filename):","BY u.email \" \"ORDER BY COUNT(qq.id) DESC\", [room_id]) ranking =","def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\"SELECT r.id, r.status","value = db.exec_query(\"SELECT id \" \"FROM quiz_question \" \"WHERE asked_user_id","(creator) VALUES (%s)\", [user_id]) return json.dumps({\"id\": room_id}) @app.route('/get_rooms') def get_rooms():","q.id AND q.room_id = %s AND a.user_id= %s\", [room_id, user_id])","WHERE room_id = %s\", [room_id]) response = [] for val","= request.args.get('user_id') values = db.exec_query(\"SELECT a.id \" \"FROM answer a","json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data","quiz_question SET correct_answer_id=%s WHERE id = %s\", [answer_id, quiz_question_id]) answers.append((answer_id,"],"string":"[\n \"JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id\",\n \"%s AND about_user_id = %s AND question_id = %s\\\", [user_id,\",\n \"text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT\",\n \"return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id\",\n \"os.path import random import re from flask import Flask, send_from_directory\",\n \"[user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id')\",\n \"def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register')\",\n \"FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email ,\",\n \"user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id,\",\n \"to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id,\",\n \"\\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value\",\n \"value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id =\",\n \"len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) >\",\n \"[room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id():\",\n \"return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"})\",\n \"asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = []\",\n \"if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask)\",\n \"return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id\",\n \"WHERE r.id = %s\\\", [room_id]) # for # SELECT id,\",\n \"db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id])\",\n \"room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE\",\n \"received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id'))\",\n \"len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id,\",\n \"user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id')\",\n \"\\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP\",\n \"(user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json\",\n \"opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id')\",\n \"len(other_users)) > 0: for i in range(min(numberOfAnswers - 1, len(other_users))):\",\n \"successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE\",\n \"return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values\",\n \"json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room():\",\n \"request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER\",\n \"json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room')\",\n \"if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions\",\n \"\\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id'\",\n \"server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id,\",\n \"== correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} })\",\n \"\\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value =\",\n \"WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] })\",\n \"quiz_question SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id,\",\n \"question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value\",\n \"SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The\",\n \"question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text =\",\n \"return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To\",\n \"= value[0][0] question_text = \\\"What did %s answer to '%s'\",\n \"\\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\"\",\n \"[room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id =\",\n \"@app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO\",\n \"db app = Flask(__name__) # Regular expression to only accept\",\n \"import flask_run import datab.social_database as db app = Flask(__name__) #\",\n \"= [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\":\",\n \"qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0]\",\n \"404 user_id = json_data[\\\"user_id\\\"] values = [] for a in\",\n \"3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE\",\n \"val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response})\",\n \"is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user})\",\n \"\\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\"\",\n \"room r WHERE r.creator=%s\\\", [user_id]) response = [] for val\",\n \"methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is None:\",\n \"db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id]) return\",\n \"VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id\",\n \"@app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE\",\n \"db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question q\",\n \"WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM room\",\n \"return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/') def index(filename):\",\n \"Flask(__name__) # Regular expression to only accept certain files fileChecker\",\n \"db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id')\",\n \"= db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id =\",\n \"get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id,\",\n \"u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s\",\n \"def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id =\",\n \"request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id =\",\n \"(%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id\",\n \"def root(): return index(\\\"index2.html\\\") @app.route('/') def index(filename): if fileChecker.match(filename): return\",\n \"\\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE a.question_id\",\n \"random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 < len(asked_about_id):\",\n \"abort(403) @app.route('/register') def register(): # To obtain the mail email\",\n \"in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST'])\",\n \"@app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status FROM\",\n \"user_id = json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]:\",\n \"for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1])\",\n \"= value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value =\",\n \"the mail email = request.args.get('email') print(email) if email is None:\",\n \"qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id\",\n \"[correct_answer_id]) if len(value) > 0: text = value[0][0] else: text\",\n \"json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id =\",\n \"values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"]))\",\n \"\\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text\",\n \"%s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\",\",\n \"[quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM\",\n \"user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id')\",\n \"> 0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id,\",\n \"AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0]\",\n \"Regular expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\")\",\n \"about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]])\",\n \"FROM room r WHERE r.creator=%s\\\", [user_id]) response = [] for\",\n \"user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def\",\n \"[] asked_about_id = [] if len(possible_questions) > 0: question_id =\",\n \"for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER\",\n \"%s \\\", [correct_answer_id]) if len(value) > 0: text = value[0][0]\",\n \"[asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did %s answer\",\n \"q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]]) question_text = value[0][0]\",\n \"id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND\",\n \"0: text = value[0][0] else: text = \\\"something when wrong\\\"\",\n \"= %s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id)\",\n \"email = request.args.get('email') print(email) if email is None: return json.dumps({})\",\n \"None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room')\",\n \"json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id)\",\n \"json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if\",\n \"successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE\",\n \"values = db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\",\",\n \"def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values =\",\n \"user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room\",\n \"WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM\",\n \"response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def\",\n \"index(\\\"index2.html\\\") @app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename)\",\n \"= u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id =\",\n \"datab.social_database as db app = Flask(__name__) # Regular expression to\",\n \"been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id =\",\n \"# for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r\",\n \"question_id = [] asked_about_id = [] if len(possible_questions) > 0:\",\n \"= %s\\\", [room_id]) response = [] for val in values:\",\n \"request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\",\",\n \"json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id\",\n \"= request.args.get('email') print(email) if email is None: return json.dumps({}) id_user\",\n \"email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room')\",\n \"answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question\",\n \"questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer():\",\n \"request, abort from flaskrun.flaskrun import flask_run import datab.social_database as db\",\n \"%s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id =\",\n \"user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id =\",\n \"%s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room():\",\n \"SELECT 'question' FROM 'Question' WHERE 'id' = 3 value =\",\n \"values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def\",\n \"= request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r\",\n \"row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id')\",\n \"quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id =\",\n \"answer a INNER JOIN question q \\\" \\\"WHERE a.question_id =\",\n \"else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in\",\n \"answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text}\",\n \"answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id,\",\n \"(u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id\",\n \"close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE\",\n \"BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in\",\n \"-*- import json import os.path import random import re from\",\n \"db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q])\",\n \"id, question FROM question WHERE room_id = %s\\\", [room_id]) response\",\n \"return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id'))\",\n \"question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id,\",\n \"room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id\",\n \"= db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first\",\n \"val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room',\",\n \"print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' = 3\",\n \"in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id\",\n \"= db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\"\",\n \"- 1, len(other_users)) > 0: for i in range(min(numberOfAnswers -\",\n \"= request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id\",\n \"= request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE\",\n \"for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\":\",\n \"import request, abort from flaskrun.flaskrun import flask_run import datab.social_database as\",\n \"def status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room\",\n \"room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0]\",\n \"correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if\",\n \"%s\\\", [room_id]) response = [] for val in values: response.append({\\\"id\\\":\",\n \"= random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask,\",\n \"= [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]})\",\n \"when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server\",\n \"quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE\",\n \"= random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions)\",\n \"asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\"\",\n \"WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id,\",\n \"correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in\",\n \"numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no cover\",\n \"%s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\",\",\n \"\\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room\",\n \"(answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE\",\n \"room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return\",\n \"\\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id\",\n \"coding: utf-8 -*- import json import os.path import random import\",\n \"request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES\",\n \"\\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id\",\n \"= request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id\",\n \"= [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]})\",\n \"will be the correct one random.shuffle(answers) answer_json = [] for\",\n \"room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id')\",\n \"= json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id,\",\n \"user_id}) @app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id')\",\n \"db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return\",\n \"val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data =\",\n \"@app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET\",\n \"%s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) >\",\n \"= json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT\",\n \"question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id =\",\n \"FROM question WHERE room_id = %s\\\", [room_id]) response = []\",\n \"return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id ==\",\n \"random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/')\",\n \"from flaskrun.flaskrun import flask_run import datab.social_database as db app =\",\n \"db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)\",\n \"else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id =\",\n \"root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root():\",\n \"# SELECT 'question' FROM 'Question' WHERE 'id' = 3 value\",\n \"text_id)) # if commented the first answer will be the\",\n \"\\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name =\",\n \"asked_about_id = [] if len(possible_questions) > 0: question_id = random.sample(possible_questions,\",\n \"id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email')\",\n \"users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members\",\n \"value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT\",\n \"values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq\",\n \"mail email = request.args.get('email') print(email) if email is None: return\",\n \"json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id\",\n \"is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\":\",\n \"\\\"INNER JOIN room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE\",\n \"users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text\",\n \"value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE\",\n \"0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id\",\n \"= request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id)\",\n \"\\\"Not available questions for this user in this room\\\"}) @app.route('/post_quiz_answer')\",\n \"len(value) > 0: text = value[0][0] else: text = \\\"something\",\n \"u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm\",\n \"json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id,\",\n \"flaskrun.flaskrun import flask_run import datab.social_database as db app = Flask(__name__)\",\n \"COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row in values:\",\n \"value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({\",\n \"if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404\",\n \"q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\",\",\n \"(qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id\",\n \"filename) abort(403) @app.route('/register') def register(): # To obtain the mail\",\n \"text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id\",\n \"= value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q\",\n \"json import os.path import random import re from flask import\",\n \"%s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT\",\n \"room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id\",\n \"db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id])\",\n \"@app.route('/answered_room') def answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values\",\n \"json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room')\",\n \"json.dumps({\\\"error\\\": \\\"Not available questions for this user in this room\\\"})\",\n \"= random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0 <\",\n \"INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM\",\n \"question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\":\",\n \"= db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) =\",\n \"if len(value) > 0: text = value[0][0] else: text =\",\n \", COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users\",\n \"db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]])\",\n \"in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers',\",\n \"in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status')\",\n \"quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s\",\n \"db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id = %s\\\", [room_id])\",\n \"= %s \\\", [correct_answer_id]) if len(value) > 0: text =\",\n \"db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id = %s\\\",\",\n \"\\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal\",\n \"question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json })\",\n \"a INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id\",\n \"> 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id,\",\n \"return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id =\",\n \"# SELECT status FROM Room WHERE id = 1 values\",\n \"@app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r\",\n \"\\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s AND\",\n \"@app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions\",\n \"ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room():\",\n \"asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if\",\n \"= %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id\",\n \"ranking = [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\":\",\n \"= re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma:\",\n \"1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return\",\n \"correct_answer_id = value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer\",\n \"else: text = \\\"something when wrong\\\" if value is None:\",\n \"(room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data\",\n \"0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email)\",\n \"r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response = []\",\n \"%s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT\",\n \"1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) #\",\n \"\\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data\",\n \"other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id)\",\n \"question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a\",\n \"# SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN\",\n \"questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id,\",\n \"JOIN question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id\",\n \"def post_room_answers(): json_data = request.get_json() if json_data is None: return\",\n \"post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET\",\n \"\\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values\",\n \"= %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id\",\n \"value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer\",\n \"found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for a\",\n \"val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data\",\n \"value[0][0] else: text = \\\"something when wrong\\\" if value is\",\n \"room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT\",\n \"# -*- coding: utf-8 -*- import json import os.path import\",\n \"= db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\",\",\n \"db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if len(possible_questions)\",\n \"room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question\",\n \"question_text = \\\"What did %s answer to '%s' ?\\\" %\",\n \"qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\",\",\n \"if email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return\",\n \"request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE\",\n \"closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id')\",\n \"received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room\",\n \"\\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id =\",\n \"answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this\",\n \"json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id,\",\n \"= request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON\",\n \"def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed'\",\n \"values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN\",\n \"asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id,\",\n \"json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no\",\n \"u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name\",\n \"request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members\",\n \"this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id =\",\n \"room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id)\",\n \"accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7)\",\n \"fill_room(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\":\",\n \"\\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\"\",\n \"return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json()\",\n \"= %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1,\",\n \"INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data\",\n \"value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\"\",\n \"correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if\",\n \"AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER\",\n \"\\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id\",\n \"def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def\",\n \"pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\")\",\n \"quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0:\",\n \"if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else:\",\n \"question FROM question WHERE room_id = %s\\\", [room_id]) response =\",\n \"\\\"started\\\"}) @app.route('/close_room') def close_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r\",\n \"@app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data is\",\n \"\\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\":\",\n \"0: for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id)\",\n \"db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id})\",\n \"[] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return\",\n \"[room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\": row[0],\",\n \"only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4\",\n \"r.status='finished' WHERE r.id = %s\\\", [room_id]) # for # SELECT\",\n \"in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question'\",\n \"return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions\",\n \"value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id\",\n \"is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id =\",\n \"in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO\",\n \"room r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return\",\n \"db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s \\\",\",\n \"if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask\",\n \"\\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\":\",\n \"Room WHERE id = 1 values = db.exec_query(\\\"SELECT status FROM\",\n \"post_room_answers(): json_data = request.get_json() if json_data is None: return json.dumps({\\\"error\\\":\",\n \"one random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers:\",\n \"[room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\":\",\n \"value[0][0] question_text = \\\"What did %s answer to '%s' ?\\\"\",\n \"# if commented the first answer will be the correct\",\n \"answer a WHERE a.id = %s \\\", [correct_answer_id]) if len(value)\",\n \"import Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun\",\n \"= db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id =\",\n \"question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users\",\n \"r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\"\",\n \"[room_id]) response = [] for val in values: response.append({\\\"id\\\": val[0],\",\n \"answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for\",\n \"possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id =\",\n \"= db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = [] if\",\n \"def register(): # To obtain the mail email = request.args.get('email')\",\n \"[] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return\",\n \"= json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"],\",\n \"= db.exec_query(\\\"SELECT status FROM room WHERE id = %s\\\", [room_id])\",\n \"VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id\",\n \"request.args.get('room_id') # SELECT status FROM Room WHERE id = 1\",\n \"answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value\",\n \"open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started'\",\n \"rm.room_id = %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY\",\n \"ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT status\",\n \"question WHERE room_id = %s\\\", [room_id]) response = [] for\",\n \"= [] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)\",\n \"if commented the first answer will be the correct one\",\n \"WHERE a.id = %s \\\", [correct_answer_id]) if len(value) > 0:\",\n \"text = \\\"something when wrong\\\" if value is None: return\",\n \"@app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is\",\n \"= [] asked_about_id = [] if len(possible_questions) > 0: question_id\",\n \"room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room():\",\n \"possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id = []\",\n \"\\\"INNER JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER\",\n \"= db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask,\",\n \"this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id =\",\n \"rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id = %s\",\n \"int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask =\",\n \"= db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u \\\" \\\"WHERE u.id=%s\\\",\",\n \"import random import re from flask import Flask, send_from_directory from\",\n \"answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE\",\n \"room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions:\",\n \"[] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) -\",\n \"question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM\",\n \"status FROM room WHERE id = %s\\\", [room_id]) return json.dumps({\",\n \"\\\" \\\"FROM answer a INNER JOIN question q \\\" \\\"WHERE\",\n \"u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = []\",\n \"0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and\",\n \"id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id\",\n \"Flask, send_from_directory from flask import request, abort from flaskrun.flaskrun import\",\n \"join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email)\",\n \"SELECT id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values\",\n \"for i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) =\",\n \"answered_room(): room_id = request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT\",\n \"= db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers\",\n \"rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id\",\n \"= db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1)\",\n \"def get_question(): room_id = int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions =\",\n \"question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions\",\n \"for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES\",\n \"return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email =\",\n \"?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text,\",\n \"%s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\"\",\n \"JOIN users u ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN\",\n \"return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else:\",\n \"values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id =\",\n \"= q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id,\",\n \"db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers =\",\n \"len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user\",\n \"text_id)) if min(numberOfAnswers - 1, len(other_users)) > 0: for i\",\n \"in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s, %s)\\\",\",\n \"qq.correct_answer_id = qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY\",\n \"cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/') def\",\n \"q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\":\",\n \"request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id])\",\n \"answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"})\",\n \"= \\\"something when wrong\\\" if value is None: return json.dumps({\\\"error\\\":\",\n \"r.id = %s\\\", [room_id]) # for # SELECT id, COUNT(a.id),\",\n \"}) else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user\",\n \"request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s\",\n \"qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id = u.id)\",\n \"request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id = %s WHERE id =\",\n \"# pragma: no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return\",\n \"json_data[\\\"user_id\\\"] values = [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id,\",\n \"len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) >\",\n \"[room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM Room\",\n \"room r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return\",\n \"value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq\",\n \"= 4 random.seed(7) def root_dir(): # pragma: no cover return\",\n \"> 0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0],\",\n \"'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id \\\"\",\n \"for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\":\",\n \"value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE\",\n \"WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers\",\n \"id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT\",\n \"def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET\",\n \"else: return json.dumps({\\\"error\\\": \\\"Not available questions for this user in\",\n \"user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES\",\n \"(room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room')\",\n \"\\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking\",\n \"json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id')\",\n \"\\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id])\",\n \"values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def\",\n \"random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0:\",\n \"certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def\",\n \"a.id \\\" \\\"FROM answer a INNER JOIN question q \\\"\",\n \"INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms')\",\n \"question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__ ==\",\n \"quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question\",\n \"\\\"The room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def\",\n \"4 random.seed(7) def root_dir(): # pragma: no cover return os.path.abspath(os.path.dirname(__file__))\",\n \"%s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question():\",\n \"len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else: possible_questions =\",\n \"id = 1 values = db.exec_query(\\\"SELECT status FROM room WHERE\",\n \"= db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a INNER JOIN question\",\n \"db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id')\",\n \"0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id)\",\n \"for this user in this room\\\"}) @app.route('/post_quiz_answer') def post_answer(): quiz_question_id\",\n \"= db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id =\",\n \"SET answered_id = %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id])\",\n \"= Flask(__name__) # Regular expression to only accept certain files\",\n \"= request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id =\",\n \"len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else: possible_users_to_ask =\",\n \"# Regular expression to only accept certain files fileChecker =\",\n \"db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s WHERE id = %s\\\",\",\n \"= 1 values = db.exec_query(\\\"SELECT status FROM room WHERE id\",\n \"VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def answered_room():\",\n \"create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator)\",\n \"To obtain the mail email = request.args.get('email') print(email) if email\",\n \"random.sample(possible_users_to_ask, 1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) >\",\n \"= value[0][0] else: text = \\\"something when wrong\\\" if value\",\n \"= %s AND about_user_id = %s AND question_id = %s\\\",\",\n \"send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): # To obtain\",\n \"WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has\",\n \"is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id =\",\n \"AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0})\",\n \"%s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id =\",\n \"values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id,\",\n \"q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question) VALUES (%s,\",\n \"question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions)\",\n \"\\\"FROM quiz_question qq \\\" \\\"WHERE qq.id = %s\\\", [quiz_question_id]) answered_id\",\n \"id, COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values =\",\n \"= value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM users u\",\n \"= %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def\",\n \"json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return\",\n \"COUNT(a.id), COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT\",\n \"request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}),\",\n \"be the correct one random.shuffle(answers) answer_json = [] for (answer_id,\",\n \"status_room(): room_id = request.args.get('room_id') # SELECT status FROM Room WHERE\",\n \"= db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = []\",\n \"request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room():\",\n \"> 0: text = value[0][0] else: text = \\\"something when\",\n \"def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question\",\n \"answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question'\",\n \"= random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) >\",\n \"= request.args.get('room_id') # SELECT status FROM Room WHERE id =\",\n \"@app.route('/register') def register(): # To obtain the mail email =\",\n \"@app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id,\",\n \"FROM room WHERE id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\":\",\n \"text_id}) print(quiz_question_id) # SELECT 'question' FROM 'Question' WHERE 'id' =\",\n \"quiz_question qq \\\" \\\"INNER JOIN users u ON (qq.asked_user_id =\",\n \"return json.dumps({\\\"error\\\": \\\"Not available questions for this user in this\",\n \"commented the first answer will be the correct one random.shuffle(answers)\",\n \"[] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return\",\n \"db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id = [] asked_about_id\",\n \"as db app = Flask(__name__) # Regular expression to only\",\n \"= value[0][1] question_id = value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM\",\n \"def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\":\",\n \"= db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id =\",\n \"-*- coding: utf-8 -*- import json import os.path import random\",\n \"db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\":\",\n \"AND about_user_id = %s AND question_id = %s\\\", [user_id, asked_about_id[0],\",\n \"def create_room(): user_id = request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room\",\n \"val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json()\",\n \"0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id) if\",\n \"values = db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\",\",\n \"@app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id,\",\n \"finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE\",\n \"1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) else:\",\n \"room_members rm ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id =\",\n \"flask import Flask, send_from_directory from flask import request, abort from\",\n \"[room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id\",\n \"db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\", [room_id])\",\n \"SELECT status FROM Room WHERE id = 1 values =\",\n \"db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer\",\n \"None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id\",\n \"r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room\",\n \"room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id})\",\n \"COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u\",\n \"the first answer will be the correct one random.shuffle(answers) answer_json\",\n \"range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id,\",\n \"register(): # To obtain the mail email = request.args.get('email') print(email)\",\n \"\\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id =\",\n \"a.id = %s \\\", [correct_answer_id]) if len(value) > 0: text\",\n \"request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id\",\n \"%s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\"\",\n \"row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id\",\n \"error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\":\",\n \"[user_id]) response = [] for val in values: response.append({\\\"id\\\": val[0],\",\n \"= request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\"\",\n \"= request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id =\",\n \"request.args.get('room_id') user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM\",\n \"[] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id})\",\n \"None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"]\",\n \"answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question():\",\n \"r.creator=%s\\\", [user_id]) response = [] for val in values: response.append({\\\"id\\\":\",\n \"json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') # SELECT\",\n \"\\\"WHERE asked_user_id = %s AND about_user_id = %s AND question_id\",\n \"1 values = db.exec_query(\\\"SELECT status FROM room WHERE id =\",\n \"\\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def\",\n \"utf-8 -*- import json import os.path import random import re\",\n \"= request.args.get('user_id') room_id = db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\",\",\n \"json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email =\",\n \"values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values =\",\n \"email is None: return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\":\",\n \"\\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s AND about_user_id =\",\n \"\\\"WHERE a.question_id = q.id AND q.room_id = %s AND a.user_id=\",\n \"if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"}) return\",\n \"def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished'\",\n \"@app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return\",\n \"id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id\",\n \"return json.dumps({\\\"info\\\": \\\"The room has been opened successfully\\\", \\\"status\\\": \\\"started\\\"})\",\n \"> 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0\",\n \"= %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email\",\n \"abort from flaskrun.flaskrun import flask_run import datab.social_database as db app\",\n \"JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = [] for\",\n \"> 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email') id_user =\",\n \"return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values\",\n \"return json.dumps({\\\"ranking\\\": ranking}) @app.route('/room_status') def status_room(): room_id = request.args.get('room_id') #\",\n \"quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question\",\n \"db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE\",\n \"ON (u.id = rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND\",\n \"random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE\",\n \"response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\":\",\n \"@app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email = request.args.get('email') user_id\",\n \"if min(numberOfAnswers - 1, len(other_users)) > 0: for i in\",\n \"> 0: question_id = random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0:\",\n \"methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data is None:\",\n \"= int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id)\",\n \"db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0],\",\n \"%s answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({\",\n \"json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer\",\n \"user_id) if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if\",\n \"question q \\\" \\\"WHERE a.question_id = q.id AND q.room_id =\",\n \"return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if\",\n \"json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question (room_id, question)\",\n \"has been opened successfully\\\", \\\"status\\\": \\\"started\\\"}) @app.route('/close_room') def close_room(): room_id\",\n \"%s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\",\",\n \"response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers(): json_data = request.get_json() if json_data\",\n \"min(numberOfAnswers - 1, len(other_users)) > 0: for i in range(min(numberOfAnswers\",\n \"db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) if\",\n \"room_id = request.args.get('room_id') # SELECT status FROM Room WHERE id\",\n \"json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def create_room(): user_id = request.args.get('user_id') room_id =\",\n \"return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\":\",\n \"'question' FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT\",\n \"db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response\",\n \"[answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users)) >\",\n \"[] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET correct_answer_id=%s\",\n \"= [] for a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values)\",\n \"user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id,\",\n \"= %s WHERE id = %s\\\", [quiz_answer_id, quiz_question_id]) value =\",\n \"value[0][0] value = db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\"\",\n \"json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO\",\n \"db.exec_query(\\\"SELECT id, question FROM question WHERE room_id = %s\\\", [room_id])\",\n \"been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id =\",\n \"values = db.exec_query(\\\"SELECT id, question FROM question WHERE room_id =\",\n \"json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values =\",\n \"flask import request, abort from flaskrun.flaskrun import flask_run import datab.social_database\",\n \"1) else: possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id\",\n \"r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been\",\n \"len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0])\",\n \"db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\", [room_id])\",\n \"obtain the mail email = request.args.get('email') print(email) if email is\",\n \"\\\"Internal server error\\\"}) return json.dumps({ \\\"correct\\\": answered_id == correct_answer_id, \\\"question\\\":\",\n \"(answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) #\",\n \"user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email\",\n \"user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\": user_id}) @app.route('/answered_room') def\",\n \"text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented the\",\n \"@app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403)\",\n \"room_id = %s\\\", [room_id]) response = [] for val in\",\n \"a WHERE a.id = %s \\\", [correct_answer_id]) if len(value) >\",\n \"return json.dumps({}) id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def\",\n \"= [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0]) db.exec_query(\\\"UPDATE quiz_question SET\",\n \"answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id) # SELECT 'question' FROM\",\n \"print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE r.id = %s\\\",\",\n \"answer will be the correct one random.shuffle(answers) answer_json = []\",\n \"= db.exec_query(\\\"SELECT r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id])\",\n \"\\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r\",\n \"qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email \\\"\",\n \"expression to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers\",\n \"get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status FROM\",\n \"\\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE\",\n \"from flask import request, abort from flaskrun.flaskrun import flask_run import\",\n \"room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='closed' WHERE r.id\",\n \"- 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id))\",\n \"\\\", [correct_answer_id]) if len(value) > 0: text = value[0][0] else:\",\n \"(%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id =\",\n \"(answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if commented\",\n \"json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id\",\n \"\\\"FROM users u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0]\",\n \"qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\" \\\"WHERE qq.id =\",\n \"\\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available\",\n \"[room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions') def get_room_question(): room_id\",\n \"'id' = 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question\",\n \"(question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/get_quiz_question')\",\n \"values: response.append({\\\"id\\\": val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST'])\",\n \"\\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data =\",\n \"question) VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"})\",\n \"\\\"question\\\": question_id, \\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__\",\n \"INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id, user_id]) return json.dumps({\\\"id\\\":\",\n \"> 0: question_id = random.sample(possible_questions, 1) else: possible_questions = db.get_all_questions(room_id)\",\n \"import re from flask import Flask, send_from_directory from flask import\",\n \"- 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\", values)\",\n \"r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for\",\n \"= %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been opened\",\n \"\\\"Data received\\\"}) @app.route('/get_quiz_question') def get_question(): room_id = int(request.args.get('room_id')) user_id =\",\n \"if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id =\",\n \"[question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT u.email \\\" \\\"FROM\",\n \"available questions for this user in this room\\\"}) @app.route('/post_quiz_answer') def\",\n \"random.shuffle(answers) answer_json = [] for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\":\",\n \"in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i])\",\n \"[quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2]\",\n \"json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms(): user_id = request.args.get('user_id') values =\",\n \"id = %s\\\", [room_id]) return json.dumps({ \\\"status\\\": values[0][0] }) @app.route('/get_room_questions')\",\n \"quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id, asked_about_id[0]) random.shuffle(other_users)\",\n \"q \\\" \\\"WHERE a.question_id = q.id AND q.room_id = %s\",\n \"if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register():\",\n \"get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT id, question FROM\",\n \"\\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for row\",\n \"\\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"] values = []\",\n \"= db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\", [room_id,\",\n \"room r SET r.status='finished' WHERE r.id = %s\\\", [room_id]) #\",\n \"}) @app.route('/get_room_questions') def get_room_question(): room_id = request.args.get('room_id') values = db.exec_query(\\\"SELECT\",\n \"val[0], \\\"text\\\": val[1]}) return json.dumps({\\\"questions\\\": response}) @app.route('/post_room_answers', methods=['POST']) def post_room_answers():\",\n \"= db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id\",\n \"request.get_json() if json_data is None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"})\",\n \"r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room\",\n \"first answer will be the correct one random.shuffle(answers) answer_json =\",\n \"print(email) if email is None: return json.dumps({}) id_user = db.register_or_get_email(email)\",\n \"db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id = %s\",\n \"= request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id =\",\n \"\\\" \\\"WHERE asked_user_id = %s AND about_user_id = %s AND\",\n \"q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id =\",\n \"AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id]) return\",\n \"INNER JOIN question q \\\" \\\"WHERE a.question_id = q.id AND\",\n \"[] if len(possible_questions) > 0: question_id = random.sample(possible_questions, 1) else:\",\n \"r.id, r.status FROM room r WHERE r.creator=%s\\\", [user_id]) response =\",\n \"= db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id, qq.question_id \\\" \\\"FROM quiz_question qq \\\"\",\n \"re from flask import Flask, send_from_directory from flask import request,\",\n \"def post_answer(): quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question\",\n \"answered_id = value[0][0] correct_answer_id = value[0][1] question_id = value[0][2] value\",\n \"random import re from flask import Flask, send_from_directory from flask\",\n \"SET r.status='finished' WHERE r.id = %s\\\", [room_id]) # for #\",\n \"to only accept certain files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers =\",\n \"FROM 'Question' WHERE 'id' = 3 value = db.exec_query(\\\"SELECT id\",\n \"text = value[0][0] else: text = \\\"something when wrong\\\" if\",\n \"print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer) VALUES(%s,%s,%s)\\\",\",\n \"= 3 value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\"\",\n \"= %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value =\",\n \"random.sample(possible_questions, 1) if len(possible_users_to_ask) > 0: asked_about_id = random.sample(possible_users_to_ask, 1)\",\n \"import os.path import random import re from flask import Flask,\",\n \"INTO question (room_id, question) VALUES (%s, %s)\\\", [room_id, q]) return\",\n \"asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id) > 0 and 0\",\n \"= request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user}) @app.route('/create_room') def\",\n \"return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def open_room(): room_id = request.args.get('room_id')\",\n \"SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The\",\n \"= qq.answered_id AND rm.room_id = %s \\\" \\\"GROUP BY u.email\",\n \"SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id))\",\n \"= %s AND question_id = %s\\\", [user_id, asked_about_id[0], question_id[0]]) quiz_question_id\",\n \"\\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking = [] for\",\n \"= value[0][2] value = db.exec_query(\\\"SELECT a.answer FROM answer a WHERE\",\n \"\\\"correct_answer\\\": {\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__ == '__main__':\",\n \"= %s \\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id)\",\n \"\\\" \\\"GROUP BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id])\",\n \"\\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN users u ON\",\n \"# To obtain the mail email = request.args.get('email') print(email) if\",\n \"return json.dumps({\\\"id\\\": id_user}) @app.route('/join_room') def join_room(): room_id = request.args.get('room_id') email\",\n \"app = Flask(__name__) # Regular expression to only accept certain\",\n \"\\\"What did %s answer to '%s' ?\\\" % (user_name, question_text)\",\n \"1, len(other_users)) > 0: for i in range(min(numberOfAnswers - 1,\",\n \"response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room():\",\n \"wrong\\\" if value is None: return json.dumps({\\\"error\\\": \\\"Internal server error\\\"})\",\n \"WHERE r.creator=%s\\\", [user_id]) response = [] for val in values:\",\n \"send_from_directory from flask import request, abort from flaskrun.flaskrun import flask_run\",\n \"status FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT\",\n \"if len(possible_questions) > 0: asked_about_id = random.sample(possible_users_to_ask, 1) if len(question_id)\",\n \"row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]}) return json.dumps({\\\"ranking\\\": ranking})\",\n \"ON (qq.asked_user_id = u.id) \\\" \\\"INNER JOIN room_members rm ON\",\n \"JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question\",\n \"= \\\"What did %s answer to '%s' ?\\\" % (user_name,\",\n \"= rm.user_id) \\\" \\\"WHERE qq.correct_answer_id = qq.answered_id AND rm.room_id =\",\n \"request.args.get('email') print(email) if email is None: return json.dumps({}) id_user =\",\n \"COUNT(a.id) FROM Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email\",\n \"for (answer_id, text_id) in answers: answer_json.append({\\\"id\\\": answer_id, \\\"text\\\": text_id}) print(quiz_question_id)\",\n \"1) else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0:\",\n \"import datab.social_database as db app = Flask(__name__) # Regular expression\",\n \"db.exec_query(\\\"SELECT u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER\",\n \"qq.id = %s\\\", [quiz_question_id]) answered_id = value[0][0] correct_answer_id = value[0][1]\",\n \"= [] for row in values: ranking.append({\\\"email\\\": row[0], \\\"correct\\\": row[1]})\",\n \"\\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What\",\n \"re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): # pragma: no\",\n \"import json import os.path import random import re from flask\",\n \"answer to '%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\":\",\n \"DESC\\\", [room_id]) ranking = [] for row in values: ranking.append({\\\"email\\\":\",\n \"for val in values: response.append({\\\"id\\\": val[0], \\\"status\\\": val[1]}) return json.dumps(response)\",\n \"quiz_question_id = request.args.get('quiz_question_id') quiz_answer_id = request.args.get('quiz_answer_id') db.exec_query(\\\"UPDATE quiz_question SET answered_id\",\n \"int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id\",\n \"u \\\" \\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text =\",\n \"files fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir():\",\n \"r WHERE r.creator=%s\\\", [user_id]) response = [] for val in\",\n \"fileChecker = re.compile(r\\\"(.*\\\\.js|.*\\\\.html|.*\\\\.png|.*\\\\.css|.*\\\\.map)$\\\") numberOfAnswers = 4 random.seed(7) def root_dir(): #\",\n \"\\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"]\",\n \"i in range(min(numberOfAnswers - 1, len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0],\",\n \"answers.append((answer_id, text_id)) # if commented the first answer will be\",\n \"room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def\",\n \"request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET r.status='finished' WHERE r.id = %s\\\",\",\n \"r SET r.status='closed' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\":\",\n \"response = [] for val in values: response.append({\\\"id\\\": val[0], \\\"text\\\":\",\n \"= db.exec_query(\\\"INSERT INTO room (creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\":\",\n \"0 and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0])\",\n \"'static'), filename) abort(403) @app.route('/register') def register(): # To obtain the\",\n \"email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id,\",\n \"asked_user_id = %s AND about_user_id = %s AND question_id =\",\n \"\\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room\",\n \"a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT INTO answer (question_id, user_id, answer)\",\n \"FROM Room WHERE id = 1 values = db.exec_query(\\\"SELECT status\",\n \"user_name = value[0][0] question_text = \\\"What did %s answer to\",\n \"id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers -\",\n \"u.id) \\\" \\\"INNER JOIN room_members rm ON (u.id = rm.user_id)\",\n \"get_user_id(): email = request.args.get('email') id_user = db.register_or_get_email(email) return json.dumps({\\\"id\\\": id_user})\",\n \"%s\\\", [answer_id, quiz_question_id]) answers.append((answer_id, text_id)) if min(numberOfAnswers - 1, len(other_users))\",\n \"user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO room_members (room_id, user_id) VALUES (%s,%s)\\\",\",\n \"user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer\",\n \"a in json_data[\\\"answers\\\"]: values.append((a[\\\"id\\\"], user_id, a[\\\"text\\\"])) print(values[len(values) - 1]) db.exec_many_query(\\\"INSERT\",\n \"JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for\",\n \"VALUES (%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room')\",\n \"possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask = db.get_non_answered_people(room_id, user_id) question_id =\",\n \"flask_run import datab.social_database as db app = Flask(__name__) # Regular\",\n \"\\\"WHERE u.id=%s\\\", [asked_about_id[0]]) user_name = value[0][0] question_text = \\\"What did\",\n \"r SET r.status='started' WHERE r.id = %s\\\", [room_id]) return json.dumps({\\\"info\\\":\",\n \"@app.route('/finish_room') def finish_room(): room_id = request.args.get('room_id') db.exec_query(\\\"UPDATE room r SET\",\n \"asked_about_id[0]) random.shuffle(other_users) answers = [] (answer_id, text_id) = db.get_answer(question_id[0], asked_about_id[0])\",\n \"room has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room():\",\n \"len(other_users))): (answer_id, text_id) = db.get_answer(question_id[0], other_users[i]) answers.append((answer_id, text_id)) # if\",\n \"from flask import Flask, send_from_directory from flask import request, abort\",\n \"% (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\":\",\n \"else: possible_users_to_ask = db.get_all_different_people(room_id, user_id) if len(possible_questions) > 0: asked_about_id\",\n \"no cover return os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/')\",\n \"db.exec_query(\\\"SELECT q.question \\\" \\\"FROM question q \\\" \\\"WHERE q.id =\",\n \"Room r INNER JOIN values = db.exec_query(\\\"SELECT u.email , COUNT(qq.id)\",\n \"a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id')\",\n \"room_id = request.args.get('room_id') print(room_id) db.exec_query(\\\"UPDATE room r SET r.status='started' WHERE\",\n \"u.email , COUNT(qq.id) \\\" \\\"FROM quiz_question qq \\\" \\\"INNER JOIN\",\n \"(%s, %s)\\\", [room_id, q]) return json.dumps({\\\"info\\\": \\\"Data received\\\"}) @app.route('/open_room') def\",\n \"index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def\",\n \"%s\\\", [room_id]) # for # SELECT id, COUNT(a.id), COUNT(a.id) FROM\",\n \"None: return json.dumps({\\\"error\\\": \\\"no JSON found\\\"}), 404 user_id = json_data[\\\"user_id\\\"]\",\n \"< len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users = db.get_all_different_people(room_id,\",\n \"did %s answer to '%s' ?\\\" % (user_name, question_text) return\",\n \"fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'), filename) abort(403) @app.route('/register') def register(): #\",\n \"[room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed successfully\\\", \\\"status\\\":\",\n \"questions = json_data[\\\"question\\\"] for q in questions: db.exec_query(\\\"INSERT INTO question\",\n \"user_id) question_id = [] asked_about_id = [] if len(possible_questions) >\",\n \"a.question_id = q.id AND q.room_id = %s AND a.user_id= %s\\\",\",\n \"has been closed successfully\\\", \\\"status\\\": \\\"closed\\\"}) @app.route('/finish_room') def finish_room(): room_id\",\n \"quiz_question_id, \\\"question\\\": question_text, \\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not\",\n \"\\\" \\\"FROM question q \\\" \\\"WHERE q.id = %s\\\", [question_id[0]])\",\n \"%s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def\",\n \"{\\\"id\\\": correct_answer_id, \\\"text\\\": text} }) if __name__ == '__main__': flask_run(app)\",\n \"found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions = json_data[\\\"question\\\"] for q\",\n \"'%s' ?\\\" % (user_name, question_text) return json.dumps({ \\\"id\\\": quiz_question_id, \\\"question\\\":\",\n \"FROM answer a WHERE a.id = %s \\\", [correct_answer_id]) if\",\n \"os.path.abspath(os.path.dirname(__file__)) @app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/') def index(filename): if\",\n \"possible_questions = db.get_all_questions(room_id) if len(possible_questions) > 0: question_id = random.sample(possible_questions,\",\n \"\\\"answers\\\": answer_json }) else: return json.dumps({\\\"error\\\": \\\"Not available questions for\",\n \"json.dumps({\\\"error\\\": \\\"no JSON found\\\"}) else: room_id = json_data[\\\"room_id\\\"] questions =\",\n \"= request.args.get('room_id') email = request.args.get('email') user_id = db.register_or_get_email(email) db.exec_query(\\\"REPLACE INTO\",\n \"the correct one random.shuffle(answers) answer_json = [] for (answer_id, text_id)\",\n \"0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users =\",\n \"id = %s\\\", [quiz_answer_id, quiz_question_id]) value = db.exec_query(\\\"SELECT qq.answered_id, qq.correct_answer_id,\",\n \"= %s AND a.user_id= %s\\\", [room_id, user_id]) return json.dumps({\\\"answered\\\": len(values)\",\n \"[user_id, asked_about_id[0], question_id[0]]) quiz_question_id = value[0][0] value = db.exec_query(\\\"SELECT q.question\",\n \"def fill_room(): json_data = request.get_json() if json_data is None: return\",\n \"= int(request.args.get('room_id')) user_id = int(request.args.get('user_id')) possible_questions = db.get_non_answered_questions(room_id, user_id) possible_users_to_ask\",\n \"return index(\\\"index2.html\\\") @app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(), 'static'),\",\n \"and 0 < len(asked_about_id): quiz_question_id = db.insert_quiz_question(user_id, asked_about_id[0], question_id[0]) other_users\",\n \"other_users[i]) answers.append((answer_id, text_id)) # if commented the first answer will\",\n \"q.id = %s\\\", [question_id[0]]) question_text = value[0][0] value = db.exec_query(\\\"SELECT\",\n \"a.answer FROM answer a WHERE a.id = %s \\\", [correct_answer_id])\",\n \"1) if len(question_id) > 0 and 0 < len(asked_about_id): quiz_question_id\",\n \"json.dumps({\\\"answered\\\": len(values) > 0}) @app.route('/get_user_id') def get_user_id(): email = request.args.get('email')\",\n \"= %s\\\", [room_id]) return json.dumps({\\\"info\\\": \\\"The room has been closed\",\n \"root(): return index(\\\"index2.html\\\") @app.route('/') def index(filename): if fileChecker.match(filename): return send_from_directory(os.path.join(root_dir(),\",\n \"= db.exec_query(\\\"SELECT a.answer FROM answer a WHERE a.id = %s\",\n \"@app.route('/') def root(): return index(\\\"index2.html\\\") @app.route('/') def index(filename): if fileChecker.match(filename):\",\n \"BY u.email \\\" \\\"ORDER BY COUNT(qq.id) DESC\\\", [room_id]) ranking =\",\n \"def get_rooms(): user_id = request.args.get('user_id') values = db.exec_query(\\\"SELECT r.id, r.status\",\n \"value = db.exec_query(\\\"SELECT id \\\" \\\"FROM quiz_question \\\" \\\"WHERE asked_user_id\",\n \"(creator) VALUES (%s)\\\", [user_id]) return json.dumps({\\\"id\\\": room_id}) @app.route('/get_rooms') def get_rooms():\",\n \"q.id AND q.room_id = %s AND a.user_id= %s\\\", [room_id, user_id])\",\n \"WHERE room_id = %s\\\", [room_id]) response = [] for val\",\n \"= request.args.get('user_id') values = db.exec_query(\\\"SELECT a.id \\\" \\\"FROM answer a\",\n \"json.dumps(response) @app.route('/fill_room', methods=['POST']) def fill_room(): json_data = request.get_json() if json_data\",\n \"quiz_question SET correct_answer_id=%s WHERE id = %s\\\", [answer_id, quiz_question_id]) answers.append((answer_id,\"\n]"}}},{"rowIdx":3498,"cells":{"ngram":{"kind":"list like","value":["\"\"\"Snapshotter keeps track of files on disk, and their hashes.","changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path","error, we rather handle it here than leave # exceptions","snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile:","change, which may require subsequent incremential snapshot and-or ignoring the","started twice at # same time. While it is technically","return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) #","in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return","FileExistsError: # This happens only if snapshot is started twice","not even work. # Then, create/update corresponding snapshotfile objects (old","import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s","from multiple threads, and the single-operation-only mode of operation is","same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked()","snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path","sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None)","*, src_dirs, dst_dirs): changes = 0 for i, relative_dir in","lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared","hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data =","<= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return","SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0","and their hashes. The hash on disk MAY change, which","try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only","# same time. While it is technically speaking upstream #","if self.src == self.dst: # The src=dst mode should be","import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f,","result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success()","else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out):","while True: data = f.read(read_buffer) if not data: break h.update(data)","st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths):","contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs,","relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path)","map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb,","_cb(snapshotfile): # src may or may not be present; dst","is not exactly flawless (the 'new operation can be started","be made with snapshotter.lock held. This is because Snapshotter is","to be built-in to Snapshotter, but having it there enables","new directory: %r\", i, relative_dir) changes += 1 return changes","not disappear between snapshot and # upload steps (e.g. Astacus","# upload steps (e.g. Astacus controls the lifecycle of the","mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost =","True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We","class Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and","object. Note that any call to public API MUST be","import increase_worth_reporting, Progress from pathlib import Path from typing import","{p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self,","self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def","%s disappeared before stat, ignoring\", lost, self.src / relative_path) continue","== snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same -","# Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() #","Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {}","can be started with old running' is intentional feature but","for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self):","old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost","== self.dst: # The src=dst mode should be used if","f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter:","self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64","+= 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing","\"\"\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile,","src may or may not be present; dst is present","that is shared across operations, possibly used from multiple threads,","details \"\"\" from astacus.common import magic, utils from astacus.common.ipc import","+= 1 return changes def snapshot(self, *, progress: Optional[Progress] =","track of files on disk, and their hashes. The hash","be present; dst is present as it is in snapshot","def _list_files(self, basepath: Path): result_files = set() for glob in","The src=dst mode should be used if and only if","and we can just use the src # directory contents","ignoring the files which have changed. The output to outside","relative_path) changes += 1 return changes def snapshot(self, *, progress:","_snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat()","= 0 disappeared = 0 changes = 0 for i,","1 return changes def snapshot(self, *, progress: Optional[Progress] = None):","directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3)","in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return","disk MAY change, which may require subsequent incremential snapshot and-or","src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path,","changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files):","snapshotter.lock held. This is because Snapshotter is process-wide utility that","def _cb(snapshotfile): # src may or may not be present;","Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {}","= self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost +=","self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path)","Path from typing import Optional import base64 import hashlib import","is process-wide utility that is shared across operations, possibly used","0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src","return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0","in # making extra symlinks and we can just use","import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from","model has empty; either plugin or configuration must supply them","it is # known that files will not disappear between","parallel): assert globs # model has empty; either plugin or","self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self,","changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially","try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if","Note that any call to public API MUST be made","+= 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\",","incremential snapshot and-or ignoring the files which have changed. The","Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add","path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in","within the file list contained in root object. Note that","= self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes +=","snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode()","FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before","return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0","relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False)","= hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success()","self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set()","from pathlib import Path from typing import Optional import base64","list contained in root object. Note that any call to","subsequent incremential snapshot and-or ignoring the files which have changed.","might not need to be built-in to Snapshotter, but having","ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new","happens only if snapshot is started twice at # same","of operation is not exactly flawless (the 'new operation can","dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files)","changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes","there is little point in # making extra symlinks and","data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest()","'new operation can be started with old running' is intentional","= self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs)","might not even work. # Then, create/update corresponding snapshotfile objects","This happens only if snapshot is started twice at #","0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst","is not # probably really worth it and due to","dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1):","if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest =","= self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns,","lost - %s disappeared before stat, ignoring\", lost, self.src /","f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest","self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs,","assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in","- %s disappeared before stat, ignoring\", lost, self.src / relative_path)","_snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0","def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for","typing import Optional import base64 import hashlib import logging import","ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest =","dirs = {p.parent for p in files} return sorted(dirs), files","= self.src / relative_path dst_path = self.dst / relative_path try:","snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress","controls the lifecycle of the # files within). In that","progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success()","which may require subsequent incremential snapshot and-or ignoring the files","Path): result_files = set() for glob in self.globs: for path","self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles","at # same time. While it is technically speaking upstream","increase_worth_reporting, Progress from pathlib import Path from typing import Optional","import logging import os import threading logger = logging.getLogger(__name__) _hash","del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path =","dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d.","self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: #","if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path)","(old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)","directory: %r\", i, relative_dir) changes += 1 return changes def","feature but new operation should eventually replace the old). The","only if it is # known that files will not","relative_paths): same = 0 lost = 0 for relative_path in","must supply them self.src = Path(src) self.dst = Path(dst) self.globs","import Path from typing import Optional import base64 import hashlib","def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st =","result_files = set() for glob in self.globs: for path in","# ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles","SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ]","= self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d.","if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path]","+= len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started","extra directories, but it is not # probably really worth","h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files","if snapshot is started twice at # same time. While","mode should be used if and only if it is","if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i -","self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file:","def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data","existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1","snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out)","as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if","return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0","progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success()","astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress","new operation should eventually replace the old). The lock itself","/ relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def","Aiven Ltd See LICENSE for details \"\"\" from astacus.common import","0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1):","old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same +=","for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst /","dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1):","= self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst","remove extra directories, but it is not # probably really","map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles,","changes += 1 return changes def snapshot(self, *, progress: Optional[Progress]","glob in self.globs: for path in basepath.glob(glob): if not path.is_file()","and # upload steps (e.g. Astacus controls the lifecycle of","ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles =","linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d.","self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock","self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile:","utility that is shared across operations, possibly used from multiple","follow_symlinks=False) except FileExistsError: # This happens only if snapshot is","API MUST be made with snapshotter.lock held. This is because","existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared):","call to public API MUST be made with snapshotter.lock held.","except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared","missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra","not need to be built-in to Snapshotter, but having it","not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent","speaking upstream # error, we rather handle it here than","self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files","is just root object's hash, as well as list of","may or may not be present; dst is present as","in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile","plugin or configuration must supply them self.src = Path(src) self.dst","little point in # making extra symlinks and we can","or may not be present; dst is present as it","relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile","globs # model has empty; either plugin or configuration must","during public API calls. \"\"\" def __init__(self, *, src, dst,","extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing","Optional import base64 import hashlib import logging import os import","hash, as well as list of other hashes which correspond","started with old running' is intentional feature but new operation","SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile)","is because Snapshotter is process-wide utility that is shared across","only if snapshot is started twice at # same time.","dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes =","_snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path","files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files","worth it and due to ignored files it # actually","held. This is because Snapshotter is process-wide utility that is","snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d.","1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared,","break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of","in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath =","file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0","snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i,","src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d.","*, progress: Optional[Progress] = None): assert self.lock.locked() if progress is","files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also","# We COULD also remove extra directories, but it is","it and due to ignored files it # actually might","of the # files within). In that case, there is","*, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer)","progress: Optional[Progress] = None): assert self.lock.locked() if progress is None:","symlinks and we can just use the src # directory","disappeared, relative_path) changes += 1 return changes def snapshot(self, *,","we can just use the src # directory contents as-is.","= None): assert self.lock.locked() if progress is None: progress =","*, src_files, dst_files): changes = 0 for i, relative_path in","disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking,","assert globs # model has empty; either plugin or configuration","on disk MAY change, which may require subsequent incremential snapshot","for details \"\"\" from astacus.common import magic, utils from astacus.common.ipc","possibly used from multiple threads, and the single-operation-only mode of","The output to outside is just root object's hash, as","be started with old running' is intentional feature but new","basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath)","dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert","handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed,","disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file:","public API MUST be made with snapshotter.lock held. This is","1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes =","= self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile)","# This happens only if snapshot is started twice at","# probably really worth it and due to ignored files","across operations, possibly used from multiple threads, and the single-operation-only","self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path,","process-wide utility that is shared across operations, possibly used from","the lifecycle of the # files within). In that case,","if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\",","magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress","twice at # same time. While it is technically speaking","FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s","exactly flawless (the 'new operation can be started with old","return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes =","MUST be made with snapshotter.lock held. This is because Snapshotter","if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d.","for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile:","operation is not exactly flawless (the 'new operation can be","= set() for glob in self.globs: for path in basepath.glob(glob):","already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared +=","same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in","that any call to public API MUST be made with","SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib","known that files will not disappear between snapshot and #","making extra symlinks and we can just use the src","0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile","# actually might not even work. # Then, create/update corresponding","if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes +=","# We initially started with 1 extra progress.add_success() return changes","result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath)","its state during public API calls. \"\"\" def __init__(self, *,","existing = 0 disappeared = 0 changes = 0 for","= self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if","dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile:","create/update corresponding snapshotfile objects (old # ones were already removed)","LICENSE for details \"\"\" from astacus.common import magic, utils from","= hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while","is # known that files will not disappear between snapshot","hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return","Snapshotter: \"\"\"Snapshotter keeps track of files on disk, and their","(the 'new operation can be started with old running' is","changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files):","%r\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self,","The lock itself might not need to be built-in to","enables asserting its state during public API calls. \"\"\" def","either plugin or configuration must supply them self.src = Path(src)","if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def","Optional[Progress] = None): assert self.lock.locked() if progress is None: progress","corresponding snapshotfile objects (old # ones were already removed) dst_dirs,","snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64","files which have changed. The output to outside is just","calls. \"\"\" def __init__(self, *, src, dst, globs, parallel): assert","really worth it and due to ignored files it #","# Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() #","in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except","dst_files): existing = 0 disappeared = 0 changes = 0","_result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles)","if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\",","+= self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra","# Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() #","import hashlib import logging import os import threading logger =","relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files)","it # actually might not even work. # Then, create/update","progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel)","dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but","1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i):","read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if","hash on disk MAY change, which may require subsequent incremential","self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self,","upload steps (e.g. Astacus controls the lifecycle of the #","their hashes. The hash on disk MAY change, which may","for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile =","return changes def snapshot(self, *, progress: Optional[Progress] = None): assert","for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src /","src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing","%r in %s is same\", same, old_snapshotfile, relative_path) continue yield","is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self):","path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break","self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src","same = 0 lost = 0 for relative_path in relative_paths:","# Then, create/update corresponding snapshotfile objects (old # ones were","dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot","= self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra","threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob","\"\"\" Copyright (c) 2020 Aiven Ltd See LICENSE for details","= Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles =","is intentional feature but new operation should eventually replace the","snapshotfile objects (old # ones were already removed) dst_dirs, dst_files","relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name ==","changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path","base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in,","same - %r in %s is same\", same, old_snapshotfile, relative_path)","dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs,","replace the old). The lock itself might not need to","if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\",","be built-in to Snapshotter, but having it there enables asserting","enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)","globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel","path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath","# making extra symlinks and we can just use