edited_code stringlengths 17 978k | original_code stringlengths 17 978k |
|---|---|
import re
import sys
import copy
import types
import inspect
import keyword
import builtins
import functools
import _thread
__all__ = ['dataclass',
'field',
'Field',
'FrozenInstanceError',
'InitVar',
'MISSING',
# Helper functions.
'fields',
'asdict',
'astuple',
'make_dataclass',
'replace',
'is_dataclass',
]
# Conditions for adding methods. The boxes indicate what action the
# dataclass decorator takes. For all of these tables, when I talk
# about init=, repr=, eq=, order=, unsafe_hash=, or frozen=, I'm
# referring to the arguments to the @dataclass decorator. When
# checking if a dunder method already exists, I mean check for an
# entry in the class's __dict__. I never check to see if an attribute
# is defined in a base class.
# Key:
# +=========+=========================================+
# + Value | Meaning |
# +=========+=========================================+
# | <blank> | No action: no method is added. |
# +---------+-----------------------------------------+
# | add | Generated method is added. |
# +---------+-----------------------------------------+
# | raise | TypeError is raised. |
# +---------+-----------------------------------------+
# | None | Attribute is set to None. |
# +=========+=========================================+
# __init__
#
# +--- init= parameter
# |
# v | | |
# | no | yes | <--- class has __init__ in __dict__?
# +=======+=======+=======+
# | False | | |
# +-------+-------+-------+
# | True | add | | <- the default
# +=======+=======+=======+
# __repr__
#
# +--- repr= parameter
# |
# v | | |
# | no | yes | <--- class has __repr__ in __dict__?
# +=======+=======+=======+
# | False | | |
# +-------+-------+-------+
# | True | add | | <- the default
# +=======+=======+=======+
# __setattr__
# __delattr__
#
# +--- frozen= parameter
# |
# v | | |
# | no | yes | <--- class has __setattr__ or __delattr__ in __dict__?
# +=======+=======+=======+
# | False | | | <- the default
# +-------+-------+-------+
# | True | add | raise |
# +=======+=======+=======+
# Raise because not adding these methods would break the "frozen-ness"
# of the class.
# __eq__
#
# +--- eq= parameter
# |
# v | | |
# | no | yes | <--- class has __eq__ in __dict__?
# +=======+=======+=======+
# | False | | |
# +-------+-------+-------+
# | True | add | | <- the default
# +=======+=======+=======+
# __lt__
# __le__
# __gt__
# __ge__
#
# +--- order= parameter
# |
# v | | |
# | no | yes | <--- class has any comparison method in __dict__?
# +=======+=======+=======+
# | False | | | <- the default
# +-------+-------+-------+
# | True | add | raise |
# +=======+=======+=======+
# Raise because to allow this case would interfere with using
# functools.total_ordering.
# __hash__
# +------------------- unsafe_hash= parameter
# | +----------- eq= parameter
# | | +--- frozen= parameter
# | | |
# v v v | | |
# | no | yes | <--- class has explicitly defined __hash__
# +=======+=======+=======+========+========+
# | False | False | False | | | No __eq__, use the base class __hash__
# +-------+-------+-------+--------+--------+
# | False | False | True | | | No __eq__, use the base class __hash__
# +-------+-------+-------+--------+--------+
# | False | True | False | None | | <-- the default, not hashable
# +-------+-------+-------+--------+--------+
# | False | True | True | add | | Frozen, so hashable, allows override
# +-------+-------+-------+--------+--------+
# | True | False | False | add | raise | Has no __eq__, but hashable
# +-------+-------+-------+--------+--------+
# | True | False | True | add | raise | Has no __eq__, but hashable
# +-------+-------+-------+--------+--------+
# | True | True | False | add | raise | Not frozen, but hashable
# +-------+-------+-------+--------+--------+
# | True | True | True | add | raise | Frozen, so hashable
# +=======+=======+=======+========+========+
# For boxes that are blank, __hash__ is untouched and therefore
# inherited from the base class. If the base is object, then
# id-based hashing is used.
#
# Note that a class may already have __hash__=None if it specified an
# __eq__ method in the class body (not one that was created by
# @dataclass).
#
# See _hash_action (below) for a coded version of this table.
# Raised when an attempt is made to modify a frozen class.
class FrozenInstanceError(AttributeError): pass
# A sentinel object for default values to signal that a default
# factory will be used. This is given a nice repr() which will appear
# in the function signature of dataclasses' constructors.
class _HAS_DEFAULT_FACTORY_CLASS:
def __repr__(self):
return '<factory>'
_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS()
# A sentinel object to detect if a parameter is supplied or not. Use
# a class to give it a better repr.
class _MISSING_TYPE:
pass
MISSING = _MISSING_TYPE()
# Since most per-field metadata will be unused, create an empty
# read-only proxy that can be shared among all fields.
_EMPTY_METADATA = types.MappingProxyType({})
# Markers for the various kinds of fields and pseudo-fields.
class _FIELD_BASE:
def __init__(self, name):
self.name = name
def __repr__(self):
return self.name
_FIELD = _FIELD_BASE('_FIELD')
_FIELD_CLASSVAR = _FIELD_BASE('_FIELD_CLASSVAR')
_FIELD_INITVAR = _FIELD_BASE('_FIELD_INITVAR')
# The name of an attribute on the class where we store the Field
# objects. Also used to check if a class is a Data Class.
_FIELDS = '__dataclass_fields__'
# The name of an attribute on the class that stores the parameters to
# @dataclass.
_PARAMS = '__dataclass_params__'
# The name of the function, that if it exists, is called at the end of
# __init__.
_POST_INIT_NAME = '__post_init__'
# String regex that string annotations for ClassVar or InitVar must match.
# Allows "identifier.identifier[" or "identifier[".
# https://bugs.python.org/issue33453 for details.
_MODULE_IDENTIFIER_RE = re.compile(r'^(?:\s*(\w+)\s*\.)?\s*(\w+)')
class _InitVarMeta(type):
def __getitem__(self, params):
return InitVar(params)
class InitVar(metaclass=_InitVarMeta):
__slots__ = ('type', )
def __init__(self, type):
self.type = type
def __repr__(self):
return f'dataclasses.InitVar[{self.type.__name__}]'
# Instances of Field are only ever created from within this module,
# and only from the field() function, although Field instances are
# exposed externally as (conceptually) read-only objects.
#
# name and type are filled in after the fact, not in __init__.
# They're not known at the time this class is instantiated, but it's
# convenient if they're available later.
#
# When cls._FIELDS is filled in with a list of Field objects, the name
# and type fields will have been populated.
class Field:
__slots__ = ('name',
'type',
'default',
'default_factory',
'repr',
'hash',
'init',
'compare',
'metadata',
'_field_type', # Private: not to be used by user code.
)
def __init__(self, default, default_factory, init, repr, hash, compare,
metadata):
self.name = None
self.type = None
self.default = default
self.default_factory = default_factory
self.init = init
self.repr = repr
self.hash = hash
self.compare = compare
self.metadata = (_EMPTY_METADATA
if metadata is None else
types.MappingProxyType(metadata))
self._field_type = None
def __repr__(self):
return ('Field('
f'name={self.name!r},'
f'type={self.type!r},'
f'default={self.default!r},'
f'default_factory={self.default_factory!r},'
f'init={self.init!r},'
f'repr={self.repr!r},'
f'hash={self.hash!r},'
f'compare={self.compare!r},'
f'metadata={self.metadata!r},'
f'_field_type={self._field_type}'
')')
# This is used to support the PEP 487 __set_name__ protocol in the
# case where we're using a field that contains a descriptor as a
# default value. For details on __set_name__, see
# https://www.python.org/dev/peps/pep-0487/#implementation-details.
#
# Note that in _process_class, this Field object is overwritten
# with the default value, so the end result is a descriptor that
# had __set_name__ called on it at the right time.
def __set_name__(self, owner, name):
func = getattr(type(self.default), '__set_name__', None)
if func:
# There is a __set_name__ method on the descriptor, call
# it.
func(self.default, owner, name)
class _DataclassParams:
__slots__ = ('init',
'repr',
'eq',
'order',
'unsafe_hash',
'frozen',
)
def __init__(self, init, repr, eq, order, unsafe_hash, frozen):
self.init = init
self.repr = repr
self.eq = eq
self.order = order
self.unsafe_hash = unsafe_hash
self.frozen = frozen
def __repr__(self):
return ('_DataclassParams('
f'init={self.init!r},'
f'repr={self.repr!r},'
f'eq={self.eq!r},'
f'order={self.order!r},'
f'unsafe_hash={self.unsafe_hash!r},'
f'frozen={self.frozen!r}'
')')
# This function is used instead of exposing Field creation directly,
# so that a type checker can be told (via overloads) that this is a
# function whose type depends on its parameters.
def field(*, default=MISSING, default_factory=MISSING, init=True, repr=True,
hash=None, compare=True, metadata=None):
"""Return an object to identify dataclass fields.
default is the default value of the field. default_factory is a
0-argument function called to initialize a field's value. If init
is True, the field will be a parameter to the class's __init__()
function. If repr is True, the field will be included in the
object's repr(). If hash is True, the field will be included in
the object's hash(). If compare is True, the field will be used
in comparison functions. metadata, if specified, must be a
mapping which is stored but not otherwise examined by dataclass.
It is an error to specify both default and default_factory.
"""
if default is not MISSING and default_factory is not MISSING:
raise ValueError('cannot specify both default and default_factory')
return Field(default, default_factory, init, repr, hash, compare,
metadata)
def _tuple_str(obj_name, fields):
# Return a string representing each field of obj_name as a tuple
# member. So, if fields is ['x', 'y'] and obj_name is "self",
# return "(self.x,self.y)".
# Special case for the 0-tuple.
if not fields:
return '()'
# Note the trailing comma, needed if this turns out to be a 1-tuple.
return f'({','.join([f'{obj_name}.{f.name}" for f in fields])},)'
# This function's logic is copied from "recursive_repr" function in
# reprlib module to avoid dependency.
def _recursive_repr(user_function):
# Decorator to make a repr function return "..." for a recursive
# call.
repr_running = set()
@functools.wraps(user_function)
def wrapper(self):
key = id(self), _thread.get_ident()
if key in repr_running:
return '...'
repr_running.add(key)
try:
result = user_function(self)
finally:
repr_running.discard(key)
return result
return wrapper
def _create_fn(name, args, body, *, globals=None, locals=None,
return_type=MISSING):
# Note that we mutate locals when exec() is called. Caller
# beware! The only callers are internal to this module, so no
# worries about external callers.
if locals is None:
locals = {}
# __builtins__ may be the "builtins" module or
# the value of its "__dict__",
# so make sure "__builtins__" is the module.
if globals is not None and '__builtins__' not in globals:
globals['__builtins__'] = builtins
return_annotation = ''
if return_type is not MISSING:
locals['_return_type'] = return_type
return_annotation = '->_return_type'
args = ','.join(args)
body = '\n'.join(f' {b}' for b in body)
# Compute the text of the entire function.
txt = f'def {name}({args}){return_annotation}:\n{body}'
exec(txt, globals, locals)
return locals[name]
def _field_assign(frozen, name, value, self_name):
# If we're a frozen class, then assign to our fields in __init__
# via object.__setattr__. Otherwise, just use a simple
# assignment.
#
# self_name is what "self" is called in this function: don't
# hard-code "self", since that might be a field name.
if frozen:
return f'__builtins__.object.__setattr__({self_name},{name!r},{value})'
return f'{self_name}.{name}={value}'
def _field_init(f, frozen, globals, self_name):
# Return the text of the line in the body of __init__ that will
# initialize this field.
default_name = f'_dflt_{f.name}'
if f.default_factory is not MISSING:
if f.init:
# This field has a default factory. If a parameter is
# given, use it. If not, call the factory.
globals[default_name] = f.default_factory
value = (f'{default_name}() '
f'if {f.name} is _HAS_DEFAULT_FACTORY '
f'else {f.name}')
else:
# This is a field that's not in the __init__ params, but
# has a default factory function. It needs to be
# initialized here by calling the factory function,
# because there's no other way to initialize it.
# For a field initialized with a default=defaultvalue, the
# class dict just has the default value
# (cls.fieldname=defaultvalue). But that won't work for a
# default factory, the factory must be called in __init__
# and we must assign that to self.fieldname. We can't
# fall back to the class dict's value, both because it's
# not set, and because it might be different per-class
# (which, after all, is why we have a factory function!).
globals[default_name] = f.default_factory
value = f'{default_name}()'
else:
# No default factory.
if f.init:
if f.default is MISSING:
# There's no default, just do an assignment.
value = f.name
elif f.default is not MISSING:
globals[default_name] = f.default
value = f.name
else:
# This field does not need initialization. Signify that
# to the caller by returning None.
return None
# Only test this now, so that we can create variables for the
# default. However, return None to signify that we're not going
# to actually do the assignment statement for InitVars.
if f._field_type is _FIELD_INITVAR:
return None
# Now, actually generate the field assignment.
return _field_assign(frozen, f.name, value, self_name)
def _init_param(f):
# Return the __init__ parameter string for this field. For
# example, the equivalent of 'x:int=3' (except instead of 'int',
# reference a variable set to int, and instead of '3', reference a
# variable set to 3).
if f.default is MISSING and f.default_factory is MISSING:
# There's no default, and no default_factory, just output the
# variable name and type.
default = ''
elif f.default is not MISSING:
# There's a default, this will be the name that's used to look
# it up.
default = f'=_dflt_{f.name}'
elif f.default_factory is not MISSING:
# There's a factory function. Set a marker.
default = '=_HAS_DEFAULT_FACTORY'
return f'{f.name}:_type_{f.name}{default}'
def _init_fn(fields, frozen, has_post_init, self_name):
# fields contains both real fields and InitVar pseudo-fields.
# Make sure we don't have fields without defaults following fields
# with defaults. This actually would be caught when exec-ing the
# function source code, but catching it here gives a better error
# message, and future-proofs us in case we build up the function
# using ast.
seen_default = False
for f in fields:
# Only consider fields in the __init__ call.
if f.init:
if not (f.default is MISSING and f.default_factory is MISSING):
seen_default = True
elif seen_default:
raise TypeError(f'non-default argument {f.name!r} '
'follows default argument')
globals = {'MISSING': MISSING,
'_HAS_DEFAULT_FACTORY': _HAS_DEFAULT_FACTORY}
body_lines = []
for f in fields:
line = _field_init(f, frozen, globals, self_name)
# line is None means that this field doesn't require
# initialization (it's a pseudo-field). Just skip it.
if line:
body_lines.append(line)
# Does this class have a post-init function?
if has_post_init:
params_str = ','.join(f.name for f in fields
if f._field_type is _FIELD_INITVAR)
body_lines.append(f'{self_name}.{_POST_INIT_NAME}({params_str})')
# If no body lines, use 'pass'.
if not body_lines:
body_lines = ['pass']
locals = {f'_type_{f.name}': f.type for f in fields}
return _create_fn('__init__',
[self_name] + [_init_param(f) for f in fields if f.init],
body_lines,
locals=locals,
globals=globals,
return_type=None)
def _repr_fn(fields):
fn = _create_fn('__repr__',
('self',),
['return self.__class__.__qualname__ + f"(' +
', '.join([f"{f.name}={{self.{f.name}!r}}"
for f in fields]) +
')"'])
return _recursive_repr(fn)
def _frozen_get_del_attr(cls, fields):
# XXX: globals is modified on the first call to _create_fn, then
# the modified version is used in the second call. Is this okay?
globals = {'cls': cls,
'FrozenInstanceError': FrozenInstanceError}
if fields:
fields_str = '(' + ','.join(repr(f.name) for f in fields) + ',)'
else:
# Special case for the zero-length tuple.
fields_str = '()'
return (_create_fn('__setattr__',
('self', 'name', 'value'),
(f'if type(self) is cls or name in {fields_str}:',
' raise FrozenInstanceError(f"cannot assign to field {name!r}")',
f'super(cls, self).__setattr__(name, value)'),
globals=globals),
_create_fn('__delattr__',
('self', 'name'),
(f'if type(self) is cls or name in {fields_str}:',
' raise FrozenInstanceError(f"cannot delete field {name!r}")',
f'super(cls, self).__delattr__(name)'),
globals=globals),
)
def _cmp_fn(name, op, self_tuple, other_tuple):
# Create a comparison function. If the fields in the object are
# named 'x' and 'y', then self_tuple is the string
# '(self.x,self.y)' and other_tuple is the string
# '(other.x,other.y)'.
return _create_fn(name,
('self', 'other'),
[ 'if other.__class__ is self.__class__:',
f' return {self_tuple}{op}{other_tuple}',
'return NotImplemented'])
def _hash_fn(fields):
self_tuple = _tuple_str('self', fields)
return _create_fn('__hash__',
('self',),
[f'return hash({self_tuple})'])
def _is_classvar(a_type, typing):
# This test uses a typing internal class, but it's the best way to
# test if this is a ClassVar.
return (a_type is typing.ClassVar
or (type(a_type) is typing._GenericAlias
and a_type.__origin__ is typing.ClassVar))
def _is_initvar(a_type, dataclasses):
# The module we're checking against is the module we're
# currently in (dataclasses.py).
return (a_type is dataclasses.InitVar
or type(a_type) is dataclasses.InitVar)
def _is_type(annotation, cls, a_module, a_type, is_type_predicate):
# Given a type annotation string, does it refer to a_type in
# a_module? For example, when checking that annotation denotes a
# ClassVar, then a_module is typing, and a_type is
# typing.ClassVar.
# It's possible to look up a_module given a_type, but it involves
# looking in sys.modules (again!), and seems like a waste since
# the caller already knows a_module.
# - annotation is a string type annotation
# - cls is the class that this annotation was found in
# - a_module is the module we want to match
# - a_type is the type in that module we want to match
# - is_type_predicate is a function called with (obj, a_module)
# that determines if obj is of the desired type.
# Since this test does not do a local namespace lookup (and
# instead only a module (global) lookup), there are some things it
# gets wrong.
# With string annotations, cv0 will be detected as a ClassVar:
# CV = ClassVar
# @dataclass
# class C0:
# cv0: CV
# But in this example cv1 will not be detected as a ClassVar:
# @dataclass
# class C1:
# CV = ClassVar
# cv1: CV
# In C1, the code in this function (_is_type) will look up "CV" in
# the module and not find it, so it will not consider cv1 as a
# ClassVar. This is a fairly obscure corner case, and the best
# way to fix it would be to eval() the string "CV" with the
# correct global and local namespaces. However that would involve
# a eval() penalty for every single field of every dataclass
# that's defined. It was judged not worth it.
match = _MODULE_IDENTIFIER_RE.match(annotation)
if match:
ns = None
module_name = match.group(1)
if not module_name:
# No module name, assume the class's module did
# "from dataclasses import InitVar".
ns = sys.modules.get(cls.__module__).__dict__
else:
# Look up module_name in the class's module.
module = sys.modules.get(cls.__module__)
if module and module.__dict__.get(module_name) is a_module:
ns = sys.modules.get(a_type.__module__).__dict__
if ns and is_type_predicate(ns.get(match.group(2)), a_module):
return True
return False
def _get_field(cls, a_name, a_type):
# Return a Field object for this field name and type. ClassVars
# and InitVars are also returned, but marked as such (see
# f._field_type).
# If the default value isn't derived from Field, then it's only a
# normal default value. Convert it to a Field().
default = getattr(cls, a_name, MISSING)
if isinstance(default, Field):
f = default
else:
if isinstance(default, types.MemberDescriptorType):
# This is a field in __slots__, so it has no default value.
default = MISSING
f = field(default=default)
# Only at this point do we know the name and the type. Set them.
f.name = a_name
f.type = a_type
# Assume it's a normal field until proven otherwise. We're next
# going to decide if it's a ClassVar or InitVar, everything else
# is just a normal field.
f._field_type = _FIELD
# In addition to checking for actual types here, also check for
# string annotations. get_type_hints() won't always work for us
# (see https://github.com/python/typing/issues/508 for example),
# plus it's expensive and would require an eval for every stirng
# annotation. So, make a best effort to see if this is a ClassVar
# or InitVar using regex's and checking that the thing referenced
# is actually of the correct type.
# For the complete discussion, see https://bugs.python.org/issue33453
# If typing has not been imported, then it's impossible for any
# annotation to be a ClassVar. So, only look for ClassVar if
# typing has been imported by any module (not necessarily cls's
# module).
typing = sys.modules.get('typing')
if typing:
if (_is_classvar(a_type, typing)
or (isinstance(f.type, str)
and _is_type(f.type, cls, typing, typing.ClassVar,
_is_classvar))):
f._field_type = _FIELD_CLASSVAR
# If the type is InitVar, or if it's a matching string annotation,
# then it's an InitVar.
if f._field_type is _FIELD:
# The module we're checking against is the module we're
# currently in (dataclasses.py).
dataclasses = sys.modules[__name__]
if (_is_initvar(a_type, dataclasses)
or (isinstance(f.type, str)
and _is_type(f.type, cls, dataclasses, dataclasses.InitVar,
_is_initvar))):
f._field_type = _FIELD_INITVAR
# Validations for individual fields. This is delayed until now,
# instead of in the Field() constructor, since only here do we
# know the field name, which allows for better error reporting.
# Special restrictions for ClassVar and InitVar.
if f._field_type in (_FIELD_CLASSVAR, _FIELD_INITVAR):
if f.default_factory is not MISSING:
raise TypeError(f'field {f.name} cannot have a '
'default factory')
# Should I check for other field settings? default_factory
# seems the most serious to check for. Maybe add others. For
# example, how about init=False (or really,
# init=<not-the-default-init-value>)? It makes no sense for
# ClassVar and InitVar to specify init=<anything>.
# For real fields, disallow mutable defaults for known types.
if f._field_type is _FIELD and isinstance(f.default, (list, dict, set)):
raise ValueError(f'mutable default {type(f.default)} for field '
f'{f.name} is not allowed: use default_factory')
return f
def _set_new_attribute(cls, name, value):
# Never overwrites an existing attribute. Returns True if the
# attribute already exists.
if name in cls.__dict__:
return True
setattr(cls, name, value)
return False
# Decide if/how we're going to create a hash function. Key is
# (unsafe_hash, eq, frozen, does-hash-exist). Value is the action to
# take. The common case is to do nothing, so instead of providing a
# function that is a no-op, use None to signify that.
def _hash_set_none(cls, fields):
return None
def _hash_add(cls, fields):
flds = [f for f in fields if (f.compare if f.hash is None else f.hash)]
return _hash_fn(flds)
def _hash_exception(cls, fields):
# Raise an exception.
raise TypeError(f'Cannot overwrite attribute __hash__ '
f'in class {cls.__name__}')
#
# +-------------------------------------- unsafe_hash?
# | +------------------------------- eq?
# | | +------------------------ frozen?
# | | | +---------------- has-explicit-hash?
# | | | |
# | | | | +------- action
# | | | | |
# v v v v v
_hash_action = {(False, False, False, False): None,
(False, False, False, True ): None,
(False, False, True, False): None,
(False, False, True, True ): None,
(False, True, False, False): _hash_set_none,
(False, True, False, True ): None,
(False, True, True, False): _hash_add,
(False, True, True, True ): None,
(True, False, False, False): _hash_add,
(True, False, False, True ): _hash_exception,
(True, False, True, False): _hash_add,
(True, False, True, True ): _hash_exception,
(True, True, False, False): _hash_add,
(True, True, False, True ): _hash_exception,
(True, True, True, False): _hash_add,
(True, True, True, True ): _hash_exception,
}
# See https://bugs.python.org/issue32929#msg312829 for an if-statement
# version of this table.
def _process_class(cls, init, repr, eq, order, unsafe_hash, frozen):
# Now that dicts retain insertion order, there's no reason to use
# an ordered dict. I am leveraging that ordering here, because
# derived class fields overwrite base class fields, but the order
# is defined by the base class, which is found first.
fields = {}
setattr(cls, _PARAMS, _DataclassParams(init, repr, eq, order,
unsafe_hash, frozen))
# Find our base classes in reverse MRO order, and exclude
# ourselves. In reversed order so that more derived classes
# override earlier field definitions in base classes. As long as
# we're iterating over them, see if any are frozen.
any_frozen_base = False
has_dataclass_bases = False
for b in cls.__mro__[-1:0:-1]:
# Only process classes that have been processed by our
# decorator. That is, they have a _FIELDS attribute.
base_fields = getattr(b, _FIELDS, None)
if base_fields:
has_dataclass_bases = True
for f in base_fields.values():
fields[f.name] = f
if getattr(b, _PARAMS).frozen:
any_frozen_base = True
# Annotations that are defined in this class (not in base
# classes). If __annotations__ isn't present, then this class
# adds no new annotations. We use this to compute fields that are
# added by this class.
#
# Fields are found from cls_annotations, which is guaranteed to be
# ordered. Default values are from class attributes, if a field
# has a default. If the default value is a Field(), then it
# contains additional info beyond (and possibly including) the
# actual default value. Pseudo-fields ClassVars and InitVars are
# included, despite the fact that they're not real fields. That's
# dealt with later.
cls_annotations = cls.__dict__.get('__annotations__', {})
# Now find fields in our class. While doing so, validate some
# things, and set the default values (as class attributes) where
# we can.
cls_fields = [_get_field(cls, name, type)
for name, type in cls_annotations.items()]
for f in cls_fields:
fields[f.name] = f
# If the class attribute (which is the default value for this
# field) exists and is of type 'Field', replace it with the
# real default. This is so that normal class introspection
# sees a real default value, not a Field.
if isinstance(getattr(cls, f.name, None), Field):
if f.default is MISSING:
# If there's no default, delete the class attribute.
# This happens if we specify field(repr=False), for
# example (that is, we specified a field object, but
# no default value). Also if we're using a default
# factory. The class attribute should not be set at
# all in the post-processed class.
delattr(cls, f.name)
else:
setattr(cls, f.name, f.default)
# Do we have any Field members that don't also have annotations?
for name, value in cls.__dict__.items():
if isinstance(value, Field) and not name in cls_annotations:
raise TypeError(f'{name!r} is a field but has no type annotation')
# Check rules that apply if we are derived from any dataclasses.
if has_dataclass_bases:
# Raise an exception if any of our bases are frozen, but we're not.
if any_frozen_base and not frozen:
raise TypeError('cannot inherit non-frozen dataclass from a '
'frozen one')
# Raise an exception if we're frozen, but none of our bases are.
if not any_frozen_base and frozen:
raise TypeError('cannot inherit frozen dataclass from a '
'non-frozen one')
# Remember all of the fields on our class (including bases). This
# also marks this class as being a dataclass.
setattr(cls, _FIELDS, fields)
# Was this class defined with an explicit __hash__? Note that if
# __eq__ is defined in this class, then python will automatically
# set __hash__ to None. This is a heuristic, as it's possible
# that such a __hash__ == None was not auto-generated, but it
# close enough.
class_hash = cls.__dict__.get('__hash__', MISSING)
has_explicit_hash = not (class_hash is MISSING or
(class_hash is None and '__eq__' in cls.__dict__))
# If we're generating ordering methods, we must be generating the
# eq methods.
if order and not eq:
raise ValueError('eq must be true if order is true')
if init:
# Does this class have a post-init function?
has_post_init = hasattr(cls, _POST_INIT_NAME)
# Include InitVars and regular fields (so, not ClassVars).
flds = [f for f in fields.values()
if f._field_type in (_FIELD, _FIELD_INITVAR)]
_set_new_attribute(cls, '__init__',
_init_fn(flds,
frozen,
has_post_init,
# The name to use for the "self"
# param in __init__. Use "self"
# if possible.
'__dataclass_self__' if 'self' in fields
else 'self',
))
# Get the fields as a list, and include only real fields. This is
# used in all of the following methods.
field_list = [f for f in fields.values() if f._field_type is _FIELD]
if repr:
flds = [f for f in field_list if f.repr]
_set_new_attribute(cls, '__repr__', _repr_fn(flds))
if eq:
# Create _eq__ method. There's no need for a __ne__ method,
# since python will call __eq__ and negate it.
flds = [f for f in field_list if f.compare]
self_tuple = _tuple_str('self', flds)
other_tuple = _tuple_str('other', flds)
_set_new_attribute(cls, '__eq__',
_cmp_fn('__eq__', '==',
self_tuple, other_tuple))
if order:
# Create and set the ordering methods.
flds = [f for f in field_list if f.compare]
self_tuple = _tuple_str('self', flds)
other_tuple = _tuple_str('other', flds)
for name, op in [('__lt__', '<'),
('__le__', '<='),
('__gt__', '>'),
('__ge__', '>='),
]:
if _set_new_attribute(cls, name,
_cmp_fn(name, op, self_tuple, other_tuple)):
raise TypeError(f'Cannot overwrite attribute {name} '
f'in class {cls.__name__}. Consider using '
'functools.total_ordering')
if frozen:
for fn in _frozen_get_del_attr(cls, field_list):
if _set_new_attribute(cls, fn.__name__, fn):
raise TypeError(f'Cannot overwrite attribute {fn.__name__} '
f'in class {cls.__name__}')
# Decide if/how we're going to create a hash function.
hash_action = _hash_action[bool(unsafe_hash),
bool(eq),
bool(frozen),
has_explicit_hash]
if hash_action:
# No need to call _set_new_attribute here, since by the time
# we're here the overwriting is unconditional.
cls.__hash__ = hash_action(cls, field_list)
if not getattr(cls, '__doc__'):
# Create a class doc-string.
cls.__doc__ = (cls.__name__ +
str(inspect.signature(cls)).replace(' -> None', ''))
return cls
def dataclass(cls=None, /, *, init=True, repr=True, eq=True, order=False,
unsafe_hash=False, frozen=False):
"""Returns the same class as was passed in, with dunder methods
added based on the fields defined in the class.
Examines PEP 526 __annotations__ to determine fields.
If init is true, an __init__() method is added to the class. If
repr is true, a __repr__() method is added. If order is true, rich
comparison dunder methods are added. If unsafe_hash is true, a
__hash__() method function is added. If frozen is true, fields may
not be assigned to after instance creation.
"""
def wrap(cls):
return _process_class(cls, init, repr, eq, order, unsafe_hash, frozen)
# See if we're being called as @dataclass or @dataclass().
if cls is None:
# We're called with parens.
return wrap
# We're called as @dataclass without parens.
return wrap(cls)
def fields(class_or_instance):
"""Return a tuple describing the fields of this dataclass.
Accepts a dataclass or an instance of one. Tuple elements are of
type Field.
"""
# Might it be worth caching this, per class?
try:
fields = getattr(class_or_instance, _FIELDS)
except AttributeError:
raise TypeError('must be called with a dataclass type or instance')
# Exclude pseudo-fields. Note that fields is sorted by insertion
# order, so the order of the tuple is as the fields were defined.
return tuple(f for f in fields.values() if f._field_type is _FIELD)
def _is_dataclass_instance(obj):
"""Returns True if obj is an instance of a dataclass."""
return not isinstance(obj, type) and hasattr(obj, _FIELDS)
def is_dataclass(obj):
"""Returns True if obj is a dataclass or an instance of a
dataclass."""
return hasattr(obj, _FIELDS)
def asdict(obj, *, dict_factory=dict):
"""Return the fields of a dataclass instance as a new dictionary mapping
field names to field values.
Example usage:
@dataclass
class C:
x: int
y: int
c = C(1, 2)
assert asdict(c) == {'x': 1, 'y': 2}
If given, 'dict_factory' will be used instead of built-in dict.
The function applies recursively to field values that are
dataclass instances. This will also look into built-in containers:
tuples, lists, and dicts.
"""
if not _is_dataclass_instance(obj):
raise TypeError("asdict() should be called on dataclass instances")
return _asdict_inner(obj, dict_factory)
def _asdict_inner(obj, dict_factory):
if _is_dataclass_instance(obj):
result = []
for f in fields(obj):
value = _asdict_inner(getattr(obj, f.name), dict_factory)
result.append((f.name, value))
return dict_factory(result)
elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
# obj is a namedtuple. Recurse into it, but the returned
# object is another namedtuple of the same type. This is
# similar to how other list- or tuple-derived classes are
# treated (see below), but we just need to create them
# differently because a namedtuple's __init__ needs to be
# called differently (see bpo-34363).
# I'm not using namedtuple's _asdict()
# method, because:
# - it does not recurse in to the namedtuple fields and
# convert them to dicts (using dict_factory).
# - I don't actually want to return a dict here. The the main
# use case here is json.dumps, and it handles converting
# namedtuples to lists. Admittedly we're losing some
# information here when we produce a json list instead of a
# dict. Note that if we returned dicts here instead of
# namedtuples, we could no longer call asdict() on a data
# structure where a namedtuple was used as a dict key.
return type(obj)(*[_asdict_inner(v, dict_factory) for v in obj])
elif isinstance(obj, (list, tuple)):
# Assume we can create an object of this type by passing in a
# generator (which is not true for namedtuples, handled
# above).
return type(obj)(_asdict_inner(v, dict_factory) for v in obj)
elif isinstance(obj, dict):
return type(obj)((_asdict_inner(k, dict_factory),
_asdict_inner(v, dict_factory))
for k, v in obj.items())
else:
return copy.deepcopy(obj)
def astuple(obj, *, tuple_factory=tuple):
"""Return the fields of a dataclass instance as a new tuple of field values.
Example usage::
@dataclass
class C:
x: int
y: int
c = C(1, 2)
assert astuple(c) == (1, 2)
If given, 'tuple_factory' will be used instead of built-in tuple.
The function applies recursively to field values that are
dataclass instances. This will also look into built-in containers:
tuples, lists, and dicts.
"""
if not _is_dataclass_instance(obj):
raise TypeError("astuple() should be called on dataclass instances")
return _astuple_inner(obj, tuple_factory)
def _astuple_inner(obj, tuple_factory):
if _is_dataclass_instance(obj):
result = []
for f in fields(obj):
value = _astuple_inner(getattr(obj, f.name), tuple_factory)
result.append(value)
return tuple_factory(result)
elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
# obj is a namedtuple. Recurse into it, but the returned
# object is another namedtuple of the same type. This is
# similar to how other list- or tuple-derived classes are
# treated (see below), but we just need to create them
# differently because a namedtuple's __init__ needs to be
# called differently (see bpo-34363).
return type(obj)(*[_astuple_inner(v, tuple_factory) for v in obj])
elif isinstance(obj, (list, tuple)):
# Assume we can create an object of this type by passing in a
# generator (which is not true for namedtuples, handled
# above).
return type(obj)(_astuple_inner(v, tuple_factory) for v in obj)
elif isinstance(obj, dict):
return type(obj)((_astuple_inner(k, tuple_factory), _astuple_inner(v, tuple_factory))
for k, v in obj.items())
else:
return copy.deepcopy(obj)
def make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True,
repr=True, eq=True, order=False, unsafe_hash=False,
frozen=False):
"""Return a new dynamically created dataclass.
The dataclass name will be 'cls_name'. 'fields' is an iterable
of either (name), (name, type) or (name, type, Field) objects. If type is
omitted, use the string 'typing.Any'. Field objects are created by
the equivalent of calling 'field(name, type [, Field-info])'.
C = make_dataclass('C', ['x', ('y', int), ('z', int, field(init=False))], bases=(Base,))
is equivalent to:
@dataclass
class C(Base):
x: 'typing.Any'
y: int
z: int = field(init=False)
For the bases and namespace parameters, see the builtin type() function.
The parameters init, repr, eq, order, unsafe_hash, and frozen are passed to
dataclass().
"""
if namespace is None:
namespace = {}
else:
# Copy namespace since we're going to mutate it.
namespace = namespace.copy()
# While we're looking through the field names, validate that they
# are identifiers, are not keywords, and not duplicates.
seen = set()
anns = {}
for item in fields:
if isinstance(item, str):
name = item
tp = 'typing.Any'
elif len(item) == 2:
name, tp, = item
elif len(item) == 3:
name, tp, spec = item
namespace[name] = spec
else:
raise TypeError(f'Invalid field: {item!r}')
if not isinstance(name, str) or not name.isidentifier():
raise TypeError(f'Field names must be valid identifiers: {name!r}')
if keyword.iskeyword(name):
raise TypeError(f'Field names must not be keywords: {name!r}')
if name in seen:
raise TypeError(f'Field name duplicated: {name!r}')
seen.add(name)
anns[name] = tp
namespace['__annotations__'] = anns
# We use `types.new_class()` instead of simply `type()` to allow dynamic creation
# of generic dataclassses.
cls = types.new_class(cls_name, bases, {}, lambda ns: ns.update(namespace))
return dataclass(cls, init=init, repr=repr, eq=eq, order=order,
unsafe_hash=unsafe_hash, frozen=frozen)
def replace(obj, /, **changes):
"""Return a new object replacing specified fields with new values.
This is especially useful for frozen classes. Example usage:
@dataclass(frozen=True)
class C:
x: int
y: int
c = C(1, 2)
c1 = replace(c, x=3)
assert c1.x == 3 and c1.y == 2
"""
# We're going to mutate 'changes', but that's okay because it's a
# new dict, even if called with 'replace(obj, **my_changes)'.
if not _is_dataclass_instance(obj):
raise TypeError("replace() should be called on dataclass instances")
# It's an error to have init=False fields in 'changes'.
# If a field is not in 'changes', read its value from the provided obj.
for f in getattr(obj, _FIELDS).values():
# Only consider normal fields or InitVars.
if f._field_type is _FIELD_CLASSVAR:
continue
if not f.init:
# Error if this field is specified in changes.
if f.name in changes:
raise ValueError(f'field {f.name} is declared with '
'init=False, it cannot be specified with '
'replace()')
continue
if f.name not in changes:
if f._field_type is _FIELD_INITVAR:
raise ValueError(f"InitVar {f.name!r} "
'must be specified with replace()')
changes[f.name] = getattr(obj, f.name)
# Create the new object, which calls __init__() and
# __post_init__() (if defined), using all of the init fields we've
# added and/or left in 'changes'. If there are values supplied in
# changes that aren't fields, this will correctly raise a
# TypeError.
return obj.__class__(**changes)
| import re
import sys
import copy
import types
import inspect
import keyword
import builtins
import functools
import _thread
__all__ = ['dataclass',
'field',
'Field',
'FrozenInstanceError',
'InitVar',
'MISSING',
# Helper functions.
'fields',
'asdict',
'astuple',
'make_dataclass',
'replace',
'is_dataclass',
]
# Conditions for adding methods. The boxes indicate what action the
# dataclass decorator takes. For all of these tables, when I talk
# about init=, repr=, eq=, order=, unsafe_hash=, or frozen=, I'm
# referring to the arguments to the @dataclass decorator. When
# checking if a dunder method already exists, I mean check for an
# entry in the class's __dict__. I never check to see if an attribute
# is defined in a base class.
# Key:
# +=========+=========================================+
# + Value | Meaning |
# +=========+=========================================+
# | <blank> | No action: no method is added. |
# +---------+-----------------------------------------+
# | add | Generated method is added. |
# +---------+-----------------------------------------+
# | raise | TypeError is raised. |
# +---------+-----------------------------------------+
# | None | Attribute is set to None. |
# +=========+=========================================+
# __init__
#
# +--- init= parameter
# |
# v | | |
# | no | yes | <--- class has __init__ in __dict__?
# +=======+=======+=======+
# | False | | |
# +-------+-------+-------+
# | True | add | | <- the default
# +=======+=======+=======+
# __repr__
#
# +--- repr= parameter
# |
# v | | |
# | no | yes | <--- class has __repr__ in __dict__?
# +=======+=======+=======+
# | False | | |
# +-------+-------+-------+
# | True | add | | <- the default
# +=======+=======+=======+
# __setattr__
# __delattr__
#
# +--- frozen= parameter
# |
# v | | |
# | no | yes | <--- class has __setattr__ or __delattr__ in __dict__?
# +=======+=======+=======+
# | False | | | <- the default
# +-------+-------+-------+
# | True | add | raise |
# +=======+=======+=======+
# Raise because not adding these methods would break the "frozen-ness"
# of the class.
# __eq__
#
# +--- eq= parameter
# |
# v | | |
# | no | yes | <--- class has __eq__ in __dict__?
# +=======+=======+=======+
# | False | | |
# +-------+-------+-------+
# | True | add | | <- the default
# +=======+=======+=======+
# __lt__
# __le__
# __gt__
# __ge__
#
# +--- order= parameter
# |
# v | | |
# | no | yes | <--- class has any comparison method in __dict__?
# +=======+=======+=======+
# | False | | | <- the default
# +-------+-------+-------+
# | True | add | raise |
# +=======+=======+=======+
# Raise because to allow this case would interfere with using
# functools.total_ordering.
# __hash__
# +------------------- unsafe_hash= parameter
# | +----------- eq= parameter
# | | +--- frozen= parameter
# | | |
# v v v | | |
# | no | yes | <--- class has explicitly defined __hash__
# +=======+=======+=======+========+========+
# | False | False | False | | | No __eq__, use the base class __hash__
# +-------+-------+-------+--------+--------+
# | False | False | True | | | No __eq__, use the base class __hash__
# +-------+-------+-------+--------+--------+
# | False | True | False | None | | <-- the default, not hashable
# +-------+-------+-------+--------+--------+
# | False | True | True | add | | Frozen, so hashable, allows override
# +-------+-------+-------+--------+--------+
# | True | False | False | add | raise | Has no __eq__, but hashable
# +-------+-------+-------+--------+--------+
# | True | False | True | add | raise | Has no __eq__, but hashable
# +-------+-------+-------+--------+--------+
# | True | True | False | add | raise | Not frozen, but hashable
# +-------+-------+-------+--------+--------+
# | True | True | True | add | raise | Frozen, so hashable
# +=======+=======+=======+========+========+
# For boxes that are blank, __hash__ is untouched and therefore
# inherited from the base class. If the base is object, then
# id-based hashing is used.
#
# Note that a class may already have __hash__=None if it specified an
# __eq__ method in the class body (not one that was created by
# @dataclass).
#
# See _hash_action (below) for a coded version of this table.
# Raised when an attempt is made to modify a frozen class.
class FrozenInstanceError(AttributeError): pass
# A sentinel object for default values to signal that a default
# factory will be used. This is given a nice repr() which will appear
# in the function signature of dataclasses' constructors.
class _HAS_DEFAULT_FACTORY_CLASS:
def __repr__(self):
return '<factory>'
_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS()
# A sentinel object to detect if a parameter is supplied or not. Use
# a class to give it a better repr.
class _MISSING_TYPE:
pass
MISSING = _MISSING_TYPE()
# Since most per-field metadata will be unused, create an empty
# read-only proxy that can be shared among all fields.
_EMPTY_METADATA = types.MappingProxyType({})
# Markers for the various kinds of fields and pseudo-fields.
class _FIELD_BASE:
def __init__(self, name):
self.name = name
def __repr__(self):
return self.name
_FIELD = _FIELD_BASE('_FIELD')
_FIELD_CLASSVAR = _FIELD_BASE('_FIELD_CLASSVAR')
_FIELD_INITVAR = _FIELD_BASE('_FIELD_INITVAR')
# The name of an attribute on the class where we store the Field
# objects. Also used to check if a class is a Data Class.
_FIELDS = '__dataclass_fields__'
# The name of an attribute on the class that stores the parameters to
# @dataclass.
_PARAMS = '__dataclass_params__'
# The name of the function, that if it exists, is called at the end of
# __init__.
_POST_INIT_NAME = '__post_init__'
# String regex that string annotations for ClassVar or InitVar must match.
# Allows "identifier.identifier[" or "identifier[".
# https://bugs.python.org/issue33453 for details.
_MODULE_IDENTIFIER_RE = re.compile(r'^(?:\s*(\w+)\s*\.)?\s*(\w+)')
class _InitVarMeta(type):
def __getitem__(self, params):
return InitVar(params)
class InitVar(metaclass=_InitVarMeta):
__slots__ = ('type', )
def __init__(self, type):
self.type = type
def __repr__(self):
return f'dataclasses.InitVar[{self.type.__name__}]'
# Instances of Field are only ever created from within this module,
# and only from the field() function, although Field instances are
# exposed externally as (conceptually) read-only objects.
#
# name and type are filled in after the fact, not in __init__.
# They're not known at the time this class is instantiated, but it's
# convenient if they're available later.
#
# When cls._FIELDS is filled in with a list of Field objects, the name
# and type fields will have been populated.
class Field:
__slots__ = ('name',
'type',
'default',
'default_factory',
'repr',
'hash',
'init',
'compare',
'metadata',
'_field_type', # Private: not to be used by user code.
)
def __init__(self, default, default_factory, init, repr, hash, compare,
metadata):
self.name = None
self.type = None
self.default = default
self.default_factory = default_factory
self.init = init
self.repr = repr
self.hash = hash
self.compare = compare
self.metadata = (_EMPTY_METADATA
if metadata is None else
types.MappingProxyType(metadata))
self._field_type = None
def __repr__(self):
return ('Field('
f'name={self.name!r},'
f'type={self.type!r},'
f'default={self.default!r},'
f'default_factory={self.default_factory!r},'
f'init={self.init!r},'
f'repr={self.repr!r},'
f'hash={self.hash!r},'
f'compare={self.compare!r},'
f'metadata={self.metadata!r},'
f'_field_type={self._field_type}'
')')
# This is used to support the PEP 487 __set_name__ protocol in the
# case where we're using a field that contains a descriptor as a
# default value. For details on __set_name__, see
# https://www.python.org/dev/peps/pep-0487/#implementation-details.
#
# Note that in _process_class, this Field object is overwritten
# with the default value, so the end result is a descriptor that
# had __set_name__ called on it at the right time.
def __set_name__(self, owner, name):
func = getattr(type(self.default), '__set_name__', None)
if func:
# There is a __set_name__ method on the descriptor, call
# it.
func(self.default, owner, name)
class _DataclassParams:
__slots__ = ('init',
'repr',
'eq',
'order',
'unsafe_hash',
'frozen',
)
def __init__(self, init, repr, eq, order, unsafe_hash, frozen):
self.init = init
self.repr = repr
self.eq = eq
self.order = order
self.unsafe_hash = unsafe_hash
self.frozen = frozen
def __repr__(self):
return ('_DataclassParams('
f'init={self.init!r},'
f'repr={self.repr!r},'
f'eq={self.eq!r},'
f'order={self.order!r},'
f'unsafe_hash={self.unsafe_hash!r},'
f'frozen={self.frozen!r}'
')')
# This function is used instead of exposing Field creation directly,
# so that a type checker can be told (via overloads) that this is a
# function whose type depends on its parameters.
def field(*, default=MISSING, default_factory=MISSING, init=True, repr=True,
hash=None, compare=True, metadata=None):
"""Return an object to identify dataclass fields.
default is the default value of the field. default_factory is a
0-argument function called to initialize a field's value. If init
is True, the field will be a parameter to the class's __init__()
function. If repr is True, the field will be included in the
object's repr(). If hash is True, the field will be included in
the object's hash(). If compare is True, the field will be used
in comparison functions. metadata, if specified, must be a
mapping which is stored but not otherwise examined by dataclass.
It is an error to specify both default and default_factory.
"""
if default is not MISSING and default_factory is not MISSING:
raise ValueError('cannot specify both default and default_factory')
return Field(default, default_factory, init, repr, hash, compare,
metadata)
def _tuple_str(obj_name, fields):
# Return a string representing each field of obj_name as a tuple
# member. So, if fields is ['x', 'y'] and obj_name is "self",
# return "(self.x,self.y)".
# Special case for the 0-tuple.
if not fields:
return '()'
# Note the trailing comma, needed if this turns out to be a 1-tuple.
return f'({",".join([f"{obj_name}.{f.name}" for f in fields])},)'
# This function's logic is copied from "recursive_repr" function in
# reprlib module to avoid dependency.
def _recursive_repr(user_function):
# Decorator to make a repr function return "..." for a recursive
# call.
repr_running = set()
@functools.wraps(user_function)
def wrapper(self):
key = id(self), _thread.get_ident()
if key in repr_running:
return '...'
repr_running.add(key)
try:
result = user_function(self)
finally:
repr_running.discard(key)
return result
return wrapper
def _create_fn(name, args, body, *, globals=None, locals=None,
return_type=MISSING):
# Note that we mutate locals when exec() is called. Caller
# beware! The only callers are internal to this module, so no
# worries about external callers.
if locals is None:
locals = {}
# __builtins__ may be the "builtins" module or
# the value of its "__dict__",
# so make sure "__builtins__" is the module.
if globals is not None and '__builtins__' not in globals:
globals['__builtins__'] = builtins
return_annotation = ''
if return_type is not MISSING:
locals['_return_type'] = return_type
return_annotation = '->_return_type'
args = ','.join(args)
body = '\n'.join(f' {b}' for b in body)
# Compute the text of the entire function.
txt = f'def {name}({args}){return_annotation}:\n{body}'
exec(txt, globals, locals)
return locals[name]
def _field_assign(frozen, name, value, self_name):
# If we're a frozen class, then assign to our fields in __init__
# via object.__setattr__. Otherwise, just use a simple
# assignment.
#
# self_name is what "self" is called in this function: don't
# hard-code "self", since that might be a field name.
if frozen:
return f'__builtins__.object.__setattr__({self_name},{name!r},{value})'
return f'{self_name}.{name}={value}'
def _field_init(f, frozen, globals, self_name):
# Return the text of the line in the body of __init__ that will
# initialize this field.
default_name = f'_dflt_{f.name}'
if f.default_factory is not MISSING:
if f.init:
# This field has a default factory. If a parameter is
# given, use it. If not, call the factory.
globals[default_name] = f.default_factory
value = (f'{default_name}() '
f'if {f.name} is _HAS_DEFAULT_FACTORY '
f'else {f.name}')
else:
# This is a field that's not in the __init__ params, but
# has a default factory function. It needs to be
# initialized here by calling the factory function,
# because there's no other way to initialize it.
# For a field initialized with a default=defaultvalue, the
# class dict just has the default value
# (cls.fieldname=defaultvalue). But that won't work for a
# default factory, the factory must be called in __init__
# and we must assign that to self.fieldname. We can't
# fall back to the class dict's value, both because it's
# not set, and because it might be different per-class
# (which, after all, is why we have a factory function!).
globals[default_name] = f.default_factory
value = f'{default_name}()'
else:
# No default factory.
if f.init:
if f.default is MISSING:
# There's no default, just do an assignment.
value = f.name
elif f.default is not MISSING:
globals[default_name] = f.default
value = f.name
else:
# This field does not need initialization. Signify that
# to the caller by returning None.
return None
# Only test this now, so that we can create variables for the
# default. However, return None to signify that we're not going
# to actually do the assignment statement for InitVars.
if f._field_type is _FIELD_INITVAR:
return None
# Now, actually generate the field assignment.
return _field_assign(frozen, f.name, value, self_name)
def _init_param(f):
# Return the __init__ parameter string for this field. For
# example, the equivalent of 'x:int=3' (except instead of 'int',
# reference a variable set to int, and instead of '3', reference a
# variable set to 3).
if f.default is MISSING and f.default_factory is MISSING:
# There's no default, and no default_factory, just output the
# variable name and type.
default = ''
elif f.default is not MISSING:
# There's a default, this will be the name that's used to look
# it up.
default = f'=_dflt_{f.name}'
elif f.default_factory is not MISSING:
# There's a factory function. Set a marker.
default = '=_HAS_DEFAULT_FACTORY'
return f'{f.name}:_type_{f.name}{default}'
def _init_fn(fields, frozen, has_post_init, self_name):
# fields contains both real fields and InitVar pseudo-fields.
# Make sure we don't have fields without defaults following fields
# with defaults. This actually would be caught when exec-ing the
# function source code, but catching it here gives a better error
# message, and future-proofs us in case we build up the function
# using ast.
seen_default = False
for f in fields:
# Only consider fields in the __init__ call.
if f.init:
if not (f.default is MISSING and f.default_factory is MISSING):
seen_default = True
elif seen_default:
raise TypeError(f'non-default argument {f.name!r} '
'follows default argument')
globals = {'MISSING': MISSING,
'_HAS_DEFAULT_FACTORY': _HAS_DEFAULT_FACTORY}
body_lines = []
for f in fields:
line = _field_init(f, frozen, globals, self_name)
# line is None means that this field doesn't require
# initialization (it's a pseudo-field). Just skip it.
if line:
body_lines.append(line)
# Does this class have a post-init function?
if has_post_init:
params_str = ','.join(f.name for f in fields
if f._field_type is _FIELD_INITVAR)
body_lines.append(f'{self_name}.{_POST_INIT_NAME}({params_str})')
# If no body lines, use 'pass'.
if not body_lines:
body_lines = ['pass']
locals = {f'_type_{f.name}': f.type for f in fields}
return _create_fn('__init__',
[self_name] + [_init_param(f) for f in fields if f.init],
body_lines,
locals=locals,
globals=globals,
return_type=None)
def _repr_fn(fields):
fn = _create_fn('__repr__',
('self',),
['return self.__class__.__qualname__ + f"(' +
', '.join([f"{f.name}={{self.{f.name}!r}}"
for f in fields]) +
')"'])
return _recursive_repr(fn)
def _frozen_get_del_attr(cls, fields):
# XXX: globals is modified on the first call to _create_fn, then
# the modified version is used in the second call. Is this okay?
globals = {'cls': cls,
'FrozenInstanceError': FrozenInstanceError}
if fields:
fields_str = '(' + ','.join(repr(f.name) for f in fields) + ',)'
else:
# Special case for the zero-length tuple.
fields_str = '()'
return (_create_fn('__setattr__',
('self', 'name', 'value'),
(f'if type(self) is cls or name in {fields_str}:',
' raise FrozenInstanceError(f"cannot assign to field {name!r}")',
f'super(cls, self).__setattr__(name, value)'),
globals=globals),
_create_fn('__delattr__',
('self', 'name'),
(f'if type(self) is cls or name in {fields_str}:',
' raise FrozenInstanceError(f"cannot delete field {name!r}")',
f'super(cls, self).__delattr__(name)'),
globals=globals),
)
def _cmp_fn(name, op, self_tuple, other_tuple):
# Create a comparison function. If the fields in the object are
# named 'x' and 'y', then self_tuple is the string
# '(self.x,self.y)' and other_tuple is the string
# '(other.x,other.y)'.
return _create_fn(name,
('self', 'other'),
[ 'if other.__class__ is self.__class__:',
f' return {self_tuple}{op}{other_tuple}',
'return NotImplemented'])
def _hash_fn(fields):
self_tuple = _tuple_str('self', fields)
return _create_fn('__hash__',
('self',),
[f'return hash({self_tuple})'])
def _is_classvar(a_type, typing):
# This test uses a typing internal class, but it's the best way to
# test if this is a ClassVar.
return (a_type is typing.ClassVar
or (type(a_type) is typing._GenericAlias
and a_type.__origin__ is typing.ClassVar))
def _is_initvar(a_type, dataclasses):
# The module we're checking against is the module we're
# currently in (dataclasses.py).
return (a_type is dataclasses.InitVar
or type(a_type) is dataclasses.InitVar)
def _is_type(annotation, cls, a_module, a_type, is_type_predicate):
# Given a type annotation string, does it refer to a_type in
# a_module? For example, when checking that annotation denotes a
# ClassVar, then a_module is typing, and a_type is
# typing.ClassVar.
# It's possible to look up a_module given a_type, but it involves
# looking in sys.modules (again!), and seems like a waste since
# the caller already knows a_module.
# - annotation is a string type annotation
# - cls is the class that this annotation was found in
# - a_module is the module we want to match
# - a_type is the type in that module we want to match
# - is_type_predicate is a function called with (obj, a_module)
# that determines if obj is of the desired type.
# Since this test does not do a local namespace lookup (and
# instead only a module (global) lookup), there are some things it
# gets wrong.
# With string annotations, cv0 will be detected as a ClassVar:
# CV = ClassVar
# @dataclass
# class C0:
# cv0: CV
# But in this example cv1 will not be detected as a ClassVar:
# @dataclass
# class C1:
# CV = ClassVar
# cv1: CV
# In C1, the code in this function (_is_type) will look up "CV" in
# the module and not find it, so it will not consider cv1 as a
# ClassVar. This is a fairly obscure corner case, and the best
# way to fix it would be to eval() the string "CV" with the
# correct global and local namespaces. However that would involve
# a eval() penalty for every single field of every dataclass
# that's defined. It was judged not worth it.
match = _MODULE_IDENTIFIER_RE.match(annotation)
if match:
ns = None
module_name = match.group(1)
if not module_name:
# No module name, assume the class's module did
# "from dataclasses import InitVar".
ns = sys.modules.get(cls.__module__).__dict__
else:
# Look up module_name in the class's module.
module = sys.modules.get(cls.__module__)
if module and module.__dict__.get(module_name) is a_module:
ns = sys.modules.get(a_type.__module__).__dict__
if ns and is_type_predicate(ns.get(match.group(2)), a_module):
return True
return False
def _get_field(cls, a_name, a_type):
# Return a Field object for this field name and type. ClassVars
# and InitVars are also returned, but marked as such (see
# f._field_type).
# If the default value isn't derived from Field, then it's only a
# normal default value. Convert it to a Field().
default = getattr(cls, a_name, MISSING)
if isinstance(default, Field):
f = default
else:
if isinstance(default, types.MemberDescriptorType):
# This is a field in __slots__, so it has no default value.
default = MISSING
f = field(default=default)
# Only at this point do we know the name and the type. Set them.
f.name = a_name
f.type = a_type
# Assume it's a normal field until proven otherwise. We're next
# going to decide if it's a ClassVar or InitVar, everything else
# is just a normal field.
f._field_type = _FIELD
# In addition to checking for actual types here, also check for
# string annotations. get_type_hints() won't always work for us
# (see https://github.com/python/typing/issues/508 for example),
# plus it's expensive and would require an eval for every stirng
# annotation. So, make a best effort to see if this is a ClassVar
# or InitVar using regex's and checking that the thing referenced
# is actually of the correct type.
# For the complete discussion, see https://bugs.python.org/issue33453
# If typing has not been imported, then it's impossible for any
# annotation to be a ClassVar. So, only look for ClassVar if
# typing has been imported by any module (not necessarily cls's
# module).
typing = sys.modules.get('typing')
if typing:
if (_is_classvar(a_type, typing)
or (isinstance(f.type, str)
and _is_type(f.type, cls, typing, typing.ClassVar,
_is_classvar))):
f._field_type = _FIELD_CLASSVAR
# If the type is InitVar, or if it's a matching string annotation,
# then it's an InitVar.
if f._field_type is _FIELD:
# The module we're checking against is the module we're
# currently in (dataclasses.py).
dataclasses = sys.modules[__name__]
if (_is_initvar(a_type, dataclasses)
or (isinstance(f.type, str)
and _is_type(f.type, cls, dataclasses, dataclasses.InitVar,
_is_initvar))):
f._field_type = _FIELD_INITVAR
# Validations for individual fields. This is delayed until now,
# instead of in the Field() constructor, since only here do we
# know the field name, which allows for better error reporting.
# Special restrictions for ClassVar and InitVar.
if f._field_type in (_FIELD_CLASSVAR, _FIELD_INITVAR):
if f.default_factory is not MISSING:
raise TypeError(f'field {f.name} cannot have a '
'default factory')
# Should I check for other field settings? default_factory
# seems the most serious to check for. Maybe add others. For
# example, how about init=False (or really,
# init=<not-the-default-init-value>)? It makes no sense for
# ClassVar and InitVar to specify init=<anything>.
# For real fields, disallow mutable defaults for known types.
if f._field_type is _FIELD and isinstance(f.default, (list, dict, set)):
raise ValueError(f'mutable default {type(f.default)} for field '
f'{f.name} is not allowed: use default_factory')
return f
def _set_new_attribute(cls, name, value):
# Never overwrites an existing attribute. Returns True if the
# attribute already exists.
if name in cls.__dict__:
return True
setattr(cls, name, value)
return False
# Decide if/how we're going to create a hash function. Key is
# (unsafe_hash, eq, frozen, does-hash-exist). Value is the action to
# take. The common case is to do nothing, so instead of providing a
# function that is a no-op, use None to signify that.
def _hash_set_none(cls, fields):
return None
def _hash_add(cls, fields):
flds = [f for f in fields if (f.compare if f.hash is None else f.hash)]
return _hash_fn(flds)
def _hash_exception(cls, fields):
# Raise an exception.
raise TypeError(f'Cannot overwrite attribute __hash__ '
f'in class {cls.__name__}')
#
# +-------------------------------------- unsafe_hash?
# | +------------------------------- eq?
# | | +------------------------ frozen?
# | | | +---------------- has-explicit-hash?
# | | | |
# | | | | +------- action
# | | | | |
# v v v v v
_hash_action = {(False, False, False, False): None,
(False, False, False, True ): None,
(False, False, True, False): None,
(False, False, True, True ): None,
(False, True, False, False): _hash_set_none,
(False, True, False, True ): None,
(False, True, True, False): _hash_add,
(False, True, True, True ): None,
(True, False, False, False): _hash_add,
(True, False, False, True ): _hash_exception,
(True, False, True, False): _hash_add,
(True, False, True, True ): _hash_exception,
(True, True, False, False): _hash_add,
(True, True, False, True ): _hash_exception,
(True, True, True, False): _hash_add,
(True, True, True, True ): _hash_exception,
}
# See https://bugs.python.org/issue32929#msg312829 for an if-statement
# version of this table.
def _process_class(cls, init, repr, eq, order, unsafe_hash, frozen):
# Now that dicts retain insertion order, there's no reason to use
# an ordered dict. I am leveraging that ordering here, because
# derived class fields overwrite base class fields, but the order
# is defined by the base class, which is found first.
fields = {}
setattr(cls, _PARAMS, _DataclassParams(init, repr, eq, order,
unsafe_hash, frozen))
# Find our base classes in reverse MRO order, and exclude
# ourselves. In reversed order so that more derived classes
# override earlier field definitions in base classes. As long as
# we're iterating over them, see if any are frozen.
any_frozen_base = False
has_dataclass_bases = False
for b in cls.__mro__[-1:0:-1]:
# Only process classes that have been processed by our
# decorator. That is, they have a _FIELDS attribute.
base_fields = getattr(b, _FIELDS, None)
if base_fields:
has_dataclass_bases = True
for f in base_fields.values():
fields[f.name] = f
if getattr(b, _PARAMS).frozen:
any_frozen_base = True
# Annotations that are defined in this class (not in base
# classes). If __annotations__ isn't present, then this class
# adds no new annotations. We use this to compute fields that are
# added by this class.
#
# Fields are found from cls_annotations, which is guaranteed to be
# ordered. Default values are from class attributes, if a field
# has a default. If the default value is a Field(), then it
# contains additional info beyond (and possibly including) the
# actual default value. Pseudo-fields ClassVars and InitVars are
# included, despite the fact that they're not real fields. That's
# dealt with later.
cls_annotations = cls.__dict__.get('__annotations__', {})
# Now find fields in our class. While doing so, validate some
# things, and set the default values (as class attributes) where
# we can.
cls_fields = [_get_field(cls, name, type)
for name, type in cls_annotations.items()]
for f in cls_fields:
fields[f.name] = f
# If the class attribute (which is the default value for this
# field) exists and is of type 'Field', replace it with the
# real default. This is so that normal class introspection
# sees a real default value, not a Field.
if isinstance(getattr(cls, f.name, None), Field):
if f.default is MISSING:
# If there's no default, delete the class attribute.
# This happens if we specify field(repr=False), for
# example (that is, we specified a field object, but
# no default value). Also if we're using a default
# factory. The class attribute should not be set at
# all in the post-processed class.
delattr(cls, f.name)
else:
setattr(cls, f.name, f.default)
# Do we have any Field members that don't also have annotations?
for name, value in cls.__dict__.items():
if isinstance(value, Field) and not name in cls_annotations:
raise TypeError(f'{name!r} is a field but has no type annotation')
# Check rules that apply if we are derived from any dataclasses.
if has_dataclass_bases:
# Raise an exception if any of our bases are frozen, but we're not.
if any_frozen_base and not frozen:
raise TypeError('cannot inherit non-frozen dataclass from a '
'frozen one')
# Raise an exception if we're frozen, but none of our bases are.
if not any_frozen_base and frozen:
raise TypeError('cannot inherit frozen dataclass from a '
'non-frozen one')
# Remember all of the fields on our class (including bases). This
# also marks this class as being a dataclass.
setattr(cls, _FIELDS, fields)
# Was this class defined with an explicit __hash__? Note that if
# __eq__ is defined in this class, then python will automatically
# set __hash__ to None. This is a heuristic, as it's possible
# that such a __hash__ == None was not auto-generated, but it
# close enough.
class_hash = cls.__dict__.get('__hash__', MISSING)
has_explicit_hash = not (class_hash is MISSING or
(class_hash is None and '__eq__' in cls.__dict__))
# If we're generating ordering methods, we must be generating the
# eq methods.
if order and not eq:
raise ValueError('eq must be true if order is true')
if init:
# Does this class have a post-init function?
has_post_init = hasattr(cls, _POST_INIT_NAME)
# Include InitVars and regular fields (so, not ClassVars).
flds = [f for f in fields.values()
if f._field_type in (_FIELD, _FIELD_INITVAR)]
_set_new_attribute(cls, '__init__',
_init_fn(flds,
frozen,
has_post_init,
# The name to use for the "self"
# param in __init__. Use "self"
# if possible.
'__dataclass_self__' if 'self' in fields
else 'self',
))
# Get the fields as a list, and include only real fields. This is
# used in all of the following methods.
field_list = [f for f in fields.values() if f._field_type is _FIELD]
if repr:
flds = [f for f in field_list if f.repr]
_set_new_attribute(cls, '__repr__', _repr_fn(flds))
if eq:
# Create _eq__ method. There's no need for a __ne__ method,
# since python will call __eq__ and negate it.
flds = [f for f in field_list if f.compare]
self_tuple = _tuple_str('self', flds)
other_tuple = _tuple_str('other', flds)
_set_new_attribute(cls, '__eq__',
_cmp_fn('__eq__', '==',
self_tuple, other_tuple))
if order:
# Create and set the ordering methods.
flds = [f for f in field_list if f.compare]
self_tuple = _tuple_str('self', flds)
other_tuple = _tuple_str('other', flds)
for name, op in [('__lt__', '<'),
('__le__', '<='),
('__gt__', '>'),
('__ge__', '>='),
]:
if _set_new_attribute(cls, name,
_cmp_fn(name, op, self_tuple, other_tuple)):
raise TypeError(f'Cannot overwrite attribute {name} '
f'in class {cls.__name__}. Consider using '
'functools.total_ordering')
if frozen:
for fn in _frozen_get_del_attr(cls, field_list):
if _set_new_attribute(cls, fn.__name__, fn):
raise TypeError(f'Cannot overwrite attribute {fn.__name__} '
f'in class {cls.__name__}')
# Decide if/how we're going to create a hash function.
hash_action = _hash_action[bool(unsafe_hash),
bool(eq),
bool(frozen),
has_explicit_hash]
if hash_action:
# No need to call _set_new_attribute here, since by the time
# we're here the overwriting is unconditional.
cls.__hash__ = hash_action(cls, field_list)
if not getattr(cls, '__doc__'):
# Create a class doc-string.
cls.__doc__ = (cls.__name__ +
str(inspect.signature(cls)).replace(' -> None', ''))
return cls
def dataclass(cls=None, /, *, init=True, repr=True, eq=True, order=False,
unsafe_hash=False, frozen=False):
"""Returns the same class as was passed in, with dunder methods
added based on the fields defined in the class.
Examines PEP 526 __annotations__ to determine fields.
If init is true, an __init__() method is added to the class. If
repr is true, a __repr__() method is added. If order is true, rich
comparison dunder methods are added. If unsafe_hash is true, a
__hash__() method function is added. If frozen is true, fields may
not be assigned to after instance creation.
"""
def wrap(cls):
return _process_class(cls, init, repr, eq, order, unsafe_hash, frozen)
# See if we're being called as @dataclass or @dataclass().
if cls is None:
# We're called with parens.
return wrap
# We're called as @dataclass without parens.
return wrap(cls)
def fields(class_or_instance):
"""Return a tuple describing the fields of this dataclass.
Accepts a dataclass or an instance of one. Tuple elements are of
type Field.
"""
# Might it be worth caching this, per class?
try:
fields = getattr(class_or_instance, _FIELDS)
except AttributeError:
raise TypeError('must be called with a dataclass type or instance')
# Exclude pseudo-fields. Note that fields is sorted by insertion
# order, so the order of the tuple is as the fields were defined.
return tuple(f for f in fields.values() if f._field_type is _FIELD)
def _is_dataclass_instance(obj):
"""Returns True if obj is an instance of a dataclass."""
return not isinstance(obj, type) and hasattr(obj, _FIELDS)
def is_dataclass(obj):
"""Returns True if obj is a dataclass or an instance of a
dataclass."""
return hasattr(obj, _FIELDS)
def asdict(obj, *, dict_factory=dict):
"""Return the fields of a dataclass instance as a new dictionary mapping
field names to field values.
Example usage:
@dataclass
class C:
x: int
y: int
c = C(1, 2)
assert asdict(c) == {'x': 1, 'y': 2}
If given, 'dict_factory' will be used instead of built-in dict.
The function applies recursively to field values that are
dataclass instances. This will also look into built-in containers:
tuples, lists, and dicts.
"""
if not _is_dataclass_instance(obj):
raise TypeError("asdict() should be called on dataclass instances")
return _asdict_inner(obj, dict_factory)
def _asdict_inner(obj, dict_factory):
if _is_dataclass_instance(obj):
result = []
for f in fields(obj):
value = _asdict_inner(getattr(obj, f.name), dict_factory)
result.append((f.name, value))
return dict_factory(result)
elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
# obj is a namedtuple. Recurse into it, but the returned
# object is another namedtuple of the same type. This is
# similar to how other list- or tuple-derived classes are
# treated (see below), but we just need to create them
# differently because a namedtuple's __init__ needs to be
# called differently (see bpo-34363).
# I'm not using namedtuple's _asdict()
# method, because:
# - it does not recurse in to the namedtuple fields and
# convert them to dicts (using dict_factory).
# - I don't actually want to return a dict here. The the main
# use case here is json.dumps, and it handles converting
# namedtuples to lists. Admittedly we're losing some
# information here when we produce a json list instead of a
# dict. Note that if we returned dicts here instead of
# namedtuples, we could no longer call asdict() on a data
# structure where a namedtuple was used as a dict key.
return type(obj)(*[_asdict_inner(v, dict_factory) for v in obj])
elif isinstance(obj, (list, tuple)):
# Assume we can create an object of this type by passing in a
# generator (which is not true for namedtuples, handled
# above).
return type(obj)(_asdict_inner(v, dict_factory) for v in obj)
elif isinstance(obj, dict):
return type(obj)((_asdict_inner(k, dict_factory),
_asdict_inner(v, dict_factory))
for k, v in obj.items())
else:
return copy.deepcopy(obj)
def astuple(obj, *, tuple_factory=tuple):
"""Return the fields of a dataclass instance as a new tuple of field values.
Example usage::
@dataclass
class C:
x: int
y: int
c = C(1, 2)
assert astuple(c) == (1, 2)
If given, 'tuple_factory' will be used instead of built-in tuple.
The function applies recursively to field values that are
dataclass instances. This will also look into built-in containers:
tuples, lists, and dicts.
"""
if not _is_dataclass_instance(obj):
raise TypeError("astuple() should be called on dataclass instances")
return _astuple_inner(obj, tuple_factory)
def _astuple_inner(obj, tuple_factory):
if _is_dataclass_instance(obj):
result = []
for f in fields(obj):
value = _astuple_inner(getattr(obj, f.name), tuple_factory)
result.append(value)
return tuple_factory(result)
elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
# obj is a namedtuple. Recurse into it, but the returned
# object is another namedtuple of the same type. This is
# similar to how other list- or tuple-derived classes are
# treated (see below), but we just need to create them
# differently because a namedtuple's __init__ needs to be
# called differently (see bpo-34363).
return type(obj)(*[_astuple_inner(v, tuple_factory) for v in obj])
elif isinstance(obj, (list, tuple)):
# Assume we can create an object of this type by passing in a
# generator (which is not true for namedtuples, handled
# above).
return type(obj)(_astuple_inner(v, tuple_factory) for v in obj)
elif isinstance(obj, dict):
return type(obj)((_astuple_inner(k, tuple_factory), _astuple_inner(v, tuple_factory))
for k, v in obj.items())
else:
return copy.deepcopy(obj)
def make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True,
repr=True, eq=True, order=False, unsafe_hash=False,
frozen=False):
"""Return a new dynamically created dataclass.
The dataclass name will be 'cls_name'. 'fields' is an iterable
of either (name), (name, type) or (name, type, Field) objects. If type is
omitted, use the string 'typing.Any'. Field objects are created by
the equivalent of calling 'field(name, type [, Field-info])'.
C = make_dataclass('C', ['x', ('y', int), ('z', int, field(init=False))], bases=(Base,))
is equivalent to:
@dataclass
class C(Base):
x: 'typing.Any'
y: int
z: int = field(init=False)
For the bases and namespace parameters, see the builtin type() function.
The parameters init, repr, eq, order, unsafe_hash, and frozen are passed to
dataclass().
"""
if namespace is None:
namespace = {}
else:
# Copy namespace since we're going to mutate it.
namespace = namespace.copy()
# While we're looking through the field names, validate that they
# are identifiers, are not keywords, and not duplicates.
seen = set()
anns = {}
for item in fields:
if isinstance(item, str):
name = item
tp = 'typing.Any'
elif len(item) == 2:
name, tp, = item
elif len(item) == 3:
name, tp, spec = item
namespace[name] = spec
else:
raise TypeError(f'Invalid field: {item!r}')
if not isinstance(name, str) or not name.isidentifier():
raise TypeError(f'Field names must be valid identifiers: {name!r}')
if keyword.iskeyword(name):
raise TypeError(f'Field names must not be keywords: {name!r}')
if name in seen:
raise TypeError(f'Field name duplicated: {name!r}')
seen.add(name)
anns[name] = tp
namespace['__annotations__'] = anns
# We use `types.new_class()` instead of simply `type()` to allow dynamic creation
# of generic dataclassses.
cls = types.new_class(cls_name, bases, {}, lambda ns: ns.update(namespace))
return dataclass(cls, init=init, repr=repr, eq=eq, order=order,
unsafe_hash=unsafe_hash, frozen=frozen)
def replace(obj, /, **changes):
"""Return a new object replacing specified fields with new values.
This is especially useful for frozen classes. Example usage:
@dataclass(frozen=True)
class C:
x: int
y: int
c = C(1, 2)
c1 = replace(c, x=3)
assert c1.x == 3 and c1.y == 2
"""
# We're going to mutate 'changes', but that's okay because it's a
# new dict, even if called with 'replace(obj, **my_changes)'.
if not _is_dataclass_instance(obj):
raise TypeError("replace() should be called on dataclass instances")
# It's an error to have init=False fields in 'changes'.
# If a field is not in 'changes', read its value from the provided obj.
for f in getattr(obj, _FIELDS).values():
# Only consider normal fields or InitVars.
if f._field_type is _FIELD_CLASSVAR:
continue
if not f.init:
# Error if this field is specified in changes.
if f.name in changes:
raise ValueError(f'field {f.name} is declared with '
'init=False, it cannot be specified with '
'replace()')
continue
if f.name not in changes:
if f._field_type is _FIELD_INITVAR:
raise ValueError(f"InitVar {f.name!r} "
'must be specified with replace()')
changes[f.name] = getattr(obj, f.name)
# Create the new object, which calls __init__() and
# __post_init__() (if defined), using all of the init fields we've
# added and/or left in 'changes'. If there are values supplied in
# changes that aren't fields, this will correctly raise a
# TypeError.
return obj.__class__(**changes)
|
from typing import Any, Dict, IO, Mapping, Optional, Sequence, Tuple, Union
import cyvcf2
import logging
import numpy as np
log = logging.getLogger(__name__)
# have to re-declare here since only exist in cyvcf2 stub and fails on execution
Text = Union[str, bytes]
Primitives = Union[int, float, bool, Text]
def _numpy_unknown_to_none(a: np.ndarray) -> list:
"""
Unknown values ('.') in integer arrays are assigned as '-inf' (e.g. for in32 the value is -2^31)
Convert array to list, and replace these values with None
"""
b = a.tolist()
n = max(a.shape)
indices = zip(*np.where(a < np.iinfo(a.dtype).min + n))
def set_value(x, i, value):
"Set value in nested lists"
if len(i) > 1:
x = set_value(x[i[0]], i[1:], value)
else:
x[i[0]] = value
for idx in indices:
set_value(b, idx, None)
return b
def numpy_to_list(a: Optional[np.ndarray]):
if a is None:
return None
if np.issubdtype(a.dtype, np.integer):
return _numpy_unknown_to_none(a)
else:
return a.tolist()
class Record(object):
variant: cyvcf2.Variant
samples: Sequence[str]
meta: Mapping[str, Any]
def __init__(self, variant: cyvcf2.Variant, samples: Sequence[str], meta: Mapping[str, Any]):
self.variant = variant
self.samples = samples
self.meta = meta
def _sample_index(self, sample_name: str):
return self.samples.index(sample_name)
def get_raw_filter(self):
"""Need to implement this here, as cyvcf2 does not distinguish between 'PASS' and '.' (both return None).
Therefore, we need to parse the VCF line to get the raw filter status."""
return str(self.variant).split("\t")[6]
def sample_genotype(self, sample_name: str):
return tuple(self.variant.genotypes[self._sample_index(sample_name)][:-1])
def has_allele(self, sample_name: str):
gt = self.sample_genotype(sample_name)
return max(gt) == 1
def get_format_sample(self, property: str, sample_name: str, scalar: bool = False):
if property == "GT":
return self.sample_genotype(sample_name)
else:
prop = self.variant.format(property)
if prop is not None:
ret = numpy_to_list(prop[self._sample_index(sample_name)])
if scalar:
assert len(ret) == 1
return ret[0]
else:
return ret
def get_format(self, property: str):
if property == "GT":
return self.variant.genotypes
else:
return numpy_to_list(self.variant.format(property))
def get_block_id(self):
return self.variant.INFO.get("OLD_MULTIALLELIC")
def is_multiallelic(self):
return self.get_block_id() is not None
def is_sample_multiallelic(self, sample_name: str):
return self.is_multiallelic() and bool(set(self.sample_genotype(sample_name)) - set([0, 1]))
def annotation(
self,
) -> Dict[str, Union[Primitives, Tuple[Primitives, ...]]]:
return dict(x for x in self.variant.INFO)
def __str__(self):
s = repr(self.variant)
if self.samples:
genotypes = []
for i, x in enumerate(self.variant.gt_bases):
genotypes.append(f"{x} ({str(self.samples[i])})")
s += f" - Genotypes: {", ".join(genotypes)}"
return s
RESERVED_GT_HEADERS = {
"AD": {"Number": "R", "Type": "Integer", "Description": "Injected. Read depth for each allele"},
"ADF": {
"Number": "R",
"Type": "Integer",
"Description": "Injected. Read depth for each allele on the forward strand",
},
"ADR": {
"Number": "R",
"Type": "Integer",
"Description": "Injected. Read depth for each allele on the reverse strand",
},
"DP": {"Number": "1", "Type": "Integer", "Description": "Injected. Read depth"},
"EC": {
"Number": "A",
"Type": "Integer",
"Description": "Injected. Expected alternate allele counts",
},
"FT": {
"Number": "1",
"Type": "String",
"Description": "Injected. Filter indicating if this genotype was “called”",
},
"GL": {"Number": "G", "Type": "Float", "Description": "Injected. Genotype likelihoods"},
"GP": {
"Number": "G",
"Type": "Float",
"Description": "Injected. Genotype posterior probabilities",
},
"GQ": {
"Number": "1",
"Type": "Integer",
"Description": "Injected. Conditional genotype quality",
},
"GT": {"Number": "1", "Type": "String", "Description": "Injected. Genotype"},
"HQ": {"Number": "2", "Type": "Integer", "Description": "Injected. Haplotype quality"},
"MQ": {"Number": "1", "Type": "Integer", "Description": "Injected. RMS mapping quality"},
"PL": {
"Number": "G",
"Type": "Integer",
"Description": "Injected. Phred-scaled genotype likelihoods rounded to the closest integer",
},
"PP": {
"Number": "G",
"Type": "Integer",
"Description": "Injected. Phred-scaled genotype posterior probabilities rounded to the closest integer",
},
"PQ": {"Number": "1", "Type": "Integer", "Description": "Injected. Phasing quality"},
"PS": {"Number": "1", "Type": "Integer", "Description": "Injected. Phase"},
}
class VcfIterator(object):
def __init__(self, path_or_fileobject: Union[str, IO], include_raw: bool = False):
self.path_or_fileobject = path_or_fileobject
self.reader = cyvcf2.Reader(self.path_or_fileobject, gts012=True)
self.include_raw = include_raw
self.samples = self.reader.samples
self.add_format_headers()
self.meta: Dict[str, list] = {}
for h in self.reader.header_iter():
if h.type not in self.meta:
self.meta[h.type] = []
self.meta[h.type].append(h.info())
def add_format_headers(self):
"Add format headers if they do not exist. This is a subset of the reserved genotype keys from https://samtools.github.io/hts-specs/VCFv4.3.pdf (table 2)"
for key, fmt in RESERVED_GT_HEADERS.items():
if key in self.reader and self.reader.get_header_type(key) == "FORMAT":
existing_header_line = self.reader[key]
if (
existing_header_line["Number"] != fmt["Number"]
or existing_header_line["Type"] != fmt["Type"]
):
log.warning(
f"Header for format field {key} in VCF does not match VCF spec. Ignoring."
)
else:
self.reader.add_format_to_header({**fmt, **{"ID": key}})
def __iter__(self):
variant: cyvcf2.Variant
if self.include_raw:
for variant in self.reader:
yield str(variant), variant
else:
for variant in self.reader:
r = Record(variant, self.samples, self.meta)
yield r
| from typing import Any, Dict, IO, Mapping, Optional, Sequence, Tuple, Union
import cyvcf2
import logging
import numpy as np
log = logging.getLogger(__name__)
# have to re-declare here since only exist in cyvcf2 stub and fails on execution
Text = Union[str, bytes]
Primitives = Union[int, float, bool, Text]
def _numpy_unknown_to_none(a: np.ndarray) -> list:
"""
Unknown values ('.') in integer arrays are assigned as '-inf' (e.g. for in32 the value is -2^31)
Convert array to list, and replace these values with None
"""
b = a.tolist()
n = max(a.shape)
indices = zip(*np.where(a < np.iinfo(a.dtype).min + n))
def set_value(x, i, value):
"Set value in nested lists"
if len(i) > 1:
x = set_value(x[i[0]], i[1:], value)
else:
x[i[0]] = value
for idx in indices:
set_value(b, idx, None)
return b
def numpy_to_list(a: Optional[np.ndarray]):
if a is None:
return None
if np.issubdtype(a.dtype, np.integer):
return _numpy_unknown_to_none(a)
else:
return a.tolist()
class Record(object):
variant: cyvcf2.Variant
samples: Sequence[str]
meta: Mapping[str, Any]
def __init__(self, variant: cyvcf2.Variant, samples: Sequence[str], meta: Mapping[str, Any]):
self.variant = variant
self.samples = samples
self.meta = meta
def _sample_index(self, sample_name: str):
return self.samples.index(sample_name)
def get_raw_filter(self):
"""Need to implement this here, as cyvcf2 does not distinguish between 'PASS' and '.' (both return None).
Therefore, we need to parse the VCF line to get the raw filter status."""
return str(self.variant).split("\t")[6]
def sample_genotype(self, sample_name: str):
return tuple(self.variant.genotypes[self._sample_index(sample_name)][:-1])
def has_allele(self, sample_name: str):
gt = self.sample_genotype(sample_name)
return max(gt) == 1
def get_format_sample(self, property: str, sample_name: str, scalar: bool = False):
if property == "GT":
return self.sample_genotype(sample_name)
else:
prop = self.variant.format(property)
if prop is not None:
ret = numpy_to_list(prop[self._sample_index(sample_name)])
if scalar:
assert len(ret) == 1
return ret[0]
else:
return ret
def get_format(self, property: str):
if property == "GT":
return self.variant.genotypes
else:
return numpy_to_list(self.variant.format(property))
def get_block_id(self):
return self.variant.INFO.get("OLD_MULTIALLELIC")
def is_multiallelic(self):
return self.get_block_id() is not None
def is_sample_multiallelic(self, sample_name: str):
return self.is_multiallelic() and bool(set(self.sample_genotype(sample_name)) - set([0, 1]))
def annotation(
self,
) -> Dict[str, Union[Primitives, Tuple[Primitives, ...]]]:
return dict(x for x in self.variant.INFO)
def __str__(self):
s = repr(self.variant)
if self.samples:
genotypes = []
for i, x in enumerate(self.variant.gt_bases):
genotypes.append(f"{x} ({str(self.samples[i])})")
s += f" - Genotypes: {', '.join(genotypes)}"
return s
RESERVED_GT_HEADERS = {
"AD": {"Number": "R", "Type": "Integer", "Description": "Injected. Read depth for each allele"},
"ADF": {
"Number": "R",
"Type": "Integer",
"Description": "Injected. Read depth for each allele on the forward strand",
},
"ADR": {
"Number": "R",
"Type": "Integer",
"Description": "Injected. Read depth for each allele on the reverse strand",
},
"DP": {"Number": "1", "Type": "Integer", "Description": "Injected. Read depth"},
"EC": {
"Number": "A",
"Type": "Integer",
"Description": "Injected. Expected alternate allele counts",
},
"FT": {
"Number": "1",
"Type": "String",
"Description": "Injected. Filter indicating if this genotype was “called”",
},
"GL": {"Number": "G", "Type": "Float", "Description": "Injected. Genotype likelihoods"},
"GP": {
"Number": "G",
"Type": "Float",
"Description": "Injected. Genotype posterior probabilities",
},
"GQ": {
"Number": "1",
"Type": "Integer",
"Description": "Injected. Conditional genotype quality",
},
"GT": {"Number": "1", "Type": "String", "Description": "Injected. Genotype"},
"HQ": {"Number": "2", "Type": "Integer", "Description": "Injected. Haplotype quality"},
"MQ": {"Number": "1", "Type": "Integer", "Description": "Injected. RMS mapping quality"},
"PL": {
"Number": "G",
"Type": "Integer",
"Description": "Injected. Phred-scaled genotype likelihoods rounded to the closest integer",
},
"PP": {
"Number": "G",
"Type": "Integer",
"Description": "Injected. Phred-scaled genotype posterior probabilities rounded to the closest integer",
},
"PQ": {"Number": "1", "Type": "Integer", "Description": "Injected. Phasing quality"},
"PS": {"Number": "1", "Type": "Integer", "Description": "Injected. Phase"},
}
class VcfIterator(object):
def __init__(self, path_or_fileobject: Union[str, IO], include_raw: bool = False):
self.path_or_fileobject = path_or_fileobject
self.reader = cyvcf2.Reader(self.path_or_fileobject, gts012=True)
self.include_raw = include_raw
self.samples = self.reader.samples
self.add_format_headers()
self.meta: Dict[str, list] = {}
for h in self.reader.header_iter():
if h.type not in self.meta:
self.meta[h.type] = []
self.meta[h.type].append(h.info())
def add_format_headers(self):
"Add format headers if they do not exist. This is a subset of the reserved genotype keys from https://samtools.github.io/hts-specs/VCFv4.3.pdf (table 2)"
for key, fmt in RESERVED_GT_HEADERS.items():
if key in self.reader and self.reader.get_header_type(key) == "FORMAT":
existing_header_line = self.reader[key]
if (
existing_header_line["Number"] != fmt["Number"]
or existing_header_line["Type"] != fmt["Type"]
):
log.warning(
f"Header for format field {key} in VCF does not match VCF spec. Ignoring."
)
else:
self.reader.add_format_to_header({**fmt, **{"ID": key}})
def __iter__(self):
variant: cyvcf2.Variant
if self.include_raw:
for variant in self.reader:
yield str(variant), variant
else:
for variant in self.reader:
r = Record(variant, self.samples, self.meta)
yield r
|
def rchop(s, ending):
return s[: -len(ending)] if s.endswith(ending) else s
def lchop(s, beginning):
return s[len(beginning) :] if s.startswith(beginning) else s
def ordinal_en(n: int):
# https://stackoverflow.com/questions/9647202/ordinal-numbers-replacement
return f'{n}{'tsnrhtdd'[(n//10%10!=1)*(n%10<4)*n%10::4]}'
def ordinal_fr(n: int):
if n == 1:
return "1er"
return f"{n}è"
def arrival_time_en(time_in_seconds: int):
if time_in_seconds == 0:
return "Here"
time_in_mins = time_in_seconds // 60
if time_in_mins == 0:
return ""
return str(time_in_mins) + (" min" if time_in_mins == 1 else " mins")
| def rchop(s, ending):
return s[: -len(ending)] if s.endswith(ending) else s
def lchop(s, beginning):
return s[len(beginning) :] if s.startswith(beginning) else s
def ordinal_en(n: int):
# https://stackoverflow.com/questions/9647202/ordinal-numbers-replacement
return f'{n}{"tsnrhtdd"[(n//10%10!=1)*(n%10<4)*n%10::4]}'
def ordinal_fr(n: int):
if n == 1:
return "1er"
return f"{n}è"
def arrival_time_en(time_in_seconds: int):
if time_in_seconds == 0:
return "Here"
time_in_mins = time_in_seconds // 60
if time_in_mins == 0:
return ""
return str(time_in_mins) + (" min" if time_in_mins == 1 else " mins")
|
import logging
import warnings
from collections import OrderedDict
import transaction
from pyramid.events import NewRequest
import pyramid.tweens
from enum import Enum
from kinto.core.utils import strip_uri_prefix
logger = logging.getLogger(__name__)
class ACTIONS(Enum):
CREATE = "create"
DELETE = "delete"
READ = "read"
UPDATE = "update"
@staticmethod
def from_string_list(elements):
return tuple(ACTIONS(el) for el in elements)
class _ResourceEvent:
def __init__(self, payload, request):
self.payload = payload
self.request = request
def __repr__(self):
return f"<{self.__class__.__name__} action={self.payload["action"]} uri={self.payload["uri"]}>"
@property
def read_records(self):
message = "`read_records` is deprecated, use `read_objects` instead."
warnings.warn(message, DeprecationWarning)
return self.read_objects
@property
def impacted_records(self):
message = "`impacted_records` is deprecated, use `impacted_objects` instead."
warnings.warn(message, DeprecationWarning)
return self.impacted_objects
class ResourceRead(_ResourceEvent):
"""Triggered when a resource is being read.
"""
def __init__(self, payload, read_objects, request):
super().__init__(payload, request)
self.read_objects = read_objects
class ResourceChanged(_ResourceEvent):
"""Triggered when a resource is being changed.
"""
def __init__(self, payload, impacted_objects, request):
super().__init__(payload, request)
self.impacted_objects = impacted_objects
class AfterResourceRead(_ResourceEvent):
"""Triggered after a resource was successfully read.
"""
def __init__(self, payload, read_objects, request):
super().__init__(payload, request)
self.read_objects = read_objects
class AfterResourceChanged(_ResourceEvent):
"""Triggered after a resource was successfully changed.
"""
def __init__(self, payload, impacted_objects, request):
super().__init__(payload, request)
self.impacted_objects = impacted_objects
class EventCollector(object):
"""A collection to gather events emitted over the course of a request.
Events are gathered by parent id, resource type, and event
type. This serves as a primitive normalization so that we can emit
fewer events.
"""
def __init__(self):
self.event_dict = OrderedDict()
"""The events as collected so far.
The key of the event_dict is a triple (resource_name,
parent_id, action). The value is a triple (impacted, request,
payload). If the same (resource_name, parent_id, action) is
encountered, we just extend the existing impacted with the new
impacted. N.B. this means all values in the payload must not
be specific to a single impacted_object. See
https://github.com/Kinto/kinto/issues/945 and
https://github.com/Kinto/kinto/issues/1731.
"""
def add_event(self, resource_name, parent_id, action, payload, impacted, request):
key = (resource_name, parent_id, action)
if key not in self.event_dict:
value = (payload, impacted, request)
self.event_dict[key] = value
else:
old_value = self.event_dict[key]
(old_payload, old_impacted, old_request) = old_value
# May be a good idea to assert that old_payload == payload here.
self.event_dict[key] = (old_payload, old_impacted + impacted, old_request)
def drain(self):
"""Return an iterator that removes elements from this EventCollector.
This can be used to process events while still allowing events
to be added (for instance, as part of a cascade where events
add other events).
Items yielded will be of a tuple suitable for using as
arguments to EventCollector.add_event.
"""
return EventCollectorDrain(self)
class EventCollectorDrain(object):
"""An iterator that drains an EventCollector.
Get one using EventCollector.drain()."""
def __init__(self, event_collector):
self.event_collector = event_collector
def __iter__(self):
return self
def __next__(self):
if self.event_collector.event_dict:
# Get the "first" key in insertion order, so as to process
# events in the same order they were queued.
key = next(iter(self.event_collector.event_dict.keys()))
value = self.event_collector.event_dict.pop(key)
return key + value
else:
raise StopIteration
def notify_resource_events_before(handler, registry):
"""pyramid_tm "commit veto" hook to run ResourceChanged events.
This hook being a "commit veto" let us tell pyramid_tm to abort
the transaction if the ResourceChanged listeners raise.
"""
def tween(request):
response = handler(request)
for event in request.get_resource_events():
request.registry.notify(event)
return response
return tween
def setup_transaction_hook(config):
"""
Resource events are plugged with the transactions of ``pyramid_tm``.
Once a transaction is committed, ``AfterResourceRead`` and
``AfterResourceChanged`` events are sent.
"""
def _notify_resource_events_after(success, request):
"""Notify the accumulated resource events if transaction succeeds.
"""
if not success: # pragma: no cover
return
for event in request.get_resource_events(after_commit=True):
try:
request.registry.notify(event)
except Exception:
logger.error("Unable to notify", exc_info=True)
def on_new_request(event):
"""When a new request comes in, hook on transaction commit.
"""
# Since there is one transaction per batch, ignore subrequests.
if hasattr(event.request, "parent"):
return
current = transaction.get()
current.addAfterCommitHook(_notify_resource_events_after, args=(event.request,))
config.add_subscriber(on_new_request, NewRequest)
config.add_tween(
"kinto.core.events.notify_resource_events_before", under=pyramid.tweens.EXCVIEW
)
def get_resource_events(request, after_commit=False):
"""Generator to iterate the list of events triggered on resources.
The list is sorted chronologically (see OrderedDict).
This drains the resource_events currently in the request, which
allows us to process new events as they are added by current
events. However, once the iteration is over, we merge all the
events we've emitted into a new resource_events, which we store on
the request so we can reprocess the same events in an after-commit
tween.
This generator must be completely consumed!
"""
by_resource = request.bound_data.get("resource_events", EventCollector())
afterwards = EventCollector()
for event_call in by_resource.drain():
afterwards.add_event(*event_call)
(_, _, action, payload, impacted, request) = event_call
if after_commit:
if action == ACTIONS.READ:
event_cls = AfterResourceRead
else:
event_cls = AfterResourceChanged
else:
if action == ACTIONS.READ:
event_cls = ResourceRead
else:
event_cls = ResourceChanged
yield event_cls(payload, impacted, request)
request.bound_data["resource_events"] = afterwards
def notify_resource_event(
request, parent_id, timestamp, data, action, old=None, resource_name=None, resource_data=None
):
"""Request helper to stack a resource event.
If a similar event (same resource, same action) already occured during the
current transaction (e.g. batch) then just extend the impacted objects of
the previous one.
:param resource_name: The name of the resource on which the event
happened (taken from the request if not provided).
:param resource_data: Information about the resource on which the
event is being emitted. Usually contains information about how
to find this object in the hierarchy (for instance,
``bucket_id`` and ``collection_id`` for a record). Taken from
the request matchdict if absent.
:type resource_data: dict
"""
if action == ACTIONS.READ:
if not isinstance(data, list):
data = [data]
impacted = data
elif action == ACTIONS.CREATE:
impacted = [{"new": data}]
elif action == ACTIONS.DELETE:
if not isinstance(data, list):
impacted = [{"new": data, "old": old}]
else:
impacted = []
for i, new in enumerate(data):
impacted.append({"new": new, "old": old[i]})
else: # ACTIONS.UPDATE:
impacted = [{"new": data, "old": old}]
# Get previously triggered events.
events = request.bound_data.setdefault("resource_events", EventCollector())
resource_name = resource_name or request.current_resource_name
matchdict = resource_data or dict(request.matchdict)
payload = {
"timestamp": timestamp,
"action": action.value,
# Deprecated: don't actually use URI (see #945).
"uri": strip_uri_prefix(request.path),
"user_id": request.prefixed_userid,
"resource_name": resource_name,
}
# Deprecated: don't actually use `resource_name_id` either (see #945).
if "id" in request.matchdict:
matchdict[resource_name + "_id"] = matchdict.pop("id")
payload.update(**matchdict)
events.add_event(resource_name, parent_id, action, payload, impacted, request)
| import logging
import warnings
from collections import OrderedDict
import transaction
from pyramid.events import NewRequest
import pyramid.tweens
from enum import Enum
from kinto.core.utils import strip_uri_prefix
logger = logging.getLogger(__name__)
class ACTIONS(Enum):
CREATE = "create"
DELETE = "delete"
READ = "read"
UPDATE = "update"
@staticmethod
def from_string_list(elements):
return tuple(ACTIONS(el) for el in elements)
class _ResourceEvent:
def __init__(self, payload, request):
self.payload = payload
self.request = request
def __repr__(self):
return f"<{self.__class__.__name__} action={self.payload['action']} uri={self.payload['uri']}>"
@property
def read_records(self):
message = "`read_records` is deprecated, use `read_objects` instead."
warnings.warn(message, DeprecationWarning)
return self.read_objects
@property
def impacted_records(self):
message = "`impacted_records` is deprecated, use `impacted_objects` instead."
warnings.warn(message, DeprecationWarning)
return self.impacted_objects
class ResourceRead(_ResourceEvent):
"""Triggered when a resource is being read.
"""
def __init__(self, payload, read_objects, request):
super().__init__(payload, request)
self.read_objects = read_objects
class ResourceChanged(_ResourceEvent):
"""Triggered when a resource is being changed.
"""
def __init__(self, payload, impacted_objects, request):
super().__init__(payload, request)
self.impacted_objects = impacted_objects
class AfterResourceRead(_ResourceEvent):
"""Triggered after a resource was successfully read.
"""
def __init__(self, payload, read_objects, request):
super().__init__(payload, request)
self.read_objects = read_objects
class AfterResourceChanged(_ResourceEvent):
"""Triggered after a resource was successfully changed.
"""
def __init__(self, payload, impacted_objects, request):
super().__init__(payload, request)
self.impacted_objects = impacted_objects
class EventCollector(object):
"""A collection to gather events emitted over the course of a request.
Events are gathered by parent id, resource type, and event
type. This serves as a primitive normalization so that we can emit
fewer events.
"""
def __init__(self):
self.event_dict = OrderedDict()
"""The events as collected so far.
The key of the event_dict is a triple (resource_name,
parent_id, action). The value is a triple (impacted, request,
payload). If the same (resource_name, parent_id, action) is
encountered, we just extend the existing impacted with the new
impacted. N.B. this means all values in the payload must not
be specific to a single impacted_object. See
https://github.com/Kinto/kinto/issues/945 and
https://github.com/Kinto/kinto/issues/1731.
"""
def add_event(self, resource_name, parent_id, action, payload, impacted, request):
key = (resource_name, parent_id, action)
if key not in self.event_dict:
value = (payload, impacted, request)
self.event_dict[key] = value
else:
old_value = self.event_dict[key]
(old_payload, old_impacted, old_request) = old_value
# May be a good idea to assert that old_payload == payload here.
self.event_dict[key] = (old_payload, old_impacted + impacted, old_request)
def drain(self):
"""Return an iterator that removes elements from this EventCollector.
This can be used to process events while still allowing events
to be added (for instance, as part of a cascade where events
add other events).
Items yielded will be of a tuple suitable for using as
arguments to EventCollector.add_event.
"""
return EventCollectorDrain(self)
class EventCollectorDrain(object):
"""An iterator that drains an EventCollector.
Get one using EventCollector.drain()."""
def __init__(self, event_collector):
self.event_collector = event_collector
def __iter__(self):
return self
def __next__(self):
if self.event_collector.event_dict:
# Get the "first" key in insertion order, so as to process
# events in the same order they were queued.
key = next(iter(self.event_collector.event_dict.keys()))
value = self.event_collector.event_dict.pop(key)
return key + value
else:
raise StopIteration
def notify_resource_events_before(handler, registry):
"""pyramid_tm "commit veto" hook to run ResourceChanged events.
This hook being a "commit veto" let us tell pyramid_tm to abort
the transaction if the ResourceChanged listeners raise.
"""
def tween(request):
response = handler(request)
for event in request.get_resource_events():
request.registry.notify(event)
return response
return tween
def setup_transaction_hook(config):
"""
Resource events are plugged with the transactions of ``pyramid_tm``.
Once a transaction is committed, ``AfterResourceRead`` and
``AfterResourceChanged`` events are sent.
"""
def _notify_resource_events_after(success, request):
"""Notify the accumulated resource events if transaction succeeds.
"""
if not success: # pragma: no cover
return
for event in request.get_resource_events(after_commit=True):
try:
request.registry.notify(event)
except Exception:
logger.error("Unable to notify", exc_info=True)
def on_new_request(event):
"""When a new request comes in, hook on transaction commit.
"""
# Since there is one transaction per batch, ignore subrequests.
if hasattr(event.request, "parent"):
return
current = transaction.get()
current.addAfterCommitHook(_notify_resource_events_after, args=(event.request,))
config.add_subscriber(on_new_request, NewRequest)
config.add_tween(
"kinto.core.events.notify_resource_events_before", under=pyramid.tweens.EXCVIEW
)
def get_resource_events(request, after_commit=False):
"""Generator to iterate the list of events triggered on resources.
The list is sorted chronologically (see OrderedDict).
This drains the resource_events currently in the request, which
allows us to process new events as they are added by current
events. However, once the iteration is over, we merge all the
events we've emitted into a new resource_events, which we store on
the request so we can reprocess the same events in an after-commit
tween.
This generator must be completely consumed!
"""
by_resource = request.bound_data.get("resource_events", EventCollector())
afterwards = EventCollector()
for event_call in by_resource.drain():
afterwards.add_event(*event_call)
(_, _, action, payload, impacted, request) = event_call
if after_commit:
if action == ACTIONS.READ:
event_cls = AfterResourceRead
else:
event_cls = AfterResourceChanged
else:
if action == ACTIONS.READ:
event_cls = ResourceRead
else:
event_cls = ResourceChanged
yield event_cls(payload, impacted, request)
request.bound_data["resource_events"] = afterwards
def notify_resource_event(
request, parent_id, timestamp, data, action, old=None, resource_name=None, resource_data=None
):
"""Request helper to stack a resource event.
If a similar event (same resource, same action) already occured during the
current transaction (e.g. batch) then just extend the impacted objects of
the previous one.
:param resource_name: The name of the resource on which the event
happened (taken from the request if not provided).
:param resource_data: Information about the resource on which the
event is being emitted. Usually contains information about how
to find this object in the hierarchy (for instance,
``bucket_id`` and ``collection_id`` for a record). Taken from
the request matchdict if absent.
:type resource_data: dict
"""
if action == ACTIONS.READ:
if not isinstance(data, list):
data = [data]
impacted = data
elif action == ACTIONS.CREATE:
impacted = [{"new": data}]
elif action == ACTIONS.DELETE:
if not isinstance(data, list):
impacted = [{"new": data, "old": old}]
else:
impacted = []
for i, new in enumerate(data):
impacted.append({"new": new, "old": old[i]})
else: # ACTIONS.UPDATE:
impacted = [{"new": data, "old": old}]
# Get previously triggered events.
events = request.bound_data.setdefault("resource_events", EventCollector())
resource_name = resource_name or request.current_resource_name
matchdict = resource_data or dict(request.matchdict)
payload = {
"timestamp": timestamp,
"action": action.value,
# Deprecated: don't actually use URI (see #945).
"uri": strip_uri_prefix(request.path),
"user_id": request.prefixed_userid,
"resource_name": resource_name,
}
# Deprecated: don't actually use `resource_name_id` either (see #945).
if "id" in request.matchdict:
matchdict[resource_name + "_id"] = matchdict.pop("id")
payload.update(**matchdict)
events.add_event(resource_name, parent_id, action, payload, impacted, request)
|
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
from awsglue.blueprint.workflow import Workflow, Entities
from awsglue.blueprint.job import Job
from awsglue.blueprint.crawler import *
import boto3
from botocore.client import ClientError
import datetime
def generate_schedule(type):
now = datetime.datetime.utcnow()
year = now.year
number_of_month = now.month
days = now.day
hours = now.hour
minutes = now.minute
days_of_week = now.weekday()
if type == 'Hourly':
return generate_cron_expression(minutes, "0/1", "*", "*", "?", "*")
elif type == 'Daily':
return generate_cron_expression(minutes, hours, "*", "*", "?", "*")
elif type == 'Weekly':
return generate_cron_expression(minutes, hours, "?", "*", days_of_week, "*")
elif type == 'Monthly':
return generate_cron_expression(minutes, hours, days, "*", "?", "*")
else:
return generate_cron_expression(minutes, hours, days, number_of_month, "?", year)
def generate_cron_expression(minutes, hours, days, number_of_month, days_of_week, year):
return "cron({0} {1} {2} {3} {4} {5})".format(minutes, hours, days, number_of_month, days_of_week, year)
def validate_params(user_params, system_params):
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "" \
and user_params['InputDataLocation'] == user_params['OutputDataLocation']:
err_msg = 'InputDataLocation is same as OutputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
def generate_layout(user_params, system_params):
file_name = "compaction_{0}_{1}.py".format(user_params['SourceDatabaseName'], user_params['SourceTableName'])
session = boto3.Session(region_name=system_params['region'])
glue = session.client('glue')
s3_client = session.client('s3')
workflow_name = user_params['WorkflowName']
# Validate params
validate_params(user_params, system_params)
# Create Source Database if it does not exists
try:
glue.create_database(
DatabaseInput={
'Name': user_params['SourceDatabaseName']
}
)
print("New database is created.")
except glue.exceptions.AlreadyExistsException:
print("Existing database is used.")
location = {'LocationConstraint': system_params['region']}
# Creating script bucket
the_script_bucket = f"aws-glue-scripts-{system_params["accountId"]}-{system_params["region"]}"
try:
s3_client.head_bucket(Bucket=the_script_bucket)
print("Script bucket already exists: ", the_script_bucket)
except ClientError as ce:
print(ce)
print(ce.response['ResponseMetadata'])
print("Creating script bucket: ", the_script_bucket)
if system_params['region'] == "us-east-1":
bucket = s3_client.create_bucket(Bucket=the_script_bucket)
else:
bucket = s3_client.create_bucket(Bucket=the_script_bucket, CreateBucketConfiguration=location)
# Creating temp bucket
the_temp_bucket = f"aws-glue-temporary-{system_params["accountId"]}-{system_params["region"]}"
the_temp_prefix = f"{workflow_name}/"
the_temp_location = f"s3://{the_temp_bucket}/{the_temp_prefix}"
try:
s3_client.head_bucket(Bucket=the_temp_bucket)
print("Temp bucket already exists: ", the_temp_bucket)
except ClientError as ce:
print(ce)
print(ce.response['ResponseMetadata'])
print("Creating temp bucket: ", the_temp_bucket)
if system_params['region'] == "us-east-1":
bucket = s3_client.create_bucket(Bucket=the_temp_bucket)
else:
bucket = s3_client.create_bucket(Bucket=the_temp_bucket, CreateBucketConfiguration=location)
# Creating manifest bucket
if user_params['EnableManifest']:
the_manifest_bucket = f"aws-glue-compaction-manifest-{system_params["accountId"]}-{system_params["region"]}"
the_manifest_prefix = f"{workflow_name}/"
the_manifest_location = f"s3://{the_manifest_bucket}/{the_manifest_prefix}"
try:
s3_client.head_bucket(Bucket=the_manifest_bucket)
print("Manifest bucket already exists: ", the_manifest_bucket)
except ClientError as ce:
print(ce)
print(ce.response['ResponseMetadata'])
print("Creating Manifest bucket: ", the_manifest_bucket)
if system_params['region'] == "us-east-1":
bucket = s3_client.create_bucket(Bucket=the_manifest_bucket)
else:
bucket = s3_client.create_bucket(Bucket=the_manifest_bucket, CreateBucketConfiguration=location)
# Upload job script to script bucket
the_script_key = f"{workflow_name}/{file_name}"
the_script_location = f"s3://{the_script_bucket}/{the_script_key}"
with open("compaction/compaction.py", "rb") as f:
s3_client.upload_fileobj(f, the_script_bucket, the_script_key)
jobs = []
crawlers = []
command = {
"Name": "glueetl",
"ScriptLocation": the_script_location,
"PythonVersion": "3"
}
arguments = {
"--region": system_params['region'],
"--TempDir": the_temp_location,
"--job-bookmark-option": "job-bookmark-disable",
"--job-language": "python",
"--enable-s3-parquet-optimized-committer": "",
"--enable-rename-algorithm-v2": "",
"--enable-metrics": "",
"--enable-continuous-cloudwatch-log": "true",
"--enable_size_control": user_params['EnableSizeControl'],
"--input_database": user_params['SourceDatabaseName'],
"--input_table": user_params['SourceTableName'],
"--input_format": user_params['InputDataFormat'],
"--output_path": user_params['OutputDataLocation'],
"--desired_size_mb": user_params['DesiredFileSizeMB'],
"--enable_manifest": user_params['EnableManifest']
}
if user_params['InputDataFormatOptions']:
arguments["--input_format_options"] = user_params['InputDataFormatOptions']
if user_params['EnableManifest']:
arguments["--manifest_path"] = the_manifest_location
crawler_source = None
try:
# Get the source table definition and validate the parameters with it.
src_table = glue.get_table(
DatabaseName=user_params['SourceDatabaseName'],
Name=user_params['SourceTableName']
)
if src_table['Table']['StorageDescriptor']['Location'] == user_params['OutputDataLocation']:
err_msg = 'Location on the source table is same as OutputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "" \
and src_table['Table']['StorageDescriptor']['Location'] != user_params['InputDataLocation']:
err_msg = 'Location on the source table is different from InputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
print("Existing table is used.")
except glue.exceptions.EntityNotFoundException:
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "":
# Create a new source table if it does not exist
glue.create_table(
DatabaseName=user_params['SourceDatabaseName'],
TableInput={
'Name': user_params['SourceTableName'],
'StorageDescriptor': {
'Location': user_params['InputDataLocation']
}
}
)
print("New table is created.")
else:
err_msg = 'Source table does not exist, and input data location is not provided.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "":
targets_source = {"CatalogTargets": [{"DatabaseName": user_params['SourceDatabaseName'], "Tables": [user_params['SourceTableName']]}]}
crawler_source = Crawler(
Name="{}_crawler_source".format(workflow_name),
Role=user_params['IAMRole'],
Grouping={
"TableGroupingPolicy": "CombineCompatibleSchemas"
},
Targets=targets_source,
SchemaChangePolicy={"DeleteBehavior": "LOG"},
)
crawlers.append(crawler_source)
if crawler_source:
transform_job = Job(
Name="{0}_compaction_{1}_{2}".format(workflow_name, user_params['SourceDatabaseName'], user_params['SourceTableName']),
Command=command,
Role=user_params['IAMRole'],
DefaultArguments=arguments,
WorkerType="G.1X",
NumberOfWorkers=user_params['NumberOfWorkers'],
GlueVersion="2.0",
DependsOn={crawler_source: "SUCCEEDED"}
)
else:
transform_job = Job(
Name="{0}_compaction_{1}_{2}".format(workflow_name, user_params['SourceDatabaseName'], user_params['SourceTableName']),
Command=command,
Role=user_params['IAMRole'],
DefaultArguments=arguments,
WorkerType="G.1X",
NumberOfWorkers=user_params['NumberOfWorkers'],
GlueVersion="2.0"
)
jobs.append(transform_job)
# Create destination database if it does not exists
try:
glue.create_database(
DatabaseInput={
'Name': user_params['DestinationDatabaseName']
}
)
print("New database is created.")
except glue.exceptions.AlreadyExistsException:
print("Existing database is used.")
try:
# Get the destination table and validate the parameters with it.
dst_table = glue.get_table(
DatabaseName=user_params['DestinationDatabaseName'],
Name=user_params['DestinationTableName']
)
if dst_table['Table']['StorageDescriptor']['Location'] != user_params['OutputDataLocation']:
err_msg = 'Location on the destination table is different from the OutputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
print("Existing table is used.")
except glue.exceptions.EntityNotFoundException:
# Create destination table if it does not exist
glue.create_table(
DatabaseName=user_params['DestinationDatabaseName'],
TableInput={
'Name': user_params['DestinationTableName'],
'StorageDescriptor': {
'Location': user_params['OutputDataLocation']
}
}
)
print("New table is created.")
targets_destination = {"CatalogTargets": [{"DatabaseName": user_params['DestinationDatabaseName'], "Tables": [user_params['DestinationTableName']]}]}
crawler_destination = Crawler(
Name="{}_crawler_destination".format(workflow_name),
Role=user_params['IAMRole'],
Targets=targets_destination,
SchemaChangePolicy={"DeleteBehavior": "LOG"},
DependsOn={transform_job: "SUCCEEDED"}
)
crawlers.append(crawler_destination)
if user_params['Frequency']:
if user_params['Frequency'] == 'Custom':
schedule = user_params['FrequencyCronFormat']
else:
schedule = generate_schedule(user_params['Frequency'])
else:
schedule = None
workflow = Workflow(Name=workflow_name, Entities=Entities(Jobs=jobs, Crawlers=crawlers), OnSchedule=schedule)
return workflow
| # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
from awsglue.blueprint.workflow import Workflow, Entities
from awsglue.blueprint.job import Job
from awsglue.blueprint.crawler import *
import boto3
from botocore.client import ClientError
import datetime
def generate_schedule(type):
now = datetime.datetime.utcnow()
year = now.year
number_of_month = now.month
days = now.day
hours = now.hour
minutes = now.minute
days_of_week = now.weekday()
if type == 'Hourly':
return generate_cron_expression(minutes, "0/1", "*", "*", "?", "*")
elif type == 'Daily':
return generate_cron_expression(minutes, hours, "*", "*", "?", "*")
elif type == 'Weekly':
return generate_cron_expression(minutes, hours, "?", "*", days_of_week, "*")
elif type == 'Monthly':
return generate_cron_expression(minutes, hours, days, "*", "?", "*")
else:
return generate_cron_expression(minutes, hours, days, number_of_month, "?", year)
def generate_cron_expression(minutes, hours, days, number_of_month, days_of_week, year):
return "cron({0} {1} {2} {3} {4} {5})".format(minutes, hours, days, number_of_month, days_of_week, year)
def validate_params(user_params, system_params):
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "" \
and user_params['InputDataLocation'] == user_params['OutputDataLocation']:
err_msg = 'InputDataLocation is same as OutputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
def generate_layout(user_params, system_params):
file_name = "compaction_{0}_{1}.py".format(user_params['SourceDatabaseName'], user_params['SourceTableName'])
session = boto3.Session(region_name=system_params['region'])
glue = session.client('glue')
s3_client = session.client('s3')
workflow_name = user_params['WorkflowName']
# Validate params
validate_params(user_params, system_params)
# Create Source Database if it does not exists
try:
glue.create_database(
DatabaseInput={
'Name': user_params['SourceDatabaseName']
}
)
print("New database is created.")
except glue.exceptions.AlreadyExistsException:
print("Existing database is used.")
location = {'LocationConstraint': system_params['region']}
# Creating script bucket
the_script_bucket = f"aws-glue-scripts-{system_params['accountId']}-{system_params['region']}"
try:
s3_client.head_bucket(Bucket=the_script_bucket)
print("Script bucket already exists: ", the_script_bucket)
except ClientError as ce:
print(ce)
print(ce.response['ResponseMetadata'])
print("Creating script bucket: ", the_script_bucket)
if system_params['region'] == "us-east-1":
bucket = s3_client.create_bucket(Bucket=the_script_bucket)
else:
bucket = s3_client.create_bucket(Bucket=the_script_bucket, CreateBucketConfiguration=location)
# Creating temp bucket
the_temp_bucket = f"aws-glue-temporary-{system_params['accountId']}-{system_params['region']}"
the_temp_prefix = f"{workflow_name}/"
the_temp_location = f"s3://{the_temp_bucket}/{the_temp_prefix}"
try:
s3_client.head_bucket(Bucket=the_temp_bucket)
print("Temp bucket already exists: ", the_temp_bucket)
except ClientError as ce:
print(ce)
print(ce.response['ResponseMetadata'])
print("Creating temp bucket: ", the_temp_bucket)
if system_params['region'] == "us-east-1":
bucket = s3_client.create_bucket(Bucket=the_temp_bucket)
else:
bucket = s3_client.create_bucket(Bucket=the_temp_bucket, CreateBucketConfiguration=location)
# Creating manifest bucket
if user_params['EnableManifest']:
the_manifest_bucket = f"aws-glue-compaction-manifest-{system_params['accountId']}-{system_params['region']}"
the_manifest_prefix = f"{workflow_name}/"
the_manifest_location = f"s3://{the_manifest_bucket}/{the_manifest_prefix}"
try:
s3_client.head_bucket(Bucket=the_manifest_bucket)
print("Manifest bucket already exists: ", the_manifest_bucket)
except ClientError as ce:
print(ce)
print(ce.response['ResponseMetadata'])
print("Creating Manifest bucket: ", the_manifest_bucket)
if system_params['region'] == "us-east-1":
bucket = s3_client.create_bucket(Bucket=the_manifest_bucket)
else:
bucket = s3_client.create_bucket(Bucket=the_manifest_bucket, CreateBucketConfiguration=location)
# Upload job script to script bucket
the_script_key = f"{workflow_name}/{file_name}"
the_script_location = f"s3://{the_script_bucket}/{the_script_key}"
with open("compaction/compaction.py", "rb") as f:
s3_client.upload_fileobj(f, the_script_bucket, the_script_key)
jobs = []
crawlers = []
command = {
"Name": "glueetl",
"ScriptLocation": the_script_location,
"PythonVersion": "3"
}
arguments = {
"--region": system_params['region'],
"--TempDir": the_temp_location,
"--job-bookmark-option": "job-bookmark-disable",
"--job-language": "python",
"--enable-s3-parquet-optimized-committer": "",
"--enable-rename-algorithm-v2": "",
"--enable-metrics": "",
"--enable-continuous-cloudwatch-log": "true",
"--enable_size_control": user_params['EnableSizeControl'],
"--input_database": user_params['SourceDatabaseName'],
"--input_table": user_params['SourceTableName'],
"--input_format": user_params['InputDataFormat'],
"--output_path": user_params['OutputDataLocation'],
"--desired_size_mb": user_params['DesiredFileSizeMB'],
"--enable_manifest": user_params['EnableManifest']
}
if user_params['InputDataFormatOptions']:
arguments["--input_format_options"] = user_params['InputDataFormatOptions']
if user_params['EnableManifest']:
arguments["--manifest_path"] = the_manifest_location
crawler_source = None
try:
# Get the source table definition and validate the parameters with it.
src_table = glue.get_table(
DatabaseName=user_params['SourceDatabaseName'],
Name=user_params['SourceTableName']
)
if src_table['Table']['StorageDescriptor']['Location'] == user_params['OutputDataLocation']:
err_msg = 'Location on the source table is same as OutputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "" \
and src_table['Table']['StorageDescriptor']['Location'] != user_params['InputDataLocation']:
err_msg = 'Location on the source table is different from InputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
print("Existing table is used.")
except glue.exceptions.EntityNotFoundException:
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "":
# Create a new source table if it does not exist
glue.create_table(
DatabaseName=user_params['SourceDatabaseName'],
TableInput={
'Name': user_params['SourceTableName'],
'StorageDescriptor': {
'Location': user_params['InputDataLocation']
}
}
)
print("New table is created.")
else:
err_msg = 'Source table does not exist, and input data location is not provided.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
if user_params['InputDataLocation'] and user_params['InputDataLocation'] != "":
targets_source = {"CatalogTargets": [{"DatabaseName": user_params['SourceDatabaseName'], "Tables": [user_params['SourceTableName']]}]}
crawler_source = Crawler(
Name="{}_crawler_source".format(workflow_name),
Role=user_params['IAMRole'],
Grouping={
"TableGroupingPolicy": "CombineCompatibleSchemas"
},
Targets=targets_source,
SchemaChangePolicy={"DeleteBehavior": "LOG"},
)
crawlers.append(crawler_source)
if crawler_source:
transform_job = Job(
Name="{0}_compaction_{1}_{2}".format(workflow_name, user_params['SourceDatabaseName'], user_params['SourceTableName']),
Command=command,
Role=user_params['IAMRole'],
DefaultArguments=arguments,
WorkerType="G.1X",
NumberOfWorkers=user_params['NumberOfWorkers'],
GlueVersion="2.0",
DependsOn={crawler_source: "SUCCEEDED"}
)
else:
transform_job = Job(
Name="{0}_compaction_{1}_{2}".format(workflow_name, user_params['SourceDatabaseName'], user_params['SourceTableName']),
Command=command,
Role=user_params['IAMRole'],
DefaultArguments=arguments,
WorkerType="G.1X",
NumberOfWorkers=user_params['NumberOfWorkers'],
GlueVersion="2.0"
)
jobs.append(transform_job)
# Create destination database if it does not exists
try:
glue.create_database(
DatabaseInput={
'Name': user_params['DestinationDatabaseName']
}
)
print("New database is created.")
except glue.exceptions.AlreadyExistsException:
print("Existing database is used.")
try:
# Get the destination table and validate the parameters with it.
dst_table = glue.get_table(
DatabaseName=user_params['DestinationDatabaseName'],
Name=user_params['DestinationTableName']
)
if dst_table['Table']['StorageDescriptor']['Location'] != user_params['OutputDataLocation']:
err_msg = 'Location on the destination table is different from the OutputDataLocation.'
raise ClientError({"Error": {"Code": "InvalidInputException", "Message": err_msg}}, 'validate_params')
print("Existing table is used.")
except glue.exceptions.EntityNotFoundException:
# Create destination table if it does not exist
glue.create_table(
DatabaseName=user_params['DestinationDatabaseName'],
TableInput={
'Name': user_params['DestinationTableName'],
'StorageDescriptor': {
'Location': user_params['OutputDataLocation']
}
}
)
print("New table is created.")
targets_destination = {"CatalogTargets": [{"DatabaseName": user_params['DestinationDatabaseName'], "Tables": [user_params['DestinationTableName']]}]}
crawler_destination = Crawler(
Name="{}_crawler_destination".format(workflow_name),
Role=user_params['IAMRole'],
Targets=targets_destination,
SchemaChangePolicy={"DeleteBehavior": "LOG"},
DependsOn={transform_job: "SUCCEEDED"}
)
crawlers.append(crawler_destination)
if user_params['Frequency']:
if user_params['Frequency'] == 'Custom':
schedule = user_params['FrequencyCronFormat']
else:
schedule = generate_schedule(user_params['Frequency'])
else:
schedule = None
workflow = Workflow(Name=workflow_name, Entities=Entities(Jobs=jobs, Crawlers=crawlers), OnSchedule=schedule)
return workflow
|
import random
from ..core import Basic, Integer
from ..core.compatibility import as_int
class GrayCode(Basic):
"""
A Gray code is essentially a Hamiltonian walk on
a n-dimensional cube with edge length of one.
The vertices of the cube are represented by vectors
whose values are binary. The Hamilton walk visits
each vertex exactly once. The Gray code for a 3d
cube is ['000','100','110','010','011','111','101',
'001'].
A Gray code solves the problem of sequentially
generating all possible subsets of n objects in such
a way that each subset is obtained from the previous
one by either deleting or adding a single object.
In the above example, 1 indicates that the object is
present, and 0 indicates that its absent.
Gray codes have applications in statistics as well when
we want to compute various statistics related to subsets
in an efficient manner.
References
==========
* Nijenhuis,A. and Wilf,H.S.(1978).
Combinatorial Algorithms. Academic Press.
* Knuth, D. (2011). The Art of Computer Programming, Vol 4
Addison Wesley
Examples
========
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> a = GrayCode(4)
>>> list(a.generate_gray())
['0000', '0001', '0011', '0010', '0110', '0111', '0101', '0100',
'1100', '1101', '1111', '1110', '1010', '1011', '1001', '1000']
"""
_skip = False
_current = 0
_rank = None
def __new__(cls, n, *args, **kw_args):
"""
Default constructor.
It takes a single argument ``n`` which gives the dimension of the Gray
code. The starting Gray code string (``start``) or the starting ``rank``
may also be given; the default is to start at rank = 0 ('0...0').
Examples
========
>>> a = GrayCode(3)
>>> a
GrayCode(3)
>>> a.n
3
>>> a = GrayCode(3, start='100')
>>> a.current
'100'
>>> a = GrayCode(4, rank=4)
>>> a.current
'0110'
>>> a.rank
4
"""
if n < 1 or int(n) != n:
raise ValueError(
f'Gray code dimension must be a positive integer, not {n:d}')
n = int(n)
args = (Integer(n),) + args
obj = Basic.__new__(cls, *args)
if 'start' in kw_args:
obj._current = kw_args['start']
if len(obj._current) > n:
raise ValueError(f'Gray code start has length {len(obj._current):d} but '
f'should not be greater than {n:d}')
elif 'rank' in kw_args:
kw_args['rank'] = as_int(kw_args['rank'])
if kw_args['rank'] <= 0:
raise ValueError('Gray code rank must be a positive integer, '
f"not {kw_args["rank"]:d}")
obj._rank = kw_args['rank'] % obj.selections
obj._current = obj.unrank(n, obj._rank)
return obj
def next(self, delta=1):
"""
Returns the Gray code a distance ``delta`` (default = 1) from the
current value in canonical order.
Examples
========
>>> a = GrayCode(3, start='110')
>>> a.next().current
'111'
>>> a.next(-1).current
'010'
"""
return GrayCode(self.n, rank=(self.rank + delta) % self.selections)
@property
def selections(self):
"""
Returns the number of bit vectors in the Gray code.
Examples
========
>>> a = GrayCode(3)
>>> a.selections
8
"""
return 2**self.n
@property
def n(self):
"""
Returns the dimension of the Gray code.
Examples
========
>>> a = GrayCode(5)
>>> a.n
5
"""
return int(self.args[0])
def generate_gray(self, **hints):
"""
Generates the sequence of bit vectors of a Gray Code.
[1] Knuth, D. (2011). The Art of Computer Programming,
Vol 4, Addison Wesley
Examples
========
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> list(a.generate_gray(start='011'))
['011', '010', '110', '111', '101', '100']
>>> list(a.generate_gray(rank=4))
['110', '111', '101', '100']
See Also
========
skip
"""
bits = self.n
start = None
if 'start' in hints:
start = hints['start']
elif 'rank' in hints:
start = GrayCode.unrank(self.n, hints['rank'])
if start is not None:
self._current = start
current = self.current
graycode_bin = gray_to_bin(current)
if len(graycode_bin) > self.n:
raise ValueError(f'Gray code start has length {len(graycode_bin):d} but should '
f'not be greater than {bits:d}')
self._current = int(current, 2)
graycode_int = int(''.join(graycode_bin), 2)
for i in range(graycode_int, 1 << bits):
if self._skip:
self._skip = False
else:
yield self.current
bbtc = (i ^ (i + 1))
gbtc = (bbtc ^ (bbtc >> 1))
self._current = (self._current ^ gbtc)
self._current = 0
def skip(self):
"""
Skips the bit generation.
Examples
========
>>> a = GrayCode(3)
>>> for i in a.generate_gray():
... if i == '010':
... a.skip()
... print(i)
...
000
001
011
010
111
101
100
See Also
========
generate_gray
"""
self._skip = True
@property
def rank(self):
"""
Ranks the Gray code.
A ranking algorithm determines the position (or rank)
of a combinatorial object among all the objects w.r.t.
a given order. For example, the 4 bit binary reflected
Gray code (BRGC) '0101' has a rank of 6 as it appears in
the 6th position in the canonical ordering of the family
of 4 bit Gray codes.
References
==========
* http://statweb.stanford.edu/~susan/courses/s208/node12.html
Examples
========
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> GrayCode(3, start='100').rank
7
>>> GrayCode(3, rank=7).current
'100'
See Also
========
unrank
"""
if self._rank is None:
self._rank = int(gray_to_bin(self.current), 2)
return self._rank
@property
def current(self):
"""
Returns the currently referenced Gray code as a bit string.
Examples
========
>>> GrayCode(3, start='100').current
'100'
"""
rv = self._current or '0'
if type(rv) is not str:
rv = bin(rv)[2:]
return rv.rjust(self.n, '0')
@classmethod
def unrank(cls, n, rank):
"""
Unranks an n-bit sized Gray code of rank k. This method exists
so that a derivative GrayCode class can define its own code of
a given rank.
The string here is generated in reverse order to allow for tail-call
optimization.
Examples
========
>>> GrayCode(5, rank=3).current
'00010'
>>> GrayCode.unrank(5, 3)
'00010'
See Also
========
rank
"""
def _unrank(k, n):
if n == 1:
return str(k % 2)
m = 2**(n - 1)
if k < m:
return '0' + _unrank(k, n - 1)
return '1' + _unrank(m - (k % m) - 1, n - 1)
return _unrank(rank, n)
def random_bitstring(n):
"""
Generates a random bitlist of length n.
Examples
========
>>> random_bitstring(3)
'110'
"""
return ''.join([random.choice('01') for i in range(n)])
def gray_to_bin(bin_list):
"""
Convert from Gray coding to binary coding.
We assume big endian encoding.
Examples
========
>>> gray_to_bin('100')
'111'
See Also
========
bin_to_gray
"""
b = [bin_list[0]]
for i in range(1, len(bin_list)):
b += str(int(b[i - 1] != bin_list[i]))
return ''.join(b)
def bin_to_gray(bin_list):
"""
Convert from binary coding to gray coding.
We assume big endian encoding.
Examples
========
>>> bin_to_gray('111')
'100'
See Also
========
gray_to_bin
"""
b = [bin_list[0]]
for i in range(len(bin_list) - 1):
b += str(int(bin_list[i]) ^ int(b[i - 1]))
return ''.join(b)
def get_subset_from_bitstring(super_set, bitstring):
"""
Gets the subset defined by the bitstring.
Examples
========
>>> get_subset_from_bitstring(['a', 'b', 'c', 'd'], '0011')
['c', 'd']
>>> get_subset_from_bitstring(['c', 'a', 'c', 'c'], '1100')
['c', 'a']
See Also
========
graycode_subsets
"""
if len(super_set) != len(bitstring):
raise ValueError('The sizes of the lists are not equal')
return [super_set[i] for i, j in enumerate(bitstring) if j == '1']
def graycode_subsets(gray_code_set):
"""
Generates the subsets as enumerated by a Gray code.
Examples
========
>>> list(graycode_subsets(['a', 'b', 'c']))
[[], ['c'], ['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'],
['a', 'c'], ['a']]
>>> list(graycode_subsets(['a', 'b', 'c', 'c']))
[[], ['c'], ['c', 'c'], ['c'], ['b', 'c'], ['b', 'c', 'c'],
['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'], ['a', 'b', 'c', 'c'],
['a', 'b', 'c'], ['a', 'c'], ['a', 'c', 'c'], ['a', 'c'], ['a']]
See Also
========
get_subset_from_bitstring
"""
for bitstring in list(GrayCode(len(gray_code_set)).generate_gray()):
yield get_subset_from_bitstring(gray_code_set, bitstring)
| import random
from ..core import Basic, Integer
from ..core.compatibility import as_int
class GrayCode(Basic):
"""
A Gray code is essentially a Hamiltonian walk on
a n-dimensional cube with edge length of one.
The vertices of the cube are represented by vectors
whose values are binary. The Hamilton walk visits
each vertex exactly once. The Gray code for a 3d
cube is ['000','100','110','010','011','111','101',
'001'].
A Gray code solves the problem of sequentially
generating all possible subsets of n objects in such
a way that each subset is obtained from the previous
one by either deleting or adding a single object.
In the above example, 1 indicates that the object is
present, and 0 indicates that its absent.
Gray codes have applications in statistics as well when
we want to compute various statistics related to subsets
in an efficient manner.
References
==========
* Nijenhuis,A. and Wilf,H.S.(1978).
Combinatorial Algorithms. Academic Press.
* Knuth, D. (2011). The Art of Computer Programming, Vol 4
Addison Wesley
Examples
========
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> a = GrayCode(4)
>>> list(a.generate_gray())
['0000', '0001', '0011', '0010', '0110', '0111', '0101', '0100',
'1100', '1101', '1111', '1110', '1010', '1011', '1001', '1000']
"""
_skip = False
_current = 0
_rank = None
def __new__(cls, n, *args, **kw_args):
"""
Default constructor.
It takes a single argument ``n`` which gives the dimension of the Gray
code. The starting Gray code string (``start``) or the starting ``rank``
may also be given; the default is to start at rank = 0 ('0...0').
Examples
========
>>> a = GrayCode(3)
>>> a
GrayCode(3)
>>> a.n
3
>>> a = GrayCode(3, start='100')
>>> a.current
'100'
>>> a = GrayCode(4, rank=4)
>>> a.current
'0110'
>>> a.rank
4
"""
if n < 1 or int(n) != n:
raise ValueError(
f'Gray code dimension must be a positive integer, not {n:d}')
n = int(n)
args = (Integer(n),) + args
obj = Basic.__new__(cls, *args)
if 'start' in kw_args:
obj._current = kw_args['start']
if len(obj._current) > n:
raise ValueError(f'Gray code start has length {len(obj._current):d} but '
f'should not be greater than {n:d}')
elif 'rank' in kw_args:
kw_args['rank'] = as_int(kw_args['rank'])
if kw_args['rank'] <= 0:
raise ValueError('Gray code rank must be a positive integer, '
f"not {kw_args['rank']:d}")
obj._rank = kw_args['rank'] % obj.selections
obj._current = obj.unrank(n, obj._rank)
return obj
def next(self, delta=1):
"""
Returns the Gray code a distance ``delta`` (default = 1) from the
current value in canonical order.
Examples
========
>>> a = GrayCode(3, start='110')
>>> a.next().current
'111'
>>> a.next(-1).current
'010'
"""
return GrayCode(self.n, rank=(self.rank + delta) % self.selections)
@property
def selections(self):
"""
Returns the number of bit vectors in the Gray code.
Examples
========
>>> a = GrayCode(3)
>>> a.selections
8
"""
return 2**self.n
@property
def n(self):
"""
Returns the dimension of the Gray code.
Examples
========
>>> a = GrayCode(5)
>>> a.n
5
"""
return int(self.args[0])
def generate_gray(self, **hints):
"""
Generates the sequence of bit vectors of a Gray Code.
[1] Knuth, D. (2011). The Art of Computer Programming,
Vol 4, Addison Wesley
Examples
========
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> list(a.generate_gray(start='011'))
['011', '010', '110', '111', '101', '100']
>>> list(a.generate_gray(rank=4))
['110', '111', '101', '100']
See Also
========
skip
"""
bits = self.n
start = None
if 'start' in hints:
start = hints['start']
elif 'rank' in hints:
start = GrayCode.unrank(self.n, hints['rank'])
if start is not None:
self._current = start
current = self.current
graycode_bin = gray_to_bin(current)
if len(graycode_bin) > self.n:
raise ValueError(f'Gray code start has length {len(graycode_bin):d} but should '
f'not be greater than {bits:d}')
self._current = int(current, 2)
graycode_int = int(''.join(graycode_bin), 2)
for i in range(graycode_int, 1 << bits):
if self._skip:
self._skip = False
else:
yield self.current
bbtc = (i ^ (i + 1))
gbtc = (bbtc ^ (bbtc >> 1))
self._current = (self._current ^ gbtc)
self._current = 0
def skip(self):
"""
Skips the bit generation.
Examples
========
>>> a = GrayCode(3)
>>> for i in a.generate_gray():
... if i == '010':
... a.skip()
... print(i)
...
000
001
011
010
111
101
100
See Also
========
generate_gray
"""
self._skip = True
@property
def rank(self):
"""
Ranks the Gray code.
A ranking algorithm determines the position (or rank)
of a combinatorial object among all the objects w.r.t.
a given order. For example, the 4 bit binary reflected
Gray code (BRGC) '0101' has a rank of 6 as it appears in
the 6th position in the canonical ordering of the family
of 4 bit Gray codes.
References
==========
* http://statweb.stanford.edu/~susan/courses/s208/node12.html
Examples
========
>>> a = GrayCode(3)
>>> list(a.generate_gray())
['000', '001', '011', '010', '110', '111', '101', '100']
>>> GrayCode(3, start='100').rank
7
>>> GrayCode(3, rank=7).current
'100'
See Also
========
unrank
"""
if self._rank is None:
self._rank = int(gray_to_bin(self.current), 2)
return self._rank
@property
def current(self):
"""
Returns the currently referenced Gray code as a bit string.
Examples
========
>>> GrayCode(3, start='100').current
'100'
"""
rv = self._current or '0'
if type(rv) is not str:
rv = bin(rv)[2:]
return rv.rjust(self.n, '0')
@classmethod
def unrank(cls, n, rank):
"""
Unranks an n-bit sized Gray code of rank k. This method exists
so that a derivative GrayCode class can define its own code of
a given rank.
The string here is generated in reverse order to allow for tail-call
optimization.
Examples
========
>>> GrayCode(5, rank=3).current
'00010'
>>> GrayCode.unrank(5, 3)
'00010'
See Also
========
rank
"""
def _unrank(k, n):
if n == 1:
return str(k % 2)
m = 2**(n - 1)
if k < m:
return '0' + _unrank(k, n - 1)
return '1' + _unrank(m - (k % m) - 1, n - 1)
return _unrank(rank, n)
def random_bitstring(n):
"""
Generates a random bitlist of length n.
Examples
========
>>> random_bitstring(3)
'110'
"""
return ''.join([random.choice('01') for i in range(n)])
def gray_to_bin(bin_list):
"""
Convert from Gray coding to binary coding.
We assume big endian encoding.
Examples
========
>>> gray_to_bin('100')
'111'
See Also
========
bin_to_gray
"""
b = [bin_list[0]]
for i in range(1, len(bin_list)):
b += str(int(b[i - 1] != bin_list[i]))
return ''.join(b)
def bin_to_gray(bin_list):
"""
Convert from binary coding to gray coding.
We assume big endian encoding.
Examples
========
>>> bin_to_gray('111')
'100'
See Also
========
gray_to_bin
"""
b = [bin_list[0]]
for i in range(len(bin_list) - 1):
b += str(int(bin_list[i]) ^ int(b[i - 1]))
return ''.join(b)
def get_subset_from_bitstring(super_set, bitstring):
"""
Gets the subset defined by the bitstring.
Examples
========
>>> get_subset_from_bitstring(['a', 'b', 'c', 'd'], '0011')
['c', 'd']
>>> get_subset_from_bitstring(['c', 'a', 'c', 'c'], '1100')
['c', 'a']
See Also
========
graycode_subsets
"""
if len(super_set) != len(bitstring):
raise ValueError('The sizes of the lists are not equal')
return [super_set[i] for i, j in enumerate(bitstring) if j == '1']
def graycode_subsets(gray_code_set):
"""
Generates the subsets as enumerated by a Gray code.
Examples
========
>>> list(graycode_subsets(['a', 'b', 'c']))
[[], ['c'], ['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'],
['a', 'c'], ['a']]
>>> list(graycode_subsets(['a', 'b', 'c', 'c']))
[[], ['c'], ['c', 'c'], ['c'], ['b', 'c'], ['b', 'c', 'c'],
['b', 'c'], ['b'], ['a', 'b'], ['a', 'b', 'c'], ['a', 'b', 'c', 'c'],
['a', 'b', 'c'], ['a', 'c'], ['a', 'c', 'c'], ['a', 'c'], ['a']]
See Also
========
get_subset_from_bitstring
"""
for bitstring in list(GrayCode(len(gray_code_set)).generate_gray()):
yield get_subset_from_bitstring(gray_code_set, bitstring)
|
#!/usr/bin/env python
import argparse
import copy
import traceback
from os import listdir
from os.path import isfile, join
#from cv_bridge import CvBridge
import math
import matplotlib.pyplot as plt
import pandas as pd
import random
# u
import numpy as np
import cv2 as cv
import rospy
# Brings in the SimpleActionClient
import actionlib
# Brings in the .action file and messages used by the move base action
from move_base_msgs.msg import MoveBaseAction, MoveBaseGoal
from squaternion import quat2euler
from squaternion import euler2quat
from sensor_msgs.msg import Image
from geometry_msgs.msg import Point
from geometry_msgs.msg import Point32
from geometry_msgs.msg import TransformStamped
from rosgraph_msgs.msg import Clock
from costmap_converter.msg import ObstacleArrayMsg
from costmap_converter.msg import ObstacleMsg
from geometry_msgs.msg import Twist
import threading
import _thread
from squaternion import quat2euler
from squaternion import euler2quat
from simple_pid import PID
import pickle
import utils
import logging
logger = logging.getLogger(__name__)
class Robot():
def __init__(self, name):
self.name = name
self.prev_call_vicon = None
self.state_ = {"position":(None, None), \
"orientation":None}
self.all_states_ = []
self.last_time_observation = None
if self.name == "robot":
rospy.Subscriber("/vicon/Robot/Robot", TransformStamped, self.vicon_cb)
elif self.name == "person":
rospy.Subscriber("/vicon/Person/Person", TransformStamped, self.vicon_cb)
def get_pos(self, idx):
if "position" in self.all_states_[idx].keys():
pos = self.all_states_[idx]["position"]
else:
pos = self.all_states_[idx]["pos"]
return pos
def get_orientation(self, idx):
return self.all_states_[idx]["orientation"]
def vicon_cb(self, pose_msg):
if self.last_time_observation is not None and abs(rospy.Time.now().to_sec() - self.last_time_observation) <0.025:
return
pos = pose_msg.transform.translation
self.last_time_observation = rospy.Time.now().to_sec()
self.state_["position"] = (pos.x, pos.y)
euler = quat2euler(pose_msg.transform.rotation.x, pose_msg.transform.rotation.y, pose_msg.transform.rotation.z, pose_msg.transform.rotation.w)
self.state_["orientation"] = euler[0]
self.all_states_.append(self.state_.copy())
def get_relative_position(self, center, idx):
relative_orientation = self.all_states_[idx]['orientation']
center_pos = np.asarray(center.get_pos(idx))
center_orientation = center.all_states_[idx]['orientation']
# transform the pos to center coordinat
relative_pos = np.asarray(self.get_pos(idx) - center_pos)
rotation_matrix = np.asarray([[np.cos(-center_orientation), np.sin(-center_orientation)], [-np.sin(-center_orientation), np.cos(-center_orientation)]])
relative_pos = np.matmul(relative_pos, rotation_matrix)
return relative_pos
def get_relative_heading_position(self, center, idx):
relative_orientation = self.all_states_[idx]['orientation']
center_pos = np.asarray(center.get_pos(idx))
center_orientation = center.all_states_[idx]['orientation']
print (np.rad2deg(relative_orientation - center_orientation))
# transform the relative to center coordinat
relative_pos = np.asarray(self.get_pos(idx) - center_pos)
relative_pos2 = np.asarray((relative_pos[0] +math.cos(relative_orientation) , relative_pos[1] + math.sin(relative_orientation)))
rotation_matrix = np.asarray([[np.cos(-center_orientation), np.sin(-center_orientation)], [-np.sin(-center_orientation), np.cos(-center_orientation)]])
relative_pos = np.matmul(relative_pos, rotation_matrix)
relative_pos2 = np.matmul(relative_pos2, rotation_matrix)
angle_relative = np.arctan2(relative_pos2[1]-relative_pos[1], relative_pos2[0]-relative_pos[0])
return angle_relative, relative_pos
def is_bag_finish(self):
if self.last_time_observation is not None and abs(rospy.Time.now().to_sec() - self.last_time_observation) > 1:
return True
return False
class Results():
def __init__(self):
self.center_pos_ = (0, 0)
self.name = ""
self.DESIRE_DISTANCE = 1.5
self.colors_visualization = cv.cvtColor(cv.applyColorMap(np.arange(0, 255, dtype=np.uint8), cv.COLORMAP_WINTER), cv.COLOR_RGB2BGR).reshape(255,3).tolist()
self.current_obsevation_image_ = np.zeros([500,500,3])
self.current_obsevation_image_.fill(255)
self.color_index = 0
self.first_call_observation = True
self.robot = Robot("robot")
self.person = Robot("person")
def add_line_observation_to_image(self, pos, pos2):
color = self.colors_visualization[self.color_index]
pos_image = utils.to_image_coordinate(pos, self.center_pos_)
pos_image2 = utils.to_image_coordinate(pos2, self.center_pos_)
if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:
rospy.logerr("problem with observation: {}".format(pos_image))
return
self.new_obsevation_image_ = cv.line(self.new_obsevation_image_, (pos_image[0], pos_image[1]), (pos_image2[0], pos_image2[1]), color, 1)
def add_triangle_observation_to_image(self, pos, orientation):
color = self.colors_visualization[self.color_index]
pos_image = utils.to_image_coordinate(pos, self.center_pos_)
pos_triangle1 = utils.to_image_coordinate((pos[0]+math.cos(orientation)*0.3, pos[1]+math.sin(orientation)*0.3), self.center_pos_)
pos_triangle2 = utils.to_image_coordinate((pos[0]+math.cos(orientation+math.pi/2)*0.1, pos[1]+math.sin(orientation+math.pi/2)*0.1), self.center_pos_)
pos_triangle3 = utils.to_image_coordinate((pos[0]+math.cos(orientation-math.pi/2)*0.1, pos[1]+math.sin(orientation-math.pi/2)*0.1), self.center_pos_)
poses = [pos_triangle1, pos_triangle2, pos_triangle3]
for pos in poses:
if pos[0] >self.current_obsevation_image_.shape[0] or pos[0] < 0 or pos[1] >self.current_obsevation_image_.shape[1] or pos[1] < 0:
rospy.logerr("problem with observation: {}".format(pos))
return
self.new_obsevation_image_ = cv.drawContours(self.new_obsevation_image_, [np.asarray(poses)], 0, color, -1)
def add_arrow_observation_to_image(self, pos, orientation):
color = self.colors_visualization[self.color_index]
pos_image = utils.to_image_coordinate(pos, self.center_pos_)
pos_image2 = utils.to_image_coordinate((pos[0]+math.cos(orientation)*0.3, pos[1]+math.sin(orientation)*0.3), self.center_pos_)
if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:
rospy.logerr("problem with observation: {}".format(pos_image))
return
self.new_obsevation_image_ = cv.arrowedLine(self.new_obsevation_image_, (pos_image[0], pos_image[1]), (pos_image2[0], pos_image2[1]), color, 2, tipLength=0.5)
def add_circle_observation_to_image(self, pos, center_pos=None, image=None):
color = self.colors_visualization[self.color_index]
if image is None:
image = self.new_obsevation_image_
if center_pos is None:
center_pos = self.center_pos_
pos_image = utils.to_image_coordinate(pos, center_pos)
if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:
rospy.logerr("problem with observation: {}".format(pos_image))
return
return (cv.circle(image , (pos_image[0], pos_image[1]), 4, color, 2))
def update_observation_image(self, idx, len_data):
self.new_obsevation_image_ = np.copy(self.current_obsevation_image_)
robot_pos = self.robot.get_pos(idx)
robot_orientation = self.robot.get_orientation(idx)
person_pos = self.person.get_pos(idx)
person_orientation = self.person.get_orientation(idx)
if person_orientation is None or robot_orientation is None:
rospy.logerr("person or robot orientation is None")
return
if self.first_call_observation:
self.first_call_observation = False
self.center_pos = person_pos
#self.add_circle_observation_to_image(robot_pos)
self.add_arrow_observation_to_image(robot_pos, robot_orientation)
self.add_triangle_observation_to_image(person_pos, person_orientation)
# self.add_line_observation_to_image(robot_pos, person_pos)
alpha = 0.50
self.current_obsevation_image_ = cv.addWeighted(self.new_obsevation_image_, alpha, self.current_obsevation_image_, 1 - alpha, 0)
self.color_index += 255//len_data
def get_current_observation_image(self):
image = self.current_obsevation_image_.astype(np.uint8)
#image = image/255.
return image
def get_angle_person_robot(self, idx):
pos_rel = self.robot.get_relative_position(self.person, idx)
angle_robot_person = math.atan2(pos_rel[1], pos_rel[0])
return (utils.wrap_pi_to_pi(angle_robot_person))
def get_dist_person_robot(self, idx):
pos_rel = self.robot.get_relative_position(self.person, idx)
return math.hypot(pos_rel[0], pos_rel[1])
def get_reward(self, idx):
reward = 0
pos_rel = self.robot.get_relative_position(self.person, idx)
angle_robot_person = math.atan2(pos_rel[1], pos_rel[0])
angle_robot_person = np.rad2deg(utils.wrap_pi_to_pi(angle_robot_person))
distance = math.hypot(pos_rel[0], pos_rel[1])
# Negative reward for being behind the person
if distance<0.4:
reward -= 1
if distance < 0.5:
reward = -1.3
elif abs(distance - self.DESIRE_DISTANCE) < 0.5:
reward += 0.5 * (0.5 - abs(distance - self.DESIRE_DISTANCE))
elif distance >= self.DESIRE_DISTANCE + 0.5:
reward -= 0.25 * (distance - self.DESIRE_DISTANCE + 0.5)
elif distance < self.DESIRE_DISTANCE - 0.5:
reward -= (self.DESIRE_DISTANCE - 0.5 - distance)/(self.DESIRE_DISTANCE - 0.5)
if abs(angle_robot_person) < 25:
reward += 0.5 * (25 - abs(angle_robot_person)) / 25
else:
reward -= 0.25 * abs(angle_robot_person) / 180
if abs(distance - self.DESIRE_DISTANCE) < 0.5 and abs(angle_robot_person) < 25:
reward += 0.25
reward = min(max(reward, -1), 1)
return reward
def save(self, name):
dic_data = {"name":name,"robot":self.robot.all_states_, "person":self.person.all_states_}
with open (name+"_.pkl", "wb") as f:
pickle.dump(dic_data, f)
def load(self, file_address, use_sim=False):
with open(file_address, "rb") as f:
dic_data = pickle.load(f)
self.name = dic_data["name"]
self.person.all_states_ = dic_data["person"][4:].copy()
self.robot.all_states_ = dic_data["robot"][4:].copy()
if use_sim:
self.person.all_states_ = [ self.person.all_states_[idx*10] for idx in range (len(self.person.all_states_)//10)]
self.robot.all_states_ = [ self.robot.all_states_[idx*10] for idx in range (len(self.robot.all_states_)//10)]
def wait_until_bag_finish(self):
while not self.robot.is_bag_finish() or not self.person.is_bag_finish():
rospy.sleep(0.1)
rospy.loginfo("waiting for bag to finish")
if len(self.person.all_states_)>0 and len(self.robot.all_states_)>0:
print(self.robot.get_relative_position(self.person, -1))
print(np.rad2deg(self.get_angle_person_robot(-1)))
print (self.robot.all_states_)
print (self.person.all_states_)
def calculate_orientation_dif(self, idx):
ori_rel, pos_rel = self.robot.get_relative_heading_position(self.person, idx)
return ori_rel
def get_metrics(self):
rewards = []
orientations = []
orientation_dif = []
distances = []
len_data = min(len(self.robot.all_states_), len(self.person.all_states_))
for idx in range (len_data):
# if idx % 10==0:
# self.update_observation_image(idx)
rewards.append(self.get_reward(idx))
distances.append(self.get_dist_person_robot(idx))
orientations.append(self.get_angle_person_robot(idx))
orientation_dif.append(self.calculate_orientation_dif(idx))
mean_orientation = np.mean(orientations)
sum_orientations_m = 0
for orientation in orientations:
sum_orientations_m += np.power(utils.wrap_pi_to_pi(mean_orientation - orientation),2)
sum_orientations_m /= len(orientations)
std = np.sqrt(sum_orientations_m)
return {"name":self.name, "orientation_mean":np.average(orientations), "orientation_std":std, \
"reward":np.sum(rewards), "distance":np.average(distances), "distance_std":np.std(distances),\
"ori_dif":np.average(orientation_dif)}
def plot_calculate_metrics(self):
rewards = []
orientations = []
distances = []
len_data = min(len(self.robot.all_states_), len(self.person.all_states_))
for idx in range (len_data):
if idx % 3==0:
self.update_observation_image(idx, len_data//3)
rewards.append(self.get_reward(idx))
distances.append(self.get_dist_person_robot(idx))
orientations.append(self.get_angle_person_robot(idx))
print (np.rad2deg(self.robot.get_relative_heading_position(self.person, 0)[0]))
img = self.get_current_observation_image()
img = cv.cvtColor(img, cv.COLOR_RGB2BGR)
print(f"\n\ndist avg: {np.average(distances)} orientation avg: {np.rad2deg(np.average(orientations))}, reward: {np.sum(rewards)} reward avg: {np.average(rewards)}")
cv.imshow("image", img)
cv.waitKey(0)
def plot_all_results( results, is_sim=False):
name = []
orientations = []
rewards = []
distances = []
orientations_std = []
distances_std = []
for result in results:
met = result.get_metrics()
name.append(met["name"])
rewards.append(met["reward"])
distances.append(met["distance"])
distances_std.append(met["distance_std"])
orientations.append(np.rad2deg(met["orientation_mean"]))
orientations_std.append(np.rad2deg(met["orientation_std"]))
print (f"{name[-1]}: Distance_avg: {distances[-1]:.2f} Distance_std: {distances_std[-1]:.2f} Orientation_avg: {orientations[-1]:.1f} Orientation_std: {orientations_std[-1]:.1f} reward: {rewards[-1]:.2f} ori_dif: {np.rad2deg(met["ori_dif"]):0.2f}")
if is_sim:
print (f"{name[-1]}: ${distances[-1]:.2f}\pm{distances_std[-1]:.1f}$ & ${orientations[-1]:.1f}\pm{orientations_std[-1]:.1f}$ & ${rewards[-1]:.2f}$")
else:
print (f"{name[-1]}: ${distances[-1]:.2f}\pm{distances_std[-1]:.1f}$ & ${orientations[-1]:.1f}\pm{orientations_std[-1]:.1f}$ & ${rewards[-1]:.2f}$")
print ("\n")
#df = pd.DataFrame({'name': name, 'assess':[x for x in range(len(name))]})
#plt.errorbar(range(len(df['name'])), orientations, orientations_std, fmt='o')
#plt.xticks(range(len(df['name'])), df['name'])
if __name__== "__main__":
parser = argparse.ArgumentParser(description='input weight file of the network')
parser.add_argument('--name', default="no_name", type=str, help='name_traj')
parser.add_argument('--file-name', default="no_name", type=str, help='name_file_to_load')
parser.add_argument('--folder-name', default="no_name", type=str, help='name_file_to_load')
parser.add_argument('--save', action='store_true')
parser.add_argument('--load-file', action='store_true')
parser.add_argument('--load-folder', action='store_true')
parser.add_argument('--plot', action='store_true')
parser.add_argument('--use-sim-data', action='store_true')
parser.add_argument('--from-bag', action='store_true')
args = parser.parse_args()
node = rospy.init_node('plot_results')
if args.load_folder:
onlyfiles = [join(args.folder_name, f) for f in listdir(args.folder_name) if isfile(join(args.folder_name, f))]
onlyfiles.sort()
all_results = []
for pkl_name in onlyfiles:
result = Results()
result.load(pkl_name)
name_list = result.name.split("_")
if not args.use_sim_data and name_list[-1] != "planner" and name_list[-1] != "line":
print ("error ")
continue
new_name = f"{name_list[-1]}_{name_list[-2]}_base_line"
result.name = new_name
result.save(new_name)
all_results.append(result)
plot_all_results(all_results, args.use_sim_data)
#plt.show()
else:
result = Results()
if args.from_bag or args.load_file:
if args.from_bag:
result.wait_until_bag_finish()
else:
result.load(args.file_name, args.use_sim_data)
else:
print("exiting you need to load or read from bag file")
exit(0)
if args.save:
result.save(args.name)
if args.plot:
result.plot_calculate_metrics()
| #!/usr/bin/env python
import argparse
import copy
import traceback
from os import listdir
from os.path import isfile, join
#from cv_bridge import CvBridge
import math
import matplotlib.pyplot as plt
import pandas as pd
import random
# u
import numpy as np
import cv2 as cv
import rospy
# Brings in the SimpleActionClient
import actionlib
# Brings in the .action file and messages used by the move base action
from move_base_msgs.msg import MoveBaseAction, MoveBaseGoal
from squaternion import quat2euler
from squaternion import euler2quat
from sensor_msgs.msg import Image
from geometry_msgs.msg import Point
from geometry_msgs.msg import Point32
from geometry_msgs.msg import TransformStamped
from rosgraph_msgs.msg import Clock
from costmap_converter.msg import ObstacleArrayMsg
from costmap_converter.msg import ObstacleMsg
from geometry_msgs.msg import Twist
import threading
import _thread
from squaternion import quat2euler
from squaternion import euler2quat
from simple_pid import PID
import pickle
import utils
import logging
logger = logging.getLogger(__name__)
class Robot():
def __init__(self, name):
self.name = name
self.prev_call_vicon = None
self.state_ = {"position":(None, None), \
"orientation":None}
self.all_states_ = []
self.last_time_observation = None
if self.name == "robot":
rospy.Subscriber("/vicon/Robot/Robot", TransformStamped, self.vicon_cb)
elif self.name == "person":
rospy.Subscriber("/vicon/Person/Person", TransformStamped, self.vicon_cb)
def get_pos(self, idx):
if "position" in self.all_states_[idx].keys():
pos = self.all_states_[idx]["position"]
else:
pos = self.all_states_[idx]["pos"]
return pos
def get_orientation(self, idx):
return self.all_states_[idx]["orientation"]
def vicon_cb(self, pose_msg):
if self.last_time_observation is not None and abs(rospy.Time.now().to_sec() - self.last_time_observation) <0.025:
return
pos = pose_msg.transform.translation
self.last_time_observation = rospy.Time.now().to_sec()
self.state_["position"] = (pos.x, pos.y)
euler = quat2euler(pose_msg.transform.rotation.x, pose_msg.transform.rotation.y, pose_msg.transform.rotation.z, pose_msg.transform.rotation.w)
self.state_["orientation"] = euler[0]
self.all_states_.append(self.state_.copy())
def get_relative_position(self, center, idx):
relative_orientation = self.all_states_[idx]['orientation']
center_pos = np.asarray(center.get_pos(idx))
center_orientation = center.all_states_[idx]['orientation']
# transform the pos to center coordinat
relative_pos = np.asarray(self.get_pos(idx) - center_pos)
rotation_matrix = np.asarray([[np.cos(-center_orientation), np.sin(-center_orientation)], [-np.sin(-center_orientation), np.cos(-center_orientation)]])
relative_pos = np.matmul(relative_pos, rotation_matrix)
return relative_pos
def get_relative_heading_position(self, center, idx):
relative_orientation = self.all_states_[idx]['orientation']
center_pos = np.asarray(center.get_pos(idx))
center_orientation = center.all_states_[idx]['orientation']
print (np.rad2deg(relative_orientation - center_orientation))
# transform the relative to center coordinat
relative_pos = np.asarray(self.get_pos(idx) - center_pos)
relative_pos2 = np.asarray((relative_pos[0] +math.cos(relative_orientation) , relative_pos[1] + math.sin(relative_orientation)))
rotation_matrix = np.asarray([[np.cos(-center_orientation), np.sin(-center_orientation)], [-np.sin(-center_orientation), np.cos(-center_orientation)]])
relative_pos = np.matmul(relative_pos, rotation_matrix)
relative_pos2 = np.matmul(relative_pos2, rotation_matrix)
angle_relative = np.arctan2(relative_pos2[1]-relative_pos[1], relative_pos2[0]-relative_pos[0])
return angle_relative, relative_pos
def is_bag_finish(self):
if self.last_time_observation is not None and abs(rospy.Time.now().to_sec() - self.last_time_observation) > 1:
return True
return False
class Results():
def __init__(self):
self.center_pos_ = (0, 0)
self.name = ""
self.DESIRE_DISTANCE = 1.5
self.colors_visualization = cv.cvtColor(cv.applyColorMap(np.arange(0, 255, dtype=np.uint8), cv.COLORMAP_WINTER), cv.COLOR_RGB2BGR).reshape(255,3).tolist()
self.current_obsevation_image_ = np.zeros([500,500,3])
self.current_obsevation_image_.fill(255)
self.color_index = 0
self.first_call_observation = True
self.robot = Robot("robot")
self.person = Robot("person")
def add_line_observation_to_image(self, pos, pos2):
color = self.colors_visualization[self.color_index]
pos_image = utils.to_image_coordinate(pos, self.center_pos_)
pos_image2 = utils.to_image_coordinate(pos2, self.center_pos_)
if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:
rospy.logerr("problem with observation: {}".format(pos_image))
return
self.new_obsevation_image_ = cv.line(self.new_obsevation_image_, (pos_image[0], pos_image[1]), (pos_image2[0], pos_image2[1]), color, 1)
def add_triangle_observation_to_image(self, pos, orientation):
color = self.colors_visualization[self.color_index]
pos_image = utils.to_image_coordinate(pos, self.center_pos_)
pos_triangle1 = utils.to_image_coordinate((pos[0]+math.cos(orientation)*0.3, pos[1]+math.sin(orientation)*0.3), self.center_pos_)
pos_triangle2 = utils.to_image_coordinate((pos[0]+math.cos(orientation+math.pi/2)*0.1, pos[1]+math.sin(orientation+math.pi/2)*0.1), self.center_pos_)
pos_triangle3 = utils.to_image_coordinate((pos[0]+math.cos(orientation-math.pi/2)*0.1, pos[1]+math.sin(orientation-math.pi/2)*0.1), self.center_pos_)
poses = [pos_triangle1, pos_triangle2, pos_triangle3]
for pos in poses:
if pos[0] >self.current_obsevation_image_.shape[0] or pos[0] < 0 or pos[1] >self.current_obsevation_image_.shape[1] or pos[1] < 0:
rospy.logerr("problem with observation: {}".format(pos))
return
self.new_obsevation_image_ = cv.drawContours(self.new_obsevation_image_, [np.asarray(poses)], 0, color, -1)
def add_arrow_observation_to_image(self, pos, orientation):
color = self.colors_visualization[self.color_index]
pos_image = utils.to_image_coordinate(pos, self.center_pos_)
pos_image2 = utils.to_image_coordinate((pos[0]+math.cos(orientation)*0.3, pos[1]+math.sin(orientation)*0.3), self.center_pos_)
if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:
rospy.logerr("problem with observation: {}".format(pos_image))
return
self.new_obsevation_image_ = cv.arrowedLine(self.new_obsevation_image_, (pos_image[0], pos_image[1]), (pos_image2[0], pos_image2[1]), color, 2, tipLength=0.5)
def add_circle_observation_to_image(self, pos, center_pos=None, image=None):
color = self.colors_visualization[self.color_index]
if image is None:
image = self.new_obsevation_image_
if center_pos is None:
center_pos = self.center_pos_
pos_image = utils.to_image_coordinate(pos, center_pos)
if pos_image[0] >self.current_obsevation_image_.shape[0] or pos_image[0] < 0 or pos_image[1] >self.current_obsevation_image_.shape[1] or pos_image[1] < 0:
rospy.logerr("problem with observation: {}".format(pos_image))
return
return (cv.circle(image , (pos_image[0], pos_image[1]), 4, color, 2))
def update_observation_image(self, idx, len_data):
self.new_obsevation_image_ = np.copy(self.current_obsevation_image_)
robot_pos = self.robot.get_pos(idx)
robot_orientation = self.robot.get_orientation(idx)
person_pos = self.person.get_pos(idx)
person_orientation = self.person.get_orientation(idx)
if person_orientation is None or robot_orientation is None:
rospy.logerr("person or robot orientation is None")
return
if self.first_call_observation:
self.first_call_observation = False
self.center_pos = person_pos
#self.add_circle_observation_to_image(robot_pos)
self.add_arrow_observation_to_image(robot_pos, robot_orientation)
self.add_triangle_observation_to_image(person_pos, person_orientation)
# self.add_line_observation_to_image(robot_pos, person_pos)
alpha = 0.50
self.current_obsevation_image_ = cv.addWeighted(self.new_obsevation_image_, alpha, self.current_obsevation_image_, 1 - alpha, 0)
self.color_index += 255//len_data
def get_current_observation_image(self):
image = self.current_obsevation_image_.astype(np.uint8)
#image = image/255.
return image
def get_angle_person_robot(self, idx):
pos_rel = self.robot.get_relative_position(self.person, idx)
angle_robot_person = math.atan2(pos_rel[1], pos_rel[0])
return (utils.wrap_pi_to_pi(angle_robot_person))
def get_dist_person_robot(self, idx):
pos_rel = self.robot.get_relative_position(self.person, idx)
return math.hypot(pos_rel[0], pos_rel[1])
def get_reward(self, idx):
reward = 0
pos_rel = self.robot.get_relative_position(self.person, idx)
angle_robot_person = math.atan2(pos_rel[1], pos_rel[0])
angle_robot_person = np.rad2deg(utils.wrap_pi_to_pi(angle_robot_person))
distance = math.hypot(pos_rel[0], pos_rel[1])
# Negative reward for being behind the person
if distance<0.4:
reward -= 1
if distance < 0.5:
reward = -1.3
elif abs(distance - self.DESIRE_DISTANCE) < 0.5:
reward += 0.5 * (0.5 - abs(distance - self.DESIRE_DISTANCE))
elif distance >= self.DESIRE_DISTANCE + 0.5:
reward -= 0.25 * (distance - self.DESIRE_DISTANCE + 0.5)
elif distance < self.DESIRE_DISTANCE - 0.5:
reward -= (self.DESIRE_DISTANCE - 0.5 - distance)/(self.DESIRE_DISTANCE - 0.5)
if abs(angle_robot_person) < 25:
reward += 0.5 * (25 - abs(angle_robot_person)) / 25
else:
reward -= 0.25 * abs(angle_robot_person) / 180
if abs(distance - self.DESIRE_DISTANCE) < 0.5 and abs(angle_robot_person) < 25:
reward += 0.25
reward = min(max(reward, -1), 1)
return reward
def save(self, name):
dic_data = {"name":name,"robot":self.robot.all_states_, "person":self.person.all_states_}
with open (name+"_.pkl", "wb") as f:
pickle.dump(dic_data, f)
def load(self, file_address, use_sim=False):
with open(file_address, "rb") as f:
dic_data = pickle.load(f)
self.name = dic_data["name"]
self.person.all_states_ = dic_data["person"][4:].copy()
self.robot.all_states_ = dic_data["robot"][4:].copy()
if use_sim:
self.person.all_states_ = [ self.person.all_states_[idx*10] for idx in range (len(self.person.all_states_)//10)]
self.robot.all_states_ = [ self.robot.all_states_[idx*10] for idx in range (len(self.robot.all_states_)//10)]
def wait_until_bag_finish(self):
while not self.robot.is_bag_finish() or not self.person.is_bag_finish():
rospy.sleep(0.1)
rospy.loginfo("waiting for bag to finish")
if len(self.person.all_states_)>0 and len(self.robot.all_states_)>0:
print(self.robot.get_relative_position(self.person, -1))
print(np.rad2deg(self.get_angle_person_robot(-1)))
print (self.robot.all_states_)
print (self.person.all_states_)
def calculate_orientation_dif(self, idx):
ori_rel, pos_rel = self.robot.get_relative_heading_position(self.person, idx)
return ori_rel
def get_metrics(self):
rewards = []
orientations = []
orientation_dif = []
distances = []
len_data = min(len(self.robot.all_states_), len(self.person.all_states_))
for idx in range (len_data):
# if idx % 10==0:
# self.update_observation_image(idx)
rewards.append(self.get_reward(idx))
distances.append(self.get_dist_person_robot(idx))
orientations.append(self.get_angle_person_robot(idx))
orientation_dif.append(self.calculate_orientation_dif(idx))
mean_orientation = np.mean(orientations)
sum_orientations_m = 0
for orientation in orientations:
sum_orientations_m += np.power(utils.wrap_pi_to_pi(mean_orientation - orientation),2)
sum_orientations_m /= len(orientations)
std = np.sqrt(sum_orientations_m)
return {"name":self.name, "orientation_mean":np.average(orientations), "orientation_std":std, \
"reward":np.sum(rewards), "distance":np.average(distances), "distance_std":np.std(distances),\
"ori_dif":np.average(orientation_dif)}
def plot_calculate_metrics(self):
rewards = []
orientations = []
distances = []
len_data = min(len(self.robot.all_states_), len(self.person.all_states_))
for idx in range (len_data):
if idx % 3==0:
self.update_observation_image(idx, len_data//3)
rewards.append(self.get_reward(idx))
distances.append(self.get_dist_person_robot(idx))
orientations.append(self.get_angle_person_robot(idx))
print (np.rad2deg(self.robot.get_relative_heading_position(self.person, 0)[0]))
img = self.get_current_observation_image()
img = cv.cvtColor(img, cv.COLOR_RGB2BGR)
print(f"\n\ndist avg: {np.average(distances)} orientation avg: {np.rad2deg(np.average(orientations))}, reward: {np.sum(rewards)} reward avg: {np.average(rewards)}")
cv.imshow("image", img)
cv.waitKey(0)
def plot_all_results( results, is_sim=False):
name = []
orientations = []
rewards = []
distances = []
orientations_std = []
distances_std = []
for result in results:
met = result.get_metrics()
name.append(met["name"])
rewards.append(met["reward"])
distances.append(met["distance"])
distances_std.append(met["distance_std"])
orientations.append(np.rad2deg(met["orientation_mean"]))
orientations_std.append(np.rad2deg(met["orientation_std"]))
print (f"{name[-1]}: Distance_avg: {distances[-1]:.2f} Distance_std: {distances_std[-1]:.2f} Orientation_avg: {orientations[-1]:.1f} Orientation_std: {orientations_std[-1]:.1f} reward: {rewards[-1]:.2f} ori_dif: {np.rad2deg(met['ori_dif']):0.2f}")
if is_sim:
print (f"{name[-1]}: ${distances[-1]:.2f}\pm{distances_std[-1]:.1f}$ & ${orientations[-1]:.1f}\pm{orientations_std[-1]:.1f}$ & ${rewards[-1]:.2f}$")
else:
print (f"{name[-1]}: ${distances[-1]:.2f}\pm{distances_std[-1]:.1f}$ & ${orientations[-1]:.1f}\pm{orientations_std[-1]:.1f}$ & ${rewards[-1]:.2f}$")
print ("\n")
#df = pd.DataFrame({'name': name, 'assess':[x for x in range(len(name))]})
#plt.errorbar(range(len(df['name'])), orientations, orientations_std, fmt='o')
#plt.xticks(range(len(df['name'])), df['name'])
if __name__== "__main__":
parser = argparse.ArgumentParser(description='input weight file of the network')
parser.add_argument('--name', default="no_name", type=str, help='name_traj')
parser.add_argument('--file-name', default="no_name", type=str, help='name_file_to_load')
parser.add_argument('--folder-name', default="no_name", type=str, help='name_file_to_load')
parser.add_argument('--save', action='store_true')
parser.add_argument('--load-file', action='store_true')
parser.add_argument('--load-folder', action='store_true')
parser.add_argument('--plot', action='store_true')
parser.add_argument('--use-sim-data', action='store_true')
parser.add_argument('--from-bag', action='store_true')
args = parser.parse_args()
node = rospy.init_node('plot_results')
if args.load_folder:
onlyfiles = [join(args.folder_name, f) for f in listdir(args.folder_name) if isfile(join(args.folder_name, f))]
onlyfiles.sort()
all_results = []
for pkl_name in onlyfiles:
result = Results()
result.load(pkl_name)
name_list = result.name.split("_")
if not args.use_sim_data and name_list[-1] != "planner" and name_list[-1] != "line":
print ("error ")
continue
new_name = f"{name_list[-1]}_{name_list[-2]}_base_line"
result.name = new_name
result.save(new_name)
all_results.append(result)
plot_all_results(all_results, args.use_sim_data)
#plt.show()
else:
result = Results()
if args.from_bag or args.load_file:
if args.from_bag:
result.wait_until_bag_finish()
else:
result.load(args.file_name, args.use_sim_data)
else:
print("exiting you need to load or read from bag file")
exit(0)
if args.save:
result.save(args.name)
if args.plot:
result.plot_calculate_metrics()
|
#!/usr/bin/env python3
import os
import re
import datetime
import json
import copy
# Parses the md file, outputs html string
def createArticle(mdFileName:str, isBlog=True):
"""
mdFileName: md file name
isBlog: boolean
Returns: { article, postTitle, postSubject, timeCreated }
article: the blog post in HTML string to be injected
postTitle: h1
postSubject: initial 50 chars of first para
timeCreated: datetime, updates when the function is called
"""
# TODO: Add tags to articles, maybe a custom syntax in the markdown file
article = ""
postTitle = ""
postSubject = None
# global variables
global timeCreated
timeCreated = datetime.datetime.now().strftime("%a %b %d %X %Y")
global thumbnail
thumbnail = ""
global numImgFound
numImgFound = -1
def isHeader(line):
return line[0:1] == "#" and line != ""
def makeHeader(line):
headerType, *text = line.split(" ")
headerText = " ".join(text)
headerId = "-".join(re.sub(r"[\W_]+", " ", headerText).strip().lower().split(" "))
# header = f"<h{len(headerType)} id=\"{headerId}\"><a href=\"#{headerId}\" class=\"topic\">{headerText}</a></h{len(headerType)}>"
header = f"<h{len(headerType)} id=\"{headerId}\" class=\"topic\">{headerText}</h{len(headerType)}>"
# only show post-info if blog post
if len(headerType) == 1 and isBlog and mdFileName != "index.md":
global timeCreated
title = headerText
header += f"<div id=\"post-info\"><p class=\"post-meta\">{timeCreated}, Author: Vikram S. Negi</p></div>"
return [header, title]
elif len(headerType) == 1:
title = headerText
return [header, title]
else:
return header
def makeLink(line):
foundLink = re.findall(r"\[(.+?)\]\((.+?)\)", line)
# print(foundLink)
for text, href in foundLink:
if href[0:1] == "#":
line = re.sub(r"\[(.+?)\]\((.+?)\)", f"<a href=\"{href}\">{text}</a>", line, count=1)
else:
line = re.sub(r"\[(.+?)\]\((.+?)\)", f"<a href=\"{href}\" target=\"_blank\" rel=\"noopener noreferrer\">{text}</a>", line, count=1)
if foundLink:
# print("found:", line, end="\n\n")
return line
def makeItalic(line):
foundItalic = re.findall(r"\*(.+?)\*", line)
for text in foundItalic:
line = re.sub(r"\*(.+?)\*", f"<em>{text}</em>", line, count=1)
if foundItalic:
return line
def makeBold(line):
foundBold = re.findall(r"\*\*(.+?)\*\*", line)
for text in foundBold:
line = re.sub(r"\*\*(.+?)\*\*", f"<strong>{text}</strong>", line, count=1)
if foundBold:
return line
def isListItem(line):
return line[0:1] == "*" and line[1:2] == " "
def makeList(line, init=True):
list = ""
bullet, *text = line.split(" ")
listItem = " ".join(text)
if init:
list += "<ul>"
list += f"<li>{listItem}</li>"
else:
list += f"<li>{listItem}</li>"
return list
def makeCode(line):
foundCode = re.findall(r"\`(.+?)\`", line)
for text in foundCode:
line = re.sub(r"\`.+?\`", f"<code>{text}</code>", line, count=1)
if foundCode:
return line
def isCodeBlock(line):
return line[0:3] == "```"
def makeCodeBlock(line):
codeBlock = ""
lang = ""
if len(line) > 3:
lang = re.findall(r"^\`\`\`(\w+)", line)[0]
codeBlock += f"<pre><code class=\"language-{lang}\">"
else:
codeBlock += f"<pre><code>"
return codeBlock
def isImg(line):
return line[0:1] == "!"
def makeImg(line):
foundImg = re.findall(r"\!\[(.+?)\]\((.+?)\)", line)
if foundImg:
global numImgFound
numImgFound += len(foundImg)
for alt, linkAndCaption in foundImg:
# print(linkAndCaption)
try:
link, *caption = linkAndCaption.split(" ")
except:
# print("no caption was found!")
link = linkAndCaption
caption = []
# print(caption)
global thumbnail
if thumbnail == "":
# print("thumbnail is blank", link)
thumbnail = link
if len(caption) == 0:
line = re.sub(r"\!\[(.+?)\]\((.+?)\)", f"<figure><img src=\"{link}\" alt=\"{alt}\" loading=\"lazy\" /></figure>", line, count=1)
else:
line = re.sub(r"\!\[(.+?)\]\((.+?)\)", f"<figure><img src=\"{link}\" alt=\"{alt}\" loading=\"lazy\" /><figcaption>Figure {numImgFound}. {" ".join(caption)[1:-1]}</figcaption></figure>", line, count=1)
if foundImg:
return line
def isHr(line):
return line[0:3] == "---"
def isBlockquote(line):
return line[0:1] == ">"
def makeBlockquote(line):
sign, *text = line.split(" ")
text = " ".join(text)
return f"<blockquote><p class=\"quote\">{text}</p></blockquote>"
# TODO: error handling if syntax does has None as the input: ![]() maybe add "" (blank sting) instead of None
# TODO: mark
# TODO: <!-- comments -->
# TODO: some functions seem repetitive, refactor those!
if isBlog:
path = os.getcwd() + f"/articles/{mdFileName}"
else:
path = os.getcwd() + f"/root_files/{mdFileName}"
print(f"reading {mdFileName}...")
with open(path, "r") as f:
prevLine = ""
inCodeBlock = False
isFirstPara = True
for line in f:
# cross site scripting (xss) security reason
# line = line.replace("<", "<").replace(">", ">")
# for some reason the following replace method, replaces "!" with "<"
if isImg(line):
article += makeImg(line)
line = ""
line = line.replace("<", "<")
if not inCodeBlock: line = line.strip()
# Check this, if any errors regarding imgs
line = makeImg(line) or line
line = makeLink(line) or line
line = makeBold(line) or line
line = makeItalic(line) or line
if isBlockquote(line):
article += makeBlockquote(line)
line = ""
# print(index, line)
if isHeader(line):
headerOut = makeHeader(line)
if type(headerOut) == list:
headerTag = headerOut[0]
postTitle += headerOut[1]
else:
headerTag = headerOut
article += headerTag
line = ""
unorderedList = ""
if isListItem(line):
try:
if not isListItem(prevLine):
unorderedList += makeList(line, init=True)
else:
unorderedList += makeList(line, init=False)
except:
print("err: prevLine blank or first in the file")
unorderedList += makeList(line, init=True)
prevLine = line
line = ""
elif not isListItem(line):
try:
if isListItem(prevLine):
unorderedList += "</ul>"
except:
unorderedList += "</ul>"
prevLine = line
article += unorderedList
codeBlock = ""
if isCodeBlock(line) and not inCodeBlock:
codeBlock += makeCodeBlock(line)
inCodeBlock = True
line = ""
elif isCodeBlock(line) and inCodeBlock:
inCodeBlock = False
codeBlock += "</code></pre>"
line = ""
elif inCodeBlock:
codeBlock += line.replace("<", "<").replace(">", ">")
line = ""
article += codeBlock
line = makeCode(line) or line
if isHr(line):
article += "<hr noshade />"
line = ""
if line != "" and isFirstPara:
if len(line) > 50:
postSubject = line[:47].strip() + "..."
else:
postSubject = line
line = f"<p>{line}</p>"
isFirstPara = False
elif line != "" and not isFirstPara:
line = f"<p>{line}</p>"
article += line
f.close()
# print(article)
fileCom = mdFileName.split(".")
fileName = "".join(fileCom[:-1])
# if isBlog:
# pathToHTMLFile = f"./blog/{fileName}.html"
# else:
# pathToHTMLFile = f"./{fileName}.html"
pathToHTMLFile = f"./{fileName}.html"
return {
"article": article,
"pathToHTMLFile": pathToHTMLFile,
"postTitle": postTitle,
"postSubject": postSubject,
"timeCreated": timeCreated,
"thumbnail": thumbnail
}
# Test func
# print(createArticle("fintech-info.md"))
def enterTags(nTags=3):
"""
Asks user to input hashtags, for blog posts
3 tags by default
Returns: an array of tags
"""
tags = []
# TODO: add confirmation of tags
while nTags > 0:
tag = input("HashTag: ").strip().lower().replace(" ", "_") or "misc"
tags.append(tag)
nTags -= 1
return tags
def updateCategoryTagsDB(HTMLPath:str, tags:list):
"""
updates the category tags db
"""
post = ".".join(HTMLPath[2:].split(".")[:-1 or None])
pathToDB = f"{os.getcwd()}/db/category-tags.json"
tagDict = None # {"posts": [], "tagFrequency": {}}
with open(pathToDB, "r") as db:
tagDict = json.load(db)
if post in tagDict["posts"]:
print(f"\nTag info of {post} is already in CategoryTagsDB\n")
else:
tagDict["posts"].append(post)
for tag in tags:
tagDict["tagFrequency"][tag] = tagDict["tagFrequency"].get(tag, 0) + 1
print(f"\n{len(tagDict["posts"])} posts info saved in CategoryTagsDB\n")
with open(pathToDB, "w") as db:
json.dump(tagDict, db)
# json db
def saveToBlogDB(data:dict):
"""
saves the post meta data, like postTitle, to db
"""
pathToDB = f"{os.getcwd()}/db/blog-info.json"
blogInfo = None
# read and load json as dict
with open(pathToDB, "r") as db:
blogInfo = json.load(db)
results = blogInfo["results"]
articlePathHTML = data["pathToHTMLFile"]
HTMLFileName = articlePathHTML.split("/")[-1]
blogFound = False
i = 0
while i < len(results):
if articlePathHTML == results[i]["pathToHTMLFile"]:
# check if the HTML file exists in the /blog dir
entirePath = f"{os.getcwd()}/public/blog/{articlePathHTML[2:]}"
if not os.path.exists(entirePath):
print(f"\nFile doesn't exists! Removing it from the DB...\n")
results.pop(i)
break
print("\nUpdating the blog post...\n")
blogFound = True
# copy the tag from previously saved data
tags = copy.deepcopy(results[i]["tags"])
data["tags"] = tags
timeCreated = results[i]["timeCreated"]
data["timeCreated"] = timeCreated
# rest everything gets updated
results[i] = data
break
i += 1
# /blog/index.html doesn't get entered into the db
if not blogFound and HTMLFileName != "index.html":
print("Adding article tags...")
data["tags"] = enterTags()
updateCategoryTagsDB(data["pathToHTMLFile"], data["tags"])
results.insert(0, data)
# update length
blogInfo["length"] = len(results)
print(f"\nNumber of posts saved in BlogInfoDB: {len(results)}\n")
# clear the data and re-write everything
with open(pathToDB, "w") as db:
json.dump(blogInfo, db)
# print(data["pathToHTMLFile"], data["tags"])
# !Important: Only for testing, clearing the blog-info.json
def clearResults():
with open(f"{os.getcwd()}/db/blog-info.json", "w") as db:
json.dump({"results": []}, db)
# Create entire HTML string
def makeHTMLString(isBlog:bool, articleHTML:dict)->str:
"""
Params: isBlog, articleHTML
Returns: returns html string
"""
print("\ncreating html string...\n")
stylePath = "./style.css"
javascriptPath = "./js/main.js"
codeBlockTags = {
"css": "<link rel=\"stylesheet\" href=\"./css/dark.min.css\">",
"js": "<script src=\"./js/highlight.min.js\"></script>"
}
if isBlog:
stylePath = "../style.css"
javascriptPath = "../js/main.js"
codeBlockTags["css"] = "<link rel=\"stylesheet\" href=\"../css/dark.min.css\">"
codeBlockTags["js"] = "<script src=\"../js/highlight.min.js\"></script>"
html = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>{articleHTML["postTitle"] or "Home"} - lostvikx</title>
<link rel="stylesheet" href="{stylePath}">
<link
rel="icon"
href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>❄️</text></svg>"
>
{codeBlockTags["css"] or ""}
{codeBlockTags["js"] or ""}
</head>
<body>
<nav>
<div class="nav-link">[ <a href="/">Home</a> ]</div>
<div class="nav-link">[ <a href="/blog">Blog</a> ]</div>
<div class="nav-link">[ <a href="/github" target="_blank" rel="noopener noreferrer">GitHub</a> ]</div>
<div class="nav-link">[ <a href="/radio">Radio</a> ]</div>
</nav>
<div id="article">{articleHTML["article"]}</div>
<div class="bottom-footer">
<hr class="footer-line" />
<footer>
<div>This website was crafted with the help of a lot of ☕ and 💪🏼</div>
<div class="contact-links">
<div><a href="mailto:viknegi0@gmail.com">Mail</a></div>
<div><a href="https://github.com/lostvikx" target="_blank" rel="noopener noreferrer">GitHub</a></div>
<div><a href="https://twitter.com/lostvikx" target="_blank" rel="noopener noreferrer">Twitter</a></div>
<div><a href="https://linkedin.com/in/vikram-singh-negi/" target="_blank" rel="noopener noreferrer">Linkedin</a></div>
</div>
</footer>
</div>
<script src="{javascriptPath}" type="module"></script>
</body>
</html>"""
# rm article (the html string) from the object
articleHTML.pop("article", None)
# only blog posts get saved to the DB
if isBlog:
saveToBlogDB(articleHTML)
return html
def saveHTMLFile(isBlog:bool, fileName:str)->None:
"""
Creates an HTML file in either the ./blog or ./ (root directory)
"""
if isBlog:
path = f"{os.getcwd()}/public/blog/{fileName}.html"
else:
path = f"{os.getcwd()}/public/{fileName}.html"
try:
HTMLString = makeHTMLString(isBlog, createArticle(f"{fileName}.md", isBlog))
except:
print("Couldn't create HTML String.")
HTMLString = ""
if HTMLString != "":
with open(path, "w") as file_handle:
file_handle.write(HTMLString)
file_handle.close()
print(f"Your HTML file: {path}")
else:
print(f"err: in writing {fileName}")
# Input a markdown file
fileName = None
while True:
# if blank
fName = input("Enter md file to convert: ") or "test"
fileCom = fName.split(".")