the","old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same):","in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True)","self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories,","as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else:","See LICENSE for details \"\"\" from astacus.common import magic, utils","same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert","to Snapshotter, but having it there enables asserting its state","logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue if","relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i):","= logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h","intentional feature but new operation should eventually replace the old).","new file: %r\", i - disappeared, relative_path) changes += 1","it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size","as well as list of other hashes which correspond to","self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile","if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile)","handle it here than leave # exceptions not handled. existing","and-or ignoring the files which have changed. The output to","sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs =","logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h =","src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The","/ relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This","extra symlinks and we can just use the src #","old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile ==","lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)","progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be","= Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile =","which have changed. The output to outside is just root","increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost,","hashes which correspond to files referred to within the file","because Snapshotter is process-wide utility that is shared across operations,","the single-operation-only mode of operation is not exactly flawless (the","src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create","1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if","were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files))","src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files),","dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files)","assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs):","use the src # directory contents as-is. dst_dirs, dst_files =","files on disk, and their hashes. The hash on disk","# error, we rather handle it here than leave #","_list_files(self, basepath: Path): result_files = set() for glob in self.globs:","= 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files),","but it is not # probably really worth it and","should eventually replace the old). The lock itself might not","leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing):","not # probably really worth it and due to ignored","and only if it is # known that files will","\"\"\" def __init__(self, *, src, dst, globs, parallel): assert globs","configuration must supply them self.src = Path(src) self.dst = Path(dst)","/ relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 =","self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest:","/ relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path,","changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove","disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i -","None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if","due to ignored files it # actually might not even","- %r in %s is same\", same, old_snapshotfile, relative_path) continue","def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes +=","snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert","if it is # known that files will not disappear","*, src, dst, globs, parallel): assert globs # model has","self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files,","_hash() while True: data = f.read(read_buffer) if not data: break","= 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path =","progress.start(1) if self.src == self.dst: # The src=dst mode should","path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if","not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already","0 lost = 0 for relative_path in relative_paths: old_snapshotfile =","if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src /","increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared,","self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs),","0 disappeared = 0 changes = 0 for i, relative_path","else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files =","technically speaking upstream # error, we rather handle it here","here than leave # exceptions not handled. existing += 1","outside is just root object's hash, as well as list","asserting its state during public API calls. \"\"\" def __init__(self,","None): assert self.lock.locked() if progress is None: progress = Progress()","case, there is little point in # making extra symlinks","point in # making extra symlinks and we can just","get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf","data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track","if progress is None: progress = Progress() src_dirs, src_files =","hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True:","_hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash()","any call to public API MUST be made with snapshotter.lock","assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def","parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath)","True: data = f.read(read_buffer) if not data: break h.update(data) return","enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst","# model has empty; either plugin or configuration must supply","basepath: Path): result_files = set() for glob in self.globs: for","yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size)","be used if and only if it is # known","%r\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self,","logger.debug(\"#%d. same - %r in %s is same\", same, old_snapshotfile,","just root object's hash, as well as list of other","= 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path =","logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000):","return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs","dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir)","self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes","continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name","of files on disk, and their hashes. The hash on","eventually replace the old). The lock itself might not need","not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps","basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p","self.src == self.dst: # The src=dst mode should be used","import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from","self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st","relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError:","disappeared before stat, ignoring\", lost, self.src / relative_path) continue if","not be present; dst is present as it is in","it is technically speaking upstream # error, we rather handle","files will not disappear between snapshot and # upload steps","= f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class","with old running' is intentional feature but new operation should","len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with","def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i,","self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path):","+= 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s","work. # Then, create/update corresponding snapshotfile objects (old # ones","= src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same","Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We","%r\", i - disappeared, relative_path) changes += 1 return changes","self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost","import base64 import hashlib import logging import os import threading","+= 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing,","and the single-operation-only mode of operation is not exactly flawless","magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile","lifecycle of the # files within). In that case, there","operations, possibly used from multiple threads, and the single-operation-only mode","list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not","= snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile):","except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost -","= self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src","to within the file list contained in root object. Note","relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files,","self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink()","fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra","is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1)","relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64","[ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf","for glob in self.globs: for path in basepath.glob(glob): if not","the file list contained in root object. Note that any","_remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if","i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *,","single-operation-only mode of operation is not exactly flawless (the 'new","changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked()","self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink():","disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes","empty; either plugin or configuration must supply them self.src =","root object's hash, as well as list of other hashes","ignoring\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if","disk, and their hashes. The hash on disk MAY change,","upstream # error, we rather handle it here than leave","used if and only if it is # known that","= base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*,","files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for","snapshot and # upload steps (e.g. Astacus controls the lifecycle","is shared across operations, possibly used from multiple threads, and","old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return","parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files =","present; dst is present as it is in snapshot with","self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1","else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories","return h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on","self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel","parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self,","This is because Snapshotter is process-wide utility that is shared","self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode","utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import","self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files,","relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i,","logging import os import threading logger = logging.getLogger(__name__) _hash =","files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile =","is technically speaking upstream # error, we rather handle it","actually might not even work. # Then, create/update corresponding snapshotfile","i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *,","to public API MUST be made with snapshotter.lock held. This","to outside is just root object's hash, as well as","with snapshotter.lock held. This is because Snapshotter is process-wide utility","self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may","list of other hashes which correspond to files referred to","same time. While it is technically speaking upstream # error,","flawless (the 'new operation can be started with old running'","files referred to within the file list contained in root","directories, but it is not # probably really worth it","return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path,","lock itself might not need to be built-in to Snapshotter,","but new operation should eventually replace the old). The lock","if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue","# The src=dst mode should be used if and only","as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files","operation should eventually replace the old). The lock itself might","API calls. \"\"\" def __init__(self, *, src, dst, globs, parallel):","to files referred to within the file list contained in","%s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile def","{} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock()","= self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError:","before stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile:","dst_path.unlink() if increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes","if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for","continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig,","continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\"#%d. %s","= {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock =","require subsequent incremential snapshot and-or ignoring the files which have","changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path","in %s is same\", same, old_snapshotfile, relative_path) continue yield snapshotfile","on disk, and their hashes. The hash on disk MAY","if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile","src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files","even work. # Then, create/update corresponding snapshotfile objects (old #","relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self,","exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s","have changed. The output to outside is just root object's","relative_path): src_path = self.src / relative_path st = src_path.stat() return","# known that files will not disappear between snapshot and","i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path","may not be present; dst is present as it is","# directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else:","== magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath:","sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def","snapshot is started twice at # same time. While it","for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst /","= 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path =","disappeared = 0 changes = 0 for i, relative_path in","hashlib import logging import os import threading logger = logging.getLogger(__name__)","correspond to files referred to within the file list contained","return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if","Snapshotter is process-wide utility that is shared across operations, possibly","files = self._list_files(basepath) dirs = {p.parent for p in files}","Path): files = self._list_files(basepath) dirs = {p.parent for p in","progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src)","also remove extra directories, but it is not # probably","set() for glob in self.globs: for path in basepath.glob(glob): if","files it # actually might not even work. # Then,","%s already existed, ignoring\", existing, src_path) continue except FileNotFoundError: disappeared","from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash,","file list contained in root object. Note that any call","*, src_files, dst_files): existing = 0 disappeared = 0 changes","n=self.parallel) # We initially started with 1 extra progress.add_success() return","progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src","snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost):","is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <=","object's hash, as well as list of other hashes which","the files which have changed. The output to outside is","old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def","in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE:","src_files, dst_files): existing = 0 disappeared = 0 changes =","(c) 2020 Aiven Ltd See LICENSE for details \"\"\" from","can just use the src # directory contents as-is. dst_dirs,","increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except","def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared =","is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as","# src may or may not be present; dst is","stat, ignoring\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest","in self.globs: for path in basepath.glob(glob): if not path.is_file() or","dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except","keeps track of files on disk, and their hashes. The","self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb,","globs, parallel): assert globs # model has empty; either plugin","within). In that case, there is little point in #","i - disappeared, relative_path) changes += 1 return changes def","relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens","if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes +=","hashes. The hash on disk MAY change, which may require","in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path =","SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost","old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same","= globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel =","we rather handle it here than leave # exceptions not","contained in root object. Note that any call to public","self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items()","SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path","old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile","files within). In that case, there is little point in","+= 1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before","i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir","- disappeared): logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path)","from typing import Optional import base64 import hashlib import logging","dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files =","_list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for","== snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path):","continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if","snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for","size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def","logger.debug(\"#%d. new file: %r\", i - disappeared, relative_path) changes +=","dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): #","Then, create/update corresponding snapshotfile objects (old # ones were already","The hash on disk MAY change, which may require subsequent","# files within). In that case, there is little point","may require subsequent incremential snapshot and-or ignoring the files which","= {p.parent for p in files} return sorted(dirs), files def","snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1","need to be built-in to Snapshotter, but having it there","= old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if","def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig,","def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *,","logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1 return","as list of other hashes which correspond to files referred","is started twice at # same time. While it is","of other hashes which correspond to files referred to within","for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue","_get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path","changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared","just use the src # directory contents as-is. dst_dirs, dst_files","increase_worth_reporting(disappeared): logger.debug(\"#%d. %s disappeared before linking, ignoring\", disappeared, src_path) continue","them self.src = Path(src) self.dst = Path(dst) self.globs = globs","root object. Note that any call to public API MUST","logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path) continue except FileNotFoundError:","or configuration must supply them self.src = Path(src) self.dst =","it there enables asserting its state during public API calls.","relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path","def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i,","os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if","files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if","[]).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del","snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r","than leave # exceptions not handled. existing += 1 if","already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles))","exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes","utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1","should be used if and only if it is #","in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile =","snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest:","snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def","itself might not need to be built-in to Snapshotter, but","1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing =","to ignored files it # actually might not even work.","MAY change, which may require subsequent incremential snapshot and-or ignoring","def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path]","# exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d.","= src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) #","snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes","existing += 1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\",","Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst:","with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 =","self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size)","rather handle it here than leave # exceptions not handled.","i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path","objects (old # ones were already removed) dst_dirs, dst_files =","from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting,","2020 Aiven Ltd See LICENSE for details \"\"\" from astacus.common","snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] ==","self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory:","shared across operations, possibly used from multiple threads, and the","between snapshot and # upload steps (e.g. Astacus controls the","return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True","old running' is intentional feature but new operation should eventually","there enables asserting its state during public API calls. \"\"\"","ignored files it # actually might not even work. #","While it is technically speaking upstream # error, we rather","Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove","= 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try:","if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values()))","built-in to Snapshotter, but having it there enables asserting its","snapshot and-or ignoring the files which have changed. The output","if and only if it is # known that files","dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst)","the # files within). In that case, there is little","snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f)","threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *,","operation can be started with old running' is intentional feature","not exactly flawless (the 'new operation can be started with","= self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new","src_path = self.src / relative_path dst_path = self.dst / relative_path","continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\", i","/ relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\",","relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [","output to outside is just root object's hash, as well","before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared):","get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs,","steps (e.g. Astacus controls the lifecycle of the # files","from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from","changed. The output to outside is just root object's hash,","or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents:","Progress from pathlib import Path from typing import Optional import","dst, globs, parallel): assert globs # model has empty; either","for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else:","increase_worth_reporting(i): logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1","that files will not disappear between snapshot and # upload","time. While it is technically speaking upstream # error, we","_snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir","which correspond to files referred to within the file list","] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self,","has empty; either plugin or configuration must supply them self.src","h.hexdigest() class Snapshotter: \"\"\"Snapshotter keeps track of files on disk,","= Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src ==","relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True,","self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success()","and due to ignored files it # actually might not","in root object. Note that any call to public API","(e.g. Astacus controls the lifecycle of the # files within).","state during public API calls. \"\"\" def __init__(self, *, src,","def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile:","used from multiple threads, and the single-operation-only mode of operation","the src # directory contents as-is. dst_dirs, dst_files = src_dirs,","progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes","We COULD also remove extra directories, but it is not","logger.debug(\"#%d. lost - %s disappeared before stat, ignoring\", lost, self.src","threads, and the single-operation-only mode of operation is not exactly","supply them self.src = Path(src) self.dst = Path(dst) self.globs =","public API calls. \"\"\" def __init__(self, *, src, dst, globs,","1): src_path = self.src / relative_path dst_path = self.dst /","None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest,","increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is same\", same,","Astacus controls the lifecycle of the # files within). In","1 if increase_worth_reporting(lost): logger.debug(\"#%d. lost - %s disappeared before stat,","snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or","sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked()","other hashes which correspond to files referred to within the","src, dst, globs, parallel): assert globs # model has empty;","break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files","assert self.lock.locked() if progress is None: progress = Progress() src_dirs,","__init__(self, *, src, dst, globs, parallel): assert globs # model","relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files,","= threading.Lock() def _list_files(self, basepath: Path): result_files = set() for","base64 import hashlib import logging import os import threading logger","= 0 lost = 0 for relative_path in relative_paths: old_snapshotfile","running' is intentional feature but new operation should eventually replace","extra file: %r\", i, relative_path) changes += 1 return changes","SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import","referred to within the file list contained in root object.","but having it there enables asserting its state during public","disappear between snapshot and # upload steps (e.g. Astacus controls","import Optional import base64 import hashlib import logging import os","os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def","1 if increase_worth_reporting(existing): logger.debug(\"#%d. %s already existed, ignoring\", existing, src_path)","probably really worth it and due to ignored files it","that case, there is little point in # making extra","missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD","h = _hash() while True: data = f.read(read_buffer) if not","snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path","0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst","/ relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if","progress.add_success() # We COULD also remove extra directories, but it","increase_worth_reporting(i): logger.debug(\"#%d. new directory: %r\", i, relative_dir) changes += 1","= old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same","file: %r\", i - disappeared, relative_path) changes += 1 return","astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing","{} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath:","old). The lock itself might not need to be built-in","- disappeared, relative_path) changes += 1 return changes def snapshot(self,","file: %r\", i, relative_path) changes += 1 return changes def","src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same =","dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile):","= list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may","src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: #","changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes","COULD also remove extra directories, but it is not #","= parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files","Snapshotter, but having it there enables asserting its state during","SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] =","directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files","src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs),","dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)","multiple threads, and the single-operation-only mode of operation is not","will not disappear between snapshot and # upload steps (e.g.","= _hash() while True: data = f.read(read_buffer) if not data:","magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path):","In that case, there is little point in # making","+= self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes +=","having it there enables asserting its state during public API","Ltd See LICENSE for details \"\"\" from astacus.common import magic,","logger.debug(\"#%d. extra file: %r\", i, relative_path) changes += 1 return","+= 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes","src=dst mode should be used if and only if it","src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\"#%d. new file: %r\",","p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile):","enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if","made with snapshotter.lock held. This is because Snapshotter is process-wide","self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile","def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if","the old). The lock itself might not need to be","lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest","changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for","def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent","mode of operation is not exactly flawless (the 'new operation","= path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR:","= self._list_files(basepath) dirs = {p.parent for p in files} return","= {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self,","1 if increase_worth_reporting(same): logger.debug(\"#%d. same - %r in %s is","Copyright (c) 2020 Aiven Ltd See LICENSE for details \"\"\"","pathlib import Path from typing import Optional import base64 import","self.dst: # The src=dst mode should be used if and","_add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile)","is little point in # making extra symlinks and we","it is not # probably really worth it and due","well as list of other hashes which correspond to files","%s disappeared before linking, ignoring\", disappeared, src_path) continue if increase_worth_reporting(i","removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def","present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f:","except FileExistsError: # This happens only if snapshot is started","snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path]","it here than leave # exceptions not handled. existing +=","if not data: break h.update(data) return h.hexdigest() class Snapshotter: \"\"\"Snapshotter","def __init__(self, *, src, dst, globs, parallel): assert globs #","astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState"],"string":"[\n \"\\\"\\\"\\\"Snapshotter keeps track of files on disk, and their hashes.\",\n \"changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path\",\n \"error, we rather handle it here than leave # exceptions\",\n \"snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile:\",\n \"change, which may require subsequent incremential snapshot and-or ignoring the\",\n \"started twice at # same time. While it is technically\",\n \"return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) #\",\n \"in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return\",\n \"FileExistsError: # This happens only if snapshot is started twice\",\n \"not even work. # Then, create/update corresponding snapshotfile objects (old\",\n \"import os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s\",\n \"from multiple threads, and the single-operation-only mode of operation is\",\n \"same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked()\",\n \"snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path\",\n \"sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None)\",\n \"*, src_dirs, dst_dirs): changes = 0 for i, relative_dir in\",\n \"lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared\",\n \"hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data =\",\n \"<= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return\",\n \"SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0\",\n \"and their hashes. The hash on disk MAY change, which\",\n \"try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only\",\n \"# same time. While it is technically speaking upstream #\",\n \"if self.src == self.dst: # The src=dst mode should be\",\n \"import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f,\",\n \"result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra progress.add_success()\",\n \"else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out):\",\n \"while True: data = f.read(read_buffer) if not data: break h.update(data)\",\n \"st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths):\",\n \"contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs,\",\n \"relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path)\",\n \"map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb,\",\n \"_cb(snapshotfile): # src may or may not be present; dst\",\n \"is not exactly flawless (the 'new operation can be started\",\n \"be made with snapshotter.lock held. This is because Snapshotter is\",\n \"to be built-in to Snapshotter, but having it there enables\",\n \"new directory: %r\\\", i, relative_dir) changes += 1 return changes\",\n \"not disappear between snapshot and # upload steps (e.g. Astacus\",\n \"# upload steps (e.g. Astacus controls the lifecycle of the\",\n \"mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost =\",\n \"True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We\",\n \"class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and\",\n \"object. Note that any call to public API MUST be\",\n \"import increase_worth_reporting, Progress from pathlib import Path from typing import\",\n \"{p.parent for p in files} return sorted(dirs), files def _add_snapshotfile(self,\",\n \"self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock() def\",\n \"%s disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue\",\n \"== snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same -\",\n \"# Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() #\",\n \"Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {}\",\n \"can be started with old running' is intentional feature but\",\n \"for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self):\",\n \"old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost\",\n \"== self.dst: # The src=dst mode should be used if\",\n \"f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class Snapshotter:\",\n \"self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64\",\n \"+= 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing\",\n \"\\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile,\",\n \"src may or may not be present; dst is present\",\n \"that is shared across operations, possibly used from multiple threads,\",\n \"details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc import\",\n \"+= 1 return changes def snapshot(self, *, progress: Optional[Progress] =\",\n \"track of files on disk, and their hashes. The hash\",\n \"be present; dst is present as it is in snapshot\",\n \"def _list_files(self, basepath: Path): result_files = set() for glob in\",\n \"The src=dst mode should be used if and only if\",\n \"and we can just use the src # directory contents\",\n \"ignoring the files which have changed. The output to outside\",\n \"relative_path) changes += 1 return changes def snapshot(self, *, progress:\",\n \"_snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st = src_path.stat()\",\n \"= 0 disappeared = 0 changes = 0 for i,\",\n \"1 return changes def snapshot(self, *, progress: Optional[Progress] = None):\",\n \"directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3)\",\n \"in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return\",\n \"disk MAY change, which may require subsequent incremential snapshot and-or\",\n \"src_path = self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path,\",\n \"changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files):\",\n \"snapshotter.lock held. This is because Snapshotter is process-wide utility that\",\n \"def _cb(snapshotfile): # src may or may not be present;\",\n \"Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {}\",\n \"= self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost +=\",\n \"self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path)\",\n \"Path from typing import Optional import base64 import hashlib import\",\n \"is process-wide utility that is shared across operations, possibly used\",\n \"0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src\",\n \"return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0\",\n \"in # making extra symlinks and we can just use\",\n \"import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from\",\n \"model has empty; either plugin or configuration must supply them\",\n \"it is # known that files will not disappear between\",\n \"parallel): assert globs # model has empty; either plugin or\",\n \"self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self,\",\n \"changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially\",\n \"try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if\",\n \"Note that any call to public API MUST be made\",\n \"+= 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\",\",\n \"incremential snapshot and-or ignoring the files which have changed. The\",\n \"Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add\",\n \"path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in\",\n \"within the file list contained in root object. Note that\",\n \"= self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes +=\",\n \"snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode()\",\n \"FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before\",\n \"return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0\",\n \"relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False)\",\n \"= hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success()\",\n \"self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files = set()\",\n \"from pathlib import Path from typing import Optional import base64\",\n \"list contained in root object. Note that any call to\",\n \"subsequent incremential snapshot and-or ignoring the files which have changed.\",\n \"might not need to be built-in to Snapshotter, but having\",\n \"ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new\",\n \"happens only if snapshot is started twice at # same\",\n \"of operation is not exactly flawless (the 'new operation can\",\n \"dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files)\",\n \"changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes\",\n \"there is little point in # making extra symlinks and\",\n \"data = f.read(read_buffer) if not data: break h.update(data) return h.hexdigest()\",\n \"'new operation can be started with old running' is intentional\",\n \"= self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs)\",\n \"might not even work. # Then, create/update corresponding snapshotfile objects\",\n \"This happens only if snapshot is started twice at #\",\n \"0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst\",\n \"is not # probably really worth it and due to\",\n \"dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1):\",\n \"if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest =\",\n \"= self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns,\",\n \"lost - %s disappeared before stat, ignoring\\\", lost, self.src /\",\n \"f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest\",\n \"self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs,\",\n \"assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in\",\n \"- %s disappeared before stat, ignoring\\\", lost, self.src / relative_path)\",\n \"_snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared = 0\",\n \"def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for\",\n \"typing import Optional import base64 import hashlib import logging import\",\n \"ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest =\",\n \"dirs = {p.parent for p in files} return sorted(dirs), files\",\n \"= self.src / relative_path dst_path = self.dst / relative_path try:\",\n \"snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if progress\",\n \"controls the lifecycle of the # files within). In that\",\n \"progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success()\",\n \"which may require subsequent incremential snapshot and-or ignoring the files\",\n \"Path): result_files = set() for glob in self.globs: for path\",\n \"self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles\",\n \"at # same time. While it is technically speaking upstream\",\n \"increase_worth_reporting, Progress from pathlib import Path from typing import Optional\",\n \"import logging import os import threading logger = logging.getLogger(__name__) _hash\",\n \"del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path =\",\n \"dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d.\",\n \"self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: #\",\n \"if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path)\",\n \"(old # ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)\",\n \"directory: %r\\\", i, relative_dir) changes += 1 return changes def\",\n \"feature but new operation should eventually replace the old). The\",\n \"only if it is # known that files will not\",\n \"relative_paths): same = 0 lost = 0 for relative_path in\",\n \"must supply them self.src = Path(src) self.dst = Path(dst) self.globs\",\n \"import Path from typing import Optional import base64 import hashlib\",\n \"def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st =\",\n \"result_files = set() for glob in self.globs: for path in\",\n \"# ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles\",\n \"SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ]\",\n \"= self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d.\",\n \"if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path]\",\n \"+= len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started\",\n \"extra directories, but it is not # probably really worth\",\n \"h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files\",\n \"if snapshot is started twice at # same time. While\",\n \"mode should be used if and only if it is\",\n \"if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i -\",\n \"self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file:\",\n \"def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True: data\",\n \"existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1\",\n \"snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out)\",\n \"as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if\",\n \"return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0\",\n \"progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success()\",\n \"astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress\",\n \"new operation should eventually replace the old). The lock itself\",\n \"/ relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def\",\n \"Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import\",\n \"0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1):\",\n \"old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same +=\",\n \"for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst /\",\n \"dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1):\",\n \"= self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst\",\n \"remove extra directories, but it is not # probably really\",\n \"map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles,\",\n \"changes += 1 return changes def snapshot(self, *, progress: Optional[Progress]\",\n \"glob in self.globs: for path in basepath.glob(glob): if not path.is_file()\",\n \"and # upload steps (e.g. Astacus controls the lifecycle of\",\n \"ones were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles =\",\n \"linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d.\",\n \"self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock\",\n \"self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile:\",\n \"utility that is shared across operations, possibly used from multiple\",\n \"follow_symlinks=False) except FileExistsError: # This happens only if snapshot is\",\n \"API MUST be made with snapshotter.lock held. This is because\",\n \"existing, src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared):\",\n \"call to public API MUST be made with snapshotter.lock held.\",\n \"except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared\",\n \"missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra\",\n \"not need to be built-in to Snapshotter, but having it\",\n \"not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent\",\n \"speaking upstream # error, we rather handle it here than\",\n \"self.lock.locked() if progress is None: progress = Progress() src_dirs, src_files\",\n \"is just root object's hash, as well as list of\",\n \"may or may not be present; dst is present as\",\n \"in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile\",\n \"plugin or configuration must supply them self.src = Path(src) self.dst\",\n \"little point in # making extra symlinks and we can\",\n \"or may not be present; dst is present as it\",\n \"relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile\",\n \"globs # model has empty; either plugin or configuration must\",\n \"during public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst,\",\n \"extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing\",\n \"Optional import base64 import hashlib import logging import os import\",\n \"hash, as well as list of other hashes which correspond\",\n \"started with old running' is intentional feature but new operation\",\n \"SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile)\",\n \"is because Snapshotter is process-wide utility that is shared across\",\n \"only if snapshot is started twice at # same time.\",\n \"dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes =\",\n \"_snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i, relative_path\",\n \"files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files\",\n \"worth it and due to ignored files it # actually\",\n \"held. This is because Snapshotter is process-wide utility that is\",\n \"snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d.\",\n \"1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared,\",\n \"break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of\",\n \"in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath =\",\n \"file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0\",\n \"snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i,\",\n \"src_path) continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d.\",\n \"*, progress: Optional[Progress] = None): assert self.lock.locked() if progress is\",\n \"files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also\",\n \"# We COULD also remove extra directories, but it is\",\n \"it and due to ignored files it # actually might\",\n \"of the # files within). In that case, there is\",\n \"*, read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer)\",\n \"progress: Optional[Progress] = None): assert self.lock.locked() if progress is None:\",\n \"symlinks and we can just use the src # directory\",\n \"disappeared, relative_path) changes += 1 return changes def snapshot(self, *,\",\n \"we can just use the src # directory contents as-is.\",\n \"= None): assert self.lock.locked() if progress is None: progress =\",\n \"*, src_files, dst_files): changes = 0 for i, relative_path in\",\n \"disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking,\",\n \"assert globs # model has empty; either plugin or configuration\",\n \"on disk MAY change, which may require subsequent incremential snapshot\",\n \"for details \\\"\\\"\\\" from astacus.common import magic, utils from astacus.common.ipc\",\n \"possibly used from multiple threads, and the single-operation-only mode of\",\n \"The output to outside is just root object's hash, as\",\n \"be started with old running' is intentional feature but new\",\n \"basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath)\",\n \"dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert\",\n \"handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed,\",\n \"disappeared, src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file:\",\n \"public API MUST be made with snapshotter.lock held. This is\",\n \"1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes =\",\n \"= self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile)\",\n \"# This happens only if snapshot is started twice at\",\n \"# probably really worth it and due to ignored files\",\n \"across operations, possibly used from multiple threads, and the single-operation-only\",\n \"self.src / relative_path dst_path = self.dst / relative_path try: os.link(src=src_path,\",\n \"process-wide utility that is shared across operations, possibly used from\",\n \"the lifecycle of the # files within). In that case,\",\n \"if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\",\",\n \"magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress\",\n \"twice at # same time. While it is technically speaking\",\n \"FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s\",\n \"exactly flawless (the 'new operation can be started with old\",\n \"return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes =\",\n \"MUST be made with snapshotter.lock held. This is because Snapshotter\",\n \"if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d.\",\n \"for p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile:\",\n \"operation is not exactly flawless (the 'new operation can be\",\n \"= set() for glob in self.globs: for path in basepath.glob(glob):\",\n \"already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared +=\",\n \"same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in\",\n \"that any call to public API MUST be made with\",\n \"SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib\",\n \"known that files will not disappear between snapshot and #\",\n \"making extra symlinks and we can just use the src\",\n \"0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile\",\n \"# actually might not even work. # Then, create/update corresponding\",\n \"if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes +=\",\n \"# We initially started with 1 extra progress.add_success() return changes\",\n \"result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath)\",\n \"its state during public API calls. \\\"\\\"\\\" def __init__(self, *,\",\n \"existing = 0 disappeared = 0 changes = 0 for\",\n \"= self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if\",\n \"dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile:\",\n \"create/update corresponding snapshotfile objects (old # ones were already removed)\",\n \"LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils from\",\n \"= hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while\",\n \"is # known that files will not disappear between snapshot\",\n \"hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return\",\n \"Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk, and their\",\n \"(the 'new operation can be started with old running' is\",\n \"changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files):\",\n \"%r\\\", i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self,\",\n \"The lock itself might not need to be built-in to\",\n \"enables asserting its state during public API calls. \\\"\\\"\\\" def\",\n \"either plugin or configuration must supply them self.src = Path(src)\",\n \"if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def\",\n \"Optional[Progress] = None): assert self.lock.locked() if progress is None: progress\",\n \"corresponding snapshotfile objects (old # ones were already removed) dst_dirs,\",\n \"snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64\",\n \"files which have changed. The output to outside is just\",\n \"calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert\",\n \"really worth it and due to ignored files it #\",\n \"# Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() #\",\n \"in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except\",\n \"dst_files): existing = 0 disappeared = 0 changes = 0\",\n \"_result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles)\",\n \"if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\",\",\n \"+= self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra\",\n \"# Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() #\",\n \"import hashlib import logging import os import threading logger =\",\n \"relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files)\",\n \"it # actually might not even work. # Then, create/update\",\n \"progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel)\",\n \"dst_files=dst_files) progress.add_success() # We COULD also remove extra directories, but\",\n \"1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i):\",\n \"read_buffer=1_000_000): h = _hash() while True: data = f.read(read_buffer) if\",\n \"hash on disk MAY change, which may require subsequent incremential\",\n \"self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self,\",\n \"upload steps (e.g. Astacus controls the lifecycle of the #\",\n \"their hashes. The hash on disk MAY change, which may\",\n \"for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile =\",\n \"return changes def snapshot(self, *, progress: Optional[Progress] = None): assert\",\n \"for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src /\",\n \"src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing\",\n \"%r in %s is same\\\", same, old_snapshotfile, relative_path) continue yield\",\n \"is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self):\",\n \"path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break\",\n \"self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src\",\n \"same = 0 lost = 0 for relative_path in relative_paths:\",\n \"# Then, create/update corresponding snapshotfile objects (old # ones were\",\n \"dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if snapshot\",\n \"= self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra\",\n \"threading.Lock() def _list_files(self, basepath: Path): result_files = set() for glob\",\n \"\\\"\\\"\\\" Copyright (c) 2020 Aiven Ltd See LICENSE for details\",\n \"= Path(dst) self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles =\",\n \"is intentional feature but new operation should eventually replace the\",\n \"snapshotfile objects (old # ones were already removed) dst_dirs, dst_files\",\n \"relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name ==\",\n \"changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path\",\n \"base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*, map_in,\",\n \"same - %r in %s is same\\\", same, old_snapshotfile, relative_path)\",\n \"dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs,\",\n \"replace the old). The lock itself might not need to\",\n \"if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\",\",\n \"be built-in to Snapshotter, but having it there enables asserting\",\n \"enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)\",\n \"globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel\",\n \"path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue relpath\",\n \"# making extra symlinks and we can just use the\",\n \"old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same):\",\n \"in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True)\",\n \"self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove extra directories,\",\n \"as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else:\",\n \"See LICENSE for details \\\"\\\"\\\" from astacus.common import magic, utils\",\n \"same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert\",\n \"to Snapshotter, but having it there enables asserting its state\",\n \"logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue if\",\n \"relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if increase_worth_reporting(i):\",\n \"= logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h\",\n \"intentional feature but new operation should eventually replace the old).\",\n \"new file: %r\\\", i - disappeared, relative_path) changes += 1\",\n \"it is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size\",\n \"as well as list of other hashes which correspond to\",\n \"self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile\",\n \"if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile)\",\n \"handle it here than leave # exceptions not handled. existing\",\n \"and-or ignoring the files which have changed. The output to\",\n \"sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs =\",\n \"logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h =\",\n \"src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The\",\n \"/ relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This\",\n \"extra symlinks and we can just use the src #\",\n \"old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile ==\",\n \"lost = 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path)\",\n \"progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not be\",\n \"= Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile =\",\n \"which have changed. The output to outside is just root\",\n \"increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost,\",\n \"hashes which correspond to files referred to within the file\",\n \"because Snapshotter is process-wide utility that is shared across operations,\",\n \"the single-operation-only mode of operation is not exactly flawless (the\",\n \"src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create\",\n \"1): dst_path = self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if\",\n \"were already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files))\",\n \"src_files, dst_files): changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files),\",\n \"dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files)\",\n \"assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs):\",\n \"use the src # directory contents as-is. dst_dirs, dst_files =\",\n \"files on disk, and their hashes. The hash on disk\",\n \"# error, we rather handle it here than leave #\",\n \"_list_files(self, basepath: Path): result_files = set() for glob in self.globs:\",\n \"= 0 changes = 0 for i, relative_path in enumerate(set(src_files).difference(dst_files),\",\n \"but it is not # probably really worth it and\",\n \"should eventually replace the old). The lock itself might not\",\n \"leave # exceptions not handled. existing += 1 if increase_worth_reporting(existing):\",\n \"not # probably really worth it and due to ignored\",\n \"and only if it is # known that files will\",\n \"\\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel): assert globs\",\n \"configuration must supply them self.src = Path(src) self.dst = Path(dst)\",\n \"/ relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 =\",\n \"self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest:\",\n \"/ relative_path dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path,\",\n \"changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD also remove\",\n \"disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i -\",\n \"None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if\",\n \"due to ignored files it # actually might not even\",\n \"- %r in %s is same\\\", same, old_snapshotfile, relative_path) continue\",\n \"def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes +=\",\n \"snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert\",\n \"if it is # known that files will not disappear\",\n \"*, src, dst, globs, parallel): assert globs # model has\",\n \"self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes += self._snapshot_remove_extra_files(src_files=src_files,\",\n \"_hash() while True: data = f.read(read_buffer) if not data: break\",\n \"= 0 for i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path =\",\n \"progress.start(1) if self.src == self.dst: # The src=dst mode should\",\n \"path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents: if\",\n \"not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already\",\n \"0 lost = 0 for relative_path in relative_paths: old_snapshotfile =\",\n \"if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src /\",\n \"increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared,\",\n \"self._list_files(basepath) dirs = {p.parent for p in files} return sorted(dirs),\",\n \"0 disappeared = 0 changes = 0 for i, relative_path\",\n \"else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files =\",\n \"technically speaking upstream # error, we rather handle it here\",\n \"here than leave # exceptions not handled. existing += 1\",\n \"outside is just root object's hash, as well as list\",\n \"asserting its state during public API calls. \\\"\\\"\\\" def __init__(self,\",\n \"None): assert self.lock.locked() if progress is None: progress = Progress()\",\n \"case, there is little point in # making extra symlinks\",\n \"point in # making extra symlinks and we can just\",\n \"get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf\",\n \"data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track\",\n \"if progress is None: progress = Progress() src_dirs, src_files =\",\n \"hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash() while True:\",\n \"_hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000): h = _hash()\",\n \"any call to public API MUST be made with snapshotter.lock\",\n \"assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def\",\n \"parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath)\",\n \"True: data = f.read(read_buffer) if not data: break h.update(data) return\",\n \"enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path = self.dst\",\n \"# model has empty; either plugin or configuration must supply\",\n \"basepath: Path): result_files = set() for glob in self.globs: for\",\n \"yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size)\",\n \"be used if and only if it is # known\",\n \"%r\\\", i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self,\",\n \"logger.debug(\\\"#%d. same - %r in %s is same\\\", same, old_snapshotfile,\",\n \"just root object's hash, as well as list of other\",\n \"= 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path =\",\n \"logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *, read_buffer=1_000_000):\",\n \"return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs\",\n \"dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir)\",\n \"self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes\",\n \"continue relpath = path.relative_to(basepath) for parent in relpath.parents: if parent.name\",\n \"of files on disk, and their hashes. The hash on\",\n \"eventually replace the old). The lock itself might not need\",\n \"not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps\",\n \"basepath: Path): files = self._list_files(basepath) dirs = {p.parent for p\",\n \"self.src == self.dst: # The src=dst mode should be used\",\n \"import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from\",\n \"self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path = self.src / relative_path st\",\n \"relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError:\",\n \"disappeared before stat, ignoring\\\", lost, self.src / relative_path) continue if\",\n \"not be present; dst is present as it is in\",\n \"it is technically speaking upstream # error, we rather handle\",\n \"files will not disappear between snapshot and # upload steps\",\n \"= f.read(read_buffer) if not data: break h.update(data) return h.hexdigest() class\",\n \"with old running' is intentional feature but new operation should\",\n \"len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with\",\n \"def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i,\",\n \"self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath: Path):\",\n \"+= 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s\",\n \"work. # Then, create/update corresponding snapshotfile objects (old # ones\",\n \"= src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same\",\n \"Add missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We\",\n \"%r\\\", i - disappeared, relative_path) changes += 1 return changes\",\n \"self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost\",\n \"import base64 import hashlib import logging import os import threading\",\n \"+= 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing,\",\n \"and the single-operation-only mode of operation is not exactly flawless\",\n \"magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile\",\n \"lifecycle of the # files within). In that case, there\",\n \"operations, possibly used from multiple threads, and the single-operation-only mode\",\n \"list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may not\",\n \"= snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile):\",\n \"except FileNotFoundError: lost += 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost -\",\n \"= self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src\",\n \"to within the file list contained in root object. Note\",\n \"relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *, src_files,\",\n \"self.dst / relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink()\",\n \"fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1 extra\",\n \"is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1)\",\n \"relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64\",\n \"[ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf\",\n \"for glob in self.globs: for path in basepath.glob(glob): if not\",\n \"the file list contained in root object. Note that any\",\n \"_remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if\",\n \"i, relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *,\",\n \"single-operation-only mode of operation is not exactly flawless (the 'new\",\n \"changes def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked()\",\n \"self.globs: for path in basepath.glob(glob): if not path.is_file() or path.is_symlink():\",\n \"disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes\",\n \"empty; either plugin or configuration must supply them self.src =\",\n \"root object's hash, as well as list of other hashes\",\n \"ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared += 1 if\",\n \"disk, and their hashes. The hash on disk MAY change,\",\n \"upstream # error, we rather handle it here than leave\",\n \"used if and only if it is # known that\",\n \"= base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def _result_cb(*,\",\n \"files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for\",\n \"snapshot and # upload steps (e.g. Astacus controls the lifecycle\",\n \"is shared across operations, possibly used from multiple threads, and\",\n \"old_snapshotfile, relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return\",\n \"parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files =\",\n \"present; dst is present as it is in snapshot with\",\n \"self.relative_path_to_snapshotfile.get(relative_path) try: snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1\",\n \"else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories\",\n \"return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on\",\n \"self.globs = globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel\",\n \"parent.name == magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self,\",\n \"This is because Snapshotter is process-wide utility that is shared\",\n \"self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: # The src=dst mode\",\n \"utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import\",\n \"self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes += self._snapshot_add_missing_files(src_files=src_files,\",\n \"relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i,\",\n \"logging import os import threading logger = logging.getLogger(__name__) _hash =\",\n \"files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile =\",\n \"is technically speaking upstream # error, we rather handle it\",\n \"actually might not even work. # Then, create/update corresponding snapshotfile\",\n \"i, relative_dir) changes += 1 return changes def _snapshot_remove_extra_files(self, *,\",\n \"to public API MUST be made with snapshotter.lock held. This\",\n \"to outside is just root object's hash, as well as\",\n \"with snapshotter.lock held. This is because Snapshotter is process-wide utility\",\n \"self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may\",\n \"list of other hashes which correspond to files referred to\",\n \"same time. While it is technically speaking upstream # error,\",\n \"flawless (the 'new operation can be started with old running'\",\n \"files referred to within the file list contained in root\",\n \"directories, but it is not # probably really worth it\",\n \"return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path,\",\n \"lock itself might not need to be built-in to Snapshotter,\",\n \"but new operation should eventually replace the old). The lock\",\n \"if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue\",\n \"# The src=dst mode should be used if and only\",\n \"as-is. dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files\",\n \"operation should eventually replace the old). The lock itself might\",\n \"API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs, parallel):\",\n \"to files referred to within the file list contained in\",\n \"%s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile def\",\n \"{} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock = threading.Lock()\",\n \"= self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError:\",\n \"before stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile:\",\n \"dst_path.unlink() if increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes\",\n \"if not path.is_file() or path.is_symlink(): continue relpath = path.relative_to(basepath) for\",\n \"continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig,\",\n \"continue except FileNotFoundError: disappeared += 1 if increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s\",\n \"= {} self.hexdigest_to_snapshotfiles = {} self.parallel = parallel self.lock =\",\n \"require subsequent incremential snapshot and-or ignoring the files which have\",\n \"changes = 0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path\",\n \"in %s is same\\\", same, old_snapshotfile, relative_path) continue yield snapshotfile\",\n \"on disk, and their hashes. The hash on disk MAY\",\n \"if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile\",\n \"src # directory contents as-is. dst_dirs, dst_files = src_dirs, src_files\",\n \"even work. # Then, create/update corresponding snapshotfile objects (old #\",\n \"relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self,\",\n \"exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s\",\n \"have changed. The output to outside is just root object's\",\n \"relative_path): src_path = self.src / relative_path st = src_path.stat() return\",\n \"# known that files will not disappear between snapshot and\",\n \"i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path\",\n \"may not be present; dst is present as it is\",\n \"# directory contents as-is. dst_dirs, dst_files = src_dirs, src_files else:\",\n \"== magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath:\",\n \"sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def\",\n \"snapshot is started twice at # same time. While it\",\n \"for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst /\",\n \"= 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path =\",\n \"disappeared = 0 changes = 0 for i, relative_path in\",\n \"hashlib import logging import os import threading logger = logging.getLogger(__name__)\",\n \"correspond to files referred to within the file list contained\",\n \"return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if\",\n \"Snapshotter is process-wide utility that is shared across operations, possibly\",\n \"files = self._list_files(basepath) dirs = {p.parent for p in files}\",\n \"Path): files = self._list_files(basepath) dirs = {p.parent for p in\",\n \"progress is None: progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src)\",\n \"also remove extra directories, but it is not # probably\",\n \"set() for glob in self.globs: for path in basepath.glob(glob): if\",\n \"files it # actually might not even work. # Then,\",\n \"%s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError: disappeared\",\n \"from astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash,\",\n \"file list contained in root object. Note that any call\",\n \"*, src_files, dst_files): existing = 0 disappeared = 0 changes\",\n \"n=self.parallel) # We initially started with 1 extra progress.add_success() return\",\n \"progress = Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src\",\n \"snapshotfile = self._snapshotfile_from_path(relative_path) except FileNotFoundError: lost += 1 if increase_worth_reporting(lost):\",\n \"is in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <=\",\n \"object's hash, as well as list of other hashes which\",\n \"the files which have changed. The output to outside is\",\n \"old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def\",\n \"in snapshot with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE:\",\n \"src_files, dst_files): existing = 0 disappeared = 0 changes =\",\n \"(c) 2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\" from\",\n \"can just use the src # directory contents as-is. dst_dirs,\",\n \"increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except\",\n \"def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared =\",\n \"is present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as\",\n \"# src may or may not be present; dst is\",\n \"stat, ignoring\\\", lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest\",\n \"in self.globs: for path in basepath.glob(glob): if not path.is_file() or\",\n \"dst_path = self.dst / relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except\",\n \"keeps track of files on disk, and their hashes. The\",\n \"self._add_snapshotfile(map_out) progress.add_success() return True changes += len(snapshotfiles) utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb,\",\n \"globs, parallel): assert globs # model has empty; either plugin\",\n \"within). In that case, there is little point in #\",\n \"i - disappeared, relative_path) changes += 1 return changes def\",\n \"relative_path try: os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens\",\n \"if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes +=\",\n \"hashes. The hash on disk MAY change, which may require\",\n \"in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path =\",\n \"SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same = 0 lost\",\n \"old_snapshotfile == snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same\",\n \"= globs self.relative_path_to_snapshotfile = {} self.hexdigest_to_snapshotfiles = {} self.parallel =\",\n \"we rather handle it here than leave # exceptions not\",\n \"contained in root object. Note that any call to public\",\n \"self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items()\",\n \"SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path\",\n \"old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile\",\n \"files within). In that case, there is little point in\",\n \"+= 1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before\",\n \"i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir\",\n \"- disappeared): logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path)\",\n \"from typing import Optional import base64 import hashlib import logging\",\n \"dst_dirs, dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files =\",\n \"_list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent for\",\n \"== snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path):\",\n \"continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if\",\n \"snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for\",\n \"size=sf[0].file_size) for dig, sf in self.hexdigest_to_snapshotfiles.items() if sf ] def\",\n \"logger.debug(\\\"#%d. new file: %r\\\", i - disappeared, relative_path) changes +=\",\n \"dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): #\",\n \"Then, create/update corresponding snapshotfile objects (old # ones were already\",\n \"The hash on disk MAY change, which may require subsequent\",\n \"# files within). In that case, there is little point\",\n \"may require subsequent incremential snapshot and-or ignoring the files which\",\n \"= {p.parent for p in files} return sorted(dirs), files def\",\n \"snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1\",\n \"need to be built-in to Snapshotter, but having it there\",\n \"= old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same += 1 if\",\n \"def get_snapshot_hashes(self): assert self.lock.locked() return [ SnapshotHash(hexdigest=dig, size=sf[0].file_size) for dig,\",\n \"def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *,\",\n \"logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1 return\",\n \"as list of other hashes which correspond to files referred\",\n \"is started twice at # same time. While it is\",\n \"of other hashes which correspond to files referred to within\",\n \"for path in basepath.glob(glob): if not path.is_file() or path.is_symlink(): continue\",\n \"_get_snapshot_hash_list(self, relative_paths): same = 0 lost = 0 for relative_path\",\n \"changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing = 0 disappeared\",\n \"just use the src # directory contents as-is. dst_dirs, dst_files\",\n \"increase_worth_reporting(disappeared): logger.debug(\\\"#%d. %s disappeared before linking, ignoring\\\", disappeared, src_path) continue\",\n \"them self.src = Path(src) self.dst = Path(dst) self.globs = globs\",\n \"root object. Note that any call to public API MUST\",\n \"logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path) continue except FileNotFoundError:\",\n \"or configuration must supply them self.src = Path(src) self.dst =\",\n \"it there enables asserting its state during public API calls.\",\n \"relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path dst_path\",\n \"def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for i,\",\n \"os.link(src=src_path, dst=dst_path, follow_symlinks=False) except FileExistsError: # This happens only if\",\n \"files def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if\",\n \"[]).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del\",\n \"snapshotfile: same += 1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r\",\n \"than leave # exceptions not handled. existing += 1 if\",\n \"already removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles))\",\n \"exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes\",\n \"utils.parallel_map_to(iterable=snapshotfiles, fun=_cb, result_callback=_result_cb, n=self.parallel) # We initially started with 1\",\n \"should be used if and only if it is #\",\n \"in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst / relative_path snapshotfile =\",\n \"snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest:\",\n \"snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f) return snapshotfile def\",\n \"itself might not need to be built-in to Snapshotter, but\",\n \"1 return changes def _snapshot_add_missing_files(self, *, src_files, dst_files): existing =\",\n \"to ignored files it # actually might not even work.\",\n \"MAY change, which may require subsequent incremential snapshot and-or ignoring\",\n \"def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] == snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path]\",\n \"# exceptions not handled. existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d.\",\n \"= src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) #\",\n \"snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True changes\",\n \"existing += 1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\",\",\n \"Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst:\",\n \"with snapshotfile.open_for_reading(self.dst) as f: if snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 =\",\n \"self.src / relative_path st = src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size)\",\n \"rather handle it here than leave # exceptions not handled.\",\n \"i, relative_path in enumerate(set(src_files).difference(dst_files), 1): src_path = self.src / relative_path\",\n \"objects (old # ones were already removed) dst_dirs, dst_files =\",\n \"from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting,\",\n \"2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common\",\n \"snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest, []).append(snapshotfile) def _remove_snapshotfile(self, snapshotfile: SnapshotFile): assert self.relative_path_to_snapshotfile[snapshotfile.relative_path] ==\",\n \"self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory:\",\n \"shared across operations, possibly used from multiple threads, and the\",\n \"between snapshot and # upload steps (e.g. Astacus controls the\",\n \"return snapshotfile def _result_cb(*, map_in, map_out): self._add_snapshotfile(map_out) progress.add_success() return True\",\n \"old running' is intentional feature but new operation should eventually\",\n \"there enables asserting its state during public API calls. \\\"\\\"\\\"\",\n \"ignored files it # actually might not even work. #\",\n \"While it is technically speaking upstream # error, we rather\",\n \"Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove\",\n \"= 0 for relative_path in relative_paths: old_snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) try:\",\n \"if sf ] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values()))\",\n \"built-in to Snapshotter, but having it there enables asserting its\",\n \"snapshot and-or ignoring the files which have changed. The output\",\n \"if and only if it is # known that files\",\n \"dst is present as it is in snapshot with snapshotfile.open_for_reading(self.dst)\",\n \"the # files within). In that case, there is little\",\n \"snapshotfile.file_size <= magic.EMBEDDED_FILE_SIZE: snapshotfile.content_b64 = base64.b64encode(f.read()).decode() else: snapshotfile.hexdigest = hash_hexdigest_readable(f)\",\n \"threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def hash_hexdigest_readable(f, *,\",\n \"operation can be started with old running' is intentional feature\",\n \"not exactly flawless (the 'new operation can be started with\",\n \"= self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new\",\n \"src_path = self.src / relative_path dst_path = self.dst / relative_path\",\n \"continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\", i\",\n \"/ relative_dir dst_path.mkdir(parents=True, exist_ok=True) if increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\",\",\n \"relative_path) continue yield snapshotfile def get_snapshot_hashes(self): assert self.lock.locked() return [\",\n \"output to outside is just root object's hash, as well\",\n \"before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i - disappeared):\",\n \"get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self, *, src_dirs,\",\n \"steps (e.g. Astacus controls the lifecycle of the # files\",\n \"from astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from\",\n \"changed. The output to outside is just root object's hash,\",\n \"or path.is_symlink(): continue relpath = path.relative_to(basepath) for parent in relpath.parents:\",\n \"Progress from pathlib import Path from typing import Optional import\",\n \"dst, globs, parallel): assert globs # model has empty; either\",\n \"for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR: break else:\",\n \"increase_worth_reporting(i): logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1\",\n \"that files will not disappear between snapshot and # upload\",\n \"time. While it is technically speaking upstream # error, we\",\n \"_snapshot_create_missing_directories(self, *, src_dirs, dst_dirs): changes = 0 for i, relative_dir\",\n \"which correspond to files referred to within the file list\",\n \"] def get_snapshot_state(self): assert self.lock.locked() return SnapshotState(root_globs=self.globs, files=sorted(self.relative_path_to_snapshotfile.values())) def _snapshot_create_missing_directories(self,\",\n \"has empty; either plugin or configuration must supply them self.src\",\n \"h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter keeps track of files on disk,\",\n \"= Progress() src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src ==\",\n \"relative_dir in enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True,\",\n \"self._list_dirs_and_files(self.dst) # Create missing directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success()\",\n \"and due to ignored files it # actually might not\",\n \"in root object. Note that any call to public API\",\n \"(e.g. Astacus controls the lifecycle of the # files within).\",\n \"state during public API calls. \\\"\\\"\\\" def __init__(self, *, src,\",\n \"def _add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile:\",\n \"used from multiple threads, and the single-operation-only mode of operation\",\n \"the src # directory contents as-is. dst_dirs, dst_files = src_dirs,\",\n \"progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) # Create missing directories changes\",\n \"We COULD also remove extra directories, but it is not\",\n \"logger.debug(\\\"#%d. lost - %s disappeared before stat, ignoring\\\", lost, self.src\",\n \"threads, and the single-operation-only mode of operation is not exactly\",\n \"supply them self.src = Path(src) self.dst = Path(dst) self.globs =\",\n \"public API calls. \\\"\\\"\\\" def __init__(self, *, src, dst, globs,\",\n \"1): src_path = self.src / relative_path dst_path = self.dst /\",\n \"None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] = snapshotfile if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles.setdefault(snapshotfile.hexdigest,\",\n \"increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is same\\\", same,\",\n \"Astacus controls the lifecycle of the # files within). In\",\n \"1 if increase_worth_reporting(lost): logger.debug(\\\"#%d. lost - %s disappeared before stat,\",\n \"snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or\",\n \"sf in self.hexdigest_to_snapshotfiles.items() if sf ] def get_snapshot_state(self): assert self.lock.locked()\",\n \"other hashes which correspond to files referred to within the\",\n \"src, dst, globs, parallel): assert globs # model has empty;\",\n \"break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path): files\",\n \"assert self.lock.locked() if progress is None: progress = Progress() src_dirs,\",\n \"__init__(self, *, src, dst, globs, parallel): assert globs # model\",\n \"relative_path) changes += 1 return changes def _snapshot_add_missing_files(self, *, src_files,\",\n \"= threading.Lock() def _list_files(self, basepath: Path): result_files = set() for\",\n \"base64 import hashlib import logging import os import threading logger\",\n \"= 0 lost = 0 for relative_path in relative_paths: old_snapshotfile\",\n \"running' is intentional feature but new operation should eventually replace\",\n \"extra file: %r\\\", i, relative_path) changes += 1 return changes\",\n \"SnapshotHash, SnapshotState from astacus.common.progress import increase_worth_reporting, Progress from pathlib import\",\n \"referred to within the file list contained in root object.