# .md file
if fileCom[-1] == "md":
fileName = "".join(fileCom[:-1])
else:
fileName = fName
# check for the .md fileName in both article and root dir
articlePath = f"{os.getcwd()}/articles/{fileName}.md"
rootFilePath = f"{os.getcwd()}/root_files/{fileName}.md"
articlePathExists = os.path.exists(articlePath)
rootFilePathExists = os.path.exists(rootFilePath)
if articlePathExists and rootFilePathExists:
print(f"\nfound [1]: {articlePath}")
print(f"found [2]: {rootFilePath}\n")
print("[1] -> blog\n[2] -> root_file")
# Is a blog post or not
while True:
try:
foundId = int(input("\nSelection: "))
# print(foundId, type(foundId))
if foundId == 1:
saveHTMLFile(True, fileName)
elif foundId == 2:
saveHTMLFile(False, fileName)
else:
print("Enter a valid option!")
continue
break
except:
print("Enter a valid option!")
continue
elif articlePathExists:
print(f"\nfound: {articlePath}")
saveHTMLFile(True, fileName)
elif rootFilePathExists:
print(f"\nfound: {rootFilePath}")
saveHTMLFile(False, fileName)
else:
print(f"Error: {fileName}.md not found!")
if articlePathExists or rootFilePathExists:
break
else:
continue
| #!/usr/bin/env python3
import os
import re
import datetime
import json
import copy
# Parses the md file, outputs html string
def createArticle(mdFileName:str, isBlog=True):
"""
mdFileName: md file name
isBlog: boolean
Returns: { article, postTitle, postSubject, timeCreated }
article: the blog post in HTML string to be injected
postTitle: h1
postSubject: initial 50 chars of first para
timeCreated: datetime, updates when the function is called
"""
# TODO: Add tags to articles, maybe a custom syntax in the markdown file
article = ""
postTitle = ""
postSubject = None
# global variables
global timeCreated
timeCreated = datetime.datetime.now().strftime("%a %b %d %X %Y")
global thumbnail
thumbnail = ""
global numImgFound
numImgFound = -1
def isHeader(line):
return line[0:1] == "#" and line != ""
def makeHeader(line):
headerType, *text = line.split(" ")
headerText = " ".join(text)
headerId = "-".join(re.sub(r"[\W_]+", " ", headerText).strip().lower().split(" "))
# header = f"<h{len(headerType)} id=\"{headerId}\"><a href=\"#{headerId}\" class=\"topic\">{headerText}</a></h{len(headerType)}>"
header = f"<h{len(headerType)} id=\"{headerId}\" class=\"topic\">{headerText}</h{len(headerType)}>"
# only show post-info if blog post
if len(headerType) == 1 and isBlog and mdFileName != "index.md":
global timeCreated
title = headerText
header += f"<div id=\"post-info\"><p class=\"post-meta\">{timeCreated}, Author: Vikram S. Negi</p></div>"
return [header, title]
elif len(headerType) == 1:
title = headerText
return [header, title]
else:
return header
def makeLink(line):
foundLink = re.findall(r"\[(.+?)\]\((.+?)\)", line)
# print(foundLink)
for text, href in foundLink:
if href[0:1] == "#":
line = re.sub(r"\[(.+?)\]\((.+?)\)", f"<a href=\"{href}\">{text}</a>", line, count=1)
else:
line = re.sub(r"\[(.+?)\]\((.+?)\)", f"<a href=\"{href}\" target=\"_blank\" rel=\"noopener noreferrer\">{text}</a>", line, count=1)
if foundLink:
# print("found:", line, end="\n\n")
return line
def makeItalic(line):
foundItalic = re.findall(r"\*(.+?)\*", line)
for text in foundItalic:
line = re.sub(r"\*(.+?)\*", f"<em>{text}</em>", line, count=1)
if foundItalic:
return line
def makeBold(line):
foundBold = re.findall(r"\*\*(.+?)\*\*", line)
for text in foundBold:
line = re.sub(r"\*\*(.+?)\*\*", f"<strong>{text}</strong>", line, count=1)
if foundBold:
return line
def isListItem(line):
return line[0:1] == "*" and line[1:2] == " "
def makeList(line, init=True):
list = ""
bullet, *text = line.split(" ")
listItem = " ".join(text)
if init:
list += "<ul>"
list += f"<li>{listItem}</li>"
else:
list += f"<li>{listItem}</li>"
return list
def makeCode(line):
foundCode = re.findall(r"\`(.+?)\`", line)
for text in foundCode:
line = re.sub(r"\`.+?\`", f"<code>{text}</code>", line, count=1)
if foundCode:
return line
def isCodeBlock(line):
return line[0:3] == "```"
def makeCodeBlock(line):
codeBlock = ""
lang = ""
if len(line) > 3:
lang = re.findall(r"^\`\`\`(\w+)", line)[0]
codeBlock += f"<pre><code class=\"language-{lang}\">"
else:
codeBlock += f"<pre><code>"
return codeBlock
def isImg(line):
return line[0:1] == "!"
def makeImg(line):
foundImg = re.findall(r"\!\[(.+?)\]\((.+?)\)", line)
if foundImg:
global numImgFound
numImgFound += len(foundImg)
for alt, linkAndCaption in foundImg:
# print(linkAndCaption)
try:
link, *caption = linkAndCaption.split(" ")
except:
# print("no caption was found!")
link = linkAndCaption
caption = []
# print(caption)
global thumbnail
if thumbnail == "":
# print("thumbnail is blank", link)
thumbnail = link
if len(caption) == 0:
line = re.sub(r"\!\[(.+?)\]\((.+?)\)", f"<figure><img src=\"{link}\" alt=\"{alt}\" loading=\"lazy\" /></figure>", line, count=1)
else:
line = re.sub(r"\!\[(.+?)\]\((.+?)\)", f"<figure><img src=\"{link}\" alt=\"{alt}\" loading=\"lazy\" /><figcaption>Figure {numImgFound}. {' '.join(caption)[1:-1]}</figcaption></figure>", line, count=1)
if foundImg:
return line
def isHr(line):
return line[0:3] == "---"
def isBlockquote(line):
return line[0:1] == ">"
def makeBlockquote(line):
sign, *text = line.split(" ")
text = " ".join(text)
return f"<blockquote><p class=\"quote\">{text}</p></blockquote>"
# TODO: error handling if syntax does has None as the input: ![]() maybe add "" (blank sting) instead of None
# TODO: mark
# TODO: <!-- comments -->
# TODO: some functions seem repetitive, refactor those!
if isBlog:
path = os.getcwd() + f"/articles/{mdFileName}"
else:
path = os.getcwd() + f"/root_files/{mdFileName}"
print(f"reading {mdFileName}...")
with open(path, "r") as f:
prevLine = ""
inCodeBlock = False
isFirstPara = True
for line in f:
# cross site scripting (xss) security reason
# line = line.replace("<", "<").replace(">", ">")
# for some reason the following replace method, replaces "!" with "<"
if isImg(line):
article += makeImg(line)
line = ""
line = line.replace("<", "<")
if not inCodeBlock: line = line.strip()
# Check this, if any errors regarding imgs
line = makeImg(line) or line
line = makeLink(line) or line
line = makeBold(line) or line
line = makeItalic(line) or line
if isBlockquote(line):
article += makeBlockquote(line)
line = ""
# print(index, line)
if isHeader(line):
headerOut = makeHeader(line)
if type(headerOut) == list:
headerTag = headerOut[0]
postTitle += headerOut[1]
else:
headerTag = headerOut
article += headerTag
line = ""
unorderedList = ""
if isListItem(line):
try:
if not isListItem(prevLine):
unorderedList += makeList(line, init=True)
else:
unorderedList += makeList(line, init=False)
except:
print("err: prevLine blank or first in the file")
unorderedList += makeList(line, init=True)
prevLine = line
line = ""
elif not isListItem(line):
try:
if isListItem(prevLine):
unorderedList += "</ul>"
except:
unorderedList += "</ul>"
prevLine = line
article += unorderedList
codeBlock = ""
if isCodeBlock(line) and not inCodeBlock:
codeBlock += makeCodeBlock(line)
inCodeBlock = True
line = ""
elif isCodeBlock(line) and inCodeBlock:
inCodeBlock = False
codeBlock += "</code></pre>"
line = ""
elif inCodeBlock:
codeBlock += line.replace("<", "<").replace(">", ">")
line = ""
article += codeBlock
line = makeCode(line) or line
if isHr(line):
article += "<hr noshade />"
line = ""
if line != "" and isFirstPara:
if len(line) > 50:
postSubject = line[:47].strip() + "..."
else:
postSubject = line
line = f"<p>{line}</p>"
isFirstPara = False
elif line != "" and not isFirstPara:
line = f"<p>{line}</p>"
article += line
f.close()
# print(article)
fileCom = mdFileName.split(".")
fileName = "".join(fileCom[:-1])
# if isBlog:
# pathToHTMLFile = f"./blog/{fileName}.html"
# else:
# pathToHTMLFile = f"./{fileName}.html"
pathToHTMLFile = f"./{fileName}.html"
return {
"article": article,
"pathToHTMLFile": pathToHTMLFile,
"postTitle": postTitle,
"postSubject": postSubject,
"timeCreated": timeCreated,
"thumbnail": thumbnail
}
# Test func
# print(createArticle("fintech-info.md"))
def enterTags(nTags=3):
"""
Asks user to input hashtags, for blog posts
3 tags by default
Returns: an array of tags
"""
tags = []
# TODO: add confirmation of tags
while nTags > 0:
tag = input("HashTag: ").strip().lower().replace(" ", "_") or "misc"
tags.append(tag)
nTags -= 1
return tags
def updateCategoryTagsDB(HTMLPath:str, tags:list):
"""
updates the category tags db
"""
post = ".".join(HTMLPath[2:].split(".")[:-1 or None])
pathToDB = f"{os.getcwd()}/db/category-tags.json"
tagDict = None # {"posts": [], "tagFrequency": {}}
with open(pathToDB, "r") as db:
tagDict = json.load(db)
if post in tagDict["posts"]:
print(f"\nTag info of {post} is already in CategoryTagsDB\n")
else:
tagDict["posts"].append(post)
for tag in tags:
tagDict["tagFrequency"][tag] = tagDict["tagFrequency"].get(tag, 0) + 1
print(f"\n{len(tagDict['posts'])} posts info saved in CategoryTagsDB\n")
with open(pathToDB, "w") as db:
json.dump(tagDict, db)
# json db
def saveToBlogDB(data:dict):
"""
saves the post meta data, like postTitle, to db
"""
pathToDB = f"{os.getcwd()}/db/blog-info.json"
blogInfo = None
# read and load json as dict
with open(pathToDB, "r") as db:
blogInfo = json.load(db)
results = blogInfo["results"]
articlePathHTML = data["pathToHTMLFile"]
HTMLFileName = articlePathHTML.split("/")[-1]
blogFound = False
i = 0
while i < len(results):
if articlePathHTML == results[i]["pathToHTMLFile"]:
# check if the HTML file exists in the /blog dir
entirePath = f"{os.getcwd()}/public/blog/{articlePathHTML[2:]}"
if not os.path.exists(entirePath):
print(f"\nFile doesn't exists! Removing it from the DB...\n")
results.pop(i)
break
print("\nUpdating the blog post...\n")
blogFound = True
# copy the tag from previously saved data
tags = copy.deepcopy(results[i]["tags"])
data["tags"] = tags
timeCreated = results[i]["timeCreated"]
data["timeCreated"] = timeCreated
# rest everything gets updated
results[i] = data
break
i += 1
# /blog/index.html doesn't get entered into the db
if not blogFound and HTMLFileName != "index.html":
print("Adding article tags...")
data["tags"] = enterTags()
updateCategoryTagsDB(data["pathToHTMLFile"], data["tags"])
results.insert(0, data)
# update length
blogInfo["length"] = len(results)
print(f"\nNumber of posts saved in BlogInfoDB: {len(results)}\n")
# clear the data and re-write everything
with open(pathToDB, "w") as db:
json.dump(blogInfo, db)
# print(data["pathToHTMLFile"], data["tags"])
# !Important: Only for testing, clearing the blog-info.json
def clearResults():
with open(f"{os.getcwd()}/db/blog-info.json", "w") as db:
json.dump({"results": []}, db)
# Create entire HTML string
def makeHTMLString(isBlog:bool, articleHTML:dict)->str:
"""
Params: isBlog, articleHTML
Returns: returns html string
"""
print("\ncreating html string...\n")
stylePath = "./style.css"
javascriptPath = "./js/main.js"
codeBlockTags = {
"css": "<link rel=\"stylesheet\" href=\"./css/dark.min.css\">",
"js": "<script src=\"./js/highlight.min.js\"></script>"
}
if isBlog:
stylePath = "../style.css"
javascriptPath = "../js/main.js"
codeBlockTags["css"] = "<link rel=\"stylesheet\" href=\"../css/dark.min.css\">"
codeBlockTags["js"] = "<script src=\"../js/highlight.min.js\"></script>"
html = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>{articleHTML["postTitle"] or "Home"} - lostvikx</title>
<link rel="stylesheet" href="{stylePath}">
<link
rel="icon"
href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>❄️</text></svg>"
>
{codeBlockTags["css"] or ""}
{codeBlockTags["js"] or ""}
</head>
<body>
<nav>
<div class="nav-link">[ <a href="/">Home</a> ]</div>
<div class="nav-link">[ <a href="/blog">Blog</a> ]</div>
<div class="nav-link">[ <a href="/github" target="_blank" rel="noopener noreferrer">GitHub</a> ]</div>
<div class="nav-link">[ <a href="/radio">Radio</a> ]</div>
</nav>
<div id="article">{articleHTML["article"]}</div>
<div class="bottom-footer">
<hr class="footer-line" />
<footer>
<div>This website was crafted with the help of a lot of ☕ and 💪🏼</div>
<div class="contact-links">
<div><a href="mailto:viknegi0@gmail.com">Mail</a></div>
<div><a href="https://github.com/lostvikx" target="_blank" rel="noopener noreferrer">GitHub</a></div>
<div><a href="https://twitter.com/lostvikx" target="_blank" rel="noopener noreferrer">Twitter</a></div>
<div><a href="https://linkedin.com/in/vikram-singh-negi/" target="_blank" rel="noopener noreferrer">Linkedin</a></div>
</div>
</footer>
</div>
<script src="{javascriptPath}" type="module"></script>
</body>
</html>"""
# rm article (the html string) from the object
articleHTML.pop("article", None)
# only blog posts get saved to the DB
if isBlog:
saveToBlogDB(articleHTML)
return html
def saveHTMLFile(isBlog:bool, fileName:str)->None:
"""
Creates an HTML file in either the ./blog or ./ (root directory)
"""
if isBlog:
path = f"{os.getcwd()}/public/blog/{fileName}.html"
else:
path = f"{os.getcwd()}/public/{fileName}.html"
try:
HTMLString = makeHTMLString(isBlog, createArticle(f"{fileName}.md", isBlog))
except:
print("Couldn't create HTML String.")
HTMLString = ""
if HTMLString != "":
with open(path, "w") as file_handle:
file_handle.write(HTMLString)
file_handle.close()
print(f"Your HTML file: {path}")
else:
print(f"err: in writing {fileName}")
# Input a markdown file
fileName = None
while True:
# if blank
fName = input("Enter md file to convert: ") or "test"
fileCom = fName.split(".")
# .md file
if fileCom[-1] == "md":
fileName = "".join(fileCom[:-1])
else:
fileName = fName
# check for the .md fileName in both article and root dir
articlePath = f"{os.getcwd()}/articles/{fileName}.md"
rootFilePath = f"{os.getcwd()}/root_files/{fileName}.md"
articlePathExists = os.path.exists(articlePath)
rootFilePathExists = os.path.exists(rootFilePath)
if articlePathExists and rootFilePathExists:
print(f"\nfound [1]: {articlePath}")
print(f"found [2]: {rootFilePath}\n")
print("[1] -> blog\n[2] -> root_file")
# Is a blog post or not
while True:
try:
foundId = int(input("\nSelection: "))
# print(foundId, type(foundId))
if foundId == 1:
saveHTMLFile(True, fileName)
elif foundId == 2:
saveHTMLFile(False, fileName)
else:
print("Enter a valid option!")
continue
break
except:
print("Enter a valid option!")
continue
elif articlePathExists:
print(f"\nfound: {articlePath}")
saveHTMLFile(True, fileName)
elif rootFilePathExists:
print(f"\nfound: {rootFilePath}")
saveHTMLFile(False, fileName)
else:
print(f"Error: {fileName}.md not found!")
if articlePathExists or rootFilePathExists:
break
else:
continue
|
# coding: utf-8
# Author: Leo BRUNEL
# Contact: contact@leobrunel.com
# This file is part of Wizard
# MIT License
# Copyright (c) 2021 Leo brunel
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# This module manages the project events
# the events are stored in the project database
# and are accessed by the 'project' module but
# this module decode what is stored in the
# event rows
# Python modules
import logging
# Wizard modules
from wizard.core import environment
from wizard.core import project
from wizard.core import assets
logger = logging.getLogger(__name__)
def add_creation_event(instance_type, instance_id):
data = (instance_type, instance_id)
title = f"Created {assets.instance_to_string((instance_type, instance_id))}"
project.add_event('creation', title, '', data)
def add_export_event(export_version_id):
title = f"Exported {assets.instance_to_string(("export_version", export_version_id))}"
data = export_version_id
export_version_row = project.get_export_version_data(export_version_id)
project.add_event('export', title, export_version_row['comment'], data, '', export_version_row['work_version_thumbnail_path'])
def add_archive_event(title, archive_path):
data = archive_path
project.add_event('archive', title, '', data)
def add_tag_event(instance_type, instance_id, comment, user):
data_dic = dict()
data_dic['instance'] = (instance_type, instance_id)
data_dic['tagged_user'] = user
title = f"Tagged {user} in a comment"
project.add_event('tag', title, comment, data_dic) | # coding: utf-8
# Author: Leo BRUNEL
# Contact: contact@leobrunel.com
# This file is part of Wizard
# MIT License
# Copyright (c) 2021 Leo brunel
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# This module manages the project events
# the events are stored in the project database
# and are accessed by the 'project' module but
# this module decode what is stored in the
# event rows
# Python modules
import logging
# Wizard modules
from wizard.core import environment
from wizard.core import project
from wizard.core import assets
logger = logging.getLogger(__name__)
def add_creation_event(instance_type, instance_id):
data = (instance_type, instance_id)
title = f"Created {assets.instance_to_string((instance_type, instance_id))}"
project.add_event('creation', title, '', data)
def add_export_event(export_version_id):
title = f"Exported {assets.instance_to_string(('export_version', export_version_id))}"
data = export_version_id
export_version_row = project.get_export_version_data(export_version_id)
project.add_event('export', title, export_version_row['comment'], data, '', export_version_row['work_version_thumbnail_path'])
def add_archive_event(title, archive_path):
data = archive_path
project.add_event('archive', title, '', data)
def add_tag_event(instance_type, instance_id, comment, user):
data_dic = dict()
data_dic['instance'] = (instance_type, instance_id)
data_dic['tagged_user'] = user
title = f"Tagged {user} in a comment"
project.add_event('tag', title, comment, data_dic) |
from financialmodelingprep.decorator import get_json_data
BASE_URL = 'https://financialmodelingprep.com'
class calendars():
BASE_URL = 'https://financialmodelingprep.com'
API_KEY = ''
def __init__(self, API_KEY):
self.API = API_KEY
@get_json_data
def earning_calendar(self):
'''
Earnings Calendar
'''
return f'{self.BASE_URL}/api/v3/earning_calendar?apikey={self.API}'
@get_json_data
def earning_calendar_period(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/earning_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}'
@get_json_data
def company_historical_earnings_calender(self, ticker: str, limit: int):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/earning_calendar/{ticker}?limit={str(limit)}?apikey={self.API}'
@get_json_data
def company_historical_earnings_calender(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/ipo_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}'
@get_json_data
def ipo_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/ipo_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}'
@get_json_data
def stock_split_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/stock_split_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}'
@get_json_data
def stock_dividend_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/stock_dividend_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}'
@get_json_data
def economic_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/economic_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}'
@get_json_data
def economic_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/economic_calendar?from={datetime_from.strftime('%Y-%m-%d')}&to={datetime_to.strftime('%Y-%m-%d')}?apikey={self.API}' | from financialmodelingprep.decorator import get_json_data
BASE_URL = 'https://financialmodelingprep.com'
class calendars():
BASE_URL = 'https://financialmodelingprep.com'
API_KEY = ''
def __init__(self, API_KEY):
self.API = API_KEY
@get_json_data
def earning_calendar(self):
'''
Earnings Calendar
'''
return f'{self.BASE_URL}/api/v3/earning_calendar?apikey={self.API}'
@get_json_data
def earning_calendar_period(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/earning_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}'
@get_json_data
def company_historical_earnings_calender(self, ticker: str, limit: int):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/earning_calendar/{ticker}?limit={str(limit)}?apikey={self.API}'
@get_json_data
def company_historical_earnings_calender(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/ipo_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}'
@get_json_data
def ipo_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/ipo_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}'
@get_json_data
def stock_split_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/stock_split_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}'
@get_json_data
def stock_dividend_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/stock_dividend_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}'
@get_json_data
def economic_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/economic_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}'
@get_json_data
def economic_calendar(self, datetime_from, datetime_to):
'''
Earnings Calendar with period
'''
return f'{self.BASE_URL}/api/v3/economic_calendar?from={datetime_from.strftime("%Y-%m-%d")}&to={datetime_to.strftime("%Y-%m-%d")}?apikey={self.API}' |
"""
CS 229 Machine Learning
Question: Reinforcement Learning - The Inverted Pendulum
"""
from __future__ import division, print_function
import matplotlib
matplotlib.use('TkAgg')
from env import CartPole, Physics
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import lfilter
"""
Parts of the code (cart and pole dynamics, and the state
discretization) are inspired from code available at the RL repository
http://www-anw.cs.umass.edu/rlr/domains.html
Briefly, the cart-pole system is described in `cart_pole.py`. The main
simulation loop in this file calls the `simulate()` function for
simulating the pole dynamics, `get_state()` for discretizing the
otherwise continuous state space in discrete states, and `show_cart()`
for display.
Some useful parameters are listed below:
`NUM_STATES`: Number of states in the discretized state space
You must assume that states are numbered 0 through `NUM_STATES` - 1. The
state numbered `NUM_STATES` - 1 (the last one) is a special state that
marks the state when the pole has been judged to have fallen (or when
the cart is out of bounds). However, you should NOT treat this state
any differently in your code. Any distinctions you need to make between
states should come automatically from your learning algorithm.
After each simulation cycle, you are supposed to update the transition
counts and rewards observed. However, you should not change either
your value function or the transition probability matrix at each
cycle.
Whenever the pole falls, a section of your code below will be
executed. At this point, you must use the transition counts and reward
observations that you have gathered to generate a new model for the MDP
(i.e. transition probabilities and state rewards). After that, you
must use value iteration to get the optimal value function for this MDP
model.
`TOLERANCE`: Controls the convergence criteria for each value iteration
run. In value iteration, you can assume convergence when the maximum
absolute change in the value function at any state in an iteration
becomes lower than `TOLERANCE.
You need to write code that chooses the best action according
to your current value function, and the current model of the MDP. The
action must be either 0 or 1 (corresponding to possible directions of
pushing the cart)
Finally, we assume that the simulation has converged when
`NO_LEARNING_THRESHOLD` consecutive value function computations all
converged within one value function iteration. Intuitively, it seems
like there will be little learning after this, so we end the simulation
here, and say the overall algorithm has converged.
Learning curves can be generated by calling a code snippet at the end
(it assumes that the learning was just executed, and the array
`time_steps_to_failure` that records the time for which the pole was
balanced before each failure is in memory). `num_failures` is a variable
that stores the number of failures (pole drops / cart out of bounds)
till now.
Other parameters in the code are described below:
`GAMMA`: Discount factor to be used
The following parameters control the simulation display; you dont
really need to know about them:
`pause_time`: Controls the pause between successive frames of the
display. Higher values make your simulation slower.
`min_trial_length_to_start_display`: Allows you to start the display only
after the pole has been successfully balanced for at least this many
trials. Setting this to zero starts the display immediately. Choosing a
reasonably high value (around 100) can allow you to rush through the
initial learning quickly, and start the display only after the
performance is reasonable.
"""
def initialize_mdp_data(num_states):
"""
Return a variable that contains all the parameters/state you need for your MDP.
Feel free to use whatever data type is most convenient for you (custom classes, tuples, dicts, etc)
Assume that no transitions or rewards have been observed.
Initialize the value function array to small random values (0 to 0.10, say).
Initialize the transition probabilities uniformly (ie, probability of
transitioning for state x to state y using action a is exactly
1/num_states).
Initialize all state rewards to zero.
Args:
num_states: The number of states
Returns: The initial MDP parameters
"""
transition_counts = np.zeros((num_states, num_states, 2)) # P_ijk = P_{s_i, a_k} (s_j) i.e. s_i -> s_j under a_k
transition_probs = np.ones((num_states, num_states, 2)) / num_states # initial uniform distribution
#Index zero is count of rewards being -1 , index 1 is count of total num state is reached
reward_counts = np.zeros((num_states, 2))
reward = np.zeros(num_states)
value = np.random.rand(num_states) * 0.1 # - 0.05 - 1/num_states
return {
'transition_counts': transition_counts,
'transition_probs': transition_probs,
'reward_counts': reward_counts,
'reward': reward,
'value': value,
'num_states': num_states,
'state_action_history': [[]]
}
def choose_action(state, mdp_data):
"""
Choose the next action (0 or 1) that is optimal according to your current
mdp_data. When there is no optimal action, return a random action.
Args:
state: The current state in the MDP
mdp_data: The parameters for your MDP. See initialize_mdp_data.
Returns:
0 or 1 that is optimal according to your current MDP
"""
# *** START CODE HERE ***
# argmax over actions a of sum_s' P_sa(s') V(s')
# s, s', a axes. times in axis s', sum over it, argmax over a and then take state s
expected_state_action_value = (mdp_data['transition_probs'] * mdp_data['value'].reshape(1, -1, 1)).sum(axis=1)
optimal = expected_state_action_value.argmax(axis=1) # argmax over the 2 actions
is_optimal = expected_state_action_value[state][int(optimal[state])] > \
expected_state_action_value[state][int(not optimal[state])]
action = optimal[state] if is_optimal else np.random.randint(2)
#print(f'state: {state}; optimal action: {action}; expected values: {expected_state_action_value[state]}')
return optimal[state] if expected_state_action_value[state][int(optimal[state])] > expected_state_action_value[state][int(not optimal[state])] else np.random.randint(2)
# *** END CODE HERE ***
def update_mdp_transition_counts_reward_counts(mdp_data, state, action, new_state, reward):
"""
Update the transition count and reward count information in your mdp_data.
Do not change the other MDP parameters (those get changed later).
Record the number of times `state, action, new_state` occurs.
Record the rewards for every `new_state`
(since rewards are -1 or 0, you just need to record number of times reward -1 is seen in 'reward_counts' index new_state,0)
Record the number of time `new_state` was reached (in 'reward_counts' index new_state,1)
Args:
mdp_data: The parameters of your MDP. See initialize_mdp_data.
state: The state that was observed at the start.
action: The action you performed.
new_state: The state after your action.
reward: The reward after your action (i.e. reward corresponding to new_state).
Returns:
Nothing
"""
# *** START CODE HERE ***
mdp_data['state_action_history'][-1].append([state, action, new_state])
if reward == -1:
mdp_data['reward_counts'][new_state][0] += 1
mdp_data['reward_counts'][new_state][1] += 1 # the number of times reaching this state regardless of reward -1 or 0
mdp_data['transition_counts'][state, new_state, action] += 1
# *** END CODE HERE ***
# This function does not return anything
return
def update_mdp_transition_probs_reward(mdp_data):
"""
Update the estimated transition probabilities and reward values in your MDP.
Make sure you account for the case when a state-action pair has never
been tried before, or the state has never been visited before. In that
case, you must not change that component (and thus keep it at the
initialized uniform distribution).
Args:
mdp_data: The data for your MDP. See initialize_mdp_data.
Returns:
Nothing
"""
# *** START CODE HERE ***
num_states = mdp_data['value'].shape[0]
transition_counts = mdp_data['transition_counts']
# i.e. some s' transition occured for s,a pair
state_actions_occurred = transition_counts.sum(axis=1) > 0
state_actions_occurred = np.tile(state_actions_occurred, (num_states, 1, 1)).transpose(1,0,2)
# where s,a pair occured take from counts, otherwise from the previous probs
transition_probs = np.where(state_actions_occurred, transition_counts, mdp_data['transition_probs'])
# counts at s' for s,a pair divided by total counts for s,a is our new prob - or same as old prob if no s,a
# pair occured
transition_probs = transition_probs/transition_probs.sum(axis=1).reshape(num_states, 1, 2)
mdp_data['transition_probs'] = transition_probs
# number of -1 rewards divided by total number of rewards (more general would be sum rewards / number times visited)
# rewards <-> new states, so did we ever reach this new state?
reward_occured = transition_counts.sum(axis=(0, 2)) > 0
reward = np.where(reward_occured, - mdp_data['reward_counts'][:, 0] / mdp_data['reward_counts'][:, 1], mdp_data['reward'])
mdp_data['reward'] = reward
# *** END CODE HERE ***
# This function does not return anything
return
def update_mdp_value(mdp_data, tolerance, gamma):
"""
Update the estimated values in your MDP.
Perform value iteration using the new estimated model for the MDP.
The convergence criterion should be based on `TOLERANCE` as described
at the top of the file.
Return true if it converges within one iteration.
Args:
mdp_data: The data for your MDP. See initialize_mdp_data.
tolerance: The tolerance to use for the convergence criterion.
gamma: Your discount factor.
Returns:
True if the value iteration converged in one iteration
"""
# *** START CODE HERE ***
action_values = (mdp_data['transition_probs'] * mdp_data['value'].reshape(1, -1, 1)).sum(axis=1)
old_value = mdp_data['value']
mdp_data['value'] = mdp_data['reward'] + gamma * action_values.max(axis=1)
max_diff = np.max(np.abs(mdp_data['value'] - old_value))
print(f'max state value diff: {max_diff}')
return max_diff < tolerance
# *** END CODE HERE ***
def plot_mdp_data(mdp_data):
plt.figure()
plt.plot(mdp_data['value'])
def main(plot=True):
# Seed the randomness of the simulation so this outputs the same thing each time
np.random.seed(3)
# Simulation parameters
pause_time = 0.0001
min_trial_length_to_start_display = 100
display_started = min_trial_length_to_start_display == 0
NUM_STATES = 163
GAMMA = 0.995
TOLERANCE = 0.01
NO_LEARNING_THRESHOLD = 20
# TOTAL_MAX_TRIALS might be useful otherwise it takes up to 300 ish iterations to converge sometimes
# Time cycle of the simulation
time = 0
# These variables perform bookkeeping (how many cycles was the pole
# balanced for before it fell). Useful for plotting learning curves.
time_steps_to_failure = []
num_failures = 0
time_at_start_of_current_trial = 0
# You should reach convergence well before this
max_failures = 500
# Initialize a cart pole
cart_pole = CartPole(Physics())
# Starting `state_tuple` is (0, 0, 0, 0)
# x, x_dot, theta, theta_dot represents the actual continuous state vector
x, x_dot, theta, theta_dot = 0.0, 0.0, 0.0, 0.0
state_tuple = (x, x_dot, theta, theta_dot)
# `state` is the number given to this state, you only need to consider
# this representation of the state
state = cart_pole.get_state(state_tuple)
if min_trial_length_to_start_display == 0 or display_started == 1:
cart_pole.show_cart(state_tuple, pause_time)
mdp_data = initialize_mdp_data(NUM_STATES)
# This is the criterion to end the simulation.
# You should change it to terminate when the previous
# 'NO_LEARNING_THRESHOLD' consecutive value function computations all
# converged within one value function iteration. Intuitively, it seems
# like there will be little learning after this, so end the simulation
# here, and say the overall algorithm has converged.
consecutive_no_learning_trials = 0
while consecutive_no_learning_trials < NO_LEARNING_THRESHOLD:
action = choose_action(state, mdp_data)
# Get the next state by simulating the dynamics
state_tuple = cart_pole.simulate(action, state_tuple)
# x, x_dot, theta, theta_dot = state_tuple
# Increment simulation time
time = time + 1
# Get the state number corresponding to new state vector
new_state = cart_pole.get_state(state_tuple)
# if display_started == 1:
# cart_pole.show_cart(state_tuple, pause_time)
#print(f'state transition prob: {state}, {action} -> {new_state}: {mdp_data['transition_probs'][state, new_state, action]}')
# reward function to use - do not change this!
if new_state == NUM_STATES - 1:
R = -1
else:
R = 0
# add to transition count for s, s', a triple and to reward count for s'
update_mdp_transition_counts_reward_counts(mdp_data, state, action, new_state, R)
# Recompute MDP model whenever pole falls
# Compute the value function V for the new model
if new_state == NUM_STATES - 1:
update_mdp_transition_probs_reward(mdp_data)
converged_in_one_iteration = update_mdp_value(mdp_data, TOLERANCE, GAMMA)
if converged_in_one_iteration:
consecutive_no_learning_trials = consecutive_no_learning_trials + 1
else:
consecutive_no_learning_trials = 0
# Do NOT change this code: Controls the simulation, and handles the case
# when the pole fell and the state must be reinitialized.
if new_state == NUM_STATES - 1:
num_failures += 1
if num_failures >= max_failures:
break
print('[INFO] Failure number {}'.format(num_failures))
# plot_mdp_data(mdp_data)
time_steps_to_failure.append(time - time_at_start_of_current_trial)
print(f'time to failure: {time_steps_to_failure[-1]}')
# print(f'history: {mdp_data['state_action_history'][-1]}')
mdp_data["state_action_history"].append([])
# time_steps_to_failure[num_failures] = time - time_at_start_of_current_trial
time_at_start_of_current_trial = time
if time_steps_to_failure[num_failures - 1] > min_trial_length_to_start_display:
display_started = 1
# Reinitialize state
# x = 0.0
x = -1.1 + np.random.uniform() * 2.2
x_dot, theta, theta_dot = 0.0, 0.0, 0.0
state_tuple = (x, x_dot, theta, theta_dot)
state = cart_pole.get_state(state_tuple)
else:
state = new_state
if plot:
plt.figure()
# plot the learning curve (time balanced vs. trial)
log_tstf = np.log(np.array(time_steps_to_failure))
plt.plot(np.arange(len(time_steps_to_failure)), log_tstf, 'k')
window = 30
w = np.array([1/window for _ in range(window)])
weights = lfilter(w, 1, log_tstf)
x = np.arange(window//2, len(log_tstf) - window//2)
plt.plot(x, weights[window:len(log_tstf)], 'r--')
plt.xlabel('Num failures')
plt.ylabel('Log of num steps to failure')
plt.savefig('./control_seed3.pdf')
return np.array(time_steps_to_failure)
if __name__ == '__main__':
main()
| """
CS 229 Machine Learning
Question: Reinforcement Learning - The Inverted Pendulum
"""
from __future__ import division, print_function
import matplotlib
matplotlib.use('TkAgg')
from env import CartPole, Physics
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import lfilter
"""
Parts of the code (cart and pole dynamics, and the state
discretization) are inspired from code available at the RL repository
http://www-anw.cs.umass.edu/rlr/domains.html
Briefly, the cart-pole system is described in `cart_pole.py`. The main
simulation loop in this file calls the `simulate()` function for
simulating the pole dynamics, `get_state()` for discretizing the
otherwise continuous state space in discrete states, and `show_cart()`
for display.
Some useful parameters are listed below:
`NUM_STATES`: Number of states in the discretized state space
You must assume that states are numbered 0 through `NUM_STATES` - 1. The
state numbered `NUM_STATES` - 1 (the last one) is a special state that
marks the state when the pole has been judged to have fallen (or when
the cart is out of bounds). However, you should NOT treat this state
any differently in your code. Any distinctions you need to make between
states should come automatically from your learning algorithm.
After each simulation cycle, you are supposed to update the transition
counts and rewards observed. However, you should not change either
your value function or the transition probability matrix at each
cycle.
Whenever the pole falls, a section of your code below will be
executed. At this point, you must use the transition counts and reward
observations that you have gathered to generate a new model for the MDP
(i.e. transition probabilities and state rewards). After that, you
must use value iteration to get the optimal value function for this MDP
model.
`TOLERANCE`: Controls the convergence criteria for each value iteration
run. In value iteration, you can assume convergence when the maximum
absolute change in the value function at any state in an iteration
becomes lower than `TOLERANCE.
You need to write code that chooses the best action according
to your current value function, and the current model of the MDP. The
action must be either 0 or 1 (corresponding to possible directions of
pushing the cart)
Finally, we assume that the simulation has converged when
`NO_LEARNING_THRESHOLD` consecutive value function computations all
converged within one value function iteration. Intuitively, it seems
like there will be little learning after this, so we end the simulation
here, and say the overall algorithm has converged.
Learning curves can be generated by calling a code snippet at the end
(it assumes that the learning was just executed, and the array
`time_steps_to_failure` that records the time for which the pole was
balanced before each failure is in memory). `num_failures` is a variable
that stores the number of failures (pole drops / cart out of bounds)
till now.
Other parameters in the code are described below:
`GAMMA`: Discount factor to be used
The following parameters control the simulation display; you dont
really need to know about them:
`pause_time`: Controls the pause between successive frames of the
display. Higher values make your simulation slower.
`min_trial_length_to_start_display`: Allows you to start the display only
after the pole has been successfully balanced for at least this many
trials. Setting this to zero starts the display immediately. Choosing a
reasonably high value (around 100) can allow you to rush through the
initial learning quickly, and start the display only after the
performance is reasonable.
"""
def initialize_mdp_data(num_states):
"""
Return a variable that contains all the parameters/state you need for your MDP.
Feel free to use whatever data type is most convenient for you (custom classes, tuples, dicts, etc)
Assume that no transitions or rewards have been observed.
Initialize the value function array to small random values (0 to 0.10, say).
Initialize the transition probabilities uniformly (ie, probability of
transitioning for state x to state y using action a is exactly
1/num_states).
Initialize all state rewards to zero.
Args:
num_states: The number of states
Returns: The initial MDP parameters
"""
transition_counts = np.zeros((num_states, num_states, 2)) # P_ijk = P_{s_i, a_k} (s_j) i.e. s_i -> s_j under a_k
transition_probs = np.ones((num_states, num_states, 2)) / num_states # initial uniform distribution
#Index zero is count of rewards being -1 , index 1 is count of total num state is reached
reward_counts = np.zeros((num_states, 2))
reward = np.zeros(num_states)
value = np.random.rand(num_states) * 0.1 # - 0.05 - 1/num_states
return {
'transition_counts': transition_counts,
'transition_probs': transition_probs,
'reward_counts': reward_counts,
'reward': reward,
'value': value,
'num_states': num_states,
'state_action_history': [[]]
}
def choose_action(state, mdp_data):
"""
Choose the next action (0 or 1) that is optimal according to your current
mdp_data. When there is no optimal action, return a random action.
Args:
state: The current state in the MDP
mdp_data: The parameters for your MDP. See initialize_mdp_data.
Returns:
0 or 1 that is optimal according to your current MDP
"""
# *** START CODE HERE ***
# argmax over actions a of sum_s' P_sa(s') V(s')
# s, s', a axes. times in axis s', sum over it, argmax over a and then take state s
expected_state_action_value = (mdp_data['transition_probs'] * mdp_data['value'].reshape(1, -1, 1)).sum(axis=1)
optimal = expected_state_action_value.argmax(axis=1) # argmax over the 2 actions
is_optimal = expected_state_action_value[state][int(optimal[state])] > \
expected_state_action_value[state][int(not optimal[state])]
action = optimal[state] if is_optimal else np.random.randint(2)
#print(f'state: {state}; optimal action: {action}; expected values: {expected_state_action_value[state]}')
return optimal[state] if expected_state_action_value[state][int(optimal[state])] > expected_state_action_value[state][int(not optimal[state])] else np.random.randint(2)
# *** END CODE HERE ***
def update_mdp_transition_counts_reward_counts(mdp_data, state, action, new_state, reward):
"""
Update the transition count and reward count information in your mdp_data.
Do not change the other MDP parameters (those get changed later).
Record the number of times `state, action, new_state` occurs.
Record the rewards for every `new_state`
(since rewards are -1 or 0, you just need to record number of times reward -1 is seen in 'reward_counts' index new_state,0)
Record the number of time `new_state` was reached (in 'reward_counts' index new_state,1)
Args:
mdp_data: The parameters of your MDP. See initialize_mdp_data.
state: The state that was observed at the start.
action: The action you performed.
new_state: The state after your action.
reward: The reward after your action (i.e. reward corresponding to new_state).
Returns:
Nothing
"""
# *** START CODE HERE ***
mdp_data['state_action_history'][-1].append([state, action, new_state])
if reward == -1:
mdp_data['reward_counts'][new_state][0] += 1
mdp_data['reward_counts'][new_state][1] += 1 # the number of times reaching this state regardless of reward -1 or 0
mdp_data['transition_counts'][state, new_state, action] += 1
# *** END CODE HERE ***
# This function does not return anything
return
def update_mdp_transition_probs_reward(mdp_data):
"""
Update the estimated transition probabilities and reward values in your MDP.
Make sure you account for the case when a state-action pair has never
been tried before, or the state has never been visited before. In that
case, you must not change that component (and thus keep it at the
initialized uniform distribution).
Args:
mdp_data: The data for your MDP. See initialize_mdp_data.
Returns:
Nothing
"""
# *** START CODE HERE ***
num_states = mdp_data['value'].shape[0]
transition_counts = mdp_data['transition_counts']
# i.e. some s' transition occured for s,a pair
state_actions_occurred = transition_counts.sum(axis=1) > 0
state_actions_occurred = np.tile(state_actions_occurred, (num_states, 1, 1)).transpose(1,0,2)
# where s,a pair occured take from counts, otherwise from the previous probs
transition_probs = np.where(state_actions_occurred, transition_counts, mdp_data['transition_probs'])
# counts at s' for s,a pair divided by total counts for s,a is our new prob - or same as old prob if no s,a
# pair occured
transition_probs = transition_probs/transition_probs.sum(axis=1).reshape(num_states, 1, 2)
mdp_data['transition_probs'] = transition_probs
# number of -1 rewards divided by total number of rewards (more general would be sum rewards / number times visited)
# rewards <-> new states, so did we ever reach this new state?
reward_occured = transition_counts.sum(axis=(0, 2)) > 0
reward = np.where(reward_occured, - mdp_data['reward_counts'][:, 0] / mdp_data['reward_counts'][:, 1], mdp_data['reward'])
mdp_data['reward'] = reward
# *** END CODE HERE ***
# This function does not return anything
return
def update_mdp_value(mdp_data, tolerance, gamma):
"""
Update the estimated values in your MDP.
Perform value iteration using the new estimated model for the MDP.
The convergence criterion should be based on `TOLERANCE` as described
at the top of the file.
Return true if it converges within one iteration.
Args:
mdp_data: The data for your MDP. See initialize_mdp_data.
tolerance: The tolerance to use for the convergence criterion.
gamma: Your discount factor.
Returns:
True if the value iteration converged in one iteration
"""
# *** START CODE HERE ***
action_values = (mdp_data['transition_probs'] * mdp_data['value'].reshape(1, -1, 1)).sum(axis=1)
old_value = mdp_data['value']
mdp_data['value'] = mdp_data['reward'] + gamma * action_values.max(axis=1)
max_diff = np.max(np.abs(mdp_data['value'] - old_value))
print(f'max state value diff: {max_diff}')
return max_diff < tolerance
# *** END CODE HERE ***
def plot_mdp_data(mdp_data):
plt.figure()
plt.plot(mdp_data['value'])
def main(plot=True):
# Seed the randomness of the simulation so this outputs the same thing each time
np.random.seed(3)
# Simulation parameters
pause_time = 0.0001
min_trial_length_to_start_display = 100
display_started = min_trial_length_to_start_display == 0
NUM_STATES = 163
GAMMA = 0.995
TOLERANCE = 0.01
NO_LEARNING_THRESHOLD = 20
# TOTAL_MAX_TRIALS might be useful otherwise it takes up to 300 ish iterations to converge sometimes
# Time cycle of the simulation
time = 0
# These variables perform bookkeeping (how many cycles was the pole
# balanced for before it fell). Useful for plotting learning curves.
time_steps_to_failure = []
num_failures = 0
time_at_start_of_current_trial = 0
# You should reach convergence well before this
max_failures = 500
# Initialize a cart pole
cart_pole = CartPole(Physics())
# Starting `state_tuple` is (0, 0, 0, 0)
# x, x_dot, theta, theta_dot represents the actual continuous state vector
x, x_dot, theta, theta_dot = 0.0, 0.0, 0.0, 0.0
state_tuple = (x, x_dot, theta, theta_dot)
# `state` is the number given to this state, you only need to consider
# this representation of the state
state = cart_pole.get_state(state_tuple)
if min_trial_length_to_start_display == 0 or display_started == 1:
cart_pole.show_cart(state_tuple, pause_time)
mdp_data = initialize_mdp_data(NUM_STATES)
# This is the criterion to end the simulation.
# You should change it to terminate when the previous
# 'NO_LEARNING_THRESHOLD' consecutive value function computations all
# converged within one value function iteration. Intuitively, it seems
# like there will be little learning after this, so end the simulation
# here, and say the overall algorithm has converged.
consecutive_no_learning_trials = 0
while consecutive_no_learning_trials < NO_LEARNING_THRESHOLD:
action = choose_action(state, mdp_data)
# Get the next state by simulating the dynamics
state_tuple = cart_pole.simulate(action, state_tuple)
# x, x_dot, theta, theta_dot = state_tuple
# Increment simulation time
time = time + 1
# Get the state number corresponding to new state vector
new_state = cart_pole.get_state(state_tuple)
# if display_started == 1:
# cart_pole.show_cart(state_tuple, pause_time)
#print(f'state transition prob: {state}, {action} -> {new_state}: {mdp_data["transition_probs"][state, new_state, action]}')
# reward function to use - do not change this!
if new_state == NUM_STATES - 1:
R = -1
else:
R = 0
# add to transition count for s, s', a triple and to reward count for s'
update_mdp_transition_counts_reward_counts(mdp_data, state, action, new_state, R)
# Recompute MDP model whenever pole falls
# Compute the value function V for the new model
if new_state == NUM_STATES - 1:
update_mdp_transition_probs_reward(mdp_data)
converged_in_one_iteration = update_mdp_value(mdp_data, TOLERANCE, GAMMA)
if converged_in_one_iteration:
consecutive_no_learning_trials = consecutive_no_learning_trials + 1
else:
consecutive_no_learning_trials = 0
# Do NOT change this code: Controls the simulation, and handles the case
# when the pole fell and the state must be reinitialized.
if new_state == NUM_STATES - 1:
num_failures += 1
if num_failures >= max_failures:
break
print('[INFO] Failure number {}'.format(num_failures))
# plot_mdp_data(mdp_data)
time_steps_to_failure.append(time - time_at_start_of_current_trial)
print(f'time to failure: {time_steps_to_failure[-1]}')
# print(f'history: {mdp_data["state_action_history"][-1]}')
mdp_data["state_action_history"].append([])
# time_steps_to_failure[num_failures] = time - time_at_start_of_current_trial
time_at_start_of_current_trial = time
if time_steps_to_failure[num_failures - 1] > min_trial_length_to_start_display:
display_started = 1
# Reinitialize state
# x = 0.0
x = -1.1 + np.random.uniform() * 2.2
x_dot, theta, theta_dot = 0.0, 0.0, 0.0
state_tuple = (x, x_dot, theta, theta_dot)
state = cart_pole.get_state(state_tuple)
else:
state = new_state
if plot:
plt.figure()
# plot the learning curve (time balanced vs. trial)
log_tstf = np.log(np.array(time_steps_to_failure))
plt.plot(np.arange(len(time_steps_to_failure)), log_tstf, 'k')
window = 30
w = np.array([1/window for _ in range(window)])
weights = lfilter(w, 1, log_tstf)
x = np.arange(window//2, len(log_tstf) - window//2)
plt.plot(x, weights[window:len(log_tstf)], 'r--')
plt.xlabel('Num failures')
plt.ylabel('Log of num steps to failure')
plt.savefig('./control_seed3.pdf')
return np.array(time_steps_to_failure)
if __name__ == '__main__':
main()
|
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import getpass
import os
import textwrap
from typing import Optional
import attr
import click
import datetime
import glob
import releasetool.filehelpers
import releasetool.git
import releasetool.github
import releasetool.secrets
import releasetool.commands.common
_CHANGELOG_TEMPLATE = """\
# Release History
[RubyGems.org history][1]
[1]: https://rubygems.org/gems/{package_name}/versions
"""
@attr.s(auto_attribs=True, slots=True)
class Context(releasetool.commands.common.GitHubContext):
last_release_version: Optional[str] = None
last_release_committish: Optional[str] = None
release_version: Optional[str] = None
release_branch: Optional[str] = None
pull_request: Optional[dict] = None
version_file: Optional[str] = None
def determine_package_name(ctx: Context) -> None:
click.secho("> Figuring out the package name.", fg="cyan")
ctx.package_name = os.path.basename(os.getcwd())
click.secho(f"Looks like we're releasing {ctx.package_name}.")
def determine_last_release(ctx: Context) -> None:
click.secho("> Figuring out what the last release was.", fg="cyan")
tags = releasetool.git.list_tags()
candidates = [tag for tag in tags if tag.startswith(ctx.package_name)]
if candidates and "google-cloud" in candidates[0]:
ctx.last_release_committish = candidates[0]
ctx.last_release_version = candidates[0].rsplit("/").pop().lstrip("v")
elif ("google-cloud" not in ctx.package_name) and tags:
ctx.last_release_committish = tags[0]
ctx.last_release_version = tags[0].rsplit("/")[-1].lstrip("v")
else:
click.secho(
f"I couldn't figure out the last release for {ctx.package_name}, "
"so I'm assuming this is the first release. Can you tell me "
"which git rev/sha to start the changelog at?",
fg="yellow",
)
ctx.last_release_committish = click.prompt("Committish")
ctx.last_release_version = "0.0.0"
click.secho(f"The last release was {ctx.last_release_version}.")
def gather_changes(ctx: Context) -> None:
click.secho(f"> Gathering changes since {ctx.last_release_version}", fg="cyan")
ctx.changes = releasetool.git.summary_log(
from_=ctx.last_release_committish, to=f"{ctx.upstream_name}/master"
)
click.secho(f"Cool, {len(ctx.changes)} changes found.")
def edit_release_notes(ctx: Context) -> None:
click.secho(f"> Opening your editor to finalize release notes.", fg="cyan")
release_notes = "\n".join(f"* {change.strip()}" for change in set(ctx.changes))
ctx.release_notes = releasetool.filehelpers.open_editor_with_tempfile(
release_notes, "release-notes.md"
).strip()
def determine_release_version(ctx: Context) -> None:
click.secho(f"> Now it's time to pick a release version!", fg="cyan")
release_notes = textwrap.indent(ctx.release_notes, "\t")
click.secho(f"Here's the release notes you wrote:\n\n{release_notes}\n")
parsed_version = [int(x) for x in ctx.last_release_version.split(".")]
if parsed_version == [0, 0, 0]:
ctx.release_version = "0.1.0"
return
selection = click.prompt(
"Is this a major, minor, or patch update (or enter the new version " "directly)"
)
if selection == "major":
parsed_version[0] += 1
parsed_version[1] = 0
parsed_version[2] = 0
elif selection == "minor":
parsed_version[1] += 1
parsed_version[2] = 0
elif selection == "patch":
parsed_version[2] += 1
else:
ctx.release_version = selection
return
ctx.release_version = "{}.{}.{}".format(*parsed_version)
click.secho(f"Got it, releasing {ctx.release_version}.")
def create_release_branch(ctx) -> None:
ctx.release_branch = f"release-{ctx.package_name}-v{ctx.release_version}"
click.secho(f"> Creating branch {ctx.release_branch}", fg="cyan")
return releasetool.git.checkout_create_branch(ctx.release_branch)
def update_changelog(ctx: Context) -> None:
changelog_filename = "CHANGELOG.md"
click.secho(f"> Updating {changelog_filename}.", fg="cyan")
if not os.path.exists(changelog_filename):
print(f"{changelog_filename} does not yet exist. Opening it for " "creation.")
releasetool.filehelpers.open_editor_with_content(
changelog_filename,
_CHANGELOG_TEMPLATE.format(package_name=ctx.package_name),
)
today = datetime.date.today()
changelog_entry = (
f"### {ctx.release_version} / {today}" f"\n\n" f"{ctx.release_notes}" f"\n\n"
)
releasetool.filehelpers.insert_before(
changelog_filename, changelog_entry, r"^### (.+)$|\Z"
)
def update_version(ctx: Context) -> None:
click.secho("> Updating version.rb.", fg="cyan")
gemspec = glob.glob("*.gemspec")[0]
version = releasetool.filehelpers.extract(gemspec, r"gem.version.*=(.*)")
if version.lower().find("version") == -1:
final = (
releasetool.filehelpers.extract(gemspec, "(gem.version.*=)")
+ f' "{ctx.release_version}"'
)
ctx.version_file = gemspec
releasetool.filehelpers.replace(ctx.version_file, r"gem.version.*", final)
else:
ctx.version_file = glob.glob("lib/**/version.rb", recursive=True)[0]
releasetool.filehelpers.replace(
ctx.version_file, r'VERSION = "(.+?)"', f'VERSION = "{ctx.release_version}"'
)
def create_release_commit(ctx: Context) -> None:
"""Create a release commit."""
click.secho("> Committing changes to CHANGELOG.md, {ctx.version_file}", fg="cyan")
releasetool.git.commit(
["CHANGELOG.md", ctx.version_file],
f"Release {ctx.package_name} {ctx.release_version}\n\n{ctx.release_notes}",
)
def push_release_branch(ctx: Context) -> None:
click.secho("> Pushing release branch.", fg="cyan")
releasetool.git.push(ctx.release_branch)
def create_release_pr(ctx: Context, autorelease: bool = True) -> None:
click.secho(f"> Creating release pull request.", fg="cyan")
if ctx.upstream_repo == ctx.origin_repo:
head = ctx.release_branch
else:
head = f"{ctx.origin_user}:{ctx.release_branch}"
ctx.pull_request = ctx.github.create_pull_request(
ctx.upstream_repo,
head=head,
title=f"Release {ctx.package_name} {ctx.release_version}",
body=f"{ctx.release_notes}\n\nThis pull request was generated using releasetool.",
)
if autorelease:
ctx.github.add_issue_labels(
ctx.upstream_repo, ctx.pull_request["number"], ["autorelease: pending"]
)
click.secho(f"Pull request is at {ctx.pull_request["html_url"]}.")
def start() -> None:
ctx = Context()
click.secho(f"o/ Hey, {getpass.getuser()}, let's release some Ruby!", fg="magenta")
releasetool.commands.common.setup_github_context(ctx)
determine_package_name(ctx)
determine_last_release(ctx)
gather_changes(ctx)
edit_release_notes(ctx)
determine_release_version(ctx)
create_release_branch(ctx)
update_changelog(ctx)
update_version(ctx)
create_release_commit(ctx)
push_release_branch(ctx)
# TODO: Confirm?
create_release_pr(ctx)
click.secho(f"\\o/ All done!", fg="magenta")
| # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import getpass
import os
import textwrap
from typing import Optional
import attr
import click
import datetime
import glob
import releasetool.filehelpers
import releasetool.git
import releasetool.github
import releasetool.secrets
import releasetool.commands.common
_CHANGELOG_TEMPLATE = """\
# Release History
[RubyGems.org history][1]
[1]: https://rubygems.org/gems/{package_name}/versions
"""
@attr.s(auto_attribs=True, slots=True)
class Context(releasetool.commands.common.GitHubContext):
last_release_version: Optional[str] = None
last_release_committish: Optional[str] = None
release_version: Optional[str] = None
release_branch: Optional[str] = None
pull_request: Optional[dict] = None
version_file: Optional[str] = None
def determine_package_name(ctx: Context) -> None:
click.secho("> Figuring out the package name.", fg="cyan")
ctx.package_name = os.path.basename(os.getcwd())
click.secho(f"Looks like we're releasing {ctx.package_name}.")
def determine_last_release(ctx: Context) -> None:
click.secho("> Figuring out what the last release was.", fg="cyan")
tags = releasetool.git.list_tags()
candidates = [tag for tag in tags if tag.startswith(ctx.package_name)]
if candidates and "google-cloud" in candidates[0]:
ctx.last_release_committish = candidates[0]
ctx.last_release_version = candidates[0].rsplit("/").pop().lstrip("v")
elif ("google-cloud" not in ctx.package_name) and tags:
ctx.last_release_committish = tags[0]
ctx.last_release_version = tags[0].rsplit("/")[-1].lstrip("v")
else:
click.secho(
f"I couldn't figure out the last release for {ctx.package_name}, "
"so I'm assuming this is the first release. Can you tell me "
"which git rev/sha to start the changelog at?",
fg="yellow",
)
ctx.last_release_committish = click.prompt("Committish")
ctx.last_release_version = "0.0.0"
click.secho(f"The last release was {ctx.last_release_version}.")
def gather_changes(ctx: Context) -> None:
click.secho(f"> Gathering changes since {ctx.last_release_version}", fg="cyan")
ctx.changes = releasetool.git.summary_log(
from_=ctx.last_release_committish, to=f"{ctx.upstream_name}/master"
)
click.secho(f"Cool, {len(ctx.changes)} changes found.")
def edit_release_notes(ctx: Context) -> None:
click.secho(f"> Opening your editor to finalize release notes.", fg="cyan")
release_notes = "\n".join(f"* {change.strip()}" for change in set(ctx.changes))
ctx.release_notes = releasetool.filehelpers.open_editor_with_tempfile(
release_notes, "release-notes.md"
).strip()
def determine_release_version(ctx: Context) -> None:
click.secho(f"> Now it's time to pick a release version!", fg="cyan")
release_notes = textwrap.indent(ctx.release_notes, "\t")
click.secho(f"Here's the release notes you wrote:\n\n{release_notes}\n")
parsed_version = [int(x) for x in ctx.last_release_version.split(".")]
if parsed_version == [0, 0, 0]:
ctx.release_version = "0.1.0"
return
selection = click.prompt(
"Is this a major, minor, or patch update (or enter the new version " "directly)"
)
if selection == "major":
parsed_version[0] += 1
parsed_version[1] = 0
parsed_version[2] = 0
elif selection == "minor":
parsed_version[1] += 1
parsed_version[2] = 0
elif selection == "patch":
parsed_version[2] += 1
else:
ctx.release_version = selection
return
ctx.release_version = "{}.{}.{}".format(*parsed_version)
click.secho(f"Got it, releasing {ctx.release_version}.")
def create_release_branch(ctx) -> None:
ctx.release_branch = f"release-{ctx.package_name}-v{ctx.release_version}"
click.secho(f"> Creating branch {ctx.release_branch}", fg="cyan")
return releasetool.git.checkout_create_branch(ctx.release_branch)
def update_changelog(ctx: Context) -> None:
changelog_filename = "CHANGELOG.md"
click.secho(f"> Updating {changelog_filename}.", fg="cyan")
if not os.path.exists(changelog_filename):
print(f"{changelog_filename} does not yet exist. Opening it for " "creation.")
releasetool.filehelpers.open_editor_with_content(
changelog_filename,
_CHANGELOG_TEMPLATE.format(package_name=ctx.package_name),
)
today = datetime.date.today()
changelog_entry = (
f"### {ctx.release_version} / {today}" f"\n\n" f"{ctx.release_notes}" f"\n\n"
)
releasetool.filehelpers.insert_before(
changelog_filename, changelog_entry, r"^### (.+)$|\Z"
)
def update_version(ctx: Context) -> None:
click.secho("> Updating version.rb.", fg="cyan")
gemspec = glob.glob("*.gemspec")[0]
version = releasetool.filehelpers.extract(gemspec, r"gem.version.*=(.*)")
if version.lower().find("version") == -1:
final = (
releasetool.filehelpers.extract(gemspec, "(gem.version.*=)")
+ f' "{ctx.release_version}"'
)
ctx.version_file = gemspec
releasetool.filehelpers.replace(ctx.version_file, r"gem.version.*", final)
else:
ctx.version_file = glob.glob("lib/**/version.rb", recursive=True)[0]
releasetool.filehelpers.replace(
ctx.version_file, r'VERSION = "(.+?)"', f'VERSION = "{ctx.release_version}"'
)
def create_release_commit(ctx: Context) -> None:
"""Create a release commit."""
click.secho("> Committing changes to CHANGELOG.md, {ctx.version_file}", fg="cyan")
releasetool.git.commit(
["CHANGELOG.md", ctx.version_file],
f"Release {ctx.package_name} {ctx.release_version}\n\n{ctx.release_notes}",
)
def push_release_branch(ctx: Context) -> None:
click.secho("> Pushing release branch.", fg="cyan")
releasetool.git.push(ctx.release_branch)
def create_release_pr(ctx: Context, autorelease: bool = True) -> None:
click.secho(f"> Creating release pull request.", fg="cyan")
if ctx.upstream_repo == ctx.origin_repo:
head = ctx.release_branch
else:
head = f"{ctx.origin_user}:{ctx.release_branch}"
ctx.pull_request = ctx.github.create_pull_request(
ctx.upstream_repo,
head=head,
title=f"Release {ctx.package_name} {ctx.release_version}",
body=f"{ctx.release_notes}\n\nThis pull request was generated using releasetool.",
)
if autorelease:
ctx.github.add_issue_labels(
ctx.upstream_repo, ctx.pull_request["number"], ["autorelease: pending"]
)
click.secho(f"Pull request is at {ctx.pull_request['html_url']}.")
def start() -> None:
ctx = Context()
click.secho(f"o/ Hey, {getpass.getuser()}, let's release some Ruby!", fg="magenta")
releasetool.commands.common.setup_github_context(ctx)
determine_package_name(ctx)
determine_last_release(ctx)
gather_changes(ctx)
edit_release_notes(ctx)
determine_release_version(ctx)
create_release_branch(ctx)
update_changelog(ctx)
update_version(ctx)
create_release_commit(ctx)
push_release_branch(ctx)
# TODO: Confirm?
create_release_pr(ctx)
click.secho(f"\\o/ All done!", fg="magenta")
|
#!/usr/bin/env python3
"""Zeff record config generator for HousePrice records."""
import logging
import urllib.parse
import csv
LOGGER = logging.getLogger("zeffclient.record.generator")
def HousePriceRecordGenerator(arg: str):
"""Return house primary key value."""
with open(arg, "r") as csvfile:
for row in csv.DictReader(csvfile):
url = f"file://{arg}/?id={row["id"]}"
yield url
if __name__ == "__main__":
from logging import basicConfig, DEBUG
from zeff.cli import load_configuration
basicConfig(level=DEBUG)
config = load_configuration()
generatorarg = config.records.records_config_arg
for config in HousePriceRecordGenerator(generatorarg):
print(config)
| #!/usr/bin/env python3
"""Zeff record config generator for HousePrice records."""
import logging
import urllib.parse
import csv
LOGGER = logging.getLogger("zeffclient.record.generator")
def HousePriceRecordGenerator(arg: str):
"""Return house primary key value."""
with open(arg, "r") as csvfile:
for row in csv.DictReader(csvfile):
url = f"file://{arg}/?id={row['id']}"
yield url
if __name__ == "__main__":
from logging import basicConfig, DEBUG
from zeff.cli import load_configuration
basicConfig(level=DEBUG)
config = load_configuration()
generatorarg = config.records.records_config_arg
for config in HousePriceRecordGenerator(generatorarg):
print(config)
|
#!/usr/bin/python
import os
import sys
import time
import signal
import importlib
import argparse
import subprocess
import xml.etree.ElementTree as ET
import xml.dom.minidom
import board
import timer
class Server(object):
"""
Othello server, implements a simple file-based playing protocol
"""
def __init__(self, p1_dir, p2_dir, delay, history, output):
"""
Initializes the Othello game server
:param p1_dir: directory where the 'agent.py' of the 1st player is located
:param p2_dir: directory where the 'agent.py' of the 2nd player is located
:param delay: time limit to make a move
:param history: file that will contain the match history (plain text)
:param output: file to save game details (includes history)
"""
self.basedir = os.path.abspath('.')
self.player_dirs = [p1_dir, p2_dir]
self.player_colors = [board.Board.BLACK, board.Board.WHITE]
self.color_names = ['black', 'white']
self.board = board.Board()
self.history = [] # a list of performed moves (tuple: ((x,y), color)
self.history_file = open(history, 'w')
self.output_file = output
self.delay = delay
self.result = None
# start and finish times of match
self.start = None
self.finish = None
# imports 'agent.py' from both players
self.player_modules = [
importlib.import_module(f"{p1_dir.strip("/")}.agent"),
importlib.import_module(f"{p2_dir.strip("/")}.agent"),
]
def __del__(self):
self.history_file.close()
def run(self):
self.start = time.localtime()
player = 0
illegal_count = [0, 0] # counts the number of illegal move attempts
print(f'---- Current match: {self.player_dirs[0]} (B) x {self.player_dirs[1]} (W) ----')
print('Initial board:')
print(self.board.decorated_str())
while True: # runs until endgame
# checks whether players have available moves
no_moves_current = len(self.board.legal_moves(self.player_colors[player])) == 0
no_moves_opponent = len(self.board.legal_moves(self.board.opponent(self.player_colors[player]))) == 0
# calculates scores
p1_score = sum([1 for char in str(self.board) if char == self.board.BLACK])
p2_score = sum([1 for char in str(self.board) if char == self.board.WHITE])
print(f'---- Current match: {self.player_dirs[0]} (B) x {self.player_dirs[1]} (W) ----')
# disqualify player if he attempts illegal moves 5 times in a row
if illegal_count[player] >= 5:
print(f'Player {player+1} ({self.player_dirs[player]}) DISQUALIFIED! Too many illegal move attempts.')
print('End of game reached!')
print('Player 1 (B): %d' % p1_score)
print('Player 2 (W): %d' % p2_score)
self.result = 1 - player
self.finish = time.localtime()
return self.result
# checks whether both players don't have available moves (end of game)
if no_moves_current and no_moves_opponent:
print('End of game reached! Scores:')
print(f'Player 1 (B - {self.player_dirs[0]}): {p1_score}')
print(f'Player 2 (W - {self.player_dirs[1]}): {p2_score}')
if p1_score > p2_score:
print(f'Player 1 (B - {self.player_dirs[0]} wins!')
elif p2_score > p1_score:
print(f'Player 2 (W - {self.player_dirs[1]}) wins!')
else:
print('Draw!')
self.result = 0 if p1_score > p2_score else 1 if p2_score > p1_score else 2
self.finish = time.localtime()
return self.result
# if current player has no moves, toggle player and continue
if no_moves_current:
print(f'Player {player+1} ({self.player_dirs[player]}) has no legal moves and will not play this turn.')
illegal_count[player] = 0
player = 1 - player
continue
# creates a copy of the board, so that player changes won't affect mine
board_copy = board.from_string(self.board.__str__())
# calls current player's make_move function with the specified timeout
function_call = timer.FunctionTimer(self.player_modules[player].make_move, (board_copy, self.player_colors[player]))
move = function_call.run(self.delay)
if move is None: # detects timeout
print('Player %d has not made a move and lost its turn.' % (player + 1))
player = 1 - player
continue
move_x, move_y = move
# saves move in history
self.history_file.write('%d,%d,%s\n' % (move_x, move_y, self.player_colors[player]))
self.history.append(((move_x, move_y), self.player_colors[player]))
if self.board.process_move((move_x, move_y), self.player_colors[player]):
illegal_count[player] = 0
print('Player %d move %d,%d accepted.' % (player + 1, move_x, move_y))
else:
illegal_count[player] += 1
print('Player %d move %d,%d ILLEGAL!' % (player + 1,move_x, move_y))
print('Current board:')
print(self.board.decorated_str())
# toggle player for next move
player = 1 - player
def write_output(self):
"""
Writes a xml file with detailed match data
:return:
"""
os.chdir(self.basedir)
root = ET.Element('othello-match')
colors = [self.board.BLACK, self.board.WHITE, 'None']
self.player_dirs.append('None') # trick for writing a draw match
timing = ET.SubElement(root, 'timing')
timing.set('start', time.asctime(self.start))
timing.set('finish', time.asctime(self.finish))
scores = [self.board.piece_count['B'], self.board.piece_count['W']]
for idx, p in enumerate(self.player_dirs[:2]):
elem = ET.SubElement(root, 'player%d' % (idx + 1))
elem.set('directory', p)
elem.set('color', colors[idx])
result = 'win' if scores[idx] > scores[idx - 1] else 'loss' if scores[idx] < scores[idx - 1] else 'draw'
elem.set('result', result)
elem.set('score', str(scores[idx]))
moves = ET.SubElement(root, 'moves')
for coords, color in self.history:
move = ET.SubElement(moves, 'move')
move.set('coord', '%d,%d' % coords)
move.set('color', color)
# preety xml thanks to: https://stackoverflow.com/a/1206856/1251716
ugly_xml = ET.tostring(root).decode('utf-8')
dom = xml.dom.minidom.parseString(ugly_xml) # or xml.dom.minidom.parseString(xml_string)
pretty_xml = dom.toprettyxml()
f = open(self.output_file, 'w')
f.write(pretty_xml)
f.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Othello server.')
parser.add_argument('players', metavar='player', type=str, nargs=2,
help='Path to player directory')
parser.add_argument('-d', '--delay', type=float, metavar='delay',
default=5.0,
help='Time allocated for players to make a move.')
parser.add_argument('-l', '--log-history', type=str, dest='history',
default='history.txt', metavar='log-history',
help='File to save game log (history).')
parser.add_argument('-o', '--output-file', type=str, dest='output',
default='results.xml', metavar='output-file',
help='File to save game details (includes history)')
args = parser.parse_args()
p1, p2 = args.players
s = Server(p1, p2, args.delay, args.history, args.output)
s.run()
s.write_output()
| #!/usr/bin/python
import os
import sys
import time
import signal
import importlib
import argparse
import subprocess
import xml.etree.ElementTree as ET
import xml.dom.minidom
import board
import timer
class Server(object):
"""
Othello server, implements a simple file-based playing protocol
"""
def __init__(self, p1_dir, p2_dir, delay, history, output):
"""
Initializes the Othello game server
:param p1_dir: directory where the 'agent.py' of the 1st player is located
:param p2_dir: directory where the 'agent.py' of the 2nd player is located
:param delay: time limit to make a move
:param history: file that will contain the match history (plain text)
:param output: file to save game details (includes history)
"""
self.basedir = os.path.abspath('.')
self.player_dirs = [p1_dir, p2_dir]
self.player_colors = [board.Board.BLACK, board.Board.WHITE]
self.color_names = ['black', 'white']
self.board = board.Board()
self.history = [] # a list of performed moves (tuple: ((x,y), color)
self.history_file = open(history, 'w')
self.output_file = output
self.delay = delay
self.result = None
# start and finish times of match
self.start = None
self.finish = None
# imports 'agent.py' from both players
self.player_modules = [
importlib.import_module(f"{p1_dir.strip('/')}.agent"),
importlib.import_module(f"{p2_dir.strip('/')}.agent"),
]
def __del__(self):
self.history_file.close()
def run(self):
self.start = time.localtime()
player = 0
illegal_count = [0, 0] # counts the number of illegal move attempts
print(f'---- Current match: {self.player_dirs[0]} (B) x {self.player_dirs[1]} (W) ----')
print('Initial board:')
print(self.board.decorated_str())
while True: # runs until endgame
# checks whether players have available moves
no_moves_current = len(self.board.legal_moves(self.player_colors[player])) == 0
no_moves_opponent = len(self.board.legal_moves(self.board.opponent(self.player_colors[player]))) == 0
# calculates scores
p1_score = sum([1 for char in str(self.board) if char == self.board.BLACK])
p2_score = sum([1 for char in str(self.board) if char == self.board.WHITE])
print(f'---- Current match: {self.player_dirs[0]} (B) x {self.player_dirs[1]} (W) ----')
# disqualify player if he attempts illegal moves 5 times in a row
if illegal_count[player] >= 5:
print(f'Player {player+1} ({self.player_dirs[player]}) DISQUALIFIED! Too many illegal move attempts.')
print('End of game reached!')
print('Player 1 (B): %d' % p1_score)
print('Player 2 (W): %d' % p2_score)
self.result = 1 - player
self.finish = time.localtime()
return self.result
# checks whether both players don't have available moves (end of game)
if no_moves_current and no_moves_opponent:
print('End of game reached! Scores:')
print(f'Player 1 (B - {self.player_dirs[0]}): {p1_score}')
print(f'Player 2 (W - {self.player_dirs[1]}): {p2_score}')
if p1_score > p2_score:
print(f'Player 1 (B - {self.player_dirs[0]} wins!')
elif p2_score > p1_score:
print(f'Player 2 (W - {self.player_dirs[1]}) wins!')
else:
print('Draw!')
self.result = 0 if p1_score > p2_score else 1 if p2_score > p1_score else 2
self.finish = time.localtime()
return self.result
# if current player has no moves, toggle player and continue
if no_moves_current:
print(f'Player {player+1} ({self.player_dirs[player]}) has no legal moves and will not play this turn.')
illegal_count[player] = 0
player = 1 - player
continue
# creates a copy of the board, so that player changes won't affect mine
board_copy = board.from_string(self.board.__str__())
# calls current player's make_move function with the specified timeout
function_call = timer.FunctionTimer(self.player_modules[player].make_move, (board_copy, self.player_colors[player]))
move = function_call.run(self.delay)
if move is None: # detects timeout
print('Player %d has not made a move and lost its turn.' % (player + 1))
player = 1 - player
continue
move_x, move_y = move
# saves move in history
self.history_file.write('%d,%d,%s\n' % (move_x, move_y, self.player_colors[player]))
self.history.append(((move_x, move_y), self.player_colors[player]))
if self.board.process_move((move_x, move_y), self.player_colors[player]):
illegal_count[player] = 0
print('Player %d move %d,%d accepted.' % (player + 1, move_x, move_y))
else:
illegal_count[player] += 1
print('Player %d move %d,%d ILLEGAL!' % (player + 1,move_x, move_y))
print('Current board:')
print(self.board.decorated_str())
# toggle player for next move
player = 1 - player
def write_output(self):
"""
Writes a xml file with detailed match data
:return:
"""
os.chdir(self.basedir)
root = ET.Element('othello-match')
colors = [self.board.BLACK, self.board.WHITE, 'None']
self.player_dirs.append('None') # trick for writing a draw match
timing = ET.SubElement(root, 'timing')
timing.set('start', time.asctime(self.start))
timing.set('finish', time.asctime(self.finish))
scores = [self.board.piece_count['B'], self.board.piece_count['W']]
for idx, p in enumerate(self.player_dirs[:2]):
elem = ET.SubElement(root, 'player%d' % (idx + 1))
elem.set('directory', p)
elem.set('color', colors[idx])
result = 'win' if scores[idx] > scores[idx - 1] else 'loss' if scores[idx] < scores[idx - 1] else 'draw'
elem.set('result', result)
elem.set('score', str(scores[idx]))
moves = ET.SubElement(root, 'moves')
for coords, color in self.history:
move = ET.SubElement(moves, 'move')
move.set('coord', '%d,%d' % coords)
move.set('color', color)
# preety xml thanks to: https://stackoverflow.com/a/1206856/1251716
ugly_xml = ET.tostring(root).decode('utf-8')
dom = xml.dom.minidom.parseString(ugly_xml) # or xml.dom.minidom.parseString(xml_string)
pretty_xml = dom.toprettyxml()
f = open(self.output_file, 'w')
f.write(pretty_xml)
f.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Othello server.')
parser.add_argument('players', metavar='player', type=str, nargs=2,
help='Path to player directory')
parser.add_argument('-d', '--delay', type=float, metavar='delay',
default=5.0,
help='Time allocated for players to make a move.')
parser.add_argument('-l', '--log-history', type=str, dest='history',
default='history.txt', metavar='log-history',
help='File to save game log (history).')
parser.add_argument('-o', '--output-file', type=str, dest='output',
default='results.xml', metavar='output-file',
help='File to save game details (includes history)')
args = parser.parse_args()
p1, p2 = args.players
s = Server(p1, p2, args.delay, args.history, args.output)
s.run()
s.write_output()
|
"""Scans directories for files that satisfy a specific mask and updates
version tags of all actions.
Example:
python3 update_actions.py live.yml action.y*ml folder/*.y*ml
"""
import argparse
import re
from pathlib import Path
from typing import Dict, List, Optional
from github import Github
ACTION_PATTERN = r"\s+action:\s*(?P<svc>gh|github):(?P<org>[\w-]+)/(?P<repo>[\w-]+)@(?P<cur_tag>[\w.]+)" # noqa: E501
def main():
args = parse_args()
patterns: List[str] = args.patterns
token: Optional[str] = args.token
if args.root:
root = Path(args.root).resolve()
else:
root = Path(__file__).parent.resolve()
update_actions(patterns, root, token)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"patterns",
metavar="PATTERN",
nargs="+",
help="Neuro-flow workflow file, which should be scanned for action updates.",
)
parser.add_argument("--token", nargs="?", help="GitHub token to use.")
parser.add_argument(
"--root",
nargs="?",
help="Directory, where to start searching for workflow files",
)
return parser.parse_args()
def update_actions(patterns: List[str], root_dir: Path, gh_token: Optional[str]):
github_client = Github(gh_token)
action_string_pattern = re.compile(ACTION_PATTERN)
# action_file_rel_path: [found_actions, updated_actions]
update_stats: Dict[str, List[int, int]] = {}
for pattern in patterns:
for file_path in root_dir.rglob(pattern):
found_actions = 0
updated_actions = 0
new_file_content = []
rel_path = str(file_path.relative_to(root_dir))
for line in file_path.read_text().splitlines(keepends=True):
match = action_string_pattern.match(line)
if match:
found_actions += 1
gh_repo = github_client.get_repo(f"{match["org"]}/{match["repo"]}")
current_tag = match["cur_tag"]
release_tags = [rel.tag_name for rel in gh_repo.get_releases()]
if not release_tags:
print(f"No releases found for '{gh_repo.full_name}' action")
elif current_tag not in release_tags:
print(
f"Ignoring '{gh_repo.full_name}' action in '{rel_path}',"
" since it is not refferenced by the release tag,"
f" but by '{current_tag}'."
)
else:
latest_tag = release_tags[0]
if latest_tag != current_tag:
updated_actions += 1
line = line.replace(current_tag, latest_tag)
new_file_content.append(line)
file_path.write_text("".join(new_file_content))
update_stats[rel_path] = [found_actions, updated_actions]
pr_body_lines = [
"::set-output name=updated_files::",
]
for filename in update_stats:
pr_body_lines.append(
f"{filename}: found {update_stats[filename][0]} "
f"updated {update_stats[filename][1]} actions; "
)
# The output afterwards will be used by GH CI to submit a PR.
print("".join(pr_body_lines))
if __name__ == "__main__":
main()
| """Scans directories for files that satisfy a specific mask and updates
version tags of all actions.
Example:
python3 update_actions.py live.yml action.y*ml folder/*.y*ml
"""
import argparse
import re
from pathlib import Path
from typing import Dict, List, Optional
from github import Github
ACTION_PATTERN = r"\s+action:\s*(?P<svc>gh|github):(?P<org>[\w-]+)/(?P<repo>[\w-]+)@(?P<cur_tag>[\w.]+)" # noqa: E501
def main():
args = parse_args()
patterns: List[str] = args.patterns
token: Optional[str] = args.token
if args.root:
root = Path(args.root).resolve()
else:
root = Path(__file__).parent.resolve()
update_actions(patterns, root, token)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"patterns",
metavar="PATTERN",
nargs="+",
help="Neuro-flow workflow file, which should be scanned for action updates.",
)
parser.add_argument("--token", nargs="?", help="GitHub token to use.")
parser.add_argument(
"--root",
nargs="?",
help="Directory, where to start searching for workflow files",
)
return parser.parse_args()
def update_actions(patterns: List[str], root_dir: Path, gh_token: Optional[str]):
github_client = Github(gh_token)
action_string_pattern = re.compile(ACTION_PATTERN)
# action_file_rel_path: [found_actions, updated_actions]
update_stats: Dict[str, List[int, int]] = {}
for pattern in patterns:
for file_path in root_dir.rglob(pattern):
found_actions = 0
updated_actions = 0
new_file_content = []
rel_path = str(file_path.relative_to(root_dir))
for line in file_path.read_text().splitlines(keepends=True):
match = action_string_pattern.match(line)
if match:
found_actions += 1
gh_repo = github_client.get_repo(f"{match['org']}/{match['repo']}")
current_tag = match["cur_tag"]
release_tags = [rel.tag_name for rel in gh_repo.get_releases()]
if not release_tags:
print(f"No releases found for '{gh_repo.full_name}' action")
elif current_tag not in release_tags:
print(
f"Ignoring '{gh_repo.full_name}' action in '{rel_path}',"
" since it is not refferenced by the release tag,"
f" but by '{current_tag}'."
)
else:
latest_tag = release_tags[0]
if latest_tag != current_tag:
updated_actions += 1
line = line.replace(current_tag, latest_tag)
new_file_content.append(line)
file_path.write_text("".join(new_file_content))
update_stats[rel_path] = [found_actions, updated_actions]
pr_body_lines = [
"::set-output name=updated_files::",
]
for filename in update_stats:
pr_body_lines.append(
f"{filename}: found {update_stats[filename][0]} "
f"updated {update_stats[filename][1]} actions; "
)
# The output afterwards will be used by GH CI to submit a PR.
print("".join(pr_body_lines))
if __name__ == "__main__":
main()
|
from pipeline import *
def make_index_html(D):# {{{
source_dir = '../../data/interim/htmls/'
for i in tqdm(D.index):
auth = D.loc[i]
papers = get_papers_from_df(auth)
df = gen_spreadsheet(auth, papers)
idx = np.argsort(df.Año.values)
df = df.loc[idx, :]
FP = np.array(auth.filter_papers.reshape([-1])[idx])
if FP.size>0:
S = []
for i, x in enumerate(FP.reshape([-1])):
ck = 'checked' if bool(x) else ''
S.append(f'{s1}{str(i+1).zfill(3)}" value="" {ck}{s2}')
df['include'] = S
else:
df['include'] = []
url = [f'{s3}{r}{s4}{t}{s5}' for r, t in zip(df.adsurl, df.Título)]
df['linkurl'] = url
title_links = df.apply(lambda x: x.linkurl.replace('link', x.Título), axis=1)
if FP.size>0:
df['title_links'] = title_links
else:
df['title_links'] = []
df['counter'] = np.arange(1,df.shape[0]+1)
dfo = df.iloc[:, [9,3,4,8,6,1,2]].copy()
for k in dfo.index:
aut = focus_authors(dfo.Autores[k], auth.auth_pos[k])
dfo.at[k, 'Autores'] = aut
aff = focus_authors(dfo.Afiliaciones[k], auth.auth_pos[k])
dfo.at[k, 'Afiliaciones'] = aff
dfo = dfo.assign(Autores=dfo.Autores.apply(lambda x: '<br>'.join(x)))
dfo = dfo.assign(Afiliaciones=dfo.Afiliaciones.apply(lambda x: '<br>'.join(x)))
N = df.shape[0]
Ni = sum(FP)
#--- template
str_io = StringIO()
dfo.to_html(buf=str_io, index=False, index_names=False, escape=False)
html_str = str_io.getvalue()
#fname = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(' ', '_')}_{auth.nombre[0]}.html')
#fout = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(' ', '_')}_{auth.nombre[0]}.txt')
filename = fnames(auth, source_dir, '.html')
fout = fnames(auth, source_dir, '.txt', False)
target = open(filename, 'w')
target.write(template_page.render(N=N,
Ni=Ni,
html_str=html_str,
auth=auth,
filedata=fout))
target.close()# }}}
def S04_make_pages(D):# {{{
source_dir = '../../models/'
template_file = 'template.html'
templateLoader = jinja2.FileSystemLoader(searchpath=source_dir)
latex_jinja_env = jinja2.Environment(
block_start_string=r"\BLOCK{",
block_end_string='}',
variable_start_string=r'\VAR{',
variable_end_string='}',
comment_start_string=r'\#{',
comment_end_string='}',
line_statement_prefix='%%',
line_comment_prefix='%#',
trim_blocks=True,
autoescape=False,
loader=templateLoader
)
template_page = latex_jinja_env.get_template(template_file)
s1 = '<input type="checkbox" name="check'
s2 = ' /><br>'
s3 = '<a href="'
s4 = '">'
s5 = '</a>'
source_dir = '../../data/interim/htmls/'
for i in tqdm(D.index):
auth = D.loc[i]
papers = get_papers_from_df(auth)
df = gen_spreadsheet(auth, papers)
idx = np.argsort(df.Año.values)
df = df.loc[idx, :]
FP = np.array(auth.filter_papers.reshape([-1])[idx])
if FP.size>0:
S = []
for i, x in enumerate(FP.reshape([-1])):
ck = 'checked' if bool(x) else ''
S.append(f'{s1}{str(i+1).zfill(3)}" value="" {ck}{s2}')
df['include'] = S
else:
df['include'] = []
url = [f'{s3}{r}{s4}{t}{s5}' for r, t in zip(df.adsurl, df.Título)]
df['linkurl'] = url
title_links = df.apply(lambda x: x.linkurl.replace('link', x.Título), axis=1)
if FP.size>0:
df['title_links'] = title_links
else:
df['title_links'] = []
df['counter'] = np.arange(1,df.shape[0]+1)
dfo = df.iloc[:, [9,3,4,8,6,1,2]].copy()
for k in dfo.index:
aut = focus_authors(dfo.Autores[k], auth.auth_pos[k])
dfo.at[k, 'Autores'] = aut
aff = focus_authors(dfo.Afiliaciones[k], auth.auth_pos[k])
dfo.at[k, 'Afiliaciones'] = aff
dfo = dfo.assign(Autores=dfo.Autores.apply(lambda x: '<br>'.join(x)))
dfo = dfo.assign(Afiliaciones=dfo.Afiliaciones.apply(lambda x: '<br>'.join(x)))
N = df.shape[0]
Ni = sum(FP)
#--- template
str_io = StringIO()
dfo.to_html(buf=str_io, index=False, index_names=False, escape=False)
html_str = str_io.getvalue()
#fname = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(' ', '_')}_{auth.nombre[0]}.html')
#fout = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(' ', '_')}_{auth.nombre[0]}.txt')
filename = fnames(auth, source_dir, '.html')
fout = fnames(auth, source_dir, '.txt', False)
target = open(filename, 'w')
target.write(template_page.render(N=N,
Ni=Ni,
html_str=html_str,
auth=auth,
filedata=fout))
target.close()# }}}
# read authors
with open('../../data/redux/astrogen_DB_labelled.pk', 'rb') as f:
D = pickle.load(f)
# select authors
conn = sqlite3.connect('../../data/redux/astrogen_DB_labelled.db')
script = """
select *, COUNT(*) as cc,
MAX(p.year) as ymx,
SUM(CASE WHEN p.inar=1 then 1 else 0 END) as N_inar,
SUM(CASE WHEN p.inar=1 then 1 else 0 END) / (1.*COUNT(*)) as q
FROM papers as p
INNER JOIN people as g
WHERE
p.ID==g.ID
AND
g.yob BETWEEN 1951 AND 2001
AND
p.journal_Q==1
AND
p.author_count<51
GROUP BY p.ID
HAVING
ymx>2016
AND
q>0.75
"""
sql_query = pd.read_sql_query (script, conn)
df = pd.DataFrame(sql_query)
conn.close()
ids = df.ID.values[:,0]
ids = np.random.permutation(ids)
ddf = D.iloc[ids, :]
D = S04_load_check_filters(ddf); ddf = next(D)
S04_make_pages(ddf)
# make index page
source_dir = '../../models/'
target_dir = './'
template_file = 'template_list.html'
templateLoader = jinja2.FileSystemLoader(searchpath=source_dir)
latex_jinja_env = jinja2.Environment(
block_start_string=r"\BLOCK{",
block_end_string='}',
variable_start_string=r'\VAR{',
variable_end_string='}',
comment_start_string=r'\#{',
comment_end_string='}',
line_statement_prefix='%%',
line_comment_prefix='%#',
trim_blocks=True,
autoescape=False,
loader=templateLoader
)
template_page = latex_jinja_env.get_template(template_file)
htmlfilename, authname, authobj = [], [], []
for i in ddf.index:
auth = ddf.loc[i]
filename = fnames(auth, target_dir, '.html')
htmlfilename.append(filename)
authname.append(f'{auth.apellido}, {auth.nombre}')
authobj.append(auth)
lst = zip(htmlfilename, authname, authobj)
filename = '../../data/interim/htmls/index.html'
target = open(filename, 'w')
target.write(template_page.render(lst=lst))
target.close()
| from pipeline import *
def make_index_html(D):# {{{
source_dir = '../../data/interim/htmls/'
for i in tqdm(D.index):
auth = D.loc[i]
papers = get_papers_from_df(auth)
df = gen_spreadsheet(auth, papers)
idx = np.argsort(df.Año.values)
df = df.loc[idx, :]
FP = np.array(auth.filter_papers.reshape([-1])[idx])
if FP.size>0:
S = []
for i, x in enumerate(FP.reshape([-1])):
ck = 'checked' if bool(x) else ''
S.append(f'{s1}{str(i+1).zfill(3)}" value="" {ck}{s2}')
df['include'] = S
else:
df['include'] = []
url = [f'{s3}{r}{s4}{t}{s5}' for r, t in zip(df.adsurl, df.Título)]
df['linkurl'] = url
title_links = df.apply(lambda x: x.linkurl.replace('link', x.Título), axis=1)
if FP.size>0:
df['title_links'] = title_links
else:
df['title_links'] = []
df['counter'] = np.arange(1,df.shape[0]+1)
dfo = df.iloc[:, [9,3,4,8,6,1,2]].copy()
for k in dfo.index:
aut = focus_authors(dfo.Autores[k], auth.auth_pos[k])
dfo.at[k, 'Autores'] = aut
aff = focus_authors(dfo.Afiliaciones[k], auth.auth_pos[k])
dfo.at[k, 'Afiliaciones'] = aff
dfo = dfo.assign(Autores=dfo.Autores.apply(lambda x: '<br>'.join(x)))
dfo = dfo.assign(Afiliaciones=dfo.Afiliaciones.apply(lambda x: '<br>'.join(x)))
N = df.shape[0]
Ni = sum(FP)
#--- template
str_io = StringIO()
dfo.to_html(buf=str_io, index=False, index_names=False, escape=False)
html_str = str_io.getvalue()
#fname = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(" ", "_")}_{auth.nombre[0]}.html')
#fout = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(" ", "_")}_{auth.nombre[0]}.txt')
filename = fnames(auth, source_dir, '.html')
fout = fnames(auth, source_dir, '.txt', False)
target = open(filename, 'w')
target.write(template_page.render(N=N,
Ni=Ni,
html_str=html_str,
auth=auth,
filedata=fout))
target.close()# }}}
def S04_make_pages(D):# {{{
source_dir = '../../models/'
template_file = 'template.html'
templateLoader = jinja2.FileSystemLoader(searchpath=source_dir)
latex_jinja_env = jinja2.Environment(
block_start_string=r"\BLOCK{",
block_end_string='}',
variable_start_string=r'\VAR{',
variable_end_string='}',
comment_start_string=r'\#{',
comment_end_string='}',
line_statement_prefix='%%',
line_comment_prefix='%#',
trim_blocks=True,
autoescape=False,
loader=templateLoader
)
template_page = latex_jinja_env.get_template(template_file)
s1 = '<input type="checkbox" name="check'
s2 = ' /><br>'
s3 = '<a href="'
s4 = '">'
s5 = '</a>'
source_dir = '../../data/interim/htmls/'
for i in tqdm(D.index):
auth = D.loc[i]
papers = get_papers_from_df(auth)
df = gen_spreadsheet(auth, papers)
idx = np.argsort(df.Año.values)
df = df.loc[idx, :]
FP = np.array(auth.filter_papers.reshape([-1])[idx])
if FP.size>0:
S = []
for i, x in enumerate(FP.reshape([-1])):
ck = 'checked' if bool(x) else ''
S.append(f'{s1}{str(i+1).zfill(3)}" value="" {ck}{s2}')
df['include'] = S
else:
df['include'] = []
url = [f'{s3}{r}{s4}{t}{s5}' for r, t in zip(df.adsurl, df.Título)]
df['linkurl'] = url
title_links = df.apply(lambda x: x.linkurl.replace('link', x.Título), axis=1)
if FP.size>0:
df['title_links'] = title_links
else:
df['title_links'] = []
df['counter'] = np.arange(1,df.shape[0]+1)
dfo = df.iloc[:, [9,3,4,8,6,1,2]].copy()
for k in dfo.index:
aut = focus_authors(dfo.Autores[k], auth.auth_pos[k])
dfo.at[k, 'Autores'] = aut
aff = focus_authors(dfo.Afiliaciones[k], auth.auth_pos[k])
dfo.at[k, 'Afiliaciones'] = aff
dfo = dfo.assign(Autores=dfo.Autores.apply(lambda x: '<br>'.join(x)))
dfo = dfo.assign(Afiliaciones=dfo.Afiliaciones.apply(lambda x: '<br>'.join(x)))
N = df.shape[0]
Ni = sum(FP)
#--- template
str_io = StringIO()
dfo.to_html(buf=str_io, index=False, index_names=False, escape=False)
html_str = str_io.getvalue()
#fname = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(" ", "_")}_{auth.nombre[0]}.html')
#fout = (f'{str(i).zfill(3)}_'
# f'{auth.apellido.replace(" ", "_")}_{auth.nombre[0]}.txt')
filename = fnames(auth, source_dir, '.html')
fout = fnames(auth, source_dir, '.txt', False)
target = open(filename, 'w')
target.write(template_page.render(N=N,
Ni=Ni,
html_str=html_str,
auth=auth,
filedata=fout))
target.close()# }}}
# read authors
with open('../../data/redux/astrogen_DB_labelled.pk', 'rb') as f:
D = pickle.load(f)
# select authors
conn = sqlite3.connect('../../data/redux/astrogen_DB_labelled.db')
script = """
select *, COUNT(*) as cc,
MAX(p.year) as ymx,
SUM(CASE WHEN p.inar=1 then 1 else 0 END) as N_inar,
SUM(CASE WHEN p.inar=1 then 1 else 0 END) / (1.*COUNT(*)) as q
FROM papers as p
INNER JOIN people as g
WHERE
p.ID==g.ID
AND
g.yob BETWEEN 1951 AND 2001
AND
p.journal_Q==1
AND
p.author_count<51
GROUP BY p.ID
HAVING
ymx>2016
AND
q>0.75
"""
sql_query = pd.read_sql_query (script, conn)
df = pd.DataFrame(sql_query)
conn.close()
ids = df.ID.values[:,0]
ids = np.random.permutation(ids)
ddf = D.iloc[ids, :]
D = S04_load_check_filters(ddf); ddf = next(D)
S04_make_pages(ddf)
# make index page
source_dir = '../../models/'
target_dir = './'
template_file = 'template_list.html'
templateLoader = jinja2.FileSystemLoader(searchpath=source_dir)
latex_jinja_env = jinja2.Environment(
block_start_string=r"\BLOCK{",
block_end_string='}',
variable_start_string=r'\VAR{',
variable_end_string='}',
comment_start_string=r'\#{',
comment_end_string='}',
line_statement_prefix='%%',
line_comment_prefix='%#',
trim_blocks=True,
autoescape=False,
loader=templateLoader
)
template_page = latex_jinja_env.get_template(template_file)
htmlfilename, authname, authobj = [], [], []
for i in ddf.index:
auth = ddf.loc[i]
filename = fnames(auth, target_dir, '.html')
htmlfilename.append(filename)
authname.append(f'{auth.apellido}, {auth.nombre}')
authobj.append(auth)
lst = zip(htmlfilename, authname, authobj)
filename = '../../data/interim/htmls/index.html'
target = open(filename, 'w')
target.write(template_page.render(lst=lst))
target.close()
|
# SPDX-License-Identifier: MIT
import itertools, fnmatch
from construct import *
from .utils import AddrLookup, FourCC, SafeGreedyRange
__all__ = ["load_adt"]
ADTPropertyStruct = Struct(
"name" / PaddedString(32, "ascii"),
"size" / Int32ul,
"value" / Bytes(this.size & 0x7fffffff)
)
ADTNodeStruct = Struct(
"property_count" / Int32ul,
"child_count" / Int32ul,
"properties" / Array(this.property_count, Aligned(4, ADTPropertyStruct)),
"children" / Array(this.child_count, LazyBound(lambda: ADTNodeStruct))
)
ADTStringList = SafeGreedyRange(CString("ascii"))
ADT2Tuple = Array(2, Hex(Int64ul))
ADT3Tuple = Array(3, Hex(Int64ul))
Function = Struct(
"phandle" / Int32ul,
"name" / FourCC,
"args" / SafeGreedyRange(Int32ul),
)
STD_PROPERTIES = {
"cpu-impl-reg": ADT2Tuple,
"name": CString("ascii"),
"compatible": ADTStringList,
"model": CString("ascii"),
"#size-cells": Int32ul,
"#address-cells": Int32ul,
"clock-ids": SafeGreedyRange(Int32ul),
"clock-gates": SafeGreedyRange(Int32ul),
"power-gates": SafeGreedyRange(Int32ul),
}
PMAPIORanges = SafeGreedyRange(Struct(
"addr" / Hex(Int64ul),
"size" / Hex(Int64ul),
"flags" / Hex(Int32ul),
"name" / FourCC,
))
PMGRPSRegs = SafeGreedyRange(Struct(
"reg" / Int32ul,
"offset" / Hex(Int32ul),
"mask" / Hex(Int32ul),
))
PMGRPWRGateRegs = SafeGreedyRange(Struct(
"reg" / Int32ul,
"offset" / Hex(Int32ul),
"mask" / Hex(Int32ul),
"unk" / Hex(Int32ul),
))
PMGRDevices = SafeGreedyRange(Struct(
"flags" / Int8ul,
"unk1_0" / Int8ul,
"unk1_1" / Int8ul,
"unk1_2" / Int8ul,
"parents" / Array(2, Int16ul),
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"psidx" / Int8ul,
"psreg" / Int8ul,
"unk2_0" / Int16ul,
"pd" / Int8ul,
"ps_cfg16" / Int8ul,
Const(0, Int32ul),
Const(0, Int32ul),
"unk2_3" / Int16ul,
"id" / Int16ul,
"unk3" / Int32ul,
"name" / PaddedString(16, "ascii")
))
PMGRClocks = SafeGreedyRange(Struct(
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"unk" / Int8ul,
"id" / Int8ul,
Const(0, Int32ul),
"name" / PaddedString(16, "ascii"),
))
PMGRPowerDomains = SafeGreedyRange(Struct(
"unk" / Const(0, Int8ul),
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"id" / Int8ul,
Const(0, Int32ul),
"name" / PaddedString(16, "ascii"),
))
PMGRDeviceBridges = SafeGreedyRange(Struct(
"idx" / Int32ub,
"subdevs" / HexDump(Bytes(0x48)),
))
PMGREvents = SafeGreedyRange(Struct(
"unk1" / Int8ul,
"unk2" / Int8ul,
"unk3" / Int8ul,
"id" / Int8ul,
"ctl2_idx" / Int8ul,
"ctl2_block" / Int8ul,
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"name" / PaddedString(16, "ascii"),
))
DEV_PROPERTIES = {
"pmgr": {
"*": {
"clusters": SafeGreedyRange(Int32ul),
"devices": PMGRDevices,
"ps-regs": PMGRPSRegs,
"pwrgate-regs": PMGRPWRGateRegs,
"power-domains": PMGRPowerDomains,
"clocks": PMGRClocks,
"device-bridges": PMGRDeviceBridges,
"voltage-states*": SafeGreedyRange(Int32ul),
"events": PMGREvents,
}
},
"clpc": {
"*": {
"events": SafeGreedyRange(Int32ul),
"devices": SafeGreedyRange(Int32ul),
}
},
"soc-tuner": {
"*": {
"device-set-*": SafeGreedyRange(Int32ul),
"mcc-configs": SafeGreedyRange(Int32ul),
}
},
"mcc": {
"*": {
"dramcfg-data": SafeGreedyRange(Int32ul),
"config-data": SafeGreedyRange(Int32ul),
}
},
"stockholm-spmi": {
"*": {
"required-functions": ADTStringList,
},
},
"arm-io": {
"*": {
"clock-frequencies": SafeGreedyRange(Int32ul),
"clock-frequencies-regs": SafeGreedyRange(Hex(Int64ul)),
"clock-frequencies-nclk": SafeGreedyRange(Int32ul),
},
},
"defaults": {
"*": {
"pmap-io-ranges": PMAPIORanges,
}
}
}
def parse_prop(node, path, node_name, name, v, is_template=False):
t = None
if is_template:
t = CString("ascii")
dev_props = DEV_PROPERTIES.get(path, DEV_PROPERTIES.get(node_name, None))
possible_match = False
if dev_props:
for compat_match, cprops in dev_props.items():
for k, pt in cprops.items():
if fnmatch.fnmatch(name, k):
possible_match = True
break
if possible_match:
try:
compat = node.compatible[0]
except AttributeError:
compat = ""
for compat_match, cprops in dev_props.items():
if fnmatch.fnmatch(compat, compat_match):
for k, pt in cprops.items():
if fnmatch.fnmatch(name, k):
t = pt
break
else:
continue
break
if v == b'' or v is None:
return None, None
if name.startswith("function-"):
if len(v) == 4:
t = FourCC
else:
t = Function
if name == "reg" and path != "/device-tree/memory":
n = node._parent
while n is not None and n._parent is not None:
if "ranges" not in n._properties:
break
n = n._parent
else:
ac, sc = node._parent.address_cells, node._parent.size_cells
at = Hex(Int64ul) if ac == 2 else Array(ac, Hex(Int32ul))
st = Hex(Int64ul) if sc == 2 else Array(sc, Hex(Int32ul))
t = SafeGreedyRange(Struct("addr" / at, "size" / st))
if len(v) % ((ac + sc) * 4):
t = None
elif name == "ranges":
try:
ac, sc = node.address_cells, node.size_cells
except AttributeError:
return None, v
pac, _ = node._parent.address_cells, node._parent.size_cells
at = Hex(Int64ul) if ac == 2 else Array(ac, Hex(Int32ul))
pat = Hex(Int64ul) if pac == 2 else Array(pac, Hex(Int32ul))
st = Hex(Int64ul) if sc == 2 else Array(sc, Hex(Int32ul))
t = SafeGreedyRange(Struct("bus_addr" / at, "parent_addr" / pat, "size" / st))
elif name == "interrupts":
# parse "interrupts" as Array of Int32ul, wrong for nodes whose
# "interrupt-parent" has "interrupt-cells" = 2
# parsing this correctly would require a second pass
t = Array(len(v) // 4, Int32ul)
if t is not None:
v = Sequence(t, Terminated).parse(v)[0]
return t, v
if name in STD_PROPERTIES:
t = STD_PROPERTIES[name]
elif v and v[-1] == 0 and all(0x20 <= i <= 0x7e for i in v[:-1]):
t = CString("ascii")
elif len(v) == 4:
t = Int32ul
elif len(v) == 8:
t = Int64ul
elif len(v) == 16 and all(v[i] == 0 for i in (6, 7, 14, 15)):
t = ADT2Tuple
if t is not None:
try:
v = Sequence(t, Terminated).parse(v)[0]
except:
print("Failed to parse:", path, name, v.hex())
raise
return t, v
def build_prop(path, name, v, t=None):
if v is None:
return b''
if t is not None:
return t.build(v)
if isinstance(v, bytes):
return v
if name in STD_PROPERTIES:
t = STD_PROPERTIES[name]
elif isinstance(v, str):
t = CString("ascii")
elif isinstance(v, int):
t = Int32ul
elif isinstance(v, tuple) and all(isinstance(i, int) for i in v):
t = Array(len(v), Int32ul)
return t.build(v)
class ADTNode:
def __init__(self, val=None, path="/", parent=None):
self._children = []
self._properties = {}
self._types = {}
self._parent_path = path
self._parent = parent
if val is not None:
for p in val.properties:
if p.name == "name":
_name = p.value.decode("ascii").rstrip("\0")
break
else:
raise ValueError(f"Node in {path} has no name!")
path = self._parent_path + _name
for p in val.properties:
is_template = bool(p.size & 0x80000000)
try:
t, v = parse_prop(self, path, _name, p.name, p.value, is_template)
self._types[p.name] = t, is_template
self._properties[p.name] = v
except Exception as e:
print(f"Exception parsing {path}.{p.name} value {p.value.hex()}:", file=sys.stderr)
raise
# Second pass
for k, (t, is_template) in self._types.items():
if t is None:
t, v = parse_prop(self, path, _name, k, self._properties[k], is_template)
self._types[k] = t, is_template
self._properties[k] = v
for c in val.children:
node = ADTNode(c, f"{self._path}/", parent=self)
self._children.append(node)
@property
def _path(self):
return self._parent_path + self.name
def __getitem__(self, item):
if isinstance(item, str):
while item.startswith("/"):
item = item[1:]
if "/" in item:
a, b = item.split("/", 1)
return self[a][b]
for i in self._children:
if i.name == item:
return i
raise KeyError(f"Child node '{item}' not found")
return self._children[item]
def __setitem__(self, item, value):
if isinstance(item, str):
while item.startswith("/"):
item = item[1:]
if "/" in item:
a, b = item.split("/", 1)
self[a][b] = value
return
for i, c in enumerate(self._children):
if c.name == item:
self._children[i] = value
break
else:
self._children.append(value)
else:
self._children[item] = value
def __delitem__(self, item):
if isinstance(item, str):
while item.startswith("/"):
item = item[1:]
if "/" in item:
a, b = item.split("/", 1)
del self[a][b]
return
for i, c in enumerate(self._children):
if c.name == item:
del self._children[i]
return
raise KeyError(f"Child node '{item}' not found")
del self._children[item]
def __getattr__(self, attr):
attr = attr.replace("_", "-")
if attr in self._properties:
return self._properties[attr]
raise AttributeError(attr)
def __setattr__(self, attr, value):
if attr[0] == "_":
self.__dict__[attr] = value
return
attr = attr.replace("_", "-")
self._properties[attr] = value
def __delattr__(self, attr):
if attr[0] == "_":
del self.__dict__[attr]
return
del self._properties[attr]
@property
def address_cells(self):
try:
return self._properties["#address-cells"]
except KeyError:
raise AttributeError("#address-cells")
@property
def size_cells(self):
try:
return self._properties["#size-cells"]
except KeyError:
raise AttributeError("#size-cells")
@property
def interrupt_cells(self):
try:
return self._properties["#interrupt-cells"]
except KeyError:
raise AttributeError("#interrupt-cells")
def _fmt_prop(self, k, v):
t, is_template = self._types[k]
if is_template:
return f"<< {v} >>"
elif isinstance(v, ListContainer):
return f"[{", ".join(self._fmt_prop(k, i) for i in v)}]"
elif isinstance(v, bytes):
if all(i == 0 for i in v):
return f"zeroes({len(v):#x})"
else:
return v.hex()
elif k.startswith("function-"):
if isinstance(v, str):
return f"{v}()"
elif v is None:
return f"None"
else:
args = []
for arg in v.args:
b = arg.to_bytes(4, "big")
is_ascii = all(0x20 <= c <= 0x7e for c in b)
args.append(f"{arg:#x}" if not is_ascii else f""{b.decode("ascii")}'")
return f"{v.phandle}:{v.name}({", ".join(args)})"
name.startswith("function-")
else:
return str(v)
def __str__(self, t=""):
return "\n".join([
t + f"{self.name} {{",
*(t + f" {k} = {self._fmt_prop(k, v)}" for k, v in self._properties.items() if k != "name"),
"",
*(i.__str__(t + " ") for i in self._children),
t + "}"
])
def __repr__(self):
return f"<ADTNode {self.name}>"
def __iter__(self):
return iter(self._children)
def get_reg(self, idx):
reg = self.reg[idx]
addr = reg.addr
size = reg.size
node = self._parent
while node is not None:
if "ranges" not in node._properties:
break
for r in node.ranges:
if r.bus_addr <= addr < (r.bus_addr + r.size):
addr = addr - r.bus_addr + r.parent_addr
break
node = node._parent
return addr, size
def tostruct(self):
properties = []
for k,v in itertools.chain(self._properties.items()):
t, is_template = self._types.get(k, (None, False))
value = build_prop(self._path, k, v, t=t)
properties.append({
"name": k,
"size": len(value) | (0x80000000 if is_template else 0),
"value": value
})
data = {
"property_count": len(self._properties),
"child_count": len(self._children),
"properties": properties,
"children": [c.tostruct() for c in self._children]
}
return data
def build(self):
return ADTNodeStruct.build(self.tostruct())
def walk_tree(self):
yield self
for child in self:
yield from child
def build_addr_lookup(self):
lookup = AddrLookup()
for node in self.walk_tree():
reg = getattr(node, 'reg', None)
if not isinstance(reg, list):
continue
for index in range(len(reg)):
try:
addr, size = node.get_reg(index)
except AttributeError:
continue
if size == 0:
continue
lookup.add(range(addr, addr + size), node.name + f"[{index}]")
return lookup
def load_adt(data):
return ADTNode(ADTNodeStruct.parse(data))
if __name__ == "__main__":
import sys, argparse, pathlib
parser = argparse.ArgumentParser(description='ADT test for m1n1')
parser.add_argument('input', type=pathlib.Path)
parser.add_argument('output', nargs='?', type=pathlib.Path)
parser.add_argument('-r', '--retrieve', help='retrieve and store the adt from m1n1', action='store_true')
parser.add_argument('-a', '--dump-addr', help='dump address lookup table', action='store_true')
args = parser.parse_args()
if args.retrieve:
if args.input.exists():
print('Error "{}" exists!'.format(args.input))
sys.exit()
from .setup import *
adt_data = u.get_adt()
args.input.write_bytes(adt_data)
else:
adt_data = args.input.read_bytes()
adt = load_adt(adt_data)
print(adt)
new_data = adt.build()
if args.output is not None:
args.output.write_bytes(new_data)
assert new_data == adt_data[:len(new_data)]
assert adt_data[len(new_data):] == bytes(len(adt_data) - len(new_data))
if args.dump_addr:
print("Address lookup table:")
print(adt.build_addr_lookup())
| # SPDX-License-Identifier: MIT
import itertools, fnmatch
from construct import *
from .utils import AddrLookup, FourCC, SafeGreedyRange
__all__ = ["load_adt"]
ADTPropertyStruct = Struct(
"name" / PaddedString(32, "ascii"),
"size" / Int32ul,
"value" / Bytes(this.size & 0x7fffffff)
)
ADTNodeStruct = Struct(
"property_count" / Int32ul,
"child_count" / Int32ul,
"properties" / Array(this.property_count, Aligned(4, ADTPropertyStruct)),
"children" / Array(this.child_count, LazyBound(lambda: ADTNodeStruct))
)
ADTStringList = SafeGreedyRange(CString("ascii"))
ADT2Tuple = Array(2, Hex(Int64ul))
ADT3Tuple = Array(3, Hex(Int64ul))
Function = Struct(
"phandle" / Int32ul,
"name" / FourCC,
"args" / SafeGreedyRange(Int32ul),
)
STD_PROPERTIES = {
"cpu-impl-reg": ADT2Tuple,
"name": CString("ascii"),
"compatible": ADTStringList,
"model": CString("ascii"),
"#size-cells": Int32ul,
"#address-cells": Int32ul,
"clock-ids": SafeGreedyRange(Int32ul),
"clock-gates": SafeGreedyRange(Int32ul),
"power-gates": SafeGreedyRange(Int32ul),
}
PMAPIORanges = SafeGreedyRange(Struct(
"addr" / Hex(Int64ul),
"size" / Hex(Int64ul),
"flags" / Hex(Int32ul),
"name" / FourCC,
))
PMGRPSRegs = SafeGreedyRange(Struct(
"reg" / Int32ul,
"offset" / Hex(Int32ul),
"mask" / Hex(Int32ul),
))
PMGRPWRGateRegs = SafeGreedyRange(Struct(
"reg" / Int32ul,
"offset" / Hex(Int32ul),
"mask" / Hex(Int32ul),
"unk" / Hex(Int32ul),
))
PMGRDevices = SafeGreedyRange(Struct(
"flags" / Int8ul,
"unk1_0" / Int8ul,
"unk1_1" / Int8ul,
"unk1_2" / Int8ul,
"parents" / Array(2, Int16ul),
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"psidx" / Int8ul,
"psreg" / Int8ul,
"unk2_0" / Int16ul,
"pd" / Int8ul,
"ps_cfg16" / Int8ul,
Const(0, Int32ul),
Const(0, Int32ul),
"unk2_3" / Int16ul,
"id" / Int16ul,
"unk3" / Int32ul,
"name" / PaddedString(16, "ascii")
))
PMGRClocks = SafeGreedyRange(Struct(
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"unk" / Int8ul,
"id" / Int8ul,
Const(0, Int32ul),
"name" / PaddedString(16, "ascii"),
))
PMGRPowerDomains = SafeGreedyRange(Struct(
"unk" / Const(0, Int8ul),
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"id" / Int8ul,
Const(0, Int32ul),
"name" / PaddedString(16, "ascii"),
))
PMGRDeviceBridges = SafeGreedyRange(Struct(
"idx" / Int32ub,
"subdevs" / HexDump(Bytes(0x48)),
))
PMGREvents = SafeGreedyRange(Struct(
"unk1" / Int8ul,
"unk2" / Int8ul,
"unk3" / Int8ul,
"id" / Int8ul,
"ctl2_idx" / Int8ul,
"ctl2_block" / Int8ul,
"ctl_idx" / Int8ul,
"ctl_block" / Int8ul,
"name" / PaddedString(16, "ascii"),
))
DEV_PROPERTIES = {
"pmgr": {
"*": {
"clusters": SafeGreedyRange(Int32ul),
"devices": PMGRDevices,
"ps-regs": PMGRPSRegs,
"pwrgate-regs": PMGRPWRGateRegs,
"power-domains": PMGRPowerDomains,
"clocks": PMGRClocks,
"device-bridges": PMGRDeviceBridges,
"voltage-states*": SafeGreedyRange(Int32ul),
"events": PMGREvents,
}
},
"clpc": {
"*": {
"events": SafeGreedyRange(Int32ul),
"devices": SafeGreedyRange(Int32ul),
}
},
"soc-tuner": {
"*": {
"device-set-*": SafeGreedyRange(Int32ul),
"mcc-configs": SafeGreedyRange(Int32ul),
}
},
"mcc": {
"*": {
"dramcfg-data": SafeGreedyRange(Int32ul),
"config-data": SafeGreedyRange(Int32ul),
}
},
"stockholm-spmi": {
"*": {
"required-functions": ADTStringList,
},
},
"arm-io": {
"*": {
"clock-frequencies": SafeGreedyRange(Int32ul),
"clock-frequencies-regs": SafeGreedyRange(Hex(Int64ul)),
"clock-frequencies-nclk": SafeGreedyRange(Int32ul),
},
},
"defaults": {
"*": {
"pmap-io-ranges": PMAPIORanges,
}
}
}
def parse_prop(node, path, node_name, name, v, is_template=False):
t = None
if is_template:
t = CString("ascii")
dev_props = DEV_PROPERTIES.get(path, DEV_PROPERTIES.get(node_name, None))
possible_match = False
if dev_props:
for compat_match, cprops in dev_props.items():
for k, pt in cprops.items():
if fnmatch.fnmatch(name, k):
possible_match = True
break
if possible_match:
try:
compat = node.compatible[0]
except AttributeError:
compat = ""
for compat_match, cprops in dev_props.items():
if fnmatch.fnmatch(compat, compat_match):
for k, pt in cprops.items():
if fnmatch.fnmatch(name, k):
t = pt
break
else:
continue
break
if v == b'' or v is None:
return None, None
if name.startswith("function-"):
if len(v) == 4:
t = FourCC
else:
t = Function
if name == "reg" and path != "/device-tree/memory":
n = node._parent
while n is not None and n._parent is not None:
if "ranges" not in n._properties:
break
n = n._parent
else:
ac, sc = node._parent.address_cells, node._parent.size_cells
at = Hex(Int64ul) if ac == 2 else Array(ac, Hex(Int32ul))
st = Hex(Int64ul) if sc == 2 else Array(sc, Hex(Int32ul))
t = SafeGreedyRange(Struct("addr" / at, "size" / st))
if len(v) % ((ac + sc) * 4):
t = None
elif name == "ranges":
try:
ac, sc = node.address_cells, node.size_cells
except AttributeError:
return None, v
pac, _ = node._parent.address_cells, node._parent.size_cells
at = Hex(Int64ul) if ac == 2 else Array(ac, Hex(Int32ul))
pat = Hex(Int64ul) if pac == 2 else Array(pac, Hex(Int32ul))
st = Hex(Int64ul) if sc == 2 else Array(sc, Hex(Int32ul))
t = SafeGreedyRange(Struct("bus_addr" / at, "parent_addr" / pat, "size" / st))
elif name == "interrupts":
# parse "interrupts" as Array of Int32ul, wrong for nodes whose
# "interrupt-parent" has "interrupt-cells" = 2
# parsing this correctly would require a second pass
t = Array(len(v) // 4, Int32ul)
if t is not None:
v = Sequence(t, Terminated).parse(v)[0]
return t, v
if name in STD_PROPERTIES:
t = STD_PROPERTIES[name]
elif v and v[-1] == 0 and all(0x20 <= i <= 0x7e for i in v[:-1]):
t = CString("ascii")
elif len(v) == 4:
t = Int32ul
elif len(v) == 8:
t = Int64ul
elif len(v) == 16 and all(v[i] == 0 for i in (6, 7, 14, 15)):
t = ADT2Tuple
if t is not None:
try:
v = Sequence(t, Terminated).parse(v)[0]
except:
print("Failed to parse:", path, name, v.hex())
raise
return t, v
def build_prop(path, name, v, t=None):
if v is None:
return b''
if t is not None:
return t.build(v)
if isinstance(v, bytes):
return v
if name in STD_PROPERTIES:
t = STD_PROPERTIES[name]
elif isinstance(v, str):
t = CString("ascii")
elif isinstance(v, int):
t = Int32ul
elif isinstance(v, tuple) and all(isinstance(i, int) for i in v):
t = Array(len(v), Int32ul)
return t.build(v)
class ADTNode:
def __init__(self, val=None, path="/", parent=None):
self._children = []
self._properties = {}
self._types = {}
self._parent_path = path
self._parent = parent
if val is not None:
for p in val.properties:
if p.name == "name":
_name = p.value.decode("ascii").rstrip("\0")
break
else:
raise ValueError(f"Node in {path} has no name!")
path = self._parent_path + _name
for p in val.properties:
is_template = bool(p.size & 0x80000000)
try:
t, v = parse_prop(self, path, _name, p.name, p.value, is_template)
self._types[p.name] = t, is_template
self._properties[p.name] = v
except Exception as e:
print(f"Exception parsing {path}.{p.name} value {p.value.hex()}:", file=sys.stderr)
raise
# Second pass
for k, (t, is_template) in self._types.items():
if t is None:
t, v = parse_prop(self, path, _name, k, self._properties[k], is_template)
self._types[k] = t, is_template
self._properties[k] = v
for c in val.children:
node = ADTNode(c, f"{self._path}/", parent=self)
self._children.append(node)
@property
def _path(self):
return self._parent_path + self.name
def __getitem__(self, item):
if isinstance(item, str):
while item.startswith("/"):
item = item[1:]
if "/" in item:
a, b = item.split("/", 1)
return self[a][b]
for i in self._children:
if i.name == item:
return i
raise KeyError(f"Child node '{item}' not found")
return self._children[item]
def __setitem__(self, item, value):
if isinstance(item, str):
while item.startswith("/"):
item = item[1:]
if "/" in item:
a, b = item.split("/", 1)
self[a][b] = value
return
for i, c in enumerate(self._children):
if c.name == item:
self._children[i] = value
break
else:
self._children.append(value)
else:
self._children[item] = value
def __delitem__(self, item):
if isinstance(item, str):
while item.startswith("/"):
item = item[1:]
if "/" in item:
a, b = item.split("/", 1)
del self[a][b]
return
for i, c in enumerate(self._children):
if c.name == item:
del self._children[i]
return
raise KeyError(f"Child node '{item}' not found")
del self._children[item]
def __getattr__(self, attr):
attr = attr.replace("_", "-")
if attr in self._properties:
return self._properties[attr]
raise AttributeError(attr)
def __setattr__(self, attr, value):
if attr[0] == "_":
self.__dict__[attr] = value
return
attr = attr.replace("_", "-")
self._properties[attr] = value
def __delattr__(self, attr):
if attr[0] == "_":
del self.__dict__[attr]
return
del self._properties[attr]
@property
def address_cells(self):
try:
return self._properties["#address-cells"]
except KeyError:
raise AttributeError("#address-cells")
@property
def size_cells(self):
try:
return self._properties["#size-cells"]
except KeyError:
raise AttributeError("#size-cells")
@property
def interrupt_cells(self):
try:
return self._properties["#interrupt-cells"]
except KeyError:
raise AttributeError("#interrupt-cells")
def _fmt_prop(self, k, v):
t, is_template = self._types[k]
if is_template:
return f"<< {v} >>"
elif isinstance(v, ListContainer):
return f"[{', '.join(self._fmt_prop(k, i) for i in v)}]"
elif isinstance(v, bytes):
if all(i == 0 for i in v):
return f"zeroes({len(v):#x})"
else:
return v.hex()
elif k.startswith("function-"):
if isinstance(v, str):
return f"{v}()"
elif v is None:
return f"None"
else:
args = []
for arg in v.args:
b = arg.to_bytes(4, "big")
is_ascii = all(0x20 <= c <= 0x7e for c in b)
args.append(f"{arg:#x}" if not is_ascii else f"'{b.decode('ascii')}'")
return f"{v.phandle}:{v.name}({', '.join(args)})"
name.startswith("function-")
else:
return str(v)
def __str__(self, t=""):
return "\n".join([
t + f"{self.name} {{",
*(t + f" {k} = {self._fmt_prop(k, v)}" for k, v in self._properties.items() if k != "name"),
"",
*(i.__str__(t + " ") for i in self._children),
t + "}"
])
def __repr__(self):
return f"<ADTNode {self.name}>"
def __iter__(self):
return iter(self._children)
def get_reg(self, idx):
reg = self.reg[idx]
addr = reg.addr
size = reg.size
node = self._parent
while node is not None:
if "ranges" not in node._properties:
break
for r in node.ranges:
if r.bus_addr <= addr < (r.bus_addr + r.size):
addr = addr - r.bus_addr + r.parent_addr
break
node = node._parent
return addr, size
def tostruct(self):
properties = []
for k,v in itertools.chain(self._properties.items()):
t, is_template = self._types.get(k, (None, False))
value = build_prop(self._path, k, v, t=t)
properties.append({
"name": k,
"size": len(value) | (0x80000000 if is_template else 0),
"value": value
})
data = {
"property_count": len(self._properties),
"child_count": len(self._children),
"properties": properties,
"children": [c.tostruct() for c in self._children]
}
return data
def build(self):
return ADTNodeStruct.build(self.tostruct())
def walk_tree(self):
yield self
for child in self:
yield from child
def build_addr_lookup(self):
lookup = AddrLookup()
for node in self.walk_tree():
reg = getattr(node, 'reg', None)
if not isinstance(reg, list):
continue
for index in range(len(reg)):
try:
addr, size = node.get_reg(index)
except AttributeError:
continue
if size == 0:
continue
lookup.add(range(addr, addr + size), node.name + f"[{index}]")
return lookup
def load_adt(data):
return ADTNode(ADTNodeStruct.parse(data))
if __name__ == "__main__":
import sys, argparse, pathlib
parser = argparse.ArgumentParser(description='ADT test for m1n1')
parser.add_argument('input', type=pathlib.Path)
parser.add_argument('output', nargs='?', type=pathlib.Path)
parser.add_argument('-r', '--retrieve', help='retrieve and store the adt from m1n1', action='store_true')
parser.add_argument('-a', '--dump-addr', help='dump address lookup table', action='store_true')
args = parser.parse_args()
if args.retrieve:
if args.input.exists():
print('Error "{}" exists!'.format(args.input))
sys.exit()
from .setup import *
adt_data = u.get_adt()
args.input.write_bytes(adt_data)
else:
adt_data = args.input.read_bytes()
adt = load_adt(adt_data)
print(adt)
new_data = adt.build()
if args.output is not None:
args.output.write_bytes(new_data)
assert new_data == adt_data[:len(new_data)]
assert adt_data[len(new_data):] == bytes(len(adt_data) - len(new_data))
if args.dump_addr:
print("Address lookup table:")
print(adt.build_addr_lookup())
|
#Import Libraries
#Web Scraping tools
from bs4 import BeautifulSoup as bs
from selenium import webdriver
#from splinter import Browser
#DataFrame tools
import pandas as pd
#Misc tools for web scraping
import time
import requests
#Function to initianilze browser.
def init_browser():
#Settings for headless mode.
options = webdriver.ChromeOptions()
options.add_argument('headless')
#Splinter option - using absolute path
#executable_path = {'executable_path': '/Users/Sebastian/Documents/GitHub/Data Visualization Bootcamp/Sebastian Homework/Web-Scraping-Challenge/chromedriver'}
#browser = Browser('chrome', **executable_path, headless = True)
#path to the driver and load the options.
browser = webdriver.Chrome("/Users/Sebastian/Documents/GitHub/Data Visualization Bootcamp/Sebastian Homework/Web-Scraping-Challenge/chromedriver",chrome_options = options)
#returns the brower.
return browser
def scrapper():
#Call browser function
browser = init_browser()
#Dictionary to store all the results.
marsInfo_dict = {}
#Code to get NASA Mars News ----------------------------------------------------------------------------------------------
try:
url = "https://mars.nasa.gov/news/?page=0&per_page=40&order=publish_date+desc%2Ccreated_at+desc&search=&year=2020%3Apublish_date&category=19%2C165%2C184%2C204&blank_scope=Latest"
#splinter option - open url
#browser.visit(url)
#Open url.
browser.get(url)
#Time to let the website load all the elements
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
#Collect the latest news title
news_title = soup.find_all('li', class_="slide")[0].find(class_="content_title").text
news_p = soup.find_all('li', class_="slide")[0].text
marsInfo_dict['news_title'] = news_title
marsInfo_dict['news_p'] = news_p
except :
print(f"Problem at website {url}")
#Code to get JPL Mars Space Images - Featured Image ---------------------------------------------------------------------------------
try:
url = "https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars"
#splinter option - open url
#browser.visit(url)
#Opens the url.
browser.get(url)
#splinter option - FULL IMAGE BUTTON
#browser.click_link_by_id("full_image")
#Interact with the FULL IMAGE BUTTON
browser.find_element_by_id("full_image").click()
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
featured_image_url = "https://www.jpl.nasa.gov/" + soup.find_all('img', class_="fancybox-image")[0]['src']
marsInfo_dict['featured_image_url'] = featured_image_url
except :
print(f"Problem at website {url}")
#Mars Weather ------------------------------------------------------------------------------------------------------------------------
try:
url = "https://twitter.com/marswxreport?lang=en"
#splinter option - open url
#browser.visit(url)
#Open the url.
browser.get(url)
#Time to let the website load all the elements
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
mars_weather = soup.find_all('article', class_="css-1dbjc4n r-1loqt21 r-18u37iz r-1ny4l3l r-o7ynqc r-6416eg")[0].text.strip().replace('Mars Weather@MarsWxReport·19hInSight ','')
marsInfo_dict['mars_weather'] = mars_weather
except :
print(mars_weather)
print(f"Problem at website {url}")
# Mars Facts--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
try:
url = 'http://space-facts.com/mars/'
#Load url to pandas read html.
tables = pd.read_html(url)
#Tables
marsFacts_df = tables[0]
earthMars_df = tables[1]
#Rename columns
marsFacts_df.columns = ['Facts', 'Values']
#Outpout
html_outputFacts = marsFacts_df.to_html(index = False)
html_outputFacts = html_outputFacts.replace('\n', '')
html_outputMarsEarth = earthMars_df.to_html(index = False)
html_outputMarsEarth = html_outputMarsEarth.replace('\n', '')
marsInfo_dict['html_outputFacts'] = html_outputFacts
marsInfo_dict['html_outputMarsEarth'] = html_outputMarsEarth
except :
print(f"Problem at website {url}")
#hemisphereImages ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
try:
temp_list = []
url = "https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars"
#splinter option - open url
#browser.visit(url)
#Opens the url.
browser.get(url)
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
# close web browser
browser.close()
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
links = soup.find_all('div', class_="description")
for link in links:
highDef_url = f"https://astrogeology.usgs.gov{link.find("a")["href"]}"
responseHighDef = requests.get(highDef_url)
soupHighDef = bs(responseHighDef.text, 'html.parser')
highDef_url = soupHighDef.find_all("div", class_="downloads")[0].find('a')['href']
title = link.find('h3').text
temp_list.append({"title" : title, "img_url" : highDef_url})
marsInfo_dict['hemisphere_image_urls'] = temp_list
except :
print(f"Problem at website {url}")
return marsInfo_dict | #Import Libraries
#Web Scraping tools
from bs4 import BeautifulSoup as bs
from selenium import webdriver
#from splinter import Browser
#DataFrame tools
import pandas as pd
#Misc tools for web scraping
import time
import requests
#Function to initianilze browser.
def init_browser():
#Settings for headless mode.
options = webdriver.ChromeOptions()
options.add_argument('headless')
#Splinter option - using absolute path
#executable_path = {'executable_path': '/Users/Sebastian/Documents/GitHub/Data Visualization Bootcamp/Sebastian Homework/Web-Scraping-Challenge/chromedriver'}
#browser = Browser('chrome', **executable_path, headless = True)
#path to the driver and load the options.
browser = webdriver.Chrome("/Users/Sebastian/Documents/GitHub/Data Visualization Bootcamp/Sebastian Homework/Web-Scraping-Challenge/chromedriver",chrome_options = options)
#returns the brower.
return browser
def scrapper():
#Call browser function
browser = init_browser()
#Dictionary to store all the results.
marsInfo_dict = {}
#Code to get NASA Mars News ----------------------------------------------------------------------------------------------
try:
url = "https://mars.nasa.gov/news/?page=0&per_page=40&order=publish_date+desc%2Ccreated_at+desc&search=&year=2020%3Apublish_date&category=19%2C165%2C184%2C204&blank_scope=Latest"
#splinter option - open url
#browser.visit(url)
#Open url.
browser.get(url)
#Time to let the website load all the elements
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
#Collect the latest news title
news_title = soup.find_all('li', class_="slide")[0].find(class_="content_title").text
news_p = soup.find_all('li', class_="slide")[0].text
marsInfo_dict['news_title'] = news_title
marsInfo_dict['news_p'] = news_p
except :
print(f"Problem at website {url}")
#Code to get JPL Mars Space Images - Featured Image ---------------------------------------------------------------------------------
try:
url = "https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars"
#splinter option - open url
#browser.visit(url)
#Opens the url.
browser.get(url)
#splinter option - FULL IMAGE BUTTON
#browser.click_link_by_id("full_image")
#Interact with the FULL IMAGE BUTTON
browser.find_element_by_id("full_image").click()
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
featured_image_url = "https://www.jpl.nasa.gov/" + soup.find_all('img', class_="fancybox-image")[0]['src']
marsInfo_dict['featured_image_url'] = featured_image_url
except :
print(f"Problem at website {url}")
#Mars Weather ------------------------------------------------------------------------------------------------------------------------
try:
url = "https://twitter.com/marswxreport?lang=en"
#splinter option - open url
#browser.visit(url)
#Open the url.
browser.get(url)
#Time to let the website load all the elements
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
mars_weather = soup.find_all('article', class_="css-1dbjc4n r-1loqt21 r-18u37iz r-1ny4l3l r-o7ynqc r-6416eg")[0].text.strip().replace('Mars Weather@MarsWxReport·19hInSight ','')
marsInfo_dict['mars_weather'] = mars_weather
except :
print(mars_weather)
print(f"Problem at website {url}")
# Mars Facts--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
try:
url = 'http://space-facts.com/mars/'
#Load url to pandas read html.
tables = pd.read_html(url)
#Tables
marsFacts_df = tables[0]
earthMars_df = tables[1]
#Rename columns
marsFacts_df.columns = ['Facts', 'Values']
#Outpout
html_outputFacts = marsFacts_df.to_html(index = False)
html_outputFacts = html_outputFacts.replace('\n', '')
html_outputMarsEarth = earthMars_df.to_html(index = False)
html_outputMarsEarth = html_outputMarsEarth.replace('\n', '')
marsInfo_dict['html_outputFacts'] = html_outputFacts
marsInfo_dict['html_outputMarsEarth'] = html_outputMarsEarth
except :
print(f"Problem at website {url}")
#hemisphereImages ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
try:
temp_list = []
url = "https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars"
#splinter option - open url
#browser.visit(url)
#Opens the url.
browser.get(url)
time.sleep(4)
#splinter option - save HTML
#html = browser.html
#save the html source.
html = browser.page_source
# close web browser
browser.close()
#Use bs4 to parse the html response.
soup = bs(html, "html.parser")
links = soup.find_all('div', class_="description")
for link in links:
highDef_url = f"https://astrogeology.usgs.gov{link.find('a')['href']}"
responseHighDef = requests.get(highDef_url)
soupHighDef = bs(responseHighDef.text, 'html.parser')
highDef_url = soupHighDef.find_all("div", class_="downloads")[0].find('a')['href']
title = link.find('h3').text
temp_list.append({"title" : title, "img_url" : highDef_url})
marsInfo_dict['hemisphere_image_urls'] = temp_list
except :
print(f"Problem at website {url}")
return marsInfo_dict |
# Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.c (the "License");
# you may not use this file except in compliance with the License.
#
# TheCyberUserBot - Luciferxz
import io
import re
import userbot.modules.sql_helper.blacklist_sql as sql
from userbot import CMD_HELP
from userbot.events import register
from requests import get
from userbot.cmdhelp import CmdHelp
# ██████ LANGUAGE CONSTANTS ██████ #
from userbot.language import get_value
LANG = get_value("blacklist")
# ████████████████████████████████ #
KUFURLER = get('https://raw.githubusercontent.com/FaridDadashzade/deploy/main/forbidden.json').json()
@register(incoming=True, disable_edited=True, disable_errors=True)
async def on_new_message(event):
name = event.raw_text
snips = sql.get_chat_blacklist(event.chat_id)
for snip in snips:
if snip == "küfür":
for kufur in KUFURLER:
pattern = r"( |^|[^\w])" + re.escape(kufur) + r"( |$|[^\w])"
if re.search(pattern, name, flags=re.IGNORECASE):
try:
await event.delete()
except:
await event.reply(LANG['FORBIDDEN_KUFUR'])
sql.rm_from_blacklist(event.chat_id, "kufur")
break
pass
continue
else:
pattern = r"( |^|[^\w])" + re.escape(snip) + r"( |$|[^\w])"
if re.search(pattern, name, flags=re.IGNORECASE):
try:
await event.delete()
except Exception as e:
await event.reply(LANG['HAVENT_PERMISSION'])
sql.rm_from_blacklist(event.chat_id, snip.lower())
break
pass
@register(outgoing=True, pattern="^.küfür ?(.*)")
@register(outgoing=True, pattern="^.otoblist ?(.*)")
@register(outgoing=True, pattern="^.s[oö]y[üu]ş ?(.*)")
async def kufur(event):
kufur = event.pattern_match.group(1)
if len(kufur) < 1:
await event.edit(LANG['USAGE_KUFUR'])
if kufur == "aç":
sql.add_to_blacklist(event.chat_id, "küfür")
await event.edit(LANG['OPENED_KUFUR'])
elif kufur == "bağla":
if sql.rm_from_blacklist(event.chat_id, "küfür"):
await event.edit(LANG['CLOSED_KUFUR'])
else:
await event.edit(LANG['ALREADY_CLOSED_KUFUR'])
@register(outgoing=True, pattern="^.addblacklist(?: |$)(.*)")
async def on_add_black_list(addbl):
if addbl.is_reply:
reply = await addbl.get_reply_message()
text = reply.text
else:
text = addbl.pattern_match.group(1)
to_blacklist = text.split()
for trigger in to_blacklist:
sql.add_to_blacklist(addbl.chat_id, trigger)
await addbl.edit("{} **{}**".format(len(to_blacklist), LANG['ADDED']))
@register(outgoing=True, pattern="^.listblacklist(?: |$)(.*)")
async def on_view_blacklist(listbl):
all_blacklisted = sql.get_chat_blacklist(listbl.chat_id)
OUT_STR = f"**{LANG["BLACKLIST"]}**\n"
if len(all_blacklisted) > 0:
for trigger in all_blacklisted:
OUT_STR += f"`{trigger}`\n"
else:
OUT_STR = LANG['NOT_FOUND']
if len(OUT_STR) > 4096:
with io.BytesIO(str.encode(OUT_STR)) as out_file:
out_file.name = "blacklist.text"
await listbl.client.send_file(
listbl.chat_id,
out_file,
force_document=True,
allow_cache=False,
caption=LANG['BLACKLIST_FILE'],
reply_to=listbl
)
await listbl.delete()
else:
await listbl.edit(OUT_STR)
@register(outgoing=True, pattern="^.rmblacklist(?: |$)(.*)")
async def on_delete_blacklist(rmbl):
text = rmbl.pattern_match.group(1)
to_unblacklist = list(set(trigger.strip() for trigger in text.split("\n") if trigger.strip()))
successful = 0
for trigger in to_unblacklist:
if sql.rm_from_blacklist(rmbl.chat_id, trigger.lower()):
successful += 1
await rmbl.edit(LANG['REMOVED'])
CmdHelp('blacklist').add_command(
'listblacklist', None, 'Bir söhbətdəki aktiv blacklist-i göstərər.'
).add_command(
'addblacklist', '<söz(lər)/cavab>', 'Mesajı \'qara list bölməsinə\' qeyd edər. \'Kara liste anahtar kelimesinden\' bahsedildiğinde bot iletiyi siler.', '.addblacklist amk'
).add_command(
'rmblacklist', '<söz>', 'Belirtilen kara listeyi durdurur.', '.rmblacklist amk'
).add_command(
'söyüş', '<aç/bağla>', 'Oto Blacklisti açar grupda söyüş söyən olsa silər', '.söyüş aç'
).add_warning('Bu işlemleri gerçekleştirmek için yönetici olmalı ve **Mesaj Silme** yetkiniz olmalı.').add()
| # Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.c (the "License");
# you may not use this file except in compliance with the License.
#
# TheCyberUserBot - Luciferxz
import io
import re
import userbot.modules.sql_helper.blacklist_sql as sql
from userbot import CMD_HELP
from userbot.events import register
from requests import get
from userbot.cmdhelp import CmdHelp
# ██████ LANGUAGE CONSTANTS ██████ #
from userbot.language import get_value
LANG = get_value("blacklist")
# ████████████████████████████████ #
KUFURLER = get('https://raw.githubusercontent.com/FaridDadashzade/deploy/main/forbidden.json').json()
@register(incoming=True, disable_edited=True, disable_errors=True)
async def on_new_message(event):
name = event.raw_text
snips = sql.get_chat_blacklist(event.chat_id)
for snip in snips:
if snip == "küfür":
for kufur in KUFURLER:
pattern = r"( |^|[^\w])" + re.escape(kufur) + r"( |$|[^\w])"
if re.search(pattern, name, flags=re.IGNORECASE):
try:
await event.delete()
except:
await event.reply(LANG['FORBIDDEN_KUFUR'])
sql.rm_from_blacklist(event.chat_id, "kufur")
break
pass
continue
else:
pattern = r"( |^|[^\w])" + re.escape(snip) + r"( |$|[^\w])"
if re.search(pattern, name, flags=re.IGNORECASE):
try:
await event.delete()
except Exception as e:
await event.reply(LANG['HAVENT_PERMISSION'])
sql.rm_from_blacklist(event.chat_id, snip.lower())
break
pass
@register(outgoing=True, pattern="^.küfür ?(.*)")
@register(outgoing=True, pattern="^.otoblist ?(.*)")
@register(outgoing=True, pattern="^.s[oö]y[üu]ş ?(.*)")
async def kufur(event):
kufur = event.pattern_match.group(1)
if len(kufur) < 1:
await event.edit(LANG['USAGE_KUFUR'])
if kufur == "aç":
sql.add_to_blacklist(event.chat_id, "küfür")
await event.edit(LANG['OPENED_KUFUR'])
elif kufur == "bağla":
if sql.rm_from_blacklist(event.chat_id, "küfür"):
await event.edit(LANG['CLOSED_KUFUR'])
else:
await event.edit(LANG['ALREADY_CLOSED_KUFUR'])
@register(outgoing=True, pattern="^.addblacklist(?: |$)(.*)")
async def on_add_black_list(addbl):
if addbl.is_reply:
reply = await addbl.get_reply_message()
text = reply.text
else:
text = addbl.pattern_match.group(1)
to_blacklist = text.split()
for trigger in to_blacklist:
sql.add_to_blacklist(addbl.chat_id, trigger)
await addbl.edit("{} **{}**".format(len(to_blacklist), LANG['ADDED']))
@register(outgoing=True, pattern="^.listblacklist(?: |$)(.*)")
async def on_view_blacklist(listbl):
all_blacklisted = sql.get_chat_blacklist(listbl.chat_id)
OUT_STR = f"**{LANG['BLACKLIST']}**\n"
if len(all_blacklisted) > 0:
for trigger in all_blacklisted:
OUT_STR += f"`{trigger}`\n"
else:
OUT_STR = LANG['NOT_FOUND']
if len(OUT_STR) > 4096:
with io.BytesIO(str.encode(OUT_STR)) as out_file:
out_file.name = "blacklist.text"
await listbl.client.send_file(
listbl.chat_id,
out_file,
force_document=True,
allow_cache=False,
caption=LANG['BLACKLIST_FILE'],
reply_to=listbl
)
await listbl.delete()
else:
await listbl.edit(OUT_STR)
@register(outgoing=True, pattern="^.rmblacklist(?: |$)(.*)")
async def on_delete_blacklist(rmbl):
text = rmbl.pattern_match.group(1)
to_unblacklist = list(set(trigger.strip() for trigger in text.split("\n") if trigger.strip()))
successful = 0
for trigger in to_unblacklist:
if sql.rm_from_blacklist(rmbl.chat_id, trigger.lower()):
successful += 1
await rmbl.edit(LANG['REMOVED'])
CmdHelp('blacklist').add_command(
'listblacklist', None, 'Bir söhbətdəki aktiv blacklist-i göstərər.'
).add_command(
'addblacklist', '<söz(lər)/cavab>', 'Mesajı \'qara list bölməsinə\' qeyd edər. \'Kara liste anahtar kelimesinden\' bahsedildiğinde bot iletiyi siler.', '.addblacklist amk'
).add_command(
'rmblacklist', '<söz>', 'Belirtilen kara listeyi durdurur.', '.rmblacklist amk'
).add_command(
'söyüş', '<aç/bağla>', 'Oto Blacklisti açar grupda söyüş söyən olsa silər', '.söyüş aç'
).add_warning('Bu işlemleri gerçekleştirmek için yönetici olmalı ve **Mesaj Silme** yetkiniz olmalı.').add()
|
import typing as t
from abc import ABC, abstractmethod
from types import SimpleNamespace
from .exceptions import BadMethod, FinalizationError, NoMethod, NotFound
from .line import Line
from .patterns import REGEX_TYPES
from .route import Route
from .tree import Tree
from .utils import parts_to_path, path_to_parts
# The below functions might be called by the compiled source code, and
# therefore should be made available here by import
import re # noqa isort:skip
from datetime import datetime # noqa isort:skip
from urllib.parse import unquote # noqa isort:skip
from uuid import UUID # noqa isort:skip
from .patterns import parse_date # noqa isort:skip
class BaseRouter(ABC):
DEFAULT_METHOD = "BASE"
ALLOWED_METHODS: t.Tuple[str, ...] = tuple()
def __init__(
self,
delimiter: str = "/",
exception: t.Type[NotFound] = NotFound,
method_handler_exception: t.Type[NoMethod] = NoMethod,
route_class: t.Type[Route] = Route,
stacking: bool = False,
cascade_not_found: bool = False,
) -> None:
self._find_route = None
self._matchers = None
self.static_routes: t.Dict[t.Tuple[str, ...], Route] = {}
self.dynamic_routes: t.Dict[t.Tuple[str, ...], Route] = {}
self.regex_routes: t.Dict[t.Tuple[str, ...], Route] = {}
self.name_index: t.Dict[str, Route] = {}
self.delimiter = delimiter
self.exception = exception
self.method_handler_exception = method_handler_exception
self.route_class = route_class
self.tree = Tree()
self.finalized = False
self.stacking = stacking
self.ctx = SimpleNamespace()
self.cascade_not_found = cascade_not_found
@abstractmethod
def get(self, **kwargs):
...
def resolve(
self,
path: str,
*,
method: t.Optional[str] = None,
orig: t.Optional[str] = None,
extra: t.Optional[t.Dict[str, str]] = None,
):
try:
route, param_basket = self.find_route(
path, self, {"__handler_idx__": 0, "__params__": {}}, extra
)
except NotFound as e:
if path.endswith(self.delimiter):
return self.resolve(
path=path[:-1],
method=method,
orig=path,
extra=extra,
)
raise self.exception(str(e), path=path)
handler = None
handler_idx = param_basket.pop("__handler_idx__")
raw_path = param_basket.pop("__raw_path__")
params = param_basket.pop("__params__")
if route.strict and orig and orig[-1] != route.path[-1]:
raise self.exception("Path not found", path=path)
handler = route.get_handler(raw_path, method, handler_idx)
return route, handler, params
def add(
self,
path: str,
handler: t.Callable,
methods: t.Optional[t.Union[t.Iterable[str], str]] = None,
name: t.Optional[str] = None,
requirements: t.Optional[t.Dict[str, t.Any]] = None,
strict: bool = False,
unquote: bool = False, # noqa
overwrite: bool = False,
) -> Route:
if not methods:
methods = [self.DEFAULT_METHOD]
if hasattr(methods, "__iter__") and not isinstance(methods, frozenset):
methods = frozenset(methods)
elif isinstance(methods, str):
methods = frozenset([methods])
if self.ALLOWED_METHODS and any(
method not in self.ALLOWED_METHODS for method in methods
):
bad = [
method
for method in methods
if method not in self.ALLOWED_METHODS
]
raise BadMethod(
f"Bad method: {bad}. Must be one of: {self.ALLOWED_METHODS}"
)
if self.finalized:
raise FinalizationError("Cannot finalize router more than once.")
static = "<" not in path and requirements is None
regex = self._is_regex(path)
if regex:
routes = self.regex_routes
elif static:
routes = self.static_routes
else:
routes = self.dynamic_routes
# Only URL encode the static parts of the path
path = parts_to_path(
path_to_parts(path, self.delimiter), self.delimiter
)
strip = path.lstrip if strict else path.strip
path = strip(self.delimiter)
route = self.route_class(
self,
path,
name or "",
strict=strict,
unquote=unquote,
static=static,
regex=regex,
)
# Catch the scenario where a route is overloaded with and
# and without requirements, first as dynamic then as static
if static and route.parts in self.dynamic_routes:
routes = self.dynamic_routes
# Catch the reverse scenario where a route is overload first as static
# and then as dynamic
if not static and route.parts in self.static_routes:
route = self.static_routes.pop(route.parts)
self.dynamic_routes[route.parts] = route
else:
if route.parts in routes:
route = routes[route.parts]
else:
routes[route.parts] = route
if name:
self.name_index[name] = route
for method in methods:
route.add_handler(path, handler, method, requirements, overwrite)
return route
def finalize(self, do_compile: bool = True):
if self.finalized:
raise FinalizationError("Cannot finalize router more than once.")
if not self.routes:
raise FinalizationError("Cannot finalize with no routes defined.")
self.finalized = True
for route in self.routes.values():
route.finalize()
self._generate_tree()
self._render(do_compile)
def reset(self):
self.finalized = False
self.tree = Tree()
self._find_route = None
for route in self.routes.values():
route.reset()
def _generate_tree(self) -> None:
self.tree.generate(self.dynamic_routes)
self.tree.finalize()
def _render(self, do_compile: bool = True) -> None:
src = [
Line("def find_route(path, router, basket, extra):", 0),
Line("parts = tuple(path[1:].split(router.delimiter))", 1),
]
delayed = []
if self.static_routes:
# TODO:
# - future improvement would be to decide which option to use
# at runtime based upon the makeup of the router since this
# potentially has an impact on performance
src += [
Line("try:", 1),
Line("route = router.static_routes[parts]", 2),
Line("basket['__raw_path__'] = path", 2),
Line("return route, basket", 2),
Line("except KeyError:", 1),
Line("pass", 2),
]
# src += [
# Line("if parts in router.static_routes:", 1),
# Line("route = router.static_routes[parts]", 2),
# Line("basket['__raw_path__'] = route.path", 2),
# Line("return route, basket", 2),
# ]
# src += [
# Line("if path in router.static_routes:", 1),
# Line("route = router.static_routes.get(path)", 2),
# Line("basket['__raw_path__'] = route.path", 2),
# Line("return route, basket", 2),
# ]
if self.dynamic_routes:
src += [Line("num = len(parts)", 1)]
src += self.tree.render()
if self.regex_routes:
routes = sorted(
self.regex_routes.values(),
key=lambda route: len(route.parts),
reverse=True,
)
delayed.append(Line("matchers = [", 0))
for idx, route in enumerate(routes):
delayed.append(Line(f"re.compile(r'^{route.pattern}$'),", 1))
src.extend(
[
Line(f"match = router.matchers[{idx}].match(path)", 1),
Line("if match:", 1),
Line("basket['__params__'] = match.groupdict()", 2),
Line(f"basket['__raw_path__'] = '{route.path}'", 2),
Line(
(
f"return router.name_index['{route.name}'], "
"basket"
),
2,
),
]
)
delayed.append(Line("]", 0))
src.append(Line("raise NotFound", 1))
src.extend(delayed)
self.optimize(src)
self.find_route_src = "".join(
map(str, filter(lambda x: x.render, src))
)
if do_compile:
try:
compiled_src = compile(
self.find_route_src,
"",
"exec",
)
except SyntaxError as se:
syntax_error = (
f"Line {se.lineno}: {se.msg}\n{se.text}"
f"{" "*max(0,int(se.offset or 0)-1) + "^"}"
)
raise FinalizationError(
f"Cannot compile route AST:\n{self.find_route_src}"
f"\n{syntax_error}"
)
ctx: t.Dict[t.Any, t.Any] = {}
exec(compiled_src, None, ctx)
self._find_route = ctx["find_route"]
self._matchers = ctx.get("matchers")
@property
def find_route(self):
return self._find_route
@property
def matchers(self):
return self._matchers
@property
def routes(self):
return {
**self.static_routes,
**self.dynamic_routes,
**self.regex_routes,
}
def optimize(self, src: t.List[Line]) -> None:
"""
Insert NotFound exceptions to be able to bail as quick as possible,
and realign lines to proper indentation
"""
offset = 0
current = 0
insert_at = set()
for num, line in enumerate(src):
if line.indent < current:
if not line.src.startswith("."):
if offset < 0:
offset += 1
else:
offset = 0
if (
line.src.startswith("if")
or line.src.startswith("elif")
or line.src.startswith("return")
or line.src.startswith("basket")
or line.src.startswith("try")
):
idnt = line.indent + 1
prev_line = src[num - 1]
while idnt < prev_line.indent:
insert_at.add((num, idnt))
idnt += 1
offset += line.offset
line.indent += offset
current = line.indent
idnt = 1
prev_line = src[-1]
while idnt < prev_line.indent:
insert_at.add((len(src), idnt))
idnt += 1
if self.cascade_not_found:
for num, indent in sorted(
insert_at, key=lambda x: (x[0] * -1, x[1])
):
src.insert(num, Line("raise NotFound", indent))
def _is_regex(self, path: str):
parts = path_to_parts(path, self.delimiter)
def requires(part):
if not part.startswith("<") or ":" not in part:
return False
_, pattern_type = part[1:-1].split(":", 1)
return (
part.endswith(":path>")
or self.delimiter in part
or pattern_type not in REGEX_TYPES
)
return any(requires(part) for part in parts)
| import typing as t
from abc import ABC, abstractmethod
from types import SimpleNamespace
from .exceptions import BadMethod, FinalizationError, NoMethod, NotFound
from .line import Line
from .patterns import REGEX_TYPES
from .route import Route
from .tree import Tree
from .utils import parts_to_path, path_to_parts
# The below functions might be called by the compiled source code, and
# therefore should be made available here by import
import re # noqa isort:skip
from datetime import datetime # noqa isort:skip
from urllib.parse import unquote # noqa isort:skip
from uuid import UUID # noqa isort:skip
from .patterns import parse_date # noqa isort:skip
class BaseRouter(ABC):
DEFAULT_METHOD = "BASE"
ALLOWED_METHODS: t.Tuple[str, ...] = tuple()
def __init__(
self,
delimiter: str = "/",
exception: t.Type[NotFound] = NotFound,
method_handler_exception: t.Type[NoMethod] = NoMethod,
route_class: t.Type[Route] = Route,
stacking: bool = False,
cascade_not_found: bool = False,
) -> None:
self._find_route = None
self._matchers = None
self.static_routes: t.Dict[t.Tuple[str, ...], Route] = {}
self.dynamic_routes: t.Dict[t.Tuple[str, ...], Route] = {}
self.regex_routes: t.Dict[t.Tuple[str, ...], Route] = {}
self.name_index: t.Dict[str, Route] = {}
self.delimiter = delimiter
self.exception = exception
self.method_handler_exception = method_handler_exception
self.route_class = route_class
self.tree = Tree()
self.finalized = False
self.stacking = stacking
self.ctx = SimpleNamespace()
self.cascade_not_found = cascade_not_found
@abstractmethod
def get(self, **kwargs):
...
def resolve(
self,
path: str,
*,
method: t.Optional[str] = None,
orig: t.Optional[str] = None,
extra: t.Optional[t.Dict[str, str]] = None,
):
try:
route, param_basket = self.find_route(
path, self, {"__handler_idx__": 0, "__params__": {}}, extra
)
except NotFound as e:
if path.endswith(self.delimiter):
return self.resolve(
path=path[:-1],
method=method,
orig=path,
extra=extra,
)
raise self.exception(str(e), path=path)
handler = None
handler_idx = param_basket.pop("__handler_idx__")
raw_path = param_basket.pop("__raw_path__")
params = param_basket.pop("__params__")
if route.strict and orig and orig[-1] != route.path[-1]:
raise self.exception("Path not found", path=path)
handler = route.get_handler(raw_path, method, handler_idx)
return route, handler, params
def add(
self,
path: str,
handler: t.Callable,
methods: t.Optional[t.Union[t.Iterable[str], str]] = None,
name: t.Optional[str] = None,
requirements: t.Optional[t.Dict[str, t.Any]] = None,
strict: bool = False,
unquote: bool = False, # noqa
overwrite: bool = False,
) -> Route:
if not methods:
methods = [self.DEFAULT_METHOD]
if hasattr(methods, "__iter__") and not isinstance(methods, frozenset):
methods = frozenset(methods)
elif isinstance(methods, str):
methods = frozenset([methods])
if self.ALLOWED_METHODS and any(
method not in self.ALLOWED_METHODS for method in methods
):
bad = [
method
for method in methods
if method not in self.ALLOWED_METHODS
]
raise BadMethod(
f"Bad method: {bad}. Must be one of: {self.ALLOWED_METHODS}"
)
if self.finalized:
raise FinalizationError("Cannot finalize router more than once.")
static = "<" not in path and requirements is None
regex = self._is_regex(path)
if regex:
routes = self.regex_routes
elif static:
routes = self.static_routes
else:
routes = self.dynamic_routes
# Only URL encode the static parts of the path
path = parts_to_path(
path_to_parts(path, self.delimiter), self.delimiter
)
strip = path.lstrip if strict else path.strip
path = strip(self.delimiter)
route = self.route_class(
self,
path,
name or "",
strict=strict,
unquote=unquote,
static=static,
regex=regex,
)
# Catch the scenario where a route is overloaded with and
# and without requirements, first as dynamic then as static
if static and route.parts in self.dynamic_routes:
routes = self.dynamic_routes
# Catch the reverse scenario where a route is overload first as static
# and then as dynamic
if not static and route.parts in self.static_routes:
route = self.static_routes.pop(route.parts)
self.dynamic_routes[route.parts] = route
else:
if route.parts in routes:
route = routes[route.parts]
else:
routes[route.parts] = route
if name:
self.name_index[name] = route
for method in methods:
route.add_handler(path, handler, method, requirements, overwrite)
return route
def finalize(self, do_compile: bool = True):
if self.finalized:
raise FinalizationError("Cannot finalize router more than once.")
if not self.routes:
raise FinalizationError("Cannot finalize with no routes defined.")
self.finalized = True
for route in self.routes.values():
route.finalize()
self._generate_tree()
self._render(do_compile)
def reset(self):
self.finalized = False
self.tree = Tree()
self._find_route = None
for route in self.routes.values():
route.reset()
def _generate_tree(self) -> None:
self.tree.generate(self.dynamic_routes)
self.tree.finalize()
def _render(self, do_compile: bool = True) -> None:
src = [
Line("def find_route(path, router, basket, extra):", 0),
Line("parts = tuple(path[1:].split(router.delimiter))", 1),
]
delayed = []
if self.static_routes:
# TODO:
# - future improvement would be to decide which option to use
# at runtime based upon the makeup of the router since this
# potentially has an impact on performance
src += [
Line("try:", 1),
Line("route = router.static_routes[parts]", 2),
Line("basket['__raw_path__'] = path", 2),
Line("return route, basket", 2),
Line("except KeyError:", 1),
Line("pass", 2),
]
# src += [
# Line("if parts in router.static_routes:", 1),
# Line("route = router.static_routes[parts]", 2),
# Line("basket['__raw_path__'] = route.path", 2),
# Line("return route, basket", 2),
# ]
# src += [
# Line("if path in router.static_routes:", 1),
# Line("route = router.static_routes.get(path)", 2),
# Line("basket['__raw_path__'] = route.path", 2),
# Line("return route, basket", 2),
# ]
if self.dynamic_routes:
src += [Line("num = len(parts)", 1)]
src += self.tree.render()
if self.regex_routes:
routes = sorted(
self.regex_routes.values(),
key=lambda route: len(route.parts),
reverse=True,
)
delayed.append(Line("matchers = [", 0))
for idx, route in enumerate(routes):
delayed.append(Line(f"re.compile(r'^{route.pattern}$'),", 1))
src.extend(
[
Line(f"match = router.matchers[{idx}].match(path)", 1),
Line("if match:", 1),
Line("basket['__params__'] = match.groupdict()", 2),
Line(f"basket['__raw_path__'] = '{route.path}'", 2),
Line(
(
f"return router.name_index['{route.name}'], "
"basket"
),
2,
),
]
)
delayed.append(Line("]", 0))
src.append(Line("raise NotFound", 1))
src.extend(delayed)
self.optimize(src)
self.find_route_src = "".join(
map(str, filter(lambda x: x.render, src))
)
if do_compile:
try:
compiled_src = compile(
self.find_route_src,
"",
"exec",
)
except SyntaxError as se:
syntax_error = (
f"Line {se.lineno}: {se.msg}\n{se.text}"
f"{' '*max(0,int(se.offset or 0)-1) + '^'}"
)
raise FinalizationError(
f"Cannot compile route AST:\n{self.find_route_src}"
f"\n{syntax_error}"
)
ctx: t.Dict[t.Any, t.Any] = {}
exec(compiled_src, None, ctx)
self._find_route = ctx["find_route"]
self._matchers = ctx.get("matchers")
@property
def find_route(self):
return self._find_route
@property
def matchers(self):
return self._matchers
@property
def routes(self):
return {
**self.static_routes,
**self.dynamic_routes,
**self.regex_routes,
}
def optimize(self, src: t.List[Line]) -> None:
"""
Insert NotFound exceptions to be able to bail as quick as possible,
and realign lines to proper indentation
"""
offset = 0
current = 0
insert_at = set()
for num, line in enumerate(src):
if line.indent < current:
if not line.src.startswith("."):
if offset < 0:
offset += 1
else:
offset = 0
if (
line.src.startswith("if")
or line.src.startswith("elif")
or line.src.startswith("return")
or line.src.startswith("basket")
or line.src.startswith("try")
):
idnt = line.indent + 1
prev_line = src[num - 1]
while idnt < prev_line.indent:
insert_at.add((num, idnt))
idnt += 1
offset += line.offset
line.indent += offset
current = line.indent
idnt = 1
prev_line = src[-1]
while idnt < prev_line.indent:
insert_at.add((len(src), idnt))
idnt += 1
if self.cascade_not_found:
for num, indent in sorted(
insert_at, key=lambda x: (x[0] * -1, x[1])
):
src.insert(num, Line("raise NotFound", indent))
def _is_regex(self, path: str):
parts = path_to_parts(path, self.delimiter)
def requires(part):
if not part.startswith("<") or ":" not in part:
return False
_, pattern_type = part[1:-1].split(":", 1)
return (
part.endswith(":path>")
or self.delimiter in part
or pattern_type not in REGEX_TYPES
)
return any(requires(part) for part in parts)
|
# Copyright (c) 2021 Cybereason Inc
# This code is licensed under MIT license (see LICENSE.md for details)
import json
import requests
import oci
import io
import base64
import logging
import hashlib
from fdk import response
from cybereason_connection import get_cybereason_connection
from cybereason_machine_suspicions import is_suspicious
from cybereason_isolate_machine import isolate_machine
from oci_utils import *
from constants import *
def get_private_ips(machine_type, signer, compartment_id, resource_id):
# Getting the instances private IPs
private_ips = []
if machine_type == MT_INSTANCE:
private_ips = get_instance_private_ips(signer,compartment_id,resource_id)
elif machine_type == MT_DATABASE:
db_system_id = get_db_system_from_database(signer, resource_id)
private_ips = get_database_ip(signer, compartment_id, db_system_id)
elif machine_type == MT_DATABASE_SYSTEM:
private_ips = get_database_ip(signer, compartment_id,resource_id)
if not private_ips:
print("No IPs found for resource: " + resource_id, flush=True)
private_ips = []
return private_ips
def send_notification(signer, ctx, body, remediated):
try:
ctx_data = dict(ctx.Config())
topic_id = ctx_data.get('ONS_TOPIC_OCID')
if not topic_id:
print('WARNING: No ONS topic OCID provided. Cannot publish message to topic.', flush=True)
return
cloud_guard_problem = body["data"]["resourceName"]
if remediated:
message_title = "Cloud Guard Problem " + cloud_guard_problem + " Isolated by Cybereason"
message_body = f'Cloud Guard has detected problem with {body['data']['resourceName'].lower()}. Cybereason also detected suspicious activity and has isolated the machine to remediate the Cloud Guard Problem.'
message_body = message_body + f'\nFor more information go to the OCI Cloud Guard Console and look for {cloud_guard_problem}'
else:
message_title = "Cloud Guard Finding " + cloud_guard_problem + " Suspicious Activity Detected by Cybereason"
message_body = f'Cloud Guard has detected a {body['data']['resourceName'].lower()} and Cybereason also detected suspicious activity.'
message_body = message_body + f'\nFor more information go to the OCI Cloud Guard Console and look for {cloud_guard_problem}'
notification_message = {"default": "Cloud Guard Finding", "body": message_body, "title": message_title}
ons = oci.ons.NotificationDataPlaneClient(config={}, signer=signer)
ons.publish_message(topic_id, notification_message)
except (Exception) as ex:
print('ERROR: Failed to publish message to topic', ex, flush=True)
raise
def machine_event_handler(ctx, handler_options, data: io.BytesIO=None):
print("Cloud Guard Response I support", flush=True)
signer = get_signer()
number_of_findings = 0 # The number of findings from the Cybereason console
private_ips = [] # Private IPs associate with the OCI Instance
body = get_request_body(data)
# Getting Instance's IP to query Cybereason
resource_id = body["data"]["additionalDetails"]["resourceId"]
compartment_id = body["data"]["compartmentId"]
private_ips = get_private_ips(handler_options['machine_type'], signer, compartment_id, resource_id)
try:
ctx_data = dict(ctx.Config())
server = ctx_data['CYBEREASON_SERVER']
port = ctx_data['CYBEREASON_PORT']
username = ctx_data['CYBEREASON_USERNAME']
password = get_password_from_secrets(signer, ctx_data['CYBEREASON_SECRET_OCID'])
cr_isolate_machine = ctx_data.get('ISOLATE_MACHINE', 'False').lower()
send_notifications = ctx_data.get('SEND_NOTIFICATIONS', 'False').lower()
except Exception as ex:
print("ERROR: Failed to retrieve function configuration data", ex, flush=True)
raise
# We can have multiple IP addresses. Loop through each one of them.
cr_connection = get_cybereason_connection(server, port, username, password)
for ipaddr in private_ips:
number_of_findings = is_suspicious(cr_connection, ipaddr)
if number_of_findings > 0:
# We have some suspicious findings from Cybereason
print('Cybereason found {number_of_findings} issues in the machine specified by ip address {ip_address}'.format(ip_address=ipaddr, number_of_findings=number_of_findings), flush=True)
if handler_options['isolate_machine'] and (cr_isolate_machine == 'true'):
isolate_machine(cr_connection, ipaddr)
cloud_guard_remediate_problem(signer, body["data"]["resourceId"])
# Determining if what notification to send to the user
if handler_options['send_notifications'] and (send_notifications == 'true') and handler_options['isolate_machine'] and (cr_isolate_machine == 'true'):
send_notification(signer, ctx, body, True)
if handler_options['send_notifications'] and (send_notifications == 'true'):
send_notification(signer, ctx, body, False)
return response.Response(
ctx,
response_data={"Private_IPs" : private_ips, "Number_of_findings" : number_of_findings},
headers={"Content-Type": "application/json"}
)
| # Copyright (c) 2021 Cybereason Inc
# This code is licensed under MIT license (see LICENSE.md for details)
import json
import requests
import oci
import io
import base64
import logging
import hashlib
from fdk import response
from cybereason_connection import get_cybereason_connection
from cybereason_machine_suspicions import is_suspicious
from cybereason_isolate_machine import isolate_machine
from oci_utils import *
from constants import *
def get_private_ips(machine_type, signer, compartment_id, resource_id):
# Getting the instances private IPs
private_ips = []
if machine_type == MT_INSTANCE:
private_ips = get_instance_private_ips(signer,compartment_id,resource_id)
elif machine_type == MT_DATABASE:
db_system_id = get_db_system_from_database(signer, resource_id)
private_ips = get_database_ip(signer, compartment_id, db_system_id)
elif machine_type == MT_DATABASE_SYSTEM:
private_ips = get_database_ip(signer, compartment_id,resource_id)
if not private_ips:
print("No IPs found for resource: " + resource_id, flush=True)
private_ips = []
return private_ips
def send_notification(signer, ctx, body, remediated):
try:
ctx_data = dict(ctx.Config())
topic_id = ctx_data.get('ONS_TOPIC_OCID')
if not topic_id:
print('WARNING: No ONS topic OCID provided. Cannot publish message to topic.', flush=True)
return
cloud_guard_problem = body["data"]["resourceName"]
if remediated:
message_title = "Cloud Guard Problem " + cloud_guard_problem + " Isolated by Cybereason"
message_body = f'Cloud Guard has detected problem with {body["data"]["resourceName"].lower()}. Cybereason also detected suspicious activity and has isolated the machine to remediate the Cloud Guard Problem.'
message_body = message_body + f'\nFor more information go to the OCI Cloud Guard Console and look for {cloud_guard_problem}'
else:
message_title = "Cloud Guard Finding " + cloud_guard_problem + " Suspicious Activity Detected by Cybereason"
message_body = f'Cloud Guard has detected a {body["data"]["resourceName"].lower()} and Cybereason also detected suspicious activity.'
message_body = message_body + f'\nFor more information go to the OCI Cloud Guard Console and look for {cloud_guard_problem}'
notification_message = {"default": "Cloud Guard Finding", "body": message_body, "title": message_title}
ons = oci.ons.NotificationDataPlaneClient(config={}, signer=signer)
ons.publish_message(topic_id, notification_message)
except (Exception) as ex:
print('ERROR: Failed to publish message to topic', ex, flush=True)
raise
def machine_event_handler(ctx, handler_options, data: io.BytesIO=None):
print("Cloud Guard Response I support", flush=True)
signer = get_signer()
number_of_findings = 0 # The number of findings from the Cybereason console
private_ips = [] # Private IPs associate with the OCI Instance
body = get_request_body(data)
# Getting Instance's IP to query Cybereason
resource_id = body["data"]["additionalDetails"]["resourceId"]
compartment_id = body["data"]["compartmentId"]
private_ips = get_private_ips(handler_options['machine_type'], signer, compartment_id, resource_id)
try:
ctx_data = dict(ctx.Config())
server = ctx_data['CYBEREASON_SERVER']
port = ctx_data['CYBEREASON_PORT']
username = ctx_data['CYBEREASON_USERNAME']
password = get_password_from_secrets(signer, ctx_data['CYBEREASON_SECRET_OCID'])
cr_isolate_machine = ctx_data.get('ISOLATE_MACHINE', 'False').lower()
send_notifications = ctx_data.get('SEND_NOTIFICATIONS', 'False').lower()
except Exception as ex:
print("ERROR: Failed to retrieve function configuration data", ex, flush=True)
raise
# We can have multiple IP addresses. Loop through each one of them.
cr_connection = get_cybereason_connection(server, port, username, password)
for ipaddr in private_ips:
number_of_findings = is_suspicious(cr_connection, ipaddr)
if number_of_findings > 0:
# We have some suspicious findings from Cybereason
print('Cybereason found {number_of_findings} issues in the machine specified by ip address {ip_address}'.format(ip_address=ipaddr, number_of_findings=number_of_findings), flush=True)
if handler_options['isolate_machine'] and (cr_isolate_machine == 'true'):
isolate_machine(cr_connection, ipaddr)
cloud_guard_remediate_problem(signer, body["data"]["resourceId"])
# Determining if what notification to send to the user
if handler_options['send_notifications'] and (send_notifications == 'true') and handler_options['isolate_machine'] and (cr_isolate_machine == 'true'):
send_notification(signer, ctx, body, True)
if handler_options['send_notifications'] and (send_notifications == 'true'):
send_notification(signer, ctx, body, False)
return response.Response(
ctx,
response_data={"Private_IPs" : private_ips, "Number_of_findings" : number_of_findings},
headers={"Content-Type": "application/json"}
)
|
import aiohttp
from flask import Flask
from aio_executor import run_with_asyncio
app = Flask(__name__)
async def get_random_quote():
async with aiohttp.ClientSession() as session:
async with session.get('https://api.quotable.io/random') as response:
quote = await response.json()
return f'{quote['content']} ({quote['author']})'
@app.route('/')
@run_with_asyncio
async def index():
return await get_random_quote()
if __name__ == '__main__':
app.run()
| import aiohttp
from flask import Flask
from aio_executor import run_with_asyncio
app = Flask(__name__)
async def get_random_quote():
async with aiohttp.ClientSession() as session:
async with session.get('https://api.quotable.io/random') as response:
quote = await response.json()
return f'{quote["content"]} ({quote["author"]})'
@app.route('/')
@run_with_asyncio
async def index():
return await get_random_quote()
if __name__ == '__main__':
app.run()
|
__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import glob
import json
import urllib.parse
import urllib.request
import webbrowser
from typing import Dict
from .checker import *
from .database import MongoDBHandler
from .helper import handle_dot_in_keys
from ..clients.python import ProgressBar
from ..excepts import PeaFailToStart
from ..helper import colored, get_readable_size, get_now_timestamp, get_full_version, random_name, expand_dict
from ..logging import get_logger
from ..logging.profile import TimeContext
if False:
import argparse
_allowed = {'name', 'description', 'author', 'url',
'documentation', 'version', 'vendor', 'license', 'avatar',
'platform', 'update', 'keywords'}
_repo_prefix = 'jinahub/'
_label_prefix = 'ai.jina.hub.'
class HubIO:
""" :class:`HubIO` provides the way to interact with Jina Hub registry.
You can use it with CLI to package a directory into a Jina Hub image and publish it to the world.
Examples:
- :command:`jina hub build my_pod/` build the image
- :command:`jina hub build my_pod/ --push` build the image and push to the public registry
- :command:`jina hub pull jinahub/pod.dummy_mwu_encoder:0.0.6` to download the image
"""
def __init__(self, args: 'argparse.Namespace'):
self.logger = get_logger(self.__class__.__name__, **vars(args))
self.args = args
try:
import docker
from docker import APIClient
self._client = docker.from_env()
# low-level client
self._raw_client = APIClient(base_url='unix://var/run/docker.sock')
except (ImportError, ModuleNotFoundError):
self.logger.critical('requires "docker" dependency, please install it via "pip install jina[docker]"')
raise
def new(self) -> None:
"""Create a new executor using cookiecutter template """
try:
from cookiecutter.main import cookiecutter
except (ImportError, ModuleNotFoundError):
self.logger.critical('requires "cookiecutter" dependency, please install it via "pip install cookiecutter"')
raise
import click
cookiecutter_template = self.args.template
if self.args.type == 'app':
cookiecutter_template = 'https://github.com/jina-ai/cookiecutter-jina.git'
elif self.args.type == 'pod':
cookiecutter_template = 'https://github.com/jina-ai/cookiecutter-jina-hub.git'
cookiecutter(cookiecutter_template, overwrite_if_exists=self.args.overwrite, output_dir=self.args.output_dir)
try:
cookiecutter(cookiecutter_template, overwrite_if_exists=self.args.overwrite,
output_dir=self.args.output_dir)
except click.exceptions.Abort:
self.logger.info('nothing is created, bye!')
def push(self, name: str = None, readme_path: str = None) -> None:
""" A wrapper of docker push
- Checks for the tempfile, returns without push if it cannot find
- Pushes to docker hub, returns withput writing to db if it fails
- Writes to the db
"""
name = name or self.args.name
file_path = get_summary_path(name)
if not os.path.isfile(file_path):
self.logger.error(f'can not find the build summary file')
return
try:
self._push_docker_hub(name, readme_path)
except:
self.logger.error('can not push to the docker hub registry')
return
with open(file_path) as f:
result = json.load(f)
if result['is_build_success']:
self._write_summary_to_db(summary=result)
def _push_docker_hub(self, name: str = None, readme_path: str = None) -> None:
""" Helper push function """
check_registry(self.args.registry, name, _repo_prefix)
self._check_docker_image(name)
self.login()
with ProgressBar(task_name=f'pushing {name}', batch_unit='') as t:
for line in self._client.images.push(name, stream=True, decode=True):
t.update(1)
self.logger.debug(line)
self.logger.success(f'🎉 {name} is now published!')
if False and readme_path:
# unfortunately Docker Hub Personal Access Tokens cannot be used as they are not supported by the API
_volumes = {os.path.dirname(os.path.abspath(readme_path)): {'bind': '/workspace'}}
_env = {
'DOCKERHUB_USERNAME': self.args.username,
'DOCKERHUB_PASSWORD': self.args.password,
'DOCKERHUB_REPOSITORY': name.split(':')[0],
'README_FILEPATH': '/workspace/README.md',
}
self._client.containers.run('peterevans/dockerhub-description:2.1',
auto_remove=True,
volumes=_volumes,
environment=_env)
share_link = f'https://api.jina.ai/hub/?jh={urllib.parse.quote_plus(name)}'
try:
webbrowser.open(share_link, new=2)
except:
pass
finally:
self.logger.info(
f'Check out the usage {colored(share_link, 'cyan', attrs=['underline'])} and share it with others!')
def pull(self) -> None:
"""A wrapper of docker pull """
check_registry(self.args.registry, self.args.name, _repo_prefix)
self.login()
try:
with TimeContext(f'pulling {self.args.name}', self.logger):
image = self._client.images.pull(self.args.name)
if isinstance(image, list):
image = image[0]
image_tag = image.tags[0] if image.tags else ''
self.logger.success(
f'🎉 pulled {image_tag} ({image.short_id}) uncompressed size: {get_readable_size(image.attrs['Size'])}')
except:
self.logger.error(f'can not pull image {self.args.name} from {self.args.registry}')
raise
def _check_docker_image(self, name: str) -> None:
# check local image
image = self._client.images.get(name)
for r in _allowed:
if f'{_label_prefix}{r}' not in image.labels.keys():
self.logger.warning(f'{r} is missing in your docker image labels, you may want to check it')
try:
if name != safe_url_name(
f'{_repo_prefix}' + '{type}.{kind}.{name}:{version}'.format(
**{k.replace(_label_prefix, ''): v for k, v in image.labels.items()})):
raise ValueError(f'image {name} does not match with label info in the image')
except KeyError:
self.logger.error('missing key in the label of the image')
raise
self.logger.info(f'✅ {name} is a valid Jina Hub image, ready to publish')
def login(self) -> None:
"""A wrapper of docker login """
if self.args.username and self.args.password:
self._client.login(username=self.args.username, password=self.args.password,
registry=self.args.registry)
else:
raise ValueError('no username/password specified, docker login failed')
def build(self) -> Dict:
"""A wrapper of docker build """
if self.args.dry_run:
result = self.dry_run()
else:
is_build_success, is_push_success = True, False
_logs = []
_excepts = []
with TimeContext(f'building {colored(self.args.path, 'green')}', self.logger) as tc:
try:
self._check_completeness()
streamer = self._raw_client.build(
decode=True,
path=self.args.path,
tag=self.tag,
pull=self.args.pull,
dockerfile=self.dockerfile_path_revised,
rm=True
)
for chunk in streamer:
if 'stream' in chunk:
for line in chunk['stream'].splitlines():
if is_error_message(line):
self.logger.critical(line)
_excepts.append(line)
elif 'warning' in line.lower():
self.logger.warning(line)
else:
self.logger.info(line)
_logs.append(line)
except Exception as ex:
# if pytest fails it should end up here as well
is_build_success = False
_excepts.append(str(ex))
if is_build_success:
# compile it again, but this time don't show the log
image, log = self._client.images.build(path=self.args.path,
tag=self.tag,
pull=self.args.pull,
dockerfile=self.dockerfile_path_revised,
rm=True)
# success
_details = {
'inspect': self._raw_client.inspect_image(image.tags[0]),
'tag': image.tags[0],
'hash': image.short_id,
'size': get_readable_size(image.attrs['Size']),
}
self.logger.success(
'🎉 built {tag} ({hash}) uncompressed size: {size}'.format_map(_details))
else:
self.logger.error(f'can not build the image, please double check the log')
_details = {}
if is_build_success:
if self.args.test_uses:
try:
is_build_success = False
from jina.flow import Flow
p_name = random_name()
with Flow().add(name=p_name, uses=image.tags[0], daemon=self.args.daemon):
pass
if self.args.daemon:
self._raw_client.stop(p_name)
self._raw_client.prune_containers()
is_build_success = True
except PeaFailToStart:
self.logger.error(f'can not use it in the Flow, please check your file bundle')
except Exception as ex:
self.logger.error(f'something wrong but it is probably not your fault. {repr(ex)}')
_version = self.manifest['version'] if 'version' in self.manifest else '0.0.1'
info, env_info = get_full_version()
_host_info = {
'jina': info,
'jina_envs': env_info,
'docker': self._raw_client.info(),
'build_args': vars(self.args)
}
_build_history = {
'time': get_now_timestamp(),
'host_info': _host_info if is_build_success and self.args.host_info else '',
'duration': tc.readable_duration,
'logs': _logs,
'exception': _excepts
}
if self.args.prune_images:
self.logger.info('deleting unused images')
self._raw_client.prune_images()
result = {
'name': getattr(self, 'canonical_name', ''),
'version': self.manifest['version'] if is_build_success and 'version' in self.manifest else '0.0.1',
'path': self.args.path,
'manifest_info': self.manifest if is_build_success else '',
'details': _details,
'is_build_success': is_build_success,
'build_history': [_build_history]
}
# only successful build (NOT dry run) writes the summary to disk
if result['is_build_success']:
self._write_summary_to_file(summary=result)
if self.args.push:
try:
self._push_docker_hub(image.tags[0], self.readme_path)
self._write_summary_to_db(summary=result)
self._write_slack_message(result, _details, _build_history)
except Exception as ex:
self.logger.error(f'can not complete the push due to {repr(ex)}')
if not result['is_build_success'] and self.args.raise_error:
# remove the very verbose build log when throw error
result['build_history'][0].pop('logs')
raise RuntimeError(result)
return result
def dry_run(self) -> Dict:
try:
s = self._check_completeness()
s['is_build_success'] = True
except Exception as ex:
s = {'is_build_success': False,
'exception': str(ex)}
return s
def _write_summary_to_db(self, summary: Dict) -> None:
""" Inserts / Updates summary document in mongodb """
if not is_db_envs_set():
self.logger.warning('MongoDB environment vars are not set! bookkeeping skipped.')
return
build_summary = handle_dot_in_keys(document=summary)
_build_query = {'name': build_summary['name'], 'version': build_summary['version']}
_current_build_history = build_summary['build_history']
with MongoDBHandler(hostname=os.environ['JINA_DB_HOSTNAME'],
username=os.environ['JINA_DB_USERNAME'],
password=os.environ['JINA_DB_PASSWORD'],
database_name=os.environ['JINA_DB_NAME'],
collection_name=os.environ['JINA_DB_COLLECTION']) as db:
existing_doc = db.find(query=_build_query)
if existing_doc:
build_summary['build_history'] = existing_doc['build_history'] + _current_build_history
_modified_count = db.replace(document=build_summary,
query=_build_query)
self.logger.debug(f'Updated the build + push summary in db. {_modified_count} documents modified')
else:
_inserted_id = db.insert(document=build_summary)
self.logger.debug(f'Inserted the build + push summary in db with id {_inserted_id}')
def _write_summary_to_file(self, summary: Dict) -> None:
file_path = get_summary_path(f'{summary['name']}:{summary['version']}')
with open(file_path, 'w+') as f:
json.dump(summary, f)
self.logger.debug(f'stored the summary from build to {file_path}')
def _check_completeness(self) -> Dict:
self.dockerfile_path = get_exist_path(self.args.path, 'Dockerfile')
self.manifest_path = get_exist_path(self.args.path, 'manifest.yml')
self.readme_path = get_exist_path(self.args.path, 'README.md')
self.requirements_path = get_exist_path(self.args.path, 'requirements.txt')
yaml_glob = glob.glob(os.path.join(self.args.path, '*.yml'))
if yaml_glob:
yaml_glob.remove(self.manifest_path)
py_glob = glob.glob(os.path.join(self.args.path, '*.py'))
test_glob = glob.glob(os.path.join(self.args.path, 'tests/test_*.py'))
completeness = {
'Dockerfile': self.dockerfile_path,
'manifest.yml': self.manifest_path,
'README.md': self.readme_path,
'requirements.txt': self.requirements_path,
'*.yml': yaml_glob,
'*.py': py_glob,
'tests': test_glob
}
self.logger.info(
f'completeness check\n' +
'\n'.join('%4s %-20s %s' % (colored('✓', 'green') if v else colored('✗', 'red'), k, v) for k, v in
completeness.items()) + '\n')
if completeness['Dockerfile'] and completeness['manifest.yml']:
pass
else:
self.logger.critical('Dockerfile or manifest.yml is not given, can not build')
raise FileNotFoundError('Dockerfile or manifest.yml is not given, can not build')
self.manifest = self._read_manifest(self.manifest_path)
self.dockerfile_path_revised = self._get_revised_dockerfile(self.dockerfile_path, self.manifest)
self.tag = safe_url_name(f'{_repo_prefix}' + '{type}.{kind}.{name}:{version}'.format(**self.manifest))
self.canonical_name = safe_url_name(f'{_repo_prefix}' + '{type}.{kind}.{name}'.format(**self.manifest))
return completeness
def _read_manifest(self, path: str, validate: bool = True) -> Dict:
with resource_stream('jina', '/'.join(('resources', 'hub-builder', 'manifest.yml'))) as fp:
tmp = yaml.load(fp) # do not expand variables at here, i.e. DO NOT USE expand_dict(yaml.load(fp))
with open(path) as fp:
tmp.update(yaml.load(fp))
if validate:
self._validate_manifest(tmp)
return tmp
def _validate_manifest(self, manifest: Dict) -> None:
required = {'name', 'type', 'version'}
# check the required field in manifest
for r in required:
if r not in manifest:
raise ValueError(f'{r} is missing in the manifest.yaml, it is required')
# check if all fields are there
for r in _allowed:
if r not in manifest:
self.logger.warning(f'{r} is missing in your manifest.yml, you may want to check it')
# check name
check_name(manifest['name'])
# check_image_type
check_image_type(manifest['type'])
# check version number
check_version(manifest['version'])
# check version number
check_license(manifest['license'])
# check platform
if not isinstance(manifest['platform'], list):
manifest['platform'] = list(manifest['platform'])
check_platform(manifest['platform'])
# replace all chars in value to safe chars
for k, v in manifest.items():
if v and isinstance(v, str):
manifest[k] = remove_control_characters(v)
# show manifest key-values
for k, v in manifest.items():
self.logger.debug(f'{k}: {v}')
def _get_revised_dockerfile(self, dockerfile_path: str, manifest: Dict) -> str:
# modify dockerfile
revised_dockerfile = []
with open(dockerfile_path) as fp:
for l in fp:
revised_dockerfile.append(l)
if l.startswith('FROM'):
revised_dockerfile.append('LABEL ')
revised_dockerfile.append(
' \\ \n'.join(f'{_label_prefix}{k}="{v}"' for k, v in manifest.items()))
f = tempfile.NamedTemporaryFile('w', delete=False).name
with open(f, 'w', encoding='utf8') as fp:
fp.writelines(revised_dockerfile)
for k in revised_dockerfile:
self.logger.debug(k)
return f
def _write_slack_message(self, *args):
def _expand_fn(v):
if isinstance(v, str):
for d in args:
try:
v = v.format(**d)
except KeyError:
pass
return v
if 'JINAHUB_SLACK_WEBHOOK' in os.environ:
with resource_stream('jina', '/'.join(('resources', 'hub-builder-success', 'slack-jinahub.json'))) as fp:
tmp = expand_dict(json.load(fp), _expand_fn, resolve_cycle_ref=False)
req = urllib.request.Request(os.environ['JINAHUB_SLACK_WEBHOOK'])
req.add_header('Content-Type', 'application/json; charset=utf-8')
jdb = json.dumps(tmp).encode('utf-8') # needs to be bytes
req.add_header('Content-Length', str(len(jdb)))
with urllib.request.urlopen(req, jdb) as f:
res = f.read()
self.logger.info(f'push to Slack: {res}')
# alias of "new" in cli
create = new
init = new
| __copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import glob
import json
import urllib.parse
import urllib.request
import webbrowser
from typing import Dict
from .checker import *
from .database import MongoDBHandler
from .helper import handle_dot_in_keys
from ..clients.python import ProgressBar
from ..excepts import PeaFailToStart
from ..helper import colored, get_readable_size, get_now_timestamp, get_full_version, random_name, expand_dict
from ..logging import get_logger
from ..logging.profile import TimeContext
if False:
import argparse
_allowed = {'name', 'description', 'author', 'url',
'documentation', 'version', 'vendor', 'license', 'avatar',
'platform', 'update', 'keywords'}
_repo_prefix = 'jinahub/'
_label_prefix = 'ai.jina.hub.'
class HubIO:
""" :class:`HubIO` provides the way to interact with Jina Hub registry.
You can use it with CLI to package a directory into a Jina Hub image and publish it to the world.
Examples:
- :command:`jina hub build my_pod/` build the image
- :command:`jina hub build my_pod/ --push` build the image and push to the public registry
- :command:`jina hub pull jinahub/pod.dummy_mwu_encoder:0.0.6` to download the image
"""
def __init__(self, args: 'argparse.Namespace'):
self.logger = get_logger(self.__class__.__name__, **vars(args))
self.args = args
try:
import docker
from docker import APIClient
self._client = docker.from_env()
# low-level client
self._raw_client = APIClient(base_url='unix://var/run/docker.sock')
except (ImportError, ModuleNotFoundError):
self.logger.critical('requires "docker" dependency, please install it via "pip install jina[docker]"')
raise
def new(self) -> None:
"""Create a new executor using cookiecutter template """
try:
from cookiecutter.main import cookiecutter
except (ImportError, ModuleNotFoundError):
self.logger.critical('requires "cookiecutter" dependency, please install it via "pip install cookiecutter"')
raise
import click
cookiecutter_template = self.args.template
if self.args.type == 'app':
cookiecutter_template = 'https://github.com/jina-ai/cookiecutter-jina.git'
elif self.args.type == 'pod':
cookiecutter_template = 'https://github.com/jina-ai/cookiecutter-jina-hub.git'
cookiecutter(cookiecutter_template, overwrite_if_exists=self.args.overwrite, output_dir=self.args.output_dir)
try:
cookiecutter(cookiecutter_template, overwrite_if_exists=self.args.overwrite,
output_dir=self.args.output_dir)
except click.exceptions.Abort:
self.logger.info('nothing is created, bye!')
def push(self, name: str = None, readme_path: str = None) -> None:
""" A wrapper of docker push
- Checks for the tempfile, returns without push if it cannot find
- Pushes to docker hub, returns withput writing to db if it fails
- Writes to the db
"""
name = name or self.args.name
file_path = get_summary_path(name)
if not os.path.isfile(file_path):
self.logger.error(f'can not find the build summary file')
return
try:
self._push_docker_hub(name, readme_path)
except:
self.logger.error('can not push to the docker hub registry')
return
with open(file_path) as f:
result = json.load(f)
if result['is_build_success']:
self._write_summary_to_db(summary=result)
def _push_docker_hub(self, name: str = None, readme_path: str = None) -> None:
""" Helper push function """
check_registry(self.args.registry, name, _repo_prefix)
self._check_docker_image(name)
self.login()
with ProgressBar(task_name=f'pushing {name}', batch_unit='') as t:
for line in self._client.images.push(name, stream=True, decode=True):
t.update(1)
self.logger.debug(line)
self.logger.success(f'🎉 {name} is now published!')
if False and readme_path:
# unfortunately Docker Hub Personal Access Tokens cannot be used as they are not supported by the API
_volumes = {os.path.dirname(os.path.abspath(readme_path)): {'bind': '/workspace'}}
_env = {
'DOCKERHUB_USERNAME': self.args.username,
'DOCKERHUB_PASSWORD': self.args.password,
'DOCKERHUB_REPOSITORY': name.split(':')[0],
'README_FILEPATH': '/workspace/README.md',
}
self._client.containers.run('peterevans/dockerhub-description:2.1',
auto_remove=True,
volumes=_volumes,
environment=_env)
share_link = f'https://api.jina.ai/hub/?jh={urllib.parse.quote_plus(name)}'
try:
webbrowser.open(share_link, new=2)
except:
pass
finally:
self.logger.info(
f'Check out the usage {colored(share_link, "cyan", attrs=["underline"])} and share it with others!')
def pull(self) -> None:
"""A wrapper of docker pull """
check_registry(self.args.registry, self.args.name, _repo_prefix)
self.login()
try:
with TimeContext(f'pulling {self.args.name}', self.logger):
image = self._client.images.pull(self.args.name)
if isinstance(image, list):
image = image[0]
image_tag = image.tags[0] if image.tags else ''
self.logger.success(
f'🎉 pulled {image_tag} ({image.short_id}) uncompressed size: {get_readable_size(image.attrs["Size"])}')
except:
self.logger.error(f'can not pull image {self.args.name} from {self.args.registry}')
raise
def _check_docker_image(self, name: str) -> None:
# check local image
image = self._client.images.get(name)
for r in _allowed:
if f'{_label_prefix}{r}' not in image.labels.keys():
self.logger.warning(f'{r} is missing in your docker image labels, you may want to check it')
try:
if name != safe_url_name(
f'{_repo_prefix}' + '{type}.{kind}.{name}:{version}'.format(
**{k.replace(_label_prefix, ''): v for k, v in image.labels.items()})):
raise ValueError(f'image {name} does not match with label info in the image')
except KeyError:
self.logger.error('missing key in the label of the image')
raise
self.logger.info(f'✅ {name} is a valid Jina Hub image, ready to publish')
def login(self) -> None:
"""A wrapper of docker login """
if self.args.username and self.args.password:
self._client.login(username=self.args.username, password=self.args.password,
registry=self.args.registry)
else:
raise ValueError('no username/password specified, docker login failed')
def build(self) -> Dict:
"""A wrapper of docker build """
if self.args.dry_run:
result = self.dry_run()
else:
is_build_success, is_push_success = True, False
_logs = []
_excepts = []
with TimeContext(f'building {colored(self.args.path, "green")}', self.logger) as tc:
try:
self._check_completeness()
streamer = self._raw_client.build(
decode=True,
path=self.args.path,
tag=self.tag,
pull=self.args.pull,
dockerfile=self.dockerfile_path_revised,
rm=True
)
for chunk in streamer:
if 'stream' in chunk:
for line in chunk['stream'].splitlines():
if is_error_message(line):
self.logger.critical(line)
_excepts.append(line)
elif 'warning' in line.lower():
self.logger.warning(line)
else:
self.logger.info(line)
_logs.append(line)
except Exception as ex:
# if pytest fails it should end up here as well
is_build_success = False
_excepts.append(str(ex))
if is_build_success:
# compile it again, but this time don't show the log
image, log = self._client.images.build(path=self.args.path,
tag=self.tag,
pull=self.args.pull,
dockerfile=self.dockerfile_path_revised,
rm=True)
# success
_details = {
'inspect': self._raw_client.inspect_image(image.tags[0]),
'tag': image.tags[0],
'hash': image.short_id,
'size': get_readable_size(image.attrs['Size']),
}
self.logger.success(
'🎉 built {tag} ({hash}) uncompressed size: {size}'.format_map(_details))
else:
self.logger.error(f'can not build the image, please double check the log')
_details = {}
if is_build_success:
if self.args.test_uses:
try:
is_build_success = False
from jina.flow import Flow
p_name = random_name()
with Flow().add(name=p_name, uses=image.tags[0], daemon=self.args.daemon):
pass
if self.args.daemon:
self._raw_client.stop(p_name)
self._raw_client.prune_containers()
is_build_success = True
except PeaFailToStart:
self.logger.error(f'can not use it in the Flow, please check your file bundle')
except Exception as ex:
self.logger.error(f'something wrong but it is probably not your fault. {repr(ex)}')
_version = self.manifest['version'] if 'version' in self.manifest else '0.0.1'
info, env_info = get_full_version()
_host_info = {
'jina': info,
'jina_envs': env_info,
'docker': self._raw_client.info(),
'build_args': vars(self.args)
}
_build_history = {
'time': get_now_timestamp(),
'host_info': _host_info if is_build_success and self.args.host_info else '',
'duration': tc.readable_duration,
'logs': _logs,
'exception': _excepts
}
if self.args.prune_images:
self.logger.info('deleting unused images')
self._raw_client.prune_images()
result = {
'name': getattr(self, 'canonical_name', ''),
'version': self.manifest['version'] if is_build_success and 'version' in self.manifest else '0.0.1',
'path': self.args.path,
'manifest_info': self.manifest if is_build_success else '',
'details': _details,
'is_build_success': is_build_success,
'build_history': [_build_history]
}
# only successful build (NOT dry run) writes the summary to disk
if result['is_build_success']:
self._write_summary_to_file(summary=result)
if self.args.push:
try:
self._push_docker_hub(image.tags[0], self.readme_path)
self._write_summary_to_db(summary=result)
self._write_slack_message(result, _details, _build_history)
except Exception as ex:
self.logger.error(f'can not complete the push due to {repr(ex)}')
if not result['is_build_success'] and self.args.raise_error:
# remove the very verbose build log when throw error
result['build_history'][0].pop('logs')
raise RuntimeError(result)
return result
def dry_run(self) -> Dict:
try:
s = self._check_completeness()
s['is_build_success'] = True
except Exception as ex:
s = {'is_build_success': False,
'exception': str(ex)}
return s
def _write_summary_to_db(self, summary: Dict) -> None:
""" Inserts / Updates summary document in mongodb """
if not is_db_envs_set():
self.logger.warning('MongoDB environment vars are not set! bookkeeping skipped.')
return
build_summary = handle_dot_in_keys(document=summary)
_build_query = {'name': build_summary['name'], 'version': build_summary['version']}
_current_build_history = build_summary['build_history']
with MongoDBHandler(hostname=os.environ['JINA_DB_HOSTNAME'],
username=os.environ['JINA_DB_USERNAME'],
password=os.environ['JINA_DB_PASSWORD'],
database_name=os.environ['JINA_DB_NAME'],
collection_name=os.environ['JINA_DB_COLLECTION']) as db:
existing_doc = db.find(query=_build_query)
if existing_doc:
build_summary['build_history'] = existing_doc['build_history'] + _current_build_history
_modified_count = db.replace(document=build_summary,
query=_build_query)
self.logger.debug(f'Updated the build + push summary in db. {_modified_count} documents modified')
else:
_inserted_id = db.insert(document=build_summary)
self.logger.debug(f'Inserted the build + push summary in db with id {_inserted_id}')
def _write_summary_to_file(self, summary: Dict) -> None:
file_path = get_summary_path(f'{summary["name"]}:{summary["version"]}')
with open(file_path, 'w+') as f:
json.dump(summary, f)
self.logger.debug(f'stored the summary from build to {file_path}')
def _check_completeness(self) -> Dict:
self.dockerfile_path = get_exist_path(self.args.path, 'Dockerfile')
self.manifest_path = get_exist_path(self.args.path, 'manifest.yml')
self.readme_path = get_exist_path(self.args.path, 'README.md')
self.requirements_path = get_exist_path(self.args.path, 'requirements.txt')
yaml_glob = glob.glob(os.path.join(self.args.path, '*.yml'))
if yaml_glob:
yaml_glob.remove(self.manifest_path)
py_glob = glob.glob(os.path.join(self.args.path, '*.py'))
test_glob = glob.glob(os.path.join(self.args.path, 'tests/test_*.py'))
completeness = {
'Dockerfile': self.dockerfile_path,
'manifest.yml': self.manifest_path,
'README.md': self.readme_path,
'requirements.txt': self.requirements_path,
'*.yml': yaml_glob,
'*.py': py_glob,
'tests': test_glob
}
self.logger.info(
f'completeness check\n' +
'\n'.join('%4s %-20s %s' % (colored('✓', 'green') if v else colored('✗', 'red'), k, v) for k, v in
completeness.items()) + '\n')
if completeness['Dockerfile'] and completeness['manifest.yml']:
pass
else:
self.logger.critical('Dockerfile or manifest.yml is not given, can not build')
raise FileNotFoundError('Dockerfile or manifest.yml is not given, can not build')
self.manifest = self._read_manifest(self.manifest_path)
self.dockerfile_path_revised = self._get_revised_dockerfile(self.dockerfile_path, self.manifest)
self.tag = safe_url_name(f'{_repo_prefix}' + '{type}.{kind}.{name}:{version}'.format(**self.manifest))
self.canonical_name = safe_url_name(f'{_repo_prefix}' + '{type}.{kind}.{name}'.format(**self.manifest))
return completeness
def _read_manifest(self, path: str, validate: bool = True) -> Dict:
with resource_stream('jina', '/'.join(('resources', 'hub-builder', 'manifest.yml'))) as fp:
tmp = yaml.load(fp) # do not expand variables at here, i.e. DO NOT USE expand_dict(yaml.load(fp))
with open(path) as fp:
tmp.update(yaml.load(fp))
if validate:
self._validate_manifest(tmp)
return tmp
def _validate_manifest(self, manifest: Dict) -> None:
required = {'name', 'type', 'version'}
# check the required field in manifest
for r in required:
if r not in manifest:
raise ValueError(f'{r} is missing in the manifest.yaml, it is required')
# check if all fields are there
for r in _allowed:
if r not in manifest:
self.logger.warning(f'{r} is missing in your manifest.yml, you may want to check it')
# check name
check_name(manifest['name'])
# check_image_type
check_image_type(manifest['type'])
# check version number
check_version(manifest['version'])
# check version number
check_license(manifest['license'])
# check platform
if not isinstance(manifest['platform'], list):
manifest['platform'] = list(manifest['platform'])
check_platform(manifest['platform'])
# replace all chars in value to safe chars
for k, v in manifest.items():
if v and isinstance(v, str):
manifest[k] = remove_control_characters(v)
# show manifest key-values
for k, v in manifest.items():
self.logger.debug(f'{k}: {v}')
def _get_revised_dockerfile(self, dockerfile_path: str, manifest: Dict) -> str:
# modify dockerfile
revised_dockerfile = []
with open(dockerfile_path) as fp:
for l in fp:
revised_dockerfile.append(l)
if l.startswith('FROM'):
revised_dockerfile.append('LABEL ')
revised_dockerfile.append(
' \\ \n'.join(f'{_label_prefix}{k}="{v}"' for k, v in manifest.items()))
f = tempfile.NamedTemporaryFile('w', delete=False).name
with open(f, 'w', encoding='utf8') as fp:
fp.writelines(revised_dockerfile)
for k in revised_dockerfile:
self.logger.debug(k)
return f
def _write_slack_message(self, *args):
def _expand_fn(v):
if isinstance(v, str):
for d in args:
try:
v = v.format(**d)
except KeyError:
pass
return v
if 'JINAHUB_SLACK_WEBHOOK' in os.environ:
with resource_stream('jina', '/'.join(('resources', 'hub-builder-success', 'slack-jinahub.json'))) as fp:
tmp = expand_dict(json.load(fp), _expand_fn, resolve_cycle_ref=False)
req = urllib.request.Request(os.environ['JINAHUB_SLACK_WEBHOOK'])
req.add_header('Content-Type', 'application/json; charset=utf-8')
jdb = json.dumps(tmp).encode('utf-8') # needs to be bytes
req.add_header('Content-Length', str(len(jdb)))
with urllib.request.urlopen(req, jdb) as f:
res = f.read()
self.logger.info(f'push to Slack: {res}')
# alias of "new" in cli
create = new
init = new
|
frase = str(input('Digite uma frase: ')).upper().strip()
print(f'A letra "A" aparece {frase.count('A')} vezes na frase.')
print(f'A primeira letra A apareceu na posição {frase.find('A')+1}.')
print(f'A última letra A apareceu na posição {frase.rfind('A')+1}.')