\",\n \"but having it there enables asserting its state during public\",\n \"disappear between snapshot and # upload steps (e.g. Astacus controls\",\n \"import Optional import base64 import hashlib import logging import os\",\n \"os import threading logger = logging.getLogger(__name__) _hash = hashlib.blake2s def\",\n \"1 if increase_worth_reporting(existing): logger.debug(\\\"#%d. %s already existed, ignoring\\\", existing, src_path)\",\n \"probably really worth it and due to ignored files it\",\n \"that case, there is little point in # making extra\",\n \"missing files changes += self._snapshot_add_missing_files(src_files=src_files, dst_files=dst_files) progress.add_success() # We COULD\",\n \"h = _hash() while True: data = f.read(read_buffer) if not\",\n \"snapshotfile del self.relative_path_to_snapshotfile[snapshotfile.relative_path] if snapshotfile.hexdigest: self.hexdigest_to_snapshotfiles[snapshotfile.hexdigest].remove(snapshotfile) def _snapshotfile_from_path(self, relative_path): src_path\",\n \"0 for i, relative_path in enumerate(set(dst_files).difference(src_files), 1): dst_path = self.dst\",\n \"/ relative_path snapshotfile = self.relative_path_to_snapshotfile.get(relative_path) if snapshotfile: self._remove_snapshotfile(snapshotfile) dst_path.unlink() if\",\n \"progress.add_success() # We COULD also remove extra directories, but it\",\n \"increase_worth_reporting(i): logger.debug(\\\"#%d. new directory: %r\\\", i, relative_dir) changes += 1\",\n \"= old_snapshotfile.hexdigest snapshotfile.content_b64 = old_snapshotfile.content_b64 if old_snapshotfile == snapshotfile: same\",\n \"file: %r\\\", i - disappeared, relative_path) changes += 1 return\",\n \"astacus.common.progress import increase_worth_reporting, Progress from pathlib import Path from typing\",\n \"{} self.parallel = parallel self.lock = threading.Lock() def _list_files(self, basepath:\",\n \"old). The lock itself might not need to be built-in\",\n \"- disappeared, relative_path) changes += 1 return changes def snapshot(self,\",\n \"file: %r\\\", i, relative_path) changes += 1 return changes def\",\n \"src_path.stat() return SnapshotFile(relative_path=relative_path, mtime_ns=st.st_mtime_ns, file_size=st.st_size) def _get_snapshot_hash_list(self, relative_paths): same =\",\n \"dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile):\",\n \"= list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def _cb(snapshotfile): # src may or may\",\n \"src_dirs, src_files = self._list_dirs_and_files(self.src) progress.start(1) if self.src == self.dst: #\",\n \"changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files changes\",\n \"COULD also remove extra directories, but it is not #\",\n \"= parallel self.lock = threading.Lock() def _list_files(self, basepath: Path): result_files\",\n \"Snapshotter, but having it there enables asserting its state during\",\n \"SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path] =\",\n \"directories changes = self._snapshot_create_missing_directories(src_dirs=src_dirs, dst_dirs=dst_dirs) progress.add_success() # Remove extra files\",\n \"src_dirs, dst_dirs): changes = 0 for i, relative_dir in enumerate(set(src_dirs).difference(dst_dirs),\",\n \"dst_files = src_dirs, src_files else: progress.add_total(3) dst_dirs, dst_files = self._list_dirs_and_files(self.dst)\",\n \"multiple threads, and the single-operation-only mode of operation is not\",\n \"will not disappear between snapshot and # upload steps (e.g.\",\n \"= _hash() while True: data = f.read(read_buffer) if not data:\",\n \"magic.ASTACUS_TMPDIR: break else: result_files.add(relpath) return sorted(result_files) def _list_dirs_and_files(self, basepath: Path):\",\n \"In that case, there is little point in # making\",\n \"+= self._snapshot_remove_extra_files(src_files=src_files, dst_files=dst_files) progress.add_success() # Add missing files changes +=\",\n \"having it there enables asserting its state during public API\",\n \"Ltd See LICENSE for details \\\"\\\"\\\" from astacus.common import magic,\",\n \"logger.debug(\\\"#%d. extra file: %r\\\", i, relative_path) changes += 1 return\",\n \"+= 1 return changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes\",\n \"src=dst mode should be used if and only if it\",\n \"src_path) continue if increase_worth_reporting(i - disappeared): logger.debug(\\\"#%d. new file: %r\\\",\",\n \"p in files} return sorted(dirs), files def _add_snapshotfile(self, snapshotfile: SnapshotFile):\",\n \"enumerate(set(src_dirs).difference(dst_dirs), 1): dst_path = self.dst / relative_dir dst_path.mkdir(parents=True, exist_ok=True) if\",\n \"made with snapshotter.lock held. This is because Snapshotter is process-wide\",\n \"self.src = Path(src) self.dst = Path(dst) self.globs = globs self.relative_path_to_snapshotfile\",\n \"def snapshot(self, *, progress: Optional[Progress] = None): assert self.lock.locked() if\",\n \"the old). The lock itself might not need to be\",\n \"lost, self.src / relative_path) continue if old_snapshotfile: snapshotfile.hexdigest = old_snapshotfile.hexdigest\",\n \"changes def _snapshot_remove_extra_files(self, *, src_files, dst_files): changes = 0 for\",\n \"def _list_dirs_and_files(self, basepath: Path): files = self._list_files(basepath) dirs = {p.parent\",\n \"mode of operation is not exactly flawless (the 'new operation\",\n \"= path.relative_to(basepath) for parent in relpath.parents: if parent.name == magic.ASTACUS_TMPDIR:\",\n \"= self._list_files(basepath) dirs = {p.parent for p in files} return\",\n \"= {} self.parallel = parallel self.lock = threading.Lock() def _list_files(self,\",\n \"1 if increase_worth_reporting(same): logger.debug(\\\"#%d. same - %r in %s is\",\n \"Copyright (c) 2020 Aiven Ltd See LICENSE for details \\\"\\\"\\\"\",\n \"pathlib import Path from typing import Optional import base64 import\",\n \"self.dst: # The src=dst mode should be used if and\",\n \"_add_snapshotfile(self, snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile)\",\n \"is little point in # making extra symlinks and we\",\n \"it is not # probably really worth it and due\",\n \"well as list of other hashes which correspond to files\",\n \"%s disappeared before linking, ignoring\\\", disappeared, src_path) continue if increase_worth_reporting(i\",\n \"removed) dst_dirs, dst_files = self._list_dirs_and_files(self.dst) snapshotfiles = list(self._get_snapshot_hash_list(dst_files)) progress.add_total(len(snapshotfiles)) def\",\n \"present as it is in snapshot with snapshotfile.open_for_reading(self.dst) as f:\",\n \"except FileExistsError: # This happens only if snapshot is started\",\n \"snapshotfile: SnapshotFile): old_snapshotfile = self.relative_path_to_snapshotfile.get(snapshotfile.relative_path, None) if old_snapshotfile: self._remove_snapshotfile(old_snapshotfile) self.relative_path_to_snapshotfile[snapshotfile.relative_path]\",\n \"it here than leave # exceptions not handled. existing +=\",\n \"if not data: break h.update(data) return h.hexdigest() class Snapshotter: \\\"\\\"\\\"Snapshotter\",\n \"def __init__(self, *, src, dst, globs, parallel): assert globs #\",\n \"astacus.common import magic, utils from astacus.common.ipc import SnapshotFile, SnapshotHash, SnapshotState\"\n]"}}},{"rowIdx":3499,"cells":{"ngram":{"kind":"list like","value":["for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir,","tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base,","return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async","bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in","[args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd +=","the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if","import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\"","if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts(","from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task","import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable","out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames","classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook(","src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames:","= args.install_base # invoke build step return await run( self.context,","= [] # add jars and classes to CLASSPATH with","Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is","# remove anything on the destination tree but not in","+= [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd","src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in","build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join(","= self.context.pkg # remove anything on the destination tree but","self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is","src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir)","update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif","self.context.pkg # remove anything on the destination tree but not","os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames =","env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as","set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in","https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,","rc and rc.returncode: return rc.returncode rc = await self._install(args, env)","nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments","build step return await run( self.context, cmd, cwd=args.build_base, env=env) async","pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') #","is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could","__init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path):","'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task","# add jars and classes to CLASSPATH with wildcards #","with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name,","colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import","out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args',","os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base),","dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames -","= dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for","in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in","package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = []","prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir,","cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd =","the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) #","= os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames","os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory","GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint):","self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames","async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False","distutils import dir_util import glob import os from pathlib import","for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for","exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree(","for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames:","parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass","os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build',","src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames","and rc.returncode: return rc.returncode rc = await self._install(args, env) if","the default task') async def build( # noqa: D102 self,","'*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',","- bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in","get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from","start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path))","import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging","run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from","os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java')","\" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of","the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base,","in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):","args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())]","as e: logger.error(str(e)) return 1 rc = await self._build(args, env)","cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle'","= dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for","'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree(","directory to the build one to avoid # polluting the","not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not","def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*',","\"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION,"," # Licensed under the Apache License, Version 2.0 from","_build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname,","but not in the source tree src_package_src_dir = os.path.join(args.path, 'src')","'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH',","dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames","Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task]","['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] =","environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step","2.0 from distutils import dir_util import glob import os from","in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return","'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env","Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import","env) if rc and rc.returncode: return rc.returncode rc = await","shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to","None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find","- src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname","bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share',","in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] #","matching other options must be prefixed by a space,\\n' 'e.g.","os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames =","dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for","import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class","from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle","args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if","= os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames","import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint","args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove","from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell","get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger","rc = await self._install(args, env) if rc and rc.returncode: return","- src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in","dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename","pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames,","'--gradle-task', help='Run a specific task instead of the default task')","files from the source directory to the build one to","additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java',","= self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames =","self._install(args, env) if rc and rc.returncode: return rc.returncode if not","source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src')","has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def","instead of the default task') async def build( # noqa:","os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree(","colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import","RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") # Gradle","if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')):","and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks +=","and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args,","skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env):","to the build one to avoid # polluting the latter","Version 2.0 from distutils import dir_util import glob import os","from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger =","additional_hooks = [] # add jars and classes to CLASSPATH","rc.returncode: return rc.returncode rc = await self._install(args, env) if rc","colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import","from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build","def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove","Apache License, Version 2.0 from distutils import dir_util import glob","tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base,","if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] #","def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for","avoid # polluting the latter during the build process dir_util.copy_tree(args.path,","has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd","be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument(","+= create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend')","Copyright 2018 # Licensed under the Apache License, Version","dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath(","rc = await self._build(args, env) if rc and rc.returncode: return","= self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks","--help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the","satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames =","args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle","'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True)","projects. ' 'Arguments matching other options must be prefixed by","add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*',","subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename),","prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir,","['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd","out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self,","args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc","'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir,","env): self.progress('install') pkg = self.context.pkg # remove anything on the","else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\")","prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames","to avoid # polluting the latter during the build process","from distutils import dir_util import glob import os from pathlib","return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102","= self.context.pkg args = self.context.args logger.info( \"Building Gradle package in","GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__()","in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir","from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task","space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific","mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except","# noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments","pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name,","cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg =","out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add(","import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run","# Gradle Task (by default 'assemble') if args.gradle_task: cmd +=","return 1 rc = await self._build(args, env) if rc and","Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else:","return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self,","import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version","import dir_util import glob import os from pathlib import Path","jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks","1 rc = await self._build(args, env) if rc and rc.returncode:","self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames","remove anything on the destination tree but not in the","dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames","try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError","the Apache License, Version 2.0 from distutils import dir_util import","\"Could not find 'gradle' or 'wrapper' executable\") # Gradle Task","): pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle","filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def","mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,","self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc =","if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None:","if rc and rc.returncode: return rc.returncode rc = await self._install(args,","'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks","cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or","'^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set()","get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return","on the destination tree but not in the build tree","build one to avoid # polluting the latter during the","None: additional_hooks = [] # add jars and classes to","one to avoid # polluting the latter during the build","os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath(","not find 'gradle' or 'wrapper' executable\") # Gradle Task (by","D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set()","cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX","= colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): #","args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree(","not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir","for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames:","# Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base","= [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE]","if additional_hooks is None: additional_hooks = [] # add jars","anything on the destination tree but not in the build","pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await","latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle","not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args,","Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks =","env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation:","options must be prefixed by a space,\\n' 'e.g. --gradle-args \"","from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle","os from pathlib import Path import shutil from colcon_core.environment import","from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell","return rc.returncode rc = await self._install(args, env) if rc and","dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames","os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames =","self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames","copy files from the source directory to the build one","noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames =","from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system","pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from","(out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument(","except RuntimeError as e: logger.error(str(e)) return 1 rc = await","src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames -","env): self.progress('build') # remove anything on the destination tree but","= [GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or","colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle","elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise","# https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',","additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'),","dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames -","'Arguments matching other options must be prefixed by a space,\\n'","noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to","\"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks","build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args):","pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook(","'--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. '","but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build',","dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames,","= self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames =","[str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else:","GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await","# Gradle Arguments cmd += (args.gradle_args or []) cmd +=","prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task',","_build(self, args, env): self.progress('build') # remove anything on the destination","wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH',","for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames:","'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir)","satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from","# copy files from the source directory to the build","'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble']","from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle","# noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg =","await self._build(args, env) if rc and rc.returncode: return rc.returncode rc","create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build')","executable\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd","prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if","os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename","prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(","+= (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base","def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self,","bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames -","in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):","[GRADLE_EXECUTABLE] else: raise RuntimeError( \"Could not find 'gradle' or 'wrapper'","+= ['assemble'] # Gradle Arguments cmd += (args.gradle_args or [])","start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames,","else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args","the destination tree but not in the build tree bld_package_jar_dir","dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames:","for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return","CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base),","prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) #","in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from","def _build(self, args, env): self.progress('build') # remove anything on the","the source directory to the build one to avoid #","import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment","import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger","'{args.path}'\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add","logger.error(str(e)) return 1 rc = await self._build(args, env) if rc","dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename","# Copyright 2018 # Licensed under the Apache License,","pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base,","metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching","self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames","= set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname","prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the","colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import","prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname))","cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments","D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle","+= ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX']","--gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a specific task instead","step return await run( self.context, cmd, cwd=args.build_base, env=env) async def","in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname),","import glob import os from pathlib import Path import shutil","in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in","set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path):","'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1","gradle packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0')","dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for","= os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames =","_install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything","polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1)","for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy","logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self):","noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg","not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs')","= self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames =","args, env): self.progress('install') pkg = self.context.pkg # remove anything on","(args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to","import os from pathlib import Path import shutil from colcon_core.environment","D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args","destination tree but not in the build tree bld_package_jar_dir =","additional_hooks is None: additional_hooks = [] # add jars and","2018 # Licensed under the Apache License, Version 2.0","dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd =","glob import os from pathlib import Path import shutil from","other options must be prefixed by a space,\\n' 'e.g. --gradle-args","Gradle projects. ' 'Arguments matching other options must be prefixed","= await self._build(args, env) if rc and rc.returncode: return rc.returncode","by a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run","or 'wrapper' executable\") # Gradle Task (by default 'assemble') if","'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies)","self.progress('build') # remove anything on the destination tree but not","must be prefixed by a space,\\n' 'e.g. --gradle-args \" --help\"')","src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames","or []) cmd += ['--stacktrace'] # Add install_base to environment","- bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname","additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything","invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env)","'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment(","a space,\\n' 'e.g. --gradle-args \" --help\"') parser.add_argument( '--gradle-task', help='Run a","prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir,","super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames","out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for","Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base #","'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes',","the destination tree but not in the source tree src_package_src_dir","filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): #","cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add","from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts","of the default task') async def build( # noqa: D102","async def _install(self, args, env): self.progress('install') pkg = self.context.pkg #","from the source directory to the build one to avoid","= set() out_filenames = set() for dirname, dirnames, filenames in","a specific task instead of the default task') async def","colcon_gradle/task/gradle/build.py0 # Copyright 2018 # Licensed under the Apache","os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser):","self.context.pkg args = self.context.args logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals()))","# polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base,","colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa:","start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa:","os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar)","on the destination tree but not in the source tree","for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for","TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from","[]) cmd += ['--stacktrace'] # Add install_base to environment in","colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import","help='Pass arguments to Gradle projects. ' 'Arguments matching other options","tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames,","parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects.","(by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd","= await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e:","await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e))","# noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames","os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname,","dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add(","[] # add jars and classes to CLASSPATH with wildcards","tree but not in the source tree src_package_src_dir = os.path.join(args.path,","build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg","colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import","+= create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'),","self.progress('install') pkg = self.context.pkg # remove anything on the destination","shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename =","colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__)","to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars',","dir_util import glob import os from pathlib import Path import","= os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name,","to Gradle projects. ' 'Arguments matching other options must be","create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend')","prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar","rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg,","task') async def build( # noqa: D102 self, *, additional_hooks=None,","def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ):","env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run(","import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook","filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname,","destination tree but not in the source tree src_package_src_dir =","Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace']","raise RuntimeError( \"Could not find 'gradle' or 'wrapper' executable\") #","to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build","self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install')","pkg = self.context.pkg # remove anything on the destination tree","jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir, jar_filename))","RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args,","'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir)","bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames","colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import","task instead of the default task') async def build( #","await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args,","subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename","if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self,","= dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename))","shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from","rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks)","env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg","arguments to Gradle projects. ' 'Arguments matching other options must","bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames:","args.install_base # invoke build step return await run( self.context, cmd,","prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir,","# Licensed under the Apache License, Version 2.0 from distutils","specific task instead of the default task') async def build(","create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from","Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks +=","Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] #","the build one to avoid # polluting the latter during","under the Apache License, Version 2.0 from distutils import dir_util","*, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args","filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames,","# invoke build step return await run( self.context, cmd, cwd=args.build_base,","GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError(","default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd +=","Licensed under the Apache License, Version 2.0 from distutils import","self._build(args, env) if rc and rc.returncode: return rc.returncode rc =","rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def","args, env): self.progress('build') # remove anything on the destination tree","during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable","prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar,","License, Version 2.0 from distutils import dir_util import glob import","cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg","*, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip,","logger.info( \"Building Gradle package in '{args.path}'\".format_map(locals())) if additional_hooks is None:","dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames,","'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames","= await self._install(args, env) if rc and rc.returncode: return rc.returncode","= self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames =","help='Run a specific task instead of the default task') async","add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762","source directory to the build one to avoid # polluting","parser.add_argument( '--gradle-task', help='Run a specific task instead of the default","process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd","anything on the destination tree but not in the source","'wrapper' executable\") # Gradle Task (by default 'assemble') if args.gradle_task:","if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source","skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \"Building","in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir =","create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try:","colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from","in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames)","' 'Arguments matching other options must be prefixed by a","prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if","os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname))","prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames","the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir =","self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args =","packages.\"\"\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def","await self._install(args, env) if rc and rc.returncode: return rc.returncode if","= dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename))","os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path))","'gradle' or 'wrapper' executable\") # Gradle Task (by default 'assemble')","Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not","import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE","colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import","class GradleBuildTask(TaskExtensionPoint): \"\"\"Build gradle packages.\"\"\" def __init__(self): # noqa: D107","default task') async def build( # noqa: D102 self, *,","prune_dirname)) # copy files from the source directory to the","pkg = self.context.pkg args = self.context.args logger.info( \"Building Gradle package","prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files","e: logger.error(str(e)) return 1 rc = await self._build(args, env) if","dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames","type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other","find 'gradle' or 'wrapper' executable\") # Gradle Task (by default","Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env =","os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir,","is None: additional_hooks = [] # add jars and classes","async def _build(self, args, env): self.progress('build') # remove anything on","create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from","bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir)","rc.returncode rc = await self._install(args, env) if rc and rc.returncode:","os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *,","install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke","run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env):","# Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE","import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable","additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info("],"string":"[\n \"for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir,\",\n \"tree but not in the build tree bld_package_jar_dir = os.path.join(args.build_base,\",\n \"return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async\",\n \"bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in\",\n \"[args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd +=\",\n \"the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if\",\n \"import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\"\",\n \"if rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts(\",\n \"from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task\",\n \"import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable\",\n \"out_dirnames = set() out_filenames = set() for dirname, dirnames, filenames\",\n \"classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook(\",\n \"src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames:\",\n \"= args.install_base # invoke build step return await run( self.context,\",\n \"= [] # add jars and classes to CLASSPATH with\",\n \"Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is\",\n \"# remove anything on the destination tree but not in\",\n \"+= [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments cmd\",\n \"src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in\",\n \"build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join(\",\n \"= self.context.pkg # remove anything on the destination tree but\",\n \"self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is\",\n \"src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir)\",\n \"update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif\",\n \"self.context.pkg # remove anything on the destination tree but not\",\n \"os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames =\",\n \"env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as\",\n \"set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname in\",\n \"https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,\",\n \"rc and rc.returncode: return rc.returncode rc = await self._install(args, env)\",\n \"nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments\",\n \"build step return await run( self.context, cmd, cwd=args.build_base, env=env) async\",\n \"pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') #\",\n \"is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could\",\n \"__init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path):\",\n \"'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task\",\n \"# add jars and classes to CLASSPATH with wildcards #\",\n \"with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name,\",\n \"colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import\",\n \"out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args',\",\n \"os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base),\",\n \"dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames -\",\n \"= dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for\",\n \"in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in\",\n \"package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = []\",\n \"prune_filenames = dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir,\",\n \"cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd =\",\n \"the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) #\",\n \"= os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames\",\n \"os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory\",\n \"GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint):\",\n \"self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames\",\n \"async def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False\",\n \"distutils import dir_util import glob import os from pathlib import\",\n \"for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for\",\n \"exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree(\",\n \"for dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames:\",\n \"parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass\",\n \"os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build',\",\n \"src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames\",\n \"and rc.returncode: return rc.returncode rc = await self._install(args, env) if\",\n \"the default task') async def build( # noqa: D102 self,\",\n \"'*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',\",\n \"- bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in\",\n \"get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from\",\n \"start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path))\",\n \"import Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging\",\n \"run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from\",\n \"os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java')\",\n \"\\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of\",\n \"the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base,\",\n \"in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):\",\n \"args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())]\",\n \"as e: logger.error(str(e)) return 1 rc = await self._build(args, env)\",\n \"cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle'\",\n \"= dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for\",\n \"'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree(\",\n \"directory to the build one to avoid # polluting the\",\n \"not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not\",\n \"def add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*',\",\n \"\\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION,\",\n \" # Licensed under the Apache License, Version 2.0 from\",\n \"_build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for dirname,\",\n \"but not in the source tree src_package_src_dir = os.path.join(args.path, 'src')\",\n \"'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH',\",\n \"dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames\",\n \"Gradle Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task]\",\n \"['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] =\",\n \"environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step\",\n \"2.0 from distutils import dir_util import glob import os from\",\n \"in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return\",\n \"'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env\",\n \"Path import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import\",\n \"env) if rc and rc.returncode: return rc.returncode rc = await\",\n \"shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source directory to\",\n \"None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find\",\n \"- src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname\",\n \"bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share',\",\n \"in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] #\",\n \"matching other options must be prefixed by a space,\\\\n' 'e.g.\",\n \"os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames =\",\n \"dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for\",\n \"import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class\",\n \"from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle\",\n \"args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if\",\n \"= os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames\",\n \"import get_command_environment from colcon_core.task import run from colcon_core.task import TaskExtensionPoint\",\n \"args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove\",\n \"from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell\",\n \"get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger\",\n \"rc = await self._install(args, env) if rc and rc.returncode: return\",\n \"- src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename in\",\n \"dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename\",\n \"pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames,\",\n \"'--gradle-task', help='Run a specific task instead of the default task')\",\n \"files from the source directory to the build one to\",\n \"additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java',\",\n \"= self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames =\",\n \"self._install(args, env) if rc and rc.returncode: return rc.returncode if not\",\n \"source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src')\",\n \"has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def\",\n \"instead of the default task') async def build( # noqa:\",\n \"os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree(\",\n \"colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import\",\n \"RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle\",\n \"if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')):\",\n \"and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks +=\",\n \"and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args,\",\n \"skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env):\",\n \"to the build one to avoid # polluting the latter\",\n \"Version 2.0 from distutils import dir_util import glob import os\",\n \"from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger =\",\n \"additional_hooks = [] # add jars and classes to CLASSPATH\",\n \"rc.returncode: return rc.returncode rc = await self._install(args, env) if rc\",\n \"colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import\",\n \"from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build\",\n \"def _install(self, args, env): self.progress('install') pkg = self.context.pkg # remove\",\n \"Apache License, Version 2.0 from distutils import dir_util import glob\",\n \"tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base,\",\n \"if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] #\",\n \"def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set() for\",\n \"avoid # polluting the latter during the build process dir_util.copy_tree(args.path,\",\n \"has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd\",\n \"be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument(\",\n \"+= create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend')\",\n \"Copyright 2018 # Licensed under the Apache License, Version\",\n \"dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath(\",\n \"rc = await self._build(args, env) if rc and rc.returncode: return\",\n \"= self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks\",\n \"--help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead of the\",\n \"satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames =\",\n \"args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle\",\n \"'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True)\",\n \"projects. ' 'Arguments matching other options must be prefixed by\",\n \"add_arguments(self, *, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*',\",\n \"subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename),\",\n \"prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir,\",\n \"['assemble'] # Gradle Arguments cmd += (args.gradle_args or []) cmd\",\n \"out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self,\",\n \"args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc\",\n \"'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir,\",\n \"env): self.progress('install') pkg = self.context.pkg # remove anything on the\",\n \"else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\")\",\n \"prune_dirnames = dst_dirnames - bld_dirnames prune_filenames = dst_filenames - bld_filenames\",\n \"to avoid # polluting the latter during the build process\",\n \"from distutils import dir_util import glob import os from pathlib\",\n \"return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102\",\n \"= self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in\",\n \"GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__()\",\n \"in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir\",\n \"from colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task\",\n \"space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific\",\n \"mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except\",\n \"# noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments\",\n \"pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name,\",\n \"cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg =\",\n \"out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add(\",\n \"import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run\",\n \"# Gradle Task (by default 'assemble') if args.gradle_task: cmd +=\",\n \"return 1 rc = await self._build(args, env) if rc and\",\n \"Task (by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else:\",\n \"return await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self,\",\n \"import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version\",\n \"import dir_util import glob import os from pathlib import Path\",\n \"jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks\",\n \"1 rc = await self._build(args, env) if rc and rc.returncode:\",\n \"self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames\",\n \"remove anything on the destination tree but not in the\",\n \"dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames\",\n \"try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError\",\n \"the Apache License, Version 2.0 from distutils import dir_util import\",\n \"\\\"Could not find 'gradle' or 'wrapper' executable\\\") # Gradle Task\",\n \"): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle\",\n \"filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def\",\n \"mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name,\",\n \"self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1 rc =\",\n \"if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None:\",\n \"if rc and rc.returncode: return rc.returncode rc = await self._install(args,\",\n \"'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks\",\n \"cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args or\",\n \"'^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames = set()\",\n \"get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return\",\n \"on the destination tree but not in the build tree\",\n \"build one to avoid # polluting the latter during the\",\n \"None: additional_hooks = [] # add jars and classes to\",\n \"one to avoid # polluting the latter during the build\",\n \"os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath(\",\n \"not find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by\",\n \"D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set()\",\n \"cmd += ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX\",\n \"= colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): #\",\n \"args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree(\",\n \"not in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir\",\n \"for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames:\",\n \"# Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base\",\n \"= [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE]\",\n \"if additional_hooks is None: additional_hooks = [] # add jars\",\n \"anything on the destination tree but not in the build\",\n \"pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await\",\n \"latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle\",\n \"not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args,\",\n \"Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks =\",\n \"env) if rc and rc.returncode: return rc.returncode if not skip_hook_creation:\",\n \"options must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\"\",\n \"from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle\",\n \"os from pathlib import Path import shutil from colcon_core.environment import\",\n \"from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell\",\n \"return rc.returncode rc = await self._install(args, env) if rc and\",\n \"dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames\",\n \"os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames =\",\n \"self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames\",\n \"copy files from the source directory to the build one\",\n \"noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames =\",\n \"from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system\",\n \"pathlib import Path import shutil from colcon_core.environment import create_environment_scripts from\",\n \"(out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa: D102 parser.add_argument(\",\n \"except RuntimeError as e: logger.error(str(e)) return 1 rc = await\",\n \"src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames -\",\n \"env): self.progress('build') # remove anything on the destination tree but\",\n \"= [GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or\",\n \"colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle\",\n \"elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise\",\n \"# https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share',\",\n \"additional_hooks += create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'),\",\n \"dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames -\",\n \"'Arguments matching other options must be prefixed by a space,\\\\n'\",\n \"noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to\",\n \"\\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks\",\n \"build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args):\",\n \"pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook(\",\n \"'--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. '\",\n \"but not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build',\",\n \"dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames,\",\n \"= self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames =\",\n \"[str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else:\",\n \"GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await\",\n \"# Gradle Arguments cmd += (args.gradle_args or []) cmd +=\",\n \"prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task',\",\n \"_build(self, args, env): self.progress('build') # remove anything on the destination\",\n \"wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH',\",\n \"for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames:\",\n \"'share', pkg.name, 'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir)\",\n \"satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from\",\n \"# copy files from the source directory to the build\",\n \"'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd += ['assemble']\",\n \"from colcon_core.task import run from colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle\",\n \"# noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg =\",\n \"await self._build(args, env) if rc and rc.returncode: return rc.returncode rc\",\n \"create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build')\",\n \"executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task: cmd\",\n \"prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if\",\n \"os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename\",\n \"prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(\",\n \"+= (args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base\",\n \"def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self,\",\n \"bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames -\",\n \"in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname):\",\n \"[GRADLE_EXECUTABLE] else: raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper'\",\n \"+= ['assemble'] # Gradle Arguments cmd += (args.gradle_args or [])\",\n \"start_path): out_dirnames = set() out_filenames = set() for dirname, dirnames,\",\n \"else: cmd += ['assemble'] # Gradle Arguments cmd += (args.gradle_args\",\n \"the destination tree but not in the build tree bld_package_jar_dir\",\n \"dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename in filenames:\",\n \"for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return\",\n \"CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars', Path(args.install_base),\",\n \"prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) #\",\n \"in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from\",\n \"def _build(self, args, env): self.progress('build') # remove anything on the\",\n \"the source directory to the build one to avoid #\",\n \"import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment\",\n \"import shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger\",\n \"'{args.path}'\\\".format_map(locals())) if additional_hooks is None: additional_hooks = [] # add\",\n \"logger.error(str(e)) return 1 rc = await self._build(args, env) if rc\",\n \"dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames for prune_filename\",\n \"# Copyright 2018 # Licensed under the Apache License,\",\n \"pkg.name, 'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base,\",\n \"metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching\",\n \"self._build_file_tree( dst_package_src_dir) prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames\",\n \"= set() for dirname, dirnames, filenames in os.walk(start_path): for subdirname\",\n \"prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the\",\n \"colcon_core.task import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import\",\n \"prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname))\",\n \"cmd += [args.gradle_task] else: cmd += ['assemble'] # Gradle Arguments\",\n \"D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle\",\n \"+= ['--stacktrace'] # Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX']\",\n \"--gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a specific task instead\",\n \"step return await run( self.context, cmd, cwd=args.build_base, env=env) async def\",\n \"in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname),\",\n \"import glob import os from pathlib import Path import shutil\",\n \"in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in\",\n \"set() out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path):\",\n \"'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e)) return 1\",\n \"gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0')\",\n \"dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for\",\n \"= os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames =\",\n \"_install(self, args, env): self.progress('install') pkg = self.context.pkg # remove anything\",\n \"polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base, update=1)\",\n \"for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy\",\n \"logger = colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self):\",\n \"noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg\",\n \"not in the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs')\",\n \"= self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames prune_filenames =\",\n \"args, env): self.progress('install') pkg = self.context.pkg # remove anything on\",\n \"(args.gradle_args or []) cmd += ['--stacktrace'] # Add install_base to\",\n \"import os from pathlib import Path import shutil from colcon_core.environment\",\n \"D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args\",\n \"destination tree but not in the build tree bld_package_jar_dir =\",\n \"additional_hooks is None: additional_hooks = [] # add jars and\",\n \"2018 # Licensed under the Apache License, Version 2.0\",\n \"dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd =\",\n \"glob import os from pathlib import Path import shutil from\",\n \"other options must be prefixed by a space,\\\\n' 'e.g. --gradle-args\",\n \"Gradle projects. ' 'Arguments matching other options must be prefixed\",\n \"= await self._build(args, env) if rc and rc.returncode: return rc.returncode\",\n \"by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run\",\n \"or 'wrapper' executable\\\") # Gradle Task (by default 'assemble') if\",\n \"'java'), mode='prepend') try: env = await get_command_environment( 'build', args.build_base, self.context.dependencies)\",\n \"self.progress('build') # remove anything on the destination tree but not\",\n \"must be prefixed by a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"')\",\n \"src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames\",\n \"or []) cmd += ['--stacktrace'] # Add install_base to environment\",\n \"- bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname\",\n \"additional_hooks=additional_hooks) async def _build(self, args, env): self.progress('build') # remove anything\",\n \"invoke build step return await run( self.context, cmd, cwd=args.build_base, env=env)\",\n \"'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env = await get_command_environment(\",\n \"a space,\\\\n' 'e.g. --gradle-args \\\" --help\\\"') parser.add_argument( '--gradle-task', help='Run a\",\n \"prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir,\",\n \"super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames = set() out_filenames\",\n \"out_filenames = set() for dirname, dirnames, filenames in os.walk(start_path): for\",\n \"Add install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base #\",\n \"'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks += create_environment_hook( 'classpath_classes',\",\n \"the destination tree but not in the source tree src_package_src_dir\",\n \"filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): #\",\n \"cmd += (args.gradle_args or []) cmd += ['--stacktrace'] # Add\",\n \"from pathlib import Path import shutil from colcon_core.environment import create_environment_scripts\",\n \"of the default task') async def build( # noqa: D102\",\n \"async def _install(self, args, env): self.progress('install') pkg = self.context.pkg #\",\n \"from the source directory to the build one to avoid\",\n \"= set() out_filenames = set() for dirname, dirnames, filenames in\",\n \"a specific task instead of the default task') async def\",\n \"colcon_gradle/task/gradle/build.py0 # Copyright 2018 # Licensed under the Apache\",\n \"os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser):\",\n \"self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals()))\",\n \"# polluting the latter during the build process dir_util.copy_tree(args.path, args.build_base,\",\n \"colcon_logger.getChild(__name__) class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa:\",\n \"start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *, parser): # noqa:\",\n \"os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar)\",\n \"on the destination tree but not in the source tree\",\n \"for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for\",\n \"TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from\",\n \"[]) cmd += ['--stacktrace'] # Add install_base to environment in\",\n \"colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import\",\n \"help='Pass arguments to Gradle projects. ' 'Arguments matching other options\",\n \"tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames,\",\n \"parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip, help='Pass arguments to Gradle projects.\",\n \"(by default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd\",\n \"= await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e:\",\n \"await get_command_environment( 'build', args.build_base, self.context.dependencies) except RuntimeError as e: logger.error(str(e))\",\n \"# noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def _build_file_tree(self, start_path): out_dirnames\",\n \"os.path.join(dirname, subdirname), start=start_path)) for filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname,\",\n \"dirname, dirnames, filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add(\",\n \"[] # add jars and classes to CLASSPATH with wildcards\",\n \"tree but not in the source tree src_package_src_dir = os.path.join(args.path,\",\n \"build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ): pkg\",\n \"colcon_core.shell import create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import\",\n \"+= create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'),\",\n \"self.progress('install') pkg = self.context.pkg # remove anything on the destination\",\n \"shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename =\",\n \"colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable logger = colcon_logger.getChild(__name__)\",\n \"to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762 additional_hooks += create_environment_hook( 'classpath_jars',\",\n \"dir_util import glob import os from pathlib import Path import\",\n \"= os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir = os.path.join( args.install_base, 'share', pkg.name,\",\n \"to Gradle projects. ' 'Arguments matching other options must be\",\n \"create_environment_hook( 'classpath_jars', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend')\",\n \"prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar\",\n \"rc and rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg,\",\n \"task') async def build( # noqa: D102 self, *, additional_hooks=None,\",\n \"def build( # noqa: D102 self, *, additional_hooks=None, skip_hook_creation=False ):\",\n \"env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build step return await run(\",\n \"import colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook\",\n \"filenames in os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname,\",\n \"destination tree but not in the source tree src_package_src_dir =\",\n \"Gradle Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace']\",\n \"raise RuntimeError( \\\"Could not find 'gradle' or 'wrapper' executable\\\") #\",\n \"to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke build\",\n \"self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install')\",\n \"pkg = self.context.pkg # remove anything on the destination tree\",\n \"jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar, os.path.join(dst_package_jar_dir, jar_filename))\",\n \"RuntimeError as e: logger.error(str(e)) return 1 rc = await self._build(args,\",\n \"'src') dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir)\",\n \"bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames\",\n \"colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from colcon_core.shell import\",\n \"task instead of the default task') async def build( #\",\n \"await run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args,\",\n \"subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path)) for filename\",\n \"if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def _build(self,\",\n \"= dst_filenames - src_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename))\",\n \"shutil from colcon_core.environment import create_environment_scripts from colcon_core.logging import colcon_logger from\",\n \"rc.returncode: return rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks)\",\n \"env=env) async def _install(self, args, env): self.progress('install') pkg = self.context.pkg\",\n \"arguments to Gradle projects. ' 'Arguments matching other options must\",\n \"bld_dirnames prune_filenames = dst_filenames - bld_filenames for prune_filename in prune_filenames:\",\n \"args.install_base # invoke build step return await run( self.context, cmd,\",\n \"prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir, prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir,\",\n \"# Licensed under the Apache License, Version 2.0 from distutils\",\n \"specific task instead of the default task') async def build(\",\n \"create_environment_scripts from colcon_core.logging import colcon_logger from colcon_core.plugin_system import satisfies_version from\",\n \"Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java', '*'), mode='prepend') additional_hooks +=\",\n \"Arguments cmd += (args.gradle_args or []) cmd += ['--stacktrace'] #\",\n \"the build one to avoid # polluting the latter during\",\n \"under the Apache License, Version 2.0 from distutils import dir_util\",\n \"*, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args\",\n \"filename in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames,\",\n \"# invoke build step return await run( self.context, cmd, cwd=args.build_base,\",\n \"GRADLE_EXECUTABLE is not None: cmd = [GRADLE_EXECUTABLE] else: raise RuntimeError(\",\n \"default 'assemble') if args.gradle_task: cmd += [args.gradle_task] else: cmd +=\",\n \"Licensed under the Apache License, Version 2.0 from distutils import\",\n \"self._build(args, env) if rc and rc.returncode: return rc.returncode rc =\",\n \"rc.returncode if not skip_hook_creation: create_environment_scripts( pkg, args, additional_hooks=additional_hooks) async def\",\n \"args, env): self.progress('build') # remove anything on the destination tree\",\n \"during the build process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable\",\n \"prune_dirname)) for jar in glob.glob(os.path.join(bld_package_jar_dir, '*.jar')): jar_filename = os.path.basename(jar) shutil.copy2(jar,\",\n \"License, Version 2.0 from distutils import dir_util import glob import\",\n \"cmd, cwd=args.build_base, env=env) async def _install(self, args, env): self.progress('install') pkg\",\n \"*, parser): # noqa: D102 parser.add_argument( '--gradle-args', nargs='*', metavar='*', type=str.lstrip,\",\n \"logger.info( \\\"Building Gradle package in '{args.path}'\\\".format_map(locals())) if additional_hooks is None:\",\n \"dst_package_src_dir = os.path.join(args.build_base, 'src') src_dirnames, src_filenames = self._build_file_tree( src_package_src_dir) dst_dirnames,\",\n \"'java') os.makedirs(dst_package_jar_dir, exist_ok=True) bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames\",\n \"= await self._install(args, env) if rc and rc.returncode: return rc.returncode\",\n \"= self._build_file_tree( src_package_src_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_src_dir) prune_dirnames =\",\n \"help='Run a specific task instead of the default task') async\",\n \"add jars and classes to CLASSPATH with wildcards # https://docs.oracle.com/javase/8/docs/technotes/tools/windows/classpath.html#A1100762\",\n \"source directory to the build one to avoid # polluting\",\n \"parser.add_argument( '--gradle-task', help='Run a specific task instead of the default\",\n \"process dir_util.copy_tree(args.path, args.build_base, update=1) # Gradle Executable if has_wrapper_executable(args): cmd\",\n \"anything on the destination tree but not in the source\",\n \"'wrapper' executable\\\") # Gradle Task (by default 'assemble') if args.gradle_task:\",\n \"if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files from the source\",\n \"skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info( \\\"Building\",\n \"in the source tree src_package_src_dir = os.path.join(args.path, 'src') dst_package_src_dir =\",\n \"create_environment_hook( 'classpath_classes', Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try:\",\n \"colcon_logger from colcon_core.plugin_system import satisfies_version from colcon_core.shell import create_environment_hook from\",\n \"in filenames: out_filenames.add( os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames)\",\n \"' 'Arguments matching other options must be prefixed by a\",\n \"prune_filename in prune_filenames: os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if\",\n \"os.remove(os.path.join(dst_package_src_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname))\",\n \"prune_dirnames = dst_dirnames - src_dirnames prune_filenames = dst_filenames - src_filenames\",\n \"the build tree bld_package_jar_dir = os.path.join(args.build_base, 'build', 'libs') dst_package_jar_dir =\",\n \"self, *, additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args =\",\n \"packages.\\\"\\\"\\\" def __init__(self): # noqa: D107 super().__init__() satisfies_version(TaskExtensionPoint.EXTENSION_POINT_VERSION, '^1.0') def\",\n \"await self._install(args, env) if rc and rc.returncode: return rc.returncode if\",\n \"= dst_filenames - bld_filenames for prune_filename in prune_filenames: os.remove(os.path.join(dst_package_jar_dir, prune_filename))\",\n \"os.walk(start_path): for subdirname in dirnames: out_dirnames.add( os.path.relpath( os.path.join(dirname, subdirname), start=start_path))\",\n \"'gradle' or 'wrapper' executable\\\") # Gradle Task (by default 'assemble')\",\n \"Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE is not\",\n \"import TaskExtensionPoint from colcon_gradle.task.gradle import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE\",\n \"colcon_core.shell import get_command_environment from colcon_core.task import run from colcon_core.task import\",\n \"class GradleBuildTask(TaskExtensionPoint): \\\"\\\"\\\"Build gradle packages.\\\"\\\"\\\" def __init__(self): # noqa: D107\",\n \"default task') async def build( # noqa: D102 self, *,\",\n \"prune_dirname)) # copy files from the source directory to the\",\n \"pkg = self.context.pkg args = self.context.args logger.info( \\\"Building Gradle package\",\n \"prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree(os.path.join(dst_package_src_dir, prune_dirname)) # copy files\",\n \"e: logger.error(str(e)) return 1 rc = await self._build(args, env) if\",\n \"dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir) prune_dirnames = dst_dirnames - bld_dirnames\",\n \"type=str.lstrip, help='Pass arguments to Gradle projects. ' 'Arguments matching other\",\n \"find 'gradle' or 'wrapper' executable\\\") # Gradle Task (by default\",\n \"Path(args.install_base), pkg.name, 'CLASSPATH', os.path.join('share', pkg.name, 'java'), mode='prepend') try: env =\",\n \"os.remove(os.path.join(dst_package_jar_dir, prune_filename)) for prune_dirname in prune_dirnames: if os.path.exists(prune_dirname): shutil.rmtree( os.path.join(dst_package_jar_dir,\",\n \"is None: additional_hooks = [] # add jars and classes\",\n \"async def _build(self, args, env): self.progress('build') # remove anything on\",\n \"create_environment_hook from colcon_core.shell import get_command_environment from colcon_core.task import run from\",\n \"bld_dirnames, bld_filenames = self._build_file_tree( bld_package_jar_dir) dst_dirnames, dst_filenames = self._build_file_tree( dst_package_jar_dir)\",\n \"rc.returncode rc = await self._install(args, env) if rc and rc.returncode:\",\n \"os.path.relpath( os.path.join(dirname, filename), start=start_path)) return (out_dirnames, out_filenames) def add_arguments(self, *,\",\n \"install_base to environment in GRADLE_INSTALL_PREFIX env['GRADLE_INSTALL_PREFIX'] = args.install_base # invoke\",\n \"run( self.context, cmd, cwd=args.build_base, env=env) async def _install(self, args, env):\",\n \"# Gradle Executable if has_wrapper_executable(args): cmd = [str(get_wrapper_executable(args).absolute())] elif GRADLE_EXECUTABLE\",\n \"import get_wrapper_executable from colcon_gradle.task.gradle import GRADLE_EXECUTABLE from colcon_gradle.task.gradle import has_wrapper_executable\",\n \"additional_hooks=None, skip_hook_creation=False ): pkg = self.context.pkg args = self.context.args logger.info(\"\n]"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":34,"numItemsPerPage":100,"numTotalItems":12866649,"offset":3400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MTI3NDI3OCwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMCIsImV4cCI6MTc3MTI3Nzg3OCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.w0XoUMlA3Q3k4JEoYnH4erRx1vdgIMl7-3eqJ5-WKNHVU2aOQiIJAUmcjBKC42Y3UkmpdnUrs-M8VM39g_yQBw","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":[]}}">
","# class CapsuleData: ## The constructor # # @param self","made available under the terms and conditions of the BSD","AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append","reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2","FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else:","UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in","16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4],","This program and the accompanying materials # are licensed and","license may be found at # http://opensource.org/licenses/bsd-license.php # # THE","None self.FvName = None self.CapsuleName = None ## generate FV","if AuthData: # the ImageFileSize need include the full authenticated","Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1],","this distribution. The full text of the license may be","payload file, the header is defined below: #pragma pack(1) typedef","self.FileName = None ## generate Afile capsule data # #","} EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version =","ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read()","# class CapsuleAnyFile (CapsuleData): ## The constructor # # @param","for capsule data # # class CapsuleAfile (CapsuleData): ## The","EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize;","from Common.Misc import SaveFileOnChange import uuid ## base class for","self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0'","ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import","self.Ffs = None self.FileName = None ## generate Afile capsule","//Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName =","self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the","Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return","accompanies this distribution. The full text of the license may","object pointer def __init__(self): pass ## generate capsule data #","# import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from","# generate capsule # # Copyright (c) 2007-2017, Intel Corporation.","= FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile","UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced","open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile =","constructor # # @param self The object pointer def __init__(self):","FdFile ## AnyFile class for capsule data # # class","Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the","= open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True","from struct import pack import os from Common.Misc import SaveFileOnChange","0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure","CapsuleData: ## The constructor # # @param self The object","## @file # generate capsule # # Copyright (c) 2007-2017,","if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer","self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile","OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## #","not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance =","of the license may be found at # http://opensource.org/licenses/bsd-license.php #","Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import","generate FFS capsule data # # @param self The object","__init__(self) : self.Ffs = None self.FvName = None ## generate","FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class","UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64","pointer # def __init__(self) : self.Ffs = None self.FileName =","bytes of MonotonicCount to last bytes of certificate. # the","'0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need","'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return Buffer","= '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize =","self.FvName = None self.CapsuleName = None ## generate FV capsule","THE BSD LICENSE ON AN \"AS IS\" BASIS, # WITHOUT","None ## generate FV capsule data # # @param self","## # Import Modules # import Ffs from GenFdsGlobalVariable import","typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8","conditions of the BSD License # which accompanies this distribution.","self.CapsuleName = None ## generate FV capsule data # #","the full authenticated info size. From first bytes of MonotonicCount","16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0,","self.Ffs.GenFfs() return FfsFile ## FV class for capsule data #","uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure #","GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for","the BSD License # which accompanies this distribution. The full","in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None","= [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed =","EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None","[] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None","text of the license may be found at # http://opensource.org/licenses/bsd-license.php","None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None","data # # class CapsuleFd (CapsuleData): ## The constructor #","# which accompanies this distribution. The full text of the","-1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile =","# Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
#","header is defined below: #pragma pack(1) typedef struct { UINT32","materials # are licensed and made available under the terms","= StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName =","self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid","= None ## generate FFS capsule data # # @param","## generate AnyFile capsule data # # @param self The","available under the terms and conditions of the BSD License","FFS class for capsule data # # class CapsuleFfs (CapsuleData):","pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # #","string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs()","None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002'","Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1:","bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize","AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF","last bytes of certificate. # the 32 bit is the","self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2],","# This program and the accompanying materials # are licensed","BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER","The object pointer # @retval string Generated file name #","if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('')","self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if","self The object pointer # def __init__(self) : self.Ffs =","int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize,","for capsule data # # class CapsuleAnyFile (CapsuleData): ## The","name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ##","= None ## generate FD capsule data # # @param","self.Ffs = None self.FvName = None ## generate FFS capsule","file content to the structure # ImageFile = open(self.ImageFile, 'rb')","capsule data # # @param self The object pointer def","= '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not","__init__(self) : self.Ffs = None self.FvName = None self.CapsuleName =","the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32","CapsuleFv (CapsuleData): ## The constructor # # @param self The","None ## generate AnyFile capsule data # # @param self","generate capsule data # # @param self The object pointer","def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data","if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj =","not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData:","# the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType","content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer","== -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer","# Append file content to the structure # ImageFile =","class for capsule data # # class CapsuleFd (CapsuleData): ##","@param self The object pointer # @retval string Generated file","at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER","bytes of certificate. # the 32 bit is the MonotonicCount,","Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData):","# # Fill structure # Guid = self.ImageTypeId.split('-') Buffer =","the accompanying materials # are licensed and made available under","# def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in","AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content","int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1],","= GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile =","## AnyFile class for capsule data # # class CapsuleAnyFile","int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16),","class CapsuleFfs (CapsuleData): ## The constructor # # @param self","FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName","self.Ffs = None self.FvName = None self.CapsuleName = None ##","The object pointer def GenCapsuleSubItem(self): pass ## FFS class for","uuid ## base class for capsule data # # class","data # # @param self The object pointer # @retval","PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS","# def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV","FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ##","file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate","= None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return","# WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS","import SaveFileOnChange import uuid ## base class for capsule data","## FD class for capsule data # # class CapsuleFd","return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile","= None self.FileName = None ## generate AnyFile capsule data","'0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance:","FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ##","the license may be found at # http://opensource.org/licenses/bsd-license.php # #","import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct","# # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE","to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer +=","The full text of the license may be found at","= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data","@retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd')","# def __init__(self) : self.Ffs = None self.FileName = None","include the full authenticated info size. From first bytes of","to last bytes of certificate. # the 32 bit is","Corporation. All rights reserved.