## .join(frase.split()) juntar tudo, remover espaços | frase = str(input('Digite uma frase: ')).upper().strip()
print(f'A letra "A" aparece {frase.count("A")} vezes na frase.')
print(f'A primeira letra A apareceu na posição {frase.find("A")+1}.')
print(f'A última letra A apareceu na posição {frase.rfind("A")+1}.')
## .join(frase.split()) juntar tudo, remover espaços |
"""Tests for the Renault integration."""
from __future__ import annotations
from types import MappingProxyType
from typing import Any
from unittest.mock import patch
from renault_api.kamereon import schemas
from renault_api.renault_account import RenaultAccount
from homeassistant.components.renault.const import DOMAIN
from homeassistant.config_entries import SOURCE_USER
from homeassistant.const import (
ATTR_ICON,
ATTR_IDENTIFIERS,
ATTR_MANUFACTURER,
ATTR_MODEL,
ATTR_NAME,
ATTR_SW_VERSION,
)
from homeassistant.core import HomeAssistant
from homeassistant.helpers import aiohttp_client
from homeassistant.helpers.device_registry import DeviceRegistry
from .const import ICON_FOR_EMPTY_VALUES, MOCK_CONFIG, MOCK_VEHICLES
from tests.common import MockConfigEntry, load_fixture
def get_mock_config_entry():
"""Create the Renault integration."""
return MockConfigEntry(
domain=DOMAIN,
source=SOURCE_USER,
data=MOCK_CONFIG,
unique_id="account_id_1",
options={},
entry_id="123456",
)
def get_fixtures(vehicle_type: str) -> dict[str, Any]:
"""Create a vehicle proxy for testing."""
mock_vehicle = MOCK_VEHICLES.get(vehicle_type, {"endpoints": {}})
return {
"battery_status": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle["endpoints"]["battery_status"]}")
if "battery_status" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleBatteryStatusDataSchema),
"charge_mode": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle["endpoints"]["charge_mode"]}")
if "charge_mode" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleChargeModeDataSchema),
"cockpit": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle["endpoints"]["cockpit"]}")
if "cockpit" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleCockpitDataSchema),
"hvac_status": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle["endpoints"]["hvac_status"]}")
if "hvac_status" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleHvacStatusDataSchema),
}
def get_no_data_icon(expected_entity: MappingProxyType):
"""Check attribute for icon for inactive sensors."""
entity_id = expected_entity["entity_id"]
return ICON_FOR_EMPTY_VALUES.get(entity_id, expected_entity.get(ATTR_ICON))
async def setup_renault_integration_simple(hass: HomeAssistant):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch("renault_api.renault_account.RenaultAccount.get_vehicles"):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
async def setup_renault_integration_vehicle(hass: HomeAssistant, vehicle_type: str):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
mock_vehicle = MOCK_VEHICLES[vehicle_type]
mock_fixtures = get_fixtures(vehicle_type)
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch(
"renault_api.renault_account.RenaultAccount.get_vehicles",
return_value=(
schemas.KamereonVehiclesResponseSchema.loads(
load_fixture(f"renault/vehicle_{vehicle_type}.json")
)
),
), patch(
"renault_api.renault_vehicle.RenaultVehicle.supports_endpoint",
side_effect=mock_vehicle["endpoints_available"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.has_contract_for_endpoint",
return_value=True,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_battery_status",
return_value=mock_fixtures["battery_status"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_charge_mode",
return_value=mock_fixtures["charge_mode"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_cockpit",
return_value=mock_fixtures["cockpit"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_hvac_status",
return_value=mock_fixtures["hvac_status"],
):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
async def setup_renault_integration_vehicle_with_no_data(
hass: HomeAssistant, vehicle_type: str
):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
mock_vehicle = MOCK_VEHICLES[vehicle_type]
mock_fixtures = get_fixtures("")
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch(
"renault_api.renault_account.RenaultAccount.get_vehicles",
return_value=(
schemas.KamereonVehiclesResponseSchema.loads(
load_fixture(f"renault/vehicle_{vehicle_type}.json")
)
),
), patch(
"renault_api.renault_vehicle.RenaultVehicle.supports_endpoint",
side_effect=mock_vehicle["endpoints_available"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.has_contract_for_endpoint",
return_value=True,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_battery_status",
return_value=mock_fixtures["battery_status"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_charge_mode",
return_value=mock_fixtures["charge_mode"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_cockpit",
return_value=mock_fixtures["cockpit"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_hvac_status",
return_value=mock_fixtures["hvac_status"],
):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
async def setup_renault_integration_vehicle_with_side_effect(
hass: HomeAssistant, vehicle_type: str, side_effect: Any
):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
mock_vehicle = MOCK_VEHICLES[vehicle_type]
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch(
"renault_api.renault_account.RenaultAccount.get_vehicles",
return_value=(
schemas.KamereonVehiclesResponseSchema.loads(
load_fixture(f"renault/vehicle_{vehicle_type}.json")
)
),
), patch(
"renault_api.renault_vehicle.RenaultVehicle.supports_endpoint",
side_effect=mock_vehicle["endpoints_available"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.has_contract_for_endpoint",
return_value=True,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_battery_status",
side_effect=side_effect,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_charge_mode",
side_effect=side_effect,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_cockpit",
side_effect=side_effect,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_hvac_status",
side_effect=side_effect,
):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
def check_device_registry(
device_registry: DeviceRegistry, expected_device: dict[str, Any]
) -> None:
"""Ensure that the expected_device is correctly registered."""
assert len(device_registry.devices) == 1
registry_entry = device_registry.async_get_device(expected_device[ATTR_IDENTIFIERS])
assert registry_entry is not None
assert registry_entry.identifiers == expected_device[ATTR_IDENTIFIERS]
assert registry_entry.manufacturer == expected_device[ATTR_MANUFACTURER]
assert registry_entry.name == expected_device[ATTR_NAME]
assert registry_entry.model == expected_device[ATTR_MODEL]
assert registry_entry.sw_version == expected_device[ATTR_SW_VERSION]
| """Tests for the Renault integration."""
from __future__ import annotations
from types import MappingProxyType
from typing import Any
from unittest.mock import patch
from renault_api.kamereon import schemas
from renault_api.renault_account import RenaultAccount
from homeassistant.components.renault.const import DOMAIN
from homeassistant.config_entries import SOURCE_USER
from homeassistant.const import (
ATTR_ICON,
ATTR_IDENTIFIERS,
ATTR_MANUFACTURER,
ATTR_MODEL,
ATTR_NAME,
ATTR_SW_VERSION,
)
from homeassistant.core import HomeAssistant
from homeassistant.helpers import aiohttp_client
from homeassistant.helpers.device_registry import DeviceRegistry
from .const import ICON_FOR_EMPTY_VALUES, MOCK_CONFIG, MOCK_VEHICLES
from tests.common import MockConfigEntry, load_fixture
def get_mock_config_entry():
"""Create the Renault integration."""
return MockConfigEntry(
domain=DOMAIN,
source=SOURCE_USER,
data=MOCK_CONFIG,
unique_id="account_id_1",
options={},
entry_id="123456",
)
def get_fixtures(vehicle_type: str) -> dict[str, Any]:
"""Create a vehicle proxy for testing."""
mock_vehicle = MOCK_VEHICLES.get(vehicle_type, {"endpoints": {}})
return {
"battery_status": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle['endpoints']['battery_status']}")
if "battery_status" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleBatteryStatusDataSchema),
"charge_mode": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle['endpoints']['charge_mode']}")
if "charge_mode" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleChargeModeDataSchema),
"cockpit": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle['endpoints']['cockpit']}")
if "cockpit" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleCockpitDataSchema),
"hvac_status": schemas.KamereonVehicleDataResponseSchema.loads(
load_fixture(f"renault/{mock_vehicle['endpoints']['hvac_status']}")
if "hvac_status" in mock_vehicle["endpoints"]
else load_fixture("renault/no_data.json")
).get_attributes(schemas.KamereonVehicleHvacStatusDataSchema),
}
def get_no_data_icon(expected_entity: MappingProxyType):
"""Check attribute for icon for inactive sensors."""
entity_id = expected_entity["entity_id"]
return ICON_FOR_EMPTY_VALUES.get(entity_id, expected_entity.get(ATTR_ICON))
async def setup_renault_integration_simple(hass: HomeAssistant):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch("renault_api.renault_account.RenaultAccount.get_vehicles"):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
async def setup_renault_integration_vehicle(hass: HomeAssistant, vehicle_type: str):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
mock_vehicle = MOCK_VEHICLES[vehicle_type]
mock_fixtures = get_fixtures(vehicle_type)
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch(
"renault_api.renault_account.RenaultAccount.get_vehicles",
return_value=(
schemas.KamereonVehiclesResponseSchema.loads(
load_fixture(f"renault/vehicle_{vehicle_type}.json")
)
),
), patch(
"renault_api.renault_vehicle.RenaultVehicle.supports_endpoint",
side_effect=mock_vehicle["endpoints_available"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.has_contract_for_endpoint",
return_value=True,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_battery_status",
return_value=mock_fixtures["battery_status"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_charge_mode",
return_value=mock_fixtures["charge_mode"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_cockpit",
return_value=mock_fixtures["cockpit"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_hvac_status",
return_value=mock_fixtures["hvac_status"],
):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
async def setup_renault_integration_vehicle_with_no_data(
hass: HomeAssistant, vehicle_type: str
):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
mock_vehicle = MOCK_VEHICLES[vehicle_type]
mock_fixtures = get_fixtures("")
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch(
"renault_api.renault_account.RenaultAccount.get_vehicles",
return_value=(
schemas.KamereonVehiclesResponseSchema.loads(
load_fixture(f"renault/vehicle_{vehicle_type}.json")
)
),
), patch(
"renault_api.renault_vehicle.RenaultVehicle.supports_endpoint",
side_effect=mock_vehicle["endpoints_available"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.has_contract_for_endpoint",
return_value=True,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_battery_status",
return_value=mock_fixtures["battery_status"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_charge_mode",
return_value=mock_fixtures["charge_mode"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_cockpit",
return_value=mock_fixtures["cockpit"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_hvac_status",
return_value=mock_fixtures["hvac_status"],
):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
async def setup_renault_integration_vehicle_with_side_effect(
hass: HomeAssistant, vehicle_type: str, side_effect: Any
):
"""Create the Renault integration."""
config_entry = get_mock_config_entry()
config_entry.add_to_hass(hass)
renault_account = RenaultAccount(
config_entry.unique_id,
websession=aiohttp_client.async_get_clientsession(hass),
)
mock_vehicle = MOCK_VEHICLES[vehicle_type]
with patch("renault_api.renault_session.RenaultSession.login"), patch(
"renault_api.renault_client.RenaultClient.get_api_account",
return_value=renault_account,
), patch(
"renault_api.renault_account.RenaultAccount.get_vehicles",
return_value=(
schemas.KamereonVehiclesResponseSchema.loads(
load_fixture(f"renault/vehicle_{vehicle_type}.json")
)
),
), patch(
"renault_api.renault_vehicle.RenaultVehicle.supports_endpoint",
side_effect=mock_vehicle["endpoints_available"],
), patch(
"renault_api.renault_vehicle.RenaultVehicle.has_contract_for_endpoint",
return_value=True,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_battery_status",
side_effect=side_effect,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_charge_mode",
side_effect=side_effect,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_cockpit",
side_effect=side_effect,
), patch(
"renault_api.renault_vehicle.RenaultVehicle.get_hvac_status",
side_effect=side_effect,
):
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
def check_device_registry(
device_registry: DeviceRegistry, expected_device: dict[str, Any]
) -> None:
"""Ensure that the expected_device is correctly registered."""
assert len(device_registry.devices) == 1
registry_entry = device_registry.async_get_device(expected_device[ATTR_IDENTIFIERS])
assert registry_entry is not None
assert registry_entry.identifiers == expected_device[ATTR_IDENTIFIERS]
assert registry_entry.manufacturer == expected_device[ATTR_MANUFACTURER]
assert registry_entry.name == expected_device[ATTR_NAME]
assert registry_entry.model == expected_device[ATTR_MODEL]
assert registry_entry.sw_version == expected_device[ATTR_SW_VERSION]
|
import re
import textwrap
from core.exceptions import InvalidMemoryAddress, MemoryLimitExceeded
class Hex:
def __init__(self, data: str = "0x00", _bytes: str = 1, *args, **kwargs) -> None:
self._bytes = _bytes
self._base = 16
self._format_spec = f"#0{2 + _bytes * 2}x"
self._format_spec_bin = f"#0{2 + _bytes * 8}b"
self._memory_limit_hex = "FF" * _bytes
self._memory_limit = int(self._memory_limit_hex, self._base)
self.data = data
return
def __call__(self, value: str) -> None:
self.data = value
def __str__(self) -> str:
return self._data
def __repr__(self) -> str:
return self._data
def __int__(self) -> int:
return int(self._data, self._base)
def __index__(self) -> int:
return int(self._data, self._base)
def __format__(self, format_spec: str = None) -> str:
if not format_spec:
format_spec = self._format_spec
return format(int(self._data, self._base), format_spec)
def __next__(self):
self._data = format(int(self._data, self._base) + 1, self._format_spec)
return self._data
def __add__(self, val: int):
return Hex(format(int(self._data, self._base) + val, self._format_spec), _bytes=self._bytes)
def __sub__(self, val: int):
return Hex(format(int(self._data, self._base) - val, self._format_spec), _bytes=self._bytes)
def __len__(self):
return self._bytes
def _verify(self, value: str):
if not re.fullmatch("^0[x|X][0-9a-fA-F]+", str(value)):
raise InvalidMemoryAddress()
if int(str(value), self._base) > self._memory_limit:
raise MemoryLimitExceeded()
def bin(self) -> str:
return format(int(self._data, self._base), self._format_spec_bin)
@property
def data(self) -> str:
return self._data
@data.setter
def data(self, val: str) -> None:
self._verify(val)
self._data = format(int(str(val), self._base), self._format_spec)
return
def read(self, *args, **kwargs) -> str:
return self
def write(self, val: str, *args, **kwargs) -> bool:
self.data = val
return True
def update(self, val: str, *args, **kwargs) -> bool:
return self.write(val, *args, **kwargs)
def replace(self, *args, **kwargs) -> None:
return self._data.replace(*args, **kwargs)
def lower(self, *args, **kwargs):
return self._data.lower(*args, **kwargs)
def upper(self, *args, **kwargs):
return self._data.upper(*args, **kwargs)
pass
class Byte(Hex):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
pass
class Memory(dict):
def __init__(self, memory_size=65535, starting_address="0x0000", _bytes=2, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self._bytes = 1
self._base = 16
self._memory_size = memory_size
self._starting_address = starting_address
self._default_mem = "0x00"
self._format_spec = f"#0{2 + _bytes * 2}x"
self._format_spec_bin = f"#0{2 + _bytes * 4}b"
self._memory_limit = int(starting_address, 16) + memory_size
self._memory_limit_hex = format(self._memory_limit, self._format_spec)
return
def __getitem__(self, addr: str) -> str:
addr = self._verify(addr)
if addr not in self:
super().__setitem__(addr, Byte(self._default_mem))
return super().__getitem__(addr)
def __setitem__(self, addr: str, value: str) -> None:
addr = self._verify(addr)
if addr not in self:
super().__setitem__(addr, Byte(value))
return super().__getitem__(addr).write(value)
def _verify(self, value: str) -> None:
if not re.fullmatch("^0[x|X][0-9a-fA-F]+", str(value)):
raise InvalidMemoryAddress()
if int(str(value), self._base) > self._memory_limit:
raise MemoryLimitExceeded()
return format(int(value, self._base), self._format_spec)
def sort(self):
return dict(sorted(self.items(), key=lambda x: int(str(x[0]), 16)))
def read(self, *args, **kwargs):
return self.__getitem__(*args, **kwargs)
def write(self, *args, **kwargs):
return self.__setitem__(*args, **kwargs)
pass
class RegisterPair:
def __init__(self, reg_1, reg_2, *args, **kwargs):
super().__init__(*args, **kwargs)
self._reg_1 = reg_1
self._reg_2 = reg_2
self._registers = {
reg_1: Byte(),
reg_2: Byte(),
}
self._bytes = 2
self._base = 16
self.keys = self._registers.keys
self.values = self._registers.values
self.items = self._registers.items
return
def __getitem__(self, key):
return self._registers.get(key).read()
def __setitem__(self, key, value):
return self._registers.get(key).write(value)
def __repr__(self):
return f"{self._registers.get(self._reg_1)} {self._registers.get(self._reg_2)}"
def read(self, addr) -> Byte:
return self._registers.get(str(addr).upper())
def read_pair(self) -> str:
bin1 = format(int(str(self._registers.get(self._reg_1).read()), self._base), f"0{self._bytes * 4}b")
bin2 = format(int(str(self._registers.get(self._reg_2).read()), self._base), f"0{self._bytes * 4}b")
bin_total = "".join(["0b", bin1, bin2])
return f'0x{format(int(bin_total, 2), f'0{self._bytes * 2}x')}'
def write(self, data, addr) -> bool:
return self._registers.get(str(addr).upper()).__call__(data)
def write_pair(self, data) -> bool:
mem_size = 8
binary_data = format(int(str(data), self._base), f"0{self._bytes*8}b")
data_1, data_2 = [
format(int(binary_data[mem_size * x : mem_size * (x + 1)], 2), f"#0{int(mem_size/2)}x")
for x in range(0, int(len(binary_data) / mem_size))
]
self._registers.get(str(self._reg_1).upper()).__call__(data_1)
self._registers.get(str(self._reg_2).upper()).__call__(data_2)
return True
class StackPointer(Byte):
def __init__(self, memory, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.memory = memory
def __add__(self, val: int, *args, **kwargs) -> str:
"""
val: `int`
"""
data_int = int(self._data, self._base) + val
if data_int > self._memory_limit:
data_int -= self._memory_limit
elif data_int < 0:
data_int += self._memory_limit
self._data = format(data_int, self._format_spec)
return self._data
def __sub__(self, val: int, *args, **kwargs) -> str:
"""
val: `int`
"""
data_int = int(self._data, self._base) - val
if data_int > self._memory_limit:
data_int -= self._memory_limit
elif data_int < 0:
data_int += self._memory_limit
self._data = format(data_int, self._format_spec)
return self._data
def __next__(self):
return self.__add__(1)
def read(self, *args, **kwargs) -> Byte:
"""
POP rp
"""
bin1 = format(int(str(self.memory[self.__add__(1)]), self._base), f"0{8}b") # single byte
bin2 = format(int(str(self.memory[self.__add__(1)]), self._base), f"0{8}b") # single byte
bin_total = "".join(["0b", bin2, bin1])
return f'0x{format(int(bin_total, 2), f'0{4}x')}'
def write(self, data, *args) -> Byte:
"""
PUSH rp
rp = BC, DE, HL, or PSW
"""
mem_size = 8
binary_data = format(int(str(data), self._base), f"0{self._bytes*8}b")
data_1, data_2 = [
format(int(binary_data[mem_size * x : mem_size * (x + 1)], 2), f"#0{int(mem_size/2)}x")
for x in range(0, int(len(binary_data) / mem_size))
]
self.memory.write(self._data, data_1)
_ = self.__sub__(1)
self.memory.write(self._data, data_2)
_ = self.__sub__(1)
return True
class ProgramCounter(Byte):
def __init__(self, memory, _bytes=2, *args, **kwargs) -> None:
super().__init__(_bytes=2, *args, **kwargs)
self.memory = memory
return
def write(self, data):
self.memory[self._data] = data
self.__next__()
return True
class SuperMemory:
def __init__(self) -> None:
self.memory = Memory(65535, "0x0000")
self.A = Byte()
self.PSW = Byte()
self.BC = RegisterPair("B", "C")
self.DE = RegisterPair("D", "E")
self.HL = RegisterPair("H", "L")
self.SP = StackPointer(self.memory, "0xFFFF", _bytes=2)
self.PC = ProgramCounter(self.memory)
setattr(self.M.__func__, "read", lambda *args: self.memory[self.HL.read_pair()])
setattr(self.M.__func__, "write", lambda data, *args: self.memory.write(self.HL.read_pair(), data))
pass
def M(self):
return
def _reg_inspect(self):
return textwrap.dedent(
f"""
Registers
---------
A/PSW = {self.A} {self.PSW}
BC = {self.BC}
DE = {self.DE}
HL = {self.HL}
SP = {self.SP}
PC = {self.PC}
"""
)
def _registers_todict(self):
return {
"A/PSW": f"{self.A} {self.PSW}",
"BC": f"{self.BC}",
"DE": f"{self.DE}",
"HL": f"{self.HL}",
"SP": f"{self.SP}",
"PC": f"{self.PC}",
}
def inspect(self):
return "\n\n".join([self._reg_inspect(), str(self.memory.sort())])
pass
| import re
import textwrap
from core.exceptions import InvalidMemoryAddress, MemoryLimitExceeded
class Hex:
def __init__(self, data: str = "0x00", _bytes: str = 1, *args, **kwargs) -> None:
self._bytes = _bytes
self._base = 16
self._format_spec = f"#0{2 + _bytes * 2}x"
self._format_spec_bin = f"#0{2 + _bytes * 8}b"
self._memory_limit_hex = "FF" * _bytes
self._memory_limit = int(self._memory_limit_hex, self._base)
self.data = data
return
def __call__(self, value: str) -> None:
self.data = value
def __str__(self) -> str:
return self._data
def __repr__(self) -> str:
return self._data
def __int__(self) -> int:
return int(self._data, self._base)
def __index__(self) -> int:
return int(self._data, self._base)
def __format__(self, format_spec: str = None) -> str:
if not format_spec:
format_spec = self._format_spec
return format(int(self._data, self._base), format_spec)
def __next__(self):
self._data = format(int(self._data, self._base) + 1, self._format_spec)
return self._data
def __add__(self, val: int):
return Hex(format(int(self._data, self._base) + val, self._format_spec), _bytes=self._bytes)
def __sub__(self, val: int):
return Hex(format(int(self._data, self._base) - val, self._format_spec), _bytes=self._bytes)
def __len__(self):
return self._bytes
def _verify(self, value: str):
if not re.fullmatch("^0[x|X][0-9a-fA-F]+", str(value)):
raise InvalidMemoryAddress()
if int(str(value), self._base) > self._memory_limit:
raise MemoryLimitExceeded()
def bin(self) -> str:
return format(int(self._data, self._base), self._format_spec_bin)
@property
def data(self) -> str:
return self._data
@data.setter
def data(self, val: str) -> None:
self._verify(val)
self._data = format(int(str(val), self._base), self._format_spec)
return
def read(self, *args, **kwargs) -> str:
return self
def write(self, val: str, *args, **kwargs) -> bool:
self.data = val
return True
def update(self, val: str, *args, **kwargs) -> bool:
return self.write(val, *args, **kwargs)
def replace(self, *args, **kwargs) -> None:
return self._data.replace(*args, **kwargs)
def lower(self, *args, **kwargs):
return self._data.lower(*args, **kwargs)
def upper(self, *args, **kwargs):
return self._data.upper(*args, **kwargs)
pass
class Byte(Hex):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
pass
class Memory(dict):
def __init__(self, memory_size=65535, starting_address="0x0000", _bytes=2, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self._bytes = 1
self._base = 16
self._memory_size = memory_size
self._starting_address = starting_address
self._default_mem = "0x00"
self._format_spec = f"#0{2 + _bytes * 2}x"
self._format_spec_bin = f"#0{2 + _bytes * 4}b"
self._memory_limit = int(starting_address, 16) + memory_size
self._memory_limit_hex = format(self._memory_limit, self._format_spec)
return
def __getitem__(self, addr: str) -> str:
addr = self._verify(addr)
if addr not in self:
super().__setitem__(addr, Byte(self._default_mem))
return super().__getitem__(addr)
def __setitem__(self, addr: str, value: str) -> None:
addr = self._verify(addr)
if addr not in self:
super().__setitem__(addr, Byte(value))
return super().__getitem__(addr).write(value)
def _verify(self, value: str) -> None:
if not re.fullmatch("^0[x|X][0-9a-fA-F]+", str(value)):
raise InvalidMemoryAddress()
if int(str(value), self._base) > self._memory_limit:
raise MemoryLimitExceeded()
return format(int(value, self._base), self._format_spec)
def sort(self):
return dict(sorted(self.items(), key=lambda x: int(str(x[0]), 16)))
def read(self, *args, **kwargs):
return self.__getitem__(*args, **kwargs)
def write(self, *args, **kwargs):
return self.__setitem__(*args, **kwargs)
pass
class RegisterPair:
def __init__(self, reg_1, reg_2, *args, **kwargs):
super().__init__(*args, **kwargs)
self._reg_1 = reg_1
self._reg_2 = reg_2
self._registers = {
reg_1: Byte(),
reg_2: Byte(),
}
self._bytes = 2
self._base = 16
self.keys = self._registers.keys
self.values = self._registers.values
self.items = self._registers.items
return
def __getitem__(self, key):
return self._registers.get(key).read()
def __setitem__(self, key, value):
return self._registers.get(key).write(value)
def __repr__(self):
return f"{self._registers.get(self._reg_1)} {self._registers.get(self._reg_2)}"
def read(self, addr) -> Byte:
return self._registers.get(str(addr).upper())
def read_pair(self) -> str:
bin1 = format(int(str(self._registers.get(self._reg_1).read()), self._base), f"0{self._bytes * 4}b")
bin2 = format(int(str(self._registers.get(self._reg_2).read()), self._base), f"0{self._bytes * 4}b")
bin_total = "".join(["0b", bin1, bin2])
return f'0x{format(int(bin_total, 2), f"0{self._bytes * 2}x")}'
def write(self, data, addr) -> bool:
return self._registers.get(str(addr).upper()).__call__(data)
def write_pair(self, data) -> bool:
mem_size = 8
binary_data = format(int(str(data), self._base), f"0{self._bytes*8}b")
data_1, data_2 = [
format(int(binary_data[mem_size * x : mem_size * (x + 1)], 2), f"#0{int(mem_size/2)}x")
for x in range(0, int(len(binary_data) / mem_size))
]
self._registers.get(str(self._reg_1).upper()).__call__(data_1)
self._registers.get(str(self._reg_2).upper()).__call__(data_2)
return True
class StackPointer(Byte):
def __init__(self, memory, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.memory = memory
def __add__(self, val: int, *args, **kwargs) -> str:
"""
val: `int`
"""
data_int = int(self._data, self._base) + val
if data_int > self._memory_limit:
data_int -= self._memory_limit
elif data_int < 0:
data_int += self._memory_limit
self._data = format(data_int, self._format_spec)
return self._data
def __sub__(self, val: int, *args, **kwargs) -> str:
"""
val: `int`
"""
data_int = int(self._data, self._base) - val
if data_int > self._memory_limit:
data_int -= self._memory_limit
elif data_int < 0:
data_int += self._memory_limit
self._data = format(data_int, self._format_spec)
return self._data
def __next__(self):
return self.__add__(1)
def read(self, *args, **kwargs) -> Byte:
"""
POP rp
"""
bin1 = format(int(str(self.memory[self.__add__(1)]), self._base), f"0{8}b") # single byte
bin2 = format(int(str(self.memory[self.__add__(1)]), self._base), f"0{8}b") # single byte
bin_total = "".join(["0b", bin2, bin1])
return f'0x{format(int(bin_total, 2), f"0{4}x")}'
def write(self, data, *args) -> Byte:
"""
PUSH rp
rp = BC, DE, HL, or PSW
"""
mem_size = 8
binary_data = format(int(str(data), self._base), f"0{self._bytes*8}b")
data_1, data_2 = [
format(int(binary_data[mem_size * x : mem_size * (x + 1)], 2), f"#0{int(mem_size/2)}x")
for x in range(0, int(len(binary_data) / mem_size))
]
self.memory.write(self._data, data_1)
_ = self.__sub__(1)
self.memory.write(self._data, data_2)
_ = self.__sub__(1)
return True
class ProgramCounter(Byte):
def __init__(self, memory, _bytes=2, *args, **kwargs) -> None:
super().__init__(_bytes=2, *args, **kwargs)
self.memory = memory
return
def write(self, data):
self.memory[self._data] = data
self.__next__()
return True
class SuperMemory:
def __init__(self) -> None:
self.memory = Memory(65535, "0x0000")
self.A = Byte()
self.PSW = Byte()
self.BC = RegisterPair("B", "C")
self.DE = RegisterPair("D", "E")
self.HL = RegisterPair("H", "L")
self.SP = StackPointer(self.memory, "0xFFFF", _bytes=2)
self.PC = ProgramCounter(self.memory)
setattr(self.M.__func__, "read", lambda *args: self.memory[self.HL.read_pair()])
setattr(self.M.__func__, "write", lambda data, *args: self.memory.write(self.HL.read_pair(), data))
pass
def M(self):
return
def _reg_inspect(self):
return textwrap.dedent(
f"""
Registers
---------
A/PSW = {self.A} {self.PSW}
BC = {self.BC}
DE = {self.DE}
HL = {self.HL}
SP = {self.SP}
PC = {self.PC}
"""
)
def _registers_todict(self):
return {
"A/PSW": f"{self.A} {self.PSW}",
"BC": f"{self.BC}",
"DE": f"{self.DE}",
"HL": f"{self.HL}",
"SP": f"{self.SP}",
"PC": f"{self.PC}",
}
def inspect(self):
return "\n\n".join([self._reg_inspect(), str(self.memory.sort())])
pass
|
from typing import TYPE_CHECKING, List, Optional, Dict, Any
from dis_snek.client.const import MISSING
from dis_snek.client.utils.attr_utils import define, field
from dis_snek.client.utils.converters import optional
from dis_snek.models.discord.asset import Asset
from dis_snek.models.discord.enums import ApplicationFlags
from dis_snek.models.discord.snowflake import Snowflake_Type, to_snowflake
from dis_snek.models.discord.team import Team
from .base import DiscordObject
if TYPE_CHECKING:
from dis_snek.client import Snake
from dis_snek.models import User
__all__ = ["Application"]
@define()
class Application(DiscordObject):
"""Represents a discord application."""
name: str = field(repr=True)
"""The name of the application"""
icon: Optional[Asset] = field(default=None)
"""The icon of the application"""
description: Optional[str] = field(default=None)
"""The description of the application"""
rpc_origins: Optional[List[str]] = field(default=None)
"""An array of rpc origin urls, if rpc is enabled"""
bot_public: bool = field(default=True)
"""When false only app owner can join the app's bot to guilds"""
bot_require_code_grant: bool = field(default=False)
"""When true the app's bot will only join upon completion of the full oauth2 code grant flow"""
terms_of_service_url: Optional[str] = field(default=None)
"""The url of the app's terms of service"""
privacy_policy_url: Optional[str] = field(default=None)
"""The url of the app's privacy policy"""
owner_id: Optional[Snowflake_Type] = field(default=None, converter=optional(to_snowflake))
"""The id of the owner of the application"""
summary: str = field()
"""If this application is a game sold on Discord, this field will be the summary field for the store page of its primary sku"""
verify_key: Optional[str] = field(default=MISSING)
"""The hex encoded key for verification in interactions and the GameSDK's GetTicket"""
team: Optional["Team"] = field(default=None)
"""If the application belongs to a team, this will be a list of the members of that team"""
guild_id: Optional["Snowflake_Type"] = field(default=None)
"""If this application is a game sold on Discord, this field will be the guild to which it has been linked"""
primary_sku_id: Optional["Snowflake_Type"] = field(default=None)
"""If this application is a game sold on Discord, this field will be the id of the "Game SKU" that is created, if exists"""
slug: Optional[str] = field(default=None)
"""If this application is a game sold on Discord, this field will be the URL slug that links to the store page"""
cover_image: Optional[str] = field(default=None)
"""The application's default rich presence invite cover image hash"""
flags: Optional["ApplicationFlags"] = field(default=None, converter=optional(ApplicationFlags))
"""The application's public flags"""
@classmethod
def _process_dict(cls, data: Dict[str, Any], client: "Snake") -> Dict[str, Any]:
if data.get("team"):
data["team"] = Team.from_dict(data["team"], client)
data["owner_id"] = data["team"].owner_user_id
else:
if "owner" in data:
owner = client.cache.place_user_data(data.pop("owner"))
data["owner_id"] = owner.id
if data.get("icon"):
data["icon"] = Asset.from_path_hash(client, f"app-icons/{data["id"]}/{{}}", data["icon"])
return data
@property
def owner(self) -> "User":
"""The user object for the owner of this application"""
return self._client.cache.get_user(self.owner_id)
| from typing import TYPE_CHECKING, List, Optional, Dict, Any
from dis_snek.client.const import MISSING
from dis_snek.client.utils.attr_utils import define, field
from dis_snek.client.utils.converters import optional
from dis_snek.models.discord.asset import Asset
from dis_snek.models.discord.enums import ApplicationFlags
from dis_snek.models.discord.snowflake import Snowflake_Type, to_snowflake
from dis_snek.models.discord.team import Team
from .base import DiscordObject
if TYPE_CHECKING:
from dis_snek.client import Snake
from dis_snek.models import User
__all__ = ["Application"]
@define()
class Application(DiscordObject):
"""Represents a discord application."""
name: str = field(repr=True)
"""The name of the application"""
icon: Optional[Asset] = field(default=None)
"""The icon of the application"""
description: Optional[str] = field(default=None)
"""The description of the application"""
rpc_origins: Optional[List[str]] = field(default=None)
"""An array of rpc origin urls, if rpc is enabled"""
bot_public: bool = field(default=True)
"""When false only app owner can join the app's bot to guilds"""
bot_require_code_grant: bool = field(default=False)
"""When true the app's bot will only join upon completion of the full oauth2 code grant flow"""
terms_of_service_url: Optional[str] = field(default=None)
"""The url of the app's terms of service"""
privacy_policy_url: Optional[str] = field(default=None)
"""The url of the app's privacy policy"""
owner_id: Optional[Snowflake_Type] = field(default=None, converter=optional(to_snowflake))
"""The id of the owner of the application"""
summary: str = field()
"""If this application is a game sold on Discord, this field will be the summary field for the store page of its primary sku"""
verify_key: Optional[str] = field(default=MISSING)
"""The hex encoded key for verification in interactions and the GameSDK's GetTicket"""
team: Optional["Team"] = field(default=None)
"""If the application belongs to a team, this will be a list of the members of that team"""
guild_id: Optional["Snowflake_Type"] = field(default=None)
"""If this application is a game sold on Discord, this field will be the guild to which it has been linked"""
primary_sku_id: Optional["Snowflake_Type"] = field(default=None)
"""If this application is a game sold on Discord, this field will be the id of the "Game SKU" that is created, if exists"""
slug: Optional[str] = field(default=None)
"""If this application is a game sold on Discord, this field will be the URL slug that links to the store page"""
cover_image: Optional[str] = field(default=None)
"""The application's default rich presence invite cover image hash"""
flags: Optional["ApplicationFlags"] = field(default=None, converter=optional(ApplicationFlags))
"""The application's public flags"""
@classmethod
def _process_dict(cls, data: Dict[str, Any], client: "Snake") -> Dict[str, Any]:
if data.get("team"):
data["team"] = Team.from_dict(data["team"], client)
data["owner_id"] = data["team"].owner_user_id
else:
if "owner" in data:
owner = client.cache.place_user_data(data.pop("owner"))
data["owner_id"] = owner.id
if data.get("icon"):
data["icon"] = Asset.from_path_hash(client, f"app-icons/{data['id']}/{{}}", data["icon"])
return data
@property
def owner(self) -> "User":
"""The user object for the owner of this application"""
return self._client.cache.get_user(self.owner_id)
|
import re
import sys
from collections import defaultdict
from typing import TYPE_CHECKING, AbstractSet, List, NamedTuple, Optional
from dagster.core.definitions.dependency import DependencyStructure, Node
from dagster.core.errors import DagsterExecutionStepNotFoundError, DagsterInvalidSubsetError
from dagster.utils import check
MAX_NUM = sys.maxsize
if TYPE_CHECKING:
from dagster.core.execution.plan.plan import ExecutionPlan
class OpSelectionData(
NamedTuple(
"_OpSelectionData",
[
("resolved_op_selection", Optional[AbstractSet[str]]),
("ignored_solids", List[Node]),
],
)
):
"""The data about op selection.
Attributes:
resolved_op_selection (Optional[AbstractSet[str]])): The names of selected ops.
ignored_solids (List[Node]): The solids in the original full graph but outside the current
selection. This is used in run config resolution to handle unsatisfied inputs correctly.
"""
def __new__(cls, resolved_op_selection=None, ignored_solids=None):
return super(OpSelectionData, cls).__new__(
cls,
resolved_op_selection=check.opt_set_param(
resolved_op_selection, "resolved_op_selection", str
),
ignored_solids=check.opt_list_param(ignored_solids, "ignored_solids", Node),
)
def generate_dep_graph(pipeline_def):
"""'pipeline to dependency graph. It currently only supports top-level solids.
Args:
pipeline (PipelineDefinition): The pipeline to execute.
Returns:
graph (Dict[str, Dict[str, Set[str]]]): the input and output dependency graph. e.g.
```
{
"upstream": {
"solid_one_1": set(),
"solid_one_2": set(),
"solid_two": {"solid_one_1", "solid_one_2"},
"solid_three": {"solid_two"},
},
"downstream": {
"solid_one_1": {"solid_two"},
"solid_one_2": {"solid_two"},
"solid_two": {"solid_three"},
"solid_three": set(),
},
}
```
"""
dependency_structure = check.inst_param(
pipeline_def.dependency_structure, "dependency_structure", DependencyStructure
)
item_names = [i.name for i in pipeline_def.solids]
# defaultdict isn't appropriate because we also want to include items without dependencies
graph = {"upstream": {}, "downstream": {}}
for item_name in item_names:
graph["upstream"][item_name] = set()
upstream_dep = dependency_structure.input_to_upstream_outputs_for_solid(item_name)
for upstreams in upstream_dep.values():
for up in upstreams:
graph["upstream"][item_name].add(up.solid_name)
graph["downstream"][item_name] = set()
downstream_dep = dependency_structure.output_to_downstream_inputs_for_solid(item_name)
for downstreams in downstream_dep.values():
for down in downstreams:
graph["downstream"][item_name].add(down.solid_name)
return graph
class Traverser:
def __init__(self, graph):
self.graph = graph
def _fetch_items(self, item_name, depth, direction):
dep_graph = self.graph[direction]
stack = [item_name]
result = set()
curr_depth = 0
while stack:
# stop when reach the given depth
if curr_depth >= depth:
break
curr_level_len = len(stack)
while stack and curr_level_len > 0:
curr_item = stack.pop()
for item in dep_graph.get(curr_item, set()):
curr_level_len -= 1
if item not in result:
stack.append(item)
result.add(item)
curr_depth += 1
return result
def fetch_upstream(self, item_name, depth):
# return a set of ancestors of the given item, up to the given depth
return self._fetch_items(item_name, depth, "upstream")
def fetch_downstream(self, item_name, depth):
# return a set of descendants of the given item, down to the given depth
return self._fetch_items(item_name, depth, "downstream")
def parse_clause(clause):
def _get_depth(part):
if part == "":
return 0
if "*" in part:
return MAX_NUM
if set(part) == set("+"):
return len(part)
return None
token_matching = re.compile(r"^(\*?\+*)?([.\w\d\[\]?_-]+)(\+*\*?)?$").search(clause.strip())
# return None if query is invalid
parts = token_matching.groups() if token_matching is not None else []
if len(parts) != 3:
return None
ancestor_part, item_name, descendant_part = parts
up_depth = _get_depth(ancestor_part)
down_depth = _get_depth(descendant_part)
return (up_depth, item_name, down_depth)
def parse_items_from_selection(selection):
items = []
for clause in selection:
parts = parse_clause(clause)
if parts is None:
continue
_u, item, _d = parts
items.append(item)
return items
def clause_to_subset(graph, clause):
"""Take a selection query and return a list of the selected and qualified items.
Args:
graph (Dict[str, Dict[str, Set[str]]]): the input and output dependency graph.
clause (str): the subselection query in model selection syntax, e.g. "*some_solid+" will
select all of some_solid's upstream dependencies and its direct downstream dependecies.
Returns:
subset_list (List[str]): a list of selected and qualified solid names, empty if input is
invalid.
"""
# parse cluase
if not isinstance(clause, str):
return []
parts = parse_clause(clause)
if parts is None:
return []
up_depth, item_name, down_depth = parts
# item_name invalid
if item_name not in graph["upstream"]:
return []
subset_list = []
traverser = Traverser(graph=graph)
subset_list.append(item_name)
# traverse graph to get up/downsteam items
subset_list += traverser.fetch_upstream(item_name, up_depth)
subset_list += traverser.fetch_downstream(item_name, down_depth)
return subset_list
def parse_solid_selection(pipeline_def, solid_selection):
"""Take pipeline definition and a list of solid selection queries (inlcuding names of solid
invocations. See syntax examples below) and return a set of the qualified solid names.
It currently only supports top-level solids.
Query syntax examples:
- "some_solid": select "some_solid" itself
- "*some_solid": select "some_solid" and all ancestors (upstream dependencies)
- "some_solid*": select "some_solid" and all descendants (downstream dependencies)
- "*some_solid*": select "some_solid" and all of its ancestors and descendants
- "+some_solid": select "some_solid" and its ancestors at 1 level up
- "some_solid+++": select "some_solid" and its descendants within 3 levels down
Note:
- If one of the query clauses is invalid, we will skip that one and continue to parse the valid
ones.
Args:
pipeline_def (PipelineDefinition): the pipeline to execute.
solid_selection (List[str]): a list of the solid selection queries (including single solid
names) to execute.
Returns:
FrozenSet[str]: a frozenset of qualified deduplicated solid names, empty if no qualified
subset selected.
"""
check.list_param(solid_selection, "solid_selection", of_type=str)
# special case: select all
if len(solid_selection) == 1 and solid_selection[0] == "*":
return frozenset(pipeline_def.graph.node_names())
graph = generate_dep_graph(pipeline_def)
solids_set = set()
# loop over clauses
for clause in solid_selection:
subset = clause_to_subset(graph, clause)
if len(subset) == 0:
raise DagsterInvalidSubsetError(
"No qualified solids to execute found for solid_selection={requested}".format(
requested=solid_selection
)
)
solids_set.update(subset)
return frozenset(solids_set)
def parse_step_selection(step_deps, step_selection):
"""Take the dependency dictionary generated while building execution plan and a list of step key
selection queries and return a set of the qualified step keys.
It currently only supports top-level solids.
Args:
step_deps (Dict[str, Set[str]]): a dictionary of execution step dependency where the key is
a step key and the value is a set of direct upstream dependency of the step.
step_selection (List[str]): a list of the step key selection queries (including single
step key) to execute.
Returns:
FrozenSet[str]: a frozenset of qualified deduplicated solid names, empty if no qualified
subset selected.
"""
check.list_param(step_selection, "step_selection", of_type=str)
# reverse step_deps to get the downstream_deps
# make sure we have all items as keys, including the ones without downstream dependencies
downstream_deps = defaultdict(set, {k: set() for k in step_deps.keys()})
for downstream_key, upstream_keys in step_deps.items():
for step_key in upstream_keys:
downstream_deps[step_key].add(downstream_key)
# generate dep graph
graph = {"upstream": step_deps, "downstream": downstream_deps}
steps_set = set()
step_keys = parse_items_from_selection(step_selection)
invalid_keys = [key for key in step_keys if key not in step_deps]
if invalid_keys:
raise DagsterExecutionStepNotFoundError(
f"Step selection refers to unknown step{"s" if len(invalid_keys)> 1 else ""}: {", ".join(invalid_keys)}",
step_keys=invalid_keys,
)
# loop over clauses
for clause in step_selection:
subset = clause_to_subset(graph, clause)
if len(subset) == 0:
raise DagsterInvalidSubsetError(
"No qualified steps to execute found for step_selection={requested}".format(
requested=step_selection
),
)
steps_set.update(subset)
return frozenset(steps_set)
| import re
import sys
from collections import defaultdict
from typing import TYPE_CHECKING, AbstractSet, List, NamedTuple, Optional
from dagster.core.definitions.dependency import DependencyStructure, Node
from dagster.core.errors import DagsterExecutionStepNotFoundError, DagsterInvalidSubsetError
from dagster.utils import check
MAX_NUM = sys.maxsize
if TYPE_CHECKING:
from dagster.core.execution.plan.plan import ExecutionPlan
class OpSelectionData(
NamedTuple(
"_OpSelectionData",
[
("resolved_op_selection", Optional[AbstractSet[str]]),
("ignored_solids", List[Node]),
],
)
):
"""The data about op selection.
Attributes:
resolved_op_selection (Optional[AbstractSet[str]])): The names of selected ops.
ignored_solids (List[Node]): The solids in the original full graph but outside the current
selection. This is used in run config resolution to handle unsatisfied inputs correctly.
"""
def __new__(cls, resolved_op_selection=None, ignored_solids=None):
return super(OpSelectionData, cls).__new__(
cls,
resolved_op_selection=check.opt_set_param(
resolved_op_selection, "resolved_op_selection", str
),
ignored_solids=check.opt_list_param(ignored_solids, "ignored_solids", Node),
)
def generate_dep_graph(pipeline_def):
"""'pipeline to dependency graph. It currently only supports top-level solids.
Args:
pipeline (PipelineDefinition): The pipeline to execute.
Returns:
graph (Dict[str, Dict[str, Set[str]]]): the input and output dependency graph. e.g.
```
{
"upstream": {
"solid_one_1": set(),
"solid_one_2": set(),
"solid_two": {"solid_one_1", "solid_one_2"},
"solid_three": {"solid_two"},
},
"downstream": {
"solid_one_1": {"solid_two"},
"solid_one_2": {"solid_two"},
"solid_two": {"solid_three"},
"solid_three": set(),
},
}
```
"""
dependency_structure = check.inst_param(
pipeline_def.dependency_structure, "dependency_structure", DependencyStructure
)
item_names = [i.name for i in pipeline_def.solids]
# defaultdict isn't appropriate because we also want to include items without dependencies
graph = {"upstream": {}, "downstream": {}}
for item_name in item_names:
graph["upstream"][item_name] = set()
upstream_dep = dependency_structure.input_to_upstream_outputs_for_solid(item_name)
for upstreams in upstream_dep.values():
for up in upstreams:
graph["upstream"][item_name].add(up.solid_name)
graph["downstream"][item_name] = set()
downstream_dep = dependency_structure.output_to_downstream_inputs_for_solid(item_name)
for downstreams in downstream_dep.values():
for down in downstreams:
graph["downstream"][item_name].add(down.solid_name)
return graph
class Traverser:
def __init__(self, graph):
self.graph = graph
def _fetch_items(self, item_name, depth, direction):
dep_graph = self.graph[direction]
stack = [item_name]
result = set()
curr_depth = 0
while stack:
# stop when reach the given depth
if curr_depth >= depth:
break
curr_level_len = len(stack)
while stack and curr_level_len > 0:
curr_item = stack.pop()
for item in dep_graph.get(curr_item, set()):
curr_level_len -= 1
if item not in result:
stack.append(item)
result.add(item)
curr_depth += 1
return result
def fetch_upstream(self, item_name, depth):
# return a set of ancestors of the given item, up to the given depth
return self._fetch_items(item_name, depth, "upstream")
def fetch_downstream(self, item_name, depth):
# return a set of descendants of the given item, down to the given depth
return self._fetch_items(item_name, depth, "downstream")
def parse_clause(clause):
def _get_depth(part):
if part == "":
return 0
if "*" in part:
return MAX_NUM
if set(part) == set("+"):
return len(part)
return None
token_matching = re.compile(r"^(\*?\+*)?([.\w\d\[\]?_-]+)(\+*\*?)?$").search(clause.strip())
# return None if query is invalid
parts = token_matching.groups() if token_matching is not None else []
if len(parts) != 3:
return None
ancestor_part, item_name, descendant_part = parts
up_depth = _get_depth(ancestor_part)
down_depth = _get_depth(descendant_part)
return (up_depth, item_name, down_depth)
def parse_items_from_selection(selection):
items = []
for clause in selection:
parts = parse_clause(clause)
if parts is None:
continue
_u, item, _d = parts
items.append(item)
return items
def clause_to_subset(graph, clause):
"""Take a selection query and return a list of the selected and qualified items.
Args:
graph (Dict[str, Dict[str, Set[str]]]): the input and output dependency graph.
clause (str): the subselection query in model selection syntax, e.g. "*some_solid+" will
select all of some_solid's upstream dependencies and its direct downstream dependecies.
Returns:
subset_list (List[str]): a list of selected and qualified solid names, empty if input is
invalid.
"""
# parse cluase
if not isinstance(clause, str):
return []
parts = parse_clause(clause)
if parts is None:
return []
up_depth, item_name, down_depth = parts
# item_name invalid
if item_name not in graph["upstream"]:
return []
subset_list = []
traverser = Traverser(graph=graph)
subset_list.append(item_name)
# traverse graph to get up/downsteam items
subset_list += traverser.fetch_upstream(item_name, up_depth)
subset_list += traverser.fetch_downstream(item_name, down_depth)
return subset_list
def parse_solid_selection(pipeline_def, solid_selection):
"""Take pipeline definition and a list of solid selection queries (inlcuding names of solid
invocations. See syntax examples below) and return a set of the qualified solid names.
It currently only supports top-level solids.
Query syntax examples:
- "some_solid": select "some_solid" itself
- "*some_solid": select "some_solid" and all ancestors (upstream dependencies)
- "some_solid*": select "some_solid" and all descendants (downstream dependencies)
- "*some_solid*": select "some_solid" and all of its ancestors and descendants
- "+some_solid": select "some_solid" and its ancestors at 1 level up
- "some_solid+++": select "some_solid" and its descendants within 3 levels down
Note:
- If one of the query clauses is invalid, we will skip that one and continue to parse the valid
ones.
Args:
pipeline_def (PipelineDefinition): the pipeline to execute.
solid_selection (List[str]): a list of the solid selection queries (including single solid
names) to execute.
Returns:
FrozenSet[str]: a frozenset of qualified deduplicated solid names, empty if no qualified
subset selected.
"""
check.list_param(solid_selection, "solid_selection", of_type=str)
# special case: select all
if len(solid_selection) == 1 and solid_selection[0] == "*":
return frozenset(pipeline_def.graph.node_names())
graph = generate_dep_graph(pipeline_def)
solids_set = set()
# loop over clauses
for clause in solid_selection:
subset = clause_to_subset(graph, clause)
if len(subset) == 0:
raise DagsterInvalidSubsetError(
"No qualified solids to execute found for solid_selection={requested}".format(
requested=solid_selection
)
)
solids_set.update(subset)
return frozenset(solids_set)
def parse_step_selection(step_deps, step_selection):
"""Take the dependency dictionary generated while building execution plan and a list of step key
selection queries and return a set of the qualified step keys.
It currently only supports top-level solids.
Args:
step_deps (Dict[str, Set[str]]): a dictionary of execution step dependency where the key is
a step key and the value is a set of direct upstream dependency of the step.
step_selection (List[str]): a list of the step key selection queries (including single
step key) to execute.
Returns:
FrozenSet[str]: a frozenset of qualified deduplicated solid names, empty if no qualified
subset selected.
"""
check.list_param(step_selection, "step_selection", of_type=str)
# reverse step_deps to get the downstream_deps
# make sure we have all items as keys, including the ones without downstream dependencies
downstream_deps = defaultdict(set, {k: set() for k in step_deps.keys()})
for downstream_key, upstream_keys in step_deps.items():
for step_key in upstream_keys:
downstream_deps[step_key].add(downstream_key)
# generate dep graph
graph = {"upstream": step_deps, "downstream": downstream_deps}
steps_set = set()
step_keys = parse_items_from_selection(step_selection)
invalid_keys = [key for key in step_keys if key not in step_deps]
if invalid_keys:
raise DagsterExecutionStepNotFoundError(
f"Step selection refers to unknown step{'s' if len(invalid_keys)> 1 else ''}: {', '.join(invalid_keys)}",
step_keys=invalid_keys,
)
# loop over clauses
for clause in step_selection:
subset = clause_to_subset(graph, clause)
if len(subset) == 0:
raise DagsterInvalidSubsetError(
"No qualified steps to execute found for step_selection={requested}".format(
requested=step_selection
),
)
steps_set.update(subset)
return frozenset(steps_set)
|
# makes a string of 50 equals signs for a line break
separator = "=" * 50
if True: print("hi") else: print("no")
# creates a 2d list with 10 rows
car_park = [[] for i in range(10)]
# filling the car park
for i in range(10):
for j in range(6):
car_park[i].append("E") # e stands for empty
# creates the interface for allowing the user to leave
def leave_menu():
print(separator)
print("Do you want to try again or exit to main menu?")
print("1 = Try again.")
print("2 = Exit to main menu.")
print(separator)
exit_choice = input(">>> ")
if exit_choice == "2":
return True
else:
return False
# goes through each index of the park and makes it "e"
def empty_park():
for i in range(10):
for j in range(6):
car_park[i][j] = "E"
print(separator)
print("Success, all spaces now are empty.")
# allows the user to remove a car based on its plate
def remove_car():
removed = False
while not(removed):
print(separator)
print("What is the number plate of the car?")
print(separator)
plate_number = input(">>> ")
for index_i, i in enumerate(car_park):
for index_j, j in enumerate(i):
if j == plate_number:
car_park[index_i][index_j] = "E";
removed = True
if not(removed):
print(separator)
print(f"Could not find car with registration '{plate_number}', please try again.")
if leave_menu():
break
else:
continue
else:
print(separator)
print(f"Congratulations car with registration '{plate_number}' has\nsuccessfully been removed.")
# allows the user to add a number plate to the garage
def park_car():
while True:
print(separator)
print("What row are you parking in?")
print("Rows must be between 1 and 10.")
print(separator)
row = input(">>> ")
print(separator)
print("What space are you parking in?")
print("Spaces must be between 1 and 6.")
print(separator)
column = input(">>> ")
print(separator)
print("What is your registration number?")
print(separator)
plate_number = input(">>> ")
print(separator)
print(f"Car with registration '{plate_number}' has\nsuccessfully been parked in row {row}, column {column}.")
try:
row = int(row)
column = int(column)
column_arr = []
car_park[row - 1][column - 1] = plate_number
for c, i in enumerate(car_park):
column_arr.append(car_park[c][column - 1])
max_length = len(max(column_arr, key=len))
for c, i in enumerate(car_park):
i[column - 1] = i[column - 1] + (" " * (max_length - len(i[column - 1])))
break
except:
print(separator)
print(f"Invalid input detected, please try again.")
if leave_menu():
break
else:
continue
# displays the entire garage in a neat format for the user
def show_park():
print(separator)
print("Parking Garage: ")
print(" ")
for c, i in enumerate(car_park):
print(f"[ {" ".join(i)} ]")
print("\n* E stands for empty.")
while True:
print(separator)
print("What would you like to do?")
print("1 = Set all spaces to empty.")
print("2 = Park a car.")
print("3 = Remove a car.")
print("4 = Display the parking grid.")
print("5 = Quit.")
print(separator)
option_chosen = input(">>> ")
if option_chosen == "1":
empty_park()
elif option_chosen == "2":
park_car()
elif option_chosen == "3":
remove_car()
elif option_chosen == "4":
show_park()
elif option_chosen == "5":
print(separator)
print("Success, program has quit.")
print(separator)
quit()
else:
print(separator)
print("Option not recognised, please try again")
| # makes a string of 50 equals signs for a line break
separator = "=" * 50
if True: print("hi") else: print("no")
# creates a 2d list with 10 rows
car_park = [[] for i in range(10)]
# filling the car park
for i in range(10):
for j in range(6):
car_park[i].append("E") # e stands for empty
# creates the interface for allowing the user to leave
def leave_menu():
print(separator)
print("Do you want to try again or exit to main menu?")
print("1 = Try again.")
print("2 = Exit to main menu.")
print(separator)
exit_choice = input(">>> ")
if exit_choice == "2":
return True
else:
return False
# goes through each index of the park and makes it "e"
def empty_park():
for i in range(10):
for j in range(6):
car_park[i][j] = "E"
print(separator)
print("Success, all spaces now are empty.")
# allows the user to remove a car based on its plate
def remove_car():
removed = False
while not(removed):
print(separator)
print("What is the number plate of the car?")
print(separator)
plate_number = input(">>> ")
for index_i, i in enumerate(car_park):
for index_j, j in enumerate(i):
if j == plate_number:
car_park[index_i][index_j] = "E";
removed = True
if not(removed):
print(separator)
print(f"Could not find car with registration '{plate_number}', please try again.")
if leave_menu():
break
else:
continue
else:
print(separator)
print(f"Congratulations car with registration '{plate_number}' has\nsuccessfully been removed.")
# allows the user to add a number plate to the garage
def park_car():
while True:
print(separator)
print("What row are you parking in?")
print("Rows must be between 1 and 10.")
print(separator)
row = input(">>> ")
print(separator)
print("What space are you parking in?")
print("Spaces must be between 1 and 6.")
print(separator)
column = input(">>> ")
print(separator)
print("What is your registration number?")
print(separator)
plate_number = input(">>> ")
print(separator)
print(f"Car with registration '{plate_number}' has\nsuccessfully been parked in row {row}, column {column}.")
try:
row = int(row)
column = int(column)
column_arr = []
car_park[row - 1][column - 1] = plate_number
for c, i in enumerate(car_park):
column_arr.append(car_park[c][column - 1])
max_length = len(max(column_arr, key=len))
for c, i in enumerate(car_park):
i[column - 1] = i[column - 1] + (" " * (max_length - len(i[column - 1])))
break
except:
print(separator)
print(f"Invalid input detected, please try again.")
if leave_menu():
break
else:
continue
# displays the entire garage in a neat format for the user
def show_park():
print(separator)
print("Parking Garage: ")
print(" ")
for c, i in enumerate(car_park):
print(f"[ {' '.join(i)} ]")
print("\n* E stands for empty.")
while True:
print(separator)
print("What would you like to do?")
print("1 = Set all spaces to empty.")
print("2 = Park a car.")
print("3 = Remove a car.")
print("4 = Display the parking grid.")
print("5 = Quit.")
print(separator)
option_chosen = input(">>> ")
if option_chosen == "1":
empty_park()
elif option_chosen == "2":
park_car()
elif option_chosen == "3":
remove_car()
elif option_chosen == "4":
show_park()
elif option_chosen == "5":
print(separator)
print("Success, program has quit.")
print(separator)
quit()
else:
print(separator)
print("Option not recognised, please try again")
|
import numpy as np
import cv2
import random
import warnings
import scipy
from scipy.linalg.basic import solve_circulant
import skimage
import skimage.transform
from distutils.version import LooseVersion
import torch
import math
import json
from torch.functional import Tensor
np.random.seed(42)
def load_points_dataset(f_p):
"""load keypoints(head/tail)from the text file
Args:
f_p ([str]): File path containing the head and tail points (x,y) of each fruit in the image. Each Image can have multiple fruits
Returns:
[dict]: Dictionary of file names as keys and corresponding fruit points as values
"""
with open(f_p, "r") as f:
all_lines = f.readlines()
points = {}
i = 0
while i < len(all_lines):
if i > len(all_lines):
break
line = all_lines[i].split(",")
label = line[0]
file = line[3]
first_point = None
second_point = None
if label == "head":
first_point = (int(line[1]), int(line[2]))
elif label == "tail":
second_point = (int(line[1]), int(line[2]))
i += 1
if i < len(all_lines):
line2 = all_lines[i].split(",")
if line2[3] == file:
if line2[0] == "head":
first_point = (int(line2[1]), int(line2[2]))
elif line2[0] == "tail":
second_point = (int(line2[1]), int(line2[2]))
i += 1
if file in points:
# file already in dictionary append the list
# print(f"Appending the file to existing one {file}")
points[file].append([first_point, second_point])
else:
points[file] = [[first_point, second_point]]
return points
def load_points_dataset_2(f_p,label_name=["head","tail"]):
"""load keypoints(head/tail)from the text file
Args:
f_p ([str]): File path containing the head and tail points (x,y) of each fruit in the image. Each Image can have multiple fruits
Returns:
[dict]: Dictionary of file names as keys and corresponding fruit points as values
"""
with open(f_p, "r") as f:
all_lines = f.readlines()
points = {}
i = 0
while i < len(all_lines):
if i > len(all_lines):
break
line = all_lines[i].split(",")
label = line[0]
file = line[3]
first_point = None
second_point = None
if label == label_name[0]:
first_point = (int(line[1]), int(line[2]))
elif label == label_name[1]:
second_point = (int(line[1]), int(line[2]))
i += 1
if i < len(all_lines):
line2 = all_lines[i].split(",")
if line2[3] == file:
if line2[0] == label_name[0]:
first_point = (int(line2[1]), int(line2[2]))
elif line2[0] == label_name[1]:
second_point = (int(line2[1]), int(line2[2]))
i += 1
if not first_point and not second_point:
continue
if file in points:
# file already in dictionary append the list
# print(f"Appending the file to existing one {file}")
points[file].append([first_point, second_point])
else:
points[file] = [[first_point, second_point]]
return points
def load_class_dataset(f_p,label_name=["rating","neck"]):
"""load keypoints(head/tail)from the text file
Args:
f_p ([str]): File path containing the head and tail points (x,y) of each fruit in the image. Each Image can have multiple fruits
Returns:
[dict]: Dictionary of file names as keys and corresponding fruit points as values
"""
with open(f_p, "r") as f:
all_lines = f.readlines()
points = {}
i = 0
while i < len(all_lines):
if i > len(all_lines):
break
line = all_lines[i].split(",")
label = line[0]
splitted_labels=label.split("_")
file = line[3]
coords= None
if splitted_labels[0] in label_name:
coords = (int(line[1]), int(line[2]))
i += 1
if coords is None:
continue
if file in points:
# file already in dictionary append the list
# print(f"Appending the file to existing one {file}")
if splitted_labels[0] in points[file]:
points[file][splitted_labels[0]].append([coords,int(splitted_labels[1])])
else:
points[file][splitted_labels[0]]=[[coords,int(splitted_labels[1])]]
else:
points[file]={splitted_labels[0]:[[coords, int(splitted_labels[1])]]}
return points
def load_segmentation_dataset(f_p,label_names=None):
""""
Returns:
[dict]: Dictionary of list with names
"""
data=load_json(f_p)
cat_map={}
for cat in data["categories"]:
if cat["name"] in label_names:
cat_map[cat['id']]=cat["name"]
image_map={}
for cat in data["images"]:
image_map[cat['id']]=cat["file_name"]
annos={}
for d in data["annotations"]:
tmp=[]
seg=d["segmentation"][0]
for i in range(0,len(seg)-1,2):
tmp.append([seg[i],seg[i+1]])
if image_map[d["image_id"]] not in annos:
annos[image_map[d["image_id"]]]=[{"class_id":cat_map[d["category_id"]],"annotation":tmp}]
else:
annos[image_map[d["image_id"]]].append({"class_id":cat_map[d["category_id"]],"annotation":tmp})
return annos
def load_backbone(filename):
with open(filename) as f:
back_annotation = json.load(f)
return back_annotation
def load_json(filename):
with open(filename) as f:
annotation = json.load(f)
return annotation
def extract_bboxes(mask):
"""Compute bounding boxes from masks.
mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
Returns: bbox array [num_instances, (y1, x1, y2, x2)].
"""
#print(np.max(mask))
#print(np.min(mask))
boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
for i in range(mask.shape[-1]):
m = mask[:, :, i]
# Bounding box.
horizontal_indicies = np.where(np.any(m, axis=0))[0]
vertical_indicies = np.where(np.any(m, axis=1))[0]
if horizontal_indicies.shape[0]:
x1, x2 = horizontal_indicies[[0, -1]]
y1, y2 = vertical_indicies[[0, -1]]
# x2 and y2 should not be part of the box. Increment by 1.
x2 += 1
y2 += 1
else:
# No mask for this instance. Might happen due to
# resizing or cropping. Set bbox to zeros
x1, x2, y1, y2 = 0, 0, 0, 0
boxes[i] = np.array([x1,y1,x2,y2])
return boxes.astype(np.int32)
def resize(
image,
output_shape,
order=1,
mode="constant",
cval=0,
clip=True,
preserve_range=False,
anti_aliasing=False,
anti_aliasing_sigma=None,
):
"""A wrapper for Scikit-Image resize().
Scikit-Image generates warnings on every call to resize() if it doesn't
receive the right parameters. The right parameters depend on the version
of skimage. This solves the problem by using different parameters per
version. And it provides a central place to control resizing defaults.
"""
if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
# New in 0.14: anti_aliasing. Default it to False for backward
# compatibility with skimage 0.13.
return skimage.transform.resize(
image,
output_shape,
order=order,
mode=mode,
cval=cval,
clip=clip,
preserve_range=preserve_range,
anti_aliasing=anti_aliasing,
anti_aliasing_sigma=anti_aliasing_sigma,
)
else:
return skimage.transform.resize(
image,
output_shape,
order=order,
mode=mode,
cval=cval,
clip=clip,
preserve_range=preserve_range,
)
def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"):
"""Resizes an image keeping the aspect ratio unchanged.
min_dim: if provided, resizes the image such that it's smaller
dimension == min_dim
max_dim: if provided, ensures that the image longest side doesn't
exceed this value.
min_scale: if provided, ensure that the image is scaled up by at least
this percent even if min_dim doesn't require it.
mode: Resizing mode.
none: No resizing. Return the image unchanged.
square: Resize and pad with zeros to get a square image
of size [max_dim, max_dim].
pad64: Pads width and height with zeros to make them multiples of 64.
If min_dim or min_scale are provided, it scales the image up
before padding. max_dim is ignored in this mode.
The multiple of 64 is needed to ensure smooth scaling of feature
maps up and down the 6 levels of the FPN pyramid (2**6=64).
crop: Picks random crops from the image. First, scales the image based
on min_dim and min_scale, then picks a random crop of
size min_dim x min_dim. Can be used in training only.
max_dim is not used in this mode.
Returns:
image: the resized image
window: (y1, x1, y2, x2). If max_dim is provided, padding might
be inserted in the returned image. If so, this window is the
coordinates of the image part of the full image (excluding
the padding). The x2, y2 pixels are not included.
scale: The scale factor used to resize the image
padding: Padding added to the image [(top, bottom), (left, right), (0, 0)]
"""
# Keep track of image dtype and return results in the same dtype
image_dtype = image.dtype
# Default window (y1, x1, y2, x2) and default scale == 1.
h, w = image.shape[:2]
window = (0, 0, h, w)
scale = 1
padding = [(0, 0), (0, 0), (0, 0)]
crop = None
if mode == "none":
return image, window, scale, padding, crop
# Scale?
if min_dim:
# Scale up but not down
scale = max(1, min_dim / min(h, w))
if min_scale and scale < min_scale:
scale = min_scale
# Does it exceed max dim?
if max_dim and mode == "square":
image_max = max(h, w)
if round(image_max * scale) > max_dim:
scale = max_dim / image_max
# Resize image using bilinear interpolation
if scale != 1:
image = resize(image, (round(h * scale), round(w * scale)), preserve_range=True)
# Need padding or cropping?
if mode == "square":
# Get new height and width
h, w = image.shape[:2]
top_pad = (max_dim - h) // 2
bottom_pad = max_dim - h - top_pad
left_pad = (max_dim - w) // 2
right_pad = max_dim - w - left_pad
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode="constant", constant_values=0)
window = (top_pad, left_pad, h + top_pad, w + left_pad)
elif mode == "pad64":
h, w = image.shape[:2]
# Both sides must be divisible by 64
assert min_dim % 64 == 0, "Minimum dimension must be a multiple of 64"
# Height
if h % 64 > 0:
max_h = h - (h % 64) + 64
top_pad = (max_h - h) // 2
bottom_pad = max_h - h - top_pad
else:
top_pad = bottom_pad = 0
# Width
if w % 64 > 0:
max_w = w - (w % 64) + 64
left_pad = (max_w - w) // 2
right_pad = max_w - w - left_pad
else:
left_pad = right_pad = 0
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode="constant", constant_values=0)
window = (top_pad, left_pad, h + top_pad, w + left_pad)
elif mode == "crop":
# Pick a random crop
h, w = image.shape[:2]
y = random.randint(0, (h - min_dim))
x = random.randint(0, (w - min_dim))
crop = (y, x, min_dim, min_dim)
image = image[y : y + min_dim, x : x + min_dim]
window = (0, 0, min_dim, min_dim)
else:
raise Exception("Mode {} not supported".format(mode))
return image.astype(image_dtype), window, scale, padding, crop
def resize_mask(mask, scale, padding, crop=None):
"""Resizes a mask using the given scale and padding.
Typically, you get the scale and padding from resize_image() to
ensure both, the image and the mask, are resized consistently.
scale: mask scaling factor
padding: Padding to add to the mask in the form
[(top, bottom), (left, right), (0, 0)]
"""
# Suppress warning from scipy 0.13.0, the output shape of zoom() is
# calculated with round() instead of int()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mask = scipy.ndimage.zoom(mask, zoom=[scale, scale, 1], order=0)
if crop is not None:
y, x, h, w = crop
mask = mask[y : y + h, x : x + w]
else:
mask = np.pad(mask, padding, mode="constant", constant_values=0)
return mask
def vis_mask(vis_img,indicies,color=(255,120,0)):
for j in range(len(indicies[0])):
x = indicies[1][j]
y = indicies[0][j]
# viusalize masks
cv2.circle(vis_img, (x, y), 1, color, 1)
def resize_images_cv(img):
scale_percent = 40 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
# resize image
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
return resized
def write_text(image,text,point=(0,0),color=(255,0,0)):
# font
font = cv2.FONT_HERSHEY_SIMPLEX
# fontScale
fontScale = 1
# Line thickness of 2 px
thickness = 3
# Using cv2.putText() method
image = cv2.putText(image, text, point, font,
fontScale, color, thickness, cv2.LINE_AA)
return image
def vis_data(image, masks, bboxs,classes,keypoints=None,**kwargs):
vis_img = (image.detach().numpy()*255).astype(np.uint8)
vis_img=np.moveaxis(vis_img, 0, -1)
vis_img=vis_img.copy()
class_color={i:[random.uniform(0,255) for _ in range(3)] for i in np.unique(classes)}
# offset for drawing text
off_x=20
off_y=50
for i in range(masks.shape[0]):
mask = masks[i][...,None].detach().numpy()
bbox = np.int0(bboxs[i].detach().numpy().copy())
indicies = np.where(mask >= 0.5)
vis_mask(vis_img,indicies=indicies,color=class_color[classes[i]])
# Visualize bounding box
cv2.rectangle(vis_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), class_color[classes[i]], 2)
# write class name
write_text(vis_img,classes[i],((bbox[0], bbox[1])))
# Visualize segm classes
if "other_masks" in kwargs and "seg_labels" in kwargs:
for j,k in enumerate(kwargs["other_masks"].keys(),start=1):
if classes[i] in kwargs["seg_labels"][j]:
m=kwargs["other_masks"][k][i][...,None].detach().numpy()
indicies = np.where(m >= 0.1)
vis_mask(vis_img,indicies=indicies,color=(0,0,255))
#other_mask_id+=1
## Visualize keypoints
if "kp_labels" in kwargs and keypoints is not None:
if classes[i] in kwargs["kp_labels"]:
keypoint = np.int0(keypoints[i].detach().numpy())
# Visualize Keypoints
cv2.circle(vis_img, (keypoint[0][0], keypoint[0][1]), 1, (0, 255, 255), 20)
write_text(vis_img,"Head",(keypoint[0][0]+off_x, keypoint[0][1]+off_y))
cv2.circle(vis_img, (keypoint[1][0], keypoint[1][1]), 1, (0, 255, 255), 20)
write_text(vis_img,"Tail",(keypoint[1][0]+off_x, keypoint[1][1]+off_y))
# visualize classification
if "clas" in kwargs and "clas_labels" in kwargs:
for j,k in enumerate(kwargs["clas"].keys(),start=0):
if classes[i] in kwargs["clas_labels"][j]:
cl=kwargs["clas"][k][i].cpu().item()
point=(bbox[0]+(bbox[2]-bbox[0])//2+j*off_x,bbox[1]+(bbox[3]-bbox[1])//2+j*off_y)
write_text(vis_img,f"{k}: {cl}",point=point,color=(0,0,255))
vis_img=resize_images_cv(vis_img)
#if"" kwargs["DEBUG"]:
if "epoch" in kwargs:
cv2.imwrite("/home/ec2-user/SageMaker/SMN/res_images/"+f"{kwargs["epoch"]}"+".png",vis_img)
#cv2.imshow("Input and labels", vis_img)
#cv2.waitKey(0)
def resize_points(points, scale, window):
# window: (y1, x1, y2, x2)
scaled_points = []
for i in range(len(points)):
two_point = np.array(points[i])
two_point = scale * two_point
two_point[0][0] = two_point[0][0] + window[1]
two_point[0][1] = two_point[0][1] + window[0]
two_point[1][0] = two_point[1][0] + window[1]
two_point[1][1] = two_point[1][1] + window[0]
scaled_points.append(two_point)
return np.int0(scaled_points)
def generate_anchors_tensor(scales, ratios, shape, feature_stride, anchor_stride,device="cpu"):
"""
scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
shape: [height, width] spatial shape of the feature map over which
to generate anchors.
feature_stride: Stride of the feature map relative to the image in pixels.
anchor_stride: Stride of anchors on the feature map. For example, if the
value is 2 then generate anchors for every other feature map pixel.
"""
# Get all combinations of scales and ratios
scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
scales = scales.flatten()
ratios = ratios.flatten()
# Enumerate heights and widths from scales and ratios
heights = scales / np.sqrt(ratios)
widths = scales * np.sqrt(ratios)
# Enumerate shifts in feature space
shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)
# Enumerate combinations of shifts, widths, and heights
box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
# Reshape to get a list of (y, x) and a list of (h, w)
box_centers = np.stack([box_centers_y, box_centers_x], axis=2).reshape([-1, 2])
box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2])
# Convert to corner coordinates (y1, x1, y2, x2)
boxes = np.concatenate(
[box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], axis=1
)
boxes=torch.tensor(boxes,dtype=torch.float32)
return boxes
def visualize_anchors(
img,
anchors,
backbone_shapes,
RPN_ANCHOR_RATIOS,
RPN_ANCHOR_STRIDE,
RPN_ANCHOR_SCALES,
):
vis_img = img.copy()
num_levels = len(backbone_shapes)
anchors_per_cell = len(RPN_ANCHOR_RATIOS)
print("Anchors Count: ", anchors.shape[0])
print("Scales: ", RPN_ANCHOR_SCALES)
print("ratios: ", RPN_ANCHOR_RATIOS)
print("Anchors per Cell: ", anchors_per_cell)
print("Levels: ", num_levels)
anchors_per_level = []
for l in range(num_levels):
num_cells = backbone_shapes[l][0] * backbone_shapes[l][1]
anchors_per_level.append(anchors_per_cell * num_cells // RPN_ANCHOR_STRIDE ** 2)
print("Anchors in Level {}: {}".format(l, anchors_per_level[l]))
for level in range(num_levels):
colors = [[0, 255, 0]]
# Compute the index of the anchors at the center of the image
level_start = sum(
anchors_per_level[:level]
) # sum of anchors of previous levels
level_anchors = anchors[level_start : level_start + anchors_per_level[level]]
print(
"Level {}. Anchors: {:6} Feature map Shape: {}".format(
level, level_anchors.shape[0], backbone_shapes[level]
)
)
center_cell = np.array(backbone_shapes[level]) // 2
center_cell_index = center_cell[0] * backbone_shapes[level][1] + center_cell[1]
level_center = center_cell_index * anchors_per_cell
center_anchor = anchors_per_cell * (
(center_cell[0] * backbone_shapes[level][1] / RPN_ANCHOR_STRIDE ** 2)
+ center_cell[1] / RPN_ANCHOR_STRIDE
)
level_center = int(center_anchor)
# Draw anchors. Brightness show the order in the array, dark to bright.
for i, rect in enumerate(
level_anchors[level_center : level_center + anchors_per_cell]
):
y1, x1, y2, x2 = rect
cv2.rectangle(
vis_img, (int(x1), int(y1)), (int(x2), int(y2)), colors[level], 2
)
cv2.imshow("Center Anchor Boxes", vis_img)
cv2.waitKey(0)
def generate_pyramid_anchors_tensor(scales, ratios, feature_shapes, feature_strides, anchor_stride,device="cpu"):
"""Generate anchors at different levels of a feature pyramid. Each scale
is associated with a level of the pyramid, but each ratio is used in
all levels of the pyramid.
Returns:
anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted
with the same order of the given scales. So, anchors of scale[0] come
first, then anchors of scale[1], and so on.
"""
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
anchors = []
for i in range(len(scales)):
anchors.append(generate_anchors_tensor(scales[i], ratios, feature_shapes[i], feature_strides[i], anchor_stride,device=device))
return torch.cat(anchors, axis=0).to(device=device)
def compute_iou_tensor(box, boxes, box_area, boxes_area):
"""Calculates IoU of the given box with the array of the given boxes.
box: 1D vector [y1, x1, y2, x2]
boxes: [boxes_count, (y1, x1, y2, x2)]
box_area: float. the area of 'box'
boxes_area: array of length boxes_count.
Note: the areas are passed in rather than calculated here for
efficiency. Calculate once in the caller to avoid duplicate work.
"""
# Calculate intersection areas
y1 = torch.maximum(box[0], boxes[:, 0])
y2 = torch.minimum(box[2], boxes[:, 2])
x1 = torch.maximum(box[1], boxes[:, 1])
x2 = torch.minimum(box[3], boxes[:, 3])
intersection = torch.maximum(x2 - x1, torch.tensor(0)) * torch.maximum(y2 - y1, torch.tensor(0))
union = box_area + boxes_area[:] - intersection[:]
iou = intersection / union
return iou
def compute_overlaps_tesnor(boxes1, boxes2,device="cpu"):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
For better performance, pass the largest set first and the smaller second.
"""
# Areas of anchors and GT boxes
area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
# Compute overlaps to generate matrix [boxes1 count, boxes2 count]
# Each cell contains the IoU value.
overlaps = torch.zeros((boxes1.shape[0], boxes2.shape[0])).to(device=device)
for i in range(overlaps.shape[1]):
box2 = boxes2[i]
overlaps[:, i] = compute_iou_tensor(box2, boxes1, area2[i], area1)
return overlaps
def apply_box_deltas_tesnor(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box.
deltas: [N, (dy, dx, log(dh), log(dw))]
"""
# Convert to y, x, h, w
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
center_y = boxes[:, 0] + 0.5 * height
center_x = boxes[:, 1] + 0.5 * width
# Apply deltas
center_y += deltas[:, 0] * height
center_x += deltas[:, 1] * width
height *= torch.exp(deltas[:, 2])
width *= torch.exp(deltas[:, 3])
# Convert back to y1, x1, y2, x2
y1 = center_y - 0.5 * height
x1 = center_x - 0.5 * width
y2 = y1 + height
x2 = x1 + width
return torch.stack([y1, x1, y2, x2], axis=1)
def vis_anchors_refined_anchors_(img, anchors, refined_anchors):
vis_img = img.copy()
for i, rect in enumerate(anchors):
y1, x1, y2, x2 = rect
cv2.rectangle(vis_img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
y1, x1, y2, x2 = refined_anchors[i]
cv2.rectangle(vis_img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
break
cv2.imshow("Matched Anchor Boxes", vis_img)
cv2.waitKey(0)
def build_rpn_targets_tensor(anchors, gt_boxes, config):
"""Given the anchors and GT boxes, compute overlaps and identify positive
anchors and deltas to refine them to match their corresponding GT boxes.
anchors: [num_anchors, (y1, x1, y2, x2)]
gt_class_ids: [num_gt_boxes] Integer class IDs.
gt_boxes: [num_gt_boxes, (y1, x1, y2, x2)]
Returns:
rpn_match: [N] (int32) matches between anchors and GT boxes.
1 = positive anchor, -1 = negative anchor, 0 = neutral
rpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
"""
# RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral
rpn_match = torch.zeros([anchors.shape[0]], dtype=torch.int32).to(device=config.DEVICE)
# RPN bounding boxes: [max anchors per image, (dy, dx, log(dh), log(dw))]
rpn_bbox = torch.zeros((config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4)).to(device=config.DEVICE)
no_crowd_bool = torch.ones([anchors.shape[0]], dtype=torch.bool).to(device=config.DEVICE)
# Compute overlaps [num_anchors, num_gt_boxes]
overlaps = compute_overlaps_tesnor(anchors, gt_boxes,config.DEVICE)
# Match anchors to GT Boxes
# If an anchor overlaps a GT box with IoU >= 0.7 then it's positive.
# If an anchor overlaps a GT box with IoU < 0.3 then it's negative.
# Neutral anchors are those that don't match the conditions above,
# and they don't influence the loss function.
# However, don't keep any GT box unmatched (rare, but happens). Instead,
# match it to the closest anchor (even if its max IoU is < 0.3).
#
# 1. Set negative anchors first. They get overwritten below if a GT box is
# matched to them. Skip boxes in crowd areas.
anchor_iou_argmax = torch.argmax(overlaps, axis=1)
anchor_iou_max = overlaps[torch.arange(overlaps.shape[0]), anchor_iou_argmax]
rpn_match[(anchor_iou_max < 0.3) & (no_crowd_bool)] = -1
# 2. Set an anchor for each GT box (regardless of IoU value).
# If multiple anchors have the same IoU match all of them
# original was argwhere
# gt_iou_argmax = torch.where(torch.tensor(overlaps == torch.max(overlaps, axis=0)))[:, 0]
a = torch.max(overlaps, axis=0)[0]
gt_iou_argmax = torch.where(overlaps == a)[0]
rpn_match[gt_iou_argmax] = 1
# 3. Set anchors with high overlap as positive.
rpn_match[anchor_iou_max >= 0.7] = 1
# Subsample to balance positive and negative anchors
# Don't let positives be more than half the anchors
ids = torch.where(rpn_match == 1)[0]
extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2)
if extra > 0:
# Reset the extra ones to neutral
unif = torch.ones(ids.shape[0]).to(device=config.DEVICE)
idx = unif.multinomial(extra, replacement=False)
ids = ids[idx]
# ids = np.random.choice(ids, extra, replace=False)
rpn_match[ids] = 0
# Same for negative proposals
ids = torch.where(rpn_match == -1)[0]
extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE - torch.sum(rpn_match == 1))
if extra > 0:
# Rest the extra ones to neutral
# ids = np.random.choice(ids, extra, replace=False)
unif = torch.ones(ids.shape[0]).to(device=config.DEVICE)
idx = unif.multinomial(extra, replacement=False)
ids = ids[idx]
rpn_match[ids] = 0
# For positive anchors, compute shift and scale needed to transform them
# to match the corresponding GT boxes.
ids = torch.where(rpn_match == 1)[0]
ix = 0 # index into rpn_bbox
for i, a in zip(ids, anchors[ids]):
# Closest gt box (it might have IoU < 0.7)
gt = gt_boxes[anchor_iou_argmax[i]]
# Convert coordinates to center plus width/height.
# GT Box
gt_h = gt[2] - gt[0]
gt_w = gt[3] - gt[1]
gt_center_y = gt[0] + 0.5 * gt_h
gt_center_x = gt[1] + 0.5 * gt_w
# Anchor
a_h = a[2] - a[0]
a_w = a[3] - a[1]
a_center_y = a[0] + 0.5 * a_h
a_center_x = a[1] + 0.5 * a_w
# Compute the bbox refinement that the RPN should predict.
rpn_bbox[ix] = torch.tensor(
[
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
torch.log(gt_h / a_h),
torch.log(gt_w / a_w),
]
)
# Normalize
#rpn_bbox[ix] /= torch.tensor(config.RPN_BBOX_STD_DEV, dtype=torch.float32).to(device=config.DEVICE)
ix += 1
return rpn_match, rpn_bbox
def box_refinement(box, gt_box):
"""Compute refinement needed to transform box to gt_box.
box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is
assumed to be outside the box.
"""
box = box.type(torch.float32)
gt_box = gt_box.type(torch.float32)
height = box[:, 2] - box[:, 0]
width = box[:, 3] - box[:, 1]
center_y = box[:, 0] + 0.5 * height
center_x = box[:, 1] + 0.5 * width
gt_height = gt_box[:, 2] - gt_box[:, 0]
gt_width = gt_box[:, 3] - gt_box[:, 1]
gt_center_y = gt_box[:, 0] + 0.5 * gt_height
gt_center_x = gt_box[:, 1] + 0.5 * gt_width
dy = (gt_center_y - center_y) / height
dx = (gt_center_x - center_x) / width
dh = torch.log(gt_height / height)
dw = torch.log(gt_width / width)
return torch.stack([dy, dx, dh, dw], axis=1)
def process_box(box, score, image_shape, min_size):
"""
Clip boxes in the image size and remove boxes which are too small.
"""
box[:, [0, 2]] = box[:, [0, 2]].clamp(0, image_shape[0])
box[:, [1, 3]] = box[:, [1, 3]].clamp(0, image_shape[1])
w, h = box[:, 2] - box[:, 0], box[:, 3] - box[:, 1]
keep = torch.where((w >= min_size) & (h >= min_size))[0]
box, score = box[keep], score[keep]
return box, score
def roi_align(
features, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio
):
if torch.__version__ >= "1.5.0":
return torch.ops.torchvision.roi_align(
features,
rois,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio,
False,
)
else:
return torch.ops.torchvision.roi_align(
features, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio
)
class RoIAlign:
"""
Performs Region of Interest (RoI) Align operator described in Mask R-CNN
"""
def __init__(self, output_size, sampling_ratio):
"""
Arguments:
output_size (Tuple[int, int]): the size of the output after the cropping
is performed, as (height, width)
sampling_ratio (int): number of sampling points in the interpolation grid
used to compute the output value of each pooled output bin. If > 0,
then exactly sampling_ratio x sampling_ratio grid points are used. If
<= 0, then an adaptive number of grid points are used (computed as
ceil(roi_width / pooled_w), and likewise for height). Default: -1
"""
self.output_size = output_size
self.sampling_ratio = sampling_ratio
self.spatial_scale = None
def setup_scale(self, feature_shape, image_shape):
if self.spatial_scale is not None:
return
possible_scales = []
for s1, s2 in zip(feature_shape, image_shape):
scale = 2 ** int(math.log2(s1 / s2))
possible_scales.append(scale)
assert possible_scales[0] == possible_scales[1]
self.spatial_scale = possible_scales[0]
def __call__(self, feature, proposal, image_shape):
"""
Arguments:
feature (Tensor[N, C, H, W])
proposal (Tensor[K, 4])
image_shape (Torch.Size([H, W]))
Returns:
output (Tensor[K, C, self.output_size[0], self.output_size[1]])
"""
idx = proposal.new_full((proposal.shape[0], 1), 0)
roi = torch.cat((idx, proposal), dim=1)
self.setup_scale(feature.shape[-2:], image_shape)
return roi_align(
feature.to(roi),
roi,
self.spatial_scale,
self.output_size[0],
self.output_size[1],
self.sampling_ratio,
)
class Matcher:
def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False):
self.high_threshold = high_threshold
self.low_threshold = low_threshold
self.allow_low_quality_matches = allow_low_quality_matches
def __call__(self, iou):
"""
Arguments:
iou (Tensor[M, N]): containing the pairwise quality between
M ground-truth boxes and N predicted boxes.
Returns:
label (Tensor[N]): positive (1) or negative (0) label for each predicted box,
-1 means ignoring this box.
matched_idx (Tensor[N]): indices of gt box matched by each predicted box.
"""
value, matched_idx = iou.max(dim=0)
label = torch.full((iou.shape[1],), -1, dtype=torch.float, device=iou.device)
label[value >= self.high_threshold] = 1
label[value < self.low_threshold] = 0
if self.allow_low_quality_matches:
highest_quality = iou.max(dim=1)[0]
gt_pred_pairs = torch.where(iou == highest_quality[:, None])[1]
label[gt_pred_pairs] = 1
return label, matched_idx
class BalancedPositiveNegativeSampler:
def __init__(self, num_samples, positive_fraction):
self.num_samples = num_samples
self.positive_fraction = positive_fraction
def __call__(self, label):
positive = torch.where(label == 1)[0]
negative = torch.where(label == 0)[0]
num_pos = int(self.num_samples * self.positive_fraction)
num_pos = min(positive.numel(), num_pos)
num_neg = self.num_samples - num_pos
num_neg = min(negative.numel(), num_neg)
pos_perm = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
neg_perm = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
pos_idx = positive[pos_perm]
neg_idx = negative[neg_perm]
return pos_idx, neg_idx
def visualize_inference(in_img, inv_normalize,results):
vis_img = in_img.clone()
vis_img = (inv_normalize(vis_img).data.numpy() * 255).astype(np.uint8)
vis_img = np.ascontiguousarray(np.moveaxis(vis_img, 0, -1))
boxes=np.int0(results["boxes"])
labels=results["labels"]
scores=results["scores"]
print(f"Labels max {labels.max()}")
print(f"label min {labels.min()}")
print(f"scores max {scores.max()}")
print(f"scores min {scores.min()}")
for i in range(boxes.shape[0]):
bbox=boxes[i]
cv2.rectangle(vis_img, (bbox[1], bbox[0]), (bbox[3], bbox[2]), (255, 255, 0), 2)
cv2.imshow("Results",vis_img)
cv2.waitKey(0)
| import numpy as np
import cv2
import random
import warnings
import scipy
from scipy.linalg.basic import solve_circulant
import skimage
import skimage.transform
from distutils.version import LooseVersion
import torch
import math
import json
from torch.functional import Tensor
np.random.seed(42)
def load_points_dataset(f_p):
"""load keypoints(head/tail)from the text file
Args:
f_p ([str]): File path containing the head and tail points (x,y) of each fruit in the image. Each Image can have multiple fruits
Returns:
[dict]: Dictionary of file names as keys and corresponding fruit points as values
"""
with open(f_p, "r") as f:
all_lines = f.readlines()
points = {}
i = 0
while i < len(all_lines):
if i > len(all_lines):
break
line = all_lines[i].split(",")
label = line[0]
file = line[3]
first_point = None
second_point = None
if label == "head":
first_point = (int(line[1]), int(line[2]))
elif label == "tail":
second_point = (int(line[1]), int(line[2]))
i += 1
if i < len(all_lines):
line2 = all_lines[i].split(",")
if line2[3] == file:
if line2[0] == "head":
first_point = (int(line2[1]), int(line2[2]))
elif line2[0] == "tail":
second_point = (int(line2[1]), int(line2[2]))
i += 1
if file in points:
# file already in dictionary append the list
# print(f"Appending the file to existing one {file}")
points[file].append([first_point, second_point])
else:
points[file] = [[first_point, second_point]]
return points
def load_points_dataset_2(f_p,label_name=["head","tail"]):
"""load keypoints(head/tail)from the text file
Args:
f_p ([str]): File path containing the head and tail points (x,y) of each fruit in the image. Each Image can have multiple fruits
Returns:
[dict]: Dictionary of file names as keys and corresponding fruit points as values
"""
with open(f_p, "r") as f:
all_lines = f.readlines()
points = {}
i = 0
while i < len(all_lines):
if i > len(all_lines):
break
line = all_lines[i].split(",")
label = line[0]
file = line[3]
first_point = None
second_point = None
if label == label_name[0]:
first_point = (int(line[1]), int(line[2]))
elif label == label_name[1]:
second_point = (int(line[1]), int(line[2]))
i += 1
if i < len(all_lines):
line2 = all_lines[i].split(",")
if line2[3] == file:
if line2[0] == label_name[0]:
first_point = (int(line2[1]), int(line2[2]))
elif line2[0] == label_name[1]:
second_point = (int(line2[1]), int(line2[2]))
i += 1
if not first_point and not second_point:
continue
if file in points:
# file already in dictionary append the list
# print(f"Appending the file to existing one {file}")
points[file].append([first_point, second_point])
else:
points[file] = [[first_point, second_point]]
return points
def load_class_dataset(f_p,label_name=["rating","neck"]):
"""load keypoints(head/tail)from the text file
Args:
f_p ([str]): File path containing the head and tail points (x,y) of each fruit in the image. Each Image can have multiple fruits
Returns:
[dict]: Dictionary of file names as keys and corresponding fruit points as values
"""
with open(f_p, "r") as f:
all_lines = f.readlines()
points = {}
i = 0
while i < len(all_lines):
if i > len(all_lines):
break
line = all_lines[i].split(",")
label = line[0]
splitted_labels=label.split("_")
file = line[3]
coords= None
if splitted_labels[0] in label_name:
coords = (int(line[1]), int(line[2]))
i += 1
if coords is None:
continue
if file in points:
# file already in dictionary append the list
# print(f"Appending the file to existing one {file}")
if splitted_labels[0] in points[file]:
points[file][splitted_labels[0]].append([coords,int(splitted_labels[1])])
else:
points[file][splitted_labels[0]]=[[coords,int(splitted_labels[1])]]
else:
points[file]={splitted_labels[0]:[[coords, int(splitted_labels[1])]]}
return points
def load_segmentation_dataset(f_p,label_names=None):
""""
Returns:
[dict]: Dictionary of list with names
"""
data=load_json(f_p)
cat_map={}
for cat in data["categories"]:
if cat["name"] in label_names:
cat_map[cat['id']]=cat["name"]
image_map={}
for cat in data["images"]:
image_map[cat['id']]=cat["file_name"]
annos={}
for d in data["annotations"]:
tmp=[]
seg=d["segmentation"][0]
for i in range(0,len(seg)-1,2):
tmp.append([seg[i],seg[i+1]])
if image_map[d["image_id"]] not in annos:
annos[image_map[d["image_id"]]]=[{"class_id":cat_map[d["category_id"]],"annotation":tmp}]
else:
annos[image_map[d["image_id"]]].append({"class_id":cat_map[d["category_id"]],"annotation":tmp})
return annos
def load_backbone(filename):
with open(filename) as f:
back_annotation = json.load(f)
return back_annotation
def load_json(filename):
with open(filename) as f:
annotation = json.load(f)
return annotation
def extract_bboxes(mask):
"""Compute bounding boxes from masks.
mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
Returns: bbox array [num_instances, (y1, x1, y2, x2)].
"""
#print(np.max(mask))
#print(np.min(mask))
boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
for i in range(mask.shape[-1]):
m = mask[:, :, i]
# Bounding box.
horizontal_indicies = np.where(np.any(m, axis=0))[0]
vertical_indicies = np.where(np.any(m, axis=1))[0]
if horizontal_indicies.shape[0]:
x1, x2 = horizontal_indicies[[0, -1]]
y1, y2 = vertical_indicies[[0, -1]]
# x2 and y2 should not be part of the box. Increment by 1.
x2 += 1
y2 += 1
else:
# No mask for this instance. Might happen due to
# resizing or cropping. Set bbox to zeros
x1, x2, y1, y2 = 0, 0, 0, 0
boxes[i] = np.array([x1,y1,x2,y2])
return boxes.astype(np.int32)
def resize(
image,
output_shape,
order=1,
mode="constant",
cval=0,
clip=True,
preserve_range=False,
anti_aliasing=False,
anti_aliasing_sigma=None,
):
"""A wrapper for Scikit-Image resize().
Scikit-Image generates warnings on every call to resize() if it doesn't
receive the right parameters. The right parameters depend on the version
of skimage. This solves the problem by using different parameters per
version. And it provides a central place to control resizing defaults.
"""
if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
# New in 0.14: anti_aliasing. Default it to False for backward
# compatibility with skimage 0.13.
return skimage.transform.resize(
image,
output_shape,
order=order,
mode=mode,
cval=cval,
clip=clip,
preserve_range=preserve_range,
anti_aliasing=anti_aliasing,
anti_aliasing_sigma=anti_aliasing_sigma,
)
else:
return skimage.transform.resize(
image,
output_shape,
order=order,
mode=mode,
cval=cval,
clip=clip,
preserve_range=preserve_range,
)
def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"):
"""Resizes an image keeping the aspect ratio unchanged.
min_dim: if provided, resizes the image such that it's smaller
dimension == min_dim
max_dim: if provided, ensures that the image longest side doesn't
exceed this value.
min_scale: if provided, ensure that the image is scaled up by at least
this percent even if min_dim doesn't require it.
mode: Resizing mode.
none: No resizing. Return the image unchanged.
square: Resize and pad with zeros to get a square image
of size [max_dim, max_dim].
pad64: Pads width and height with zeros to make them multiples of 64.
If min_dim or min_scale are provided, it scales the image up
before padding. max_dim is ignored in this mode.
The multiple of 64 is needed to ensure smooth scaling of feature
maps up and down the 6 levels of the FPN pyramid (2**6=64).
crop: Picks random crops from the image. First, scales the image based
on min_dim and min_scale, then picks a random crop of
size min_dim x min_dim. Can be used in training only.
max_dim is not used in this mode.
Returns:
image: the resized image
window: (y1, x1, y2, x2). If max_dim is provided, padding might
be inserted in the returned image. If so, this window is the
coordinates of the image part of the full image (excluding
the padding). The x2, y2 pixels are not included.
scale: The scale factor used to resize the image
padding: Padding added to the image [(top, bottom), (left, right), (0, 0)]
"""
# Keep track of image dtype and return results in the same dtype
image_dtype = image.dtype
# Default window (y1, x1, y2, x2) and default scale == 1.
h, w = image.shape[:2]
window = (0, 0, h, w)
scale = 1
padding = [(0, 0), (0, 0), (0, 0)]
crop = None
if mode == "none":
return image, window, scale, padding, crop
# Scale?
if min_dim:
# Scale up but not down
scale = max(1, min_dim / min(h, w))
if min_scale and scale < min_scale:
scale = min_scale
# Does it exceed max dim?
if max_dim and mode == "square":
image_max = max(h, w)
if round(image_max * scale) > max_dim:
scale = max_dim / image_max
# Resize image using bilinear interpolation
if scale != 1:
image = resize(image, (round(h * scale), round(w * scale)), preserve_range=True)
# Need padding or cropping?
if mode == "square":
# Get new height and width
h, w = image.shape[:2]
top_pad = (max_dim - h) // 2
bottom_pad = max_dim - h - top_pad
left_pad = (max_dim - w) // 2
right_pad = max_dim - w - left_pad
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode="constant", constant_values=0)
window = (top_pad, left_pad, h + top_pad, w + left_pad)
elif mode == "pad64":
h, w = image.shape[:2]
# Both sides must be divisible by 64
assert min_dim % 64 == 0, "Minimum dimension must be a multiple of 64"
# Height
if h % 64 > 0:
max_h = h - (h % 64) + 64
top_pad = (max_h - h) // 2
bottom_pad = max_h - h - top_pad
else:
top_pad = bottom_pad = 0
# Width
if w % 64 > 0:
max_w = w - (w % 64) + 64
left_pad = (max_w - w) // 2
right_pad = max_w - w - left_pad
else:
left_pad = right_pad = 0
padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
image = np.pad(image, padding, mode="constant", constant_values=0)
window = (top_pad, left_pad, h + top_pad, w + left_pad)
elif mode == "crop":
# Pick a random crop
h, w = image.shape[:2]
y = random.randint(0, (h - min_dim))
x = random.randint(0, (w - min_dim))
crop = (y, x, min_dim, min_dim)
image = image[y : y + min_dim, x : x + min_dim]
window = (0, 0, min_dim, min_dim)
else:
raise Exception("Mode {} not supported".format(mode))
return image.astype(image_dtype), window, scale, padding, crop
def resize_mask(mask, scale, padding, crop=None):
"""Resizes a mask using the given scale and padding.
Typically, you get the scale and padding from resize_image() to
ensure both, the image and the mask, are resized consistently.
scale: mask scaling factor
padding: Padding to add to the mask in the form
[(top, bottom), (left, right), (0, 0)]
"""
# Suppress warning from scipy 0.13.0, the output shape of zoom() is
# calculated with round() instead of int()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mask = scipy.ndimage.zoom(mask, zoom=[scale, scale, 1], order=0)
if crop is not None:
y, x, h, w = crop
mask = mask[y : y + h, x : x + w]
else:
mask = np.pad(mask, padding, mode="constant", constant_values=0)
return mask
def vis_mask(vis_img,indicies,color=(255,120,0)):
for j in range(len(indicies[0])):
x = indicies[1][j]
y = indicies[0][j]
# viusalize masks
cv2.circle(vis_img, (x, y), 1, color, 1)
def resize_images_cv(img):
scale_percent = 40 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
# resize image
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
return resized
def write_text(image,text,point=(0,0),color=(255,0,0)):
# font
font = cv2.FONT_HERSHEY_SIMPLEX
# fontScale
fontScale = 1
# Line thickness of 2 px
thickness = 3
# Using cv2.putText() method
image = cv2.putText(image, text, point, font,
fontScale, color, thickness, cv2.LINE_AA)
return image
def vis_data(image, masks, bboxs,classes,keypoints=None,**kwargs):
vis_img = (image.detach().numpy()*255).astype(np.uint8)
vis_img=np.moveaxis(vis_img, 0, -1)
vis_img=vis_img.copy()
class_color={i:[random.uniform(0,255) for _ in range(3)] for i in np.unique(classes)}
# offset for drawing text
off_x=20
off_y=50
for i in range(masks.shape[0]):
mask = masks[i][...,None].detach().numpy()
bbox = np.int0(bboxs[i].detach().numpy().copy())
indicies = np.where(mask >= 0.5)
vis_mask(vis_img,indicies=indicies,color=class_color[classes[i]])
# Visualize bounding box
cv2.rectangle(vis_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), class_color[classes[i]], 2)
# write class name
write_text(vis_img,classes[i],((bbox[0], bbox[1])))
# Visualize segm classes
if "other_masks" in kwargs and "seg_labels" in kwargs:
for j,k in enumerate(kwargs["other_masks"].keys(),start=1):
if classes[i] in kwargs["seg_labels"][j]:
m=kwargs["other_masks"][k][i][...,None].detach().numpy()
indicies = np.where(m >= 0.1)
vis_mask(vis_img,indicies=indicies,color=(0,0,255))
#other_mask_id+=1
## Visualize keypoints
if "kp_labels" in kwargs and keypoints is not None:
if classes[i] in kwargs["kp_labels"]:
keypoint = np.int0(keypoints[i].detach().numpy())
# Visualize Keypoints
cv2.circle(vis_img, (keypoint[0][0], keypoint[0][1]), 1, (0, 255, 255), 20)
write_text(vis_img,"Head",(keypoint[0][0]+off_x, keypoint[0][1]+off_y))
cv2.circle(vis_img, (keypoint[1][0], keypoint[1][1]), 1, (0, 255, 255), 20)
write_text(vis_img,"Tail",(keypoint[1][0]+off_x, keypoint[1][1]+off_y))
# visualize classification
if "clas" in kwargs and "clas_labels" in kwargs:
for j,k in enumerate(kwargs["clas"].keys(),start=0):
if classes[i] in kwargs["clas_labels"][j]:
cl=kwargs["clas"][k][i].cpu().item()
point=(bbox[0]+(bbox[2]-bbox[0])//2+j*off_x,bbox[1]+(bbox[3]-bbox[1])//2+j*off_y)
write_text(vis_img,f"{k}: {cl}",point=point,color=(0,0,255))
vis_img=resize_images_cv(vis_img)
#if"" kwargs["DEBUG"]:
if "epoch" in kwargs:
cv2.imwrite("/home/ec2-user/SageMaker/SMN/res_images/"+f"{kwargs['epoch']}"+".png",vis_img)
#cv2.imshow("Input and labels", vis_img)
#cv2.waitKey(0)
def resize_points(points, scale, window):
# window: (y1, x1, y2, x2)
scaled_points = []
for i in range(len(points)):
two_point = np.array(points[i])
two_point = scale * two_point
two_point[0][0] = two_point[0][0] + window[1]
two_point[0][1] = two_point[0][1] + window[0]
two_point[1][0] = two_point[1][0] + window[1]
two_point[1][1] = two_point[1][1] + window[0]
scaled_points.append(two_point)
return np.int0(scaled_points)
def generate_anchors_tensor(scales, ratios, shape, feature_stride, anchor_stride,device="cpu"):
"""
scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
shape: [height, width] spatial shape of the feature map over which
to generate anchors.
feature_stride: Stride of the feature map relative to the image in pixels.
anchor_stride: Stride of anchors on the feature map. For example, if the
value is 2 then generate anchors for every other feature map pixel.
"""
# Get all combinations of scales and ratios
scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
scales = scales.flatten()
ratios = ratios.flatten()
# Enumerate heights and widths from scales and ratios
heights = scales / np.sqrt(ratios)
widths = scales * np.sqrt(ratios)
# Enumerate shifts in feature space
shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)
# Enumerate combinations of shifts, widths, and heights
box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
# Reshape to get a list of (y, x) and a list of (h, w)
box_centers = np.stack([box_centers_y, box_centers_x], axis=2).reshape([-1, 2])
box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2])
# Convert to corner coordinates (y1, x1, y2, x2)
boxes = np.concatenate(
[box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], axis=1
)
boxes=torch.tensor(boxes,dtype=torch.float32)
return boxes
def visualize_anchors(
img,
anchors,
backbone_shapes,
RPN_ANCHOR_RATIOS,
RPN_ANCHOR_STRIDE,
RPN_ANCHOR_SCALES,
):
vis_img = img.copy()
num_levels = len(backbone_shapes)
anchors_per_cell = len(RPN_ANCHOR_RATIOS)
print("Anchors Count: ", anchors.shape[0])
print("Scales: ", RPN_ANCHOR_SCALES)
print("ratios: ", RPN_ANCHOR_RATIOS)
print("Anchors per Cell: ", anchors_per_cell)
print("Levels: ", num_levels)
anchors_per_level = []
for l in range(num_levels):
num_cells = backbone_shapes[l][0] * backbone_shapes[l][1]
anchors_per_level.append(anchors_per_cell * num_cells // RPN_ANCHOR_STRIDE ** 2)
print("Anchors in Level {}: {}".format(l, anchors_per_level[l]))
for level in range(num_levels):
colors = [[0, 255, 0]]
# Compute the index of the anchors at the center of the image
level_start = sum(
anchors_per_level[:level]
) # sum of anchors of previous levels
level_anchors = anchors[level_start : level_start + anchors_per_level[level]]
print(
"Level {}. Anchors: {:6} Feature map Shape: {}".format(
level, level_anchors.shape[0], backbone_shapes[level]
)
)
center_cell = np.array(backbone_shapes[level]) // 2
center_cell_index = center_cell[0] * backbone_shapes[level][1] + center_cell[1]
level_center = center_cell_index * anchors_per_cell
center_anchor = anchors_per_cell * (
(center_cell[0] * backbone_shapes[level][1] / RPN_ANCHOR_STRIDE ** 2)
+ center_cell[1] / RPN_ANCHOR_STRIDE
)
level_center = int(center_anchor)
# Draw anchors. Brightness show the order in the array, dark to bright.
for i, rect in enumerate(
level_anchors[level_center : level_center + anchors_per_cell]
):
y1, x1, y2, x2 = rect
cv2.rectangle(
vis_img, (int(x1), int(y1)), (int(x2), int(y2)), colors[level], 2
)
cv2.imshow("Center Anchor Boxes", vis_img)
cv2.waitKey(0)
def generate_pyramid_anchors_tensor(scales, ratios, feature_shapes, feature_strides, anchor_stride,device="cpu"):
"""Generate anchors at different levels of a feature pyramid. Each scale
is associated with a level of the pyramid, but each ratio is used in
all levels of the pyramid.
Returns:
anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted
with the same order of the given scales. So, anchors of scale[0] come
first, then anchors of scale[1], and so on.
"""
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
anchors = []
for i in range(len(scales)):
anchors.append(generate_anchors_tensor(scales[i], ratios, feature_shapes[i], feature_strides[i], anchor_stride,device=device))
return torch.cat(anchors, axis=0).to(device=device)
def compute_iou_tensor(box, boxes, box_area, boxes_area):
"""Calculates IoU of the given box with the array of the given boxes.
box: 1D vector [y1, x1, y2, x2]
boxes: [boxes_count, (y1, x1, y2, x2)]
box_area: float. the area of 'box'
boxes_area: array of length boxes_count.
Note: the areas are passed in rather than calculated here for
efficiency. Calculate once in the caller to avoid duplicate work.
"""
# Calculate intersection areas
y1 = torch.maximum(box[0], boxes[:, 0])
y2 = torch.minimum(box[2], boxes[:, 2])
x1 = torch.maximum(box[1], boxes[:, 1])
x2 = torch.minimum(box[3], boxes[:, 3])
intersection = torch.maximum(x2 - x1, torch.tensor(0)) * torch.maximum(y2 - y1, torch.tensor(0))
union = box_area + boxes_area[:] - intersection[:]
iou = intersection / union
return iou
def compute_overlaps_tesnor(boxes1, boxes2,device="cpu"):
"""Computes IoU overlaps between two sets of boxes.
boxes1, boxes2: [N, (y1, x1, y2, x2)].
For better performance, pass the largest set first and the smaller second.
"""
# Areas of anchors and GT boxes
area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
# Compute overlaps to generate matrix [boxes1 count, boxes2 count]
# Each cell contains the IoU value.
overlaps = torch.zeros((boxes1.shape[0], boxes2.shape[0])).to(device=device)
for i in range(overlaps.shape[1]):
box2 = boxes2[i]
overlaps[:, i] = compute_iou_tensor(box2, boxes1, area2[i], area1)
return overlaps
def apply_box_deltas_tesnor(boxes, deltas):
"""Applies the given deltas to the given boxes.
boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box.
deltas: [N, (dy, dx, log(dh), log(dw))]
"""
# Convert to y, x, h, w
height = boxes[:, 2] - boxes[:, 0]
width = boxes[:, 3] - boxes[:, 1]
center_y = boxes[:, 0] + 0.5 * height
center_x = boxes[:, 1] + 0.5 * width
# Apply deltas
center_y += deltas[:, 0] * height
center_x += deltas[:, 1] * width
height *= torch.exp(deltas[:, 2])
width *= torch.exp(deltas[:, 3])
# Convert back to y1, x1, y2, x2
y1 = center_y - 0.5 * height
x1 = center_x - 0.5 * width
y2 = y1 + height
x2 = x1 + width
return torch.stack([y1, x1, y2, x2], axis=1)
def vis_anchors_refined_anchors_(img, anchors, refined_anchors):
vis_img = img.copy()
for i, rect in enumerate(anchors):
y1, x1, y2, x2 = rect
cv2.rectangle(vis_img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
y1, x1, y2, x2 = refined_anchors[i]
cv2.rectangle(vis_img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
break
cv2.imshow("Matched Anchor Boxes", vis_img)
cv2.waitKey(0)
def build_rpn_targets_tensor(anchors, gt_boxes, config):
"""Given the anchors and GT boxes, compute overlaps and identify positive
anchors and deltas to refine them to match their corresponding GT boxes.
anchors: [num_anchors, (y1, x1, y2, x2)]
gt_class_ids: [num_gt_boxes] Integer class IDs.
gt_boxes: [num_gt_boxes, (y1, x1, y2, x2)]
Returns:
rpn_match: [N] (int32) matches between anchors and GT boxes.
1 = positive anchor, -1 = negative anchor, 0 = neutral
rpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
"""
# RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral
rpn_match = torch.zeros([anchors.shape[0]], dtype=torch.int32).to(device=config.DEVICE)
# RPN bounding boxes: [max anchors per image, (dy, dx, log(dh), log(dw))]
rpn_bbox = torch.zeros((config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4)).to(device=config.DEVICE)
no_crowd_bool = torch.ones([anchors.shape[0]], dtype=torch.bool).to(device=config.DEVICE)
# Compute overlaps [num_anchors, num_gt_boxes]
overlaps = compute_overlaps_tesnor(anchors, gt_boxes,config.DEVICE)
# Match anchors to GT Boxes
# If an anchor overlaps a GT box with IoU >= 0.7 then it's positive.
# If an anchor overlaps a GT box with IoU < 0.3 then it's negative.
# Neutral anchors are those that don't match the conditions above,
# and they don't influence the loss function.
# However, don't keep any GT box unmatched (rare, but happens). Instead,
# match it to the closest anchor (even if its max IoU is < 0.3).
#
# 1. Set negative anchors first. They get overwritten below if a GT box is
# matched to them. Skip boxes in crowd areas.
anchor_iou_argmax = torch.argmax(overlaps, axis=1)
anchor_iou_max = overlaps[torch.arange(overlaps.shape[0]), anchor_iou_argmax]
rpn_match[(anchor_iou_max < 0.3) & (no_crowd_bool)] = -1
# 2. Set an anchor for each GT box (regardless of IoU value).
# If multiple anchors have the same IoU match all of them
# original was argwhere
# gt_iou_argmax = torch.where(torch.tensor(overlaps == torch.max(overlaps, axis=0)))[:, 0]
a = torch.max(overlaps, axis=0)[0]
gt_iou_argmax = torch.where(overlaps == a)[0]
rpn_match[gt_iou_argmax] = 1
# 3. Set anchors with high overlap as positive.
rpn_match[anchor_iou_max >= 0.7] = 1
# Subsample to balance positive and negative anchors
# Don't let positives be more than half the anchors
ids = torch.where(rpn_match == 1)[0]
extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2)
if extra > 0:
# Reset the extra ones to neutral
unif = torch.ones(ids.shape[0]).to(device=config.DEVICE)
idx = unif.multinomial(extra, replacement=False)
ids = ids[idx]
# ids = np.random.choice(ids, extra, replace=False)
rpn_match[ids] = 0
# Same for negative proposals
ids = torch.where(rpn_match == -1)[0]
extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE - torch.sum(rpn_match == 1))
if extra > 0:
# Rest the extra ones to neutral
# ids = np.random.choice(ids, extra, replace=False)
unif = torch.ones(ids.shape[0]).to(device=config.DEVICE)
idx = unif.multinomial(extra, replacement=False)
ids = ids[idx]
rpn_match[ids] = 0
# For positive anchors, compute shift and scale needed to transform them
# to match the corresponding GT boxes.
ids = torch.where(rpn_match == 1)[0]
ix = 0 # index into rpn_bbox
for i, a in zip(ids, anchors[ids]):
# Closest gt box (it might have IoU < 0.7)
gt = gt_boxes[anchor_iou_argmax[i]]
# Convert coordinates to center plus width/height.
# GT Box
gt_h = gt[2] - gt[0]
gt_w = gt[3] - gt[1]
gt_center_y = gt[0] + 0.5 * gt_h
gt_center_x = gt[1] + 0.5 * gt_w
# Anchor
a_h = a[2] - a[0]
a_w = a[3] - a[1]
a_center_y = a[0] + 0.5 * a_h
a_center_x = a[1] + 0.5 * a_w
# Compute the bbox refinement that the RPN should predict.
rpn_bbox[ix] = torch.tensor(
[
(gt_center_y - a_center_y) / a_h,
(gt_center_x - a_center_x) / a_w,
torch.log(gt_h / a_h),
torch.log(gt_w / a_w),
]
)
# Normalize
#rpn_bbox[ix] /= torch.tensor(config.RPN_BBOX_STD_DEV, dtype=torch.float32).to(device=config.DEVICE)
ix += 1
return rpn_match, rpn_bbox
def box_refinement(box, gt_box):
"""Compute refinement needed to transform box to gt_box.
box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is
assumed to be outside the box.
"""
box = box.type(torch.float32)
gt_box = gt_box.type(torch.float32)
height = box[:, 2] - box[:, 0]
width = box[:, 3] - box[:, 1]
center_y = box[:, 0] + 0.5 * height
center_x = box[:, 1] + 0.5 * width
gt_height = gt_box[:, 2] - gt_box[:, 0]
gt_width = gt_box[:, 3] - gt_box[:, 1]
gt_center_y = gt_box[:, 0] + 0.5 * gt_height
gt_center_x = gt_box[:, 1] + 0.5 * gt_width
dy = (gt_center_y - center_y) / height
dx = (gt_center_x - center_x) / width
dh = torch.log(gt_height / height)
dw = torch.log(gt_width / width)
return torch.stack([dy, dx, dh, dw], axis=1)
def process_box(box, score, image_shape, min_size):
"""
Clip boxes in the image size and remove boxes which are too small.
"""
box[:, [0, 2]] = box[:, [0, 2]].clamp(0, image_shape[0])
box[:, [1, 3]] = box[:, [1, 3]].clamp(0, image_shape[1])
w, h = box[:, 2] - box[:, 0], box[:, 3] - box[:, 1]
keep = torch.where((w >= min_size) & (h >= min_size))[0]
box, score = box[keep], score[keep]
return box, score
def roi_align(
features, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio
):
if torch.__version__ >= "1.5.0":
return torch.ops.torchvision.roi_align(
features,
rois,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio,
False,
)
else:
return torch.ops.torchvision.roi_align(
features, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio
)
class RoIAlign:
"""
Performs Region of Interest (RoI) Align operator described in Mask R-CNN
"""
def __init__(self, output_size, sampling_ratio):
"""
Arguments:
output_size (Tuple[int, int]): the size of the output after the cropping
is performed, as (height, width)
sampling_ratio (int): number of sampling points in the interpolation grid
used to compute the output value of each pooled output bin. If > 0,
then exactly sampling_ratio x sampling_ratio grid points are used. If
<= 0, then an adaptive number of grid points are used (computed as
ceil(roi_width / pooled_w), and likewise for height). Default: -1
"""
self.output_size = output_size
self.sampling_ratio = sampling_ratio
self.spatial_scale = None
def setup_scale(self, feature_shape, image_shape):
if self.spatial_scale is not None:
return
possible_scales = []
for s1, s2 in zip(feature_shape, image_shape):
scale = 2 ** int(math.log2(s1 / s2))
possible_scales.append(scale)
assert possible_scales[0] == possible_scales[1]
self.spatial_scale = possible_scales[0]
def __call__(self, feature, proposal, image_shape):
"""
Arguments:
feature (Tensor[N, C, H, W])
proposal (Tensor[K, 4])
image_shape (Torch.Size([H, W]))
Returns:
output (Tensor[K, C, self.output_size[0], self.output_size[1]])
"""
idx = proposal.new_full((proposal.shape[0], 1), 0)
roi = torch.cat((idx, proposal), dim=1)
self.setup_scale(feature.shape[-2:], image_shape)
return roi_align(
feature.to(roi),
roi,
self.spatial_scale,
self.output_size[0],
self.output_size[1],
self.sampling_ratio,
)
class Matcher:
def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False):
self.high_threshold = high_threshold
self.low_threshold = low_threshold
self.allow_low_quality_matches = allow_low_quality_matches
def __call__(self, iou):
"""
Arguments:
iou (Tensor[M, N]): containing the pairwise quality between
M ground-truth boxes and N predicted boxes.
Returns:
label (Tensor[N]): positive (1) or negative (0) label for each predicted box,
-1 means ignoring this box.
matched_idx (Tensor[N]): indices of gt box matched by each predicted box.
"""
value, matched_idx = iou.max(dim=0)
label = torch.full((iou.shape[1],), -1, dtype=torch.float, device=iou.device)
label[value >= self.high_threshold] = 1
label[value < self.low_threshold] = 0
if self.allow_low_quality_matches:
highest_quality = iou.max(dim=1)[0]
gt_pred_pairs = torch.where(iou == highest_quality[:, None])[1]
label[gt_pred_pairs] = 1
return label, matched_idx
class BalancedPositiveNegativeSampler:
def __init__(self, num_samples, positive_fraction):
self.num_samples = num_samples
self.positive_fraction = positive_fraction
def __call__(self, label):
positive = torch.where(label == 1)[0]
negative = torch.where(label == 0)[0]
num_pos = int(self.num_samples * self.positive_fraction)
num_pos = min(positive.numel(), num_pos)
num_neg = self.num_samples - num_pos
num_neg = min(negative.numel(), num_neg)
pos_perm = torch.randperm(positive.numel(), device=positive.device)[:num_pos]
neg_perm = torch.randperm(negative.numel(), device=negative.device)[:num_neg]
pos_idx = positive[pos_perm]
neg_idx = negative[neg_perm]
return pos_idx, neg_idx
def visualize_inference(in_img, inv_normalize,results):
vis_img = in_img.clone()
vis_img = (inv_normalize(vis_img).data.numpy() * 255).astype(np.uint8)
vis_img = np.ascontiguousarray(np.moveaxis(vis_img, 0, -1))
boxes=np.int0(results["boxes"])
labels=results["labels"]
scores=results["scores"]
print(f"Labels max {labels.max()}")
print(f"label min {labels.min()}")
print(f"scores max {scores.max()}")
print(f"scores min {scores.min()}")
for i in range(boxes.shape[0]):
bbox=boxes[i]
cv2.rectangle(vis_img, (bbox[1], bbox[0]), (bbox[3], bbox[2]), (255, 255, 0), 2)
cv2.imshow("Results",vis_img)
cv2.waitKey(0)
|
from celescope.mut.__init__ import __ASSAY__
from celescope.tools.multi import Multi
class Multi_mut(Multi):
def mapping_mut(self, sample):
step = 'mapping_mut'
fq = f'{self.outdir_dic[sample]['cutadapt']}/{sample}_clean_2.fq{self.fq_suffix}'
cmd = (
f'{self.__APP__} '
f'{self.__ASSAY__} '
f'{step } '
f'--outdir {self.outdir_dic[sample][step]} '
f'--sample {sample} '
f'--assay {self.__ASSAY__} '
f'--fq {fq} '
f'--thread {self.thread} '
f'--indel_genomeDir {self.indel_genomeDir} '
)
self.process_cmd(cmd, step, sample, m=self.args.starMem, x=self.args.thread)
def count_mut(self, sample):
step = 'count_mut'
bam = f'{self.outdir_dic[sample]['mapping_mut']}/{sample}_Aligned.sortedByCoord.out.bam'
cmd = (
f'{self.__APP__} '
f'{self.__ASSAY__} '
f'{step } '
f'--outdir {self.outdir_dic[sample][step]} '
f'--sample {sample} '
f'--assay {self.__ASSAY__} '
f'--bam {bam} '
f'--mut_file {self.mut_file} '
f'--match_dir {self.col4_dict[sample]} '
f'--shift_base {self.shift_base} '
)
self.process_cmd(cmd, step, sample, m=8, x=1)
def main():
multi = Multi_mut(__ASSAY__)
multi.run()
if __name__ == '__main__':
main()
|
from celescope.mut.__init__ import __ASSAY__
from celescope.tools.multi import Multi
class Multi_mut(Multi):
def mapping_mut(self, sample):
step = 'mapping_mut'
fq = f'{self.outdir_dic[sample]["cutadapt"]}/{sample}_clean_2.fq{self.fq_suffix}'
cmd = (
f'{self.__APP__} '
f'{self.__ASSAY__} '
f'{step } '
f'--outdir {self.outdir_dic[sample][step]} '
f'--sample {sample} '
f'--assay {self.__ASSAY__} '
f'--fq {fq} '
f'--thread {self.thread} '
f'--indel_genomeDir {self.indel_genomeDir} '
)
self.process_cmd(cmd, step, sample, m=self.args.starMem, x=self.args.thread)
def count_mut(self, sample):
step = 'count_mut'
bam = f'{self.outdir_dic[sample]["mapping_mut"]}/{sample}_Aligned.sortedByCoord.out.bam'
cmd = (
f'{self.__APP__} '
f'{self.__ASSAY__} '
f'{step } '
f'--outdir {self.outdir_dic[sample][step]} '
f'--sample {sample} '
f'--assay {self.__ASSAY__} '
f'--bam {bam} '
f'--mut_file {self.mut_file} '
f'--match_dir {self.col4_dict[sample]} '
f'--shift_base {self.shift_base} '
)
self.process_cmd(cmd, step, sample, m=8, x=1)
def main():
multi = Multi_mut(__ASSAY__)
multi.run()
if __name__ == '__main__':
main()
|
from flask import Flask, jsonify, request, render_template, Response, send_file
# from prefix_and_wsgi_proxy_fix import ReverseProxied
from base64 import urlsafe_b64encode
import gpxpy.gpx
import geojson
import werkzeug.exceptions
import os
from werkzeug.datastructures import Headers
from db_functions import get_waypoints_by_trip, set_db_path, create_new_trip, get_nakarte_url_by_trip
from time_functions import str_ts_to_UTC_ts
from datetime import timedelta, timezone
app = Flask(__name__)
APP_PATH = os.path.dirname(os.path.realpath(__file__))
sqlite_db_path = os.path.join(APP_PATH, 'db', 'tracks_prod.db')
set_db_path(sqlite_db_path)
# This is just a test route. It is autotested after deploy
@app.route('/test_app_is_working_kQK74RxmgPPm69')
def test_app_is_working():
return "Yup! The app is working!\n"
@app.errorhandler(werkzeug.exceptions.BadRequest)
def bad_request_error_handler(e=None):
message = {
'status': 400,
'message': 'Bad request or API method not found: ' + request.url,
'return': {'debug': str(e)}
}
response = jsonify(message)
response.status_code = 400
return response
@app.errorhandler(werkzeug.exceptions.InternalServerError)
def internal_error_handler(e=None):
message = {
'status': 500,
'message': 'Internal server error: ' + request.url,
'return': {'debug': str(e)}
}
response = jsonify(message)
response.status_code = 500
return response
@app.route('/get_waypoints')
def get_waypoints(*args, **kwargs):
args = dict(request.args)
if 'trip_name' not in args:
return bad_request_error_handler(NameError(f'Key trip_name not found'))
trip_name = args['trip_name']
waypoints = get_waypoints_by_trip(trip_name)
features = []
for i in range(len(waypoints)):
id, fms_key_id, id_fms, lat, long, alt, ts, bs, msg = waypoints[i]
ts = str_ts_to_UTC_ts(ts)
cur_point = geojson.Point((lat, long, alt))
features.append(geojson.Feature(geometry=cur_point, properties={'BatteryState': bs,
'Message': msg,
'TimeStamp': ts}))
message = {
'status': 200,
'message': 'OK',
'cnt': len(waypoints),
'return': geojson.FeatureCollection(features)
}
response = jsonify(message)
response.status_code = 200
return response
@app.route('/u')
def generate_iframe_html(*args, **kwargs):
args = dict(request.args)
if 'trip_name' not in args and 't' not in args:
return bad_request_error_handler(NameError(f'Key trip_name not found'))
trip_name = ''.join((args.get('trip_name', None) or args['t']))
nakarte_url = get_nakarte_url_by_trip(trip_name) or 'https://nakarte.me/#l=Otm'
print('nakarte_url: ', nakarte_url)
urlbase64 = f'[{{'n':'{trip_name}","c":3,"m":true,"u":"https://pyfindmespot.proj179.ru/gw?t={trip_name}"}}]'
urlbase64 = urlsafe_b64encode(urlbase64.encode('utf-8')).decode('utf-8')
nakarte_url += f'&nktj={urlbase64}'
return render_template('nakarte.html', nakarte_url=nakarte_url)
@app.route('/gw')
@app.route('/get_gpx_waypoints')
def generate_gpx(*args, **kwargs):
args = dict(request.args)
if 'trip_name' not in args and 't' not in args:
return bad_request_error_handler(NameError(f'Key trip_name not found'))
trip_name = ''.join((args.get('trip_name', None) or args['t']))
waypoints = get_waypoints_by_trip(trip_name)
gpx = gpxpy.gpx.GPX()
gpx_track = gpxpy.gpx.GPXTrack()
gpx.tracks.append(gpx_track)
gpx_segment = gpxpy.gpx.GPXTrackSegment()
gpx_track.segments.append(gpx_segment)
for i in range(len(waypoints)):
id, fms_key_id, id_fms, lat, long, alt, ts, bs, msg = waypoints[i]
ts = str_ts_to_UTC_ts(ts)
if msg:
ts_msg = ts.astimezone(timezone(offset=timedelta(hours=+3))).strftime(" (%m-%d %H:%M)")
gpx.waypoints.append(gpxpy.gpx.GPXWaypoint(latitude=lat, longitude=long, elevation=alt, comment=msg + ts_msg, time=ts, name=msg + ts_msg))
cur_pnt = gpxpy.gpx.GPXTrackPoint(latitude=lat, longitude=long, elevation=alt, comment=msg, time=ts)
cur_pnt.description = f"Время: {ts} Заряд батареи {bs}"
gpx_segment.points.append(cur_pnt)
# Добавляем точку с временем последнего сообщения
if waypoints:
ts_msg = ts.astimezone(timezone(offset=timedelta(hours=+3))).strftime("%Y-%m-%d %H:%M:%S")
gpx.waypoints.append(gpxpy.gpx.GPXWaypoint(latitude=lat, longitude=long, elevation=alt, comment=ts_msg, time=ts, name=ts_msg))
hdrs = Headers()
hdrs.add('Content-Type', 'application/gpx+xml')
hdrs.add('Content-Disposition', 'attachment', filename='track.gpx')
return Response(gpx.to_xml(), headers=hdrs)
@app.route('/create_track')
def create_track(*args, **kwargs):
args = dict(request.args)
for parm in ['trip_name', 'fms_key', 'date_s', 'date_e']:
if parm not in args:
return bad_request_error_handler(NameError(f'Key {parm} not found'))
trip_name = args['trip_name']
fms_key = args['fms_key']
print(args)
date_s = str_ts_to_UTC_ts(args['date_s'])
date_e = str_ts_to_UTC_ts(args['date_e'])
create_new_trip(trip_name, fms_key, date_s, date_e)
message = {
'status': 200,
'message': 'OK'
}
response = jsonify(message)
response.status_code = 200
return response
@app.route('/')
def just_index():
return render_template('index.html')
# app.wsgi_app = ReverseProxied(app.wsgi_app)
if __name__ == "__main__":
app.run(host="0.0.0.0")
| from flask import Flask, jsonify, request, render_template, Response, send_file
# from prefix_and_wsgi_proxy_fix import ReverseProxied
from base64 import urlsafe_b64encode
import gpxpy.gpx
import geojson
import werkzeug.exceptions
import os
from werkzeug.datastructures import Headers
from db_functions import get_waypoints_by_trip, set_db_path, create_new_trip, get_nakarte_url_by_trip
from time_functions import str_ts_to_UTC_ts
from datetime import timedelta, timezone
app = Flask(__name__)
APP_PATH = os.path.dirname(os.path.realpath(__file__))
sqlite_db_path = os.path.join(APP_PATH, 'db', 'tracks_prod.db')
set_db_path(sqlite_db_path)
# This is just a test route. It is autotested after deploy
@app.route('/test_app_is_working_kQK74RxmgPPm69')
def test_app_is_working():
return "Yup! The app is working!\n"
@app.errorhandler(werkzeug.exceptions.BadRequest)
def bad_request_error_handler(e=None):
message = {
'status': 400,
'message': 'Bad request or API method not found: ' + request.url,
'return': {'debug': str(e)}
}
response = jsonify(message)
response.status_code = 400
return response
@app.errorhandler(werkzeug.exceptions.InternalServerError)
def internal_error_handler(e=None):
message = {
'status': 500,
'message': 'Internal server error: ' + request.url,
'return': {'debug': str(e)}
}
response = jsonify(message)
response.status_code = 500
return response
@app.route('/get_waypoints')
def get_waypoints(*args, **kwargs):
args = dict(request.args)
if 'trip_name' not in args:
return bad_request_error_handler(NameError(f'Key trip_name not found'))
trip_name = args['trip_name']
waypoints = get_waypoints_by_trip(trip_name)
features = []
for i in range(len(waypoints)):
id, fms_key_id, id_fms, lat, long, alt, ts, bs, msg = waypoints[i]
ts = str_ts_to_UTC_ts(ts)
cur_point = geojson.Point((lat, long, alt))
features.append(geojson.Feature(geometry=cur_point, properties={'BatteryState': bs,
'Message': msg,
'TimeStamp': ts}))
message = {
'status': 200,
'message': 'OK',
'cnt': len(waypoints),
'return': geojson.FeatureCollection(features)
}
response = jsonify(message)
response.status_code = 200
return response
@app.route('/u')
def generate_iframe_html(*args, **kwargs):
args = dict(request.args)
if 'trip_name' not in args and 't' not in args:
return bad_request_error_handler(NameError(f'Key trip_name not found'))
trip_name = ''.join((args.get('trip_name', None) or args['t']))
nakarte_url = get_nakarte_url_by_trip(trip_name) or 'https://nakarte.me/#l=Otm'
print('nakarte_url: ', nakarte_url)
urlbase64 = f'[{{"n":"{trip_name}","c":3,"m":true,"u":"https://pyfindmespot.proj179.ru/gw?t={trip_name}"}}]'
urlbase64 = urlsafe_b64encode(urlbase64.encode('utf-8')).decode('utf-8')
nakarte_url += f'&nktj={urlbase64}'
return render_template('nakarte.html', nakarte_url=nakarte_url)
@app.route('/gw')
@app.route('/get_gpx_waypoints')
def generate_gpx(*args, **kwargs):
args = dict(request.args)
if 'trip_name' not in args and 't' not in args:
return bad_request_error_handler(NameError(f'Key trip_name not found'))
trip_name = ''.join((args.get('trip_name', None) or args['t']))
waypoints = get_waypoints_by_trip(trip_name)
gpx = gpxpy.gpx.GPX()
gpx_track = gpxpy.gpx.GPXTrack()
gpx.tracks.append(gpx_track)
gpx_segment = gpxpy.gpx.GPXTrackSegment()
gpx_track.segments.append(gpx_segment)
for i in range(len(waypoints)):
id, fms_key_id, id_fms, lat, long, alt, ts, bs, msg = waypoints[i]
ts = str_ts_to_UTC_ts(ts)
if msg:
ts_msg = ts.astimezone(timezone(offset=timedelta(hours=+3))).strftime(" (%m-%d %H:%M)")
gpx.waypoints.append(gpxpy.gpx.GPXWaypoint(latitude=lat, longitude=long, elevation=alt, comment=msg + ts_msg, time=ts, name=msg + ts_msg))
cur_pnt = gpxpy.gpx.GPXTrackPoint(latitude=lat, longitude=long, elevation=alt, comment=msg, time=ts)
cur_pnt.description = f"Время: {ts} Заряд батареи {bs}"
gpx_segment.points.append(cur_pnt)
# Добавляем точку с временем последнего сообщения
if waypoints:
ts_msg = ts.astimezone(timezone(offset=timedelta(hours=+3))).strftime("%Y-%m-%d %H:%M:%S")
gpx.waypoints.append(gpxpy.gpx.GPXWaypoint(latitude=lat, longitude=long, elevation=alt, comment=ts_msg, time=ts, name=ts_msg))
hdrs = Headers()
hdrs.add('Content-Type', 'application/gpx+xml')
hdrs.add('Content-Disposition', 'attachment', filename='track.gpx')
return Response(gpx.to_xml(), headers=hdrs)
@app.route('/create_track')
def create_track(*args, **kwargs):
args = dict(request.args)
for parm in ['trip_name', 'fms_key', 'date_s', 'date_e']:
if parm not in args:
return bad_request_error_handler(NameError(f'Key {parm} not found'))
trip_name = args['trip_name']
fms_key = args['fms_key']
print(args)
date_s = str_ts_to_UTC_ts(args['date_s'])
date_e = str_ts_to_UTC_ts(args['date_e'])
create_new_trip(trip_name, fms_key, date_s, date_e)
message = {
'status': 200,
'message': 'OK'
}
response = jsonify(message)
response.status_code = 200
return response
@app.route('/')
def just_index():
return render_template('index.html')
# app.wsgi_app = ReverseProxied(app.wsgi_app)
if __name__ == "__main__":
app.run(host="0.0.0.0")
|
from InquirerPy import prompt, inquirer
from InquirerPy.separator import Separator
from ...flair_management.skin_manager.skin_manager import Skin_Manager
from .weapon_config_prompts import Prompts
class Weight_Editor:
@staticmethod
def weights_entrypoint():
weapon_data, skin_data, skin_choice, weapon_choice, weapon_skin_data = Prompts.select_weapon_type(change_all=False, weights=True)
while weapon_data is not None:
weapon_data['skins'][skin_choice]["weight"] = Weight_Editor.set_weight(weapon_skin_data)
Skin_Manager.modify_skin_data(skin_data)
weapon_data, skin_data, skin_choice, weapon_choice, weapon_skin_data = Prompts.select_skin(skin_data, weapon_choice, change_all=False, weights=True)
def set_weight(skin_data):
current_weight = str(skin_data["weight"])
new_weight = inquirer.text(
message=f"[{skin_data["display_name"]}] Selecione o peso para a aleatorização (peso atual {current_weight})",
default=current_weight,
validate=lambda result: result.isdigit(),
filter=lambda result: int(result)
).execute()
return new_weight
| from InquirerPy import prompt, inquirer
from InquirerPy.separator import Separator
from ...flair_management.skin_manager.skin_manager import Skin_Manager
from .weapon_config_prompts import Prompts
class Weight_Editor:
@staticmethod
def weights_entrypoint():
weapon_data, skin_data, skin_choice, weapon_choice, weapon_skin_data = Prompts.select_weapon_type(change_all=False, weights=True)
while weapon_data is not None:
weapon_data['skins'][skin_choice]["weight"] = Weight_Editor.set_weight(weapon_skin_data)
Skin_Manager.modify_skin_data(skin_data)
weapon_data, skin_data, skin_choice, weapon_choice, weapon_skin_data = Prompts.select_skin(skin_data, weapon_choice, change_all=False, weights=True)
def set_weight(skin_data):
current_weight = str(skin_data["weight"])
new_weight = inquirer.text(
message=f"[{skin_data['display_name']}] Selecione o peso para a aleatorização (peso atual {current_weight})",
default=current_weight,
validate=lambda result: result.isdigit(),
filter=lambda result: int(result)
).execute()
return new_weight
|
# -*- coding: UTF-8 -*-
#
# copyright: 2020-2021, Frederico Martins
# author: Frederico Martins <http://github.com/fscm>
# license: SPDX-License-Identifier: MIT
"""Discogs data handler.
This module handles Discogs data.
The following is a simple usage example::
from .discogs import Discogs
d = Discogs('my_discogs_secret_token')
r = discogs.get_ratings()
print(r)
The module contains the following public classes:
- Discogs -- The main entry point. As the example above shows, the
Discogs() class can be used to load data from Discogs.
All other classes in this module are considered implementation details.
"""
import json
import re
from time import time, sleep
from progress.bar import Bar
from requests import sessions
class Discogs:
"""Data handler.
This class loads data from Discogs.
Args:
key (str): Discogs API key.
logger (logger.Logger, optional): Logger to use. Defaults to None.
"""
API_BASEURL = 'https://api.discogs.com'
API_FORMAT = 'application/vnd.discogs.v2.plaintext+json'
API_LIMIT = 100
API_RATELIMIT_STATUS = 429
API_RATELIMIT_TIME = 61
def __init__(self, key, logger=None):
self.__api_last_block_time = time()
self.__headers = {
'Accept': f'{self.API_FORMAT}',
'Accept-Encoding': 'gzip',
'Content-Type': 'application/json',
'User-Agent': f'{__package__}'}
self.__key = key
self.__logger = logger
self.__params = {
'token': f'{self.__key}',
'per_page': self.API_LIMIT}
self.__session = sessions.Session()
self.__identity = self.__request(f'{self.API_BASEURL}/oauth/identity')
def __request(self, url, params=None):
"""Private method to perform a request to the Discogs API.
Args:
url (str): Request URL.
params (dict[str, Any], optional): Extra requests params.
Defaults to None.
Returns:
dict[str, Any]: Discogs API data.
"""
response = self.__session.get(
url,
params={**self.__params, **params} if params else self.__params,
headers=self.__headers)
headers = response.headers
status_code = response.status_code
remaining_queries = int(headers.get('X-Discogs-Ratelimit-Remaining', 60))
if (remaining_queries < 2) or (status_code == self.API_RATELIMIT_STATUS):
if self.__logger:
self.__logger.warning('API rate limit reacehd.')
now = time()
sleep(max(
2,
self.API_RATELIMIT_TIME - (now - self.__api_last_block_time)))
self.__api_last_block_time = now
return self.__request(url=url, params=params)
return json.loads(response.content)
def get_ratings(self, ratings=None):
"""Fetch Discogs ratings from the user's collection.
Args:
ratings (dict[str, Any], optional): Ratings. If provided this
ratings will be updated. Defaults to None.
Returns:
dict[str, Any]: Ratings.
"""
if self.__logger:
self.__logger.info('Fetching ratings from Discogs.')
last_updated = int(time())
collection_info = self.__request(
url=f'{self.__identity['resource_url']}/collection/folders/0',
params={'page': 1})
total_albums = int(collection_info.get('count', 0))
total_pages = -(-total_albums // self.API_LIMIT)
if ratings:
ratings = ratings.get('ratings', {})
else:
ratings = {}
show_progress = True
if self.__logger and self.__logger.level < self.__logger.Level.INFO:
show_progress = False
for step in Bar('Processing').iter(
range(total_pages)) if show_progress else range(total_pages):
page = step + 1
if self.__logger:
self.__logger.debug(f'Fetching page {page}')
content = self.__request(
f'{self.__identity['resource_url']}/collection/folders/0/releases',
params={'page': page})
releases = content['releases']
for release in releases:
release_album_rating = int(release['rating'])
release_album = release['basic_information']['title'].title()
release_artist = ' - '.join(map(
lambda x: re.sub(r'\(\d+\)', '', x['name']).strip(),
release['basic_information']['artists'])).title()
if self.__logger:
self.__logger.debug(
f'{release_artist} - [{release_album_rating}] {release_album} ')
ratings.setdefault(release_artist, {})
ratings[release_artist].setdefault(release_album, {})
ratings[release_artist][release_album].setdefault(
'rating',
release_album_rating)
return {'last_updated': last_updated, 'ratings': ratings}
| # -*- coding: UTF-8 -*-
#
# copyright: 2020-2021, Frederico Martins
# author: Frederico Martins <http://github.com/fscm>
# license: SPDX-License-Identifier: MIT
"""Discogs data handler.
This module handles Discogs data.
The following is a simple usage example::
from .discogs import Discogs
d = Discogs('my_discogs_secret_token')
r = discogs.get_ratings()
print(r)
The module contains the following public classes:
- Discogs -- The main entry point. As the example above shows, the
Discogs() class can be used to load data from Discogs.
All other classes in this module are considered implementation details.
"""
import json
import re
from time import time, sleep
from progress.bar import Bar
from requests import sessions
class Discogs:
"""Data handler.
This class loads data from Discogs.
Args:
key (str): Discogs API key.
logger (logger.Logger, optional): Logger to use. Defaults to None.
"""
API_BASEURL = 'https://api.discogs.com'
API_FORMAT = 'application/vnd.discogs.v2.plaintext+json'
API_LIMIT = 100
API_RATELIMIT_STATUS = 429
API_RATELIMIT_TIME = 61
def __init__(self, key, logger=None):
self.__api_last_block_time = time()
self.__headers = {
'Accept': f'{self.API_FORMAT}',
'Accept-Encoding': 'gzip',
'Content-Type': 'application/json',
'User-Agent': f'{__package__}'}
self.__key = key
self.__logger = logger
self.__params = {
'token': f'{self.__key}',
'per_page': self.API_LIMIT}
self.__session = sessions.Session()
self.__identity = self.__request(f'{self.API_BASEURL}/oauth/identity')
def __request(self, url, params=None):
"""Private method to perform a request to the Discogs API.
Args:
url (str): Request URL.
params (dict[str, Any], optional): Extra requests params.
Defaults to None.
Returns:
dict[str, Any]: Discogs API data.
"""
response = self.__session.get(
url,
params={**self.__params, **params} if params else self.__params,
headers=self.__headers)
headers = response.headers
status_code = response.status_code
remaining_queries = int(headers.get('X-Discogs-Ratelimit-Remaining', 60))
if (remaining_queries < 2) or (status_code == self.API_RATELIMIT_STATUS):
if self.__logger:
self.__logger.warning('API rate limit reacehd.')
now = time()
sleep(max(
2,
self.API_RATELIMIT_TIME - (now - self.__api_last_block_time)))
self.__api_last_block_time = now
return self.__request(url=url, params=params)
return json.loads(response.content)
def get_ratings(self, ratings=None):
"""Fetch Discogs ratings from the user's collection.
Args:
ratings (dict[str, Any], optional): Ratings. If provided this
ratings will be updated. Defaults to None.
Returns:
dict[str, Any]: Ratings.
"""
if self.__logger:
self.__logger.info('Fetching ratings from Discogs.')
last_updated = int(time())
collection_info = self.__request(
url=f'{self.__identity["resource_url"]}/collection/folders/0',
params={'page': 1})
total_albums = int(collection_info.get('count', 0))
total_pages = -(-total_albums // self.API_LIMIT)
if ratings:
ratings = ratings.get('ratings', {})
else:
ratings = {}
show_progress = True
if self.__logger and self.__logger.level < self.__logger.Level.INFO:
show_progress = False
for step in Bar('Processing').iter(
range(total_pages)) if show_progress else range(total_pages):
page = step + 1
if self.__logger:
self.__logger.debug(f'Fetching page {page}')
content = self.__request(
f'{self.__identity["resource_url"]}/collection/folders/0/releases',
params={'page': page})
releases = content['releases']
for release in releases:
release_album_rating = int(release['rating'])
release_album = release['basic_information']['title'].title()
release_artist = ' - '.join(map(
lambda x: re.sub(r'\(\d+\)', '', x['name']).strip(),
release['basic_information']['artists'])).title()
if self.__logger:
self.__logger.debug(
f'{release_artist} - [{release_album_rating}] {release_album} ')
ratings.setdefault(release_artist, {})
ratings[release_artist].setdefault(release_album, {})
ratings[release_artist][release_album].setdefault(
'rating',
release_album_rating)
return {'last_updated': last_updated, 'ratings': ratings}
|
"""
Define typing templates
"""
from abc import ABC, abstractmethod
import functools
import sys
import inspect
import os.path
from collections import namedtuple
from collections.abc import Sequence
from types import MethodType, FunctionType
import numba
from numba.core import types, utils
from numba.core.errors import TypingError, InternalError
from numba.core.cpu_options import InlineOptions
# info store for inliner callback functions e.g. cost model
_inline_info = namedtuple('inline_info',
'func_ir typemap calltypes signature')
class Signature(object):
"""
The signature of a function call or operation, i.e. its argument types
and return type.
"""
# XXX Perhaps the signature should be a BoundArguments, instead
# of separate args and pysig...
__slots__ = '_return_type', '_args', '_recvr', '_pysig'
def __init__(self, return_type, args, recvr, pysig=None):
if isinstance(args, list):
args = tuple(args)
self._return_type = return_type
self._args = args
self._recvr = recvr
self._pysig = pysig
@property
def return_type(self):
return self._return_type
@property
def args(self):
return self._args
@property
def recvr(self):
return self._recvr
@property
def pysig(self):
return self._pysig
def replace(self, **kwargs):
"""Copy and replace the given attributes provided as keyword arguments.
Returns an updated copy.
"""
curstate = dict(return_type=self.return_type,
args=self.args,
recvr=self.recvr,
pysig=self.pysig)
curstate.update(kwargs)
return Signature(**curstate)
def __getstate__(self):
"""
Needed because of __slots__.
"""
return self._return_type, self._args, self._recvr, self._pysig
def __setstate__(self, state):
"""
Needed because of __slots__.
"""
self._return_type, self._args, self._recvr, self._pysig = state
def __hash__(self):
return hash((self.args, self.return_type))
def __eq__(self, other):
if isinstance(other, Signature):
return (self.args == other.args and
self.return_type == other.return_type and
self.recvr == other.recvr and
self.pysig == other.pysig)
def __ne__(self, other):
return not (self == other)
def __repr__(self):
return "%s -> %s" % (self.args, self.return_type)
@property
def is_method(self):
"""
Whether this signature represents a bound method or a regular
function.
"""
return self.recvr is not None
def as_method(self):
"""
Convert this signature to a bound method signature.
"""
if self.recvr is not None:
return self
sig = signature(self.return_type, *self.args[1:],
recvr=self.args[0])
# Adjust the python signature
params = list(self.pysig.parameters.values())[1:]
sig = sig.replace(
pysig=utils.pySignature(
parameters=params,
return_annotation=self.pysig.return_annotation,
),
)
return sig
def as_function(self):
"""
Convert this signature to a regular function signature.
"""
if self.recvr is None:
return self
sig = signature(self.return_type, *((self.recvr,) + self.args))
return sig
def as_type(self):
"""
Convert this signature to a first-class function type.
"""
return types.FunctionType(self)
def __unliteral__(self):
return signature(types.unliteral(self.return_type),
*map(types.unliteral, self.args))
def dump(self, tab=''):
c = self.as_type()._code
print(f'{tab}DUMP {type(self).__name__} [type code: {c}]')
print(f'{tab} Argument types:')
for a in self.args:
a.dump(tab=tab + ' | ')
print(f'{tab} Return type:')
self.return_type.dump(tab=tab + ' | ')
print(f'{tab}END DUMP')
def is_precise(self):
for atype in self.args:
if not atype.is_precise():
return False
return self.return_type.is_precise()
def make_concrete_template(name, key, signatures):
baseclasses = (ConcreteTemplate,)
gvars = dict(key=key, cases=list(signatures))
return type(name, baseclasses, gvars)
def make_callable_template(key, typer, recvr=None):
"""
Create a callable template with the given key and typer function.
"""
def generic(self):
return typer
name = "%s_CallableTemplate" % (key,)
bases = (CallableTemplate,)
class_dict = dict(key=key, generic=generic, recvr=recvr)
return type(name, bases, class_dict)
def signature(return_type, *args, **kws):
recvr = kws.pop('recvr', None)
assert not kws
return Signature(return_type, args, recvr=recvr)
def fold_arguments(pysig, args, kws, normal_handler, default_handler,
stararg_handler):
"""
Given the signature *pysig*, explicit *args* and *kws*, resolve
omitted arguments and keyword arguments. A tuple of positional
arguments is returned.
Various handlers allow to process arguments:
- normal_handler(index, param, value) is called for normal arguments
- default_handler(index, param, default) is called for omitted arguments
- stararg_handler(index, param, values) is called for a "*args" argument
"""
if isinstance(kws, Sequence):
# Normalize dict kws
kws = dict(kws)
# deal with kwonly args
params = pysig.parameters
kwonly = []
for name, p in params.items():
if p.kind == p.KEYWORD_ONLY:
kwonly.append(name)
if kwonly:
bind_args = args[:-len(kwonly)]
else:
bind_args = args
bind_kws = kws.copy()
if kwonly:
for idx, n in enumerate(kwonly):
bind_kws[n] = args[len(kwonly) + idx]
# now bind
ba = pysig.bind(*bind_args, **bind_kws)
for i, param in enumerate(pysig.parameters.values()):
name = param.name
default = param.default
if param.kind == param.VAR_POSITIONAL:
# stararg may be omitted, in which case its "default" value
# is simply the empty tuple
if name in ba.arguments:
argval = ba.arguments[name]
# NOTE: avoid wrapping the tuple type for stararg in another
# tuple.
if (len(argval) == 1 and
isinstance(argval[0], (types.StarArgTuple,
types.StarArgUniTuple))):
argval = tuple(argval[0])
else:
argval = ()
out = stararg_handler(i, param, argval)
ba.arguments[name] = out
elif name in ba.arguments:
# Non-stararg, present
ba.arguments[name] = normal_handler(i, param, ba.arguments[name])
else:
# Non-stararg, omitted
assert default is not param.empty
ba.arguments[name] = default_handler(i, param, default)
# Collect args in the right order
args = tuple(ba.arguments[param.name]
for param in pysig.parameters.values())
return args
class FunctionTemplate(ABC):
# Set to true to disable unsafe cast.
# subclass overide-able
unsafe_casting = True
# Set to true to require exact match without casting.
# subclass overide-able
exact_match_required = False
# Set to true to prefer literal arguments.
# Useful for definitions that specialize on literal but also support
# non-literals.
# subclass overide-able
prefer_literal = False
def __init__(self, context):
self.context = context
def _select(self, cases, args, kws):
options = {
'unsafe_casting': self.unsafe_casting,
'exact_match_required': self.exact_match_required,
}
selected = self.context.resolve_overload(self.key, cases, args, kws,
**options)
return selected
def get_impl_key(self, sig):
"""
Return the key for looking up the implementation for the given
signature on the target context.
"""
# Lookup the key on the class, to avoid binding it with `self`.
key = type(self).key
# On Python 2, we must also take care about unbound methods
if isinstance(key, MethodType):
assert key.im_self is None
key = key.im_func
return key
@classmethod
def get_source_code_info(cls, impl):
"""
Gets the source information about function impl.
Returns:
code - str: source code as a string
firstlineno - int: the first line number of the function impl
path - str: the path to file containing impl
if any of the above are not available something generic is returned
"""
try:
code, firstlineno = inspect.getsourcelines(impl)
except OSError: # missing source, probably a string
code = "None available (built from string?)"
firstlineno = 0
path = inspect.getsourcefile(impl)
if path is None:
path = "<unknown> (built from string?)"
return code, firstlineno, path
@abstractmethod
def get_template_info(self):
"""
Returns a dictionary with information specific to the template that will
govern how error messages are displayed to users. The dictionary must
be of the form:
info = {
'kind': "unknown", # str: The kind of template, e.g. "Overload"
'name': "unknown", # str: The name of the source function
'sig': "unknown", # str: The signature(s) of the source function
'filename': "unknown", # str: The filename of the source function
'lines': ("start", "end"), # tuple(int, int): The start and
end line of the source function.
'docstring': "unknown" # str: The docstring of the source function
}
"""
pass
def __str__(self):
info = self.get_template_info()
srcinfo = f"{info["filename"]}:{info["lines"][0]}"
return f"<{self.__class__.__name__} {srcinfo}>"
__repr__ = __str__
class AbstractTemplate(FunctionTemplate):
"""
Defines method ``generic(self, args, kws)`` which compute a possible
signature base on input types. The signature does not have to match the
input types. It is compared against the input types afterwards.
"""
def apply(self, args, kws):
generic = getattr(self, "generic")
sig = generic(args, kws)
# Enforce that *generic()* must return None or Signature
if sig is not None:
if not isinstance(sig, Signature):
raise AssertionError(
"generic() must return a Signature or None. "
"{} returned {}".format(generic, type(sig)),
)
# Unpack optional type if no matching signature
if not sig and any(isinstance(x, types.Optional) for x in args):
def unpack_opt(x):
if isinstance(x, types.Optional):
return x.type
else:
return x
args = list(map(unpack_opt, args))
assert not kws # Not supported yet
sig = generic(args, kws)
return sig
def get_template_info(self):
impl = getattr(self, "generic")
basepath = os.path.dirname(os.path.dirname(numba.__file__))
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
class CallableTemplate(FunctionTemplate):
"""
Base class for a template defining a ``generic(self)`` method
returning a callable to be called with the actual ``*args`` and
``**kwargs`` representing the call signature. The callable has
to return a return type, a full signature, or None. The signature
does not have to match the input types. It is compared against the
input types afterwards.
"""
recvr = None
def apply(self, args, kws):
generic = getattr(self, "generic")
typer = generic()
sig = typer(*args, **kws)
# Unpack optional type if no matching signature
if sig is None:
if any(isinstance(x, types.Optional) for x in args):
def unpack_opt(x):
if isinstance(x, types.Optional):
return x.type
else:
return x
args = list(map(unpack_opt, args))
sig = typer(*args, **kws)
if sig is None:
return
# Get the pysig
try:
pysig = typer.pysig
except AttributeError:
pysig = utils.pysignature(typer)
# Fold any keyword arguments
bound = pysig.bind(*args, **kws)
if bound.kwargs:
raise TypingError("unsupported call signature")
if not isinstance(sig, Signature):
# If not a signature, `sig` is assumed to be the return type
if not isinstance(sig, types.Type):
raise TypeError("invalid return type for callable template: "
"got %r" % (sig,))
sig = signature(sig, *bound.args)
if self.recvr is not None:
sig = sig.replace(recvr=self.recvr)
# Hack any omitted parameters out of the typer's pysig,
# as lowering expects an exact match between formal signature
# and actual args.
if len(bound.args) < len(pysig.parameters):
parameters = list(pysig.parameters.values())[:len(bound.args)]
pysig = pysig.replace(parameters=parameters)
sig = sig.replace(pysig=pysig)
cases = [sig]
return self._select(cases, bound.args, bound.kwargs)
def get_template_info(self):
impl = getattr(self, "generic")
basepath = os.path.dirname(os.path.dirname(numba.__file__))
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(self.key, '__name__',
getattr(impl, '__qualname__', impl.__name__),),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
class ConcreteTemplate(FunctionTemplate):
"""
Defines attributes "cases" as a list of signature to match against the
given input types.
"""
def apply(self, args, kws):
cases = getattr(self, 'cases')
return self._select(cases, args, kws)
def get_template_info(self):
import operator
name = getattr(self.key, '__name__', "unknown")
op_func = getattr(operator, name, None)
kind = "Type restricted function"
if op_func is not None:
if self.key is op_func:
kind = "operator overload"
info = {
'kind': kind,
'name': name,
'sig': "unknown",
'filename': "unknown",
'lines': ("unknown", "unknown"),
'docstring': "unknown"
}
return info
class _EmptyImplementationEntry(InternalError):
def __init__(self, reason):
super(_EmptyImplementationEntry, self).__init__(
"_EmptyImplementationEntry({!r})".format(reason),
)
class _OverloadFunctionTemplate(AbstractTemplate):
"""
A base class of templates for overload functions.
"""
def _validate_sigs(self, typing_func, impl_func):
# check that the impl func and the typing func have the same signature!
typing_sig = utils.pysignature(typing_func)
impl_sig = utils.pysignature(impl_func)
# the typing signature is considered golden and must be adhered to by
# the implementation...
# Things that are valid:
# 1. args match exactly
# 2. kwargs match exactly in name and default value
# 3. Use of *args in the same location by the same name in both typing
# and implementation signature
# 4. Use of *args in the implementation signature to consume any number
# of arguments in the typing signature.
# Things that are invalid:
# 5. Use of *args in the typing signature that is not replicated
# in the implementing signature
# 6. Use of **kwargs
def get_args_kwargs(sig):
kws = []
args = []
pos_arg = None
for x in sig.parameters.values():
if x.default == utils.pyParameter.empty:
args.append(x)
if x.kind == utils.pyParameter.VAR_POSITIONAL:
pos_arg = x
elif x.kind == utils.pyParameter.VAR_KEYWORD:
msg = ("The use of VAR_KEYWORD (e.g. **kwargs) is "
"unsupported. (offending argument name is '%s')")
raise InternalError(msg % x)
else:
kws.append(x)
return args, kws, pos_arg
ty_args, ty_kws, ty_pos = get_args_kwargs(typing_sig)
im_args, im_kws, im_pos = get_args_kwargs(impl_sig)
sig_fmt = ("Typing signature: %s\n"
"Implementation signature: %s")
sig_str = sig_fmt % (typing_sig, impl_sig)
err_prefix = "Typing and implementation arguments differ in "
a = ty_args
b = im_args
if ty_pos:
if not im_pos:
# case 5. described above
msg = ("VAR_POSITIONAL (e.g. *args) argument kind (offending "
"argument name is '%s') found in the typing function "
"signature, but is not in the implementing function "
"signature.\n%s") % (ty_pos, sig_str)
raise InternalError(msg)
else:
if im_pos:
# no *args in typing but there's a *args in the implementation
# this is case 4. described above
b = im_args[:im_args.index(im_pos)]
try:
a = ty_args[:ty_args.index(b[-1]) + 1]
except ValueError:
# there's no b[-1] arg name in the ty_args, something is
# very wrong, we can't work out a diff (*args consumes
# unknown quantity of args) so just report first error
specialized = "argument names.\n%s\nFirst difference: '%s'"
msg = err_prefix + specialized % (sig_str, b[-1])
raise InternalError(msg)
def gen_diff(typing, implementing):
diff = set(typing) ^ set(implementing)
return "Difference: %s" % diff
if a != b:
specialized = "argument names.\n%s\n%s" % (sig_str, gen_diff(a, b))
raise InternalError(err_prefix + specialized)
# ensure kwargs are the same
ty = [x.name for x in ty_kws]
im = [x.name for x in im_kws]
if ty != im:
specialized = "keyword argument names.\n%s\n%s"
msg = err_prefix + specialized % (sig_str, gen_diff(ty_kws, im_kws))
raise InternalError(msg)
same = [x.default for x in ty_kws] == [x.default for x in im_kws]
if not same:
specialized = "keyword argument default values.\n%s\n%s"
msg = err_prefix + specialized % (sig_str, gen_diff(ty_kws, im_kws))
raise InternalError(msg)
def generic(self, args, kws):
"""
Type the overloaded function by compiling the appropriate
implementation for the given args.
"""
disp, new_args = self._get_impl(args, kws)
if disp is None:
return
# Compile and type it for the given types
disp_type = types.Dispatcher(disp)
# Store the compiled overload for use in the lowering phase if there's
# no inlining required (else functions are being compiled which will
# never be used as they are inlined)
if not self._inline.is_never_inline:
# need to run the compiler front end up to type inference to compute
# a signature
from numba.core import typed_passes, compiler
from numba.core.inline_closurecall import InlineWorker
fcomp = disp._compiler
flags = compiler.Flags()
# Updating these causes problems?!
#fcomp.targetdescr.options.parse_as_flags(flags,
# fcomp.targetoptions)
#flags = fcomp._customize_flags(flags)
# spoof a compiler pipline like the one that will be in use
tyctx = fcomp.targetdescr.typing_context
tgctx = fcomp.targetdescr.target_context
compiler_inst = fcomp.pipeline_class(tyctx, tgctx, None, None, None,
flags, None, )
inline_worker = InlineWorker(tyctx, tgctx, fcomp.locals,
compiler_inst, flags, None,)
# If the inlinee contains something to trigger literal arg dispatch
# then the pipeline call will unconditionally fail due to a raised
# ForceLiteralArg exception. Therefore `resolve` is run first, as
# type resolution must occur at some point, this will hit any
# `literally` calls and because it's going via the dispatcher will
# handle them correctly i.e. ForceLiteralArg propagates. This having
# the desired effect of ensuring the pipeline call is only made in
# situations that will succeed. For context see #5887.
resolve = disp_type.dispatcher.get_call_template
template, pysig, folded_args, kws = resolve(new_args, kws)
ir = inline_worker.run_untyped_passes(disp_type.dispatcher.py_func)
typemap, return_type, calltypes = typed_passes.type_inference_stage(
self.context, ir, folded_args, None)
sig = Signature(return_type, folded_args, None)
# this stores a load of info for the cost model function if supplied
# it by default is None
self._inline_overloads[sig.args] = {'folded_args': folded_args}
# this stores the compiled overloads, if there's no compiled
# overload available i.e. function is always inlined, the key still
# needs to exist for type resolution
# NOTE: If lowering is failing on a `_EmptyImplementationEntry`,
# the inliner has failed to inline this entry corretly.
impl_init = _EmptyImplementationEntry('always inlined')
self._compiled_overloads[sig.args] = impl_init
if not self._inline.is_always_inline:
# this branch is here because a user has supplied a function to
# determine whether to inline or not. As a result both compiled
# function and inliner info needed, delaying the computation of
# this leads to an internal state mess at present. TODO: Fix!
sig = disp_type.get_call_type(self.context, new_args, kws)
self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
# store the inliner information, it's used later in the cost
# model function call
iinfo = _inline_info(ir, typemap, calltypes, sig)
self._inline_overloads[sig.args] = {'folded_args': folded_args,
'iinfo': iinfo}
else:
sig = disp_type.get_call_type(self.context, new_args, kws)
self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
return sig
def _get_impl(self, args, kws):
"""Get implementation given the argument types.
Returning a Dispatcher object. The Dispatcher object is cached
internally in `self._impl_cache`.
"""
cache_key = self.context, tuple(args), tuple(kws.items())
try:
impl, args = self._impl_cache[cache_key]
except KeyError:
impl, args = self._build_impl(cache_key, args, kws)
return impl, args
def _build_impl(self, cache_key, args, kws):
"""Build and cache the implementation.
Given the positional (`args`) and keyword arguments (`kws`), obtains
the `overload` implementation and wrap it in a Dispatcher object.
The expected argument types are returned for use by type-inference.
The expected argument types are only different from the given argument
types if there is an imprecise type in the given argument types.
Parameters
----------
cache_key : hashable
The key used for caching the implementation.
args : Tuple[Type]
Types of positional argument.
kws : Dict[Type]
Types of keyword argument.
Returns
-------
disp, args :
On success, returns `(Dispatcher, Tuple[Type])`.
On failure, returns `(None, None)`.
"""
from numba import jit
# Get the overload implementation for the given types
ovf_result = self._overload_func(*args, **kws)
if ovf_result is None:
# No implementation => fail typing
self._impl_cache[cache_key] = None, None
return None, None
elif isinstance(ovf_result, tuple):
# The implementation returned a signature that the type-inferencer
# should be using.
sig, pyfunc = ovf_result
args = sig.args
kws = {}
cache_key = None # don't cache
else:
# Regular case
pyfunc = ovf_result
# Check type of pyfunc
if not isinstance(pyfunc, FunctionType):
msg = ("Implementator function returned by `@overload` "
"has an unexpected type. Got {}")
raise AssertionError(msg.format(pyfunc))
# check that the typing and impl sigs match up
if self._strict:
self._validate_sigs(self._overload_func, pyfunc)
# Make dispatcher
jitdecor = jit(nopython=True, **self._jit_options)
disp = jitdecor(pyfunc)
# Make sure that the implementation can be fully compiled
disp_type = types.Dispatcher(disp)
disp_type.get_call_type(self.context, args, kws)
if cache_key is not None:
self._impl_cache[cache_key] = disp, args
return disp, args
def get_impl_key(self, sig):
"""
Return the key for looking up the implementation for the given
signature on the target context.
"""
return self._compiled_overloads[sig.args]
@classmethod
def get_source_info(cls):
"""Return a dictionary with information about the source code of the
implementation.
Returns
-------
info : dict
- "kind" : str
The implementation kind.
- "name" : str
The name of the function that provided the definition.
- "sig" : str
The formatted signature of the function.
- "filename" : str
The name of the source file.
- "lines": tuple (int, int)
First and list line number.
- "docstring": str
The docstring of the definition.
"""
basepath = os.path.dirname(os.path.dirname(numba.__file__))
impl = cls._overload_func
code, firstlineno, path = cls.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
def get_template_info(self):
basepath = os.path.dirname(os.path.dirname(numba.__file__))
impl = self._overload_func
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
def make_overload_template(func, overload_func, jit_options, strict,
inline, prefer_literal=False):
"""
Make a template class for function *func* overloaded by *overload_func*.
Compiler options are passed as a dictionary to *jit_options*.
"""
func_name = getattr(func, '__name__', str(func))
name = "OverloadTemplate_%s" % (func_name,)
base = _OverloadFunctionTemplate
dct = dict(key=func, _overload_func=staticmethod(overload_func),
_impl_cache={}, _compiled_overloads={}, _jit_options=jit_options,
_strict=strict, _inline=staticmethod(InlineOptions(inline)),
_inline_overloads={}, prefer_literal=prefer_literal)
return type(base)(name, (base,), dct)
class _IntrinsicTemplate(AbstractTemplate):
"""
A base class of templates for intrinsic definition
"""
def generic(self, args, kws):
"""
Type the intrinsic by the arguments.
"""
from numba.core.imputils import lower_builtin
cache_key = self.context, args, tuple(kws.items())
try:
return self._impl_cache[cache_key]
except KeyError:
result = self._definition_func(self.context, *args, **kws)
if result is None:
return
[sig, imp] = result
pysig = utils.pysignature(self._definition_func)
# omit context argument from user function
parameters = list(pysig.parameters.values())[1:]
sig = sig.replace(pysig=pysig.replace(parameters=parameters))
self._impl_cache[cache_key] = sig
self._overload_cache[sig.args] = imp
# register the lowering
lower_builtin(imp, *sig.args)(imp)
return sig
def get_impl_key(self, sig):
"""
Return the key for looking up the implementation for the given
signature on the target context.
"""
return self._overload_cache[sig.args]
def get_template_info(self):
basepath = os.path.dirname(os.path.dirname(numba.__file__))
impl = self._definition_func
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "intrinsic",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
def make_intrinsic_template(handle, defn, name):
"""
Make a template class for a intrinsic handle *handle* defined by the
function *defn*. The *name* is used for naming the new template class.
"""
base = _IntrinsicTemplate
name = "_IntrinsicTemplate_%s" % (name)
dct = dict(key=handle, _definition_func=staticmethod(defn),
_impl_cache={}, _overload_cache={})
return type(base)(name, (base,), dct)
class AttributeTemplate(object):
_initialized = False
def __init__(self, context):
self._lazy_class_init()
self.context = context
def resolve(self, value, attr):
return self._resolve(value, attr)
@classmethod
def _lazy_class_init(cls):
if not cls._initialized:
cls.do_class_init()
cls._initialized = True
@classmethod
def do_class_init(cls):
"""
Class-wide initialization. Can be overridden by subclasses to
register permanent typing or target hooks.
"""
def _resolve(self, value, attr):
fn = getattr(self, "resolve_%s" % attr, None)
if fn is None:
fn = self.generic_resolve
if fn is NotImplemented:
if isinstance(value, types.Module):
return self.context.resolve_module_constants(value, attr)
else:
return None
else:
return fn(value, attr)
else:
return fn(value)
generic_resolve = NotImplemented
class _OverloadAttributeTemplate(AttributeTemplate):
"""
A base class of templates for @overload_attribute functions.
"""
is_method = False
def __init__(self, context):
super(_OverloadAttributeTemplate, self).__init__(context)
self.context = context
@classmethod
def do_class_init(cls):
"""
Register attribute implementation.
"""
from numba.core.imputils import lower_getattr
attr = cls._attr
@lower_getattr(cls.key, attr)
def getattr_impl(context, builder, typ, value):
typingctx = context.typing_context
fnty = cls._get_function_type(typingctx, typ)
sig = cls._get_signature(typingctx, fnty, (typ,), {})
call = context.get_function(fnty, sig)
return call(builder, (value,))
def _resolve(self, typ, attr):
if self._attr != attr:
return None
fnty = self._get_function_type(self.context, typ)
sig = self._get_signature(self.context, fnty, (typ,), {})
# There should only be one template
for template in fnty.templates:
self._inline_overloads.update(template._inline_overloads)
return sig.return_type
@classmethod
def _get_signature(cls, typingctx, fnty, args, kws):
sig = fnty.get_call_type(typingctx, args, kws)
sig = sig.replace(pysig=utils.pysignature(cls._overload_func))
return sig
@classmethod
def _get_function_type(cls, typingctx, typ):
return typingctx.resolve_value_type(cls._overload_func)
class _OverloadMethodTemplate(_OverloadAttributeTemplate):
"""
A base class of templates for @overload_method functions.
"""
is_method = True
@classmethod
def do_class_init(cls):
"""
Register generic method implementation.
"""
from numba.core.imputils import lower_builtin
attr = cls._attr
@lower_builtin((cls.key, attr), cls.key, types.VarArg(types.Any))
def method_impl(context, builder, sig, args):
typ = sig.args[0]
typing_context = context.typing_context
fnty = cls._get_function_type(typing_context, typ)
sig = cls._get_signature(typing_context, fnty, sig.args, {})
call = context.get_function(fnty, sig)
# Link dependent library
context.add_linking_libs(getattr(call, 'libs', ()))
return call(builder, args)
def _resolve(self, typ, attr):
if self._attr != attr:
return None
assert isinstance(typ, self.key)
class MethodTemplate(AbstractTemplate):
key = (self.key, attr)
_inline = self._inline
_overload_func = staticmethod(self._overload_func)
_inline_overloads = self._inline_overloads
prefer_literal = self.prefer_literal
def generic(_, args, kws):
args = (typ,) + tuple(args)
fnty = self._get_function_type(self.context, typ)
sig = self._get_signature(self.context, fnty, args, kws)
sig = sig.replace(pysig=utils.pysignature(self._overload_func))
for template in fnty.templates:
self._inline_overloads.update(template._inline_overloads)
if sig is not None:
return sig.as_method()
return types.BoundFunction(MethodTemplate, typ)
def make_overload_attribute_template(typ, attr, overload_func, inline,
prefer_literal=False,
base=_OverloadAttributeTemplate):
"""
Make a template class for attribute *attr* of *typ* overloaded by
*overload_func*.
"""
assert isinstance(typ, types.Type) or issubclass(typ, types.Type)
name = "OverloadAttributeTemplate_%s_%s" % (typ, attr)
# Note the implementation cache is subclass-specific
dct = dict(key=typ, _attr=attr, _impl_cache={},
_inline=staticmethod(InlineOptions(inline)),
_inline_overloads={},
_overload_func=staticmethod(overload_func),
prefer_literal=prefer_literal,
)
obj = type(base)(name, (base,), dct)
return obj
def make_overload_method_template(typ, attr, overload_func, inline,
prefer_literal=False):
"""
Make a template class for method *attr* of *typ* overloaded by
*overload_func*.
"""
return make_overload_attribute_template(
typ, attr, overload_func, inline=inline,
base=_OverloadMethodTemplate, prefer_literal=prefer_literal,
)
def bound_function(template_key):
"""
Wrap an AttributeTemplate resolve_* method to allow it to
resolve an instance method's signature rather than a instance attribute.
The wrapped method must return the resolved method's signature
according to the given self type, args, and keywords.
It is used thusly:
class ComplexAttributes(AttributeTemplate):
@bound_function("complex.conjugate")
def resolve_conjugate(self, ty, args, kwds):
return ty
*template_key* (e.g. "complex.conjugate" above) will be used by the
target to look up the method's implementation, as a regular function.
"""
def wrapper(method_resolver):
@functools.wraps(method_resolver)
def attribute_resolver(self, ty):
class MethodTemplate(AbstractTemplate):
key = template_key
def generic(_, args, kws):
sig = method_resolver(self, ty, args, kws)
if sig is not None and sig.recvr is None:
sig = sig.replace(recvr=ty)
return sig
return types.BoundFunction(MethodTemplate, ty)
return attribute_resolver
return wrapper
class MacroTemplate(object):
pass
# -----------------------------
class Registry(object):
"""
A registry of typing declarations. The registry stores such declarations
for functions, attributes and globals.
"""
def __init__(self):
self.functions = []
self.attributes = []
self.globals = []
def register(self, item):
assert issubclass(item, FunctionTemplate)
self.functions.append(item)
return item
def register_attr(self, item):
assert issubclass(item, AttributeTemplate)
self.attributes.append(item)
return item
def register_global(self, val=None, typ=None, **kwargs):
"""
Register the typing of a global value.
Functional usage with a Numba type::
register_global(value, typ)
Decorator usage with a template class::
@register_global(value, typing_key=None)
class Template:
...
"""
if typ is not None:
# register_global(val, typ)
assert val is not None
assert not kwargs
self.globals.append((val, typ))
else:
def decorate(cls, typing_key):
class Template(cls):
key = typing_key
if callable(val):
typ = types.Function(Template)
else:
raise TypeError("cannot infer type for global value %r")
self.globals.append((val, typ))
return cls
# register_global(val, typing_key=None)(<template class>)
assert val is not None
typing_key = kwargs.pop('typing_key', val)
assert not kwargs
if typing_key is val:
# Check the value is globally reachable, as it is going
# to be used as the key.
mod = sys.modules[val.__module__]
if getattr(mod, val.__name__) is not val:
raise ValueError("%r is not globally reachable as '%s.%s'"
% (mod, val.__module__, val.__name__))
def decorator(cls):
return decorate(cls, typing_key)
return decorator
class BaseRegistryLoader(object):
"""
An incremental loader for a registry. Each new call to
new_registrations() will iterate over the not yet seen registrations.
The reason for this object is multiple:
- there can be several contexts
- each context wants to install all registrations
- registrations can be added after the first installation, so contexts
must be able to get the "new" installations
Therefore each context maintains its own loaders for each existing
registry, without duplicating the registries themselves.
"""
def __init__(self, registry):
self._registrations = dict(
(name, utils.stream_list(getattr(registry, name)))
for name in self.registry_items)
def new_registrations(self, name):
for item in next(self._registrations[name]):
yield item
class RegistryLoader(BaseRegistryLoader):
"""
An incremental loader for a typing registry.
"""
registry_items = ('functions', 'attributes', 'globals')
builtin_registry = Registry()
infer = builtin_registry.register
infer_getattr = builtin_registry.register_attr
infer_global = builtin_registry.register_global
| """
Define typing templates
"""
from abc import ABC, abstractmethod
import functools
import sys
import inspect
import os.path
from collections import namedtuple
from collections.abc import Sequence
from types import MethodType, FunctionType
import numba
from numba.core import types, utils
from numba.core.errors import TypingError, InternalError
from numba.core.cpu_options import InlineOptions
# info store for inliner callback functions e.g. cost model
_inline_info = namedtuple('inline_info',
'func_ir typemap calltypes signature')
class Signature(object):
"""
The signature of a function call or operation, i.e. its argument types
and return type.
"""
# XXX Perhaps the signature should be a BoundArguments, instead
# of separate args and pysig...
__slots__ = '_return_type', '_args', '_recvr', '_pysig'
def __init__(self, return_type, args, recvr, pysig=None):
if isinstance(args, list):
args = tuple(args)
self._return_type = return_type
self._args = args
self._recvr = recvr
self._pysig = pysig
@property
def return_type(self):
return self._return_type
@property
def args(self):
return self._args
@property
def recvr(self):
return self._recvr
@property
def pysig(self):
return self._pysig
def replace(self, **kwargs):
"""Copy and replace the given attributes provided as keyword arguments.
Returns an updated copy.
"""
curstate = dict(return_type=self.return_type,
args=self.args,
recvr=self.recvr,
pysig=self.pysig)
curstate.update(kwargs)
return Signature(**curstate)
def __getstate__(self):
"""
Needed because of __slots__.
"""
return self._return_type, self._args, self._recvr, self._pysig
def __setstate__(self, state):
"""
Needed because of __slots__.
"""
self._return_type, self._args, self._recvr, self._pysig = state
def __hash__(self):
return hash((self.args, self.return_type))
def __eq__(self, other):
if isinstance(other, Signature):
return (self.args == other.args and
self.return_type == other.return_type and
self.recvr == other.recvr and
self.pysig == other.pysig)
def __ne__(self, other):
return not (self == other)
def __repr__(self):
return "%s -> %s" % (self.args, self.return_type)
@property
def is_method(self):
"""
Whether this signature represents a bound method or a regular
function.
"""
return self.recvr is not None
def as_method(self):
"""
Convert this signature to a bound method signature.
"""
if self.recvr is not None:
return self
sig = signature(self.return_type, *self.args[1:],
recvr=self.args[0])
# Adjust the python signature
params = list(self.pysig.parameters.values())[1:]
sig = sig.replace(
pysig=utils.pySignature(
parameters=params,
return_annotation=self.pysig.return_annotation,
),
)
return sig
def as_function(self):
"""
Convert this signature to a regular function signature.
"""
if self.recvr is None:
return self
sig = signature(self.return_type, *((self.recvr,) + self.args))
return sig
def as_type(self):
"""
Convert this signature to a first-class function type.
"""
return types.FunctionType(self)
def __unliteral__(self):
return signature(types.unliteral(self.return_type),
*map(types.unliteral, self.args))
def dump(self, tab=''):
c = self.as_type()._code
print(f'{tab}DUMP {type(self).__name__} [type code: {c}]')
print(f'{tab} Argument types:')
for a in self.args:
a.dump(tab=tab + ' | ')
print(f'{tab} Return type:')
self.return_type.dump(tab=tab + ' | ')
print(f'{tab}END DUMP')
def is_precise(self):
for atype in self.args:
if not atype.is_precise():
return False
return self.return_type.is_precise()
def make_concrete_template(name, key, signatures):
baseclasses = (ConcreteTemplate,)
gvars = dict(key=key, cases=list(signatures))
return type(name, baseclasses, gvars)
def make_callable_template(key, typer, recvr=None):
"""
Create a callable template with the given key and typer function.
"""
def generic(self):
return typer
name = "%s_CallableTemplate" % (key,)
bases = (CallableTemplate,)
class_dict = dict(key=key, generic=generic, recvr=recvr)
return type(name, bases, class_dict)
def signature(return_type, *args, **kws):
recvr = kws.pop('recvr', None)
assert not kws
return Signature(return_type, args, recvr=recvr)
def fold_arguments(pysig, args, kws, normal_handler, default_handler,
stararg_handler):
"""
Given the signature *pysig*, explicit *args* and *kws*, resolve
omitted arguments and keyword arguments. A tuple of positional
arguments is returned.
Various handlers allow to process arguments:
- normal_handler(index, param, value) is called for normal arguments
- default_handler(index, param, default) is called for omitted arguments
- stararg_handler(index, param, values) is called for a "*args" argument
"""
if isinstance(kws, Sequence):
# Normalize dict kws
kws = dict(kws)
# deal with kwonly args
params = pysig.parameters
kwonly = []
for name, p in params.items():
if p.kind == p.KEYWORD_ONLY:
kwonly.append(name)
if kwonly:
bind_args = args[:-len(kwonly)]
else:
bind_args = args
bind_kws = kws.copy()
if kwonly:
for idx, n in enumerate(kwonly):
bind_kws[n] = args[len(kwonly) + idx]
# now bind
ba = pysig.bind(*bind_args, **bind_kws)
for i, param in enumerate(pysig.parameters.values()):
name = param.name
default = param.default
if param.kind == param.VAR_POSITIONAL:
# stararg may be omitted, in which case its "default" value
# is simply the empty tuple
if name in ba.arguments:
argval = ba.arguments[name]
# NOTE: avoid wrapping the tuple type for stararg in another
# tuple.
if (len(argval) == 1 and
isinstance(argval[0], (types.StarArgTuple,
types.StarArgUniTuple))):
argval = tuple(argval[0])
else:
argval = ()
out = stararg_handler(i, param, argval)
ba.arguments[name] = out
elif name in ba.arguments:
# Non-stararg, present
ba.arguments[name] = normal_handler(i, param, ba.arguments[name])
else:
# Non-stararg, omitted
assert default is not param.empty
ba.arguments[name] = default_handler(i, param, default)
# Collect args in the right order
args = tuple(ba.arguments[param.name]
for param in pysig.parameters.values())
return args
class FunctionTemplate(ABC):
# Set to true to disable unsafe cast.
# subclass overide-able
unsafe_casting = True
# Set to true to require exact match without casting.
# subclass overide-able
exact_match_required = False
# Set to true to prefer literal arguments.
# Useful for definitions that specialize on literal but also support
# non-literals.
# subclass overide-able
prefer_literal = False
def __init__(self, context):
self.context = context
def _select(self, cases, args, kws):
options = {
'unsafe_casting': self.unsafe_casting,
'exact_match_required': self.exact_match_required,
}
selected = self.context.resolve_overload(self.key, cases, args, kws,
**options)
return selected
def get_impl_key(self, sig):
"""
Return the key for looking up the implementation for the given
signature on the target context.
"""
# Lookup the key on the class, to avoid binding it with `self`.
key = type(self).key
# On Python 2, we must also take care about unbound methods
if isinstance(key, MethodType):
assert key.im_self is None
key = key.im_func
return key
@classmethod
def get_source_code_info(cls, impl):
"""
Gets the source information about function impl.
Returns:
code - str: source code as a string
firstlineno - int: the first line number of the function impl
path - str: the path to file containing impl
if any of the above are not available something generic is returned
"""
try:
code, firstlineno = inspect.getsourcelines(impl)
except OSError: # missing source, probably a string
code = "None available (built from string?)"
firstlineno = 0
path = inspect.getsourcefile(impl)
if path is None:
path = "<unknown> (built from string?)"
return code, firstlineno, path
@abstractmethod
def get_template_info(self):
"""
Returns a dictionary with information specific to the template that will
govern how error messages are displayed to users. The dictionary must
be of the form:
info = {
'kind': "unknown", # str: The kind of template, e.g. "Overload"
'name': "unknown", # str: The name of the source function
'sig': "unknown", # str: The signature(s) of the source function
'filename': "unknown", # str: The filename of the source function
'lines': ("start", "end"), # tuple(int, int): The start and
end line of the source function.
'docstring': "unknown" # str: The docstring of the source function
}
"""
pass
def __str__(self):
info = self.get_template_info()
srcinfo = f"{info['filename']}:{info['lines'][0]}"
return f"<{self.__class__.__name__} {srcinfo}>"
__repr__ = __str__
class AbstractTemplate(FunctionTemplate):
"""
Defines method ``generic(self, args, kws)`` which compute a possible
signature base on input types. The signature does not have to match the
input types. It is compared against the input types afterwards.
"""
def apply(self, args, kws):
generic = getattr(self, "generic")
sig = generic(args, kws)
# Enforce that *generic()* must return None or Signature
if sig is not None:
if not isinstance(sig, Signature):
raise AssertionError(
"generic() must return a Signature or None. "
"{} returned {}".format(generic, type(sig)),
)
# Unpack optional type if no matching signature
if not sig and any(isinstance(x, types.Optional) for x in args):
def unpack_opt(x):
if isinstance(x, types.Optional):
return x.type
else:
return x
args = list(map(unpack_opt, args))
assert not kws # Not supported yet
sig = generic(args, kws)
return sig
def get_template_info(self):
impl = getattr(self, "generic")
basepath = os.path.dirname(os.path.dirname(numba.__file__))
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
class CallableTemplate(FunctionTemplate):
"""
Base class for a template defining a ``generic(self)`` method
returning a callable to be called with the actual ``*args`` and
``**kwargs`` representing the call signature. The callable has
to return a return type, a full signature, or None. The signature
does not have to match the input types. It is compared against the
input types afterwards.
"""
recvr = None
def apply(self, args, kws):
generic = getattr(self, "generic")
typer = generic()
sig = typer(*args, **kws)
# Unpack optional type if no matching signature
if sig is None:
if any(isinstance(x, types.Optional) for x in args):
def unpack_opt(x):
if isinstance(x, types.Optional):
return x.type
else:
return x
args = list(map(unpack_opt, args))
sig = typer(*args, **kws)
if sig is None:
return
# Get the pysig
try:
pysig = typer.pysig
except AttributeError:
pysig = utils.pysignature(typer)
# Fold any keyword arguments
bound = pysig.bind(*args, **kws)
if bound.kwargs:
raise TypingError("unsupported call signature")
if not isinstance(sig, Signature):
# If not a signature, `sig` is assumed to be the return type
if not isinstance(sig, types.Type):
raise TypeError("invalid return type for callable template: "
"got %r" % (sig,))
sig = signature(sig, *bound.args)
if self.recvr is not None:
sig = sig.replace(recvr=self.recvr)
# Hack any omitted parameters out of the typer's pysig,
# as lowering expects an exact match between formal signature
# and actual args.
if len(bound.args) < len(pysig.parameters):
parameters = list(pysig.parameters.values())[:len(bound.args)]
pysig = pysig.replace(parameters=parameters)
sig = sig.replace(pysig=pysig)
cases = [sig]
return self._select(cases, bound.args, bound.kwargs)
def get_template_info(self):
impl = getattr(self, "generic")
basepath = os.path.dirname(os.path.dirname(numba.__file__))
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(self.key, '__name__',
getattr(impl, '__qualname__', impl.__name__),),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
class ConcreteTemplate(FunctionTemplate):
"""
Defines attributes "cases" as a list of signature to match against the
given input types.
"""
def apply(self, args, kws):
cases = getattr(self, 'cases')
return self._select(cases, args, kws)
def get_template_info(self):
import operator
name = getattr(self.key, '__name__', "unknown")
op_func = getattr(operator, name, None)
kind = "Type restricted function"
if op_func is not None:
if self.key is op_func:
kind = "operator overload"
info = {
'kind': kind,
'name': name,
'sig': "unknown",
'filename': "unknown",
'lines': ("unknown", "unknown"),
'docstring': "unknown"
}
return info
class _EmptyImplementationEntry(InternalError):
def __init__(self, reason):
super(_EmptyImplementationEntry, self).__init__(
"_EmptyImplementationEntry({!r})".format(reason),
)
class _OverloadFunctionTemplate(AbstractTemplate):
"""
A base class of templates for overload functions.
"""
def _validate_sigs(self, typing_func, impl_func):
# check that the impl func and the typing func have the same signature!
typing_sig = utils.pysignature(typing_func)
impl_sig = utils.pysignature(impl_func)
# the typing signature is considered golden and must be adhered to by
# the implementation...
# Things that are valid:
# 1. args match exactly
# 2. kwargs match exactly in name and default value
# 3. Use of *args in the same location by the same name in both typing
# and implementation signature
# 4. Use of *args in the implementation signature to consume any number
# of arguments in the typing signature.
# Things that are invalid:
# 5. Use of *args in the typing signature that is not replicated
# in the implementing signature
# 6. Use of **kwargs
def get_args_kwargs(sig):
kws = []
args = []
pos_arg = None
for x in sig.parameters.values():
if x.default == utils.pyParameter.empty:
args.append(x)
if x.kind == utils.pyParameter.VAR_POSITIONAL:
pos_arg = x
elif x.kind == utils.pyParameter.VAR_KEYWORD:
msg = ("The use of VAR_KEYWORD (e.g. **kwargs) is "
"unsupported. (offending argument name is '%s')")
raise InternalError(msg % x)
else:
kws.append(x)
return args, kws, pos_arg
ty_args, ty_kws, ty_pos = get_args_kwargs(typing_sig)
im_args, im_kws, im_pos = get_args_kwargs(impl_sig)
sig_fmt = ("Typing signature: %s\n"
"Implementation signature: %s")
sig_str = sig_fmt % (typing_sig, impl_sig)
err_prefix = "Typing and implementation arguments differ in "
a = ty_args
b = im_args
if ty_pos:
if not im_pos:
# case 5. described above
msg = ("VAR_POSITIONAL (e.g. *args) argument kind (offending "
"argument name is '%s') found in the typing function "
"signature, but is not in the implementing function "
"signature.\n%s") % (ty_pos, sig_str)
raise InternalError(msg)
else:
if im_pos:
# no *args in typing but there's a *args in the implementation
# this is case 4. described above
b = im_args[:im_args.index(im_pos)]
try:
a = ty_args[:ty_args.index(b[-1]) + 1]
except ValueError:
# there's no b[-1] arg name in the ty_args, something is
# very wrong, we can't work out a diff (*args consumes
# unknown quantity of args) so just report first error
specialized = "argument names.\n%s\nFirst difference: '%s'"
msg = err_prefix + specialized % (sig_str, b[-1])
raise InternalError(msg)
def gen_diff(typing, implementing):
diff = set(typing) ^ set(implementing)
return "Difference: %s" % diff
if a != b:
specialized = "argument names.\n%s\n%s" % (sig_str, gen_diff(a, b))
raise InternalError(err_prefix + specialized)
# ensure kwargs are the same
ty = [x.name for x in ty_kws]
im = [x.name for x in im_kws]
if ty != im:
specialized = "keyword argument names.\n%s\n%s"
msg = err_prefix + specialized % (sig_str, gen_diff(ty_kws, im_kws))
raise InternalError(msg)
same = [x.default for x in ty_kws] == [x.default for x in im_kws]
if not same:
specialized = "keyword argument default values.\n%s\n%s"
msg = err_prefix + specialized % (sig_str, gen_diff(ty_kws, im_kws))
raise InternalError(msg)
def generic(self, args, kws):
"""
Type the overloaded function by compiling the appropriate
implementation for the given args.
"""
disp, new_args = self._get_impl(args, kws)
if disp is None:
return
# Compile and type it for the given types
disp_type = types.Dispatcher(disp)
# Store the compiled overload for use in the lowering phase if there's
# no inlining required (else functions are being compiled which will
# never be used as they are inlined)
if not self._inline.is_never_inline:
# need to run the compiler front end up to type inference to compute
# a signature
from numba.core import typed_passes, compiler
from numba.core.inline_closurecall import InlineWorker
fcomp = disp._compiler
flags = compiler.Flags()
# Updating these causes problems?!
#fcomp.targetdescr.options.parse_as_flags(flags,
# fcomp.targetoptions)
#flags = fcomp._customize_flags(flags)
# spoof a compiler pipline like the one that will be in use
tyctx = fcomp.targetdescr.typing_context
tgctx = fcomp.targetdescr.target_context
compiler_inst = fcomp.pipeline_class(tyctx, tgctx, None, None, None,
flags, None, )
inline_worker = InlineWorker(tyctx, tgctx, fcomp.locals,
compiler_inst, flags, None,)
# If the inlinee contains something to trigger literal arg dispatch
# then the pipeline call will unconditionally fail due to a raised
# ForceLiteralArg exception. Therefore `resolve` is run first, as
# type resolution must occur at some point, this will hit any
# `literally` calls and because it's going via the dispatcher will
# handle them correctly i.e. ForceLiteralArg propagates. This having
# the desired effect of ensuring the pipeline call is only made in
# situations that will succeed. For context see #5887.
resolve = disp_type.dispatcher.get_call_template
template, pysig, folded_args, kws = resolve(new_args, kws)
ir = inline_worker.run_untyped_passes(disp_type.dispatcher.py_func)
typemap, return_type, calltypes = typed_passes.type_inference_stage(
self.context, ir, folded_args, None)
sig = Signature(return_type, folded_args, None)
# this stores a load of info for the cost model function if supplied
# it by default is None
self._inline_overloads[sig.args] = {'folded_args': folded_args}
# this stores the compiled overloads, if there's no compiled
# overload available i.e. function is always inlined, the key still
# needs to exist for type resolution
# NOTE: If lowering is failing on a `_EmptyImplementationEntry`,
# the inliner has failed to inline this entry corretly.
impl_init = _EmptyImplementationEntry('always inlined')
self._compiled_overloads[sig.args] = impl_init
if not self._inline.is_always_inline:
# this branch is here because a user has supplied a function to
# determine whether to inline or not. As a result both compiled
# function and inliner info needed, delaying the computation of
# this leads to an internal state mess at present. TODO: Fix!
sig = disp_type.get_call_type(self.context, new_args, kws)
self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
# store the inliner information, it's used later in the cost
# model function call
iinfo = _inline_info(ir, typemap, calltypes, sig)
self._inline_overloads[sig.args] = {'folded_args': folded_args,
'iinfo': iinfo}
else:
sig = disp_type.get_call_type(self.context, new_args, kws)
self._compiled_overloads[sig.args] = disp_type.get_overload(sig)
return sig
def _get_impl(self, args, kws):
"""Get implementation given the argument types.
Returning a Dispatcher object. The Dispatcher object is cached
internally in `self._impl_cache`.
"""
cache_key = self.context, tuple(args), tuple(kws.items())
try:
impl, args = self._impl_cache[cache_key]
except KeyError:
impl, args = self._build_impl(cache_key, args, kws)
return impl, args
def _build_impl(self, cache_key, args, kws):
"""Build and cache the implementation.
Given the positional (`args`) and keyword arguments (`kws`), obtains
the `overload` implementation and wrap it in a Dispatcher object.
The expected argument types are returned for use by type-inference.
The expected argument types are only different from the given argument
types if there is an imprecise type in the given argument types.
Parameters
----------
cache_key : hashable
The key used for caching the implementation.
args : Tuple[Type]
Types of positional argument.
kws : Dict[Type]
Types of keyword argument.
Returns
-------
disp, args :
On success, returns `(Dispatcher, Tuple[Type])`.
On failure, returns `(None, None)`.
"""
from numba import jit
# Get the overload implementation for the given types
ovf_result = self._overload_func(*args, **kws)
if ovf_result is None:
# No implementation => fail typing
self._impl_cache[cache_key] = None, None
return None, None
elif isinstance(ovf_result, tuple):
# The implementation returned a signature that the type-inferencer
# should be using.
sig, pyfunc = ovf_result
args = sig.args
kws = {}
cache_key = None # don't cache
else:
# Regular case
pyfunc = ovf_result
# Check type of pyfunc
if not isinstance(pyfunc, FunctionType):
msg = ("Implementator function returned by `@overload` "
"has an unexpected type. Got {}")
raise AssertionError(msg.format(pyfunc))
# check that the typing and impl sigs match up
if self._strict:
self._validate_sigs(self._overload_func, pyfunc)
# Make dispatcher
jitdecor = jit(nopython=True, **self._jit_options)
disp = jitdecor(pyfunc)
# Make sure that the implementation can be fully compiled
disp_type = types.Dispatcher(disp)
disp_type.get_call_type(self.context, args, kws)
if cache_key is not None:
self._impl_cache[cache_key] = disp, args
return disp, args
def get_impl_key(self, sig):
"""
Return the key for looking up the implementation for the given
signature on the target context.
"""
return self._compiled_overloads[sig.args]
@classmethod
def get_source_info(cls):
"""Return a dictionary with information about the source code of the
implementation.
Returns
-------
info : dict
- "kind" : str
The implementation kind.
- "name" : str
The name of the function that provided the definition.
- "sig" : str
The formatted signature of the function.
- "filename" : str
The name of the source file.
- "lines": tuple (int, int)
First and list line number.
- "docstring": str
The docstring of the definition.
"""
basepath = os.path.dirname(os.path.dirname(numba.__file__))
impl = cls._overload_func
code, firstlineno, path = cls.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
def get_template_info(self):
basepath = os.path.dirname(os.path.dirname(numba.__file__))
impl = self._overload_func
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "overload",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
def make_overload_template(func, overload_func, jit_options, strict,
inline, prefer_literal=False):
"""
Make a template class for function *func* overloaded by *overload_func*.
Compiler options are passed as a dictionary to *jit_options*.
"""
func_name = getattr(func, '__name__', str(func))
name = "OverloadTemplate_%s" % (func_name,)
base = _OverloadFunctionTemplate
dct = dict(key=func, _overload_func=staticmethod(overload_func),
_impl_cache={}, _compiled_overloads={}, _jit_options=jit_options,
_strict=strict, _inline=staticmethod(InlineOptions(inline)),
_inline_overloads={}, prefer_literal=prefer_literal)
return type(base)(name, (base,), dct)
class _IntrinsicTemplate(AbstractTemplate):
"""
A base class of templates for intrinsic definition
"""
def generic(self, args, kws):
"""
Type the intrinsic by the arguments.
"""
from numba.core.imputils import lower_builtin
cache_key = self.context, args, tuple(kws.items())
try:
return self._impl_cache[cache_key]
except KeyError:
result = self._definition_func(self.context, *args, **kws)
if result is None:
return
[sig, imp] = result
pysig = utils.pysignature(self._definition_func)
# omit context argument from user function
parameters = list(pysig.parameters.values())[1:]
sig = sig.replace(pysig=pysig.replace(parameters=parameters))
self._impl_cache[cache_key] = sig
self._overload_cache[sig.args] = imp
# register the lowering
lower_builtin(imp, *sig.args)(imp)
return sig
def get_impl_key(self, sig):
"""
Return the key for looking up the implementation for the given
signature on the target context.
"""
return self._overload_cache[sig.args]
def get_template_info(self):
basepath = os.path.dirname(os.path.dirname(numba.__file__))
impl = self._definition_func
code, firstlineno, path = self.get_source_code_info(impl)
sig = str(utils.pysignature(impl))
info = {
'kind': "intrinsic",
'name': getattr(impl, '__qualname__', impl.__name__),
'sig': sig,
'filename': utils.safe_relpath(path, start=basepath),
'lines': (firstlineno, firstlineno + len(code) - 1),
'docstring': impl.__doc__
}
return info
def make_intrinsic_template(handle, defn, name):
"""
Make a template class for a intrinsic handle *handle* defined by the
function *defn*. The *name* is used for naming the new template class.
"""
base = _IntrinsicTemplate
name = "_IntrinsicTemplate_%s" % (name)
dct = dict(key=handle, _definition_func=staticmethod(defn),
_impl_cache={}, _overload_cache={})
return type(base)(name, (base,), dct)
class AttributeTemplate(object):
_initialized = False
def __init__(self, context):
self._lazy_class_init()
self.context = context
def resolve(self, value, attr):
return self._resolve(value, attr)
@classmethod
def _lazy_class_init(cls):
if not cls._initialized:
cls.do_class_init()
cls._initialized = True
@classmethod
def do_class_init(cls):
"""
Class-wide initialization. Can be overridden by subclasses to
register permanent typing or target hooks.
"""
def _resolve(self, value, attr):
fn = getattr(self, "resolve_%s" % attr, None)
if fn is None:
fn = self.generic_resolve
if fn is NotImplemented:
if isinstance(value, types.Module):
return self.context.resolve_module_constants(value, attr)
else:
return None
else:
return fn(value, attr)
else:
return fn(value)
generic_resolve = NotImplemented
class _OverloadAttributeTemplate(AttributeTemplate):
"""
A base class of templates for @overload_attribute functions.
"""
is_method = False
def __init__(self, context):
super(_OverloadAttributeTemplate, self).__init__(context)
self.context = context
@classmethod
def do_class_init(cls):
"""
Register attribute implementation.
"""
from numba.core.imputils import lower_getattr
attr = cls._attr
@lower_getattr(cls.key, attr)
def getattr_impl(context, builder, typ, value):
typingctx = context.typing_context
fnty = cls._get_function_type(typingctx, typ)
sig = cls._get_signature(typingctx, fnty, (typ,), {})
call = context.get_function(fnty, sig)
return call(builder, (value,))
def _resolve(self, typ, attr):
if self._attr != attr:
return None
fnty = self._get_function_type(self.context, typ)
sig = self._get_signature(self.context, fnty, (typ,), {})
# There should only be one template
for template in fnty.templates:
self._inline_overloads.update(template._inline_overloads)
return sig.return_type
@classmethod
def _get_signature(cls, typingctx, fnty, args, kws):
sig = fnty.get_call_type(typingctx, args, kws)
sig = sig.replace(pysig=utils.pysignature(cls._overload_func))
return sig
@classmethod
def _get_function_type(cls, typingctx, typ):
return typingctx.resolve_value_type(cls._overload_func)
class _OverloadMethodTemplate(_OverloadAttributeTemplate):
"""
A base class of templates for @overload_method functions.
"""
is_method = True
@classmethod
def do_class_init(cls):
"""
Register generic method implementation.
"""
from numba.core.imputils import lower_builtin
attr = cls._attr
@lower_builtin((cls.key, attr), cls.key, types.VarArg(types.Any))
def method_impl(context, builder, sig, args):
typ = sig.args[0]
typing_context = context.typing_context
fnty = cls._get_function_type(typing_context, typ)
sig = cls._get_signature(typing_context, fnty, sig.args, {})
call = context.get_function(fnty, sig)
# Link dependent library
context.add_linking_libs(getattr(call, 'libs', ()))
return call(builder, args)
def _resolve(self, typ, attr):
if self._attr != attr:
return None
assert isinstance(typ, self.key)
class MethodTemplate(AbstractTemplate):
key = (self.key, attr)
_inline = self._inline
_overload_func = staticmethod(self._overload_func)
_inline_overloads = self._inline_overloads
prefer_literal = self.prefer_literal
def generic(_, args, kws):
args = (typ,) + tuple(args)
fnty = self._get_function_type(self.context, typ)
sig = self._get_signature(self.context, fnty, args, kws)
sig = sig.replace(pysig=utils.pysignature(self._overload_func))
for template in fnty.templates:
self._inline_overloads.update(template._inline_overloads)
if sig is not None:
return sig.as_method()
return types.BoundFunction(MethodTemplate, typ)
def make_overload_attribute_template(typ, attr, overload_func, inline,
prefer_literal=False,
base=_OverloadAttributeTemplate):
"""
Make a template class for attribute *attr* of *typ* overloaded by
*overload_func*.
"""
assert isinstance(typ, types.Type) or issubclass(typ, types.Type)
name = "OverloadAttributeTemplate_%s_%s" % (typ, attr)
# Note the implementation cache is subclass-specific
dct = dict(key=typ, _attr=attr, _impl_cache={},
_inline=staticmethod(InlineOptions(inline)),
_inline_overloads={},
_overload_func=staticmethod(overload_func),
prefer_literal=prefer_literal,
)
obj = type(base)(name, (base,), dct)
return obj
def make_overload_method_template(typ, attr, overload_func, inline,
prefer_literal=False):
"""
Make a template class for method *attr* of *typ* overloaded by
*overload_func*.
"""
return make_overload_attribute_template(
typ, attr, overload_func, inline=inline,
base=_OverloadMethodTemplate, prefer_literal=prefer_literal,
)
def bound_function(template_key):
"""
Wrap an AttributeTemplate resolve_* method to allow it to
resolve an instance method's signature rather than a instance attribute.
The wrapped method must return the resolved method's signature
according to the given self type, args, and keywords.
It is used thusly:
class ComplexAttributes(AttributeTemplate):
@bound_function("complex.conjugate")
def resolve_conjugate(self, ty, args, kwds):
return ty
*template_key* (e.g. "complex.conjugate" above) will be used by the
target to look up the method's implementation, as a regular function.
"""
def wrapper(method_resolver):
@functools.wraps(method_resolver)
def attribute_resolver(self, ty):
class MethodTemplate(AbstractTemplate):
key = template_key
def generic(_, args, kws):
sig = method_resolver(self, ty, args, kws)
if sig is not None and sig.recvr is None:
sig = sig.replace(recvr=ty)
return sig
return types.BoundFunction(MethodTemplate, ty)
return attribute_resolver
return wrapper
class MacroTemplate(object):
pass
# -----------------------------
class Registry(object):
"""
A registry of typing declarations. The registry stores such declarations
for functions, attributes and globals.
"""
def __init__(self):
self.functions = []
self.attributes = []
self.globals = []
def register(self, item):
assert issubclass(item, FunctionTemplate)
self.functions.append(item)
return item
def register_attr(self, item):
assert issubclass(item, AttributeTemplate)
self.attributes.append(item)
return item
def register_global(self, val=None, typ=None, **kwargs):
"""
Register the typing of a global value.
Functional usage with a Numba type::
register_global(value, typ)
Decorator usage with a template class::
@register_global(value, typing_key=None)
class Template:
...
"""
if typ is not None:
# register_global(val, typ)
assert val is not None
assert not kwargs
self.globals.append((val, typ))
else:
def decorate(cls, typing_key):
class Template(cls):
key = typing_key
if callable(val):
typ = types.Function(Template)
else:
raise TypeError("cannot infer type for global value %r")
self.globals.append((val, typ))
return cls
# register_global(val, typing_key=None)(<template class>)
assert val is not None
typing_key = kwargs.pop('typing_key', val)
assert not kwargs
if typing_key is val:
# Check the value is globally reachable, as it is going
# to be used as the key.
mod = sys.modules[val.__module__]
if getattr(mod, val.__name__) is not val:
raise ValueError("%r is not globally reachable as '%s.%s'"
% (mod, val.__module__, val.__name__))
def decorator(cls):
return decorate(cls, typing_key)
return decorator
class BaseRegistryLoader(object):
"""
An incremental loader for a registry. Each new call to
new_registrations() will iterate over the not yet seen registrations.
The reason for this object is multiple:
- there can be several contexts
- each context wants to install all registrations
- registrations can be added after the first installation, so contexts
must be able to get the "new" installations
Therefore each context maintains its own loaders for each existing
registry, without duplicating the registries themselves.
"""
def __init__(self, registry):
self._registrations = dict(
(name, utils.stream_list(getattr(registry, name)))
for name in self.registry_items)
def new_registrations(self, name):
for item in next(self._registrations[name]):
yield item
class RegistryLoader(BaseRegistryLoader):
"""
An incremental loader for a typing registry.
"""
registry_items = ('functions', 'attributes', 'globals')
builtin_registry = Registry()
infer = builtin_registry.register
infer_getattr = builtin_registry.register_attr
infer_global = builtin_registry.register_global
|
import os
import re
import sys
import importlib
from aq.cli.commands import AQCommand
class Command(AQCommand):
description = "Displays this help message"
def run(self):
print("\nAzure Query CLI\n")
print("This tool provides an easy to use CLI implementation of the Microsoft Graph API REST interface")
print(f"Usage: {os.path.basename(sys.argv[0])} [subcommand] [options]\n")
print("List of Sub Commands:")
sub_command_path = os.path.dirname(__file__)
max_length = 0
sub_commands = []
for sub_command in os.listdir(sub_command_path):
if re.match("^__.*", sub_command) or not re.match('.*\.py$', sub_command):
continue
else:
sub_commands.append(sub_command[:-3])
if len(sub_command) > max_length:
max_length = len(sub_command)
for sub_command in sub_commands:
command_module = importlib.import_module(f"aq.cli.commands.{sub_command}")
description = command_module.Command.description
print(f"{sub_command.rjust(max_length + 3, " ")} : {description}")
| import os
import re
import sys
import importlib
from aq.cli.commands import AQCommand
class Command(AQCommand):
description = "Displays this help message"
def run(self):
print("\nAzure Query CLI\n")
print("This tool provides an easy to use CLI implementation of the Microsoft Graph API REST interface")
print(f"Usage: {os.path.basename(sys.argv[0])} [subcommand] [options]\n")
print("List of Sub Commands:")
sub_command_path = os.path.dirname(__file__)
max_length = 0
sub_commands = []
for sub_command in os.listdir(sub_command_path):
if re.match("^__.*", sub_command) or not re.match('.*\.py$', sub_command):
continue
else:
sub_commands.append(sub_command[:-3])
if len(sub_command) > max_length:
max_length = len(sub_command)
for sub_command in sub_commands:
command_module = importlib.import_module(f"aq.cli.commands.{sub_command}")
description = command_module.Command.description
print(f"{sub_command.rjust(max_length + 3, ' ')} : {description}")
|
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import uuid
from dataclasses import dataclass
from dataclasses import field
from typing import List, Iterable
import requests
from metadata.generated.schema.entity.data.chart import Chart
from metadata.generated.schema.type.entityReference import EntityReference
from metadata.ingestion.api.common import ConfigModel, Record, WorkflowContext
from metadata.ingestion.api.source import Source
from metadata.ingestion.api.source import SourceStatus
from metadata.ingestion.models.table_metadata import Dashboard
from metadata.ingestion.ometa.openmetadata_rest import MetadataServerConfig
from redash_toolbelt import Redash
from metadata.utils.helpers import get_dashboard_service_or_create
from metadata.generated.schema.entity.services.dashboardService import DashboardServiceType
class RedashSourceConfig(ConfigModel):
uri: str = "http://localhost:5000"
username: str = ""
api_key: str
service_name: str
service_type: str = "Redash"
@dataclass
class RedashSourceStatus(SourceStatus):
items_scanned: int = 0
filtered: List[str] = field(default_factory=list)
def item_scanned_status(self) -> None:
self.items_scanned += 1
def item_dropped_status(self, item: str) -> None:
self.filtered.append(item)
class RedashSource(Source):
config: RedashSourceConfig
metadata_config: MetadataServerConfig
status: RedashSourceStatus
platform = "redash"
def __init__(self, config: RedashSourceConfig, metadata_config: MetadataServerConfig, ctx: WorkflowContext):
super().__init__(ctx)
self.config = config
self.metadata_config = metadata_config
self.status = RedashSourceStatus()
self.client = Redash(self.config.uri, self.config.api_key)
self.service = get_dashboard_service_or_create(config.service_name,
DashboardServiceType.Redash.name,
config.username,
config.api_key,
config.uri,
metadata_config)
@classmethod
def create(cls, config_dict: dict, metadata_config_dict: dict, ctx: WorkflowContext):
config = RedashSourceConfig.parse_obj(config_dict)
metadata_config = MetadataServerConfig.parse_obj(metadata_config_dict)
return cls(config, metadata_config, ctx)
def prepare(self):
pass
def next_record(self) -> Iterable[Record]:
yield from self.get_redash_charts()
yield from self.get_redash_dashboard()
def get_redash_charts(self) -> Chart:
query_info = self.client.queries()
for query_info in query_info["results"]:
query_id = query_info["id"]
query_name = query_info["name"]
query_data = requests.get(f"{self.config.uri}/api/queries/{query_id}").json()
for visualization in query_data.get("Visualizations", []):
chart_type = visualization.get("type", "")
chart_description = visualization.get("description", "") if visualization.get("description", "") else ""
yield Chart(
id=uuid.uuid4(),
name=query_id,
displayName=query_name,
chartType=chart_type,
service=EntityReference(id=self.service.id, type="dashboardService"),
description=chart_description,
)
def get_redash_dashboard(self) -> Dashboard:
charts: List[Chart] = []
dashboard_info = self.client.dashboards()
for dashboard_info in dashboard_info["results"]:
dashboard_id = dashboard_info["id"]
if dashboard_info["id"] is not None:
self.status.item_scanned_status()
dashboard_data = self.client.dashboard(dashboard_id)
dashboard_url = f"{self.config.uri}/dashboard/{dashboard_data.get("slug", "")}"
for widgets in dashboard_data.get("widgets", []):
dashboard_description = widgets.get("text")
yield Dashboard(
id=uuid.uuid4(),
name=dashboard_info["id"],
displayName=dashboard_info["name"],
description=dashboard_description if dashboard_info else "",
charts=charts,
usageSummary=None,
service=EntityReference(id=self.service.id, type="dashboardService"),
url=dashboard_url
)
def get_status(self) -> SourceStatus:
return self.status
| # Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import uuid
from dataclasses import dataclass
from dataclasses import field
from typing import List, Iterable
import requests
from metadata.generated.schema.entity.data.chart import Chart
from metadata.generated.schema.type.entityReference import EntityReference
from metadata.ingestion.api.common import ConfigModel, Record, WorkflowContext
from metadata.ingestion.api.source import Source
from metadata.ingestion.api.source import SourceStatus
from metadata.ingestion.models.table_metadata import Dashboard
from metadata.ingestion.ometa.openmetadata_rest import MetadataServerConfig
from redash_toolbelt import Redash
from metadata.utils.helpers import get_dashboard_service_or_create
from metadata.generated.schema.entity.services.dashboardService import DashboardServiceType
class RedashSourceConfig(ConfigModel):
uri: str = "http://localhost:5000"
username: str = ""
api_key: str
service_name: str
service_type: str = "Redash"
@dataclass
class RedashSourceStatus(SourceStatus):
items_scanned: int = 0
filtered: List[str] = field(default_factory=list)
def item_scanned_status(self) -> None:
self.items_scanned += 1
def item_dropped_status(self, item: str) -> None:
self.filtered.append(item)
class RedashSource(Source):
config: RedashSourceConfig
metadata_config: MetadataServerConfig
status: RedashSourceStatus
platform = "redash"
def __init__(self, config: RedashSourceConfig, metadata_config: MetadataServerConfig, ctx: WorkflowContext):
super().__init__(ctx)
self.config = config
self.metadata_config = metadata_config
self.status = RedashSourceStatus()
self.client = Redash(self.config.uri, self.config.api_key)
self.service = get_dashboard_service_or_create(config.service_name,
DashboardServiceType.Redash.name,
config.username,
config.api_key,
config.uri,
metadata_config)
@classmethod
def create(cls, config_dict: dict, metadata_config_dict: dict, ctx: WorkflowContext):
config = RedashSourceConfig.parse_obj(config_dict)
metadata_config = MetadataServerConfig.parse_obj(metadata_config_dict)
return cls(config, metadata_config, ctx)
def prepare(self):
pass
def next_record(self) -> Iterable[Record]:
yield from self.get_redash_charts()
yield from self.get_redash_dashboard()
def get_redash_charts(self) -> Chart:
query_info = self.client.queries()
for query_info in query_info["results"]:
query_id = query_info["id"]
query_name = query_info["name"]
query_data = requests.get(f"{self.config.uri}/api/queries/{query_id}").json()
for visualization in query_data.get("Visualizations", []):
chart_type = visualization.get("type", "")
chart_description = visualization.get("description", "") if visualization.get("description", "") else ""
yield Chart(
id=uuid.uuid4(),
name=query_id,
displayName=query_name,
chartType=chart_type,
service=EntityReference(id=self.service.id, type="dashboardService"),
description=chart_description,
)
def get_redash_dashboard(self) -> Dashboard:
charts: List[Chart] = []
dashboard_info = self.client.dashboards()
for dashboard_info in dashboard_info["results"]:
dashboard_id = dashboard_info["id"]
if dashboard_info["id"] is not None:
self.status.item_scanned_status()
dashboard_data = self.client.dashboard(dashboard_id)
dashboard_url = f"{self.config.uri}/dashboard/{dashboard_data.get('slug', '')}"
for widgets in dashboard_data.get("widgets", []):
dashboard_description = widgets.get("text")
yield Dashboard(
id=uuid.uuid4(),
name=dashboard_info["id"],
displayName=dashboard_info["name"],
description=dashboard_description if dashboard_info else "",
charts=charts,
usageSummary=None,
service=EntityReference(id=self.service.id, type="dashboardService"),
url=dashboard_url
)
def get_status(self) -> SourceStatus:
return self.status
|
# Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.c (the "License");
# you may not use this file except in compliance with the License.
#
""" Userbot module for kanging stickers or making new ones. Thanks @rupansh"""
import io
import math
import random
import urllib.request
from os import remove
from PIL import Image
from telethon.tl.functions.messages import GetStickerSetRequest
from telethon.tl.types import (
DocumentAttributeFilename,
DocumentAttributeSticker,
InputStickerSetID,
MessageMediaPhoto,
)
from userbot import CMD_HELP, bot
from userbot.events import register
KANGING_STR = [
"Eh... Koq bagus... aku curry ahhh :3",
"Aku curry ya kakak :)",
"Curry Sticker dulu yee kan",
"ehh, mantep nih.....aku ambil ya kaka",
"Bagus eaaaa....\nAmbil ahh....",
"Ini Sticker aku ambil yaa\nDUARR!",
"leh ugha ni Sticker\nCurry ahh~",
"Pim Pim Pom!!!\nni Sticker punya aing sekarang hehe",
"Bentar boss, ane curry dulu",
"Ihh, bagus nih\nCurry ahh~",
"Curry lagi yee kan.....",
"CURRY TROSS!!!",
"Curry Sticker ahh.....",
"Curry dolo boss",
"Swiper jangan mencurry",
]
@register(outgoing=True, pattern=r"^\.curry")
async def kang(args):
"""For .kang command, kangs stickers or creates new ones."""
user = await bot.get_me()
if not user.username:
user.username = user.first_name
message = await args.get_reply_message()
photo = None
emojibypass = False
is_anim = False
emoji = None
if not message or not message.media:
return await args.edit("`I can't kang that...`")
if isinstance(message.media, MessageMediaPhoto):
await args.edit(f"`{random.choice(KANGING_STR)}`")
photo = io.BytesIO()
photo = await bot.download_media(message.photo, photo)
elif "image" in message.media.document.mime_type.split("/"):
await args.edit(f"`{random.choice(KANGING_STR)}`")
photo = io.BytesIO()
await bot.download_file(message.media.document, photo)
if (
DocumentAttributeFilename(file_name="sticker.webp")
in message.media.document.attributes
):
emoji = message.media.document.attributes[1].alt
if emoji != "":
emojibypass = True
elif "tgsticker" in message.media.document.mime_type:
await args.edit(f"`{random.choice(KANGING_STR)}`")
await bot.download_file(message.media.document, "AnimatedSticker.tgs")
attributes = message.media.document.attributes
for attribute in attributes:
if isinstance(attribute, DocumentAttributeSticker):
emoji = attribute.alt
emojibypass = True
is_anim = True
photo = 1
else:
return await args.edit("`Unsupported File!`")
if photo:
splat = args.text.split()
if not emojibypass:
emoji = "🤔"
pack = 1
if len(splat) == 3:
pack = splat[2] # User sent both
emoji = splat[1]
elif len(splat) == 2:
if splat[1].isnumeric():
# User wants to push into different pack, but is okay with
# thonk as emote.
pack = int(splat[1])
else:
# User sent just custom emote, wants to push to default
# pack
emoji = splat[1]
packname = f"a{user.id}_by_{user.username}_{pack}"
packnick = f"@{user.username}'s kang pack Vol.{pack}"
cmd = "/newpack"
file = io.BytesIO()
if not is_anim:
image = await resize_photo(photo)
file.name = "sticker.png"
image.save(file, "PNG")
else:
packname += "_anim"
packnick += " (Animated)"
cmd = "/newanimated"
response = urllib.request.urlopen(
urllib.request.Request(f"http://t.me/addstickers/{packname}")
)
htmlstr = response.read().decode("utf8").split("\n")
if (
" A <strong>Telegram</strong> user has created the <strong>Sticker Set</strong>."
not in htmlstr
):
async with bot.conversation("Stickers") as conv:
await conv.send_message("/addsticker")
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message(packname)
x = await conv.get_response()
while "120" in x.text:
pack += 1
packname = f"a{user.id}_by_{user.username}_{pack}"
packnick = f"@{user.username}'s kang pack Vol.{pack}"
await args.edit(
"`Switching to Pack "
+ str(pack)
+ " due to insufficient space`"
)
await conv.send_message(packname)
x = await conv.get_response()
if x.text == "Invalid pack selected.":
await conv.send_message(cmd)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message(packnick)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
if is_anim:
await conv.send_file("AnimatedSticker.tgs")
remove("AnimatedSticker.tgs")
else:
file.seek(0)
await conv.send_file(file, force_document=True)
await conv.get_response()
await conv.send_message(emoji)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message("/publish")
if is_anim:
await conv.get_response()
await conv.send_message(f"<{packnick}>")
# Ensure user doesn't get spamming notifications
await conv.get_response()
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message("/skip")
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message(packname)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
return await args.edit(
"`Sticker added in a Different Pack !"
"\nThis Pack is Newly created!"
f"\nYour pack can be found [here](t.me/addstickers/{packname})",
parse_mode="md",
)
if is_anim:
await conv.send_file("AnimatedSticker.tgs")
remove("AnimatedSticker.tgs")
else:
file.seek(0)
await conv.send_file(file, force_document=True)
rsp = await conv.get_response()
if "Sorry, the file type is invalid." in rsp.text:
return await args.edit(
"`Failed to add sticker, use` @Stickers `bot to add the sticker manually.`"
)
await conv.send_message(emoji)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message("/done")
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
else:
await args.edit("`Brewing a new Pack...`")
async with bot.conversation("Stickers") as conv:
await conv.send_message(cmd)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message(packnick)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
if is_anim:
await conv.send_file("AnimatedSticker.tgs")
remove("AnimatedSticker.tgs")
else:
file.seek(0)
await conv.send_file(file, force_document=True)
rsp = await conv.get_response()
if "Sorry, the file type is invalid." in rsp.text:
return await args.edit(
"`Failed to add sticker, use` @Stickers `bot to add the sticker manually.`"
)
await conv.send_message(emoji)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message("/publish")
if is_anim:
await conv.get_response()
await conv.send_message(f"<{packnick}>")
# Ensure user doesn't get spamming notifications
await conv.get_response()
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message("/skip")
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message(packname)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await args.edit(
"Curry Success!" f"\n[Klik Disini](t.me/addstickers/{packname})",
parse_mode="md",
)
async def resize_photo(photo):
"""Resize the given photo to 512x512"""
image = Image.open(photo)
if (image.width and image.height) < 512:
size1 = image.width
size2 = image.height
if image.width > image.height:
scale = 512 / size1
size1new = 512
size2new = size2 * scale
else:
scale = 512 / size2
size1new = size1 * scale
size2new = 512
size1new = math.floor(size1new)
size2new = math.floor(size2new)
sizenew = (size1new, size2new)
image = image.resize(sizenew)
else:
maxsize = (512, 512)
image.thumbnail(maxsize)
return image
@register(outgoing=True, pattern=r"^\.stkrinfo$")
async def get_pack_info(event):
if not event.is_reply:
return await event.edit("`I can't fetch info from nothing, can I ?!`")
rep_msg = await event.get_reply_message()
if not rep_msg.document:
return await event.edit("`Reply to a sticker to get the pack details`")
try:
stickerset_attr = rep_msg.document.attributes[1]
await event.edit("`Fetching details of the sticker pack, please wait..`")
except BaseException:
return await event.edit("`This is not a sticker. Reply to a sticker.`")
if not isinstance(stickerset_attr, DocumentAttributeSticker):
return await event.edit("`This is not a sticker. Reply to a sticker.`")
get_stickerset = await bot(
GetStickerSetRequest(
InputStickerSetID(
id=stickerset_attr.stickerset.id,
access_hash=stickerset_attr.stickerset.access_hash,
)
)
)
pack_emojis = []
for document_sticker in get_stickerset.packs:
if document_sticker.emoticon not in pack_emojis:
pack_emojis.append(document_sticker.emoticon)
OUTPUT = (
f"**Sticker Title:** `{get_stickerset.set.title}\n`"
f"**Sticker Short Name:** `{get_stickerset.set.short_name}`\n"
f"**Official:** `{get_stickerset.set.official}`\n"
f"**Archived:** `{get_stickerset.set.archived}`\n"
f"**Stickers In Pack:** `{len(get_stickerset.packs)}`\n"
f"**Emojis In Pack:**\n{" ".join(pack_emojis)}"
)
await event.edit(OUTPUT)
@register(outgoing=True, pattern=r"^\.getsticker$")
async def sticker_to_png(sticker):
if not sticker.is_reply:
await sticker.edit("`NULL information to fetch...`")
return False
img = await sticker.get_reply_message()
if not img.document:
await sticker.edit("`Reply to a sticker...`")
return False
try:
img.document.attributes[1]
except Exception:
await sticker.edit("`This is not a sticker...`")
return
with io.BytesIO() as image:
await sticker.client.download_media(img, image)
image.name = "sticker.png"
image.seek(0)
try:
await img.reply(file=image, force_document=True)
except Exception:
await sticker.edit("`Err, can't send file...`")
else:
await sticker.delete()
return
CMD_HELP.update(
{
"stickers": ">`.curry`"
"\nUsage: Reply .curry to a sticker or an image to put it to your sticker pack."
"\n\n>`.curry (emoji['s]]?` [number]?"
"\nUsage: Curry the sticker/image to the specified pack. You can specify the emoji too. "
"(Default: 🤔)"
"\n\n>`.stkrinfo`"
"\nUsage: Gets info about the sticker pack."
"\n\n>`.getsticker`"
"\nUsage: Reply to a sticker to get 'PNG' file of sticker."
}
)
| # Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.c (the "License");
# you may not use this file except in compliance with the License.
#
""" Userbot module for kanging stickers or making new ones. Thanks @rupansh"""
import io
import math
import random
import urllib.request
from os import remove
from PIL import Image
from telethon.tl.functions.messages import GetStickerSetRequest
from telethon.tl.types import (
DocumentAttributeFilename,
DocumentAttributeSticker,
InputStickerSetID,
MessageMediaPhoto,
)
from userbot import CMD_HELP, bot
from userbot.events import register
KANGING_STR = [
"Eh... Koq bagus... aku curry ahhh :3",
"Aku curry ya kakak :)",
"Curry Sticker dulu yee kan",
"ehh, mantep nih.....aku ambil ya kaka",
"Bagus eaaaa....\nAmbil ahh....",
"Ini Sticker aku ambil yaa\nDUARR!",
"leh ugha ni Sticker\nCurry ahh~",
"Pim Pim Pom!!!\nni Sticker punya aing sekarang hehe",
"Bentar boss, ane curry dulu",
"Ihh, bagus nih\nCurry ahh~",
"Curry lagi yee kan.....",
"CURRY TROSS!!!",
"Curry Sticker ahh.....",
"Curry dolo boss",
"Swiper jangan mencurry",
]
@register(outgoing=True, pattern=r"^\.curry")
async def kang(args):
"""For .kang command, kangs stickers or creates new ones."""
user = await bot.get_me()
if not user.username:
user.username = user.first_name
message = await args.get_reply_message()
photo = None
emojibypass = False
is_anim = False
emoji = None
if not message or not message.media:
return await args.edit("`I can't kang that...`")
if isinstance(message.media, MessageMediaPhoto):
await args.edit(f"`{random.choice(KANGING_STR)}`")
photo = io.BytesIO()
photo = await bot.download_media(message.photo, photo)
elif "image" in message.media.document.mime_type.split("/"):
await args.edit(f"`{random.choice(KANGING_STR)}`")
photo = io.BytesIO()
await bot.download_file(message.media.document, photo)
if (
DocumentAttributeFilename(file_name="sticker.webp")
in message.media.document.attributes
):
emoji = message.media.document.attributes[1].alt
if emoji != "":
emojibypass = True
elif "tgsticker" in message.media.document.mime_type:
await args.edit(f"`{random.choice(KANGING_STR)}`")
await bot.download_file(message.media.document, "AnimatedSticker.tgs")
attributes = message.media.document.attributes
for attribute in attributes:
if isinstance(attribute, DocumentAttributeSticker):
emoji = attribute.alt
emojibypass = True
is_anim = True
photo = 1
else:
return await args.edit("`Unsupported File!`")
if photo:
splat = args.text.split()
if not emojibypass:
emoji = "🤔"
pack = 1
if len(splat) == 3:
pack = splat[2] # User sent both
emoji = splat[1]
elif len(splat) == 2:
if splat[1].isnumeric():
# User wants to push into different pack, but is okay with
# thonk as emote.
pack = int(splat[1])
else:
# User sent just custom emote, wants to push to default
# pack
emoji = splat[1]
packname = f"a{user.id}_by_{user.username}_{pack}"
packnick = f"@{user.username}'s kang pack Vol.{pack}"
cmd = "/newpack"
file = io.BytesIO()
if not is_anim:
image = await resize_photo(photo)
file.name = "sticker.png"
image.save(file, "PNG")
else:
packname += "_anim"
packnick += " (Animated)"
cmd = "/newanimated"
response = urllib.request.urlopen(
urllib.request.Request(f"http://t.me/addstickers/{packname}")
)
htmlstr = response.read().decode("utf8").split("\n")
if (
" A <strong>Telegram</strong> user has created the <strong>Sticker Set</strong>."
not in htmlstr
):
async with bot.conversation("Stickers") as conv:
await conv.send_message("/addsticker")
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message(packname)
x = await conv.get_response()
while "120" in x.text:
pack += 1
packname = f"a{user.id}_by_{user.username}_{pack}"
packnick = f"@{user.username}'s kang pack Vol.{pack}"
await args.edit(
"`Switching to Pack "
+ str(pack)
+ " due to insufficient space`"
)
await conv.send_message(packname)
x = await conv.get_response()
if x.text == "Invalid pack selected.":
await conv.send_message(cmd)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message(packnick)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
if is_anim:
await conv.send_file("AnimatedSticker.tgs")
remove("AnimatedSticker.tgs")
else:
file.seek(0)
await conv.send_file(file, force_document=True)
await conv.get_response()
await conv.send_message(emoji)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message("/publish")
if is_anim:
await conv.get_response()
await conv.send_message(f"<{packnick}>")
# Ensure user doesn't get spamming notifications
await conv.get_response()
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message("/skip")
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message(packname)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
return await args.edit(
"`Sticker added in a Different Pack !"
"\nThis Pack is Newly created!"
f"\nYour pack can be found [here](t.me/addstickers/{packname})",
parse_mode="md",
)
if is_anim:
await conv.send_file("AnimatedSticker.tgs")
remove("AnimatedSticker.tgs")
else:
file.seek(0)
await conv.send_file(file, force_document=True)
rsp = await conv.get_response()
if "Sorry, the file type is invalid." in rsp.text:
return await args.edit(
"`Failed to add sticker, use` @Stickers `bot to add the sticker manually.`"
)
await conv.send_message(emoji)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message("/done")
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
else:
await args.edit("`Brewing a new Pack...`")
async with bot.conversation("Stickers") as conv:
await conv.send_message(cmd)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message(packnick)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
if is_anim:
await conv.send_file("AnimatedSticker.tgs")
remove("AnimatedSticker.tgs")
else:
file.seek(0)
await conv.send_file(file, force_document=True)
rsp = await conv.get_response()
if "Sorry, the file type is invalid." in rsp.text:
return await args.edit(
"`Failed to add sticker, use` @Stickers `bot to add the sticker manually.`"
)
await conv.send_message(emoji)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message("/publish")
if is_anim:
await conv.get_response()
await conv.send_message(f"<{packnick}>")
# Ensure user doesn't get spamming notifications
await conv.get_response()
await bot.send_read_acknowledge(conv.chat_id)
await conv.send_message("/skip")
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
await conv.send_message(packname)
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await conv.get_response()
# Ensure user doesn't get spamming notifications
await bot.send_read_acknowledge(conv.chat_id)
await args.edit(
"Curry Success!" f"\n[Klik Disini](t.me/addstickers/{packname})",
parse_mode="md",
)
async def resize_photo(photo):
"""Resize the given photo to 512x512"""
image = Image.open(photo)
if (image.width and image.height) < 512:
size1 = image.width
size2 = image.height
if image.width > image.height:
scale = 512 / size1
size1new = 512
size2new = size2 * scale
else:
scale = 512 / size2
size1new = size1 * scale
size2new = 512
size1new = math.floor(size1new)
size2new = math.floor(size2new)
sizenew = (size1new, size2new)
image = image.resize(sizenew)
else:
maxsize = (512, 512)
image.thumbnail(maxsize)
return image
@register(outgoing=True, pattern=r"^\.stkrinfo$")
async def get_pack_info(event):
if not event.is_reply:
return await event.edit("`I can't fetch info from nothing, can I ?!`")
rep_msg = await event.get_reply_message()
if not rep_msg.document:
return await event.edit("`Reply to a sticker to get the pack details`")
try:
stickerset_attr = rep_msg.document.attributes[1]
await event.edit("`Fetching details of the sticker pack, please wait..`")
except BaseException:
return await event.edit("`This is not a sticker. Reply to a sticker.`")
if not isinstance(stickerset_attr, DocumentAttributeSticker):
return await event.edit("`This is not a sticker. Reply to a sticker.`")
get_stickerset = await bot(
GetStickerSetRequest(
InputStickerSetID(
id=stickerset_attr.stickerset.id,
access_hash=stickerset_attr.stickerset.access_hash,
)
)
)
pack_emojis = []
for document_sticker in get_stickerset.packs:
if document_sticker.emoticon not in pack_emojis:
pack_emojis.append(document_sticker.emoticon)
OUTPUT = (
f"**Sticker Title:** `{get_stickerset.set.title}\n`"
f"**Sticker Short Name:** `{get_stickerset.set.short_name}`\n"
f"**Official:** `{get_stickerset.set.official}`\n"
f"**Archived:** `{get_stickerset.set.archived}`\n"
f"**Stickers In Pack:** `{len(get_stickerset.packs)}`\n"
f"**Emojis In Pack:**\n{' '.join(pack_emojis)}"
)
await event.edit(OUTPUT)
@register(outgoing=True, pattern=r"^\.getsticker$")
async def sticker_to_png(sticker):
if not sticker.is_reply:
await sticker.edit("`NULL information to fetch...`")
return False
img = await sticker.get_reply_message()
if not img.document:
await sticker.edit("`Reply to a sticker...`")
return False
try:
img.document.attributes[1]
except Exception:
await sticker.edit("`This is not a sticker...`")
return
with io.BytesIO() as image:
await sticker.client.download_media(img, image)
image.name = "sticker.png"
image.seek(0)
try:
await img.reply(file=image, force_document=True)
except Exception:
await sticker.edit("`Err, can't send file...`")
else:
await sticker.delete()
return
CMD_HELP.update(
{
"stickers": ">`.curry`"
"\nUsage: Reply .curry to a sticker or an image to put it to your sticker pack."
"\n\n>`.curry (emoji['s]]?` [number]?"
"\nUsage: Curry the sticker/image to the specified pack. You can specify the emoji too. "
"(Default: 🤔)"
"\n\n>`.stkrinfo`"
"\nUsage: Gets info about the sticker pack."
"\n\n>`.getsticker`"
"\nUsage: Reply to a sticker to get 'PNG' file of sticker."
}
)
|
from __future__ import unicode_literals
import datetime
import logging
from inspect import isclass
from django.core.exceptions import ImproperlyConfigured, FieldDoesNotExist
from django.db.models import Q, ForeignKey
from .fields import SlickReportField
from .helpers import get_field_from_query_text
from .registry import field_registry
logger = logging.getLogger(__name__)
class ReportGenerator(object):
"""
The main class responsible generating the report and managing the flow
"""
field_registry_class = field_registry
"""You can have a custom computation field locator! It only needs a `get_field_by_name(string)`
and returns a ReportField`"""
report_model = None
"""The main model where data is """
"""
Class to generate a Json Object containing report data.
"""
date_field = None
"""Main date field to use whenever date filter is needed"""
print_flag = None
list_display_links = []
group_by = None
"""The field to use for grouping, if not set then the report is expected to be a sub version of the report model"""
columns = None
"""A list of column names.
Columns names can be
1. A Computation Field
2. If group_by is set, then any field on teh group_by model
3. If group_by is not set, then any field name on the report_model / queryset
4. A callable on the generator
5. Special __time_series__, and __crosstab__
Those can be use to control the position of the time series inside the columns, defaults it's appended at the end
Example:
columns = ['product_id', '__time_series__', 'col_b']
Same is true with __crosstab__
"""
time_series_pattern = ''
"""
If set the Report will compute a time series.
Possible options are: daily, weekly, semimonthly, monthly, quarterly, semiannually, annually and custom.
if `custom` is set, you'd need to override `get_custom_time_series_dates`
"""
time_series_columns = None
"""
a list of Calculation Field names which will be included in the series calculation.
Example: ['__total__', '__total_quantity__'] with compute those 2 fields for all the series
"""
time_series_custom_dates = None
"""
Used with `time_series_pattern` set to 'custom'
It's a list of tuple, each tuple represent start date & end date
Example: [ (start_date_1, end_date_1), (start_date_2, end_date_2), ....]
"""
crosstab_model = None
"""
If set, a cross tab over this model selected ids (via `crosstab_ids`)
"""
crosstab_columns = None
"""The computation fields which will be computed for each crosstab-ed ids """
crosstab_ids = None
"""A list is the ids to create a crosstab report on"""
crosstab_compute_reminder = True
"""Include an an extra crosstab_columns for the outer group ( ie: all expects those `crosstab_ids`) """
show_empty_records = True
"""
If group_by is set, this option control if the report result will include all objects regardless of appearing in the report_model/qs.
If set False, only those objects which are found in the report_model/qs
Example: Say you group by client
show_empty_records = True will get the computation fields for all clients in the Client model (including those who
didnt make a transaction.
show_empty_records = False will get the computation fields for all clients in the Client model (including those who
didnt make a transaction.
"""
limit_records = None
"""Serves are a main limit to the returned data of teh report_model.
Can be beneficial if the results may be huge.
"""
swap_sign = False
def __init__(self, report_model=None, main_queryset=None, start_date=None, end_date=None, date_field=None,
q_filters=None, kwargs_filters=None,
group_by=None, columns=None,
time_series_pattern=None, time_series_columns=None, time_series_custom_dates=None,
crosstab_model=None, crosstab_columns=None, crosstab_ids=None, crosstab_compute_reminder=None,
swap_sign=False, show_empty_records=None,
print_flag=False,
doc_type_plus_list=None, doc_type_minus_list=None, limit_records=False, format_row_func=None):
"""
:param report_model: Main model containing the data
:param main_queryset: Default to report_model.objects
:param start_date:
:param end_date:
:param date_field:
:param q_filters:
:param kwargs_filters:
:param group_by:
:param columns:
:param time_series_pattern:
:param time_series_columns:
:param crosstab_model:
:param crosstab_columns:
:param crosstab_ids:
:param crosstab_compute_reminder:
:param swap_sign:
:param show_empty_records:
:param base_model:
:param print_flag:
:param doc_type_plus_list:
:param doc_type_minus_list:
:param limit_records:
"""
from .app_settings import SLICK_REPORTING_DEFAULT_START_DATE, SLICK_REPORTING_DEFAULT_END_DATE
super(ReportGenerator, self).__init__()
self.report_model = self.report_model or report_model
if not self.report_model:
raise ImproperlyConfigured('report_model must be set on a class level or via init')
self.start_date = start_date or datetime.datetime.combine(SLICK_REPORTING_DEFAULT_START_DATE.date(),
SLICK_REPORTING_DEFAULT_START_DATE.time())
self.end_date = end_date or datetime.datetime.combine(SLICK_REPORTING_DEFAULT_END_DATE.date(),
SLICK_REPORTING_DEFAULT_END_DATE.time())
self.date_field = self.date_field or date_field
if not self.date_field:
raise ImproperlyConfigured('date_field must be set on a class level or via init')
self.q_filters = q_filters or []
self.kwargs_filters = kwargs_filters or {}
self.crosstab_model = self.crosstab_model or crosstab_model
self.crosstab_columns = crosstab_columns or self.crosstab_columns or []
self.crosstab_ids = self.crosstab_ids or crosstab_ids or []
self.crosstab_compute_reminder = self.crosstab_compute_reminder if crosstab_compute_reminder is None else crosstab_compute_reminder
self.format_row = format_row_func or self._default_format_row
main_queryset = main_queryset or self.report_model.objects
main_queryset = main_queryset.order_by()
self.columns = self.columns or columns or []
self.group_by = self.group_by or group_by
self.time_series_pattern = self.time_series_pattern or time_series_pattern
self.time_series_columns = self.time_series_columns or time_series_columns
self.time_series_custom_dates = self.time_series_custom_dates or time_series_custom_dates
self._prepared_results = {}
self.report_fields_classes = {}
self._report_fields_dependencies = {'time_series': {}, 'crosstab': {}, 'normal': {}}
self.existing_dependencies = {'series': [], 'matrix': [], 'normal': []}
self.print_flag = print_flag or self.print_flag
# todo validate columns is not empty (if no time series / cross tab)
if self.group_by:
group_by_split = self.group_by.split('__')
search_field = group_by_split[0]
try:
self.group_by_field = [x for x in self.report_model._meta.get_fields() if x.name == search_field][0]
except IndexError:
raise ImproperlyConfigured(
f'Can not find group_by field:{self.group_by} in report_model {self.report_model} ')
self.focus_field_as_key = self.group_by_field
if '__' not in self.group_by:
self.group_by_field_attname = self.group_by_field.attname
else:
self.group_by_field_attname = self.group_by
else:
self.focus_field_as_key = None
self.group_by_field_attname = None
# doc_types = form.get_doc_type_plus_minus_lists()
doc_types = [], []
self.doc_type_plus_list = list(doc_type_plus_list) if doc_type_plus_list else doc_types[0]
self.doc_type_minus_list = list(doc_type_minus_list) if doc_type_minus_list else doc_types[1]
self.swap_sign = self.swap_sign or swap_sign
self.limit_records = self.limit_records or limit_records
# passed to the report fields
# self.date_field = date_field or self.date_field
# in case of a group by, do we show a grouped by model data regardless of their appearance in the results
# a client who didnt make a transaction during the date period.
self.show_empty_records = False # show_empty_records if show_empty_records else self.show_empty_records
# Looks like this options is harder then what i thought as it interfere with the usual filtering of the report
# Preparing actions
self._parse()
if self.group_by:
if self.show_empty_records:
pass
# group_by_filter = self.kwargs_filters.get(self.group_by, '')
# qs = self.group_by_field.related_model.objects
# if group_by_filter:
# lookup = 'pk__in' if isinstance(group_by_filter, Iterable) else 'pk'
# qs = qs.filter(**{lookup: group_by_filter})
# self.main_queryset = qs.values()
else:
self.main_queryset = self._apply_queryset_options(main_queryset)
if type(self.group_by_field) is ForeignKey and '__' not in self.group_by:
ids = self.main_queryset.values_list(self.group_by_field_attname).distinct()
self.main_queryset = self.group_by_field.related_model.objects.filter(pk__in=ids).values()
else:
self.main_queryset = self.main_queryset.distinct().values(self.group_by_field_attname)
else:
if self.time_series_pattern:
self.main_queryset = [{}]
else:
self.main_queryset = self._apply_queryset_options(main_queryset, self.get_database_columns())
self._prepare_report_dependencies()
def _apply_queryset_options(self, query, fields=None):
"""
Apply the filters to the main queryset which will computed results be mapped to
:param query:
:param fields:
:return:
"""
filters = {
f'{self.date_field}__gt': self.start_date,
f'{self.date_field}__lte': self.end_date,
}
filters.update(self.kwargs_filters)
if filters:
query = query.filter(**filters)
if fields:
return query.values(*fields)
return query.values()
def _construct_crosstab_filter(self, col_data):
"""
In charge of adding the needed crosstab filter, specific to the case of is_reminder or not
:param col_data:
:return:
"""
if col_data['is_reminder']:
filters = [~Q(**{f"{col_data["model"]}_id__in": self.crosstab_ids})]
else:
filters = [Q(**{f"{col_data["model"]}_id": col_data['id']})]
return filters
def _prepare_report_dependencies(self):
from .fields import SlickReportField
all_columns = (
('normal', self._parsed_columns),
('time_series', self._time_series_parsed_columns),
('crosstab', self._crosstab_parsed_columns),
)
for window, window_cols in all_columns:
for col_data in window_cols:
klass = col_data['ref']
if isclass(klass) and issubclass(klass, SlickReportField):
dependencies_names = klass.get_full_dependency_list()
# check if any of this dependencies is on the report
fields_on_report = [x for x in window_cols if x['ref'] in dependencies_names]
for field in fields_on_report:
self._report_fields_dependencies[window][field['name']] = col_data['name']
for col_data in window_cols:
klass = col_data['ref']
name = col_data['name']
# if column has a dependency then skip it
if not (isclass(klass) and issubclass(klass, SlickReportField)):
continue
if self._report_fields_dependencies[window].get(name, False):
continue
report_class = klass(self.doc_type_plus_list, self.doc_type_minus_list,
group_by=self.group_by,
report_model=self.report_model, date_field=self.date_field)
q_filters = None
date_filter = {
f'{self.date_field}__gt': col_data.get('start_date', self.start_date),
f'{self.date_field}__lte': col_data.get('end_date', self.end_date),
}
date_filter.update(self.kwargs_filters)
if window == 'crosstab':
q_filters = self._construct_crosstab_filter(col_data)
report_class.init_preparation(q_filters, date_filter)
self.report_fields_classes[name] = report_class
def _get_record_data(self, obj, columns):
"""
the function is run for every obj in the main_queryset
:param obj: current row
:param: columns： The columns we iterate on
:return: a dict object containing all needed data
"""
# todo , if columns are empty for whatever reason this will throw an error
display_link = self.list_display_links or columns[0]
data = {}
group_by_val = None
if self.group_by:
column_data = obj.get(self.group_by_field_attname, obj.get('id'))
group_by_val = str(column_data)
for window, window_cols in columns:
for col_data in window_cols:
name = col_data['name']
if (col_data.get('source', '') == 'magic_field' and self.group_by) or (
self.time_series_pattern and not self.group_by):
source = self._report_fields_dependencies[window].get(name, False)
if source:
computation_class = self.report_fields_classes[source]
value = computation_class.get_dependency_value(group_by_val,
col_data['ref'].name)
else:
try:
computation_class = self.report_fields_classes[name]
except KeyError:
continue
value = computation_class.resolve(group_by_val, data)
if self.swap_sign: value = -value
data[name] = value
else:
data[name] = obj.get(name, '')
# if self.group_by and name in display_link:
# data[name] = make_linkable_field(self.group_by_field.related_model, group_by_val, data[name])
return data
def get_report_data(self):
main_queryset = self.main_queryset[:self.limit_records] if self.limit_records else self.main_queryset
all_columns = (
('normal', self._parsed_columns),
('time_series', self._time_series_parsed_columns),
('crosstab', self._crosstab_parsed_columns),
)
get_record_data = self._get_record_data
format_row = self.format_row
data = [format_row(get_record_data(obj, all_columns)) for obj in main_queryset]
return data
def _default_format_row(self, row_obj):
"""
Hook where you can format row values like properly format a date
:param row_obj:
:return:
"""
return row_obj
@classmethod
def check_columns(cls, columns, group_by, report_model, ):
"""
Check and parse the columns, throw errors in case an item in the columns cant not identified
:param columns: List of columns
:param group_by: group by field if any
:param report_model: the report model
:return: List of dict, each dict contains relevant data to the respective field in `columns`
"""
group_by_model = None
if group_by:
group_by_field = [x for x in report_model._meta.get_fields() if x.name == group_by.split('__')[0]][0]
if group_by_field.is_relation:
group_by_model = group_by_field.related_model
else:
group_by_model = report_model
parsed_columns = []
for col in columns:
if col in ['__time_series__', '__crosstab__']:
# These are placeholder not real computation field
continue
magic_field_class = None
attr = None
if type(col) is str:
attr = getattr(cls, col, None)
elif issubclass(col, SlickReportField):
magic_field_class = col
try:
magic_field_class = magic_field_class or field_registry.get_field_by_name(col)
except KeyError:
magic_field_class = None
if attr:
# todo Add testing here
col_data = {'name': col,
'verbose_name': getattr(attr, 'verbose_name', col),
# 'type': 'method',
'ref': attr,
'type': 'text'
}
elif magic_field_class:
# a magic field
if col in ['__time_series__', '__crosstab__']:
# These are placeholder not real computation field
continue
col_data = {'name': magic_field_class.name,
'verbose_name': magic_field_class.verbose_name,
'source': 'magic_field',
'ref': magic_field_class,
'type': magic_field_class.type,
'is_summable': magic_field_class.is_summable
}
else:
# A database field
model_to_use = group_by_model if group_by and '__' not in group_by else report_model
try:
if '__' in col:
# A traversing link order__client__email
field = get_field_from_query_text(col, model_to_use)
else:
field = model_to_use._meta.get_field(col)
except FieldDoesNotExist:
raise FieldDoesNotExist(
f'Field "{col}" not found either as an attribute to the generator class {cls}, '
f'or a computation field, or a database column for the model "{model_to_use}"')
col_data = {'name': col,
'verbose_name': getattr(field, 'verbose_name', col),
'source': 'database',
'ref': field,
'type': field.get_internal_type()
}
parsed_columns.append(col_data)
return parsed_columns
def _parse(self):
self.parsed_columns = self.check_columns(self.columns, self.group_by, self.report_model)
self._parsed_columns = list(self.parsed_columns)
self._time_series_parsed_columns = self.get_time_series_parsed_columns()
self._crosstab_parsed_columns = self.get_crosstab_parsed_columns()
def get_database_columns(self):
return [col['name'] for col in self.parsed_columns if col['source'] == 'database']
def get_method_columns(self):
return [col['name'] for col in self.parsed_columns if col['type'] == 'method']
def get_list_display_columns(self):
columns = self.parsed_columns
if self.time_series_pattern:
time_series_columns = self.get_time_series_parsed_columns()
try:
index = self.columns.index('__time_series__')
columns[index:index] = time_series_columns
except ValueError:
columns += time_series_columns
if self.crosstab_model:
crosstab_columns = self.get_crosstab_parsed_columns()
try:
index = self.columns.index('__crosstab__')
columns[index:index] = crosstab_columns
except ValueError:
columns += crosstab_columns
return columns
def get_time_series_parsed_columns(self):
"""
Return time series columns with all needed data attached
:param plain: if True it returns '__total__' instead of '__total_TS011212'
:return: List if columns
"""
_values = []
cols = self.time_series_columns or []
series = self._get_time_series_dates(self.time_series_pattern)
for index, dt in enumerate(series):
for col in cols:
magic_field_class = None
if type(col) is str:
magic_field_class = field_registry.get_field_by_name(col)
elif issubclass(col, SlickReportField):
magic_field_class = col
_values.append({
'name': magic_field_class.name + 'TS' + dt[1].strftime('%Y%m%d'),
'original_name': magic_field_class.name,
'verbose_name': self.get_time_series_field_verbose_name(magic_field_class, dt, index, series),
'ref': magic_field_class,
'start_date': dt[0],
'end_date': dt[1],
'source': 'magic_field' if magic_field_class else '',
'is_summable': magic_field_class.is_summable,
})
return _values
def get_time_series_field_verbose_name(self, computation_class, date_period, index, series, pattern=None):
"""
Sent the column data to construct a verbose name.
Default implementation is delegated to the ReportField.get_time_series_field_verbose_name
(which is name + the end date %Y%m%d)
:param computation_class: the computation field_name
:param date_period: a tuple of (start_date, end_date)
:return: a verbose string
"""
pattern = pattern or self.time_series_pattern
return computation_class.get_time_series_field_verbose_name(date_period, index, series,
pattern)
def get_custom_time_series_dates(self):
"""
Hook to get custom , maybe separated date periods
:return: [ (date1,date2) , (date3,date4), .... ]
"""
return self.time_series_custom_dates or []
def _get_time_series_dates(self, series=None, start_date=None, end_date=None):
from dateutil.relativedelta import relativedelta
series = series or self.time_series_pattern
start_date = start_date or self.start_date
end_date = end_date or self.end_date
_values = []
if series:
if series == 'daily':
time_delta = datetime.timedelta(days=1)
elif series == 'weekly':
time_delta = relativedelta(weeks=1)
elif series == 'semimonthly':
time_delta = relativedelta(weeks=2)
elif series == 'monthly':
time_delta = relativedelta(months=1)
elif series == 'quarterly':
time_delta = relativedelta(months=3)
elif series == 'semiannually':
time_delta = relativedelta(months=6)
elif series == 'annually':
time_delta = relativedelta(years=1)
elif series == 'custom':
return self.get_custom_time_series_dates()
else:
raise NotImplementedError(f'"{series}" is not implemented for time_series_pattern')
done = False
while not done:
to_date = start_date + time_delta
_values.append((start_date, to_date))
start_date = to_date
if to_date >= end_date:
done = True
return _values
def get_crosstab_parsed_columns(self):
"""
Return a list of the columns analyzed , with reference to computation field and everything
:return:
"""
report_columns = self.crosstab_columns or []
ids = list(self.crosstab_ids)
if self.crosstab_compute_reminder:
ids.append('----')
output_cols = []
ids_length = len(ids) - 1
for counter, id in enumerate(ids):
for col in report_columns:
magic_field_class = None
if type(col) is str:
magic_field_class = field_registry.get_field_by_name(col)
elif issubclass(col, SlickReportField):
magic_field_class = col
output_cols.append({
'name': f'{magic_field_class.name}CT{id}',
'original_name': magic_field_class.name,
'verbose_name': self.get_crosstab_field_verbose_name(magic_field_class, self.crosstab_model, id),
'ref': magic_field_class,
'id': id,
'model': self.crosstab_model,
'is_reminder': counter == ids_length,
'source': 'magic_field' if magic_field_class else '',
'is_summable': magic_field_class.is_summable,
})
return output_cols
def get_crosstab_field_verbose_name(self, computation_class, model, id):
"""
Hook to change the crosstab field verbose name, default it delegate this function to the ReportField
:param computation_class: ReportField Class
:param model: the model name as string
:param id: the current crosstab id
:return: a verbose string
"""
return computation_class.get_crosstab_field_verbose_name(model, id)
| from __future__ import unicode_literals
import datetime
import logging
from inspect import isclass
from django.core.exceptions import ImproperlyConfigured, FieldDoesNotExist
from django.db.models import Q, ForeignKey
from .fields import SlickReportField
from .helpers import get_field_from_query_text
from .registry import field_registry
logger = logging.getLogger(__name__)
class ReportGenerator(object):
"""
The main class responsible generating the report and managing the flow
"""
field_registry_class = field_registry
"""You can have a custom computation field locator! It only needs a `get_field_by_name(string)`
and returns a ReportField`"""
report_model = None
"""The main model where data is """
"""
Class to generate a Json Object containing report data.
"""
date_field = None
"""Main date field to use whenever date filter is needed"""
print_flag = None
list_display_links = []
group_by = None
"""The field to use for grouping, if not set then the report is expected to be a sub version of the report model"""
columns = None
"""A list of column names.
Columns names can be
1. A Computation Field
2. If group_by is set, then any field on teh group_by model
3. If group_by is not set, then any field name on the report_model / queryset
4. A callable on the generator
5. Special __time_series__, and __crosstab__
Those can be use to control the position of the time series inside the columns, defaults it's appended at the end
Example:
columns = ['product_id', '__time_series__', 'col_b']
Same is true with __crosstab__
"""
time_series_pattern = ''
"""
If set the Report will compute a time series.
Possible options are: daily, weekly, semimonthly, monthly, quarterly, semiannually, annually and custom.
if `custom` is set, you'd need to override `get_custom_time_series_dates`
"""
time_series_columns = None
"""
a list of Calculation Field names which will be included in the series calculation.
Example: ['__total__', '__total_quantity__'] with compute those 2 fields for all the series
"""
time_series_custom_dates = None
"""
Used with `time_series_pattern` set to 'custom'
It's a list of tuple, each tuple represent start date & end date
Example: [ (start_date_1, end_date_1), (start_date_2, end_date_2), ....]
"""
crosstab_model = None
"""
If set, a cross tab over this model selected ids (via `crosstab_ids`)
"""
crosstab_columns = None
"""The computation fields which will be computed for each crosstab-ed ids """
crosstab_ids = None
"""A list is the ids to create a crosstab report on"""
crosstab_compute_reminder = True
"""Include an an extra crosstab_columns for the outer group ( ie: all expects those `crosstab_ids`) """
show_empty_records = True
"""
If group_by is set, this option control if the report result will include all objects regardless of appearing in the report_model/qs.
If set False, only those objects which are found in the report_model/qs
Example: Say you group by client
show_empty_records = True will get the computation fields for all clients in the Client model (including those who
didnt make a transaction.
show_empty_records = False will get the computation fields for all clients in the Client model (including those who
didnt make a transaction.
"""
limit_records = None
"""Serves are a main limit to the returned data of teh report_model.
Can be beneficial if the results may be huge.
"""
swap_sign = False
def __init__(self, report_model=None, main_queryset=None, start_date=None, end_date=None, date_field=None,
q_filters=None, kwargs_filters=None,
group_by=None, columns=None,
time_series_pattern=None, time_series_columns=None, time_series_custom_dates=None,
crosstab_model=None, crosstab_columns=None, crosstab_ids=None, crosstab_compute_reminder=None,
swap_sign=False, show_empty_records=None,
print_flag=False,
doc_type_plus_list=None, doc_type_minus_list=None, limit_records=False, format_row_func=None):
"""
:param report_model: Main model containing the data
:param main_queryset: Default to report_model.objects
:param start_date:
:param end_date:
:param date_field:
:param q_filters:
:param kwargs_filters:
:param group_by:
:param columns:
:param time_series_pattern:
:param time_series_columns:
:param crosstab_model:
:param crosstab_columns:
:param crosstab_ids:
:param crosstab_compute_reminder:
:param swap_sign:
:param show_empty_records:
:param base_model:
:param print_flag:
:param doc_type_plus_list:
:param doc_type_minus_list:
:param limit_records:
"""
from .app_settings import SLICK_REPORTING_DEFAULT_START_DATE, SLICK_REPORTING_DEFAULT_END_DATE
super(ReportGenerator, self).__init__()
self.report_model = self.report_model or report_model
if not self.report_model:
raise ImproperlyConfigured('report_model must be set on a class level or via init')
self.start_date = start_date or datetime.datetime.combine(SLICK_REPORTING_DEFAULT_START_DATE.date(),
SLICK_REPORTING_DEFAULT_START_DATE.time())
self.end_date = end_date or datetime.datetime.combine(SLICK_REPORTING_DEFAULT_END_DATE.date(),
SLICK_REPORTING_DEFAULT_END_DATE.time())
self.date_field = self.date_field or date_field
if not self.date_field:
raise ImproperlyConfigured('date_field must be set on a class level or via init')
self.q_filters = q_filters or []
self.kwargs_filters = kwargs_filters or {}
self.crosstab_model = self.crosstab_model or crosstab_model
self.crosstab_columns = crosstab_columns or self.crosstab_columns or []
self.crosstab_ids = self.crosstab_ids or crosstab_ids or []
self.crosstab_compute_reminder = self.crosstab_compute_reminder if crosstab_compute_reminder is None else crosstab_compute_reminder
self.format_row = format_row_func or self._default_format_row
main_queryset = main_queryset or self.report_model.objects
main_queryset = main_queryset.order_by()
self.columns = self.columns or columns or []
self.group_by = self.group_by or group_by
self.time_series_pattern = self.time_series_pattern or time_series_pattern
self.time_series_columns = self.time_series_columns or time_series_columns
self.time_series_custom_dates = self.time_series_custom_dates or time_series_custom_dates
self._prepared_results = {}
self.report_fields_classes = {}
self._report_fields_dependencies = {'time_series': {}, 'crosstab': {}, 'normal': {}}
self.existing_dependencies = {'series': [], 'matrix': [], 'normal': []}
self.print_flag = print_flag or self.print_flag
# todo validate columns is not empty (if no time series / cross tab)
if self.group_by:
group_by_split = self.group_by.split('__')
search_field = group_by_split[0]
try:
self.group_by_field = [x for x in self.report_model._meta.get_fields() if x.name == search_field][0]
except IndexError:
raise ImproperlyConfigured(
f'Can not find group_by field:{self.group_by} in report_model {self.report_model} ')
self.focus_field_as_key = self.group_by_field
if '__' not in self.group_by:
self.group_by_field_attname = self.group_by_field.attname
else:
self.group_by_field_attname = self.group_by
else:
self.focus_field_as_key = None
self.group_by_field_attname = None
# doc_types = form.get_doc_type_plus_minus_lists()
doc_types = [], []
self.doc_type_plus_list = list(doc_type_plus_list) if doc_type_plus_list else doc_types[0]
self.doc_type_minus_list = list(doc_type_minus_list) if doc_type_minus_list else doc_types[1]
self.swap_sign = self.swap_sign or swap_sign
self.limit_records = self.limit_records or limit_records
# passed to the report fields
# self.date_field = date_field or self.date_field
# in case of a group by, do we show a grouped by model data regardless of their appearance in the results
# a client who didnt make a transaction during the date period.
self.show_empty_records = False # show_empty_records if show_empty_records else self.show_empty_records
# Looks like this options is harder then what i thought as it interfere with the usual filtering of the report
# Preparing actions
self._parse()
if self.group_by:
if self.show_empty_records:
pass
# group_by_filter = self.kwargs_filters.get(self.group_by, '')
# qs = self.group_by_field.related_model.objects
# if group_by_filter:
# lookup = 'pk__in' if isinstance(group_by_filter, Iterable) else 'pk'
# qs = qs.filter(**{lookup: group_by_filter})
# self.main_queryset = qs.values()
else:
self.main_queryset = self._apply_queryset_options(main_queryset)
if type(self.group_by_field) is ForeignKey and '__' not in self.group_by:
ids = self.main_queryset.values_list(self.group_by_field_attname).distinct()
self.main_queryset = self.group_by_field.related_model.objects.filter(pk__in=ids).values()
else:
self.main_queryset = self.main_queryset.distinct().values(self.group_by_field_attname)
else:
if self.time_series_pattern:
self.main_queryset = [{}]
else:
self.main_queryset = self._apply_queryset_options(main_queryset, self.get_database_columns())
self._prepare_report_dependencies()
def _apply_queryset_options(self, query, fields=None):
"""
Apply the filters to the main queryset which will computed results be mapped to
:param query:
:param fields:
:return:
"""
filters = {
f'{self.date_field}__gt': self.start_date,
f'{self.date_field}__lte': self.end_date,
}
filters.update(self.kwargs_filters)
if filters:
query = query.filter(**filters)
if fields:
return query.values(*fields)
return query.values()
def _construct_crosstab_filter(self, col_data):
"""
In charge of adding the needed crosstab filter, specific to the case of is_reminder or not
:param col_data:
:return:
"""
if col_data['is_reminder']:
filters = [~Q(**{f"{col_data['model']}_id__in": self.crosstab_ids})]
else:
filters = [Q(**{f"{col_data['model']}_id": col_data['id']})]
return filters
def _prepare_report_dependencies(self):
from .fields import SlickReportField
all_columns = (
('normal', self._parsed_columns),
('time_series', self._time_series_parsed_columns),
('crosstab', self._crosstab_parsed_columns),
)
for window, window_cols in all_columns:
for col_data in window_cols:
klass = col_data['ref']
if isclass(klass) and issubclass(klass, SlickReportField):
dependencies_names = klass.get_full_dependency_list()
# check if any of this dependencies is on the report
fields_on_report = [x for x in window_cols if x['ref'] in dependencies_names]
for field in fields_on_report:
self._report_fields_dependencies[window][field['name']] = col_data['name']
for col_data in window_cols:
klass = col_data['ref']
name = col_data['name']
# if column has a dependency then skip it
if not (isclass(klass) and issubclass(klass, SlickReportField)):
continue
if self._report_fields_dependencies[window].get(name, False):
continue
report_class = klass(self.doc_type_plus_list, self.doc_type_minus_list,
group_by=self.group_by,
report_model=self.report_model, date_field=self.date_field)
q_filters = None
date_filter = {
f'{self.date_field}__gt': col_data.get('start_date', self.start_date),
f'{self.date_field}__lte': col_data.get('end_date', self.end_date),
}
date_filter.update(self.kwargs_filters)
if window == 'crosstab':
q_filters = self._construct_crosstab_filter(col_data)
report_class.init_preparation(q_filters, date_filter)
self.report_fields_classes[name] = report_class
def _get_record_data(self, obj, columns):
"""
the function is run for every obj in the main_queryset
:param obj: current row
:param: columns： The columns we iterate on
:return: a dict object containing all needed data
"""
# todo , if columns are empty for whatever reason this will throw an error
display_link = self.list_display_links or columns[0]
data = {}
group_by_val = None
if self.group_by:
column_data = obj.get(self.group_by_field_attname, obj.get('id'))
group_by_val = str(column_data)
for window, window_cols in columns:
for col_data in window_cols:
name = col_data['name']
if (col_data.get('source', '') == 'magic_field' and self.group_by) or (
self.time_series_pattern and not self.group_by):
source = self._report_fields_dependencies[window].get(name, False)
if source:
computation_class = self.report_fields_classes[source]
value = computation_class.get_dependency_value(group_by_val,
col_data['ref'].name)
else:
try:
computation_class = self.report_fields_classes[name]
except KeyError:
continue
value = computation_class.resolve(group_by_val, data)
if self.swap_sign: value = -value
data[name] = value
else:
data[name] = obj.get(name, '')
# if self.group_by and name in display_link:
# data[name] = make_linkable_field(self.group_by_field.related_model, group_by_val, data[name])
return data
def get_report_data(self):
main_queryset = self.main_queryset[:self.limit_records] if self.limit_records else self.main_queryset
all_columns = (
('normal', self._parsed_columns),
('time_series', self._time_series_parsed_columns),
('crosstab', self._crosstab_parsed_columns),
)
get_record_data = self._get_record_data
format_row = self.format_row
data = [format_row(get_record_data(obj, all_columns)) for obj in main_queryset]
return data
def _default_format_row(self, row_obj):
"""
Hook where you can format row values like properly format a date
:param row_obj:
:return:
"""
return row_obj
@classmethod
def check_columns(cls, columns, group_by, report_model, ):
"""
Check and parse the columns, throw errors in case an item in the columns cant not identified
:param columns: List of columns
:param group_by: group by field if any
:param report_model: the report model
:return: List of dict, each dict contains relevant data to the respective field in `columns`
"""
group_by_model = None
if group_by:
group_by_field = [x for x in report_model._meta.get_fields() if x.name == group_by.split('__')[0]][0]
if group_by_field.is_relation:
group_by_model = group_by_field.related_model
else:
group_by_model = report_model
parsed_columns = []
for col in columns:
if col in ['__time_series__', '__crosstab__']:
# These are placeholder not real computation field
continue
magic_field_class = None
attr = None
if type(col) is str:
attr = getattr(cls, col, None)
elif issubclass(col, SlickReportField):
magic_field_class = col
try:
magic_field_class = magic_field_class or field_registry.get_field_by_name(col)
except KeyError:
magic_field_class = None
if attr:
# todo Add testing here
col_data = {'name': col,
'verbose_name': getattr(attr, 'verbose_name', col),
# 'type': 'method',
'ref': attr,
'type': 'text'
}
elif magic_field_class:
# a magic field
if col in ['__time_series__', '__crosstab__']:
# These are placeholder not real computation field
continue
col_data = {'name': magic_field_class.name,
'verbose_name': magic_field_class.verbose_name,
'source': 'magic_field',
'ref': magic_field_class,
'type': magic_field_class.type,
'is_summable': magic_field_class.is_summable
}
else:
# A database field
model_to_use = group_by_model if group_by and '__' not in group_by else report_model
try:
if '__' in col:
# A traversing link order__client__email
field = get_field_from_query_text(col, model_to_use)
else:
field = model_to_use._meta.get_field(col)
except FieldDoesNotExist:
raise FieldDoesNotExist(
f'Field "{col}" not found either as an attribute to the generator class {cls}, '
f'or a computation field, or a database column for the model "{model_to_use}"')
col_data = {'name': col,
'verbose_name': getattr(field, 'verbose_name', col),
'source': 'database',
'ref': field,
'type': field.get_internal_type()
}
parsed_columns.append(col_data)
return parsed_columns
def _parse(self):
self.parsed_columns = self.check_columns(self.columns, self.group_by, self.report_model)
self._parsed_columns = list(self.parsed_columns)
self._time_series_parsed_columns = self.get_time_series_parsed_columns()
self._crosstab_parsed_columns = self.get_crosstab_parsed_columns()
def get_database_columns(self):
return [col['name'] for col in self.parsed_columns if col['source'] == 'database']
def get_method_columns(self):
return [col['name'] for col in self.parsed_columns if col['type'] == 'method']
def get_list_display_columns(self):
columns = self.parsed_columns
if self.time_series_pattern:
time_series_columns = self.get_time_series_parsed_columns()
try:
index = self.columns.index('__time_series__')
columns[index:index] = time_series_columns
except ValueError:
columns += time_series_columns
if self.crosstab_model:
crosstab_columns = self.get_crosstab_parsed_columns()
try:
index = self.columns.index('__crosstab__')
columns[index:index] = crosstab_columns
except ValueError:
columns += crosstab_columns
return columns
def get_time_series_parsed_columns(self):
"""
Return time series columns with all needed data attached
:param plain: if True it returns '__total__' instead of '__total_TS011212'
:return: List if columns
"""
_values = []
cols = self.time_series_columns or []
series = self._get_time_series_dates(self.time_series_pattern)
for index, dt in enumerate(series):
for col in cols:
magic_field_class = None
if type(col) is str:
magic_field_class = field_registry.get_field_by_name(col)
elif issubclass(col, SlickReportField):
magic_field_class = col
_values.append({
'name': magic_field_class.name + 'TS' + dt[1].strftime('%Y%m%d'),
'original_name': magic_field_class.name,
'verbose_name': self.get_time_series_field_verbose_name(magic_field_class, dt, index, series),
'ref': magic_field_class,
'start_date': dt[0],
'end_date': dt[1],
'source': 'magic_field' if magic_field_class else '',
'is_summable': magic_field_class.is_summable,
})
return _values
def get_time_series_field_verbose_name(self, computation_class, date_period, index, series, pattern=None):
"""
Sent the column data to construct a verbose name.
Default implementation is delegated to the ReportField.get_time_series_field_verbose_name
(which is name + the end date %Y%m%d)
:param computation_class: the computation field_name
:param date_period: a tuple of (start_date, end_date)
:return: a verbose string
"""
pattern = pattern or self.time_series_pattern
return computation_class.get_time_series_field_verbose_name(date_period, index, series,
pattern)
def get_custom_time_series_dates(self):
"""
Hook to get custom , maybe separated date periods
:return: [ (date1,date2) , (date3,date4), .... ]
"""
return self.time_series_custom_dates or []
def _get_time_series_dates(self, series=None, start_date=None, end_date=None):
from dateutil.relativedelta import relativedelta
series = series or self.time_series_pattern
start_date = start_date or self.start_date
end_date = end_date or self.end_date
_values = []
if series:
if series == 'daily':
time_delta = datetime.timedelta(days=1)
elif series == 'weekly':
time_delta = relativedelta(weeks=1)
elif series == 'semimonthly':
time_delta = relativedelta(weeks=2)
elif series == 'monthly':
time_delta = relativedelta(months=1)
elif series == 'quarterly':
time_delta = relativedelta(months=3)
elif series == 'semiannually':
time_delta = relativedelta(months=6)
elif series == 'annually':
time_delta = relativedelta(years=1)
elif series == 'custom':
return self.get_custom_time_series_dates()
else:
raise NotImplementedError(f'"{series}" is not implemented for time_series_pattern')
done = False
while not done:
to_date = start_date + time_delta
_values.append((start_date, to_date))
start_date = to_date
if to_date >= end_date:
done = True
return _values
def get_crosstab_parsed_columns(self):
"""
Return a list of the columns analyzed , with reference to computation field and everything
:return:
"""
report_columns = self.crosstab_columns or []
ids = list(self.crosstab_ids)
if self.crosstab_compute_reminder:
ids.append('----')
output_cols = []
ids_length = len(ids) - 1
for counter, id in enumerate(ids):
for col in report_columns:
magic_field_class = None
if type(col) is str:
magic_field_class = field_registry.get_field_by_name(col)
elif issubclass(col, SlickReportField):
magic_field_class = col
output_cols.append({
'name': f'{magic_field_class.name}CT{id}',
'original_name': magic_field_class.name,
'verbose_name': self.get_crosstab_field_verbose_name(magic_field_class, self.crosstab_model, id),
'ref': magic_field_class,
'id': id,
'model': self.crosstab_model,
'is_reminder': counter == ids_length,
'source': 'magic_field' if magic_field_class else '',
'is_summable': magic_field_class.is_summable,
})
return output_cols
def get_crosstab_field_verbose_name(self, computation_class, model, id):
"""
Hook to change the crosstab field verbose name, default it delegate this function to the ReportField
:param computation_class: ReportField Class
:param model: the model name as string
:param id: the current crosstab id
:return: a verbose string
"""
return computation_class.get_crosstab_field_verbose_name(model, id)
|
# flake8: noqa E501
import json
conditional_token_abi = json.loads(
'[{"constant":true,"inputs":[{"name":"owner","type":"address"},{"name":"id","type":"uint256"}],"name":"balanceOf","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"bytes32"},{"name":"","type":"uint256"}],"name":"payoutNumerators","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"from","type":"address"},{"name":"to","type":"address"},{"name":"ids","type":"uint256[]"},{"name":"values","type":"uint256[]"},{"name":"data","type":"bytes"}],"name":"safeBatchTransferFrom","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"owners","type":"address[]"},{"name":"ids","type":"uint256[]"}],"name":"balanceOfBatch","outputs":[{"name":"","type":"uint256[]"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"operator","type":"address"},{"name":"approved","type":"bool"}],"name":"setApprovalForAll","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"","type":"bytes32"}],"name":"payoutDenominator","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"owner","type":"address"},{"name":"operator","type":"address"}],"name":"isApprovedForAll","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"from","type":"address"},{"name":"to","type":"address"},{"name":"id","type":"uint256"},{"name":"value","type":"uint256"},{"name":"data","type":"bytes"}],"name":"safeTransferFrom","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"anonymous":false,"inputs":[{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":true,"name":"oracle","type":"address"},{"indexed":true,"name":"questionId","type":"bytes32"},{"indexed":false,"name":"outcomeSlotCount","type":"uint256"}],"name":"ConditionPreparation","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":true,"name":"oracle","type":"address"},{"indexed":true,"name":"questionId","type":"bytes32"},{"indexed":false,"name":"outcomeSlotCount","type":"uint256"},{"indexed":false,"name":"payoutNumerators","type":"uint256[]"}],"name":"ConditionResolution","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"stakeholder","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"partition","type":"uint256[]"},{"indexed":false,"name":"amount","type":"uint256"}],"name":"PositionSplit","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"stakeholder","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"partition","type":"uint256[]"},{"indexed":false,"name":"amount","type":"uint256"}],"name":"PositionsMerge","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"redeemer","type":"address"},{"indexed":true,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":false,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"indexSets","type":"uint256[]"},{"indexed":false,"name":"payout","type":"uint256"}],"name":"PayoutRedemption","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"operator","type":"address"},{"indexed":true,"name":"from","type":"address"},{"indexed":true,"name":"to","type":"address"},{"indexed":false,"name":"id","type":"uint256"},{"indexed":false,"name":"value","type":"uint256"}],"name":"TransferSingle","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"operator","type":"address"},{"indexed":true,"name":"from","type":"address"},{"indexed":true,"name":"to","type":"address"},{"indexed":false,"name":"ids","type":"uint256[]"},{"indexed":false,"name":"values","type":"uint256[]"}],"name":"TransferBatch","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"owner","type":"address"},{"indexed":true,"name":"operator","type":"address"},{"indexed":false,"name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"value","type":"string"},{"indexed":true,"name":"id","type":"uint256"}],"name":"URI","type":"event"},{"constant":false,"inputs":[{"name":"oracle","type":"address"},{"name":"questionId","type":"bytes32"},{"name":"outcomeSlotCount","type":"uint256"}],"name":"prepareCondition","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"questionId","type":"bytes32"},{"name":"payouts","type":"uint256[]"}],"name":"reportPayouts","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"partition","type":"uint256[]"},{"name":"amount","type":"uint256"}],"name":"splitPosition","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"partition","type":"uint256[]"},{"name":"amount","type":"uint256"}],"name":"mergePositions","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"indexSets","type":"uint256[]"}],"name":"redeemPositions","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"conditionId","type":"bytes32"}],"name":"getOutcomeSlotCount","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"oracle","type":"address"},{"name":"questionId","type":"bytes32"},{"name":"outcomeSlotCount","type":"uint256"}],"name":"getConditionId","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"pure","type":"function"},{"constant":true,"inputs":[{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"indexSet","type":"uint256"}],"name":"getCollectionId","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"collateralToken","type":"address"},{"name":"collectionId","type":"bytes32"}],"name":"getPositionId","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"pure","type":"function"}]'
)
market_maker_abi = json.loads(
'[{"constant":true,"inputs":[{"name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[],"name":"resume","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"pmSystem","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"outcomeTokenAmounts","type":"int256[]"},{"name":"collateralLimit","type":"int256"}],"name":"trade","outputs":[{"name":"netCost","type":"int256"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"close","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"withdrawFees","outputs":[{"name":"fees","type":"uint256"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"renounceOwnership","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"pause","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"fundingChange","type":"int256"}],"name":"changeFunding","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"owner","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"isOwner","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"whitelist","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"outcomeTokenCost","type":"uint256"}],"name":"calcMarketFee","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"collateralToken","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"_operator","type":"address"},{"name":"","type":"address"},{"name":"","type":"uint256[]"},{"name":"","type":"uint256[]"},{"name":"","type":"bytes"}],"name":"onERC1155BatchReceived","outputs":[{"name":"","type":"bytes4"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"stage","outputs":[{"name":"","type":"uint8"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"funding","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"uint256"}],"name":"conditionIds","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"atomicOutcomeSlotCount","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"fee","outputs":[{"name":"","type":"uint64"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"_fee","type":"uint64"}],"name":"changeFee","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"operator","type":"address"},{"name":"","type":"address"},{"name":"","type":"uint256"},{"name":"","type":"uint256"},{"name":"","type":"bytes"}],"name":"onERC1155Received","outputs":[{"name":"","type":"bytes4"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"newOwner","type":"address"}],"name":"transferOwnership","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"FEE_RANGE","outputs":[{"name":"","type":"uint64"}],"payable":false,"stateMutability":"view","type":"function"},{"anonymous":false,"inputs":[{"indexed":false,"name":"initialFunding","type":"uint256"}],"name":"AMMCreated","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMPaused","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMResumed","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMClosed","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"fundingChange","type":"int256"}],"name":"AMMFundingChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"newFee","type":"uint64"}],"name":"AMMFeeChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"fees","type":"uint256"}],"name":"AMMFeeWithdrawal","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"transactor","type":"address"},{"indexed":false,"name":"outcomeTokenAmounts","type":"int256[]"},{"indexed":false,"name":"outcomeTokenNetCost","type":"int256"},{"indexed":false,"name":"marketFees","type":"uint256"}],"name":"AMMOutcomeTokenTrade","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"previousOwner","type":"address"},{"indexed":true,"name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"constant":true,"inputs":[{"name":"outcomeTokenAmounts","type":"int256[]"}],"name":"calcNetCost","outputs":[{"name":"netCost","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"outcomeTokenIndex","type":"uint8"}],"name":"calcMarginalPrice","outputs":[{"name":"price","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"}]'
)
market_maker_factory_abi = json.loads(
'[{"constant":true,"inputs":[],"name":"implementationMaster","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"inputs":[],"payable":false,"stateMutability":"nonpayable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"name":"creator","type":"address"},{"indexed":false,"name":"lmsrMarketMaker","type":"address"},{"indexed":false,"name":"pmSystem","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":false,"name":"conditionIds","type":"bytes32[]"},{"indexed":false,"name":"fee","type":"uint64"},{"indexed":false,"name":"funding","type":"uint256"}],"name":"LMSRMarketMakerCreation","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"previousOwner","type":"address"},{"indexed":true,"name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"initialFunding","type":"uint256"}],"name":"AMMCreated","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"target","type":"address"},{"indexed":false,"name":"clone","type":"address"}],"name":"CloneCreated","type":"event"},{"constant":false,"inputs":[{"name":"consData","type":"bytes"}],"name":"cloneConstructor","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"pmSystem","type":"address"},{"name":"collateralToken","type":"address"},{"name":"conditionIds","type":"bytes32[]"},{"name":"fee","type":"uint64"},{"name":"whitelist","type":"address"},{"name":"funding","type":"uint256"}],"name":"createLMSRMarketMaker","outputs":[{"name":"lmsrMarketMaker","type":"address"}],"payable":false,"stateMutability":"nonpayable","type":"function"}]'
)
| # flake8: noqa E501
import json
conditional_token_abi = json.loads(
'[{"constant":true,"inputs":[{"name":"owner","type":"address"},{"name":"id","type":"uint256"}],"name":"balanceOf","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"bytes32"},{"name":"","type":"uint256"}],"name":"payoutNumerators","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"from","type":"address"},{"name":"to","type":"address"},{"name":"ids","type":"uint256[]"},{"name":"values","type":"uint256[]"},{"name":"data","type":"bytes"}],"name":"safeBatchTransferFrom","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"owners","type":"address[]"},{"name":"ids","type":"uint256[]"}],"name":"balanceOfBatch","outputs":[{"name":"","type":"uint256[]"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"operator","type":"address"},{"name":"approved","type":"bool"}],"name":"setApprovalForAll","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"","type":"bytes32"}],"name":"payoutDenominator","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"owner","type":"address"},{"name":"operator","type":"address"}],"name":"isApprovedForAll","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"from","type":"address"},{"name":"to","type":"address"},{"name":"id","type":"uint256"},{"name":"value","type":"uint256"},{"name":"data","type":"bytes"}],"name":"safeTransferFrom","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"anonymous":false,"inputs":[{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":true,"name":"oracle","type":"address"},{"indexed":true,"name":"questionId","type":"bytes32"},{"indexed":false,"name":"outcomeSlotCount","type":"uint256"}],"name":"ConditionPreparation","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":true,"name":"oracle","type":"address"},{"indexed":true,"name":"questionId","type":"bytes32"},{"indexed":false,"name":"outcomeSlotCount","type":"uint256"},{"indexed":false,"name":"payoutNumerators","type":"uint256[]"}],"name":"ConditionResolution","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"stakeholder","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"partition","type":"uint256[]"},{"indexed":false,"name":"amount","type":"uint256"}],"name":"PositionSplit","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"stakeholder","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":true,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"partition","type":"uint256[]"},{"indexed":false,"name":"amount","type":"uint256"}],"name":"PositionsMerge","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"redeemer","type":"address"},{"indexed":true,"name":"collateralToken","type":"address"},{"indexed":true,"name":"parentCollectionId","type":"bytes32"},{"indexed":false,"name":"conditionId","type":"bytes32"},{"indexed":false,"name":"indexSets","type":"uint256[]"},{"indexed":false,"name":"payout","type":"uint256"}],"name":"PayoutRedemption","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"operator","type":"address"},{"indexed":true,"name":"from","type":"address"},{"indexed":true,"name":"to","type":"address"},{"indexed":false,"name":"id","type":"uint256"},{"indexed":false,"name":"value","type":"uint256"}],"name":"TransferSingle","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"operator","type":"address"},{"indexed":true,"name":"from","type":"address"},{"indexed":true,"name":"to","type":"address"},{"indexed":false,"name":"ids","type":"uint256[]"},{"indexed":false,"name":"values","type":"uint256[]"}],"name":"TransferBatch","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"owner","type":"address"},{"indexed":true,"name":"operator","type":"address"},{"indexed":false,"name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"value","type":"string"},{"indexed":true,"name":"id","type":"uint256"}],"name":"URI","type":"event"},{"constant":false,"inputs":[{"name":"oracle","type":"address"},{"name":"questionId","type":"bytes32"},{"name":"outcomeSlotCount","type":"uint256"}],"name":"prepareCondition","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"questionId","type":"bytes32"},{"name":"payouts","type":"uint256[]"}],"name":"reportPayouts","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"partition","type":"uint256[]"},{"name":"amount","type":"uint256"}],"name":"splitPosition","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"partition","type":"uint256[]"},{"name":"amount","type":"uint256"}],"name":"mergePositions","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"collateralToken","type":"address"},{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"indexSets","type":"uint256[]"}],"name":"redeemPositions","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[{"name":"conditionId","type":"bytes32"}],"name":"getOutcomeSlotCount","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"oracle","type":"address"},{"name":"questionId","type":"bytes32"},{"name":"outcomeSlotCount","type":"uint256"}],"name":"getConditionId","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"pure","type":"function"},{"constant":true,"inputs":[{"name":"parentCollectionId","type":"bytes32"},{"name":"conditionId","type":"bytes32"},{"name":"indexSet","type":"uint256"}],"name":"getCollectionId","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"collateralToken","type":"address"},{"name":"collectionId","type":"bytes32"}],"name":"getPositionId","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"pure","type":"function"}]'
)
market_maker_abi = json.loads(
'[{"constant":true,"inputs":[{"name":"interfaceId","type":"bytes4"}],"name":"supportsInterface","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[],"name":"resume","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"pmSystem","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"outcomeTokenAmounts","type":"int256[]"},{"name":"collateralLimit","type":"int256"}],"name":"trade","outputs":[{"name":"netCost","type":"int256"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"close","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"withdrawFees","outputs":[{"name":"fees","type":"uint256"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"renounceOwnership","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"pause","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"fundingChange","type":"int256"}],"name":"changeFunding","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"owner","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"isOwner","outputs":[{"name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"whitelist","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"outcomeTokenCost","type":"uint256"}],"name":"calcMarketFee","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"collateralToken","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"_operator","type":"address"},{"name":"","type":"address"},{"name":"","type":"uint256[]"},{"name":"","type":"uint256[]"},{"name":"","type":"bytes"}],"name":"onERC1155BatchReceived","outputs":[{"name":"","type":"bytes4"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"stage","outputs":[{"name":"","type":"uint8"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"funding","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"","type":"uint256"}],"name":"conditionIds","outputs":[{"name":"","type":"bytes32"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"atomicOutcomeSlotCount","outputs":[{"name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[],"name":"fee","outputs":[{"name":"","type":"uint64"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"name":"_fee","type":"uint64"}],"name":"changeFee","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"operator","type":"address"},{"name":"","type":"address"},{"name":"","type":"uint256"},{"name":"","type":"uint256"},{"name":"","type":"bytes"}],"name":"onERC1155Received","outputs":[{"name":"","type":"bytes4"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"newOwner","type":"address"}],"name":"transferOwnership","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"FEE_RANGE","outputs":[{"name":"","type":"uint64"}],"payable":false,"stateMutability":"view","type":"function"},{"anonymous":false,"inputs":[{"indexed":false,"name":"initialFunding","type":"uint256"}],"name":"AMMCreated","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMPaused","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMResumed","type":"event"},{"anonymous":false,"inputs":[],"name":"AMMClosed","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"fundingChange","type":"int256"}],"name":"AMMFundingChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"newFee","type":"uint64"}],"name":"AMMFeeChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"fees","type":"uint256"}],"name":"AMMFeeWithdrawal","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"transactor","type":"address"},{"indexed":false,"name":"outcomeTokenAmounts","type":"int256[]"},{"indexed":false,"name":"outcomeTokenNetCost","type":"int256"},{"indexed":false,"name":"marketFees","type":"uint256"}],"name":"AMMOutcomeTokenTrade","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"previousOwner","type":"address"},{"indexed":true,"name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"constant":true,"inputs":[{"name":"outcomeTokenAmounts","type":"int256[]"}],"name":"calcNetCost","outputs":[{"name":"netCost","type":"int256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"name":"outcomeTokenIndex","type":"uint8"}],"name":"calcMarginalPrice","outputs":[{"name":"price","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"}]'
)
market_maker_factory_abi = json.loads(
'[{"constant":true,"inputs":[],"name":"implementationMaster","outputs":[{"name":"","type":"address"}],"payable":false,"stateMutability":"view","type":"function"},{"inputs":[],"payable":false,"stateMutability":"nonpayable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"name":"creator","type":"address"},{"indexed":false,"name":"lmsrMarketMaker","type":"address"},{"indexed":false,"name":"pmSystem","type":"address"},{"indexed":false,"name":"collateralToken","type":"address"},{"indexed":false,"name":"conditionIds","type":"bytes32[]"},{"indexed":false,"name":"fee","type":"uint64"},{"indexed":false,"name":"funding","type":"uint256"}],"name":"LMSRMarketMakerCreation","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"previousOwner","type":"address"},{"indexed":true,"name":"newOwner","type":"address"}],"name":"OwnershipTransferred","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"name":"initialFunding","type":"uint256"}],"name":"AMMCreated","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"name":"target","type":"address"},{"indexed":false,"name":"clone","type":"address"}],"name":"CloneCreated","type":"event"},{"constant":false,"inputs":[{"name":"consData","type":"bytes"}],"name":"cloneConstructor","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"name":"pmSystem","type":"address"},{"name":"collateralToken","type":"address"},{"name":"conditionIds","type":"bytes32[]"},{"name":"fee","type":"uint64"},{"name":"whitelist","type":"address"},{"name":"funding","type":"uint256"}],"name":"createLMSRMarketMaker","outputs":[{"name":"lmsrMarketMaker","type":"address"}],"payable":false,"stateMutability":"nonpayable","type":"function"}]'
)
|
"""
Prepare all X-ray structures for FAH
# Projects
13430 : apo Mpro monomer His41(0) Cys145(0)
13431 : apo Mpro monomer His41(+) Cys145(-)
13432 : holo Mpro monomer His41(0) Cys145(0)
13433 : holo Mpro monomer His41(+) Cys145(-)
13434 : apo Mpro dimer His41(0) Cys145(0)
13435 : apo Mpro dimer His41(+) Cys145(-)
13436 : holo Mpro dimer His41(0) Cys145(0)
13437 : holo Mpro dimer His41(+) Cys145(-)
Each RUN corresponds to a different fragment structure
# Manifest
`../structures/metadata.csv` : master index of fragment IDs and RUNs
```
,crystal_name,RealCrystalName,smiles,new_smiles,alternate_name,site_name,pdb_entry
1,Mpro-1q2w-2020-04-Bonanno_0,Mpro-1q2w-2020-04-Bonanno,C[C@H](O)CC(C)(C)O,NA,NA,Mpro-SARS1,1Q2W
3,Mpro-1wof-2020-04-Yang_0,Mpro-1wof-2020-04-Yang,CCOC(O)CC[C@H](C[C@@H]1CCNC1O)N[C@H](O)[C@H](CC(C)C)NC(O)[C@@H](NC(O)[C@H](C)NC(O)C1CC(C)ON1)C(C)C,NA,NA,Mpro-SARS1,1WOF
4,Mpro-2a5i-2020-04-Lee_0,Mpro-2a5i-2020-04-Lee,CCOC(O)[C@@H](O)C[C@@H](O)N(CCC(N)O)NC(O)[C@H](CC1CCCCC1)N[C@H](O)[C@H](CC(C)C)N[C@H](O)OCC1CCCCC1,NA,NA,Mpro-SARS1,2A5I
...
```
First column is used to identify RUN:
* `RUN0` is skipped
* `RUN1` is Mpro-1q2w-2020-04-Bonanno_0
* `RUN2` is skipped
* `RUN3` is Mpro-1wof-2020-04-Yang_0
...
"""
import os
import time
import argparse
import csv
from collections import OrderedDict
import tempfile
import traceback, sys
from simtk import unit, openmm
from openff.toolkit.topology import Molecule
from simtk.openmm import app
from openmmforcefields.generators import SystemGenerator
import mdtraj as md
import bz2
from openmmtools import integrators
import numpy as np
from rich.progress import track
from openeye import oechem
import yaml
def setup_fah_run(destination_path, protein_pdb_filename, oemol=None, cache=None, restrain_rmsd=False,
biounit='monomer'):
"""
Prepare simulation
Parameters
----------
destination_path : str
The path to the RUN to be created
protein_pdb_filename : str
Path to protein PDB file
oemol : openeye.oechem.OEMol, optional, default=None
The molecule to parameterize, with SDData attached
If None, don't include the small molecule
restrain_rmsd : bool, optional, default=False
If True, restrain RMSD during first equilibration phase
biounit : str, optional, default='monomer'
'monomer' or 'dimer'
"""
# Parameters
protein_forcefield = 'amber14/protein.ff14SB.xml'
solvent_forcefield = 'amber14/tip3p.xml'
small_molecule_forcefield = 'openff-1.3.0'
water_model = 'tip3p'
solvent_padding = 10.0 * unit.angstrom
ionic_strength = 70 * unit.millimolar # assay buffer: 20 mM HEPES pH 7.3, 1 mM TCEP, 50 mM NaCl, 0.01% Tween-20, 10% glycerol
pressure = 1.0 * unit.atmospheres
collision_rate = 1.0 / unit.picoseconds
temperature = 300.0 * unit.kelvin
timestep = 4.0 * unit.femtoseconds
iterations = 1000 # 1 ns equilibration
nsteps_per_iteration = 250
nsteps_per_snapshot = 250000 # 1 ns
nsnapshots_per_wu = 20 # number of snapshots per WU
# Prepare phases
system_xml_filename = os.path.join(destination_path, 'system.xml.bz2')
integrator_xml_filename = os.path.join(destination_path, 'integrator.xml.bz2')
state_xml_filename = os.path.join(destination_path, 'state.xml.bz2')
# Check if we can skip setup
openmm_files_exist = os.path.exists(system_xml_filename) and os.path.exists(state_xml_filename) and os.path.exists(
integrator_xml_filename)
if openmm_files_exist:
return
# Create barostat
barostat = openmm.MonteCarloBarostat(pressure, temperature)
# Create RUN directory if it does not yet exist
os.makedirs(destination_path, exist_ok=True)
# Load any molecule(s)
molecule = None
molecules = []
if oemol is not None:
molecule = Molecule.from_openeye(oemol, allow_undefined_stereo=True)
molecule.name = 'MOL' # Ensure residue is MOL
print([res for res in molecule.to_topology().to_openmm().residues()])
molecules = [molecule]
# Create SystemGenerator
forcefield_kwargs = {'removeCMMotion': False, 'hydrogenMass': 3.0 * unit.amu, 'constraints': app.HBonds,
'rigidWater': True}
periodic_kwargs = {'nonbondedMethod': app.PME, 'ewaldErrorTolerance': 2.5e-04}
forcefields = [protein_forcefield, solvent_forcefield]
openmm_system_generator = SystemGenerator(
forcefields=forcefields,
molecules=molecules, small_molecule_forcefield=small_molecule_forcefield, cache=cache,
barostat=barostat,
forcefield_kwargs=forcefield_kwargs, periodic_forcefield_kwargs=periodic_kwargs)
# Read protein
print(f'Reading protein from {protein_pdb_filename}...')
pdbfile = app.PDBFile(protein_pdb_filename)
modeller = app.Modeller(pdbfile.topology, pdbfile.positions)
if oemol is not None:
# Add small molecule to the system
modeller.add(molecule.to_topology().to_openmm(), molecule.conformers[0])
# Extract protein and molecule chains and indices before adding solvent
mdtop = md.Topology.from_openmm(modeller.topology) # excludes solvent and ions
protein_atom_indices = mdtop.select('protein and (mass > 1)')
molecule_atom_indices = mdtop.select('(not protein) and (not water) and (mass > 1)')
protein_chainids = list(
set([atom.residue.chain.index for atom in mdtop.atoms if atom.index in protein_atom_indices]))
n_protein_chains = len(protein_chainids)
protein_chain_atom_indices = dict()
for chainid in protein_chainids:
protein_chain_atom_indices[chainid] = mdtop.select(f'protein and chainid {chainid}')
# Add solvent
print('Adding solvent...')
kwargs = {'padding': solvent_padding}
modeller.addSolvent(openmm_system_generator.forcefield, model='tip3p', ionicStrength=ionic_strength, **kwargs)
# Write initial model and select atom subsets and chains
with bz2.open(os.path.join(destination_path, 'initial-model.pdb.bz2'), 'wt') as outfile:
app.PDBFile.writeFile(modeller.topology, modeller.positions, outfile, keepIds=True)
# Create an OpenMM system
print('Creating OpenMM system...')
system = openmm_system_generator.create_system(modeller.topology)
#
# Add virtual bonds to ensure protein subunits and ligand are imaged together
#
virtual_bond_force = openmm.CustomBondForce('0')
system.addForce(virtual_bond_force)
# Add a virtual bond between protein chains
if (n_protein_chains > 1):
chainid = protein_chainids[0]
iatom = protein_chain_atom_indices[chainid][0]
for chainid in protein_chainids[1:]:
jatom = protein_chain_atom_indices[chainid][0]
print(f'Creating virtual bond between atoms {iatom} and {jatom}')
virtual_bond_force.addBond(int(iatom), int(jatom), [])
# Add a virtual bond between protein and ligand to make sure they are not imaged separately
if oemol is not None:
ligand_atom_indices = mdtop.select('((resname MOL) and (mass > 1))') # ligand heavy atoms
protein_atom_index = int(protein_atom_indices[0])
ligand_atom_index = int(ligand_atom_indices[0])
print(f'Creating virtual bond between atoms {protein_atom_index} and {ligand_atom_index}')
virtual_bond_force.addBond(int(protein_atom_index), int(ligand_atom_index), [])
# Add RMSD restraints if requested
if restrain_rmsd:
print('Adding RMSD restraint...')
kB = unit.AVOGADRO_CONSTANT_NA * unit.BOLTZMANN_CONSTANT_kB
kT = kB * temperature
rmsd_atom_indices = mdtop.select(
'(protein and (name CA)) or ((resname MOL) and (mass > 1))') # CA atoms and ligand heavy atoms
rmsd_atom_indices = [int(index) for index in rmsd_atom_indices]
custom_cv_force = openmm.CustomCVForce('(K_RMSD/2)*RMSD^2')
custom_cv_force.addGlobalParameter('K_RMSD', kT / unit.angstrom ** 2)
rmsd_force = openmm.RMSDForce(modeller.positions, rmsd_atom_indices)
custom_cv_force.addCollectiveVariable('RMSD', rmsd_force)
force_index = system.addForce(custom_cv_force)
# Create OpenM Context
platform = openmm.Platform.getPlatformByName('CPU')
# platform = openmm.Platform.findPlatform()
# platform.setPropertyDefaultValue('Precision', 'mixed')
integrator = integrators.LangevinIntegrator(temperature, collision_rate, timestep)
context = openmm.Context(system, integrator, platform)
context.setPositions(modeller.positions)
# Report initial potential energy
state = context.getState(getEnergy=True)
print(f'Initial potential energy is {state.getPotentialEnergy() / unit.kilocalories_per_mole:.3f} kcal/mol')
# Store snapshots in MDTraj trajectory to examine RMSD
mdtop = md.Topology.from_openmm(pdbfile.topology)
atom_indices = mdtop.select('all') # all solute atoms
protein_atom_indices = mdtop.select('protein and (mass > 1)') # heavy solute atoms
if oemol is not None:
ligand_atom_indices = mdtop.select('(resname MOL) and (mass > 1)') # ligand heavy atoms
trajectory = md.Trajectory(np.zeros([iterations + 1, len(atom_indices), 3], np.float32), mdtop)
trajectory.xyz[0, :, :] = context.getState(getPositions=True).getPositions(asNumpy=True)[
atom_indices] / unit.nanometers
# Minimize
print('Minimizing...')
openmm.LocalEnergyMinimizer.minimize(context)
# Equilibrate (with RMSD restraint if needed)
initial_time = time.time()
for iteration in track(range(iterations), 'Equilibrating...'):
integrator.step(nsteps_per_iteration)
trajectory.xyz[iteration + 1, :, :] = context.getState(getPositions=True).getPositions(asNumpy=True)[
atom_indices] / unit.nanometers
elapsed_time = (time.time() - initial_time) * unit.seconds
ns_per_day = (context.getState().getTime() / elapsed_time) / (unit.nanoseconds / unit.day)
print(f'Performance: {ns_per_day:8.3f} ns/day')
if restrain_rmsd:
# Disable RMSD restraint
context.setParameter('K_RMSD', 0.0)
print('Minimizing...')
openmm.LocalEnergyMinimizer.minimize(context)
for iteration in track(range(iterations), 'Equilibrating without RMSD restraint...'):
integrator.step(nsteps_per_iteration)
# Retrieve state
state = context.getState(getPositions=True, getVelocities=True, getEnergy=True, getForces=True)
system.setDefaultPeriodicBoxVectors(*state.getPeriodicBoxVectors())
modeller.topology.setPeriodicBoxVectors(state.getPeriodicBoxVectors())
print(f'Final potential energy is {state.getPotentialEnergy() / unit.kilocalories_per_mole:.3f} kcal/mol')
# Equilibrate again if we restrained the RMSD
if restrain_rmsd:
print('Removing RMSD restraint from system...')
system.removeForce(force_index)
# if oemol is not None:
# # Check final RMSD
# print('checking RMSD...')
# trajectory.superpose(trajectory, atom_indices=protein_atom_indices)
# protein_rmsd = md.rmsd(trajectory, trajectory[-1], atom_indices=protein_atom_indices)[-1] * 10 # Angstroms
# oechem.OESetSDData(oemol, 'equil_protein_rmsd', f'{protein_rmsd:.2f} A')
# ligand_rmsd = md.rmsd(trajectory, trajectory[-1], atom_indices=ligand_atom_indices)[-1] * 10 # Angstroms
# oechem.OESetSDData(oemol, 'equil_ligand_rmsd', f'{ligand_rmsd:.2f} A')
# print('RMSD after equilibration: protein {protein_rmsd:8.2f} A | ligand {ligand_rmsd:8.3f} A')
# Save as OpenMM
print('Exporting for OpenMM FAH simulation...')
with bz2.open(integrator_xml_filename, 'wt') as f:
f.write(openmm.XmlSerializer.serialize(integrator))
with bz2.open(state_xml_filename, 'wt') as f:
f.write(openmm.XmlSerializer.serialize(state))
with bz2.open(system_xml_filename, 'wt') as f:
f.write(openmm.XmlSerializer.serialize(system))
with bz2.open(os.path.join(destination_path, 'equilibrated-all.pdb.bz2'), 'wt') as f:
app.PDBFile.writeFile(modeller.topology, state.getPositions(), f, keepIds=True)
with open(os.path.join(destination_path, 'equilibrated-solute.pdb'), 'wt') as f:
mdtraj_topology = mdtraj.Topology.from_openmm(modeller.topology)
mdtraj_trajectory = mdtraj.Trajectory([state.getPositions(asNumpy=True) / unit.nanometers], mdtraj_topology)
selection = mdtraj_topology.select('not water')
mdtraj_trajectory = mdtraj_trajectory.atom_slice(selection)
app.PDBFile.writeFile(mdtraj_trajectory.topology.to_openmm(), mdtraj_trajectory.openmm_positions(0), f,
keepIds=True)
with open(os.path.join(destination_path, 'core.xml'), 'wt') as f:
f.write(f'<config>\n')
f.write(f' <numSteps>{nsteps_per_snapshot * nsnapshots_per_wu}</numSteps>\n')
f.write(f' <xtcFreq>{nsteps_per_snapshot}</xtcFreq>\n')
f.write(f' <precision>mixed</precision>\n')
f.write(f' <xtcAtoms>{','.join([str(index) for index in selection])}</xtcAtoms>\n')
f.write(f'</config>\n')
if oemol is not None:
# Write molecule as SDF, SMILES, and mol2
for extension in ['sdf', 'mol2', 'smi', 'csv']:
filename = os.path.join(destination_path, f'molecule.{extension}')
with oechem.oemolostream(filename) as ofs:
oechem.OEWriteMolecule(ofs, oemol)
# Clean up
del context, integrator
if __name__ == '__main__':
# Parse arguments
parser = argparse.ArgumentParser(
description='Prepare the specified RUN for FAH by preparing all X-ray structure variants of a specific fragment')
parser.add_argument('--receptors', dest='receptors_path', type=str, default='../receptors',
help='directory of receptor conformations (default: ../receptors)')
parser.add_argument('--metadata', dest='metadata_filename', type=str, default='../fah-xray/fah-metadata.csv',
help='metadata (default: ../fah-xray/fah-metadata.csv)')
parser.add_argument('--run', dest='run', type=str, required=True,
help='RUN index to prepare (zero-indexes first column contents in Mpro.zip metadata.csv)')
parser.add_argument('--output', dest='output_path', type=str, default='projects',
help='base directory for produced output (default: projects/)')
args = parser.parse_args()
# Read DiamondMX/XChem structure medatadata
metadata = OrderedDict()
with open(args.metadata_filename, newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
run = row['RUN']
metadata[run] = row
# Extract relevant metadata
run = f'RUN{args.run}'
if run not in metadata:
raise Exception(f'{run} not found in metadata.csv')
print(f'Preparing {run}')
metadata = metadata[run]
# Extract crystal_name
crystal_name = metadata['crystal_name']
# Read molecule in the appropriate protonation state
try:
oemol = oechem.OEMol()
molecule_filename = os.path.join(args.receptors_path, 'monomer', f'{crystal_name}_bound-ligand.mol2')
if not os.path.exists(molecule_filename):
msg = f'{molecule_filename} does not exist'
print(msg)
with oechem.oemolistream(molecule_filename) as ifs:
oechem.OEReadMolecule(ifs, oemol)
# Rename the molecule
title = metadata['alternate_name']
print(f'Setting title to {title}')
oemol.SetTitle(title)
# Remove dummy atoms
for atom in oemol.GetAtoms():
if atom.GetName().startswith('Du'):
print('Removing dummy atom.')
oemol.DeleteAtom(atom)
# Attach all structure metadata to the molecule
for key in metadata:
oechem.OESetSDData(oemol, key, metadata[key])
except Exception as e:
print(e)
oemol = None
# Set up all variants
# TODO: Generalize this to just use just one protonation state
# and maybe constant-pH simulations?
with tempfile.TemporaryDirectory() as tmpdir:
cache = os.path.join(tmpdir, 'cache.json')
def prepare_variant(project, run, crystal_name, biounit, dyad_state, oemol):
assert biounit in ['monomer', 'dimer']
try:
print('')
print(f'PROJ{project}')
if dyad_state == 'His41(0) Cys145(0)':
protein_pdb_filename = os.path.join(args.receptors_path, biounit,
f'{crystal_name}_bound-protein.pdb')
elif dyad_state == 'His41(+) Cys145(-)':
protein_pdb_filename = os.path.join(args.receptors_path, biounit,
f'{crystal_name}_bound-protein-thiolate.pdb')
else:
raise Exception("dyad_state must be one of ['His41(0) Cys145(0)', 'His41(+) Cys145(-)']")
destination_path = os.path.join(args.output_path, project, 'RUNS', f'RUN{run}')
# Create RUN directory if it does not yet exist
os.makedirs(destination_path, exist_ok=True)
# Write metadata
with open(os.path.join(destination_path, 'metadata.yaml'), 'wt') as outfile:
yaml.dump(dict(metadata), outfile)
# Set up RUN
setup_fah_run(destination_path, protein_pdb_filename, oemol=oemol, cache=cache, biounit=biounit)
print('')
except Exception as e:
traceback.print_exc(file=sys.stdout)
print(e)
# prepare_variant('13430', args.run, crystal_name, 'monomer', 'His41(0) Cys145(0)', None)
prepare_variant('13431', args.run, crystal_name, 'monomer', 'His41(+) Cys145(-)', None)
if oemol is not None:
# prepare_variant('13432', args.run, crystal_name, 'monomer', 'His41(0) Cys145(0)', oemol)
prepare_variant('13433', args.run, crystal_name, 'monomer', 'His41(+) Cys145(-)', oemol)
# prepare_variant('13434', args.run, crystal_name, 'dimer', 'His41(0) Cys145(0)', None)
prepare_variant('13435', args.run, crystal_name, 'dimer', 'His41(+) Cys145(-)', None)
if oemol is not None:
# prepare_variant('13436', args.run, crystal_name, 'dimer', 'His41(0) Cys145(0)', oemol)
prepare_variant('13437', args.run, crystal_name, 'dimer', 'His41(+) Cys145(-)', oemol)
| """
Prepare all X-ray structures for FAH
# Projects
13430 : apo Mpro monomer His41(0) Cys145(0)
13431 : apo Mpro monomer His41(+) Cys145(-)
13432 : holo Mpro monomer His41(0) Cys145(0)
13433 : holo Mpro monomer His41(+) Cys145(-)
13434 : apo Mpro dimer His41(0) Cys145(0)
13435 : apo Mpro dimer His41(+) Cys145(-)
13436 : holo Mpro dimer His41(0) Cys145(0)
13437 : holo Mpro dimer His41(+) Cys145(-)
Each RUN corresponds to a different fragment structure
# Manifest
`../structures/metadata.csv` : master index of fragment IDs and RUNs
```
,crystal_name,RealCrystalName,smiles,new_smiles,alternate_name,site_name,pdb_entry
1,Mpro-1q2w-2020-04-Bonanno_0,Mpro-1q2w-2020-04-Bonanno,C[C@H](O)CC(C)(C)O,NA,NA,Mpro-SARS1,1Q2W
3,Mpro-1wof-2020-04-Yang_0,Mpro-1wof-2020-04-Yang,CCOC(O)CC[C@H](C[C@@H]1CCNC1O)N[C@H](O)[C@H](CC(C)C)NC(O)[C@@H](NC(O)[C@H](C)NC(O)C1CC(C)ON1)C(C)C,NA,NA,Mpro-SARS1,1WOF
4,Mpro-2a5i-2020-04-Lee_0,Mpro-2a5i-2020-04-Lee,CCOC(O)[C@@H](O)C[C@@H](O)N(CCC(N)O)NC(O)[C@H](CC1CCCCC1)N[C@H](O)[C@H](CC(C)C)N[C@H](O)OCC1CCCCC1,NA,NA,Mpro-SARS1,2A5I
...
```
First column is used to identify RUN:
* `RUN0` is skipped
* `RUN1` is Mpro-1q2w-2020-04-Bonanno_0
* `RUN2` is skipped
* `RUN3` is Mpro-1wof-2020-04-Yang_0
...
"""
import os
import time
import argparse
import csv
from collections import OrderedDict
import tempfile
import traceback, sys
from simtk import unit, openmm
from openff.toolkit.topology import Molecule
from simtk.openmm import app
from openmmforcefields.generators import SystemGenerator
import mdtraj as md
import bz2
from openmmtools import integrators
import numpy as np
from rich.progress import track
from openeye import oechem
import yaml
def setup_fah_run(destination_path, protein_pdb_filename, oemol=None, cache=None, restrain_rmsd=False,
biounit='monomer'):
"""
Prepare simulation
Parameters
----------
destination_path : str
The path to the RUN to be created
protein_pdb_filename : str
Path to protein PDB file
oemol : openeye.oechem.OEMol, optional, default=None
The molecule to parameterize, with SDData attached
If None, don't include the small molecule
restrain_rmsd : bool, optional, default=False
If True, restrain RMSD during first equilibration phase
biounit : str, optional, default='monomer'
'monomer' or 'dimer'
"""
# Parameters
protein_forcefield = 'amber14/protein.ff14SB.xml'
solvent_forcefield = 'amber14/tip3p.xml'
small_molecule_forcefield = 'openff-1.3.0'
water_model = 'tip3p'
solvent_padding = 10.0 * unit.angstrom
ionic_strength = 70 * unit.millimolar # assay buffer: 20 mM HEPES pH 7.3, 1 mM TCEP, 50 mM NaCl, 0.01% Tween-20, 10% glycerol
pressure = 1.0 * unit.atmospheres
collision_rate = 1.0 / unit.picoseconds
temperature = 300.0 * unit.kelvin
timestep = 4.0 * unit.femtoseconds
iterations = 1000 # 1 ns equilibration
nsteps_per_iteration = 250
nsteps_per_snapshot = 250000 # 1 ns
nsnapshots_per_wu = 20 # number of snapshots per WU
# Prepare phases
system_xml_filename = os.path.join(destination_path, 'system.xml.bz2')
integrator_xml_filename = os.path.join(destination_path, 'integrator.xml.bz2')
state_xml_filename = os.path.join(destination_path, 'state.xml.bz2')
# Check if we can skip setup
openmm_files_exist = os.path.exists(system_xml_filename) and os.path.exists(state_xml_filename) and os.path.exists(
integrator_xml_filename)
if openmm_files_exist:
return
# Create barostat
barostat = openmm.MonteCarloBarostat(pressure, temperature)
# Create RUN directory if it does not yet exist
os.makedirs(destination_path, exist_ok=True)
# Load any molecule(s)
molecule = None
molecules = []
if oemol is not None:
molecule = Molecule.from_openeye(oemol, allow_undefined_stereo=True)
molecule.name = 'MOL' # Ensure residue is MOL
print([res for res in molecule.to_topology().to_openmm().residues()])
molecules = [molecule]
# Create SystemGenerator
forcefield_kwargs = {'removeCMMotion': False, 'hydrogenMass': 3.0 * unit.amu, 'constraints': app.HBonds,
'rigidWater': True}
periodic_kwargs = {'nonbondedMethod': app.PME, 'ewaldErrorTolerance': 2.5e-04}
forcefields = [protein_forcefield, solvent_forcefield]
openmm_system_generator = SystemGenerator(
forcefields=forcefields,
molecules=molecules, small_molecule_forcefield=small_molecule_forcefield, cache=cache,
barostat=barostat,
forcefield_kwargs=forcefield_kwargs, periodic_forcefield_kwargs=periodic_kwargs)
# Read protein
print(f'Reading protein from {protein_pdb_filename}...')
pdbfile = app.PDBFile(protein_pdb_filename)
modeller = app.Modeller(pdbfile.topology, pdbfile.positions)
if oemol is not None:
# Add small molecule to the system
modeller.add(molecule.to_topology().to_openmm(), molecule.conformers[0])
# Extract protein and molecule chains and indices before adding solvent
mdtop = md.Topology.from_openmm(modeller.topology) # excludes solvent and ions
protein_atom_indices = mdtop.select('protein and (mass > 1)')
molecule_atom_indices = mdtop.select('(not protein) and (not water) and (mass > 1)')
protein_chainids = list(
set([atom.residue.chain.index for atom in mdtop.atoms if atom.index in protein_atom_indices]))
n_protein_chains = len(protein_chainids)
protein_chain_atom_indices = dict()
for chainid in protein_chainids:
protein_chain_atom_indices[chainid] = mdtop.select(f'protein and chainid {chainid}')
# Add solvent
print('Adding solvent...')
kwargs = {'padding': solvent_padding}
modeller.addSolvent(openmm_system_generator.forcefield, model='tip3p', ionicStrength=ionic_strength, **kwargs)
# Write initial model and select atom subsets and chains
with bz2.open(os.path.join(destination_path, 'initial-model.pdb.bz2'), 'wt') as outfile:
app.PDBFile.writeFile(modeller.topology, modeller.positions, outfile, keepIds=True)
# Create an OpenMM system
print('Creating OpenMM system...')
system = openmm_system_generator.create_system(modeller.topology)
#
# Add virtual bonds to ensure protein subunits and ligand are imaged together
#
virtual_bond_force = openmm.CustomBondForce('0')
system.addForce(virtual_bond_force)
# Add a virtual bond between protein chains
if (n_protein_chains > 1):
chainid = protein_chainids[0]
iatom = protein_chain_atom_indices[chainid][0]
for chainid in protein_chainids[1:]:
jatom = protein_chain_atom_indices[chainid][0]
print(f'Creating virtual bond between atoms {iatom} and {jatom}')
virtual_bond_force.addBond(int(iatom), int(jatom), [])
# Add a virtual bond between protein and ligand to make sure they are not imaged separately
if oemol is not None:
ligand_atom_indices = mdtop.select('((resname MOL) and (mass > 1))') # ligand heavy atoms
protein_atom_index = int(protein_atom_indices[0])
ligand_atom_index = int(ligand_atom_indices[0])
print(f'Creating virtual bond between atoms {protein_atom_index} and {ligand_atom_index}')
virtual_bond_force.addBond(int(protein_atom_index), int(ligand_atom_index), [])
# Add RMSD restraints if requested
if restrain_rmsd:
print('Adding RMSD restraint...')
kB = unit.AVOGADRO_CONSTANT_NA * unit.BOLTZMANN_CONSTANT_kB
kT = kB * temperature
rmsd_atom_indices = mdtop.select(
'(protein and (name CA)) or ((resname MOL) and (mass > 1))') # CA atoms and ligand heavy atoms
rmsd_atom_indices = [int(index) for index in rmsd_atom_indices]
custom_cv_force = openmm.CustomCVForce('(K_RMSD/2)*RMSD^2')
custom_cv_force.addGlobalParameter('K_RMSD', kT / unit.angstrom ** 2)
rmsd_force = openmm.RMSDForce(modeller.positions, rmsd_atom_indices)
custom_cv_force.addCollectiveVariable('RMSD', rmsd_force)
force_index = system.addForce(custom_cv_force)
# Create OpenM Context
platform = openmm.Platform.getPlatformByName('CPU')
# platform = openmm.Platform.findPlatform()
# platform.setPropertyDefaultValue('Precision', 'mixed')
integrator = integrators.LangevinIntegrator(temperature, collision_rate, timestep)
context = openmm.Context(system, integrator, platform)
context.setPositions(modeller.positions)
# Report initial potential energy
state = context.getState(getEnergy=True)
print(f'Initial potential energy is {state.getPotentialEnergy() / unit.kilocalories_per_mole:.3f} kcal/mol')
# Store snapshots in MDTraj trajectory to examine RMSD
mdtop = md.Topology.from_openmm(pdbfile.topology)
atom_indices = mdtop.select('all') # all solute atoms
protein_atom_indices = mdtop.select('protein and (mass > 1)') # heavy solute atoms
if oemol is not None:
ligand_atom_indices = mdtop.select('(resname MOL) and (mass > 1)') # ligand heavy atoms
trajectory = md.Trajectory(np.zeros([iterations + 1, len(atom_indices), 3], np.float32), mdtop)
trajectory.xyz[0, :, :] = context.getState(getPositions=True).getPositions(asNumpy=True)[
atom_indices] / unit.nanometers
# Minimize
print('Minimizing...')
openmm.LocalEnergyMinimizer.minimize(context)
# Equilibrate (with RMSD restraint if needed)
initial_time = time.time()
for iteration in track(range(iterations), 'Equilibrating...'):
integrator.step(nsteps_per_iteration)
trajectory.xyz[iteration + 1, :, :] = context.getState(getPositions=True).getPositions(asNumpy=True)[
atom_indices] / unit.nanometers
elapsed_time = (time.time() - initial_time) * unit.seconds
ns_per_day = (context.getState().getTime() / elapsed_time) / (unit.nanoseconds / unit.day)
print(f'Performance: {ns_per_day:8.3f} ns/day')
if restrain_rmsd:
# Disable RMSD restraint
context.setParameter('K_RMSD', 0.0)
print('Minimizing...')
openmm.LocalEnergyMinimizer.minimize(context)
for iteration in track(range(iterations), 'Equilibrating without RMSD restraint...'):
integrator.step(nsteps_per_iteration)
# Retrieve state
state = context.getState(getPositions=True, getVelocities=True, getEnergy=True, getForces=True)
system.setDefaultPeriodicBoxVectors(*state.getPeriodicBoxVectors())
modeller.topology.setPeriodicBoxVectors(state.getPeriodicBoxVectors())
print(f'Final potential energy is {state.getPotentialEnergy() / unit.kilocalories_per_mole:.3f} kcal/mol')
# Equilibrate again if we restrained the RMSD
if restrain_rmsd:
print('Removing RMSD restraint from system...')
system.removeForce(force_index)
# if oemol is not None:
# # Check final RMSD
# print('checking RMSD...')
# trajectory.superpose(trajectory, atom_indices=protein_atom_indices)
# protein_rmsd = md.rmsd(trajectory, trajectory[-1], atom_indices=protein_atom_indices)[-1] * 10 # Angstroms
# oechem.OESetSDData(oemol, 'equil_protein_rmsd', f'{protein_rmsd:.2f} A')
# ligand_rmsd = md.rmsd(trajectory, trajectory[-1], atom_indices=ligand_atom_indices)[-1] * 10 # Angstroms
# oechem.OESetSDData(oemol, 'equil_ligand_rmsd', f'{ligand_rmsd:.2f} A')
# print('RMSD after equilibration: protein {protein_rmsd:8.2f} A | ligand {ligand_rmsd:8.3f} A')
# Save as OpenMM
print('Exporting for OpenMM FAH simulation...')
with bz2.open(integrator_xml_filename, 'wt') as f:
f.write(openmm.XmlSerializer.serialize(integrator))
with bz2.open(state_xml_filename, 'wt') as f:
f.write(openmm.XmlSerializer.serialize(state))
with bz2.open(system_xml_filename, 'wt') as f:
f.write(openmm.XmlSerializer.serialize(system))
with bz2.open(os.path.join(destination_path, 'equilibrated-all.pdb.bz2'), 'wt') as f:
app.PDBFile.writeFile(modeller.topology, state.getPositions(), f, keepIds=True)
with open(os.path.join(destination_path, 'equilibrated-solute.pdb'), 'wt') as f:
mdtraj_topology = mdtraj.Topology.from_openmm(modeller.topology)
mdtraj_trajectory = mdtraj.Trajectory([state.getPositions(asNumpy=True) / unit.nanometers], mdtraj_topology)
selection = mdtraj_topology.select('not water')
mdtraj_trajectory = mdtraj_trajectory.atom_slice(selection)
app.PDBFile.writeFile(mdtraj_trajectory.topology.to_openmm(), mdtraj_trajectory.openmm_positions(0), f,
keepIds=True)
with open(os.path.join(destination_path, 'core.xml'), 'wt') as f:
f.write(f'<config>\n')
f.write(f' <numSteps>{nsteps_per_snapshot * nsnapshots_per_wu}</numSteps>\n')
f.write(f' <xtcFreq>{nsteps_per_snapshot}</xtcFreq>\n')
f.write(f' <precision>mixed</precision>\n')
f.write(f' <xtcAtoms>{",".join([str(index) for index in selection])}</xtcAtoms>\n')
f.write(f'</config>\n')
if oemol is not None:
# Write molecule as SDF, SMILES, and mol2
for extension in ['sdf', 'mol2', 'smi', 'csv']:
filename = os.path.join(destination_path, f'molecule.{extension}')
with oechem.oemolostream(filename) as ofs:
oechem.OEWriteMolecule(ofs, oemol)
# Clean up
del context, integrator
if __name__ == '__main__':
# Parse arguments
parser = argparse.ArgumentParser(
description='Prepare the specified RUN for FAH by preparing all X-ray structure variants of a specific fragment')
parser.add_argument('--receptors', dest='receptors_path', type=str, default='../receptors',
help='directory of receptor conformations (default: ../receptors)')
parser.add_argument('--metadata', dest='metadata_filename', type=str, default='../fah-xray/fah-metadata.csv',
help='metadata (default: ../fah-xray/fah-metadata.csv)')
parser.add_argument('--run', dest='run', type=str, required=True,
help='RUN index to prepare (zero-indexes first column contents in Mpro.zip metadata.csv)')
parser.add_argument('--output', dest='output_path', type=str, default='projects',
help='base directory for produced output (default: projects/)')
args = parser.parse_args()
# Read DiamondMX/XChem structure medatadata
metadata = OrderedDict()
with open(args.metadata_filename, newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
run = row['RUN']
metadata[run] = row
# Extract relevant metadata
run = f'RUN{args.run}'
if run not in metadata:
raise Exception(f'{run} not found in metadata.csv')
print(f'Preparing {run}')
metadata = metadata[run]
# Extract crystal_name
crystal_name = metadata['crystal_name']
# Read molecule in the appropriate protonation state
try:
oemol = oechem.OEMol()
molecule_filename = os.path.join(args.receptors_path, 'monomer', f'{crystal_name}_bound-ligand.mol2')
if not os.path.exists(molecule_filename):
msg = f'{molecule_filename} does not exist'
print(msg)
with oechem.oemolistream(molecule_filename) as ifs:
oechem.OEReadMolecule(ifs, oemol)
# Rename the molecule
title = metadata['alternate_name']
print(f'Setting title to {title}')
oemol.SetTitle(title)
# Remove dummy atoms
for atom in oemol.GetAtoms():
if atom.GetName().startswith('Du'):
print('Removing dummy atom.')
oemol.DeleteAtom(atom)
# Attach all structure metadata to the molecule
for key in metadata:
oechem.OESetSDData(oemol, key, metadata[key])
except Exception as e:
print(e)
oemol = None
# Set up all variants
# TODO: Generalize this to just use just one protonation state
# and maybe constant-pH simulations?
with tempfile.TemporaryDirectory() as tmpdir:
cache = os.path.join(tmpdir, 'cache.json')
def prepare_variant(project, run, crystal_name, biounit, dyad_state, oemol):
assert biounit in ['monomer', 'dimer']
try:
print('')
print(f'PROJ{project}')
if dyad_state == 'His41(0) Cys145(0)':
protein_pdb_filename = os.path.join(args.receptors_path, biounit,
f'{crystal_name}_bound-protein.pdb')
elif dyad_state == 'His41(+) Cys145(-)':
protein_pdb_filename = os.path.join(args.receptors_path, biounit,
f'{crystal_name}_bound-protein-thiolate.pdb')
else:
raise Exception("dyad_state must be one of ['His41(0) Cys145(0)', 'His41(+) Cys145(-)']")
destination_path = os.path.join(args.output_path, project, 'RUNS', f'RUN{run}')
# Create RUN directory if it does not yet exist
os.makedirs(destination_path, exist_ok=True)
# Write metadata
with open(os.path.join(destination_path, 'metadata.yaml'), 'wt') as outfile:
yaml.dump(dict(metadata), outfile)
# Set up RUN
setup_fah_run(destination_path, protein_pdb_filename, oemol=oemol, cache=cache, biounit=biounit)
print('')
except Exception as e:
traceback.print_exc(file=sys.stdout)
print(e)
# prepare_variant('13430', args.run, crystal_name, 'monomer', 'His41(0) Cys145(0)', None)
prepare_variant('13431', args.run, crystal_name, 'monomer', 'His41(+) Cys145(-)', None)
if oemol is not None:
# prepare_variant('13432', args.run, crystal_name, 'monomer', 'His41(0) Cys145(0)', oemol)
prepare_variant('13433', args.run, crystal_name, 'monomer', 'His41(+) Cys145(-)', oemol)
# prepare_variant('13434', args.run, crystal_name, 'dimer', 'His41(0) Cys145(0)', None)
prepare_variant('13435', args.run, crystal_name, 'dimer', 'His41(+) Cys145(-)', None)
if oemol is not None:
# prepare_variant('13436', args.run, crystal_name, 'dimer', 'His41(0) Cys145(0)', oemol)
prepare_variant('13437', args.run, crystal_name, 'dimer', 'His41(+) Cys145(-)', oemol)
|
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016
from random import shuffle
from unittest import TestCase
import warnings
from tsfresh.feature_extraction.feature_calculators import *
from tsfresh.feature_extraction.feature_calculators import _roll
from tsfresh.feature_extraction.feature_calculators import _get_length_sequences_where
from tsfresh.feature_extraction.feature_calculators import _estimate_friedrich_coefficients
from tsfresh.feature_extraction.feature_calculators import _aggregate_on_chunks
from tsfresh.feature_extraction.feature_calculators import _into_subchunks
from tsfresh.examples.driftbif_simulation import velocity
import math
class FeatureCalculationTestCase(TestCase):
def setUp(self):
# There will be a lot of warnings in the feature calculators.
# Just ignore all of them in these tests
warnings.simplefilter("ignore")
def tearDown(self):
warnings.resetwarnings()
def assertIsNaN(self, result):
self.assertTrue(np.isnan(result), msg="{} is not np.NaN")
def assertEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):
expected_result = f(input_to_f, *args, **kwargs)
self.assertEqual(expected_result, result,
msg="Not equal for lists: {} != {}".format(expected_result, result))
expected_result = f(np.array(input_to_f), *args, **kwargs)
self.assertEqual(expected_result, result,
msg="Not equal for numpy.arrays: {} != {}".format(expected_result, result))
expected_result = f(pd.Series(input_to_f, dtype="float64"), *args, **kwargs)
self.assertEqual(expected_result, result,
msg="Not equal for pandas.Series: {} != {}".format(expected_result, result))
def assertTrueOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertTrue(f(input_to_f, *args, **kwargs), msg="Not true for lists")
self.assertTrue(f(np.array(input_to_f), *args, **kwargs), msg="Not true for numpy.arrays")
self.assertTrue(f(pd.Series(input_to_f), *args, **kwargs), msg="Not true for pandas.Series")
def assertAllTrueOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertTrue(all(dict(f(input_to_f, *args, **kwargs)).values()), msg="Not true for lists")
self.assertTrue(all(dict(f(np.array(input_to_f), *args, **kwargs)).values()), msg="Not true for numpy.arrays")
self.assertTrue(all(dict(f(pd.Series(input_to_f), *args, **kwargs)).values()), msg="Not true for pandas.Series")
def assertFalseOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertFalse(f(input_to_f, *args, **kwargs), msg="Not false for lists")
self.assertFalse(f(np.array(input_to_f), *args, **kwargs), msg="Not false for numpy.arrays")
self.assertFalse(f(pd.Series(input_to_f), *args, **kwargs), msg="Not false for pandas.Series")
def assertAllFalseOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertFalse(any(dict(f(input_to_f, *args, **kwargs)).values()), msg="Not false for lists")
self.assertFalse(any(dict(f(np.array(input_to_f), *args, **kwargs)).values()),
msg="Not false for numpy.arrays")
self.assertFalse(any(dict(f(pd.Series(input_to_f), *args, **kwargs)).values()),
msg="Not false for pandas.Series")
def assertAlmostEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):
expected_result = f(input_to_f, *args, **kwargs)
self.assertAlmostEqual(expected_result, result,
msg="Not almost equal for lists: {} != {}".format(expected_result, result))
expected_result = f(np.array(input_to_f), *args, **kwargs)
self.assertAlmostEqual(expected_result, result,
msg="Not almost equal for numpy.arrays: {} != {}".format(expected_result, result))
expected_result = f(pd.Series(input_to_f, dtype="float64"), *args, **kwargs)
self.assertAlmostEqual(expected_result, result,
msg="Not almost equal for pandas.Series: {} != {}".format(expected_result, result))
def assertIsNanOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertTrue(np.isnan(f(input_to_f, *args, **kwargs)), msg="Not NaN for lists")
self.assertTrue(np.isnan(f(np.array(input_to_f), *args, **kwargs)), msg="Not NaN for numpy.arrays")
self.assertTrue(np.isnan(f(pd.Series(input_to_f, dtype="float64"), *args, **kwargs)),
msg="Not NaN for pandas.Series")
def assertEqualPandasSeriesWrapper(self, f, input_to_f, result, *args, **kwargs):
self.assertEqual(f(pd.Series(input_to_f), *args, **kwargs), result,
msg="Not equal for pandas.Series: {} != {}".format(
f(pd.Series(input_to_f), *args, **kwargs), result))
def test__roll(self):
x = np.random.normal(size=30)
for shift in [0, 1, 10, 11, 30, 31, 50, 51, 150, 151]:
np.testing.assert_array_equal(_roll(x, shift), np.roll(x, shift))
np.testing.assert_array_equal(_roll(x, -shift), np.roll(x, -shift))
def test___get_length_sequences_where(self):
self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1],
[1, 3, 1, 2])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where,
[0, True, 0, 0, True, True, True, 0, 0, True, 0, True, True],
[1, 3, 1, 2])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where,
[0, True, 0, 0, 1, True, 1, 0, 0, True, 0, 1, True], [1, 3, 1, 2])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [0] * 10, [0])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [], [0])
def test__into_subchunks(self):
np.testing.assert_array_equal(_into_subchunks(range(7), 3, 2), np.array([[0, 1, 2], [2, 3, 4], [4, 5, 6]]))
np.testing.assert_array_equal(_into_subchunks(range(5), 3), np.array([[0, 1, 2], [1, 2, 3], [2, 3, 4]]))
def test_variance_larger_than_standard_deviation(self):
self.assertFalseOnAllArrayTypes(variance_larger_than_standard_deviation, [-1, -1, 1, 1, 1])
self.assertTrueOnAllArrayTypes(variance_larger_than_standard_deviation, [-1, -1, 1, 1, 2])
def test_large_standard_deviation(self):
self.assertFalseOnAllArrayTypes(large_standard_deviation, [1, 1, 1, 1], r=0)
self.assertFalseOnAllArrayTypes(large_standard_deviation, [1, 1, 1, 1], r=0)
self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0)
self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.25)
self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.3)
self.assertFalseOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.5)
def test_symmetry_looking(self):
self.assertAllTrueOnAllArrayTypes(symmetry_looking, [-1, -1, 1, 1],
[dict(r=0.05), dict(r=0.75)])
self.assertAllFalseOnAllArrayTypes(symmetry_looking, [-1, -1, 1, 1], [dict(r=0)])
self.assertAllFalseOnAllArrayTypes(symmetry_looking, [-1, -1, -1, -1, 1], [dict(r=0.05)])
self.assertAllTrueOnAllArrayTypes(symmetry_looking, [-2, -2, -2, -1, -1, -1], [dict(r=0.05)])
self.assertAllTrueOnAllArrayTypes(symmetry_looking, [-0.9, -0.900001], [dict(r=0.05)])
def test_has_duplicate_max(self):
self.assertTrueOnAllArrayTypes(has_duplicate_max, [2.1, 0, 0, 2.1, 1.1])
self.assertFalseOnAllArrayTypes(has_duplicate_max, np.array([2.1, 0, 0, 2, 1.1]))
self.assertTrueOnAllArrayTypes(has_duplicate_max, [1, 1, 1, 1])
self.assertFalseOnAllArrayTypes(has_duplicate_max, np.array([0]))
self.assertTrueOnAllArrayTypes(has_duplicate_max, np.array([1, 1]))
def test_has_duplicate_min(self):
self.assertTrueOnAllArrayTypes(has_duplicate_min, [-2.1, 0, 0, -2.1, 1.1])
self.assertFalseOnAllArrayTypes(has_duplicate_min, [2.1, 0, -1, 2, 1.1])
self.assertTrueOnAllArrayTypes(has_duplicate_min, np.array([1, 1, 1, 1]))
self.assertFalseOnAllArrayTypes(has_duplicate_min, np.array([0]))
self.assertTrueOnAllArrayTypes(has_duplicate_min, np.array([1, 1]))
def test_has_duplicate(self):
self.assertTrueOnAllArrayTypes(has_duplicate, np.array([-2.1, 0, 0, -2.1]))
self.assertTrueOnAllArrayTypes(has_duplicate, [-2.1, 2.1, 2.1, 2.1])
self.assertFalseOnAllArrayTypes(has_duplicate, [1.1, 1.2, 1.3, 1.4])
self.assertFalseOnAllArrayTypes(has_duplicate, [1])
self.assertFalseOnAllArrayTypes(has_duplicate, [])
def test_sum(self):
self.assertEqualOnAllArrayTypes(sum_values, [1, 2, 3, 4.1], 10.1)
self.assertEqualOnAllArrayTypes(sum_values, [-1.2, -2, -3, -4], -10.2)
self.assertEqualOnAllArrayTypes(sum_values, [], 0)
def test_agg_autocorrelation_returns_correct_values(self):
param = [{"f_agg": "mean", "maxlag": 10}]
x = [1, 1, 1, 1, 1, 1, 1]
expected_res = 0
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
x = [1, 2, -3]
expected_res = 1 / np.var(x) * (((1 * 2 + 2 * (-3)) / 2 + (1 * -3)) / 2)
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
np.random.seed(42)
x = np.random.normal(size=3000)
expected_res = 0
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=2)
param = [{"f_agg": "median", "maxlag": 10}]
x = [1, 1, 1, 1, 1, 1, 1]
expected_res = 0
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"median\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
x = [1, 2, -3]
expected_res = 1 / np.var(x) * (((1 * 2 + 2 * (-3)) / 2 + (1 * -3)) / 2)
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"median\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
def test_agg_autocorrelation_returns_max_lag_does_not_affect_other_results(self):
param = [{"f_agg": "mean", "maxlag": 1},
{"f_agg": "mean", "maxlag": 10}]
x = range(10)
res1 = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_1"]
res10 = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res1, 0.77777777, places=4)
self.assertAlmostEqual(res10, -0.64983164983165, places=4)
param = [{"f_agg": "mean", "maxlag": 1}]
x = range(10)
res1 = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_1"]
self.assertAlmostEqual(res1, 0.77777777, places=4)
def test_partial_autocorrelation(self):
# Test for altering time series
# len(x) < max_lag
param = [{"lag": lag} for lag in range(10)]
x = [1, 2, 1, 2, 1, 2]
expected_res = [("lag_0", 1.0), ("lag_1", -1.0), ("lag_2", np.nan)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=4)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=4)
self.assertIsNaN(res[2][1])
# Linear signal
param = [{"lag": lag} for lag in range(10)]
x = np.linspace(0, 1, 3000)
expected_res = [("lag_0", 1.0), ("lag_1", 1.0), ("lag_2", 0)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=2)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=2)
self.assertAlmostEqual(res[2][1], expected_res[2][1], places=2)
# Random noise
np.random.seed(42)
x = np.random.normal(size=3000)
param = [{"lag": lag} for lag in range(10)]
expected_res = [("lag_0", 1.0), ("lag_1", 0), ("lag_2", 0)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=1)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=1)
self.assertAlmostEqual(res[2][1], expected_res[2][1], places=1)
# On a simulated AR process
np.random.seed(42)
param = [{"lag": lag} for lag in range(10)]
# Simulate AR process
T = 3000
epsilon = np.random.randn(T)
x = np.repeat(1.0, T)
for t in range(T - 1):
x[t + 1] = 0.5 * x[t] + 2 + epsilon[t]
expected_res = [("lag_0", 1.0), ("lag_1", 0.5), ("lag_2", 0)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=1)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=1)
self.assertAlmostEqual(res[2][1], expected_res[2][1], places=1)
# Some pathological cases
param = [{"lag": lag} for lag in range(10)]
# List of length 1
res = partial_autocorrelation([1], param=param)
for lag_no, lag_val in res:
self.assertIsNaN(lag_val)
# Empty list
res = partial_autocorrelation([], param=param)
for lag_no, lag_val in res:
self.assertIsNaN(lag_val)
# List contains only zeros
res = partial_autocorrelation(np.zeros(100), param=param)
for lag_no, lag_val in res:
if lag_no == "lag_0":
self.assertEqual(lag_val, 1.0)
else:
self.assertIsNaN(lag_val)
def test_augmented_dickey_fuller(self):
# todo: add unit test for the values of the test statistic
# the adf hypothesis test checks for unit roots,
# so H_0 = {random drift} vs H_1 = {AR(1) model}
# H0 is true
np.random.seed(seed=42)
x = np.cumsum(np.random.uniform(size=100))
param = [
{"autolag": "BIC", "attr": "teststat"},
{"autolag": "BIC", "attr": "pvalue"},
{"autolag": "BIC", "attr": "usedlag"}
]
expected_index = [
'attr_"teststat"__autolag_"BIC"',
'attr_"pvalue"__autolag_"BIC"',
'attr_"usedlag"__autolag_"BIC"',
]
res = augmented_dickey_fuller(x=x, param=param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertGreater(res['attr_"pvalue"__autolag_"BIC"'], 0.10)
self.assertEqual(res['attr_"usedlag"__autolag_"BIC"'], 0)
# H0 should be rejected for AR(1) model with x_{t} = 1/2 x_{t-1} + e_{t}
np.random.seed(seed=42)
e = np.random.normal(0.1, 0.1, size=100)
m = 50
x = [0] * m
x[0] = 100
for i in range(1, m):
x[i] = x[i - 1] * 0.5 + e[i]
param = [
{"autolag": "AIC", "attr": "teststat"},
{"autolag": "AIC", "attr": "pvalue"},
{"autolag": "AIC", "attr": "usedlag"}
]
expected_index = [
'attr_"teststat"__autolag_"AIC"',
'attr_"pvalue"__autolag_"AIC"',
'attr_"usedlag"__autolag_"AIC"',
]
res = augmented_dickey_fuller(x=x, param=param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertLessEqual(res['attr_"pvalue"__autolag_"AIC"'], 0.05)
self.assertEqual(res['attr_"usedlag"__autolag_"AIC"'], 0)
# Check if LinAlgError and ValueError are catched
res_linalg_error = augmented_dickey_fuller(x=np.repeat(np.nan, 100), param=param)
res_value_error = augmented_dickey_fuller(x=[], param=param)
for index, val in res_linalg_error:
self.assertIsNaN(val)
for index, val in res_value_error:
self.assertIsNaN(val)
# Should return NaN if "attr" is unknown
res_attr_error = augmented_dickey_fuller(x=x, param=[{"autolag": "AIC", "attr": ""}])
for index, val in res_attr_error:
self.assertIsNaN(val)
def test_abs_energy(self):
self.assertEqualOnAllArrayTypes(abs_energy, [1, 1, 1], 3)
self.assertEqualOnAllArrayTypes(abs_energy, [1, 2, 3], 14)
self.assertEqualOnAllArrayTypes(abs_energy, [-1, 2, -3], 14)
self.assertAlmostEqualOnAllArrayTypes(abs_energy, [-1, 1.3], 2.69)
self.assertEqualOnAllArrayTypes(abs_energy, [1], 1)
def test_cid_ce(self):
self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [0, 4], 2, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [100, 104], 2, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=False)
self.assertEqualOnAllArrayTypes(cid_ce, [0.5, 3.5, 7.5], 5, normalize=False)
self.assertEqualOnAllArrayTypes(cid_ce, [-4.33, -1.33, 2.67], 5, normalize=False)
def test_lempel_ziv_complexity(self):
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1], 2. / 3, bins=2)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1], 2. / 3, bins=5)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1, 1, 1, 1, 1],
0.4285714285, bins=2)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1, 2, 1, 1, 1],
0.5714285714, bins=2)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity,
[-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
0.8, bins=10)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity,
[-1, np.nan, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
0.4, bins=10)
def test_fourier_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 2, 1], 0.693147180, bins=2)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 2, 1], 0.693147180, bins=5)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 1, 2, 1, 1, 1, 1],
0.5623351446188083, bins=5)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 1, 1, 1, 2, 1, 1],
1.0397207708399179, bins=5)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy,
[-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
1.5607104090414063, bins=10)
self.assertIsNanOnAllArrayTypes(fourier_entropy,
[-1, np.nan, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
bins=10)
def test_permutation_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy, [4, 7, 9, 10, 6, 11, 3], 1.054920167,
dimension=3, tau=1)
# should grow
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy, [1, -1, 1, -1, 1, -1, 1, -1],
0.6931471805599453, dimension=3, tau=1)
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy, [1, -1, 1, -1, 1, 1, 1, -1],
1.3296613488547582, dimension=3, tau=1)
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy,
[-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
1.0397207708399179, dimension=3, tau=2)
# nan is treated like any other number
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy,
[-1, 4.3, 5, 1, -4.5, 1, 5, np.nan, -3.4, 6],
1.0397207708399179, dimension=3, tau=2)
# if too short, return nan
self.assertIsNanOnAllArrayTypes(permutation_entropy, [1, -1], dimension=3, tau=1)
def test_ratio_beyond_r_sigma(self):
x = [0, 1] * 10 + [10, 20, -30] # std of x is 7.21, mean 3.04
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 3. / len(x), r=1)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 2. / len(x), r=2)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 1. / len(x), r=3)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 0, r=20)
def test_mean_abs_change(self):
self.assertEqualOnAllArrayTypes(mean_abs_change, [-2, 2, 5], 3.5)
self.assertEqualOnAllArrayTypes(mean_abs_change, [1, 2, -1], 2)
def test_mean_change(self):
self.assertEqualOnAllArrayTypes(mean_change, [-2, 2, 5], 3.5)
self.assertEqualOnAllArrayTypes(mean_change, [1, 2, -1], -1)
self.assertEqualOnAllArrayTypes(mean_change, [10, 20], 10)
self.assertIsNanOnAllArrayTypes(mean_change, [1])
self.assertIsNanOnAllArrayTypes(mean_change, [])
def test_mean_second_derivate_central(self):
self.assertEqualOnAllArrayTypes(mean_second_derivative_central, list(range(10)), 0)
self.assertEqualOnAllArrayTypes(mean_second_derivative_central, [1, 3, 5], 0)
self.assertEqualOnAllArrayTypes(mean_second_derivative_central, [1, 3, 7, -3], -3)
def test_median(self):
self.assertEqualOnAllArrayTypes(median, [1, 1, 2, 2], 1.5)
self.assertEqualOnAllArrayTypes(median, [0.5, 0.5, 2, 3.5, 10], 2)
self.assertEqualOnAllArrayTypes(median, [0.5], 0.5)
self.assertIsNanOnAllArrayTypes(median, [])
def test_mean(self):
self.assertEqualOnAllArrayTypes(mean, [1, 1, 2, 2], 1.5)
self.assertEqualOnAllArrayTypes(mean, [0.5, 0.5, 2, 3.5, 10], 3.3)
self.assertEqualOnAllArrayTypes(mean, [0.5], 0.5)
self.assertIsNanOnAllArrayTypes(mean, [])
def test_length(self):
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, 4], 4)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3], 3)
self.assertEqualOnAllArrayTypes(length, [1, 2], 2)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, np.NaN], 4)
self.assertEqualOnAllArrayTypes(length, [], 0)
def test_standard_deviation(self):
self.assertAlmostEqualOnAllArrayTypes(standard_deviation, [1, 1, -1, -1], 1)
self.assertAlmostEqualOnAllArrayTypes(standard_deviation, [1, 2, -2, -1], 1.58113883008)
self.assertIsNanOnAllArrayTypes(standard_deviation, [])
def test_variation_coefficient(self):
self.assertIsNanOnAllArrayTypes(variation_coefficient, [1, 1, -1, -1], )
self.assertAlmostEqualOnAllArrayTypes(variation_coefficient, [1, 2, -3, -1], -7.681145747868608)
self.assertAlmostEqualOnAllArrayTypes(variation_coefficient, [1, 2, 4, -1], 1.2018504251546631)
self.assertIsNanOnAllArrayTypes(variation_coefficient, [])
def test_variance(self):
self.assertAlmostEqualOnAllArrayTypes(variance, [1, 1, -1, -1], 1)
self.assertAlmostEqualOnAllArrayTypes(variance, [1, 2, -2, -1], 2.5)
self.assertIsNanOnAllArrayTypes(variance, [])
def test_skewness(self):
self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1, 2, 2, 2], 0)
self.assertAlmostEqualOnAllArrayTypes(skewness, [1, 1, 1, 2, 2], 0.6085806194501855)
self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1], 0)
self.assertIsNanOnAllArrayTypes(skewness, [1, 1])
def test_kurtosis(self):
self.assertAlmostEqualOnAllArrayTypes(kurtosis, [1, 1, 1, 2, 2], -3.333333333333333)
self.assertAlmostEqualOnAllArrayTypes(kurtosis, [1, 1, 1, 1], 0)
self.assertIsNanOnAllArrayTypes(kurtosis, [1, 1, 1])
def test_absolute_sum_of_changes(self):
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, 1, 1, 1, 2, 1], 2)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, -1, 1, -1], 6)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1], 0)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [], 0)
def test_longest_strike_below_mean(self):
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 1, 1, 1, 2, 2, 2], 3)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 3, 4, 5, 6], 3)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 3, 4, 5], 2)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 1], 1)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [], 0)
def test_longest_strike_above_mean(self):
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 1, 2, 1, 2, 2, 1], 2)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 3, 4, 5, 6], 3)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 3, 4, 5], 2)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 1], 1)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [], 0)
def test_count_above_mean(self):
self.assertEqualOnAllArrayTypes(count_above_mean, [1, 2, 1, 2, 1, 2], 3)
self.assertEqualOnAllArrayTypes(count_above_mean, [1, 1, 1, 1, 1, 2], 1)
self.assertEqualOnAllArrayTypes(count_above_mean, [1, 1, 1, 1, 1], 0)
self.assertEqualOnAllArrayTypes(count_above_mean, [], 0)
def test_count_below_mean(self):
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 2, 1, 2, 1, 2], 3)
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1, 2], 5)
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1], 0)
self.assertEqualOnAllArrayTypes(count_below_mean, [], 0)
def test_last_location_maximum(self):
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1, 2, 1, 2, 1], 0.8)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1, 2, 1, 1, 2], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [2, 1, 1, 1, 1], 0.2)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1, 1, 1, 1, 1], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1], 1.0)
self.assertIsNanOnAllArrayTypes(last_location_of_maximum, [])
def test_first_location_of_maximum(self):
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1, 2, 1, 2, 1], 0.2)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1, 2, 1, 1, 2], 0.2)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [2, 1, 1, 1, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1, 1, 1, 1, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1], 0.0)
self.assertIsNanOnAllArrayTypes(first_location_of_maximum, [])
def test_last_location_of_minimum(self):
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1, 2, 1, 2, 1], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1, 2, 1, 2, 2], 0.6)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [2, 1, 1, 1, 2], 0.8)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1, 1, 1, 1, 1], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1], 1.0)
self.assertIsNanOnAllArrayTypes(last_location_of_minimum, [])
def test_first_location_of_minimum(self):
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1, 2, 1, 2, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [2, 2, 1, 2, 2], 0.4)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [2, 1, 1, 1, 2], 0.2)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1, 1, 1, 1, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1], 0.0)
self.assertIsNanOnAllArrayTypes(first_location_of_minimum, [])
def test_percentage_of_doubled_datapoints(self):
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1, 2, 3, 4],
0.4)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints,
[1.111, -2.45, 1.111, 2.45], 0.5)
self.assertIsNanOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [])
def test_ratio_of_doubled_values(self):
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1, 2, 3, 4], 0.25)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values,
[1.111, -2.45, 1.111, 2.45], 1.0 / 3.0)
self.assertIsNanOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [])
def test_sum_of_reoccurring_values(self):
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1, 1, 2, 3, 4, 4], 5)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1.111, -2.45, 1.111, 2.45], 1.111)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [], 0)
def test_sum_of_reoccurring_data_points(self):
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1, 1, 2, 3, 4, 4], 10)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1.111, -2.45, 1.111, 2.45], 2.222)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [], 0)
def test_uniqueness_factor(self):
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1, 1, 2, 3, 4], 0.8)
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1, 1.5, 2, 3], 1)
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1], 1)
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1.111, -2.45, 1.111, 2.45],
0.75)
self.assertIsNanOnAllArrayTypes(ratio_value_number_to_time_series_length, [])
def test_fft_coefficient(self):
x = range(10)
param = [{"coeff": 0, "attr": "real"}, {"coeff": 1, "attr": "real"}, {"coeff": 2, "attr": "real"},
{"coeff": 0, "attr": "imag"}, {"coeff": 1, "attr": "imag"}, {"coeff": 2, "attr": "imag"},
{"coeff": 0, "attr": "angle"}, {"coeff": 1, "attr": "angle"}, {"coeff": 2, "attr": "angle"},
{"coeff": 0, "attr": "abs"}, {"coeff": 1, "attr": "abs"}, {"coeff": 2, "attr": "abs"}]
expected_index = ['attr_"real"__coeff_0', 'attr_"real"__coeff_1', 'attr_"real"__coeff_2',
'attr_"imag"__coeff_0', 'attr_"imag"__coeff_1', 'attr_"imag"__coeff_2',
'attr_"angle"__coeff_0', 'attr_"angle"__coeff_1', 'attr_"angle"__coeff_2',
'attr_"abs"__coeff_0', 'attr_"abs"__coeff_1', 'attr_"abs"__coeff_2']
res = pd.Series(dict(fft_coefficient(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['attr_"imag"__coeff_0'], 0, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_0'], sum(x), places=6)
self.assertAlmostEqual(res['attr_"angle"__coeff_0'], 0, places=6)
self.assertAlmostEqual(res['attr_"abs"__coeff_0'], sum(x), places=6)
x = [0, 1, 0, 0]
res = pd.Series(dict(fft_coefficient(x, param)))
# see documentation of fft in numpy
# should return array([1. + 0.j, 0. - 1.j, -1. + 0.j])
self.assertAlmostEqual(res['attr_"imag"__coeff_0'], 0, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_0'], 1, places=6)
self.assertAlmostEqual(res['attr_"imag"__coeff_1'], -1, places=6)
self.assertAlmostEqual(res['attr_"angle"__coeff_1'], -90, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_1'], 0, places=6)
self.assertAlmostEqual(res['attr_"imag"__coeff_2'], 0, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_2'], -1, places=6)
# test what happens if coeff is biger than time series lenght
x = range(5)
param = [{"coeff": 10, "attr": "real"}]
expected_index = ['attr_"real"__coeff_10']
res = pd.Series(dict(fft_coefficient(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertIsNaN(res['attr_"real"__coeff_10'])
def test_fft_aggregated(self):
param = [
{"aggtype": "centroid"},
{"aggtype": "variance"},
{"aggtype": "skew"},
{"aggtype": "kurtosis"}
]
expected_index = ['aggtype_"centroid"', 'aggtype_"variance"', 'aggtype_"skew"', 'aggtype_"kurtosis"']
x = np.arange(10)
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['aggtype_"centroid"'], 1.135, places=3)
self.assertAlmostEqual(res['aggtype_"variance"'], 2.368, places=3)
self.assertAlmostEqual(res['aggtype_"skew"'], 1.249, places=3)
self.assertAlmostEqual(res['aggtype_"kurtosis"'], 3.643, places=3)
# Scalar multiplying the distribution should not change the results:
x = 10 * x
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['aggtype_"centroid"'], 1.135, places=3)
self.assertAlmostEqual(res['aggtype_"variance"'], 2.368, places=3)
self.assertAlmostEqual(res['aggtype_"skew"'], 1.249, places=3)
self.assertAlmostEqual(res['aggtype_"kurtosis"'], 3.643, places=3)
# The fft of a sign wave is a dirac delta, variance and skew should be near zero, kurtosis should be near 3:
# However, in the discrete limit, skew and kurtosis blow up in a manner that is noise dependent and are
# therefore bad features, therefore an nan should be returned for these values
x = np.sin(2 * np.pi / 10 * np.arange(30))
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['aggtype_"centroid"'], 3., places=5)
self.assertAlmostEqual(res['aggtype_"variance"'], 0., places=5)
self.assertIsNaN(res['aggtype_"skew"'])
self.assertIsNaN(res['aggtype_"kurtosis"'])
# Gaussian test:
def normal(y, mean_, sigma_):
return 1 / (2 * np.pi * sigma_ ** 2) * np.exp(-(y - mean_) ** 2 / (2 * sigma_ ** 2))
mean_ = 500.
sigma_ = 1.
range_ = int(2 * mean_)
x = list(map(lambda x: normal(x, mean_, sigma_), range(range_)))
# The fourier transform of a Normal dist in the positive halfspace is a half normal,
# Hand calculated values of centroid and variance based for the half-normal dist:
# (Ref: https://en.wikipedia.org/wiki/Half-normal_distribution)
expected_fft_centroid = (range_ / (2 * np.pi * sigma_)) * np.sqrt(2 / np.pi)
expected_fft_var = (range_ / (2 * np.pi * sigma_)) ** 2 * (1 - 2 / np.pi)
# Calculate values for unit test:
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
# Compare against hand calculated values:
rel_diff_allowed = 0.02
self.assertAlmostEqual(
res['aggtype_"centroid"'], expected_fft_centroid,
delta=rel_diff_allowed * expected_fft_centroid
)
self.assertAlmostEqual(
res['aggtype_"variance"'], expected_fft_var,
delta=rel_diff_allowed * expected_fft_var
)
def test_number_peaks(self):
x = np.array([0, 1, 2, 1, 0, 1, 2, 3, 4, 5, 4, 3, 2, 1])
self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 1)
self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 2)
self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 3)
self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 4)
self.assertEqualOnAllArrayTypes(number_peaks, x, 0, 5)
self.assertEqualOnAllArrayTypes(number_peaks, x, 0, 6)
def test_mass_quantile(self):
x = [1] * 101
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.5"], 0.5, places=1)
# Test for parts of pandas series
x = pd.Series([0] * 55 + [1] * 101)
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x[x > 0], param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.5"], 0.5, places=1)
x = [0] * 1000 + [1]
param = [{"q": 0.5}, {"q": 0.99}]
expected_index = ["q_0.5", "q_0.99"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.5"], 1, places=1)
self.assertAlmostEqual(res["q_0.99"], 1, places=1)
x = [0, 1, 1, 0, 0, 1, 0, 0]
param = [{"q": 0.30}, {"q": 0.60}, {"q": 0.90}]
expected_index = ["q_0.3", "q_0.6",
"q_0.9"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.3"], 0.25, places=1)
self.assertAlmostEqual(res["q_0.6"], 0.375, places=1)
self.assertAlmostEqual(res["q_0.9"], 0.75, places=1)
x = [0, 0, 0]
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.isnan(res["q_0.5"]))
x = []
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.isnan(res["q_0.5"]))
def test_number_cwt_peaks(self):
x = [1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1]
self.assertEqualOnAllArrayTypes(number_cwt_peaks, x, 2, 2)
def test_spkt_welch_density(self):
# todo: improve tests
x = range(10)
param = [{"coeff": 1}, {"coeff": 10}]
expected_index = ["coeff_1", "coeff_10"]
res = pd.Series(dict(spkt_welch_density(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertIsNaN(res["coeff_10"])
def test_cwt_coefficients(self):
x = [0.1, 0.2, 0.3]
param = [{"widths": (1, 2, 3), "coeff": 2, "w": 1},
{"widths": (1, 3), "coeff": 2, "w": 3},
{"widths": (1, 3), "coeff": 5, "w": 3}]
shuffle(param)
expected_index = ["coeff_2__w_1__widths_(1, 2, 3)",
"coeff_2__w_3__widths_(1, 3)",
"coeff_5__w_3__widths_(1, 3)"]
res = cwt_coefficients(x, param)
res = pd.Series(dict(res))
# todo: add unit test for the values
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(math.isnan(res["coeff_5__w_3__widths_(1, 3)"]))
def test_ar_coefficient(self):
# Test for X_i = 2.5 * X_{i-1} + 1
param = [{"k": 1, "coeff": 0}, {"k": 1, "coeff": 1}]
shuffle(param)
x = [1] + 9 * [0]
for i in range(1, len(x)):
x[i] = 2.5 * x[i - 1] + 1
res = ar_coefficient(x, param)
expected_index = ["coeff_0__k_1", "coeff_1__k_1"]
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["coeff_0__k_1"], 1, places=2)
self.assertAlmostEqual(res["coeff_1__k_1"], 2.5, places=2)
# Test for X_i = 1.4 * X_{i-1} - 1 X_{i-2} + 1
param = [{"k": 1, "coeff": 0}, {"k": 1, "coeff": 1},
{"k": 2, "coeff": 0}, {"k": 2, "coeff": 1}, {"k": 2, "coeff": 2}, {"k": 2, "coeff": 3}]
shuffle(param)
x = [1, 1] + 5 * [0]
for i in range(2, len(x)):
x[i] = (-2) * x[i - 2] + 3.5 * x[i - 1] + 1
res = ar_coefficient(x, param)
expected_index = ["coeff_0__k_1", "coeff_1__k_1",
"coeff_0__k_2", "coeff_1__k_2",
"coeff_2__k_2", "coeff_3__k_2"]
res = pd.Series(dict(res))
self.assertIsInstance(res, pd.Series)
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["coeff_0__k_2"], 1, places=2)
self.assertAlmostEqual(res["coeff_1__k_2"], 3.5, places=2)
self.assertAlmostEqual(res["coeff_2__k_2"], -2, places=2)
self.assertTrue(np.isnan(res["coeff_3__k_2"]))
def test_time_reversal_asymmetry_statistic(self):
x = [1] * 10
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 0)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 1)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 2)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 3)
x = [1, 2, -3, 4]
# 1/2 * ( (4^2 * -3 + 3 * 2^2) + (3^2*2)-(2*1^1)) = 1/2 * (-48+12+18-2) = 20/2
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, -10, 1)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 2)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 3)
def test_number_crossing_m(self):
x = [10, -10, 10, -10]
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 3, 0)
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 0, 10)
x = [10, 20, 20, 30]
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 0, 0)
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 1, 15)
def test_c3(self):
x = [1] * 10
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 0)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 1)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 2)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 3)
x = [1, 2, -3, 4]
# 1/2 *(1*2*(-3)+2*(-3)*4) = 1/2 *(-6-24) = -30/2
self.assertAlmostEqualOnAllArrayTypes(c3, x, -15, 1)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 0, 2)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 0, 3)
def test_binned_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 100, 0, 10)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 10 + [1], - (10 / 11 * np.math.log(10 / 11) +
1 / 11 * np.math.log(1 / 11)), 10)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 10 + [1], - (10 / 11 * np.math.log(10 / 11) +
1 / 11 * np.math.log(1 / 11)), 10)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 10 + [1], - (10 / 11 * np.math.log(10 / 11) +
1 / 11 * np.math.log(1 / 11)), 100)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, list(range(10)), - np.math.log(1 / 10), 100)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, list(range(100)), - np.math.log(1 / 2), 2)
def test_sample_entropy(self):
# "random" list -> large entropy
ts = [1, 4, 5, 1, 7, 3, 1, 2, 5, 8, 9, 7, 3, 7, 9, 5, 4, 3, 9, 1, 2, 3, 4, 2, 9, 6, 7, 4, 9, 2, 9, 9, 6, 5, 1,
3, 8, 1, 5, 3, 8, 4, 1, 2, 2, 1, 6, 5, 3, 6, 5, 4, 8, 9, 6, 7, 5, 3, 2, 5, 4, 2, 5, 1, 6, 5, 3, 5, 6, 7,
8, 5, 2, 8, 6, 3, 8, 2, 7, 1, 7, 3, 5, 6, 2, 1, 3, 7, 3, 5, 3, 7, 6, 7, 7, 2, 3, 1, 7, 8]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 2.38262780)
# This is not very complex, so it gives a small value
ts = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.25131442)
# however adding a 2 increases complexity
ts = [1, 1, 2, 1, 1, 1, 1, 1, 1, 1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.74193734)
# and it does not matter where
ts = [1, 1, 1, 2, 1, 1, 1, 1, 1, 1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.74193734)
# negative numbers also work
ts = [1, -1, 1, -1, 1, -1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.69314718)
# nan gives nan
ts = [1, -1, 1, np.nan, 1, -1]
self.assertIsNanOnAllArrayTypes(sample_entropy, ts)
# this is not a very "random" list, so it should give a small entropy
ts = list(range(1000))
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.0010314596066622707)
def test_autocorrelation(self):
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], -1, 1)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 1, 2)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], -1, 3)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 1, 4)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, pd.Series([0, 1, 2, 0, 1, 2]), -0.75, 2)
# Autocorrelation lag is larger than length of the time series
self.assertIsNanOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 200)
self.assertIsNanOnAllArrayTypes(autocorrelation, [np.nan], 0)
self.assertIsNanOnAllArrayTypes(autocorrelation, [], 0)
# time series with length 1 has no variance, therefore no result for autocorrelation at lag 0
self.assertIsNanOnAllArrayTypes(autocorrelation, [1], 0)
def test_quantile(self):
self.assertAlmostEqualOnAllArrayTypes(quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 1.0, 0.2)
self.assertAlmostEqualOnAllArrayTypes(quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 0.9)
self.assertAlmostEqualOnAllArrayTypes(quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 1.0)
self.assertAlmostEqualOnAllArrayTypes(quantile, [1], 1, 0.5)
self.assertIsNanOnAllArrayTypes(quantile, [], 0.5)
def test_mean_abs_change_quantiles(self):
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 1,
ql=0.1, qh=0.9, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.15, qh=0.18, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0.5,
ql=0, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0.5,
ql=0.1, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0.1, qh=0.6, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 5,
ql=0, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 0.5,
ql=0.1, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0, 1, 0], 0.75,
ql=0.1, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 1,
ql=0.1, qh=0.9, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.15, qh=0.18, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0.1, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0.1, qh=0.6, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 0,
ql=0, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 0.5,
ql=0.1, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0, 1, 0], 0.25,
ql=0.1, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.1, qh=0.9, isabs=True, f_agg="std")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0.5,
ql=0, qh=1, isabs=True, f_agg="std")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.1, qh=0.9, isabs=False, f_agg="std")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 1, 0], 1,
ql=0, qh=1, isabs=False, f_agg="std")
def test_value_count(self):
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 10, value=1)
self.assertEqualPandasSeriesWrapper(value_count, list(range(10)), 1, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 0, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.NaN, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.NINF, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.PINF, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [0.1, 0.2, 0.3] * 3, 3, value=0.2)
self.assertEqualPandasSeriesWrapper(value_count, [np.NaN, 0, 1] * 3, 3, value=np.NaN)
self.assertEqualPandasSeriesWrapper(value_count, [np.NINF, 0, 1] * 3, 3, value=np.NINF)
self.assertEqualPandasSeriesWrapper(value_count, [np.PINF, 0, 1] * 3, 3, value=np.PINF)
def test_range_count(self):
self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 0, min=1, max=1)
self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 0, min=0.9, max=1)
self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 10, min=1, max=1.1)
self.assertEqualPandasSeriesWrapper(range_count, list(range(10)), 9, min=0, max=9)
self.assertEqualPandasSeriesWrapper(range_count, list(range(10)), 10, min=0, max=10)
self.assertEqualPandasSeriesWrapper(range_count, list(range(0, -10, -1)), 9, min=-10, max=0)
self.assertEqualPandasSeriesWrapper(range_count, [np.NaN, np.PINF, np.NINF] + list(range(10)), 10, min=0,
max=10)
def test_approximate_entropy(self):
self.assertEqualOnAllArrayTypes(approximate_entropy, [1], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)
self.assertAlmostEqualOnAllArrayTypes(approximate_entropy, [12, 13, 15, 16, 17] * 10, 0.282456191, m=2, r=0.9)
self.assertRaises(ValueError, approximate_entropy, x=[12, 13, 15, 16, 17] * 10, m=2, r=-0.5)
def test_max_langevin_fixed_point(self):
"""
Estimating the intrinsic velocity of a dissipative soliton
"""
default_params = {"m": 3, "r": 30}
# active Brownian motion
ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(100000, v0=np.zeros(1))
v0 = max_langevin_fixed_point(v[:, 0], **default_params)
self.assertLess(abs(ds.deterministic - v0), 0.001)
# Brownian motion
ds = velocity(tau=2.0 / 0.3 - 3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
v0 = max_langevin_fixed_point(v[:, 0], **default_params)
self.assertLess(v0, 0.001)
def test_linear_trend(self):
# check linear up trend
x = range(10)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend(x, param)
res = pd.Series(dict(res))
expected_index = ["attr_\"pvalue\"", "attr_\"intercept\"",
"attr_\"rvalue\"", "attr_\"slope\"",
"attr_\"stderr\""]
self.assertEqual(len(res), 5)
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["attr_\"pvalue\""], 0)
self.assertAlmostEqual(res["attr_\"stderr\""], 0)
self.assertAlmostEqual(res["attr_\"intercept\""], 0)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0)
# check p value for random trend
np.random.seed(42)
x = np.random.uniform(size=100)
param = [{"attr": "rvalue"}]
res = linear_trend(x, param)
res = pd.Series(dict(res))
self.assertLess(abs(res["attr_\"rvalue\""]), 0.1)
# check slope and intercept decreasing trend with intercept
x = [42 - 2 * x for x in range(10)]
param = [{"attr": "intercept"}, {"attr": "slope"}]
res = linear_trend(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"intercept\""], 42)
self.assertAlmostEqual(res["attr_\"slope\""], -2)
def test__aggregate_on_chunks(self):
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="max", chunk_len=2), [1, 3])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([1, 1, 3, 3]), f_agg="max", chunk_len=2), [1, 3])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="min", chunk_len=2), [0, 2])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3, 5]), f_agg="min", chunk_len=2), [0, 2, 5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="mean", chunk_len=2),
[0.5, 2.5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 0, 4, 5]), f_agg="mean", chunk_len=2),
[0.5, 2, 5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 0, 4, 5]), f_agg="mean", chunk_len=3),
[1 / 3, 4.5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3, 5, -2]),
f_agg="median", chunk_len=2), [0.5, 2.5, 1.5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([-10, 5, 3, -3, 4, -6]),
f_agg="median", chunk_len=3), [3, -3])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, np.NaN, 5]),
f_agg="median", chunk_len=2), [0.5, 2, 5])
def test_agg_linear_trend(self):
x = pd.Series(range(9), index=range(9))
param = [{"attr": "intercept", "chunk_len": 3, "f_agg": "max"},
{"attr": "slope", "chunk_len": 3, "f_agg": "max"},
{"attr": "intercept", "chunk_len": 3, "f_agg": "min"},
{"attr": "slope", "chunk_len": 3, "f_agg": "min"},
{"attr": "intercept", "chunk_len": 3, "f_agg": "mean"},
{"attr": "slope", "chunk_len": 3, "f_agg": "mean"},
{"attr": "intercept", "chunk_len": 3, "f_agg": "median"},
{"attr": "slope", "chunk_len": 3, "f_agg": "median"}]
expected_index = ['attr_"intercept"__chunk_len_3__f_agg_"max"',
'attr_"slope"__chunk_len_3__f_agg_"max"',
'attr_"intercept"__chunk_len_3__f_agg_"min"',
'attr_"slope"__chunk_len_3__f_agg_"min"',
'attr_"intercept"__chunk_len_3__f_agg_"mean"',
'attr_"slope"__chunk_len_3__f_agg_"mean"',
'attr_"intercept"__chunk_len_3__f_agg_"median"',
'attr_"slope"__chunk_len_3__f_agg_"median"']
res = agg_linear_trend(x=x, param=param)
res = pd.Series(dict(res))
self.assertEqual(len(res), 8)
self.maxDiff = 2000
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"max"'], 2)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"max"'], 3)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"min"'], 0)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"min"'], 3)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"mean"'], 1)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"mean"'], 3)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"median"'], 1)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"median"'], 3)
x = pd.Series([np.NaN, np.NaN, np.NaN, -3, -3, -3])
res = agg_linear_trend(x=x, param=param)
res = pd.Series(dict(res))
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"max"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"max"'])
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"min"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"min"'])
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"mean"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"mean"'])
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"median"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"median"'])
x = pd.Series([np.NaN, np.NaN, -3, -3, -3, -3])
res = agg_linear_trend(x=x, param=param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"max"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"max"'], 0)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"min"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"min"'], 0)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"mean"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"mean"'], 0)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"median"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"median"'], 0)
def test_energy_ratio_by_chunks(self):
x = pd.Series(range(90), index=range(90))
param = [{"num_segments": 6, "segment_focus": i} for i in range(6)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertAlmostEqual(output[0][1], 0.0043, places=3)
self.assertAlmostEqual(output[1][1], 0.0316, places=3)
self.assertAlmostEqual(output[2][1], 0.0871, places=3)
self.assertAlmostEqual(output[3][1], 0.1709, places=3)
self.assertAlmostEqual(output[4][1], 0.2829, places=3)
self.assertAlmostEqual(output[5][1], 0.4232, places=3)
# Sum of the ratios should be 1.0
sum = 0.0
for name, dat in output:
sum = sum + dat
self.assertAlmostEqual(sum, 1.0)
x = pd.Series(1, index=range(10))
param = [{"num_segments": 3, "segment_focus": i} for i in range(3)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertAlmostEqual(output[0][1], 0.4, places=3)
self.assertAlmostEqual(output[1][1], 0.3, places=3)
self.assertAlmostEqual(output[2][1], 0.3, places=3)
# Sum of the ratios should be 1.0
sum = 0.0
for name, dat in output:
sum = sum + dat
self.assertAlmostEqual(sum, 1.0)
x = pd.Series(0, index=range(10))
param = [{"num_segments": 3, "segment_focus": i} for i in range(3)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertIsNaN(output[0][1])
self.assertIsNaN(output[1][1])
self.assertIsNaN(output[2][1])
def test_linear_trend_timewise_hours(self):
"""Test linear_trend_timewise function with hour intervals."""
x = pd.Series(
[0, 1, 3, 6],
index=pd.DatetimeIndex([
'2018-01-01 04:00:00', '2018-01-01 05:00:00',
'2018-01-01 07:00:00', '2018-01-01 10:00:00'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
expected_index = ["attr_\"pvalue\"", "attr_\"intercept\"",
"attr_\"rvalue\"", "attr_\"slope\"",
"attr_\"stderr\""]
self.assertEqual(len(res), 5)
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_linear_trend_timewise_days(self):
"""Test linear_trend_timewise function with day intervals."""
# Try with different days
x = pd.Series(
[0, 24, 48, 72],
index=pd.DatetimeIndex([
'2018-01-01 04:00:00', '2018-01-02 04:00:00',
'2018-01-03 04:00:00', '2018-01-04 04:00:00'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_linear_trend_timewise_seconds(self):
"""Test linear_trend_timewise function with second intervals."""
# Try with different days
x = pd.Series(
[0, 1 / float(3600), 2 / float(3600), 3 / float(3600)],
index=pd.DatetimeIndex([
'2018-01-01 04:00:01', '2018-01-01 04:00:02',
'2018-01-01 04:00:03', '2018-01-01 04:00:04'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_linear_trend_timewise_years(self):
"""Test linear_trend_timewise function with year intervals."""