# # This program and the","# # @param self The object pointer def GenCapsuleSubItem(self): pass","struct import pack import os from Common.Misc import SaveFileOnChange import","= None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance =","# class CapsuleFfs (CapsuleData): ## The constructor # # @param","FvFile ## FD class for capsule data # # class","VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid =","GenCapsuleSubItem(self): pass ## FFS class for capsule data # #","32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) #","[] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False","else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for","GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName","in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile","self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid","else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for","# @param self The object pointer def __init__(self): pass ##","= os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the","FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName)","16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0,","if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd()","= None self.FdName = None self.CapsuleName = None ## generate",") if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3])","None self.FileName = None ## generate AnyFile capsule data #","class for capsule data # # class CapsuleFv (CapsuleData): ##","@retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv')","full authenticated info size. From first bytes of MonotonicCount to","+= 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile)","and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile:","= self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16),","info size. From first bytes of MonotonicCount to last bytes","UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance;","pointer # def __init__(self) : self.Ffs = None self.FvName =","CapsuleAfile (CapsuleData): ## The constructor # # @param self The","capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights","pack import os from Common.Misc import SaveFileOnChange import uuid ##","self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance","name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper()","self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined","DISTRIBUTED UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS,","wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if","CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma","None self.MonotonicCount = None self.Existed = False self.Buffer = None","# # @param self The object pointer def __init__(self): pass","size. From first bytes of MonotonicCount to last bytes of","None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None","may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM","Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16),",": self.Ffs = None self.FileName = None ## generate Afile","OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. #","0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer +=","int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16),","CapsuleFd (CapsuleData): ## The constructor # # @param self The","FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule","= open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile","0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer","## The constructor # # @param self The object pointer","class CapsuleFv (CapsuleData): ## The constructor # # @param self","## generate FV capsule data # # @param self The","# # This program and the accompanying materials # are","which accompanies this distribution. The full text of the license","object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule","be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS","file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class","for capsule data # # class CapsuleData: ## The constructor","== -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile","FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule","= 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill","int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16),","@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName","FfsFile ## FV class for capsule data # # class","self.FileName = None ## generate AnyFile capsule data # #","# # @param self The object pointer # @retval string","= None self.CapsuleName = None ## generate FV capsule data","UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize;","@param self The object pointer def __init__(self): pass ## generate","FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class","None ## generate Afile capsule data # # @param self","class CapsuleFd (CapsuleData): ## The constructor # # @param self","#pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8","pointer # def __init__(self) : self.Ffs = None self.FdName =","object pointer # @retval string Generated file name # def","string Generated file name # def GenCapsuleSubItem(self): return self.FileName ##","found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED","FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close()","UNDER THE BSD LICENSE ON AN \"AS IS\" BASIS, #","MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize","False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version:","int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:],","# def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in","OR IMPLIED. # ## # Import Modules # import Ffs","FD class for capsule data # # class CapsuleFd (CapsuleData):","AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file","self The object pointer # @retval string Generated file name","is defined below: #pragma pack(1) typedef struct { UINT32 Version;","Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1:","Afile class for capsule data # # class CapsuleAfile (CapsuleData):","__init__(self): pass ## generate capsule data # # @param self","the header is defined below: #pragma pack(1) typedef struct {","= None self.CapsuleName = None ## generate FD capsule data","def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName","2007-2017, Intel Corporation. All rights reserved.
# # This program","# @param self The object pointer # @retval string Generated","= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data","base class for capsule data # # class CapsuleData: ##","'0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile)","GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data #","# Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable","import pack import os from Common.Misc import SaveFileOnChange import uuid","16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8],","= None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid =","# http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE","GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj","= None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version =","Append file content to the structure # ImageFile = open(self.ImageFile,","= None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self,","IMPLIED. # ## # Import Modules # import Ffs from","certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision,","of certificate. # the 32 bit is the MonotonicCount, dwLength,","reserved.
# # This program and the accompanying materials #","generate FD capsule data # # @param self The object","capsule data # # class CapsuleAfile (CapsuleData): ## The constructor","def __init__(self) : self.Ffs = None self.FileName = None ##","# # class CapsuleData: ## The constructor # # @param","GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else:","EITHER EXPRESS OR IMPLIED. # ## # Import Modules #","Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
# #","in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName =","= None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile =","## generate Afile capsule data # # @param self The","{ UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32","class CapsuleAfile (CapsuleData): ## The constructor # # @param self","return FvFile ## FD class for capsule data # #","self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile","capsule data # # class CapsuleFv (CapsuleData): ## The constructor","distribution. The full text of the license may be found","FV capsule data # # @param self The object pointer","AnyFile capsule data # # @param self The object pointer","# @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile","int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16),","for capsule data # # class CapsuleFv (CapsuleData): ## The","# def __init__(self) : self.Ffs = None self.FvName = None","authenticated info size. From first bytes of MonotonicCount to last","None self.FvName = None ## generate FFS capsule data #","structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0],","dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize =","self.FileName ## Afile class for capsule data # # class","= [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount =","self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: #","16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if","# # class CapsuleFfs (CapsuleData): ## The constructor # #","## generate FFS capsule data # # @param self The","@retval string Generated file name # def GenCapsuleSubItem(self): FfsFile =","Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO","class for capsule data # # class CapsuleAfile (CapsuleData): ##","0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData:","Common.Misc import SaveFileOnChange import uuid ## base class for capsule","IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND,","= None ## generate AnyFile capsule data # # @param","# @retval string Generated file name # def GenCapsuleSubItem(self): if","structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close()","self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile","## base class for capsule data # # class CapsuleData:","Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32","return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD","''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId","# @param self The object pointer # def __init__(self) :","@param self The object pointer def GenCapsuleSubItem(self): pass ## FFS","terms and conditions of the BSD License # which accompanies","import StringIO from struct import pack import os from Common.Misc","'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile,","Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile","WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.","int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16),","# the ImageFileSize need include the full authenticated info size.","FV class for capsule data # # class CapsuleFv (CapsuleData):","None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = []","# # @param self The object pointer # def __init__(self)","= self.Ffs.GenFfs() return FfsFile ## FV class for capsule data","class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below:","# Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ',","AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer +=","__init__(self): self.UiName = None self.Version = None self.ImageTypeId = None","pointer def __init__(self): pass ## generate capsule data # #","if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex","are licensed and made available under the terms and conditions","16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16)","string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') ==","ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer +=","self The object pointer def __init__(self): pass ## generate capsule","UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER;","= None ## generate FV capsule data # # @param","class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self","16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize,","= False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not","AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData):","full text of the license may be found at #","'''Generate payload file, the header is defined below: #pragma pack(1)","+= uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure","16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2],","None ## generate FFS capsule data # # @param self","# class CapsuleAfile (CapsuleData): ## The constructor # # @param","int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16),","# def __init__(self) : self.Ffs = None self.FdName = None","self.Ffs = None self.FdName = None self.CapsuleName = None ##","self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed","the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and","os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer","# class CapsuleFv (CapsuleData): ## The constructor # # @param","self.CapsuleName = None ## generate FD capsule data # #","import os from Common.Misc import SaveFileOnChange import uuid ## base","self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version","if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if","generate FV capsule data # # @param self The object","@param self The object pointer # def __init__(self) : self.Ffs","(CapsuleData): ## The constructor # # @param self The object","# # class CapsuleAfile (CapsuleData): ## The constructor # #","StringIO from struct import pack import os from Common.Misc import","self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1'","GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data #","self.FdName = None self.CapsuleName = None ## generate FD capsule","FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile","None self.FileName = None ## generate Afile capsule data #","UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; '''","v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version","name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper()","16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10],","# THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON","UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self):","= None self.FileName = None ## generate Afile capsule data","def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if","return FdFile ## AnyFile class for capsule data # #","def __init__(self): pass ## generate capsule data # # @param","+= pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() #","licensed and made available under the terms and conditions of","32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType","Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb')","ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile:","16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:],","if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj =","None ## generate FD capsule data # # @param self","import GenFdsGlobalVariable import StringIO from struct import pack import os","pass ## FFS class for capsule data # # class","SaveFileOnChange import uuid ## base class for capsule data #","data # # class CapsuleAfile (CapsuleData): ## The constructor #","= None self.ImageIndex = None self.HardwareInstance = None self.ImageFile =","wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0","KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules","## generate FD capsule data # # @param self The","is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize +=","os from Common.Misc import SaveFileOnChange import uuid ## base class","class for capsule data # # class CapsuleAnyFile (CapsuleData): ##","pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data","self The object pointer def GenCapsuleSubItem(self): pass ## FFS class","FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule","MonotonicCount to last bytes of certificate. # the 32 bit","## generate capsule data # # @param self The object","None self.CapsuleName = None ## generate FD capsule data #","def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys():","and made available under the terms and conditions of the","and the accompanying materials # are licensed and made available","capsule data # # class CapsuleData: ## The constructor #","FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile =","UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def","of MonotonicCount to last bytes of certificate. # the 32","int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) )",": self.Ffs = None self.FvName = None self.CapsuleName = None","Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le()","class for capsule data # # class CapsuleData: ## The","for capsule data # # class CapsuleFfs (CapsuleData): ## The",": self.Ffs = None self.FdName = None self.CapsuleName = None","@file # generate capsule # # Copyright (c) 2007-2017, Intel","program and the accompanying materials # are licensed and made","os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full","-1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer =","below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId;","first bytes of MonotonicCount to last bytes of certificate. #","Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16),","pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex;","CapsuleFfs (CapsuleData): ## The constructor # # @param self The","## Afile class for capsule data # # class CapsuleAfile","= pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2],","if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close()","and conditions of the BSD License # which accompanies this","from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack","= os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-')","def __init__(self) : self.Ffs = None self.FvName = None ##","return self.FileName ## Afile class for capsule data # #","data # # @param self The object pointer def GenCapsuleSubItem(self):","Afile capsule data # # @param self The object pointer","need include the full authenticated info size. From first bytes","WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR","Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import","The object pointer # def __init__(self) : self.Ffs = None","self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if","From first bytes of MonotonicCount to last bytes of certificate.",": self.Ffs = None self.FvName = None ## generate FFS","# ## # Import Modules # import Ffs from GenFdsGlobalVariable","VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0],","class for capsule data # # class CapsuleFfs (CapsuleData): ##","def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the","None self.Version = None self.ImageTypeId = None self.ImageIndex = None","# def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file,","+= ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer","open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return","16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2],","REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ##","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY","AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex:","return FfsFile ## FV class for capsule data # #","file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if","# # class CapsuleFd (CapsuleData): ## The constructor # #","capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor","= None self.FvName = None self.CapsuleName = None ## generate","# @retval string Generated file name # def GenCapsuleSubItem(self): return","All rights reserved.
# # This program and the accompanying","pass ## generate capsule data # # @param self The","## FFS class for capsule data # # class CapsuleFfs","16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex,","Intel Corporation. All rights reserved.
# # This program and","= FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile","accompanying materials # are licensed and made available under the","self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex","the terms and conditions of the BSD License # which","file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile","self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper())","self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed","CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize","rights reserved.
# # This program and the accompanying materials","FD capsule data # # @param self The object pointer","object pointer # def __init__(self) : self.Ffs = None self.FdName","FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return","generate Afile capsule data # # @param self The object","ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include","BSD License # which accompanies this distribution. The full text","def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName","None self.CapsuleName = None ## generate FV capsule data #","= None self.MonotonicCount = None self.Existed = False self.Buffer =","capsule data # # @param self The object pointer #","StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None","capsule data # # class CapsuleFd (CapsuleData): ## The constructor","self.FvName = None ## generate FFS capsule data # #","self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount","AuthData: # the ImageFileSize need include the full authenticated info","## FV class for capsule data # # class CapsuleFv","16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6],","int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0,","= None self.Version = None self.ImageTypeId = None self.ImageIndex =","the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read()","= None ## generate Afile capsule data # # @param","GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName)","# class CapsuleFd (CapsuleData): ## The constructor # # @param","The constructor # # @param self The object pointer #","file, the header is defined below: #pragma pack(1) typedef struct","ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS","file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if","# Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16),","The object pointer def __init__(self): pass ## generate capsule data","# # Append file content to the structure # ImageFile","int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance,","name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload","= self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return","AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to","data # # class CapsuleData: ## The constructor # #","if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure #","class CapsuleData: ## The constructor # # @param self The","(c) 2007-2017, Intel Corporation. All rights reserved.
# # This","= None self.FvName = None ## generate FFS capsule data","UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 }","string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') ==","LICENSE ON AN \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","# def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule","data # # class CapsuleFfs (CapsuleData): ## The constructor #","self.MonotonicCount = None self.Existed = False self.Buffer = None def","GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data #","None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = []","GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header","= GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile =","# # class CapsuleAnyFile (CapsuleData): ## The constructor # #","object pointer # def __init__(self) : self.Ffs = None self.FvName","GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer)","int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16),","data # # class CapsuleFv (CapsuleData): ## The constructor #","string Generated file name # def GenCapsuleSubItem(self): return self.FileName class","of the BSD License # which accompanies this distribution. The","__init__(self) : self.Ffs = None self.FileName = None ## generate","GenFdsGlobalVariable import StringIO from struct import pack import os from","def GenCapsuleSubItem(self): pass ## FFS class for capsule data #","pointer # @retval string Generated file name # def GenCapsuleSubItem(self):","# ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if","self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize"],"string":"[\n \"return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is\",\n \"UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName\",\n \"self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False self.Buffer\",\n \"= '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize\",\n \"# @param self The object pointer def GenCapsuleSubItem(self): pass ##\",\n \"ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH',\",\n \"The constructor # # @param self The object pointer def\",\n \"generate capsule # # Copyright (c) 2007-2017, Intel Corporation. All\",\n \"FFS capsule data # # @param self The object pointer\",\n \"def __init__(self): self.UiName = None self.Version = None self.ImageTypeId =\",\n \"def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys():\",\n \"capsule data # # class CapsuleFfs (CapsuleData): ## The constructor\",\n \"VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed =\",\n \"int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16),\",\n \"IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\"\",\n \"if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance\",\n \"GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack import\",\n \"VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # #\",\n \"pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16),\",\n \"not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex =\",\n \"EXPRESS OR IMPLIED. # ## # Import Modules # import\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FileName\",\n \"self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper())\",\n \"constructor # # @param self The object pointer # def\",\n \"FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile\",\n \"generate AnyFile capsule data # # @param self The object\",\n \"http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE BSD\",\n \"CapsuleAnyFile (CapsuleData): ## The constructor # # @param self The\",\n \"def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class\",\n \"self.Ffs = None self.FileName = None ## generate AnyFile capsule\",\n \"GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj\",\n \"# are licensed and made available under the terms and\",\n \"__init__(self) : self.Ffs = None self.FdName = None self.CapsuleName =\",\n \"THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN\",\n \": self.Ffs = None self.FileName = None ## generate AnyFile\",\n \"defined below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID\",\n \"for capsule data # # class CapsuleFd (CapsuleData): ## The\",\n \"License # which accompanies this distribution. The full text of\",\n \"the ImageFileSize need include the full authenticated info size. From\",\n \"self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return\",\n \"struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3];\",\n \"import uuid ## base class for capsule data # #\",\n \"None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]):\",\n \"under the terms and conditions of the BSD License #\",\n \"name # def GenCapsuleSubItem(self): return self.FileName ## Afile class for\",\n \"ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize =\",\n \"GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if not\",\n \"None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile\",\n \"None self.FdName = None self.CapsuleName = None ## generate FD\",\n \"# # class CapsuleFv (CapsuleData): ## The constructor # #\",\n \"ImageFileSize need include the full authenticated info size. From first\",\n \"self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName\",\n \"BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"data # # class CapsuleAnyFile (CapsuleData): ## The constructor #\",\n \"# # Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
\",\n \"# class CapsuleData: ## The constructor # # @param self\",\n \"made available under the terms and conditions of the BSD\",\n \"AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append\",\n \"reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2\",\n \"FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else:\",\n \"UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in\",\n \"16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4],\",\n \"This program and the accompanying materials # are licensed and\",\n \"license may be found at # http://opensource.org/licenses/bsd-license.php # # THE\",\n \"None self.FvName = None self.CapsuleName = None ## generate FV\",\n \"if AuthData: # the ImageFileSize need include the full authenticated\",\n \"Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1],\",\n \"this distribution. The full text of the license may be\",\n \"payload file, the header is defined below: #pragma pack(1) typedef\",\n \"self.FileName = None ## generate Afile capsule data # #\",\n \"} EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version =\",\n \"ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read()\",\n \"# class CapsuleAnyFile (CapsuleData): ## The constructor # # @param\",\n \"for capsule data # # class CapsuleAfile (CapsuleData): ## The\",\n \"EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize;\",\n \"from Common.Misc import SaveFileOnChange import uuid ## base class for\",\n \"self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0'\",\n \"ANY KIND, EITHER EXPRESS OR IMPLIED. # ## # Import\",\n \"self.Ffs = None self.FileName = None ## generate Afile capsule\",\n \"//Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName =\",\n \"self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the\",\n \"Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return\",\n \"accompanies this distribution. The full text of the license may\",\n \"object pointer def __init__(self): pass ## generate capsule data #\",\n \"# import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from\",\n \"# generate capsule # # Copyright (c) 2007-2017, Intel Corporation.\",\n \"= FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile\",\n \"UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced\",\n \"open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile =\",\n \"constructor # # @param self The object pointer def __init__(self):\",\n \"FdFile ## AnyFile class for capsule data # # class\",\n \"Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the\",\n \"= open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True\",\n \"from struct import pack import os from Common.Misc import SaveFileOnChange\",\n \"0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure\",\n \"CapsuleData: ## The constructor # # @param self The object\",\n \"## @file # generate capsule # # Copyright (c) 2007-2017,\",\n \"if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer\",\n \"self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None self.ImageFile\",\n \"OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ## #\",\n \"not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance =\",\n \"of the license may be found at # http://opensource.org/licenses/bsd-license.php #\",\n \"Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import\",\n \"generate FFS capsule data # # @param self The object\",\n \"__init__(self) : self.Ffs = None self.FvName = None ## generate\",\n \"FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class\",\n \"UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64\",\n \"pointer # def __init__(self) : self.Ffs = None self.FileName =\",\n \"bytes of MonotonicCount to last bytes of certificate. # the\",\n \"'0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need\",\n \"'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return Buffer\",\n \"= '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize =\",\n \"self.FvName = None self.CapsuleName = None ## generate FV capsule\",\n \"THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"None ## generate FV capsule data # # @param self\",\n \"## # Import Modules # import Ffs from GenFdsGlobalVariable import\",\n \"typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8\",\n \"conditions of the BSD License # which accompanies this distribution.\",\n \"self.CapsuleName = None ## generate FV capsule data # #\",\n \"the full authenticated info size. From first bytes of MonotonicCount\",\n \"16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0,\",\n \"self.Ffs.GenFfs() return FfsFile ## FV class for capsule data #\",\n \"uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure #\",\n \"GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for\",\n \"the BSD License # which accompanies this distribution. The full\",\n \"in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None\",\n \"= [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed =\",\n \"EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version = None\",\n \"[] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None\",\n \"text of the license may be found at # http://opensource.org/licenses/bsd-license.php\",\n \"None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None\",\n \"data # # class CapsuleFd (CapsuleData): ## The constructor #\",\n \"# which accompanies this distribution. The full text of the\",\n \"-1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile =\",\n \"# Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
#\",\n \"header is defined below: #pragma pack(1) typedef struct { UINT32\",\n \"materials # are licensed and made available under the terms\",\n \"= StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName =\",\n \"self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid\",\n \"= None ## generate FFS capsule data # # @param\",\n \"## generate AnyFile capsule data # # @param self The\",\n \"available under the terms and conditions of the BSD License\",\n \"FFS class for capsule data # # class CapsuleFfs (CapsuleData):\",\n \"pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # #\",\n \"string Generated file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs()\",\n \"None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002'\",\n \"Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1:\",\n \"bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize\",\n \"AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF\",\n \"last bytes of certificate. # the 32 bit is the\",\n \"self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2],\",\n \"# This program and the accompanying materials # are licensed\",\n \"BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER\",\n \"The object pointer # @retval string Generated file name #\",\n \"if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('')\",\n \"self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if\",\n \"self The object pointer # def __init__(self) : self.Ffs =\",\n \"int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize,\",\n \"for capsule data # # class CapsuleAnyFile (CapsuleData): ## The\",\n \"name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ##\",\n \"= None ## generate FD capsule data # # @param\",\n \"self.Ffs = None self.FvName = None ## generate FFS capsule\",\n \"file content to the structure # ImageFile = open(self.ImageFile, 'rb')\",\n \"capsule data # # @param self The object pointer def\",\n \"= '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not\",\n \"__init__(self) : self.Ffs = None self.FvName = None self.CapsuleName =\",\n \"the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32\",\n \"CapsuleFv (CapsuleData): ## The constructor # # @param self The\",\n \"None ## generate AnyFile capsule data # # @param self\",\n \"generate capsule data # # @param self The object pointer\",\n \"def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data\",\n \"if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj =\",\n \"not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData:\",\n \"# the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType\",\n \"content to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer\",\n \"== -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer\",\n \"# Append file content to the structure # ImageFile =\",\n \"class for capsule data # # class CapsuleFd (CapsuleData): ##\",\n \"@param self The object pointer # @retval string Generated file\",\n \"at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER\",\n \"bytes of certificate. # the 32 bit is the MonotonicCount,\",\n \"Generated file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData):\",\n \"# # Fill structure # Guid = self.ImageTypeId.split('-') Buffer =\",\n \"the accompanying materials # are licensed and made available under\",\n \"# def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in\",\n \"AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content\",\n \"int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1],\",\n \"= GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile =\",\n \"## AnyFile class for capsule data # # class CapsuleAnyFile\",\n \"int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16),\",\n \"class CapsuleFfs (CapsuleData): ## The constructor # # @param self\",\n \"FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName\",\n \"self.Ffs = None self.FvName = None self.CapsuleName = None ##\",\n \"The object pointer def GenCapsuleSubItem(self): pass ## FFS class for\",\n \"uuid ## base class for capsule data # # class\",\n \"data # # @param self The object pointer # @retval\",\n \"PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS\",\n \"# def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile ## FV\",\n \"FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ##\",\n \"file name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate\",\n \"= None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return\",\n \"# WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS\",\n \"import SaveFileOnChange import uuid ## base class for capsule data\",\n \"## FD class for capsule data # # class CapsuleFd\",\n \"return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile\",\n \"= None self.FileName = None ## generate AnyFile capsule data\",\n \"'0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1' if not self.HardwareInstance:\",\n \"FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ##\",\n \"the license may be found at # http://opensource.org/licenses/bsd-license.php # #\",\n \"import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct\",\n \"# # THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE\",\n \"to the structure # ImageFile = open(self.ImageFile, 'rb') Buffer +=\",\n \"The full text of the license may be found at\",\n \"= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd')\",\n \"# def __init__(self) : self.Ffs = None self.FileName = None\",\n \"include the full authenticated info size. From first bytes of\",\n \"to last bytes of certificate. # the 32 bit is\",\n \"Corporation. All rights reserved.
# # This program and the\",\n \"# # @param self The object pointer def GenCapsuleSubItem(self): pass\",\n \"struct import pack import os from Common.Misc import SaveFileOnChange import\",\n \"= None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance =\",\n \"# class CapsuleFfs (CapsuleData): ## The constructor # # @param\",\n \"FvFile ## FD class for capsule data # # class\",\n \"VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid =\",\n \"GenCapsuleSubItem(self): pass ## FFS class for capsule data # #\",\n \"32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) #\",\n \"[] self.Certificate_Guid = None self.MonotonicCount = None self.Existed = False\",\n \"else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName\",\n \"in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile\",\n \"self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid\",\n \"else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for\",\n \"# @param self The object pointer def __init__(self): pass ##\",\n \"= os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the\",\n \"FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName)\",\n \"16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0,\",\n \"if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd()\",\n \"= None self.FdName = None self.CapsuleName = None ## generate\",\n \") if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3])\",\n \"None self.FileName = None ## generate AnyFile capsule data #\",\n \"class for capsule data # # class CapsuleFv (CapsuleData): ##\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv')\",\n \"full authenticated info size. From first bytes of MonotonicCount to\",\n \"+= 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile)\",\n \"and CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile:\",\n \"= self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16),\",\n \"info size. From first bytes of MonotonicCount to last bytes\",\n \"UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance;\",\n \"pointer # def __init__(self) : self.Ffs = None self.FvName =\",\n \"CapsuleAfile (CapsuleData): ## The constructor # # @param self The\",\n \"capsule # # Copyright (c) 2007-2017, Intel Corporation. All rights\",\n \"pack import os from Common.Misc import SaveFileOnChange import uuid ##\",\n \"self.Version = None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance\",\n \"name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper()\",\n \"self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined\",\n \"DISTRIBUTED UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS,\",\n \"wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0 if\",\n \"CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below: #pragma\",\n \"None self.MonotonicCount = None self.Existed = False self.Buffer = None\",\n \"# # @param self The object pointer def __init__(self): pass\",\n \"size. From first bytes of MonotonicCount to last bytes of\",\n \"None self.ImageTypeId = None self.ImageIndex = None self.HardwareInstance = None\",\n \"may be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM\",\n \"Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16),\",\n \": self.Ffs = None self.FileName = None ## generate Afile\",\n \"OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. #\",\n \"0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer +=\",\n \"int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16),\",\n \"CapsuleFd (CapsuleData): ## The constructor # # @param self The\",\n \"FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule\",\n \"= open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile\",\n \"0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer\",\n \"## The constructor # # @param self The object pointer\",\n \"class CapsuleFv (CapsuleData): ## The constructor # # @param self\",\n \"## generate FV capsule data # # @param self The\",\n \"# # This program and the accompanying materials # are\",\n \"which accompanies this distribution. The full text of the license\",\n \"object pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule\",\n \"be found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS\",\n \"file name # def GenCapsuleSubItem(self): return self.FileName ## Afile class\",\n \"for capsule data # # class CapsuleData: ## The constructor\",\n \"== -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile\",\n \"FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule\",\n \"= 0 if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill\",\n \"int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16),\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): return self.FileName\",\n \"FfsFile ## FV class for capsule data # # class\",\n \"self.FileName = None ## generate AnyFile capsule data # #\",\n \"# # @param self The object pointer # @retval string\",\n \"= None self.CapsuleName = None ## generate FV capsule data\",\n \"UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize;\",\n \"@param self The object pointer def __init__(self): pass ## generate\",\n \"FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class\",\n \"None ## generate Afile capsule data # # @param self\",\n \"class CapsuleFd (CapsuleData): ## The constructor # # @param self\",\n \"#pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8\",\n \"pointer # def __init__(self) : self.Ffs = None self.FdName =\",\n \"object pointer # @retval string Generated file name # def\",\n \"string Generated file name # def GenCapsuleSubItem(self): return self.FileName ##\",\n \"found at # http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED\",\n \"FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close()\",\n \"UNDER THE BSD LICENSE ON AN \\\"AS IS\\\" BASIS, #\",\n \"MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize\",\n \"False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version:\",\n \"int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:],\",\n \"# def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in\",\n \"OR IMPLIED. # ## # Import Modules # import Ffs\",\n \"FD class for capsule data # # class CapsuleFd (CapsuleData):\",\n \"AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file\",\n \"self The object pointer # @retval string Generated file name\",\n \"is defined below: #pragma pack(1) typedef struct { UINT32 Version;\",\n \"Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1:\",\n \"Afile class for capsule data # # class CapsuleAfile (CapsuleData):\",\n \"__init__(self): pass ## generate capsule data # # @param self\",\n \"the header is defined below: #pragma pack(1) typedef struct {\",\n \"= None self.CapsuleName = None ## generate FD capsule data\",\n \"def __init__(self) : self.Ffs = None self.FdName = None self.CapsuleName\",\n \"2007-2017, Intel Corporation. All rights reserved.