# Try with different days
x = pd.Series(
[0, 365 * 24, 365 * 48, 365 * 72 + 24], # Add 24 to the last one since it's a leap year
index=pd.DatetimeIndex([
'2018-01-01 04:00:00', '2019-01-01 04:00:00',
'2020-01-01 04:00:00', '2021-01-01 04:00:00'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_change_quantiles(self):
"""Test change_quantiles function when changing from `sum` to `np.sum`."""
np.random.seed(0)
res = change_quantiles(np.random.rand(10000) * 1000, 0.1, 0.2, False, 'mean')
self.assertAlmostEqual(res, -0.9443846621365727)
def test_count_above(self):
self.assertEqualPandasSeriesWrapper(count_above, [1] * 10, 1, t=1)
self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 1, t=0)
self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 0.5, t=5)
self.assertEqualPandasSeriesWrapper(count_above, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2)
self.assertEqualPandasSeriesWrapper(count_above, [np.NaN, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.NINF, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.PINF, 0, 1] * 3, 1, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.NaN, 0, 1] * 3, 0, t=np.NaN)
self.assertEqualPandasSeriesWrapper(count_above, [np.NINF, 0, np.PINF] * 3, 1, t=np.NINF)
self.assertEqualPandasSeriesWrapper(count_above, [np.PINF, 0, 1] * 3, 1 / 3, t=np.PINF)
def test_count_below(self):
self.assertEqualPandasSeriesWrapper(count_below, [1] * 10, 1, t=1)
self.assertEqualPandasSeriesWrapper(count_below, list(range(10)), 1 / 10, t=0)
self.assertEqualPandasSeriesWrapper(count_below, list(range(10)), 6 / 10, t=5)
self.assertEqualPandasSeriesWrapper(count_below, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2)
self.assertEqualPandasSeriesWrapper(count_below, [np.NaN, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_below, [np.NINF, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_below, [np.PINF, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_below, [np.NaN, 0, 1] * 3, 0, t=np.NaN)
self.assertEqualPandasSeriesWrapper(count_below, [np.NINF, 0, np.PINF] * 3, 1 / 3, t=np.NINF)
self.assertEqualPandasSeriesWrapper(count_below, [np.PINF, 0, 1] * 3, 1, t=np.PINF)
def test_benford_correlation(self):
# A test with list of random values
np.random.seed(42)
random_list = np.random.uniform(size=100)
# Fibonacci series is known to match the Newcomb-Benford's Distribution
fibonacci_list = [0, 1]
for i in range(2, 200):
fibonacci_list.append(fibonacci_list[i - 1] + fibonacci_list[i - 2])
# A list of equally distributed digits (returns NaN)
equal_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
# A list containing NaN
list_with_nan = [1.354, 0.058, 0.055, 0.99, 3.15, np.nan, 0.3, 2.3, 0, 0.59, 0.74]
self.assertAlmostEqual(benford_correlation(random_list), 0.39458056)
self.assertAlmostEqual(benford_correlation(fibonacci_list), 0.998003988)
self.assertAlmostEqual(benford_correlation(list_with_nan), 0.10357511)
self.assertIsNaN(benford_correlation(equal_list))
class FriedrichTestCase(TestCase):
def test_estimate_friedrich_coefficients(self):
"""
Estimate friedrich coefficients
"""
default_params = {"m": 3, "r": 30}
# active Brownian motion
ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
coeff = _estimate_friedrich_coefficients(v[:, 0], **default_params)
self.assertLess(abs(coeff[-1]), 0.0001)
# Brownian motion
ds = velocity(tau=2.0 / 0.3 - 3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
coeff = _estimate_friedrich_coefficients(v[:, 0], **default_params)
self.assertLess(abs(coeff[-1]), 0.0001)
def test_friedrich_coefficients(self):
# Test binning error returns vector of NaNs
param = [{"coeff": coeff, "m": 2, "r": 30} for coeff in range(4)]
x = np.zeros(100)
res = pd.Series(dict(friedrich_coefficients(x, param)))
expected_index = ["coeff_0__m_2__r_30", "coeff_1__m_2__r_30", "coeff_2__m_2__r_30", "coeff_3__m_2__r_30"]
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.sum(np.isnan(res)), 3)
def test_friedrich_number_of_returned_features_is_equal_to_number_of_parameters(self):
""" unit test for issue 501 """
param = [{'m': 3, 'r': 5, 'coeff': 2}, {'m': 3, 'r': 5, 'coeff': 3}, {'m': 3, 'r': 2, 'coeff': 3}]
x = np.zeros(100)
res = pd.Series(dict(friedrich_coefficients(x, param)))
expected_index = ["coeff_2__m_3__r_5", "coeff_3__m_3__r_5", "coeff_3__m_3__r_2"]
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.sum(np.isnan(res)), 3)
def test_friedrich_equal_to_snapshot(self):
param = [{"coeff": coeff, "m": 2, "r": 30} for coeff in range(4)]
x = np.array([-0.53, -0.61, -1.26, -0.88, -0.34, 0.58, 2.86, -0.47, 0.78,
-0.45, -0.27, 0.43, 1.72, 0.26, 1.02, -0.09, 0.65, 1.49,
-0.95, -1.02, -0.64, -1.63, -0.71, -0.43, -1.69, 0.05, 1.58,
1.1, 0.55, -1.02])
res = pd.Series(dict(friedrich_coefficients(x, param)))
self.assertAlmostEqual(res['coeff_0__m_2__r_30'], -0.24536975738843042)
self.assertAlmostEqual(res['coeff_1__m_2__r_30'], -0.533309548662685)
self.assertAlmostEqual(res['coeff_2__m_2__r_30'], 0.2759399238199404)
| # -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016
from random import shuffle
from unittest import TestCase
import warnings
from tsfresh.feature_extraction.feature_calculators import *
from tsfresh.feature_extraction.feature_calculators import _roll
from tsfresh.feature_extraction.feature_calculators import _get_length_sequences_where
from tsfresh.feature_extraction.feature_calculators import _estimate_friedrich_coefficients
from tsfresh.feature_extraction.feature_calculators import _aggregate_on_chunks
from tsfresh.feature_extraction.feature_calculators import _into_subchunks
from tsfresh.examples.driftbif_simulation import velocity
import math
class FeatureCalculationTestCase(TestCase):
def setUp(self):
# There will be a lot of warnings in the feature calculators.
# Just ignore all of them in these tests
warnings.simplefilter("ignore")
def tearDown(self):
warnings.resetwarnings()
def assertIsNaN(self, result):
self.assertTrue(np.isnan(result), msg="{} is not np.NaN")
def assertEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):
expected_result = f(input_to_f, *args, **kwargs)
self.assertEqual(expected_result, result,
msg="Not equal for lists: {} != {}".format(expected_result, result))
expected_result = f(np.array(input_to_f), *args, **kwargs)
self.assertEqual(expected_result, result,
msg="Not equal for numpy.arrays: {} != {}".format(expected_result, result))
expected_result = f(pd.Series(input_to_f, dtype="float64"), *args, **kwargs)
self.assertEqual(expected_result, result,
msg="Not equal for pandas.Series: {} != {}".format(expected_result, result))
def assertTrueOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertTrue(f(input_to_f, *args, **kwargs), msg="Not true for lists")
self.assertTrue(f(np.array(input_to_f), *args, **kwargs), msg="Not true for numpy.arrays")
self.assertTrue(f(pd.Series(input_to_f), *args, **kwargs), msg="Not true for pandas.Series")
def assertAllTrueOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertTrue(all(dict(f(input_to_f, *args, **kwargs)).values()), msg="Not true for lists")
self.assertTrue(all(dict(f(np.array(input_to_f), *args, **kwargs)).values()), msg="Not true for numpy.arrays")
self.assertTrue(all(dict(f(pd.Series(input_to_f), *args, **kwargs)).values()), msg="Not true for pandas.Series")
def assertFalseOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertFalse(f(input_to_f, *args, **kwargs), msg="Not false for lists")
self.assertFalse(f(np.array(input_to_f), *args, **kwargs), msg="Not false for numpy.arrays")
self.assertFalse(f(pd.Series(input_to_f), *args, **kwargs), msg="Not false for pandas.Series")
def assertAllFalseOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertFalse(any(dict(f(input_to_f, *args, **kwargs)).values()), msg="Not false for lists")
self.assertFalse(any(dict(f(np.array(input_to_f), *args, **kwargs)).values()),
msg="Not false for numpy.arrays")
self.assertFalse(any(dict(f(pd.Series(input_to_f), *args, **kwargs)).values()),
msg="Not false for pandas.Series")
def assertAlmostEqualOnAllArrayTypes(self, f, input_to_f, result, *args, **kwargs):
expected_result = f(input_to_f, *args, **kwargs)
self.assertAlmostEqual(expected_result, result,
msg="Not almost equal for lists: {} != {}".format(expected_result, result))
expected_result = f(np.array(input_to_f), *args, **kwargs)
self.assertAlmostEqual(expected_result, result,
msg="Not almost equal for numpy.arrays: {} != {}".format(expected_result, result))
expected_result = f(pd.Series(input_to_f, dtype="float64"), *args, **kwargs)
self.assertAlmostEqual(expected_result, result,
msg="Not almost equal for pandas.Series: {} != {}".format(expected_result, result))
def assertIsNanOnAllArrayTypes(self, f, input_to_f, *args, **kwargs):
self.assertTrue(np.isnan(f(input_to_f, *args, **kwargs)), msg="Not NaN for lists")
self.assertTrue(np.isnan(f(np.array(input_to_f), *args, **kwargs)), msg="Not NaN for numpy.arrays")
self.assertTrue(np.isnan(f(pd.Series(input_to_f, dtype="float64"), *args, **kwargs)),
msg="Not NaN for pandas.Series")
def assertEqualPandasSeriesWrapper(self, f, input_to_f, result, *args, **kwargs):
self.assertEqual(f(pd.Series(input_to_f), *args, **kwargs), result,
msg="Not equal for pandas.Series: {} != {}".format(
f(pd.Series(input_to_f), *args, **kwargs), result))
def test__roll(self):
x = np.random.normal(size=30)
for shift in [0, 1, 10, 11, 30, 31, 50, 51, 150, 151]:
np.testing.assert_array_equal(_roll(x, shift), np.roll(x, shift))
np.testing.assert_array_equal(_roll(x, -shift), np.roll(x, -shift))
def test___get_length_sequences_where(self):
self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1],
[1, 3, 1, 2])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where,
[0, True, 0, 0, True, True, True, 0, 0, True, 0, True, True],
[1, 3, 1, 2])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where,
[0, True, 0, 0, 1, True, 1, 0, 0, True, 0, 1, True], [1, 3, 1, 2])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [0] * 10, [0])
self.assertEqualOnAllArrayTypes(_get_length_sequences_where, [], [0])
def test__into_subchunks(self):
np.testing.assert_array_equal(_into_subchunks(range(7), 3, 2), np.array([[0, 1, 2], [2, 3, 4], [4, 5, 6]]))
np.testing.assert_array_equal(_into_subchunks(range(5), 3), np.array([[0, 1, 2], [1, 2, 3], [2, 3, 4]]))
def test_variance_larger_than_standard_deviation(self):
self.assertFalseOnAllArrayTypes(variance_larger_than_standard_deviation, [-1, -1, 1, 1, 1])
self.assertTrueOnAllArrayTypes(variance_larger_than_standard_deviation, [-1, -1, 1, 1, 2])
def test_large_standard_deviation(self):
self.assertFalseOnAllArrayTypes(large_standard_deviation, [1, 1, 1, 1], r=0)
self.assertFalseOnAllArrayTypes(large_standard_deviation, [1, 1, 1, 1], r=0)
self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0)
self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.25)
self.assertTrueOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.3)
self.assertFalseOnAllArrayTypes(large_standard_deviation, [-1, -1, 1, 1], r=0.5)
def test_symmetry_looking(self):
self.assertAllTrueOnAllArrayTypes(symmetry_looking, [-1, -1, 1, 1],
[dict(r=0.05), dict(r=0.75)])
self.assertAllFalseOnAllArrayTypes(symmetry_looking, [-1, -1, 1, 1], [dict(r=0)])
self.assertAllFalseOnAllArrayTypes(symmetry_looking, [-1, -1, -1, -1, 1], [dict(r=0.05)])
self.assertAllTrueOnAllArrayTypes(symmetry_looking, [-2, -2, -2, -1, -1, -1], [dict(r=0.05)])
self.assertAllTrueOnAllArrayTypes(symmetry_looking, [-0.9, -0.900001], [dict(r=0.05)])
def test_has_duplicate_max(self):
self.assertTrueOnAllArrayTypes(has_duplicate_max, [2.1, 0, 0, 2.1, 1.1])
self.assertFalseOnAllArrayTypes(has_duplicate_max, np.array([2.1, 0, 0, 2, 1.1]))
self.assertTrueOnAllArrayTypes(has_duplicate_max, [1, 1, 1, 1])
self.assertFalseOnAllArrayTypes(has_duplicate_max, np.array([0]))
self.assertTrueOnAllArrayTypes(has_duplicate_max, np.array([1, 1]))
def test_has_duplicate_min(self):
self.assertTrueOnAllArrayTypes(has_duplicate_min, [-2.1, 0, 0, -2.1, 1.1])
self.assertFalseOnAllArrayTypes(has_duplicate_min, [2.1, 0, -1, 2, 1.1])
self.assertTrueOnAllArrayTypes(has_duplicate_min, np.array([1, 1, 1, 1]))
self.assertFalseOnAllArrayTypes(has_duplicate_min, np.array([0]))
self.assertTrueOnAllArrayTypes(has_duplicate_min, np.array([1, 1]))
def test_has_duplicate(self):
self.assertTrueOnAllArrayTypes(has_duplicate, np.array([-2.1, 0, 0, -2.1]))
self.assertTrueOnAllArrayTypes(has_duplicate, [-2.1, 2.1, 2.1, 2.1])
self.assertFalseOnAllArrayTypes(has_duplicate, [1.1, 1.2, 1.3, 1.4])
self.assertFalseOnAllArrayTypes(has_duplicate, [1])
self.assertFalseOnAllArrayTypes(has_duplicate, [])
def test_sum(self):
self.assertEqualOnAllArrayTypes(sum_values, [1, 2, 3, 4.1], 10.1)
self.assertEqualOnAllArrayTypes(sum_values, [-1.2, -2, -3, -4], -10.2)
self.assertEqualOnAllArrayTypes(sum_values, [], 0)
def test_agg_autocorrelation_returns_correct_values(self):
param = [{"f_agg": "mean", "maxlag": 10}]
x = [1, 1, 1, 1, 1, 1, 1]
expected_res = 0
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
x = [1, 2, -3]
expected_res = 1 / np.var(x) * (((1 * 2 + 2 * (-3)) / 2 + (1 * -3)) / 2)
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
np.random.seed(42)
x = np.random.normal(size=3000)
expected_res = 0
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=2)
param = [{"f_agg": "median", "maxlag": 10}]
x = [1, 1, 1, 1, 1, 1, 1]
expected_res = 0
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"median\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
x = [1, 2, -3]
expected_res = 1 / np.var(x) * (((1 * 2 + 2 * (-3)) / 2 + (1 * -3)) / 2)
res = dict(agg_autocorrelation(x, param=param))["f_agg_\"median\"__maxlag_10"]
self.assertAlmostEqual(res, expected_res, places=4)
def test_agg_autocorrelation_returns_max_lag_does_not_affect_other_results(self):
param = [{"f_agg": "mean", "maxlag": 1},
{"f_agg": "mean", "maxlag": 10}]
x = range(10)
res1 = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_1"]
res10 = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_10"]
self.assertAlmostEqual(res1, 0.77777777, places=4)
self.assertAlmostEqual(res10, -0.64983164983165, places=4)
param = [{"f_agg": "mean", "maxlag": 1}]
x = range(10)
res1 = dict(agg_autocorrelation(x, param=param))["f_agg_\"mean\"__maxlag_1"]
self.assertAlmostEqual(res1, 0.77777777, places=4)
def test_partial_autocorrelation(self):
# Test for altering time series
# len(x) < max_lag
param = [{"lag": lag} for lag in range(10)]
x = [1, 2, 1, 2, 1, 2]
expected_res = [("lag_0", 1.0), ("lag_1", -1.0), ("lag_2", np.nan)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=4)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=4)
self.assertIsNaN(res[2][1])
# Linear signal
param = [{"lag": lag} for lag in range(10)]
x = np.linspace(0, 1, 3000)
expected_res = [("lag_0", 1.0), ("lag_1", 1.0), ("lag_2", 0)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=2)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=2)
self.assertAlmostEqual(res[2][1], expected_res[2][1], places=2)
# Random noise
np.random.seed(42)
x = np.random.normal(size=3000)
param = [{"lag": lag} for lag in range(10)]
expected_res = [("lag_0", 1.0), ("lag_1", 0), ("lag_2", 0)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=1)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=1)
self.assertAlmostEqual(res[2][1], expected_res[2][1], places=1)
# On a simulated AR process
np.random.seed(42)
param = [{"lag": lag} for lag in range(10)]
# Simulate AR process
T = 3000
epsilon = np.random.randn(T)
x = np.repeat(1.0, T)
for t in range(T - 1):
x[t + 1] = 0.5 * x[t] + 2 + epsilon[t]
expected_res = [("lag_0", 1.0), ("lag_1", 0.5), ("lag_2", 0)]
res = partial_autocorrelation(x, param=param)
self.assertAlmostEqual(res[0][1], expected_res[0][1], places=1)
self.assertAlmostEqual(res[1][1], expected_res[1][1], places=1)
self.assertAlmostEqual(res[2][1], expected_res[2][1], places=1)
# Some pathological cases
param = [{"lag": lag} for lag in range(10)]
# List of length 1
res = partial_autocorrelation([1], param=param)
for lag_no, lag_val in res:
self.assertIsNaN(lag_val)
# Empty list
res = partial_autocorrelation([], param=param)
for lag_no, lag_val in res:
self.assertIsNaN(lag_val)
# List contains only zeros
res = partial_autocorrelation(np.zeros(100), param=param)
for lag_no, lag_val in res:
if lag_no == "lag_0":
self.assertEqual(lag_val, 1.0)
else:
self.assertIsNaN(lag_val)
def test_augmented_dickey_fuller(self):
# todo: add unit test for the values of the test statistic
# the adf hypothesis test checks for unit roots,
# so H_0 = {random drift} vs H_1 = {AR(1) model}
# H0 is true
np.random.seed(seed=42)
x = np.cumsum(np.random.uniform(size=100))
param = [
{"autolag": "BIC", "attr": "teststat"},
{"autolag": "BIC", "attr": "pvalue"},
{"autolag": "BIC", "attr": "usedlag"}
]
expected_index = [
'attr_"teststat"__autolag_"BIC"',
'attr_"pvalue"__autolag_"BIC"',
'attr_"usedlag"__autolag_"BIC"',
]
res = augmented_dickey_fuller(x=x, param=param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertGreater(res['attr_"pvalue"__autolag_"BIC"'], 0.10)
self.assertEqual(res['attr_"usedlag"__autolag_"BIC"'], 0)
# H0 should be rejected for AR(1) model with x_{t} = 1/2 x_{t-1} + e_{t}
np.random.seed(seed=42)
e = np.random.normal(0.1, 0.1, size=100)
m = 50
x = [0] * m
x[0] = 100
for i in range(1, m):
x[i] = x[i - 1] * 0.5 + e[i]
param = [
{"autolag": "AIC", "attr": "teststat"},
{"autolag": "AIC", "attr": "pvalue"},
{"autolag": "AIC", "attr": "usedlag"}
]
expected_index = [
'attr_"teststat"__autolag_"AIC"',
'attr_"pvalue"__autolag_"AIC"',
'attr_"usedlag"__autolag_"AIC"',
]
res = augmented_dickey_fuller(x=x, param=param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertLessEqual(res['attr_"pvalue"__autolag_"AIC"'], 0.05)
self.assertEqual(res['attr_"usedlag"__autolag_"AIC"'], 0)
# Check if LinAlgError and ValueError are catched
res_linalg_error = augmented_dickey_fuller(x=np.repeat(np.nan, 100), param=param)
res_value_error = augmented_dickey_fuller(x=[], param=param)
for index, val in res_linalg_error:
self.assertIsNaN(val)
for index, val in res_value_error:
self.assertIsNaN(val)
# Should return NaN if "attr" is unknown
res_attr_error = augmented_dickey_fuller(x=x, param=[{"autolag": "AIC", "attr": ""}])
for index, val in res_attr_error:
self.assertIsNaN(val)
def test_abs_energy(self):
self.assertEqualOnAllArrayTypes(abs_energy, [1, 1, 1], 3)
self.assertEqualOnAllArrayTypes(abs_energy, [1, 2, 3], 14)
self.assertEqualOnAllArrayTypes(abs_energy, [-1, 2, -3], 14)
self.assertAlmostEqualOnAllArrayTypes(abs_energy, [-1, 1.3], 2.69)
self.assertEqualOnAllArrayTypes(abs_energy, [1], 1)
def test_cid_ce(self):
self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [0, 4], 2, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [100, 104], 2, normalize=True)
self.assertEqualOnAllArrayTypes(cid_ce, [1, 1, 1], 0, normalize=False)
self.assertEqualOnAllArrayTypes(cid_ce, [0.5, 3.5, 7.5], 5, normalize=False)
self.assertEqualOnAllArrayTypes(cid_ce, [-4.33, -1.33, 2.67], 5, normalize=False)
def test_lempel_ziv_complexity(self):
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1], 2. / 3, bins=2)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1], 2. / 3, bins=5)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1, 1, 1, 1, 1],
0.4285714285, bins=2)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity, [1, 1, 1, 2, 1, 1, 1],
0.5714285714, bins=2)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity,
[-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
0.8, bins=10)
self.assertAlmostEqualOnAllArrayTypes(lempel_ziv_complexity,
[-1, np.nan, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
0.4, bins=10)
def test_fourier_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 2, 1], 0.693147180, bins=2)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 2, 1], 0.693147180, bins=5)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 1, 2, 1, 1, 1, 1],
0.5623351446188083, bins=5)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy, [1, 1, 1, 1, 2, 1, 1],
1.0397207708399179, bins=5)
self.assertAlmostEqualOnAllArrayTypes(fourier_entropy,
[-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
1.5607104090414063, bins=10)
self.assertIsNanOnAllArrayTypes(fourier_entropy,
[-1, np.nan, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
bins=10)
def test_permutation_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy, [4, 7, 9, 10, 6, 11, 3], 1.054920167,
dimension=3, tau=1)
# should grow
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy, [1, -1, 1, -1, 1, -1, 1, -1],
0.6931471805599453, dimension=3, tau=1)
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy, [1, -1, 1, -1, 1, 1, 1, -1],
1.3296613488547582, dimension=3, tau=1)
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy,
[-1, 4.3, 5, 1, -4.5, 1, 5, 7, -3.4, 6],
1.0397207708399179, dimension=3, tau=2)
# nan is treated like any other number
self.assertAlmostEqualOnAllArrayTypes(permutation_entropy,
[-1, 4.3, 5, 1, -4.5, 1, 5, np.nan, -3.4, 6],
1.0397207708399179, dimension=3, tau=2)
# if too short, return nan
self.assertIsNanOnAllArrayTypes(permutation_entropy, [1, -1], dimension=3, tau=1)
def test_ratio_beyond_r_sigma(self):
x = [0, 1] * 10 + [10, 20, -30] # std of x is 7.21, mean 3.04
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 3. / len(x), r=1)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 2. / len(x), r=2)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 1. / len(x), r=3)
self.assertEqualOnAllArrayTypes(ratio_beyond_r_sigma, x, 0, r=20)
def test_mean_abs_change(self):
self.assertEqualOnAllArrayTypes(mean_abs_change, [-2, 2, 5], 3.5)
self.assertEqualOnAllArrayTypes(mean_abs_change, [1, 2, -1], 2)
def test_mean_change(self):
self.assertEqualOnAllArrayTypes(mean_change, [-2, 2, 5], 3.5)
self.assertEqualOnAllArrayTypes(mean_change, [1, 2, -1], -1)
self.assertEqualOnAllArrayTypes(mean_change, [10, 20], 10)
self.assertIsNanOnAllArrayTypes(mean_change, [1])
self.assertIsNanOnAllArrayTypes(mean_change, [])
def test_mean_second_derivate_central(self):
self.assertEqualOnAllArrayTypes(mean_second_derivative_central, list(range(10)), 0)
self.assertEqualOnAllArrayTypes(mean_second_derivative_central, [1, 3, 5], 0)
self.assertEqualOnAllArrayTypes(mean_second_derivative_central, [1, 3, 7, -3], -3)
def test_median(self):
self.assertEqualOnAllArrayTypes(median, [1, 1, 2, 2], 1.5)
self.assertEqualOnAllArrayTypes(median, [0.5, 0.5, 2, 3.5, 10], 2)
self.assertEqualOnAllArrayTypes(median, [0.5], 0.5)
self.assertIsNanOnAllArrayTypes(median, [])
def test_mean(self):
self.assertEqualOnAllArrayTypes(mean, [1, 1, 2, 2], 1.5)
self.assertEqualOnAllArrayTypes(mean, [0.5, 0.5, 2, 3.5, 10], 3.3)
self.assertEqualOnAllArrayTypes(mean, [0.5], 0.5)
self.assertIsNanOnAllArrayTypes(mean, [])
def test_length(self):
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, 4], 4)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3], 3)
self.assertEqualOnAllArrayTypes(length, [1, 2], 2)
self.assertEqualOnAllArrayTypes(length, [1, 2, 3, np.NaN], 4)
self.assertEqualOnAllArrayTypes(length, [], 0)
def test_standard_deviation(self):
self.assertAlmostEqualOnAllArrayTypes(standard_deviation, [1, 1, -1, -1], 1)
self.assertAlmostEqualOnAllArrayTypes(standard_deviation, [1, 2, -2, -1], 1.58113883008)
self.assertIsNanOnAllArrayTypes(standard_deviation, [])
def test_variation_coefficient(self):
self.assertIsNanOnAllArrayTypes(variation_coefficient, [1, 1, -1, -1], )
self.assertAlmostEqualOnAllArrayTypes(variation_coefficient, [1, 2, -3, -1], -7.681145747868608)
self.assertAlmostEqualOnAllArrayTypes(variation_coefficient, [1, 2, 4, -1], 1.2018504251546631)
self.assertIsNanOnAllArrayTypes(variation_coefficient, [])
def test_variance(self):
self.assertAlmostEqualOnAllArrayTypes(variance, [1, 1, -1, -1], 1)
self.assertAlmostEqualOnAllArrayTypes(variance, [1, 2, -2, -1], 2.5)
self.assertIsNanOnAllArrayTypes(variance, [])
def test_skewness(self):
self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1, 2, 2, 2], 0)
self.assertAlmostEqualOnAllArrayTypes(skewness, [1, 1, 1, 2, 2], 0.6085806194501855)
self.assertEqualOnAllArrayTypes(skewness, [1, 1, 1], 0)
self.assertIsNanOnAllArrayTypes(skewness, [1, 1])
def test_kurtosis(self):
self.assertAlmostEqualOnAllArrayTypes(kurtosis, [1, 1, 1, 2, 2], -3.333333333333333)
self.assertAlmostEqualOnAllArrayTypes(kurtosis, [1, 1, 1, 1], 0)
self.assertIsNanOnAllArrayTypes(kurtosis, [1, 1, 1])
def test_absolute_sum_of_changes(self):
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, 1, 1, 1, 2, 1], 2)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1, -1, 1, -1], 6)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [1], 0)
self.assertEqualOnAllArrayTypes(absolute_sum_of_changes, [], 0)
def test_longest_strike_below_mean(self):
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 1, 1, 1, 2, 2, 2], 3)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 3, 4, 5, 6], 3)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 3, 4, 5], 2)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [1, 2, 1], 1)
self.assertEqualOnAllArrayTypes(longest_strike_below_mean, [], 0)
def test_longest_strike_above_mean(self):
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 1, 2, 1, 2, 2, 1], 2)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 3, 4, 5, 6], 3)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 3, 4, 5], 2)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [1, 2, 1], 1)
self.assertEqualOnAllArrayTypes(longest_strike_above_mean, [], 0)
def test_count_above_mean(self):
self.assertEqualOnAllArrayTypes(count_above_mean, [1, 2, 1, 2, 1, 2], 3)
self.assertEqualOnAllArrayTypes(count_above_mean, [1, 1, 1, 1, 1, 2], 1)
self.assertEqualOnAllArrayTypes(count_above_mean, [1, 1, 1, 1, 1], 0)
self.assertEqualOnAllArrayTypes(count_above_mean, [], 0)
def test_count_below_mean(self):
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 2, 1, 2, 1, 2], 3)
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1, 2], 5)
self.assertEqualOnAllArrayTypes(count_below_mean, [1, 1, 1, 1, 1], 0)
self.assertEqualOnAllArrayTypes(count_below_mean, [], 0)
def test_last_location_maximum(self):
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1, 2, 1, 2, 1], 0.8)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1, 2, 1, 1, 2], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [2, 1, 1, 1, 1], 0.2)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1, 1, 1, 1, 1], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_maximum, [1], 1.0)
self.assertIsNanOnAllArrayTypes(last_location_of_maximum, [])
def test_first_location_of_maximum(self):
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1, 2, 1, 2, 1], 0.2)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1, 2, 1, 1, 2], 0.2)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [2, 1, 1, 1, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1, 1, 1, 1, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_maximum, [1], 0.0)
self.assertIsNanOnAllArrayTypes(first_location_of_maximum, [])
def test_last_location_of_minimum(self):
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1, 2, 1, 2, 1], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1, 2, 1, 2, 2], 0.6)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [2, 1, 1, 1, 2], 0.8)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1, 1, 1, 1, 1], 1.0)
self.assertAlmostEqualOnAllArrayTypes(last_location_of_minimum, [1], 1.0)
self.assertIsNanOnAllArrayTypes(last_location_of_minimum, [])
def test_first_location_of_minimum(self):
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1, 2, 1, 2, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [2, 2, 1, 2, 2], 0.4)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [2, 1, 1, 1, 2], 0.2)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1, 1, 1, 1, 1], 0.0)
self.assertAlmostEqualOnAllArrayTypes(first_location_of_minimum, [1], 0.0)
self.assertIsNanOnAllArrayTypes(first_location_of_minimum, [])
def test_percentage_of_doubled_datapoints(self):
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1, 2, 3, 4],
0.4)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints,
[1.111, -2.45, 1.111, 2.45], 0.5)
self.assertIsNanOnAllArrayTypes(percentage_of_reoccurring_datapoints_to_all_datapoints, [])
def test_ratio_of_doubled_values(self):
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1, 2, 3, 4], 0.25)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values,
[1.111, -2.45, 1.111, 2.45], 1.0 / 3.0)
self.assertIsNanOnAllArrayTypes(percentage_of_reoccurring_values_to_all_values, [])
def test_sum_of_reoccurring_values(self):
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1, 1, 2, 3, 4, 4], 5)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [1.111, -2.45, 1.111, 2.45], 1.111)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_values, [], 0)
def test_sum_of_reoccurring_data_points(self):
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1, 1, 2, 3, 4, 4], 10)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1, 1.5, 2, 3], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1], 0)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [1.111, -2.45, 1.111, 2.45], 2.222)
self.assertAlmostEqualOnAllArrayTypes(sum_of_reoccurring_data_points, [], 0)
def test_uniqueness_factor(self):
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1, 1, 2, 3, 4], 0.8)
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1, 1.5, 2, 3], 1)
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1], 1)
self.assertAlmostEqualOnAllArrayTypes(ratio_value_number_to_time_series_length, [1.111, -2.45, 1.111, 2.45],
0.75)
self.assertIsNanOnAllArrayTypes(ratio_value_number_to_time_series_length, [])
def test_fft_coefficient(self):
x = range(10)
param = [{"coeff": 0, "attr": "real"}, {"coeff": 1, "attr": "real"}, {"coeff": 2, "attr": "real"},
{"coeff": 0, "attr": "imag"}, {"coeff": 1, "attr": "imag"}, {"coeff": 2, "attr": "imag"},
{"coeff": 0, "attr": "angle"}, {"coeff": 1, "attr": "angle"}, {"coeff": 2, "attr": "angle"},
{"coeff": 0, "attr": "abs"}, {"coeff": 1, "attr": "abs"}, {"coeff": 2, "attr": "abs"}]
expected_index = ['attr_"real"__coeff_0', 'attr_"real"__coeff_1', 'attr_"real"__coeff_2',
'attr_"imag"__coeff_0', 'attr_"imag"__coeff_1', 'attr_"imag"__coeff_2',
'attr_"angle"__coeff_0', 'attr_"angle"__coeff_1', 'attr_"angle"__coeff_2',
'attr_"abs"__coeff_0', 'attr_"abs"__coeff_1', 'attr_"abs"__coeff_2']
res = pd.Series(dict(fft_coefficient(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['attr_"imag"__coeff_0'], 0, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_0'], sum(x), places=6)
self.assertAlmostEqual(res['attr_"angle"__coeff_0'], 0, places=6)
self.assertAlmostEqual(res['attr_"abs"__coeff_0'], sum(x), places=6)
x = [0, 1, 0, 0]
res = pd.Series(dict(fft_coefficient(x, param)))
# see documentation of fft in numpy
# should return array([1. + 0.j, 0. - 1.j, -1. + 0.j])
self.assertAlmostEqual(res['attr_"imag"__coeff_0'], 0, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_0'], 1, places=6)
self.assertAlmostEqual(res['attr_"imag"__coeff_1'], -1, places=6)
self.assertAlmostEqual(res['attr_"angle"__coeff_1'], -90, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_1'], 0, places=6)
self.assertAlmostEqual(res['attr_"imag"__coeff_2'], 0, places=6)
self.assertAlmostEqual(res['attr_"real"__coeff_2'], -1, places=6)
# test what happens if coeff is biger than time series lenght
x = range(5)
param = [{"coeff": 10, "attr": "real"}]
expected_index = ['attr_"real"__coeff_10']
res = pd.Series(dict(fft_coefficient(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertIsNaN(res['attr_"real"__coeff_10'])
def test_fft_aggregated(self):
param = [
{"aggtype": "centroid"},
{"aggtype": "variance"},
{"aggtype": "skew"},
{"aggtype": "kurtosis"}
]
expected_index = ['aggtype_"centroid"', 'aggtype_"variance"', 'aggtype_"skew"', 'aggtype_"kurtosis"']
x = np.arange(10)
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['aggtype_"centroid"'], 1.135, places=3)
self.assertAlmostEqual(res['aggtype_"variance"'], 2.368, places=3)
self.assertAlmostEqual(res['aggtype_"skew"'], 1.249, places=3)
self.assertAlmostEqual(res['aggtype_"kurtosis"'], 3.643, places=3)
# Scalar multiplying the distribution should not change the results:
x = 10 * x
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['aggtype_"centroid"'], 1.135, places=3)
self.assertAlmostEqual(res['aggtype_"variance"'], 2.368, places=3)
self.assertAlmostEqual(res['aggtype_"skew"'], 1.249, places=3)
self.assertAlmostEqual(res['aggtype_"kurtosis"'], 3.643, places=3)
# The fft of a sign wave is a dirac delta, variance and skew should be near zero, kurtosis should be near 3:
# However, in the discrete limit, skew and kurtosis blow up in a manner that is noise dependent and are
# therefore bad features, therefore an nan should be returned for these values
x = np.sin(2 * np.pi / 10 * np.arange(30))
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['aggtype_"centroid"'], 3., places=5)
self.assertAlmostEqual(res['aggtype_"variance"'], 0., places=5)
self.assertIsNaN(res['aggtype_"skew"'])
self.assertIsNaN(res['aggtype_"kurtosis"'])
# Gaussian test:
def normal(y, mean_, sigma_):
return 1 / (2 * np.pi * sigma_ ** 2) * np.exp(-(y - mean_) ** 2 / (2 * sigma_ ** 2))
mean_ = 500.
sigma_ = 1.
range_ = int(2 * mean_)
x = list(map(lambda x: normal(x, mean_, sigma_), range(range_)))
# The fourier transform of a Normal dist in the positive halfspace is a half normal,
# Hand calculated values of centroid and variance based for the half-normal dist:
# (Ref: https://en.wikipedia.org/wiki/Half-normal_distribution)
expected_fft_centroid = (range_ / (2 * np.pi * sigma_)) * np.sqrt(2 / np.pi)
expected_fft_var = (range_ / (2 * np.pi * sigma_)) ** 2 * (1 - 2 / np.pi)
# Calculate values for unit test:
res = pd.Series(dict(fft_aggregated(x, param)))
self.assertCountEqual(list(res.index), expected_index)
# Compare against hand calculated values:
rel_diff_allowed = 0.02
self.assertAlmostEqual(
res['aggtype_"centroid"'], expected_fft_centroid,
delta=rel_diff_allowed * expected_fft_centroid
)
self.assertAlmostEqual(
res['aggtype_"variance"'], expected_fft_var,
delta=rel_diff_allowed * expected_fft_var
)
def test_number_peaks(self):
x = np.array([0, 1, 2, 1, 0, 1, 2, 3, 4, 5, 4, 3, 2, 1])
self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 1)
self.assertEqualOnAllArrayTypes(number_peaks, x, 2, 2)
self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 3)
self.assertEqualOnAllArrayTypes(number_peaks, x, 1, 4)
self.assertEqualOnAllArrayTypes(number_peaks, x, 0, 5)
self.assertEqualOnAllArrayTypes(number_peaks, x, 0, 6)
def test_mass_quantile(self):
x = [1] * 101
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.5"], 0.5, places=1)
# Test for parts of pandas series
x = pd.Series([0] * 55 + [1] * 101)
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x[x > 0], param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.5"], 0.5, places=1)
x = [0] * 1000 + [1]
param = [{"q": 0.5}, {"q": 0.99}]
expected_index = ["q_0.5", "q_0.99"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.5"], 1, places=1)
self.assertAlmostEqual(res["q_0.99"], 1, places=1)
x = [0, 1, 1, 0, 0, 1, 0, 0]
param = [{"q": 0.30}, {"q": 0.60}, {"q": 0.90}]
expected_index = ["q_0.3", "q_0.6",
"q_0.9"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["q_0.3"], 0.25, places=1)
self.assertAlmostEqual(res["q_0.6"], 0.375, places=1)
self.assertAlmostEqual(res["q_0.9"], 0.75, places=1)
x = [0, 0, 0]
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.isnan(res["q_0.5"]))
x = []
param = [{"q": 0.5}]
expected_index = ["q_0.5"]
res = index_mass_quantile(x, param)
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.isnan(res["q_0.5"]))
def test_number_cwt_peaks(self):
x = [1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1]
self.assertEqualOnAllArrayTypes(number_cwt_peaks, x, 2, 2)
def test_spkt_welch_density(self):
# todo: improve tests
x = range(10)
param = [{"coeff": 1}, {"coeff": 10}]
expected_index = ["coeff_1", "coeff_10"]
res = pd.Series(dict(spkt_welch_density(x, param)))
self.assertCountEqual(list(res.index), expected_index)
self.assertIsNaN(res["coeff_10"])
def test_cwt_coefficients(self):
x = [0.1, 0.2, 0.3]
param = [{"widths": (1, 2, 3), "coeff": 2, "w": 1},
{"widths": (1, 3), "coeff": 2, "w": 3},
{"widths": (1, 3), "coeff": 5, "w": 3}]
shuffle(param)
expected_index = ["coeff_2__w_1__widths_(1, 2, 3)",
"coeff_2__w_3__widths_(1, 3)",
"coeff_5__w_3__widths_(1, 3)"]
res = cwt_coefficients(x, param)
res = pd.Series(dict(res))
# todo: add unit test for the values
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(math.isnan(res["coeff_5__w_3__widths_(1, 3)"]))
def test_ar_coefficient(self):
# Test for X_i = 2.5 * X_{i-1} + 1
param = [{"k": 1, "coeff": 0}, {"k": 1, "coeff": 1}]
shuffle(param)
x = [1] + 9 * [0]
for i in range(1, len(x)):
x[i] = 2.5 * x[i - 1] + 1
res = ar_coefficient(x, param)
expected_index = ["coeff_0__k_1", "coeff_1__k_1"]
res = pd.Series(dict(res))
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["coeff_0__k_1"], 1, places=2)
self.assertAlmostEqual(res["coeff_1__k_1"], 2.5, places=2)
# Test for X_i = 1.4 * X_{i-1} - 1 X_{i-2} + 1
param = [{"k": 1, "coeff": 0}, {"k": 1, "coeff": 1},
{"k": 2, "coeff": 0}, {"k": 2, "coeff": 1}, {"k": 2, "coeff": 2}, {"k": 2, "coeff": 3}]
shuffle(param)
x = [1, 1] + 5 * [0]
for i in range(2, len(x)):
x[i] = (-2) * x[i - 2] + 3.5 * x[i - 1] + 1
res = ar_coefficient(x, param)
expected_index = ["coeff_0__k_1", "coeff_1__k_1",
"coeff_0__k_2", "coeff_1__k_2",
"coeff_2__k_2", "coeff_3__k_2"]
res = pd.Series(dict(res))
self.assertIsInstance(res, pd.Series)
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["coeff_0__k_2"], 1, places=2)
self.assertAlmostEqual(res["coeff_1__k_2"], 3.5, places=2)
self.assertAlmostEqual(res["coeff_2__k_2"], -2, places=2)
self.assertTrue(np.isnan(res["coeff_3__k_2"]))
def test_time_reversal_asymmetry_statistic(self):
x = [1] * 10
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 0)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 1)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 2)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 3)
x = [1, 2, -3, 4]
# 1/2 * ( (4^2 * -3 + 3 * 2^2) + (3^2*2)-(2*1^1)) = 1/2 * (-48+12+18-2) = 20/2
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, -10, 1)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 2)
self.assertAlmostEqualOnAllArrayTypes(time_reversal_asymmetry_statistic, x, 0, 3)
def test_number_crossing_m(self):
x = [10, -10, 10, -10]
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 3, 0)
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 0, 10)
x = [10, 20, 20, 30]
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 0, 0)
self.assertEqualOnAllArrayTypes(number_crossing_m, x, 1, 15)
def test_c3(self):
x = [1] * 10
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 0)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 1)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 2)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 1, 3)
x = [1, 2, -3, 4]
# 1/2 *(1*2*(-3)+2*(-3)*4) = 1/2 *(-6-24) = -30/2
self.assertAlmostEqualOnAllArrayTypes(c3, x, -15, 1)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 0, 2)
self.assertAlmostEqualOnAllArrayTypes(c3, x, 0, 3)
def test_binned_entropy(self):
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 100, 0, 10)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 10 + [1], - (10 / 11 * np.math.log(10 / 11) +
1 / 11 * np.math.log(1 / 11)), 10)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 10 + [1], - (10 / 11 * np.math.log(10 / 11) +
1 / 11 * np.math.log(1 / 11)), 10)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, [10] * 10 + [1], - (10 / 11 * np.math.log(10 / 11) +
1 / 11 * np.math.log(1 / 11)), 100)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, list(range(10)), - np.math.log(1 / 10), 100)
self.assertAlmostEqualOnAllArrayTypes(binned_entropy, list(range(100)), - np.math.log(1 / 2), 2)
def test_sample_entropy(self):
# "random" list -> large entropy
ts = [1, 4, 5, 1, 7, 3, 1, 2, 5, 8, 9, 7, 3, 7, 9, 5, 4, 3, 9, 1, 2, 3, 4, 2, 9, 6, 7, 4, 9, 2, 9, 9, 6, 5, 1,
3, 8, 1, 5, 3, 8, 4, 1, 2, 2, 1, 6, 5, 3, 6, 5, 4, 8, 9, 6, 7, 5, 3, 2, 5, 4, 2, 5, 1, 6, 5, 3, 5, 6, 7,
8, 5, 2, 8, 6, 3, 8, 2, 7, 1, 7, 3, 5, 6, 2, 1, 3, 7, 3, 5, 3, 7, 6, 7, 7, 2, 3, 1, 7, 8]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 2.38262780)
# This is not very complex, so it gives a small value
ts = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.25131442)
# however adding a 2 increases complexity
ts = [1, 1, 2, 1, 1, 1, 1, 1, 1, 1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.74193734)
# and it does not matter where
ts = [1, 1, 1, 2, 1, 1, 1, 1, 1, 1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.74193734)
# negative numbers also work
ts = [1, -1, 1, -1, 1, -1]
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.69314718)
# nan gives nan
ts = [1, -1, 1, np.nan, 1, -1]
self.assertIsNanOnAllArrayTypes(sample_entropy, ts)
# this is not a very "random" list, so it should give a small entropy
ts = list(range(1000))
self.assertAlmostEqualOnAllArrayTypes(sample_entropy, ts, 0.0010314596066622707)
def test_autocorrelation(self):
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], -1, 1)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 1, 2)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], -1, 3)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 1, 4)
self.assertAlmostEqualOnAllArrayTypes(autocorrelation, pd.Series([0, 1, 2, 0, 1, 2]), -0.75, 2)
# Autocorrelation lag is larger than length of the time series
self.assertIsNanOnAllArrayTypes(autocorrelation, [1, 2, 1, 2, 1, 2], 200)
self.assertIsNanOnAllArrayTypes(autocorrelation, [np.nan], 0)
self.assertIsNanOnAllArrayTypes(autocorrelation, [], 0)
# time series with length 1 has no variance, therefore no result for autocorrelation at lag 0
self.assertIsNanOnAllArrayTypes(autocorrelation, [1], 0)
def test_quantile(self):
self.assertAlmostEqualOnAllArrayTypes(quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 1.0, 0.2)
self.assertAlmostEqualOnAllArrayTypes(quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 0.9)
self.assertAlmostEqualOnAllArrayTypes(quantile, [1, 1, 1, 3, 4, 7, 9, 11, 13, 13], 13, 1.0)
self.assertAlmostEqualOnAllArrayTypes(quantile, [1], 1, 0.5)
self.assertIsNanOnAllArrayTypes(quantile, [], 0.5)
def test_mean_abs_change_quantiles(self):
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 1,
ql=0.1, qh=0.9, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.15, qh=0.18, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0.5,
ql=0, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0.5,
ql=0.1, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0.1, qh=0.6, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 5,
ql=0, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 0.5,
ql=0.1, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0, 1, 0], 0.75,
ql=0.1, qh=1, isabs=True, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 1,
ql=0.1, qh=0.9, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.15, qh=0.18, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0.1, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0,
ql=0.1, qh=0.6, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 0,
ql=0, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0], 0.5,
ql=0.1, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, -9, 0, 0, 1, 0], 0.25,
ql=0.1, qh=1, isabs=False, f_agg="mean")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.1, qh=0.9, isabs=True, f_agg="std")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 0, 0], 0.5,
ql=0, qh=1, isabs=True, f_agg="std")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, list(range(10)), 0,
ql=0.1, qh=0.9, isabs=False, f_agg="std")
self.assertAlmostEqualOnAllArrayTypes(change_quantiles, [0, 1, 0, 1, 0], 1,
ql=0, qh=1, isabs=False, f_agg="std")
def test_value_count(self):
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 10, value=1)
self.assertEqualPandasSeriesWrapper(value_count, list(range(10)), 1, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [1] * 10, 0, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.NaN, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.NINF, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [np.PINF, 0, 1] * 3, 3, value=0)
self.assertEqualPandasSeriesWrapper(value_count, [0.1, 0.2, 0.3] * 3, 3, value=0.2)
self.assertEqualPandasSeriesWrapper(value_count, [np.NaN, 0, 1] * 3, 3, value=np.NaN)
self.assertEqualPandasSeriesWrapper(value_count, [np.NINF, 0, 1] * 3, 3, value=np.NINF)
self.assertEqualPandasSeriesWrapper(value_count, [np.PINF, 0, 1] * 3, 3, value=np.PINF)
def test_range_count(self):
self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 0, min=1, max=1)
self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 0, min=0.9, max=1)
self.assertEqualPandasSeriesWrapper(range_count, [1] * 10, 10, min=1, max=1.1)
self.assertEqualPandasSeriesWrapper(range_count, list(range(10)), 9, min=0, max=9)
self.assertEqualPandasSeriesWrapper(range_count, list(range(10)), 10, min=0, max=10)
self.assertEqualPandasSeriesWrapper(range_count, list(range(0, -10, -1)), 9, min=-10, max=0)
self.assertEqualPandasSeriesWrapper(range_count, [np.NaN, np.PINF, np.NINF] + list(range(10)), 10, min=0,
max=10)
def test_approximate_entropy(self):
self.assertEqualOnAllArrayTypes(approximate_entropy, [1], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)
self.assertEqualOnAllArrayTypes(approximate_entropy, [1, 2, 3], 0, m=2, r=0.5)
self.assertAlmostEqualOnAllArrayTypes(approximate_entropy, [12, 13, 15, 16, 17] * 10, 0.282456191, m=2, r=0.9)
self.assertRaises(ValueError, approximate_entropy, x=[12, 13, 15, 16, 17] * 10, m=2, r=-0.5)
def test_max_langevin_fixed_point(self):
"""
Estimating the intrinsic velocity of a dissipative soliton
"""
default_params = {"m": 3, "r": 30}
# active Brownian motion
ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(100000, v0=np.zeros(1))
v0 = max_langevin_fixed_point(v[:, 0], **default_params)
self.assertLess(abs(ds.deterministic - v0), 0.001)
# Brownian motion
ds = velocity(tau=2.0 / 0.3 - 3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
v0 = max_langevin_fixed_point(v[:, 0], **default_params)
self.assertLess(v0, 0.001)
def test_linear_trend(self):
# check linear up trend
x = range(10)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend(x, param)
res = pd.Series(dict(res))
expected_index = ["attr_\"pvalue\"", "attr_\"intercept\"",
"attr_\"rvalue\"", "attr_\"slope\"",
"attr_\"stderr\""]
self.assertEqual(len(res), 5)
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["attr_\"pvalue\""], 0)
self.assertAlmostEqual(res["attr_\"stderr\""], 0)
self.assertAlmostEqual(res["attr_\"intercept\""], 0)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0)
# check p value for random trend
np.random.seed(42)
x = np.random.uniform(size=100)
param = [{"attr": "rvalue"}]
res = linear_trend(x, param)
res = pd.Series(dict(res))
self.assertLess(abs(res["attr_\"rvalue\""]), 0.1)
# check slope and intercept decreasing trend with intercept
x = [42 - 2 * x for x in range(10)]
param = [{"attr": "intercept"}, {"attr": "slope"}]
res = linear_trend(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"intercept\""], 42)
self.assertAlmostEqual(res["attr_\"slope\""], -2)
def test__aggregate_on_chunks(self):
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="max", chunk_len=2), [1, 3])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([1, 1, 3, 3]), f_agg="max", chunk_len=2), [1, 3])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="min", chunk_len=2), [0, 2])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3, 5]), f_agg="min", chunk_len=2), [0, 2, 5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3]), f_agg="mean", chunk_len=2),
[0.5, 2.5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 0, 4, 5]), f_agg="mean", chunk_len=2),
[0.5, 2, 5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 0, 4, 5]), f_agg="mean", chunk_len=3),
[1 / 3, 4.5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, 3, 5, -2]),
f_agg="median", chunk_len=2), [0.5, 2.5, 1.5])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([-10, 5, 3, -3, 4, -6]),
f_agg="median", chunk_len=3), [3, -3])
self.assertListEqual(_aggregate_on_chunks(x=pd.Series([0, 1, 2, np.NaN, 5]),
f_agg="median", chunk_len=2), [0.5, 2, 5])
def test_agg_linear_trend(self):
x = pd.Series(range(9), index=range(9))
param = [{"attr": "intercept", "chunk_len": 3, "f_agg": "max"},
{"attr": "slope", "chunk_len": 3, "f_agg": "max"},
{"attr": "intercept", "chunk_len": 3, "f_agg": "min"},
{"attr": "slope", "chunk_len": 3, "f_agg": "min"},
{"attr": "intercept", "chunk_len": 3, "f_agg": "mean"},
{"attr": "slope", "chunk_len": 3, "f_agg": "mean"},
{"attr": "intercept", "chunk_len": 3, "f_agg": "median"},
{"attr": "slope", "chunk_len": 3, "f_agg": "median"}]
expected_index = ['attr_"intercept"__chunk_len_3__f_agg_"max"',
'attr_"slope"__chunk_len_3__f_agg_"max"',
'attr_"intercept"__chunk_len_3__f_agg_"min"',
'attr_"slope"__chunk_len_3__f_agg_"min"',
'attr_"intercept"__chunk_len_3__f_agg_"mean"',
'attr_"slope"__chunk_len_3__f_agg_"mean"',
'attr_"intercept"__chunk_len_3__f_agg_"median"',
'attr_"slope"__chunk_len_3__f_agg_"median"']
res = agg_linear_trend(x=x, param=param)
res = pd.Series(dict(res))
self.assertEqual(len(res), 8)
self.maxDiff = 2000
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"max"'], 2)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"max"'], 3)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"min"'], 0)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"min"'], 3)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"mean"'], 1)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"mean"'], 3)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"median"'], 1)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"median"'], 3)
x = pd.Series([np.NaN, np.NaN, np.NaN, -3, -3, -3])
res = agg_linear_trend(x=x, param=param)
res = pd.Series(dict(res))
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"max"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"max"'])
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"min"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"min"'])
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"mean"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"mean"'])
self.assertIsNaN(res['attr_"intercept"__chunk_len_3__f_agg_"median"'])
self.assertIsNaN(res['attr_"slope"__chunk_len_3__f_agg_"median"'])
x = pd.Series([np.NaN, np.NaN, -3, -3, -3, -3])
res = agg_linear_trend(x=x, param=param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"max"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"max"'], 0)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"min"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"min"'], 0)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"mean"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"mean"'], 0)
self.assertAlmostEqual(res['attr_"intercept"__chunk_len_3__f_agg_"median"'], -3)
self.assertAlmostEqual(res['attr_"slope"__chunk_len_3__f_agg_"median"'], 0)
def test_energy_ratio_by_chunks(self):
x = pd.Series(range(90), index=range(90))
param = [{"num_segments": 6, "segment_focus": i} for i in range(6)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertAlmostEqual(output[0][1], 0.0043, places=3)
self.assertAlmostEqual(output[1][1], 0.0316, places=3)
self.assertAlmostEqual(output[2][1], 0.0871, places=3)
self.assertAlmostEqual(output[3][1], 0.1709, places=3)
self.assertAlmostEqual(output[4][1], 0.2829, places=3)
self.assertAlmostEqual(output[5][1], 0.4232, places=3)
# Sum of the ratios should be 1.0
sum = 0.0
for name, dat in output:
sum = sum + dat
self.assertAlmostEqual(sum, 1.0)
x = pd.Series(1, index=range(10))
param = [{"num_segments": 3, "segment_focus": i} for i in range(3)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertAlmostEqual(output[0][1], 0.4, places=3)
self.assertAlmostEqual(output[1][1], 0.3, places=3)
self.assertAlmostEqual(output[2][1], 0.3, places=3)
# Sum of the ratios should be 1.0
sum = 0.0
for name, dat in output:
sum = sum + dat
self.assertAlmostEqual(sum, 1.0)
x = pd.Series(0, index=range(10))
param = [{"num_segments": 3, "segment_focus": i} for i in range(3)]
output = energy_ratio_by_chunks(x=x, param=param)
self.assertIsNaN(output[0][1])
self.assertIsNaN(output[1][1])
self.assertIsNaN(output[2][1])
def test_linear_trend_timewise_hours(self):
"""Test linear_trend_timewise function with hour intervals."""
x = pd.Series(
[0, 1, 3, 6],
index=pd.DatetimeIndex([
'2018-01-01 04:00:00', '2018-01-01 05:00:00',
'2018-01-01 07:00:00', '2018-01-01 10:00:00'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
expected_index = ["attr_\"pvalue\"", "attr_\"intercept\"",
"attr_\"rvalue\"", "attr_\"slope\"",
"attr_\"stderr\""]
self.assertEqual(len(res), 5)
self.assertCountEqual(list(res.index), expected_index)
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_linear_trend_timewise_days(self):
"""Test linear_trend_timewise function with day intervals."""
# Try with different days
x = pd.Series(
[0, 24, 48, 72],
index=pd.DatetimeIndex([
'2018-01-01 04:00:00', '2018-01-02 04:00:00',
'2018-01-03 04:00:00', '2018-01-04 04:00:00'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_linear_trend_timewise_seconds(self):
"""Test linear_trend_timewise function with second intervals."""
# Try with different days
x = pd.Series(
[0, 1 / float(3600), 2 / float(3600), 3 / float(3600)],
index=pd.DatetimeIndex([
'2018-01-01 04:00:01', '2018-01-01 04:00:02',
'2018-01-01 04:00:03', '2018-01-01 04:00:04'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_linear_trend_timewise_years(self):
"""Test linear_trend_timewise function with year intervals."""
# Try with different days
x = pd.Series(
[0, 365 * 24, 365 * 48, 365 * 72 + 24], # Add 24 to the last one since it's a leap year
index=pd.DatetimeIndex([
'2018-01-01 04:00:00', '2019-01-01 04:00:00',
'2020-01-01 04:00:00', '2021-01-01 04:00:00'
]),
)
param = [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"}, {"attr": "slope"}, {"attr": "stderr"}]
res = linear_trend_timewise(x, param)
res = pd.Series(dict(res))
self.assertAlmostEqual(res["attr_\"pvalue\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"stderr\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"intercept\""], 0, places=3)
self.assertAlmostEqual(res["attr_\"slope\""], 1.0, places=3)
def test_change_quantiles(self):
"""Test change_quantiles function when changing from `sum` to `np.sum`."""
np.random.seed(0)
res = change_quantiles(np.random.rand(10000) * 1000, 0.1, 0.2, False, 'mean')
self.assertAlmostEqual(res, -0.9443846621365727)
def test_count_above(self):
self.assertEqualPandasSeriesWrapper(count_above, [1] * 10, 1, t=1)
self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 1, t=0)
self.assertEqualPandasSeriesWrapper(count_above, list(range(10)), 0.5, t=5)
self.assertEqualPandasSeriesWrapper(count_above, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2)
self.assertEqualPandasSeriesWrapper(count_above, [np.NaN, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.NINF, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.PINF, 0, 1] * 3, 1, t=0)
self.assertEqualPandasSeriesWrapper(count_above, [np.NaN, 0, 1] * 3, 0, t=np.NaN)
self.assertEqualPandasSeriesWrapper(count_above, [np.NINF, 0, np.PINF] * 3, 1, t=np.NINF)
self.assertEqualPandasSeriesWrapper(count_above, [np.PINF, 0, 1] * 3, 1 / 3, t=np.PINF)
def test_count_below(self):
self.assertEqualPandasSeriesWrapper(count_below, [1] * 10, 1, t=1)
self.assertEqualPandasSeriesWrapper(count_below, list(range(10)), 1 / 10, t=0)
self.assertEqualPandasSeriesWrapper(count_below, list(range(10)), 6 / 10, t=5)
self.assertEqualPandasSeriesWrapper(count_below, [0.1, 0.2, 0.3] * 3, 2 / 3, t=0.2)
self.assertEqualPandasSeriesWrapper(count_below, [np.NaN, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_below, [np.NINF, 0, 1] * 3, 2 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_below, [np.PINF, 0, 1] * 3, 1 / 3, t=0)
self.assertEqualPandasSeriesWrapper(count_below, [np.NaN, 0, 1] * 3, 0, t=np.NaN)
self.assertEqualPandasSeriesWrapper(count_below, [np.NINF, 0, np.PINF] * 3, 1 / 3, t=np.NINF)
self.assertEqualPandasSeriesWrapper(count_below, [np.PINF, 0, 1] * 3, 1, t=np.PINF)
def test_benford_correlation(self):
# A test with list of random values
np.random.seed(42)
random_list = np.random.uniform(size=100)
# Fibonacci series is known to match the Newcomb-Benford's Distribution
fibonacci_list = [0, 1]
for i in range(2, 200):
fibonacci_list.append(fibonacci_list[i - 1] + fibonacci_list[i - 2])
# A list of equally distributed digits (returns NaN)
equal_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
# A list containing NaN
list_with_nan = [1.354, 0.058, 0.055, 0.99, 3.15, np.nan, 0.3, 2.3, 0, 0.59, 0.74]
self.assertAlmostEqual(benford_correlation(random_list), 0.39458056)
self.assertAlmostEqual(benford_correlation(fibonacci_list), 0.998003988)
self.assertAlmostEqual(benford_correlation(list_with_nan), 0.10357511)
self.assertIsNaN(benford_correlation(equal_list))
class FriedrichTestCase(TestCase):
def test_estimate_friedrich_coefficients(self):
"""
Estimate friedrich coefficients
"""
default_params = {"m": 3, "r": 30}
# active Brownian motion
ds = velocity(tau=3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
coeff = _estimate_friedrich_coefficients(v[:, 0], **default_params)
self.assertLess(abs(coeff[-1]), 0.0001)
# Brownian motion
ds = velocity(tau=2.0 / 0.3 - 3.8, delta_t=0.05, R=3e-4, seed=0)
v = ds.simulate(10000, v0=np.zeros(1))
coeff = _estimate_friedrich_coefficients(v[:, 0], **default_params)
self.assertLess(abs(coeff[-1]), 0.0001)
def test_friedrich_coefficients(self):
# Test binning error returns vector of NaNs
param = [{"coeff": coeff, "m": 2, "r": 30} for coeff in range(4)]
x = np.zeros(100)
res = pd.Series(dict(friedrich_coefficients(x, param)))
expected_index = ["coeff_0__m_2__r_30", "coeff_1__m_2__r_30", "coeff_2__m_2__r_30", "coeff_3__m_2__r_30"]
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.sum(np.isnan(res)), 3)
def test_friedrich_number_of_returned_features_is_equal_to_number_of_parameters(self):
""" unit test for issue 501 """
param = [{'m': 3, 'r': 5, 'coeff': 2}, {'m': 3, 'r': 5, 'coeff': 3}, {'m': 3, 'r': 2, 'coeff': 3}]
x = np.zeros(100)
res = pd.Series(dict(friedrich_coefficients(x, param)))
expected_index = ["coeff_2__m_3__r_5", "coeff_3__m_3__r_5", "coeff_3__m_3__r_2"]
self.assertCountEqual(list(res.index), expected_index)
self.assertTrue(np.sum(np.isnan(res)), 3)
def test_friedrich_equal_to_snapshot(self):
param = [{"coeff": coeff, "m": 2, "r": 30} for coeff in range(4)]
x = np.array([-0.53, -0.61, -1.26, -0.88, -0.34, 0.58, 2.86, -0.47, 0.78,
-0.45, -0.27, 0.43, 1.72, 0.26, 1.02, -0.09, 0.65, 1.49,
-0.95, -1.02, -0.64, -1.63, -0.71, -0.43, -1.69, 0.05, 1.58,
1.1, 0.55, -1.02])
res = pd.Series(dict(friedrich_coefficients(x, param)))
self.assertAlmostEqual(res['coeff_0__m_2__r_30'], -0.24536975738843042)
self.assertAlmostEqual(res['coeff_1__m_2__r_30'], -0.533309548662685)
self.assertAlmostEqual(res['coeff_2__m_2__r_30'], 0.2759399238199404)
|
"""
Copyright 2021 Nirlep_5252_
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import discord
import traceback
import json
from discord.ext import commands
from utils.embed import (
replace_things_in_string_fancy_lemao,
process_embeds_from_json,
error_embed
)
from config import (
OWNERS, EMOJIS, MAIN_COLOR, SUPPORT_SERVER_LINK,
VOTE_LINK, RED_COLOR
)
from utils.random import gen_random_string
from utils.custom_checks import NotVoted, NotBotMod, OptedOut, PrivateCommand
from utils.converters import ImportantCategory, InvalidTimeZone, InvalidCategory
from humanfriendly import format_timespan
from utils.bot import EpicBot
class ErrorHandling(commands.Cog):
def __init__(self, client: EpicBot):
self.client = client
self.cd_mapping = commands.CooldownMapping.from_cooldown(5, 20, commands.BucketType.user)
self.nice_spam_idiot = commands.CooldownMapping.from_cooldown(2, 10, commands.BucketType.user)
async def process_custom_cmds(self, ctx: commands.Context, cmd_name):
interseting_allowed_mentions = discord.AllowedMentions(
everyone=False,
roles=False,
replied_user=False,
users=True
)
guild_config = await self.client.get_guild_config(ctx.guild.id)
if "custom_cmds" not in guild_config:
guild_config.update({"custom_cmds": []})
custom_cmds_list = guild_config["custom_cmds"]
for e in custom_cmds_list:
if e['name'] == cmd_name:
if not e['embed']:
h = await replace_things_in_string_fancy_lemao(self.client, [ctx.author, ctx.guild], e['reply'])
await ctx.send(h, allowed_mentions=interseting_allowed_mentions)
else:
embed_json = json.loads(e['reply'])
thing = await process_embeds_from_json(self.client, [ctx.author, ctx.guild], embed_json)
if thing[0] is not None:
await ctx.send(thing[0], embed=thing[1]) # use the function from utils.embed
else:
await ctx.send(embed=thing[1])
return
@commands.Cog.listener()
async def on_command_error(self, ctx: commands.Context, error):
bucket_pain = self.nice_spam_idiot.get_bucket(ctx.message)
retry_after_pain = bucket_pain.update_rate_limit()
prefix = ctx.clean_prefix
if retry_after_pain:
return
if isinstance(error, commands.CommandNotFound):
bucket = self.cd_mapping.get_bucket(ctx.message)
retry_after = bucket.update_rate_limit()
if retry_after and ctx.author.id not in OWNERS:
return await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Calm down!",
f"Please try again after **{format_timespan(round(error.retry_after, 2))}**."),
delete_after=5
)
await self.process_custom_cmds(ctx, ctx.invoked_with)
elif isinstance(error, commands.CommandOnCooldown):
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Calm down!",
f"Please try again after **{format_timespan(round(error.retry_after, 2))}**.".format(error.retry_after)),
delete_after=5
)
elif isinstance(error, commands.MaxConcurrencyReached):
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Limit reached!",
f"An instance of this command is already running...\nYou can only run `{error.number}` instances at the same time."
))
elif isinstance(error, commands.MissingPermissions):
if ctx.author.id == 558861606063308822:
return await ctx.reinvoke()
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Nah bro!",
"You need **{}** perms to run this command.".format(' '.join(error.missing_permissions[0].split('_')).title())
))
elif isinstance(error, commands.BotMissingPermissions):
ctx.command.reset_cooldown(ctx)
if error.missing_permissions[0] == 'send_messages':
return
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Error!",
"I am missing **{}** permissions.".format(' '.join(error.missing_permissions[0].split('_')).title())
))
elif isinstance(error, commands.NSFWChannelRequired):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Go away horny!",
"This command can only be used in a **NSFW** channel."
))
elif isinstance(error, commands.NotOwner):
await self.client.get_channel(800252938869669898).send(
embed=discord.Embed(
title="Someone tried to use Owner only command!",
description=f"```{ctx.message.content}```",
color=MAIN_COLOR
).add_field(name="User", value=f"{ctx.author.mention}```{ctx.author} ({ctx.author.id})```", inline=False)
.add_field(name="Server", value=f"```{ctx.guild} ({ctx.guild.id})```", inline=False)
)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} No!",
"Sowwi cutie but you cannot use this command!~"
))
elif isinstance(error, commands.MemberNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Not found!",
"I wasn't able to find **{}**, please try again.".format(error.argument)
))
elif isinstance(error, commands.UserNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Not found!",
"I wasn't able to find **{}**, please try again.".format(error.argument)
))
elif isinstance(error, commands.ChannelNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Not found!",
"No channel named **{}** was found, please try again.".format(error.argument)
))
elif isinstance(error, commands.RoleNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Not found!",
"No role named **{}** was found, please try again.".format(error.argument)
))
elif isinstance(error, commands.EmojiNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Not found!",
f"I wasn't able to find any emoji named: `{error.argument}`."
))
elif isinstance(error, commands.PartialEmojiConversionFailure):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Not found!",
f"I wasn't able to find any emoji named: `{error.argument}`."
))
elif isinstance(error, NotVoted):
await ctx.reply(embed=error_embed(
f"{EMOJIS["weirdchamp"]} Voter only!",
f"This command is restricted to voters only.\nClick **[here]({VOTE_LINK})** to vote!"
))
elif isinstance(error, NotBotMod):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} No!",
"Only bot moderators can use this command!"
))
elif isinstance(error, OptedOut):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} No!",
f"You cannot snipe, because you opted out!\nPlease use `{prefix}optout` to be able to snipe again."
))
elif isinstance(error, InvalidTimeZone):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Invalid Timezone!",
f"Please use a valid timezone.\nClick **[here](https://github.com/nirlep5252/epicbot/tree/main/other/timezones.txt)** to see the list of valid timezones.\n\nYou can also set your timezone using `{ctx.clean_prefix}settimezone <timezone>` for all commands."
))
elif isinstance(error, InvalidCategory):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Invalid Category!",
f"The category `{error.category}` is not a valid category!\nPlease use `{prefix}help` to see the list of valid categories."
))
elif isinstance(error, ImportantCategory):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Important Category!",
f"You cannot disable the `{error.category}` category!\nIt has contains the core features of epicbot\nFor more info join our [Support Server]({SUPPORT_SERVER_LINK})."
))
elif isinstance(error, PrivateCommand):
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} Private Command!",
"This command is private and you cannot use it."
))
elif isinstance(error, commands.CheckFailure):
ctx.command.reset_cooldown(ctx)
if not self.client.beta:
await ctx.message.add_reaction('❌')
else:
random_error_id = gen_random_string(10)
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS["tick_no"]} An unknown error occured!",
error
).set_footer(text=f"ERROR ID: {random_error_id}"))
error_text = "".join(traceback.format_exception(etype=type(error), value=error, tb=error.__traceback__))[:2000]
error_embed_ = discord.Embed(
title="Traceback",
description=("```py\n" + error_text + "\n```"),
color=RED_COLOR
).add_field(name="Command", value=f"```{ctx.message.content}```", inline=False
).add_field(name="User", value=f"{ctx.author.mention} ```{ctx.author} ({ctx.author.id})```", inline=False
).add_field(name="Server", value=f"```{ctx.guild}({ctx.guild.id})```", inline=False
).set_footer(text=f"ERROR ID: {random_error_id}")
try:
webhooks = self.client.get_cog("Webhooks").webhooks
webhook = webhooks.get("cmd_error")
await webhook.send(embed=error_embed_)
except Exception:
traceback.print_exception(etype=type(error), value=error, tb=error.__traceback__)
def setup(client):
client.add_cog(ErrorHandling(client))
| """
Copyright 2021 Nirlep_5252_
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import discord
import traceback
import json
from discord.ext import commands
from utils.embed import (
replace_things_in_string_fancy_lemao,
process_embeds_from_json,
error_embed
)
from config import (
OWNERS, EMOJIS, MAIN_COLOR, SUPPORT_SERVER_LINK,
VOTE_LINK, RED_COLOR
)
from utils.random import gen_random_string
from utils.custom_checks import NotVoted, NotBotMod, OptedOut, PrivateCommand
from utils.converters import ImportantCategory, InvalidTimeZone, InvalidCategory
from humanfriendly import format_timespan
from utils.bot import EpicBot
class ErrorHandling(commands.Cog):
def __init__(self, client: EpicBot):
self.client = client
self.cd_mapping = commands.CooldownMapping.from_cooldown(5, 20, commands.BucketType.user)
self.nice_spam_idiot = commands.CooldownMapping.from_cooldown(2, 10, commands.BucketType.user)
async def process_custom_cmds(self, ctx: commands.Context, cmd_name):
interseting_allowed_mentions = discord.AllowedMentions(
everyone=False,
roles=False,
replied_user=False,
users=True
)
guild_config = await self.client.get_guild_config(ctx.guild.id)
if "custom_cmds" not in guild_config:
guild_config.update({"custom_cmds": []})
custom_cmds_list = guild_config["custom_cmds"]
for e in custom_cmds_list:
if e['name'] == cmd_name:
if not e['embed']:
h = await replace_things_in_string_fancy_lemao(self.client, [ctx.author, ctx.guild], e['reply'])
await ctx.send(h, allowed_mentions=interseting_allowed_mentions)
else:
embed_json = json.loads(e['reply'])
thing = await process_embeds_from_json(self.client, [ctx.author, ctx.guild], embed_json)
if thing[0] is not None:
await ctx.send(thing[0], embed=thing[1]) # use the function from utils.embed
else:
await ctx.send(embed=thing[1])
return
@commands.Cog.listener()
async def on_command_error(self, ctx: commands.Context, error):
bucket_pain = self.nice_spam_idiot.get_bucket(ctx.message)
retry_after_pain = bucket_pain.update_rate_limit()
prefix = ctx.clean_prefix
if retry_after_pain:
return
if isinstance(error, commands.CommandNotFound):
bucket = self.cd_mapping.get_bucket(ctx.message)
retry_after = bucket.update_rate_limit()
if retry_after and ctx.author.id not in OWNERS:
return await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Calm down!",
f"Please try again after **{format_timespan(round(error.retry_after, 2))}**."),
delete_after=5
)
await self.process_custom_cmds(ctx, ctx.invoked_with)
elif isinstance(error, commands.CommandOnCooldown):
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Calm down!",
f"Please try again after **{format_timespan(round(error.retry_after, 2))}**.".format(error.retry_after)),
delete_after=5
)
elif isinstance(error, commands.MaxConcurrencyReached):
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Limit reached!",
f"An instance of this command is already running...\nYou can only run `{error.number}` instances at the same time."
))
elif isinstance(error, commands.MissingPermissions):
if ctx.author.id == 558861606063308822:
return await ctx.reinvoke()
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Nah bro!",
"You need **{}** perms to run this command.".format(' '.join(error.missing_permissions[0].split('_')).title())
))
elif isinstance(error, commands.BotMissingPermissions):
ctx.command.reset_cooldown(ctx)
if error.missing_permissions[0] == 'send_messages':
return
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Error!",
"I am missing **{}** permissions.".format(' '.join(error.missing_permissions[0].split('_')).title())
))
elif isinstance(error, commands.NSFWChannelRequired):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Go away horny!",
"This command can only be used in a **NSFW** channel."
))
elif isinstance(error, commands.NotOwner):
await self.client.get_channel(800252938869669898).send(
embed=discord.Embed(
title="Someone tried to use Owner only command!",
description=f"```{ctx.message.content}```",
color=MAIN_COLOR
).add_field(name="User", value=f"{ctx.author.mention}```{ctx.author} ({ctx.author.id})```", inline=False)
.add_field(name="Server", value=f"```{ctx.guild} ({ctx.guild.id})```", inline=False)
)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} No!",
"Sowwi cutie but you cannot use this command!~"
))
elif isinstance(error, commands.MemberNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Not found!",
"I wasn't able to find **{}**, please try again.".format(error.argument)
))
elif isinstance(error, commands.UserNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Not found!",
"I wasn't able to find **{}**, please try again.".format(error.argument)
))
elif isinstance(error, commands.ChannelNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Not found!",
"No channel named **{}** was found, please try again.".format(error.argument)
))
elif isinstance(error, commands.RoleNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Not found!",
"No role named **{}** was found, please try again.".format(error.argument)
))
elif isinstance(error, commands.EmojiNotFound):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Not found!",
f"I wasn't able to find any emoji named: `{error.argument}`."
))
elif isinstance(error, commands.PartialEmojiConversionFailure):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Not found!",
f"I wasn't able to find any emoji named: `{error.argument}`."
))
elif isinstance(error, NotVoted):
await ctx.reply(embed=error_embed(
f"{EMOJIS['weirdchamp']} Voter only!",
f"This command is restricted to voters only.\nClick **[here]({VOTE_LINK})** to vote!"
))
elif isinstance(error, NotBotMod):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} No!",
"Only bot moderators can use this command!"
))
elif isinstance(error, OptedOut):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} No!",
f"You cannot snipe, because you opted out!\nPlease use `{prefix}optout` to be able to snipe again."
))
elif isinstance(error, InvalidTimeZone):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Invalid Timezone!",
f"Please use a valid timezone.\nClick **[here](https://github.com/nirlep5252/epicbot/tree/main/other/timezones.txt)** to see the list of valid timezones.\n\nYou can also set your timezone using `{ctx.clean_prefix}settimezone <timezone>` for all commands."
))
elif isinstance(error, InvalidCategory):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Invalid Category!",
f"The category `{error.category}` is not a valid category!\nPlease use `{prefix}help` to see the list of valid categories."
))
elif isinstance(error, ImportantCategory):
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Important Category!",
f"You cannot disable the `{error.category}` category!\nIt has contains the core features of epicbot\nFor more info join our [Support Server]({SUPPORT_SERVER_LINK})."
))
elif isinstance(error, PrivateCommand):
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} Private Command!",
"This command is private and you cannot use it."
))
elif isinstance(error, commands.CheckFailure):
ctx.command.reset_cooldown(ctx)
if not self.client.beta:
await ctx.message.add_reaction('❌')
else:
random_error_id = gen_random_string(10)
ctx.command.reset_cooldown(ctx)
await ctx.reply(embed=error_embed(
f"{EMOJIS['tick_no']} An unknown error occured!",
error
).set_footer(text=f"ERROR ID: {random_error_id}"))
error_text = "".join(traceback.format_exception(etype=type(error), value=error, tb=error.__traceback__))[:2000]
error_embed_ = discord.Embed(
title="Traceback",
description=("```py\n" + error_text + "\n```"),
color=RED_COLOR
).add_field(name="Command", value=f"```{ctx.message.content}```", inline=False
).add_field(name="User", value=f"{ctx.author.mention} ```{ctx.author} ({ctx.author.id})```", inline=False
).add_field(name="Server", value=f"```{ctx.guild}({ctx.guild.id})```", inline=False
).set_footer(text=f"ERROR ID: {random_error_id}")
try:
webhooks = self.client.get_cog("Webhooks").webhooks
webhook = webhooks.get("cmd_error")
await webhook.send(embed=error_embed_)
except Exception:
traceback.print_exception(etype=type(error), value=error, tb=error.__traceback__)
def setup(client):
client.add_cog(ErrorHandling(client))
|
r"""
From previous experiments, we saw that ephemeral pseudo-labelling helped boost accuracy
despite starting with only 20 points. We could kick-start BALD with 85% accuracy with 24 iterations
but it seems like using 80% accuracy at 10 iterations is a good trade-off. It's harder to gain more
accuracy as the number of iteration increases.
This experiment kick-starts BALD10 acquisition by warming the model to 80% accuracy (with 10 iterations
of ephemeral pseudo-labelling). However, the acquisition loop will NOT run ephemeral P.L. as we've seen
a decrease in performance when doing so. There are two possibilities: (1) warm-starting the model
has caused it to lower its entropy on the pool dataset, hence causing it to actually perform worse.
(2) warm-starting it actually helped! my bet is (unfortunately) on the former, given previous observations
(i.e. ephemeral bald10 performs worse than bald10 -- but i'm hopeful, notwithstanding.).
"""
from collections import defaultdict
from alr.utils import manual_seed, eval_fwd_exp, timeop
from alr.acquisition import BALD
from alr import MCDropout
from alr.data.datasets import Dataset
from alr.training.samplers import RandomFixedLengthSampler
from alr.data import UnlabelledDataset, DataManager
from alr.training import Trainer
from alr.training.repeated_acquisition_utils import (
get_confident_indices,
RelabelledDataset,
)
import torch
import torch.utils.data as torchdata
import pickle
from torch.nn import functional as F
from pathlib import Path
def main(b, threshold, warm_start_iters, log_every):
manual_seed(42)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
kwargs = dict(num_workers=4, pin_memory=True)
# --- constants ---
BATCH_SIZE = 64
EPOCHS = 200
REPS = 6
ITERS = 23
# +1 because of the structure of our loop
warm_start_iters += 1
VAL_SIZE = 5_000
MIN_TRAIN_LEN = 12_500
# --- setup ---
train, pool, test = Dataset.MNIST.get_fixed()
val, pool = torchdata.random_split(pool, (VAL_SIZE, len(pool) - VAL_SIZE))
pool = UnlabelledDataset(pool, debug=True)
model = MCDropout(Dataset.MNIST.model, forward=20, fast=True).to(device)
bald = BALD(eval_fwd_exp(model), device=device, batch_size=1024, **kwargs)
dm = DataManager(train, pool, bald)
val_loader = torchdata.DataLoader(
val,
batch_size=1024,
shuffle=False,
**kwargs,
)
test_loader = torchdata.DataLoader(
test,
batch_size=1024,
shuffle=False,
**kwargs,
)
warm_start_accs = []
accs = defaultdict(list)
template = f"wsi={warm_start_iters}_b={b}_thresh={threshold}"
pl_metrics = Path("pl_metrics") / template
metrics = Path("metrics") / template
saved_models = Path("saved_models") / template
metrics.mkdir(parents=True)
saved_models.mkdir(parents=True)
for r in range(1, REPS + 1):
print(f"- Repeat {r} of {REPS} -")
dm.reset()
ws_accs_r = {}
# store temporarily labelled points (will be union-ed with the training dataset)
pseudo_labelled_points = None
for i in range(1, warm_start_iters + 1):
if pseudo_labelled_points is not None:
full_train_dataset = torchdata.ConcatDataset(
(dm.labelled, pseudo_labelled_points)
)
else:
full_train_dataset = dm.labelled
train_length = len(full_train_dataset)
print(
f"=== Warm start iteration {i} of {warm_start_iters} ({i / warm_start_iters:.2%}) ==="
)
print(
f"\ttrain: {train_length}; "
f"pool: {dm.n_unlabelled}; "
f"val: {len(val)}; "
f"test: {len(test)}"
)
model.reset_weights()
# -- stage 1: train --
trainer = Trainer(
model, F.nll_loss, "Adam", patience=3, reload_best=True, device=device
)
train_loader = torchdata.DataLoader(
full_train_dataset,
batch_size=BATCH_SIZE,
sampler=RandomFixedLengthSampler(
full_train_dataset, MIN_TRAIN_LEN, shuffle=True
),
**kwargs,
)
with timeop() as t:
history = trainer.fit(train_loader, val_loader, epochs=EPOCHS)
test_metrics = trainer.evaluate(test_loader)
ws_accs_r[train_length] = test_metrics["acc"]
print(
f"\t[test] loss, acc: ({test_metrics["loss"]:.4f}, {test_metrics["acc"]:.4f}); time: {t}"
)
with open(
metrics / f"repeat_{r}_dsize_{train_length}_metrics.pkl", "wb"
) as fp:
payload = {
"history": history,
"test_metrics": test_metrics,
}
pickle.dump(payload, fp)
if (i - 1) % log_every == 0:
torch.save(
model.state_dict(),
saved_models / f"repeat_{r}_dsize_{train_length}_weights.pth",
)
# skip if this is the last iteration
if i == warm_start_iters:
accs[dm.n_labelled].append(test_metrics["acc"])
continue
# -- stage 2: acquire more data into the training set --
# -- acquire using pseudo-labels --
dm.unlabelled.debug = True
idxs, plabs = get_confident_indices(
model=model,
dataset=dm.unlabelled,
threshold=threshold,
root=((pl_metrics / f"repeat_{r}") if r == 1 else None),
step=i,
device=device,
**kwargs,
)
if idxs.shape[0]:
truth = torchdata.Subset(dm.unlabelled, idxs)
# replace true labels with pseudo-labels
pseudo_labelled_points = RelabelledDataset(truth, plabs)
assert len(pseudo_labelled_points) == idxs.shape[0]
else:
print(
f"\tSelf-labelling didn't happen because none of the pseudo-labels are confident enough."
)
warm_start_accs.append(ws_accs_r)
dm.unlabelled.debug = False
print(
f"Warm-started with {warm_start_iters} iterations. Beginning AL acquisitions"
)
for i in range(1, ITERS + 1):
dm.acquire(b=b)
print(f"=== Iteration {i} of {ITERS} ({i / ITERS:.2%}) ===")
print(
f"\ttrain: {dm.n_labelled}; val: {len(val)}; "
f"pool: {dm.n_unlabelled}; test: {len(test)}"
)
# model.reset_weights() # leverage p.l. from before, DON'T reset!
trainer = Trainer(
model,
F.nll_loss,
optimiser="Adam",
patience=3,
reload_best=True,
device=device,
)
train_loader = torchdata.DataLoader(
dm.labelled,
batch_size=BATCH_SIZE,
sampler=RandomFixedLengthSampler(
dm.labelled, MIN_TRAIN_LEN, shuffle=True
),
**kwargs,
)
with timeop() as t:
trainer.fit(train_loader, val_loader, epochs=EPOCHS)
test_metric = trainer.evaluate(test_loader)
print(f"\t[test] acc: {test_metric["acc"]}, time: {t}")
accs[dm.n_labelled].append(test_metric["acc"])
with open(f"{template}_warm_start_accs.pkl", "wb") as fp:
pickle.dump(warm_start_accs, fp)
with open(f"{template}_accs.pkl", "wb") as fp:
pickle.dump(accs, fp)
if __name__ == "__main__":
main(b=10, threshold=0.9, warm_start_iters=10, log_every=2)
| r"""
From previous experiments, we saw that ephemeral pseudo-labelling helped boost accuracy
despite starting with only 20 points. We could kick-start BALD with 85% accuracy with 24 iterations
but it seems like using 80% accuracy at 10 iterations is a good trade-off. It's harder to gain more
accuracy as the number of iteration increases.
This experiment kick-starts BALD10 acquisition by warming the model to 80% accuracy (with 10 iterations
of ephemeral pseudo-labelling). However, the acquisition loop will NOT run ephemeral P.L. as we've seen
a decrease in performance when doing so. There are two possibilities: (1) warm-starting the model
has caused it to lower its entropy on the pool dataset, hence causing it to actually perform worse.
(2) warm-starting it actually helped! my bet is (unfortunately) on the former, given previous observations
(i.e. ephemeral bald10 performs worse than bald10 -- but i'm hopeful, notwithstanding.).
"""
from collections import defaultdict
from alr.utils import manual_seed, eval_fwd_exp, timeop
from alr.acquisition import BALD
from alr import MCDropout
from alr.data.datasets import Dataset
from alr.training.samplers import RandomFixedLengthSampler
from alr.data import UnlabelledDataset, DataManager
from alr.training import Trainer
from alr.training.repeated_acquisition_utils import (
get_confident_indices,
RelabelledDataset,
)
import torch
import torch.utils.data as torchdata
import pickle
from torch.nn import functional as F
from pathlib import Path
def main(b, threshold, warm_start_iters, log_every):
manual_seed(42)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
kwargs = dict(num_workers=4, pin_memory=True)
# --- constants ---
BATCH_SIZE = 64
EPOCHS = 200
REPS = 6
ITERS = 23
# +1 because of the structure of our loop
warm_start_iters += 1
VAL_SIZE = 5_000
MIN_TRAIN_LEN = 12_500
# --- setup ---
train, pool, test = Dataset.MNIST.get_fixed()
val, pool = torchdata.random_split(pool, (VAL_SIZE, len(pool) - VAL_SIZE))
pool = UnlabelledDataset(pool, debug=True)
model = MCDropout(Dataset.MNIST.model, forward=20, fast=True).to(device)
bald = BALD(eval_fwd_exp(model), device=device, batch_size=1024, **kwargs)
dm = DataManager(train, pool, bald)
val_loader = torchdata.DataLoader(
val,
batch_size=1024,
shuffle=False,
**kwargs,
)
test_loader = torchdata.DataLoader(
test,
batch_size=1024,
shuffle=False,
**kwargs,
)
warm_start_accs = []
accs = defaultdict(list)
template = f"wsi={warm_start_iters}_b={b}_thresh={threshold}"
pl_metrics = Path("pl_metrics") / template
metrics = Path("metrics") / template
saved_models = Path("saved_models") / template
metrics.mkdir(parents=True)
saved_models.mkdir(parents=True)
for r in range(1, REPS + 1):
print(f"- Repeat {r} of {REPS} -")
dm.reset()
ws_accs_r = {}
# store temporarily labelled points (will be union-ed with the training dataset)
pseudo_labelled_points = None
for i in range(1, warm_start_iters + 1):
if pseudo_labelled_points is not None:
full_train_dataset = torchdata.ConcatDataset(
(dm.labelled, pseudo_labelled_points)
)
else:
full_train_dataset = dm.labelled
train_length = len(full_train_dataset)
print(
f"=== Warm start iteration {i} of {warm_start_iters} ({i / warm_start_iters:.2%}) ==="
)
print(
f"\ttrain: {train_length}; "
f"pool: {dm.n_unlabelled}; "
f"val: {len(val)}; "
f"test: {len(test)}"
)
model.reset_weights()
# -- stage 1: train --
trainer = Trainer(
model, F.nll_loss, "Adam", patience=3, reload_best=True, device=device
)
train_loader = torchdata.DataLoader(
full_train_dataset,
batch_size=BATCH_SIZE,
sampler=RandomFixedLengthSampler(
full_train_dataset, MIN_TRAIN_LEN, shuffle=True
),
**kwargs,
)
with timeop() as t:
history = trainer.fit(train_loader, val_loader, epochs=EPOCHS)
test_metrics = trainer.evaluate(test_loader)
ws_accs_r[train_length] = test_metrics["acc"]
print(
f"\t[test] loss, acc: ({test_metrics['loss']:.4f}, {test_metrics['acc']:.4f}); time: {t}"
)
with open(
metrics / f"repeat_{r}_dsize_{train_length}_metrics.pkl", "wb"
) as fp:
payload = {
"history": history,
"test_metrics": test_metrics,
}
pickle.dump(payload, fp)
if (i - 1) % log_every == 0:
torch.save(
model.state_dict(),
saved_models / f"repeat_{r}_dsize_{train_length}_weights.pth",
)
# skip if this is the last iteration
if i == warm_start_iters:
accs[dm.n_labelled].append(test_metrics["acc"])
continue
# -- stage 2: acquire more data into the training set --
# -- acquire using pseudo-labels --
dm.unlabelled.debug = True
idxs, plabs = get_confident_indices(
model=model,
dataset=dm.unlabelled,
threshold=threshold,
root=((pl_metrics / f"repeat_{r}") if r == 1 else None),
step=i,
device=device,
**kwargs,
)
if idxs.shape[0]:
truth = torchdata.Subset(dm.unlabelled, idxs)
# replace true labels with pseudo-labels
pseudo_labelled_points = RelabelledDataset(truth, plabs)
assert len(pseudo_labelled_points) == idxs.shape[0]
else:
print(
f"\tSelf-labelling didn't happen because none of the pseudo-labels are confident enough."
)
warm_start_accs.append(ws_accs_r)
dm.unlabelled.debug = False
print(
f"Warm-started with {warm_start_iters} iterations. Beginning AL acquisitions"
)
for i in range(1, ITERS + 1):
dm.acquire(b=b)
print(f"=== Iteration {i} of {ITERS} ({i / ITERS:.2%}) ===")
print(
f"\ttrain: {dm.n_labelled}; val: {len(val)}; "
f"pool: {dm.n_unlabelled}; test: {len(test)}"
)
# model.reset_weights() # leverage p.l. from before, DON'T reset!
trainer = Trainer(
model,
F.nll_loss,
optimiser="Adam",
patience=3,
reload_best=True,
device=device,
)
train_loader = torchdata.DataLoader(
dm.labelled,
batch_size=BATCH_SIZE,
sampler=RandomFixedLengthSampler(
dm.labelled, MIN_TRAIN_LEN, shuffle=True
),
**kwargs,
)
with timeop() as t:
trainer.fit(train_loader, val_loader, epochs=EPOCHS)
test_metric = trainer.evaluate(test_loader)
print(f"\t[test] acc: {test_metric['acc']}, time: {t}")
accs[dm.n_labelled].append(test_metric["acc"])
with open(f"{template}_warm_start_accs.pkl", "wb") as fp:
pickle.dump(warm_start_accs, fp)
with open(f"{template}_accs.pkl", "wb") as fp:
pickle.dump(accs, fp)
if __name__ == "__main__":
main(b=10, threshold=0.9, warm_start_iters=10, log_every=2)
|
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
import logging
import os
import sys
import time
from contextlib import contextmanager
from threading import Lock
from typing import Dict, Tuple
from pants.base.exiter import PANTS_FAILED_EXIT_CODE, ExitCode
from pants.bin.local_pants_runner import LocalPantsRunner
from pants.engine.internals.native import Native, RawFdRunner
from pants.init.logging import (
clear_logging_handlers,
get_logging_handlers,
set_logging_handlers,
setup_logging,
)
from pants.init.util import clean_global_runtime_state
from pants.option.options_bootstrapper import OptionsBootstrapper
from pants.pantsd.pants_daemon_core import PantsDaemonCore
from pants.util.contextutil import argv_as, hermetic_environment_as, stdio_as
logger = logging.getLogger(__name__)
class ExclusiveRequestTimeout(Exception):
"""Represents a timeout while waiting for another request to complete."""
class DaemonPantsRunner(RawFdRunner):
"""A RawFdRunner (callable) that will be called for each client request to Pantsd."""
def __init__(self, core: PantsDaemonCore) -> None:
super().__init__()
self._core = core
self._run_lock = Lock()
@staticmethod
def _send_stderr(stderr_fd: int, msg: str) -> None:
"""Used to send stderr on a raw filehandle _before_ stdio replacement.
After stdio replacement has happened via `stdio_as` (which mutates sys.std*, and thus cannot
happen until the request lock has been acquired), sys.std* should be used directly.
"""
with os.fdopen(stderr_fd, mode="w", closefd=False) as stderr:
print(msg, file=stderr, flush=True)
@contextmanager
def _one_run_at_a_time(self, stderr_fd: int, timeout: float):
"""Acquires exclusive access within the daemon.
Periodically prints a message on the given stderr_fd while exclusive access cannot be
acquired.
"""
should_poll_forever = timeout <= 0
start = time.time()
deadline = None if should_poll_forever else start + timeout
def should_keep_polling(now):
return not deadline or deadline > now
acquired = self._run_lock.acquire(blocking=False)
if not acquired:
# If we don't acquire immediately, send an explanation.
length = "forever" if should_poll_forever else "up to {} seconds".format(timeout)
self._send_stderr(
stderr_fd,
f"Another pants invocation is running. Will wait {length} for it to finish before giving up.\n"
"If you don't want to wait for the first run to finish, please press Ctrl-C and run "
"this command with PANTS_CONCURRENT=True in the environment.\n",
)
while True:
now = time.time()
if acquired:
try:
yield
break
finally:
self._run_lock.release()
elif should_keep_polling(now):
self._send_stderr(
stderr_fd,
f"Waiting for invocation to finish (waited for {int(now - start)}s so far)...\n",
)
acquired = self._run_lock.acquire(blocking=True, timeout=5)
else:
raise ExclusiveRequestTimeout(
"Timed out while waiting for another pants invocation to finish."
)
@contextmanager
def _stderr_logging(self, global_bootstrap_options):
"""Temporarily replaces existing handlers (ie, the pantsd handler) with a stderr handler.
In the context of pantsd, there will be an existing handler for the pantsd log, which we
temporarily replace. Making them additive would cause per-run logs to go to pantsd, which
we don't want.
TODO: It would be good to handle logging destinations entirely via the threadlocal state
rather than via handler mutations.
"""
handlers = get_logging_handlers()
try:
clear_logging_handlers()
Native().override_thread_logging_destination_to_just_stderr()
setup_logging(global_bootstrap_options, stderr_logging=True)
yield
finally:
Native().override_thread_logging_destination_to_just_pantsd()
set_logging_handlers(handlers)
def single_daemonized_run(self, working_dir: str) -> ExitCode:
"""Run a single daemonized run of Pants.
All aspects of the `sys` global should already have been replaced in `__call__`, so this
method should not need any special handling for the fact that it's running in a proxied
environment.
"""
# Capture the client's start time, which we propagate here in order to get an accurate
# view of total time.
env_start_time = os.environ.get("PANTSD_RUNTRACKER_CLIENT_START_TIME", None)
start_time = float(env_start_time) if env_start_time else time.time()
# Clear global mutable state before entering `LocalPantsRunner`. Note that we use
# `sys.argv` and `os.environ`, since they have been mutated to maintain the illusion
# of a local run: once we allow for concurrent runs, this information should be
# propagated down from the caller.
# see https://github.com/pantsbuild/pants/issues/7654
clean_global_runtime_state(reset_subsystem=True)
options_bootstrapper = OptionsBootstrapper.create(
env=os.environ, args=sys.argv, allow_pantsrc=True
)
bootstrap_options = options_bootstrapper.bootstrap_options
global_bootstrap_options = bootstrap_options.for_global_scope()
# Run using the pre-warmed Session.
with self._stderr_logging(global_bootstrap_options):
try:
scheduler = self._core.prepare_scheduler(options_bootstrapper)
runner = LocalPantsRunner.create(
os.environ, options_bootstrapper, scheduler=scheduler
)
return runner.run(start_time)
except Exception as e:
logger.exception(e)
return PANTS_FAILED_EXIT_CODE
except KeyboardInterrupt:
print("Interrupted by user.\n", file=sys.stderr)
return PANTS_FAILED_EXIT_CODE
def __call__(
self,
command: str,
args: Tuple[str, ...],
env: Dict[str, str],
working_directory: bytes,
stdin_fd: int,
stdout_fd: int,
stderr_fd: int,
) -> ExitCode:
request_timeout = float(env.get("PANTSD_REQUEST_TIMEOUT_LIMIT", -1))
# NB: Order matters: we acquire a lock before mutating either `sys.std*`, `os.environ`, etc.
with self._one_run_at_a_time(stderr_fd, timeout=request_timeout), stdio_as(
stdin_fd=stdin_fd, stdout_fd=stdout_fd, stderr_fd=stderr_fd
), hermetic_environment_as(**env), argv_as((command,) + args):
# NB: Run implements exception handling, so only the most primitive errors will escape
# this function, where they will be logged to the pantsd.log by the server.
logger.info(f"handling request: `{" ".join(args)}`")
try:
return self.single_daemonized_run(working_directory.decode())
finally:
logger.info(f"request completed: `{" ".join(args)}`")
| # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
import logging
import os
import sys
import time
from contextlib import contextmanager
from threading import Lock
from typing import Dict, Tuple
from pants.base.exiter import PANTS_FAILED_EXIT_CODE, ExitCode
from pants.bin.local_pants_runner import LocalPantsRunner
from pants.engine.internals.native import Native, RawFdRunner
from pants.init.logging import (
clear_logging_handlers,
get_logging_handlers,
set_logging_handlers,
setup_logging,
)
from pants.init.util import clean_global_runtime_state
from pants.option.options_bootstrapper import OptionsBootstrapper
from pants.pantsd.pants_daemon_core import PantsDaemonCore
from pants.util.contextutil import argv_as, hermetic_environment_as, stdio_as
logger = logging.getLogger(__name__)
class ExclusiveRequestTimeout(Exception):
"""Represents a timeout while waiting for another request to complete."""
class DaemonPantsRunner(RawFdRunner):
"""A RawFdRunner (callable) that will be called for each client request to Pantsd."""
def __init__(self, core: PantsDaemonCore) -> None:
super().__init__()
self._core = core
self._run_lock = Lock()
@staticmethod
def _send_stderr(stderr_fd: int, msg: str) -> None:
"""Used to send stderr on a raw filehandle _before_ stdio replacement.
After stdio replacement has happened via `stdio_as` (which mutates sys.std*, and thus cannot
happen until the request lock has been acquired), sys.std* should be used directly.
"""
with os.fdopen(stderr_fd, mode="w", closefd=False) as stderr:
print(msg, file=stderr, flush=True)
@contextmanager
def _one_run_at_a_time(self, stderr_fd: int, timeout: float):
"""Acquires exclusive access within the daemon.
Periodically prints a message on the given stderr_fd while exclusive access cannot be
acquired.
"""
should_poll_forever = timeout <= 0
start = time.time()
deadline = None if should_poll_forever else start + timeout
def should_keep_polling(now):
return not deadline or deadline > now
acquired = self._run_lock.acquire(blocking=False)
if not acquired:
# If we don't acquire immediately, send an explanation.
length = "forever" if should_poll_forever else "up to {} seconds".format(timeout)
self._send_stderr(
stderr_fd,
f"Another pants invocation is running. Will wait {length} for it to finish before giving up.\n"
"If you don't want to wait for the first run to finish, please press Ctrl-C and run "
"this command with PANTS_CONCURRENT=True in the environment.\n",
)
while True:
now = time.time()
if acquired:
try:
yield
break
finally:
self._run_lock.release()
elif should_keep_polling(now):
self._send_stderr(
stderr_fd,
f"Waiting for invocation to finish (waited for {int(now - start)}s so far)...\n",
)
acquired = self._run_lock.acquire(blocking=True, timeout=5)
else:
raise ExclusiveRequestTimeout(
"Timed out while waiting for another pants invocation to finish."
)
@contextmanager
def _stderr_logging(self, global_bootstrap_options):
"""Temporarily replaces existing handlers (ie, the pantsd handler) with a stderr handler.
In the context of pantsd, there will be an existing handler for the pantsd log, which we
temporarily replace. Making them additive would cause per-run logs to go to pantsd, which
we don't want.
TODO: It would be good to handle logging destinations entirely via the threadlocal state
rather than via handler mutations.
"""
handlers = get_logging_handlers()
try:
clear_logging_handlers()
Native().override_thread_logging_destination_to_just_stderr()
setup_logging(global_bootstrap_options, stderr_logging=True)
yield
finally:
Native().override_thread_logging_destination_to_just_pantsd()
set_logging_handlers(handlers)
def single_daemonized_run(self, working_dir: str) -> ExitCode:
"""Run a single daemonized run of Pants.
All aspects of the `sys` global should already have been replaced in `__call__`, so this
method should not need any special handling for the fact that it's running in a proxied
environment.
"""
# Capture the client's start time, which we propagate here in order to get an accurate
# view of total time.
env_start_time = os.environ.get("PANTSD_RUNTRACKER_CLIENT_START_TIME", None)
start_time = float(env_start_time) if env_start_time else time.time()
# Clear global mutable state before entering `LocalPantsRunner`. Note that we use
# `sys.argv` and `os.environ`, since they have been mutated to maintain the illusion
# of a local run: once we allow for concurrent runs, this information should be
# propagated down from the caller.
# see https://github.com/pantsbuild/pants/issues/7654
clean_global_runtime_state(reset_subsystem=True)
options_bootstrapper = OptionsBootstrapper.create(
env=os.environ, args=sys.argv, allow_pantsrc=True
)
bootstrap_options = options_bootstrapper.bootstrap_options
global_bootstrap_options = bootstrap_options.for_global_scope()
# Run using the pre-warmed Session.
with self._stderr_logging(global_bootstrap_options):
try:
scheduler = self._core.prepare_scheduler(options_bootstrapper)
runner = LocalPantsRunner.create(
os.environ, options_bootstrapper, scheduler=scheduler
)
return runner.run(start_time)
except Exception as e:
logger.exception(e)
return PANTS_FAILED_EXIT_CODE
except KeyboardInterrupt:
print("Interrupted by user.\n", file=sys.stderr)
return PANTS_FAILED_EXIT_CODE
def __call__(
self,
command: str,
args: Tuple[str, ...],
env: Dict[str, str],
working_directory: bytes,
stdin_fd: int,
stdout_fd: int,
stderr_fd: int,
) -> ExitCode:
request_timeout = float(env.get("PANTSD_REQUEST_TIMEOUT_LIMIT", -1))
# NB: Order matters: we acquire a lock before mutating either `sys.std*`, `os.environ`, etc.
with self._one_run_at_a_time(stderr_fd, timeout=request_timeout), stdio_as(
stdin_fd=stdin_fd, stdout_fd=stdout_fd, stderr_fd=stderr_fd
), hermetic_environment_as(**env), argv_as((command,) + args):
# NB: Run implements exception handling, so only the most primitive errors will escape
# this function, where they will be logged to the pantsd.log by the server.
logger.info(f"handling request: `{' '.join(args)}`")
try:
return self.single_daemonized_run(working_directory.decode())
finally:
logger.info(f"request completed: `{' '.join(args)}`")
|
from snappy import ProductIO, HashMap, GPF
import os
def apply_orbit_file(product):
parameters = HashMap()
parameters.put("Apply-Orbit-File", True)
operator_name = "Apply-Orbit-File"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def thermal_noise_removal(product, remove_thermal_noise=True):
parameters = HashMap()
parameters.put("removeThermalNoise", remove_thermal_noise)
operator_name = "ThermalNoiseRemoval"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def border_noise_remove(product, border_margin_limit=500, thresold=0.5):
parameters = HashMap()
parameters.put("borderMarginLimit", border_margin_limit)
parameters.put("Threshold", thresold)
operator_name = "Remove-GRD-Border-Noise"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def calibration(product, output_type="sigma0", polarization="both"):
"""
Passes the snap product from border_noise_removal and calibrates it to either sigma0, beta0 or gamma0 depending on
the users selection.
:param product: The product from ProductIO.
:param output_type: The type of calibration to undertake, choose from ["sigma0", "beta0", "gamma0"].
:param polarization: The polarization of the images being passed to calibration. Choose from:
["both", "vv", "vh"].
:return: The calibrated product.
"""
parameters = HashMap()
polarization = polarization.lower()
output_type = output_type.lower()
if polarization not in ["both", "vv", "vh"]:
raise ValueError("The polarization chosen is not supported, choose from 'both', 'vv' or 'vh'")
if output_type not in ["sigma0", "beta0", "gamma0"]:
raise ValueError("The output type chosen isn't possible, choose from 'sigma0', 'beta0' or 'gamma0'")
# Choose calibration to undertake.
if output_type == "sigma0":
parameters.put("outputSigmaBand", True)
elif output_type == "gamma0":
parameters.put("outputGammaBand", True)
else:
parameters.put("outputBetaBand", True)
# Choose polarizations to use.
if polarization == "both":
parameters.put("sourceBands", "Intensity_VH,Intensity_VV")
elif polarization == "vh":
parameters.put("sourceBands", "Intensity_VH")
elif polarization == "vv":
parameters.put("sourceBands", "Intensity_VV")
operator_name = "Calibration"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def terrain_correction(product):
parameters = HashMap()
parameters.put("demName", "ACE30")
parameters.put("imgResamplingMethod", "BICUBIC_INTERPOLATION")
parameters.put("saveProjectedLocalIncidenceAngle", True)
parameters.put('saveSelectedSourceBand', True)
operator_name = "Terrain-Correction"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def speckle_filtering(product, filter_type="lee", filter_size=5):
"""
Attempts to filter the speckle from the image, without too much loss of edges.
:param product: The input product processed to calibration or terrain correction level.
:param filter_type: Choose from lee, lee_sigma, refined_lee, median and cnn. It should be noted that cnn is a tool
external from the SNAP toolbox and therefore if CNN is chosen here, we move into numpy arrays.
:param filter_size: An odd number is required, the base size is 5 but it's recommended to change this for some
settings.
:return:
"""
if filter_size % 2 == 0:
raise ValueError("The filter size must be an odd value.")
if filter_type not in ["lee", "lee_sigma", "refined_lee", "median", "cnn"]:
raise ValueError("The filter type chosen is not valid in this pipeline, choose from 'lee', 'lee_sigma',"
" 'refine_lee', 'median', 'cnn'")
parameters = HashMap()
parameters.put("filterSizeX", filter_size)
parameters.put("filterSizeY", filter_size)
# Apply the chosen filter.
if filter_type == "lee":
parameters.put("filter", "Lee")
elif filter_type == "lee_sigma":
parameters.put("filter", "LeeSigma")
elif filter_type == "refine_lee":
parameters.put("filter", "RefineLee")
elif filter_type == "median":
parameters.put("filter", "Median")
else:
print("This is not ready yet.")
operator_name = "Speckle-Filter"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def run_sar_pipeline(path_to_product: str, out_path: str, filter_image=False, remove_thermal=True,
border_margin_limit=500, threshold=0.5, output_type="sigma0", polarization="both",
filter_type="lee", filter_size=5):
"""
Runs the sar pipeline to process a single input.
:param path_to_product:
:param out_path:
:param filter_image:
:param remove_thermal:
:param border_margin_limit:
:param threshold:
:param output_type:
:param polarization:
:param filter_type:
:param filter_size:
:return:
"""
# Load
s1_img = ProductIO.readProduct(path_to_product)
# Process the image.
# Apply orbit file.
s1_img = apply_orbit_file(s1_img)
# Remove thermal noise.
s1_img = thermal_noise_removal(s1_img, remove_thermal)
# Remove border noise.
s1_img = border_noise_remove(s1_img, border_margin_limit, threshold)
# Calibrate.
s1_img = calibration(s1_img, output_type, polarization)
# Terrain correction.
s1_img = terrain_correction(s1_img)
# Filtering.
if filter_image:
speckle_filtering(s1_img, filter_type, filter_size)
# Write the imagery.
ProductIO.writeProduct(s1_img, out_path, "BEAM-DIMAP")
run_sar_pipeline("D:/sar/s1_denmark/S1B_IW_GRDH_1SDV_20190305T053954_20190305T054019_015215_01C76B_406C.SAFE",
"D:/sar/20190305T053954.dim")
if __name__ == '__main__':
for product in os.listdir("D:/sar/s1_denmark"):
out_name = f"{out_name.split("_")[4]}.dim"
out_path = f"D:/sar/s1_denmark_processed/{out_name}"
run_sar_pipeline(f"D:/sar/s1_denmark/{product}",
out_path) | from snappy import ProductIO, HashMap, GPF
import os
def apply_orbit_file(product):
parameters = HashMap()
parameters.put("Apply-Orbit-File", True)
operator_name = "Apply-Orbit-File"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def thermal_noise_removal(product, remove_thermal_noise=True):
parameters = HashMap()
parameters.put("removeThermalNoise", remove_thermal_noise)
operator_name = "ThermalNoiseRemoval"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def border_noise_remove(product, border_margin_limit=500, thresold=0.5):
parameters = HashMap()
parameters.put("borderMarginLimit", border_margin_limit)
parameters.put("Threshold", thresold)
operator_name = "Remove-GRD-Border-Noise"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def calibration(product, output_type="sigma0", polarization="both"):
"""
Passes the snap product from border_noise_removal and calibrates it to either sigma0, beta0 or gamma0 depending on
the users selection.
:param product: The product from ProductIO.
:param output_type: The type of calibration to undertake, choose from ["sigma0", "beta0", "gamma0"].
:param polarization: The polarization of the images being passed to calibration. Choose from:
["both", "vv", "vh"].
:return: The calibrated product.
"""
parameters = HashMap()
polarization = polarization.lower()
output_type = output_type.lower()
if polarization not in ["both", "vv", "vh"]:
raise ValueError("The polarization chosen is not supported, choose from 'both', 'vv' or 'vh'")
if output_type not in ["sigma0", "beta0", "gamma0"]:
raise ValueError("The output type chosen isn't possible, choose from 'sigma0', 'beta0' or 'gamma0'")
# Choose calibration to undertake.
if output_type == "sigma0":
parameters.put("outputSigmaBand", True)
elif output_type == "gamma0":
parameters.put("outputGammaBand", True)
else:
parameters.put("outputBetaBand", True)
# Choose polarizations to use.
if polarization == "both":
parameters.put("sourceBands", "Intensity_VH,Intensity_VV")
elif polarization == "vh":
parameters.put("sourceBands", "Intensity_VH")
elif polarization == "vv":
parameters.put("sourceBands", "Intensity_VV")
operator_name = "Calibration"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def terrain_correction(product):
parameters = HashMap()
parameters.put("demName", "ACE30")
parameters.put("imgResamplingMethod", "BICUBIC_INTERPOLATION")
parameters.put("saveProjectedLocalIncidenceAngle", True)
parameters.put('saveSelectedSourceBand', True)
operator_name = "Terrain-Correction"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def speckle_filtering(product, filter_type="lee", filter_size=5):
"""
Attempts to filter the speckle from the image, without too much loss of edges.
:param product: The input product processed to calibration or terrain correction level.
:param filter_type: Choose from lee, lee_sigma, refined_lee, median and cnn. It should be noted that cnn is a tool
external from the SNAP toolbox and therefore if CNN is chosen here, we move into numpy arrays.
:param filter_size: An odd number is required, the base size is 5 but it's recommended to change this for some
settings.
:return:
"""
if filter_size % 2 == 0:
raise ValueError("The filter size must be an odd value.")
if filter_type not in ["lee", "lee_sigma", "refined_lee", "median", "cnn"]:
raise ValueError("The filter type chosen is not valid in this pipeline, choose from 'lee', 'lee_sigma',"
" 'refine_lee', 'median', 'cnn'")
parameters = HashMap()
parameters.put("filterSizeX", filter_size)
parameters.put("filterSizeY", filter_size)
# Apply the chosen filter.
if filter_type == "lee":
parameters.put("filter", "Lee")
elif filter_type == "lee_sigma":
parameters.put("filter", "LeeSigma")
elif filter_type == "refine_lee":
parameters.put("filter", "RefineLee")
elif filter_type == "median":
parameters.put("filter", "Median")
else:
print("This is not ready yet.")
operator_name = "Speckle-Filter"
target_product = GPF.createProduct(operator_name, parameters, product)
return target_product
def run_sar_pipeline(path_to_product: str, out_path: str, filter_image=False, remove_thermal=True,
border_margin_limit=500, threshold=0.5, output_type="sigma0", polarization="both",
filter_type="lee", filter_size=5):
"""
Runs the sar pipeline to process a single input.
:param path_to_product:
:param out_path:
:param filter_image:
:param remove_thermal:
:param border_margin_limit:
:param threshold:
:param output_type:
:param polarization:
:param filter_type:
:param filter_size:
:return:
"""
# Load
s1_img = ProductIO.readProduct(path_to_product)
# Process the image.
# Apply orbit file.
s1_img = apply_orbit_file(s1_img)
# Remove thermal noise.
s1_img = thermal_noise_removal(s1_img, remove_thermal)
# Remove border noise.
s1_img = border_noise_remove(s1_img, border_margin_limit, threshold)
# Calibrate.
s1_img = calibration(s1_img, output_type, polarization)
# Terrain correction.
s1_img = terrain_correction(s1_img)
# Filtering.
if filter_image:
speckle_filtering(s1_img, filter_type, filter_size)
# Write the imagery.
ProductIO.writeProduct(s1_img, out_path, "BEAM-DIMAP")
run_sar_pipeline("D:/sar/s1_denmark/S1B_IW_GRDH_1SDV_20190305T053954_20190305T054019_015215_01C76B_406C.SAFE",
"D:/sar/20190305T053954.dim")
if __name__ == '__main__':
for product in os.listdir("D:/sar/s1_denmark"):
out_name = f"{out_name.split('_')[4]}.dim"
out_path = f"D:/sar/s1_denmark_processed/{out_name}"
run_sar_pipeline(f"D:/sar/s1_denmark/{product}",
out_path) |
# -*- coding: utf-8 -*-
# author: https://github.com/Zfour
import json
import yaml
from bs4 import BeautifulSoup
from request_data import request
data_pool = []
def load_config():
f = open('_config.yml', 'r', encoding='utf-8')
ystr = f.read()
ymllist = yaml.load(ystr, Loader=yaml.FullLoader)
return ymllist
def github_issuse():
print('\n')
print('------- github issues start ----------')
baselink = 'https://github.com/'
config = load_config()
try:
for number in range(1, 100):
print(number)
if config['issues']['label']:
label_plus = '+label%3A' + config['issues']['label']
else:
label_plus = ''
github = request.get_data(
f"https://github.com/{config["issues"]["repo"]}/issues?q=is%3A{config["issues"]["state"]}{str(label_plus)}&page={str(number)}")
soup = BeautifulSoup(github, 'html.parser')
main_content = soup.find_all('div', {'aria-label': 'Issues'})
linklist = main_content[0].find_all('a', {'class': 'Link--primary'})
if len(linklist) == 0:
print('> end')
break
for item in linklist:
issueslink = baselink + item['href']
issues_page = request.get_data(issueslink)
issues_soup = BeautifulSoup(issues_page, 'html.parser')
try:
issues_linklist = issues_soup.find_all('pre')
source = issues_linklist[0].text
if "{" in source:
source = json.loads(source)
print(source)
data_pool.append(source)
except:
continue
except:
print('> end')
print('------- github issues end ----------')
print('\n')
# 友链规则
github_issuse()
filename = 'generator/output/v1/data.json'
with open(filename, 'w', encoding='utf-8') as file_obj:
json.dump(data_pool, file_obj, ensure_ascii=False)
| # -*- coding: utf-8 -*-
# author: https://github.com/Zfour
import json
import yaml
from bs4 import BeautifulSoup
from request_data import request
data_pool = []
def load_config():
f = open('_config.yml', 'r', encoding='utf-8')
ystr = f.read()
ymllist = yaml.load(ystr, Loader=yaml.FullLoader)
return ymllist
def github_issuse():
print('\n')
print('------- github issues start ----------')
baselink = 'https://github.com/'
config = load_config()
try:
for number in range(1, 100):
print(number)
if config['issues']['label']:
label_plus = '+label%3A' + config['issues']['label']
else:
label_plus = ''
github = request.get_data(
f"https://github.com/{config['issues']['repo']}/issues?q=is%3A{config['issues']['state']}{str(label_plus)}&page={str(number)}")
soup = BeautifulSoup(github, 'html.parser')
main_content = soup.find_all('div', {'aria-label': 'Issues'})
linklist = main_content[0].find_all('a', {'class': 'Link--primary'})
if len(linklist) == 0:
print('> end')
break
for item in linklist:
issueslink = baselink + item['href']
issues_page = request.get_data(issueslink)
issues_soup = BeautifulSoup(issues_page, 'html.parser')
try:
issues_linklist = issues_soup.find_all('pre')
source = issues_linklist[0].text
if "{" in source:
source = json.loads(source)
print(source)
data_pool.append(source)
except:
continue
except:
print('> end')
print('------- github issues end ----------')
print('\n')
# 友链规则
github_issuse()
filename = 'generator/output/v1/data.json'
with open(filename, 'w', encoding='utf-8') as file_obj:
json.dump(data_pool, file_obj, ensure_ascii=False)
|
#!/usr/bin/env python
import logging
import numpy as np
import time
from flask import Flask, request, jsonify
from os import getenv
import sentry_sdk
sentry_sdk.init(getenv("SENTRY_DSN"))
logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO)
logger = logging.getLogger(__name__)
app = Flask(__name__)
@app.route("/respond", methods=["POST"])
def respond():
st_time = time.time()
dialogs = request.json["dialogs"]
response_candidates = [dialog["utterances"][-1]["hypotheses"] for dialog in dialogs]
logger.error(dialogs)
logger.error(response_candidates)
selected_skill_names = []
selected_responses = []
selected_confidences = []
selected_human_attributes = []
selected_bot_attributes = []
for i, dialog in enumerate(dialogs):
confidences = []
responses = []
skill_names = []
human_attributes = []
bot_attributes = []
for skill_data in response_candidates[i]:
if skill_data["text"] and skill_data["confidence"]:
logger.info(f"Skill {skill_data["skill_name"]} returned non-empty hypothesis with non-zero confidence.")
confidences += [skill_data["confidence"]]
responses += [skill_data["text"]]
skill_names += [skill_data["skill_name"]]
human_attributes += [skill_data.get("human_attributes", {})]
bot_attributes += [skill_data.get("bot_attributes", {})]
if skill_data["skill_name"] == "dff_bot_persona_2_skill" and skill_data["confidence"] == 1.0:
confidences[-1] = 100.0
logger.info("DFF Persona was superpowered!")
logger.error(confidences)
best_id = np.argmax(confidences)
selected_skill_names.append(skill_names[best_id])
selected_responses.append(responses[best_id])
selected_confidences.append(confidences[best_id])
selected_human_attributes.append(human_attributes[best_id])
selected_bot_attributes.append(bot_attributes[best_id])
total_time = time.time() - st_time
logger.info(f"rule_based_response_selector exec time = {total_time:.3f}s")
return jsonify(list(zip(selected_skill_names, selected_responses, selected_confidences, selected_human_attributes, selected_bot_attributes)))
if __name__ == "__main__":
app.run(debug=False, host="0.0.0.0", port=3003)
| #!/usr/bin/env python
import logging
import numpy as np
import time
from flask import Flask, request, jsonify
from os import getenv
import sentry_sdk
sentry_sdk.init(getenv("SENTRY_DSN"))
logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO)
logger = logging.getLogger(__name__)
app = Flask(__name__)
@app.route("/respond", methods=["POST"])
def respond():
st_time = time.time()
dialogs = request.json["dialogs"]
response_candidates = [dialog["utterances"][-1]["hypotheses"] for dialog in dialogs]
logger.error(dialogs)
logger.error(response_candidates)
selected_skill_names = []
selected_responses = []
selected_confidences = []
selected_human_attributes = []
selected_bot_attributes = []
for i, dialog in enumerate(dialogs):
confidences = []
responses = []
skill_names = []
human_attributes = []
bot_attributes = []
for skill_data in response_candidates[i]:
if skill_data["text"] and skill_data["confidence"]:
logger.info(f"Skill {skill_data['skill_name']} returned non-empty hypothesis with non-zero confidence.")
confidences += [skill_data["confidence"]]
responses += [skill_data["text"]]
skill_names += [skill_data["skill_name"]]
human_attributes += [skill_data.get("human_attributes", {})]
bot_attributes += [skill_data.get("bot_attributes", {})]
if skill_data["skill_name"] == "dff_bot_persona_2_skill" and skill_data["confidence"] == 1.0:
confidences[-1] = 100.0
logger.info("DFF Persona was superpowered!")
logger.error(confidences)
best_id = np.argmax(confidences)
selected_skill_names.append(skill_names[best_id])
selected_responses.append(responses[best_id])
selected_confidences.append(confidences[best_id])
selected_human_attributes.append(human_attributes[best_id])
selected_bot_attributes.append(bot_attributes[best_id])
total_time = time.time() - st_time
logger.info(f"rule_based_response_selector exec time = {total_time:.3f}s")
return jsonify(list(zip(selected_skill_names, selected_responses, selected_confidences, selected_human_attributes, selected_bot_attributes)))
if __name__ == "__main__":
app.run(debug=False, host="0.0.0.0", port=3003)
|
#!/usr/bin/env python
import argparse
import logging
import os
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import gevent
from gevent.lock import Semaphore
from typing_extensions import Literal
from rotkehlchen.accounting.accountant import Accountant
from rotkehlchen.assets.asset import Asset, EthereumToken
from rotkehlchen.assets.resolver import AssetResolver
from rotkehlchen.balances.manual import account_for_manually_tracked_balances
from rotkehlchen.chain.ethereum.manager import EthereumManager
from rotkehlchen.chain.manager import BlockchainBalancesUpdate, ChainManager
from rotkehlchen.config import default_data_directory
from rotkehlchen.constants.assets import A_USD
from rotkehlchen.data.importer import DataImporter
from rotkehlchen.data_handler import DataHandler
from rotkehlchen.db.settings import DBSettings, ModifiableDBSettings
from rotkehlchen.errors import (
EthSyncError,
InputError,
PremiumAuthenticationError,
RemoteError,
SystemPermissionError,
)
from rotkehlchen.exchanges.manager import ExchangeManager
from rotkehlchen.externalapis.alethio import Alethio
from rotkehlchen.externalapis.cryptocompare import Cryptocompare
from rotkehlchen.externalapis.etherscan import Etherscan
from rotkehlchen.fval import FVal
from rotkehlchen.greenlets import GreenletManager
from rotkehlchen.history import PriceHistorian, TradesHistorian
from rotkehlchen.inquirer import Inquirer
from rotkehlchen.logging import DEFAULT_ANONYMIZED_LOGS, LoggingSettings, RotkehlchenLogsAdapter
from rotkehlchen.premium.premium import Premium, PremiumCredentials, premium_create_and_verify
from rotkehlchen.premium.sync import PremiumSyncManager
from rotkehlchen.transactions import EthereumAnalyzer
from rotkehlchen.typing import (
ApiKey,
ApiSecret,
BlockchainAccountData,
ListOfBlockchainAddresses,
SupportedBlockchain,
Timestamp,
)
from rotkehlchen.usage_analytics import maybe_submit_usage_analytics
from rotkehlchen.user_messages import MessagesAggregator
from rotkehlchen.utils.misc import combine_stat_dicts, dict_get_sumof, merge_dicts
logger = logging.getLogger(__name__)
log = RotkehlchenLogsAdapter(logger)
MAIN_LOOP_SECS_DELAY = 15
class Rotkehlchen():
def __init__(self, args: argparse.Namespace) -> None:
"""Initialize the Rotkehlchen object
May Raise:
- SystemPermissionError if the given data directory's permissions
are not correct.
"""
self.lock = Semaphore()
self.lock.acquire()
# Can also be None after unlock if premium credentials did not
# authenticate or premium server temporarily offline
self.premium: Optional[Premium] = None
self.user_is_logged_in = False
logfilename = None
if args.logtarget == 'file':
logfilename = args.logfile
if args.loglevel == 'debug':
loglevel = logging.DEBUG
elif args.loglevel == 'info':
loglevel = logging.INFO
elif args.loglevel == 'warn':
loglevel = logging.WARN
elif args.loglevel == 'error':
loglevel = logging.ERROR
elif args.loglevel == 'critical':
loglevel = logging.CRITICAL
else:
raise AssertionError('Should never get here. Illegal log value')
logging.basicConfig(
filename=logfilename,
filemode='w',
level=loglevel,
format='%(asctime)s -- %(levelname)s:%(name)s:%(message)s',
datefmt='%d/%m/%Y %H:%M:%S %Z',
)
if not args.logfromothermodules:
logging.getLogger('urllib3').setLevel(logging.CRITICAL)
logging.getLogger('urllib3.connectionpool').setLevel(logging.CRITICAL)
self.sleep_secs = args.sleep_secs
if args.data_dir is None:
self.data_dir = default_data_directory()
else:
self.data_dir = Path(args.data_dir)
if not os.access(self.data_dir, os.W_OK | os.R_OK):
raise SystemPermissionError(
f'The given data directory {self.data_dir} is not readable or writable',
)
self.args = args
self.msg_aggregator = MessagesAggregator()
self.greenlet_manager = GreenletManager(msg_aggregator=self.msg_aggregator)
self.exchange_manager = ExchangeManager(msg_aggregator=self.msg_aggregator)
self.all_eth_tokens = AssetResolver().get_all_eth_tokens()
self.data = DataHandler(self.data_dir, self.msg_aggregator)
self.cryptocompare = Cryptocompare(data_directory=self.data_dir, database=None)
# Initialize the Inquirer singleton
Inquirer(data_dir=self.data_dir, cryptocompare=self.cryptocompare)
self.lock.release()
self.shutdown_event = gevent.event.Event()
def reset_after_failed_account_creation_or_login(self) -> None:
"""If the account creation or login failed make sure that the Rotki instance is clear
Tricky instances are when after either failed premium credentials or user refusal
to sync premium databases we relogged in.
"""
self.cryptocompare.db = None
def unlock_user(
self,
user: str,
password: str,
create_new: bool,
sync_approval: Literal['yes', 'no', 'unknown'],
premium_credentials: Optional[PremiumCredentials],
initial_settings: Optional[ModifiableDBSettings] = None,
) -> None:
"""Unlocks an existing user or creates a new one if `create_new` is True
May raise:
- PremiumAuthenticationError if the password can't unlock the database.
- AuthenticationError if premium_credentials are given and are invalid
or can't authenticate with the server
- DBUpgradeError if the rotki DB version is newer than the software or
there is a DB upgrade and there is an error.
- SystemPermissionError if the directory or DB file can not be accessed
"""
log.info(
'Unlocking user',
user=user,
create_new=create_new,
sync_approval=sync_approval,
initial_settings=initial_settings,
)
# unlock or create the DB
self.password = password
self.user_directory = self.data.unlock(user, password, create_new, initial_settings)
self.data_importer = DataImporter(db=self.data.db)
self.last_data_upload_ts = self.data.db.get_last_data_upload_ts()
self.premium_sync_manager = PremiumSyncManager(data=self.data, password=password)
# set the DB in the external services instances that need it
self.cryptocompare.set_database(self.data.db)
# Anything that was set above here has to be cleaned in case of failure in the next step
# by reset_after_failed_account_creation_or_login()
try:
self.premium = self.premium_sync_manager.try_premium_at_start(
given_premium_credentials=premium_credentials,
username=user,
create_new=create_new,
sync_approval=sync_approval,
)
except PremiumAuthenticationError:
# Reraise it only if this is during the creation of a new account where
# the premium credentials were given by the user
if create_new:
raise
# else let's just continue. User signed in succesfully, but he just
# has unauthenticable/invalid premium credentials remaining in his DB
settings = self.get_settings()
maybe_submit_usage_analytics(settings.submit_usage_analytics)
self.etherscan = Etherscan(database=self.data.db, msg_aggregator=self.msg_aggregator)
alethio = Alethio(
database=self.data.db,
msg_aggregator=self.msg_aggregator,
all_eth_tokens=self.all_eth_tokens,
)
historical_data_start = settings.historical_data_start
eth_rpc_endpoint = settings.eth_rpc_endpoint
# Initialize the price historian singleton
PriceHistorian(
data_directory=self.data_dir,
history_date_start=historical_data_start,
cryptocompare=self.cryptocompare,
)
self.accountant = Accountant(
db=self.data.db,
user_directory=self.user_directory,
msg_aggregator=self.msg_aggregator,
create_csv=True,
)
# Initialize the rotkehlchen logger
LoggingSettings(anonymized_logs=settings.anonymized_logs)
exchange_credentials = self.data.db.get_exchange_credentials()
self.exchange_manager.initialize_exchanges(
exchange_credentials=exchange_credentials,
database=self.data.db,
)
# Initialize blockchain querying modules
ethereum_manager = EthereumManager(
ethrpc_endpoint=eth_rpc_endpoint,
etherscan=self.etherscan,
msg_aggregator=self.msg_aggregator,
)
self.chain_manager = ChainManager(
blockchain_accounts=self.data.db.get_blockchain_accounts(),
owned_eth_tokens=self.data.db.get_owned_tokens(),
ethereum_manager=ethereum_manager,
msg_aggregator=self.msg_aggregator,
alethio=alethio,
greenlet_manager=self.greenlet_manager,
premium=self.premium,
eth_modules=settings.active_modules,
)
self.ethereum_analyzer = EthereumAnalyzer(
ethereum_manager=ethereum_manager,
database=self.data.db,
)
self.trades_historian = TradesHistorian(
user_directory=self.user_directory,
db=self.data.db,
msg_aggregator=self.msg_aggregator,
exchange_manager=self.exchange_manager,
chain_manager=self.chain_manager,
)
self.user_is_logged_in = True
def logout(self) -> None:
if not self.user_is_logged_in:
return
user = self.data.username
log.info(
'Logging out user',
user=user,
)
del self.chain_manager
self.exchange_manager.delete_all_exchanges()
# Reset rotkehlchen logger to default
LoggingSettings(anonymized_logs=DEFAULT_ANONYMIZED_LOGS)
del self.accountant
del self.trades_historian
del self.data_importer
if self.premium is not None:
del self.premium
self.data.logout()
self.password = ''
self.cryptocompare.unset_database()
# Make sure no messages leak to other user sessions
self.msg_aggregator.consume_errors()
self.msg_aggregator.consume_warnings()
self.user_is_logged_in = False
log.info(
'User successfully logged out',
user=user,
)
def set_premium_credentials(self, credentials: PremiumCredentials) -> None:
"""
Sets the premium credentials for Rotki
Raises PremiumAuthenticationError if the given key is rejected by the Rotkehlchen server
"""
log.info('Setting new premium credentials')
if self.premium is not None:
self.premium.set_credentials(credentials)
else:
self.premium = premium_create_and_verify(credentials)
self.data.db.set_rotkehlchen_premium(credentials)
def delete_premium_credentials(self, name: str) -> Tuple[bool, str]:
"""Deletes the premium credentials for Rotki"""
success: bool
msg = ''
if name != self.data.username:
msg = f'Provided user "{name}" is not the logged in user'
success = False
success = self.data.db.del_rotkehlchen_premium()
if success is False:
msg = 'The database was unable to delete the Premium keys for the logged-in user'
self.deactivate_premium_status()
return success, msg
def deactivate_premium_status(self) -> None:
"""Deactivate premium in the current session"""
self.premium = None
self.premium_sync_manager.premium = None
self.chain_manager.deactivate_premium_status()
def start(self) -> gevent.Greenlet:
return gevent.spawn(self.main_loop)
def main_loop(self) -> None:
"""Rotki main loop that fires often and manages many different tasks
Each task remembers the last time it run sucesfully and know how often it
should run. So each task manages itself.
"""
while self.shutdown_event.wait(MAIN_LOOP_SECS_DELAY) is not True:
if self.user_is_logged_in:
log.debug('Main loop start')
self.premium_sync_manager.maybe_upload_data_to_server()
self.ethereum_analyzer.analyze_ethereum_transactions()
log.debug('Main loop end')
def add_blockchain_accounts(
self,
blockchain: SupportedBlockchain,
account_data: List[BlockchainAccountData],
) -> BlockchainBalancesUpdate:
"""Adds new blockchain accounts
Adds the accounts to the blockchain instance and queries them to get the
updated balances. Also adds them in the DB
May raise:
- EthSyncError from modify_blockchain_account
- InputError if the given accounts list is empty.
- TagConstraintError if any of the given account data contain unknown tags.
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
"""
self.data.db.ensure_tags_exist(
given_data=account_data,
action='adding',
data_type='blockchain accounts',
)
address_type = blockchain.get_address_type()
updated_balances = self.chain_manager.add_blockchain_accounts(
blockchain=blockchain,
accounts=[address_type(entry.address) for entry in account_data],
)
self.data.db.add_blockchain_accounts(
blockchain=blockchain,
account_data=account_data,
)
return updated_balances
def edit_blockchain_accounts(
self,
blockchain: SupportedBlockchain,
account_data: List[BlockchainAccountData],
) -> None:
"""Edits blockchain accounts
Edits blockchain account data for the given accounts
May raise:
- InputError if the given accounts list is empty or if
any of the accounts to edit do not exist.
- TagConstraintError if any of the given account data contain unknown tags.
"""
# First check for validity of account data addresses
if len(account_data) == 0:
raise InputError('Empty list of blockchain account data to edit was given')
accounts = [x.address for x in account_data]
unknown_accounts = set(accounts).difference(self.chain_manager.accounts.get(blockchain))
if len(unknown_accounts) != 0:
raise InputError(
f'Tried to edit unknown {blockchain.value} '
f'accounts {','.join(unknown_accounts)}',
)
self.data.db.ensure_tags_exist(
given_data=account_data,
action='editing',
data_type='blockchain accounts',
)
# Finally edit the accounts
self.data.db.edit_blockchain_accounts(
blockchain=blockchain,
account_data=account_data,
)
return None
def remove_blockchain_accounts(
self,
blockchain: SupportedBlockchain,
accounts: ListOfBlockchainAddresses,
) -> BlockchainBalancesUpdate:
"""Removes blockchain accounts
Removes the accounts from the blockchain instance and queries them to get
the updated balances. Also removes them from the DB
May raise:
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
- InputError if a non-existing account was given to remove
"""
balances_update = self.chain_manager.remove_blockchain_accounts(
blockchain=blockchain,
accounts=accounts,
)
self.data.db.remove_blockchain_accounts(blockchain, accounts)
return balances_update
def add_owned_eth_tokens(
self,
tokens: List[EthereumToken],
) -> BlockchainBalancesUpdate:
"""Adds tokens to the blockchain state and updates balance of all accounts
May raise:
- InputError if some of the tokens already exist
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
- EthSyncError if querying the token balances through a provided ethereum
client and the chain is not synced
"""
new_data = self.chain_manager.track_new_tokens(tokens)
self.data.write_owned_eth_tokens(self.chain_manager.owned_eth_tokens)
return new_data
def remove_owned_eth_tokens(
self,
tokens: List[EthereumToken],
) -> BlockchainBalancesUpdate:
"""
Removes tokens from the state and stops their balance from being tracked
for each account
May raise:
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
- EthSyncError if querying the token balances through a provided ethereum
client and the chain is not synced
"""
new_data = self.chain_manager.remove_eth_tokens(tokens)
self.data.write_owned_eth_tokens(self.chain_manager.owned_eth_tokens)
return new_data
def process_history(
self,
start_ts: Timestamp,
end_ts: Timestamp,
) -> Tuple[Dict[str, Any], str]:
(
error_or_empty,
history,
loan_history,
asset_movements,
eth_transactions,
defi_events,
) = self.trades_historian.get_history(
start_ts=start_ts,
end_ts=end_ts,
has_premium=self.premium is not None,
)
result = self.accountant.process_history(
start_ts=start_ts,
end_ts=end_ts,
trade_history=history,
loan_history=loan_history,
asset_movements=asset_movements,
eth_transactions=eth_transactions,
defi_events=defi_events,
)
return result, error_or_empty
def query_fiat_balances(self) -> Dict[Asset, Dict[str, FVal]]:
result = {}
balances = self.data.get_fiat_balances()
for currency, str_amount in balances.items():
amount = FVal(str_amount)
usd_rate = Inquirer().query_fiat_pair(currency, A_USD)
result[currency] = {
'amount': amount,
'usd_value': amount * usd_rate,
}
return result
def query_balances(
self,
requested_save_data: bool = False,
timestamp: Timestamp = None,
ignore_cache: bool = False,
) -> Dict[str, Any]:
"""Query all balances rotkehlchen can see.
If requested_save_data is True then the data are always saved in the DB,
if it is False then data are saved if self.data.should_save_balances()
is True.
If timestamp is None then the current timestamp is used.
If a timestamp is given then that is the time that the balances are going
to be saved in the DB
If ignore_cache is True then all underlying calls that have a cache ignore it
Returns a dictionary with the queried balances.
"""
log.info('query_balances called', requested_save_data=requested_save_data)
balances = {}
problem_free = True
for _, exchange in self.exchange_manager.connected_exchanges.items():
exchange_balances, _ = exchange.query_balances(ignore_cache=ignore_cache)
# If we got an error, disregard that exchange but make sure we don't save data
if not isinstance(exchange_balances, dict):
problem_free = False
else:
balances[exchange.name] = exchange_balances
try:
blockchain_result = self.chain_manager.query_balances(
blockchain=None,
ignore_cache=ignore_cache,
)
balances['blockchain'] = {
asset: balance.to_dict() for asset, balance in blockchain_result.totals.items()
}
except (RemoteError, EthSyncError) as e:
problem_free = False
log.error(f'Querying blockchain balances failed due to: {str(e)}')
result = self.query_fiat_balances()
if result != {}:
balances['banks'] = result
balances = account_for_manually_tracked_balances(db=self.data.db, balances=balances)
combined = combine_stat_dicts([v for k, v in balances.items()])
total_usd_per_location = [(k, dict_get_sumof(v, 'usd_value')) for k, v in balances.items()]
# calculate net usd value
net_usd = FVal(0)
for _, v in combined.items():
net_usd += FVal(v['usd_value'])
stats: Dict[str, Any] = {
'location': {
},
'net_usd': net_usd,
}
for entry in total_usd_per_location:
name = entry[0]
total = entry[1]
if net_usd != FVal(0):
percentage = (total / net_usd).to_percentage()
else:
percentage = '0%'
stats['location'][name] = {
'usd_value': total,
'percentage_of_net_value': percentage,
}
for k, v in combined.items():
if net_usd != FVal(0):
percentage = (v['usd_value'] / net_usd).to_percentage()
else:
percentage = '0%'
combined[k]['percentage_of_net_value'] = percentage
result_dict = merge_dicts(combined, stats)
allowed_to_save = requested_save_data or self.data.should_save_balances()
if problem_free and allowed_to_save:
if not timestamp:
timestamp = Timestamp(int(time.time()))
self.data.save_balances_data(data=result_dict, timestamp=timestamp)
log.debug('query_balances data saved')
else:
log.debug(
'query_balances data not saved',
allowed_to_save=allowed_to_save,
problem_free=problem_free,
)
# After adding it to the saved file we can overlay additional data that
# is not required to be saved in the history file
try:
details = self.accountant.events.details
for asset, (tax_free_amount, average_buy_value) in details.items():
if asset not in result_dict:
continue
result_dict[asset]['tax_free_amount'] = tax_free_amount
result_dict[asset]['average_buy_value'] = average_buy_value
current_price = result_dict[asset]['usd_value'] / result_dict[asset]['amount']
if average_buy_value != FVal(0):
result_dict[asset]['percent_change'] = (
((current_price - average_buy_value) / average_buy_value) * 100
)
else:
result_dict[asset]['percent_change'] = 'INF'
except AttributeError:
pass
return result_dict
def set_settings(self, settings: ModifiableDBSettings) -> Tuple[bool, str]:
"""Tries to set new settings. Returns True in success or False with message if error"""
with self.lock:
if settings.eth_rpc_endpoint is not None:
result, msg = self.chain_manager.set_eth_rpc_endpoint(settings.eth_rpc_endpoint)
if not result:
return False, msg
if settings.kraken_account_type is not None:
kraken = self.exchange_manager.get('kraken')
if kraken:
kraken.set_account_type(settings.kraken_account_type) # type: ignore
self.data.db.set_settings(settings)
return True, ''
def get_settings(self) -> DBSettings:
"""Returns the db settings with a check whether premium is active or not"""
db_settings = self.data.db.get_settings(have_premium=self.premium is not None)
return db_settings
def setup_exchange(
self,
name: str,
api_key: ApiKey,
api_secret: ApiSecret,
passphrase: Optional[str] = None,
) -> Tuple[bool, str]:
"""
Setup a new exchange with an api key and an api secret and optionally a passphrase
By default the api keys are always validated unless validate is False.
"""
is_success, msg = self.exchange_manager.setup_exchange(
name=name,
api_key=api_key,
api_secret=api_secret,
database=self.data.db,
passphrase=passphrase,
)
if is_success:
# Success, save the result in the DB
self.data.db.add_exchange(name, api_key, api_secret, passphrase=passphrase)
return is_success, msg
def remove_exchange(self, name: str) -> Tuple[bool, str]:
if not self.exchange_manager.has_exchange(name):
return False, 'Exchange {} is not registered'.format(name)
self.exchange_manager.delete_exchange(name)
# Success, remove it also from the DB
self.data.db.remove_exchange(name)
self.data.db.delete_used_query_range_for_exchange(name)
return True, ''
def query_periodic_data(self) -> Dict[str, Union[bool, Timestamp]]:
"""Query for frequently changing data"""
result: Dict[str, Union[bool, Timestamp]] = {}
if self.user_is_logged_in:
result['last_balance_save'] = self.data.db.get_last_balance_save_time()
result['eth_node_connection'] = self.chain_manager.ethereum.web3 is not None
result['history_process_start_ts'] = self.accountant.started_processing_timestamp
result['history_process_current_ts'] = self.accountant.currently_processing_timestamp
return result
def shutdown(self) -> None:
self.logout()
self.shutdown_event.set()
| #!/usr/bin/env python
import argparse
import logging
import os
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import gevent
from gevent.lock import Semaphore
from typing_extensions import Literal
from rotkehlchen.accounting.accountant import Accountant
from rotkehlchen.assets.asset import Asset, EthereumToken
from rotkehlchen.assets.resolver import AssetResolver
from rotkehlchen.balances.manual import account_for_manually_tracked_balances
from rotkehlchen.chain.ethereum.manager import EthereumManager
from rotkehlchen.chain.manager import BlockchainBalancesUpdate, ChainManager
from rotkehlchen.config import default_data_directory
from rotkehlchen.constants.assets import A_USD
from rotkehlchen.data.importer import DataImporter
from rotkehlchen.data_handler import DataHandler
from rotkehlchen.db.settings import DBSettings, ModifiableDBSettings
from rotkehlchen.errors import (
EthSyncError,
InputError,
PremiumAuthenticationError,
RemoteError,
SystemPermissionError,
)
from rotkehlchen.exchanges.manager import ExchangeManager
from rotkehlchen.externalapis.alethio import Alethio
from rotkehlchen.externalapis.cryptocompare import Cryptocompare
from rotkehlchen.externalapis.etherscan import Etherscan
from rotkehlchen.fval import FVal
from rotkehlchen.greenlets import GreenletManager
from rotkehlchen.history import PriceHistorian, TradesHistorian
from rotkehlchen.inquirer import Inquirer
from rotkehlchen.logging import DEFAULT_ANONYMIZED_LOGS, LoggingSettings, RotkehlchenLogsAdapter
from rotkehlchen.premium.premium import Premium, PremiumCredentials, premium_create_and_verify
from rotkehlchen.premium.sync import PremiumSyncManager
from rotkehlchen.transactions import EthereumAnalyzer
from rotkehlchen.typing import (
ApiKey,
ApiSecret,
BlockchainAccountData,
ListOfBlockchainAddresses,
SupportedBlockchain,
Timestamp,
)
from rotkehlchen.usage_analytics import maybe_submit_usage_analytics
from rotkehlchen.user_messages import MessagesAggregator
from rotkehlchen.utils.misc import combine_stat_dicts, dict_get_sumof, merge_dicts
logger = logging.getLogger(__name__)
log = RotkehlchenLogsAdapter(logger)
MAIN_LOOP_SECS_DELAY = 15
class Rotkehlchen():
def __init__(self, args: argparse.Namespace) -> None:
"""Initialize the Rotkehlchen object
May Raise:
- SystemPermissionError if the given data directory's permissions
are not correct.
"""
self.lock = Semaphore()
self.lock.acquire()
# Can also be None after unlock if premium credentials did not
# authenticate or premium server temporarily offline
self.premium: Optional[Premium] = None
self.user_is_logged_in = False
logfilename = None
if args.logtarget == 'file':
logfilename = args.logfile
if args.loglevel == 'debug':
loglevel = logging.DEBUG
elif args.loglevel == 'info':
loglevel = logging.INFO
elif args.loglevel == 'warn':
loglevel = logging.WARN
elif args.loglevel == 'error':
loglevel = logging.ERROR
elif args.loglevel == 'critical':
loglevel = logging.CRITICAL
else:
raise AssertionError('Should never get here. Illegal log value')
logging.basicConfig(
filename=logfilename,
filemode='w',
level=loglevel,
format='%(asctime)s -- %(levelname)s:%(name)s:%(message)s',
datefmt='%d/%m/%Y %H:%M:%S %Z',
)
if not args.logfromothermodules:
logging.getLogger('urllib3').setLevel(logging.CRITICAL)
logging.getLogger('urllib3.connectionpool').setLevel(logging.CRITICAL)
self.sleep_secs = args.sleep_secs
if args.data_dir is None:
self.data_dir = default_data_directory()
else:
self.data_dir = Path(args.data_dir)
if not os.access(self.data_dir, os.W_OK | os.R_OK):
raise SystemPermissionError(
f'The given data directory {self.data_dir} is not readable or writable',
)
self.args = args
self.msg_aggregator = MessagesAggregator()
self.greenlet_manager = GreenletManager(msg_aggregator=self.msg_aggregator)
self.exchange_manager = ExchangeManager(msg_aggregator=self.msg_aggregator)
self.all_eth_tokens = AssetResolver().get_all_eth_tokens()
self.data = DataHandler(self.data_dir, self.msg_aggregator)
self.cryptocompare = Cryptocompare(data_directory=self.data_dir, database=None)
# Initialize the Inquirer singleton
Inquirer(data_dir=self.data_dir, cryptocompare=self.cryptocompare)
self.lock.release()
self.shutdown_event = gevent.event.Event()
def reset_after_failed_account_creation_or_login(self) -> None:
"""If the account creation or login failed make sure that the Rotki instance is clear
Tricky instances are when after either failed premium credentials or user refusal
to sync premium databases we relogged in.
"""
self.cryptocompare.db = None
def unlock_user(
self,
user: str,
password: str,
create_new: bool,
sync_approval: Literal['yes', 'no', 'unknown'],
premium_credentials: Optional[PremiumCredentials],
initial_settings: Optional[ModifiableDBSettings] = None,
) -> None:
"""Unlocks an existing user or creates a new one if `create_new` is True
May raise:
- PremiumAuthenticationError if the password can't unlock the database.
- AuthenticationError if premium_credentials are given and are invalid
or can't authenticate with the server
- DBUpgradeError if the rotki DB version is newer than the software or
there is a DB upgrade and there is an error.
- SystemPermissionError if the directory or DB file can not be accessed
"""
log.info(
'Unlocking user',
user=user,
create_new=create_new,
sync_approval=sync_approval,
initial_settings=initial_settings,
)
# unlock or create the DB
self.password = password
self.user_directory = self.data.unlock(user, password, create_new, initial_settings)
self.data_importer = DataImporter(db=self.data.db)
self.last_data_upload_ts = self.data.db.get_last_data_upload_ts()
self.premium_sync_manager = PremiumSyncManager(data=self.data, password=password)
# set the DB in the external services instances that need it
self.cryptocompare.set_database(self.data.db)
# Anything that was set above here has to be cleaned in case of failure in the next step
# by reset_after_failed_account_creation_or_login()
try:
self.premium = self.premium_sync_manager.try_premium_at_start(
given_premium_credentials=premium_credentials,
username=user,
create_new=create_new,
sync_approval=sync_approval,
)
except PremiumAuthenticationError:
# Reraise it only if this is during the creation of a new account where
# the premium credentials were given by the user
if create_new:
raise
# else let's just continue. User signed in succesfully, but he just
# has unauthenticable/invalid premium credentials remaining in his DB
settings = self.get_settings()
maybe_submit_usage_analytics(settings.submit_usage_analytics)
self.etherscan = Etherscan(database=self.data.db, msg_aggregator=self.msg_aggregator)
alethio = Alethio(
database=self.data.db,
msg_aggregator=self.msg_aggregator,
all_eth_tokens=self.all_eth_tokens,
)
historical_data_start = settings.historical_data_start
eth_rpc_endpoint = settings.eth_rpc_endpoint
# Initialize the price historian singleton
PriceHistorian(
data_directory=self.data_dir,
history_date_start=historical_data_start,
cryptocompare=self.cryptocompare,
)
self.accountant = Accountant(
db=self.data.db,
user_directory=self.user_directory,
msg_aggregator=self.msg_aggregator,
create_csv=True,
)
# Initialize the rotkehlchen logger
LoggingSettings(anonymized_logs=settings.anonymized_logs)
exchange_credentials = self.data.db.get_exchange_credentials()
self.exchange_manager.initialize_exchanges(
exchange_credentials=exchange_credentials,
database=self.data.db,
)
# Initialize blockchain querying modules
ethereum_manager = EthereumManager(
ethrpc_endpoint=eth_rpc_endpoint,
etherscan=self.etherscan,
msg_aggregator=self.msg_aggregator,
)
self.chain_manager = ChainManager(
blockchain_accounts=self.data.db.get_blockchain_accounts(),
owned_eth_tokens=self.data.db.get_owned_tokens(),
ethereum_manager=ethereum_manager,
msg_aggregator=self.msg_aggregator,
alethio=alethio,
greenlet_manager=self.greenlet_manager,
premium=self.premium,
eth_modules=settings.active_modules,
)
self.ethereum_analyzer = EthereumAnalyzer(
ethereum_manager=ethereum_manager,
database=self.data.db,
)
self.trades_historian = TradesHistorian(
user_directory=self.user_directory,
db=self.data.db,
msg_aggregator=self.msg_aggregator,
exchange_manager=self.exchange_manager,
chain_manager=self.chain_manager,
)
self.user_is_logged_in = True
def logout(self) -> None:
if not self.user_is_logged_in:
return
user = self.data.username
log.info(
'Logging out user',
user=user,
)
del self.chain_manager
self.exchange_manager.delete_all_exchanges()
# Reset rotkehlchen logger to default
LoggingSettings(anonymized_logs=DEFAULT_ANONYMIZED_LOGS)
del self.accountant
del self.trades_historian
del self.data_importer
if self.premium is not None:
del self.premium
self.data.logout()
self.password = ''
self.cryptocompare.unset_database()
# Make sure no messages leak to other user sessions
self.msg_aggregator.consume_errors()
self.msg_aggregator.consume_warnings()
self.user_is_logged_in = False
log.info(
'User successfully logged out',
user=user,
)
def set_premium_credentials(self, credentials: PremiumCredentials) -> None:
"""
Sets the premium credentials for Rotki
Raises PremiumAuthenticationError if the given key is rejected by the Rotkehlchen server
"""
log.info('Setting new premium credentials')
if self.premium is not None:
self.premium.set_credentials(credentials)
else:
self.premium = premium_create_and_verify(credentials)
self.data.db.set_rotkehlchen_premium(credentials)
def delete_premium_credentials(self, name: str) -> Tuple[bool, str]:
"""Deletes the premium credentials for Rotki"""
success: bool
msg = ''
if name != self.data.username:
msg = f'Provided user "{name}" is not the logged in user'
success = False
success = self.data.db.del_rotkehlchen_premium()
if success is False:
msg = 'The database was unable to delete the Premium keys for the logged-in user'
self.deactivate_premium_status()
return success, msg
def deactivate_premium_status(self) -> None:
"""Deactivate premium in the current session"""
self.premium = None
self.premium_sync_manager.premium = None
self.chain_manager.deactivate_premium_status()
def start(self) -> gevent.Greenlet:
return gevent.spawn(self.main_loop)
def main_loop(self) -> None:
"""Rotki main loop that fires often and manages many different tasks
Each task remembers the last time it run sucesfully and know how often it
should run. So each task manages itself.
"""
while self.shutdown_event.wait(MAIN_LOOP_SECS_DELAY) is not True:
if self.user_is_logged_in:
log.debug('Main loop start')
self.premium_sync_manager.maybe_upload_data_to_server()
self.ethereum_analyzer.analyze_ethereum_transactions()
log.debug('Main loop end')
def add_blockchain_accounts(
self,
blockchain: SupportedBlockchain,
account_data: List[BlockchainAccountData],
) -> BlockchainBalancesUpdate:
"""Adds new blockchain accounts
Adds the accounts to the blockchain instance and queries them to get the
updated balances. Also adds them in the DB
May raise:
- EthSyncError from modify_blockchain_account
- InputError if the given accounts list is empty.
- TagConstraintError if any of the given account data contain unknown tags.
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
"""
self.data.db.ensure_tags_exist(
given_data=account_data,
action='adding',
data_type='blockchain accounts',
)
address_type = blockchain.get_address_type()
updated_balances = self.chain_manager.add_blockchain_accounts(
blockchain=blockchain,
accounts=[address_type(entry.address) for entry in account_data],
)
self.data.db.add_blockchain_accounts(
blockchain=blockchain,
account_data=account_data,
)
return updated_balances
def edit_blockchain_accounts(
self,
blockchain: SupportedBlockchain,
account_data: List[BlockchainAccountData],
) -> None:
"""Edits blockchain accounts
Edits blockchain account data for the given accounts
May raise:
- InputError if the given accounts list is empty or if
any of the accounts to edit do not exist.
- TagConstraintError if any of the given account data contain unknown tags.
"""
# First check for validity of account data addresses
if len(account_data) == 0:
raise InputError('Empty list of blockchain account data to edit was given')
accounts = [x.address for x in account_data]
unknown_accounts = set(accounts).difference(self.chain_manager.accounts.get(blockchain))
if len(unknown_accounts) != 0:
raise InputError(
f'Tried to edit unknown {blockchain.value} '
f'accounts {",".join(unknown_accounts)}',
)
self.data.db.ensure_tags_exist(
given_data=account_data,
action='editing',
data_type='blockchain accounts',
)
# Finally edit the accounts
self.data.db.edit_blockchain_accounts(
blockchain=blockchain,
account_data=account_data,
)
return None
def remove_blockchain_accounts(
self,
blockchain: SupportedBlockchain,
accounts: ListOfBlockchainAddresses,
) -> BlockchainBalancesUpdate:
"""Removes blockchain accounts
Removes the accounts from the blockchain instance and queries them to get
the updated balances. Also removes them from the DB
May raise:
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
- InputError if a non-existing account was given to remove
"""
balances_update = self.chain_manager.remove_blockchain_accounts(
blockchain=blockchain,
accounts=accounts,
)
self.data.db.remove_blockchain_accounts(blockchain, accounts)
return balances_update
def add_owned_eth_tokens(
self,
tokens: List[EthereumToken],
) -> BlockchainBalancesUpdate:
"""Adds tokens to the blockchain state and updates balance of all accounts
May raise:
- InputError if some of the tokens already exist
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
- EthSyncError if querying the token balances through a provided ethereum
client and the chain is not synced
"""
new_data = self.chain_manager.track_new_tokens(tokens)
self.data.write_owned_eth_tokens(self.chain_manager.owned_eth_tokens)
return new_data
def remove_owned_eth_tokens(
self,
tokens: List[EthereumToken],
) -> BlockchainBalancesUpdate:
"""
Removes tokens from the state and stops their balance from being tracked
for each account
May raise:
- RemoteError if an external service such as Etherscan is queried and
there is a problem with its query.
- EthSyncError if querying the token balances through a provided ethereum
client and the chain is not synced
"""
new_data = self.chain_manager.remove_eth_tokens(tokens)
self.data.write_owned_eth_tokens(self.chain_manager.owned_eth_tokens)
return new_data
def process_history(
self,
start_ts: Timestamp,
end_ts: Timestamp,
) -> Tuple[Dict[str, Any], str]:
(
error_or_empty,
history,
loan_history,
asset_movements,
eth_transactions,
defi_events,
) = self.trades_historian.get_history(
start_ts=start_ts,
end_ts=end_ts,
has_premium=self.premium is not None,
)
result = self.accountant.process_history(
start_ts=start_ts,
end_ts=end_ts,
trade_history=history,
loan_history=loan_history,
asset_movements=asset_movements,
eth_transactions=eth_transactions,
defi_events=defi_events,
)
return result, error_or_empty
def query_fiat_balances(self) -> Dict[Asset, Dict[str, FVal]]:
result = {}
balances = self.data.get_fiat_balances()
for currency, str_amount in balances.items():
amount = FVal(str_amount)
usd_rate = Inquirer().query_fiat_pair(currency, A_USD)
result[currency] = {
'amount': amount,
'usd_value': amount * usd_rate,
}
return result
def query_balances(
self,
requested_save_data: bool = False,
timestamp: Timestamp = None,
ignore_cache: bool = False,
) -> Dict[str, Any]:
"""Query all balances rotkehlchen can see.
If requested_save_data is True then the data are always saved in the DB,
if it is False then data are saved if self.data.should_save_balances()
is True.
If timestamp is None then the current timestamp is used.
If a timestamp is given then that is the time that the balances are going
to be saved in the DB
If ignore_cache is True then all underlying calls that have a cache ignore it
Returns a dictionary with the queried balances.
"""
log.info('query_balances called', requested_save_data=requested_save_data)
balances = {}
problem_free = True
for _, exchange in self.exchange_manager.connected_exchanges.items():
exchange_balances, _ = exchange.query_balances(ignore_cache=ignore_cache)
# If we got an error, disregard that exchange but make sure we don't save data
if not isinstance(exchange_balances, dict):
problem_free = False
else:
balances[exchange.name] = exchange_balances
try:
blockchain_result = self.chain_manager.query_balances(
blockchain=None,
ignore_cache=ignore_cache,
)
balances['blockchain'] = {
asset: balance.to_dict() for asset, balance in blockchain_result.totals.items()
}
except (RemoteError, EthSyncError) as e:
problem_free = False
log.error(f'Querying blockchain balances failed due to: {str(e)}')
result = self.query_fiat_balances()
if result != {}:
balances['banks'] = result
balances = account_for_manually_tracked_balances(db=self.data.db, balances=balances)
combined = combine_stat_dicts([v for k, v in balances.items()])
total_usd_per_location = [(k, dict_get_sumof(v, 'usd_value')) for k, v in balances.items()]
# calculate net usd value
net_usd = FVal(0)
for _, v in combined.items():
net_usd += FVal(v['usd_value'])
stats: Dict[str, Any] = {
'location': {
},
'net_usd': net_usd,
}
for entry in total_usd_per_location:
name = entry[0]
total = entry[1]
if net_usd != FVal(0):
percentage = (total / net_usd).to_percentage()
else:
percentage = '0%'
stats['location'][name] = {
'usd_value': total,
'percentage_of_net_value': percentage,
}
for k, v in combined.items():
if net_usd != FVal(0):
percentage = (v['usd_value'] / net_usd).to_percentage()
else:
percentage = '0%'
combined[k]['percentage_of_net_value'] = percentage
result_dict = merge_dicts(combined, stats)
allowed_to_save = requested_save_data or self.data.should_save_balances()
if problem_free and allowed_to_save:
if not timestamp:
timestamp = Timestamp(int(time.time()))
self.data.save_balances_data(data=result_dict, timestamp=timestamp)
log.debug('query_balances data saved')
else:
log.debug(
'query_balances data not saved',
allowed_to_save=allowed_to_save,
problem_free=problem_free,
)
# After adding it to the saved file we can overlay additional data that
# is not required to be saved in the history file
try:
details = self.accountant.events.details
for asset, (tax_free_amount, average_buy_value) in details.items():
if asset not in result_dict:
continue
result_dict[asset]['tax_free_amount'] = tax_free_amount
result_dict[asset]['average_buy_value'] = average_buy_value
current_price = result_dict[asset]['usd_value'] / result_dict[asset]['amount']
if average_buy_value != FVal(0):
result_dict[asset]['percent_change'] = (
((current_price - average_buy_value) / average_buy_value) * 100
)
else:
result_dict[asset]['percent_change'] = 'INF'
except AttributeError:
pass
return result_dict
def set_settings(self, settings: ModifiableDBSettings) -> Tuple[bool, str]:
"""Tries to set new settings. Returns True in success or False with message if error"""
with self.lock:
if settings.eth_rpc_endpoint is not None:
result, msg = self.chain_manager.set_eth_rpc_endpoint(settings.eth_rpc_endpoint)
if not result:
return False, msg
if settings.kraken_account_type is not None:
kraken = self.exchange_manager.get('kraken')
if kraken:
kraken.set_account_type(settings.kraken_account_type) # type: ignore
self.data.db.set_settings(settings)
return True, ''
def get_settings(self) -> DBSettings:
"""Returns the db settings with a check whether premium is active or not"""
db_settings = self.data.db.get_settings(have_premium=self.premium is not None)
return db_settings
def setup_exchange(
self,
name: str,
api_key: ApiKey,
api_secret: ApiSecret,
passphrase: Optional[str] = None,
) -> Tuple[bool, str]:
"""
Setup a new exchange with an api key and an api secret and optionally a passphrase
By default the api keys are always validated unless validate is False.
"""
is_success, msg = self.exchange_manager.setup_exchange(
name=name,
api_key=api_key,
api_secret=api_secret,
database=self.data.db,
passphrase=passphrase,
)
if is_success:
# Success, save the result in the DB
self.data.db.add_exchange(name, api_key, api_secret, passphrase=passphrase)
return is_success, msg
def remove_exchange(self, name: str) -> Tuple[bool, str]:
if not self.exchange_manager.has_exchange(name):
return False, 'Exchange {} is not registered'.format(name)
self.exchange_manager.delete_exchange(name)
# Success, remove it also from the DB
self.data.db.remove_exchange(name)
self.data.db.delete_used_query_range_for_exchange(name)
return True, ''
def query_periodic_data(self) -> Dict[str, Union[bool, Timestamp]]:
"""Query for frequently changing data"""
result: Dict[str, Union[bool, Timestamp]] = {}
if self.user_is_logged_in:
result['last_balance_save'] = self.data.db.get_last_balance_save_time()
result['eth_node_connection'] = self.chain_manager.ethereum.web3 is not None
result['history_process_start_ts'] = self.accountant.started_processing_timestamp
result['history_process_current_ts'] = self.accountant.currently_processing_timestamp
return result
def shutdown(self) -> None:
self.logout()
self.shutdown_event.set()
|
"""
MIT License
Copyright (c) 2020 Airbyte
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import csv
import io
import json
import pkgutil
import time
from typing import Dict, List, Optional, Tuple, Union
import msal
import requests
from airbyte_protocol import AirbyteStream
from msal.exceptions import MsalServiceError
class Client:
"""
Microsoft Teams API Reference: https://docs.microsoft.com/en-us/graph/api/resources/teams-api-overview?view=graph-rest-1.0
"""
MICROSOFT_GRAPH_BASE_API_URL: str = "https://graph.microsoft.com/"
MICROSOFT_GRAPH_API_VERSION: str = "v1.0"
PAGINATION_COUNT: Optional[int] = 20
def __init__(self, config: json):
self.ENTITY_MAP = {
"users": self.get_users,
"groups": self.get_groups,
"group_members": self.get_group_members,
"group_owners": self.get_group_owners,
"channels": self.get_channels,
"channel_members": self.get_channel_members,
"channel_tabs": self.get_channel_tabs,
"conversations": self.get_conversations,
"conversation_threads": self.get_conversation_threads,
"conversation_posts": self.get_conversation_posts,
"team_drives": self.get_team_drives,
"team_device_usage_report": self.get_team_device_usage_report,
}
self.configs = config
self._group_ids = None
self.msal_app = msal.ConfidentialClientApplication(
self.configs["client_id"],
authority=f"https://login.microsoftonline.com/" f"{self.configs["tenant_id"]}",
client_credential=self.configs["client_secret"],
)
def _get_api_url(self, endpoint: str) -> str:
api_url = f"{self.MICROSOFT_GRAPH_BASE_API_URL}{self.MICROSOFT_GRAPH_API_VERSION}/{endpoint}/"
return api_url
def _get_access_token(self) -> str:
scope = ["https://graph.microsoft.com/.default"]
# First, the code looks up a token from the cache.
result = self.msal_app.acquire_token_silent(scope, account=None)
# If no suitable token exists in cache. Let's get a new one from AAD.
if not result:
result = self.msal_app.acquire_token_for_client(scopes=scope)
if "access_token" in result:
return result["access_token"]
else:
raise MsalServiceError(error=result.get("error"), error_description=result.get("error_description"))
def _make_request(self, api_url: str, params: Optional[Dict] = None) -> Union[Dict, object]:
access_token = self._get_access_token()
headers = {"Authorization": f"Bearer {access_token}"}
response = requests.get(api_url, headers=headers, params=params)
if response.status_code == 429:
if "Retry-After" in response.headers:
pause_time = float(response.headers["Retry-After"])
time.sleep(pause_time)
response = requests.get(api_url, headers=headers, params=params)
if response.status_code != 200:
raise requests.exceptions.RequestException(response.text)
if response.headers["Content-Type"] == "application/octet-stream":
raw_response = response.content
else:
raw_response = response.json()
return raw_response
@staticmethod
def _get_response_value_unsafe(raw_response: Dict) -> List:
if "value" not in raw_response:
raise requests.exceptions.RequestException()
value = raw_response["value"]
return value
def _get_request_params(self, params: Optional[Dict] = None, pagination: bool = True) -> Dict:
if self.PAGINATION_COUNT and pagination:
params = params if params else {}
if "$top" not in params:
params["$top"] = self.PAGINATION_COUNT
return params
def _fetch_data(self, endpoint: str, params: Optional[Dict] = None, pagination: bool = True):
api_url = self._get_api_url(endpoint)
params = self._get_request_params(params, pagination)
while True:
raw_response = self._make_request(api_url, params)
value = self._get_response_value_unsafe(raw_response)
yield value
if "@odata.nextLink" not in raw_response:
break
params = None
api_url = raw_response["@odata.nextLink"]
def health_check(self) -> Tuple[bool, object]:
try:
self._get_access_token()
return True, None
except MsalServiceError as err:
return False, err.args[0]
def get_streams(self):
streams = []
for schema, method in self.ENTITY_MAP.items():
raw_schema = json.loads(pkgutil.get_data(self.__class__.__module__.split(".")[0], f"schemas/{schema}.json"))
streams.append(AirbyteStream(name=schema, json_schema=raw_schema))
return streams
def get_users(self):
for users in self._fetch_data("users"):
yield users
def get_groups(self):
for groups in self._fetch_data("groups"):
yield filter(lambda item: "Team" in item["resourceProvisioningOptions"], groups)
def _get_group_ids(self):
if not self._group_ids:
api_url = self._get_api_url("groups")
params = {"$select": "id,resourceProvisioningOptions"}
groups = self._get_response_value_unsafe(self._make_request(api_url, params=params))
self._group_ids = [item["id"] for item in groups if "Team" in item["resourceProvisioningOptions"]]
return self._group_ids
def get_group_members(self):
for group_id in self._get_group_ids():
for members in self._fetch_data(f"groups/{group_id}/members"):
yield members
def get_group_owners(self):
for group_id in self._get_group_ids():
for owners in self._fetch_data(f"groups/{group_id}/owners"):
yield owners
def get_channels(self):
for group_id in self._get_group_ids():
for channels in self._fetch_data(f"teams/{group_id}/channels", pagination=False):
yield channels
def _get_channel_ids(self, group_id: str):
api_url = self._get_api_url(f"teams/{group_id}/channels")
params = {"$select": "id"}
channels_ids = self._get_response_value_unsafe(self._make_request(api_url, params=params))
return channels_ids
def get_channel_members(self):
for group_id in self._get_group_ids():
channels = self._get_channel_ids(group_id=group_id)
for channel in channels:
for members in self._fetch_data(f'teams/{group_id}/channels/{channel['id']}/members'):
yield members
def get_channel_tabs(self):
for group_id in self._get_group_ids():
channels = self._get_channel_ids(group_id=group_id)
for channel in channels:
for tabs in self._fetch_data(f'teams/{group_id}/channels/{channel['id']}/tabs', pagination=False):
yield tabs
def get_conversations(self):
for group_id in self._get_group_ids():
for conversations in self._fetch_data(f"groups/{group_id}/conversations"):
yield conversations
def _get_conversation_ids(self, group_id: str):
api_url = self._get_api_url(f"groups/{group_id}/conversations")
params = {"$select": "id"}
conversation_ids = self._get_response_value_unsafe(self._make_request(api_url, params=params))
return conversation_ids
def get_conversation_threads(self):
for group_id in self._get_group_ids():
conversations = self._get_conversation_ids(group_id=group_id)
for conversation in conversations:
for threads in self._fetch_data(f'groups/{group_id}/conversations/{conversation['id']}/threads'):
yield threads
def _get_thread_ids(self, group_id: str, conversation_id: str):
api_url = self._get_api_url(f"groups/{group_id}/conversations/{conversation_id}/threads")
params = {"$select": "id"}
thread_ids = self._get_response_value_unsafe(self._make_request(api_url, params=params))
return thread_ids
def get_conversation_posts(self):
for group_id in self._get_group_ids():
conversations = self._get_conversation_ids(group_id=group_id)
for conversation in conversations:
threads = self._get_thread_ids(group_id, conversation["id"])
for thread in threads:
for posts in self._fetch_data(f'groups/{group_id}/conversations/{conversation['id']}/threads/{thread['id']}/posts'):
yield posts
def get_team_drives(self):
for group_id in self._get_group_ids():
for drives in self._fetch_data(f"groups/{group_id}/drives"):
yield drives
def get_team_device_usage_report(self):
period = self.configs["period"]
api_url = self._get_api_url(f"reports/getTeamsDeviceUsageUserDetail(period='{period}')")
csv_response = io.BytesIO(self._make_request(api_url))
csv_response.readline()
with io.TextIOWrapper(csv_response, encoding="utf-8-sig") as text_file:
field_names = [
"report_refresh_date",
"user_principal_name",
"last_activity_date",
"is_deleted",
"deleted_date",
"used_web",
"used_windows_phone",
"used_i_os",
"used_mac",
"used_android_phone",
"used_windows",
"report_period",
]
reader = csv.DictReader(text_file, fieldnames=field_names)
for row in reader:
yield [
row,
]
| """
MIT License
Copyright (c) 2020 Airbyte
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import csv
import io
import json
import pkgutil
import time
from typing import Dict, List, Optional, Tuple, Union
import msal
import requests
from airbyte_protocol import AirbyteStream
from msal.exceptions import MsalServiceError
class Client:
"""
Microsoft Teams API Reference: https://docs.microsoft.com/en-us/graph/api/resources/teams-api-overview?view=graph-rest-1.0
"""
MICROSOFT_GRAPH_BASE_API_URL: str = "https://graph.microsoft.com/"
MICROSOFT_GRAPH_API_VERSION: str = "v1.0"
PAGINATION_COUNT: Optional[int] = 20
def __init__(self, config: json):
self.ENTITY_MAP = {
"users": self.get_users,
"groups": self.get_groups,
"group_members": self.get_group_members,
"group_owners": self.get_group_owners,
"channels": self.get_channels,
"channel_members": self.get_channel_members,
"channel_tabs": self.get_channel_tabs,
"conversations": self.get_conversations,
"conversation_threads": self.get_conversation_threads,
"conversation_posts": self.get_conversation_posts,
"team_drives": self.get_team_drives,
"team_device_usage_report": self.get_team_device_usage_report,
}
self.configs = config
self._group_ids = None
self.msal_app = msal.ConfidentialClientApplication(
self.configs["client_id"],
authority=f"https://login.microsoftonline.com/" f"{self.configs['tenant_id']}",
client_credential=self.configs["client_secret"],
)
def _get_api_url(self, endpoint: str) -> str:
api_url = f"{self.MICROSOFT_GRAPH_BASE_API_URL}{self.MICROSOFT_GRAPH_API_VERSION}/{endpoint}/"
return api_url
def _get_access_token(self) -> str:
scope = ["https://graph.microsoft.com/.default"]
# First, the code looks up a token from the cache.
result = self.msal_app.acquire_token_silent(scope, account=None)
# If no suitable token exists in cache. Let's get a new one from AAD.
if not result:
result = self.msal_app.acquire_token_for_client(scopes=scope)
if "access_token" in result:
return result["access_token"]
else:
raise MsalServiceError(error=result.get("error"), error_description=result.get("error_description"))
def _make_request(self, api_url: str, params: Optional[Dict] = None) -> Union[Dict, object]:
access_token = self._get_access_token()
headers = {"Authorization": f"Bearer {access_token}"}
response = requests.get(api_url, headers=headers, params=params)
if response.status_code == 429:
if "Retry-After" in response.headers:
pause_time = float(response.headers["Retry-After"])
time.sleep(pause_time)
response = requests.get(api_url, headers=headers, params=params)
if response.status_code != 200:
raise requests.exceptions.RequestException(response.text)
if response.headers["Content-Type"] == "application/octet-stream":
raw_response = response.content
else:
raw_response = response.json()
return raw_response
@staticmethod
def _get_response_value_unsafe(raw_response: Dict) -> List:
if "value" not in raw_response:
raise requests.exceptions.RequestException()
value = raw_response["value"]
return value
def _get_request_params(self, params: Optional[Dict] = None, pagination: bool = True) -> Dict:
if self.PAGINATION_COUNT and pagination:
params = params if params else {}
if "$top" not in params:
params["$top"] = self.PAGINATION_COUNT
return params
def _fetch_data(self, endpoint: str, params: Optional[Dict] = None, pagination: bool = True):
api_url = self._get_api_url(endpoint)
params = self._get_request_params(params, pagination)
while True:
raw_response = self._make_request(api_url, params)
value = self._get_response_value_unsafe(raw_response)
yield value
if "@odata.nextLink" not in raw_response:
break
params = None
api_url = raw_response["@odata.nextLink"]
def health_check(self) -> Tuple[bool, object]:
try:
self._get_access_token()
return True, None
except MsalServiceError as err:
return False, err.args[0]
def get_streams(self):
streams = []
for schema, method in self.ENTITY_MAP.items():
raw_schema = json.loads(pkgutil.get_data(self.__class__.__module__.split(".")[0], f"schemas/{schema}.json"))
streams.append(AirbyteStream(name=schema, json_schema=raw_schema))
return streams
def get_users(self):
for users in self._fetch_data("users"):
yield users
def get_groups(self):
for groups in self._fetch_data("groups"):
yield filter(lambda item: "Team" in item["resourceProvisioningOptions"], groups)
def _get_group_ids(self):
if not self._group_ids:
api_url = self._get_api_url("groups")
params = {"$select": "id,resourceProvisioningOptions"}
groups = self._get_response_value_unsafe(self._make_request(api_url, params=params))
self._group_ids = [item["id"] for item in groups if "Team" in item["resourceProvisioningOptions"]]
return self._group_ids
def get_group_members(self):
for group_id in self._get_group_ids():
for members in self._fetch_data(f"groups/{group_id}/members"):
yield members
def get_group_owners(self):
for group_id in self._get_group_ids():
for owners in self._fetch_data(f"groups/{group_id}/owners"):
yield owners
def get_channels(self):
for group_id in self._get_group_ids():
for channels in self._fetch_data(f"teams/{group_id}/channels", pagination=False):
yield channels
def _get_channel_ids(self, group_id: str):
api_url = self._get_api_url(f"teams/{group_id}/channels")
params = {"$select": "id"}
channels_ids = self._get_response_value_unsafe(self._make_request(api_url, params=params))
return channels_ids
def get_channel_members(self):
for group_id in self._get_group_ids():
channels = self._get_channel_ids(group_id=group_id)
for channel in channels:
for members in self._fetch_data(f'teams/{group_id}/channels/{channel["id"]}/members'):
yield members
def get_channel_tabs(self):
for group_id in self._get_group_ids():
channels = self._get_channel_ids(group_id=group_id)
for channel in channels:
for tabs in self._fetch_data(f'teams/{group_id}/channels/{channel["id"]}/tabs', pagination=False):
yield tabs
def get_conversations(self):
for group_id in self._get_group_ids():
for conversations in self._fetch_data(f"groups/{group_id}/conversations"):
yield conversations
def _get_conversation_ids(self, group_id: str):
api_url = self._get_api_url(f"groups/{group_id}/conversations")
params = {"$select": "id"}
conversation_ids = self._get_response_value_unsafe(self._make_request(api_url, params=params))
return conversation_ids
def get_conversation_threads(self):
for group_id in self._get_group_ids():
conversations = self._get_conversation_ids(group_id=group_id)
for conversation in conversations:
for threads in self._fetch_data(f'groups/{group_id}/conversations/{conversation["id"]}/threads'):
yield threads
def _get_thread_ids(self, group_id: str, conversation_id: str):
api_url = self._get_api_url(f"groups/{group_id}/conversations/{conversation_id}/threads")
params = {"$select": "id"}
thread_ids = self._get_response_value_unsafe(self._make_request(api_url, params=params))
return thread_ids
def get_conversation_posts(self):
for group_id in self._get_group_ids():
conversations = self._get_conversation_ids(group_id=group_id)
for conversation in conversations:
threads = self._get_thread_ids(group_id, conversation["id"])
for thread in threads:
for posts in self._fetch_data(f'groups/{group_id}/conversations/{conversation["id"]}/threads/{thread["id"]}/posts'):
yield posts
def get_team_drives(self):
for group_id in self._get_group_ids():
for drives in self._fetch_data(f"groups/{group_id}/drives"):
yield drives
def get_team_device_usage_report(self):
period = self.configs["period"]
api_url = self._get_api_url(f"reports/getTeamsDeviceUsageUserDetail(period='{period}')")
csv_response = io.BytesIO(self._make_request(api_url))
csv_response.readline()
with io.TextIOWrapper(csv_response, encoding="utf-8-sig") as text_file:
field_names = [
"report_refresh_date",
"user_principal_name",
"last_activity_date",
"is_deleted",
"deleted_date",
"used_web",
"used_windows_phone",
"used_i_os",
"used_mac",
"used_android_phone",
"used_windows",
"report_period",
]
reader = csv.DictReader(text_file, fieldnames=field_names)
for row in reader:
yield [
row,
]
|
import sqlite3
from os import listdir
import pandas as pd
from transfer_data import pick_path
def database_pipeline(path):
connection = sqlite3.connect("./baseData/allPlayerStats.db")
cursor = connection.cursor()
# See this for various ways to import CSV into sqlite using Python. Pandas used here because files are not prohibitively large.
# https://stackoverflow.com/questions/2887878/importing-a-csv-file-into-a-sqlite3-database-table-using-python
print("SQL scripts starting...")
# Drop old tables, might not be necessary since we're dropping them
sql_file = open("./scripts/SQL/drop_old_tables.sql")
try:
sql_as_string = sql_file.read()
cursor.executescript(sql_as_string)
sql_file.close()
except Exception:
pass
# Decide whether to have user pick path or just set it automatically...
for fileName in listdir(path):
if fileName.endswith('.csv'): # Avoid any accidents
df = pd.read_csv(f'{path}/{fileName}')
df.to_sql(
f'{fileName.replace('.csv','').split('_')[0]}', connection, if_exists='replace', index=False)
try:
date = f'{fileName.replace('.csv','').split('_')[1]}'
except Exception:
pass
# Make changes to tables
sql_file = open("./scripts/SQL/prep_tables_for_extraction.sql")
try:
sql_as_string = sql_file.read()
cursor.executescript(sql_as_string)
except Exception:
pass
sql_file.close()
# Extract this season's qualified players
sql_file = open("./scripts/SQL/players2022_dbeaver.sql")
df_output = pd.read_sql_query(sql_file.read(), connection)
sql_file.close()
# sql_as_string = sql_file.read()
# cursor.executescript(sql_as_string)
print(df_output)
df_output.to_csv(f'{path}/stats_{date}.csv', index=False)
print("SQL scripts complete.")
def main():
data_path = pick_path()
database_pipeline(data_path)
if __name__ == '__main__':
main()
| import sqlite3
from os import listdir
import pandas as pd
from transfer_data import pick_path
def database_pipeline(path):
connection = sqlite3.connect("./baseData/allPlayerStats.db")
cursor = connection.cursor()
# See this for various ways to import CSV into sqlite using Python. Pandas used here because files are not prohibitively large.
# https://stackoverflow.com/questions/2887878/importing-a-csv-file-into-a-sqlite3-database-table-using-python
print("SQL scripts starting...")
# Drop old tables, might not be necessary since we're dropping them
sql_file = open("./scripts/SQL/drop_old_tables.sql")
try:
sql_as_string = sql_file.read()
cursor.executescript(sql_as_string)
sql_file.close()
except Exception:
pass
# Decide whether to have user pick path or just set it automatically...
for fileName in listdir(path):
if fileName.endswith('.csv'): # Avoid any accidents
df = pd.read_csv(f'{path}/{fileName}')
df.to_sql(
f'{fileName.replace(".csv","").split("_")[0]}', connection, if_exists='replace', index=False)
try:
date = f'{fileName.replace(".csv","").split("_")[1]}'
except Exception:
pass
# Make changes to tables
sql_file = open("./scripts/SQL/prep_tables_for_extraction.sql")
try:
sql_as_string = sql_file.read()
cursor.executescript(sql_as_string)
except Exception:
pass
sql_file.close()
# Extract this season's qualified players
sql_file = open("./scripts/SQL/players2022_dbeaver.sql")
df_output = pd.read_sql_query(sql_file.read(), connection)
sql_file.close()
# sql_as_string = sql_file.read()
# cursor.executescript(sql_as_string)
print(df_output)
df_output.to_csv(f'{path}/stats_{date}.csv', index=False)
print("SQL scripts complete.")
def main():
data_path = pick_path()
database_pipeline(data_path)
if __name__ == '__main__':
main()
|
import re
from io import StringIO
from pathlib import Path
import warnings
from typing import TextIO, Optional
def dump_parameters_text(PARAMETERS: dict, file: Optional[TextIO] = None):
from .parameters import Parameter, SequenceParameter, PlaceholderParameter
for path, param in PARAMETERS.items():
path: str
param: Parameter
is_list = isinstance(param, SequenceParameter)
if len(param.types) == 1:
type_str = param.types[0].__name__
if is_list:
type_str = f"of {type_str}"
else:
type_str = ", ".join(t.__name__ for t in param.types)
if is_list:
type_str = f"of one of {type_str}"
if is_list:
length_str = " or ".join(str(l) for l in param.lengths)
type_str = f"list of length {length_str} {type_str}"
print(f"{path} (type: {type_str})", file=file)
def dump_parameters_md(file: Optional[TextIO] = None):
from .parameters import PARAMETERS, Parameter, SequenceParameter, PlaceholderParameter
from .expression import EXPRESSION_ARGS
for path, param in PARAMETERS.items():
path: str
param: Parameter
is_list = isinstance(param, SequenceParameter)
is_section = isinstance(param, PlaceholderParameter)
is_single_param = not any(filter(
lambda p: p.startswith(path + "."),
PARAMETERS.keys()
)) and (
path.startswith("targets.transforms.") and len(path.split(".")) == 3
)
is_new_section = is_section and len(path.split(".")) <= 2
if is_new_section:
print("\n\n---\n\n", file=file)
if is_section:
print(f"### `{path}`\n", file=file)
if param.doc:
print(prepare_doc_string(param.doc) + "\n", file=file)
else:
warnings.warn(f"No documentation of '{path}'")
continue
if is_single_param:
print(f"### `{path}`\n", file=file)
else:
print(f"#### `{path}`\n", file=file)
if len(param.types) == 1:
type_str = param.types[0].__name__
if is_list:
type_str = f"of {type_str}"
else:
type_str = ", ".join(t.__name__ for t in param.types)
if is_list:
type_str = f"of one of {type_str}"
if is_list:
length_str = " or ".join(str(l) for l in param.lengths)
type_str = f"list of length {length_str} {type_str}"
if param.default is None:
default_str = "no default"
else:
default_str = f"default: **`{param.default}`**"
print(f"`{type_str}` {default_str}\n", file=file)
if param.expression_groups:
group_names = [
EXPRESSION_ARGS[n]["name"]
for n in sorted(set(param.expression_groups))
]
print("\nexpression variables: " + ", ".join(
f"[{n}](expressions.md#{n.replace(" ", "-")}-variables)"
for n in group_names
) + "\n", file=file)
if param.doc:
print(prepare_doc_string(param.doc) + "\n", file=file)
else:
warnings.warn(f"No documentation of '{path}'")
def prepare_doc_string(doc: str, indent: int = 0) -> str:
doc = strip_doc(doc)
links = {
"CLIPig": "https://github.com/defgsus/CLIPig/",
"CLIP": "https://github.com/openai/CLIP/",
"gaussian blur": "https://en.wikipedia.org/wiki/Gaussian_blur",
}
def _repl(m):
key, suffix = m.groups()
return f"[{key}]({links[key]}){suffix}"
for key, href in links.items():
doc = re.sub(f"({key})([\s\-\.'])", _repl, doc)
if indent:
doc = "\n".join(
" " * indent + line
for line in doc.splitlines()
)
return doc
def strip_doc(doc: Optional[str]) -> Optional[str]:
if not doc:
return doc
min_lstrip = min(
len(line) - len(line.lstrip())
for line in doc.splitlines()
if line.strip()
)
doc = "\n".join(
line[min_lstrip:]
for line in doc.splitlines()
)
return doc.strip()
def render_markdown_documentation(template: str) -> str:
template = prepare_doc_string(template)
for key, render_func in (
("transforms", dump_transforms),
("constraints", dump_constraints),
("variables", dump_expression_variables),
("reference", dump_parameters_md),
):
file = StringIO()
render_func(file=file)
file.seek(0)
text = file.read()
template = template.replace("{{%s}}" % key, text)
return template
def dump_constraints(file: Optional[TextIO] = None):
from .constraints import constraints
for name in sorted(constraints):
klass = constraints[name]
text = klass.__doc__.strip()
if "\n\n" in text:
text = text[:text.index("\n\n")]
print(f"- [{name}](reference.md#targetsconstraints{name}): {text}", file=file)
def dump_transforms(file: Optional[TextIO] = None):
from .transforms import transformations
for name in sorted(transformations):
klass = transformations[name]
text = klass.__doc__.strip()
if "\n\n" in text:
text = text[:text.index("\n\n")]
print(f"- [{name}](reference.md#targetstransforms{name}): {text}", file=file)
def dump_expression_variables(file: Optional[TextIO] = None):
from .expression import EXPRESSION_ARGS
for group_id, group in EXPRESSION_ARGS.items():
print(f"### {group["name"]} variables\n", file=file)
print(prepare_doc_string(group["doc"]) + "\n", file=file)
for variable_name, variable in group["args"].items():
if not variable.get("doc"):
continue
print(f"#### `{variable_name}` variable\n", file=file)
print(f"type: `{variable["type"]}`\n", file=file)
print(prepare_doc_string(variable["doc"]), file=file)
| import re
from io import StringIO
from pathlib import Path
import warnings
from typing import TextIO, Optional
def dump_parameters_text(PARAMETERS: dict, file: Optional[TextIO] = None):
from .parameters import Parameter, SequenceParameter, PlaceholderParameter
for path, param in PARAMETERS.items():
path: str
param: Parameter
is_list = isinstance(param, SequenceParameter)
if len(param.types) == 1:
type_str = param.types[0].__name__
if is_list:
type_str = f"of {type_str}"
else:
type_str = ", ".join(t.__name__ for t in param.types)
if is_list:
type_str = f"of one of {type_str}"
if is_list:
length_str = " or ".join(str(l) for l in param.lengths)
type_str = f"list of length {length_str} {type_str}"
print(f"{path} (type: {type_str})", file=file)
def dump_parameters_md(file: Optional[TextIO] = None):
from .parameters import PARAMETERS, Parameter, SequenceParameter, PlaceholderParameter
from .expression import EXPRESSION_ARGS
for path, param in PARAMETERS.items():
path: str
param: Parameter
is_list = isinstance(param, SequenceParameter)
is_section = isinstance(param, PlaceholderParameter)
is_single_param = not any(filter(
lambda p: p.startswith(path + "."),
PARAMETERS.keys()
)) and (
path.startswith("targets.transforms.") and len(path.split(".")) == 3
)
is_new_section = is_section and len(path.split(".")) <= 2
if is_new_section:
print("\n\n---\n\n", file=file)
if is_section:
print(f"### `{path}`\n", file=file)
if param.doc:
print(prepare_doc_string(param.doc) + "\n", file=file)
else:
warnings.warn(f"No documentation of '{path}'")
continue
if is_single_param:
print(f"### `{path}`\n", file=file)
else:
print(f"#### `{path}`\n", file=file)
if len(param.types) == 1:
type_str = param.types[0].__name__
if is_list:
type_str = f"of {type_str}"
else:
type_str = ", ".join(t.__name__ for t in param.types)
if is_list:
type_str = f"of one of {type_str}"
if is_list:
length_str = " or ".join(str(l) for l in param.lengths)
type_str = f"list of length {length_str} {type_str}"
if param.default is None:
default_str = "no default"
else:
default_str = f"default: **`{param.default}`**"
print(f"`{type_str}` {default_str}\n", file=file)
if param.expression_groups:
group_names = [
EXPRESSION_ARGS[n]["name"]
for n in sorted(set(param.expression_groups))
]
print("\nexpression variables: " + ", ".join(
f"[{n}](expressions.md#{n.replace(' ', '-')}-variables)"
for n in group_names
) + "\n", file=file)
if param.doc:
print(prepare_doc_string(param.doc) + "\n", file=file)
else:
warnings.warn(f"No documentation of '{path}'")
def prepare_doc_string(doc: str, indent: int = 0) -> str:
doc = strip_doc(doc)
links = {
"CLIPig": "https://github.com/defgsus/CLIPig/",
"CLIP": "https://github.com/openai/CLIP/",
"gaussian blur": "https://en.wikipedia.org/wiki/Gaussian_blur",
}
def _repl(m):
key, suffix = m.groups()
return f"[{key}]({links[key]}){suffix}"
for key, href in links.items():
doc = re.sub(f"({key})([\s\-\.'])", _repl, doc)
if indent:
doc = "\n".join(
" " * indent + line
for line in doc.splitlines()
)
return doc
def strip_doc(doc: Optional[str]) -> Optional[str]:
if not doc:
return doc
min_lstrip = min(
len(line) - len(line.lstrip())
for line in doc.splitlines()
if line.strip()
)
doc = "\n".join(
line[min_lstrip:]
for line in doc.splitlines()
)
return doc.strip()
def render_markdown_documentation(template: str) -> str:
template = prepare_doc_string(template)
for key, render_func in (
("transforms", dump_transforms),
("constraints", dump_constraints),
("variables", dump_expression_variables),
("reference", dump_parameters_md),
):
file = StringIO()
render_func(file=file)
file.seek(0)
text = file.read()
template = template.replace("{{%s}}" % key, text)
return template
def dump_constraints(file: Optional[TextIO] = None):
from .constraints import constraints
for name in sorted(constraints):
klass = constraints[name]
text = klass.__doc__.strip()
if "\n\n" in text:
text = text[:text.index("\n\n")]
print(f"- [{name}](reference.md#targetsconstraints{name}): {text}", file=file)
def dump_transforms(file: Optional[TextIO] = None):
from .transforms import transformations
for name in sorted(transformations):
klass = transformations[name]
text = klass.__doc__.strip()
if "\n\n" in text:
text = text[:text.index("\n\n")]
print(f"- [{name}](reference.md#targetstransforms{name}): {text}", file=file)
def dump_expression_variables(file: Optional[TextIO] = None):
from .expression import EXPRESSION_ARGS
for group_id, group in EXPRESSION_ARGS.items():
print(f"### {group['name']} variables\n", file=file)
print(prepare_doc_string(group["doc"]) + "\n", file=file)
for variable_name, variable in group["args"].items():
if not variable.get("doc"):
continue
print(f"#### `{variable_name}` variable\n", file=file)
print(f"type: `{variable['type']}`\n", file=file)
print(prepare_doc_string(variable["doc"]), file=file)
|
# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
from neptune_load.sigv4_signer.sigv4_signer import SigV4Signer
from neptune_load.bulk_loader.bulk_loader import BulkLoader
import logging
import os
import sys
logger = logging.getLogger("bulk_load")
logger.setLevel(logging.INFO)
def kill_all_active(loader: BulkLoader):
loads = loader.get_active_loads()
logger.info(f"Loading {loads}")
for load in loads:
loader._load_id = load
try:
loader.cancel_load()
except Exception as e:
logger.warn(f"Failed to cancel {load} {e}")
loader._load_id = None
return
if __name__ == "__main__":
# parse_input_and_query_neptune()
host = f'{os.getenv('NEPTUNE_ENDPOINT')}:8182'
source_bucket = os.getenv("S3_BUCKET")
loader_role = os.getenv("NEPTUNE_LOADER_IAM_ROLE")
region = os.getenv("SERVICE_REGION")
file_name = os.getenv("TRIPLE_NAME")
source_string = f"s3://{source_bucket}/{file_name}"
signer = SigV4Signer()
loader = BulkLoader(
signer=signer,
iam_role_arn=loader_role,
region=region,
source=source_string,
neptune_endpoint=host,
)
loads = loader.get_active_loads()
logger.info(f"Loading {loads}")
kill_all_active(loader)
try:
loader.wait_for_bulk_load_from_s3()
except KeyboardInterrupt as ke:
logger.info(f"Cancellation requested")
loader.cancel_load()
logger.info(f"Final status \n {loader.status.raw}")
sys.exit()
logger.info(f"Load complete")
logger.info(f"Results {loader.status.raw}")
| # Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
from neptune_load.sigv4_signer.sigv4_signer import SigV4Signer
from neptune_load.bulk_loader.bulk_loader import BulkLoader
import logging
import os
import sys
logger = logging.getLogger("bulk_load")
logger.setLevel(logging.INFO)
def kill_all_active(loader: BulkLoader):
loads = loader.get_active_loads()
logger.info(f"Loading {loads}")
for load in loads:
loader._load_id = load
try:
loader.cancel_load()
except Exception as e:
logger.warn(f"Failed to cancel {load} {e}")
loader._load_id = None
return
if __name__ == "__main__":
# parse_input_and_query_neptune()
host = f'{os.getenv("NEPTUNE_ENDPOINT")}:8182'
source_bucket = os.getenv("S3_BUCKET")
loader_role = os.getenv("NEPTUNE_LOADER_IAM_ROLE")
region = os.getenv("SERVICE_REGION")
file_name = os.getenv("TRIPLE_NAME")
source_string = f"s3://{source_bucket}/{file_name}"
signer = SigV4Signer()
loader = BulkLoader(
signer=signer,
iam_role_arn=loader_role,
region=region,
source=source_string,
neptune_endpoint=host,
)
loads = loader.get_active_loads()
logger.info(f"Loading {loads}")
kill_all_active(loader)
try:
loader.wait_for_bulk_load_from_s3()
except KeyboardInterrupt as ke:
logger.info(f"Cancellation requested")
loader.cancel_load()
logger.info(f"Final status \n {loader.status.raw}")
sys.exit()
logger.info(f"Load complete")
logger.info(f"Results {loader.status.raw}")
|
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available.
Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
BK-BASE 蓝鲸基础平台 is licensed under the MIT License.
License for BK-BASE 蓝鲸基础平台:
--------------------------------------------------------------------
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial
portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
"""
对主机信息进行批量对账检查
"""
import logging
from collection.cmdb.datacheck.base import CMDBBaseCheckerMixin
from collection.cmdb.set_info import CMDBSetInfoCollector
from collection.conf.constants import BKDATA_BIZ_ID, CMDB_SET_TABLE_NAME
logger = logging.getLogger(__name__)
collect_cmdb_set_config = {"bk_biz_id": 591, "raw_data_name": "bkpub_cmdb_set"}
class CMDBSetInfoChecker(CMDBSetInfoCollector, CMDBBaseCheckerMixin):
key_name = "bk_set_id"
def __init__(self, config=None, init_producer=False):
super(CMDBSetInfoChecker, self).__init__(config, init_producer=init_producer)
if "rt_id" in config:
self.rt_id = config["rt_id"]
else:
self.rt_id = f"{config["bk_biz_id"]}_{config["raw_data_name"]}"
def check_cmdb_set_info(params=None):
if not params:
params = {"bk_biz_id": BKDATA_BIZ_ID, "raw_data_name": CMDB_SET_TABLE_NAME}
c = CMDBSetInfoChecker(config=params)
c.check_all_biz()
def check_cmdb_set_info_by_one(bk_biz_id):
params = {"bk_biz_id": BKDATA_BIZ_ID, "raw_data_name": CMDB_SET_TABLE_NAME}
c = CMDBSetInfoChecker(config=params)
c.check_biz(bk_biz_id)
if __name__ == "__main__":
check_cmdb_set_info()
| # -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available.
Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
BK-BASE 蓝鲸基础平台 is licensed under the MIT License.
License for BK-BASE 蓝鲸基础平台:
--------------------------------------------------------------------
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial
portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
"""
对主机信息进行批量对账检查
"""
import logging
from collection.cmdb.datacheck.base import CMDBBaseCheckerMixin
from collection.cmdb.set_info import CMDBSetInfoCollector
from collection.conf.constants import BKDATA_BIZ_ID, CMDB_SET_TABLE_NAME
logger = logging.getLogger(__name__)
collect_cmdb_set_config = {"bk_biz_id": 591, "raw_data_name": "bkpub_cmdb_set"}
class CMDBSetInfoChecker(CMDBSetInfoCollector, CMDBBaseCheckerMixin):
key_name = "bk_set_id"
def __init__(self, config=None, init_producer=False):
super(CMDBSetInfoChecker, self).__init__(config, init_producer=init_producer)
if "rt_id" in config:
self.rt_id = config["rt_id"]
else:
self.rt_id = f"{config['bk_biz_id']}_{config['raw_data_name']}"
def check_cmdb_set_info(params=None):
if not params:
params = {"bk_biz_id": BKDATA_BIZ_ID, "raw_data_name": CMDB_SET_TABLE_NAME}
c = CMDBSetInfoChecker(config=params)
c.check_all_biz()
def check_cmdb_set_info_by_one(bk_biz_id):
params = {"bk_biz_id": BKDATA_BIZ_ID, "raw_data_name": CMDB_SET_TABLE_NAME}
c = CMDBSetInfoChecker(config=params)
c.check_biz(bk_biz_id)
if __name__ == "__main__":
check_cmdb_set_info()
|
# Crie um programa que tenha uma Tupla unica com nomes de produtos e seus respectivos preços na sequencia.
# No final, mostre uma listagem de preços, organizando os dados em forma tabular.
'''Dá para fazer em 2 linhas...
for i in range(0, len(prod), 2):
print(f'{prod[i]:.<30}R${prod[i + 1]:7.2f}')'''
produtos = ('caneta', 2, 'Relogio', 23, 'remedio', 12, 'mouse', 10,
'pão', 2, "mascara", 3, "gabinete", 245, "alcool", 11)
print(f'{'Listagem de preços':^30}') # Centralizando o topo
for i in range(0, len(produtos), 2): # FAZENDO O PRINT DAS INFORMAÇÕES COM FOR PULANDO DE 2 EM 2 PRA PEGAR A ORDEM CORRETA
print(f'{str(produtos[i]).upper():.<24}', end='') # TRANSFORMADO EM STR, CENTRALIZADO A DIREITA COM PONTO ENCHENDO O ESPAÇO
print(f'R${produtos[i+1]:>6.2f} ') # ADICIONADO 1 NO INDICE PRA COMEÇAR DO SEGUINTE E MSOTRAR TODOS OS PREÇOS.
| # Crie um programa que tenha uma Tupla unica com nomes de produtos e seus respectivos preços na sequencia.
# No final, mostre uma listagem de preços, organizando os dados em forma tabular.
'''Dá para fazer em 2 linhas...
for i in range(0, len(prod), 2):
print(f'{prod[i]:.<30}R${prod[i + 1]:7.2f}')'''
produtos = ('caneta', 2, 'Relogio', 23, 'remedio', 12, 'mouse', 10,
'pão', 2, "mascara", 3, "gabinete", 245, "alcool", 11)
print(f'{"Listagem de preços":^30}') # Centralizando o topo
for i in range(0, len(produtos), 2): # FAZENDO O PRINT DAS INFORMAÇÕES COM FOR PULANDO DE 2 EM 2 PRA PEGAR A ORDEM CORRETA
print(f'{str(produtos[i]).upper():.<24}', end='') # TRANSFORMADO EM STR, CENTRALIZADO A DIREITA COM PONTO ENCHENDO O ESPAÇO
print(f'R${produtos[i+1]:>6.2f} ') # ADICIONADO 1 NO INDICE PRA COMEÇAR DO SEGUINTE E MSOTRAR TODOS OS PREÇOS.
|
# Inspired in
# https://github.com/grizzlypeaksoftware/Flask-Stock-Widget
# https://github.com/rubenafo/yfMongo
import sys, os
import re
import csv
import json
from datetime import datetime, date, time, timedelta
from itertools import zip_longest
import numpy as np
import pytz
import yfinance as yf
import ast
import copy
from flask import jsonify
from pymongo import *
from pandas_datareader import data as pdr
class mongoYfinance:
mongoClient = None
yfdb = None
verbose = False
#
# Used to print messages only if the verbose flag was enabled
#
def sprint(self, msg):
if self.verbose:
print(msg)
#
# Generic function to check all user input dates
# The format must be dd/mm/yyyy and cannot be a date in the future.
# In case of error the execution of the application is stopped.
#
def __checkDate(self, date):
try:
inputDate = datetime.strptime(date, "%Y/%m/%d")
currentTime = datetime.now()
if (inputDate > currentTime):
self.sprint("Error: provided date (" + date + ") is in the future")
exit()
except ValueError:
self.sprint("Error: invalid provided date format (expected yyyy/mm/dd)")
exit()
#
# Given a symbol document in the mongodb this returns the date it contains.
#
def __getFormattedDate(self, symbol):
try:
# print(symbol['Datetime'])
# return datetime.date(symbol['Datetime'], "%Y-%m-%d")
return symbol['_id']['Datetime']
except ValueError:
self.sprint("Error: invalid provided date format (expected yyyy/mm/dd)")
#
# Initialises the ddbb
#
def __init__(self, user="admin", password="", hostname="localhost", database="yfmongo", verbose=True):
userAndPass = ""
if user and password:
userAndPass = user + ":" + str(password) + "@"
url = "mongodb+srv://" + userAndPass + hostname
self.mongoClient = MongoClient(url)
self.yfdb = self.mongoClient[database];
self.verbose = verbose
#
# Removes all content in the database (Caution!)
#
def clear(self, keepSymbols=False):
if keepSymbols:
self.sprint("Removing data ... done")
self.yfdb.timeline.delete_many({});
else:
self.sprint("Removing all collections [symbols and timeline] ... done")
self.yfdb.timeline.delete_many({});
self.yfdb.symbols.delete_many({});
def add(self, symbol, startDate=None, endDate=None):
exists = self.yfdb.symbols.count_documents({'_id.sym': symbol})
if not exists:
quote = yf.Ticker(symbol)
if "shortName" not in quote.info:
return {'symbolExists': False, 'added': False, 'message': 'Symbol ' + symbol + ' not found in API'}
self.yfdb.symbols.replace_one({'_id': {'sym': symbol}},
{'_id': {'sym': symbol}, 'shortName': quote.info['shortName']}, upsert=True)
self.sprint("'" + symbol + "'" + " added to the database")
oldestDate = datetime.today() - timedelta(days=6)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
symbol=symbol)
result = {'symbolExists': True, 'added': True, 'message': 'Symbol ' + symbol + ' was successfully added',
'sym': symbol, 'shortName': quote.info['shortName']}
else:
symbols = self.yfdb.symbols.find({'_id.sym': symbol})
for s in symbols:
result = {'symbolExists': True, 'added': False,
'message': 'Symbol ' + symbol + ' is already in database',
'sym': symbol, 'shortName': s['shortName']}
if startDate != None:
if endDate != None:
self.fetchInterval(startDate, endDate, symbol)
else:
self.fetch(startDate, symbol)
return result
#
# Removes a symbol from the ddbb, including all timeline entries
#
def remove(self, symbol):
if not symbol:
return {'removed': False, 'message': 'Missing symbol name'}
exists = self.yfdb.symbols.count_documents({'_id.sym': symbol})
if not exists:
self.sprint("Error: symbol'" + symbol + "' not in the database")
return {'removed': False, 'message': 'Symbol ' + symbol + ' not found in database'}
else:
self.yfdb.symbols.delete_many({'_id.sym': symbol})
self.yfdb.timeline.delete_many({'_id.sym': symbol})
self.sprint("'" + symbol + "'" + " removed from the database")
return {'removed': True, 'message': symbol + ' removed from the database'}
#
# Prints information regarding the admin info (start and end dates)
# and the symbols contained in the database
#
def info(self):
symbols = self.yfdb.symbols.find();
for symb in symbols:
print(symb['sym'])
print("Timeline size: " + str(self.yfdb.timeline.find().count()))
print("Symbols: " + str(symbols.count()))
dates = []
symbols = self.yfdb.timeline.find()
for symb in symbols:
date = self.__getFormattedDate(symb)
dates.append(date)
if dates:
print("Oldest record: " + min(dates).strftime("%Y/%m/%d"))
print("Most recent record: " + max(dates).strftime("%Y/%m/%d"))
def listSymbols(self):
symbols = self.yfdb.symbols.find()
symList = {}
count = 0
for s in symbols:
print(s)
symList[count] = {'sym': s['_id']['sym'], 'shortName': s['shortName']}
count += 1
return symList
#
# Updates the database fetching data for all symbols since last
# date in the data until today
#
def update(self):
tickers = self.yfdb.symbols.find()
for ticker in tickers:
tickerTimeline = list(self.yfdb.timeline.find({'_id.sym': ticker["_id"]["sym"]}))
if len(tickerTimeline) > 0:
dateToday = datetime.today()
oldestDate = max(map(lambda s: self.__getFormattedDate(s), tickerTimeline))
delta = dateToday - oldestDate
endDate = oldestDate
week_period = delta.days // 6
day_period = delta.days % 6
if week_period > 0:
for i in range(1, week_period):
if oldestDate is not None:
endDate = endDate+timedelta(days=6)
print("oldestDate:", oldestDate, "endDate:", endDate, "week_period:", week_period)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
endDate.strftime("%Y/%m/%d"),
symbol=ticker["_id"]["sym"])
if week_period > 0 and day_period > 0:
if oldestDate is not None:
endDate = endDate + timedelta(days=day_period)
print("oldestDate:", oldestDate, "endDate:", endDate, "day_period:", day_period)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
endDate.strftime("%Y/%m/%d"),
symbol=ticker["_id"]["sym"])
# print(tickerTimeline)
oldestDate = max(map(lambda s: self.__getFormattedDate(s), tickerTimeline))
print(oldestDate)
if oldestDate is not None:
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
None,
symbol=ticker["_id"]["sym"])
else:
oldestDate = datetime.today() - timedelta(days=6)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
None,
symbol=ticker["_id"]["sym"])
# Fetches symbol data for the interval between startDate and endDate
# If the symbol is set None, all symbols found in the database are
# updated.
def fetchInterval(self, startDate, endDate=None, symbol=None, interval='1m'):
timezone = pytz.timezone("UTC")
if symbol is None:
symbols = self.yfdb.symbols.find()
else:
symbols = self.yfdb.symbols.find(({'_id.sym': symbol}))
for symbol in symbols:
# download dataframe
quote = yf.Ticker(symbol['_id']['sym'])
# data = quote.history(start=startDate.replace("/", "-"), end=endDate.replace("/", "-"), interval=interval)
if endDate is not None:
data = quote.history(start=startDate.replace("/", "-"), end=endDate.replace("/", "-"), interval=interval)
else:
data = quote.history(start=startDate.replace("/", "-"), interval=interval)
# set index to column in pandas DataFrame
data.reset_index(inplace=True)
data.dropna(inplace=True)
self.sprint(data)
if "Datetime" in data:
lastTicker = self.getLastTicker(symbol['_id']['sym'])
tickersNotRounded = data[data['Datetime'].dt.second > 0].index
data.drop(tickersNotRounded, inplace=True)
if len(data) > 0:
# self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# + symbol['_id']['sym'] + "' (" + str(len(data)) + " entries)")
dictData = data.to_dict(orient='records')
for data in dictData:
data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": data["Datetime"]}
data.pop('Datetime', None)
ids = [dt.pop("_id") for dt in dictData]
operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
zip(ids, dictData)]
self.yfdb.timeline.bulk_write(operations)
if "Date" in data:
if len(data) > 0:
# self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# + symbol['_id']['sym'] + "' (" + str(len(data)) + " entries)")
dictData = data.to_dict(orient='records')
for data in dictData:
date = datetime.combine(data["Date"], datetime.min.time())
data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": date}
data.pop('Date', None)
self.sprint(data)
ids = [dt.pop("_id") for dt in dictData]
operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
zip(ids, dictData)]
self.yfdb.timeline.bulk_write(operations)
# update already exists in database
# if lastTicker:
# # storedData = timezone.localize(self.getLastTicker(symbol['sym']))
# # apiData = data["Datetime"].iat[-1].to_pydatetime().astimezone(timezone)
#
# print(apiData.timestamp() - storedData.timestamp())
#
# if len(data) > 0 and apiData.timestamp() - storedData.timestamp() > 120:
# # self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# # + symbol['sym'] + "' (" + str(len(data)) + " entries)")
# dictData = data.to_dict(orient='records')
#
# for data in dictData:
# data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": data["Datetime"]}
# data.pop('Datetime', None)
#
# ids = [dt.pop("_id") for dt in dictData]
#
# operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
# zip(ids, dictData)]
#
# self.yfdb.timeline.bulk_write(operations)
#
# # insert new data
# else:
# if len(data) > 0:
# # self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# # + symbol['_id']['sym'] + "' (" + str(len(data)) + " entries)")
# dictData = data.to_dict(orient='records')
#
# for data in dictData:
# data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": data["Datetime"]}
# data.pop('Datetime', None)
#
# ids = [dt.pop("_id") for dt in dictData]
#
# operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
# zip(ids, dictData)]
#
# self.yfdb.timeline.bulk_write(operations)
def getTicker(self, symbol):
# self.add(symbol)
self.update()
symbols = self.yfdb.timeline.find({'_id.sym': symbol}).sort('_id.Datetime', 1)
volume = {}
close = {}
cleanSymbols = {}
for s in symbols:
datetimeStock = int(s['_id']['Datetime'].timestamp() * 1000)
volume[datetimeStock] = s['Volume']
close[datetimeStock] = s['Close']
cleanSymbols["Close"] = close
cleanSymbols["Volume"] = volume
return cleanSymbols
def getLastTicker(self, symbol):
symbols = self.yfdb.timeline.find({'_id.sym': symbol}).sort('_id', -1).limit(1);
symbolsList = list(symbols)
if len(symbolsList) == 0:
return None
elif 'Datetime' in symbolsList[0]:
return symbolsList[0]['_id']['Datetime']
else:
return None
def periodInterval(self, x):
"""
Return two variables with interval and period to be fetched from API
Parameters:
x - interval from each close
Return:
interval - interval in minutes to calculate the indicators
days - period in days to fetch data from API
"""
match x:
# case 'period':
# return period in minutes, interval in days to be fetched
case '1m':
return 1, 6
case '5m':
return 5, 59
case '15m':
return 15, 59
case '30m':
return 30, 59
case '1hr':
return 60, 300
case '2hr':
return 2 * 60, 300
case '4hr':
return 4 * 60, 300
case '12hr':
return 12 * 60, 300
case '1d':
return 1 * 24 * 60, 300
case '5d':
return 5 * 24 * 60, 1500
case '1wk':
return 7 * 24 * 60, 2100
case '1mo':
return 30 * 24 * 60, 9000
case _:
return 5, 59 # 5, 59 is the default case if x is not found
# https://www.mongodb.com/developer/article/time-series-macd-rsi/
def getIndicators(self, symbol, interval='5m'):
intervalInMinutes, days = self.periodInterval(interval)
self.sprint(intervalInMinutes)
self.sprint(days)
self.sprint(1000 * 60 * intervalInMinutes)
date = datetime.today() - timedelta(days=days)
self.fetchInterval(date.strftime("%Y/%m/%d"), None, symbol, interval)
indicators = self.yfdb.timeline.aggregate([
{
"$match": {
"_id.sym": symbol,
}
},
{
"$group": {
"_id": {
"sym": "$_id.sym",
"Datetime": {
"$subtract": [
{"$toLong": "$_id.Datetime"},
{"$mod": [{"$toLong": "$_id.Datetime"}, 1000 * 60 * intervalInMinutes]}
]
}
},
"close": {"$last": "$Close"},
"volume": {"$last": "$Volume"},
},
},
{
"$sort": {
"_id.Datetime": 1,
},
},
{
"$project": {
"_id": 1,
"price": "$close",
"volume": "$volume"
}
},
{
"$setWindowFields": {
"partitionBy": "$id.sym",
"sortBy": {"quantity": -1},
"output": {
"count": {
"$documentNumber": {}
}
}
}
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"ema_10": {
"$expMovingAvg": {"input": "$price", "N": 10},
},
"ema_20": {
"$expMovingAvg": {"input": "$price", "N": 20},
},
"ema_50": {
"$expMovingAvg": {"input": "$price", "N": 50},
},
"ema_100": {
"$expMovingAvg": {"input": "$price", "N": 100},
},
"ema_200": {
"$expMovingAvg": {"input": "$price", "N": 200},
},
"ema_12": {
"$expMovingAvg": {"input": "$price", "N": 12},
},
"ema_26": {
"$expMovingAvg": {"input": "$price", "N": 26},
},
},
},
},
{"$addFields": {
"ema_10": {
"$cond": {
"if": {"$gte": ["$count", 10]},
"then": "$ema_10",
"else": None
}
},
"ema_20": {
"$cond": {
"if": {"$gte": ["$count", 20]},
"then": "$ema_20",
"else": None
}
},
"ema_12": {
"$cond": {
"if": {"$gte": ["$count", 12]},
"then": "$ema_12",
"else": None
}
},
"ema_26": {
"$cond": {
"if": {"$gte": ["$count", 26]},
"then": "$ema_26",
"else": None
}
},
"ema_50": {
"$cond": {
"if": {"$gte": ["$count", 50]},
"then": "$ema_50",
"else": None
}
},
"ema_100": {
"$cond": {
"if": {"$gte": ["$count", 100]},
"then": "$ema_100",
"else": None
}
},
"ema_200": {
"$cond": {
"if": {"$gte": ["$count", 200]},
"then": "$ema_200",
"else": None
}
},
}},
{"$addFields": {"macdLine": {"$subtract": ["$ema_12", "$ema_26"]}}},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"macdSignal": {
"$expMovingAvg": {"input": "$macdLine", "N": 9},
},
},
},
},
{
"$addFields": {"macdHistogram": {"$subtract": ["$macdLine", "$macdSignal"]}},
}, {
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"previousPrice": {"$shift": {"by": -1, "output": "$price"}},
},
},
},
# MACD Indicator
# NOR, Safwan Mohd; WICKREMASINGHE, Guneratne. The profitability of
# MACD and RSI trading rules in the Australian stock market.
# Investment management and financial innovations,
# n. 11, Iss. 4 (contin.), p. 196, 2014.
{
"$addFields": {
"macd_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$macdLine", None]},
{"$eq": ["$macdSignal", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$gt": ["$macdLine", "$macdSignal"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$lt": ["$macdLine", "$macdSignal"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
# End MACD Indicator
{
"$addFields": {
"diff": {
"$subtract": ["$price", {"$ifNull": ["$previousPrice", "$price"]}],
},
},
},
{
"$addFields": {
"gain": {"$cond": {"if": {"$gte": ["$diff", 0]}, "then": "$diff", "else": 0}},
"loss": {
"$cond": {
"if": {"$lte": ["$diff", 0]}, "then": {"$abs": "$diff"}, "else": 0
},
},
},
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"avgGain": {
"$avg": "$gain",
"window": {"documents": [-14, 0]},
},
"avgLoss": {
"$avg": "$loss",
"window": {"documents": [-14, 0]},
},
},
},
},
{
"$addFields": {
"relativeStrength": {
"$cond": {
"if": {
"$gt": ["$avgLoss", 0],
},
"then": {
"$cond": [
{"$eq": ["$avgLoss", -1]},
"$avgGain",
{"$divide": ["$avgGain", "$avgLoss"]}
]
},
"else": "$avgGain",
},
},
},
},
{
"$addFields": {
"rsi": {
"$cond": {
"if": {"$gt": ["$count", 14]},
"then": {
"$cond": [ # Avoid division by zero
{"$eq": ["$relativeStrength", -1]},
None,
{
"$subtract": [
100,
{"$divide": [100, {"$add": [1, "$relativeStrength"]}]},
]
}
]
},
"else": None,
},
},
},
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"previousRsi": {"$shift": {"by": -1, "output": "$rsi"}},
},
},
},
# Chande Momentum Oscillator
# CHANDE, Tushar S.; KROLL, Stanley.
# The new technical trader: boost your profit by plugging into the latest indicators.
# John Wiley & Sons Incorporated, p. 100, 1994.
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"cmoUp": {
"$sum": "$gain",
"window": {"documents": [-9, 0]},
},
"cmoDown": {
"$sum": "$loss",
"window": {"documents": [-9, 0]},
},
},
},
},
{
"$addFields": {
"cmo_9": {
"$cond": {
"if": {"$gt": ["$count", 9]},
"then": {
"$cond": [ # Avoid division by zero
{
"$eq": [
{"$add": ["$cmoUp", "$cmoDown"]}, 0
]
},
None,
{
"$multiply": [100,
{
"$divide": [
{"$subtract": ["$cmoUp", "$cmoDown"]},
{"$add": ["$cmoUp", "$cmoDown"]}
]
},
]
}
]
},
"else": None,
},
},
},
},
{
"$addFields": {
"cmo_9_indicator": {
"$switch": {
"branches": [
{
"case": {
"$eq": ["$cmo_9", None]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$and": [
{"$lt": ["$cmo_9", -70]},
{"$ifNull": ["$cmo_9", False]}
]},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$and": [
{"$gt": ["$cmo_9", 70]},
{"$ifNull": ["$cmo_9", False]}
]},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
# End Chande Momentum Oscillator
# EMA's Indicators
# DI LORENZO, Renato. Basic technical analysis of financial markets.
# Milan, Italy: Springer, p. 58, 2013.
{
"$addFields": {
"ema_10_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_20", None]},
{"$eq": ["$ema_10", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_20", "$ema_10"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_20", "$ema_10"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
"ema_20_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_50", None]},
{"$eq": ["$ema_20", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_50", "$ema_20"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_50", "$ema_20"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
"ema_50_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_100", None]},
{"$eq": ["$ema_50", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_100", "$ema_50"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_100", "$ema_50"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
"ema_100_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_200", None]},
{"$eq": ["$ema_100", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_200", "$ema_100"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_200", "$ema_100"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
# End EMA's Indicators
# RSI Indicator
# ANDERSON, Bing; LI, Shuyun. An investigation of the relative strength index.
# Banks & bank systems, n. 10, Iss. 1, p. 92-96, 2015.
# "Surprisingly, the trading simulation with RSI at 40 and
# 60 being the buy/sell threshold performs the best
# among all the parameter combinations we have tested
# so far. The total profit is 5206 pips. There are 125
# trades in total. The trade with the biggest loss has a
# loss of 1876 pips."
{
"$addFields": {
"rsi_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$rsi", None]},
{"$eq": ["$previousRsi", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$and": [
{"$gt": ["$rsi", 60]},
{"$gt": ["$previousRsi", "$rsi"]}
]
},
"then": {"weight": -1, "recommendation": "Sell"}},
{
"case": {
"$and": [
{"$lt": ["$rsi", 40]},
{"$lt": ["$previousRsi", "$rsi"]}
]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
#End RSI Oscillator
# Stochastic RSI Oscillator
# CHANDE, Tushar S.; KROLL, Stanley.
# The new technical trader: boost your profit by plugging into the latest indicators.
# John Wiley & Sons Incorporated, p. 124, 1994.
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"rsi_stoch_low": {
"$min": "$rsi",
"window": {"documents": [-14, 0]},
},
"rsi_stoch_high": {
"$max": "$rsi",
"window": {"documents": [-14, 0]},
},
},
},
},
{
"$addFields": {
"rsi_stoch": {
"$cond": {
"if": {
"$and": [
{"$gt": ["$count", 14]},
{"$gt": [{"$subtract": ["$rsi_stoch_high", "$rsi_stoch_low"]}, 0]}
]
},
"then": {
"$cond": [ # Avoid division by zero
{
"$eq": [{"$subtract": ["$rsi_stoch_high", "$rsi_stoch_low"]}, 0]
},
None,
{
"$divide": [
{"$subtract": ["$rsi", "$rsi_stoch_low"]},
{"$subtract": ["$rsi_stoch_high", "$rsi_stoch_low"]},
]
}
]
},
"else": None,
},
}
},
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"previousRsiStoch": {"$shift": {"by": -1, "output": "$rsi_stoch"}},
},
},
},
{
"$addFields": {
"rsi_stoch_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$rsi_stoch", None]},
{"$eq": ["$previousRsiStoch", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$and": [
{"$gt": ["$rsi_stoch", 0.8]},
{"$gt": ["$previousRsiStoch", "$rsi_stoch"]},
{"$ifNull": ["$rsi_stoch", False]}
]
},
"then": {"weight": -1, "recommendation": "Sell"}},
{
"case": {
"$and": [
{"$lt": ["$rsi_stoch", 0.2]},
{"$lt": ["$previousRsiStoch", "$rsi_stoch"]},
{"$ifNull": ["$rsi_stoch", False]}
]
},
"then": {"weight": 1, "recommendation": "Buy"}},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
}
},
},
# End Stochastic RSI Oscillator
{
"$addFields": {
"indicators_tendency":
{
"$sum": [
"$macd_indicator.weight",
"$cmo_9_indicator.weight",
"$ema_10_indicator.weight",
"$ema_20_indicator.weight",
"$ema_50_indicator.weight",
"$ema_100_indicator.weight",
"$rsi_indicator.weight",
"$rsi_stoch_indicator.weight",
]
}
},
},
{
"$addFields": {
"indicators_recommendation": {
"$switch": {
"branches": [
{
"case": {
"$gt": ["$indicators_tendency", 4]
},
"then": "Strong Buy"
},
{
"case": {
"$gt": ["$indicators_tendency", 0]
},
"then": "Buy"
},
{
"case": {
"$lt": ["$indicators_tendency", -4]
},
"then": "Strong Sell"
},
{
"case": {
"$lt": ["$indicators_tendency", 0]
},
"then": "Sell"
},
],
"default": "Neutral"
}
},
},
},
{
"$addFields": {
"indicators_up": {
"$sum": [
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$macd_indicator.weight", 0]},
{"$ifNull": ["$macd_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$cmo_9_indicator.weight", 0]},
{"$ifNull": ["$cmo_9_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_10_indicator.weight", 0]},
{"$ifNull": ["$ema_10_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_20_indicator.weight", 0]},
{"$ifNull": ["$ema_20_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_50_indicator.weight", 0]},
{"$ifNull": ["$ema_50_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_100_indicator.weight", 0]},
{"$ifNull": ["$ema_100_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$rsi_indicator.weight", 0]},
{"$ifNull": ["$rsi_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$rsi_stoch_indicator.weight", 0]},
{"$ifNull": ["$rsi_stoch_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
]
},
"indicators_down": {
"$sum": [
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$macd_indicator.weight", 0]},
{"$ifNull": ["$macd_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$cmo_9_indicator.weight", 0]},
{"$ifNull": ["$cmo_9_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_10_indicator.weight", 0]},
{"$ifNull": ["$ema_10_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_20_indicator.weight", 0]},
{"$ifNull": ["$ema_20_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_50_indicator.weight", 0]},
{"$ifNull": ["$ema_50_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_100_indicator.weight", 0]},
{"$ifNull": ["$ema_100_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$rsi_indicator.weight", 0]},
{"$ifNull": ["$rsi_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$rsi_stoch_indicator.weight", 0]},
{"$ifNull": ["$rsi_stoch_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
]
},
"indicators_neutral": {
"$sum": [
{
"$cond": {
"if": {
"$eq": ["$macd_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$cmo_9_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_10_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_20_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_50_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_100_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$rsi_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$rsi_stoch_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
]
},
},
}
])
# self.sprint(list(indicators))
return list(indicators)
#
# -6 Strong Sell
#
# -3 Sell
#
# 0 Neutral
#
# + 3 Buy
#
# + 6 Strong Buy | # Inspired in
# https://github.com/grizzlypeaksoftware/Flask-Stock-Widget
# https://github.com/rubenafo/yfMongo
import sys, os
import re
import csv
import json
from datetime import datetime, date, time, timedelta
from itertools import zip_longest
import numpy as np
import pytz
import yfinance as yf
import ast
import copy
from flask import jsonify
from pymongo import *
from pandas_datareader import data as pdr
class mongoYfinance:
mongoClient = None
yfdb = None
verbose = False
#
# Used to print messages only if the verbose flag was enabled
#
def sprint(self, msg):
if self.verbose:
print(msg)
#
# Generic function to check all user input dates
# The format must be dd/mm/yyyy and cannot be a date in the future.
# In case of error the execution of the application is stopped.
#
def __checkDate(self, date):
try:
inputDate = datetime.strptime(date, "%Y/%m/%d")
currentTime = datetime.now()
if (inputDate > currentTime):
self.sprint("Error: provided date (" + date + ") is in the future")
exit()
except ValueError:
self.sprint("Error: invalid provided date format (expected yyyy/mm/dd)")
exit()
#
# Given a symbol document in the mongodb this returns the date it contains.
#
def __getFormattedDate(self, symbol):
try:
# print(symbol['Datetime'])
# return datetime.date(symbol['Datetime'], "%Y-%m-%d")
return symbol['_id']['Datetime']
except ValueError:
self.sprint("Error: invalid provided date format (expected yyyy/mm/dd)")
#
# Initialises the ddbb
#
def __init__(self, user="admin", password="", hostname="localhost", database="yfmongo", verbose=True):
userAndPass = ""
if user and password:
userAndPass = user + ":" + str(password) + "@"
url = "mongodb+srv://" + userAndPass + hostname
self.mongoClient = MongoClient(url)
self.yfdb = self.mongoClient[database];
self.verbose = verbose
#
# Removes all content in the database (Caution!)
#
def clear(self, keepSymbols=False):
if keepSymbols:
self.sprint("Removing data ... done")
self.yfdb.timeline.delete_many({});
else:
self.sprint("Removing all collections [symbols and timeline] ... done")
self.yfdb.timeline.delete_many({});
self.yfdb.symbols.delete_many({});
def add(self, symbol, startDate=None, endDate=None):
exists = self.yfdb.symbols.count_documents({'_id.sym': symbol})
if not exists:
quote = yf.Ticker(symbol)
if "shortName" not in quote.info:
return {'symbolExists': False, 'added': False, 'message': 'Symbol ' + symbol + ' not found in API'}
self.yfdb.symbols.replace_one({'_id': {'sym': symbol}},
{'_id': {'sym': symbol}, 'shortName': quote.info['shortName']}, upsert=True)
self.sprint("'" + symbol + "'" + " added to the database")
oldestDate = datetime.today() - timedelta(days=6)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
symbol=symbol)
result = {'symbolExists': True, 'added': True, 'message': 'Symbol ' + symbol + ' was successfully added',
'sym': symbol, 'shortName': quote.info['shortName']}
else:
symbols = self.yfdb.symbols.find({'_id.sym': symbol})
for s in symbols:
result = {'symbolExists': True, 'added': False,
'message': 'Symbol ' + symbol + ' is already in database',
'sym': symbol, 'shortName': s['shortName']}
if startDate != None:
if endDate != None:
self.fetchInterval(startDate, endDate, symbol)
else:
self.fetch(startDate, symbol)
return result
#
# Removes a symbol from the ddbb, including all timeline entries
#
def remove(self, symbol):
if not symbol:
return {'removed': False, 'message': 'Missing symbol name'}
exists = self.yfdb.symbols.count_documents({'_id.sym': symbol})
if not exists:
self.sprint("Error: symbol'" + symbol + "' not in the database")
return {'removed': False, 'message': 'Symbol ' + symbol + ' not found in database'}
else:
self.yfdb.symbols.delete_many({'_id.sym': symbol})
self.yfdb.timeline.delete_many({'_id.sym': symbol})
self.sprint("'" + symbol + "'" + " removed from the database")
return {'removed': True, 'message': symbol + ' removed from the database'}
#
# Prints information regarding the admin info (start and end dates)
# and the symbols contained in the database
#
def info(self):
symbols = self.yfdb.symbols.find();
for symb in symbols:
print(symb['sym'])
print("Timeline size: " + str(self.yfdb.timeline.find().count()))
print("Symbols: " + str(symbols.count()))
dates = []
symbols = self.yfdb.timeline.find()
for symb in symbols:
date = self.__getFormattedDate(symb)
dates.append(date)
if dates:
print("Oldest record: " + min(dates).strftime("%Y/%m/%d"))
print("Most recent record: " + max(dates).strftime("%Y/%m/%d"))
def listSymbols(self):
symbols = self.yfdb.symbols.find()
symList = {}
count = 0
for s in symbols:
print(s)
symList[count] = {'sym': s['_id']['sym'], 'shortName': s['shortName']}
count += 1
return symList
#
# Updates the database fetching data for all symbols since last
# date in the data until today
#
def update(self):
tickers = self.yfdb.symbols.find()
for ticker in tickers:
tickerTimeline = list(self.yfdb.timeline.find({'_id.sym': ticker["_id"]["sym"]}))
if len(tickerTimeline) > 0:
dateToday = datetime.today()
oldestDate = max(map(lambda s: self.__getFormattedDate(s), tickerTimeline))
delta = dateToday - oldestDate
endDate = oldestDate
week_period = delta.days // 6
day_period = delta.days % 6
if week_period > 0:
for i in range(1, week_period):
if oldestDate is not None:
endDate = endDate+timedelta(days=6)
print("oldestDate:", oldestDate, "endDate:", endDate, "week_period:", week_period)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
endDate.strftime("%Y/%m/%d"),
symbol=ticker["_id"]["sym"])
if week_period > 0 and day_period > 0:
if oldestDate is not None:
endDate = endDate + timedelta(days=day_period)
print("oldestDate:", oldestDate, "endDate:", endDate, "day_period:", day_period)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
endDate.strftime("%Y/%m/%d"),
symbol=ticker["_id"]["sym"])
# print(tickerTimeline)
oldestDate = max(map(lambda s: self.__getFormattedDate(s), tickerTimeline))
print(oldestDate)
if oldestDate is not None:
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
None,
symbol=ticker["_id"]["sym"])
else:
oldestDate = datetime.today() - timedelta(days=6)
self.fetchInterval(oldestDate.strftime("%Y/%m/%d"),
None,
symbol=ticker["_id"]["sym"])
# Fetches symbol data for the interval between startDate and endDate
# If the symbol is set None, all symbols found in the database are
# updated.
def fetchInterval(self, startDate, endDate=None, symbol=None, interval='1m'):
timezone = pytz.timezone("UTC")
if symbol is None:
symbols = self.yfdb.symbols.find()
else:
symbols = self.yfdb.symbols.find(({'_id.sym': symbol}))
for symbol in symbols:
# download dataframe
quote = yf.Ticker(symbol['_id']['sym'])
# data = quote.history(start=startDate.replace("/", "-"), end=endDate.replace("/", "-"), interval=interval)
if endDate is not None:
data = quote.history(start=startDate.replace("/", "-"), end=endDate.replace("/", "-"), interval=interval)
else:
data = quote.history(start=startDate.replace("/", "-"), interval=interval)
# set index to column in pandas DataFrame
data.reset_index(inplace=True)
data.dropna(inplace=True)
self.sprint(data)
if "Datetime" in data:
lastTicker = self.getLastTicker(symbol['_id']['sym'])
tickersNotRounded = data[data['Datetime'].dt.second > 0].index
data.drop(tickersNotRounded, inplace=True)
if len(data) > 0:
# self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# + symbol['_id']['sym'] + "' (" + str(len(data)) + " entries)")
dictData = data.to_dict(orient='records')
for data in dictData:
data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": data["Datetime"]}
data.pop('Datetime', None)
ids = [dt.pop("_id") for dt in dictData]
operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
zip(ids, dictData)]
self.yfdb.timeline.bulk_write(operations)
if "Date" in data:
if len(data) > 0:
# self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# + symbol['_id']['sym'] + "' (" + str(len(data)) + " entries)")
dictData = data.to_dict(orient='records')
for data in dictData:
date = datetime.combine(data["Date"], datetime.min.time())
data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": date}
data.pop('Date', None)
self.sprint(data)
ids = [dt.pop("_id") for dt in dictData]
operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
zip(ids, dictData)]
self.yfdb.timeline.bulk_write(operations)
# update already exists in database
# if lastTicker:
# # storedData = timezone.localize(self.getLastTicker(symbol['sym']))
# # apiData = data["Datetime"].iat[-1].to_pydatetime().astimezone(timezone)
#
# print(apiData.timestamp() - storedData.timestamp())
#
# if len(data) > 0 and apiData.timestamp() - storedData.timestamp() > 120:
# # self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# # + symbol['sym'] + "' (" + str(len(data)) + " entries)")
# dictData = data.to_dict(orient='records')
#
# for data in dictData:
# data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": data["Datetime"]}
# data.pop('Datetime', None)
#
# ids = [dt.pop("_id") for dt in dictData]
#
# operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
# zip(ids, dictData)]
#
# self.yfdb.timeline.bulk_write(operations)
#
# # insert new data
# else:
# if len(data) > 0:
# # self.sprint("Adding '[" + startDate + ", " + endDate + "]' data for symbol '"
# # + symbol['_id']['sym'] + "' (" + str(len(data)) + " entries)")
# dictData = data.to_dict(orient='records')
#
# for data in dictData:
# data["_id"] = {"sym": symbol['_id']['sym'], "Datetime": data["Datetime"]}
# data.pop('Datetime', None)
#
# ids = [dt.pop("_id") for dt in dictData]
#
# operations = [UpdateOne({"_id": idn}, {'$set': dt}, upsert=True) for idn, dt in
# zip(ids, dictData)]
#
# self.yfdb.timeline.bulk_write(operations)
def getTicker(self, symbol):
# self.add(symbol)
self.update()
symbols = self.yfdb.timeline.find({'_id.sym': symbol}).sort('_id.Datetime', 1)
volume = {}
close = {}
cleanSymbols = {}
for s in symbols:
datetimeStock = int(s['_id']['Datetime'].timestamp() * 1000)
volume[datetimeStock] = s['Volume']
close[datetimeStock] = s['Close']
cleanSymbols["Close"] = close
cleanSymbols["Volume"] = volume
return cleanSymbols
def getLastTicker(self, symbol):
symbols = self.yfdb.timeline.find({'_id.sym': symbol}).sort('_id', -1).limit(1);
symbolsList = list(symbols)
if len(symbolsList) == 0:
return None
elif 'Datetime' in symbolsList[0]:
return symbolsList[0]['_id']['Datetime']
else:
return None
def periodInterval(self, x):
"""
Return two variables with interval and period to be fetched from API
Parameters:
x - interval from each close
Return:
interval - interval in minutes to calculate the indicators
days - period in days to fetch data from API
"""
match x:
# case 'period':
# return period in minutes, interval in days to be fetched
case '1m':
return 1, 6
case '5m':
return 5, 59
case '15m':
return 15, 59
case '30m':
return 30, 59
case '1hr':
return 60, 300
case '2hr':
return 2 * 60, 300
case '4hr':
return 4 * 60, 300
case '12hr':
return 12 * 60, 300
case '1d':
return 1 * 24 * 60, 300
case '5d':
return 5 * 24 * 60, 1500
case '1wk':
return 7 * 24 * 60, 2100
case '1mo':
return 30 * 24 * 60, 9000
case _:
return 5, 59 # 5, 59 is the default case if x is not found
# https://www.mongodb.com/developer/article/time-series-macd-rsi/
def getIndicators(self, symbol, interval='5m'):
intervalInMinutes, days = self.periodInterval(interval)
self.sprint(intervalInMinutes)
self.sprint(days)
self.sprint(1000 * 60 * intervalInMinutes)
date = datetime.today() - timedelta(days=days)
self.fetchInterval(date.strftime("%Y/%m/%d"), None, symbol, interval)
indicators = self.yfdb.timeline.aggregate([
{
"$match": {
"_id.sym": symbol,
}
},
{
"$group": {
"_id": {
"sym": "$_id.sym",
"Datetime": {
"$subtract": [
{"$toLong": "$_id.Datetime"},
{"$mod": [{"$toLong": "$_id.Datetime"}, 1000 * 60 * intervalInMinutes]}
]
}
},
"close": {"$last": "$Close"},
"volume": {"$last": "$Volume"},
},
},
{
"$sort": {
"_id.Datetime": 1,
},
},
{
"$project": {
"_id": 1,
"price": "$close",
"volume": "$volume"
}
},
{
"$setWindowFields": {
"partitionBy": "$id.sym",
"sortBy": {"quantity": -1},
"output": {
"count": {
"$documentNumber": {}
}
}
}
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"ema_10": {
"$expMovingAvg": {"input": "$price", "N": 10},
},
"ema_20": {
"$expMovingAvg": {"input": "$price", "N": 20},
},
"ema_50": {
"$expMovingAvg": {"input": "$price", "N": 50},
},
"ema_100": {
"$expMovingAvg": {"input": "$price", "N": 100},
},
"ema_200": {
"$expMovingAvg": {"input": "$price", "N": 200},
},
"ema_12": {
"$expMovingAvg": {"input": "$price", "N": 12},
},
"ema_26": {
"$expMovingAvg": {"input": "$price", "N": 26},
},
},
},
},
{"$addFields": {
"ema_10": {
"$cond": {
"if": {"$gte": ["$count", 10]},
"then": "$ema_10",
"else": None
}
},
"ema_20": {
"$cond": {
"if": {"$gte": ["$count", 20]},
"then": "$ema_20",
"else": None
}
},
"ema_12": {
"$cond": {
"if": {"$gte": ["$count", 12]},
"then": "$ema_12",
"else": None
}
},
"ema_26": {
"$cond": {
"if": {"$gte": ["$count", 26]},
"then": "$ema_26",
"else": None
}
},
"ema_50": {
"$cond": {
"if": {"$gte": ["$count", 50]},
"then": "$ema_50",
"else": None
}
},
"ema_100": {
"$cond": {
"if": {"$gte": ["$count", 100]},
"then": "$ema_100",
"else": None
}
},
"ema_200": {
"$cond": {
"if": {"$gte": ["$count", 200]},
"then": "$ema_200",
"else": None
}
},
}},
{"$addFields": {"macdLine": {"$subtract": ["$ema_12", "$ema_26"]}}},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"macdSignal": {
"$expMovingAvg": {"input": "$macdLine", "N": 9},
},
},
},
},
{
"$addFields": {"macdHistogram": {"$subtract": ["$macdLine", "$macdSignal"]}},
}, {
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"previousPrice": {"$shift": {"by": -1, "output": "$price"}},
},
},
},
# MACD Indicator
# NOR, Safwan Mohd; WICKREMASINGHE, Guneratne. The profitability of
# MACD and RSI trading rules in the Australian stock market.
# Investment management and financial innovations,
# n. 11, Iss. 4 (contin.), p. 196, 2014.
{
"$addFields": {
"macd_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$macdLine", None]},
{"$eq": ["$macdSignal", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$gt": ["$macdLine", "$macdSignal"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$lt": ["$macdLine", "$macdSignal"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
# End MACD Indicator
{
"$addFields": {
"diff": {
"$subtract": ["$price", {"$ifNull": ["$previousPrice", "$price"]}],
},
},
},
{
"$addFields": {
"gain": {"$cond": {"if": {"$gte": ["$diff", 0]}, "then": "$diff", "else": 0}},
"loss": {
"$cond": {
"if": {"$lte": ["$diff", 0]}, "then": {"$abs": "$diff"}, "else": 0
},
},
},
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"avgGain": {
"$avg": "$gain",
"window": {"documents": [-14, 0]},
},
"avgLoss": {
"$avg": "$loss",
"window": {"documents": [-14, 0]},
},
},
},
},
{
"$addFields": {
"relativeStrength": {
"$cond": {
"if": {
"$gt": ["$avgLoss", 0],
},
"then": {
"$cond": [
{"$eq": ["$avgLoss", -1]},
"$avgGain",
{"$divide": ["$avgGain", "$avgLoss"]}
]
},
"else": "$avgGain",
},
},
},
},
{
"$addFields": {
"rsi": {
"$cond": {
"if": {"$gt": ["$count", 14]},
"then": {
"$cond": [ # Avoid division by zero
{"$eq": ["$relativeStrength", -1]},
None,
{
"$subtract": [
100,
{"$divide": [100, {"$add": [1, "$relativeStrength"]}]},
]
}
]
},
"else": None,
},
},
},
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"previousRsi": {"$shift": {"by": -1, "output": "$rsi"}},
},
},
},
# Chande Momentum Oscillator
# CHANDE, Tushar S.; KROLL, Stanley.
# The new technical trader: boost your profit by plugging into the latest indicators.
# John Wiley & Sons Incorporated, p. 100, 1994.
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"cmoUp": {
"$sum": "$gain",
"window": {"documents": [-9, 0]},
},
"cmoDown": {
"$sum": "$loss",
"window": {"documents": [-9, 0]},
},
},
},
},
{
"$addFields": {
"cmo_9": {
"$cond": {
"if": {"$gt": ["$count", 9]},
"then": {
"$cond": [ # Avoid division by zero
{
"$eq": [
{"$add": ["$cmoUp", "$cmoDown"]}, 0
]
},
None,
{
"$multiply": [100,
{
"$divide": [
{"$subtract": ["$cmoUp", "$cmoDown"]},
{"$add": ["$cmoUp", "$cmoDown"]}
]
},
]
}
]
},
"else": None,
},
},
},
},
{
"$addFields": {
"cmo_9_indicator": {
"$switch": {
"branches": [
{
"case": {
"$eq": ["$cmo_9", None]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$and": [
{"$lt": ["$cmo_9", -70]},
{"$ifNull": ["$cmo_9", False]}
]},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$and": [
{"$gt": ["$cmo_9", 70]},
{"$ifNull": ["$cmo_9", False]}
]},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
# End Chande Momentum Oscillator
# EMA's Indicators
# DI LORENZO, Renato. Basic technical analysis of financial markets.
# Milan, Italy: Springer, p. 58, 2013.
{
"$addFields": {
"ema_10_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_20", None]},
{"$eq": ["$ema_10", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_20", "$ema_10"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_20", "$ema_10"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
"ema_20_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_50", None]},
{"$eq": ["$ema_20", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_50", "$ema_20"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_50", "$ema_20"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
"ema_50_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_100", None]},
{"$eq": ["$ema_50", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_100", "$ema_50"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_100", "$ema_50"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
"ema_100_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$ema_200", None]},
{"$eq": ["$ema_100", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$lt": ["$ema_200", "$ema_100"]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
{
"case": {
"$gt": ["$ema_200", "$ema_100"]
},
"then": {"weight": -1, "recommendation": "Sell"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
# End EMA's Indicators
# RSI Indicator
# ANDERSON, Bing; LI, Shuyun. An investigation of the relative strength index.
# Banks & bank systems, n. 10, Iss. 1, p. 92-96, 2015.
# "Surprisingly, the trading simulation with RSI at 40 and
# 60 being the buy/sell threshold performs the best
# among all the parameter combinations we have tested
# so far. The total profit is 5206 pips. There are 125
# trades in total. The trade with the biggest loss has a
# loss of 1876 pips."
{
"$addFields": {
"rsi_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$rsi", None]},
{"$eq": ["$previousRsi", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$and": [
{"$gt": ["$rsi", 60]},
{"$gt": ["$previousRsi", "$rsi"]}
]
},
"then": {"weight": -1, "recommendation": "Sell"}},
{
"case": {
"$and": [
{"$lt": ["$rsi", 40]},
{"$lt": ["$previousRsi", "$rsi"]}
]
},
"then": {"weight": 1, "recommendation": "Buy"}
},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
},
},
},
#End RSI Oscillator
# Stochastic RSI Oscillator
# CHANDE, Tushar S.; KROLL, Stanley.
# The new technical trader: boost your profit by plugging into the latest indicators.
# John Wiley & Sons Incorporated, p. 124, 1994.
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"rsi_stoch_low": {
"$min": "$rsi",
"window": {"documents": [-14, 0]},
},
"rsi_stoch_high": {
"$max": "$rsi",
"window": {"documents": [-14, 0]},
},
},
},
},
{
"$addFields": {
"rsi_stoch": {
"$cond": {
"if": {
"$and": [
{"$gt": ["$count", 14]},
{"$gt": [{"$subtract": ["$rsi_stoch_high", "$rsi_stoch_low"]}, 0]}
]
},
"then": {
"$cond": [ # Avoid division by zero
{
"$eq": [{"$subtract": ["$rsi_stoch_high", "$rsi_stoch_low"]}, 0]
},
None,
{
"$divide": [
{"$subtract": ["$rsi", "$rsi_stoch_low"]},
{"$subtract": ["$rsi_stoch_high", "$rsi_stoch_low"]},
]
}
]
},
"else": None,
},
}
},
},
{
"$setWindowFields": {
"partitionBy": "$_id.sym",
"sortBy": {"_id.Datetime": 1},
"output": {
"previousRsiStoch": {"$shift": {"by": -1, "output": "$rsi_stoch"}},
},
},
},
{
"$addFields": {
"rsi_stoch_indicator": {
"$switch": {
"branches": [
{
"case": {
"$or": [
{"$eq": ["$rsi_stoch", None]},
{"$eq": ["$previousRsiStoch", None]}
]
},
"then": {"weight": None, "recommendation": None}
},
{
"case": {
"$and": [
{"$gt": ["$rsi_stoch", 0.8]},
{"$gt": ["$previousRsiStoch", "$rsi_stoch"]},
{"$ifNull": ["$rsi_stoch", False]}
]
},
"then": {"weight": -1, "recommendation": "Sell"}},
{
"case": {
"$and": [
{"$lt": ["$rsi_stoch", 0.2]},
{"$lt": ["$previousRsiStoch", "$rsi_stoch"]},
{"$ifNull": ["$rsi_stoch", False]}
]
},
"then": {"weight": 1, "recommendation": "Buy"}},
],
"default": {"weight": 0, "recommendation": "Neutral"}
}
}
},
},
# End Stochastic RSI Oscillator
{
"$addFields": {
"indicators_tendency":
{
"$sum": [
"$macd_indicator.weight",
"$cmo_9_indicator.weight",
"$ema_10_indicator.weight",
"$ema_20_indicator.weight",
"$ema_50_indicator.weight",
"$ema_100_indicator.weight",
"$rsi_indicator.weight",
"$rsi_stoch_indicator.weight",
]
}
},
},
{
"$addFields": {
"indicators_recommendation": {
"$switch": {
"branches": [
{
"case": {
"$gt": ["$indicators_tendency", 4]
},
"then": "Strong Buy"
},
{
"case": {
"$gt": ["$indicators_tendency", 0]
},
"then": "Buy"
},
{
"case": {
"$lt": ["$indicators_tendency", -4]
},
"then": "Strong Sell"
},
{
"case": {
"$lt": ["$indicators_tendency", 0]
},
"then": "Sell"
},
],
"default": "Neutral"
}
},
},
},
{
"$addFields": {
"indicators_up": {
"$sum": [
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$macd_indicator.weight", 0]},
{"$ifNull": ["$macd_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$cmo_9_indicator.weight", 0]},
{"$ifNull": ["$cmo_9_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_10_indicator.weight", 0]},
{"$ifNull": ["$ema_10_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_20_indicator.weight", 0]},
{"$ifNull": ["$ema_20_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_50_indicator.weight", 0]},
{"$ifNull": ["$ema_50_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$ema_100_indicator.weight", 0]},
{"$ifNull": ["$ema_100_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$rsi_indicator.weight", 0]},
{"$ifNull": ["$rsi_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$gt": ["$rsi_stoch_indicator.weight", 0]},
{"$ifNull": ["$rsi_stoch_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
]
},
"indicators_down": {
"$sum": [
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$macd_indicator.weight", 0]},
{"$ifNull": ["$macd_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$cmo_9_indicator.weight", 0]},
{"$ifNull": ["$cmo_9_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_10_indicator.weight", 0]},
{"$ifNull": ["$ema_10_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_20_indicator.weight", 0]},
{"$ifNull": ["$ema_20_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_50_indicator.weight", 0]},
{"$ifNull": ["$ema_50_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$ema_100_indicator.weight", 0]},
{"$ifNull": ["$ema_100_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$rsi_indicator.weight", 0]},
{"$ifNull": ["$rsi_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$and": [
{"$lt": ["$rsi_stoch_indicator.weight", 0]},
{"$ifNull": ["$rsi_stoch_indicator.weight", False]}
]
},
"then": 1, "else": 0
}
},
]
},
"indicators_neutral": {
"$sum": [
{
"$cond": {
"if": {
"$eq": ["$macd_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$cmo_9_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_10_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_20_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_50_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$ema_100_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$rsi_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
{
"$cond": {
"if": {
"$eq": ["$rsi_stoch_indicator.weight", 0]
},
"then": 1, "else": 0
}
},
]
},
},
}
])
# self.sprint(list(indicators))
return list(indicators)
#
# -6 Strong Sell
#
# -3 Sell
#
# 0 Neutral
#
# + 3 Buy
#
# + 6 Strong Buy |
import logging
import os
from typing import Text, Optional, Dict, List, Union
import rasa.shared.data
import rasa.shared.utils.io
from rasa.shared.core.domain import Domain
from rasa.shared.core.training_data.story_reader.markdown_story_reader import (
MarkdownStoryReader,
)
from rasa.shared.core.training_data.story_reader.story_reader import StoryReader
from rasa.shared.core.training_data.story_reader.yaml_story_reader import (
YAMLStoryReader,
)
from rasa.shared.core.training_data.structures import StoryStep
from rasa.shared.data import YAML_FILE_EXTENSIONS, MARKDOWN_FILE_EXTENSIONS
logger = logging.getLogger(__name__)
def _get_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if rasa.shared.data.is_likely_markdown_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
elif rasa.shared.data.is_likely_yaml_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
else:
# This is a use case for uploading the story over REST API.
# The source file has a random name.
return _guess_reader(filename, domain, template_variables, use_e2e)
def _guess_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if YAMLStoryReader.is_stories_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
elif MarkdownStoryReader.is_stories_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
raise ValueError(
f"Failed to find a reader class for the story file `{filename}`. "
f"Supported formats are "
f"{", ".join(MARKDOWN_FILE_EXTENSIONS + YAML_FILE_EXTENSIONS)}."
)
async def load_data_from_resource(
resource: Union[Text],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified folder.
Args:
resource: Folder/File with core training data files.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies if the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
if not os.path.exists(resource):
raise ValueError(f"Resource '{resource}' does not exist.")
return await load_data_from_files(
rasa.shared.utils.io.list_files(resource),
domain,
template_variables,
use_e2e,
exclusion_percentage,
)
async def load_data_from_files(
story_files: List[Text],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified files.
Args:
story_files: List of files with training data in it.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies whether the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
story_steps = []
for story_file in story_files:
reader = _get_reader(story_file, domain, template_variables, use_e2e)
steps = reader.read_from_file(story_file)
story_steps.extend(steps)
if exclusion_percentage and exclusion_percentage != 100:
import random
idx = int(round(exclusion_percentage / 100.0 * len(story_steps)))
random.shuffle(story_steps)
story_steps = story_steps[:-idx]
return story_steps
| import logging
import os
from typing import Text, Optional, Dict, List, Union
import rasa.shared.data
import rasa.shared.utils.io
from rasa.shared.core.domain import Domain
from rasa.shared.core.training_data.story_reader.markdown_story_reader import (
MarkdownStoryReader,
)
from rasa.shared.core.training_data.story_reader.story_reader import StoryReader
from rasa.shared.core.training_data.story_reader.yaml_story_reader import (
YAMLStoryReader,
)
from rasa.shared.core.training_data.structures import StoryStep
from rasa.shared.data import YAML_FILE_EXTENSIONS, MARKDOWN_FILE_EXTENSIONS
logger = logging.getLogger(__name__)
def _get_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if rasa.shared.data.is_likely_markdown_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
elif rasa.shared.data.is_likely_yaml_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
else:
# This is a use case for uploading the story over REST API.
# The source file has a random name.
return _guess_reader(filename, domain, template_variables, use_e2e)
def _guess_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if YAMLStoryReader.is_stories_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
elif MarkdownStoryReader.is_stories_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
raise ValueError(
f"Failed to find a reader class for the story file `{filename}`. "
f"Supported formats are "
f"{', '.join(MARKDOWN_FILE_EXTENSIONS + YAML_FILE_EXTENSIONS)}."
)
async def load_data_from_resource(
resource: Union[Text],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified folder.
Args:
resource: Folder/File with core training data files.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies if the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
if not os.path.exists(resource):
raise ValueError(f"Resource '{resource}' does not exist.")
return await load_data_from_files(
rasa.shared.utils.io.list_files(resource),
domain,
template_variables,
use_e2e,
exclusion_percentage,
)
async def load_data_from_files(
story_files: List[Text],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified files.
Args:
story_files: List of files with training data in it.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies whether the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
story_steps = []
for story_file in story_files:
reader = _get_reader(story_file, domain, template_variables, use_e2e)
steps = reader.read_from_file(story_file)
story_steps.extend(steps)
if exclusion_percentage and exclusion_percentage != 100:
import random
idx = int(round(exclusion_percentage / 100.0 * len(story_steps)))
random.shuffle(story_steps)
story_steps = story_steps[:-idx]
return story_steps
|
#!/usr/bin/env python3
from contextlib import contextmanager
from os import path as osp
import joblib
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
DEMO_DIR = osp.abspath(osp.dirname(__file__))
DATA_DIR = osp.join(DEMO_DIR, "data")
MODELS_DIR = osp.join(DEMO_DIR, "models")
@contextmanager
def load_data(train=True):
df = pd.read_csv(osp.join(DATA_DIR, f'iris_{'train' if train else 'test'}.csv'), header=None)
df.columns = ["sepal length", "sepal width", "petal length", "petal width", "label"]
X = df.drop(["label"], axis=1)
y = pd.factorize(df["label"], sort=True)[0]
yield X, y
def main():
with load_data(train=True) as (X, y):
model_a = SVC(gamma="scale")
model_a.fit(X, y)
model_b = AdaBoostClassifier()
model_b.fit(X, y)
print("train")
print(f"├─ model A score: {model_a.score(X, y):.3f}")
print(f"└─ model B score: {model_b.score(X, y):.3f}")
with load_data(train=False) as (X, y):
print("\ntest (debugging only. you wouldn't see these irl)")
print(f"├─ model A score: {model_a.score(X, y):.3f}")
print(f"└─ model B score: {model_b.score(X, y):.3f}")
joblib.dump(model_a, osp.join(MODELS_DIR, "model_a.joblib"))
joblib.dump(model_b, osp.join(MODELS_DIR, "model_b.joblib"))
if __name__ == "__main__":
main()
| #!/usr/bin/env python3
from contextlib import contextmanager
from os import path as osp
import joblib
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
DEMO_DIR = osp.abspath(osp.dirname(__file__))
DATA_DIR = osp.join(DEMO_DIR, "data")
MODELS_DIR = osp.join(DEMO_DIR, "models")
@contextmanager
def load_data(train=True):
df = pd.read_csv(osp.join(DATA_DIR, f'iris_{"train" if train else "test"}.csv'), header=None)
df.columns = ["sepal length", "sepal width", "petal length", "petal width", "label"]
X = df.drop(["label"], axis=1)
y = pd.factorize(df["label"], sort=True)[0]
yield X, y
def main():
with load_data(train=True) as (X, y):
model_a = SVC(gamma="scale")
model_a.fit(X, y)
model_b = AdaBoostClassifier()
model_b.fit(X, y)
print("train")
print(f"├─ model A score: {model_a.score(X, y):.3f}")
print(f"└─ model B score: {model_b.score(X, y):.3f}")
with load_data(train=False) as (X, y):
print("\ntest (debugging only. you wouldn't see these irl)")
print(f"├─ model A score: {model_a.score(X, y):.3f}")
print(f"└─ model B score: {model_b.score(X, y):.3f}")
joblib.dump(model_a, osp.join(MODELS_DIR, "model_a.joblib"))
joblib.dump(model_b, osp.join(MODELS_DIR, "model_b.joblib"))
if __name__ == "__main__":
main()
|
import pandas as pd
estados = ['acre|ac', 'alagoas|al', 'amapá|ap', 'amazonas|am', 'bahia|ba', 'ceará|ce', 'espírito santo|es', 'goiás|go', 'maranhão|ma', 'mato grosso|mt', 'mato grosso do sul|ms', 'goiás|go',
'maranhão|ma', 'minas gerais|mg', 'pará|pa', 'paraíba|pb', 'paraná|pr', 'pernambuco|pe', 'piauí|pi', 'rio de janeiro|rj', 'rio grande do norte|rn', 'rio grande do sul|rs',
'rondônia|ro', 'roraima|rr', 'santa catarina|sc', 'são paulo|sp', 'sergipe|se', 'tocantins|to', 'distrito federal|df']
def contagem_palavras_especificas(arquivo, palavras):
"""Conta como mais de um caso os termos tenham sido mencionados no mesmo tweet. Além disso, Mato Grosso conta os dados de MS"""
dados = {}
df = pd.read_csv(arquivo)
df = df[['date', 'tweet']]
df['count'] = 1
df['tweet'] = df['tweet'].str.lower()
for palavra in palavras:
termo = df.loc[df['tweet'].str.contains(fr"\b({palavra})\b")].sum()
termo['estado'] = palavra
dados[f'{termo['estado']}'] = termo['count']
for i in sorted(dados, key= dados.get, reverse=True):
print(i, dados[i])
#contagem.to_csv(novo_doc)
print('Tarcisio')
contagem_palavras_especificas('tarcisio.csv', estados)
print('\n')
print('onyx')
contagem_palavras_especificas('onyx.csv', estados)
print('\n')
print('marinho')
contagem_palavras_especificas('marinho.csv', estados)
print('\n')
print('TerezaCrisMS')
contagem_palavras_especificas('tereza.csv', estados)
print('\n')
print('andersongtorres')
contagem_palavras_especificas('torres.csv', estados)
print('\n')
print('João Roma')
contagem_palavras_especificas('joao_roma.csv', estados)
print('\n')
print('fabiofaria')
contagem_palavras_especificas('fabiofaria.csv', estados)
| import pandas as pd
estados = ['acre|ac', 'alagoas|al', 'amapá|ap', 'amazonas|am', 'bahia|ba', 'ceará|ce', 'espírito santo|es', 'goiás|go', 'maranhão|ma', 'mato grosso|mt', 'mato grosso do sul|ms', 'goiás|go',
'maranhão|ma', 'minas gerais|mg', 'pará|pa', 'paraíba|pb', 'paraná|pr', 'pernambuco|pe', 'piauí|pi', 'rio de janeiro|rj', 'rio grande do norte|rn', 'rio grande do sul|rs',
'rondônia|ro', 'roraima|rr', 'santa catarina|sc', 'são paulo|sp', 'sergipe|se', 'tocantins|to', 'distrito federal|df']
def contagem_palavras_especificas(arquivo, palavras):
"""Conta como mais de um caso os termos tenham sido mencionados no mesmo tweet. Além disso, Mato Grosso conta os dados de MS"""
dados = {}
df = pd.read_csv(arquivo)
df = df[['date', 'tweet']]
df['count'] = 1
df['tweet'] = df['tweet'].str.lower()
for palavra in palavras:
termo = df.loc[df['tweet'].str.contains(fr"\b({palavra})\b")].sum()
termo['estado'] = palavra
dados[f'{termo["estado"]}'] = termo['count']
for i in sorted(dados, key= dados.get, reverse=True):
print(i, dados[i])
#contagem.to_csv(novo_doc)
print('Tarcisio')
contagem_palavras_especificas('tarcisio.csv', estados)
print('\n')
print('onyx')
contagem_palavras_especificas('onyx.csv', estados)
print('\n')
print('marinho')
contagem_palavras_especificas('marinho.csv', estados)
print('\n')
print('TerezaCrisMS')
contagem_palavras_especificas('tereza.csv', estados)
print('\n')
print('andersongtorres')
contagem_palavras_especificas('torres.csv', estados)
print('\n')
print('João Roma')
contagem_palavras_especificas('joao_roma.csv', estados)
print('\n')
print('fabiofaria')
contagem_palavras_especificas('fabiofaria.csv', estados)
|
from onelang_core import *
import OneLang.Parsers.Common.Reader as read
import OneLang.Parsers.Common.ExpressionParser as exprPars
import OneLang.Parsers.Common.NodeManager as nodeMan
import OneLang.Parsers.Common.IParser as iPars
import OneLang.One.Ast.AstTypes as astTypes
import OneLang.One.Ast.Expressions as exprs
import OneLang.One.Ast.Statements as stats
import OneLang.One.Ast.Types as types
import OneLang.One.Ast.Interfaces as ints
import re
class TypeAndInit:
def __init__(self, type, init):
self.type = type
self.init = init
class MethodSignature:
def __init__(self, params, fields, body, returns, super_call_args):
self.params = params
self.fields = fields
self.body = body
self.returns = returns
self.super_call_args = super_call_args
class TypeScriptParser2:
def __init__(self, source, path = None):
self.context = []
self.reader = None
self.expression_parser = None
self.node_manager = None
self.export_scope = None
self.missing_return_type_is_void = False
self.allow_dollar_ids = False
self.path = path
self.reader = read.Reader(source)
self.reader.hooks = self
self.node_manager = nodeMan.NodeManager(self.reader)
self.expression_parser = self.create_expression_parser(self.reader, self.node_manager)
self.export_scope = types.ExportScopeRef(self.path.pkg.name, re.sub("\\.ts$", "", self.path.path) if self.path.path != None else None) if self.path != None else None
def create_expression_parser(self, reader, node_manager = None):
expression_parser = exprPars.ExpressionParser(reader, self, node_manager)
expression_parser.string_literal_type = astTypes.UnresolvedType("TsString", [])
expression_parser.numeric_literal_type = astTypes.UnresolvedType("TsNumber", [])
return expression_parser
def error_callback(self, error):
raise Error(f'''[TypeScriptParser] {error.message} at {error.cursor.line}:{error.cursor.column} (context: {'/'.join(self.context)})\n{self.reader.line_preview(error.cursor)}''')
def infix_prehook(self, left):
if isinstance(left, exprs.PropertyAccessExpression) and self.reader.peek_regex("<[A-Za-z0-9_<>]*?>\\(") != None:
type_args = self.parse_type_args()
self.reader.expect_token("(")
args = self.expression_parser.parse_call_arguments()
return exprs.UnresolvedCallExpression(left, type_args, args)
elif self.reader.read_token("instanceof"):
type = self.parse_type()
return exprs.InstanceOfExpression(left, type)
elif isinstance(left, exprs.Identifier) and self.reader.read_token("=>"):
block = self.parse_lambda_block()
return types.Lambda([types.MethodParameter(left.text, None, None, None)], block)
return None
def parse_lambda_params(self):
if not self.reader.read_token("("):
return None
params = []
if not self.reader.read_token(")"):
while True:
param_name = self.reader.expect_identifier()
type = self.parse_type() if self.reader.read_token(":") else None
params.append(types.MethodParameter(param_name, type, None, None))
if not (self.reader.read_token(",")):
break
self.reader.expect_token(")")
return params
def parse_type(self):
if self.reader.read_token("{"):
self.reader.expect_token("[")
self.reader.read_identifier()
self.reader.expect_token(":")
self.reader.expect_token("string")
self.reader.expect_token("]")
self.reader.expect_token(":")
map_value_type = self.parse_type()
self.reader.read_token(";")
self.reader.expect_token("}")
return astTypes.UnresolvedType("TsMap", [map_value_type])
if self.reader.peek_token("("):
params = self.parse_lambda_params()
self.reader.expect_token("=>")
return_type = self.parse_type()
return astTypes.LambdaType(params, return_type)
type_name = self.reader.expect_identifier()
start_pos = self.reader.prev_token_offset
if type_name == "string":
type = astTypes.UnresolvedType("TsString", [])
elif type_name == "boolean":
type = astTypes.UnresolvedType("TsBoolean", [])
elif type_name == "number":
type = astTypes.UnresolvedType("TsNumber", [])
elif type_name == "any":
type = astTypes.AnyType.instance
elif type_name == "void":
type = astTypes.VoidType.instance
else:
type_arguments = self.parse_type_args()
type = astTypes.UnresolvedType(type_name, type_arguments)
self.node_manager.add_node(type, start_pos)
while self.reader.read_token("[]"):
type = astTypes.UnresolvedType("TsArray", [type])
self.node_manager.add_node(type, start_pos)
return type
def parse_expression(self):
return self.expression_parser.parse()
def unary_prehook(self):
if self.reader.read_token("null"):
return exprs.NullLiteral()
elif self.reader.read_token("true"):
return exprs.BooleanLiteral(True)
elif self.reader.read_token("false"):
return exprs.BooleanLiteral(False)
elif self.reader.read_token("`"):
parts = []
lit_part = ""
while True:
if self.reader.read_exactly("`"):
if lit_part != "":
parts.append(exprs.TemplateStringPart.literal(lit_part))
lit_part = ""
break
elif self.reader.read_exactly("${"):
if lit_part != "":
parts.append(exprs.TemplateStringPart.literal(lit_part))
lit_part = ""
expr = self.parse_expression()
parts.append(exprs.TemplateStringPart.expression(expr))
self.reader.expect_token("}")
elif self.allow_dollar_ids and self.reader.read_exactly("$"):
if lit_part != "":
parts.append(exprs.TemplateStringPart.literal(lit_part))
lit_part = ""
id = self.reader.read_identifier()
parts.append(exprs.TemplateStringPart.expression(exprs.Identifier(id)))
elif self.reader.read_exactly("\\"):
chr = self.reader.read_char()
if chr == "n":
lit_part += "\n"
elif chr == "r":
lit_part += "\r"
elif chr == "t":
lit_part += "\t"
elif chr == "`":
lit_part += "`"
elif chr == "$":
lit_part += "$"
elif chr == "\\":
lit_part += "\\"
else:
self.reader.fail("invalid escape", self.reader.offset - 1)
else:
chr = self.reader.read_char()
chr_code = ord(chr[0])
if not (32 <= chr_code and chr_code <= 126) or chr == "`" or chr == "\\":
self.reader.fail(f'''not allowed character (code={chr_code})''', self.reader.offset - 1)
lit_part += chr
return exprs.TemplateString(parts)
elif self.reader.read_token("new"):
type = self.parse_type()
if isinstance(type, astTypes.UnresolvedType):
self.reader.expect_token("(")
args = self.expression_parser.parse_call_arguments()
return exprs.UnresolvedNewExpression(type, args)
else:
raise Error(f'''[TypeScriptParser2] Expected UnresolvedType here!''')
elif self.reader.read_token("<"):
new_type = self.parse_type()
self.reader.expect_token(">")
expression = self.parse_expression()
return exprs.CastExpression(new_type, expression)
elif self.reader.read_token("/"):
pattern = ""
while True:
chr = self.reader.read_char()
if chr == "\\":
chr2 = self.reader.read_char()
pattern += "/" if chr2 == "/" else "\\" + chr2
elif chr == "/":
break
else:
pattern += chr
modifiers = self.reader.read_modifiers(["g", "i"])
return exprs.RegexLiteral(pattern, "i" in modifiers, "g" in modifiers)
elif self.reader.read_token("typeof"):
expr = self.expression_parser.parse(self.expression_parser.prefix_precedence)
self.reader.expect_token("===")
check = self.reader.expect_string()
ts_type = None
if check == "string":
ts_type = "TsString"
elif check == "boolean":
ts_type = "TsBoolean"
elif check == "object":
ts_type = "Object"
elif check == "function":
# TODO: ???
ts_type = "Function"
elif check == "undefined":
# TODO: ???
ts_type = "Object"
else:
self.reader.fail("unexpected typeof comparison")
return exprs.InstanceOfExpression(expr, astTypes.UnresolvedType(ts_type, []))
elif self.reader.peek_regex("\\([A-Za-z0-9_]+\\s*[:,]|\\(\\)") != None:
params = self.parse_lambda_params()
self.reader.expect_token("=>")
block = self.parse_lambda_block()
return types.Lambda(params, block)
elif self.reader.read_token("await"):
expression = self.parse_expression()
return exprs.AwaitExpression(expression)
map_literal = self.expression_parser.parse_map_literal()
if map_literal != None:
return map_literal
array_literal = self.expression_parser.parse_array_literal()
if array_literal != None:
return array_literal
return None
def parse_lambda_block(self):
block = self.parse_block()
if block != None:
return block
return_expr = self.parse_expression()
if isinstance(return_expr, exprs.ParenthesizedExpression):
return_expr = return_expr.expression
return stats.Block([stats.ReturnStatement(return_expr)])
def parse_type_and_init(self):
type = self.parse_type() if self.reader.read_token(":") else None
init = self.parse_expression() if self.reader.read_token("=") else None
if type == None and init == None:
self.reader.fail(f'''expected type declaration or initializer''')
return TypeAndInit(type, init)
def expect_block_or_statement(self):
block = self.parse_block()
if block != None:
return block
stmts = []
stmt = self.expect_statement()
if stmt != None:
stmts.append(stmt)
return stats.Block(stmts)
def expect_statement(self):
statement = None
leading_trivia = self.reader.read_leading_trivia()
start_pos = self.reader.offset
requires_closing = True
var_decl_matches = self.reader.read_regex("(const|let|var)\\b")
if var_decl_matches != None:
name = self.reader.expect_identifier("expected variable name")
type_and_init = self.parse_type_and_init()
statement = stats.VariableDeclaration(name, type_and_init.type, type_and_init.init)
elif self.reader.read_token("delete"):
statement = stats.UnsetStatement(self.parse_expression())
elif self.reader.read_token("if"):
requires_closing = False
self.reader.expect_token("(")
condition = self.parse_expression()
self.reader.expect_token(")")
then = self.expect_block_or_statement()
else_ = self.expect_block_or_statement() if self.reader.read_token("else") else None
statement = stats.IfStatement(condition, then, else_)
elif self.reader.read_token("while"):
requires_closing = False
self.reader.expect_token("(")
condition = self.parse_expression()
self.reader.expect_token(")")
body = self.expect_block_or_statement()
statement = stats.WhileStatement(condition, body)
elif self.reader.read_token("do"):
requires_closing = False
body = self.expect_block_or_statement()
self.reader.expect_token("while")
self.reader.expect_token("(")
condition = self.parse_expression()
self.reader.expect_token(")")
statement = stats.DoStatement(condition, body)
elif self.reader.read_token("for"):
requires_closing = False
self.reader.expect_token("(")
var_decl_mod = self.reader.read_any_of(["const", "let", "var"])
item_var_name = None if var_decl_mod == None else self.reader.expect_identifier()
if item_var_name != None and self.reader.read_token("of"):
items = self.parse_expression()
self.reader.expect_token(")")
body = self.expect_block_or_statement()
statement = stats.ForeachStatement(stats.ForeachVariable(item_var_name), items, body)
else:
for_var = None
if item_var_name != None:
type_and_init = self.parse_type_and_init()
for_var = stats.ForVariable(item_var_name, type_and_init.type, type_and_init.init)
self.reader.expect_token(";")
condition = self.parse_expression()
self.reader.expect_token(";")
incrementor = self.parse_expression()
self.reader.expect_token(")")
body = self.expect_block_or_statement()
statement = stats.ForStatement(for_var, condition, incrementor, body)
elif self.reader.read_token("try"):
block = self.expect_block("try body is missing")
catch_var = None
catch_body = None
if self.reader.read_token("catch"):
self.reader.expect_token("(")
catch_var = stats.CatchVariable(self.reader.expect_identifier(), None)
self.reader.expect_token(")")
catch_body = self.expect_block("catch body is missing")
finally_body = self.expect_block() if self.reader.read_token("finally") else None
return stats.TryStatement(block, catch_var, catch_body, finally_body)
elif self.reader.read_token("return"):
expr = None if self.reader.peek_token(";") else self.parse_expression()
statement = stats.ReturnStatement(expr)
elif self.reader.read_token("throw"):
expr = self.parse_expression()
statement = stats.ThrowStatement(expr)
elif self.reader.read_token("break"):
statement = stats.BreakStatement()
elif self.reader.read_token("continue"):
statement = stats.ContinueStatement()
elif self.reader.read_token("debugger;"):
return None
else:
expr = self.parse_expression()
statement = stats.ExpressionStatement(expr)
is_binary_set = isinstance(expr, exprs.BinaryExpression) and expr.operator in ["=", "+=", "-="]
is_unary_set = isinstance(expr, exprs.UnaryExpression) and expr.operator in ["++", "--"]
if not (isinstance(expr, exprs.UnresolvedCallExpression) or is_binary_set or is_unary_set or isinstance(expr, exprs.AwaitExpression)):
self.reader.fail("this expression is not allowed as statement")
if statement == None:
self.reader.fail("unknown statement")
statement.leading_trivia = leading_trivia
self.node_manager.add_node(statement, start_pos)
statement_last_line = self.reader.ws_line_counter
if not self.reader.read_token(";") and requires_closing and self.reader.ws_line_counter == statement_last_line:
self.reader.fail("statement is not closed", self.reader.ws_offset)
return statement
def parse_block(self):
if not self.reader.read_token("{"):
return None
start_pos = self.reader.prev_token_offset
statements = []
if not self.reader.read_token("}"):
while True:
statement = self.expect_statement()
if statement != None:
statements.append(statement)
if not (not self.reader.read_token("}")):
break
block = stats.Block(statements)
self.node_manager.add_node(block, start_pos)
return block
def expect_block(self, error_msg = None):
block = self.parse_block()
if block == None:
self.reader.fail(error_msg or "expected block here")
return block
def parse_type_args(self):
type_arguments = []
if self.reader.read_token("<"):
while True:
generics = self.parse_type()
type_arguments.append(generics)
if not (self.reader.read_token(",")):
break
self.reader.expect_token(">")
return type_arguments
def parse_generics_args(self):
type_arguments = []
if self.reader.read_token("<"):
while True:
generics = self.reader.expect_identifier()
type_arguments.append(generics)
if not (self.reader.read_token(",")):
break
self.reader.expect_token(">")
return type_arguments
def parse_expr_stmt_from_string(self, expression):
expr = self.create_expression_parser(read.Reader(expression)).parse()
return stats.ExpressionStatement(expr)
def parse_method_signature(self, is_constructor, declaration_only):
params = []
fields = []
if not self.reader.read_token(")"):
while True:
leading_trivia = self.reader.read_leading_trivia()
param_start = self.reader.offset
is_public = self.reader.read_token("public")
if is_public and not is_constructor:
self.reader.fail("public modifier is only allowed in constructor definition")
param_name = self.reader.expect_identifier()
self.context.append(f'''arg:{param_name}''')
type_and_init = self.parse_type_and_init()
param = types.MethodParameter(param_name, type_and_init.type, type_and_init.init, leading_trivia)
params.append(param)
# init should be used as only the constructor's method parameter, but not again as a field initializer too
# (otherwise it would called twice if cloned or cause AST error is just referenced from two separate places)
if is_public:
field = types.Field(param_name, type_and_init.type, None, types.VISIBILITY.PUBLIC, False, param, param.leading_trivia)
fields.append(field)
param.field_decl = field
self.node_manager.add_node(param, param_start)
self.context.pop()
if not (self.reader.read_token(",")):
break
self.reader.expect_token(")")
returns = None
if not is_constructor:
# in case of constructor, "returns" won't be used
returns = self.parse_type() if self.reader.read_token(":") else astTypes.VoidType.instance if self.missing_return_type_is_void else None
body = None
super_call_args = None
if declaration_only:
self.reader.expect_token(";")
else:
body = self.expect_block("method body is missing")
first_stmt = body.statements[0] if len(body.statements) > 0 else None
if isinstance(first_stmt, stats.ExpressionStatement) and isinstance(first_stmt.expression, exprs.UnresolvedCallExpression) and isinstance(first_stmt.expression.func, exprs.Identifier) and first_stmt.expression.func.text == "super":
super_call_args = first_stmt.expression.args
body.statements.pop(0)
return MethodSignature(params, fields, body, returns, super_call_args)
def parse_identifier_or_string(self):
return self.reader.read_string() or self.reader.expect_identifier()
def parse_interface(self, leading_trivia, is_exported):
if not self.reader.read_token("interface"):
return None
intf_start = self.reader.prev_token_offset
intf_name = self.reader.expect_identifier("expected identifier after 'interface' keyword")
self.context.append(f'''I:{intf_name}''')
intf_type_args = self.parse_generics_args()
base_interfaces = []
if self.reader.read_token("extends"):
while True:
base_interfaces.append(self.parse_type())
if not (self.reader.read_token(",")):
break
methods = []
fields = []
self.reader.expect_token("{")
while not self.reader.read_token("}"):
member_leading_trivia = self.reader.read_leading_trivia()
member_start = self.reader.offset
member_name = self.parse_identifier_or_string()
if self.reader.read_token(":"):
self.context.append(f'''F:{member_name}''')
field_type = self.parse_type()
self.reader.expect_token(";")
field = types.Field(member_name, field_type, None, types.VISIBILITY.PUBLIC, False, None, member_leading_trivia)
fields.append(field)
self.node_manager.add_node(field, member_start)
self.context.pop()
else:
self.context.append(f'''M:{member_name}''')
method_type_args = self.parse_generics_args()
self.reader.expect_token("(")
# method
sig = self.parse_method_signature(False, True)
method = types.Method(member_name, method_type_args, sig.params, sig.body, types.VISIBILITY.PUBLIC, False, sig.returns, False, member_leading_trivia)
methods.append(method)
self.node_manager.add_node(method, member_start)
self.context.pop()
intf = types.Interface(intf_name, intf_type_args, base_interfaces, fields, methods, is_exported, leading_trivia)
self.node_manager.add_node(intf, intf_start)
self.context.pop()
return intf
def parse_specified_type(self):
type_name = self.reader.read_identifier()
type_args = self.parse_type_args()
return astTypes.UnresolvedType(type_name, type_args)
def parse_class(self, leading_trivia, is_exported, declaration_only):
cls_modifiers = self.reader.read_modifiers(["abstract"])
if not self.reader.read_token("class"):
return None
cls_start = self.reader.prev_token_offset
cls_name = self.reader.expect_identifier("expected identifier after 'class' keyword")
self.context.append(f'''C:{cls_name}''')
type_args = self.parse_generics_args()
base_class = self.parse_specified_type() if self.reader.read_token("extends") else None
base_interfaces = []
if self.reader.read_token("implements"):
while True:
base_interfaces.append(self.parse_specified_type())
if not (self.reader.read_token(",")):
break
constructor = None
fields = []
methods = []
properties = []
self.reader.expect_token("{")
while not self.reader.read_token("}"):
member_leading_trivia = self.reader.read_leading_trivia()
member_start = self.reader.offset
modifiers = self.reader.read_modifiers(["static", "public", "protected", "private", "readonly", "async", "abstract"])
is_static = "static" in modifiers
is_async = "async" in modifiers
is_abstract = "abstract" in modifiers
visibility = types.VISIBILITY.PRIVATE if "private" in modifiers else types.VISIBILITY.PROTECTED if "protected" in modifiers else types.VISIBILITY.PUBLIC
member_name = self.parse_identifier_or_string()
method_type_args = self.parse_generics_args()
if self.reader.read_token("("):
# method
is_constructor = member_name == "constructor"
sig = self.parse_method_signature(is_constructor, declaration_only or is_abstract)
if is_constructor:
member = constructor = types.Constructor(sig.params, sig.body, sig.super_call_args, member_leading_trivia)
for field in sig.fields:
fields.append(field)
else:
method = types.Method(member_name, method_type_args, sig.params, sig.body, visibility, is_static, sig.returns, is_async, member_leading_trivia)
methods.append(method)
member = method
self.node_manager.add_node(member, member_start)
elif member_name == "get" or member_name == "set":
# property
prop_name = self.reader.expect_identifier()
prop = next(filter(lambda x: x.name == prop_name, properties), None)
prop_type = None
getter = None
setter = None
if member_name == "get":
# get propName(): propType { return ... }
self.context.append(f'''P[G]:{prop_name}''')
self.reader.expect_token("()", "expected '()' after property getter name")
prop_type = self.parse_type() if self.reader.read_token(":") else None
if declaration_only:
if prop_type == None:
self.reader.fail("Type is missing for property in declare class")
self.reader.expect_token(";")
else:
getter = self.expect_block("property getter body is missing")
if prop != None:
prop.getter = getter
elif member_name == "set":
# set propName(value: propType) { ... }
self.context.append(f'''P[S]:{prop_name}''')
self.reader.expect_token("(", "expected '(' after property setter name")
self.reader.expect_identifier()
prop_type = self.parse_type() if self.reader.read_token(":") else None
self.reader.expect_token(")")
if declaration_only:
if prop_type == None:
self.reader.fail("Type is missing for property in declare class")
self.reader.expect_token(";")
else:
setter = self.expect_block("property setter body is missing")
if prop != None:
prop.setter = setter
if prop == None:
prop = types.Property(prop_name, prop_type, getter, setter, visibility, is_static, member_leading_trivia)
properties.append(prop)
self.node_manager.add_node(prop, member_start)
self.context.pop()
else:
self.context.append(f'''F:{member_name}''')
type_and_init = self.parse_type_and_init()
self.reader.expect_token(";")
field = types.Field(member_name, type_and_init.type, type_and_init.init, visibility, is_static, None, member_leading_trivia)
fields.append(field)
self.node_manager.add_node(field, member_start)
self.context.pop()
cls_ = types.Class(cls_name, type_args, base_class, base_interfaces, fields, properties, constructor, methods, is_exported, leading_trivia)
self.node_manager.add_node(cls_, cls_start)
self.context.pop()
return cls_
def parse_enum(self, leading_trivia, is_exported):
if not self.reader.read_token("enum"):
return None
enum_start = self.reader.prev_token_offset
name = self.reader.expect_identifier("expected identifier after 'enum' keyword")
self.context.append(f'''E:{name}''')
members = []
self.reader.expect_token("{")
if not self.reader.read_token("}"):
while True:
if self.reader.peek_token("}"):
break
# eg. "enum { A, B, }" (but multiline)
enum_member = types.EnumMember(self.reader.expect_identifier())
members.append(enum_member)
self.node_manager.add_node(enum_member, self.reader.prev_token_offset)
# TODO: generated code compatibility
self.reader.read_token(f'''= "{enum_member.name}"''')
if not (self.reader.read_token(",")):
break
self.reader.expect_token("}")
enum_obj = types.Enum(name, members, is_exported, leading_trivia)
self.node_manager.add_node(enum_obj, enum_start)
self.context.pop()
return enum_obj
@classmethod
def calculate_relative_path(cls, curr_file, rel_path):
if not rel_path.startswith("."):
raise Error(f'''relPath must start with \'.\', but got \'{rel_path}\'''')
curr = re.split("/", curr_file)
curr.pop()
# filename does not matter
for part in re.split("/", rel_path):
if part == "":
raise Error(f'''relPath should not contain multiple \'/\' next to each other (relPath=\'{rel_path}\')''')
if part == ".":
# "./" == stay in current directory
continue
elif part == "..":
# "../" == parent directory
if len(curr) == 0:
raise Error(f'''relPath goes out of root (curr=\'{curr_file}\', relPath=\'{rel_path}\')''')
curr.pop()
else:
curr.append(part)
return "/".join(curr)
@classmethod
def calculate_import_scope(cls, curr_scope, import_file):
if import_file.startswith("."):
# relative
return types.ExportScopeRef(curr_scope.package_name, TypeScriptParser2.calculate_relative_path(curr_scope.scope_name, import_file))
else:
path = re.split("/", import_file)
pkg_name = path.pop(0)
return types.ExportScopeRef(pkg_name, types.Package.index if len(path) == 0 else "/".join(path))
def read_identifier(self):
raw_id = self.reader.read_identifier()
return re.sub("_+$", "", raw_id)
def parse_import(self, leading_trivia):
if not self.reader.read_token("import"):
return None
import_start = self.reader.prev_token_offset
import_all_alias = None
import_parts = []
if self.reader.read_token("*"):
self.reader.expect_token("as")
import_all_alias = self.reader.expect_identifier()
else:
self.reader.expect_token("{")
while True:
if self.reader.peek_token("}"):
break
imp = self.reader.expect_identifier()
if self.reader.read_token("as"):
self.reader.fail("This is not yet supported")
import_parts.append(types.UnresolvedImport(imp))
self.node_manager.add_node(imp, self.reader.prev_token_offset)
if not (self.reader.read_token(",")):
break
self.reader.expect_token("}")
self.reader.expect_token("from")
module_name = self.reader.expect_string()
self.reader.expect_token(";")
import_scope = TypeScriptParser2.calculate_import_scope(self.export_scope, module_name) if self.export_scope != None else None
imports = []
if len(import_parts) > 0:
imports.append(types.Import(import_scope, False, import_parts, None, leading_trivia))
if import_all_alias != None:
imports.append(types.Import(import_scope, True, None, import_all_alias, leading_trivia))
#this.nodeManager.addNode(imports, importStart);
return imports
def parse_source_file(self):
imports = []
enums = []
intfs = []
classes = []
funcs = []
while True:
leading_trivia = self.reader.read_leading_trivia()
if self.reader.get_eof():
break
imps = self.parse_import(leading_trivia)
if imps != None:
for imp in imps:
imports.append(imp)
continue
modifiers = self.reader.read_modifiers(["export", "declare"])
is_exported = "export" in modifiers
is_declaration = "declare" in modifiers
cls_ = self.parse_class(leading_trivia, is_exported, is_declaration)
if cls_ != None:
classes.append(cls_)
continue
enum_obj = self.parse_enum(leading_trivia, is_exported)
if enum_obj != None:
enums.append(enum_obj)
continue
intf = self.parse_interface(leading_trivia, is_exported)
if intf != None:
intfs.append(intf)
continue
if self.reader.read_token("function"):
func_name = self.read_identifier()
self.reader.expect_token("(")
sig = self.parse_method_signature(False, is_declaration)
funcs.append(types.GlobalFunction(func_name, sig.params, sig.body, sig.returns, is_exported, leading_trivia))
continue
break
self.reader.skip_whitespace()
stmts = []
while True:
leading_trivia = self.reader.read_leading_trivia()
if self.reader.get_eof():
break
stmt = self.expect_statement()
if stmt == None:
continue
stmt.leading_trivia = leading_trivia
stmts.append(stmt)
return types.SourceFile(imports, intfs, classes, enums, funcs, stats.Block(stmts), self.path, self.export_scope)
def parse(self):
return self.parse_source_file()
@classmethod
def parse_file(cls, source, path = None):
return TypeScriptParser2(source, path).parse_source_file() | from onelang_core import *
import OneLang.Parsers.Common.Reader as read
import OneLang.Parsers.Common.ExpressionParser as exprPars
import OneLang.Parsers.Common.NodeManager as nodeMan
import OneLang.Parsers.Common.IParser as iPars
import OneLang.One.Ast.AstTypes as astTypes
import OneLang.One.Ast.Expressions as exprs
import OneLang.One.Ast.Statements as stats
import OneLang.One.Ast.Types as types
import OneLang.One.Ast.Interfaces as ints
import re
class TypeAndInit:
def __init__(self, type, init):
self.type = type
self.init = init
class MethodSignature:
def __init__(self, params, fields, body, returns, super_call_args):
self.params = params
self.fields = fields
self.body = body
self.returns = returns
self.super_call_args = super_call_args
class TypeScriptParser2:
def __init__(self, source, path = None):
self.context = []
self.reader = None
self.expression_parser = None
self.node_manager = None
self.export_scope = None
self.missing_return_type_is_void = False
self.allow_dollar_ids = False
self.path = path
self.reader = read.Reader(source)
self.reader.hooks = self
self.node_manager = nodeMan.NodeManager(self.reader)
self.expression_parser = self.create_expression_parser(self.reader, self.node_manager)
self.export_scope = types.ExportScopeRef(self.path.pkg.name, re.sub("\\.ts$", "", self.path.path) if self.path.path != None else None) if self.path != None else None
def create_expression_parser(self, reader, node_manager = None):
expression_parser = exprPars.ExpressionParser(reader, self, node_manager)
expression_parser.string_literal_type = astTypes.UnresolvedType("TsString", [])
expression_parser.numeric_literal_type = astTypes.UnresolvedType("TsNumber", [])
return expression_parser
def error_callback(self, error):
raise Error(f'''[TypeScriptParser] {error.message} at {error.cursor.line}:{error.cursor.column} (context: {"/".join(self.context)})\n{self.reader.line_preview(error.cursor)}''')
def infix_prehook(self, left):
if isinstance(left, exprs.PropertyAccessExpression) and self.reader.peek_regex("<[A-Za-z0-9_<>]*?>\\(") != None:
type_args = self.parse_type_args()
self.reader.expect_token("(")
args = self.expression_parser.parse_call_arguments()
return exprs.UnresolvedCallExpression(left, type_args, args)
elif self.reader.read_token("instanceof"):
type = self.parse_type()
return exprs.InstanceOfExpression(left, type)
elif isinstance(left, exprs.Identifier) and self.reader.read_token("=>"):
block = self.parse_lambda_block()
return types.Lambda([types.MethodParameter(left.text, None, None, None)], block)
return None
def parse_lambda_params(self):
if not self.reader.read_token("("):
return None
params = []
if not self.reader.read_token(")"):
while True:
param_name = self.reader.expect_identifier()
type = self.parse_type() if self.reader.read_token(":") else None
params.append(types.MethodParameter(param_name, type, None, None))
if not (self.reader.read_token(",")):
break
self.reader.expect_token(")")
return params
def parse_type(self):
if self.reader.read_token("{"):
self.reader.expect_token("[")
self.reader.read_identifier()
self.reader.expect_token(":")
self.reader.expect_token("string")
self.reader.expect_token("]")
self.reader.expect_token(":")
map_value_type = self.parse_type()
self.reader.read_token(";")
self.reader.expect_token("}")
return astTypes.UnresolvedType("TsMap", [map_value_type])
if self.reader.peek_token("("):
params = self.parse_lambda_params()
self.reader.expect_token("=>")
return_type = self.parse_type()
return astTypes.LambdaType(params, return_type)
type_name = self.reader.expect_identifier()
start_pos = self.reader.prev_token_offset
if type_name == "string":
type = astTypes.UnresolvedType("TsString", [])
elif type_name == "boolean":
type = astTypes.UnresolvedType("TsBoolean", [])
elif type_name == "number":
type = astTypes.UnresolvedType("TsNumber", [])
elif type_name == "any":
type = astTypes.AnyType.instance
elif type_name == "void":
type = astTypes.VoidType.instance
else:
type_arguments = self.parse_type_args()
type = astTypes.UnresolvedType(type_name, type_arguments)
self.node_manager.add_node(type, start_pos)
while self.reader.read_token("[]"):
type = astTypes.UnresolvedType("TsArray", [type])
self.node_manager.add_node(type, start_pos)
return type
def parse_expression(self):
return self.expression_parser.parse()
def unary_prehook(self):
if self.reader.read_token("null"):
return exprs.NullLiteral()
elif self.reader.read_token("true"):
return exprs.BooleanLiteral(True)
elif self.reader.read_token("false"):
return exprs.BooleanLiteral(False)
elif self.reader.read_token("`"):
parts = []
lit_part = ""
while True:
if self.reader.read_exactly("`"):
if lit_part != "":
parts.append(exprs.TemplateStringPart.literal(lit_part))
lit_part = ""
break
elif self.reader.read_exactly("${"):
if lit_part != "":
parts.append(exprs.TemplateStringPart.literal(lit_part))
lit_part = ""
expr = self.parse_expression()
parts.append(exprs.TemplateStringPart.expression(expr))
self.reader.expect_token("}")
elif self.allow_dollar_ids and self.reader.read_exactly("$"):
if lit_part != "":
parts.append(exprs.TemplateStringPart.literal(lit_part))
lit_part = ""
id = self.reader.read_identifier()
parts.append(exprs.TemplateStringPart.expression(exprs.Identifier(id)))
elif self.reader.read_exactly("\\"):
chr = self.reader.read_char()
if chr == "n":
lit_part += "\n"
elif chr == "r":
lit_part += "\r"
elif chr == "t":
lit_part += "\t"
elif chr == "`":
lit_part += "`"
elif chr == "$":
lit_part += "$"
elif chr == "\\":
lit_part += "\\"
else:
self.reader.fail("invalid escape", self.reader.offset - 1)
else:
chr = self.reader.read_char()
chr_code = ord(chr[0])
if not (32 <= chr_code and chr_code <= 126) or chr == "`" or chr == "\\":
self.reader.fail(f'''not allowed character (code={chr_code})''', self.reader.offset - 1)
lit_part += chr
return exprs.TemplateString(parts)
elif self.reader.read_token("new"):
type = self.parse_type()
if isinstance(type, astTypes.UnresolvedType):
self.reader.expect_token("(")
args = self.expression_parser.parse_call_arguments()
return exprs.UnresolvedNewExpression(type, args)
else:
raise Error(f'''[TypeScriptParser2] Expected UnresolvedType here!''')
elif self.reader.read_token("<"):
new_type = self.parse_type()
self.reader.expect_token(">")
expression = self.parse_expression()
return exprs.CastExpression(new_type, expression)
elif self.reader.read_token("/"):
pattern = ""
while True:
chr = self.reader.read_char()
if chr == "\\":
chr2 = self.reader.read_char()
pattern += "/" if chr2 == "/" else "\\" + chr2
elif chr == "/":
break
else:
pattern += chr
modifiers = self.reader.read_modifiers(["g", "i"])
return exprs.RegexLiteral(pattern, "i" in modifiers, "g" in modifiers)
elif self.reader.read_token("typeof"):
expr = self.expression_parser.parse(self.expression_parser.prefix_precedence)
self.reader.expect_token("===")
check = self.reader.expect_string()
ts_type = None
if check == "string":
ts_type = "TsString"
elif check == "boolean":
ts_type = "TsBoolean"
elif check == "object":
ts_type = "Object"
elif check == "function":
# TODO: ???
ts_type = "Function"
elif check == "undefined":
# TODO: ???
ts_type = "Object"
else:
self.reader.fail("unexpected typeof comparison")
return exprs.InstanceOfExpression(expr, astTypes.UnresolvedType(ts_type, []))
elif self.reader.peek_regex("\\([A-Za-z0-9_]+\\s*[:,]|\\(\\)") != None:
params = self.parse_lambda_params()
self.reader.expect_token("=>")
block = self.parse_lambda_block()
return types.Lambda(params, block)
elif self.reader.read_token("await"):
expression = self.parse_expression()
return exprs.AwaitExpression(expression)
map_literal = self.expression_parser.parse_map_literal()
if map_literal != None:
return map_literal
array_literal = self.expression_parser.parse_array_literal()
if array_literal != None:
return array_literal
return None
def parse_lambda_block(self):
block = self.parse_block()
if block != None:
return block
return_expr = self.parse_expression()
if isinstance(return_expr, exprs.ParenthesizedExpression):
return_expr = return_expr.expression
return stats.Block([stats.ReturnStatement(return_expr)])
def parse_type_and_init(self):
type = self.parse_type() if self.reader.read_token(":") else None
init = self.parse_expression() if self.reader.read_token("=") else None
if type == None and init == None:
self.reader.fail(f'''expected type declaration or initializer''')
return TypeAndInit(type, init)
def expect_block_or_statement(self):
block = self.parse_block()
if block != None:
return block
stmts = []
stmt = self.expect_statement()
if stmt != None:
stmts.append(stmt)
return stats.Block(stmts)
def expect_statement(self):
statement = None
leading_trivia = self.reader.read_leading_trivia()
start_pos = self.reader.offset
requires_closing = True
var_decl_matches = self.reader.read_regex("(const|let|var)\\b")
if var_decl_matches != None:
name = self.reader.expect_identifier("expected variable name")
type_and_init = self.parse_type_and_init()
statement = stats.VariableDeclaration(name, type_and_init.type, type_and_init.init)
elif self.reader.read_token("delete"):
statement = stats.UnsetStatement(self.parse_expression())
elif self.reader.read_token("if"):
requires_closing = False
self.reader.expect_token("(")
condition = self.parse_expression()
self.reader.expect_token(")")
then = self.expect_block_or_statement()
else_ = self.expect_block_or_statement() if self.reader.read_token("else") else None
statement = stats.IfStatement(condition, then, else_)
elif self.reader.read_token("while"):
requires_closing = False
self.reader.expect_token("(")
condition = self.parse_expression()
self.reader.expect_token(")")
body = self.expect_block_or_statement()
statement = stats.WhileStatement(condition, body)
elif self.reader.read_token("do"):
requires_closing = False
body = self.expect_block_or_statement()
self.reader.expect_token("while")
self.reader.expect_token("(")
condition = self.parse_expression()
self.reader.expect_token(")")
statement = stats.DoStatement(condition, body)
elif self.reader.read_token("for"):
requires_closing = False
self.reader.expect_token("(")
var_decl_mod = self.reader.read_any_of(["const", "let", "var"])
item_var_name = None if var_decl_mod == None else self.reader.expect_identifier()
if item_var_name != None and self.reader.read_token("of"):
items = self.parse_expression()
self.reader.expect_token(")")
body = self.expect_block_or_statement()
statement = stats.ForeachStatement(stats.ForeachVariable(item_var_name), items, body)
else:
for_var = None
if item_var_name != None:
type_and_init = self.parse_type_and_init()
for_var = stats.ForVariable(item_var_name, type_and_init.type, type_and_init.init)
self.reader.expect_token(";")
condition = self.parse_expression()
self.reader.expect_token(";")
incrementor = self.parse_expression()
self.reader.expect_token(")")
body = self.expect_block_or_statement()
statement = stats.ForStatement(for_var, condition, incrementor, body)
elif self.reader.read_token("try"):
block = self.expect_block("try body is missing")
catch_var = None
catch_body = None
if self.reader.read_token("catch"):
self.reader.expect_token("(")
catch_var = stats.CatchVariable(self.reader.expect_identifier(), None)
self.reader.expect_token(")")
catch_body = self.expect_block("catch body is missing")
finally_body = self.expect_block() if self.reader.read_token("finally") else None
return stats.TryStatement(block, catch_var, catch_body, finally_body)
elif self.reader.read_token("return"):
expr = None if self.reader.peek_token(";") else self.parse_expression()
statement = stats.ReturnStatement(expr)
elif self.reader.read_token("throw"):
expr = self.parse_expression()
statement = stats.ThrowStatement(expr)
elif self.reader.read_token("break"):
statement = stats.BreakStatement()
elif self.reader.read_token("continue"):
statement = stats.ContinueStatement()
elif self.reader.read_token("debugger;"):
return None
else:
expr = self.parse_expression()
statement = stats.ExpressionStatement(expr)
is_binary_set = isinstance(expr, exprs.BinaryExpression) and expr.operator in ["=", "+=", "-="]
is_unary_set = isinstance(expr, exprs.UnaryExpression) and expr.operator in ["++", "--"]
if not (isinstance(expr, exprs.UnresolvedCallExpression) or is_binary_set or is_unary_set or isinstance(expr, exprs.AwaitExpression)):
self.reader.fail("this expression is not allowed as statement")
if statement == None:
self.reader.fail("unknown statement")
statement.leading_trivia = leading_trivia
self.node_manager.add_node(statement, start_pos)
statement_last_line = self.reader.ws_line_counter
if not self.reader.read_token(";") and requires_closing and self.reader.ws_line_counter == statement_last_line:
self.reader.fail("statement is not closed", self.reader.ws_offset)
return statement
def parse_block(self):
if not self.reader.read_token("{"):
return None
start_pos = self.reader.prev_token_offset
statements = []
if not self.reader.read_token("}"):
while True:
statement = self.expect_statement()
if statement != None:
statements.append(statement)
if not (not self.reader.read_token("}")):
break
block = stats.Block(statements)
self.node_manager.add_node(block, start_pos)
return block
def expect_block(self, error_msg = None):
block = self.parse_block()
if block == None:
self.reader.fail(error_msg or "expected block here")
return block
def parse_type_args(self):
type_arguments = []
if self.reader.read_token("<"):
while True:
generics = self.parse_type()
type_arguments.append(generics)
if not (self.reader.read_token(",")):
break
self.reader.expect_token(">")
return type_arguments
def parse_generics_args(self):
type_arguments = []
if self.reader.read_token("<"):
while True:
generics = self.reader.expect_identifier()
type_arguments.append(generics)
if not (self.reader.read_token(",")):
break
self.reader.expect_token(">")
return type_arguments
def parse_expr_stmt_from_string(self, expression):
expr = self.create_expression_parser(read.Reader(expression)).parse()
return stats.ExpressionStatement(expr)
def parse_method_signature(self, is_constructor, declaration_only):
params = []
fields = []
if not self.reader.read_token(")"):
while True:
leading_trivia = self.reader.read_leading_trivia()
param_start = self.reader.offset
is_public = self.reader.read_token("public")
if is_public and not is_constructor:
self.reader.fail("public modifier is only allowed in constructor definition")
param_name = self.reader.expect_identifier()
self.context.append(f'''arg:{param_name}''')
type_and_init = self.parse_type_and_init()
param = types.MethodParameter(param_name, type_and_init.type, type_and_init.init, leading_trivia)
params.append(param)
# init should be used as only the constructor's method parameter, but not again as a field initializer too
# (otherwise it would called twice if cloned or cause AST error is just referenced from two separate places)
if is_public:
field = types.Field(param_name, type_and_init.type, None, types.VISIBILITY.PUBLIC, False, param, param.leading_trivia)
fields.append(field)
param.field_decl = field
self.node_manager.add_node(param, param_start)
self.context.pop()
if not (self.reader.read_token(",")):
break
self.reader.expect_token(")")
returns = None
if not is_constructor:
# in case of constructor, "returns" won't be used
returns = self.parse_type() if self.reader.read_token(":") else astTypes.VoidType.instance if self.missing_return_type_is_void else None
body = None
super_call_args = None
if declaration_only:
self.reader.expect_token(";")
else:
body = self.expect_block("method body is missing")
first_stmt = body.statements[0] if len(body.statements) > 0 else None
if isinstance(first_stmt, stats.ExpressionStatement) and isinstance(first_stmt.expression, exprs.UnresolvedCallExpression) and isinstance(first_stmt.expression.func, exprs.Identifier) and first_stmt.expression.func.text == "super":
super_call_args = first_stmt.expression.args
body.statements.pop(0)
return MethodSignature(params, fields, body, returns, super_call_args)
def parse_identifier_or_string(self):
return self.reader.read_string() or self.reader.expect_identifier()
def parse_interface(self, leading_trivia, is_exported):
if not self.reader.read_token("interface"):
return None
intf_start = self.reader.prev_token_offset
intf_name = self.reader.expect_identifier("expected identifier after 'interface' keyword")
self.context.append(f'''I:{intf_name}''')
intf_type_args = self.parse_generics_args()
base_interfaces = []
if self.reader.read_token("extends"):
while True:
base_interfaces.append(self.parse_type())
if not (self.reader.read_token(",")):
break
methods = []
fields = []
self.reader.expect_token("{")
while not self.reader.read_token("}"):
member_leading_trivia = self.reader.read_leading_trivia()
member_start = self.reader.offset
member_name = self.parse_identifier_or_string()
if self.reader.read_token(":"):
self.context.append(f'''F:{member_name}''')
field_type = self.parse_type()
self.reader.expect_token(";")
field = types.Field(member_name, field_type, None, types.VISIBILITY.PUBLIC, False, None, member_leading_trivia)
fields.append(field)
self.node_manager.add_node(field, member_start)
self.context.pop()
else:
self.context.append(f'''M:{member_name}''')
method_type_args = self.parse_generics_args()
self.reader.expect_token("(")
# method
sig = self.parse_method_signature(False, True)
method = types.Method(member_name, method_type_args, sig.params, sig.body, types.VISIBILITY.PUBLIC, False, sig.returns, False, member_leading_trivia)
methods.append(method)
self.node_manager.add_node(method, member_start)
self.context.pop()
intf = types.Interface(intf_name, intf_type_args, base_interfaces, fields, methods, is_exported, leading_trivia)
self.node_manager.add_node(intf, intf_start)
self.context.pop()
return intf
def parse_specified_type(self):
type_name = self.reader.read_identifier()
type_args = self.parse_type_args()
return astTypes.UnresolvedType(type_name, type_args)
def parse_class(self, leading_trivia, is_exported, declaration_only):
cls_modifiers = self.reader.read_modifiers(["abstract"])
if not self.reader.read_token("class"):
return None
cls_start = self.reader.prev_token_offset
cls_name = self.reader.expect_identifier("expected identifier after 'class' keyword")
self.context.append(f'''C:{cls_name}''')
type_args = self.parse_generics_args()
base_class = self.parse_specified_type() if self.reader.read_token("extends") else None
base_interfaces = []
if self.reader.read_token("implements"):
while True:
base_interfaces.append(self.parse_specified_type())
if not (self.reader.read_token(",")):
break
constructor = None
fields = []
methods = []
properties = []
self.reader.expect_token("{")
while not self.reader.read_token("}"):
member_leading_trivia = self.reader.read_leading_trivia()
member_start = self.reader.offset
modifiers = self.reader.read_modifiers(["static", "public", "protected", "private", "readonly", "async", "abstract"])
is_static = "static" in modifiers
is_async = "async" in modifiers
is_abstract = "abstract" in modifiers
visibility = types.VISIBILITY.PRIVATE if "private" in modifiers else types.VISIBILITY.PROTECTED if "protected" in modifiers else types.VISIBILITY.PUBLIC
member_name = self.parse_identifier_or_string()
method_type_args = self.parse_generics_args()
if self.reader.read_token("("):
# method
is_constructor = member_name == "constructor"
sig = self.parse_method_signature(is_constructor, declaration_only or is_abstract)
if is_constructor:
member = constructor = types.Constructor(sig.params, sig.body, sig.super_call_args, member_leading_trivia)
for field in sig.fields:
fields.append(field)
else:
method = types.Method(member_name, method_type_args, sig.params, sig.body, visibility, is_static, sig.returns, is_async, member_leading_trivia)
methods.append(method)
member = method
self.node_manager.add_node(member, member_start)
elif member_name == "get" or member_name == "set":
# property
prop_name = self.reader.expect_identifier()
prop = next(filter(lambda x: x.name == prop_name, properties), None)
prop_type = None
getter = None
setter = None
if member_name == "get":
# get propName(): propType { return ... }
self.context.append(f'''P[G]:{prop_name}''')
self.reader.expect_token("()", "expected '()' after property getter name")
prop_type = self.parse_type() if self.reader.read_token(":") else None
if declaration_only:
if prop_type == None:
self.reader.fail("Type is missing for property in declare class")
self.reader.expect_token(";")
else:
getter = self.expect_block("property getter body is missing")
if prop != None:
prop.getter = getter
elif member_name == "set":
# set propName(value: propType) { ... }
self.context.append(f'''P[S]:{prop_name}''')
self.reader.expect_token("(", "expected '(' after property setter name")
self.reader.expect_identifier()
prop_type = self.parse_type() if self.reader.read_token(":") else None
self.reader.expect_token(")")
if declaration_only:
if prop_type == None:
self.reader.fail("Type is missing for property in declare class")
self.reader.expect_token(";")
else:
setter = self.expect_block("property setter body is missing")
if prop != None:
prop.setter = setter
if prop == None:
prop = types.Property(prop_name, prop_type, getter, setter, visibility, is_static, member_leading_trivia)
properties.append(prop)
self.node_manager.add_node(prop, member_start)
self.context.pop()
else:
self.context.append(f'''F:{member_name}''')
type_and_init = self.parse_type_and_init()
self.reader.expect_token(";")
field = types.Field(member_name, type_and_init.type, type_and_init.init, visibility, is_static, None, member_leading_trivia)
fields.append(field)
self.node_manager.add_node(field, member_start)
self.context.pop()
cls_ = types.Class(cls_name, type_args, base_class, base_interfaces, fields, properties, constructor, methods, is_exported, leading_trivia)
self.node_manager.add_node(cls_, cls_start)
self.context.pop()
return cls_
def parse_enum(self, leading_trivia, is_exported):
if not self.reader.read_token("enum"):
return None
enum_start = self.reader.prev_token_offset
name = self.reader.expect_identifier("expected identifier after 'enum' keyword")
self.context.append(f'''E:{name}''')
members = []
self.reader.expect_token("{")
if not self.reader.read_token("}"):
while True:
if self.reader.peek_token("}"):
break
# eg. "enum { A, B, }" (but multiline)
enum_member = types.EnumMember(self.reader.expect_identifier())
members.append(enum_member)
self.node_manager.add_node(enum_member, self.reader.prev_token_offset)
# TODO: generated code compatibility
self.reader.read_token(f'''= "{enum_member.name}"''')
if not (self.reader.read_token(",")):
break
self.reader.expect_token("}")
enum_obj = types.Enum(name, members, is_exported, leading_trivia)
self.node_manager.add_node(enum_obj, enum_start)
self.context.pop()
return enum_obj
@classmethod
def calculate_relative_path(cls, curr_file, rel_path):
if not rel_path.startswith("."):
raise Error(f'''relPath must start with \'.\', but got \'{rel_path}\'''')
curr = re.split("/", curr_file)
curr.pop()
# filename does not matter
for part in re.split("/", rel_path):
if part == "":
raise Error(f'''relPath should not contain multiple \'/\' next to each other (relPath=\'{rel_path}\')''')
if part == ".":
# "./" == stay in current directory
continue
elif part == "..":
# "../" == parent directory
if len(curr) == 0:
raise Error(f'''relPath goes out of root (curr=\'{curr_file}\', relPath=\'{rel_path}\')''')
curr.pop()
else:
curr.append(part)
return "/".join(curr)
@classmethod
def calculate_import_scope(cls, curr_scope, import_file):
if import_file.startswith("."):
# relative
return types.ExportScopeRef(curr_scope.package_name, TypeScriptParser2.calculate_relative_path(curr_scope.scope_name, import_file))
else:
path = re.split("/", import_file)
pkg_name = path.pop(0)
return types.ExportScopeRef(pkg_name, types.Package.index if len(path) == 0 else "/".join(path))
def read_identifier(self):
raw_id = self.reader.read_identifier()
return re.sub("_+$", "", raw_id)
def parse_import(self, leading_trivia):
if not self.reader.read_token("import"):
return None
import_start = self.reader.prev_token_offset
import_all_alias = None
import_parts = []
if self.reader.read_token("*"):
self.reader.expect_token("as")
import_all_alias = self.reader.expect_identifier()
else:
self.reader.expect_token("{")
while True:
if self.reader.peek_token("}"):
break
imp = self.reader.expect_identifier()
if self.reader.read_token("as"):
self.reader.fail("This is not yet supported")
import_parts.append(types.UnresolvedImport(imp))
self.node_manager.add_node(imp, self.reader.prev_token_offset)
if not (self.reader.read_token(",")):
break
self.reader.expect_token("}")
self.reader.expect_token("from")
module_name = self.reader.expect_string()
self.reader.expect_token(";")
import_scope = TypeScriptParser2.calculate_import_scope(self.export_scope, module_name) if self.export_scope != None else None
imports = []
if len(import_parts) > 0:
imports.append(types.Import(import_scope, False, import_parts, None, leading_trivia))
if import_all_alias != None:
imports.append(types.Import(import_scope, True, None, import_all_alias, leading_trivia))
#this.nodeManager.addNode(imports, importStart);
return imports
def parse_source_file(self):
imports = []
enums = []
intfs = []
classes = []
funcs = []
while True:
leading_trivia = self.reader.read_leading_trivia()
if self.reader.get_eof():
break
imps = self.parse_import(leading_trivia)
if imps != None:
for imp in imps:
imports.append(imp)
continue
modifiers = self.reader.read_modifiers(["export", "declare"])
is_exported = "export" in modifiers
is_declaration = "declare" in modifiers
cls_ = self.parse_class(leading_trivia, is_exported, is_declaration)
if cls_ != None:
classes.append(cls_)
continue
enum_obj = self.parse_enum(leading_trivia, is_exported)
if enum_obj != None:
enums.append(enum_obj)
continue
intf = self.parse_interface(leading_trivia, is_exported)
if intf != None:
intfs.append(intf)
continue
if self.reader.read_token("function"):
func_name = self.read_identifier()
self.reader.expect_token("(")
sig = self.parse_method_signature(False, is_declaration)
funcs.append(types.GlobalFunction(func_name, sig.params, sig.body, sig.returns, is_exported, leading_trivia))
continue
break
self.reader.skip_whitespace()
stmts = []
while True:
leading_trivia = self.reader.read_leading_trivia()
if self.reader.get_eof():
break
stmt = self.expect_statement()
if stmt == None:
continue
stmt.leading_trivia = leading_trivia
stmts.append(stmt)
return types.SourceFile(imports, intfs, classes, enums, funcs, stats.Block(stmts), self.path, self.export_scope)
def parse(self):
return self.parse_source_file()
@classmethod
def parse_file(cls, source, path = None):
return TypeScriptParser2(source, path).parse_source_file() |
#!/usr/bin/env python3
import json
import logging
import argparse
from project.default import get_homedir
def validate_generic_config_file():
sample_config = get_homedir() / 'config' / 'generic.json.sample'
with sample_config.open() as f:
generic_config_sample = json.load(f)
# Check documentation
for key in generic_config_sample.keys():
if key == '_notes':
continue
if key not in generic_config_sample['_notes']:
raise Exception(f'###### - Documentation missing for {key}')
user_config = get_homedir() / 'config' / 'generic.json'
if not user_config.exists():
# The config file was never created, copy the sample.
with user_config.open('w') as _fw:
json.dump(generic_config_sample, _fw)
with user_config.open() as f:
generic_config = json.load(f)
# Check all entries in the sample files are in the user file, and they have the same type
for key in generic_config_sample.keys():
if key == '_notes':
continue
if generic_config.get(key) is None:
logger.warning(f'Entry missing in user config file: {key}. Will default to: {generic_config_sample[key]}')
continue
if not isinstance(generic_config[key], type(generic_config_sample[key])):
raise Exception(f'Invalid type for {key}. Got: {type(generic_config[key])} ({generic_config[key]}), expected: {type(generic_config_sample[key])} ({generic_config_sample[key]})')
if isinstance(generic_config[key], dict):
# Check entries
for sub_key in generic_config_sample[key].keys():
if sub_key not in generic_config[key]:
raise Exception(f'{sub_key} is missing in generic_config[key]. Default from sample file: {generic_config_sample[key][sub_key]}')
if not isinstance(generic_config[key][sub_key], type(generic_config_sample[key][sub_key])):
raise Exception(f'Invalid type for {sub_key} in {key}. Got: {type(generic_config[key][sub_key])} ({generic_config[key][sub_key]}), expected: {type(generic_config_sample[key][sub_key])} ({generic_config_sample[key][sub_key]})')
# Make sure the user config file doesn't have entries missing in the sample config
for key in generic_config.keys():
if key not in generic_config_sample:
raise Exception(f'{key} is missing in the sample config file. You need to compare {user_config} with {sample_config}.')
return True
def update_user_configs():
for file_name in ['generic']:
with (get_homedir() / 'config' / f'{file_name}.json').open() as f:
try:
generic_config = json.load(f)
except Exception:
generic_config = {}
with (get_homedir() / 'config' / f'{file_name}.json.sample').open() as f:
generic_config_sample = json.load(f)
has_new_entry = False
for key in generic_config_sample.keys():
if key == '_notes':
continue
if generic_config.get(key) is None:
print(f'{key} was missing in {file_name}, adding it.')
print(f"Description: {generic_config_sample["_notes"][key]}")
generic_config[key] = generic_config_sample[key]
has_new_entry = True
elif isinstance(generic_config[key], dict):
for sub_key in generic_config_sample[key].keys():
if sub_key not in generic_config[key]:
print(f'{sub_key} was missing in {key} from {file_name}, adding it.')
generic_config[key][sub_key] = generic_config_sample[key][sub_key]
has_new_entry = True
if has_new_entry:
with (get_homedir() / 'config' / f'{file_name}.json').open('w') as fw:
json.dump(generic_config, fw, indent=2, sort_keys=True)
return has_new_entry
if __name__ == '__main__':
logger = logging.getLogger('Config validator')
parser = argparse.ArgumentParser(description='Check the config files.')
parser.add_argument('--check', default=False, action='store_true', help='Check if the sample config and the user config are in-line')
parser.add_argument('--update', default=False, action='store_true', help='Update the user config with the entries from the sample config if entries are missing')
args = parser.parse_args()
if args.check:
if validate_generic_config_file():
print(f"The entries in {get_homedir() / "config" / "generic.json"} are valid.")
if args.update:
if not update_user_configs():
print(f"No updates needed in {get_homedir() / "config" / "generic.json"}.")
| #!/usr/bin/env python3
import json
import logging
import argparse
from project.default import get_homedir
def validate_generic_config_file():
sample_config = get_homedir() / 'config' / 'generic.json.sample'
with sample_config.open() as f:
generic_config_sample = json.load(f)
# Check documentation
for key in generic_config_sample.keys():
if key == '_notes':
continue
if key not in generic_config_sample['_notes']:
raise Exception(f'###### - Documentation missing for {key}')
user_config = get_homedir() / 'config' / 'generic.json'
if not user_config.exists():
# The config file was never created, copy the sample.
with user_config.open('w') as _fw:
json.dump(generic_config_sample, _fw)
with user_config.open() as f:
generic_config = json.load(f)
# Check all entries in the sample files are in the user file, and they have the same type
for key in generic_config_sample.keys():
if key == '_notes':
continue
if generic_config.get(key) is None:
logger.warning(f'Entry missing in user config file: {key}. Will default to: {generic_config_sample[key]}')
continue
if not isinstance(generic_config[key], type(generic_config_sample[key])):
raise Exception(f'Invalid type for {key}. Got: {type(generic_config[key])} ({generic_config[key]}), expected: {type(generic_config_sample[key])} ({generic_config_sample[key]})')
if isinstance(generic_config[key], dict):
# Check entries
for sub_key in generic_config_sample[key].keys():
if sub_key not in generic_config[key]:
raise Exception(f'{sub_key} is missing in generic_config[key]. Default from sample file: {generic_config_sample[key][sub_key]}')
if not isinstance(generic_config[key][sub_key], type(generic_config_sample[key][sub_key])):
raise Exception(f'Invalid type for {sub_key} in {key}. Got: {type(generic_config[key][sub_key])} ({generic_config[key][sub_key]}), expected: {type(generic_config_sample[key][sub_key])} ({generic_config_sample[key][sub_key]})')
# Make sure the user config file doesn't have entries missing in the sample config
for key in generic_config.keys():
if key not in generic_config_sample:
raise Exception(f'{key} is missing in the sample config file. You need to compare {user_config} with {sample_config}.')
return True
def update_user_configs():
for file_name in ['generic']:
with (get_homedir() / 'config' / f'{file_name}.json').open() as f:
try:
generic_config = json.load(f)
except Exception:
generic_config = {}
with (get_homedir() / 'config' / f'{file_name}.json.sample').open() as f:
generic_config_sample = json.load(f)
has_new_entry = False
for key in generic_config_sample.keys():
if key == '_notes':
continue
if generic_config.get(key) is None:
print(f'{key} was missing in {file_name}, adding it.')
print(f"Description: {generic_config_sample['_notes'][key]}")
generic_config[key] = generic_config_sample[key]
has_new_entry = True
elif isinstance(generic_config[key], dict):
for sub_key in generic_config_sample[key].keys():
if sub_key not in generic_config[key]:
print(f'{sub_key} was missing in {key} from {file_name}, adding it.')
generic_config[key][sub_key] = generic_config_sample[key][sub_key]
has_new_entry = True
if has_new_entry:
with (get_homedir() / 'config' / f'{file_name}.json').open('w') as fw:
json.dump(generic_config, fw, indent=2, sort_keys=True)
return has_new_entry
if __name__ == '__main__':
logger = logging.getLogger('Config validator')
parser = argparse.ArgumentParser(description='Check the config files.')
parser.add_argument('--check', default=False, action='store_true', help='Check if the sample config and the user config are in-line')
parser.add_argument('--update', default=False, action='store_true', help='Update the user config with the entries from the sample config if entries are missing')
args = parser.parse_args()
if args.check:
if validate_generic_config_file():
print(f"The entries in {get_homedir() / 'config' / 'generic.json'} are valid.")
if args.update:
if not update_user_configs():
print(f"No updates needed in {get_homedir() / 'config' / 'generic.json'}.")
|
import datetime
import logging
import tornado.escape
import tornado.web
from icubam.backoffice.handlers import base, home, icus, users
from icubam.db import store
from icubam.messaging import client
class ListMessagesHandler(base.AdminHandler):
ROUTE = "list_messages"
def initialize(self):
super().initialize()
self.client = client.MessageServerClient(self.config)
def prepare_for_table(self, msg):
result = [
{
'key': 'user',
'value': msg['user_name'],
'link': f'{users.UserHandler.ROUTE}?id={msg['user_id']}'
},
{
'key': 'ICU',
'value': msg["icu_name"],
'link': f'{icus.ICUHandler.ROUTE}?id={msg['icu_id']}'
},
]
msg_dict = {}
msg_dict['telephone'] = msg['phone']
msg_dict['scheduled'] = '{0:%Y/%m/%d at %H:%M:%S}'.format(
datetime.datetime.fromtimestamp(msg['when'])
)
msg_dict['attempts'] = msg['attempts']
msg_dict['first sent'] = 'not yet'
if msg['first_sent'] is not None:
msg_dict['first sent'] = '{0:%Y/%m/%d at %H:%M:%S}'.format(
datetime.datetime.fromtimestamp(msg['first_sent'])
)
result.extend(self.format_list_item(msg_dict))
result.append({'key': 'url', 'value': 'link', 'link': msg['url']})
return result
@tornado.web.authenticated
async def get(self):
try:
messages = await self.client.get_scheduled_messages(self.user.user_id)
except Exception as e:
logging.error(f'Cannot contact message server: {e}')
return self.redirect(self.root_path)
data = [self.prepare_for_table(msg) for msg in messages]
self.render_list(
data=data, objtype='Scheduled Messages', create_handler=None
)
| import datetime
import logging
import tornado.escape
import tornado.web
from icubam.backoffice.handlers import base, home, icus, users
from icubam.db import store
from icubam.messaging import client
class ListMessagesHandler(base.AdminHandler):
ROUTE = "list_messages"
def initialize(self):
super().initialize()
self.client = client.MessageServerClient(self.config)
def prepare_for_table(self, msg):
result = [
{
'key': 'user',
'value': msg['user_name'],
'link': f'{users.UserHandler.ROUTE}?id={msg["user_id"]}'
},
{
'key': 'ICU',
'value': msg["icu_name"],
'link': f'{icus.ICUHandler.ROUTE}?id={msg["icu_id"]}'
},
]
msg_dict = {}
msg_dict['telephone'] = msg['phone']
msg_dict['scheduled'] = '{0:%Y/%m/%d at %H:%M:%S}'.format(
datetime.datetime.fromtimestamp(msg['when'])
)
msg_dict['attempts'] = msg['attempts']
msg_dict['first sent'] = 'not yet'
if msg['first_sent'] is not None:
msg_dict['first sent'] = '{0:%Y/%m/%d at %H:%M:%S}'.format(
datetime.datetime.fromtimestamp(msg['first_sent'])
)
result.extend(self.format_list_item(msg_dict))
result.append({'key': 'url', 'value': 'link', 'link': msg['url']})
return result
@tornado.web.authenticated
async def get(self):
try:
messages = await self.client.get_scheduled_messages(self.user.user_id)
except Exception as e:
logging.error(f'Cannot contact message server: {e}')
return self.redirect(self.root_path)
data = [self.prepare_for_table(msg) for msg in messages]
self.render_list(
data=data, objtype='Scheduled Messages', create_handler=None
)
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
This module implements a friendly (well, friendlier) interface between the raw JSON
responses from Jira and the Resource/dict abstractions provided by this library. Users
will construct a JIRA object as described below. Full API documentation can be found
at: https://jira.readthedocs.io/en/latest/
"""
import calendar
import copy
import datetime
import hashlib
import imghdr
import json
import logging as _logging
import mimetypes
import os
import re
import sys
import time
import warnings
from collections import OrderedDict
from collections.abc import Iterable
from functools import lru_cache, wraps
from io import BufferedReader
from numbers import Number
from typing import (
Any,
Callable,
Dict,
Generic,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
cast,
no_type_check,
)
from urllib.parse import urlparse
import requests
from pkg_resources import parse_version
from requests import Response
from requests.auth import AuthBase
from requests.utils import get_netrc_auth
from jira import __version__
# GreenHopper specific resources
from jira.exceptions import JIRAError
from jira.resilientsession import ResilientSession, raise_on_error
# Jira-specific resources
from jira.resources import (
Attachment,
Board,
Comment,
Component,
Customer,
CustomFieldOption,
Dashboard,
Filter,
GreenHopperResource,
Group,
Issue,
IssueLink,
IssueLinkType,
IssueType,
Priority,
Project,
RemoteLink,
RequestType,
Resolution,
Resource,
Role,
SecurityLevel,
ServiceDesk,
Sprint,
Status,
StatusCategory,
User,
Version,
Votes,
Watchers,
Worklog,
)
from jira.utils import CaseInsensitiveDict, json_loads, threaded_requests
try:
# noinspection PyUnresolvedReferences
from requests_toolbelt import MultipartEncoder
except ImportError:
pass
try:
from requests_jwt import JWTAuth
except ImportError:
pass
LOG = _logging.getLogger("jira")
LOG.addHandler(_logging.NullHandler())
def translate_resource_args(func: Callable):
"""Decorator that converts Issue and Project resources to their keys when used as arguments."""
@wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
arg_list = []
for arg in args:
if isinstance(arg, (Issue, Project)):
arg_list.append(arg.key)
else:
arg_list.append(arg)
result = func(*arg_list, **kwargs)
return result
return wrapper
def _field_worker(
fields: Dict[str, Any] = None, **fieldargs: Any
) -> Union[Dict[str, Dict[str, Any]], Dict[str, Dict[str, str]]]:
if fields is not None:
return {"fields": fields}
return {"fields": fieldargs}
ResourceType = TypeVar("ResourceType", contravariant=True, bound=Resource)
class ResultList(list, Generic[ResourceType]):
def __init__(
self,
iterable: Iterable = None,
_startAt: int = 0,
_maxResults: int = 0,
_total: Optional[int] = None,
_isLast: Optional[bool] = None,
) -> None:
"""
Args:
iterable (Iterable): [description]. Defaults to None.
_startAt (int): Start page. Defaults to 0.
_maxResults (int): Max results per page. Defaults to 0.
_total (Optional[int]): Total results from query. Defaults to 0.
_isLast (Optional[bool]): Last Page? Defaults to None.
"""
if iterable is not None:
list.__init__(self, iterable)
else:
list.__init__(self)
self.startAt = _startAt
self.maxResults = _maxResults
# Optional parameters:
self.isLast = _isLast
self.total = _total if _total is not None else len(self)
self.iterable: List = list(iterable) if iterable else []
self.current = self.startAt
def __next__(self) -> Type[ResourceType]:
self.current += 1
if self.current > self.total:
raise StopIteration
else:
return self.iterable[self.current - 1]
class QshGenerator(object):
def __init__(self, context_path):
self.context_path = context_path
def __call__(self, req):
parse_result = urlparse(req.url)
path = (
parse_result.path[len(self.context_path) :]
if len(self.context_path) > 1
else parse_result.path
)
# Per Atlassian docs, use %20 for whitespace when generating qsh for URL
# https://developer.atlassian.com/cloud/jira/platform/understanding-jwt/#qsh
query = "&".join(sorted(parse_result.query.split("&"))).replace("+", "%20")
qsh = f"{req.method.upper()}&{path}&{query}"
return hashlib.sha256(qsh.encode("utf-8")).hexdigest()
class JiraCookieAuth(AuthBase):
"""Jira Cookie Authentication
Allows using cookie authentication as described by
https://developer.atlassian.com/jiradev/jira-apis/jira-rest-apis/jira-rest-api-tutorials/jira-rest-api-example-cookie-based-authentication
"""
def __init__(
self, session: ResilientSession, _get_session: Callable, auth: Tuple[str, str]
):
"""Cookie Based Authentication
Args:
session (ResilientSession): The Session object to communicate with the API.
_get_session (Callable): The function that returns a :py_class:``User``
auth (Tuple[str, str]): The username, password tuple
"""
self._session = session
self._get_session = _get_session
self.__auth = auth
def handle_401(self, response, **kwargs):
if response.status_code != 401:
return response
self.init_session()
response = self.process_original_request(response.request.copy())
return response
def process_original_request(self, original_request):
self.update_cookies(original_request)
return self.send_request(original_request)
def update_cookies(self, original_request):
# Cookie header needs first to be deleted for the header to be updated using
# the prepare_cookies method. See request.PrepareRequest.prepare_cookies
if "Cookie" in original_request.headers:
del original_request.headers["Cookie"]
original_request.prepare_cookies(self.cookies)
def init_session(self):
self.start_session()
def __call__(self, request):
request.register_hook("response", self.handle_401)
return request
def send_request(self, request):
return self._session.send(request)
@property
def cookies(self):
return self._session.cookies
def start_session(self):
self._get_session(self.__auth)
class JIRA(object):
"""User interface to Jira.
Clients interact with Jira by constructing an instance of this object and calling its methods. For addressable
resources in Jira -- those with "self" links -- an appropriate subclass of :py:class:`jira.resources.Resource` will be returned
with customized ``update()`` and ``delete()`` methods, along with attribute access to fields. This means that calls
of the form ``issue.fields.summary`` will be resolved into the proper lookups to return the JSON value at that
mapping. Methods that do not return resources will return a dict constructed from the JSON response or a scalar
value; see each method's documentation for details on what that method returns.
Without any arguments, this client will connect anonymously to the Jira instance
started by the Atlassian Plugin SDK from one of the 'atlas-run', ``atlas-debug``,
or ``atlas-run-standalone`` commands. By default, this instance runs at
``http://localhost:2990/jira``. The ``options`` argument can be used to set the Jira instance to use.
Authentication is handled with the ``basic_auth`` argument. If authentication is supplied (and is
accepted by Jira), the client will remember it for subsequent requests.
For quick command line access to a server, see the ``jirashell`` script included with this distribution.
The easiest way to instantiate is using ``j = JIRA("https://jira.atlassian.com")``
"""
DEFAULT_OPTIONS = {
"server": "http://localhost:2990/jira",
"auth_url": "/rest/auth/1/session",
"context_path": "/",
"rest_path": "api",
"rest_api_version": "2",
"agile_rest_path": GreenHopperResource.GREENHOPPER_REST_PATH,
"agile_rest_api_version": "1.0",
"verify": True,
"resilient": True,
"async": False,
"async_workers": 5,
"client_cert": None,
"check_update": False,
# amount of seconds to wait for loading a resource after updating it
# used to avoid server side caching issues, used to be 4 seconds.
"delay_reload": 0,
"headers": {
"Cache-Control": "no-cache",
# 'Accept': 'application/json;charset=UTF-8', # default for REST
"Content-Type": "application/json", # ;charset=UTF-8',
# 'Accept': 'application/json', # default for REST
# 'Pragma': 'no-cache',
# 'Expires': 'Thu, 01 Jan 1970 00:00:00 GMT'
"X-Atlassian-Token": "no-check",
},
}
checked_version = False
# TODO(ssbarnea): remove these two variables and use the ones defined in resources
JIRA_BASE_URL = Resource.JIRA_BASE_URL
AGILE_BASE_URL = GreenHopperResource.AGILE_BASE_URL
def __init__(
self,
server: str = None,
options: Dict[str, Union[str, bool, Any]] = None,
basic_auth: Union[None, Tuple[str, str]] = None,
oauth: Dict[str, Any] = None,
jwt: Dict[str, Any] = None,
kerberos=False,
kerberos_options: Dict[str, Any] = None,
validate=False,
get_server_info: bool = True,
async_: bool = False,
async_workers: int = 5,
logging: bool = True,
max_retries: int = 3,
proxies: Any = None,
timeout: Optional[Union[Union[float, int], Tuple[float, float]]] = None,
auth: Tuple[str, str] = None,
):
"""Construct a Jira client instance.
Without any arguments, this client will connect anonymously to the Jira instance
started by the Atlassian Plugin SDK from one of the 'atlas-run', ``atlas-debug``,
or ``atlas-run-standalone`` commands. By default, this instance runs at
``http://localhost:2990/jira``. The ``options`` argument can be used to set the Jira instance to use.
Authentication is handled with the ``basic_auth`` argument. If authentication is supplied (and is
accepted by Jira), the client will remember it for subsequent requests.
For quick command line access to a server, see the ``jirashell`` script included with this distribution.
The easiest way to instantiate is using ``j = JIRA("https://jira.atlasian.com")``
Args:
server (Optional[str]): The server address and context path to use. Defaults to ``http://localhost:2990/jira``.
options (Optional[Dict[str, Any]]): Specify the server and properties this client will use.
Use a dict with any of the following properties:
* server -- the server address and context path to use. Defaults to ``http://localhost:2990/jira``.
* rest_path -- the root REST path to use. Defaults to ``api``, where the Jira REST resources live.
* rest_api_version -- the version of the REST resources under rest_path to use. Defaults to ``2``.
* agile_rest_path - the REST path to use for Jira Agile requests. Defaults to ``greenhopper`` (old, private
API). Check :py:class:`jira.resources.GreenHopperResource` for other supported values.
* verify -- Verify SSL certs. Defaults to ``True``.
* client_cert -- a tuple of (cert,key) for the requests library for client side SSL
* check_update -- Check whether using the newest python-jira library version.
basic_auth (Union[None, Tuple[str, str]]): A tuple of username and password to use when
establishing a session via HTTP BASIC authentication.
oauth (Optional[Any]): A dict of properties for OAuth authentication. The following properties are required:
* access_token -- OAuth access token for the user
* access_token_secret -- OAuth access token secret to sign with the key
* consumer_key -- key of the OAuth application link defined in Jira
* key_cert -- private key file to sign requests with (should be the pair of the public key supplied to
Jira in the OAuth application link)
kerberos (bool): If true it will enable Kerberos authentication.
kerberos_options (Optional[Dict[str,str]]): A dict of properties for Kerberos authentication.
The following properties are possible:
* mutual_authentication -- string DISABLED or OPTIONAL.
Example kerberos_options structure: ``{'mutual_authentication': 'DISABLED'}``
jwt (Optional[Any]): A dict of properties for JWT authentication supported by Atlassian Connect.
The following properties are required:
* secret -- shared secret as delivered during 'installed' lifecycle event
(see https://developer.atlassian.com/static/connect/docs/latest/modules/lifecycle.html for details)
* payload -- dict of fields to be inserted in the JWT payload, e.g. 'iss'
Example jwt structure: ``{'secret': SHARED_SECRET, 'payload': {'iss': PLUGIN_KEY}}``
validate (bool): If true it will validate your credentials first. Remember that if you are accessing Jira
as anonymous it will fail to instantiate.
get_server_info (bool): If true it will fetch server version info first to determine if some API calls
are available.
async_ (bool): To enable async requests for those actions where we implemented it, like issue update() or delete().
async_workers (int): Set the number of worker threads for async operations.
timeout (Optional[Union[Union[float, int], Tuple[float, float]]]): Set a read/connect timeout for the underlying
calls to Jira (default: None).
Obviously this means that you cannot rely on the return code when this is enabled.
max_retries (int): Sets the amount Retries for the HTTP sessions initiated by the client. (Default: 3)
proxies (Optional[Any]): Sets the proxies for the HTTP session.
auth (Optional[Tuple[str,str]]): Set a cookie auth token if this is required.
logging (bool): Determine whether or not logging should be enabled. (Default: True)
"""
# force a copy of the tuple to be used in __del__() because
# sys.version_info could have already been deleted in __del__()
self.sys_version_info = tuple([i for i in sys.version_info])
if options is None:
options = {}
if server and isinstance(server, dict):
warnings.warn(
"Old API usage, use JIRA(url) or JIRA(options={'server': url}, when using dictionary always use named parameters.",
DeprecationWarning,
)
options = server
server = ""
if server:
options["server"] = server
if async_:
options["async"] = async_
options["async_workers"] = async_workers
LOG.setLevel(_logging.INFO if logging else _logging.CRITICAL)
self.log = LOG
self._options: Dict[str, Any] = copy.copy(JIRA.DEFAULT_OPTIONS)
self._options.update(options)
self._rank = None
# Rip off trailing slash since all urls depend on that
assert isinstance(self._options["server"], str) # to help mypy
if self._options["server"].endswith("/"):
self._options["server"] = self._options["server"][:-1]
context_path = urlparse(self.server_url).path
if len(context_path) > 0:
self._options["context_path"] = context_path
self._try_magic()
assert isinstance(self._options["headers"], dict) # for mypy benefit
self._session: ResilientSession # for mypy benefit
if oauth:
self._create_oauth_session(oauth, timeout)
elif basic_auth:
self._create_http_basic_session(*basic_auth, timeout=timeout)
self._session.headers.update(self._options["headers"])
elif jwt:
self._create_jwt_session(jwt, timeout)
elif kerberos:
self._create_kerberos_session(timeout, kerberos_options=kerberos_options)
elif auth:
self._create_cookie_auth(auth, timeout)
# always log in for cookie based auth, as we need a first request to be logged in
validate = True
else:
verify = bool(self._options["verify"])
self._session = ResilientSession(timeout=timeout)
self._session.verify = verify
self._session.headers.update(self._options["headers"])
if "cookies" in self._options:
self._session.cookies.update(self._options["cookies"])
self._session.max_retries = max_retries
if proxies:
self._session.proxies = proxies
self.auth = auth
if validate:
# This will raise an Exception if you are not allowed to login.
# It's better to fail faster than later.
user = self.session()
if user.raw is None:
auth_method = (
oauth or basic_auth or jwt or kerberos or auth or "anonymous"
)
raise JIRAError(f"Can not log in with {str(auth_method)}")
self.deploymentType = None
if get_server_info:
# We need version in order to know what API calls are available or not
si = self.server_info()
try:
self._version = tuple(si["versionNumbers"])
except Exception as e:
self.log.error("invalid server_info: %s", si)
raise e
self.deploymentType = si.get("deploymentType")
else:
self._version = (0, 0, 0)
if self._options["check_update"] and not JIRA.checked_version:
self._check_update_()
JIRA.checked_version = True
self._fields = {}
for f in self.fields():
if "clauseNames" in f:
for name in f["clauseNames"]:
self._fields[name] = f["id"]
@property
def server_url(self) -> str:
"""Return the server url"""
return str(self._options["server"])
def _create_cookie_auth(
self,
auth: Tuple[str, str],
timeout: Optional[Union[Union[float, int], Tuple[float, float]]],
):
self._session = ResilientSession(timeout=timeout)
self._session.auth = JiraCookieAuth(self._session, self.session, auth)
self._session.verify = bool(self._options["verify"])
client_cert: Tuple[str, str] = self._options["client_cert"] # to help mypy
self._session.cert = client_cert
def _check_update_(self):
"""Check if the current version of the library is outdated."""
try:
data = requests.get(
"https://pypi.python.org/pypi/jira/json", timeout=2.001
).json()
released_version = data["info"]["version"]
if parse_version(released_version) > parse_version(__version__):
warnings.warn(
"You are running an outdated version of Jira Python %s. Current version is %s. Do not file any bugs against older versions."
% (__version__, released_version)
)
except requests.RequestException:
pass
except Exception as e:
self.log.warning(e)
def __del__(self):
"""Destructor for JIRA instance."""
self.close()
def close(self):
session = getattr(self, "_session", None)
if session is not None:
try:
session.close()
except TypeError:
# TypeError: "'NoneType' object is not callable"
# Could still happen here because other references are also
# in the process to be torn down, see warning section in
# https://docs.python.org/2/reference/datamodel.html#object.__del__
pass
self._session = None
def _check_for_html_error(self, content: str):
# Jira has the bad habit of returning errors in pages with 200 and
# embedding the error in a huge webpage.
if "<!-- SecurityTokenMissing -->" in content:
self.log.warning("Got SecurityTokenMissing")
raise JIRAError(f"SecurityTokenMissing: {content}")
return False
return True
def _get_sprint_field_id(self):
sprint_field_name = "Sprint"
sprint_field_id = [
f["schema"]["customId"]
for f in self.fields()
if f["name"] == sprint_field_name
][0]
return sprint_field_id
def _fetch_pages(
self,
item_type: Type[ResourceType],
items_key: Optional[str],
request_path: str,
startAt: int = 0,
maxResults: int = 50,
params: Dict[str, Any] = None,
base: str = JIRA_BASE_URL,
) -> ResultList[ResourceType]:
"""Fetch from a paginated end point.
Args:
item_type (Type[Resource]): Type of single item. ResultList of such items will be returned.
items_key (Optional[str]): Path to the items in JSON returned from server.
Set it to None, if response is an array, and not a JSON object.
request_path (str): path in request URL
startAt (int): index of the first record to be fetched. (Default: 0)
maxResults (int): Maximum number of items to return.
If maxResults evaluates as False, it will try to get all items in batches. (Default:50)
params (Dict[str, Any]): Params to be used in all requests. Should not contain startAt and maxResults,
as they will be added for each request created from this function.
base (str): base URL to use for the requests.
Returns:
ResultList
"""
async_workers = None
async_class = None
if self._options["async"]:
try:
from requests_futures.sessions import FuturesSession
async_class = FuturesSession
except ImportError:
pass
async_workers = self._options.get("async_workers")
page_params = params.copy() if params else {}
if startAt:
page_params["startAt"] = startAt
if maxResults:
page_params["maxResults"] = maxResults
resource = self._get_json(request_path, params=page_params, base=base)
next_items_page = self._get_items_from_page(item_type, items_key, resource)
items = next_items_page
if True: # isinstance(resource, dict):
if isinstance(resource, dict):
total = resource.get("total")
total = int(total) if total is not None else total
# 'isLast' is the optional key added to responses in Jira Agile 6.7.6. So far not used in basic Jira API.
is_last = resource.get("isLast", False)
start_at_from_response = resource.get("startAt", 0)
max_results_from_response = resource.get("maxResults", 1)
else:
# if is a list
total = 1
is_last = True
start_at_from_response = 0
max_results_from_response = 1
# If maxResults evaluates as False, get all items in batches
if not maxResults:
page_size = max_results_from_response or len(items)
page_start = (startAt or start_at_from_response or 0) + page_size
if (
async_class is not None
and not is_last
and (total is not None and len(items) < total)
):
async_fetches = []
future_session = async_class(
session=self._session, max_workers=async_workers
)
for start_index in range(page_start, total, page_size):
page_params = params.copy() if params else {}
page_params["startAt"] = start_index
page_params["maxResults"] = page_size
url = self._get_url(request_path)
r = future_session.get(url, params=page_params)
async_fetches.append(r)
for future in async_fetches:
response = future.result()
resource = json_loads(response)
if resource:
next_items_page = self._get_items_from_page(
item_type, items_key, resource
)
items.extend(next_items_page)
while (
async_class is None
and not is_last
and (total is None or page_start < total)
and len(next_items_page) == page_size
):
page_params["startAt"] = page_start
page_params["maxResults"] = page_size
resource = self._get_json(
request_path, params=page_params, base=base
)
if resource:
next_items_page = self._get_items_from_page(
item_type, items_key, resource
)
items.extend(next_items_page)
page_start += page_size
else:
# if resource is an empty dictionary we assume no-results
break
return ResultList(
items, start_at_from_response, max_results_from_response, total, is_last
)
else: # TODO: unreachable
# it seems that search_users can return a list() containing a single user!
return ResultList(
[item_type(self._options, self._session, resource)], 0, 1, 1, True
)
def _get_items_from_page(
self,
item_type: Type[ResourceType],
items_key: Optional[str],
resource: Dict[str, Any],
) -> List[ResourceType]:
try:
return [
# We need to ignore the type here, as 'Resource' is an option
item_type(self._options, self._session, raw_issue_json) # type: ignore
for raw_issue_json in (resource[items_key] if items_key else resource)
]
except KeyError as e:
# improving the error text so we know why it happened
raise KeyError(str(e) + " : " + json.dumps(resource))
# Information about this client
def client_info(self) -> str:
"""Get the server this client is connected to."""
return self.server_url
# Universal resource loading
def find(
self, resource_format: str, ids: Union[Tuple[str, str], int, str] = ""
) -> Resource:
"""Find Resource object for any addressable resource on the server.
This method is a universal resource locator for any REST-ful resource in Jira. The
argument ``resource_format`` is a string of the form ``resource``, ``resource/{0}``,
``resource/{0}/sub``, ``resource/{0}/sub/{1}``, etc. The format placeholders will be
populated from the ``ids`` argument if present. The existing authentication session
will be used.
The return value is an untyped Resource object, which will not support specialized
:py:meth:`.Resource.update` or :py:meth:`.Resource.delete` behavior. Moreover, it will
not know to return an issue Resource if the client uses the resource issue path. For this
reason, it is intended to support resources that are not included in the standard
Atlassian REST API.
Args:
resource_format (str): the subpath to the resource string
ids (Optional[Tuple]): values to substitute in the ``resource_format`` string
Returns:
Resource
"""
resource = Resource(resource_format, self._options, self._session)
resource.find(ids)
return resource
@no_type_check # FIXME: This function fails type checking, probably a bug or two
def async_do(self, size: int = 10):
"""Execute all asynchronous jobs and wait for them to finish. By default it will run on 10 threads.
Args:
size (int): number of threads to run on.
"""
if hasattr(self._session, "_async_jobs"):
self.log.info(
"Executing asynchronous %s jobs found in queue by using %s threads..."
% (len(self._session._async_jobs), size)
)
threaded_requests.map(self._session._async_jobs, size=size)
# Application properties
# non-resource
def application_properties(
self, key: str = None
) -> Union[Dict[str, str], List[Dict[str, str]]]:
"""Return the mutable server application properties.
Args:
key (Optional[str]): the single property to return a value for
Returns:
Union[Dict[str, str], List[Dict[str, str]]]
"""
params = {}
if key is not None:
params["key"] = key
return self._get_json("application-properties", params=params)
def set_application_property(self, key: str, value: str):
"""Set the application property.
Args:
key (str): key of the property to set
value (str): value to assign to the property
"""
url = self._get_latest_url("application-properties/" + key)
payload = {"id": key, "value": value}
return self._session.put(url, data=json.dumps(payload))
def applicationlinks(self, cached: bool = True) -> List:
"""List of application links.
Returns:
List[Dict]: json, or empty list
"""
self._applicationlinks: List[Dict] # for mypy benefit
# if cached, return the last result
if cached and hasattr(self, "_applicationlinks"):
return self._applicationlinks
# url = self._options['server'] + '/rest/applinks/latest/applicationlink'
url = self.server_url + "/rest/applinks/latest/listApplicationlinks"
r = self._session.get(url)
o = json_loads(r)
if "list" in o and isinstance(o, dict):
self._applicationlinks = o["list"]
else:
self._applicationlinks = []
return self._applicationlinks
# Attachments
def attachment(self, id: str) -> Attachment:
"""Get an attachment Resource from the server for the specified ID.
Args:
id (str): The Attachment ID
Returns:
Attachment
"""
return self._find_for_resource(Attachment, id)
# non-resource
def attachment_meta(self) -> Dict[str, int]:
"""Get the attachment metadata.
Return:
Dict[str, int]
"""
return self._get_json("attachment/meta")
@translate_resource_args
def add_attachment(
self, issue: str, attachment: Union[str, BufferedReader], filename: str = None
) -> Attachment:
"""Attach an attachment to an issue and returns a Resource for it.
The client will *not* attempt to open or validate the attachment; it expects a file-like object to be ready
for its use. The user is still responsible for tidying up (e.g., closing the file, killing the socket, etc.)
Args:
issue (str): the issue to attach the attachment to
attachment (Union[str,BufferedReader]): file-like object to attach to the issue, also works if it is a string with the filename.
filename (str): optional name for the attached file. If omitted, the file object's ``name`` attribute
is used. If you acquired the file-like object by any other method than ``open()``, make sure
that a name is specified in one way or the other.
Returns:
Attachment
"""
close_attachment = False
if isinstance(attachment, str):
attachment: BufferedReader = open(attachment, "rb") # type: ignore
attachment = cast(BufferedReader, attachment)
close_attachment = True
elif isinstance(attachment, BufferedReader) and attachment.mode != "rb":
self.log.warning(
"%s was not opened in 'rb' mode, attaching file may fail."
% attachment.name
)
url = self._get_url("issue/" + str(issue) + "/attachments")
fname = filename
if not fname and isinstance(attachment, BufferedReader):
fname = os.path.basename(attachment.name)
if "MultipartEncoder" not in globals():
method = "old"
try:
r = self._session.post(
url,
files={"file": (fname, attachment, "application/octet-stream")},
headers=CaseInsensitiveDict(
{"content-type": None, "X-Atlassian-Token": "no-check"}
),
)
finally:
if close_attachment:
attachment.close()
else:
method = "MultipartEncoder"
def file_stream() -> MultipartEncoder:
"""Returns files stream of attachment."""
return MultipartEncoder(
fields={"file": (fname, attachment, "application/octet-stream")}
)
m = file_stream()
try:
r = self._session.post(
url,
data=m,
headers=CaseInsensitiveDict(
{
"content-type": m.content_type,
"X-Atlassian-Token": "no-check",
}
),
retry_data=file_stream,
)
finally:
if close_attachment:
attachment.close()
js: Union[Dict[str, Any], List[Dict[str, Any]]] = json_loads(r)
if not js or not isinstance(js, Iterable):
raise JIRAError(f"Unable to parse JSON: {js}")
jira_attachment = Attachment(
self._options, self._session, js[0] if isinstance(js, List) else js
)
if jira_attachment.size == 0:
raise JIRAError(
"Added empty attachment via %s method?!: r: %s\nattachment: %s"
% (method, r, jira_attachment)
)
return jira_attachment
def delete_attachment(self, id: str) -> Response:
"""Delete attachment by id.
Args:
id (str): ID of the attachment to delete
Returns:
Response
"""
url = self._get_url("attachment/" + str(id))
return self._session.delete(url)
# Components
def component(self, id: str):
"""Get a component Resource from the server.
Args:
id (str): ID of the component to get
"""
return self._find_for_resource(Component, id)
@translate_resource_args
def create_component(
self,
name: str,
project: str,
description=None,
leadUserName=None,
assigneeType=None,
isAssigneeTypeValid=False,
) -> Component:
"""Create a component inside a project and return a Resource for it.
Args:
name (str): name of the component
project (str): key of the project to create the component in
description (str): a description of the component
leadUserName (Optional[str]): the username of the user responsible for this component
assigneeType (Optional[str]): see the ComponentBean.AssigneeType class for valid values
isAssigneeTypeValid (bool): boolean specifying whether the assignee type is acceptable (Default: False)
Returns:
Component
"""
data = {
"name": name,
"project": project,
"isAssigneeTypeValid": isAssigneeTypeValid,
}
if description is not None:
data["description"] = description
if leadUserName is not None:
data["leadUserName"] = leadUserName
if assigneeType is not None:
data["assigneeType"] = assigneeType
url = self._get_url("component")
r = self._session.post(url, data=json.dumps(data))
component = Component(self._options, self._session, raw=json_loads(r))
return component
def component_count_related_issues(self, id: str):
"""Get the count of related issues for a component.
Args:
id (str): ID of the component to use
"""
data: Dict[str, Any] = self._get_json(
"component/" + str(id) + "/relatedIssueCounts"
)
return data["issueCount"]
def delete_component(self, id: str) -> Response:
"""Delete component by id.
Args:
id (str): ID of the component to use
Returns:
Response
"""
url = self._get_url("component/" + str(id))
return self._session.delete(url)
# Custom field options
def custom_field_option(self, id: str) -> CustomFieldOption:
"""Get a custom field option Resource from the server.
Args:
id (str): ID of the custom field to use
Returns:
CustomFieldOption
"""
return self._find_for_resource(CustomFieldOption, id)
# Dashboards
def dashboards(
self, filter=None, startAt=0, maxResults=20
) -> ResultList[Dashboard]:
"""Return a ResultList of Dashboard resources and a ``total`` count.
Args:
filter (Optional[str]): either "favourite" or "my", the type of dashboards to return
startAt (int): index of the first dashboard to return (Default: 0)
maxResults (int): maximum number of dashboards to return. If maxResults evaluates as False, it will try to get all items in batches. (Default: 20)
Returns:
ResultList
"""
params = {}
if filter is not None:
params["filter"] = filter
return self._fetch_pages(
Dashboard, "dashboards", "dashboard", startAt, maxResults, params
)
def dashboard(self, id: str) -> Dashboard:
"""Get a dashboard Resource from the server.
Args:
id (str): ID of the dashboard to get.
Returns:
Dashboard
"""
return self._find_for_resource(Dashboard, id)
# Fields
# non-resource
def fields(self) -> List[Dict[str, Any]]:
"""Return a list of all issue fields.
Returns:
List[Dict[str, Any]]
"""
return self._get_json("field")
# Filters
def filter(self, id: str) -> Filter:
"""Get a filter Resource from the server.
Args:
id (str): ID of the filter to get.
Returns:
Filter
"""
return self._find_for_resource(Filter, id)
def favourite_filters(self) -> List[Filter]:
"""Get a list of filter Resources which are the favourites of the currently authenticated user.
Returns:
List[Filter]
"""
r_json: List[Dict[str, Any]] = self._get_json("filter/favourite")
filters = [
Filter(self._options, self._session, raw_filter_json)
for raw_filter_json in r_json
]
return filters
def create_filter(
self,
name: str = None,
description: str = None,
jql: str = None,
favourite: bool = None,
):
"""Create a new filter and return a filter Resource for it.
Args:
name (str): name of the new filter
description (str): useful human readable description of the new filter
jql (str): query string that defines the filter
favourite (bool): whether to add this filter to the current user's favorites
Returns:
Filter
"""
data: Dict[str, Any] = {}
if name is not None:
data["name"] = name
if description is not None:
data["description"] = description
if jql is not None:
data["jql"] = jql
if favourite is not None:
data["favourite"] = favourite
url = self._get_url("filter")
r = self._session.post(url, data=json.dumps(data))
raw_filter_json: Dict[str, Any] = json_loads(r)
return Filter(self._options, self._session, raw=raw_filter_json)
def update_filter(
self,
filter_id,
name: str = None,
description: str = None,
jql: str = None,
favourite: bool = None,
):
"""Update a filter and return a filter Resource for it.
Args:
name (Optional[str]): name of the new filter
description (Optional[str]): useful human readable description of the new filter
jql (Optional[str]): query string that defines the filter
favourite (Optional[bool]): whether to add this filter to the current user's favorites
"""
filter = self.filter(filter_id)
data = {}
data["name"] = name or filter.name
data["description"] = description or filter.description
data["jql"] = jql or filter.jql
data["favourite"] = favourite or filter.favourite
url = self._get_url(f"filter/{filter_id}")
r = self._session.put(
url, headers={"content-type": "application/json"}, data=json.dumps(data)
)
raw_filter_json = json.loads(r.text)
return Filter(self._options, self._session, raw=raw_filter_json)
# Groups
def group(self, id: str, expand: Any = None) -> Group:
"""Get a group Resource from the server.
Args:
id (str): ID of the group to get
expand (Optional[Any]): Extra information to fetch inside each resource
Returns:
Group
"""
group = Group(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
group.find(id, params=params)
return group
# non-resource
def groups(
self,
query: Optional[str] = None,
exclude: Optional[Any] = None,
maxResults: int = 9999,
) -> List[str]:
"""Return a list of groups matching the specified criteria.
Args:
query (Optional[str]): filter groups by name with this string
exclude (Optional[Any]): filter out groups by name with this string
maxResults (int): maximum results to return. (Default: 9999)
Returns:
List[str]
"""
params: Dict[str, Any] = {}
groups = []
if query is not None:
params["query"] = query
if exclude is not None:
params["exclude"] = exclude
if maxResults is not None:
params["maxResults"] = maxResults
for group in self._get_json("groups/picker", params=params)["groups"]:
groups.append(group["name"])
return sorted(groups)
def group_members(self, group: str) -> OrderedDict:
"""Return a hash or users with their information. Requires Jira 6.0 or will raise NotImplemented.
Args:
group (str): Name of the group.
"""
if self._version < (6, 0, 0):
raise NotImplementedError(
"Group members is not implemented in Jira before version 6.0, upgrade the instance, if possible."
)
params = {"groupname": group, "expand": "users"}
r = self._get_json("group", params=params)
size = r["users"]["size"]
end_index = r["users"]["end-index"]
while end_index < size - 1:
params = {
"groupname": group,
"expand": f"users[{end_index + 1}:{end_index + 50}]",
}
r2 = self._get_json("group", params=params)
for user in r2["users"]["items"]:
r["users"]["items"].append(user)
end_index = r2["users"]["end-index"]
size = r["users"]["size"]
result = {}
for user in r["users"]["items"]:
result[user["id"]] = {
"name": user.get("name"),
"id": user.get("id"),
"accountId": user.get("accountId"),
"fullname": user.get("displayName"),
"email": user.get("emailAddress", "hidden"),
"active": user.get("active"),
"timezone": user.get("timezone"),
}
return OrderedDict(sorted(result.items(), key=lambda t: t[0]))
def add_group(self, groupname: str) -> bool:
"""Create a new group in Jira.
Args:
groupname (str): The name of the group you wish to create.
Returns:
bool: True if successful.
"""
url = self._get_latest_url("group")
# implementation based on
# https://docs.atlassian.com/jira/REST/ondemand/#d2e5173
x = OrderedDict()
x["name"] = groupname
payload = json.dumps(x)
self._session.post(url, data=payload)
return True
def remove_group(self, groupname: str) -> bool:
"""Delete a group from the Jira instance.
Args:
groupname (str): The group to be deleted from the Jira instance.
Returns:
bool: Returns True on success.
"""
# implementation based on
# https://docs.atlassian.com/jira/REST/ondemand/#d2e5173
url = self._get_latest_url("group")
x = {"groupname": groupname}
self._session.delete(url, params=x)
return True
# Issues
def issue(
self,
id: Union[Issue, str],
fields: Optional[str] = None,
expand: Optional[str] = None,
) -> Issue:
"""Get an issue Resource from the server.
Args:
id (Union[Issue, str]): ID or key of the issue to get
fields (Optional[str]): comma-separated string of issue fields to include in the results
expand (Optional[str]): extra information to fetch inside each resource
Returns:
Issue
"""
# this allows us to pass Issue objects to issue()
if isinstance(id, Issue):
return id
issue = Issue(self._options, self._session)
params = {}
if fields is not None:
params["fields"] = fields
if expand is not None:
params["expand"] = expand
issue.find(id, params=params)
return issue
def create_issue(
self,
fields: Optional[Dict[str, Any]] = None,
prefetch: bool = True,
**fieldargs,
) -> Issue:
"""Create a new issue and return an issue Resource for it.
Each keyword argument (other than the predefined ones) is treated as a field name and the argument's value
is treated as the intended value for that field -- if the fields argument is used, all other keyword arguments
will be ignored.
By default, the client will immediately reload the issue Resource created by this method in order to return
a complete Issue object to the caller; this behavior can be controlled through the 'prefetch' argument.
Jira projects may contain many different issue types. Some issue screens have different requirements for
fields in a new issue. This information is available through the 'createmeta' method. Further examples are
available here: https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+Example+-+Create+Issue
Args:
fields (Optional[Dict[str, Any]]): a dict containing field names and the values to use. If present, all other keyword arguments
will be ignored
prefetch (bool): whether to reload the created issue Resource so that all of its data is present in the value
returned from this method
Returns:
Issue
"""
data: Dict[str, Any] = _field_worker(fields, **fieldargs)
p = data["fields"]["project"]
if isinstance(p, str) or isinstance(p, int):
data["fields"]["project"] = {"id": self.project(str(p)).id}
p = data["fields"]["issuetype"]
if isinstance(p, int):
data["fields"]["issuetype"] = {"id": p}
if isinstance(p, str) or isinstance(p, int):
data["fields"]["issuetype"] = {"id": self.issue_type_by_name(str(p)).id}
url = self._get_url("issue")
r = self._session.post(url, data=json.dumps(data))
raw_issue_json = json_loads(r)
if "key" not in raw_issue_json:
raise JIRAError(
status_code=r.status_code, response=r, url=url, text=json.dumps(data)
)
if prefetch:
return self.issue(raw_issue_json["key"])
else:
return Issue(self._options, self._session, raw=raw_issue_json)
def create_issues(
self, field_list: List[Dict[str, Any]], prefetch: bool = True
) -> List[Dict[str, Any]]:
"""Bulk create new issues and return an issue Resource for each successfully created issue.
See `create_issue` documentation for field information.
Args:
field_list (List[Dict[str, Any]]): a list of dicts each containing field names and the values to use. Each dict
is an individual issue to create and is subject to its minimum requirements.
prefetch (bool): whether to reload the created issue Resource for each created issue so that all
of its data is present in the value returned from this method.
Returns:
List[Dict[str, Any]]
"""
data: Dict[str, List] = {"issueUpdates": []}
for field_dict in field_list:
issue_data: Dict[str, Any] = _field_worker(field_dict)
p = issue_data["fields"]["project"]
if isinstance(p, str) or isinstance(p, int):
issue_data["fields"]["project"] = {"id": self.project(str(p)).id}
p = issue_data["fields"]["issuetype"]
if isinstance(p, int):
issue_data["fields"]["issuetype"] = {"id": p}
if isinstance(p, str):
issue_data["fields"]["issuetype"] = {
"id": self.issue_type_by_name(str(p)).id
}
data["issueUpdates"].append(issue_data)
url = self._get_url("issue/bulk")
try:
r = self._session.post(url, data=json.dumps(data))
raw_issue_json = json_loads(r)
# Catching case where none of the issues has been created. See https://github.com/pycontribs/jira/issues/350
except JIRAError as je:
if je.status_code == 400 and je.response:
raw_issue_json = json.loads(je.response.text)
else:
raise
issue_list = []
errors = {}
for error in raw_issue_json["errors"]:
errors[error["failedElementNumber"]] = error["elementErrors"]["errors"]
for index, fields in enumerate(field_list):
if index in errors:
issue_list.append(
{
"status": "Error",
"error": errors[index],
"issue": None,
"input_fields": fields,
}
)
else:
issue = raw_issue_json["issues"].pop(0)
if prefetch:
issue = self.issue(issue["key"])
else:
issue = Issue(self._options, self._session, raw=issue)
issue_list.append(
{
"status": "Success",
"issue": issue,
"error": None,
"input_fields": fields,
}
)
return issue_list
def supports_service_desk(self):
"""Returns whether or not the Jira instance supports service desk.
Returns:
bool
"""
url = self.server_url + "/rest/servicedeskapi/info"
headers = {"X-ExperimentalApi": "opt-in"}
try:
r = self._session.get(url, headers=headers)
return r.status_code == 200
except JIRAError:
return False
def create_customer(self, email: str, displayName: str) -> Customer:
"""Create a new customer and return an issue Resource for it.
Args:
email (str): Customer Email
displayName (str): Customer display name
Returns:
Customer
"""
url = self.server_url + "/rest/servicedeskapi/customer"
headers = {"X-ExperimentalApi": "opt-in"}
r = self._session.post(
url,
headers=headers,
data=json.dumps({"email": email, "displayName": displayName}),
)
raw_customer_json = json_loads(r)
if r.status_code != 201:
raise JIRAError(status_code=r.status_code, request=r)
return Customer(self._options, self._session, raw=raw_customer_json)
def service_desks(self) -> List[ServiceDesk]:
"""Get a list of ServiceDesk Resources from the server visible to the current authenticated user.
Returns:
List[ServiceDesk]
"""
url = self.server_url + "/rest/servicedeskapi/servicedesk"
headers = {"X-ExperimentalApi": "opt-in"}
r_json = json_loads(self._session.get(url, headers=headers))
print(r_json)
projects = [
ServiceDesk(self._options, self._session, raw_project_json)
for raw_project_json in r_json["values"]
]
return projects
def service_desk(self, id: str) -> ServiceDesk:
"""Get a Service Desk Resource from the server.
Args:
id (str): ID or key of the Service Desk to get
Returns:
ServiceDesk
"""
return self._find_for_resource(ServiceDesk, id)
@no_type_check # FIXME: This function does not do what it wants to with fieldargs
def create_customer_request(
self, fields: Dict[str, Any] = None, prefetch: bool = True, **fieldargs
) -> Issue:
"""Create a new customer request and return an issue Resource for it.
Each keyword argument (other than the predefined ones) is treated as a field name and the argument's value
is treated as the intended value for that field -- if the fields argument is used, all other keyword arguments
will be ignored.
By default, the client will immediately reload the issue Resource created by this method in order to return
a complete Issue object to the caller; this behavior can be controlled through the 'prefetch' argument.
Jira projects may contain many different issue types. Some issue screens have different requirements for
fields in a new issue. This information is available through the 'createmeta' method. Further examples are
available here: https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+Example+-+Create+Issue
Args:
fields (Dict[str, Any]): a dict containing field names and the values to use. If present, all other keyword arguments
will be ignored
prefetch (bool): whether to reload the created issue Resource so that all of its data is present in the value
returned from this method
Returns:
Issue
"""
data = fields
p = data["serviceDeskId"]
service_desk = None
if isinstance(p, str) or isinstance(p, int):
service_desk = self.service_desk(p)
elif isinstance(p, ServiceDesk):
service_desk = p
data["serviceDeskId"] = service_desk.id
p = data["requestTypeId"]
if isinstance(p, int):
data["requestTypeId"] = p
elif isinstance(p, str):
data["requestTypeId"] = self.request_type_by_name(service_desk, p).id
url = self.server_url + "/rest/servicedeskapi/request"
headers = {"X-ExperimentalApi": "opt-in"}
r = self._session.post(url, headers=headers, data=json.dumps(data))
raw_issue_json = json_loads(r)
if "issueKey" not in raw_issue_json:
raise JIRAError(status_code=r.status_code, request=r)
if prefetch:
return self.issue(raw_issue_json["issueKey"])
else:
return Issue(self._options, self._session, raw=raw_issue_json)
def createmeta(
self,
projectKeys: Optional[Union[Tuple[str, str], str]] = None,
projectIds: Union[List, Tuple[str, str]] = [],
issuetypeIds: Optional[List[str]] = None,
issuetypeNames: Optional[str] = None,
expand: Optional[str] = None,
) -> Dict[str, Any]:
"""Get the metadata required to create issues, optionally filtered by projects and issue types.
Args:
projectKeys (Optional[Union[Tuple[str, str], str]]): keys of the projects to filter the results with.
Can be a single value or a comma-delimited string. May be combined
with projectIds.
projectIds (Union[List, Tuple[str, str]]): IDs of the projects to filter the results with. Can
be a single value or a comma-delimited string. May be combined with
projectKeys.
issuetypeIds (Optional[List[str]]): IDs of the issue types to filter the results with.
Can be a single value or a comma-delimited string. May be combined
with issuetypeNames.
issuetypeNames (Optional[str]): Names of the issue types to filter the results
with. Can be a single value or a comma-delimited string. May be
combined with issuetypeIds.
expand (Optional[str]): extra information to fetch inside each resource.
Returns:
Dict[str, Any]
"""
params: Dict[str, Any] = {}
if projectKeys is not None:
params["projectKeys"] = projectKeys
if projectIds is not None:
if isinstance(projectIds, str):
projectIds = projectIds.split(",")
params["projectIds"] = projectIds
if issuetypeIds is not None:
params["issuetypeIds"] = issuetypeIds
if issuetypeNames is not None:
params["issuetypeNames"] = issuetypeNames
if expand is not None:
params["expand"] = expand
return self._get_json("issue/createmeta", params)
def _get_user_key(self, user: str) -> str:
"""Internal method for translating an user (str) to an key."""
try:
key = self.search_users(user, maxResults=1)[0].key
except Exception as e:
raise JIRAError(str(e))
return key
# non-resource
@translate_resource_args
def assign_issue(self, issue: Union[int, str], assignee: str) -> bool:
"""Assign an issue to a user. None will set it to unassigned. -1 will set it to Automatic.
Args:
issue (Union[int,str]): the issue ID or key to assign
assignee (str): the user to assign the issue to
Returns:
bool
"""
url = self._get_latest_url("issue/{}/assignee".format(str(issue)))
payload = {"name": self._get_user_key(assignee)}
# 'key' and 'name' are deprecated in favor of accountId
r = self._session.put(url, data=json.dumps(payload))
raise_on_error(r)
return True
@translate_resource_args
def comments(self, issue: str, expand: Optional[str] = None) -> List[Comment]:
"""Get a list of comment Resources.
:param issue: the issue to get comments from
:type issue: str
:param expand: extra information to fetch for each comment
such as renderedBody and properties.
:type expand: str
:rtype: List[Comment]
"""
params = {}
if expand is not None:
params["expand"] = expand
r_json = self._get_json("issue/{}/comment".format(str(issue)), params=params)
comments = [
Comment(self._options, self._session, raw_comment_json)
for raw_comment_json in r_json["comments"]
]
return comments
@translate_resource_args
def comment(
self, issue: str, comment: str, expand: Optional[str] = None
) -> Comment:
"""Get a comment Resource from the server for the specified ID.
:param issue: ID or key of the issue to get the comment from
:param comment: ID of the comment to get
:param expand: extra information to fetch for comment
such as renderedBody and properties.
"""
return self._find_for_resource(Comment, (issue, comment), expand=expand)
@translate_resource_args
def add_comment(
self,
issue: str,
body: str,
visibility: Optional[Dict[str, str]] = None,
is_internal: bool = False,
) -> Comment:
"""Add a comment from the current authenticated user on the specified issue and return a Resource for it.
The issue identifier and comment body are required.
Args:
issue (str): ID or key of the issue to add the comment to
body (str): Text of the comment to add
visibility (Optional[Dict[str, str]]): a dict containing two entries: "type" and "value".
"type" is 'role' (or 'group' if the Jira server has configured
comment visibility for groups) and 'value' is the name of the role
(or group) to which viewing of this comment will be restricted.
is_internal (bool): Defines whether a comment has to be marked as 'Internal' in Jira Service Desk (Default: False)
Returns:
Comment: the created comment
"""
data: Dict[str, Any] = {"body": body}
if is_internal:
data.update(
{
"properties": [
{"key": "sd.public.comment", "value": {"internal": is_internal}}
]
}
)
if visibility is not None:
data["visibility"] = visibility
url = self._get_url("issue/" + str(issue) + "/comment")
r = self._session.post(url, data=json.dumps(data))
comment = Comment(self._options, self._session, raw=json_loads(r))
return comment
# non-resource
@translate_resource_args
def editmeta(self, issue: Union[str, int]):
"""Get the edit metadata for an issue.
Args:
issue (str): the issue to get metadata for
Returns:
Dict[str, Dict[str, Dict[str, Any]]]
"""
return self._get_json("issue/" + str(issue) + "/editmeta")
@translate_resource_args
def remote_links(self, issue: Union[str, int]) -> List[RemoteLink]:
"""Get a list of remote link Resources from an issue.
Args:
issue (str): the issue to get remote links from
"""
r_json = self._get_json("issue/" + str(issue) + "/remotelink")
remote_links = [
RemoteLink(self._options, self._session, raw_remotelink_json)
for raw_remotelink_json in r_json
]
return remote_links
@translate_resource_args
def remote_link(self, issue: str, id: str) -> RemoteLink:
"""Get a remote link Resource from the server.
Args:
issue (str): the issue holding the remote link
id (str): ID of the remote link
"""
return self._find_for_resource(RemoteLink, (issue, id))
# removed the @translate_resource_args because it prevents us from finding
# information for building a proper link
def add_remote_link(
self,
issue: str,
destination: Union[Issue, Dict[str, Any]],
globalId: Optional[str] = None,
application: Optional[Dict[str, Any]] = None,
relationship: Optional[str] = None,
) -> RemoteLink:
"""Add a remote link from an issue to an external application and returns a remote link Resource for it.
``destination`` should be a dict containing at least ``url`` to the linked external URL and
``title`` to display for the link inside Jira.
For definitions of the allowable fields for ``object`` and the keyword arguments ``globalId``, ``application``
and ``relationship``, see https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+for+Remote+Issue+Links.
Args:
issue (str): the issue to add the remote link to
destination (Union[Issue, Dict[str, Any]]): the link details to add (see the above link for details)
globalId (Optional[str]): unique ID for the link (see the above link for details)
application (Optional[Dict[str,Any]]): application information for the link (see the above link for details)
relationship (Optional[str]): relationship description for the link (see the above link for details)
Returns:
RemoteLink: the added remote lint
"""
try:
applicationlinks: List[Dict] = self.applicationlinks()
except JIRAError as e:
applicationlinks = []
# In many (if not most) configurations, non-admin users are
# not allowed to list applicationlinks; if we aren't allowed,
# let's let people try to add remote links anyway, we just
# won't be able to be quite as helpful.
warnings.warn(
"Unable to gather applicationlinks; you will not be able "
"to add links to remote issues: (%s) %s" % (e.status_code, e.text),
Warning,
)
data: Dict[str, Any] = {}
if isinstance(destination, Issue) and destination.raw:
data["object"] = {"title": str(destination), "url": destination.permalink()}
for x in applicationlinks:
if x["application"]["displayUrl"] == destination._options["server"]:
data["globalId"] = "appId=%s&issueId=%s" % (
x["application"]["id"],
destination.raw["id"],
)
data["application"] = {
"name": x["application"]["name"],
"type": "com.atlassian.jira",
}
break
if "globalId" not in data:
raise NotImplementedError("Unable to identify the issue to link to.")
else:
if globalId is not None:
data["globalId"] = globalId
if application is not None:
data["application"] = application
data["object"] = destination
if relationship is not None:
data["relationship"] = relationship
# check if the link comes from one of the configured application links
if isinstance(destination, Issue) and destination.raw:
for x in applicationlinks:
if x["application"]["displayUrl"] == self.server_url:
data["globalId"] = "appId=%s&issueId=%s" % (
x["application"]["id"],
destination.raw["id"], # .raw only present on Issue
)
data["application"] = {
"name": x["application"]["name"],
"type": "com.atlassian.jira",
}
break
url = self._get_url("issue/" + str(issue) + "/remotelink")
r = self._session.post(url, data=json.dumps(data))
remote_link = RemoteLink(self._options, self._session, raw=json_loads(r))
return remote_link
def add_simple_link(self, issue: str, object: Dict[str, Any]):
"""Add a simple remote link from an issue to web resource.
This avoids the admin access problems from add_remote_link by just
using a simple object and presuming all fields are correct and not
requiring more complex ``application`` data.
``object`` should be a dict containing at least ``url`` to the
linked external URL and ``title`` to display for the link inside Jira.
For definitions of the allowable fields for ``object`` , see https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+for+Remote+Issue+Links.
Args:
issue (str): the issue to add the remote link to
object (Dict[str,Any]): the dictionary used to create remotelink data
Returns:
RemoteLint
"""
data = {"object": object}
url = self._get_url("issue/" + str(issue) + "/remotelink")
r = self._session.post(url, data=json.dumps(data))
simple_link = RemoteLink(self._options, self._session, raw=json_loads(r))
return simple_link
# non-resource
@translate_resource_args
def transitions(self, issue: str, id: Optional[str] = None, expand=None):
"""Get a list of the transitions available on the specified issue to the current user.
Args:
issue (str): ID or key of the issue to get the transitions from
id (Optional[str]): if present, get only the transition matching this ID
expand (Optional): extra information to fetch inside each transition
Returns:
Any: json of response
"""
params = {}
if id is not None:
params["transitionId"] = id
if expand is not None:
params["expand"] = expand
return self._get_json("issue/" + str(issue) + "/transitions", params=params)[
"transitions"
]
def find_transitionid_by_name(
self, issue: str, transition_name: str
) -> Optional[int]:
"""Get a transitionid available on the specified issue to the current user.
Look at https://developer.atlassian.com/static/rest/jira/6.1.html#d2e1074 for json reference
Args:
issue (str): ID or key of the issue to get the transitions from
trans_name (str): iname of transition we are looking for
"""
transitions_json = self.transitions(issue)
id: Optional[int] = None
for transition in transitions_json:
if transition["name"].lower() == transition_name.lower():
id = transition["id"]
break
return id
@translate_resource_args
def transition_issue(
self,
issue: str,
transition: str,
fields: Optional[Dict[str, Any]] = None,
comment: Optional[str] = None,
worklog: Optional[str] = None,
**fieldargs,
):
"""Perform a transition on an issue.
Each keyword argument (other than the predefined ones) is treated as a field name and the argument's value
is treated as the intended value for that field -- if the fields argument is used, all other keyword arguments
will be ignored. Field values will be set on the issue as part of the transition process.
Args:
issue (str): ID or key of the issue to perform the transition on
transition (str): ID or name of the transition to perform
fields (Optional[Dict[str,Any]]): a dict containing field names and the values to use.
comment (Optional[str]): String to add as comment to the issue when performing the transition.
workload (Optional[str]): String to add as time spent on the issue when performing the transition.
**fieldargs: If present, all other keyword arguments will be ignored
"""
transitionId: Optional[int] = None
try:
transitionId = int(transition)
except Exception:
# cannot cast to int, so try to find transitionId by name
transitionId = self.find_transitionid_by_name(issue, transition)
if transitionId is None:
raise JIRAError(f"Invalid transition name. {transition}")
data: Dict[str, Any] = {"transition": {"id": transitionId}}
if comment:
data["update"] = {"comment": [{"add": {"body": comment}}]}
if worklog:
data["update"] = {"worklog": [{"add": {"timeSpent": worklog}}]}
if fields is not None:
data["fields"] = fields
else:
fields_dict = {}
for field in fieldargs:
fields_dict[field] = fieldargs[field]
data["fields"] = fields_dict
url = self._get_url("issue/" + str(issue) + "/transitions")
r = self._session.post(url, data=json.dumps(data))
try:
r_json = json_loads(r)
except ValueError as e:
self.log.error(f"{e}\n{r.text}")
raise e
return r_json
@translate_resource_args
def votes(self, issue: str) -> Votes:
"""Get a votes Resource from the server.
Args:
issue (str): ID or key of the issue to get the votes for
Returns:
Votes
"""
return self._find_for_resource(Votes, issue)
@translate_resource_args
def add_vote(self, issue: str) -> Response:
"""Register a vote for the current authenticated user on an issue.
Args:
issue (str): ID or key of the issue to vote on
Returns:
Response
"""
url = self._get_url("issue/" + str(issue) + "/votes")
return self._session.post(url)
@translate_resource_args
def remove_vote(self, issue: str):
"""Remove the current authenticated user's vote from an issue.
Args:
issue (str): ID or key of the issue to remove vote on
"""
url = self._get_url("issue/" + str(issue) + "/votes")
self._session.delete(url)
@translate_resource_args
def watchers(self, issue: str) -> Watchers:
"""Get a watchers Resource from the server for an issue.
Args:
issue (str): ID or key of the issue to get the watchers for
Returns:
Watchers
"""
return self._find_for_resource(Watchers, issue)
@translate_resource_args
def add_watcher(self, issue: str, watcher: str) -> Response:
"""Add a user to an issue's watchers list.
Args:
issue (str): ID or key of the issue affected
watcher (str): key of the user to add to the watchers list
"""
url = self._get_url("issue/" + str(issue) + "/watchers")
return self._session.post(url, data=json.dumps(watcher))
@translate_resource_args
def remove_watcher(self, issue: str, watcher: str) -> Response:
"""Remove a user from an issue's watch list.
Args:
issue (str): ID or key of the issue affected
watcher (str): key of the user to remove from the watchers list
Returns:
Response
"""
url = self._get_url("issue/" + str(issue) + "/watchers")
# https://docs.atlassian.com/software/jira/docs/api/REST/8.13.6/#api/2/issue-removeWatcher
params = {"username": watcher}
result = self._session.delete(url, params=params)
return result
@translate_resource_args
def worklogs(self, issue: str) -> List[Worklog]:
"""Get a list of worklog Resources from the server for an issue.
Args:
issue (str): ID or key of the issue to get worklogs from
Returns:
List[Worklog]
"""
r_json = self._get_json("issue/" + str(issue) + "/worklog")
worklogs = [
Worklog(self._options, self._session, raw_worklog_json)
for raw_worklog_json in r_json["worklogs"]
]
return worklogs
@translate_resource_args
def worklog(self, issue: str, id: str) -> Worklog:
"""Get a specific worklog Resource from the server.
Args:
issue (str): ID or key of the issue to get the worklog from
id (str): ID of the worklog to get
Returns:
Worklog
"""
return self._find_for_resource(Worklog, (issue, id))
@translate_resource_args
def add_worklog(
self,
issue,
timeSpent: (Optional[str]) = None,
timeSpentSeconds: (Optional[str]) = None,
adjustEstimate: (Optional[str]) = None,
newEstimate: (Optional[str]) = None,
reduceBy: (Optional[str]) = None,
comment: (Optional[str]) = None,
started: (Optional[datetime.datetime]) = None,
user: (Optional[str]) = None,
) -> Worklog:
"""Add a new worklog entry on an issue and return a Resource for it.
Args:
issue (str): the issue to add the worklog to
timeSpent (Optional[str]): a worklog entry with this amount of time spent, e.g. "2d"
timeSpentSeconds (Optional[str]): a worklog entry with this amount of time spent in seconds
adjustEstimate (Optional[str]): allows the user to provide specific instructions to update
the remaining time estimate of the issue. The value can either be ``new``, ``leave``, ``manual`` or ``auto`` (default).
newEstimate (Optional[str]): the new value for the remaining estimate field. e.g. "2d"
reduceBy (Optional[str]): the amount to reduce the remaining estimate by e.g. "2d"
comment (Optional[str]): optional worklog comment
started (Optional[datetime.datetime]): Moment when the work is logged, if not specified will default to now
user (Optional[str]): the user ID or name to use for this worklog
Returns:
Worklog
"""
params = {}
if adjustEstimate is not None:
params["adjustEstimate"] = adjustEstimate
if newEstimate is not None:
params["newEstimate"] = newEstimate
if reduceBy is not None:
params["reduceBy"] = reduceBy
data: Dict[str, Any] = {}
if timeSpent is not None:
data["timeSpent"] = timeSpent
if timeSpentSeconds is not None:
data["timeSpentSeconds"] = timeSpentSeconds
if comment is not None:
data["comment"] = comment
elif user:
# we log user inside comment as it doesn't always work
data["comment"] = user
if started is not None:
# based on REST Browser it needs: "2014-06-03T08:21:01.273+0000"
if started.tzinfo is None:
data["started"] = started.strftime("%Y-%m-%dT%H:%M:%S.000+0000")
else:
data["started"] = started.strftime("%Y-%m-%dT%H:%M:%S.000%z")
if user is not None:
data["author"] = {
"name": user,
"self": self.JIRA_BASE_URL + "/rest/api/latest/user?username=" + user,
"displayName": user,
"active": False,
}
data["updateAuthor"] = data["author"]
# report bug to Atlassian: author and updateAuthor parameters are
# ignored.
url = self._get_url(f"issue/{issue}/worklog")
r = self._session.post(url, params=params, data=json.dumps(data))
return Worklog(self._options, self._session, json_loads(r))
# Issue links
@translate_resource_args
def create_issue_link(
self,
type: Union[str, IssueLinkType],
inwardIssue: str,
outwardIssue: str,
comment: Optional[Dict[str, Any]] = None,
) -> Response:
"""Create a link between two issues.
Args:
type (Union[str,IssueLinkType]): the type of link to create
inwardIssue: the issue to link from
outwardIssue: the issue to link to
comment (Optional[Dict[str, Any]]): a comment to add to the issues with the link.
Should be a dict containing ``body`` and ``visibility`` fields: ``body`` being
the text of the comment and ``visibility`` being a dict containing
two entries: ``type`` and ``value``. ``type`` is ``role`` (or
``group`` if the Jira server has configured comment visibility for
groups) and ``value`` is the name of the role (or group) to which
viewing of this comment will be restricted.
Returns:
Response
"""
# let's see if we have the right issue link 'type' and fix it if needed
issue_link_types = self.issue_link_types()
if type not in issue_link_types:
for lt in issue_link_types:
if lt.outward == type:
# we are smart to figure it out what he meant
type = lt.name
break
elif lt.inward == type:
# so that's the reverse, so we fix the request
type = lt.name
inwardIssue, outwardIssue = outwardIssue, inwardIssue
break
data = {
"type": {"name": type},
"inwardIssue": {"key": inwardIssue},
"outwardIssue": {"key": outwardIssue},
"comment": comment,
}
url = self._get_url("issueLink")
return self._session.post(url, data=json.dumps(data))
def delete_issue_link(self, id: str):
"""Delete a link between two issues.