# # This program\",\n \"# @param self The object pointer # @retval string Generated\",\n \"= GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data\",\n \"base class for capsule data # # class CapsuleData: ##\",\n \"'0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile)\",\n \"GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD class for capsule data #\",\n \"# Import Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable\",\n \"import pack import os from Common.Misc import SaveFileOnChange import uuid\",\n \"16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8],\",\n \"= None self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid =\",\n \"# http://opensource.org/licenses/bsd-license.php # # THE PROGRAM IS DISTRIBUTED UNDER THE\",\n \"GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj\",\n \"= None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version =\",\n \"Append file content to the structure # ImageFile = open(self.ImageFile,\",\n \"= None self.Existed = False self.Buffer = None def GenCapsuleSubItem(self,\",\n \"IMPLIED. # ## # Import Modules # import Ffs from\",\n \"certificate. # the 32 bit is the MonotonicCount, dwLength, wRevision,\",\n \"of certificate. # the 32 bit is the MonotonicCount, dwLength,\",\n \"reserved.
# # This program and the accompanying materials #\",\n \"generate FD capsule data # # @param self The object\",\n \"capsule data # # class CapsuleAfile (CapsuleData): ## The constructor\",\n \"def __init__(self) : self.Ffs = None self.FileName = None ##\",\n \"# # class CapsuleData: ## The constructor # # @param\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else:\",\n \"EITHER EXPRESS OR IMPLIED. # ## # Import Modules #\",\n \"Copyright (c) 2007-2017, Intel Corporation. All rights reserved.
# #\",\n \"in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName =\",\n \"= None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile =\",\n \"## generate Afile capsule data # # @param self The\",\n \"{ UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32\",\n \"class CapsuleAfile (CapsuleData): ## The constructor # # @param self\",\n \"return FvFile ## FD class for capsule data # #\",\n \"self.ImageIndex = None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile\",\n \"capsule data # # class CapsuleFv (CapsuleData): ## The constructor\",\n \"distribution. The full text of the license may be found\",\n \"FV capsule data # # @param self The object pointer\",\n \"AnyFile capsule data # # @param self The object pointer\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): FfsFile\",\n \"int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16),\",\n \"for capsule data # # class CapsuleFv (CapsuleData): ## The\",\n \"# def __init__(self) : self.Ffs = None self.FvName = None\",\n \"authenticated info size. From first bytes of MonotonicCount to last\",\n \"None self.FvName = None ## generate FFS capsule data #\",\n \"structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0],\",\n \"dwLength, wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize =\",\n \"self.FileName ## Afile class for capsule data # # class\",\n \"= [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount =\",\n \"self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: #\",\n \"16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if\",\n \"# # class CapsuleFfs (CapsuleData): ## The constructor # #\",\n \"## generate FFS capsule data # # @param self The\",\n \"@retval string Generated file name # def GenCapsuleSubItem(self): FfsFile =\",\n \"Modules # import Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO\",\n \"class for capsule data # # class CapsuleAfile (CapsuleData): ##\",\n \"0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData:\",\n \"Common.Misc import SaveFileOnChange import uuid ## base class for capsule\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND,\",\n \"= None ## generate AnyFile capsule data # # @param\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): if\",\n \"structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close()\",\n \"self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile\",\n \"## base class for capsule data # # class CapsuleData:\",\n \"Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex; UINT8 reserved_bytes[3]; UINT32 UpdateImageSize; UINT32\",\n \"return FvFile else: FvFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FvName) return FvFile ## FD\",\n \"''' def __init__(self): self.UiName = None self.Version = None self.ImageTypeId\",\n \"# @param self The object pointer # def __init__(self) :\",\n \"@param self The object pointer def GenCapsuleSubItem(self): pass ## FFS\",\n \"terms and conditions of the BSD License # which accompanies\",\n \"import StringIO from struct import pack import os from Common.Misc\",\n \"'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile,\",\n \"Generated file name # def GenCapsuleSubItem(self): return self.FileName ## Afile\",\n \"WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED.\",\n \"int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16),\",\n \"# the ImageFileSize need include the full authenticated info size.\",\n \"FV class for capsule data # # class CapsuleFv (CapsuleData):\",\n \"None self.ImageIndex = None self.HardwareInstance = None self.ImageFile = []\",\n \"# # @param self The object pointer # def __init__(self)\",\n \"= self.Ffs.GenFfs() return FfsFile ## FV class for capsule data\",\n \"class CapsulePayload(CapsuleData): '''Generate payload file, the header is defined below:\",\n \"# Fill structure # Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ',\",\n \"AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer +=\",\n \"__init__(self): self.UiName = None self.Version = None self.ImageTypeId = None\",\n \"pointer def __init__(self): pass ## generate capsule data # #\",\n \"if not self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex\",\n \"are licensed and made available under the terms and conditions\",\n \"16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16)\",\n \"string Generated file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') ==\",\n \"ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer +=\",\n \"self The object pointer def __init__(self): pass ## generate capsule\",\n \"UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER;\",\n \"= None ## generate FV capsule data # # @param\",\n \"class CapsuleAnyFile (CapsuleData): ## The constructor # # @param self\",\n \"16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize,\",\n \"= False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not\",\n \"AnyFile class for capsule data # # class CapsuleAnyFile (CapsuleData):\",\n \"full text of the license may be found at #\",\n \"'''Generate payload file, the header is defined below: #pragma pack(1)\",\n \"+= uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to the structure\",\n \"16) ) if AuthData: Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2],\",\n \"None ## generate FFS capsule data # # @param self\",\n \"# class CapsuleAfile (CapsuleData): ## The constructor # # @param\",\n \"int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16),\",\n \"# def __init__(self) : self.Ffs = None self.FdName = None\",\n \"self.Ffs = None self.FdName = None self.CapsuleName = None ##\",\n \"self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed\",\n \"the 32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and\",\n \"os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-') Buffer\",\n \"# class CapsuleFv (CapsuleData): ## The constructor # # @param\",\n \"self.CapsuleName = None ## generate FD capsule data # #\",\n \"import os from Common.Misc import SaveFileOnChange import uuid ## base\",\n \"self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version\",\n \"if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize = os.path.getsize(self.ImageFile) if\",\n \"generate FV capsule data # # @param self The object\",\n \"@param self The object pointer # def __init__(self) : self.Ffs\",\n \"(CapsuleData): ## The constructor # # @param self The object\",\n \"# # class CapsuleAfile (CapsuleData): ## The constructor # #\",\n \"StringIO from struct import pack import os from Common.Misc import\",\n \"self.Version: self.Version = '0x00000002' if not self.ImageIndex: self.ImageIndex = '0x1'\",\n \"GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return FdFile ## AnyFile class for capsule data #\",\n \"self.FdName = None self.CapsuleName = None ## generate FD capsule\",\n \"FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile\",\n \"None self.FileName = None ## generate Afile capsule data #\",\n \"UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; '''\",\n \"v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self): self.UiName = None self.Version\",\n \"name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if self.FvName.upper()\",\n \"16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10],\",\n \"# THE PROGRAM IS DISTRIBUTED UNDER THE BSD LICENSE ON\",\n \"UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def __init__(self):\",\n \"= None self.FileName = None ## generate Afile capsule data\",\n \"def GenCapsuleSubItem(self, AuthData=[]): if not self.Version: self.Version = '0x00000002' if\",\n \"return FdFile ## AnyFile class for capsule data # #\",\n \"def __init__(self): pass ## generate capsule data # # @param\",\n \"+= pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() #\",\n \"licensed and made available under the terms and conditions of\",\n \"32 bit is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType\",\n \"Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb')\",\n \"ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if self.VendorCodeFile:\",\n \"16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:],\",\n \"if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj =\",\n \"None ## generate FD capsule data # # @param self\",\n \"import GenFdsGlobalVariable import StringIO from struct import pack import os\",\n \"pass ## FFS class for capsule data # # class\",\n \"SaveFileOnChange import uuid ## base class for capsule data #\",\n \"data # # class CapsuleAfile (CapsuleData): ## The constructor #\",\n \"= None self.ImageIndex = None self.HardwareInstance = None self.ImageFile =\",\n \"wRevision, wCertificateType and CertType ImageFileSize += 32 VendorFileSize = 0\",\n \"KIND, EITHER EXPRESS OR IMPLIED. # ## # Import Modules\",\n \"## generate FD capsule data # # @param self The\",\n \"is the MonotonicCount, dwLength, wRevision, wCertificateType and CertType ImageFileSize +=\",\n \"os from Common.Misc import SaveFileOnChange import uuid ## base class\",\n \"class for capsule data # # class CapsuleAnyFile (CapsuleData): ##\",\n \"pointer def GenCapsuleSubItem(self): pass ## FFS class for capsule data\",\n \"self The object pointer def GenCapsuleSubItem(self): pass ## FFS class\",\n \"FfsFile = self.Ffs.GenFfs() return FfsFile ## FV class for capsule\",\n \"MonotonicCount to last bytes of certificate. # the 32 bit\",\n \"## generate capsule data # # @param self The object\",\n \"None self.CapsuleName = None ## generate FD capsule data #\",\n \"def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys():\",\n \"and made available under the terms and conditions of the\",\n \"and the accompanying materials # are licensed and made available\",\n \"capsule data # # class CapsuleData: ## The constructor #\",\n \"FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile =\",\n \"UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 } EFI_FIRMWARE_MANAGEMENT_CAPSULE_IMAGE_HEADER; ''' def\",\n \"of MonotonicCount to last bytes of certificate. # the 32\",\n \"int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance, 16) )\",\n \": self.Ffs = None self.FvName = None self.CapsuleName = None\",\n \"Buffer += pack('QIHH', AuthData[0], AuthData[1], AuthData[2], AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le()\",\n \"class for capsule data # # class CapsuleData: ## The\",\n \"for capsule data # # class CapsuleFfs (CapsuleData): ## The\",\n \": self.Ffs = None self.FdName = None self.CapsuleName = None\",\n \"@file # generate capsule # # Copyright (c) 2007-2017, Intel\",\n \"program and the accompanying materials # are licensed and made\",\n \"os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include the full\",\n \"-1: if self.FvName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FvDict.keys(): FvObj = GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer =\",\n \"below: #pragma pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId;\",\n \"first bytes of MonotonicCount to last bytes of certificate. #\",\n \"Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16),\",\n \"pack(1) typedef struct { UINT32 Version; EFI_GUID UpdateImageTypeId; UINT8 UpdateImageIndex;\",\n \"CapsuleFfs (CapsuleData): ## The constructor # # @param self The\",\n \"## Afile class for capsule data # # class CapsuleAfile\",\n \"= pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16), int(Guid[1], 16), int(Guid[2], 16), int(Guid[3][-4:-2],\",\n \"if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close()\",\n \"and conditions of the BSD License # which accompanies this\",\n \"from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import pack\",\n \"= os.path.getsize(self.VendorCodeFile) # # Fill structure # Guid = self.ImageTypeId.split('-')\",\n \"def __init__(self) : self.Ffs = None self.FvName = None ##\",\n \"return self.FileName ## Afile class for capsule data # #\",\n \"data # # @param self The object pointer def GenCapsuleSubItem(self):\",\n \"Afile capsule data # # @param self The object pointer\",\n \"need include the full authenticated info size. From first bytes\",\n \"WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR\",\n \"Ffs from GenFdsGlobalVariable import GenFdsGlobalVariable import StringIO from struct import\",\n \"The object pointer # def __init__(self) : self.Ffs = None\",\n \"self.Existed = False self.Buffer = None def GenCapsuleSubItem(self, AuthData=[]): if\",\n \"From first bytes of MonotonicCount to last bytes of certificate.\",\n \": self.Ffs = None self.FvName = None ## generate FFS\",\n \"# ## # Import Modules # import Ffs from GenFdsGlobalVariable\",\n \"VendorFileSize, int(self.HardwareInstance, 16) ) if AuthData: Buffer += pack('QIHH', AuthData[0],\",\n \"class for capsule data # # class CapsuleFfs (CapsuleData): ##\",\n \"def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the\",\n \"None self.Version = None self.ImageTypeId = None self.ImageIndex = None\",\n \"# def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file,\",\n \"+= ImageFile.read() ImageFile.close() if self.VendorCodeFile: VendorFile = open(self.VendorCodeFile, 'rb') Buffer\",\n \"open(self.VendorCodeFile, 'rb') Buffer += VendorFile.read() VendorFile.close() self.Existed = True return\",\n \"16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2],\",\n \"REPRESENTATIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED. # ##\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS OF ANY\",\n \"AuthData=[]): if not self.Version: self.Version = '0x00000002' if not self.ImageIndex:\",\n \"return FfsFile ## FV class for capsule data # #\",\n \"file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') == -1: if\",\n \"# # class CapsuleFd (CapsuleData): ## The constructor # #\",\n \"capsule data # # class CapsuleAnyFile (CapsuleData): ## The constructor\",\n \"= None self.FvName = None self.CapsuleName = None ## generate\",\n \"# @retval string Generated file name # def GenCapsuleSubItem(self): return\",\n \"All rights reserved.
# # This program and the accompanying\",\n \"pass ## generate capsule data # # @param self The\",\n \"## FFS class for capsule data # # class CapsuleFfs\",\n \"16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex,\",\n \"Intel Corporation. All rights reserved.
# # This program and\",\n \"= FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return FvFile else: FvFile\",\n \"accompanying materials # are licensed and made available under the\",\n \"self.UiName = None self.Version = None self.ImageTypeId = None self.ImageIndex\",\n \"the terms and conditions of the BSD License # which\",\n \"file name # def GenCapsuleSubItem(self): FfsFile = self.Ffs.GenFfs() return FfsFile\",\n \"self.FdName.find('.fd') == -1: if self.FdName.upper() in GenFdsGlobalVariable.FdfParser.Profile.FdDict.keys(): FdObj = GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper())\",\n \"self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount = None self.Existed\",\n \"CertType ImageFileSize += 32 VendorFileSize = 0 if self.VendorCodeFile: VendorFileSize\",\n \"rights reserved.
# # This program and the accompanying materials\",\n \"FD capsule data # # @param self The object pointer\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FdName\",\n \"FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName) return\",\n \"generate Afile capsule data # # @param self The object\",\n \"ImageFileSize = os.path.getsize(self.ImageFile) if AuthData: # the ImageFileSize need include\",\n \"BSD License # which accompanies this distribution. The full text\",\n \"def __init__(self) : self.Ffs = None self.FvName = None self.CapsuleName\",\n \"None self.CapsuleName = None ## generate FV capsule data #\",\n \"= None self.MonotonicCount = None self.Existed = False self.Buffer =\",\n \"capsule data # # @param self The object pointer #\",\n \"StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None\",\n \"capsule data # # class CapsuleFd (CapsuleData): ## The constructor\",\n \"self.FvName = None ## generate FFS capsule data # #\",\n \"self.ImageFile = [] self.VendorCodeFile = [] self.Certificate_Guid = None self.MonotonicCount\",\n \"AuthData: # the ImageFileSize need include the full authenticated info\",\n \"## FV class for capsule data # # class CapsuleFv\",\n \"16), int(Guid[3][-4:-2], 16), int(Guid[3][-2:], 16), int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6],\",\n \"int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16), int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0,\",\n \"= None self.Version = None self.ImageTypeId = None self.ImageIndex =\",\n \"the structure # ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read()\",\n \"= None ## generate Afile capsule data # # @param\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile = GenFdsGlobalVariable.ReplaceWorkspaceMacro(self.FdName)\",\n \"# class CapsuleFd (CapsuleData): ## The constructor # # @param\",\n \"The constructor # # @param self The object pointer #\",\n \"file, the header is defined below: #pragma pack(1) typedef struct\",\n \"ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR REPRESENTATIONS\",\n \"file name # def GenCapsuleSubItem(self): if self.FvName.find('.fv') == -1: if\",\n \"# Guid = self.ImageTypeId.split('-') Buffer = pack('=ILHHBBBBBBBBBBBBIIQ', int(self.Version,16), int(Guid[0], 16),\",\n \"The object pointer def __init__(self): pass ## generate capsule data\",\n \"# # Append file content to the structure # ImageFile\",\n \"int(Guid[4][-2:], 16), int(self.ImageIndex, 16), 0, 0, 0, ImageFileSize, VendorFileSize, int(self.HardwareInstance,\",\n \"name # def GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload\",\n \"= self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer) FvObj.CapsuleName = None FdBuffer.close() return\",\n \"AuthData[3]) Buffer += uuid.UUID(AuthData[4]).get_bytes_le() # # Append file content to\",\n \"data # # class CapsuleData: ## The constructor # #\",\n \"if self.VendorCodeFile: VendorFileSize = os.path.getsize(self.VendorCodeFile) # # Fill structure #\",\n \"class CapsuleData: ## The constructor # # @param self The\",\n \"(c) 2007-2017, Intel Corporation. All rights reserved.
# # This\",\n \"= None self.FvName = None ## generate FFS capsule data\",\n \"UINT32 UpdateImageSize; UINT32 UpdateVendorCodeSize; UINT64 UpdateHardwareInstance; //Introduced in v2 }\",\n \"string Generated file name # def GenCapsuleSubItem(self): if self.FdName.find('.fd') ==\",\n \"LICENSE ON AN \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"# def GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule\",\n \"data # # class CapsuleFfs (CapsuleData): ## The constructor #\",\n \"self.MonotonicCount = None self.Existed = False self.Buffer = None def\",\n \"GenCapsuleSubItem(self): return self.FileName ## Afile class for capsule data #\",\n \"None self.HardwareInstance = None self.ImageFile = [] self.VendorCodeFile = []\",\n \"GenCapsuleSubItem(self): return self.FileName class CapsulePayload(CapsuleData): '''Generate payload file, the header\",\n \"= GenFdsGlobalVariable.FdfParser.Profile.FdDict.get(self.FdName.upper()) FdFile = FdObj.GenFd() return FdFile else: FdFile =\",\n \"# # class CapsuleAnyFile (CapsuleData): ## The constructor # #\",\n \"object pointer # def __init__(self) : self.Ffs = None self.FvName\",\n \"GenFdsGlobalVariable.FdfParser.Profile.FvDict.get(self.FvName.upper()) FdBuffer = StringIO.StringIO('') FvObj.CapsuleName = self.CapsuleName FvFile = FvObj.AddToBuffer(FdBuffer)\",\n \"int(Guid[4][-12:-10], 16), int(Guid[4][-10:-8], 16), int(Guid[4][-8:-6], 16), int(Guid[4][-6:-4], 16), int(Guid[4][-4:-2], 16),\",\n \"data # # class CapsuleFv (CapsuleData): ## The constructor #\",\n \"string Generated file name # def GenCapsuleSubItem(self): return self.FileName class\",\n \"of the BSD License # which accompanies this distribution. The\",\n \"__init__(self) : self.Ffs = None self.FileName = None ## generate\",\n \"GenFdsGlobalVariable import StringIO from struct import pack import os from\",\n \"def GenCapsuleSubItem(self): pass ## FFS class for capsule data #\",\n \"pointer # @retval string Generated file name # def GenCapsuleSubItem(self):\",\n \"# ImageFile = open(self.ImageFile, 'rb') Buffer += ImageFile.read() ImageFile.close() if\",\n \"self.ImageIndex = '0x1' if not self.HardwareInstance: self.HardwareInstance = '0x0' ImageFileSize\"\n]"}}},{"rowIdx":3492,"cells":{"ngram":{"kind":"list like","value":["size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,","[] time = [] with open(\"pi_linear.txt\") as file: for line","plt import numpy as np # Read data size =","line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots()","Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes',","Read data size = [] time = [] with open(\"pi_linear.txt\")","numpy as np # Read data size = [] time","with open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y","import matplotlib.pyplot as plt import numpy as np # Read","import numpy as np # Read data size = []","as np # Read data size = [] time =","= [] with open(\"pi_linear.txt\") as file: for line in file.readlines():","[] with open(\"pi_linear.txt\") as file: for line in file.readlines(): x,","open(\"pi_linear.txt\") as file: for line in file.readlines(): x, y =","ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi","time = [] with open(\"pi_linear.txt\") as file: for line in","= plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear')","x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig,","data size = [] time = [] with open(\"pi_linear.txt\") as","ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\"pi_linear.png\")","plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid()","as file: for line in file.readlines(): x, y = line.split(',')","size = [] time = [] with open(\"pi_linear.txt\") as file:","matplotlib.pyplot as plt import numpy as np # Read data","= [] time = [] with open(\"pi_linear.txt\") as file: for","data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time","as plt import numpy as np # Read data size","np # Read data size = [] time = []","# Read data size = [] time = [] with","= line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax =","file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip()))","time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\"pi_linear.png\") plt.show()","time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time)","in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot","y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax","line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #","for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip()))","file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data","fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)',","# Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num."],"string":"[\n \"size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size,\",\n \"[] time = [] with open(\\\"pi_linear.txt\\\") as file: for line\",\n \"plt import numpy as np # Read data size =\",\n \"line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax = plt.subplots()\",\n \"Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes',\",\n \"Read data size = [] time = [] with open(\\\"pi_linear.txt\\\")\",\n \"numpy as np # Read data size = [] time\",\n \"with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y\",\n \"import matplotlib.pyplot as plt import numpy as np # Read\",\n \"import numpy as np # Read data size = []\",\n \"as np # Read data size = [] time =\",\n \"= [] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines():\",\n \"[] with open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x,\",\n \"open(\\\"pi_linear.txt\\\") as file: for line in file.readlines(): x, y =\",\n \"ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi\",\n \"time = [] with open(\\\"pi_linear.txt\\\") as file: for line in\",\n \"= plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear')\",\n \"x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig,\",\n \"data size = [] time = [] with open(\\\"pi_linear.txt\\\") as\",\n \"ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\\\"pi_linear.png\\\")\",\n \"plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid()\",\n \"as file: for line in file.readlines(): x, y = line.split(',')\",\n \"size = [] time = [] with open(\\\"pi_linear.txt\\\") as file:\",\n \"matplotlib.pyplot as plt import numpy as np # Read data\",\n \"= [] time = [] with open(\\\"pi_linear.txt\\\") as file: for\",\n \"data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time\",\n \"as plt import numpy as np # Read data size\",\n \"np # Read data size = [] time = []\",\n \"# Read data size = [] time = [] with\",\n \"= line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax =\",\n \"file: for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip()))\",\n \"time) ax.set(xlabel='Num. processes', ylabel='Time (s)', title='Pi linear') #ax.grid() fig.savefig(\\\"pi_linear.png\\\") plt.show()\",\n \"time.append(float(y.strip())) # Plot data fig, ax = plt.subplots() ax.plot(size, time)\",\n \"in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot\",\n \"y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data fig, ax\",\n \"line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) #\",\n \"for line in file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip()))\",\n \"file.readlines(): x, y = line.split(',') size.append(int(x.strip())) time.append(float(y.strip())) # Plot data\",\n \"fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num. processes', ylabel='Time (s)',\",\n \"# Plot data fig, ax = plt.subplots() ax.plot(size, time) ax.set(xlabel='Num.\"\n]"}}},{"rowIdx":3493,"cells":{"ngram":{"kind":"list like","value":["each partition :type partition_result: List[PartitionResult] \"\"\" self.topic = topic self.partition_result","election results, or an empty array if the requester did","partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult: topic:","name :type topic: str :param partition_result: The results for each",":type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms self.replica_election_results = replica_election_results","The result error, or zero if there was no error.","def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]): \"\"\" :param throttle_time_ms: The","typing import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode","str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\"","error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]):","self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult] api_key:","List, Optional from ...constants import ApiKey, ErrorCode from ..base import","error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic:","class PartitionResult: partition_id: int error_code: ErrorCode error_message: Optional[str] def __init__(self,","ErrorCode :param error_message: The result message, or null if there","which the request was throttled due to a quota violation,","for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms = throttle_time_ms","__init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The topic","a quota violation, or zero if the request did not",":param partition_id: The partition id :type partition_id: int :param error_code:","error_message: The result message, or null if there was no","results for each partition :type partition_result: List[PartitionResult] \"\"\" self.topic =","quota. :type throttle_time_ms: int :param replica_election_results: The election results, or",":param throttle_time_ms: The duration in milliseconds for which the request","api_key: ClassVar[ApiKey] = ApiKey.ELECT_PREFERRED_LEADERS def __init__(self, throttle_time_ms: int, replica_election_results: List[ReplicaElectionResult]):","The election results, or an empty array if the requester","self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def","did not have permission and the request asks for all","error_code self.error_message = error_message class ReplicaElectionResult: topic: str partition_result: List[PartitionResult]","partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\" :param partition_id: The","ErrorCode error_message: Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message:","there was no error. :type error_message: Optional[str] \"\"\" self.partition_id =",":type throttle_time_ms: int :param replica_election_results: The election results, or an","ReplicaElectionResult: topic: str partition_result: List[PartitionResult] def __init__(self, topic: str, partition_result:","int :param replica_election_results: The election results, or an empty array","def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic: The",":param topic: The topic name :type topic: str :param partition_result:","not have permission and the request asks for all partitions.","topic: The topic name :type topic: str :param partition_result: The",":param error_message: The result message, or null if there was","the request did not violate any quota. :type throttle_time_ms: int","\"\"\" self.topic = topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms:","permission and the request asks for all partitions. :type replica_election_results:","= partition_id self.error_code = error_code self.error_message = error_message class ReplicaElectionResult:","asks for all partitions. :type replica_election_results: List[ReplicaElectionResult] \"\"\" self.throttle_time_ms =","List[PartitionResult] def __init__(self, topic: str, partition_result: List[PartitionResult]): \"\"\" :param topic:","import ClassVar, List, Optional from ...constants import ApiKey, ErrorCode from","Optional[str] def __init__(self, partition_id: int, error_code: ErrorCode, error_message: Optional[str]): \"\"\"","topic self.partition_result = partition_result class ElectPreferredLeadersResponseData(ResponseData): throttle_time_ms: int replica_election_results: List[ReplicaElectionResult]","