Args:
id (str): ID of the issue link to delete
"""
url = self._get_url("issueLink") + "/" + id
return self._session.delete(url)
def issue_link(self, id: str):
"""Get an issue link Resource from the server.
Args:
id (str): ID of the issue link to get
"""
return self._find_for_resource(IssueLink, id)
# Issue link types
def issue_link_types(self, force: bool = False) -> List[IssueLinkType]:
"""Get a list of issue link type Resources from the server.
Returns:
List[IssueLinkType]
"""
if not hasattr(self, "self._cached_issue_link_types") or force:
r_json = self._get_json("issueLinkType")
self._cached_issue_link_types = [
IssueLinkType(self._options, self._session, raw_link_json)
for raw_link_json in r_json["issueLinkTypes"]
]
return self._cached_issue_link_types
def issue_link_type(self, id: str) -> IssueLinkType:
"""Get an issue link type Resource from the server.
Args:
id (str): ID of the issue link type to get
Returns:
IssueLinkType
"""
return self._find_for_resource(IssueLinkType, id)
# Issue types
def issue_types(self) -> List[IssueType]:
"""Get a list of issue type Resources from the server.
Returns:
List[IssueType]
"""
r_json = self._get_json("issuetype")
issue_types = [
IssueType(self._options, self._session, raw_type_json)
for raw_type_json in r_json
]
return issue_types
def issue_type(self, id: str) -> IssueType:
"""Get an issue type Resource from the server.
Args:
id (str): ID of the issue type to get
Returns:
IssueType
"""
return self._find_for_resource(IssueType, id)
def issue_type_by_name(self, name: str) -> IssueType:
"""
Args:
name (str): Name of the issue type
Returns:
IssueType
"""
matching_issue_types = [it for it in self.issue_types() if it.name == name]
if len(matching_issue_types) == 1:
return matching_issue_types[0]
elif len(matching_issue_types) == 0:
raise KeyError(f"Issue type '{name}' is unknown.")
else:
raise KeyError(f"Issue type '{name}' appears more than once.")
def request_types(self, service_desk: ServiceDesk) -> List[RequestType]:
"""Returns request types supported by a service desk instance.
Args:
service_desk (ServiceDesk): The service desk instance.
Returns:
List[RequestType]
"""
if hasattr(service_desk, "id"):
service_desk = service_desk.id
url = (
self.server_url
+ f"/rest/servicedeskapi/servicedesk/{service_desk}/requesttype"
)
headers = {"X-ExperimentalApi": "opt-in"}
r_json = json_loads(self._session.get(url, headers=headers))
request_types = [
RequestType(self._options, self._session, raw_type_json)
for raw_type_json in r_json["values"]
]
return request_types
def request_type_by_name(self, service_desk: ServiceDesk, name: str):
request_types = self.request_types(service_desk)
try:
request_type = [rt for rt in request_types if rt.name == name][0]
except IndexError:
raise KeyError(f"Request type '{name}' is unknown.")
return request_type
# User permissions
# non-resource
def my_permissions(
self,
projectKey: Optional[str] = None,
projectId: Optional[str] = None,
issueKey: Optional[str] = None,
issueId: Optional[str] = None,
) -> Dict[str, Dict[str, Dict[str, str]]]:
"""Get a dict of all available permissions on the server.
Args:
projectKey (Optional[str]): limit returned permissions to the specified project
projectId (Optional[str]): limit returned permissions to the specified project
issueKey (Optional[str]): limit returned permissions to the specified issue
issueId (Optional[str]): limit returned permissions to the specified issue
Returns:
Dict[str, Dict[str, Dict[str, str]]]
"""
params = {}
if projectKey is not None:
params["projectKey"] = projectKey
if projectId is not None:
params["projectId"] = projectId
if issueKey is not None:
params["issueKey"] = issueKey
if issueId is not None:
params["issueId"] = issueId
return self._get_json("mypermissions", params=params)
# Priorities
def priorities(self):
"""Get a list of priority Resources from the server.
Returns:
List[Priority]
"""
r_json = self._get_json("priority")
priorities = [
Priority(self._options, self._session, raw_priority_json)
for raw_priority_json in r_json
]
return priorities
def priority(self, id: str) -> Priority:
"""Get a priority Resource from the server.
Args:
id (str): ID of the priority to get
Returns:
Priority
"""
return self._find_for_resource(Priority, id)
# Projects
def projects(self) -> List[Project]:
"""Get a list of project Resources from the server visible to the current authenticated user.
Returns:
List[Project]
"""
r_json = self._get_json("project")
projects = [
Project(self._options, self._session, raw_project_json)
for raw_project_json in r_json
]
return projects
def project(self, id: str) -> Project:
"""Get a project Resource from the server.
Args:
id (str): ID or key of the project to get
Returns:
Project
"""
return self._find_for_resource(Project, id)
# non-resource
@translate_resource_args
def project_avatars(self, project: str):
"""Get a dict of all avatars for a project visible to the current authenticated user.
Args:
project (str): ID or key of the project to get avatars for
"""
return self._get_json("project/" + project + "/avatars")
@translate_resource_args
def create_temp_project_avatar(
self,
project: str,
filename: str,
size: int,
avatar_img: bytes,
contentType: str = None,
auto_confirm: bool = False,
):
"""Register an image file as a project avatar.
The avatar created is temporary and must be confirmed before it can be used.
Avatar images are specified by a filename, size, and file object. By default, the client will attempt to
autodetect the picture's content type: this mechanism relies on libmagic and will not work out of the box
on Windows systems (see https://filemagic.readthedocs.io/en/latest/guide.html for details on how to install
support). The ``contentType`` argument can be used to explicitly set the value (note that Jira will reject any
type other than the well-known ones for images, e.g. ``image/jpg``, ``image/png``, etc.)
This method returns a dict of properties that can be used to crop a subarea of a larger image for use. This
dict should be saved and passed to :py:meth:`confirm_project_avatar` to finish the avatar creation process. If
you want to cut out the middleman and confirm the avatar with Jira's default cropping, pass the 'auto_confirm'
argument with a truthy value and :py:meth:`confirm_project_avatar` will be called for you before this method
returns.
Args:
project (str): ID or key of the project to create the avatar in
filename (str): name of the avatar file
size (int): size of the avatar file
avatar_img (bytes): file-like object holding the avatar
contentType (str): explicit specification for the avatar image's content-type
auto_confirm (bool): whether to automatically confirm the temporary avatar by calling
:py:meth:`confirm_project_avatar` with the return value of this method. (Default: False)
"""
size_from_file = os.path.getsize(filename)
if size != size_from_file:
size = size_from_file
params = {"filename": filename, "size": size}
headers: Dict[str, Any] = {"X-Atlassian-Token": "no-check"}
if contentType is not None:
headers["content-type"] = contentType
else:
# try to detect content-type, this may return None
headers["content-type"] = self._get_mime_type(avatar_img)
url = self._get_url("project/" + project + "/avatar/temporary")
r = self._session.post(url, params=params, headers=headers, data=avatar_img)
cropping_properties: Dict[str, Any] = json_loads(r)
if auto_confirm:
return self.confirm_project_avatar(project, cropping_properties)
else:
return cropping_properties
@translate_resource_args
def confirm_project_avatar(self, project: str, cropping_properties: Dict[str, Any]):
"""Confirm the temporary avatar image previously uploaded with the specified cropping.
After a successful registry with :py:meth:`create_temp_project_avatar`, use this method to confirm the avatar
for use. The final avatar can be a subarea of the uploaded image, which is customized with the
``cropping_properties``: the return value of :py:meth:`create_temp_project_avatar` should be used for this
argument.
Args:
project (str): ID or key of the project to confirm the avatar in
cropping_properties (Dict[str,Any]): a dict of cropping properties from :py:meth:`create_temp_project_avatar`
"""
data = cropping_properties
url = self._get_url("project/" + project + "/avatar")
r = self._session.post(url, data=json.dumps(data))
return json_loads(r)
@translate_resource_args
def set_project_avatar(self, project: str, avatar: str):
"""Set a project's avatar.
Args:
project (str): ID or key of the project to set the avatar on
avatar (str): ID of the avatar to set
"""
self._set_avatar(None, self._get_url("project/" + project + "/avatar"), avatar)
@translate_resource_args
def delete_project_avatar(self, project: str, avatar: str) -> Response:
"""Delete a project's avatar.
Args:
project (str): ID or key of the project to delete the avatar from
avatar (str): ID of the avatar to delete
"""
url = self._get_url("project/" + project + "/avatar/" + avatar)
return self._session.delete(url)
@translate_resource_args
def project_components(self, project: str) -> List[Component]:
"""Get a list of component Resources present on a project.
Args:
project (str): ID or key of the project to get components from
Returns:
List[Component]
"""
r_json = self._get_json("project/" + project + "/components")
components = [
Component(self._options, self._session, raw_comp_json)
for raw_comp_json in r_json
]
return components
@translate_resource_args
def project_versions(self, project: str) -> List[Version]:
"""Get a list of version Resources present on a project.
Args:
project (str): ID or key of the project to get versions from
Returns:
List[Version]
"""
r_json = self._get_json("project/" + project + "/versions")
versions = [
Version(self._options, self._session, raw_ver_json)
for raw_ver_json in r_json
]
return versions
@translate_resource_args
def get_project_version_by_name(
self, project: str, version_name: str
) -> Optional[Version]:
"""Get a version Resource by its name present on a project.
Args:
project (str): ID or key of the project to get versions from
version_name (str): name of the version to search for
Returns:
Optional[Version]
"""
versions: List[Version] = self.project_versions(project)
for version in versions:
if version.name == version_name:
return version
return None
@translate_resource_args
def rename_version(self, project: str, old_name: str, new_name: str) -> None:
"""Rename a version Resource on a project.
Args:
project (str): ID or key of the project to get versions from
old_name (str): old name of the version to rename
new_name (str): new name of the version to rename
Returns:
None
"""
version = self.get_project_version_by_name(project, old_name)
if version:
version.update(name=new_name)
# non-resource
@translate_resource_args
def project_roles(self, project: str) -> Dict[str, Dict[str, str]]:
"""Get a dict of role names to resource locations for a project.
Args:
project (str): ID or key of the project to get roles from
"""
path = "project/" + project + "/role"
_rolesdict: Dict[str, str] = self._get_json(path)
rolesdict: Dict[str, Dict[str, str]] = {}
for k, v in _rolesdict.items():
tmp: Dict[str, str] = {}
tmp["id"] = v.split("/")[-1]
tmp["url"] = v
rolesdict[k] = tmp
return rolesdict
# TODO(ssbarnea): return a list of Roles()
@translate_resource_args
def project_role(self, project: str, id: str) -> Role:
"""Get a role Resource.
Args:
project (str): ID or key of the project to get the role from
id (str): ID of the role to get
"""
if isinstance(id, Number):
id = f"{id}"
return self._find_for_resource(Role, (project, id))
# Resolutions
def resolutions(self) -> List[Resolution]:
"""Get a list of resolution Resources from the server.
Returns:
List[Resolution]
"""
r_json = self._get_json("resolution")
resolutions = [
Resolution(self._options, self._session, raw_res_json)
for raw_res_json in r_json
]
return resolutions
def resolution(self, id: str) -> Resolution:
"""Get a resolution Resource from the server.
Args:
id (str): ID of the resolution to get
Returns:
Resolution
"""
return self._find_for_resource(Resolution, id)
# Search
def search_issues(
self,
jql_str: str,
startAt: int = 0,
maxResults: int = 50,
validate_query: bool = True,
fields: Optional[Union[str, List[str]]] = None,
expand: Optional[str] = None,
json_result: bool = False,
) -> Union[List[Dict[str, Any]], ResultList[Issue]]:
"""Get a :class:`~jira.client.ResultList` of issue Resources matching a JQL search string.
Args:
jql_str (str): The JQL search string.
startAt (int): Index of the first issue to return. (Default: 0)
maxResults (int): Maximum number of issues to return. Total number of results
is available in the ``total`` attribute of the returned :class:`~jira.client.ResultList`.
If maxResults evaluates as False, it will try to get all issues in batches. (Default: 50)
validate_query (bool): Whether or not the query should be validated. (Default: True)
fields (Optional[Union[str, List[str]]]): comma-separated string or list of issue fields to include in the results.
Default is to include all fields.
expand (Optional[str]): extra information to fetch inside each resource
json_result (bool): JSON response will be returned when this parameter is set to True.
Otherwise, :class:`~jira.client.ResultList` will be returned.
Returns:
Union[Dict,ResultList]: Dict if ``json_result=True``
"""
if isinstance(fields, str):
fields = fields.split(",")
else:
fields = list(fields or [])
# this will translate JQL field names to REST API Name
# most people do know the JQL names so this will help them use the API easier
untranslate = {} # use to add friendly aliases when we get the results back
if self._fields:
for i, field in enumerate(fields):
if field in self._fields:
untranslate[self._fields[field]] = fields[i]
fields[i] = self._fields[field]
search_params = {
"jql": jql_str,
"startAt": startAt,
"validateQuery": validate_query,
"fields": fields,
"expand": expand,
}
if json_result:
search_params["maxResults"] = maxResults
if not maxResults:
warnings.warn(
"All issues cannot be fetched at once, when json_result parameter is set",
Warning,
)
r_json: List[Dict[str, Any]] = self._get_json(
"search", params=search_params
)
return r_json
issues = self._fetch_pages(
Issue, "issues", "search", startAt, maxResults, search_params
)
if untranslate:
iss: Issue
for iss in issues:
for k, v in untranslate.items():
if iss.raw:
if k in iss.raw.get("fields", {}):
iss.raw["fields"][v] = iss.raw["fields"][k]
return issues
# Security levels
def security_level(self, id: str) -> SecurityLevel:
"""Get a security level Resource.
Args:
id (str): ID of the security level to get
"""
return self._find_for_resource(SecurityLevel, id)
# Server info
# non-resource
def server_info(self) -> Dict[str, Any]:
"""Get a dict of server information for this Jira instance.
Returns:
Dict[str, Any]
"""
retry = 0
j = self._get_json("serverInfo")
while not j and retry < 3:
self.log.warning(
"Bug https://jira.atlassian.com/browse/JRA-59676 trying again..."
)
retry += 1
j = self._get_json("serverInfo")
return j
def myself(self) -> Dict[str, Any]:
"""Get a dict of server information for this Jira instance."""
return self._get_json("myself")
# Status
def statuses(self) -> List[Status]:
"""Get a list of status Resources from the server.
Returns:
List[Status]
"""
r_json = self._get_json("status")
statuses = [
Status(self._options, self._session, raw_stat_json)
for raw_stat_json in r_json
]
return statuses
def status(self, id: str) -> Status:
"""Get a status Resource from the server.
Args:
id (str): ID of the status resource to get
Returns:
Status
"""
return self._find_for_resource(Status, id)
# Category
def statuscategories(self) -> List[StatusCategory]:
"""Get a list of status category Resources from the server.
Returns:
List[StatusCategory]
"""
r_json = self._get_json("statuscategory")
statuscategories = [
StatusCategory(self._options, self._session, raw_stat_json)
for raw_stat_json in r_json
]
return statuscategories
def statuscategory(self, id: int) -> StatusCategory:
"""Get a status category Resource from the server.
Args:
id (int): ID of the status category resource to get
Returns:
StatusCategory
"""
return self._find_for_resource(StatusCategory, id)
# Users
def user(self, id: str, expand: Optional[Any] = None) -> User:
"""Get a user Resource from the server.
Args:
id (str): ID of the user to get
expand (Optional[Any]): Extra information to fetch inside each resource
Returns:
User
"""
user = User(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
user.find(id, params=params)
return user
def search_assignable_users_for_projects(
self, username: str, projectKeys: str, startAt: int = 0, maxResults: int = 50
) -> ResultList:
"""Get a list of user Resources that match the search string and can be assigned issues for projects.
Args:
username (str): A string to match usernames against
projectKeys (str): Comma-separated list of project keys to check for issue assignment permissions
startAt (int): Index of the first user to return (Default: 0)
maxResults (int): Maximum number of users to return.
If maxResults evaluates as False, it will try to get all users in batches. (Default: 50)
Returns:
ResultList
"""
params = {"username": username, "projectKeys": projectKeys}
return self._fetch_pages(
User,
None,
"user/assignable/multiProjectSearch",
startAt,
maxResults,
params,
)
def search_assignable_users_for_issues(
self,
username: str,
project: Optional[str] = None,
issueKey: Optional[str] = None,
expand: Optional[Any] = None,
startAt: int = 0,
maxResults: int = 50,
):
"""Get a list of user Resources that match the search string for assigning or creating issues.
This method is intended to find users that are eligible to create issues in a project or be assigned
to an existing issue. When searching for eligible creators, specify a project. When searching for eligible
assignees, specify an issue key.
Args:
username (str): A string to match usernames against
project (Optional[str]): Filter returned users by permission in this project
(expected if a result will be used to create an issue)
issueKey (Optional[str]): Filter returned users by this issue
(expected if a result will be used to edit this issue)
expand (Optional[Any]): Extra information to fetch inside each resource
startAt (int): Index of the first user to return (Default: 0)
maxResults (int): maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches. (Default: 50)
Returns:
ResultList
"""
params = {"username": username}
if project is not None:
params["project"] = project
if issueKey is not None:
params["issueKey"] = issueKey
if expand is not None:
params["expand"] = expand
return self._fetch_pages(
User, None, "user/assignable/search", startAt, maxResults, params
)
# non-resource
def user_avatars(self, username: str) -> Dict[str, Any]:
"""Get a dict of avatars for the specified user.
Args:
username (str): the username to get avatars for
"""
return self._get_json("user/avatars", params={"username": username})
def create_temp_user_avatar(
self,
user: str,
filename: str,
size: int,
avatar_img: bytes,
contentType: Any = None,
auto_confirm: bool = False,
):
"""Register an image file as a user avatar.
The avatar created is temporary and must be confirmed before it can
be used.
Avatar images are specified by a filename, size, and file object. By default, the client will attempt to
autodetect the picture's content type: this mechanism relies on ``libmagic`` and will not work out of the box
on Windows systems (see http://filemagic.readthedocs.org/en/latest/guide.html for details on how to install
support). The ``contentType`` argument can be used to explicitly set the value (note that Jira will reject any
type other than the well-known ones for images, e.g. ``image/jpg``, ``image/png``, etc.)
This method returns a dict of properties that can be used to crop a subarea of a larger image for use. This
dict should be saved and passed to :py:meth:`confirm_user_avatar` to finish the avatar creation process. If you
want to cut out the middleman and confirm the avatar with Jira's default cropping, pass the ``auto_confirm``
argument with a truthy value and :py:meth:`confirm_user_avatar` will be called for you before this method
returns.
Args:
user (str): User to register the avatar for
filename (str): name of the avatar file
size (int): size of the avatar file
avatar_img (bytes): file-like object containing the avatar
contentType (Optional[Any]): explicit specification for the avatar image's content-type
auto_confirm (bool): whether to automatically confirm the temporary avatar by calling
:py:meth:`confirm_user_avatar` with the return value of this method. (Default: False)
"""
size_from_file = os.path.getsize(filename)
if size != size_from_file:
size = size_from_file
# remove path from filename
filename = os.path.split(filename)[1]
params = {"username": user, "filename": filename, "size": size}
headers: Dict[str, Any]
headers = {"X-Atlassian-Token": "no-check"}
if contentType is not None:
headers["content-type"] = contentType
else:
# try to detect content-type, this may return None
headers["content-type"] = self._get_mime_type(avatar_img)
url = self._get_url("user/avatar/temporary")
r = self._session.post(url, params=params, headers=headers, data=avatar_img)
cropping_properties: Dict[str, Any] = json_loads(r)
if auto_confirm:
return self.confirm_user_avatar(user, cropping_properties)
else:
return cropping_properties
def confirm_user_avatar(self, user: str, cropping_properties: Dict[str, Any]):
"""Confirm the temporary avatar image previously uploaded with the specified cropping.
After a successful registry with :py:meth:`create_temp_user_avatar`, use this method to confirm the avatar for
use. The final avatar can be a subarea of the uploaded image, which is customized with the
``cropping_properties``: the return value of :py:meth:`create_temp_user_avatar` should be used for this
argument.
Args:
user (str): the user to confirm the avatar for
cropping_properties (Dict[str,Any]): a dict of cropping properties from :py:meth:`create_temp_user_avatar`
"""
data = cropping_properties
url = self._get_url("user/avatar")
r = self._session.post(url, params={"username": user}, data=json.dumps(data))
return json_loads(r)
def set_user_avatar(self, username: str, avatar: str) -> Response:
"""Set a user's avatar.
Args:
username (str): the user to set the avatar for
avatar (str): ID of the avatar to set
"""
return self._set_avatar(
{"username": username}, self._get_url("user/avatar"), avatar
)
def delete_user_avatar(self, username: str, avatar: str):
"""Delete a user's avatar.
Args:
username (str): the user to delete the avatar from
avatar (str): ID of the avatar to remove
"""
params = {"username": username}
url = self._get_url("user/avatar/" + avatar)
return self._session.delete(url, params=params)
def search_users(
self,
user: Optional[str] = None,
startAt: int = 0,
maxResults: int = 50,
includeActive: bool = True,
includeInactive: bool = False,
query: Optional[str] = None,
) -> ResultList[User]:
"""Get a list of user Resources that match the specified search string.
"username" query parameter is deprecated in Jira Cloud; the expected parameter now is "query", which can just be the full
email again. But the "user" parameter is kept for backwards compatibility, i.e. Jira Server/Data Center.
Args:
user (Optional[str]): a string to match usernames, name or email against.
startAt (int): index of the first user to return.
maxResults (int): maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches.
includeActive (bool): If true, then active users are included in the results. (Default: True)
includeInactive (bool): If true, then inactive users are included in the results. (Default: False)
query (Optional[str]): Search term. It can just be the email.
Returns:
ResultList[User]
"""
if not user and not query:
raise ValueError("Either 'user' or 'query' arguments must be specified.")
params = {
"username": user,
"query": query,
"includeActive": includeActive,
"includeInactive": includeInactive,
}
return self._fetch_pages(User, None, "user/search", startAt, maxResults, params)
def search_allowed_users_for_issue(
self,
user: str,
issueKey: str = None,
projectKey: str = None,
startAt: int = 0,
maxResults: int = 50,
) -> ResultList:
"""Get a list of user Resources that match a username string and have browse permission for the issue or project.
Args:
user (str): a string to match usernames against.
issueKey (Optional[str]): find users with browse permission for this issue.
projectKey (Optional[str]): find users with browse permission for this project.
startAt (int): index of the first user to return. (Default: 0)
maxResults (int): maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches. (Default: 50)
Returns:
ResultList
"""
params = {"username": user}
if issueKey is not None:
params["issueKey"] = issueKey
if projectKey is not None:
params["projectKey"] = projectKey
return self._fetch_pages(
User, None, "user/viewissue/search", startAt, maxResults, params
)
# Versions
@translate_resource_args
def create_version(
self,
name: str,
project: str,
description: str = None,
releaseDate: Any = None,
startDate: Any = None,
archived: bool = False,
released: bool = False,
) -> Version:
"""Create a version in a project and return a Resource for it.
Args:
name (str): name of the version to create
project (str): key of the project to create the version in
description (str): a description of the version
releaseDate (Optional[Any]): the release date assigned to the version
startDate (Optional[Any]): The start date for the version
archived (bool): Denotes whether a version should be archived. (Default: False)
released (bool): Denotes whether a version is released. (Default: False)
Returns:
Version
"""
data = {
"name": name,
"project": project,
"archived": archived,
"released": released,
}
if description is not None:
data["description"] = description
if releaseDate is not None:
data["releaseDate"] = releaseDate
if startDate is not None:
data["startDate"] = startDate
url = self._get_url("version")
r = self._session.post(url, data=json.dumps(data))
time.sleep(1)
version = Version(self._options, self._session, raw=json_loads(r))
return version
def move_version(self, id: str, after: str = None, position: str = None) -> Version:
"""Move a version within a project's ordered version list and return a new version Resource for it.
One, but not both, of ``after`` and ``position`` must be specified.
Args:
id (str): ID of the version to move
after (str): the self attribute of a version to place the specified version after (that is, higher in the list)
position (Optional[str]): the absolute position to move this version to:
must be one of ``First``, ``Last``, ``Earlier``, or ``Later``
Returns:
Version
"""
data = {}
if after is not None:
data["after"] = after
elif position is not None:
data["position"] = position
url = self._get_url("version/" + id + "/move")
r = self._session.post(url, data=json.dumps(data))
version = Version(self._options, self._session, raw=json_loads(r))
return version
def version(self, id: str, expand: Any = None) -> Version:
"""Get a version Resource.
Args:
id (str): ID of the version to get
expand (Optional[Any]): extra information to fetch inside each resource
Returns:
Version
"""
version = Version(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
version.find(id, params=params)
return version
def version_count_related_issues(self, id: str):
"""Get a dict of the counts of issues fixed and affected by a version.
Args:
id (str): the version to count issues for
"""
r_json: Dict[str, Any] = self._get_json("version/" + id + "/relatedIssueCounts")
del r_json["self"] # this isn't really an addressable resource
return r_json
def version_count_unresolved_issues(self, id: str):
"""Get the number of unresolved issues for a version.
Args:
id (str): ID of the version to count issues for
"""
r_json: Dict[str, Any] = self._get_json(
"version/" + id + "/unresolvedIssueCount"
)
return r_json["issuesUnresolvedCount"]
# Session authentication
def session(self) -> User:
"""Get a dict of the current authenticated user's session information.
Returns:
User
"""
url = "{server}{auth_url}".format(**self._options)
r = self._session.get(url)
user = User(self._options, self._session, json_loads(r))
return user
def kill_session(self) -> Response:
"""Destroy the session of the current authenticated user."""
url = self.server_url + "/rest/auth/latest/session"
return self._session.delete(url)
# Websudo
def kill_websudo(self) -> Optional[Response]:
"""Destroy the user's current WebSudo session.
Works only for non-cloud deployments, for others does nothing.
Returns:
Optional[Response]
"""
if self.deploymentType != "Cloud":
url = self.server_url + "/rest/auth/1/websudo"
return self._session.delete(url)
return None
# Utilities
def _create_http_basic_session(
self,
username: str,
password: str,
timeout: Optional[Union[Union[float, int], Tuple[float, float]]] = None,
):
"""Creates a basic http session.
Args:
username (str): Username for the session
password (str): Password for the username
timeout (Optional[int]): If set determines the timeout period for the Session.
Returns:
ResilientSession
"""
verify = bool(self._options["verify"])
self._session = ResilientSession(timeout=timeout)
self._session.verify = verify
self._session.auth = (username, password)
client_cert: Tuple[str, str] = self._options["client_cert"] # to help mypy
self._session.cert = client_cert
def _create_oauth_session(
self, oauth, timeout: Optional[Union[Union[float, int], Tuple[float, float]]]
):
verify = bool(self._options["verify"])
from oauthlib.oauth1 import SIGNATURE_RSA
from requests_oauthlib import OAuth1
oauth_instance = OAuth1(
oauth["consumer_key"],
rsa_key=oauth["key_cert"],
signature_method=SIGNATURE_RSA,
resource_owner_key=oauth["access_token"],
resource_owner_secret=oauth["access_token_secret"],
)
self._session = ResilientSession(timeout)
self._session.verify = verify
self._session.auth = oauth_instance
def _create_kerberos_session(
self,
timeout: Optional[Union[Union[float, int], Tuple[float, float]]],
kerberos_options=None,
):
verify = bool(self._options["verify"])
if kerberos_options is None:
kerberos_options = {}
from requests_kerberos import DISABLED, OPTIONAL, HTTPKerberosAuth
if kerberos_options.get("mutual_authentication", "OPTIONAL") == "OPTIONAL":
mutual_authentication = OPTIONAL
elif kerberos_options.get("mutual_authentication") == "DISABLED":
mutual_authentication = DISABLED
else:
raise ValueError(
"Unknown value for mutual_authentication: %s"
% kerberos_options["mutual_authentication"]
)
self._session = ResilientSession(timeout=timeout)
self._session.verify = verify
self._session.auth = HTTPKerberosAuth(
mutual_authentication=mutual_authentication
)
@staticmethod
def _timestamp(dt: datetime.timedelta = None):
t = datetime.datetime.utcnow()
if dt is not None:
t += dt
return calendar.timegm(t.timetuple())
def _create_jwt_session(
self, jwt, timeout: Optional[Union[Union[float, int], Tuple[float, float]]]
):
try:
jwt_auth = JWTAuth(jwt["secret"], alg="HS256")
except NameError as e:
self.log.error("JWT authentication requires requests_jwt")
raise e
jwt_auth.set_header_format("JWT %s")
jwt_auth.add_field("iat", lambda req: JIRA._timestamp())
jwt_auth.add_field(
"exp", lambda req: JIRA._timestamp(datetime.timedelta(minutes=3))
)
jwt_auth.add_field("qsh", QshGenerator(self._options["context_path"]))
for f in jwt["payload"].items():
jwt_auth.add_field(f[0], f[1])
self._session = ResilientSession(timeout=timeout)
self._session.verify = bool(self._options["verify"])
self._session.auth = jwt_auth
def _set_avatar(self, params, url, avatar):
data = {"id": avatar}
return self._session.put(url, params=params, data=json.dumps(data))
def _get_url(self, path: str, base: str = JIRA_BASE_URL) -> str:
"""Returns the full url based on Jira base url and the path provided.
Using the API version specified during the __init__.
Args:
path (str): The subpath desired.
base (Optional[str]): The base url which should be prepended to the path
Returns:
str: Fully qualified URL
"""
options = self._options.copy()
options.update({"path": path})
return base.format(**options)
def _get_latest_url(self, path: str, base: str = JIRA_BASE_URL) -> str:
"""Returns the full url based on Jira base url and the path provided.
Using the latest API endpoint.
Args:
path (str): The subpath desired.
base (Optional[str]): The base url which should be prepended to the path
Returns:
str: Fully qualified URL
"""
options = self._options.copy()
options.update({"path": path, "rest_api_version": "latest"})
return base.format(**options)
def _get_json(
self, path: str, params: Dict[str, Any] = None, base: str = JIRA_BASE_URL
):
"""Get the json for a given path and params.
Args:
path (str): The subpath required
params (Optional[Dict[str, Any]]): Parameters to filter the json query.
base (Optional[str]): The Base Jira URL, defaults to the instance base.
Returns:
Union[Dict[str, Any], List[Dict[str, str]]]
"""
url = self._get_url(path, base)
r = self._session.get(url, params=params)
try:
r_json = json_loads(r)
except ValueError as e:
self.log.error(f"{e}\n{r.text if r else r}")
raise e
return r_json
def _find_for_resource(
self, resource_cls: Any, ids: Union[Tuple[str, str], int, str], expand=None
) -> Any:
"""Uses the find method of the provided Resource class
Args:
resource_cls (Any): Any instance of :py:class`Resource`
ids (Union[Tuple[str, str], int, str]): The arguments to the Resource's ``find()``
expand ([type], optional): The value for the expand property in the Resource's
``find()`` params. Defaults to None.
Raises:
JIRAError: If the Resource cannot be found
Returns:
Any: A class of the same type as ``resource_cls``
"""
resource = resource_cls(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
resource.find(id=ids, params=params)
if not resource:
raise JIRAError("Unable to find resource %s(%s)", resource_cls, str(ids))
return resource
def _try_magic(self):
try:
import weakref
import magic
except ImportError:
self._magic = None
else:
try:
_magic = magic.Magic(flags=magic.MAGIC_MIME_TYPE)
def cleanup(x):
_magic.close()
self._magic_weakref = weakref.ref(self, cleanup)
self._magic = _magic
except TypeError:
self._magic = None
except AttributeError:
self._magic = None
def _get_mime_type(self, buff: bytes) -> Optional[str]:
"""Get the MIME type for a given stream of bytes
Args:
buff (bytes): Stream of bytes
Returns:
Optional[str]: the MIME type
"""
if self._magic is not None:
return self._magic.id_buffer(buff)
else:
try:
return mimetypes.guess_type("f." + str(imghdr.what(0, buff)))[0]
except (IOError, TypeError):
self.log.warning(
"Couldn't detect content type of avatar image"
". Specify the 'contentType' parameter explicitly."
)
return None
def rename_user(self, old_user: str, new_user: str):
"""Rename a Jira user.
Args:
old_user (str): Old username login
new_user (str): New username login
"""
if self._version > (6, 0, 0):
url = self._get_latest_url("user")
payload = {"name": new_user}
params = {"username": old_user}
# raw displayName
self.log.debug(f"renaming {self.user(old_user).emailAddress}")
r = self._session.put(url, params=params, data=json.dumps(payload))
raise_on_error(r)
else:
raise NotImplementedError(
"Support for renaming users in Jira " "< 6.0.0 has been removed."
)
def delete_user(self, username: str) -> bool:
"""Deletes a Jira User.
Args:
username (str): Username to delete
Returns:
bool: Success of user deletion
"""
url = self._get_latest_url(f"user/?username={username}")
r = self._session.delete(url)
if 200 <= r.status_code <= 299:
return True
else:
self.log.error(r.status_code)
return False
def deactivate_user(self, username: str) -> Union[str, int]:
"""Disable/deactivate the user.
Args:
username (str): User to be deactivated.
Returns:
Union[str, int]
"""
if self.deploymentType == "Cloud":
# Disabling users now needs cookie auth in the Cloud - see https://jira.atlassian.com/browse/ID-6230
if "authCookie" not in vars(self):
user = self.session()
if user.raw is None:
raise JIRAError("Can not log in!")
self.authCookie = "%s=%s" % (
user.raw["session"]["name"],
user.raw["session"]["value"],
)
url = (
self._options["server"]
+ f"/admin/rest/um/1/user/deactivate?username={username}"
)
# We can't use our existing session here - this endpoint is fragile and objects to extra headers
try:
r = requests.post(
url,
headers={
"Cookie": self.authCookie,
"Content-Type": "application/json",
},
proxies=self._session.proxies,
data={},
)
if r.status_code == 200:
return True
else:
self.log.warning(
f"Got response from deactivating {username}: {r.status_code}"
)
return r.status_code
except Exception as e:
self.log.error(f"Error Deactivating {username}: {e}")
raise JIRAError(f"Error Deactivating {username}: {e}")
else:
url = self.server_url + "/secure/admin/user/EditUser.jspa"
self._options["headers"][
"Content-Type"
] = "application/x-www-form-urlencoded; charset=UTF-8"
user = self.user(username)
userInfo = {
"inline": "true",
"decorator": "dialog",
"username": user.name,
"fullName": user.displayName,
"email": user.emailAddress,
"editName": user.name,
}
try:
r = self._session.post(
url, headers=self._options["headers"], data=userInfo
)
if r.status_code == 200:
return True
else:
self.log.warning(
f"Got response from deactivating {username}: {r.status_code}"
)
return r.status_code
except Exception as e:
self.log.error(f"Error Deactivating {username}: {e}")
raise JIRAError(f"Error Deactivating {username}: {e}")
def reindex(self, force: bool = False, background: bool = True) -> bool:
"""Start jira re-indexing. Returns True if reindexing is in progress or not needed, or False.
If you call reindex() without any parameters it will perform a background reindex only if Jira thinks it should do it.
Args:
force (bool): reindex even if Jira doesn't say this is needed, False by default.
background (bool): reindex in background, slower but does not impact the users, defaults to True.
Returns:
bool: Returns True if reindexing is in progress or not needed, or False.
"""
# /secure/admin/IndexAdmin.jspa
# /secure/admin/jira/IndexProgress.jspa?taskId=1
if background:
indexingStrategy = "background"
else:
indexingStrategy = "stoptheworld"
url = self.server_url + "/secure/admin/jira/IndexReIndex.jspa"
r = self._session.get(url, headers=self._options["headers"])
if r.status_code == 503:
# self.log.warning("Jira returned 503, this could mean that a full reindex is in progress.")
return 503 # type: ignore # FIXME: is this a bug?
if (
not r.text.find("To perform the re-index now, please go to the")
and force is False
):
return True
if r.text.find("All issues are being re-indexed"):
self.log.warning("Jira re-indexing is already running.")
return True # still reindexing is considered still a success
if r.text.find("To perform the re-index now, please go to the") or force:
r = self._session.post(
url,
headers=self._options["headers"],
params={"indexingStrategy": indexingStrategy, "reindex": "Re-Index"},
)
if r.text.find("All issues are being re-indexed") != -1:
return True
self.log.error("Failed to reindex jira, probably a bug.")
return False
def backup(self, filename: str = "backup.zip", attachments: bool = False):
"""Will call jira export to backup as zipped xml. Returning with success does not mean that the backup process finished."""
payload: Any # _session.post is pretty open
if self.deploymentType == "Cloud":
url = self.server_url + "/rest/backup/1/export/runbackup"
payload = json.dumps({"cbAttachments": attachments})
self._options["headers"]["X-Requested-With"] = "XMLHttpRequest"
else:
url = self.server_url + "/secure/admin/XmlBackup.jspa"
payload = {"filename": filename}
try:
r = self._session.post(url, headers=self._options["headers"], data=payload)
if r.status_code == 200:
return True
else:
self.log.warning(f"Got {r.status_code} response from calling backup.")
return r.status_code
except Exception as e:
self.log.error("I see %s", e)
def backup_progress(self):
"""Return status of cloud backup as a dict.
Is there a way to get progress for Server version?
"""
epoch_time = int(time.time() * 1000)
if self.deploymentType == "Cloud":
url = self.server_url + "/rest/obm/1.0/getprogress?_=%i" % epoch_time
else:
self.log.warning("This functionality is not available in Server version")
return None
r = self._session.get(url, headers=self._options["headers"])
# This is weird. I used to get xml, but now I'm getting json
try:
return json.loads(r.text)
except Exception:
import defusedxml.ElementTree as etree
progress = {}
try:
root = etree.fromstring(r.text)
except etree.ParseError as pe:
self.log.warning(
"Unable to find backup info. You probably need to initiate a new backup. %s"
% pe
)
return None
for k in root.keys():
progress[k] = root.get(k)
return progress
def backup_complete(self) -> Optional[bool]:
"""Return boolean based on 'alternativePercentage' and 'size' returned from backup_progress (cloud only)."""
if self.deploymentType != "Cloud":
self.log.warning("This functionality is not available in Server version")
return None
status = self.backup_progress()
perc_search = re.search(r"\s([0-9]*)\s", status["alternativePercentage"])
perc_complete = int(
perc_search.group(1) # type: ignore # ignore that re.search can return None
)
file_size = int(status["size"])
return perc_complete >= 100 and file_size > 0
def backup_download(self, filename: str = None):
"""Download backup file from WebDAV (cloud only)."""
if self.deploymentType != "Cloud":
self.log.warning("This functionality is not available in Server version")
return None
remote_file = self.backup_progress()["fileName"]
local_file = filename or remote_file
url = self.server_url + "/webdav/backupmanager/" + remote_file
try:
self.log.debug(f"Writing file to {local_file}")
with open(local_file, "wb") as file:
try:
resp = self._session.get(
url, headers=self._options["headers"], stream=True
)
except Exception:
raise JIRAError()
if not resp.ok:
self.log.error(f"Something went wrong with download: {resp.text}")
raise JIRAError(resp.text)
for block in resp.iter_content(1024):
file.write(block)
except JIRAError as je:
self.log.error(f"Unable to access remote backup file: {je}")
except IOError as ioe:
self.log.error(ioe)
return None
def current_user(self, field: str = "key") -> str:
"""Returns the username or emailAddress of the current user. For anonymous
users it will return a value that evaluates as False.
Returns:
str
"""
if not hasattr(self, "_myself"):
url = self._get_url("myself")
r = self._session.get(url, headers=self._options["headers"])
r_json: Dict[str, str] = json_loads(r)
self._myself = r_json
return self._myself[field]
def delete_project(self, pid: Union[str, Project]) -> Optional[bool]:
"""Delete project from Jira.
Args:
pid (Union[str, Project]): Jira projectID or Project or slug
Raises:
JIRAError: If project not found or not enough permissions
ValueError: If pid parameter is not Project, slug or ProjectID
Returns:
bool: True if project was deleted
"""
# allows us to call it with Project objects
if isinstance(pid, Project) and hasattr(pid, "id"):
pid = str(pid.id)
url = self._get_url(f"project/{pid}")
r = self._session.delete(url)
if r.status_code == 403:
raise JIRAError("Not enough permissions to delete project")
if r.status_code == 404:
raise JIRAError("Project not found in Jira")
return r.ok
def _gain_sudo_session(self, options, destination):
url = self.server_url + "/secure/admin/WebSudoAuthenticate.jspa"
if not self._session.auth:
self._session.auth = get_netrc_auth(url)
payload = {
"webSudoPassword": self._session.auth[1],
"webSudoDestination": destination,
"webSudoIsPost": "true",
}
payload.update(options)
return self._session.post(
url,
headers=CaseInsensitiveDict(
{"content-type": "application/x-www-form-urlencoded"}
),
data=payload,
)
@lru_cache(maxsize=None)
def templates(self) -> Dict:
url = self.server_url + "/rest/project-templates/latest/templates"
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
templates = {}
if "projectTemplatesGroupedByType" in data:
for group in data["projectTemplatesGroupedByType"]:
for t in group["projectTemplates"]:
templates[t["name"]] = t
# pprint(templates.keys())
return templates
@lru_cache(maxsize=None)
def permissionschemes(self):
url = self._get_url("permissionscheme")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["permissionSchemes"]
@lru_cache(maxsize=None)
def issuesecurityschemes(self):
url = self._get_url("issuesecurityschemes")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["issueSecuritySchemes"]
@lru_cache(maxsize=None)
def projectcategories(self):
url = self._get_url("projectCategory")
r = self._session.get(url)
data = json_loads(r)
return data
@lru_cache(maxsize=None)
def avatars(self, entity="project"):
url = self._get_url(f"avatar/{entity}/system")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["system"]
@lru_cache(maxsize=None)
def notificationschemes(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("notificationscheme")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["values"]
@lru_cache(maxsize=None)
def screens(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("screens")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["values"]
@lru_cache(maxsize=None)
def workflowscheme(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("workflowschemes")
r = self._session.get(url)
data = json_loads(r)
return data # ['values']
@lru_cache(maxsize=None)
def workflows(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("workflow")
r = self._session.get(url)
data = json_loads(r)
return data # ['values']
def delete_screen(self, id: str):
url = self._get_url(f"screens/{id}")
r = self._session.delete(url)
data = json_loads(r)
self.screens.cache_clear()
return data
def delete_permissionscheme(self, id: str):
url = self._get_url(f"permissionscheme/{id}")
r = self._session.delete(url)
data = json_loads(r)
self.permissionschemes.cache_clear()
return data
def create_project(
self,
key: str,
name: str = None,
assignee: str = None,
ptype: str = "software",
template_name: str = None,
avatarId=None,
issueSecurityScheme=None,
permissionScheme=None,
projectCategory=None,
notificationScheme=10000,
categoryId=None,
url: str = "",
):
"""Create a project with the specified parameters.
Args:
key (str): Mandatory. Must match Jira project key requirements, usually only 2-10 uppercase characters.
name (Optional[str]): If not specified it will use the key value.
assignee (Optional[str]): key of the lead, if not specified it will use current user.
ptype (Optional[str]): Determines the type of project should be created.
template_name (Optional[str]): is used to create a project based on one of the existing project templates.
If `template_name` is not specified, then it should use one of the default values.
Returns:
Union[bool,int]: Should evaluate to False if it fails otherwise it will be the new project id.
"""
template_key = None
if assignee is None:
assignee = self.current_user()
if name is None:
name = key
ps_list: List[Dict[str, Any]]
if not permissionScheme:
ps_list = self.permissionschemes()
for sec in ps_list:
if sec["name"] == "Default Permission Scheme":
permissionScheme = sec["id"]
break
if not permissionScheme:
permissionScheme = ps_list[0]["id"]
if not issueSecurityScheme:
ps_list = self.issuesecurityschemes()
for sec in ps_list:
if sec["name"] == "Default": # no idea which one is default
issueSecurityScheme = sec["id"]
break
if not issueSecurityScheme and ps_list:
issueSecurityScheme = ps_list[0]["id"]
if not projectCategory:
ps_list = self.projectcategories()
for sec in ps_list:
if sec["name"] == "Default": # no idea which one is default
projectCategory = sec["id"]
break
if not projectCategory and ps_list:
projectCategory = ps_list[0]["id"]
# <beep> Atlassian for failing to provide an API to get projectTemplateKey values
# Possible values are just hardcoded and obviously depending on Jira version.
# https://developer.atlassian.com/cloud/jira/platform/rest/v3/?_ga=2.88310429.766596084.1562439833-992274574.1559129176#api-rest-api-3-project-post
# https://jira.atlassian.com/browse/JRASERVER-59658
# preference list for picking a default template
if not template_name:
# https://confluence.atlassian.com/jirakb/creating-projects-via-rest-api-in-jira-963651978.html
template_key = (
"com.pyxis.greenhopper.jira:basic-software-development-template"
)
# https://developer.atlassian.com/cloud/jira/platform/rest/v2/api-group-projects/#api-rest-api-2-project-get
# template_keys = [
# "com.pyxis.greenhopper.jira:gh-simplified-agility-kanban",
# "com.pyxis.greenhopper.jira:gh-simplified-agility-scrum",
# "com.pyxis.greenhopper.jira:gh-simplified-basic",
# "com.pyxis.greenhopper.jira:gh-simplified-kanban-classic",
# "com.pyxis.greenhopper.jira:gh-simplified-scrum-classic",
# "com.atlassian.servicedesk:simplified-it-service-desk",
# "com.atlassian.servicedesk:simplified-internal-service-desk",
# "com.atlassian.servicedesk:simplified-external-service-desk",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-content-management",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-document-approval",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-lead-tracking",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-process-control",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-procurement",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-project-management",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-recruitment",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-task-",
# "com.atlassian.jira.jira-incident-management-plugin:im-incident-management",
# ]
# possible_templates = [
# "Scrum software development", # have Bug
# "Agility", # cannot set summary
# "Bug tracking",
# "JIRA Classic",
# "JIRA Default Schemes",
# "Basic software development",
# "Project management",
# "Kanban software development",
# "Task management",
# "Basic", # does not have Bug
# "Content Management",
# "Customer service",
# "Document Approval",
# "IT Service Desk",
# "Lead Tracking",
# "Process management",
# "Procurement",
# "Recruitment",
# ]
# templates = self.templates()
# if not template_name:
# for k, v in templates.items():
# if v['projectTypeKey'] == type:
# template_name = k
# template_name = next((t for t in templates if t['projectTypeKey'] == 'x'))
# template_key = templates[template_name]["projectTemplateModuleCompleteKey"]
# project_type_key = templates[template_name]["projectTypeKey"]
# https://confluence.atlassian.com/jirakb/creating-a-project-via-rest-based-on-jira-default-schemes-744325852.html
# see https://confluence.atlassian.com/jirakb/creating-projects-via-rest-api-in-jira-963651978.html
payload = {
"name": name,
"key": key,
"projectTypeKey": ptype,
"projectTemplateKey": template_key,
"lead": assignee,
# "leadAccountId": assignee,
"assigneeType": "PROJECT_LEAD",
"description": "",
# "avatarId": 13946,
"permissionScheme": int(permissionScheme),
"notificationScheme": notificationScheme,
"url": url,
}
if issueSecurityScheme:
payload["issueSecurityScheme"] = int(issueSecurityScheme)
if projectCategory:
payload["categoryId"] = int(projectCategory)
url = self._get_url("project")
r = self._session.post(url, data=json.dumps(payload))
r.raise_for_status()
r_json = json_loads(r)
return r_json
def add_user(
self,
username: str,
email: str,
directoryId: int = 1,
password: str = None,
fullname: str = None,
notify: bool = False,
active: bool = True,
ignore_existing: bool = False,
application_keys: Optional[List] = None,
):
"""Create a new Jira user.
Args:
username (str): the username of the new user
email (str): email address of the new user
directoryId (int): The directory ID the new user should be a part of (Default: 1)
password (Optional[str]): Optional, the password for the new user
fullname (Optional[str]): Optional, the full name of the new user
notify (bool): Whether or not to send a notification to the new user. (Default: False)
active (bool): Whether or not to make the new user active upon creation. (Default: True)
ignore_existing (bool): Whether or not to ignore and existing user. (Default: False)
applicationKeys (Optional[list]): Keys of products user should have access to
Raises:
JIRAError: If username already exists and `ignore_existing` has not been set to `True`.
Returns:
bool: Whether or not the user creation was successful.
"""
if not fullname:
fullname = username
# TODO(ssbarnea): default the directoryID to the first directory in jira instead
# of 1 which is the internal one.
url = self._get_latest_url("user")
# implementation based on
# https://docs.atlassian.com/jira/REST/ondemand/#d2e5173
x: Dict[str, Any] = OrderedDict()
x["displayName"] = fullname
x["emailAddress"] = email
x["name"] = username
if password:
x["password"] = password
if notify:
x["notification"] = "True"
if application_keys is not None:
x["applicationKeys"] = application_keys
payload = json.dumps(x)
try:
self._session.post(url, data=payload)
except JIRAError as e:
if e.response:
err = e.response.json()["errors"]
if (
"username" in err
and err["username"] == "A user with that username already exists."
and ignore_existing
):
return True
raise e
return True
def add_user_to_group(
self, username: str, group: str
) -> Union[bool, Dict[str, Any]]:
"""Add a user to an existing group.
Args:
username (str): Username that will be added to specified group.
group (str): Group that the user will be added to.
Returns:
Union[bool,Dict[str,Any]]: json response from Jira server for success or a value that evaluates as False in case of failure.
"""
url = self._get_latest_url("group/user")
x = {"groupname": group}
y = {"name": username}
payload = json.dumps(y)
r: Dict[str, Any] = json_loads(self._session.post(url, params=x, data=payload))
if "name" not in r or r["name"] != group:
return False
else:
return r
def remove_user_from_group(self, username: str, groupname: str):
"""Remove a user from a group.
Args:
username (str): The user to remove from the group.
groupname (str): The group that the user will be removed from.
"""
url = self._get_latest_url("group/user")
x = {"groupname": groupname, "username": username}
self._session.delete(url, params=x)
return True
def role(self) -> List[Dict[str, Any]]:
"""Return Jira role information.
Returns:
List[Dict[str,Any]]: List of current user roles
"""
# https://developer.atlassian.com/cloud/jira/platform/rest/v3/?utm_source=%2Fcloud%2Fjira%2Fplatform%2Frest%2F&utm_medium=302#api-rest-api-3-role-get
url = self._get_latest_url("role")
r = self._session.get(url)
data: List[Dict[str, Any]] = json_loads(r)
return data
# Experimental
# Experimental support for iDalko Grid, expect API to change as it's using private APIs currently
# https://support.idalko.com/browse/IGRID-1017
def get_igrid(self, issueid: str, customfield: str, schemeid: str):
url = self.server_url + "/rest/idalko-igrid/1.0/datagrid/data"
if str(customfield).isdigit():
customfield = f"customfield_{customfield}"
params = {
"_issueId": issueid,
"_fieldId": customfield,
"_confSchemeId": schemeid,
}
r = self._session.get(url, headers=self._options["headers"], params=params)
return json_loads(r)
# Jira Agile specific methods (GreenHopper)
"""
Define the functions that interact with GreenHopper.
"""
@translate_resource_args
def boards(
self,
startAt: int = 0,
maxResults: int = 50,
type: str = None,
name: str = None,
projectKeyOrID=None,
) -> ResultList[Board]:
"""Get a list of board resources.
Args:
startAt: The starting index of the returned boards. Base index: 0.
maxResults: The maximum number of boards to return per page. Default: 50
type: Filters results to boards of the specified type. Valid values: scrum, kanban.
name: Filters results to boards that match or partially match the specified name.
projectKeyOrID: Filters results to boards that match the specified project key or ID.
Returns:
ResultList[Board]
When old GreenHopper private API is used, paging is not enabled and all parameters are ignored.
"""
params = {}
if type:
params["type"] = type
if name:
params["name"] = name
if projectKeyOrID:
params["projectKeyOrId"] = projectKeyOrID
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
# Old, private API did not support pagination, all records were present in response,
# and no parameters were supported.
if startAt or maxResults or params:
warnings.warn(
"Old private GreenHopper API is used, all parameters will be ignored.",
Warning,
)
r_json: Dict[str, Any] = self._get_json(
"rapidviews/list", base=self.AGILE_BASE_URL
)
boards = [
Board(self._options, self._session, raw_boards_json)
for raw_boards_json in r_json["views"]
]
return ResultList(boards, 0, len(boards), len(boards), True)
else:
return self._fetch_pages(
Board,
"values",
"board",
startAt,
maxResults,
params,
base=self.AGILE_BASE_URL,
)
@translate_resource_args
def sprints(
self,
board_id: int,
extended: bool = False,
startAt: int = 0,
maxResults: int = 50,
state: str = None,
) -> ResultList[Sprint]:
"""Get a list of sprint GreenHopperResources.
Args:
board_id (int): the board to get sprints from
extended (bool): Used only by old GreenHopper API to fetch additional information like
startDate, endDate, completeDate, much slower because it requires an additional requests for each sprint.
New Jira Agile API always returns this information without a need for additional requests.
startAt (int): the index of the first sprint to return (0 based)
maxResults (int): the maximum number of sprints to return
state (str): Filters results to sprints in specified states. Valid values: `future`, `active`, `closed`.
You can define multiple states separated by commas
Returns:
ResultList[Sprint]: (content depends on API version, but always contains id, name, state, startDate and endDate)
When old GreenHopper private API is used, paging is not enabled,
and `startAt`, `maxResults` and `state` parameters are ignored.
"""
params = {}
if state:
params["state"] = state
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
r_json: Dict[str, Any] = self._get_json(
"sprintquery/%s?includeHistoricSprints=true&includeFutureSprints=true"
% board_id,
base=self.AGILE_BASE_URL,
)
if params:
warnings.warn(
"Old private GreenHopper API is used, parameters %s will be ignored."
% params,
Warning,
)
if extended:
sprints = [
Sprint(
self._options,
self._session,
self.sprint_info("", raw_sprints_json["id"]),
)
for raw_sprints_json in r_json["sprints"]
]
else:
sprints = [
Sprint(self._options, self._session, raw_sprints_json)
for raw_sprints_json in r_json["sprints"]
]
return ResultList(sprints, 0, len(sprints), len(sprints), True)
else:
return self._fetch_pages(
Sprint,
"values",
f"board/{board_id}/sprint",
startAt,
maxResults,
params,
self.AGILE_BASE_URL,
)
def sprints_by_name(self, id, extended=False):
sprints = {}
for s in self.sprints(id, extended=extended):
if s.name not in sprints:
sprints[s.name] = s.raw
else:
raise Exception
return sprints
def update_sprint(self, id, name=None, startDate=None, endDate=None, state=None):
payload = {}
if name:
payload["name"] = name
if startDate:
payload["startDate"] = startDate
if endDate:
payload["endDate"] = endDate
if state:
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
raise NotImplementedError(
"Public Jira API does not support state update"
)
payload["state"] = state
url = self._get_url(f"sprint/{id}", base=self.AGILE_BASE_URL)
r = self._session.put(url, data=json.dumps(payload))
return json_loads(r)
def incompletedIssuesEstimateSum(self, board_id: str, sprint_id: str):
"""Return the total incompleted points this sprint."""
data: Dict[str, Any] = self._get_json(
f"rapid/charts/sprintreport?rapidViewId={board_id}&sprintId={sprint_id}",
base=self.AGILE_BASE_URL,
)
return data["contents"]["incompletedIssuesEstimateSum"]["value"]
def removed_issues(self, board_id: str, sprint_id: str):
"""Return the completed issues for the sprint."""
r_json: Dict[str, Any] = self._get_json(
f"rapid/charts/sprintreport?rapidViewId={board_id}&sprintId={sprint_id}",
base=self.AGILE_BASE_URL,
)
issues = [
Issue(self._options, self._session, raw_issues_json)
for raw_issues_json in r_json["contents"]["puntedIssues"]
]
return issues
def removedIssuesEstimateSum(self, board_id: str, sprint_id: str):
"""Return the total incompleted points this sprint."""
data: Dict[str, Any] = self._get_json(
f"rapid/charts/sprintreport?rapidViewId={board_id}&sprintId={sprint_id}",
base=self.AGILE_BASE_URL,
)
return data["contents"]["puntedIssuesEstimateSum"]["value"]
# TODO(ssbarnea): remove sprint_info() method, sprint() method suit the convention more
def sprint_info(self, board_id: str, sprint_id: str) -> Optional[Dict[str, Any]]:
"""Return the information about a sprint.
Args:
board_id (str): the board retrieving issues from. Deprecated and ignored.
sprint_id (str): the sprint retrieving issues from
"""
sprint = Sprint(self._options, self._session)
sprint.find(sprint_id)
return sprint.raw
def sprint(self, id: int) -> Sprint:
"""Return the information about a sprint.
Args:
sprint_id (int): the sprint retrieving issues from
Returns:
Sprint
"""
sprint = Sprint(self._options, self._session)
sprint.find(id)
return sprint
# TODO(ssbarnea): remove this as we do have Board.delete()
def delete_board(self, id):
"""Delete an agile board."""
board = Board(self._options, self._session, raw={"id": id})
board.delete()
def create_board(
self,
name: str,
project_ids: Union[str, List[str]],
preset: str = "scrum",
location_type: str = "user",
location_id: Optional[str] = None,
) -> Board:
"""Create a new board for the ``project_ids``.
Args:
name (str): name of the board
project_ids (str): the projects to create the board in
preset (str): What preset to use for this board, options: kanban, scrum, diy. (Default: scrum)
location_type (str): the location type. Available in cloud. (Default: user)
location_id (Optional[str]): the id of project that the board should be located under.
Omit this for a 'user' location_type. Available in cloud.
Returns:
Board: The newly created board
"""
if (
self._options["agile_rest_path"]
!= GreenHopperResource.GREENHOPPER_REST_PATH
):
raise NotImplementedError(
"Jira Agile Public API does not support this request"
)
payload: Dict[str, Any] = {}
if isinstance(project_ids, str):
ids = []
for p in project_ids.split(","):
ids.append(self.project(p).id)
project_ids = ",".join(ids)
if location_id is not None:
location_id = self.project(location_id).id
payload["name"] = name
if isinstance(project_ids, str):
project_ids = project_ids.split(",") # type: ignore # re-use of variable
payload["projectIds"] = project_ids
payload["preset"] = preset
if self.deploymentType == "Cloud":
payload["locationType"] = location_type
payload["locationId"] = location_id
url = self._get_url("rapidview/create/presets", base=self.AGILE_BASE_URL)
r = self._session.post(url, data=json.dumps(payload))
raw_issue_json = json_loads(r)
return Board(self._options, self._session, raw=raw_issue_json)
def create_sprint(
self,
name: str,
board_id: int,
startDate: Optional[Any] = None,
endDate: Optional[Any] = None,
) -> Sprint:
"""Create a new sprint for the ``board_id``.
Args:
name (str): Name of the sprint
board_id (int): Which board the sprint should be assigned.
startDate (Optional[Any]): Start date for the sprint.
endDate (Optional[Any]): End date for the sprint.
Returns:
Sprint: The newly created Sprint
"""
payload: Dict[str, Any] = {"name": name}
if startDate:
payload["startDate"] = startDate
if endDate:
payload["endDate"] = endDate
raw_issue_json: Dict[str, Any]
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
url = self._get_url(f"sprint/{board_id}", base=self.AGILE_BASE_URL)
r = self._session.post(url)
raw_issue_json = json_loads(r)
""" now r contains something like:
{
"id": 742,
"name": "Sprint 89",
"state": "FUTURE",
"linkedPagesCount": 0,
"startDate": "None",
"endDate": "None",
"completeDate": "None",
"remoteLinks": []
}"""
url = self._get_url(
f"sprint/{raw_issue_json["id"]}", base=self.AGILE_BASE_URL
)
r = self._session.put(url, data=json.dumps(payload))
raw_issue_json = json_loads(r)
else:
url = self._get_url("sprint", base=self.AGILE_BASE_URL)
payload["originBoardId"] = board_id
r = self._session.post(url, data=json.dumps(payload))
raw_issue_json = json_loads(r)
return Sprint(self._options, self._session, raw=raw_issue_json)
def add_issues_to_sprint(self, sprint_id: int, issue_keys: List[str]) -> Response:
"""Add the issues in ``issue_keys`` to the ``sprint_id``.
The sprint must be started but not completed.
If a sprint was completed, then have to also edit the history of the
issue so that it was added to the sprint before it was completed,
preferably before it started. A completed sprint's issues also all have
a resolution set before the completion date.
If a sprint was not started, then have to edit the marker and copy the
rank of each issue too.
Args:
sprint_id (int): the sprint to add issues to
issue_keys (List[str]): the issues to add to the sprint
Returns:
Response
"""
if self._options["agile_rest_path"] == GreenHopperResource.AGILE_BASE_REST_PATH:
url = self._get_url(f"sprint/{sprint_id}/issue", base=self.AGILE_BASE_URL)
payload = {"issues": issue_keys}
try:
return self._session.post(url, data=json.dumps(payload))
except JIRAError as e:
if e.status_code == 404:
warnings.warn(
"Status code 404 may mean, that too old Jira Agile version is installed."
" At least version 6.7.10 is required."
)
raise
elif (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
# In old, private API the function does not exist anymore and we need to use
# issue.update() to perform this operation
# Workaround based on https://answers.atlassian.com/questions/277651/jira-agile-rest-api-example
sprint_field_id = self._get_sprint_field_id()
data = {
"idOrKeys": issue_keys,
"customFieldId": sprint_field_id,
"sprintId": sprint_id,
"addToBacklog": False,
}
url = self._get_url("sprint/rank", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
else:
raise NotImplementedError(
'No API for adding issues to sprint for agile_rest_path="%s"'
% self._options["agile_rest_path"]
)
def add_issues_to_epic(
self, epic_id: str, issue_keys: str, ignore_epics: bool = True
) -> Response:
"""Add the issues in ``issue_keys`` to the ``epic_id``.
Args:
epic_id (str): The ID for the epic where issues should be added.
issue_keys (str): The issues to add to the epic
ignore_epics (bool): ignore any issues listed in ``issue_keys`` that are epics. (Default: True)
"""
if (
self._options["agile_rest_path"]
!= GreenHopperResource.GREENHOPPER_REST_PATH
):
# TODO(ssbarnea): simulate functionality using issue.update()?
raise NotImplementedError(
"Jira Agile Public API does not support this request"
)
data: Dict[str, Any] = {}
data["issueKeys"] = issue_keys
data["ignoreEpics"] = ignore_epics
url = self._get_url(f"epics/{epic_id}/add", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
# TODO(ssbarnea): Both GreenHopper and new Jira Agile API support moving more than one issue.
def rank(self, issue: str, next_issue: str) -> Response:
"""Rank an issue before another using the default Ranking field, the one named 'Rank'.
Args:
issue (str): issue key of the issue to be ranked before the second one.
next_issue (str): issue key of the second issue.
"""
if not self._rank:
for field in self.fields():
if field["name"] == "Rank":
if (
field["schema"]["custom"]
== "com.pyxis.greenhopper.jira:gh-lexo-rank"
):
self._rank = field["schema"]["customId"]
break
elif (
field["schema"]["custom"]
== "com.pyxis.greenhopper.jira:gh-global-rank"
):
# Obsolete since Jira v6.3.13.1
self._rank = field["schema"]["customId"]
if self._options["agile_rest_path"] == GreenHopperResource.AGILE_BASE_REST_PATH:
url = self._get_url("issue/rank", base=self.AGILE_BASE_URL)
payload = {
"issues": [issue],
"rankBeforeIssue": next_issue,
"rankCustomFieldId": self._rank,
}
try:
return self._session.put(url, data=json.dumps(payload))
except JIRAError as e:
if e.status_code == 404:
warnings.warn(
"Status code 404 may mean, that too old Jira Agile version is installed."
" At least version 6.7.10 is required."
)
raise
elif (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
data = {
"issueKeys": [issue],
"rankBeforeKey": next_issue,
"customFieldId": self._rank,
}
url = self._get_url("rank", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
else:
raise NotImplementedError(
'No API for ranking issues for agile_rest_path="%s"'
% self._options["agile_rest_path"]
)
def move_to_backlog(self, issue_keys: str) -> Response:
"""Move issues in ``issue_keys`` to the backlog, removing them from all sprints that have not been completed.
Args:
issue_keys (str): the issues to move to the backlog
Raises:
JIRAError: If moving issues to backlog fails
"""
if self._options["agile_rest_path"] == GreenHopperResource.AGILE_BASE_REST_PATH:
url = self._get_url("backlog/issue", base=self.AGILE_BASE_URL)
payload = {"issues": issue_keys}
try:
return self._session.post(url, data=json.dumps(payload))
except JIRAError as e:
if e.status_code == 404:
warnings.warn(
"Status code 404 may mean, that too old Jira Agile version is installed."
" At least version 6.7.10 is required."
)
raise
elif (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
# In old, private API the function does not exist anymore and we need to use
# issue.update() to perform this operation
# Workaround based on https://answers.atlassian.com/questions/277651/jira-agile-rest-api-example
sprint_field_id = self._get_sprint_field_id()
data = {
"idOrKeys": issue_keys,
"customFieldId": sprint_field_id,
"addToBacklog": True,
}
url = self._get_url("sprint/rank", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
else:
raise NotImplementedError(
'No API for moving issues to backlog for agile_rest_path="%s"'
% self._options["agile_rest_path"]
)
class GreenHopper(JIRA):
def __init__(self, options=None, basic_auth=None, oauth=None, async_=None):
warnings.warn(
"GreenHopper() class is deprecated, just use JIRA() instead.",
DeprecationWarning,
)
JIRA.__init__(
self, options=options, basic_auth=basic_auth, oauth=oauth, async_=async_
)
| #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
This module implements a friendly (well, friendlier) interface between the raw JSON
responses from Jira and the Resource/dict abstractions provided by this library. Users
will construct a JIRA object as described below. Full API documentation can be found
at: https://jira.readthedocs.io/en/latest/
"""
import calendar
import copy
import datetime
import hashlib
import imghdr
import json
import logging as _logging
import mimetypes
import os
import re
import sys
import time
import warnings
from collections import OrderedDict
from collections.abc import Iterable
from functools import lru_cache, wraps
from io import BufferedReader
from numbers import Number
from typing import (
Any,
Callable,
Dict,
Generic,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
cast,
no_type_check,
)
from urllib.parse import urlparse
import requests
from pkg_resources import parse_version
from requests import Response
from requests.auth import AuthBase
from requests.utils import get_netrc_auth
from jira import __version__
# GreenHopper specific resources
from jira.exceptions import JIRAError
from jira.resilientsession import ResilientSession, raise_on_error
# Jira-specific resources
from jira.resources import (
Attachment,
Board,
Comment,
Component,
Customer,
CustomFieldOption,
Dashboard,
Filter,
GreenHopperResource,
Group,
Issue,
IssueLink,
IssueLinkType,
IssueType,
Priority,
Project,
RemoteLink,
RequestType,
Resolution,
Resource,
Role,
SecurityLevel,
ServiceDesk,
Sprint,
Status,
StatusCategory,
User,
Version,
Votes,
Watchers,
Worklog,
)
from jira.utils import CaseInsensitiveDict, json_loads, threaded_requests
try:
# noinspection PyUnresolvedReferences
from requests_toolbelt import MultipartEncoder
except ImportError:
pass
try:
from requests_jwt import JWTAuth
except ImportError:
pass
LOG = _logging.getLogger("jira")
LOG.addHandler(_logging.NullHandler())
def translate_resource_args(func: Callable):
"""Decorator that converts Issue and Project resources to their keys when used as arguments."""
@wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
arg_list = []
for arg in args:
if isinstance(arg, (Issue, Project)):
arg_list.append(arg.key)
else:
arg_list.append(arg)
result = func(*arg_list, **kwargs)
return result
return wrapper
def _field_worker(
fields: Dict[str, Any] = None, **fieldargs: Any
) -> Union[Dict[str, Dict[str, Any]], Dict[str, Dict[str, str]]]:
if fields is not None:
return {"fields": fields}
return {"fields": fieldargs}
ResourceType = TypeVar("ResourceType", contravariant=True, bound=Resource)
class ResultList(list, Generic[ResourceType]):
def __init__(
self,
iterable: Iterable = None,
_startAt: int = 0,
_maxResults: int = 0,
_total: Optional[int] = None,
_isLast: Optional[bool] = None,
) -> None:
"""
Args:
iterable (Iterable): [description]. Defaults to None.
_startAt (int): Start page. Defaults to 0.
_maxResults (int): Max results per page. Defaults to 0.
_total (Optional[int]): Total results from query. Defaults to 0.
_isLast (Optional[bool]): Last Page? Defaults to None.
"""
if iterable is not None:
list.__init__(self, iterable)
else:
list.__init__(self)
self.startAt = _startAt
self.maxResults = _maxResults
# Optional parameters:
self.isLast = _isLast
self.total = _total if _total is not None else len(self)
self.iterable: List = list(iterable) if iterable else []
self.current = self.startAt
def __next__(self) -> Type[ResourceType]:
self.current += 1
if self.current > self.total:
raise StopIteration
else:
return self.iterable[self.current - 1]
class QshGenerator(object):
def __init__(self, context_path):
self.context_path = context_path
def __call__(self, req):
parse_result = urlparse(req.url)
path = (
parse_result.path[len(self.context_path) :]
if len(self.context_path) > 1
else parse_result.path
)
# Per Atlassian docs, use %20 for whitespace when generating qsh for URL
# https://developer.atlassian.com/cloud/jira/platform/understanding-jwt/#qsh
query = "&".join(sorted(parse_result.query.split("&"))).replace("+", "%20")
qsh = f"{req.method.upper()}&{path}&{query}"
return hashlib.sha256(qsh.encode("utf-8")).hexdigest()
class JiraCookieAuth(AuthBase):
"""Jira Cookie Authentication
Allows using cookie authentication as described by
https://developer.atlassian.com/jiradev/jira-apis/jira-rest-apis/jira-rest-api-tutorials/jira-rest-api-example-cookie-based-authentication
"""
def __init__(
self, session: ResilientSession, _get_session: Callable, auth: Tuple[str, str]
):
"""Cookie Based Authentication
Args:
session (ResilientSession): The Session object to communicate with the API.
_get_session (Callable): The function that returns a :py_class:``User``
auth (Tuple[str, str]): The username, password tuple
"""
self._session = session
self._get_session = _get_session
self.__auth = auth
def handle_401(self, response, **kwargs):
if response.status_code != 401:
return response
self.init_session()
response = self.process_original_request(response.request.copy())
return response
def process_original_request(self, original_request):
self.update_cookies(original_request)
return self.send_request(original_request)
def update_cookies(self, original_request):
# Cookie header needs first to be deleted for the header to be updated using
# the prepare_cookies method. See request.PrepareRequest.prepare_cookies
if "Cookie" in original_request.headers:
del original_request.headers["Cookie"]
original_request.prepare_cookies(self.cookies)
def init_session(self):
self.start_session()
def __call__(self, request):
request.register_hook("response", self.handle_401)
return request
def send_request(self, request):
return self._session.send(request)
@property
def cookies(self):
return self._session.cookies
def start_session(self):
self._get_session(self.__auth)
class JIRA(object):
"""User interface to Jira.
Clients interact with Jira by constructing an instance of this object and calling its methods. For addressable
resources in Jira -- those with "self" links -- an appropriate subclass of :py:class:`jira.resources.Resource` will be returned
with customized ``update()`` and ``delete()`` methods, along with attribute access to fields. This means that calls
of the form ``issue.fields.summary`` will be resolved into the proper lookups to return the JSON value at that
mapping. Methods that do not return resources will return a dict constructed from the JSON response or a scalar
value; see each method's documentation for details on what that method returns.
Without any arguments, this client will connect anonymously to the Jira instance
started by the Atlassian Plugin SDK from one of the 'atlas-run', ``atlas-debug``,
or ``atlas-run-standalone`` commands. By default, this instance runs at
``http://localhost:2990/jira``. The ``options`` argument can be used to set the Jira instance to use.
Authentication is handled with the ``basic_auth`` argument. If authentication is supplied (and is
accepted by Jira), the client will remember it for subsequent requests.
For quick command line access to a server, see the ``jirashell`` script included with this distribution.
The easiest way to instantiate is using ``j = JIRA("https://jira.atlassian.com")``
"""
DEFAULT_OPTIONS = {
"server": "http://localhost:2990/jira",
"auth_url": "/rest/auth/1/session",
"context_path": "/",
"rest_path": "api",
"rest_api_version": "2",
"agile_rest_path": GreenHopperResource.GREENHOPPER_REST_PATH,
"agile_rest_api_version": "1.0",
"verify": True,
"resilient": True,
"async": False,
"async_workers": 5,
"client_cert": None,
"check_update": False,
# amount of seconds to wait for loading a resource after updating it
# used to avoid server side caching issues, used to be 4 seconds.
"delay_reload": 0,
"headers": {
"Cache-Control": "no-cache",
# 'Accept': 'application/json;charset=UTF-8', # default for REST
"Content-Type": "application/json", # ;charset=UTF-8',
# 'Accept': 'application/json', # default for REST
# 'Pragma': 'no-cache',
# 'Expires': 'Thu, 01 Jan 1970 00:00:00 GMT'
"X-Atlassian-Token": "no-check",
},
}
checked_version = False
# TODO(ssbarnea): remove these two variables and use the ones defined in resources
JIRA_BASE_URL = Resource.JIRA_BASE_URL
AGILE_BASE_URL = GreenHopperResource.AGILE_BASE_URL
def __init__(
self,
server: str = None,
options: Dict[str, Union[str, bool, Any]] = None,
basic_auth: Union[None, Tuple[str, str]] = None,
oauth: Dict[str, Any] = None,
jwt: Dict[str, Any] = None,
kerberos=False,
kerberos_options: Dict[str, Any] = None,
validate=False,
get_server_info: bool = True,
async_: bool = False,
async_workers: int = 5,
logging: bool = True,
max_retries: int = 3,
proxies: Any = None,
timeout: Optional[Union[Union[float, int], Tuple[float, float]]] = None,
auth: Tuple[str, str] = None,
):
"""Construct a Jira client instance.
Without any arguments, this client will connect anonymously to the Jira instance
started by the Atlassian Plugin SDK from one of the 'atlas-run', ``atlas-debug``,
or ``atlas-run-standalone`` commands. By default, this instance runs at
``http://localhost:2990/jira``. The ``options`` argument can be used to set the Jira instance to use.
Authentication is handled with the ``basic_auth`` argument. If authentication is supplied (and is
accepted by Jira), the client will remember it for subsequent requests.
For quick command line access to a server, see the ``jirashell`` script included with this distribution.
The easiest way to instantiate is using ``j = JIRA("https://jira.atlasian.com")``
Args:
server (Optional[str]): The server address and context path to use. Defaults to ``http://localhost:2990/jira``.
options (Optional[Dict[str, Any]]): Specify the server and properties this client will use.
Use a dict with any of the following properties:
* server -- the server address and context path to use. Defaults to ``http://localhost:2990/jira``.
* rest_path -- the root REST path to use. Defaults to ``api``, where the Jira REST resources live.
* rest_api_version -- the version of the REST resources under rest_path to use. Defaults to ``2``.
* agile_rest_path - the REST path to use for Jira Agile requests. Defaults to ``greenhopper`` (old, private
API). Check :py:class:`jira.resources.GreenHopperResource` for other supported values.
* verify -- Verify SSL certs. Defaults to ``True``.
* client_cert -- a tuple of (cert,key) for the requests library for client side SSL
* check_update -- Check whether using the newest python-jira library version.
basic_auth (Union[None, Tuple[str, str]]): A tuple of username and password to use when
establishing a session via HTTP BASIC authentication.
oauth (Optional[Any]): A dict of properties for OAuth authentication. The following properties are required:
* access_token -- OAuth access token for the user
* access_token_secret -- OAuth access token secret to sign with the key
* consumer_key -- key of the OAuth application link defined in Jira
* key_cert -- private key file to sign requests with (should be the pair of the public key supplied to
Jira in the OAuth application link)
kerberos (bool): If true it will enable Kerberos authentication.
kerberos_options (Optional[Dict[str,str]]): A dict of properties for Kerberos authentication.
The following properties are possible:
* mutual_authentication -- string DISABLED or OPTIONAL.
Example kerberos_options structure: ``{'mutual_authentication': 'DISABLED'}``
jwt (Optional[Any]): A dict of properties for JWT authentication supported by Atlassian Connect.
The following properties are required:
* secret -- shared secret as delivered during 'installed' lifecycle event
(see https://developer.atlassian.com/static/connect/docs/latest/modules/lifecycle.html for details)
* payload -- dict of fields to be inserted in the JWT payload, e.g. 'iss'
Example jwt structure: ``{'secret': SHARED_SECRET, 'payload': {'iss': PLUGIN_KEY}}``
validate (bool): If true it will validate your credentials first. Remember that if you are accessing Jira
as anonymous it will fail to instantiate.
get_server_info (bool): If true it will fetch server version info first to determine if some API calls
are available.
async_ (bool): To enable async requests for those actions where we implemented it, like issue update() or delete().
async_workers (int): Set the number of worker threads for async operations.
timeout (Optional[Union[Union[float, int], Tuple[float, float]]]): Set a read/connect timeout for the underlying
calls to Jira (default: None).
Obviously this means that you cannot rely on the return code when this is enabled.
max_retries (int): Sets the amount Retries for the HTTP sessions initiated by the client. (Default: 3)
proxies (Optional[Any]): Sets the proxies for the HTTP session.
auth (Optional[Tuple[str,str]]): Set a cookie auth token if this is required.
logging (bool): Determine whether or not logging should be enabled. (Default: True)
"""
# force a copy of the tuple to be used in __del__() because
# sys.version_info could have already been deleted in __del__()
self.sys_version_info = tuple([i for i in sys.version_info])
if options is None:
options = {}
if server and isinstance(server, dict):
warnings.warn(
"Old API usage, use JIRA(url) or JIRA(options={'server': url}, when using dictionary always use named parameters.",
DeprecationWarning,
)
options = server
server = ""
if server:
options["server"] = server
if async_:
options["async"] = async_
options["async_workers"] = async_workers
LOG.setLevel(_logging.INFO if logging else _logging.CRITICAL)
self.log = LOG
self._options: Dict[str, Any] = copy.copy(JIRA.DEFAULT_OPTIONS)
self._options.update(options)
self._rank = None
# Rip off trailing slash since all urls depend on that
assert isinstance(self._options["server"], str) # to help mypy
if self._options["server"].endswith("/"):
self._options["server"] = self._options["server"][:-1]
context_path = urlparse(self.server_url).path
if len(context_path) > 0:
self._options["context_path"] = context_path
self._try_magic()
assert isinstance(self._options["headers"], dict) # for mypy benefit
self._session: ResilientSession # for mypy benefit
if oauth:
self._create_oauth_session(oauth, timeout)
elif basic_auth:
self._create_http_basic_session(*basic_auth, timeout=timeout)
self._session.headers.update(self._options["headers"])
elif jwt:
self._create_jwt_session(jwt, timeout)
elif kerberos:
self._create_kerberos_session(timeout, kerberos_options=kerberos_options)
elif auth:
self._create_cookie_auth(auth, timeout)
# always log in for cookie based auth, as we need a first request to be logged in
validate = True
else:
verify = bool(self._options["verify"])
self._session = ResilientSession(timeout=timeout)
self._session.verify = verify
self._session.headers.update(self._options["headers"])
if "cookies" in self._options:
self._session.cookies.update(self._options["cookies"])
self._session.max_retries = max_retries
if proxies:
self._session.proxies = proxies
self.auth = auth
if validate:
# This will raise an Exception if you are not allowed to login.
# It's better to fail faster than later.
user = self.session()
if user.raw is None:
auth_method = (
oauth or basic_auth or jwt or kerberos or auth or "anonymous"
)
raise JIRAError(f"Can not log in with {str(auth_method)}")
self.deploymentType = None
if get_server_info:
# We need version in order to know what API calls are available or not
si = self.server_info()
try:
self._version = tuple(si["versionNumbers"])
except Exception as e:
self.log.error("invalid server_info: %s", si)
raise e
self.deploymentType = si.get("deploymentType")
else:
self._version = (0, 0, 0)
if self._options["check_update"] and not JIRA.checked_version:
self._check_update_()
JIRA.checked_version = True
self._fields = {}
for f in self.fields():
if "clauseNames" in f:
for name in f["clauseNames"]:
self._fields[name] = f["id"]
@property
def server_url(self) -> str:
"""Return the server url"""
return str(self._options["server"])
def _create_cookie_auth(
self,
auth: Tuple[str, str],
timeout: Optional[Union[Union[float, int], Tuple[float, float]]],
):
self._session = ResilientSession(timeout=timeout)
self._session.auth = JiraCookieAuth(self._session, self.session, auth)
self._session.verify = bool(self._options["verify"])
client_cert: Tuple[str, str] = self._options["client_cert"] # to help mypy
self._session.cert = client_cert
def _check_update_(self):
"""Check if the current version of the library is outdated."""
try:
data = requests.get(
"https://pypi.python.org/pypi/jira/json", timeout=2.001
).json()
released_version = data["info"]["version"]
if parse_version(released_version) > parse_version(__version__):
warnings.warn(
"You are running an outdated version of Jira Python %s. Current version is %s. Do not file any bugs against older versions."
% (__version__, released_version)
)
except requests.RequestException:
pass
except Exception as e:
self.log.warning(e)
def __del__(self):
"""Destructor for JIRA instance."""
self.close()
def close(self):
session = getattr(self, "_session", None)
if session is not None:
try:
session.close()
except TypeError:
# TypeError: "'NoneType' object is not callable"
# Could still happen here because other references are also
# in the process to be torn down, see warning section in
# https://docs.python.org/2/reference/datamodel.html#object.__del__
pass
self._session = None
def _check_for_html_error(self, content: str):
# Jira has the bad habit of returning errors in pages with 200 and
# embedding the error in a huge webpage.
if "<!-- SecurityTokenMissing -->" in content:
self.log.warning("Got SecurityTokenMissing")
raise JIRAError(f"SecurityTokenMissing: {content}")
return False
return True
def _get_sprint_field_id(self):
sprint_field_name = "Sprint"
sprint_field_id = [
f["schema"]["customId"]
for f in self.fields()
if f["name"] == sprint_field_name
][0]
return sprint_field_id
def _fetch_pages(
self,
item_type: Type[ResourceType],
items_key: Optional[str],
request_path: str,
startAt: int = 0,
maxResults: int = 50,
params: Dict[str, Any] = None,
base: str = JIRA_BASE_URL,
) -> ResultList[ResourceType]:
"""Fetch from a paginated end point.
Args:
item_type (Type[Resource]): Type of single item. ResultList of such items will be returned.
items_key (Optional[str]): Path to the items in JSON returned from server.
Set it to None, if response is an array, and not a JSON object.
request_path (str): path in request URL
startAt (int): index of the first record to be fetched. (Default: 0)
maxResults (int): Maximum number of items to return.
If maxResults evaluates as False, it will try to get all items in batches. (Default:50)
params (Dict[str, Any]): Params to be used in all requests. Should not contain startAt and maxResults,
as they will be added for each request created from this function.
base (str): base URL to use for the requests.
Returns:
ResultList
"""
async_workers = None
async_class = None
if self._options["async"]:
try:
from requests_futures.sessions import FuturesSession
async_class = FuturesSession
except ImportError:
pass
async_workers = self._options.get("async_workers")
page_params = params.copy() if params else {}
if startAt:
page_params["startAt"] = startAt
if maxResults:
page_params["maxResults"] = maxResults
resource = self._get_json(request_path, params=page_params, base=base)
next_items_page = self._get_items_from_page(item_type, items_key, resource)
items = next_items_page
if True: # isinstance(resource, dict):
if isinstance(resource, dict):
total = resource.get("total")
total = int(total) if total is not None else total
# 'isLast' is the optional key added to responses in Jira Agile 6.7.6. So far not used in basic Jira API.
is_last = resource.get("isLast", False)
start_at_from_response = resource.get("startAt", 0)
max_results_from_response = resource.get("maxResults", 1)
else:
# if is a list
total = 1
is_last = True
start_at_from_response = 0
max_results_from_response = 1
# If maxResults evaluates as False, get all items in batches
if not maxResults:
page_size = max_results_from_response or len(items)
page_start = (startAt or start_at_from_response or 0) + page_size
if (
async_class is not None
and not is_last
and (total is not None and len(items) < total)
):
async_fetches = []
future_session = async_class(
session=self._session, max_workers=async_workers
)
for start_index in range(page_start, total, page_size):
page_params = params.copy() if params else {}
page_params["startAt"] = start_index
page_params["maxResults"] = page_size
url = self._get_url(request_path)
r = future_session.get(url, params=page_params)
async_fetches.append(r)
for future in async_fetches:
response = future.result()
resource = json_loads(response)
if resource:
next_items_page = self._get_items_from_page(
item_type, items_key, resource
)
items.extend(next_items_page)
while (
async_class is None
and not is_last
and (total is None or page_start < total)
and len(next_items_page) == page_size
):
page_params["startAt"] = page_start
page_params["maxResults"] = page_size
resource = self._get_json(
request_path, params=page_params, base=base
)
if resource:
next_items_page = self._get_items_from_page(
item_type, items_key, resource
)
items.extend(next_items_page)
page_start += page_size
else:
# if resource is an empty dictionary we assume no-results
break
return ResultList(
items, start_at_from_response, max_results_from_response, total, is_last
)
else: # TODO: unreachable
# it seems that search_users can return a list() containing a single user!
return ResultList(
[item_type(self._options, self._session, resource)], 0, 1, 1, True
)
def _get_items_from_page(
self,
item_type: Type[ResourceType],
items_key: Optional[str],
resource: Dict[str, Any],
) -> List[ResourceType]:
try:
return [
# We need to ignore the type here, as 'Resource' is an option
item_type(self._options, self._session, raw_issue_json) # type: ignore
for raw_issue_json in (resource[items_key] if items_key else resource)
]
except KeyError as e:
# improving the error text so we know why it happened
raise KeyError(str(e) + " : " + json.dumps(resource))
# Information about this client
def client_info(self) -> str:
"""Get the server this client is connected to."""
return self.server_url
# Universal resource loading
def find(
self, resource_format: str, ids: Union[Tuple[str, str], int, str] = ""
) -> Resource:
"""Find Resource object for any addressable resource on the server.
This method is a universal resource locator for any REST-ful resource in Jira. The
argument ``resource_format`` is a string of the form ``resource``, ``resource/{0}``,
``resource/{0}/sub``, ``resource/{0}/sub/{1}``, etc. The format placeholders will be
populated from the ``ids`` argument if present. The existing authentication session
will be used.
The return value is an untyped Resource object, which will not support specialized
:py:meth:`.Resource.update` or :py:meth:`.Resource.delete` behavior. Moreover, it will
not know to return an issue Resource if the client uses the resource issue path. For this
reason, it is intended to support resources that are not included in the standard
Atlassian REST API.
Args:
resource_format (str): the subpath to the resource string
ids (Optional[Tuple]): values to substitute in the ``resource_format`` string
Returns:
Resource
"""
resource = Resource(resource_format, self._options, self._session)
resource.find(ids)
return resource
@no_type_check # FIXME: This function fails type checking, probably a bug or two
def async_do(self, size: int = 10):
"""Execute all asynchronous jobs and wait for them to finish. By default it will run on 10 threads.
Args:
size (int): number of threads to run on.
"""
if hasattr(self._session, "_async_jobs"):
self.log.info(
"Executing asynchronous %s jobs found in queue by using %s threads..."
% (len(self._session._async_jobs), size)
)
threaded_requests.map(self._session._async_jobs, size=size)
# Application properties
# non-resource
def application_properties(
self, key: str = None
) -> Union[Dict[str, str], List[Dict[str, str]]]:
"""Return the mutable server application properties.
Args:
key (Optional[str]): the single property to return a value for
Returns:
Union[Dict[str, str], List[Dict[str, str]]]
"""
params = {}
if key is not None:
params["key"] = key
return self._get_json("application-properties", params=params)
def set_application_property(self, key: str, value: str):
"""Set the application property.
Args:
key (str): key of the property to set
value (str): value to assign to the property
"""
url = self._get_latest_url("application-properties/" + key)
payload = {"id": key, "value": value}
return self._session.put(url, data=json.dumps(payload))
def applicationlinks(self, cached: bool = True) -> List:
"""List of application links.
Returns:
List[Dict]: json, or empty list
"""
self._applicationlinks: List[Dict] # for mypy benefit
# if cached, return the last result
if cached and hasattr(self, "_applicationlinks"):
return self._applicationlinks
# url = self._options['server'] + '/rest/applinks/latest/applicationlink'
url = self.server_url + "/rest/applinks/latest/listApplicationlinks"
r = self._session.get(url)
o = json_loads(r)
if "list" in o and isinstance(o, dict):
self._applicationlinks = o["list"]
else:
self._applicationlinks = []
return self._applicationlinks
# Attachments
def attachment(self, id: str) -> Attachment:
"""Get an attachment Resource from the server for the specified ID.
Args:
id (str): The Attachment ID
Returns:
Attachment
"""
return self._find_for_resource(Attachment, id)
# non-resource
def attachment_meta(self) -> Dict[str, int]:
"""Get the attachment metadata.
Return:
Dict[str, int]
"""
return self._get_json("attachment/meta")
@translate_resource_args
def add_attachment(
self, issue: str, attachment: Union[str, BufferedReader], filename: str = None
) -> Attachment:
"""Attach an attachment to an issue and returns a Resource for it.
The client will *not* attempt to open or validate the attachment; it expects a file-like object to be ready
for its use. The user is still responsible for tidying up (e.g., closing the file, killing the socket, etc.)
Args:
issue (str): the issue to attach the attachment to
attachment (Union[str,BufferedReader]): file-like object to attach to the issue, also works if it is a string with the filename.
filename (str): optional name for the attached file. If omitted, the file object's ``name`` attribute
is used. If you acquired the file-like object by any other method than ``open()``, make sure
that a name is specified in one way or the other.
Returns:
Attachment
"""
close_attachment = False
if isinstance(attachment, str):
attachment: BufferedReader = open(attachment, "rb") # type: ignore
attachment = cast(BufferedReader, attachment)
close_attachment = True
elif isinstance(attachment, BufferedReader) and attachment.mode != "rb":
self.log.warning(
"%s was not opened in 'rb' mode, attaching file may fail."
% attachment.name
)
url = self._get_url("issue/" + str(issue) + "/attachments")
fname = filename
if not fname and isinstance(attachment, BufferedReader):
fname = os.path.basename(attachment.name)
if "MultipartEncoder" not in globals():
method = "old"
try:
r = self._session.post(
url,
files={"file": (fname, attachment, "application/octet-stream")},
headers=CaseInsensitiveDict(
{"content-type": None, "X-Atlassian-Token": "no-check"}
),
)
finally:
if close_attachment:
attachment.close()
else:
method = "MultipartEncoder"
def file_stream() -> MultipartEncoder:
"""Returns files stream of attachment."""
return MultipartEncoder(
fields={"file": (fname, attachment, "application/octet-stream")}
)
m = file_stream()
try:
r = self._session.post(
url,
data=m,
headers=CaseInsensitiveDict(
{
"content-type": m.content_type,
"X-Atlassian-Token": "no-check",
}
),
retry_data=file_stream,
)
finally:
if close_attachment:
attachment.close()
js: Union[Dict[str, Any], List[Dict[str, Any]]] = json_loads(r)
if not js or not isinstance(js, Iterable):
raise JIRAError(f"Unable to parse JSON: {js}")
jira_attachment = Attachment(
self._options, self._session, js[0] if isinstance(js, List) else js
)
if jira_attachment.size == 0:
raise JIRAError(
"Added empty attachment via %s method?!: r: %s\nattachment: %s"
% (method, r, jira_attachment)
)
return jira_attachment
def delete_attachment(self, id: str) -> Response:
"""Delete attachment by id.
Args:
id (str): ID of the attachment to delete
Returns:
Response
"""
url = self._get_url("attachment/" + str(id))
return self._session.delete(url)
# Components
def component(self, id: str):
"""Get a component Resource from the server.
Args:
id (str): ID of the component to get
"""
return self._find_for_resource(Component, id)
@translate_resource_args
def create_component(
self,
name: str,
project: str,
description=None,
leadUserName=None,
assigneeType=None,
isAssigneeTypeValid=False,
) -> Component:
"""Create a component inside a project and return a Resource for it.
Args:
name (str): name of the component
project (str): key of the project to create the component in
description (str): a description of the component
leadUserName (Optional[str]): the username of the user responsible for this component
assigneeType (Optional[str]): see the ComponentBean.AssigneeType class for valid values
isAssigneeTypeValid (bool): boolean specifying whether the assignee type is acceptable (Default: False)
Returns:
Component
"""
data = {
"name": name,
"project": project,
"isAssigneeTypeValid": isAssigneeTypeValid,
}
if description is not None:
data["description"] = description
if leadUserName is not None:
data["leadUserName"] = leadUserName
if assigneeType is not None:
data["assigneeType"] = assigneeType
url = self._get_url("component")
r = self._session.post(url, data=json.dumps(data))
component = Component(self._options, self._session, raw=json_loads(r))
return component
def component_count_related_issues(self, id: str):
"""Get the count of related issues for a component.
Args:
id (str): ID of the component to use
"""
data: Dict[str, Any] = self._get_json(
"component/" + str(id) + "/relatedIssueCounts"
)
return data["issueCount"]
def delete_component(self, id: str) -> Response:
"""Delete component by id.
Args:
id (str): ID of the component to use
Returns:
Response
"""
url = self._get_url("component/" + str(id))
return self._session.delete(url)
# Custom field options
def custom_field_option(self, id: str) -> CustomFieldOption:
"""Get a custom field option Resource from the server.
Args:
id (str): ID of the custom field to use
Returns:
CustomFieldOption
"""
return self._find_for_resource(CustomFieldOption, id)
# Dashboards
def dashboards(
self, filter=None, startAt=0, maxResults=20
) -> ResultList[Dashboard]:
"""Return a ResultList of Dashboard resources and a ``total`` count.
Args:
filter (Optional[str]): either "favourite" or "my", the type of dashboards to return
startAt (int): index of the first dashboard to return (Default: 0)
maxResults (int): maximum number of dashboards to return. If maxResults evaluates as False, it will try to get all items in batches. (Default: 20)
Returns:
ResultList
"""
params = {}
if filter is not None:
params["filter"] = filter
return self._fetch_pages(
Dashboard, "dashboards", "dashboard", startAt, maxResults, params
)
def dashboard(self, id: str) -> Dashboard:
"""Get a dashboard Resource from the server.
Args:
id (str): ID of the dashboard to get.
Returns:
Dashboard
"""
return self._find_for_resource(Dashboard, id)
# Fields
# non-resource
def fields(self) -> List[Dict[str, Any]]:
"""Return a list of all issue fields.
Returns:
List[Dict[str, Any]]
"""
return self._get_json("field")
# Filters
def filter(self, id: str) -> Filter:
"""Get a filter Resource from the server.
Args:
id (str): ID of the filter to get.
Returns:
Filter
"""
return self._find_for_resource(Filter, id)
def favourite_filters(self) -> List[Filter]:
"""Get a list of filter Resources which are the favourites of the currently authenticated user.
Returns:
List[Filter]
"""
r_json: List[Dict[str, Any]] = self._get_json("filter/favourite")
filters = [
Filter(self._options, self._session, raw_filter_json)
for raw_filter_json in r_json
]
return filters
def create_filter(
self,
name: str = None,
description: str = None,
jql: str = None,
favourite: bool = None,
):
"""Create a new filter and return a filter Resource for it.
Args:
name (str): name of the new filter
description (str): useful human readable description of the new filter
jql (str): query string that defines the filter
favourite (bool): whether to add this filter to the current user's favorites
Returns:
Filter
"""
data: Dict[str, Any] = {}
if name is not None:
data["name"] = name
if description is not None:
data["description"] = description
if jql is not None:
data["jql"] = jql
if favourite is not None:
data["favourite"] = favourite
url = self._get_url("filter")
r = self._session.post(url, data=json.dumps(data))
raw_filter_json: Dict[str, Any] = json_loads(r)
return Filter(self._options, self._session, raw=raw_filter_json)
def update_filter(
self,
filter_id,
name: str = None,
description: str = None,
jql: str = None,
favourite: bool = None,
):
"""Update a filter and return a filter Resource for it.
Args:
name (Optional[str]): name of the new filter
description (Optional[str]): useful human readable description of the new filter
jql (Optional[str]): query string that defines the filter
favourite (Optional[bool]): whether to add this filter to the current user's favorites
"""
filter = self.filter(filter_id)
data = {}
data["name"] = name or filter.name
data["description"] = description or filter.description
data["jql"] = jql or filter.jql
data["favourite"] = favourite or filter.favourite
url = self._get_url(f"filter/{filter_id}")
r = self._session.put(
url, headers={"content-type": "application/json"}, data=json.dumps(data)
)
raw_filter_json = json.loads(r.text)
return Filter(self._options, self._session, raw=raw_filter_json)
# Groups
def group(self, id: str, expand: Any = None) -> Group:
"""Get a group Resource from the server.
Args:
id (str): ID of the group to get
expand (Optional[Any]): Extra information to fetch inside each resource
Returns:
Group
"""
group = Group(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
group.find(id, params=params)
return group
# non-resource
def groups(
self,
query: Optional[str] = None,
exclude: Optional[Any] = None,
maxResults: int = 9999,
) -> List[str]:
"""Return a list of groups matching the specified criteria.
Args:
query (Optional[str]): filter groups by name with this string
exclude (Optional[Any]): filter out groups by name with this string
maxResults (int): maximum results to return. (Default: 9999)
Returns:
List[str]
"""
params: Dict[str, Any] = {}
groups = []
if query is not None:
params["query"] = query
if exclude is not None:
params["exclude"] = exclude
if maxResults is not None:
params["maxResults"] = maxResults
for group in self._get_json("groups/picker", params=params)["groups"]:
groups.append(group["name"])
return sorted(groups)
def group_members(self, group: str) -> OrderedDict:
"""Return a hash or users with their information. Requires Jira 6.0 or will raise NotImplemented.
Args:
group (str): Name of the group.
"""
if self._version < (6, 0, 0):
raise NotImplementedError(
"Group members is not implemented in Jira before version 6.0, upgrade the instance, if possible."
)
params = {"groupname": group, "expand": "users"}
r = self._get_json("group", params=params)
size = r["users"]["size"]
end_index = r["users"]["end-index"]
while end_index < size - 1:
params = {
"groupname": group,
"expand": f"users[{end_index + 1}:{end_index + 50}]",
}
r2 = self._get_json("group", params=params)
for user in r2["users"]["items"]:
r["users"]["items"].append(user)
end_index = r2["users"]["end-index"]
size = r["users"]["size"]
result = {}
for user in r["users"]["items"]:
result[user["id"]] = {
"name": user.get("name"),
"id": user.get("id"),
"accountId": user.get("accountId"),
"fullname": user.get("displayName"),
"email": user.get("emailAddress", "hidden"),
"active": user.get("active"),
"timezone": user.get("timezone"),
}
return OrderedDict(sorted(result.items(), key=lambda t: t[0]))
def add_group(self, groupname: str) -> bool:
"""Create a new group in Jira.
Args:
groupname (str): The name of the group you wish to create.
Returns:
bool: True if successful.
"""
url = self._get_latest_url("group")
# implementation based on
# https://docs.atlassian.com/jira/REST/ondemand/#d2e5173
x = OrderedDict()
x["name"] = groupname
payload = json.dumps(x)
self._session.post(url, data=payload)
return True
def remove_group(self, groupname: str) -> bool:
"""Delete a group from the Jira instance.
Args:
groupname (str): The group to be deleted from the Jira instance.
Returns:
bool: Returns True on success.
"""
# implementation based on
# https://docs.atlassian.com/jira/REST/ondemand/#d2e5173
url = self._get_latest_url("group")
x = {"groupname": groupname}
self._session.delete(url, params=x)
return True
# Issues
def issue(
self,
id: Union[Issue, str],
fields: Optional[str] = None,
expand: Optional[str] = None,
) -> Issue:
"""Get an issue Resource from the server.
Args:
id (Union[Issue, str]): ID or key of the issue to get
fields (Optional[str]): comma-separated string of issue fields to include in the results
expand (Optional[str]): extra information to fetch inside each resource
Returns:
Issue
"""
# this allows us to pass Issue objects to issue()
if isinstance(id, Issue):
return id
issue = Issue(self._options, self._session)
params = {}
if fields is not None:
params["fields"] = fields
if expand is not None:
params["expand"] = expand
issue.find(id, params=params)
return issue
def create_issue(
self,
fields: Optional[Dict[str, Any]] = None,
prefetch: bool = True,
**fieldargs,
) -> Issue:
"""Create a new issue and return an issue Resource for it.
Each keyword argument (other than the predefined ones) is treated as a field name and the argument's value
is treated as the intended value for that field -- if the fields argument is used, all other keyword arguments
will be ignored.
By default, the client will immediately reload the issue Resource created by this method in order to return
a complete Issue object to the caller; this behavior can be controlled through the 'prefetch' argument.
Jira projects may contain many different issue types. Some issue screens have different requirements for
fields in a new issue. This information is available through the 'createmeta' method. Further examples are
available here: https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+Example+-+Create+Issue
Args:
fields (Optional[Dict[str, Any]]): a dict containing field names and the values to use. If present, all other keyword arguments
will be ignored
prefetch (bool): whether to reload the created issue Resource so that all of its data is present in the value
returned from this method
Returns:
Issue
"""
data: Dict[str, Any] = _field_worker(fields, **fieldargs)
p = data["fields"]["project"]
if isinstance(p, str) or isinstance(p, int):
data["fields"]["project"] = {"id": self.project(str(p)).id}
p = data["fields"]["issuetype"]
if isinstance(p, int):
data["fields"]["issuetype"] = {"id": p}
if isinstance(p, str) or isinstance(p, int):
data["fields"]["issuetype"] = {"id": self.issue_type_by_name(str(p)).id}
url = self._get_url("issue")
r = self._session.post(url, data=json.dumps(data))
raw_issue_json = json_loads(r)
if "key" not in raw_issue_json:
raise JIRAError(
status_code=r.status_code, response=r, url=url, text=json.dumps(data)
)
if prefetch:
return self.issue(raw_issue_json["key"])
else:
return Issue(self._options, self._session, raw=raw_issue_json)
def create_issues(
self, field_list: List[Dict[str, Any]], prefetch: bool = True
) -> List[Dict[str, Any]]:
"""Bulk create new issues and return an issue Resource for each successfully created issue.
See `create_issue` documentation for field information.
Args:
field_list (List[Dict[str, Any]]): a list of dicts each containing field names and the values to use. Each dict
is an individual issue to create and is subject to its minimum requirements.
prefetch (bool): whether to reload the created issue Resource for each created issue so that all
of its data is present in the value returned from this method.
Returns:
List[Dict[str, Any]]
"""
data: Dict[str, List] = {"issueUpdates": []}
for field_dict in field_list:
issue_data: Dict[str, Any] = _field_worker(field_dict)
p = issue_data["fields"]["project"]
if isinstance(p, str) or isinstance(p, int):
issue_data["fields"]["project"] = {"id": self.project(str(p)).id}
p = issue_data["fields"]["issuetype"]
if isinstance(p, int):
issue_data["fields"]["issuetype"] = {"id": p}
if isinstance(p, str):
issue_data["fields"]["issuetype"] = {
"id": self.issue_type_by_name(str(p)).id
}
data["issueUpdates"].append(issue_data)
url = self._get_url("issue/bulk")
try:
r = self._session.post(url, data=json.dumps(data))
raw_issue_json = json_loads(r)
# Catching case where none of the issues has been created. See https://github.com/pycontribs/jira/issues/350
except JIRAError as je:
if je.status_code == 400 and je.response:
raw_issue_json = json.loads(je.response.text)
else:
raise
issue_list = []
errors = {}
for error in raw_issue_json["errors"]:
errors[error["failedElementNumber"]] = error["elementErrors"]["errors"]
for index, fields in enumerate(field_list):
if index in errors:
issue_list.append(
{
"status": "Error",
"error": errors[index],
"issue": None,
"input_fields": fields,
}
)
else:
issue = raw_issue_json["issues"].pop(0)
if prefetch:
issue = self.issue(issue["key"])
else:
issue = Issue(self._options, self._session, raw=issue)
issue_list.append(
{
"status": "Success",
"issue": issue,
"error": None,
"input_fields": fields,
}
)
return issue_list
def supports_service_desk(self):
"""Returns whether or not the Jira instance supports service desk.
Returns:
bool
"""
url = self.server_url + "/rest/servicedeskapi/info"
headers = {"X-ExperimentalApi": "opt-in"}
try:
r = self._session.get(url, headers=headers)
return r.status_code == 200
except JIRAError:
return False
def create_customer(self, email: str, displayName: str) -> Customer:
"""Create a new customer and return an issue Resource for it.
Args:
email (str): Customer Email
displayName (str): Customer display name
Returns:
Customer
"""
url = self.server_url + "/rest/servicedeskapi/customer"
headers = {"X-ExperimentalApi": "opt-in"}
r = self._session.post(
url,
headers=headers,
data=json.dumps({"email": email, "displayName": displayName}),
)
raw_customer_json = json_loads(r)
if r.status_code != 201:
raise JIRAError(status_code=r.status_code, request=r)
return Customer(self._options, self._session, raw=raw_customer_json)
def service_desks(self) -> List[ServiceDesk]:
"""Get a list of ServiceDesk Resources from the server visible to the current authenticated user.
Returns:
List[ServiceDesk]
"""
url = self.server_url + "/rest/servicedeskapi/servicedesk"
headers = {"X-ExperimentalApi": "opt-in"}
r_json = json_loads(self._session.get(url, headers=headers))
print(r_json)
projects = [
ServiceDesk(self._options, self._session, raw_project_json)
for raw_project_json in r_json["values"]
]
return projects
def service_desk(self, id: str) -> ServiceDesk:
"""Get a Service Desk Resource from the server.
Args:
id (str): ID or key of the Service Desk to get
Returns:
ServiceDesk
"""
return self._find_for_resource(ServiceDesk, id)
@no_type_check # FIXME: This function does not do what it wants to with fieldargs
def create_customer_request(
self, fields: Dict[str, Any] = None, prefetch: bool = True, **fieldargs
) -> Issue:
"""Create a new customer request and return an issue Resource for it.
Each keyword argument (other than the predefined ones) is treated as a field name and the argument's value
is treated as the intended value for that field -- if the fields argument is used, all other keyword arguments
will be ignored.
By default, the client will immediately reload the issue Resource created by this method in order to return
a complete Issue object to the caller; this behavior can be controlled through the 'prefetch' argument.
Jira projects may contain many different issue types. Some issue screens have different requirements for
fields in a new issue. This information is available through the 'createmeta' method. Further examples are
available here: https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+Example+-+Create+Issue
Args:
fields (Dict[str, Any]): a dict containing field names and the values to use. If present, all other keyword arguments
will be ignored
prefetch (bool): whether to reload the created issue Resource so that all of its data is present in the value
returned from this method
Returns:
Issue
"""
data = fields
p = data["serviceDeskId"]
service_desk = None
if isinstance(p, str) or isinstance(p, int):
service_desk = self.service_desk(p)
elif isinstance(p, ServiceDesk):
service_desk = p
data["serviceDeskId"] = service_desk.id
p = data["requestTypeId"]
if isinstance(p, int):
data["requestTypeId"] = p
elif isinstance(p, str):
data["requestTypeId"] = self.request_type_by_name(service_desk, p).id
url = self.server_url + "/rest/servicedeskapi/request"
headers = {"X-ExperimentalApi": "opt-in"}
r = self._session.post(url, headers=headers, data=json.dumps(data))
raw_issue_json = json_loads(r)
if "issueKey" not in raw_issue_json:
raise JIRAError(status_code=r.status_code, request=r)
if prefetch:
return self.issue(raw_issue_json["issueKey"])
else:
return Issue(self._options, self._session, raw=raw_issue_json)
def createmeta(
self,
projectKeys: Optional[Union[Tuple[str, str], str]] = None,
projectIds: Union[List, Tuple[str, str]] = [],
issuetypeIds: Optional[List[str]] = None,
issuetypeNames: Optional[str] = None,
expand: Optional[str] = None,
) -> Dict[str, Any]:
"""Get the metadata required to create issues, optionally filtered by projects and issue types.
Args:
projectKeys (Optional[Union[Tuple[str, str], str]]): keys of the projects to filter the results with.
Can be a single value or a comma-delimited string. May be combined
with projectIds.
projectIds (Union[List, Tuple[str, str]]): IDs of the projects to filter the results with. Can
be a single value or a comma-delimited string. May be combined with
projectKeys.
issuetypeIds (Optional[List[str]]): IDs of the issue types to filter the results with.
Can be a single value or a comma-delimited string. May be combined
with issuetypeNames.
issuetypeNames (Optional[str]): Names of the issue types to filter the results
with. Can be a single value or a comma-delimited string. May be
combined with issuetypeIds.
expand (Optional[str]): extra information to fetch inside each resource.
Returns:
Dict[str, Any]
"""
params: Dict[str, Any] = {}
if projectKeys is not None:
params["projectKeys"] = projectKeys
if projectIds is not None:
if isinstance(projectIds, str):
projectIds = projectIds.split(",")
params["projectIds"] = projectIds
if issuetypeIds is not None:
params["issuetypeIds"] = issuetypeIds
if issuetypeNames is not None:
params["issuetypeNames"] = issuetypeNames
if expand is not None:
params["expand"] = expand
return self._get_json("issue/createmeta", params)
def _get_user_key(self, user: str) -> str:
"""Internal method for translating an user (str) to an key."""
try:
key = self.search_users(user, maxResults=1)[0].key
except Exception as e:
raise JIRAError(str(e))
return key
# non-resource
@translate_resource_args
def assign_issue(self, issue: Union[int, str], assignee: str) -> bool:
"""Assign an issue to a user. None will set it to unassigned. -1 will set it to Automatic.
Args:
issue (Union[int,str]): the issue ID or key to assign
assignee (str): the user to assign the issue to
Returns:
bool
"""
url = self._get_latest_url("issue/{}/assignee".format(str(issue)))
payload = {"name": self._get_user_key(assignee)}
# 'key' and 'name' are deprecated in favor of accountId
r = self._session.put(url, data=json.dumps(payload))
raise_on_error(r)
return True
@translate_resource_args
def comments(self, issue: str, expand: Optional[str] = None) -> List[Comment]:
"""Get a list of comment Resources.
:param issue: the issue to get comments from
:type issue: str
:param expand: extra information to fetch for each comment
such as renderedBody and properties.
:type expand: str
:rtype: List[Comment]
"""
params = {}
if expand is not None:
params["expand"] = expand
r_json = self._get_json("issue/{}/comment".format(str(issue)), params=params)
comments = [
Comment(self._options, self._session, raw_comment_json)
for raw_comment_json in r_json["comments"]
]
return comments
@translate_resource_args
def comment(
self, issue: str, comment: str, expand: Optional[str] = None
) -> Comment:
"""Get a comment Resource from the server for the specified ID.
:param issue: ID or key of the issue to get the comment from
:param comment: ID of the comment to get
:param expand: extra information to fetch for comment
such as renderedBody and properties.
"""
return self._find_for_resource(Comment, (issue, comment), expand=expand)
@translate_resource_args
def add_comment(
self,
issue: str,
body: str,
visibility: Optional[Dict[str, str]] = None,
is_internal: bool = False,
) -> Comment:
"""Add a comment from the current authenticated user on the specified issue and return a Resource for it.
The issue identifier and comment body are required.
Args:
issue (str): ID or key of the issue to add the comment to
body (str): Text of the comment to add
visibility (Optional[Dict[str, str]]): a dict containing two entries: "type" and "value".
"type" is 'role' (or 'group' if the Jira server has configured
comment visibility for groups) and 'value' is the name of the role
(or group) to which viewing of this comment will be restricted.
is_internal (bool): Defines whether a comment has to be marked as 'Internal' in Jira Service Desk (Default: False)
Returns:
Comment: the created comment
"""
data: Dict[str, Any] = {"body": body}
if is_internal:
data.update(
{
"properties": [
{"key": "sd.public.comment", "value": {"internal": is_internal}}
]
}
)
if visibility is not None:
data["visibility"] = visibility
url = self._get_url("issue/" + str(issue) + "/comment")
r = self._session.post(url, data=json.dumps(data))
comment = Comment(self._options, self._session, raw=json_loads(r))
return comment
# non-resource
@translate_resource_args
def editmeta(self, issue: Union[str, int]):
"""Get the edit metadata for an issue.
Args:
issue (str): the issue to get metadata for
Returns:
Dict[str, Dict[str, Dict[str, Any]]]
"""
return self._get_json("issue/" + str(issue) + "/editmeta")
@translate_resource_args
def remote_links(self, issue: Union[str, int]) -> List[RemoteLink]:
"""Get a list of remote link Resources from an issue.
Args:
issue (str): the issue to get remote links from
"""
r_json = self._get_json("issue/" + str(issue) + "/remotelink")
remote_links = [
RemoteLink(self._options, self._session, raw_remotelink_json)
for raw_remotelink_json in r_json
]
return remote_links
@translate_resource_args
def remote_link(self, issue: str, id: str) -> RemoteLink:
"""Get a remote link Resource from the server.
Args:
issue (str): the issue holding the remote link
id (str): ID of the remote link
"""
return self._find_for_resource(RemoteLink, (issue, id))
# removed the @translate_resource_args because it prevents us from finding
# information for building a proper link
def add_remote_link(
self,
issue: str,
destination: Union[Issue, Dict[str, Any]],
globalId: Optional[str] = None,
application: Optional[Dict[str, Any]] = None,
relationship: Optional[str] = None,
) -> RemoteLink:
"""Add a remote link from an issue to an external application and returns a remote link Resource for it.
``destination`` should be a dict containing at least ``url`` to the linked external URL and
``title`` to display for the link inside Jira.
For definitions of the allowable fields for ``object`` and the keyword arguments ``globalId``, ``application``
and ``relationship``, see https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+for+Remote+Issue+Links.
Args:
issue (str): the issue to add the remote link to
destination (Union[Issue, Dict[str, Any]]): the link details to add (see the above link for details)
globalId (Optional[str]): unique ID for the link (see the above link for details)
application (Optional[Dict[str,Any]]): application information for the link (see the above link for details)
relationship (Optional[str]): relationship description for the link (see the above link for details)
Returns:
RemoteLink: the added remote lint
"""
try:
applicationlinks: List[Dict] = self.applicationlinks()
except JIRAError as e:
applicationlinks = []
# In many (if not most) configurations, non-admin users are
# not allowed to list applicationlinks; if we aren't allowed,
# let's let people try to add remote links anyway, we just
# won't be able to be quite as helpful.
warnings.warn(
"Unable to gather applicationlinks; you will not be able "
"to add links to remote issues: (%s) %s" % (e.status_code, e.text),
Warning,
)
data: Dict[str, Any] = {}
if isinstance(destination, Issue) and destination.raw:
data["object"] = {"title": str(destination), "url": destination.permalink()}
for x in applicationlinks:
if x["application"]["displayUrl"] == destination._options["server"]:
data["globalId"] = "appId=%s&issueId=%s" % (
x["application"]["id"],
destination.raw["id"],
)
data["application"] = {
"name": x["application"]["name"],
"type": "com.atlassian.jira",
}
break
if "globalId" not in data:
raise NotImplementedError("Unable to identify the issue to link to.")
else:
if globalId is not None:
data["globalId"] = globalId
if application is not None:
data["application"] = application
data["object"] = destination
if relationship is not None:
data["relationship"] = relationship
# check if the link comes from one of the configured application links
if isinstance(destination, Issue) and destination.raw:
for x in applicationlinks:
if x["application"]["displayUrl"] == self.server_url:
data["globalId"] = "appId=%s&issueId=%s" % (
x["application"]["id"],
destination.raw["id"], # .raw only present on Issue
)
data["application"] = {
"name": x["application"]["name"],
"type": "com.atlassian.jira",
}
break
url = self._get_url("issue/" + str(issue) + "/remotelink")
r = self._session.post(url, data=json.dumps(data))
remote_link = RemoteLink(self._options, self._session, raw=json_loads(r))
return remote_link
def add_simple_link(self, issue: str, object: Dict[str, Any]):
"""Add a simple remote link from an issue to web resource.
This avoids the admin access problems from add_remote_link by just
using a simple object and presuming all fields are correct and not
requiring more complex ``application`` data.
``object`` should be a dict containing at least ``url`` to the
linked external URL and ``title`` to display for the link inside Jira.
For definitions of the allowable fields for ``object`` , see https://developer.atlassian.com/display/JIRADEV/JIRA+REST+API+for+Remote+Issue+Links.
Args:
issue (str): the issue to add the remote link to
object (Dict[str,Any]): the dictionary used to create remotelink data
Returns:
RemoteLint
"""
data = {"object": object}
url = self._get_url("issue/" + str(issue) + "/remotelink")
r = self._session.post(url, data=json.dumps(data))
simple_link = RemoteLink(self._options, self._session, raw=json_loads(r))
return simple_link
# non-resource
@translate_resource_args
def transitions(self, issue: str, id: Optional[str] = None, expand=None):
"""Get a list of the transitions available on the specified issue to the current user.
Args:
issue (str): ID or key of the issue to get the transitions from
id (Optional[str]): if present, get only the transition matching this ID
expand (Optional): extra information to fetch inside each transition
Returns:
Any: json of response
"""
params = {}
if id is not None:
params["transitionId"] = id
if expand is not None:
params["expand"] = expand
return self._get_json("issue/" + str(issue) + "/transitions", params=params)[
"transitions"
]
def find_transitionid_by_name(
self, issue: str, transition_name: str
) -> Optional[int]:
"""Get a transitionid available on the specified issue to the current user.
Look at https://developer.atlassian.com/static/rest/jira/6.1.html#d2e1074 for json reference
Args:
issue (str): ID or key of the issue to get the transitions from
trans_name (str): iname of transition we are looking for
"""
transitions_json = self.transitions(issue)
id: Optional[int] = None
for transition in transitions_json:
if transition["name"].lower() == transition_name.lower():
id = transition["id"]
break
return id
@translate_resource_args
def transition_issue(
self,
issue: str,
transition: str,
fields: Optional[Dict[str, Any]] = None,
comment: Optional[str] = None,
worklog: Optional[str] = None,
**fieldargs,
):
"""Perform a transition on an issue.
Each keyword argument (other than the predefined ones) is treated as a field name and the argument's value
is treated as the intended value for that field -- if the fields argument is used, all other keyword arguments
will be ignored. Field values will be set on the issue as part of the transition process.
Args:
issue (str): ID or key of the issue to perform the transition on
transition (str): ID or name of the transition to perform
fields (Optional[Dict[str,Any]]): a dict containing field names and the values to use.
comment (Optional[str]): String to add as comment to the issue when performing the transition.
workload (Optional[str]): String to add as time spent on the issue when performing the transition.
**fieldargs: If present, all other keyword arguments will be ignored
"""
transitionId: Optional[int] = None
try:
transitionId = int(transition)
except Exception:
# cannot cast to int, so try to find transitionId by name
transitionId = self.find_transitionid_by_name(issue, transition)
if transitionId is None:
raise JIRAError(f"Invalid transition name. {transition}")
data: Dict[str, Any] = {"transition": {"id": transitionId}}
if comment:
data["update"] = {"comment": [{"add": {"body": comment}}]}
if worklog:
data["update"] = {"worklog": [{"add": {"timeSpent": worklog}}]}
if fields is not None:
data["fields"] = fields
else:
fields_dict = {}
for field in fieldargs:
fields_dict[field] = fieldargs[field]
data["fields"] = fields_dict
url = self._get_url("issue/" + str(issue) + "/transitions")
r = self._session.post(url, data=json.dumps(data))
try:
r_json = json_loads(r)
except ValueError as e:
self.log.error(f"{e}\n{r.text}")
raise e
return r_json
@translate_resource_args
def votes(self, issue: str) -> Votes:
"""Get a votes Resource from the server.
Args:
issue (str): ID or key of the issue to get the votes for
Returns:
Votes
"""
return self._find_for_resource(Votes, issue)
@translate_resource_args
def add_vote(self, issue: str) -> Response:
"""Register a vote for the current authenticated user on an issue.
Args:
issue (str): ID or key of the issue to vote on
Returns:
Response
"""
url = self._get_url("issue/" + str(issue) + "/votes")
return self._session.post(url)
@translate_resource_args
def remove_vote(self, issue: str):
"""Remove the current authenticated user's vote from an issue.
Args:
issue (str): ID or key of the issue to remove vote on
"""
url = self._get_url("issue/" + str(issue) + "/votes")
self._session.delete(url)
@translate_resource_args
def watchers(self, issue: str) -> Watchers:
"""Get a watchers Resource from the server for an issue.
Args:
issue (str): ID or key of the issue to get the watchers for
Returns:
Watchers
"""
return self._find_for_resource(Watchers, issue)
@translate_resource_args
def add_watcher(self, issue: str, watcher: str) -> Response:
"""Add a user to an issue's watchers list.
Args:
issue (str): ID or key of the issue affected
watcher (str): key of the user to add to the watchers list
"""
url = self._get_url("issue/" + str(issue) + "/watchers")
return self._session.post(url, data=json.dumps(watcher))
@translate_resource_args
def remove_watcher(self, issue: str, watcher: str) -> Response:
"""Remove a user from an issue's watch list.
Args:
issue (str): ID or key of the issue affected
watcher (str): key of the user to remove from the watchers list
Returns:
Response
"""
url = self._get_url("issue/" + str(issue) + "/watchers")
# https://docs.atlassian.com/software/jira/docs/api/REST/8.13.6/#api/2/issue-removeWatcher
params = {"username": watcher}
result = self._session.delete(url, params=params)
return result
@translate_resource_args
def worklogs(self, issue: str) -> List[Worklog]:
"""Get a list of worklog Resources from the server for an issue.
Args:
issue (str): ID or key of the issue to get worklogs from
Returns:
List[Worklog]
"""
r_json = self._get_json("issue/" + str(issue) + "/worklog")
worklogs = [
Worklog(self._options, self._session, raw_worklog_json)
for raw_worklog_json in r_json["worklogs"]
]
return worklogs
@translate_resource_args
def worklog(self, issue: str, id: str) -> Worklog:
"""Get a specific worklog Resource from the server.
Args:
issue (str): ID or key of the issue to get the worklog from
id (str): ID of the worklog to get
Returns:
Worklog
"""
return self._find_for_resource(Worklog, (issue, id))
@translate_resource_args
def add_worklog(
self,
issue,
timeSpent: (Optional[str]) = None,
timeSpentSeconds: (Optional[str]) = None,
adjustEstimate: (Optional[str]) = None,
newEstimate: (Optional[str]) = None,
reduceBy: (Optional[str]) = None,
comment: (Optional[str]) = None,
started: (Optional[datetime.datetime]) = None,
user: (Optional[str]) = None,
) -> Worklog:
"""Add a new worklog entry on an issue and return a Resource for it.
Args:
issue (str): the issue to add the worklog to
timeSpent (Optional[str]): a worklog entry with this amount of time spent, e.g. "2d"
timeSpentSeconds (Optional[str]): a worklog entry with this amount of time spent in seconds
adjustEstimate (Optional[str]): allows the user to provide specific instructions to update
the remaining time estimate of the issue. The value can either be ``new``, ``leave``, ``manual`` or ``auto`` (default).
newEstimate (Optional[str]): the new value for the remaining estimate field. e.g. "2d"
reduceBy (Optional[str]): the amount to reduce the remaining estimate by e.g. "2d"
comment (Optional[str]): optional worklog comment
started (Optional[datetime.datetime]): Moment when the work is logged, if not specified will default to now
user (Optional[str]): the user ID or name to use for this worklog
Returns:
Worklog
"""
params = {}
if adjustEstimate is not None:
params["adjustEstimate"] = adjustEstimate
if newEstimate is not None:
params["newEstimate"] = newEstimate
if reduceBy is not None:
params["reduceBy"] = reduceBy
data: Dict[str, Any] = {}
if timeSpent is not None:
data["timeSpent"] = timeSpent
if timeSpentSeconds is not None:
data["timeSpentSeconds"] = timeSpentSeconds
if comment is not None:
data["comment"] = comment
elif user:
# we log user inside comment as it doesn't always work
data["comment"] = user
if started is not None:
# based on REST Browser it needs: "2014-06-03T08:21:01.273+0000"
if started.tzinfo is None:
data["started"] = started.strftime("%Y-%m-%dT%H:%M:%S.000+0000")
else:
data["started"] = started.strftime("%Y-%m-%dT%H:%M:%S.000%z")
if user is not None:
data["author"] = {
"name": user,
"self": self.JIRA_BASE_URL + "/rest/api/latest/user?username=" + user,
"displayName": user,
"active": False,
}
data["updateAuthor"] = data["author"]
# report bug to Atlassian: author and updateAuthor parameters are
# ignored.
url = self._get_url(f"issue/{issue}/worklog")
r = self._session.post(url, params=params, data=json.dumps(data))
return Worklog(self._options, self._session, json_loads(r))
# Issue links
@translate_resource_args
def create_issue_link(
self,
type: Union[str, IssueLinkType],
inwardIssue: str,
outwardIssue: str,
comment: Optional[Dict[str, Any]] = None,
) -> Response:
"""Create a link between two issues.
Args:
type (Union[str,IssueLinkType]): the type of link to create
inwardIssue: the issue to link from
outwardIssue: the issue to link to
comment (Optional[Dict[str, Any]]): a comment to add to the issues with the link.
Should be a dict containing ``body`` and ``visibility`` fields: ``body`` being
the text of the comment and ``visibility`` being a dict containing
two entries: ``type`` and ``value``. ``type`` is ``role`` (or
``group`` if the Jira server has configured comment visibility for
groups) and ``value`` is the name of the role (or group) to which
viewing of this comment will be restricted.
Returns:
Response
"""
# let's see if we have the right issue link 'type' and fix it if needed
issue_link_types = self.issue_link_types()
if type not in issue_link_types:
for lt in issue_link_types:
if lt.outward == type:
# we are smart to figure it out what he meant
type = lt.name
break
elif lt.inward == type:
# so that's the reverse, so we fix the request
type = lt.name
inwardIssue, outwardIssue = outwardIssue, inwardIssue
break
data = {
"type": {"name": type},
"inwardIssue": {"key": inwardIssue},
"outwardIssue": {"key": outwardIssue},
"comment": comment,
}
url = self._get_url("issueLink")
return self._session.post(url, data=json.dumps(data))
def delete_issue_link(self, id: str):
"""Delete a link between two issues.
Args:
id (str): ID of the issue link to delete
"""
url = self._get_url("issueLink") + "/" + id
return self._session.delete(url)
def issue_link(self, id: str):
"""Get an issue link Resource from the server.
Args:
id (str): ID of the issue link to get
"""
return self._find_for_resource(IssueLink, id)
# Issue link types
def issue_link_types(self, force: bool = False) -> List[IssueLinkType]:
"""Get a list of issue link type Resources from the server.
Returns:
List[IssueLinkType]
"""
if not hasattr(self, "self._cached_issue_link_types") or force:
r_json = self._get_json("issueLinkType")
self._cached_issue_link_types = [
IssueLinkType(self._options, self._session, raw_link_json)
for raw_link_json in r_json["issueLinkTypes"]
]
return self._cached_issue_link_types
def issue_link_type(self, id: str) -> IssueLinkType:
"""Get an issue link type Resource from the server.
Args:
id (str): ID of the issue link type to get
Returns:
IssueLinkType
"""
return self._find_for_resource(IssueLinkType, id)
# Issue types
def issue_types(self) -> List[IssueType]:
"""Get a list of issue type Resources from the server.
Returns:
List[IssueType]
"""
r_json = self._get_json("issuetype")
issue_types = [
IssueType(self._options, self._session, raw_type_json)
for raw_type_json in r_json
]
return issue_types
def issue_type(self, id: str) -> IssueType:
"""Get an issue type Resource from the server.
Args:
id (str): ID of the issue type to get
Returns:
IssueType
"""
return self._find_for_resource(IssueType, id)
def issue_type_by_name(self, name: str) -> IssueType:
"""
Args:
name (str): Name of the issue type
Returns:
IssueType
"""
matching_issue_types = [it for it in self.issue_types() if it.name == name]
if len(matching_issue_types) == 1:
return matching_issue_types[0]
elif len(matching_issue_types) == 0:
raise KeyError(f"Issue type '{name}' is unknown.")
else:
raise KeyError(f"Issue type '{name}' appears more than once.")
def request_types(self, service_desk: ServiceDesk) -> List[RequestType]:
"""Returns request types supported by a service desk instance.
Args:
service_desk (ServiceDesk): The service desk instance.
Returns:
List[RequestType]
"""
if hasattr(service_desk, "id"):
service_desk = service_desk.id
url = (
self.server_url
+ f"/rest/servicedeskapi/servicedesk/{service_desk}/requesttype"
)
headers = {"X-ExperimentalApi": "opt-in"}
r_json = json_loads(self._session.get(url, headers=headers))
request_types = [
RequestType(self._options, self._session, raw_type_json)
for raw_type_json in r_json["values"]
]
return request_types
def request_type_by_name(self, service_desk: ServiceDesk, name: str):
request_types = self.request_types(service_desk)
try:
request_type = [rt for rt in request_types if rt.name == name][0]
except IndexError:
raise KeyError(f"Request type '{name}' is unknown.")
return request_type
# User permissions
# non-resource
def my_permissions(
self,
projectKey: Optional[str] = None,
projectId: Optional[str] = None,
issueKey: Optional[str] = None,
issueId: Optional[str] = None,
) -> Dict[str, Dict[str, Dict[str, str]]]:
"""Get a dict of all available permissions on the server.
Args:
projectKey (Optional[str]): limit returned permissions to the specified project
projectId (Optional[str]): limit returned permissions to the specified project
issueKey (Optional[str]): limit returned permissions to the specified issue
issueId (Optional[str]): limit returned permissions to the specified issue
Returns:
Dict[str, Dict[str, Dict[str, str]]]
"""
params = {}
if projectKey is not None:
params["projectKey"] = projectKey
if projectId is not None:
params["projectId"] = projectId
if issueKey is not None:
params["issueKey"] = issueKey
if issueId is not None:
params["issueId"] = issueId
return self._get_json("mypermissions", params=params)
# Priorities
def priorities(self):
"""Get a list of priority Resources from the server.
Returns:
List[Priority]
"""
r_json = self._get_json("priority")
priorities = [
Priority(self._options, self._session, raw_priority_json)
for raw_priority_json in r_json
]
return priorities
def priority(self, id: str) -> Priority:
"""Get a priority Resource from the server.
Args:
id (str): ID of the priority to get
Returns:
Priority
"""
return self._find_for_resource(Priority, id)
# Projects
def projects(self) -> List[Project]:
"""Get a list of project Resources from the server visible to the current authenticated user.
Returns:
List[Project]
"""
r_json = self._get_json("project")
projects = [
Project(self._options, self._session, raw_project_json)
for raw_project_json in r_json
]
return projects
def project(self, id: str) -> Project:
"""Get a project Resource from the server.
Args:
id (str): ID or key of the project to get
Returns:
Project
"""
return self._find_for_resource(Project, id)
# non-resource
@translate_resource_args
def project_avatars(self, project: str):
"""Get a dict of all avatars for a project visible to the current authenticated user.
Args:
project (str): ID or key of the project to get avatars for
"""
return self._get_json("project/" + project + "/avatars")
@translate_resource_args
def create_temp_project_avatar(
self,
project: str,
filename: str,
size: int,
avatar_img: bytes,
contentType: str = None,
auto_confirm: bool = False,
):
"""Register an image file as a project avatar.
The avatar created is temporary and must be confirmed before it can be used.
Avatar images are specified by a filename, size, and file object. By default, the client will attempt to
autodetect the picture's content type: this mechanism relies on libmagic and will not work out of the box
on Windows systems (see https://filemagic.readthedocs.io/en/latest/guide.html for details on how to install
support). The ``contentType`` argument can be used to explicitly set the value (note that Jira will reject any
type other than the well-known ones for images, e.g. ``image/jpg``, ``image/png``, etc.)
This method returns a dict of properties that can be used to crop a subarea of a larger image for use. This
dict should be saved and passed to :py:meth:`confirm_project_avatar` to finish the avatar creation process. If
you want to cut out the middleman and confirm the avatar with Jira's default cropping, pass the 'auto_confirm'
argument with a truthy value and :py:meth:`confirm_project_avatar` will be called for you before this method
returns.
Args:
project (str): ID or key of the project to create the avatar in
filename (str): name of the avatar file
size (int): size of the avatar file
avatar_img (bytes): file-like object holding the avatar
contentType (str): explicit specification for the avatar image's content-type
auto_confirm (bool): whether to automatically confirm the temporary avatar by calling
:py:meth:`confirm_project_avatar` with the return value of this method. (Default: False)
"""
size_from_file = os.path.getsize(filename)
if size != size_from_file:
size = size_from_file
params = {"filename": filename, "size": size}
headers: Dict[str, Any] = {"X-Atlassian-Token": "no-check"}
if contentType is not None:
headers["content-type"] = contentType
else:
# try to detect content-type, this may return None
headers["content-type"] = self._get_mime_type(avatar_img)
url = self._get_url("project/" + project + "/avatar/temporary")
r = self._session.post(url, params=params, headers=headers, data=avatar_img)
cropping_properties: Dict[str, Any] = json_loads(r)
if auto_confirm:
return self.confirm_project_avatar(project, cropping_properties)
else:
return cropping_properties
@translate_resource_args
def confirm_project_avatar(self, project: str, cropping_properties: Dict[str, Any]):
"""Confirm the temporary avatar image previously uploaded with the specified cropping.
After a successful registry with :py:meth:`create_temp_project_avatar`, use this method to confirm the avatar
for use. The final avatar can be a subarea of the uploaded image, which is customized with the
``cropping_properties``: the return value of :py:meth:`create_temp_project_avatar` should be used for this
argument.
Args:
project (str): ID or key of the project to confirm the avatar in
cropping_properties (Dict[str,Any]): a dict of cropping properties from :py:meth:`create_temp_project_avatar`
"""
data = cropping_properties
url = self._get_url("project/" + project + "/avatar")
r = self._session.post(url, data=json.dumps(data))
return json_loads(r)
@translate_resource_args
def set_project_avatar(self, project: str, avatar: str):
"""Set a project's avatar.
Args:
project (str): ID or key of the project to set the avatar on
avatar (str): ID of the avatar to set
"""
self._set_avatar(None, self._get_url("project/" + project + "/avatar"), avatar)
@translate_resource_args
def delete_project_avatar(self, project: str, avatar: str) -> Response:
"""Delete a project's avatar.
Args:
project (str): ID or key of the project to delete the avatar from
avatar (str): ID of the avatar to delete
"""
url = self._get_url("project/" + project + "/avatar/" + avatar)
return self._session.delete(url)
@translate_resource_args
def project_components(self, project: str) -> List[Component]:
"""Get a list of component Resources present on a project.
Args:
project (str): ID or key of the project to get components from
Returns:
List[Component]
"""
r_json = self._get_json("project/" + project + "/components")
components = [
Component(self._options, self._session, raw_comp_json)
for raw_comp_json in r_json
]
return components
@translate_resource_args
def project_versions(self, project: str) -> List[Version]:
"""Get a list of version Resources present on a project.
Args:
project (str): ID or key of the project to get versions from
Returns:
List[Version]
"""
r_json = self._get_json("project/" + project + "/versions")
versions = [
Version(self._options, self._session, raw_ver_json)
for raw_ver_json in r_json
]
return versions
@translate_resource_args
def get_project_version_by_name(
self, project: str, version_name: str
) -> Optional[Version]:
"""Get a version Resource by its name present on a project.
Args:
project (str): ID or key of the project to get versions from
version_name (str): name of the version to search for
Returns:
Optional[Version]
"""
versions: List[Version] = self.project_versions(project)
for version in versions:
if version.name == version_name:
return version
return None
@translate_resource_args
def rename_version(self, project: str, old_name: str, new_name: str) -> None:
"""Rename a version Resource on a project.
Args:
project (str): ID or key of the project to get versions from
old_name (str): old name of the version to rename
new_name (str): new name of the version to rename
Returns:
None
"""
version = self.get_project_version_by_name(project, old_name)
if version:
version.update(name=new_name)
# non-resource
@translate_resource_args
def project_roles(self, project: str) -> Dict[str, Dict[str, str]]:
"""Get a dict of role names to resource locations for a project.
Args:
project (str): ID or key of the project to get roles from
"""
path = "project/" + project + "/role"
_rolesdict: Dict[str, str] = self._get_json(path)
rolesdict: Dict[str, Dict[str, str]] = {}
for k, v in _rolesdict.items():
tmp: Dict[str, str] = {}
tmp["id"] = v.split("/")[-1]
tmp["url"] = v
rolesdict[k] = tmp
return rolesdict
# TODO(ssbarnea): return a list of Roles()
@translate_resource_args
def project_role(self, project: str, id: str) -> Role:
"""Get a role Resource.
Args:
project (str): ID or key of the project to get the role from
id (str): ID of the role to get
"""
if isinstance(id, Number):
id = f"{id}"
return self._find_for_resource(Role, (project, id))
# Resolutions
def resolutions(self) -> List[Resolution]:
"""Get a list of resolution Resources from the server.
Returns:
List[Resolution]
"""
r_json = self._get_json("resolution")
resolutions = [
Resolution(self._options, self._session, raw_res_json)
for raw_res_json in r_json
]
return resolutions
def resolution(self, id: str) -> Resolution:
"""Get a resolution Resource from the server.
Args:
id (str): ID of the resolution to get
Returns:
Resolution
"""
return self._find_for_resource(Resolution, id)
# Search
def search_issues(
self,
jql_str: str,
startAt: int = 0,
maxResults: int = 50,
validate_query: bool = True,
fields: Optional[Union[str, List[str]]] = None,
expand: Optional[str] = None,
json_result: bool = False,
) -> Union[List[Dict[str, Any]], ResultList[Issue]]:
"""Get a :class:`~jira.client.ResultList` of issue Resources matching a JQL search string.
Args:
jql_str (str): The JQL search string.
startAt (int): Index of the first issue to return. (Default: 0)
maxResults (int): Maximum number of issues to return. Total number of results
is available in the ``total`` attribute of the returned :class:`~jira.client.ResultList`.
If maxResults evaluates as False, it will try to get all issues in batches. (Default: 50)
validate_query (bool): Whether or not the query should be validated. (Default: True)
fields (Optional[Union[str, List[str]]]): comma-separated string or list of issue fields to include in the results.
Default is to include all fields.
expand (Optional[str]): extra information to fetch inside each resource
json_result (bool): JSON response will be returned when this parameter is set to True.
Otherwise, :class:`~jira.client.ResultList` will be returned.
Returns:
Union[Dict,ResultList]: Dict if ``json_result=True``
"""
if isinstance(fields, str):
fields = fields.split(",")
else:
fields = list(fields or [])
# this will translate JQL field names to REST API Name
# most people do know the JQL names so this will help them use the API easier
untranslate = {} # use to add friendly aliases when we get the results back
if self._fields:
for i, field in enumerate(fields):
if field in self._fields:
untranslate[self._fields[field]] = fields[i]
fields[i] = self._fields[field]
search_params = {
"jql": jql_str,
"startAt": startAt,
"validateQuery": validate_query,
"fields": fields,
"expand": expand,
}
if json_result:
search_params["maxResults"] = maxResults
if not maxResults:
warnings.warn(
"All issues cannot be fetched at once, when json_result parameter is set",
Warning,
)
r_json: List[Dict[str, Any]] = self._get_json(
"search", params=search_params
)
return r_json
issues = self._fetch_pages(
Issue, "issues", "search", startAt, maxResults, search_params
)
if untranslate:
iss: Issue
for iss in issues:
for k, v in untranslate.items():
if iss.raw:
if k in iss.raw.get("fields", {}):
iss.raw["fields"][v] = iss.raw["fields"][k]
return issues
# Security levels
def security_level(self, id: str) -> SecurityLevel:
"""Get a security level Resource.
Args:
id (str): ID of the security level to get
"""
return self._find_for_resource(SecurityLevel, id)
# Server info
# non-resource
def server_info(self) -> Dict[str, Any]:
"""Get a dict of server information for this Jira instance.
Returns:
Dict[str, Any]
"""
retry = 0
j = self._get_json("serverInfo")
while not j and retry < 3:
self.log.warning(
"Bug https://jira.atlassian.com/browse/JRA-59676 trying again..."
)
retry += 1
j = self._get_json("serverInfo")
return j
def myself(self) -> Dict[str, Any]:
"""Get a dict of server information for this Jira instance."""
return self._get_json("myself")
# Status
def statuses(self) -> List[Status]:
"""Get a list of status Resources from the server.
Returns:
List[Status]
"""
r_json = self._get_json("status")
statuses = [
Status(self._options, self._session, raw_stat_json)
for raw_stat_json in r_json
]
return statuses
def status(self, id: str) -> Status:
"""Get a status Resource from the server.
Args:
id (str): ID of the status resource to get
Returns:
Status
"""
return self._find_for_resource(Status, id)
# Category
def statuscategories(self) -> List[StatusCategory]:
"""Get a list of status category Resources from the server.
Returns:
List[StatusCategory]
"""
r_json = self._get_json("statuscategory")
statuscategories = [
StatusCategory(self._options, self._session, raw_stat_json)
for raw_stat_json in r_json
]
return statuscategories
def statuscategory(self, id: int) -> StatusCategory:
"""Get a status category Resource from the server.
Args:
id (int): ID of the status category resource to get
Returns:
StatusCategory
"""
return self._find_for_resource(StatusCategory, id)
# Users
def user(self, id: str, expand: Optional[Any] = None) -> User:
"""Get a user Resource from the server.
Args:
id (str): ID of the user to get
expand (Optional[Any]): Extra information to fetch inside each resource
Returns:
User
"""
user = User(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
user.find(id, params=params)
return user
def search_assignable_users_for_projects(
self, username: str, projectKeys: str, startAt: int = 0, maxResults: int = 50
) -> ResultList:
"""Get a list of user Resources that match the search string and can be assigned issues for projects.
Args:
username (str): A string to match usernames against
projectKeys (str): Comma-separated list of project keys to check for issue assignment permissions
startAt (int): Index of the first user to return (Default: 0)
maxResults (int): Maximum number of users to return.
If maxResults evaluates as False, it will try to get all users in batches. (Default: 50)
Returns:
ResultList
"""
params = {"username": username, "projectKeys": projectKeys}
return self._fetch_pages(
User,
None,
"user/assignable/multiProjectSearch",
startAt,
maxResults,
params,
)
def search_assignable_users_for_issues(
self,
username: str,
project: Optional[str] = None,
issueKey: Optional[str] = None,
expand: Optional[Any] = None,
startAt: int = 0,
maxResults: int = 50,
):
"""Get a list of user Resources that match the search string for assigning or creating issues.
This method is intended to find users that are eligible to create issues in a project or be assigned
to an existing issue. When searching for eligible creators, specify a project. When searching for eligible
assignees, specify an issue key.
Args:
username (str): A string to match usernames against
project (Optional[str]): Filter returned users by permission in this project
(expected if a result will be used to create an issue)
issueKey (Optional[str]): Filter returned users by this issue
(expected if a result will be used to edit this issue)
expand (Optional[Any]): Extra information to fetch inside each resource
startAt (int): Index of the first user to return (Default: 0)
maxResults (int): maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches. (Default: 50)
Returns:
ResultList
"""
params = {"username": username}
if project is not None:
params["project"] = project
if issueKey is not None:
params["issueKey"] = issueKey
if expand is not None:
params["expand"] = expand
return self._fetch_pages(
User, None, "user/assignable/search", startAt, maxResults, params
)
# non-resource
def user_avatars(self, username: str) -> Dict[str, Any]:
"""Get a dict of avatars for the specified user.
Args:
username (str): the username to get avatars for
"""
return self._get_json("user/avatars", params={"username": username})
def create_temp_user_avatar(
self,
user: str,
filename: str,
size: int,
avatar_img: bytes,
contentType: Any = None,
auto_confirm: bool = False,
):
"""Register an image file as a user avatar.
The avatar created is temporary and must be confirmed before it can
be used.
Avatar images are specified by a filename, size, and file object. By default, the client will attempt to
autodetect the picture's content type: this mechanism relies on ``libmagic`` and will not work out of the box
on Windows systems (see http://filemagic.readthedocs.org/en/latest/guide.html for details on how to install
support). The ``contentType`` argument can be used to explicitly set the value (note that Jira will reject any
type other than the well-known ones for images, e.g. ``image/jpg``, ``image/png``, etc.)
This method returns a dict of properties that can be used to crop a subarea of a larger image for use. This
dict should be saved and passed to :py:meth:`confirm_user_avatar` to finish the avatar creation process. If you
want to cut out the middleman and confirm the avatar with Jira's default cropping, pass the ``auto_confirm``
argument with a truthy value and :py:meth:`confirm_user_avatar` will be called for you before this method
returns.
Args:
user (str): User to register the avatar for
filename (str): name of the avatar file
size (int): size of the avatar file
avatar_img (bytes): file-like object containing the avatar
contentType (Optional[Any]): explicit specification for the avatar image's content-type
auto_confirm (bool): whether to automatically confirm the temporary avatar by calling
:py:meth:`confirm_user_avatar` with the return value of this method. (Default: False)
"""
size_from_file = os.path.getsize(filename)
if size != size_from_file:
size = size_from_file
# remove path from filename
filename = os.path.split(filename)[1]
params = {"username": user, "filename": filename, "size": size}
headers: Dict[str, Any]
headers = {"X-Atlassian-Token": "no-check"}
if contentType is not None:
headers["content-type"] = contentType
else:
# try to detect content-type, this may return None
headers["content-type"] = self._get_mime_type(avatar_img)
url = self._get_url("user/avatar/temporary")
r = self._session.post(url, params=params, headers=headers, data=avatar_img)
cropping_properties: Dict[str, Any] = json_loads(r)
if auto_confirm:
return self.confirm_user_avatar(user, cropping_properties)
else:
return cropping_properties
def confirm_user_avatar(self, user: str, cropping_properties: Dict[str, Any]):
"""Confirm the temporary avatar image previously uploaded with the specified cropping.
After a successful registry with :py:meth:`create_temp_user_avatar`, use this method to confirm the avatar for
use. The final avatar can be a subarea of the uploaded image, which is customized with the
``cropping_properties``: the return value of :py:meth:`create_temp_user_avatar` should be used for this
argument.
Args:
user (str): the user to confirm the avatar for
cropping_properties (Dict[str,Any]): a dict of cropping properties from :py:meth:`create_temp_user_avatar`
"""
data = cropping_properties
url = self._get_url("user/avatar")
r = self._session.post(url, params={"username": user}, data=json.dumps(data))
return json_loads(r)
def set_user_avatar(self, username: str, avatar: str) -> Response:
"""Set a user's avatar.
Args:
username (str): the user to set the avatar for
avatar (str): ID of the avatar to set
"""
return self._set_avatar(
{"username": username}, self._get_url("user/avatar"), avatar
)
def delete_user_avatar(self, username: str, avatar: str):
"""Delete a user's avatar.
Args:
username (str): the user to delete the avatar from
avatar (str): ID of the avatar to remove
"""
params = {"username": username}
url = self._get_url("user/avatar/" + avatar)
return self._session.delete(url, params=params)
def search_users(
self,
user: Optional[str] = None,
startAt: int = 0,
maxResults: int = 50,
includeActive: bool = True,
includeInactive: bool = False,
query: Optional[str] = None,
) -> ResultList[User]:
"""Get a list of user Resources that match the specified search string.
"username" query parameter is deprecated in Jira Cloud; the expected parameter now is "query", which can just be the full
email again. But the "user" parameter is kept for backwards compatibility, i.e. Jira Server/Data Center.
Args:
user (Optional[str]): a string to match usernames, name or email against.
startAt (int): index of the first user to return.
maxResults (int): maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches.
includeActive (bool): If true, then active users are included in the results. (Default: True)
includeInactive (bool): If true, then inactive users are included in the results. (Default: False)
query (Optional[str]): Search term. It can just be the email.
Returns:
ResultList[User]
"""
if not user and not query:
raise ValueError("Either 'user' or 'query' arguments must be specified.")
params = {
"username": user,
"query": query,
"includeActive": includeActive,
"includeInactive": includeInactive,
}
return self._fetch_pages(User, None, "user/search", startAt, maxResults, params)
def search_allowed_users_for_issue(
self,
user: str,
issueKey: str = None,
projectKey: str = None,
startAt: int = 0,
maxResults: int = 50,
) -> ResultList:
"""Get a list of user Resources that match a username string and have browse permission for the issue or project.
Args:
user (str): a string to match usernames against.
issueKey (Optional[str]): find users with browse permission for this issue.
projectKey (Optional[str]): find users with browse permission for this project.
startAt (int): index of the first user to return. (Default: 0)
maxResults (int): maximum number of users to return.
If maxResults evaluates as False, it will try to get all items in batches. (Default: 50)
Returns:
ResultList
"""
params = {"username": user}
if issueKey is not None:
params["issueKey"] = issueKey
if projectKey is not None:
params["projectKey"] = projectKey
return self._fetch_pages(
User, None, "user/viewissue/search", startAt, maxResults, params
)
# Versions
@translate_resource_args
def create_version(
self,
name: str,
project: str,
description: str = None,
releaseDate: Any = None,
startDate: Any = None,
archived: bool = False,
released: bool = False,
) -> Version:
"""Create a version in a project and return a Resource for it.
Args:
name (str): name of the version to create
project (str): key of the project to create the version in
description (str): a description of the version
releaseDate (Optional[Any]): the release date assigned to the version
startDate (Optional[Any]): The start date for the version
archived (bool): Denotes whether a version should be archived. (Default: False)
released (bool): Denotes whether a version is released. (Default: False)
Returns:
Version
"""
data = {
"name": name,
"project": project,
"archived": archived,
"released": released,
}
if description is not None:
data["description"] = description
if releaseDate is not None:
data["releaseDate"] = releaseDate
if startDate is not None:
data["startDate"] = startDate
url = self._get_url("version")
r = self._session.post(url, data=json.dumps(data))
time.sleep(1)
version = Version(self._options, self._session, raw=json_loads(r))
return version
def move_version(self, id: str, after: str = None, position: str = None) -> Version:
"""Move a version within a project's ordered version list and return a new version Resource for it.
One, but not both, of ``after`` and ``position`` must be specified.
Args:
id (str): ID of the version to move
after (str): the self attribute of a version to place the specified version after (that is, higher in the list)
position (Optional[str]): the absolute position to move this version to:
must be one of ``First``, ``Last``, ``Earlier``, or ``Later``
Returns:
Version
"""
data = {}
if after is not None:
data["after"] = after
elif position is not None:
data["position"] = position
url = self._get_url("version/" + id + "/move")
r = self._session.post(url, data=json.dumps(data))
version = Version(self._options, self._session, raw=json_loads(r))
return version
def version(self, id: str, expand: Any = None) -> Version:
"""Get a version Resource.
Args:
id (str): ID of the version to get
expand (Optional[Any]): extra information to fetch inside each resource
Returns:
Version
"""
version = Version(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
version.find(id, params=params)
return version
def version_count_related_issues(self, id: str):
"""Get a dict of the counts of issues fixed and affected by a version.
Args:
id (str): the version to count issues for
"""
r_json: Dict[str, Any] = self._get_json("version/" + id + "/relatedIssueCounts")
del r_json["self"] # this isn't really an addressable resource
return r_json
def version_count_unresolved_issues(self, id: str):
"""Get the number of unresolved issues for a version.
Args:
id (str): ID of the version to count issues for
"""
r_json: Dict[str, Any] = self._get_json(
"version/" + id + "/unresolvedIssueCount"
)
return r_json["issuesUnresolvedCount"]
# Session authentication
def session(self) -> User:
"""Get a dict of the current authenticated user's session information.
Returns:
User
"""
url = "{server}{auth_url}".format(**self._options)
r = self._session.get(url)
user = User(self._options, self._session, json_loads(r))
return user
def kill_session(self) -> Response:
"""Destroy the session of the current authenticated user."""
url = self.server_url + "/rest/auth/latest/session"
return self._session.delete(url)
# Websudo
def kill_websudo(self) -> Optional[Response]:
"""Destroy the user's current WebSudo session.
Works only for non-cloud deployments, for others does nothing.
Returns:
Optional[Response]
"""
if self.deploymentType != "Cloud":
url = self.server_url + "/rest/auth/1/websudo"
return self._session.delete(url)
return None
# Utilities
def _create_http_basic_session(
self,
username: str,
password: str,
timeout: Optional[Union[Union[float, int], Tuple[float, float]]] = None,
):
"""Creates a basic http session.
Args:
username (str): Username for the session
password (str): Password for the username
timeout (Optional[int]): If set determines the timeout period for the Session.
Returns:
ResilientSession
"""
verify = bool(self._options["verify"])
self._session = ResilientSession(timeout=timeout)
self._session.verify = verify
self._session.auth = (username, password)
client_cert: Tuple[str, str] = self._options["client_cert"] # to help mypy
self._session.cert = client_cert
def _create_oauth_session(
self, oauth, timeout: Optional[Union[Union[float, int], Tuple[float, float]]]
):
verify = bool(self._options["verify"])
from oauthlib.oauth1 import SIGNATURE_RSA
from requests_oauthlib import OAuth1
oauth_instance = OAuth1(
oauth["consumer_key"],
rsa_key=oauth["key_cert"],
signature_method=SIGNATURE_RSA,
resource_owner_key=oauth["access_token"],
resource_owner_secret=oauth["access_token_secret"],
)
self._session = ResilientSession(timeout)
self._session.verify = verify
self._session.auth = oauth_instance
def _create_kerberos_session(
self,
timeout: Optional[Union[Union[float, int], Tuple[float, float]]],
kerberos_options=None,
):
verify = bool(self._options["verify"])
if kerberos_options is None:
kerberos_options = {}
from requests_kerberos import DISABLED, OPTIONAL, HTTPKerberosAuth
if kerberos_options.get("mutual_authentication", "OPTIONAL") == "OPTIONAL":
mutual_authentication = OPTIONAL
elif kerberos_options.get("mutual_authentication") == "DISABLED":
mutual_authentication = DISABLED
else:
raise ValueError(
"Unknown value for mutual_authentication: %s"
% kerberos_options["mutual_authentication"]
)
self._session = ResilientSession(timeout=timeout)
self._session.verify = verify
self._session.auth = HTTPKerberosAuth(
mutual_authentication=mutual_authentication
)
@staticmethod
def _timestamp(dt: datetime.timedelta = None):
t = datetime.datetime.utcnow()
if dt is not None:
t += dt
return calendar.timegm(t.timetuple())
def _create_jwt_session(
self, jwt, timeout: Optional[Union[Union[float, int], Tuple[float, float]]]
):
try:
jwt_auth = JWTAuth(jwt["secret"], alg="HS256")
except NameError as e:
self.log.error("JWT authentication requires requests_jwt")
raise e
jwt_auth.set_header_format("JWT %s")
jwt_auth.add_field("iat", lambda req: JIRA._timestamp())
jwt_auth.add_field(
"exp", lambda req: JIRA._timestamp(datetime.timedelta(minutes=3))
)
jwt_auth.add_field("qsh", QshGenerator(self._options["context_path"]))
for f in jwt["payload"].items():
jwt_auth.add_field(f[0], f[1])
self._session = ResilientSession(timeout=timeout)
self._session.verify = bool(self._options["verify"])
self._session.auth = jwt_auth
def _set_avatar(self, params, url, avatar):
data = {"id": avatar}
return self._session.put(url, params=params, data=json.dumps(data))
def _get_url(self, path: str, base: str = JIRA_BASE_URL) -> str:
"""Returns the full url based on Jira base url and the path provided.
Using the API version specified during the __init__.
Args:
path (str): The subpath desired.
base (Optional[str]): The base url which should be prepended to the path
Returns:
str: Fully qualified URL
"""
options = self._options.copy()
options.update({"path": path})
return base.format(**options)
def _get_latest_url(self, path: str, base: str = JIRA_BASE_URL) -> str:
"""Returns the full url based on Jira base url and the path provided.
Using the latest API endpoint.
Args:
path (str): The subpath desired.
base (Optional[str]): The base url which should be prepended to the path
Returns:
str: Fully qualified URL
"""
options = self._options.copy()
options.update({"path": path, "rest_api_version": "latest"})
return base.format(**options)
def _get_json(
self, path: str, params: Dict[str, Any] = None, base: str = JIRA_BASE_URL
):
"""Get the json for a given path and params.
Args:
path (str): The subpath required
params (Optional[Dict[str, Any]]): Parameters to filter the json query.
base (Optional[str]): The Base Jira URL, defaults to the instance base.
Returns:
Union[Dict[str, Any], List[Dict[str, str]]]
"""
url = self._get_url(path, base)
r = self._session.get(url, params=params)
try:
r_json = json_loads(r)
except ValueError as e:
self.log.error(f"{e}\n{r.text if r else r}")
raise e
return r_json
def _find_for_resource(
self, resource_cls: Any, ids: Union[Tuple[str, str], int, str], expand=None
) -> Any:
"""Uses the find method of the provided Resource class
Args:
resource_cls (Any): Any instance of :py:class`Resource`
ids (Union[Tuple[str, str], int, str]): The arguments to the Resource's ``find()``
expand ([type], optional): The value for the expand property in the Resource's
``find()`` params. Defaults to None.
Raises:
JIRAError: If the Resource cannot be found
Returns:
Any: A class of the same type as ``resource_cls``
"""
resource = resource_cls(self._options, self._session)
params = {}
if expand is not None:
params["expand"] = expand
resource.find(id=ids, params=params)
if not resource:
raise JIRAError("Unable to find resource %s(%s)", resource_cls, str(ids))
return resource
def _try_magic(self):
try:
import weakref
import magic
except ImportError:
self._magic = None
else:
try:
_magic = magic.Magic(flags=magic.MAGIC_MIME_TYPE)
def cleanup(x):
_magic.close()
self._magic_weakref = weakref.ref(self, cleanup)
self._magic = _magic
except TypeError:
self._magic = None
except AttributeError:
self._magic = None
def _get_mime_type(self, buff: bytes) -> Optional[str]:
"""Get the MIME type for a given stream of bytes
Args:
buff (bytes): Stream of bytes
Returns:
Optional[str]: the MIME type
"""
if self._magic is not None:
return self._magic.id_buffer(buff)
else:
try:
return mimetypes.guess_type("f." + str(imghdr.what(0, buff)))[0]
except (IOError, TypeError):
self.log.warning(
"Couldn't detect content type of avatar image"
". Specify the 'contentType' parameter explicitly."
)
return None
def rename_user(self, old_user: str, new_user: str):
"""Rename a Jira user.
Args:
old_user (str): Old username login
new_user (str): New username login
"""
if self._version > (6, 0, 0):
url = self._get_latest_url("user")
payload = {"name": new_user}
params = {"username": old_user}
# raw displayName
self.log.debug(f"renaming {self.user(old_user).emailAddress}")
r = self._session.put(url, params=params, data=json.dumps(payload))
raise_on_error(r)
else:
raise NotImplementedError(
"Support for renaming users in Jira " "< 6.0.0 has been removed."
)
def delete_user(self, username: str) -> bool:
"""Deletes a Jira User.
Args:
username (str): Username to delete
Returns:
bool: Success of user deletion
"""
url = self._get_latest_url(f"user/?username={username}")
r = self._session.delete(url)
if 200 <= r.status_code <= 299:
return True
else:
self.log.error(r.status_code)
return False
def deactivate_user(self, username: str) -> Union[str, int]:
"""Disable/deactivate the user.
Args:
username (str): User to be deactivated.
Returns:
Union[str, int]
"""
if self.deploymentType == "Cloud":
# Disabling users now needs cookie auth in the Cloud - see https://jira.atlassian.com/browse/ID-6230
if "authCookie" not in vars(self):
user = self.session()
if user.raw is None:
raise JIRAError("Can not log in!")
self.authCookie = "%s=%s" % (
user.raw["session"]["name"],
user.raw["session"]["value"],
)
url = (
self._options["server"]
+ f"/admin/rest/um/1/user/deactivate?username={username}"
)
# We can't use our existing session here - this endpoint is fragile and objects to extra headers
try:
r = requests.post(
url,
headers={
"Cookie": self.authCookie,
"Content-Type": "application/json",
},
proxies=self._session.proxies,
data={},
)
if r.status_code == 200:
return True
else:
self.log.warning(
f"Got response from deactivating {username}: {r.status_code}"
)
return r.status_code
except Exception as e:
self.log.error(f"Error Deactivating {username}: {e}")
raise JIRAError(f"Error Deactivating {username}: {e}")
else:
url = self.server_url + "/secure/admin/user/EditUser.jspa"
self._options["headers"][
"Content-Type"
] = "application/x-www-form-urlencoded; charset=UTF-8"
user = self.user(username)
userInfo = {
"inline": "true",
"decorator": "dialog",
"username": user.name,
"fullName": user.displayName,
"email": user.emailAddress,
"editName": user.name,
}
try:
r = self._session.post(
url, headers=self._options["headers"], data=userInfo
)
if r.status_code == 200:
return True
else:
self.log.warning(
f"Got response from deactivating {username}: {r.status_code}"
)
return r.status_code
except Exception as e:
self.log.error(f"Error Deactivating {username}: {e}")
raise JIRAError(f"Error Deactivating {username}: {e}")
def reindex(self, force: bool = False, background: bool = True) -> bool:
"""Start jira re-indexing. Returns True if reindexing is in progress or not needed, or False.
If you call reindex() without any parameters it will perform a background reindex only if Jira thinks it should do it.
Args:
force (bool): reindex even if Jira doesn't say this is needed, False by default.
background (bool): reindex in background, slower but does not impact the users, defaults to True.
Returns:
bool: Returns True if reindexing is in progress or not needed, or False.
"""
# /secure/admin/IndexAdmin.jspa
# /secure/admin/jira/IndexProgress.jspa?taskId=1
if background:
indexingStrategy = "background"
else:
indexingStrategy = "stoptheworld"
url = self.server_url + "/secure/admin/jira/IndexReIndex.jspa"
r = self._session.get(url, headers=self._options["headers"])
if r.status_code == 503:
# self.log.warning("Jira returned 503, this could mean that a full reindex is in progress.")
return 503 # type: ignore # FIXME: is this a bug?
if (
not r.text.find("To perform the re-index now, please go to the")
and force is False
):
return True
if r.text.find("All issues are being re-indexed"):
self.log.warning("Jira re-indexing is already running.")
return True # still reindexing is considered still a success
if r.text.find("To perform the re-index now, please go to the") or force:
r = self._session.post(
url,
headers=self._options["headers"],
params={"indexingStrategy": indexingStrategy, "reindex": "Re-Index"},
)
if r.text.find("All issues are being re-indexed") != -1:
return True
self.log.error("Failed to reindex jira, probably a bug.")
return False
def backup(self, filename: str = "backup.zip", attachments: bool = False):
"""Will call jira export to backup as zipped xml. Returning with success does not mean that the backup process finished."""
payload: Any # _session.post is pretty open
if self.deploymentType == "Cloud":
url = self.server_url + "/rest/backup/1/export/runbackup"
payload = json.dumps({"cbAttachments": attachments})
self._options["headers"]["X-Requested-With"] = "XMLHttpRequest"
else:
url = self.server_url + "/secure/admin/XmlBackup.jspa"
payload = {"filename": filename}
try:
r = self._session.post(url, headers=self._options["headers"], data=payload)
if r.status_code == 200:
return True
else:
self.log.warning(f"Got {r.status_code} response from calling backup.")
return r.status_code
except Exception as e:
self.log.error("I see %s", e)
def backup_progress(self):
"""Return status of cloud backup as a dict.
Is there a way to get progress for Server version?
"""
epoch_time = int(time.time() * 1000)
if self.deploymentType == "Cloud":
url = self.server_url + "/rest/obm/1.0/getprogress?_=%i" % epoch_time
else:
self.log.warning("This functionality is not available in Server version")
return None
r = self._session.get(url, headers=self._options["headers"])
# This is weird. I used to get xml, but now I'm getting json
try:
return json.loads(r.text)
except Exception:
import defusedxml.ElementTree as etree
progress = {}
try:
root = etree.fromstring(r.text)
except etree.ParseError as pe:
self.log.warning(
"Unable to find backup info. You probably need to initiate a new backup. %s"
% pe
)
return None
for k in root.keys():
progress[k] = root.get(k)
return progress
def backup_complete(self) -> Optional[bool]:
"""Return boolean based on 'alternativePercentage' and 'size' returned from backup_progress (cloud only)."""
if self.deploymentType != "Cloud":
self.log.warning("This functionality is not available in Server version")
return None
status = self.backup_progress()
perc_search = re.search(r"\s([0-9]*)\s", status["alternativePercentage"])
perc_complete = int(
perc_search.group(1) # type: ignore # ignore that re.search can return None
)
file_size = int(status["size"])
return perc_complete >= 100 and file_size > 0
def backup_download(self, filename: str = None):
"""Download backup file from WebDAV (cloud only)."""
if self.deploymentType != "Cloud":
self.log.warning("This functionality is not available in Server version")
return None
remote_file = self.backup_progress()["fileName"]
local_file = filename or remote_file
url = self.server_url + "/webdav/backupmanager/" + remote_file
try:
self.log.debug(f"Writing file to {local_file}")
with open(local_file, "wb") as file:
try:
resp = self._session.get(
url, headers=self._options["headers"], stream=True
)
except Exception:
raise JIRAError()
if not resp.ok:
self.log.error(f"Something went wrong with download: {resp.text}")
raise JIRAError(resp.text)
for block in resp.iter_content(1024):
file.write(block)
except JIRAError as je:
self.log.error(f"Unable to access remote backup file: {je}")
except IOError as ioe:
self.log.error(ioe)
return None
def current_user(self, field: str = "key") -> str:
"""Returns the username or emailAddress of the current user. For anonymous
users it will return a value that evaluates as False.
Returns:
str
"""
if not hasattr(self, "_myself"):
url = self._get_url("myself")
r = self._session.get(url, headers=self._options["headers"])
r_json: Dict[str, str] = json_loads(r)
self._myself = r_json
return self._myself[field]
def delete_project(self, pid: Union[str, Project]) -> Optional[bool]:
"""Delete project from Jira.
Args:
pid (Union[str, Project]): Jira projectID or Project or slug
Raises:
JIRAError: If project not found or not enough permissions
ValueError: If pid parameter is not Project, slug or ProjectID
Returns:
bool: True if project was deleted
"""
# allows us to call it with Project objects
if isinstance(pid, Project) and hasattr(pid, "id"):
pid = str(pid.id)
url = self._get_url(f"project/{pid}")
r = self._session.delete(url)
if r.status_code == 403:
raise JIRAError("Not enough permissions to delete project")
if r.status_code == 404:
raise JIRAError("Project not found in Jira")
return r.ok
def _gain_sudo_session(self, options, destination):
url = self.server_url + "/secure/admin/WebSudoAuthenticate.jspa"
if not self._session.auth:
self._session.auth = get_netrc_auth(url)
payload = {
"webSudoPassword": self._session.auth[1],
"webSudoDestination": destination,
"webSudoIsPost": "true",
}
payload.update(options)
return self._session.post(
url,
headers=CaseInsensitiveDict(
{"content-type": "application/x-www-form-urlencoded"}
),
data=payload,
)
@lru_cache(maxsize=None)
def templates(self) -> Dict:
url = self.server_url + "/rest/project-templates/latest/templates"
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
templates = {}
if "projectTemplatesGroupedByType" in data:
for group in data["projectTemplatesGroupedByType"]:
for t in group["projectTemplates"]:
templates[t["name"]] = t
# pprint(templates.keys())
return templates
@lru_cache(maxsize=None)
def permissionschemes(self):
url = self._get_url("permissionscheme")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["permissionSchemes"]
@lru_cache(maxsize=None)
def issuesecurityschemes(self):
url = self._get_url("issuesecurityschemes")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["issueSecuritySchemes"]
@lru_cache(maxsize=None)
def projectcategories(self):
url = self._get_url("projectCategory")
r = self._session.get(url)
data = json_loads(r)
return data
@lru_cache(maxsize=None)
def avatars(self, entity="project"):
url = self._get_url(f"avatar/{entity}/system")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["system"]
@lru_cache(maxsize=None)
def notificationschemes(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("notificationscheme")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["values"]
@lru_cache(maxsize=None)
def screens(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("screens")
r = self._session.get(url)
data: Dict[str, Any] = json_loads(r)
return data["values"]
@lru_cache(maxsize=None)
def workflowscheme(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("workflowschemes")
r = self._session.get(url)
data = json_loads(r)
return data # ['values']
@lru_cache(maxsize=None)
def workflows(self):
# TODO(ssbarnea): implement pagination support
url = self._get_url("workflow")
r = self._session.get(url)
data = json_loads(r)
return data # ['values']
def delete_screen(self, id: str):
url = self._get_url(f"screens/{id}")
r = self._session.delete(url)
data = json_loads(r)
self.screens.cache_clear()
return data
def delete_permissionscheme(self, id: str):
url = self._get_url(f"permissionscheme/{id}")
r = self._session.delete(url)
data = json_loads(r)
self.permissionschemes.cache_clear()
return data
def create_project(
self,
key: str,
name: str = None,
assignee: str = None,
ptype: str = "software",
template_name: str = None,
avatarId=None,
issueSecurityScheme=None,
permissionScheme=None,
projectCategory=None,
notificationScheme=10000,
categoryId=None,
url: str = "",
):
"""Create a project with the specified parameters.
Args:
key (str): Mandatory. Must match Jira project key requirements, usually only 2-10 uppercase characters.
name (Optional[str]): If not specified it will use the key value.
assignee (Optional[str]): key of the lead, if not specified it will use current user.
ptype (Optional[str]): Determines the type of project should be created.
template_name (Optional[str]): is used to create a project based on one of the existing project templates.
If `template_name` is not specified, then it should use one of the default values.
Returns:
Union[bool,int]: Should evaluate to False if it fails otherwise it will be the new project id.
"""
template_key = None
if assignee is None:
assignee = self.current_user()
if name is None:
name = key
ps_list: List[Dict[str, Any]]
if not permissionScheme:
ps_list = self.permissionschemes()
for sec in ps_list:
if sec["name"] == "Default Permission Scheme":
permissionScheme = sec["id"]
break
if not permissionScheme:
permissionScheme = ps_list[0]["id"]
if not issueSecurityScheme:
ps_list = self.issuesecurityschemes()
for sec in ps_list:
if sec["name"] == "Default": # no idea which one is default
issueSecurityScheme = sec["id"]
break
if not issueSecurityScheme and ps_list:
issueSecurityScheme = ps_list[0]["id"]
if not projectCategory:
ps_list = self.projectcategories()
for sec in ps_list:
if sec["name"] == "Default": # no idea which one is default
projectCategory = sec["id"]
break
if not projectCategory and ps_list:
projectCategory = ps_list[0]["id"]
# <beep> Atlassian for failing to provide an API to get projectTemplateKey values
# Possible values are just hardcoded and obviously depending on Jira version.
# https://developer.atlassian.com/cloud/jira/platform/rest/v3/?_ga=2.88310429.766596084.1562439833-992274574.1559129176#api-rest-api-3-project-post
# https://jira.atlassian.com/browse/JRASERVER-59658
# preference list for picking a default template
if not template_name:
# https://confluence.atlassian.com/jirakb/creating-projects-via-rest-api-in-jira-963651978.html
template_key = (
"com.pyxis.greenhopper.jira:basic-software-development-template"
)
# https://developer.atlassian.com/cloud/jira/platform/rest/v2/api-group-projects/#api-rest-api-2-project-get
# template_keys = [
# "com.pyxis.greenhopper.jira:gh-simplified-agility-kanban",
# "com.pyxis.greenhopper.jira:gh-simplified-agility-scrum",
# "com.pyxis.greenhopper.jira:gh-simplified-basic",
# "com.pyxis.greenhopper.jira:gh-simplified-kanban-classic",
# "com.pyxis.greenhopper.jira:gh-simplified-scrum-classic",
# "com.atlassian.servicedesk:simplified-it-service-desk",
# "com.atlassian.servicedesk:simplified-internal-service-desk",
# "com.atlassian.servicedesk:simplified-external-service-desk",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-content-management",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-document-approval",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-lead-tracking",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-process-control",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-procurement",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-project-management",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-recruitment",
# "com.atlassian.jira-core-project-templates:jira-core-simplified-task-",
# "com.atlassian.jira.jira-incident-management-plugin:im-incident-management",
# ]
# possible_templates = [
# "Scrum software development", # have Bug
# "Agility", # cannot set summary
# "Bug tracking",
# "JIRA Classic",
# "JIRA Default Schemes",
# "Basic software development",
# "Project management",
# "Kanban software development",
# "Task management",
# "Basic", # does not have Bug
# "Content Management",
# "Customer service",
# "Document Approval",
# "IT Service Desk",
# "Lead Tracking",
# "Process management",
# "Procurement",
# "Recruitment",
# ]
# templates = self.templates()
# if not template_name:
# for k, v in templates.items():
# if v['projectTypeKey'] == type:
# template_name = k
# template_name = next((t for t in templates if t['projectTypeKey'] == 'x'))
# template_key = templates[template_name]["projectTemplateModuleCompleteKey"]
# project_type_key = templates[template_name]["projectTypeKey"]
# https://confluence.atlassian.com/jirakb/creating-a-project-via-rest-based-on-jira-default-schemes-744325852.html
# see https://confluence.atlassian.com/jirakb/creating-projects-via-rest-api-in-jira-963651978.html
payload = {
"name": name,
"key": key,
"projectTypeKey": ptype,
"projectTemplateKey": template_key,
"lead": assignee,
# "leadAccountId": assignee,
"assigneeType": "PROJECT_LEAD",
"description": "",
# "avatarId": 13946,
"permissionScheme": int(permissionScheme),
"notificationScheme": notificationScheme,
"url": url,
}
if issueSecurityScheme:
payload["issueSecurityScheme"] = int(issueSecurityScheme)
if projectCategory:
payload["categoryId"] = int(projectCategory)
url = self._get_url("project")
r = self._session.post(url, data=json.dumps(payload))
r.raise_for_status()
r_json = json_loads(r)
return r_json
def add_user(
self,
username: str,
email: str,
directoryId: int = 1,
password: str = None,
fullname: str = None,
notify: bool = False,
active: bool = True,
ignore_existing: bool = False,
application_keys: Optional[List] = None,
):
"""Create a new Jira user.
Args:
username (str): the username of the new user
email (str): email address of the new user
directoryId (int): The directory ID the new user should be a part of (Default: 1)
password (Optional[str]): Optional, the password for the new user
fullname (Optional[str]): Optional, the full name of the new user
notify (bool): Whether or not to send a notification to the new user. (Default: False)
active (bool): Whether or not to make the new user active upon creation. (Default: True)
ignore_existing (bool): Whether or not to ignore and existing user. (Default: False)
applicationKeys (Optional[list]): Keys of products user should have access to
Raises:
JIRAError: If username already exists and `ignore_existing` has not been set to `True`.
Returns:
bool: Whether or not the user creation was successful.
"""
if not fullname:
fullname = username
# TODO(ssbarnea): default the directoryID to the first directory in jira instead
# of 1 which is the internal one.
url = self._get_latest_url("user")
# implementation based on
# https://docs.atlassian.com/jira/REST/ondemand/#d2e5173
x: Dict[str, Any] = OrderedDict()
x["displayName"] = fullname
x["emailAddress"] = email
x["name"] = username
if password:
x["password"] = password
if notify:
x["notification"] = "True"
if application_keys is not None:
x["applicationKeys"] = application_keys
payload = json.dumps(x)
try:
self._session.post(url, data=payload)
except JIRAError as e:
if e.response:
err = e.response.json()["errors"]
if (
"username" in err
and err["username"] == "A user with that username already exists."
and ignore_existing
):
return True
raise e
return True
def add_user_to_group(
self, username: str, group: str
) -> Union[bool, Dict[str, Any]]:
"""Add a user to an existing group.
Args:
username (str): Username that will be added to specified group.
group (str): Group that the user will be added to.
Returns:
Union[bool,Dict[str,Any]]: json response from Jira server for success or a value that evaluates as False in case of failure.
"""
url = self._get_latest_url("group/user")
x = {"groupname": group}
y = {"name": username}
payload = json.dumps(y)
r: Dict[str, Any] = json_loads(self._session.post(url, params=x, data=payload))
if "name" not in r or r["name"] != group:
return False
else:
return r
def remove_user_from_group(self, username: str, groupname: str):
"""Remove a user from a group.
Args:
username (str): The user to remove from the group.
groupname (str): The group that the user will be removed from.
"""
url = self._get_latest_url("group/user")
x = {"groupname": groupname, "username": username}
self._session.delete(url, params=x)
return True
def role(self) -> List[Dict[str, Any]]:
"""Return Jira role information.
Returns:
List[Dict[str,Any]]: List of current user roles
"""
# https://developer.atlassian.com/cloud/jira/platform/rest/v3/?utm_source=%2Fcloud%2Fjira%2Fplatform%2Frest%2F&utm_medium=302#api-rest-api-3-role-get
url = self._get_latest_url("role")
r = self._session.get(url)
data: List[Dict[str, Any]] = json_loads(r)
return data
# Experimental
# Experimental support for iDalko Grid, expect API to change as it's using private APIs currently
# https://support.idalko.com/browse/IGRID-1017
def get_igrid(self, issueid: str, customfield: str, schemeid: str):
url = self.server_url + "/rest/idalko-igrid/1.0/datagrid/data"
if str(customfield).isdigit():
customfield = f"customfield_{customfield}"
params = {
"_issueId": issueid,
"_fieldId": customfield,
"_confSchemeId": schemeid,
}
r = self._session.get(url, headers=self._options["headers"], params=params)
return json_loads(r)
# Jira Agile specific methods (GreenHopper)
"""
Define the functions that interact with GreenHopper.
"""
@translate_resource_args
def boards(
self,
startAt: int = 0,
maxResults: int = 50,
type: str = None,
name: str = None,
projectKeyOrID=None,
) -> ResultList[Board]:
"""Get a list of board resources.
Args:
startAt: The starting index of the returned boards. Base index: 0.
maxResults: The maximum number of boards to return per page. Default: 50
type: Filters results to boards of the specified type. Valid values: scrum, kanban.
name: Filters results to boards that match or partially match the specified name.
projectKeyOrID: Filters results to boards that match the specified project key or ID.
Returns:
ResultList[Board]
When old GreenHopper private API is used, paging is not enabled and all parameters are ignored.
"""
params = {}
if type:
params["type"] = type
if name:
params["name"] = name
if projectKeyOrID:
params["projectKeyOrId"] = projectKeyOrID
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
# Old, private API did not support pagination, all records were present in response,
# and no parameters were supported.
if startAt or maxResults or params:
warnings.warn(
"Old private GreenHopper API is used, all parameters will be ignored.",
Warning,
)
r_json: Dict[str, Any] = self._get_json(
"rapidviews/list", base=self.AGILE_BASE_URL
)
boards = [
Board(self._options, self._session, raw_boards_json)
for raw_boards_json in r_json["views"]
]
return ResultList(boards, 0, len(boards), len(boards), True)
else:
return self._fetch_pages(
Board,
"values",
"board",
startAt,
maxResults,
params,
base=self.AGILE_BASE_URL,
)
@translate_resource_args
def sprints(
self,
board_id: int,
extended: bool = False,
startAt: int = 0,
maxResults: int = 50,
state: str = None,
) -> ResultList[Sprint]:
"""Get a list of sprint GreenHopperResources.
Args:
board_id (int): the board to get sprints from
extended (bool): Used only by old GreenHopper API to fetch additional information like
startDate, endDate, completeDate, much slower because it requires an additional requests for each sprint.
New Jira Agile API always returns this information without a need for additional requests.
startAt (int): the index of the first sprint to return (0 based)
maxResults (int): the maximum number of sprints to return
state (str): Filters results to sprints in specified states. Valid values: `future`, `active`, `closed`.
You can define multiple states separated by commas
Returns:
ResultList[Sprint]: (content depends on API version, but always contains id, name, state, startDate and endDate)
When old GreenHopper private API is used, paging is not enabled,
and `startAt`, `maxResults` and `state` parameters are ignored.
"""
params = {}
if state:
params["state"] = state
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
r_json: Dict[str, Any] = self._get_json(
"sprintquery/%s?includeHistoricSprints=true&includeFutureSprints=true"
% board_id,
base=self.AGILE_BASE_URL,
)
if params:
warnings.warn(
"Old private GreenHopper API is used, parameters %s will be ignored."
% params,
Warning,
)
if extended:
sprints = [
Sprint(
self._options,
self._session,
self.sprint_info("", raw_sprints_json["id"]),
)
for raw_sprints_json in r_json["sprints"]
]
else:
sprints = [
Sprint(self._options, self._session, raw_sprints_json)
for raw_sprints_json in r_json["sprints"]
]
return ResultList(sprints, 0, len(sprints), len(sprints), True)
else:
return self._fetch_pages(
Sprint,
"values",
f"board/{board_id}/sprint",
startAt,
maxResults,
params,
self.AGILE_BASE_URL,
)
def sprints_by_name(self, id, extended=False):
sprints = {}
for s in self.sprints(id, extended=extended):
if s.name not in sprints:
sprints[s.name] = s.raw
else:
raise Exception
return sprints
def update_sprint(self, id, name=None, startDate=None, endDate=None, state=None):
payload = {}
if name:
payload["name"] = name
if startDate:
payload["startDate"] = startDate
if endDate:
payload["endDate"] = endDate
if state:
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
raise NotImplementedError(
"Public Jira API does not support state update"
)
payload["state"] = state
url = self._get_url(f"sprint/{id}", base=self.AGILE_BASE_URL)
r = self._session.put(url, data=json.dumps(payload))
return json_loads(r)
def incompletedIssuesEstimateSum(self, board_id: str, sprint_id: str):
"""Return the total incompleted points this sprint."""
data: Dict[str, Any] = self._get_json(
f"rapid/charts/sprintreport?rapidViewId={board_id}&sprintId={sprint_id}",
base=self.AGILE_BASE_URL,
)
return data["contents"]["incompletedIssuesEstimateSum"]["value"]
def removed_issues(self, board_id: str, sprint_id: str):
"""Return the completed issues for the sprint."""
r_json: Dict[str, Any] = self._get_json(
f"rapid/charts/sprintreport?rapidViewId={board_id}&sprintId={sprint_id}",
base=self.AGILE_BASE_URL,
)
issues = [
Issue(self._options, self._session, raw_issues_json)
for raw_issues_json in r_json["contents"]["puntedIssues"]
]
return issues
def removedIssuesEstimateSum(self, board_id: str, sprint_id: str):
"""Return the total incompleted points this sprint."""
data: Dict[str, Any] = self._get_json(
f"rapid/charts/sprintreport?rapidViewId={board_id}&sprintId={sprint_id}",
base=self.AGILE_BASE_URL,
)
return data["contents"]["puntedIssuesEstimateSum"]["value"]
# TODO(ssbarnea): remove sprint_info() method, sprint() method suit the convention more
def sprint_info(self, board_id: str, sprint_id: str) -> Optional[Dict[str, Any]]:
"""Return the information about a sprint.
Args:
board_id (str): the board retrieving issues from. Deprecated and ignored.
sprint_id (str): the sprint retrieving issues from
"""
sprint = Sprint(self._options, self._session)
sprint.find(sprint_id)
return sprint.raw
def sprint(self, id: int) -> Sprint:
"""Return the information about a sprint.
Args:
sprint_id (int): the sprint retrieving issues from
Returns:
Sprint
"""
sprint = Sprint(self._options, self._session)
sprint.find(id)
return sprint
# TODO(ssbarnea): remove this as we do have Board.delete()
def delete_board(self, id):
"""Delete an agile board."""
board = Board(self._options, self._session, raw={"id": id})
board.delete()
def create_board(
self,
name: str,
project_ids: Union[str, List[str]],
preset: str = "scrum",
location_type: str = "user",
location_id: Optional[str] = None,
) -> Board:
"""Create a new board for the ``project_ids``.
Args:
name (str): name of the board
project_ids (str): the projects to create the board in
preset (str): What preset to use for this board, options: kanban, scrum, diy. (Default: scrum)
location_type (str): the location type. Available in cloud. (Default: user)
location_id (Optional[str]): the id of project that the board should be located under.
Omit this for a 'user' location_type. Available in cloud.
Returns:
Board: The newly created board
"""
if (
self._options["agile_rest_path"]
!= GreenHopperResource.GREENHOPPER_REST_PATH
):
raise NotImplementedError(
"Jira Agile Public API does not support this request"
)
payload: Dict[str, Any] = {}
if isinstance(project_ids, str):
ids = []
for p in project_ids.split(","):
ids.append(self.project(p).id)
project_ids = ",".join(ids)
if location_id is not None:
location_id = self.project(location_id).id
payload["name"] = name
if isinstance(project_ids, str):
project_ids = project_ids.split(",") # type: ignore # re-use of variable
payload["projectIds"] = project_ids
payload["preset"] = preset
if self.deploymentType == "Cloud":
payload["locationType"] = location_type
payload["locationId"] = location_id
url = self._get_url("rapidview/create/presets", base=self.AGILE_BASE_URL)
r = self._session.post(url, data=json.dumps(payload))
raw_issue_json = json_loads(r)
return Board(self._options, self._session, raw=raw_issue_json)
def create_sprint(
self,
name: str,
board_id: int,
startDate: Optional[Any] = None,
endDate: Optional[Any] = None,
) -> Sprint:
"""Create a new sprint for the ``board_id``.
Args:
name (str): Name of the sprint
board_id (int): Which board the sprint should be assigned.
startDate (Optional[Any]): Start date for the sprint.
endDate (Optional[Any]): End date for the sprint.
Returns:
Sprint: The newly created Sprint
"""
payload: Dict[str, Any] = {"name": name}
if startDate:
payload["startDate"] = startDate
if endDate:
payload["endDate"] = endDate
raw_issue_json: Dict[str, Any]
if (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
url = self._get_url(f"sprint/{board_id}", base=self.AGILE_BASE_URL)
r = self._session.post(url)
raw_issue_json = json_loads(r)
""" now r contains something like:
{
"id": 742,
"name": "Sprint 89",
"state": "FUTURE",
"linkedPagesCount": 0,
"startDate": "None",
"endDate": "None",
"completeDate": "None",
"remoteLinks": []
}"""
url = self._get_url(
f"sprint/{raw_issue_json['id']}", base=self.AGILE_BASE_URL
)
r = self._session.put(url, data=json.dumps(payload))
raw_issue_json = json_loads(r)
else:
url = self._get_url("sprint", base=self.AGILE_BASE_URL)
payload["originBoardId"] = board_id
r = self._session.post(url, data=json.dumps(payload))
raw_issue_json = json_loads(r)
return Sprint(self._options, self._session, raw=raw_issue_json)
def add_issues_to_sprint(self, sprint_id: int, issue_keys: List[str]) -> Response:
"""Add the issues in ``issue_keys`` to the ``sprint_id``.
The sprint must be started but not completed.
If a sprint was completed, then have to also edit the history of the
issue so that it was added to the sprint before it was completed,
preferably before it started. A completed sprint's issues also all have
a resolution set before the completion date.
If a sprint was not started, then have to edit the marker and copy the
rank of each issue too.
Args:
sprint_id (int): the sprint to add issues to
issue_keys (List[str]): the issues to add to the sprint
Returns:
Response
"""
if self._options["agile_rest_path"] == GreenHopperResource.AGILE_BASE_REST_PATH:
url = self._get_url(f"sprint/{sprint_id}/issue", base=self.AGILE_BASE_URL)
payload = {"issues": issue_keys}
try:
return self._session.post(url, data=json.dumps(payload))
except JIRAError as e:
if e.status_code == 404:
warnings.warn(
"Status code 404 may mean, that too old Jira Agile version is installed."
" At least version 6.7.10 is required."
)
raise
elif (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
# In old, private API the function does not exist anymore and we need to use
# issue.update() to perform this operation
# Workaround based on https://answers.atlassian.com/questions/277651/jira-agile-rest-api-example
sprint_field_id = self._get_sprint_field_id()
data = {
"idOrKeys": issue_keys,
"customFieldId": sprint_field_id,
"sprintId": sprint_id,
"addToBacklog": False,
}
url = self._get_url("sprint/rank", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
else:
raise NotImplementedError(
'No API for adding issues to sprint for agile_rest_path="%s"'
% self._options["agile_rest_path"]
)
def add_issues_to_epic(
self, epic_id: str, issue_keys: str, ignore_epics: bool = True
) -> Response:
"""Add the issues in ``issue_keys`` to the ``epic_id``.
Args:
epic_id (str): The ID for the epic where issues should be added.
issue_keys (str): The issues to add to the epic
ignore_epics (bool): ignore any issues listed in ``issue_keys`` that are epics. (Default: True)
"""
if (
self._options["agile_rest_path"]
!= GreenHopperResource.GREENHOPPER_REST_PATH
):
# TODO(ssbarnea): simulate functionality using issue.update()?
raise NotImplementedError(
"Jira Agile Public API does not support this request"
)
data: Dict[str, Any] = {}
data["issueKeys"] = issue_keys
data["ignoreEpics"] = ignore_epics
url = self._get_url(f"epics/{epic_id}/add", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
# TODO(ssbarnea): Both GreenHopper and new Jira Agile API support moving more than one issue.
def rank(self, issue: str, next_issue: str) -> Response:
"""Rank an issue before another using the default Ranking field, the one named 'Rank'.
Args:
issue (str): issue key of the issue to be ranked before the second one.
next_issue (str): issue key of the second issue.
"""
if not self._rank:
for field in self.fields():
if field["name"] == "Rank":
if (
field["schema"]["custom"]
== "com.pyxis.greenhopper.jira:gh-lexo-rank"
):
self._rank = field["schema"]["customId"]
break
elif (
field["schema"]["custom"]
== "com.pyxis.greenhopper.jira:gh-global-rank"
):
# Obsolete since Jira v6.3.13.1
self._rank = field["schema"]["customId"]
if self._options["agile_rest_path"] == GreenHopperResource.AGILE_BASE_REST_PATH:
url = self._get_url("issue/rank", base=self.AGILE_BASE_URL)
payload = {
"issues": [issue],
"rankBeforeIssue": next_issue,
"rankCustomFieldId": self._rank,
}
try:
return self._session.put(url, data=json.dumps(payload))
except JIRAError as e:
if e.status_code == 404:
warnings.warn(
"Status code 404 may mean, that too old Jira Agile version is installed."
" At least version 6.7.10 is required."
)
raise
elif (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
data = {
"issueKeys": [issue],
"rankBeforeKey": next_issue,
"customFieldId": self._rank,
}
url = self._get_url("rank", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
else:
raise NotImplementedError(
'No API for ranking issues for agile_rest_path="%s"'
% self._options["agile_rest_path"]
)
def move_to_backlog(self, issue_keys: str) -> Response:
"""Move issues in ``issue_keys`` to the backlog, removing them from all sprints that have not been completed.
Args:
issue_keys (str): the issues to move to the backlog
Raises:
JIRAError: If moving issues to backlog fails
"""
if self._options["agile_rest_path"] == GreenHopperResource.AGILE_BASE_REST_PATH:
url = self._get_url("backlog/issue", base=self.AGILE_BASE_URL)
payload = {"issues": issue_keys}
try:
return self._session.post(url, data=json.dumps(payload))
except JIRAError as e:
if e.status_code == 404:
warnings.warn(
"Status code 404 may mean, that too old Jira Agile version is installed."
" At least version 6.7.10 is required."
)
raise
elif (
self._options["agile_rest_path"]
== GreenHopperResource.GREENHOPPER_REST_PATH
):
# In old, private API the function does not exist anymore and we need to use
# issue.update() to perform this operation
# Workaround based on https://answers.atlassian.com/questions/277651/jira-agile-rest-api-example
sprint_field_id = self._get_sprint_field_id()
data = {
"idOrKeys": issue_keys,
"customFieldId": sprint_field_id,
"addToBacklog": True,
}
url = self._get_url("sprint/rank", base=self.AGILE_BASE_URL)
return self._session.put(url, data=json.dumps(data))
else:
raise NotImplementedError(
'No API for moving issues to backlog for agile_rest_path="%s"'
% self._options["agile_rest_path"]
)
class GreenHopper(JIRA):
def __init__(self, options=None, basic_auth=None, oauth=None, async_=None):
warnings.warn(
"GreenHopper() class is deprecated, just use JIRA() instead.",
DeprecationWarning,
)
JIRA.__init__(
self, options=options, basic_auth=basic_auth, oauth=oauth, async_=async_
)
|
# cmu_112_graphics.py
# version 0.9.0
# Pre-release for CMU 15-112-s21
# Require Python 3.6 or later
import sys
if (sys.version_info[0] != 3) or (sys.version_info[1] < 6):
raise Exception("cmu_112_graphics.py requires Python version 3.6 or later.")
# Track version and file update timestamp
import datetime
MAJOR_VERSION = 0
MINOR_VERSION = 9.0 # version 0.9.0
LAST_UPDATED = datetime.date(year=2021, month=4, day=12)
# Pending changes:
# * Fix Windows-only bug: Position popup dialog box over app window (already works fine on Macs)
# * Add documentation
# * integrate sounds (probably from pyGame)
# * Improved methodIsOverridden to TopLevelApp and ModalApp
# * Save to animated gif and/or mp4 (with audio capture?)
# Deferred changes:
# * replace/augment tkinter canvas with PIL/Pillow imageDraw (perhaps with our own fn names)
# Changes in v0.9.0
# * added simpler top-level modes implementation that does not include mode objects
# * added ImageDraw and ImageFont to PIL imports
# Changes in v0.8.8
# * added __repr__ methods so:
# * print(event) works and prints event.key or event.x + event.y
# * print(app) works and prints just the user defined app fields
# Changes in v0.8.7
# * removed modes (for now)
# Changes in v0.8.6
# * s21
# Changes in v0.8.5
# * Support loadImage from Modes
# Changes in v0.8.3 + v0.8.4
# * Use default empty Mode if none is provided
# * Add KeyRelease event binding
# * Drop user32.SetProcessDPIAware (caused window to be really tiny on some Windows machines)
# Changes in v0.8.1 + v0.8.2
# * print version number and last-updated date on load
# * restrict modifiers to just control key (was confusing with NumLock, etc)
# * replace hasModifiers with 'control-' prefix, as in 'control-A'
# * replace app._paused with app.paused, etc (use app._ for private variables)
# * use improved ImageGrabber import for linux
# Changes in v0.8.0
# * suppress more modifier keys (Super_L, Super_R, ...)
# * raise exception on event.keysym or event.char + works with key = 'Enter'
# * remove tryToInstall
# Changes in v0.7.4
# * renamed drawAll back to redrawAll :-)
# Changes in v0.7.3
# * Ignore mousepress-drag-release and defer configure events for drags in titlebar
# * Extend deferredRedrawAll to 100ms with replace=True and do not draw while deferred
# (together these hopefully fix Windows-only bug: file dialog makes window not moveable)
# * changed sizeChanged to not take event (use app.width and app.height)
# Changes in v0.7.2
# * Singleton App._theRoot instance (hopefully fixes all those pesky Tkinter errors-on-exit)
# * Use user32.SetProcessDPIAware to get resolution of screen grabs right on Windows-only (fine on Macs)
# * Replaces showGraphics() with runApp(...), which is a veneer for App(...) [more intuitive for pre-OOP part of course]
# * Fixes/updates images:
# * disallows loading images in redrawAll (raises exception)
# * eliminates cache from loadImage
# * eliminates app.getTkinterImage, so user now directly calls ImageTk.PhotoImage(image))
# * also create_image allows magic pilImage=image instead of image=ImageTk.PhotoImage(app.image)
# Changes in v0.7.1
# * Added keyboard shortcut:
# * cmd/ctrl/alt-x: hard exit (uses os._exit() to exit shell without tkinter error messages)
# * Fixed bug: shortcut keys stopped working after an MVC violation (or other exception)
# * In app.saveSnapshot(), add .png to path if missing
# * Added: Print scripts to copy-paste into shell to install missing modules (more automated approaches proved too brittle)
# Changes in v0.7
# * Added some image handling (requires PIL (retained) and pyscreenshot (later removed):
# * app.loadImage() # loads PIL/Pillow image from file, with file dialog, or from URL (http or https)
# * app.scaleImage() # scales a PIL/Pillow image
# * app.getTkinterImage() # converts PIL/Pillow image to Tkinter PhotoImage for use in create_image(...)
# * app.getSnapshot() # get a snapshot of the canvas as a PIL/Pillow image
# * app.saveSnapshot() # get and save a snapshot
# * Added app._paused, app.togglePaused(), and paused highlighting (red outline around canvas when paused)
# * Added keyboard shortcuts:
# * cmd/ctrl/alt-s: save a snapshot
# * cmd/ctrl/alt-p: pause/unpause
# * cmd/ctrl/alt-q: quit
# Changes in v0.6:
# * Added fnPrefix option to TopLevelApp (so multiple TopLevelApp's can be in one file)
# * Added showGraphics(drawFn) (for graphics-only drawings before we introduce animations)
# Changes in v0.5:
# * Added:
# * app.winx and app.winy (and add winx,winy parameters to app.__init__, and sets these on configure events)
# * app.setSize(width, height)
# * app.setPosition(x, y)
# * app.quit()
# * app.showMessage(message)
# * app.getUserInput(prompt)
# * App.lastUpdated (instance of datetime.date)
# * Show popup dialog box on all exceptions (not just for MVC violations)
# * Draw (in canvas) "Exception! App Stopped! (See console for details)" for any exception
# * Replace callUserMethod() with more-general @_safeMethod decorator (also handles exceptions outside user methods)
# * Only include lines from user's code (and not our framework nor tkinter) in stack traces
# * Require Python version (3.6 or greater)
# Changes in v0.4:
# * Added __setattr__ to enforce Type 1A MVC Violations (setting app.x in redrawAll) with better stack trace
# * Added app._deferredRedrawAll() (avoids resizing drawing/crashing bug on some platforms)
# * Added deferredMethodCall() and app._afterIdMap to generalize afterId handling
# * Use (_ is None) instead of (_ == None)
# Changes in v0.3:
# * Fixed "event not defined" bug in sizeChanged handlers.
# * draw "MVC Violation" on Type 2 violation (calling draw methods outside redrawAll)
# Changes in v0.2:
# * Handles another MVC violation (now detects drawing on canvas outside of redrawAll)
# * App stops running when an exception occurs (in user code) (stops cascading errors)
# Changes in v0.1:
# * OOPy + supports inheritance + supports multiple apps in one file + etc
# * uses import instead of copy-paste-edit starter code + no "do not edit code below here!"
# * no longer uses Struct (which was non-Pythonic and a confusing way to sort-of use OOP)
# * Includes an early version of MVC violation handling (detects model changes in redrawAll)
# * added events:
# * appStarted (no init-vs-__init__ confusion)
# * appStopped (for cleanup)
# * keyReleased (well, sort of works) + mouseReleased
# * mouseMoved + mouseDragged
# * sizeChanged (when resizing window)
# * improved key names (just use event.key instead of event.char and/or event.keysym + use names for 'Enter', 'Escape', ...)
# * improved function names (renamed redrawAll to drawAll)
# * improved (if not perfect) exiting without that irksome Tkinter error/bug
# * app has a title in the titlebar (also shows window's dimensions)
# * supports Modes and ModalApp (see ModalApp and Mode, and also see TestModalApp example)
# * supports TopLevelApp (using top-level functions instead of subclasses and methods)
# * supports version checking with App.majorVersion, App.minorVersion, and App.version
# * logs drawing calls to support autograding views (still must write that autograder, but this is a very helpful first step)
from tkinter import *
from tkinter import messagebox, simpledialog, filedialog
import inspect, traceback
import sys, os
from io import BytesIO
def failedImport(importName, installName=None):
installName = installName or importName
print("**********************************************************")
print(
f"** Cannot import {importName} -- it seems you need to install {installName}"
)
print(
f"** This may result in limited functionality or even a runtime error."
)
print("**********************************************************")
print()
try:
from PIL import Image, ImageTk, ImageDraw, ImageFont
except ModuleNotFoundError:
failedImport("PIL", "pillow")
if sys.platform.startswith("linux"):
try:
import pyscreenshot as ImageGrabber
except ModuleNotFoundError:
failedImport("pyscreenshot")
else:
try:
from PIL import ImageGrab as ImageGrabber
except ModuleNotFoundError:
pass # Our PIL warning is already printed above
try:
import requests
except ModuleNotFoundError:
failedImport("requests")
def getHash(obj):
# This is used to detect MVC violations in redrawAll
# @TODO: Make this more robust and efficient
try:
return getHash(obj.__dict__)
except:
if isinstance(obj, list):
return getHash(tuple([getHash(v) for v in obj]))
elif isinstance(obj, set):
return getHash(sorted(obj))
elif isinstance(obj, dict):
return getHash(tuple([obj[key] for key in sorted(obj)]))
else:
try:
return hash(obj)
except:
return getHash(repr(obj))
class WrappedCanvas(Canvas):
# Enforces MVC: no drawing outside calls to redrawAll
# Logs draw calls (for autograder) in canvas.loggedDrawingCalls
def __init__(wrappedCanvas, app):
wrappedCanvas.loggedDrawingCalls = []
wrappedCanvas.logDrawingCalls = True
wrappedCanvas.inRedrawAll = False
wrappedCanvas.app = app
super().__init__(app._root, width=app.width, height=app.height)
def log(self, methodName, args, kwargs):
if not self.inRedrawAll:
self.app._mvcViolation(
"you may not use the canvas (the view) outside of redrawAll"
)
if self.logDrawingCalls:
self.loggedDrawingCalls.append((methodName, args, kwargs))
def create_arc(self, *args, **kwargs):
self.log("create_arc", args, kwargs)
return super().create_arc(*args, **kwargs)
def create_bitmap(self, *args, **kwargs):
self.log("create_bitmap", args, kwargs)
return super().create_bitmap(*args, **kwargs)
def create_line(self, *args, **kwargs):
self.log("create_line", args, kwargs)
return super().create_line(*args, **kwargs)
def create_oval(self, *args, **kwargs):
self.log("create_oval", args, kwargs)
return super().create_oval(*args, **kwargs)
def create_polygon(self, *args, **kwargs):
self.log("create_polygon", args, kwargs)
return super().create_polygon(*args, **kwargs)
def create_rectangle(self, *args, **kwargs):
self.log("create_rectangle", args, kwargs)
return super().create_rectangle(*args, **kwargs)
def create_text(self, *args, **kwargs):
self.log("create_text", args, kwargs)
return super().create_text(*args, **kwargs)
def create_window(self, *args, **kwargs):
self.log("create_window", args, kwargs)
return super().create_window(*args, **kwargs)
def create_image(self, *args, **kwargs):
self.log("create_image", args, kwargs)
usesImage = "image" in kwargs
usesPilImage = "pilImage" in kwargs
if (not usesImage) and (not usesPilImage):
raise Exception("create_image requires an image to draw")
elif usesImage and usesPilImage:
raise Exception(
"create_image cannot use both an image and a pilImage"
)
elif usesPilImage:
pilImage = kwargs["pilImage"]
del kwargs["pilImage"]
if not isinstance(pilImage, Image.Image):
raise Exception(
"create_image: pilImage value is not an instance of a PIL/Pillow image"
)
image = ImageTk.PhotoImage(pilImage)
else:
image = kwargs["image"]
if isinstance(image, Image.Image):
raise Exception(
"create_image: image must not be an instance of a PIL/Pillow image\n"
+ "You perhaps meant to convert from PIL to Tkinter, like so:\n"
+ " canvas.create_image(x, y, image=ImageTk.PhotoImage(image))"
)
kwargs["image"] = image
return super().create_image(*args, **kwargs)
class App(object):
majorVersion = MAJOR_VERSION
minorVersion = MINOR_VERSION
version = f"{majorVersion}.{minorVersion}"
lastUpdated = LAST_UPDATED
_theRoot = None # singleton Tkinter root object
####################################
# User Methods:
####################################
def redrawAll(app, canvas):
pass # draw (view) the model in the canvas
def appStarted(app):
pass # initialize the model (app.xyz)
def appStopped(app):
pass # cleanup after app is done running
def keyPressed(app, event):
pass # use event.key
def keyReleased(app, event):
pass # use event.key
def mousePressed(app, event):
pass # use event.x and event.y
def mouseReleased(app, event):
pass # use event.x and event.y
def mouseMoved(app, event):
pass # use event.x and event.y
def mouseDragged(app, event):
pass # use event.x and event.y
def timerFired(app):
pass # respond to timer events
def sizeChanged(app):
pass # respond to window size changes
####################################
# Implementation:
####################################
def __init__(
app,
width=300,
height=300,
x=0,
y=0,
title=None,
autorun=True,
mvcCheck=True,
logDrawingCalls=True,
):
app.winx, app.winy, app.width, app.height = x, y, width, height
app.timerDelay = 100 # milliseconds
app.mouseMovedDelay = 50 # ditto
app._title = title
app._mvcCheck = mvcCheck
app._logDrawingCalls = logDrawingCalls
app._running = app._paused = False
app._mousePressedOutsideWindow = False
if autorun:
app.run()
def __repr__(app):
keys = set(app.__dict__.keys())
keyValues = []
for key in sorted(keys - app._ignoredFields):
keyValues.append(f"{key}={app.__dict__[key]}")
return f'App({', '.join(keyValues)})'
def setSize(app, width, height):
app._root.geometry(f"{width}x{height}")
def setPosition(app, x, y):
app._root.geometry(f"+{x}+{y}")
def showMessage(app, message):
messagebox.showinfo("showMessage", message, parent=app._root)
def getUserInput(app, prompt):
return simpledialog.askstring("getUserInput", prompt)
def loadImage(app, path=None):
if app._canvas.inRedrawAll:
raise Exception("Cannot call loadImage in redrawAll")
if path is None:
path = filedialog.askopenfilename(
initialdir=os.getcwd(),
title="Select file: ",
filetypes=(
("Image files", "*.png *.gif *.jpg"),
("all files", "*.*"),
),
)
if not path:
return None
if path.startswith("http"):
response = requests.request("GET", path) # path is a URL!
image = Image.open(BytesIO(response.content))
else:
image = Image.open(path)
return image
def scaleImage(app, image, scale, antialias=False):
# antialiasing is higher-quality but slower
resample = Image.ANTIALIAS if antialias else Image.NEAREST
return image.resize(
(round(image.width * scale), round(image.height * scale)),
resample=resample,
)
def getSnapshot(app):
app._showRootWindow()
x0 = app._root.winfo_rootx() + app._canvas.winfo_x()
y0 = app._root.winfo_rooty() + app._canvas.winfo_y()
result = ImageGrabber.grab((x0, y0, x0 + app.width, y0 + app.height))
return result
def saveSnapshot(app):
path = filedialog.asksaveasfilename(
initialdir=os.getcwd(),
title="Select file: ",
filetypes=(("png files", "*.png"), ("all files", "*.*")),
)
if path:
# defer call to let filedialog close (and not grab those pixels)
if not path.endswith(".png"):
path += ".png"
app._deferredMethodCall(
afterId="saveSnapshot",
afterDelay=0,
afterFn=lambda: app.getSnapshot().save(path),
)
def _togglePaused(app):
app._paused = not app._paused
def quit(app):
app._running = False
app._root.quit() # break out of root.mainloop() without closing window!
def __setattr__(app, attr, val):
d = app.__dict__
d[attr] = val
canvas = d.get("_canvas", None)
if (
d.get("running", False)
and d.get("mvcCheck", False)
and (canvas is not None)
and canvas.inRedrawAll
):
app._mvcViolation(
f"you may not change app.{attr} in the model while in redrawAll (the view)"
)
def _printUserTraceback(app, exception, tb):
stack = traceback.extract_tb(tb)
lines = traceback.format_list(stack)
inRedrawAllWrapper = False
printLines = []
for line in lines:
if (
('"cmu_112_graphics.py"' not in line)
and ("/cmu_112_graphics.py" not in line)
and ("\\cmu_112_graphics.py" not in line)
and ("/tkinter/" not in line)
and ("\\tkinter\\" not in line)
):
printLines.append(line)
if "redrawAllWrapper" in line:
inRedrawAllWrapper = True
if len(printLines) == 0:
# No user code in trace, so we have to use all the code (bummer),
# but not if we are in a redrawAllWrapper...
if inRedrawAllWrapper:
printLines = [
" No traceback available. Error occurred in redrawAll.\n"
]
else:
printLines = lines
print("Traceback (most recent call last):")
for line in printLines:
print(line, end="")
print(f"Exception: {exception}")
def _safeMethod(appMethod):
def m(*args, **kwargs):
app = args[0]
try:
return appMethod(*args, **kwargs)
except Exception as e:
app._running = False
app._printUserTraceback(e, sys.exc_info()[2])
if "_canvas" in app.__dict__:
app._canvas.inRedrawAll = (
True # not really, but stops recursive MVC Violations!
)
app._canvas.create_rectangle(
0,
0,
app.width,
app.height,
fill=None,
width=10,
outline="red",
)
app._canvas.create_rectangle(
10,
app.height - 50,
app.width - 10,
app.height - 10,
fill="white",
outline="red",
width=4,
)
app._canvas.create_text(
app.width / 2,
app.height - 40,
text=f"Exception! App Stopped!",
fill="red",
font="Arial 12 bold",
)
app._canvas.create_text(
app.width / 2,
app.height - 20,
text=f"See console for details",
fill="red",
font="Arial 12 bold",
)
app._canvas.update()
app.showMessage(
f"Exception: {e}\nClick ok then see console for details."
)
return m
def _methodIsOverridden(app, methodName):
return getattr(type(app), methodName) is not getattr(App, methodName)
def _mvcViolation(app, errMsg):
app._running = False
raise Exception("MVC Violation: " + errMsg)
@_safeMethod
def _redrawAllWrapper(app):
if not app._running:
return
if "deferredRedrawAll" in app._afterIdMap:
return # wait for pending call
app._canvas.inRedrawAll = True
app._canvas.delete(ALL)
width, outline = (10, "red") if app._paused else (0, "white")
app._canvas.create_rectangle(
0,
0,
app.width,
app.height,
fill="white",
width=width,
outline=outline,
)
app._canvas.loggedDrawingCalls = []
app._canvas.logDrawingCalls = app._logDrawingCalls
hash1 = getHash(app) if app._mvcCheck else None
try:
app.redrawAll(app._canvas)
hash2 = getHash(app) if app._mvcCheck else None
if hash1 != hash2:
app._mvcViolation(
"you may not change the app state (the model) in redrawAll (the view)"
)
finally:
app._canvas.inRedrawAll = False
app._canvas.update()
def _deferredMethodCall(app, afterId, afterDelay, afterFn, replace=False):
def afterFnWrapper():
app._afterIdMap.pop(afterId, None)
afterFn()
id = app._afterIdMap.get(afterId, None)
if (id is None) or replace:
if id:
app._root.after_cancel(id)
app._afterIdMap[afterId] = app._root.after(
afterDelay, afterFnWrapper
)
def _deferredRedrawAll(app):
app._deferredMethodCall(
afterId="deferredRedrawAll",
afterDelay=100,
afterFn=app._redrawAllWrapper,
replace=True,
)
@_safeMethod
def _appStartedWrapper(app):
app.appStarted()
app._redrawAllWrapper()
_keyNameMap = {
"\t": "Tab",
"\n": "Enter",
"\r": "Enter",
"\b": "Backspace",
chr(127): "Delete",
chr(27): "Escape",
" ": "Space",
}
@staticmethod
def _useEventKey(attr):
raise Exception(f"Use event.key instead of event.{attr}")
@staticmethod
def _getEventKeyInfo(event, keysym, char):
key = c = char
hasControlKey = event.state & 0x4 != 0
if (c in [None, ""]) or (len(c) > 1) or (ord(c) > 255):
key = keysym
if (
key.endswith("_L")
or key.endswith("_R")
or key.endswith("_Lock")
):
key = "Modifier_Key"
elif c in App._keyNameMap:
key = App._keyNameMap[c]
elif (len(c) == 1) and (1 <= ord(c) <= 26):
key = chr(ord("a") - 1 + ord(c))
hasControlKey = True
if hasControlKey and (len(key) == 1):
# don't add control- prefix to Enter, Tab, Escape, ...
key = "control-" + key
return key
class EventWrapper(Event):
def __init__(self, event):
for key in event.__dict__:
if not key.startswith("__"):
self.__dict__[key] = event.__dict__[key]
class MouseEventWrapper(EventWrapper):
def __repr__(self):
return f"Event(x={self.x}, y={self.y})"
class KeyEventWrapper(EventWrapper):
def __init__(self, event):
keysym, char = event.keysym, event.char
del event.keysym
del event.char
super().__init__(event)
self.key = App._getEventKeyInfo(event, keysym, char)
def __repr__(self):
return f"Event(key={repr(self.key)})"
keysym = property(
lambda *args: App._useEventKey("keysym"),
lambda *args: App._useEventKey("keysym"),
)
char = property(
lambda *args: App._useEventKey("char"),
lambda *args: App._useEventKey("char"),
)
@_safeMethod
def _keyPressedWrapper(app, event):
event = App.KeyEventWrapper(event)
if event.key == "control-s":
app.saveSnapshot()
elif event.key == "control-p":
app._togglePaused()
app._redrawAllWrapper()
elif event.key == "control-q":
app.quit()
elif event.key == "control-x":
os._exit(0) # hard exit avoids tkinter error messages
elif (
app._running
and (not app._paused)
and app._methodIsOverridden("keyPressed")
and (not event.key == "Modifier_Key")
):
app.keyPressed(event)
app._redrawAllWrapper()
@_safeMethod
def _keyReleasedWrapper(app, event):
if (
(not app._running)
or app._paused
or (not app._methodIsOverridden("keyReleased"))
):
return
event = App.KeyEventWrapper(event)
if not event.key == "Modifier_Key":
app.keyReleased(event)
app._redrawAllWrapper()
@_safeMethod
def _mousePressedWrapper(app, event):
if (not app._running) or app._paused:
return
if (
(event.x < 0)
or (event.x > app.width)
or (event.y < 0)
or (event.y > app.height)
):
app._mousePressedOutsideWindow = True
else:
app._mousePressedOutsideWindow = False
app._mouseIsPressed = True
app._lastMousePosn = (event.x, event.y)
if app._methodIsOverridden("mousePressed"):
event = App.MouseEventWrapper(event)
app.mousePressed(event)
app._redrawAllWrapper()
@_safeMethod
def _mouseReleasedWrapper(app, event):
if (not app._running) or app._paused:
return
app._mouseIsPressed = False
if app._mousePressedOutsideWindow:
app._mousePressedOutsideWindow = False
app._sizeChangedWrapper()
else:
app._lastMousePosn = (event.x, event.y)
if app._methodIsOverridden("mouseReleased"):
event = App.MouseEventWrapper(event)
app.mouseReleased(event)
app._redrawAllWrapper()
@_safeMethod
def _timerFiredWrapper(app):
if (not app._running) or (not app._methodIsOverridden("timerFired")):
return
if not app._paused:
app.timerFired()
app._redrawAllWrapper()
app._deferredMethodCall(
afterId="_timerFiredWrapper",
afterDelay=app.timerDelay,
afterFn=app._timerFiredWrapper,
)
@_safeMethod
def _sizeChangedWrapper(app, event=None):
if not app._running:
return
if event and ((event.width < 2) or (event.height < 2)):
return
if app._mousePressedOutsideWindow:
return
app.width, app.height, app.winx, app.winy = [
int(v)
for v in app._root.winfo_geometry().replace("x", "+").split("+")
]
if app._lastWindowDims is None:
app._lastWindowDims = (app.width, app.height, app.winx, app.winy)
else:
newDims = (app.width, app.height, app.winx, app.winy)
if app._lastWindowDims != newDims:
app._lastWindowDims = newDims
app.updateTitle()
app.sizeChanged()
app._deferredRedrawAll() # avoid resize crashing on some platforms
@_safeMethod
def _mouseMotionWrapper(app):
if not app._running:
return
mouseMovedExists = app._methodIsOverridden("mouseMoved")
mouseDraggedExists = app._methodIsOverridden("mouseDragged")
if (
(not app._paused)
and (not app._mousePressedOutsideWindow)
and (
((not app._mouseIsPressed) and mouseMovedExists)
or (app._mouseIsPressed and mouseDraggedExists)
)
):
class MouseMotionEvent(object):
pass
event = MouseMotionEvent()
root = app._root
event.x = root.winfo_pointerx() - root.winfo_rootx()
event.y = root.winfo_pointery() - root.winfo_rooty()
event = App.MouseEventWrapper(event)
if (
(app._lastMousePosn != (event.x, event.y))
and (event.x >= 0)
and (event.x <= app.width)
and (event.y >= 0)
and (event.y <= app.height)
):
if app._mouseIsPressed:
app.mouseDragged(event)
else:
app.mouseMoved(event)
app._lastMousePosn = (event.x, event.y)
app._redrawAllWrapper()
if mouseMovedExists or mouseDraggedExists:
app._deferredMethodCall(
afterId="mouseMotionWrapper",
afterDelay=app.mouseMovedDelay,
afterFn=app._mouseMotionWrapper,
)
def updateTitle(app):
app._title = app._title or type(app).__name__
app._root.title(f"{app._title} ({app.width} x {app.height})")
def getQuitMessage(app):
appLabel = type(app).__name__
if app._title != appLabel:
if app._title.startswith(appLabel):
appLabel = app._title
else:
appLabel += f" '{app._title}'"
return f"*** Closing {appLabel}. Bye! ***\n"
def _showRootWindow(app):
root = app._root
root.update()
root.deiconify()
root.lift()
root.focus()
def _hideRootWindow(app):
root = app._root
root.withdraw()
@_safeMethod
def run(app):
app._mouseIsPressed = False
app._lastMousePosn = (-1, -1)
app._lastWindowDims = None # set in sizeChangedWrapper
app._afterIdMap = dict()
# create the singleton root window
if App._theRoot is None:
App._theRoot = Tk()
App._theRoot.createcommand(
"exit", lambda: ""
) # when user enters cmd-q, ignore here (handled in keyPressed)
App._theRoot.protocol(
"WM_DELETE_WINDOW", lambda: App._theRoot.app.quit()
) # when user presses 'x' in title bar
App._theRoot.bind(
"<Button-1>",
lambda event: App._theRoot.app._mousePressedWrapper(event),
)
App._theRoot.bind(
"<B1-ButtonRelease>",
lambda event: App._theRoot.app._mouseReleasedWrapper(event),
)
App._theRoot.bind(
"<KeyPress>",
lambda event: App._theRoot.app._keyPressedWrapper(event),
)
App._theRoot.bind(
"<KeyRelease>",
lambda event: App._theRoot.app._keyReleasedWrapper(event),
)
App._theRoot.bind(
"<Configure>",
lambda event: App._theRoot.app._sizeChangedWrapper(event),
)
else:
App._theRoot.canvas.destroy()
app._root = root = App._theRoot # singleton root!
root.app = app
root.geometry(f"{app.width}x{app.height}+{app.winx}+{app.winy}")
app.updateTitle()
# create the canvas
root.canvas = app._canvas = WrappedCanvas(app)
app._canvas.pack(fill=BOTH, expand=YES)
# initialize, start the timer, and launch the app
app._running = True
app._paused = False
app._ignoredFields = set(app.__dict__.keys()) | {"_ignoredFields"}
app._appStartedWrapper()
app._timerFiredWrapper()
app._mouseMotionWrapper()
app._showRootWindow()
root.mainloop()
app._hideRootWindow()
app._running = False
for afterId in app._afterIdMap:
app._root.after_cancel(app._afterIdMap[afterId])
app._afterIdMap.clear() # for safety
app.appStopped()
print(app.getQuitMessage())
####################################
# TopLevelApp:
# (with top-level functions not subclassses and methods)
####################################
class TopLevelApp(App):
_apps = dict() # maps fnPrefix to app
def __init__(app, fnPrefix="", **kwargs):
if fnPrefix in TopLevelApp._apps:
print(f"Quitting previous version of {fnPrefix} TopLevelApp.")
TopLevelApp._apps[fnPrefix].quit()
if (fnPrefix != "") and ("title" not in kwargs):
kwargs["title"] = f"TopLevelApp '{fnPrefix}'"
TopLevelApp._apps[fnPrefix] = app
app._fnPrefix = fnPrefix
app._callersGlobals = inspect.stack()[1][0].f_globals
app.mode = None
super().__init__(**kwargs)
def _callFn(app, fn, *args):
if (app.mode is not None) and (app.mode != ""):
fn = app.mode + "_" + fn
fn = app._fnPrefix + fn
if fn in app._callersGlobals:
app._callersGlobals[fn](*args)
def redrawAll(app, canvas):
app._callFn("redrawAll", app, canvas)
def appStarted(app):
app._callFn("appStarted", app)
def appStopped(app):
app._callFn("appStopped", app)
def keyPressed(app, event):
app._callFn("keyPressed", app, event)
def keyReleased(app, event):
app._callFn("keyReleased", app, event)
def mousePressed(app, event):
app._callFn("mousePressed", app, event)
def mouseReleased(app, event):
app._callFn("mouseReleased", app, event)
def mouseMoved(app, event):
app._callFn("mouseMoved", app, event)
def mouseDragged(app, event):
app._callFn("mouseDragged", app, event)
def timerFired(app):
app._callFn("timerFired", app)
def sizeChanged(app):
app._callFn("sizeChanged", app)
####################################
# ModalApp + Mode:
####################################
"""
# For now, only include modes in top-level apps (see above).
class Mode(object):
def __repr__(self): return f'<{self.__class__.__name__} object>'
class ModalApp(App):
def __init__(app, *args, **kwargs):
app._mode = None
super().__init__(*args, **kwargs)
def setMode(app, mode):
if (not isinstance(mode, Mode)):
raise Exception('mode must be an instance of Mode')
app._mode = mode
def _callFn(app, fn, *args):
if (app._mode == None):
raise Exception('ModalApp must have a mode (use app.setMode())')
mode = app._mode
# method = getattr(mode, fn, None)
method = mode.__class__.__dict__.get(fn) # get method as fn
if (method != None):
method(*args)
def redrawAll(app, canvas): app._callFn('redrawAll', app, canvas)
#def appStarted(app): app._callFn('appStarted', app)
#def appStopped(app): app._callFn('appStopped', app)
def keyPressed(app, event): app._callFn('keyPressed', app, event)
def keyReleased(app, event): app._callFn('keyReleased', app, event)
def mousePressed(app, event): app._callFn('mousePressed', app, event)
def mouseReleased(app, event): app._callFn('mouseReleased', app, event)
def mouseMoved(app, event): app._callFn('mouseMoved', app, event)
def mouseDragged(app, event): app._callFn('mouseDragged', app, event)
def timerFired(app): app._callFn('timerFired', app)
def sizeChanged(app): app._callFn('sizeChanged', app)
"""
####################################
# runApp()
####################################
"""
def showGraphics(drawFn, **kwargs):
class GraphicsApp(App):
def __init__(app, **kwargs):
if ('title' not in kwargs):
kwargs['title'] = drawFn.__name__
super().__init__(**kwargs)
def redrawAll(app, canvas):
drawFn(app, canvas)
app = GraphicsApp(**kwargs)
"""
runApp = TopLevelApp
print(
f"Loaded cmu_112_graphics version {App.version} (last updated {App.lastUpdated})"
)
if __name__ == "__main__":
try:
import cmu_112_graphics_tests
except:
pass
| # cmu_112_graphics.py
# version 0.9.0
# Pre-release for CMU 15-112-s21
# Require Python 3.6 or later
import sys
if (sys.version_info[0] != 3) or (sys.version_info[1] < 6):
raise Exception("cmu_112_graphics.py requires Python version 3.6 or later.")
# Track version and file update timestamp
import datetime
MAJOR_VERSION = 0
MINOR_VERSION = 9.0 # version 0.9.0
LAST_UPDATED = datetime.date(year=2021, month=4, day=12)
# Pending changes:
# * Fix Windows-only bug: Position popup dialog box over app window (already works fine on Macs)
# * Add documentation
# * integrate sounds (probably from pyGame)
# * Improved methodIsOverridden to TopLevelApp and ModalApp
# * Save to animated gif and/or mp4 (with audio capture?)
# Deferred changes:
# * replace/augment tkinter canvas with PIL/Pillow imageDraw (perhaps with our own fn names)
# Changes in v0.9.0
# * added simpler top-level modes implementation that does not include mode objects
# * added ImageDraw and ImageFont to PIL imports
# Changes in v0.8.8
# * added __repr__ methods so:
# * print(event) works and prints event.key or event.x + event.y
# * print(app) works and prints just the user defined app fields
# Changes in v0.8.7
# * removed modes (for now)
# Changes in v0.8.6
# * s21
# Changes in v0.8.5
# * Support loadImage from Modes
# Changes in v0.8.3 + v0.8.4
# * Use default empty Mode if none is provided
# * Add KeyRelease event binding
# * Drop user32.SetProcessDPIAware (caused window to be really tiny on some Windows machines)
# Changes in v0.8.1 + v0.8.2
# * print version number and last-updated date on load
# * restrict modifiers to just control key (was confusing with NumLock, etc)
# * replace hasModifiers with 'control-' prefix, as in 'control-A'
# * replace app._paused with app.paused, etc (use app._ for private variables)
# * use improved ImageGrabber import for linux
# Changes in v0.8.0
# * suppress more modifier keys (Super_L, Super_R, ...)
# * raise exception on event.keysym or event.char + works with key = 'Enter'
# * remove tryToInstall
# Changes in v0.7.4
# * renamed drawAll back to redrawAll :-)
# Changes in v0.7.3
# * Ignore mousepress-drag-release and defer configure events for drags in titlebar
# * Extend deferredRedrawAll to 100ms with replace=True and do not draw while deferred
# (together these hopefully fix Windows-only bug: file dialog makes window not moveable)
# * changed sizeChanged to not take event (use app.width and app.height)
# Changes in v0.7.2
# * Singleton App._theRoot instance (hopefully fixes all those pesky Tkinter errors-on-exit)
# * Use user32.SetProcessDPIAware to get resolution of screen grabs right on Windows-only (fine on Macs)
# * Replaces showGraphics() with runApp(...), which is a veneer for App(...) [more intuitive for pre-OOP part of course]
# * Fixes/updates images:
# * disallows loading images in redrawAll (raises exception)
# * eliminates cache from loadImage
# * eliminates app.getTkinterImage, so user now directly calls ImageTk.PhotoImage(image))
# * also create_image allows magic pilImage=image instead of image=ImageTk.PhotoImage(app.image)
# Changes in v0.7.1
# * Added keyboard shortcut:
# * cmd/ctrl/alt-x: hard exit (uses os._exit() to exit shell without tkinter error messages)
# * Fixed bug: shortcut keys stopped working after an MVC violation (or other exception)
# * In app.saveSnapshot(), add .png to path if missing
# * Added: Print scripts to copy-paste into shell to install missing modules (more automated approaches proved too brittle)
# Changes in v0.7
# * Added some image handling (requires PIL (retained) and pyscreenshot (later removed):
# * app.loadImage() # loads PIL/Pillow image from file, with file dialog, or from URL (http or https)
# * app.scaleImage() # scales a PIL/Pillow image
# * app.getTkinterImage() # converts PIL/Pillow image to Tkinter PhotoImage for use in create_image(...)
# * app.getSnapshot() # get a snapshot of the canvas as a PIL/Pillow image
# * app.saveSnapshot() # get and save a snapshot
# * Added app._paused, app.togglePaused(), and paused highlighting (red outline around canvas when paused)
# * Added keyboard shortcuts:
# * cmd/ctrl/alt-s: save a snapshot
# * cmd/ctrl/alt-p: pause/unpause
# * cmd/ctrl/alt-q: quit
# Changes in v0.6:
# * Added fnPrefix option to TopLevelApp (so multiple TopLevelApp's can be in one file)
# * Added showGraphics(drawFn) (for graphics-only drawings before we introduce animations)
# Changes in v0.5:
# * Added:
# * app.winx and app.winy (and add winx,winy parameters to app.__init__, and sets these on configure events)
# * app.setSize(width, height)
# * app.setPosition(x, y)
# * app.quit()
# * app.showMessage(message)
# * app.getUserInput(prompt)
# * App.lastUpdated (instance of datetime.date)
# * Show popup dialog box on all exceptions (not just for MVC violations)
# * Draw (in canvas) "Exception! App Stopped! (See console for details)" for any exception
# * Replace callUserMethod() with more-general @_safeMethod decorator (also handles exceptions outside user methods)
# * Only include lines from user's code (and not our framework nor tkinter) in stack traces
# * Require Python version (3.6 or greater)
# Changes in v0.4:
# * Added __setattr__ to enforce Type 1A MVC Violations (setting app.x in redrawAll) with better stack trace
# * Added app._deferredRedrawAll() (avoids resizing drawing/crashing bug on some platforms)
# * Added deferredMethodCall() and app._afterIdMap to generalize afterId handling
# * Use (_ is None) instead of (_ == None)
# Changes in v0.3:
# * Fixed "event not defined" bug in sizeChanged handlers.
# * draw "MVC Violation" on Type 2 violation (calling draw methods outside redrawAll)
# Changes in v0.2:
# * Handles another MVC violation (now detects drawing on canvas outside of redrawAll)
# * App stops running when an exception occurs (in user code) (stops cascading errors)
# Changes in v0.1:
# * OOPy + supports inheritance + supports multiple apps in one file + etc
# * uses import instead of copy-paste-edit starter code + no "do not edit code below here!"
# * no longer uses Struct (which was non-Pythonic and a confusing way to sort-of use OOP)
# * Includes an early version of MVC violation handling (detects model changes in redrawAll)
# * added events:
# * appStarted (no init-vs-__init__ confusion)
# * appStopped (for cleanup)
# * keyReleased (well, sort of works) + mouseReleased
# * mouseMoved + mouseDragged
# * sizeChanged (when resizing window)
# * improved key names (just use event.key instead of event.char and/or event.keysym + use names for 'Enter', 'Escape', ...)
# * improved function names (renamed redrawAll to drawAll)
# * improved (if not perfect) exiting without that irksome Tkinter error/bug
# * app has a title in the titlebar (also shows window's dimensions)
# * supports Modes and ModalApp (see ModalApp and Mode, and also see TestModalApp example)
# * supports TopLevelApp (using top-level functions instead of subclasses and methods)
# * supports version checking with App.majorVersion, App.minorVersion, and App.version
# * logs drawing calls to support autograding views (still must write that autograder, but this is a very helpful first step)
from tkinter import *
from tkinter import messagebox, simpledialog, filedialog
import inspect, traceback
import sys, os
from io import BytesIO
def failedImport(importName, installName=None):
installName = installName or importName
print("**********************************************************")
print(
f"** Cannot import {importName} -- it seems you need to install {installName}"
)
print(
f"** This may result in limited functionality or even a runtime error."
)
print("**********************************************************")
print()
try:
from PIL import Image, ImageTk, ImageDraw, ImageFont
except ModuleNotFoundError:
failedImport("PIL", "pillow")
if sys.platform.startswith("linux"):
try:
import pyscreenshot as ImageGrabber
except ModuleNotFoundError:
failedImport("pyscreenshot")
else:
try:
from PIL import ImageGrab as ImageGrabber
except ModuleNotFoundError:
pass # Our PIL warning is already printed above
try:
import requests
except ModuleNotFoundError:
failedImport("requests")
def getHash(obj):
# This is used to detect MVC violations in redrawAll
# @TODO: Make this more robust and efficient
try:
return getHash(obj.__dict__)
except:
if isinstance(obj, list):
return getHash(tuple([getHash(v) for v in obj]))
elif isinstance(obj, set):
return getHash(sorted(obj))
elif isinstance(obj, dict):
return getHash(tuple([obj[key] for key in sorted(obj)]))
else:
try:
return hash(obj)
except:
return getHash(repr(obj))
class WrappedCanvas(Canvas):
# Enforces MVC: no drawing outside calls to redrawAll
# Logs draw calls (for autograder) in canvas.loggedDrawingCalls
def __init__(wrappedCanvas, app):
wrappedCanvas.loggedDrawingCalls = []
wrappedCanvas.logDrawingCalls = True
wrappedCanvas.inRedrawAll = False
wrappedCanvas.app = app
super().__init__(app._root, width=app.width, height=app.height)
def log(self, methodName, args, kwargs):
if not self.inRedrawAll:
self.app._mvcViolation(
"you may not use the canvas (the view) outside of redrawAll"
)
if self.logDrawingCalls:
self.loggedDrawingCalls.append((methodName, args, kwargs))
def create_arc(self, *args, **kwargs):
self.log("create_arc", args, kwargs)
return super().create_arc(*args, **kwargs)
def create_bitmap(self, *args, **kwargs):
self.log("create_bitmap", args, kwargs)
return super().create_bitmap(*args, **kwargs)
def create_line(self, *args, **kwargs):
self.log("create_line", args, kwargs)
return super().create_line(*args, **kwargs)
def create_oval(self, *args, **kwargs):
self.log("create_oval", args, kwargs)
return super().create_oval(*args, **kwargs)
def create_polygon(self, *args, **kwargs):
self.log("create_polygon", args, kwargs)
return super().create_polygon(*args, **kwargs)
def create_rectangle(self, *args, **kwargs):
self.log("create_rectangle", args, kwargs)
return super().create_rectangle(*args, **kwargs)
def create_text(self, *args, **kwargs):
self.log("create_text", args, kwargs)
return super().create_text(*args, **kwargs)
def create_window(self, *args, **kwargs):
self.log("create_window", args, kwargs)
return super().create_window(*args, **kwargs)
def create_image(self, *args, **kwargs):
self.log("create_image", args, kwargs)
usesImage = "image" in kwargs
usesPilImage = "pilImage" in kwargs
if (not usesImage) and (not usesPilImage):
raise Exception("create_image requires an image to draw")
elif usesImage and usesPilImage:
raise Exception(
"create_image cannot use both an image and a pilImage"
)
elif usesPilImage:
pilImage = kwargs["pilImage"]
del kwargs["pilImage"]
if not isinstance(pilImage, Image.Image):
raise Exception(
"create_image: pilImage value is not an instance of a PIL/Pillow image"
)
image = ImageTk.PhotoImage(pilImage)
else:
image = kwargs["image"]
if isinstance(image, Image.Image):
raise Exception(
"create_image: image must not be an instance of a PIL/Pillow image\n"
+ "You perhaps meant to convert from PIL to Tkinter, like so:\n"
+ " canvas.create_image(x, y, image=ImageTk.PhotoImage(image))"
)
kwargs["image"] = image
return super().create_image(*args, **kwargs)
class App(object):
majorVersion = MAJOR_VERSION
minorVersion = MINOR_VERSION
version = f"{majorVersion}.{minorVersion}"
lastUpdated = LAST_UPDATED
_theRoot = None # singleton Tkinter root object
####################################
# User Methods:
####################################
def redrawAll(app, canvas):
pass # draw (view) the model in the canvas
def appStarted(app):
pass # initialize the model (app.xyz)
def appStopped(app):
pass # cleanup after app is done running
def keyPressed(app, event):
pass # use event.key
def keyReleased(app, event):
pass # use event.key
def mousePressed(app, event):
pass # use event.x and event.y
def mouseReleased(app, event):
pass # use event.x and event.y
def mouseMoved(app, event):
pass # use event.x and event.y
def mouseDragged(app, event):
pass # use event.x and event.y
def timerFired(app):
pass # respond to timer events
def sizeChanged(app):
pass # respond to window size changes
####################################
# Implementation:
####################################
def __init__(
app,
width=300,
height=300,
x=0,
y=0,
title=None,
autorun=True,
mvcCheck=True,
logDrawingCalls=True,
):
app.winx, app.winy, app.width, app.height = x, y, width, height
app.timerDelay = 100 # milliseconds
app.mouseMovedDelay = 50 # ditto
app._title = title
app._mvcCheck = mvcCheck
app._logDrawingCalls = logDrawingCalls
app._running = app._paused = False
app._mousePressedOutsideWindow = False
if autorun:
app.run()
def __repr__(app):
keys = set(app.__dict__.keys())
keyValues = []
for key in sorted(keys - app._ignoredFields):
keyValues.append(f"{key}={app.__dict__[key]}")
return f'App({", ".join(keyValues)})'
def setSize(app, width, height):
app._root.geometry(f"{width}x{height}")
def setPosition(app, x, y):
app._root.geometry(f"+{x}+{y}")
def showMessage(app, message):
messagebox.showinfo("showMessage", message, parent=app._root)
def getUserInput(app, prompt):
return simpledialog.askstring("getUserInput", prompt)
def loadImage(app, path=None):
if app._canvas.inRedrawAll:
raise Exception("Cannot call loadImage in redrawAll")
if path is None:
path = filedialog.askopenfilename(
initialdir=os.getcwd(),
title="Select file: ",
filetypes=(
("Image files", "*.png *.gif *.jpg"),
("all files", "*.*"),
),
)
if not path:
return None
if path.startswith("http"):
response = requests.request("GET", path) # path is a URL!
image = Image.open(BytesIO(response.content))
else:
image = Image.open(path)
return image
def scaleImage(app, image, scale, antialias=False):
# antialiasing is higher-quality but slower
resample = Image.ANTIALIAS if antialias else Image.NEAREST
return image.resize(
(round(image.width * scale), round(image.height * scale)),
resample=resample,
)
def getSnapshot(app):
app._showRootWindow()
x0 = app._root.winfo_rootx() + app._canvas.winfo_x()
y0 = app._root.winfo_rooty() + app._canvas.winfo_y()
result = ImageGrabber.grab((x0, y0, x0 + app.width, y0 + app.height))
return result
def saveSnapshot(app):
path = filedialog.asksaveasfilename(
initialdir=os.getcwd(),
title="Select file: ",
filetypes=(("png files", "*.png"), ("all files", "*.*")),
)
if path:
# defer call to let filedialog close (and not grab those pixels)
if not path.endswith(".png"):
path += ".png"
app._deferredMethodCall(
afterId="saveSnapshot",
afterDelay=0,
afterFn=lambda: app.getSnapshot().save(path),
)
def _togglePaused(app):
app._paused = not app._paused
def quit(app):
app._running = False
app._root.quit() # break out of root.mainloop() without closing window!
def __setattr__(app, attr, val):
d = app.__dict__
d[attr] = val
canvas = d.get("_canvas", None)
if (
d.get("running", False)
and d.get("mvcCheck", False)
and (canvas is not None)
and canvas.inRedrawAll
):
app._mvcViolation(
f"you may not change app.{attr} in the model while in redrawAll (the view)"
)
def _printUserTraceback(app, exception, tb):
stack = traceback.extract_tb(tb)
lines = traceback.format_list(stack)
inRedrawAllWrapper = False
printLines = []
for line in lines:
if (
('"cmu_112_graphics.py"' not in line)
and ("/cmu_112_graphics.py" not in line)
and ("\\cmu_112_graphics.py" not in line)
and ("/tkinter/" not in line)
and ("\\tkinter\\" not in line)
):
printLines.append(line)
if "redrawAllWrapper" in line:
inRedrawAllWrapper = True
if len(printLines) == 0:
# No user code in trace, so we have to use all the code (bummer),
# but not if we are in a redrawAllWrapper...
if inRedrawAllWrapper:
printLines = [
" No traceback available. Error occurred in redrawAll.\n"
]
else:
printLines = lines
print("Traceback (most recent call last):")
for line in printLines:
print(line, end="")
print(f"Exception: {exception}")
def _safeMethod(appMethod):
def m(*args, **kwargs):
app = args[0]
try:
return appMethod(*args, **kwargs)
except Exception as e:
app._running = False
app._printUserTraceback(e, sys.exc_info()[2])
if "_canvas" in app.__dict__:
app._canvas.inRedrawAll = (
True # not really, but stops recursive MVC Violations!
)
app._canvas.create_rectangle(
0,
0,
app.width,
app.height,
fill=None,
width=10,
outline="red",
)
app._canvas.create_rectangle(
10,
app.height - 50,
app.width - 10,
app.height - 10,
fill="white",
outline="red",
width=4,
)
app._canvas.create_text(
app.width / 2,
app.height - 40,
text=f"Exception! App Stopped!",
fill="red",
font="Arial 12 bold",
)
app._canvas.create_text(
app.width / 2,
app.height - 20,
text=f"See console for details",
fill="red",
font="Arial 12 bold",
)
app._canvas.update()
app.showMessage(
f"Exception: {e}\nClick ok then see console for details."
)
return m
def _methodIsOverridden(app, methodName):
return getattr(type(app), methodName) is not getattr(App, methodName)
def _mvcViolation(app, errMsg):
app._running = False
raise Exception("MVC Violation: " + errMsg)
@_safeMethod
def _redrawAllWrapper(app):
if not app._running:
return
if "deferredRedrawAll" in app._afterIdMap:
return # wait for pending call
app._canvas.inRedrawAll = True
app._canvas.delete(ALL)
width, outline = (10, "red") if app._paused else (0, "white")
app._canvas.create_rectangle(
0,
0,
app.width,
app.height,
fill="white",
width=width,
outline=outline,
)
app._canvas.loggedDrawingCalls = []
app._canvas.logDrawingCalls = app._logDrawingCalls
hash1 = getHash(app) if app._mvcCheck else None
try:
app.redrawAll(app._canvas)
hash2 = getHash(app) if app._mvcCheck else None
if hash1 != hash2:
app._mvcViolation(
"you may not change the app state (the model) in redrawAll (the view)"
)
finally:
app._canvas.inRedrawAll = False
app._canvas.update()
def _deferredMethodCall(app, afterId, afterDelay, afterFn, replace=False):
def afterFnWrapper():
app._afterIdMap.pop(afterId, None)
afterFn()
id = app._afterIdMap.get(afterId, None)
if (id is None) or replace:
if id:
app._root.after_cancel(id)
app._afterIdMap[afterId] = app._root.after(
afterDelay, afterFnWrapper
)
def _deferredRedrawAll(app):
app._deferredMethodCall(
afterId="deferredRedrawAll",
afterDelay=100,
afterFn=app._redrawAllWrapper,
replace=True,
)
@_safeMethod
def _appStartedWrapper(app):
app.appStarted()
app._redrawAllWrapper()
_keyNameMap = {
"\t": "Tab",
"\n": "Enter",
"\r": "Enter",
"\b": "Backspace",
chr(127): "Delete",
chr(27): "Escape",
" ": "Space",
}
@staticmethod
def _useEventKey(attr):
raise Exception(f"Use event.key instead of event.{attr}")
@staticmethod
def _getEventKeyInfo(event, keysym, char):
key = c = char
hasControlKey = event.state & 0x4 != 0
if (c in [None, ""]) or (len(c) > 1) or (ord(c) > 255):
key = keysym
if (
key.endswith("_L")
or key.endswith("_R")
or key.endswith("_Lock")
):
key = "Modifier_Key"
elif c in App._keyNameMap:
key = App._keyNameMap[c]
elif (len(c) == 1) and (1 <= ord(c) <= 26):
key = chr(ord("a") - 1 + ord(c))
hasControlKey = True
if hasControlKey and (len(key) == 1):
# don't add control- prefix to Enter, Tab, Escape, ...
key = "control-" + key
return key
class EventWrapper(Event):
def __init__(self, event):
for key in event.__dict__:
if not key.startswith("__"):
self.__dict__[key] = event.__dict__[key]
class MouseEventWrapper(EventWrapper):
def __repr__(self):
return f"Event(x={self.x}, y={self.y})"
class KeyEventWrapper(EventWrapper):
def __init__(self, event):
keysym, char = event.keysym, event.char
del event.keysym
del event.char
super().__init__(event)
self.key = App._getEventKeyInfo(event, keysym, char)
def __repr__(self):
return f"Event(key={repr(self.key)})"
keysym = property(
lambda *args: App._useEventKey("keysym"),
lambda *args: App._useEventKey("keysym"),
)
char = property(
lambda *args: App._useEventKey("char"),
lambda *args: App._useEventKey("char"),
)
@_safeMethod
def _keyPressedWrapper(app, event):
event = App.KeyEventWrapper(event)
if event.key == "control-s":
app.saveSnapshot()
elif event.key == "control-p":
app._togglePaused()
app._redrawAllWrapper()
elif event.key == "control-q":
app.quit()
elif event.key == "control-x":
os._exit(0) # hard exit avoids tkinter error messages
elif (
app._running
and (not app._paused)
and app._methodIsOverridden("keyPressed")
and (not event.key == "Modifier_Key")
):
app.keyPressed(event)
app._redrawAllWrapper()
@_safeMethod
def _keyReleasedWrapper(app, event):
if (
(not app._running)
or app._paused
or (not app._methodIsOverridden("keyReleased"))
):
return
event = App.KeyEventWrapper(event)
if not event.key == "Modifier_Key":
app.keyReleased(event)
app._redrawAllWrapper()
@_safeMethod
def _mousePressedWrapper(app, event):
if (not app._running) or app._paused:
return
if (
(event.x < 0)
or (event.x > app.width)
or (event.y < 0)
or (event.y > app.height)
):
app._mousePressedOutsideWindow = True
else:
app._mousePressedOutsideWindow = False
app._mouseIsPressed = True
app._lastMousePosn = (event.x, event.y)
if app._methodIsOverridden("mousePressed"):
event = App.MouseEventWrapper(event)
app.mousePressed(event)
app._redrawAllWrapper()
@_safeMethod
def _mouseReleasedWrapper(app, event):
if (not app._running) or app._paused:
return
app._mouseIsPressed = False
if app._mousePressedOutsideWindow:
app._mousePressedOutsideWindow = False
app._sizeChangedWrapper()
else:
app._lastMousePosn = (event.x, event.y)
if app._methodIsOverridden("mouseReleased"):
event = App.MouseEventWrapper(event)
app.mouseReleased(event)
app._redrawAllWrapper()
@_safeMethod
def _timerFiredWrapper(app):
if (not app._running) or (not app._methodIsOverridden("timerFired")):
return
if not app._paused:
app.timerFired()
app._redrawAllWrapper()
app._deferredMethodCall(
afterId="_timerFiredWrapper",
afterDelay=app.timerDelay,
afterFn=app._timerFiredWrapper,
)
@_safeMethod
def _sizeChangedWrapper(app, event=None):
if not app._running:
return
if event and ((event.width < 2) or (event.height < 2)):
return
if app._mousePressedOutsideWindow:
return
app.width, app.height, app.winx, app.winy = [
int(v)
for v in app._root.winfo_geometry().replace("x", "+").split("+")
]
if app._lastWindowDims is None:
app._lastWindowDims = (app.width, app.height, app.winx, app.winy)
else:
newDims = (app.width, app.height, app.winx, app.winy)
if app._lastWindowDims != newDims:
app._lastWindowDims = newDims
app.updateTitle()
app.sizeChanged()
app._deferredRedrawAll() # avoid resize crashing on some platforms
@_safeMethod
def _mouseMotionWrapper(app):
if not app._running:
return
mouseMovedExists = app._methodIsOverridden("mouseMoved")
mouseDraggedExists = app._methodIsOverridden("mouseDragged")
if (
(not app._paused)
and (not app._mousePressedOutsideWindow)
and (
((not app._mouseIsPressed) and mouseMovedExists)
or (app._mouseIsPressed and mouseDraggedExists)
)
):
class MouseMotionEvent(object):
pass
event = MouseMotionEvent()
root = app._root
event.x = root.winfo_pointerx() - root.winfo_rootx()
event.y = root.winfo_pointery() - root.winfo_rooty()
event = App.MouseEventWrapper(event)
if (
(app._lastMousePosn != (event.x, event.y))
and (event.x >= 0)
and (event.x <= app.width)
and (event.y >= 0)
and (event.y <= app.height)
):
if app._mouseIsPressed:
app.mouseDragged(event)
else:
app.mouseMoved(event)
app._lastMousePosn = (event.x, event.y)
app._redrawAllWrapper()
if mouseMovedExists or mouseDraggedExists:
app._deferredMethodCall(
afterId="mouseMotionWrapper",
afterDelay=app.mouseMovedDelay,
afterFn=app._mouseMotionWrapper,
)
def updateTitle(app):
app._title = app._title or type(app).__name__
app._root.title(f"{app._title} ({app.width} x {app.height})")
def getQuitMessage(app):
appLabel = type(app).__name__
if app._title != appLabel:
if app._title.startswith(appLabel):
appLabel = app._title
else:
appLabel += f" '{app._title}'"
return f"*** Closing {appLabel}. Bye! ***\n"
def _showRootWindow(app):
root = app._root
root.update()
root.deiconify()
root.lift()
root.focus()
def _hideRootWindow(app):
root = app._root
root.withdraw()
@_safeMethod
def run(app):
app._mouseIsPressed = False
app._lastMousePosn = (-1, -1)
app._lastWindowDims = None # set in sizeChangedWrapper
app._afterIdMap = dict()
# create the singleton root window
if App._theRoot is None:
App._theRoot = Tk()
App._theRoot.createcommand(
"exit", lambda: ""
) # when user enters cmd-q, ignore here (handled in keyPressed)
App._theRoot.protocol(
"WM_DELETE_WINDOW", lambda: App._theRoot.app.quit()
) # when user presses 'x' in title bar
App._theRoot.bind(
"<Button-1>",
lambda event: App._theRoot.app._mousePressedWrapper(event),
)
App._theRoot.bind(
"<B1-ButtonRelease>",
lambda event: App._theRoot.app._mouseReleasedWrapper(event),
)
App._theRoot.bind(
"<KeyPress>",
lambda event: App._theRoot.app._keyPressedWrapper(event),
)
App._theRoot.bind(
"<KeyRelease>",
lambda event: App._theRoot.app._keyReleasedWrapper(event),
)
App._theRoot.bind(
"<Configure>",
lambda event: App._theRoot.app._sizeChangedWrapper(event),
)
else:
App._theRoot.canvas.destroy()
app._root = root = App._theRoot # singleton root!
root.app = app
root.geometry(f"{app.width}x{app.height}+{app.winx}+{app.winy}")
app.updateTitle()
# create the canvas
root.canvas = app._canvas = WrappedCanvas(app)
app._canvas.pack(fill=BOTH, expand=YES)
# initialize, start the timer, and launch the app
app._running = True
app._paused = False
app._ignoredFields = set(app.__dict__.keys()) | {"_ignoredFields"}
app._appStartedWrapper()
app._timerFiredWrapper()
app._mouseMotionWrapper()
app._showRootWindow()
root.mainloop()
app._hideRootWindow()
app._running = False
for afterId in app._afterIdMap:
app._root.after_cancel(app._afterIdMap[afterId])
app._afterIdMap.clear() # for safety
app.appStopped()
print(app.getQuitMessage())
####################################
# TopLevelApp:
# (with top-level functions not subclassses and methods)
####################################
class TopLevelApp(App):
_apps = dict() # maps fnPrefix to app
def __init__(app, fnPrefix="", **kwargs):
if fnPrefix in TopLevelApp._apps:
print(f"Quitting previous version of {fnPrefix} TopLevelApp.")
TopLevelApp._apps[fnPrefix].quit()
if (fnPrefix != "") and ("title" not in kwargs):
kwargs["title"] = f"TopLevelApp '{fnPrefix}'"
TopLevelApp._apps[fnPrefix] = app
app._fnPrefix = fnPrefix
app._callersGlobals = inspect.stack()[1][0].f_globals
app.mode = None
super().__init__(**kwargs)
def _callFn(app, fn, *args):
if (app.mode is not None) and (app.mode != ""):
fn = app.mode + "_" + fn
fn = app._fnPrefix + fn
if fn in app._callersGlobals:
app._callersGlobals[fn](*args)
def redrawAll(app, canvas):
app._callFn("redrawAll", app, canvas)
def appStarted(app):
app._callFn("appStarted", app)
def appStopped(app):
app._callFn("appStopped", app)
def keyPressed(app, event):
app._callFn("keyPressed", app, event)
def keyReleased(app, event):
app._callFn("keyReleased", app, event)
def mousePressed(app, event):
app._callFn("mousePressed", app, event)
def mouseReleased(app, event):
app._callFn("mouseReleased", app, event)
def mouseMoved(app, event):
app._callFn("mouseMoved", app, event)
def mouseDragged(app, event):
app._callFn("mouseDragged", app, event)
def timerFired(app):
app._callFn("timerFired", app)
def sizeChanged(app):
app._callFn("sizeChanged", app)
####################################
# ModalApp + Mode:
####################################
"""
# For now, only include modes in top-level apps (see above).
class Mode(object):
def __repr__(self): return f'<{self.__class__.__name__} object>'
class ModalApp(App):
def __init__(app, *args, **kwargs):
app._mode = None
super().__init__(*args, **kwargs)
def setMode(app, mode):
if (not isinstance(mode, Mode)):
raise Exception('mode must be an instance of Mode')
app._mode = mode
def _callFn(app, fn, *args):
if (app._mode == None):
raise Exception('ModalApp must have a mode (use app.setMode())')
mode = app._mode
# method = getattr(mode, fn, None)
method = mode.__class__.__dict__.get(fn) # get method as fn
if (method != None):
method(*args)
def redrawAll(app, canvas): app._callFn('redrawAll', app, canvas)
#def appStarted(app): app._callFn('appStarted', app)
#def appStopped(app): app._callFn('appStopped', app)
def keyPressed(app, event): app._callFn('keyPressed', app, event)
def keyReleased(app, event): app._callFn('keyReleased', app, event)
def mousePressed(app, event): app._callFn('mousePressed', app, event)
def mouseReleased(app, event): app._callFn('mouseReleased', app, event)
def mouseMoved(app, event): app._callFn('mouseMoved', app, event)
def mouseDragged(app, event): app._callFn('mouseDragged', app, event)
def timerFired(app): app._callFn('timerFired', app)
def sizeChanged(app): app._callFn('sizeChanged', app)
"""
####################################
# runApp()
####################################
"""
def showGraphics(drawFn, **kwargs):
class GraphicsApp(App):
def __init__(app, **kwargs):
if ('title' not in kwargs):
kwargs['title'] = drawFn.__name__
super().__init__(**kwargs)
def redrawAll(app, canvas):
drawFn(app, canvas)
app = GraphicsApp(**kwargs)
"""
runApp = TopLevelApp
print(
f"Loaded cmu_112_graphics version {App.version} (last updated {App.lastUpdated})"
)
if __name__ == "__main__":
try:
import cmu_112_graphics_tests
except:
pass
|
from datetime import datetime
from airflow.models import DAG
from airflow.providers.apache.spark.operators.spark_jdbc import SparkJDBCOperator
from airflow.providers.apache.spark.operators.spark_sql import SparkSqlOperator
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
import os
with DAG(
dag_id='spark_test',
schedule_interval=None,
start_date=datetime(2021, 1, 1),
catchup=False,
tags=['FreeUni'],
) as dag:
# [START howto_operator_spark_submit]
submit_job = SparkSubmitOperator(
application="/airflow/jobs/test_job.py", task_id="submit_job"
)
# [END howto_operator_spark_submit]
submit_job_2 = SparkSubmitOperator(
application=f"{os.getenv("SPARK_HOME")}/examples/src/main/python/pi.py", task_id="submit_job_2"
)
submit_job_3 = SparkSubmitOperator(
application=f"/airflow/jobs/breaking_news.py", task_id="breaking_news"
)
[submit_job, submit_job_2] >> submit_job_3 | from datetime import datetime
from airflow.models import DAG
from airflow.providers.apache.spark.operators.spark_jdbc import SparkJDBCOperator
from airflow.providers.apache.spark.operators.spark_sql import SparkSqlOperator
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
import os
with DAG(
dag_id='spark_test',
schedule_interval=None,
start_date=datetime(2021, 1, 1),
catchup=False,
tags=['FreeUni'],
) as dag:
# [START howto_operator_spark_submit]
submit_job = SparkSubmitOperator(
application="/airflow/jobs/test_job.py", task_id="submit_job"
)
# [END howto_operator_spark_submit]
submit_job_2 = SparkSubmitOperator(
application=f"{os.getenv('SPARK_HOME')}/examples/src/main/python/pi.py", task_id="submit_job_2"
)
submit_job_3 = SparkSubmitOperator(
application=f"/airflow/jobs/breaking_news.py", task_id="breaking_news"
)
[submit_job, submit_job_2] >> submit_job_3 |
import os
import asyncio
import logging
import configparser
from contextlib import suppress
from eventkit import Event
from ib_insync.objects import Object
from ib_insync.contract import Forex
from ib_insync.ib import IB
import ib_insync.util as util
__all__ = ['IBC', 'IBController', 'Watchdog']
class IBC(Object):
"""
Programmatic control over starting and stopping TWS/Gateway
using IBC (https://github.com/IbcAlpha/IBC).
Args:
twsVersion (int): (required) The major version number for
TWS or gateway.
gateway (bool):
* True = gateway
* False = TWS
tradingMode (str): 'live' or 'paper'.
userid (str): IB account username. It is recommended to set the real
username/password in a secured IBC config file.
password (str): IB account password.
twsPath (str): Path to the TWS installation folder.
Defaults:
* Linux: ~/Jts
* OS X: ~/Applications
* Windows: C:\\\\Jts
twsSettingsPath (str): Path to the TWS settings folder.
Defaults:
* Linux: ~/Jts
* OS X: ~/Jts
* Windows: Not available
ibcPath (str): Path to the IBC installation folder.
Defaults:
* Linux: /opt/ibc
* OS X: /opt/ibc
* Windows: C:\\\\IBC
ibcIni (str): Path to the IBC configuration file.
Defaults:
* Linux: ~/ibc/config.ini
* OS X: ~/ibc/config.ini
* Windows: %%HOMEPATH%%\\\\Documents\\\\IBC\\\\config.ini
javaPath (str): Path to Java executable.
Default is to use the Java VM included with TWS/gateway.
fixuserid (str): FIX account user id (gateway only).
fixpassword (str): FIX account password (gateway only).
This is not intended to be run in a notebook.
To use IBC on Windows, the proactor (or quamash) event loop
must have been set:
.. code-block:: python
import asyncio
asyncio.set_event_loop(asyncio.ProactorEventLoop())
Example usage:
.. code-block:: python
ibc = IBC(974, gateway=True, tradingMode='live',
userid='edemo', password='demouser')
ibc.start()
IB.run()
"""
IbcLogLevel = logging.DEBUG
_Args = dict(
# key=(Default, UnixArg, WindowsArg)
twsVersion=(None, '', ''),
gateway=(None, '--gateway', '/Gateway'),
tradingMode=(None, '--mode=', '/Mode:'),
twsPath=(None, '--tws-path=', '/TwsPath:'),
twsSettingsPath=(None, '--tws-settings-path=', ''),
ibcPath=(None, '--ibc-path=', '/IbcPath:'),
ibcIni=(None, '--ibc-ini=', '/Config:'),
javaPath=(None, '--java-path=', '/JavaPath:'),
userid=(None, '--user=', '/User:'),
password=(None, '--pw=', '/PW:'),
fixuserid=(None, '--fix-user=', '/FIXUser:'),
fixpassword=(None, '--fix-pw=', '/FIXPW:'))
defaults = {k: v[0] for k, v in _Args.items()}
__slots__ = list(defaults) + ['_proc', '_logger', '_monitor']
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
if not self.ibcPath:
self.ibcPath = '/opt/ibc' if os.sys.platform != 'win32' \
else 'C:\\IBC'
self._proc = None
self._monitor = None
self._logger = logging.getLogger('ib_insync.IBC')
def __enter__(self):
self.start()
return self
def __exit__(self, *_exc):
self.terminate()
def start(self):
"""
Launch TWS/IBG.
"""
util.run(self.startAsync())
def terminate(self):
"""
Terminate TWS/IBG.
"""
util.run(self.terminateAsync())
async def startAsync(self):
if self._proc:
return
self._logger.info('Starting')
# create shell command
win32 = os.sys.platform == 'win32'
cmd = [
f'{self.ibcPath}\\scripts\\StartIBC.bat' if win32 else
#TODO: add 'runIncontainer' option to class
f'/usr/bin/xvfb-run', f'-a', f'{self.ibcPath}/scripts/ibcstart.sh']
for k, v in self.dict().items():
arg = IBC._Args[k][2 if win32 else 1]
if v:
if arg.endswith('=') or arg.endswith(':'):
cmd.append(f'{arg}{v}')
elif arg:
cmd.append(arg)
else:
cmd.append(str(v))
# run shell command
self._proc = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE)
self._monitor = asyncio.ensure_future(self.monitorAsync())
async def terminateAsync(self):
if not self._proc:
return
self._logger.info('Terminating')
if self._monitor:
self._monitor.cancel()
self._monitor = None
if os.sys.platform == 'win32':
import subprocess
subprocess.call(
['taskkill', '/F', '/T', '/PID', str(self._proc.pid)])
else:
with suppress(ProcessLookupError):
self._proc.terminate()
await self._proc.wait()
self._proc = None
async def monitorAsync(self):
while self._proc:
line = await self._proc.stdout.readline()
if not line:
break
self._logger.log(IBC.IbcLogLevel, line.strip().decode())
class IBController(Object):
"""
For new installations it is recommended to use IBC instead.
Programmatic control over starting and stopping TWS/Gateway
using IBController (https://github.com/ib-controller/ib-controller).
On Windows the the proactor (or quamash) event loop must have been set:
.. code-block:: python
import asyncio
asyncio.set_event_loop(asyncio.ProactorEventLoop())
This is not intended to be run in a notebook.
"""
defaults = dict(
APP='TWS', # 'TWS' or 'GATEWAY'
TWS_MAJOR_VRSN='969',
TRADING_MODE='live', # 'live' or 'paper'
IBC_INI='~/IBController/IBController.ini',
IBC_PATH='~/IBController',
TWS_PATH='~/Jts',
LOG_PATH='~/IBController/Logs',
TWSUSERID='',
TWSPASSWORD='',
JAVA_PATH='',
TWS_CONFIG_PATH='')
__slots__ = list(defaults) + ['_proc', '_logger', '_monitor']
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
self._proc = None
self._monitor = None
self._logger = logging.getLogger('ib_insync.IBController')
def __enter__(self):
self.start()
return self
def __exit__(self, *_exc):
self.terminate()
def start(self):
"""
Launch TWS/IBG.
"""
util.run(self.startAsync())
def stop(self):
"""
Cleanly shutdown TWS/IBG.
"""
util.run(self.stopAsync())
def terminate(self):
"""
Terminate TWS/IBG.
"""
util.run(self.terminateAsync())
async def startAsync(self):
if self._proc:
return
self._logger.info('Starting')
# expand paths
d = self.dict()
for k, v in d.items():
if k.endswith('_PATH') or k.endswith('_INI'):
d[k] = os.path.expanduser(v)
if not d['TWS_CONFIG_PATH']:
d['TWS_CONFIG_PATH'] = d['TWS_PATH']
self.update(**d)
# run shell command
ext = 'bat' if os.sys.platform == 'win32' else 'sh'
cmd = f'{d['IBC_PATH']}/Scripts/DisplayBannerAndLaunch.{ext}'
env = {**os.environ, **d}
self._proc = await asyncio.create_subprocess_exec(
cmd, env=env, stdout=asyncio.subprocess.PIPE)
self._monitor = asyncio.ensure_future(self.monitorAsync())
async def stopAsync(self):
if not self._proc:
return
self._logger.info('Stopping')
# read ibcontroller ini file to get controller port
txt = '[section]' + open(self.IBC_INI).read()
config = configparser.ConfigParser()
config.read_string(txt)
contrPort = config.getint('section', 'IbControllerPort')
_reader, writer = await asyncio.open_connection('127.0.0.1', contrPort)
writer.write(b'STOP')
await writer.drain()
writer.close()
await self._proc.wait()
self._proc = None
self._monitor.cancel()
self._monitor = None
async def terminateAsync(self):
if not self._proc:
return
self._logger.info('Terminating')
self._monitor.cancel()
self._monitor = None
with suppress(ProcessLookupError):
self._proc.terminate()
await self._proc.wait()
self._proc = None
async def monitorAsync(self):
while self._proc:
line = await self._proc.stdout.readline()
if not line:
break
self._logger.info(line.strip().decode())
class Watchdog(Object):
"""
Start, connect and watch over the TWS or gateway app and try to keep it
up and running. It is intended to be used in an event-driven
application that properly initializes itself upon (re-)connect.
It is not intended to be used in a notebook or in imperative-style code.
Do not expect Watchdog to magically shield you from reality. Do not use
Watchdog unless you understand what it does and doesn't do.
Args:
controller (Union[IBC, IBController]): (required) IBC or IBController
instance.
ib (IB): (required) IB instance to be used. Do no connect this
instance as Watchdog takes care of that.
host (str): Used for connecting IB instance.
port (int): Used for connecting IB instance.
clientId (int): Used for connecting IB instance.
connectTimeout (float): Used for connecting IB instance.
appStartupTime (float): Time (in seconds) that the app is given
to start up. Make sure that it is given ample time.
appTimeout (float): Timeout (in seconds) for network traffic idle time.
retryDelay (float): Time (in seconds) to restart app after a
previous failure.
The idea is to wait until there is no traffic coming from the app for
a certain amount of time (the ``appTimeout`` parameter). This triggers
a historical request to be placed just to see if the app is still alive
and well. If yes, then continue, if no then restart the whole app
and reconnect. Restarting will also occur directly on error 1100.
Example usage:
.. code-block:: python
def onConnected():
print(ib.accountValues())
ibc = IBC(974, gateway=True, tradingMode='paper')
ib = IB()
ib.connectedEvent += onConnected
watchdog = Watchdog(ibc, ib, port=4002)
watchdog.start()
IB.run()
Events:
* ``startingEvent`` (watchdog: :class:`.Watchdog`)
* ``startedEvent`` (watchdog: :class:`.Watchdog`)
* ``stoppingEvent`` (watchdog: :class:`.Watchdog`)
* ``stoppedEvent`` (watchdog: :class:`.Watchdog`)
* ``softTimeoutEvent`` (watchdog: :class:`.Watchdog`)
* ``hardTimeoutEvent`` (watchdog: :class:`.Watchdog`)
"""
events = [
'startingEvent', 'startedEvent', 'stoppingEvent', 'stoppedEvent',
'softTimeoutEvent', 'hardTimeoutEvent']
defaults = dict(
controller=None,
ib=None,
host='127.0.0.1',
port='7497',
clientId=1,
connectTimeout=2,
appStartupTime=30,
appTimeout=20,
retryDelay=2)
__slots__ = list(defaults.keys()) + events + ['_runner', '_logger']
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
Event.init(self, Watchdog.events)
if not self.controller:
raise ValueError('No controller supplied')
if not self.ib:
raise ValueError('No IB instance supplied')
if self.ib.isConnected():
raise ValueError('IB instance must not be connected')
assert 0 < self.appTimeout < 60
assert self.retryDelay > 0
self._runner = None
self._logger = logging.getLogger('ib_insync.Watchdog')
def start(self):
self._logger.info('Starting')
self.startingEvent.emit(self)
self._runner = asyncio.ensure_future(self.runAsync())
def stop(self):
self._logger.info('Stopping')
self.stoppingEvent.emit(self)
self.ib.disconnect()
self._runner = None
async def runAsync(self):
def onTimeout(idlePeriod):
if not waiter.done():
waiter.set_result(None)
def onError(reqId, errorCode, errorString, contract):
if errorCode == 1100 and not waiter.done():
waiter.set_exception(Warning('Error 1100'))
def onDisconnected():
if not waiter.done():
waiter.set_exception(Warning('Disconnected'))
while self._runner:
try:
await self.controller.startAsync()
await asyncio.sleep(self.appStartupTime)
await self.ib.connectAsync(
self.host, self.port, self.clientId, self.connectTimeout)
self.startedEvent.emit(self)
self.ib.setTimeout(self.appTimeout)
self.ib.timeoutEvent += onTimeout
self.ib.errorEvent += onError
self.ib.disconnectedEvent += onDisconnected
while self._runner:
waiter = asyncio.Future()
await waiter
# soft timeout, probe the app with a historical request
self._logger.debug('Soft timeout')
self.softTimeoutEvent.emit(self)
probe = self.ib.reqHistoricalDataAsync(
Forex('EURUSD'), '', '30 S', '5 secs',
'MIDPOINT', False)
bars = None
with suppress(asyncio.TimeoutError):
bars = await asyncio.wait_for(probe, 4)
if not bars:
self.hardTimeoutEvent.emit(self)
raise Warning('Hard timeout')
self.ib.setTimeout(self.appTimeout)
except ConnectionRefusedError:
pass
except Warning as w:
self._logger.warning(w)
except Exception as e:
self._logger.exception(e)
finally:
self.ib.timeoutEvent -= onTimeout
self.ib.errorEvent -= onError
self.ib.disconnectedEvent -= onDisconnected
await self.controller.terminateAsync()
self.stoppedEvent.emit(self)
if self._runner:
await asyncio.sleep(self.retryDelay)
if __name__ == '__main__':
asyncio.get_event_loop().set_debug(True)
util.logToConsole(logging.DEBUG)
ibc = IBC(974, gateway=True, tradingMode='paper')
# userid='edemo', password='demouser')
ib = IB()
app = Watchdog(ibc, ib, port=4002, appStartupTime=15, appTimeout=10)
app.start()
IB.run()
| import os
import asyncio
import logging
import configparser
from contextlib import suppress
from eventkit import Event
from ib_insync.objects import Object
from ib_insync.contract import Forex
from ib_insync.ib import IB
import ib_insync.util as util
__all__ = ['IBC', 'IBController', 'Watchdog']
class IBC(Object):
"""
Programmatic control over starting and stopping TWS/Gateway
using IBC (https://github.com/IbcAlpha/IBC).
Args:
twsVersion (int): (required) The major version number for
TWS or gateway.
gateway (bool):
* True = gateway
* False = TWS
tradingMode (str): 'live' or 'paper'.
userid (str): IB account username. It is recommended to set the real
username/password in a secured IBC config file.
password (str): IB account password.
twsPath (str): Path to the TWS installation folder.
Defaults:
* Linux: ~/Jts
* OS X: ~/Applications
* Windows: C:\\\\Jts
twsSettingsPath (str): Path to the TWS settings folder.
Defaults:
* Linux: ~/Jts
* OS X: ~/Jts
* Windows: Not available
ibcPath (str): Path to the IBC installation folder.
Defaults:
* Linux: /opt/ibc
* OS X: /opt/ibc
* Windows: C:\\\\IBC
ibcIni (str): Path to the IBC configuration file.
Defaults:
* Linux: ~/ibc/config.ini
* OS X: ~/ibc/config.ini
* Windows: %%HOMEPATH%%\\\\Documents\\\\IBC\\\\config.ini
javaPath (str): Path to Java executable.
Default is to use the Java VM included with TWS/gateway.
fixuserid (str): FIX account user id (gateway only).
fixpassword (str): FIX account password (gateway only).
This is not intended to be run in a notebook.
To use IBC on Windows, the proactor (or quamash) event loop
must have been set:
.. code-block:: python
import asyncio
asyncio.set_event_loop(asyncio.ProactorEventLoop())
Example usage:
.. code-block:: python
ibc = IBC(974, gateway=True, tradingMode='live',
userid='edemo', password='demouser')
ibc.start()
IB.run()
"""
IbcLogLevel = logging.DEBUG
_Args = dict(
# key=(Default, UnixArg, WindowsArg)
twsVersion=(None, '', ''),
gateway=(None, '--gateway', '/Gateway'),
tradingMode=(None, '--mode=', '/Mode:'),
twsPath=(None, '--tws-path=', '/TwsPath:'),
twsSettingsPath=(None, '--tws-settings-path=', ''),
ibcPath=(None, '--ibc-path=', '/IbcPath:'),
ibcIni=(None, '--ibc-ini=', '/Config:'),
javaPath=(None, '--java-path=', '/JavaPath:'),
userid=(None, '--user=', '/User:'),
password=(None, '--pw=', '/PW:'),
fixuserid=(None, '--fix-user=', '/FIXUser:'),
fixpassword=(None, '--fix-pw=', '/FIXPW:'))
defaults = {k: v[0] for k, v in _Args.items()}
__slots__ = list(defaults) + ['_proc', '_logger', '_monitor']
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
if not self.ibcPath:
self.ibcPath = '/opt/ibc' if os.sys.platform != 'win32' \
else 'C:\\IBC'
self._proc = None
self._monitor = None
self._logger = logging.getLogger('ib_insync.IBC')
def __enter__(self):
self.start()
return self
def __exit__(self, *_exc):
self.terminate()
def start(self):
"""
Launch TWS/IBG.
"""
util.run(self.startAsync())
def terminate(self):
"""
Terminate TWS/IBG.
"""
util.run(self.terminateAsync())
async def startAsync(self):
if self._proc:
return
self._logger.info('Starting')
# create shell command
win32 = os.sys.platform == 'win32'
cmd = [
f'{self.ibcPath}\\scripts\\StartIBC.bat' if win32 else
#TODO: add 'runIncontainer' option to class
f'/usr/bin/xvfb-run', f'-a', f'{self.ibcPath}/scripts/ibcstart.sh']
for k, v in self.dict().items():
arg = IBC._Args[k][2 if win32 else 1]
if v:
if arg.endswith('=') or arg.endswith(':'):
cmd.append(f'{arg}{v}')
elif arg:
cmd.append(arg)
else:
cmd.append(str(v))
# run shell command
self._proc = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE)
self._monitor = asyncio.ensure_future(self.monitorAsync())
async def terminateAsync(self):
if not self._proc:
return
self._logger.info('Terminating')
if self._monitor:
self._monitor.cancel()
self._monitor = None
if os.sys.platform == 'win32':
import subprocess
subprocess.call(
['taskkill', '/F', '/T', '/PID', str(self._proc.pid)])
else:
with suppress(ProcessLookupError):
self._proc.terminate()
await self._proc.wait()
self._proc = None
async def monitorAsync(self):
while self._proc:
line = await self._proc.stdout.readline()
if not line:
break
self._logger.log(IBC.IbcLogLevel, line.strip().decode())
class IBController(Object):
"""
For new installations it is recommended to use IBC instead.
Programmatic control over starting and stopping TWS/Gateway
using IBController (https://github.com/ib-controller/ib-controller).
On Windows the the proactor (or quamash) event loop must have been set:
.. code-block:: python
import asyncio
asyncio.set_event_loop(asyncio.ProactorEventLoop())
This is not intended to be run in a notebook.
"""
defaults = dict(
APP='TWS', # 'TWS' or 'GATEWAY'
TWS_MAJOR_VRSN='969',
TRADING_MODE='live', # 'live' or 'paper'
IBC_INI='~/IBController/IBController.ini',
IBC_PATH='~/IBController',
TWS_PATH='~/Jts',
LOG_PATH='~/IBController/Logs',
TWSUSERID='',
TWSPASSWORD='',
JAVA_PATH='',
TWS_CONFIG_PATH='')
__slots__ = list(defaults) + ['_proc', '_logger', '_monitor']
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
self._proc = None
self._monitor = None
self._logger = logging.getLogger('ib_insync.IBController')
def __enter__(self):
self.start()
return self
def __exit__(self, *_exc):
self.terminate()
def start(self):
"""
Launch TWS/IBG.
"""
util.run(self.startAsync())
def stop(self):
"""
Cleanly shutdown TWS/IBG.
"""
util.run(self.stopAsync())
def terminate(self):
"""
Terminate TWS/IBG.
"""
util.run(self.terminateAsync())
async def startAsync(self):
if self._proc:
return
self._logger.info('Starting')
# expand paths
d = self.dict()
for k, v in d.items():
if k.endswith('_PATH') or k.endswith('_INI'):
d[k] = os.path.expanduser(v)
if not d['TWS_CONFIG_PATH']:
d['TWS_CONFIG_PATH'] = d['TWS_PATH']
self.update(**d)
# run shell command
ext = 'bat' if os.sys.platform == 'win32' else 'sh'
cmd = f'{d["IBC_PATH"]}/Scripts/DisplayBannerAndLaunch.{ext}'
env = {**os.environ, **d}
self._proc = await asyncio.create_subprocess_exec(
cmd, env=env, stdout=asyncio.subprocess.PIPE)
self._monitor = asyncio.ensure_future(self.monitorAsync())
async def stopAsync(self):
if not self._proc:
return
self._logger.info('Stopping')
# read ibcontroller ini file to get controller port
txt = '[section]' + open(self.IBC_INI).read()
config = configparser.ConfigParser()
config.read_string(txt)
contrPort = config.getint('section', 'IbControllerPort')
_reader, writer = await asyncio.open_connection('127.0.0.1', contrPort)
writer.write(b'STOP')
await writer.drain()
writer.close()
await self._proc.wait()
self._proc = None
self._monitor.cancel()
self._monitor = None
async def terminateAsync(self):
if not self._proc:
return
self._logger.info('Terminating')
self._monitor.cancel()
self._monitor = None
with suppress(ProcessLookupError):
self._proc.terminate()
await self._proc.wait()
self._proc = None
async def monitorAsync(self):
while self._proc:
line = await self._proc.stdout.readline()
if not line:
break
self._logger.info(line.strip().decode())
class Watchdog(Object):
"""
Start, connect and watch over the TWS or gateway app and try to keep it
up and running. It is intended to be used in an event-driven
application that properly initializes itself upon (re-)connect.
It is not intended to be used in a notebook or in imperative-style code.
Do not expect Watchdog to magically shield you from reality. Do not use
Watchdog unless you understand what it does and doesn't do.
Args:
controller (Union[IBC, IBController]): (required) IBC or IBController
instance.
ib (IB): (required) IB instance to be used. Do no connect this
instance as Watchdog takes care of that.
host (str): Used for connecting IB instance.
port (int): Used for connecting IB instance.
clientId (int): Used for connecting IB instance.
connectTimeout (float): Used for connecting IB instance.
appStartupTime (float): Time (in seconds) that the app is given
to start up. Make sure that it is given ample time.
appTimeout (float): Timeout (in seconds) for network traffic idle time.
retryDelay (float): Time (in seconds) to restart app after a
previous failure.
The idea is to wait until there is no traffic coming from the app for
a certain amount of time (the ``appTimeout`` parameter). This triggers
a historical request to be placed just to see if the app is still alive
and well. If yes, then continue, if no then restart the whole app
and reconnect. Restarting will also occur directly on error 1100.
Example usage:
.. code-block:: python
def onConnected():
print(ib.accountValues())
ibc = IBC(974, gateway=True, tradingMode='paper')
ib = IB()
ib.connectedEvent += onConnected
watchdog = Watchdog(ibc, ib, port=4002)
watchdog.start()
IB.run()
Events:
* ``startingEvent`` (watchdog: :class:`.Watchdog`)
* ``startedEvent`` (watchdog: :class:`.Watchdog`)
* ``stoppingEvent`` (watchdog: :class:`.Watchdog`)
* ``stoppedEvent`` (watchdog: :class:`.Watchdog`)
* ``softTimeoutEvent`` (watchdog: :class:`.Watchdog`)
* ``hardTimeoutEvent`` (watchdog: :class:`.Watchdog`)
"""
events = [
'startingEvent', 'startedEvent', 'stoppingEvent', 'stoppedEvent',
'softTimeoutEvent', 'hardTimeoutEvent']
defaults = dict(
controller=None,
ib=None,
host='127.0.0.1',
port='7497',
clientId=1,
connectTimeout=2,
appStartupTime=30,
appTimeout=20,
retryDelay=2)
__slots__ = list(defaults.keys()) + events + ['_runner', '_logger']
def __init__(self, *args, **kwargs):
Object.__init__(self, *args, **kwargs)
Event.init(self, Watchdog.events)
if not self.controller:
raise ValueError('No controller supplied')
if not self.ib:
raise ValueError('No IB instance supplied')
if self.ib.isConnected():
raise ValueError('IB instance must not be connected')
assert 0 < self.appTimeout < 60
assert self.retryDelay > 0
self._runner = None
self._logger = logging.getLogger('ib_insync.Watchdog')
def start(self):
self._logger.info('Starting')
self.startingEvent.emit(self)
self._runner = asyncio.ensure_future(self.runAsync())
def stop(self):
self._logger.info('Stopping')
self.stoppingEvent.emit(self)
self.ib.disconnect()
self._runner = None
async def runAsync(self):
def onTimeout(idlePeriod):
if not waiter.done():
waiter.set_result(None)
def onError(reqId, errorCode, errorString, contract):
if errorCode == 1100 and not waiter.done():
waiter.set_exception(Warning('Error 1100'))
def onDisconnected():
if not waiter.done():
waiter.set_exception(Warning('Disconnected'))
while self._runner:
try:
await self.controller.startAsync()
await asyncio.sleep(self.appStartupTime)
await self.ib.connectAsync(
self.host, self.port, self.clientId, self.connectTimeout)
self.startedEvent.emit(self)
self.ib.setTimeout(self.appTimeout)
self.ib.timeoutEvent += onTimeout
self.ib.errorEvent += onError
self.ib.disconnectedEvent += onDisconnected
while self._runner:
waiter = asyncio.Future()
await waiter
# soft timeout, probe the app with a historical request
self._logger.debug('Soft timeout')
self.softTimeoutEvent.emit(self)
probe = self.ib.reqHistoricalDataAsync(
Forex('EURUSD'), '', '30 S', '5 secs',
'MIDPOINT', False)
bars = None
with suppress(asyncio.TimeoutError):
bars = await asyncio.wait_for(probe, 4)
if not bars:
self.hardTimeoutEvent.emit(self)
raise Warning('Hard timeout')
self.ib.setTimeout(self.appTimeout)
except ConnectionRefusedError:
pass
except Warning as w:
self._logger.warning(w)
except Exception as e:
self._logger.exception(e)
finally:
self.ib.timeoutEvent -= onTimeout
self.ib.errorEvent -= onError
self.ib.disconnectedEvent -= onDisconnected
await self.controller.terminateAsync()
self.stoppedEvent.emit(self)
if self._runner:
await asyncio.sleep(self.retryDelay)
if __name__ == '__main__':
asyncio.get_event_loop().set_debug(True)
util.logToConsole(logging.DEBUG)
ibc = IBC(974, gateway=True, tradingMode='paper')
# userid='edemo', password='demouser')
ib = IB()
app = Watchdog(ibc, ib, port=4002, appStartupTime=15, appTimeout=10)
app.start()
IB.run()
|
"""Helper for adding a projectum to the projecta collection.
Projecta are small bite-sized project quanta that typically will result in
one manuscript.
"""
import datetime as dt
import dateutil.parser as date_parser
from dateutil.relativedelta import relativedelta
from regolith.helpers.basehelper import DbHelperBase
from regolith.fsclient import _id_key
from regolith.tools import (
all_docs_from_collection,
get_pi_id,
)
from gooey import GooeyParser
TARGET_COLL = "projecta"
def subparser(subpi):
date_kwargs = {}
if isinstance(subpi, GooeyParser):
date_kwargs['widget'] = 'DateChooser'
subpi.add_argument("name", help="A short but unique name for the projectum",
default=None)
subpi.add_argument("lead", help="id of the group lead or tbd",
default=None)
subpi.add_argument("-d", "--description",
help="Slightly longer description of the projectum"
)
subpi.add_argument("-c", "--collaborators", nargs="+",
help="list of outside collaborator ids separated by spaces, "
"'aeinstein efermi'. Builders will get the full names "
"from the contacts collection"
)
subpi.add_argument("-m", "--group_members", nargs="+",
help="list of group member ids, e.g., 'astudent acolleague'. "
"Builders will get full names from people collection."
"Do not add the lead or the group"
"the pi who are added by default."
)
subpi.add_argument("-g", "--grants", nargs="+",
help="grant or (occasionally) list of grants that support this work"
)
subpi.add_argument("-u", "--due_date",
help="proposed due date for the deliverable",
**date_kwargs
)
subpi.add_argument("--checklist", action='store_true',
help="Use this to turn the prum into a paper submission"
"checklist."
)
# Do not delete --database arg
subpi.add_argument("--database",
help="The database that will be updated. Defaults to "
"first database in the regolithrc.json file."
)
# Do not delete --date arg
subpi.add_argument("--date",
help="The begin_date for the projectum Defaults to "
"today's date.",
**date_kwargs
)
return subpi
class ProjectumAdderHelper(DbHelperBase):
"""Helper for adding a projectum to the projecta collection.
Projecta are small bite-sized project quanta that typically will result in
one manuscript.
"""
# btype must be the same as helper target in helper.py
btype = "a_projectum"
needed_dbs = [f'{TARGET_COLL}', 'groups', 'people']
def construct_global_ctx(self):
"""Constructs the global context"""
super().construct_global_ctx()
gtx = self.gtx
rc = self.rc
rc.pi_id = get_pi_id(rc)
rc.coll = f"{TARGET_COLL}"
if not rc.database:
rc.database = rc.databases[0]["name"]
gtx[rc.coll] = sorted(
all_docs_from_collection(rc.client, rc.coll), key=_id_key
)
gtx["all_docs_from_collection"] = all_docs_from_collection
gtx["float"] = float
gtx["str"] = str
gtx["zip"] = zip
def db_updater(self):
rc = self.rc
if not rc.date:
now = dt.date.today()
else:
now = date_parser.parse(rc.date).date()
if not rc.due_date:
due_date = now + relativedelta(years=1)
else:
due_date = date_parser.parse(rc.due_date).date()
key = f"{rc.lead[:2]}_{"".join(rc.name.casefold().split()).strip()}"
coll = self.gtx[rc.coll]
pdocl = list(filter(lambda doc: doc["_id"] == key, coll))
if len(pdocl) > 0:
raise RuntimeError(
"This entry appears to already exist in the collection")
else:
pdoc = {}
pdoc.update({
'begin_date': now,
'log_url': '',
'name': rc.name,
'pi_id': rc.pi_id,
'lead': rc.lead,
})
if rc.lead == "tbd":
pdoc.update({
'status': 'proposed'
})
else:
pdoc.update({
'status': 'started'
})
if rc.description:
pdoc.update({
'description': rc.description,
})
if rc.grants:
if isinstance(rc.grants, str):
rc.grants = [rc.grants]
pdoc.update({'grants': rc.grants})
else:
pdoc.update({'grants': ["tbd"]})
if rc.group_members:
if isinstance(rc.group_members, str):
rc.group_members = [rc.group_members]
pdoc.update({'group_members': rc.group_members})
else:
pdoc.update({'group_members': []})
if rc.collaborators:
if isinstance(rc.collaborators, str):
rc.collaborators = [rc.collaborators]
pdoc.update({
'collaborators': rc.collaborators,
})
else:
pdoc.update({
'collaborators': [],
})
pdoc.update({"_id": key})
pdoc.update({"deliverable": {
"due_date": due_date,
"audience": ["beginning grad in chemistry"],
"success_def": "audience is happy",
"scope": [
"UCs that are supported or some other scope description if it software",
"sketch of science story if it is paper"],
"platform": "description of how and where the audience will access the deliverable. journal if it is a paper",
"roll_out": [
"steps that the audience will take to access and interact with the deliverable",
"not needed for paper submissions"],
"status": "proposed"}
})
pdoc.update({"kickoff": {
"due_date": now + relativedelta(days=7),
"audience": ["lead", "pi", "group_members"],
"name": "Kick off meeting",
"objective": "introduce project to the lead",
"status": "proposed"
}})
secondm = {'due_date': now + relativedelta(days=21),
'name': 'Project lead presentation',
'objective': 'to act as an example milestone. The date is the date it was finished. delete the field until it is finished. In this case, the lead will present what they think is the project after their reading. Add more milestones as needed.',
'audience': ['lead', 'pi', 'group_members'],
'status': 'proposed',
'type': 'meeting'
}
pdoc.update({"milestones": [secondm]})
if rc.checklist:
pdoc = self.insert_checklists(pdoc, now)
rc.client.insert_one(rc.database, rc.coll, pdoc)
print(f"{key} has been added in {TARGET_COLL}")
return
def insert_checklists(self, pdoc, now):
"""Create manuscript checklist, one item as one milestone."""
presubmission_checklist = [
("Create slide figures", "Create Inkscape graphics (Inkscape is preferrable over ppt) for the slides and place in a ``figures`` directory in the slides directory. These may then be used either in beamer or ppt. Iterate with Simon to convergence. (to get started with Inkscape download and install it, then run the program and navigate to Help-->Tutorials. The first two ('Basic' and 'Shapes') should probably be enough for someone to get basic functionality.)."),
("Create slides", "Create a 'slides' folder in the paper repo or a Google slides deck for a series of talk slides. Iterate the slide skeleton with Simon to convergence. (For a beamer template: https://gitlab.thebillingegroup.com/talks/beamerTalkTemplate)."),
("Create a highlight slide", "Create a 'highlight' folder in the paper repo. Create a single 'highlight' slide that describes the result following NSF/DOE guidelines. Place it in the 'highlight' folder. Iterate with Simon to convergence (highlight templates and examples can be found in https://gitlab.thebillingegroup.com/papers/highlights)"),
("Create public-summary", "Create a public summary in a text file. Place it in the 'highlight' folder. Iterate with Simon to convergence. (The kudos template and example can be found at https://docs.google.com/document/d/1j4ZsM8zS_nZo03s7T48uwzDbh8xTPksAQM3ZLgJ-g-Y/edit?usp=sharing)."),
]
submission_checklist = [
("Check the author list", "Check the author list. Last chance to check for missing authors. Does each author agree to submit to the specific journal? Make sure all authors have approved submission."),
("Check publisher accounts", "Check that all of the authors have accounts for the publisher you are submitting to (if possible) and that they have their ORCID IDs associated with their accounts (ie. for ACS, authors need to link their Paragon accounts with their ORCID IDs)."),
("Check institution", "Is author's name, institution correct? Last chance to avoid embarrassing typos."),
("Check acknowledgement", "Are beamlines, grants, fundings properly acknowledged at the end of the paper? Double check this with Simon and use the ackno statements in the Group Google group."),
("Check figures and tables", "Are all the figures, tables in the paper correct (the ones you intended)?"),
("Check figure captions", "Check the Figure captions for errors. If they refer to a green line, is the relevant line green, and so on."),
("Check figure axis labels", "Check that the figure axis labels are correctly labeled. Make sure it doesn't say G when F is plotted. Make sure the units are correct. Make sure it says 'G (A^-2)' and NOT 'G(r)' (common mistake)."),
("Check table captions", "Check the table caption is correct. Are all the items in the table properly defined in the caption. If it is a crystal structure, are the space group and special positions mentioned in the caption? Is all the info correct?"),
("Check numbers in the table", "Check all the numbers in the tables for errors."),
("Check any question marks", "Check all the question marks in the text. Is there any 'FIG.???' or unrecognized character?"),
("Check figure references", "Check references to the all figures and tables. Does reference to Figure 4 refer to the right figure for example."),
("Check references", "Go through the references and find all the errors. Correct errors in the bibliographic database (citations.yml, or the Zotero collection, for example), not just in the local bib file. Did all the journal names compile correctly? Are they all consistently in abbreviated form (or full form if that is the style, though that is rare). Volume, year and page numbers appear for all references? Hard to find errors in these numbers, but when you do, definitely correct the database!"),
("Check reference style", "Is reference's style in accordance with journal's requirement?"),
("Check journal submission requirements", "Check the journal submission requirements for cover letters, table of contents pictures, list of referees, etc.."),
("Check arxiv", "Check with Simon; will the paper be submitted to arXiv?"),
("Get approval", "Get final approval from all authors for the final version of the manuscript, cover letter, referees list, etc., submission to arXiv if appropriate."),
("Check cover letter", "In the cover letter, does it contain editor-in-chief's name and institution (usually at the top left of the letter) ? Is the content of letter concise and eye-catching? Are (three) suggested reviewers' information in the letter?"),
("Insert bbl", "If it is LaTeX, insert the .bbl file into the main tex file and comment out the \\thebibliography and \\bibliographystyle lines."),
("Commit and push", "Commit all the changes to your local repo, then push the changes to gitlab."),
("Submit to journal", "Go ahead and make the submission, usually online."),
("Push a tag", "If during the submission process you need to make any changes, do it in your local repo and make another commit and push. When the submission is finalized, tag the repo that points to THIS VERSION IS THE SUBMITTED VERSION. create the submission tag. If the current version of your local repo is the submitted version, type, e.g., `git tag -l` to list previous tags (try and keep the tag name formatting consistent) `git tag -a 20180525PRLsubmitted -m <initial submission to PRL>` `git push origin <tag_name>`."),
("Modify tag if needed", "If you forgot to tag and made some changes to the repo and need to point the tag to an earlier version, or want to view all the different tags, or do some other complicated thing, more info about tagging git repos is here: https://git-scm.com/book/en/v2/Git-Basics-Tagging"),
("Submit to arxiv if needed", "Submit to arxiv if appropriate."),
("Push an arxiv tag", "Make a new tag of the version submitted to arXiv with the name arXivSubmitted20170610."),
("Get arxiv reference", "Wait a day to get the full arXiv reference."),
("If submitted to arxiv, enter in citations collection in rg-db-public", "If submit to arxiv, create an entry of the paper in citations.yml at rg-db-public billingeGroup public github repository. Check, double check, and triple check that the tag for the grant is correct and the tags for the facilities are correct. Fill in the arXiv citation information in citations.yml in the bibliography reference. Any questions, ask Simon. Create a PR to merge to the billingeGroup repository."),
("If not submitted to arxiv, enter in citations collection in rg-db-group", "If not submit to arxiv, create an entry of the paper in citations.yml at rg-db-group billingeGroup private github repository. Check, double check, and triple check that the tag for the grant is correct and the tags for the facilities are correct. Any questions, ask Simon. Create a PR to merge to the billingeGroup repository."),
("Check db errors", "In your rg-db-public/local directory, run `regolith build publist --people lyang` (replace `lyang` with your own name ID in the group) to make sure that you publist is building properly. Make sure that the publication appears correctly with no errors and fix anything. If there are problems with the latex building, run the commands with --no-pdf, which yields the latex source but doesn't build it, then build the latex manually. The complied tex and pdf files are located in the `_build` folder. If any problem about installing regolith and databases, please refer to [rg-db-group wiki](https://github.com/Billingegroup/rg-db-group/wiki/Set-up-regolith-and-databases)."),
("Email coauthors", "Send an email to coauthors letting them know the arXiv citation information."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
resubmission_checklist = [
("Work on the changes", "Make changes to the manuscript in the repo. You don't need to save it to a new name or anything because we can recover the old version by checking out the tagged version."),
("Write rebuttal letter", "Write a proper rebuttal letter based on reviewers' comments and place in the repo. In this letter address all of the referee's points, one by one. Place a copy of the referee's comments in the repo. Give it a unique filename in case there are more referee comments from later submissions!"),
("Check author list", "This is a good time to check that the author list is correct (don't need to add anyone or remove anyone) and that the acknowledgements have been done correctly, the figures are correct, the figure captions and table captions are correct and all the figures and tables are correctly referenced, and there are no compilation errors. Check all references for errors and update any 'unpublished' references if they have been published."),
("Diff the changes", "create a diff.pdf file that shows changes to the manuscript between the version in the tag of the previous submission and the current version, and include this in the resubmission."),
("Send to coauthors", "Send the final version to your coauthors. Tell them you will 'submit on' where is somewhere around 48 hours later and ask for any final corrections etc. from them. Offer them the chance to extend the deadline if they need more time, i.e., write 'if you need more time, lease let me know.' However, it is assumed that all the authors have been involved in the correction process up to this point so they only have to give it one final thought..."),
("Git commit changes", "Commit all the changes to your local repo, then push the changes to gitlab."),
("Resubmit", "Resubmit following the instructions of the journal."),
("Commit any additional changes", "If during the submission process you need to make any changes, do it in your local repo and make another commit and push."),
("Push a resubmission tag", "Make a new resubmission tag (see above for details)"),
("Check db entry", "Check the entry in citations.yml doesn't need to be updated, and update if it does."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
accepted_checklist = [
("Share the news", "Congratulations on the acceptance of the paper. Let all coauthors know the great news!"),
("Share with BNL if needed", "If it is a BNL paper (check with Simon, but it should acknowledge BNL funding-not just beamtime), send a pdf copy of the accepted version of the paper from the repo to Arlene at BNL to get a BNL publication number. If you are not sure what this means, ask Simon"),
("Share the proof", "When you receive the proofs, share them quickly with all the authors. Request comments back in 24 hours. Proofs should be responded to within 48 hours in normal circumstances."),
("Respond the editor", "Go through and answer any questions from the editor."),
("Check author names", "Last chance to check that all the authors' names are correct and there are no missing authors."),
("Check institutions", "Check all authors' institutions are correct."),
("Check acknowledgement", "Make sure that all funding and all beamlines used are correctly acknowledged. Usually this is done by the bosses, but more eyes catch more mistakes."),
("Update the db entry", "In citations.yml, (the reference should have been added during the submission step) double check the grants{}, facilities{}, nb{} field entries. Any questions, ask Simon. Put 'to be published' in the note{} section. If it has not been submitted to arxiv before, move the entry from rg-db-group to rg-db-public github repo. Otherwise, it should be at rg-db-public already. Create a PR to merge to the billingeGroup repository for edits if necessary."),
("Check db errors", "In your rg-db-public/local directory, run `regolith build publist --people lyang` (replace lyang with your name) to make sure that you publist is building properly. Make sure that the publication appears correctly with no errors and fix anything. If there are problems with the latex building, run the commands with --no-pdf, which yields the latex source but doesn't build it, then build the latex manually. If any problem about installing regolith and databases, please refer to [rg-db-group wiki](https://github.com/Billingegroup/rg-db-group/wiki/Set-up-regolith-and-databases)."),
("Check figures and tables", "Are all the figures, tables in the paper correct (the ones you intended)?"),
("Check the figure caption", "Check the Figure captions for errors. If they refer to a green line, is the relevant line green, and so on."),
("Check figure axis labels", "Check that the figure axis labels are correctly labeled. Make sure it doesn't say G when F is plotted. Make sure the units are correct. Make sure it says 'G (A^-2)' and NOT 'G(r)' (common mistake)"),
("Check table captions", "Check the table caption is correct. Are all the items in the table properly defined in the caption. If it is a crystal structure, are the space group and special positions mentioned in the caption? Is all the info correct?"),
("Check numbers in the table", "Check all the numbers in the tables for errors."),
("Check figure references", "Check references to the all figures and tables. Does reference to Figure 4 refer to the right figure for example"),
("Check references", "Go through the references and find all the errors. Correct errors in the bibliographic database AS WELL AS on the proofs the manuscript. Did all the journal names compile correctly? Are they all consistently in abbreviated form (or full form if that is the style, though that is rare). Volume, year and page numbers appear for all references? Hard to find errors in these numbers, but when you do, definitely correct the database!"),
("Check unpublished references", "If any references are listed as unpublished, on arXiv or submitted or something, check if they have appeared and give the full reference if at all possible. To do this, update the bibliographic database (e.g., citations.yml) with this information and then recompile the references, then copy paste the new bbl file back into the TeX source."),
("Check reference titles if needed", "If the manuscript style has titles in the references, make sure there are no capitalization or other compilation errors. Again, correct these in the database using {braces} around words where you want to preserve the capitalization as well as on the proof."),
("Read the paper", "Finally, after you have done all these 'mechanical' checks, read through the paper and try and find any typos or other problems. Resist the temptation to do any rewriting here...you are looking for mispellings and missing or extra words and so on."),
("Apply corrections from coauthors", "Collect all the corrections from the other authors and add any additional ones to the proof and return it."),
("Email coauthors", "Send an email to your coauthors that this was successfully resubmitted."),
("Revisit talk slides", "Revisit the set of talk slides that summarize the result in a few slides if they need to be updated. Iterate with Simon to convergence."),
("Revisit the highlight slide", "Create a single 'highlight' slide that describes the result following NSF/DOE guidelines. Place it in the 'highlight' folder. Iterate with Simon to convergence (highlight templates and examples can be found in http://gitlab.thebillingegroup.com/highlights/highlightTemplate)"),
("Create web news", "Create a web news story for thebillingegroup.com site. Place it in the 'highlight' folder. Iterate with Simon to convergence"),
("Revisit kudos", "Revisit the Kudos summary if it needs to be updated. Iterate with Simon to convergence."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
published_checklist = [
("Congrats", "Phew, it is over! Pat yourself on the back and celebrate!"),
("Let coauthors know", "Let your coauthors know the link to the final paper and the final reference."),
("Update db entry", "Update citations.yml at rg-db-public github repo with the correct reference information. Commit your edited citations.yml and create a PR to merge to the billingeGroup repository."),
("Check db entry", "CAREFULLY double and triple check the meta-data associated with the paper in citations.yml:"),
("Check grants in the db entry", "grant{} lists just the billinge-group grants that appeared in the acknowledgement section. They have standard abbreviations that are listed at the top of the citations.yml file, e.g., fwp, EFRC10, etc.. Use the right standard or the whole system becomes broken! If not sure.....ask Simon. List all grants in a comma-separated list."),
("Check the facility in the db entry", "facility{} is every beamline that was used for data collection. Again, use the standard abbreviations at the top of the file. Use two levels of granularity for each, so X17A would be: 'nsls, x17a', if X17A and X7B were used it would be 'nsls, x17a, x7b' and so on."),
("Check the nb in the db entry", "nb is some other tags, also listed at the top of the file. 'art' for a regular article and 'hilite' if it is one of our top top papers are the most common."),
("Check the tags in the db entry", "tags should reflect the content so we can automatically build reading lists by subject. Most papers are PDF papers, so no need to say pdf, be more targeted."),
("Check db errors", "In your rg-db-public/local directory, run `regolith build publist --people lyang` (replace lyang with your name) to make sure that you publist is building properly. Make sure that the publication appears correctly with no errors and fix anything. If there are problems with the latex building, run the commands with --no-pdf, which yields the latex source but doesn't build it, then build the latex manually."),
("Add/update to Zotero", "Add or update the published reference to the billinge-group-bib folder in our group Zotero account"),
("Finalize the highlight slide", "Make a highlight of your work and put it in gitlab/highlights (if not done already during the accepted paper checklist). Look in there for standards to work from. This is an important activity. Now you have done your great work, this is how you can advertise it to others. Top papers we send these highlights to the funding agencies. Iterate the highlight with Simon till it is converged."),
("Finalize figures and talk slides", "Make figures and talk slides that will be used in talks and place these on gitlab on talks/figures. Iterate this with Simon till it is converged."),
("Update arXiv if necessary", "If the paper was listed on a preprint server like arXiv, submit a note to arXiv that the paper has appeared and give the full reference. If the journal copyright allows you can post the published version here, but normally that is not alllowed! Still, it is important that people who find the paper on arXiv get directed to the correct reference."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
checklistm_list = []
checklist_delay_days = [7]*len(presubmission_checklist) + [14]*len(submission_checklist) + [74]*len(resubmission_checklist) + [134]*len(accepted_checklist) + [194]*len(published_checklist)
checklist_names = ["presubmission"]*len(presubmission_checklist) + ["submission"]*len(submission_checklist) + ["resubmission"]*len(resubmission_checklist) + ["accepted"]*len(accepted_checklist) + ["published"]*len(published_checklist)
checklists = presubmission_checklist + submission_checklist + accepted_checklist + published_checklist
for (name, objective), checklist_name, delay_days in zip(checklists, checklist_names, checklist_delay_days):
checklistm = {'due_date': now + relativedelta(days=delay_days),
'name': f"{checklist_name} - {name}",
'objective': objective,
'audience': [],
'notes': [],
'status': 'converged',
'type': 'pr'
}
checklistm_list.append(checklistm)
pdoc.update({"milestones": checklistm_list})
# update the deliverable to fit checklist prum
pdoc.update({"deliverable": {
"due_date": now + relativedelta(days=checklist_delay_days[-1]),
"audience": ["simon"],
"success_def": "audience is happy",
"scope": [
"checklist",
"All publication data and metadata are correct and complete"],
"platform": "regolith publication collection in rg-db-public",
"roll_out": [
"simon merging PRs"],
"status": "converged"}
})
# update the kickoff to fit checklist prum
pdoc.update({"kickoff": {
"due_date": now,
"audience": ["lead", "pi", "group_members"],
"name": "Kick off meeting",
"objective": "introduce project to the lead",
"status": "finished"
}})
return pdoc
| """Helper for adding a projectum to the projecta collection.
Projecta are small bite-sized project quanta that typically will result in
one manuscript.
"""
import datetime as dt
import dateutil.parser as date_parser
from dateutil.relativedelta import relativedelta
from regolith.helpers.basehelper import DbHelperBase
from regolith.fsclient import _id_key
from regolith.tools import (
all_docs_from_collection,
get_pi_id,
)
from gooey import GooeyParser
TARGET_COLL = "projecta"
def subparser(subpi):
date_kwargs = {}
if isinstance(subpi, GooeyParser):
date_kwargs['widget'] = 'DateChooser'
subpi.add_argument("name", help="A short but unique name for the projectum",
default=None)
subpi.add_argument("lead", help="id of the group lead or tbd",
default=None)
subpi.add_argument("-d", "--description",
help="Slightly longer description of the projectum"
)
subpi.add_argument("-c", "--collaborators", nargs="+",
help="list of outside collaborator ids separated by spaces, "
"'aeinstein efermi'. Builders will get the full names "
"from the contacts collection"
)
subpi.add_argument("-m", "--group_members", nargs="+",
help="list of group member ids, e.g., 'astudent acolleague'. "
"Builders will get full names from people collection."
"Do not add the lead or the group"
"the pi who are added by default."
)
subpi.add_argument("-g", "--grants", nargs="+",
help="grant or (occasionally) list of grants that support this work"
)
subpi.add_argument("-u", "--due_date",
help="proposed due date for the deliverable",
**date_kwargs
)
subpi.add_argument("--checklist", action='store_true',
help="Use this to turn the prum into a paper submission"
"checklist."
)
# Do not delete --database arg
subpi.add_argument("--database",
help="The database that will be updated. Defaults to "
"first database in the regolithrc.json file."
)
# Do not delete --date arg
subpi.add_argument("--date",
help="The begin_date for the projectum Defaults to "
"today's date.",
**date_kwargs
)
return subpi
class ProjectumAdderHelper(DbHelperBase):
"""Helper for adding a projectum to the projecta collection.
Projecta are small bite-sized project quanta that typically will result in
one manuscript.
"""
# btype must be the same as helper target in helper.py
btype = "a_projectum"
needed_dbs = [f'{TARGET_COLL}', 'groups', 'people']
def construct_global_ctx(self):
"""Constructs the global context"""
super().construct_global_ctx()
gtx = self.gtx
rc = self.rc
rc.pi_id = get_pi_id(rc)
rc.coll = f"{TARGET_COLL}"
if not rc.database:
rc.database = rc.databases[0]["name"]
gtx[rc.coll] = sorted(
all_docs_from_collection(rc.client, rc.coll), key=_id_key
)
gtx["all_docs_from_collection"] = all_docs_from_collection
gtx["float"] = float
gtx["str"] = str
gtx["zip"] = zip
def db_updater(self):
rc = self.rc
if not rc.date:
now = dt.date.today()
else:
now = date_parser.parse(rc.date).date()
if not rc.due_date:
due_date = now + relativedelta(years=1)
else:
due_date = date_parser.parse(rc.due_date).date()
key = f"{rc.lead[:2]}_{''.join(rc.name.casefold().split()).strip()}"
coll = self.gtx[rc.coll]
pdocl = list(filter(lambda doc: doc["_id"] == key, coll))
if len(pdocl) > 0:
raise RuntimeError(
"This entry appears to already exist in the collection")
else:
pdoc = {}
pdoc.update({
'begin_date': now,
'log_url': '',
'name': rc.name,
'pi_id': rc.pi_id,
'lead': rc.lead,
})
if rc.lead == "tbd":
pdoc.update({
'status': 'proposed'
})
else:
pdoc.update({
'status': 'started'
})
if rc.description:
pdoc.update({
'description': rc.description,
})
if rc.grants:
if isinstance(rc.grants, str):
rc.grants = [rc.grants]
pdoc.update({'grants': rc.grants})
else:
pdoc.update({'grants': ["tbd"]})
if rc.group_members:
if isinstance(rc.group_members, str):
rc.group_members = [rc.group_members]
pdoc.update({'group_members': rc.group_members})
else:
pdoc.update({'group_members': []})
if rc.collaborators:
if isinstance(rc.collaborators, str):
rc.collaborators = [rc.collaborators]
pdoc.update({
'collaborators': rc.collaborators,
})
else:
pdoc.update({
'collaborators': [],
})
pdoc.update({"_id": key})
pdoc.update({"deliverable": {
"due_date": due_date,
"audience": ["beginning grad in chemistry"],
"success_def": "audience is happy",
"scope": [
"UCs that are supported or some other scope description if it software",
"sketch of science story if it is paper"],
"platform": "description of how and where the audience will access the deliverable. journal if it is a paper",
"roll_out": [
"steps that the audience will take to access and interact with the deliverable",
"not needed for paper submissions"],
"status": "proposed"}
})
pdoc.update({"kickoff": {
"due_date": now + relativedelta(days=7),
"audience": ["lead", "pi", "group_members"],
"name": "Kick off meeting",
"objective": "introduce project to the lead",
"status": "proposed"
}})
secondm = {'due_date': now + relativedelta(days=21),
'name': 'Project lead presentation',
'objective': 'to act as an example milestone. The date is the date it was finished. delete the field until it is finished. In this case, the lead will present what they think is the project after their reading. Add more milestones as needed.',
'audience': ['lead', 'pi', 'group_members'],
'status': 'proposed',
'type': 'meeting'
}
pdoc.update({"milestones": [secondm]})
if rc.checklist:
pdoc = self.insert_checklists(pdoc, now)
rc.client.insert_one(rc.database, rc.coll, pdoc)
print(f"{key} has been added in {TARGET_COLL}")
return
def insert_checklists(self, pdoc, now):
"""Create manuscript checklist, one item as one milestone."""
presubmission_checklist = [
("Create slide figures", "Create Inkscape graphics (Inkscape is preferrable over ppt) for the slides and place in a ``figures`` directory in the slides directory. These may then be used either in beamer or ppt. Iterate with Simon to convergence. (to get started with Inkscape download and install it, then run the program and navigate to Help-->Tutorials. The first two ('Basic' and 'Shapes') should probably be enough for someone to get basic functionality.)."),
("Create slides", "Create a 'slides' folder in the paper repo or a Google slides deck for a series of talk slides. Iterate the slide skeleton with Simon to convergence. (For a beamer template: https://gitlab.thebillingegroup.com/talks/beamerTalkTemplate)."),
("Create a highlight slide", "Create a 'highlight' folder in the paper repo. Create a single 'highlight' slide that describes the result following NSF/DOE guidelines. Place it in the 'highlight' folder. Iterate with Simon to convergence (highlight templates and examples can be found in https://gitlab.thebillingegroup.com/papers/highlights)"),
("Create public-summary", "Create a public summary in a text file. Place it in the 'highlight' folder. Iterate with Simon to convergence. (The kudos template and example can be found at https://docs.google.com/document/d/1j4ZsM8zS_nZo03s7T48uwzDbh8xTPksAQM3ZLgJ-g-Y/edit?usp=sharing)."),
]
submission_checklist = [
("Check the author list", "Check the author list. Last chance to check for missing authors. Does each author agree to submit to the specific journal? Make sure all authors have approved submission."),
("Check publisher accounts", "Check that all of the authors have accounts for the publisher you are submitting to (if possible) and that they have their ORCID IDs associated with their accounts (ie. for ACS, authors need to link their Paragon accounts with their ORCID IDs)."),
("Check institution", "Is author's name, institution correct? Last chance to avoid embarrassing typos."),
("Check acknowledgement", "Are beamlines, grants, fundings properly acknowledged at the end of the paper? Double check this with Simon and use the ackno statements in the Group Google group."),
("Check figures and tables", "Are all the figures, tables in the paper correct (the ones you intended)?"),
("Check figure captions", "Check the Figure captions for errors. If they refer to a green line, is the relevant line green, and so on."),
("Check figure axis labels", "Check that the figure axis labels are correctly labeled. Make sure it doesn't say G when F is plotted. Make sure the units are correct. Make sure it says 'G (A^-2)' and NOT 'G(r)' (common mistake)."),
("Check table captions", "Check the table caption is correct. Are all the items in the table properly defined in the caption. If it is a crystal structure, are the space group and special positions mentioned in the caption? Is all the info correct?"),
("Check numbers in the table", "Check all the numbers in the tables for errors."),
("Check any question marks", "Check all the question marks in the text. Is there any 'FIG.???' or unrecognized character?"),
("Check figure references", "Check references to the all figures and tables. Does reference to Figure 4 refer to the right figure for example."),
("Check references", "Go through the references and find all the errors. Correct errors in the bibliographic database (citations.yml, or the Zotero collection, for example), not just in the local bib file. Did all the journal names compile correctly? Are they all consistently in abbreviated form (or full form if that is the style, though that is rare). Volume, year and page numbers appear for all references? Hard to find errors in these numbers, but when you do, definitely correct the database!"),
("Check reference style", "Is reference's style in accordance with journal's requirement?"),
("Check journal submission requirements", "Check the journal submission requirements for cover letters, table of contents pictures, list of referees, etc.."),
("Check arxiv", "Check with Simon; will the paper be submitted to arXiv?"),
("Get approval", "Get final approval from all authors for the final version of the manuscript, cover letter, referees list, etc., submission to arXiv if appropriate."),
("Check cover letter", "In the cover letter, does it contain editor-in-chief's name and institution (usually at the top left of the letter) ? Is the content of letter concise and eye-catching? Are (three) suggested reviewers' information in the letter?"),
("Insert bbl", "If it is LaTeX, insert the .bbl file into the main tex file and comment out the \\thebibliography and \\bibliographystyle lines."),
("Commit and push", "Commit all the changes to your local repo, then push the changes to gitlab."),
("Submit to journal", "Go ahead and make the submission, usually online."),
("Push a tag", "If during the submission process you need to make any changes, do it in your local repo and make another commit and push. When the submission is finalized, tag the repo that points to THIS VERSION IS THE SUBMITTED VERSION. create the submission tag. If the current version of your local repo is the submitted version, type, e.g., `git tag -l` to list previous tags (try and keep the tag name formatting consistent) `git tag -a 20180525PRLsubmitted -m <initial submission to PRL>` `git push origin <tag_name>`."),
("Modify tag if needed", "If you forgot to tag and made some changes to the repo and need to point the tag to an earlier version, or want to view all the different tags, or do some other complicated thing, more info about tagging git repos is here: https://git-scm.com/book/en/v2/Git-Basics-Tagging"),
("Submit to arxiv if needed", "Submit to arxiv if appropriate."),
("Push an arxiv tag", "Make a new tag of the version submitted to arXiv with the name arXivSubmitted20170610."),
("Get arxiv reference", "Wait a day to get the full arXiv reference."),
("If submitted to arxiv, enter in citations collection in rg-db-public", "If submit to arxiv, create an entry of the paper in citations.yml at rg-db-public billingeGroup public github repository. Check, double check, and triple check that the tag for the grant is correct and the tags for the facilities are correct. Fill in the arXiv citation information in citations.yml in the bibliography reference. Any questions, ask Simon. Create a PR to merge to the billingeGroup repository."),
("If not submitted to arxiv, enter in citations collection in rg-db-group", "If not submit to arxiv, create an entry of the paper in citations.yml at rg-db-group billingeGroup private github repository. Check, double check, and triple check that the tag for the grant is correct and the tags for the facilities are correct. Any questions, ask Simon. Create a PR to merge to the billingeGroup repository."),
("Check db errors", "In your rg-db-public/local directory, run `regolith build publist --people lyang` (replace `lyang` with your own name ID in the group) to make sure that you publist is building properly. Make sure that the publication appears correctly with no errors and fix anything. If there are problems with the latex building, run the commands with --no-pdf, which yields the latex source but doesn't build it, then build the latex manually. The complied tex and pdf files are located in the `_build` folder. If any problem about installing regolith and databases, please refer to [rg-db-group wiki](https://github.com/Billingegroup/rg-db-group/wiki/Set-up-regolith-and-databases)."),
("Email coauthors", "Send an email to coauthors letting them know the arXiv citation information."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
resubmission_checklist = [
("Work on the changes", "Make changes to the manuscript in the repo. You don't need to save it to a new name or anything because we can recover the old version by checking out the tagged version."),
("Write rebuttal letter", "Write a proper rebuttal letter based on reviewers' comments and place in the repo. In this letter address all of the referee's points, one by one. Place a copy of the referee's comments in the repo. Give it a unique filename in case there are more referee comments from later submissions!"),
("Check author list", "This is a good time to check that the author list is correct (don't need to add anyone or remove anyone) and that the acknowledgements have been done correctly, the figures are correct, the figure captions and table captions are correct and all the figures and tables are correctly referenced, and there are no compilation errors. Check all references for errors and update any 'unpublished' references if they have been published."),
("Diff the changes", "create a diff.pdf file that shows changes to the manuscript between the version in the tag of the previous submission and the current version, and include this in the resubmission."),
("Send to coauthors", "Send the final version to your coauthors. Tell them you will 'submit on' where is somewhere around 48 hours later and ask for any final corrections etc. from them. Offer them the chance to extend the deadline if they need more time, i.e., write 'if you need more time, lease let me know.' However, it is assumed that all the authors have been involved in the correction process up to this point so they only have to give it one final thought..."),
("Git commit changes", "Commit all the changes to your local repo, then push the changes to gitlab."),
("Resubmit", "Resubmit following the instructions of the journal."),
("Commit any additional changes", "If during the submission process you need to make any changes, do it in your local repo and make another commit and push."),
("Push a resubmission tag", "Make a new resubmission tag (see above for details)"),
("Check db entry", "Check the entry in citations.yml doesn't need to be updated, and update if it does."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
accepted_checklist = [
("Share the news", "Congratulations on the acceptance of the paper. Let all coauthors know the great news!"),
("Share with BNL if needed", "If it is a BNL paper (check with Simon, but it should acknowledge BNL funding-not just beamtime), send a pdf copy of the accepted version of the paper from the repo to Arlene at BNL to get a BNL publication number. If you are not sure what this means, ask Simon"),
("Share the proof", "When you receive the proofs, share them quickly with all the authors. Request comments back in 24 hours. Proofs should be responded to within 48 hours in normal circumstances."),
("Respond the editor", "Go through and answer any questions from the editor."),
("Check author names", "Last chance to check that all the authors' names are correct and there are no missing authors."),
("Check institutions", "Check all authors' institutions are correct."),
("Check acknowledgement", "Make sure that all funding and all beamlines used are correctly acknowledged. Usually this is done by the bosses, but more eyes catch more mistakes."),
("Update the db entry", "In citations.yml, (the reference should have been added during the submission step) double check the grants{}, facilities{}, nb{} field entries. Any questions, ask Simon. Put 'to be published' in the note{} section. If it has not been submitted to arxiv before, move the entry from rg-db-group to rg-db-public github repo. Otherwise, it should be at rg-db-public already. Create a PR to merge to the billingeGroup repository for edits if necessary."),
("Check db errors", "In your rg-db-public/local directory, run `regolith build publist --people lyang` (replace lyang with your name) to make sure that you publist is building properly. Make sure that the publication appears correctly with no errors and fix anything. If there are problems with the latex building, run the commands with --no-pdf, which yields the latex source but doesn't build it, then build the latex manually. If any problem about installing regolith and databases, please refer to [rg-db-group wiki](https://github.com/Billingegroup/rg-db-group/wiki/Set-up-regolith-and-databases)."),
("Check figures and tables", "Are all the figures, tables in the paper correct (the ones you intended)?"),
("Check the figure caption", "Check the Figure captions for errors. If they refer to a green line, is the relevant line green, and so on."),
("Check figure axis labels", "Check that the figure axis labels are correctly labeled. Make sure it doesn't say G when F is plotted. Make sure the units are correct. Make sure it says 'G (A^-2)' and NOT 'G(r)' (common mistake)"),
("Check table captions", "Check the table caption is correct. Are all the items in the table properly defined in the caption. If it is a crystal structure, are the space group and special positions mentioned in the caption? Is all the info correct?"),
("Check numbers in the table", "Check all the numbers in the tables for errors."),
("Check figure references", "Check references to the all figures and tables. Does reference to Figure 4 refer to the right figure for example"),
("Check references", "Go through the references and find all the errors. Correct errors in the bibliographic database AS WELL AS on the proofs the manuscript. Did all the journal names compile correctly? Are they all consistently in abbreviated form (or full form if that is the style, though that is rare). Volume, year and page numbers appear for all references? Hard to find errors in these numbers, but when you do, definitely correct the database!"),
("Check unpublished references", "If any references are listed as unpublished, on arXiv or submitted or something, check if they have appeared and give the full reference if at all possible. To do this, update the bibliographic database (e.g., citations.yml) with this information and then recompile the references, then copy paste the new bbl file back into the TeX source."),
("Check reference titles if needed", "If the manuscript style has titles in the references, make sure there are no capitalization or other compilation errors. Again, correct these in the database using {braces} around words where you want to preserve the capitalization as well as on the proof."),
("Read the paper", "Finally, after you have done all these 'mechanical' checks, read through the paper and try and find any typos or other problems. Resist the temptation to do any rewriting here...you are looking for mispellings and missing or extra words and so on."),
("Apply corrections from coauthors", "Collect all the corrections from the other authors and add any additional ones to the proof and return it."),
("Email coauthors", "Send an email to your coauthors that this was successfully resubmitted."),
("Revisit talk slides", "Revisit the set of talk slides that summarize the result in a few slides if they need to be updated. Iterate with Simon to convergence."),
("Revisit the highlight slide", "Create a single 'highlight' slide that describes the result following NSF/DOE guidelines. Place it in the 'highlight' folder. Iterate with Simon to convergence (highlight templates and examples can be found in http://gitlab.thebillingegroup.com/highlights/highlightTemplate)"),
("Create web news", "Create a web news story for thebillingegroup.com site. Place it in the 'highlight' folder. Iterate with Simon to convergence"),
("Revisit kudos", "Revisit the Kudos summary if it needs to be updated. Iterate with Simon to convergence."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
published_checklist = [
("Congrats", "Phew, it is over! Pat yourself on the back and celebrate!"),
("Let coauthors know", "Let your coauthors know the link to the final paper and the final reference."),
("Update db entry", "Update citations.yml at rg-db-public github repo with the correct reference information. Commit your edited citations.yml and create a PR to merge to the billingeGroup repository."),
("Check db entry", "CAREFULLY double and triple check the meta-data associated with the paper in citations.yml:"),
("Check grants in the db entry", "grant{} lists just the billinge-group grants that appeared in the acknowledgement section. They have standard abbreviations that are listed at the top of the citations.yml file, e.g., fwp, EFRC10, etc.. Use the right standard or the whole system becomes broken! If not sure.....ask Simon. List all grants in a comma-separated list."),
("Check the facility in the db entry", "facility{} is every beamline that was used for data collection. Again, use the standard abbreviations at the top of the file. Use two levels of granularity for each, so X17A would be: 'nsls, x17a', if X17A and X7B were used it would be 'nsls, x17a, x7b' and so on."),
("Check the nb in the db entry", "nb is some other tags, also listed at the top of the file. 'art' for a regular article and 'hilite' if it is one of our top top papers are the most common."),
("Check the tags in the db entry", "tags should reflect the content so we can automatically build reading lists by subject. Most papers are PDF papers, so no need to say pdf, be more targeted."),
("Check db errors", "In your rg-db-public/local directory, run `regolith build publist --people lyang` (replace lyang with your name) to make sure that you publist is building properly. Make sure that the publication appears correctly with no errors and fix anything. If there are problems with the latex building, run the commands with --no-pdf, which yields the latex source but doesn't build it, then build the latex manually."),
("Add/update to Zotero", "Add or update the published reference to the billinge-group-bib folder in our group Zotero account"),
("Finalize the highlight slide", "Make a highlight of your work and put it in gitlab/highlights (if not done already during the accepted paper checklist). Look in there for standards to work from. This is an important activity. Now you have done your great work, this is how you can advertise it to others. Top papers we send these highlights to the funding agencies. Iterate the highlight with Simon till it is converged."),
("Finalize figures and talk slides", "Make figures and talk slides that will be used in talks and place these on gitlab on talks/figures. Iterate this with Simon till it is converged."),
("Update arXiv if necessary", "If the paper was listed on a preprint server like arXiv, submit a note to arXiv that the paper has appeared and give the full reference. If the journal copyright allows you can post the published version here, but normally that is not alllowed! Still, it is important that people who find the paper on arXiv get directed to the correct reference."),
("Ask Simon if anything unfinished", "Ask Simon about any items unfinished."),
("Email Simon", "Email Simon if finish the above."),
]
checklistm_list = []
checklist_delay_days = [7]*len(presubmission_checklist) + [14]*len(submission_checklist) + [74]*len(resubmission_checklist) + [134]*len(accepted_checklist) + [194]*len(published_checklist)
checklist_names = ["presubmission"]*len(presubmission_checklist) + ["submission"]*len(submission_checklist) + ["resubmission"]*len(resubmission_checklist) + ["accepted"]*len(accepted_checklist) + ["published"]*len(published_checklist)
checklists = presubmission_checklist + submission_checklist + accepted_checklist + published_checklist
for (name, objective), checklist_name, delay_days in zip(checklists, checklist_names, checklist_delay_days):
checklistm = {'due_date': now + relativedelta(days=delay_days),
'name': f"{checklist_name} - {name}",
'objective': objective,
'audience': [],
'notes': [],
'status': 'converged',
'type': 'pr'
}
checklistm_list.append(checklistm)
pdoc.update({"milestones": checklistm_list})
# update the deliverable to fit checklist prum
pdoc.update({"deliverable": {
"due_date": now + relativedelta(days=checklist_delay_days[-1]),
"audience": ["simon"],
"success_def": "audience is happy",
"scope": [
"checklist",
"All publication data and metadata are correct and complete"],
"platform": "regolith publication collection in rg-db-public",
"roll_out": [
"simon merging PRs"],
"status": "converged"}
})
# update the kickoff to fit checklist prum
pdoc.update({"kickoff": {
"due_date": now,
"audience": ["lead", "pi", "group_members"],
"name": "Kick off meeting",
"objective": "introduce project to the lead",
"status": "finished"
}})
return pdoc
|
from bilibili import bilibili
from statistics import Statistics
import printer
from printer import Printer
import rafflehandler
from configloader import ConfigLoader
import utils
import asyncio
import struct
import json
import sys
import aiohttp
class BaseDanmu():
__slots__ = ('ws', 'roomid', 'area_id', 'client')
structer = struct.Struct('!I2H2I')
def __init__(self, roomid=None, area_id=None):
self.client = aiohttp.ClientSession()
if roomid is None:
self.roomid = ConfigLoader().dic_user['other_control']['default_monitor_roomid']
self.area_id = 0
else:
self.roomid = roomid
self.area_id = area_id
# 待确认
async def close_connection(self):
try:
await self.ws.close()
except:
print('请联系开发者', sys.exc_info()[0], sys.exc_info()[1])
printer.info([f'{self.area_id}号弹幕收尾模块状态{self.ws.closed}'], True)
async def CheckArea(self):
try:
while True:
area_id = await asyncio.shield(utils.FetchRoomArea(self.roomid))
if area_id != self.area_id:
printer.info([f'{self.roomid}更换分区{self.area_id}为{area_id}，即将切换房间'], True)
return
await asyncio.sleep(300)
except asyncio.CancelledError:
printer.info([f'{self.area_id}号弹幕监控分区检测模块主动取消'], True)
async def connectServer(self):
try:
url = 'wss://broadcastlv.chat.bilibili.com:443/sub'
self.ws = await asyncio.wait_for(self.client.ws_connect(url), timeout=3)
except:
print("# 连接无法建立，请检查本地网络状况")
print(sys.exc_info()[0], sys.exc_info()[1])
return False
printer.info([f'{self.area_id}号弹幕监控已连接b站服务器'], True)
body = f'{{'uid':0,'roomid':{self.roomid},"protover":1,"platform":"web","clientver":"1.3.3"}}'
return (await self.SendSocketData(opt=7, body=body))
async def HeartbeatLoop(self):
printer.info([f'{self.area_id}号弹幕监控开始心跳（心跳间隔30s，后续不再提示）'], True)
try:
while True:
if not (await self.SendSocketData(opt=2, body='')):
return
await asyncio.sleep(30)
except asyncio.CancelledError:
printer.info([f'{self.area_id}号弹幕监控心跳模块主动取消'], True)
async def SendSocketData(self, opt, body, len_header=16, ver=1, seq=1):
remain_data = body.encode('utf-8')
len_data = len(remain_data) + len_header
header = self.structer.pack(len_data, len_header, ver, opt, seq)
data = header + remain_data
try:
await self.ws.send_bytes(data)
except asyncio.CancelledError:
printer.info([f'{self.area_id}号弹幕监控发送模块主动取消'], True)
return False
except:
print(sys.exc_info()[0], sys.exc_info()[1])
return False
return True
async def ReadSocketData(self):
bytes_data = None
try:
msg = await asyncio.wait_for(self.ws.receive(), timeout=35.0)
bytes_data = msg.data
except asyncio.TimeoutError:
print('# 由于心跳包30s一次，但是发现35内没有收到任何包，说明已经悄悄失联了，主动断开')
return None
except:
print(sys.exc_info()[0], sys.exc_info()[1])
print('请联系开发者')
return None
return bytes_data
async def ReceiveMessageLoop(self):
while True:
bytes_datas = await self.ReadSocketData()
if bytes_datas is None:
break
len_read = 0
len_bytes_datas = len(bytes_datas)
loop_time = 0
while len_read != len_bytes_datas:
loop_time += 1
if loop_time > 100:
print('请联系作者', bytes_datas)
state = None
split_header = self.structer.unpack(bytes_datas[len_read:16+len_read])
len_data, len_header, ver, opt, seq = split_header
remain_data = bytes_datas[len_read+16:len_read+len_data]
# 人气值/心跳 3s间隔
if opt == 3:
# self._UserCount, = struct.unpack('!I', remain_data)
printer.debug(f'弹幕心跳检测{self.area_id}')
# cmd
elif opt == 5:
messages = remain_data.decode('utf-8')
dic = json.loads(messages)
state = await self.handle_danmu(dic)
# 握手确认
elif opt == 8:
printer.info([f'{self.area_id}号弹幕监控进入房间（{self.roomid}）'], True)
else:
printer.warn(bytes_datas[len_read:len_read + len_data])
if state is not None and not state:
return
len_read += len_data
async def handle_danmu(self, dic):
await asyncio.sleep(0)
return True
class DanmuPrinter(BaseDanmu):
def handle_danmu(self, dic):
cmd = dic['cmd']
# print(cmd)
if cmd == 'DANMU_MSG':
# print(dic)
Printer().print_danmu(dic)
return
class DanmuRaffleHandler(BaseDanmu):
def handle_danmu(self, dic):
cmd = dic['cmd']
if cmd == 'PREPARING':
printer.info([f'{self.area_id}号弹幕监控房间下播({self.roomid})'], True)
return False
elif cmd == 'SYS_GIFT':
if 'giftId' in dic:
if dic['giftId'] == 39:
printer.info(["节奏风暴"], True)
roomid = dic['roomid']
rafflehandler.Rafflehandler.Put2Queue((roomid,), rafflehandler.handle_1_room_storm)
Statistics.append2pushed_raffle('节奏风暴', area_id=self.area_id)
else:
text1 = dic['real_roomid']
text2 = dic['url']
printer.info([dic, "请联系开发者"])
try:
giftId = dic['giftId']
printer.info(["检测到房间{:^9}的{}活动抽奖".format(text1, bilibili.get_giftids_raffle(str(giftId)))], True)
rafflehandler.Rafflehandler.Put2Queue((giftId, text1, text2), rafflehandler.handle_1_room_activity)
Statistics.append2pushed_raffle('活动', area_id=self.area_id)
except:
printer.info([dic, "请联系开发者"])
else:
printer.info(['普通送礼提示', dic['msg_text']])
return
elif cmd == 'SYS_MSG':
if 'real_roomid' in dic:
real_roomid = dic['real_roomid']
type_text = (dic['msg'].split(':?')[-1]).split('，')[0][2:]
printer.info([f'{self.area_id}号弹幕监控检测到{real_roomid:^9}的{type_text}'], True)
rafflehandler.Rafflehandler.Put2Queue((real_roomid,), rafflehandler.handle_1_room_TV)
rafflehandler.Rafflehandler.Put2Queue((real_roomid,), rafflehandler.handle_1_room_activity)
Statistics.append2pushed_raffle(type_text, area_id=self.area_id)
elif cmd == 'GUARD_MSG':
if 'buy_type' in dic and dic['buy_type'] == 1:
roomid = dic['roomid']
printer.info([f'{self.area_id}号弹幕监控检测到{roomid:^9}的总督'], True)
rafflehandler.Rafflehandler.Put2Queue((roomid,), rafflehandler.handle_1_room_guard)
Statistics.append2pushed_raffle('总督', area_id=self.area_id)
if 'buy_type' in dic and dic['buy_type'] != 1:
print(dic)
# roomid = dic['roomid']
printer.info([f'{self.area_id}号弹幕监控检测到{self.roomid:^9}的提督/舰长'], True)
rafflehandler.Rafflehandler.Put2Queue((self.roomid,), rafflehandler.handle_1_room_guard)
Statistics.append2pushed_raffle('提督/舰长', area_id=self.area_id)
class YjMonitorHandler(BaseDanmu):
def handle_danmu(self, dic):
cmd = dic['cmd']
# print(cmd)
if cmd == 'DANMU_MSG':
msg = dic['info'][1]
if '-' in msg:
list_word = msg.split('-')
try:
roomid = int(list_word[0])
raffleid = int(list_word[1])
printer.info([f'弹幕监控检测到{roomid:^9}的提督/舰长{raffleid}'], True)
rafflehandler.Rafflehandler.Put2Queue((1, roomid, raffleid), rafflehandler.handle_1_guard_raffle)
Statistics.append2pushed_raffle('提督/舰长', area_id=1)
except ValueError:
print(msg)
Printer().print_danmu(dic)
| from bilibili import bilibili
from statistics import Statistics
import printer
from printer import Printer
import rafflehandler
from configloader import ConfigLoader
import utils
import asyncio
import struct
import json
import sys
import aiohttp
class BaseDanmu():
__slots__ = ('ws', 'roomid', 'area_id', 'client')
structer = struct.Struct('!I2H2I')
def __init__(self, roomid=None, area_id=None):
self.client = aiohttp.ClientSession()
if roomid is None:
self.roomid = ConfigLoader().dic_user['other_control']['default_monitor_roomid']
self.area_id = 0
else:
self.roomid = roomid
self.area_id = area_id
# 待确认
async def close_connection(self):
try:
await self.ws.close()
except:
print('请联系开发者', sys.exc_info()[0], sys.exc_info()[1])
printer.info([f'{self.area_id}号弹幕收尾模块状态{self.ws.closed}'], True)
async def CheckArea(self):
try:
while True:
area_id = await asyncio.shield(utils.FetchRoomArea(self.roomid))
if area_id != self.area_id:
printer.info([f'{self.roomid}更换分区{self.area_id}为{area_id}，即将切换房间'], True)
return
await asyncio.sleep(300)
except asyncio.CancelledError:
printer.info([f'{self.area_id}号弹幕监控分区检测模块主动取消'], True)
async def connectServer(self):
try:
url = 'wss://broadcastlv.chat.bilibili.com:443/sub'
self.ws = await asyncio.wait_for(self.client.ws_connect(url), timeout=3)
except:
print("# 连接无法建立，请检查本地网络状况")
print(sys.exc_info()[0], sys.exc_info()[1])
return False
printer.info([f'{self.area_id}号弹幕监控已连接b站服务器'], True)
body = f'{{"uid":0,"roomid":{self.roomid},"protover":1,"platform":"web","clientver":"1.3.3"}}'
return (await self.SendSocketData(opt=7, body=body))
async def HeartbeatLoop(self):
printer.info([f'{self.area_id}号弹幕监控开始心跳（心跳间隔30s，后续不再提示）'], True)
try:
while True:
if not (await self.SendSocketData(opt=2, body='')):
return
await asyncio.sleep(30)
except asyncio.CancelledError:
printer.info([f'{self.area_id}号弹幕监控心跳模块主动取消'], True)
async def SendSocketData(self, opt, body, len_header=16, ver=1, seq=1):
remain_data = body.encode('utf-8')
len_data = len(remain_data) + len_header
header = self.structer.pack(len_data, len_header, ver, opt, seq)
data = header + remain_data
try:
await self.ws.send_bytes(data)
except asyncio.CancelledError:
printer.info([f'{self.area_id}号弹幕监控发送模块主动取消'], True)
return False
except:
print(sys.exc_info()[0], sys.exc_info()[1])
return False
return True
async def ReadSocketData(self):
bytes_data = None
try:
msg = await asyncio.wait_for(self.ws.receive(), timeout=35.0)
bytes_data = msg.data
except asyncio.TimeoutError:
print('# 由于心跳包30s一次，但是发现35内没有收到任何包，说明已经悄悄失联了，主动断开')
return None
except:
print(sys.exc_info()[0], sys.exc_info()[1])
print('请联系开发者')
return None
return bytes_data
async def ReceiveMessageLoop(self):
while True:
bytes_datas = await self.ReadSocketData()
if bytes_datas is None:
break
len_read = 0
len_bytes_datas = len(bytes_datas)
loop_time = 0
while len_read != len_bytes_datas:
loop_time += 1
if loop_time > 100:
print('请联系作者', bytes_datas)
state = None
split_header = self.structer.unpack(bytes_datas[len_read:16+len_read])
len_data, len_header, ver, opt, seq = split_header
remain_data = bytes_datas[len_read+16:len_read+len_data]
# 人气值/心跳 3s间隔
if opt == 3:
# self._UserCount, = struct.unpack('!I', remain_data)
printer.debug(f'弹幕心跳检测{self.area_id}')
# cmd
elif opt == 5:
messages = remain_data.decode('utf-8')
dic = json.loads(messages)
state = await self.handle_danmu(dic)
# 握手确认
elif opt == 8:
printer.info([f'{self.area_id}号弹幕监控进入房间（{self.roomid}）'], True)
else:
printer.warn(bytes_datas[len_read:len_read + len_data])
if state is not None and not state:
return
len_read += len_data
async def handle_danmu(self, dic):
await asyncio.sleep(0)
return True
class DanmuPrinter(BaseDanmu):
def handle_danmu(self, dic):
cmd = dic['cmd']
# print(cmd)
if cmd == 'DANMU_MSG':
# print(dic)
Printer().print_danmu(dic)
return
class DanmuRaffleHandler(BaseDanmu):
def handle_danmu(self, dic):
cmd = dic['cmd']
if cmd == 'PREPARING':
printer.info([f'{self.area_id}号弹幕监控房间下播({self.roomid})'], True)
return False
elif cmd == 'SYS_GIFT':
if 'giftId' in dic:
if dic['giftId'] == 39:
printer.info(["节奏风暴"], True)
roomid = dic['roomid']
rafflehandler.Rafflehandler.Put2Queue((roomid,), rafflehandler.handle_1_room_storm)
Statistics.append2pushed_raffle('节奏风暴', area_id=self.area_id)
else:
text1 = dic['real_roomid']
text2 = dic['url']
printer.info([dic, "请联系开发者"])
try:
giftId = dic['giftId']
printer.info(["检测到房间{:^9}的{}活动抽奖".format(text1, bilibili.get_giftids_raffle(str(giftId)))], True)
rafflehandler.Rafflehandler.Put2Queue((giftId, text1, text2), rafflehandler.handle_1_room_activity)
Statistics.append2pushed_raffle('活动', area_id=self.area_id)
except:
printer.info([dic, "请联系开发者"])
else:
printer.info(['普通送礼提示', dic['msg_text']])
return
elif cmd == 'SYS_MSG':
if 'real_roomid' in dic:
real_roomid = dic['real_roomid']
type_text = (dic['msg'].split(':?')[-1]).split('，')[0][2:]
printer.info([f'{self.area_id}号弹幕监控检测到{real_roomid:^9}的{type_text}'], True)
rafflehandler.Rafflehandler.Put2Queue((real_roomid,), rafflehandler.handle_1_room_TV)
rafflehandler.Rafflehandler.Put2Queue((real_roomid,), rafflehandler.handle_1_room_activity)
Statistics.append2pushed_raffle(type_text, area_id=self.area_id)
elif cmd == 'GUARD_MSG':
if 'buy_type' in dic and dic['buy_type'] == 1:
roomid = dic['roomid']
printer.info([f'{self.area_id}号弹幕监控检测到{roomid:^9}的总督'], True)
rafflehandler.Rafflehandler.Put2Queue((roomid,), rafflehandler.handle_1_room_guard)
Statistics.append2pushed_raffle('总督', area_id=self.area_id)
if 'buy_type' in dic and dic['buy_type'] != 1:
print(dic)
# roomid = dic['roomid']
printer.info([f'{self.area_id}号弹幕监控检测到{self.roomid:^9}的提督/舰长'], True)
rafflehandler.Rafflehandler.Put2Queue((self.roomid,), rafflehandler.handle_1_room_guard)
Statistics.append2pushed_raffle('提督/舰长', area_id=self.area_id)
class YjMonitorHandler(BaseDanmu):
def handle_danmu(self, dic):
cmd = dic['cmd']
# print(cmd)
if cmd == 'DANMU_MSG':
msg = dic['info'][1]
if '-' in msg:
list_word = msg.split('-')
try:
roomid = int(list_word[0])
raffleid = int(list_word[1])
printer.info([f'弹幕监控检测到{roomid:^9}的提督/舰长{raffleid}'], True)
rafflehandler.Rafflehandler.Put2Queue((1, roomid, raffleid), rafflehandler.handle_1_guard_raffle)
Statistics.append2pushed_raffle('提督/舰长', area_id=1)
except ValueError:
print(msg)
Printer().print_danmu(dic)
|
import datetime
import discord
from discord.ext import commands
from discord.ext.commands import Bot, Context
from tortoise.functions import Sum
import config
from db.models.stats import Stats
from db.models.user import User
from db.redis import RedisDB
from models.command import CommandInfo
from rpc.client import RPCClient
from util.discord.channel import ChannelUtil
from util.discord.messages import Messages
from util.env import Env
## Command documentation
TIPSTATS_INFO = CommandInfo(
triggers = ["tipstats"],
overview = "Display your personal tipping stats for a specific server.",
details = f"This will display your personal tipping statistics from the server you send the command from. This command can't be used in DM"
)
TOPTIPS_INFO = CommandInfo(
triggers = ["toptips"],
overview = "Display biggest tips for a specific server.",
details = f"This will display the biggest tip of all time, of the current month, and of the day for the current server. This command can't be used in DM"
)
LEADERBOARD_INFO = CommandInfo(
triggers = ["ballers", "leaderboard"],
overview = "Show a list of the top 15 tippers this year.",
details = f"This will display a list of the top 15 tippers on the current server. This command can't be used in DM\n" +
f"These stats are reset once a year - for all time stats use `{config.Config.instance().command_prefix}legacyboard`"
)
LEGACYBOARD_INFO = CommandInfo(
triggers = ["legacyboard", "oldballs"],
overview = "Show a list of the top 15 tippers all time.",
details = f"This will display a list of the top 15 tippers of all time on the current server. This command can't be used in DM"
)
class StatsCog(commands.Cog):
def __init__(self, bot: Bot):
self.bot = bot
async def cog_before_invoke(self, ctx: Context):
ctx.error = False
# Only allow tip commands in public channels
msg = ctx.message
if ChannelUtil.is_private(msg.channel) and ctx.command.name != 'blocks_cmd':
await Messages.send_error_dm(msg.author, "You can only view statistics in a server, not via DM.")
ctx.error = True
return
else:
# Determine if user is admin
ctx.god = msg.author.id in config.Config.instance().get_admin_ids()
if not ctx.god:
ctx.admin = False
for g in self.bot.guilds:
member = g.get_member(msg.author.id)
if member is not None:
for role in member.roles:
if role.id in config.Config.instance().get_admin_roles():
ctx.admin = True
break
if ctx.admin:
break
else:
ctx.admin = True
# Can't spam stats commands
if msg.channel.id in config.Config.instance().get_no_spam_channels():
ctx.error = True
await Messages.send_error_dm(msg.author, "I can't post stats in that channel.")
return
if ctx.command.name in ['tipstats_cmd']:
# Make sure user exists in DB
user = await User.get_user(msg.author)
if user is None:
ctx.error = True
await Messages.send_error_dm(msg.author, f"You should create an account with me first, send me `{config.Config.instance().command_prefix}help` to get started.")
return
# Update name, if applicable
await user.update_name(msg.author.name)
ctx.user = user
@commands.command(aliases=TIPSTATS_INFO.triggers)
async def tipstats_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
user: User = ctx.user
if not ctx.god and await RedisDB.instance().exists(f"tipstatsspam{msg.author.id}{msg.guild.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before trying to get your tipstats again")
return
stats: Stats = await user.get_stats(server_id=msg.guild.id)
if stats.banned:
await Messages.add_x_reaction(msg)
await Messages.send_error_dm(msg.author, "You are stats banned, contact an admin if you want to be unbanned")
return
response = ""
if stats is None or stats.total_tips == 0:
response = f"<@{msg.author.id}> You haven't sent any tips in this server yet, tip some people and then check your stats later"
else:
response = f"<@{msg.author.id}> You have sent **{stats.total_tips}** tips totaling **{Env.format_float(stats.legacy_total_tipped_amount)} {Env.currency_symbol()}**. Your biggest tip of all time is **{Env.format_float(stats.top_tip)} {Env.currency_symbol()}**"
await msg.channel.send(response)
await RedisDB.instance().set(f"tipstatsspam{msg.author.id}{msg.guild.id}", "as", expires=300)
@commands.command(aliases=TOPTIPS_INFO.triggers)
async def toptips_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
if not ctx.god and await RedisDB.instance().exists(f"toptipsspam{msg.channel.id}"):
await Messages.add_timer_reaction(msg)
return
# This would be better to be 1 query but, i'm not proficient enough with tortoise-orm
top_tip = await Stats.filter(
server_id=msg.guild.id,
banned=False
).order_by('-top_tip').prefetch_related('user').limit(1).first()
if top_tip is None:
await RedisDB.instance().set(f"toptipsspam{msg.channel.id}", "as", expires=300)
await msg.channel.send("There are no stats for this server yet. Send some tips first!")
return
# Get datetime object representing first day of this month
now = datetime.datetime.utcnow()
month = str(now.month).zfill(2)
year = now.year
first_day_of_month = datetime.datetime.strptime(f'{month}/01/{year} 00:00:00', '%m/%d/%Y %H:%M:%S')
# Find top tip of the month
top_tip_month = await Stats.filter(
server_id=msg.guild.id,
top_tip_month_at__gte=first_day_of_month,
banned=False
).order_by('-top_tip_month').prefetch_related('user').limit(1).first()
# Get datetime object representing 24 hours ago
past_24h = now - datetime.timedelta(hours=24)
# Find top tip of the month
top_tip_day = await Stats.filter(
server_id=msg.guild.id,
top_tip_day_at__gte=past_24h,
banned=False
).order_by('-top_tip_day').prefetch_related('user').limit(1).first()
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name='Biggest Tips', icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
new_line = '\n' # Can't use this directly inside f-expression, so store it in a variable
if top_tip_day is not None:
embed.description = f"**Last 24 Hours**\n```{Env.format_float(top_tip_day.top_tip_day)} {Env.currency_symbol()} - by {top_tip_day.user.name}```"
if top_tip_month is not None:
embed.description += f"{new_line if top_tip_day is not None else ""}**In {now.strftime("%B")}**\n```{Env.format_float(top_tip_month.top_tip_month)} {Env.currency_symbol()} - by {top_tip_month.user.name}```"
embed.description += f"{new_line if top_tip_day is not None or top_tip_month is not None else ""}**All Time**\n```{Env.format_float(top_tip.top_tip)} {Env.currency_symbol()} - by {top_tip.user.name}```"
# No spam
await RedisDB.instance().set(f"toptipsspam{msg.channel.id}", "as", expires=300)
await msg.channel.send(embed=embed)
@commands.command(aliases=LEADERBOARD_INFO.triggers)
async def leaderboard_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
if not ctx.god and await RedisDB.instance().exists(f"ballerspam{msg.channel.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before checking the ballers list again")
return
# Get list
ballers = await Stats.filter(server_id=msg.guild.id, banned=False).order_by('-total_tipped_amount').prefetch_related('user').limit(15).all()
if len(ballers) == 0:
await msg.channel.send(f"<@{msg.author.id}> There are no stats for this server yet, send some tips!")
return
response_msg = "```"
# Get biggest tip to adjust the padding
biggest_num = 0
for stats in ballers:
length = len(f"{Env.format_float(stats.total_tipped_amount)} {Env.currency_symbol()}")
if length > biggest_num:
biggest_num = length
for rank, stats in enumerate(ballers, start=1):
adj_rank = str(rank) if rank >= 10 else f" {rank}"
user_name = stats.user.name
amount_str = f"{Env.format_float(stats.total_tipped_amount)} {Env.currency_symbol()}"
response_msg += f"{adj_rank}. {amount_str.ljust(biggest_num)} - by {user_name}\n"
response_msg += "```"
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name=f"Here are the top {len(ballers)} tippers \U0001F44F", icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
embed.description = response_msg
embed.set_footer(text=f"Use {config.Config.instance().command_prefix}legacyboard for all-time stats")
await RedisDB.instance().set(f"ballerspam{msg.channel.id}", "as", expires=300)
await msg.channel.send(f"<@{msg.author.id}>", embed=embed)
@commands.command(aliases=LEGACYBOARD_INFO.triggers)
async def legacyboard_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
if not ctx.god and await RedisDB.instance().exists(f"ballerspam{msg.channel.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before checking the ballers list again")
return
# Get list
ballers = await Stats.filter(server_id=msg.guild.id, banned=False).order_by('-legacy_total_tipped_amount').prefetch_related('user').limit(15).all()
if len(ballers) == 0:
await msg.channel.send(f"<@{msg.author.id}> There are no stats for this server yet, send some tips!")
return
response_msg = "```"
# Get biggest tip to adjust the padding
biggest_num = 0
for stats in ballers:
# TODO change to stats.tip_sum
length = len(f"{Env.format_float(stats.legacy_total_tipped_amount)} {Env.currency_symbol()}")
if length > biggest_num:
biggest_num = length
for rank, stats in enumerate(ballers, start=1):
adj_rank = str(rank) if rank >= 10 else f" {rank}"
user_name = stats.user.name
amount_str = f"{Env.format_float(stats.legacy_total_tipped_amount)} {Env.currency_symbol()}"
response_msg += f"{adj_rank}. {amount_str.ljust(biggest_num)} - by {user_name}\n"
response_msg += "```"
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name=f"Here are the top {len(ballers)} tippers of all time\U0001F44F", icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
embed.description = response_msg
await RedisDB.instance().set(f"ballerspam{msg.channel.id}", "as", expires=300)
await msg.channel.send(f"<@{msg.author.id}>", embed=embed)
@commands.command(aliases=["blocks"])
async def blocks_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
is_private = ChannelUtil.is_private(msg.channel)
if not ctx.god and await RedisDB.instance().exists(f"blocksspam{msg.channel.id if not is_private else msg.author.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before checking the block count again?")
return
count, unchecked = await RPCClient.instance().block_count()
if count is None or unchecked is None:
await Messages.send_error_dm(msg.author, "I couldn't retrieve the current block count")
return
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name=f"Here's how many blocks I have", icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
embed.description = f"```Count: {count:,}\nUnchecked: {unchecked:,}```"
await RedisDB.instance().set(f"blocksspam{msg.channel.id if not is_private else msg.author.id}", "as", expires=120)
if is_private:
await msg.author.send(embed=embed)
else:
await msg.channel.send(f"<@{msg.author.id}>", embed=embed)
| import datetime
import discord
from discord.ext import commands
from discord.ext.commands import Bot, Context
from tortoise.functions import Sum
import config
from db.models.stats import Stats
from db.models.user import User
from db.redis import RedisDB
from models.command import CommandInfo
from rpc.client import RPCClient
from util.discord.channel import ChannelUtil
from util.discord.messages import Messages
from util.env import Env
## Command documentation
TIPSTATS_INFO = CommandInfo(
triggers = ["tipstats"],
overview = "Display your personal tipping stats for a specific server.",
details = f"This will display your personal tipping statistics from the server you send the command from. This command can't be used in DM"
)
TOPTIPS_INFO = CommandInfo(
triggers = ["toptips"],
overview = "Display biggest tips for a specific server.",
details = f"This will display the biggest tip of all time, of the current month, and of the day for the current server. This command can't be used in DM"
)
LEADERBOARD_INFO = CommandInfo(
triggers = ["ballers", "leaderboard"],
overview = "Show a list of the top 15 tippers this year.",
details = f"This will display a list of the top 15 tippers on the current server. This command can't be used in DM\n" +
f"These stats are reset once a year - for all time stats use `{config.Config.instance().command_prefix}legacyboard`"
)
LEGACYBOARD_INFO = CommandInfo(
triggers = ["legacyboard", "oldballs"],
overview = "Show a list of the top 15 tippers all time.",
details = f"This will display a list of the top 15 tippers of all time on the current server. This command can't be used in DM"
)
class StatsCog(commands.Cog):
def __init__(self, bot: Bot):
self.bot = bot
async def cog_before_invoke(self, ctx: Context):
ctx.error = False
# Only allow tip commands in public channels
msg = ctx.message
if ChannelUtil.is_private(msg.channel) and ctx.command.name != 'blocks_cmd':
await Messages.send_error_dm(msg.author, "You can only view statistics in a server, not via DM.")
ctx.error = True
return
else:
# Determine if user is admin
ctx.god = msg.author.id in config.Config.instance().get_admin_ids()
if not ctx.god:
ctx.admin = False
for g in self.bot.guilds:
member = g.get_member(msg.author.id)
if member is not None:
for role in member.roles:
if role.id in config.Config.instance().get_admin_roles():
ctx.admin = True
break
if ctx.admin:
break
else:
ctx.admin = True
# Can't spam stats commands
if msg.channel.id in config.Config.instance().get_no_spam_channels():
ctx.error = True
await Messages.send_error_dm(msg.author, "I can't post stats in that channel.")
return
if ctx.command.name in ['tipstats_cmd']:
# Make sure user exists in DB
user = await User.get_user(msg.author)
if user is None:
ctx.error = True
await Messages.send_error_dm(msg.author, f"You should create an account with me first, send me `{config.Config.instance().command_prefix}help` to get started.")
return
# Update name, if applicable
await user.update_name(msg.author.name)
ctx.user = user
@commands.command(aliases=TIPSTATS_INFO.triggers)
async def tipstats_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
user: User = ctx.user
if not ctx.god and await RedisDB.instance().exists(f"tipstatsspam{msg.author.id}{msg.guild.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before trying to get your tipstats again")
return
stats: Stats = await user.get_stats(server_id=msg.guild.id)
if stats.banned:
await Messages.add_x_reaction(msg)
await Messages.send_error_dm(msg.author, "You are stats banned, contact an admin if you want to be unbanned")
return
response = ""
if stats is None or stats.total_tips == 0:
response = f"<@{msg.author.id}> You haven't sent any tips in this server yet, tip some people and then check your stats later"
else:
response = f"<@{msg.author.id}> You have sent **{stats.total_tips}** tips totaling **{Env.format_float(stats.legacy_total_tipped_amount)} {Env.currency_symbol()}**. Your biggest tip of all time is **{Env.format_float(stats.top_tip)} {Env.currency_symbol()}**"
await msg.channel.send(response)
await RedisDB.instance().set(f"tipstatsspam{msg.author.id}{msg.guild.id}", "as", expires=300)
@commands.command(aliases=TOPTIPS_INFO.triggers)
async def toptips_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
if not ctx.god and await RedisDB.instance().exists(f"toptipsspam{msg.channel.id}"):
await Messages.add_timer_reaction(msg)
return
# This would be better to be 1 query but, i'm not proficient enough with tortoise-orm
top_tip = await Stats.filter(
server_id=msg.guild.id,
banned=False
).order_by('-top_tip').prefetch_related('user').limit(1).first()
if top_tip is None:
await RedisDB.instance().set(f"toptipsspam{msg.channel.id}", "as", expires=300)
await msg.channel.send("There are no stats for this server yet. Send some tips first!")
return
# Get datetime object representing first day of this month
now = datetime.datetime.utcnow()
month = str(now.month).zfill(2)
year = now.year
first_day_of_month = datetime.datetime.strptime(f'{month}/01/{year} 00:00:00', '%m/%d/%Y %H:%M:%S')
# Find top tip of the month
top_tip_month = await Stats.filter(
server_id=msg.guild.id,
top_tip_month_at__gte=first_day_of_month,
banned=False
).order_by('-top_tip_month').prefetch_related('user').limit(1).first()
# Get datetime object representing 24 hours ago
past_24h = now - datetime.timedelta(hours=24)
# Find top tip of the month
top_tip_day = await Stats.filter(
server_id=msg.guild.id,
top_tip_day_at__gte=past_24h,
banned=False
).order_by('-top_tip_day').prefetch_related('user').limit(1).first()
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name='Biggest Tips', icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
new_line = '\n' # Can't use this directly inside f-expression, so store it in a variable
if top_tip_day is not None:
embed.description = f"**Last 24 Hours**\n```{Env.format_float(top_tip_day.top_tip_day)} {Env.currency_symbol()} - by {top_tip_day.user.name}```"
if top_tip_month is not None:
embed.description += f"{new_line if top_tip_day is not None else ''}**In {now.strftime('%B')}**\n```{Env.format_float(top_tip_month.top_tip_month)} {Env.currency_symbol()} - by {top_tip_month.user.name}```"
embed.description += f"{new_line if top_tip_day is not None or top_tip_month is not None else ''}**All Time**\n```{Env.format_float(top_tip.top_tip)} {Env.currency_symbol()} - by {top_tip.user.name}```"
# No spam
await RedisDB.instance().set(f"toptipsspam{msg.channel.id}", "as", expires=300)
await msg.channel.send(embed=embed)
@commands.command(aliases=LEADERBOARD_INFO.triggers)
async def leaderboard_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
if not ctx.god and await RedisDB.instance().exists(f"ballerspam{msg.channel.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before checking the ballers list again")
return
# Get list
ballers = await Stats.filter(server_id=msg.guild.id, banned=False).order_by('-total_tipped_amount').prefetch_related('user').limit(15).all()
if len(ballers) == 0:
await msg.channel.send(f"<@{msg.author.id}> There are no stats for this server yet, send some tips!")
return
response_msg = "```"
# Get biggest tip to adjust the padding
biggest_num = 0
for stats in ballers:
length = len(f"{Env.format_float(stats.total_tipped_amount)} {Env.currency_symbol()}")
if length > biggest_num:
biggest_num = length
for rank, stats in enumerate(ballers, start=1):
adj_rank = str(rank) if rank >= 10 else f" {rank}"
user_name = stats.user.name
amount_str = f"{Env.format_float(stats.total_tipped_amount)} {Env.currency_symbol()}"
response_msg += f"{adj_rank}. {amount_str.ljust(biggest_num)} - by {user_name}\n"
response_msg += "```"
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name=f"Here are the top {len(ballers)} tippers \U0001F44F", icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
embed.description = response_msg
embed.set_footer(text=f"Use {config.Config.instance().command_prefix}legacyboard for all-time stats")
await RedisDB.instance().set(f"ballerspam{msg.channel.id}", "as", expires=300)
await msg.channel.send(f"<@{msg.author.id}>", embed=embed)
@commands.command(aliases=LEGACYBOARD_INFO.triggers)
async def legacyboard_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
if not ctx.god and await RedisDB.instance().exists(f"ballerspam{msg.channel.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before checking the ballers list again")
return
# Get list
ballers = await Stats.filter(server_id=msg.guild.id, banned=False).order_by('-legacy_total_tipped_amount').prefetch_related('user').limit(15).all()
if len(ballers) == 0:
await msg.channel.send(f"<@{msg.author.id}> There are no stats for this server yet, send some tips!")
return
response_msg = "```"
# Get biggest tip to adjust the padding
biggest_num = 0
for stats in ballers:
# TODO change to stats.tip_sum
length = len(f"{Env.format_float(stats.legacy_total_tipped_amount)} {Env.currency_symbol()}")
if length > biggest_num:
biggest_num = length
for rank, stats in enumerate(ballers, start=1):
adj_rank = str(rank) if rank >= 10 else f" {rank}"
user_name = stats.user.name
amount_str = f"{Env.format_float(stats.legacy_total_tipped_amount)} {Env.currency_symbol()}"
response_msg += f"{adj_rank}. {amount_str.ljust(biggest_num)} - by {user_name}\n"
response_msg += "```"
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name=f"Here are the top {len(ballers)} tippers of all time\U0001F44F", icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
embed.description = response_msg
await RedisDB.instance().set(f"ballerspam{msg.channel.id}", "as", expires=300)
await msg.channel.send(f"<@{msg.author.id}>", embed=embed)
@commands.command(aliases=["blocks"])
async def blocks_cmd(self, ctx: Context):
if ctx.error:
await Messages.add_x_reaction(ctx.message)
return
msg = ctx.message
is_private = ChannelUtil.is_private(msg.channel)
if not ctx.god and await RedisDB.instance().exists(f"blocksspam{msg.channel.id if not is_private else msg.author.id}"):
await Messages.add_timer_reaction(msg)
await Messages.send_error_dm(msg.author, "Why don't you wait awhile before checking the block count again?")
return
count, unchecked = await RPCClient.instance().block_count()
if count is None or unchecked is None:
await Messages.send_error_dm(msg.author, "I couldn't retrieve the current block count")
return
embed = discord.Embed(colour=0xC6E459)
embed.set_author(name=f"Here's how many blocks I have", icon_url="https://github.com/AnanosCommunity/graham_discord_bot/raw/master/assets/ananos_logo.png")
embed.description = f"```Count: {count:,}\nUnchecked: {unchecked:,}```"
await RedisDB.instance().set(f"blocksspam{msg.channel.id if not is_private else msg.author.id}", "as", expires=120)
if is_private:
await msg.author.send(embed=embed)
else:
await msg.channel.send(f"<@{msg.author.id}>", embed=embed)
|
def topla(a, b): return a + b
print(topla(2, 3))
topla2 = lambda a, b: a + b
print(topla2(2, 3))
def listeyiGoster(liste, gosteriFonksiyonu):
for i in liste:
print(gosteriFonksiyonu(i))
list = [
{"id": 1, "ad": "Alper", "soyad": "Konuralp" },
{"id": 2, "ad": "Burcu", "soyad": "Konuralp"},
{"id": 3, "ad": "Yağmur", "soyad": "Konuralp"},
]
listeyiGoster(list, lambda satir: f"{satir["ad"]} {satir["soyad"]}")
|
def topla(a, b): return a + b
print(topla(2, 3))
topla2 = lambda a, b: a + b
print(topla2(2, 3))
def listeyiGoster(liste, gosteriFonksiyonu):
for i in liste:
print(gosteriFonksiyonu(i))
list = [
{"id": 1, "ad": "Alper", "soyad": "Konuralp" },
{"id": 2, "ad": "Burcu", "soyad": "Konuralp"},
{"id": 3, "ad": "Yağmur", "soyad": "Konuralp"},
]
listeyiGoster(list, lambda satir: f"{satir['ad']} {satir['soyad']}")
|
from contextlib import closing
import json
import logging
import boto3
from lambda_logs import JSONFormatter, custom_lambda_logs
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.handlers[0].setFormatter(JSONFormatter())
class QCFailed(Exception):
def __init__(self, message: str):
self.message = message
def lambda_handler(event: dict, context: object):
with custom_lambda_logs(**event["logging"]):
logger.info(f"event: {str(event)}")
s3_path = f"{event["repo"]}/{event["qc_result_file"]}"
bucket, key = s3_path.split("/", 3)[2:]
s3 = boto3.client("s3")
response = s3.get_object(Bucket=bucket, Key=key)
with closing(response["Body"]) as fp:
qc_object = json.load(fp)
logger.info(f"input: {str(qc_object)}")
result = eval(event["qc_expression"], globals(), qc_object)
if result:
logger.warning("failed QC check")
sfn = boto3.client("stepfunctions")
sfn.stop_execution(
executionArn=event["execution_id"],
error=f"Job {event["logging"]["job_file_key"]} failed QC check at step {event["logging"]["step_name"]}",
cause=f"failed condition: {event["qc_expression"]}"
)
raise QCFailed(f"QC check failed ({event["qc_expression"]})")
else:
logger.info("passed QC check")
| from contextlib import closing
import json
import logging
import boto3
from lambda_logs import JSONFormatter, custom_lambda_logs
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.handlers[0].setFormatter(JSONFormatter())
class QCFailed(Exception):
def __init__(self, message: str):
self.message = message
def lambda_handler(event: dict, context: object):
with custom_lambda_logs(**event["logging"]):
logger.info(f"event: {str(event)}")
s3_path = f"{event['repo']}/{event['qc_result_file']}"
bucket, key = s3_path.split("/", 3)[2:]
s3 = boto3.client("s3")
response = s3.get_object(Bucket=bucket, Key=key)
with closing(response["Body"]) as fp:
qc_object = json.load(fp)
logger.info(f"input: {str(qc_object)}")
result = eval(event["qc_expression"], globals(), qc_object)
if result:
logger.warning("failed QC check")
sfn = boto3.client("stepfunctions")
sfn.stop_execution(
executionArn=event["execution_id"],
error=f"Job {event['logging']['job_file_key']} failed QC check at step {event['logging']['step_name']}",
cause=f"failed condition: {event['qc_expression']}"
)
raise QCFailed(f"QC check failed ({event['qc_expression']})")
else:
logger.info("passed QC check")
|
#!/usr/bin/env python3
import copy
import difflib
import logging
from pathlib import Path
from typing import Dict, Optional, Union
import click
import requests
import rich
import toml
from packaging.specifiers import Specifier
from packaging.version import Version
from rich.logging import RichHandler
from rich.syntax import Syntax
from cibuildwheel.extra import InlineArrayDictEncoder
from cibuildwheel.typing import Final, Literal, TypedDict
log = logging.getLogger("cibw")
# Looking up the dir instead of using utils.resources_dir
# since we want to write to it.
DIR: Final[Path] = Path(__file__).parent.parent.resolve()
RESOURCES_DIR: Final[Path] = DIR / "cibuildwheel/resources"
ArchStr = Literal["32", "64"]
class ConfigWinCP(TypedDict):
identifier: str
version: str
arch: str
class ConfigWinPP(TypedDict):
identifier: str
version: str
arch: str
url: str
class ConfigMacOS(TypedDict):
identifier: str
version: str
url: str
AnyConfig = Union[ConfigWinCP, ConfigWinPP, ConfigMacOS]
# The following set of "Versions" classes allow the initial call to the APIs to
# be cached and reused in the `update_version_*` methods.
class WindowsVersions:
def __init__(self, arch_str: ArchStr) -> None:
response = requests.get("https://api.nuget.org/v3/index.json")
response.raise_for_status()
api_info = response.json()
for resource in api_info["resources"]:
if resource["@type"] == "PackageBaseAddress/3.0.0":
endpoint = resource["@id"]
ARCH_DICT = {"32": "win32", "64": "win_amd64"}
PACKAGE_DICT = {"32": "pythonx86", "64": "python"}
self.arch_str = arch_str
self.arch = ARCH_DICT[arch_str]
package = PACKAGE_DICT[arch_str]
response = requests.get(f"{endpoint}{package}/index.json")
response.raise_for_status()
cp_info = response.json()
versions = (Version(v) for v in cp_info["versions"])
self.versions = sorted(v for v in versions if not v.is_devrelease)
def update_version_windows(self, spec: Specifier) -> Optional[ConfigWinCP]:
versions = sorted(v for v in self.versions if spec.contains(v))
if not all(v.is_prerelease for v in versions):
versions = [v for v in versions if not v.is_prerelease]
log.debug(f"Windows {self.arch} {spec} has {", ".join(str(v) for v in versions)}")
if not versions:
return None
version = versions[-1]
identifier = f"cp{version.major}{version.minor}-{self.arch}"
result = ConfigWinCP(
identifier=identifier,
version=str(version),
arch=self.arch_str,
)
return result
class PyPyVersions:
def __init__(self, arch_str: ArchStr):
response = requests.get("https://downloads.python.org/pypy/versions.json")
response.raise_for_status()
releases = [r for r in response.json() if r["pypy_version"] != "nightly"]
for release in releases:
release["pypy_version"] = Version(release["pypy_version"])
release["python_version"] = Version(release["python_version"])
self.releases = [
r for r in releases if not r["pypy_version"].is_prerelease and not r["pypy_version"].is_devrelease
]
self.arch = arch_str
def update_version_windows(self, spec: Specifier) -> ConfigWinCP:
if self.arch != "32":
raise RuntimeError("64 bit releases not supported yet on Windows")
releases = [r for r in self.releases if spec.contains(r["python_version"])]
releases = sorted(releases, key=lambda r: r["pypy_version"])
if not releases:
raise RuntimeError(f"PyPy Win {self.arch} not found for {spec}! {self.releases}")
release = releases[-1]
version = release["python_version"]
identifier = f"pp{version.major}{version.minor}-win32"
(url,) = [rf["download_url"] for rf in release["files"] if "" in rf["platform"] == "win32"]
return ConfigWinPP(
identifier=identifier,
version=f"{version.major}.{version.minor}",
arch="32",
url=url,
)
def update_version_macos(self, spec: Specifier) -> ConfigMacOS:
if self.arch != "64":
raise RuntimeError("Other archs not supported yet on macOS")
releases = [r for r in self.releases if spec.contains(r["python_version"])]
releases = sorted(releases, key=lambda r: r["pypy_version"])
if not releases:
raise RuntimeError(f"PyPy macOS {self.arch} not found for {spec}!")
release = releases[-1]
version = release["python_version"]
identifier = f"pp{version.major}{version.minor}-macosx_x86_64"
(url,) = [
rf["download_url"] for rf in release["files"] if "" in rf["platform"] == "darwin" and rf["arch"] == "x64"
]
return ConfigMacOS(
identifier=identifier,
version=f"{version.major}.{version.minor}",
url=url,
)
class CPythonVersions:
def __init__(self) -> None:
response = requests.get("https://www.python.org/api/v2/downloads/release/?is_published=true")
response.raise_for_status()
releases_info = response.json()
self.versions_dict: Dict[Version, int] = {}
for release in releases_info:
# Removing the prefix, Python 3.9 would use: release["name"].removeprefix("Python ")
version = Version(release["name"][7:])
if not version.is_prerelease and not version.is_devrelease:
uri = int(release["resource_uri"].rstrip("/").split("/")[-1])
self.versions_dict[version] = uri
def update_version_macos(self, identifier: str, spec: Specifier) -> Optional[ConfigMacOS]:
file_idents = ("macos11.0.pkg", "macosx10.9.pkg", "macosx10.6.pkg")
sorted_versions = sorted(v for v in self.versions_dict if spec.contains(v))
for version in reversed(sorted_versions):
# Find the first patch version that contains the requested file
uri = self.versions_dict[version]
response = requests.get(f"https://www.python.org/api/v2/downloads/release_file/?release={uri}")
response.raise_for_status()
file_info = response.json()
for file_ident in file_idents:
urls = [rf["url"] for rf in file_info if file_ident in rf["url"]]
if urls:
return ConfigMacOS(
identifier=identifier,
version=f"{version.major}.{version.minor}",
url=urls[0],
)
return None
# This is a universal interface to all the above Versions classes. Given an
# identifier, it updates a config dict.
class AllVersions:
def __init__(self) -> None:
self.windows_32 = WindowsVersions("32")
self.windows_64 = WindowsVersions("64")
self.windows_pypy = PyPyVersions("32")
self.macos_cpython = CPythonVersions()
self.macos_pypy = PyPyVersions("64")
def update_config(self, config: Dict[str, str]) -> None:
identifier = config["identifier"]
version = Version(config["version"])
spec = Specifier(f"=={version.major}.{version.minor}.*")
log.info(f"Reading in '{identifier}' -> {spec} @ {version}")
orig_config = copy.copy(config)
config_update: Optional[AnyConfig]
# We need to use ** in update due to MyPy (probably a bug)
if "macos" in identifier:
if identifier.startswith("pp"):
config_update = self.macos_pypy.update_version_macos(spec)
else:
config_update = self.macos_cpython.update_version_macos(identifier, spec)
assert config_update is not None, f"MacOS {spec} not found!"
config.update(**config_update)
elif "win32" in identifier:
if identifier.startswith("pp"):
config.update(**self.windows_pypy.update_version_windows(spec))
else:
config_update = self.windows_32.update_version_windows(spec)
if config_update:
config.update(**config_update)
elif "win_amd64" in identifier:
config_update = self.windows_64.update_version_windows(spec)
if config_update:
config.update(**config_update)
if config != orig_config:
log.info(f" Updated {orig_config} to {config}")
@click.command()
@click.option("--force", is_flag=True)
@click.option("--level", default="INFO", type=click.Choice(["INFO", "DEBUG", "TRACE"], case_sensitive=False))
def update_pythons(force: bool, level: str) -> None:
logging.basicConfig(
level="INFO",
format="%(message)s",
datefmt="[%X]",
handlers=[RichHandler(rich_tracebacks=True, markup=True)],
)
log.setLevel(level)
all_versions = AllVersions()
toml_file_path = RESOURCES_DIR / "build-platforms.toml"
original_toml = toml_file_path.read_text()
configs = toml.loads(original_toml)
for config in configs["windows"]["python_configurations"]:
all_versions.update_config(config)
for config in configs["macos"]["python_configurations"]:
all_versions.update_config(config)
result_toml = toml.dumps(configs, encoder=InlineArrayDictEncoder()) # type: ignore
rich.print() # spacer
if original_toml == result_toml:
rich.print("[green]Check complete, Python configurations unchanged.")
return
rich.print("Python configurations updated.")
rich.print("Changes:")
rich.print()
toml_relpath = toml_file_path.relative_to(DIR).as_posix()
diff_lines = difflib.unified_diff(
original_toml.splitlines(keepends=True),
result_toml.splitlines(keepends=True),
fromfile=toml_relpath,
tofile=toml_relpath,
)
rich.print(Syntax("".join(diff_lines), "diff", theme="ansi_light"))
rich.print()
if force:
toml_file_path.write_text(result_toml)
rich.print("[green]TOML file updated.")
else:
rich.print("[yellow]File left unchanged. Use --force flag to update.")
if __name__ == "__main__":
update_pythons()
| #!/usr/bin/env python3
import copy
import difflib
import logging
from pathlib import Path
from typing import Dict, Optional, Union
import click
import requests
import rich
import toml
from packaging.specifiers import Specifier
from packaging.version import Version
from rich.logging import RichHandler
from rich.syntax import Syntax
from cibuildwheel.extra import InlineArrayDictEncoder
from cibuildwheel.typing import Final, Literal, TypedDict
log = logging.getLogger("cibw")
# Looking up the dir instead of using utils.resources_dir
# since we want to write to it.
DIR: Final[Path] = Path(__file__).parent.parent.resolve()
RESOURCES_DIR: Final[Path] = DIR / "cibuildwheel/resources"
ArchStr = Literal["32", "64"]
class ConfigWinCP(TypedDict):
identifier: str
version: str
arch: str
class ConfigWinPP(TypedDict):
identifier: str
version: str
arch: str
url: str
class ConfigMacOS(TypedDict):
identifier: str
version: str
url: str
AnyConfig = Union[ConfigWinCP, ConfigWinPP, ConfigMacOS]
# The following set of "Versions" classes allow the initial call to the APIs to
# be cached and reused in the `update_version_*` methods.
class WindowsVersions:
def __init__(self, arch_str: ArchStr) -> None:
response = requests.get("https://api.nuget.org/v3/index.json")
response.raise_for_status()
api_info = response.json()
for resource in api_info["resources"]:
if resource["@type"] == "PackageBaseAddress/3.0.0":
endpoint = resource["@id"]
ARCH_DICT = {"32": "win32", "64": "win_amd64"}
PACKAGE_DICT = {"32": "pythonx86", "64": "python"}
self.arch_str = arch_str
self.arch = ARCH_DICT[arch_str]
package = PACKAGE_DICT[arch_str]
response = requests.get(f"{endpoint}{package}/index.json")
response.raise_for_status()
cp_info = response.json()
versions = (Version(v) for v in cp_info["versions"])
self.versions = sorted(v for v in versions if not v.is_devrelease)
def update_version_windows(self, spec: Specifier) -> Optional[ConfigWinCP]:
versions = sorted(v for v in self.versions if spec.contains(v))
if not all(v.is_prerelease for v in versions):
versions = [v for v in versions if not v.is_prerelease]
log.debug(f"Windows {self.arch} {spec} has {', '.join(str(v) for v in versions)}")
if not versions:
return None
version = versions[-1]
identifier = f"cp{version.major}{version.minor}-{self.arch}"
result = ConfigWinCP(
identifier=identifier,
version=str(version),
arch=self.arch_str,
)
return result
class PyPyVersions:
def __init__(self, arch_str: ArchStr):
response = requests.get("https://downloads.python.org/pypy/versions.json")
response.raise_for_status()
releases = [r for r in response.json() if r["pypy_version"] != "nightly"]
for release in releases:
release["pypy_version"] = Version(release["pypy_version"])
release["python_version"] = Version(release["python_version"])
self.releases = [
r for r in releases if not r["pypy_version"].is_prerelease and not r["pypy_version"].is_devrelease
]
self.arch = arch_str
def update_version_windows(self, spec: Specifier) -> ConfigWinCP:
if self.arch != "32":
raise RuntimeError("64 bit releases not supported yet on Windows")
releases = [r for r in self.releases if spec.contains(r["python_version"])]
releases = sorted(releases, key=lambda r: r["pypy_version"])
if not releases:
raise RuntimeError(f"PyPy Win {self.arch} not found for {spec}! {self.releases}")
release = releases[-1]
version = release["python_version"]
identifier = f"pp{version.major}{version.minor}-win32"
(url,) = [rf["download_url"] for rf in release["files"] if "" in rf["platform"] == "win32"]
return ConfigWinPP(
identifier=identifier,
version=f"{version.major}.{version.minor}",
arch="32",
url=url,
)
def update_version_macos(self, spec: Specifier) -> ConfigMacOS:
if self.arch != "64":
raise RuntimeError("Other archs not supported yet on macOS")
releases = [r for r in self.releases if spec.contains(r["python_version"])]
releases = sorted(releases, key=lambda r: r["pypy_version"])
if not releases:
raise RuntimeError(f"PyPy macOS {self.arch} not found for {spec}!")
release = releases[-1]
version = release["python_version"]
identifier = f"pp{version.major}{version.minor}-macosx_x86_64"
(url,) = [
rf["download_url"] for rf in release["files"] if "" in rf["platform"] == "darwin" and rf["arch"] == "x64"
]
return ConfigMacOS(
identifier=identifier,
version=f"{version.major}.{version.minor}",
url=url,
)
class CPythonVersions:
def __init__(self) -> None:
response = requests.get("https://www.python.org/api/v2/downloads/release/?is_published=true")
response.raise_for_status()
releases_info = response.json()
self.versions_dict: Dict[Version, int] = {}
for release in releases_info:
# Removing the prefix, Python 3.9 would use: release["name"].removeprefix("Python ")
version = Version(release["name"][7:])
if not version.is_prerelease and not version.is_devrelease:
uri = int(release["resource_uri"].rstrip("/").split("/")[-1])
self.versions_dict[version] = uri
def update_version_macos(self, identifier: str, spec: Specifier) -> Optional[ConfigMacOS]:
file_idents = ("macos11.0.pkg", "macosx10.9.pkg", "macosx10.6.pkg")
sorted_versions = sorted(v for v in self.versions_dict if spec.contains(v))
for version in reversed(sorted_versions):
# Find the first patch version that contains the requested file
uri = self.versions_dict[version]
response = requests.get(f"https://www.python.org/api/v2/downloads/release_file/?release={uri}")
response.raise_for_status()
file_info = response.json()
for file_ident in file_idents:
urls = [rf["url"] for rf in file_info if file_ident in rf["url"]]
if urls:
return ConfigMacOS(
identifier=identifier,
version=f"{version.major}.{version.minor}",
url=urls[0],
)
return None
# This is a universal interface to all the above Versions classes. Given an
# identifier, it updates a config dict.
class AllVersions:
def __init__(self) -> None:
self.windows_32 = WindowsVersions("32")
self.windows_64 = WindowsVersions("64")
self.windows_pypy = PyPyVersions("32")
self.macos_cpython = CPythonVersions()
self.macos_pypy = PyPyVersions("64")
def update_config(self, config: Dict[str, str]) -> None:
identifier = config["identifier"]
version = Version(config["version"])
spec = Specifier(f"=={version.major}.{version.minor}.*")
log.info(f"Reading in '{identifier}' -> {spec} @ {version}")
orig_config = copy.copy(config)
config_update: Optional[AnyConfig]
# We need to use ** in update due to MyPy (probably a bug)
if "macos" in identifier:
if identifier.startswith("pp"):
config_update = self.macos_pypy.update_version_macos(spec)
else:
config_update = self.macos_cpython.update_version_macos(identifier, spec)
assert config_update is not None, f"MacOS {spec} not found!"
config.update(**config_update)
elif "win32" in identifier:
if identifier.startswith("pp"):
config.update(**self.windows_pypy.update_version_windows(spec))
else:
config_update = self.windows_32.update_version_windows(spec)
if config_update:
config.update(**config_update)
elif "win_amd64" in identifier:
config_update = self.windows_64.update_version_windows(spec)
if config_update:
config.update(**config_update)
if config != orig_config:
log.info(f" Updated {orig_config} to {config}")
@click.command()
@click.option("--force", is_flag=True)
@click.option("--level", default="INFO", type=click.Choice(["INFO", "DEBUG", "TRACE"], case_sensitive=False))
def update_pythons(force: bool, level: str) -> None:
logging.basicConfig(
level="INFO",
format="%(message)s",
datefmt="[%X]",
handlers=[RichHandler(rich_tracebacks=True, markup=True)],
)
log.setLevel(level)
all_versions = AllVersions()
toml_file_path = RESOURCES_DIR / "build-platforms.toml"
original_toml = toml_file_path.read_text()
configs = toml.loads(original_toml)
for config in configs["windows"]["python_configurations"]:
all_versions.update_config(config)
for config in configs["macos"]["python_configurations"]:
all_versions.update_config(config)
result_toml = toml.dumps(configs, encoder=InlineArrayDictEncoder()) # type: ignore
rich.print() # spacer
if original_toml == result_toml:
rich.print("[green]Check complete, Python configurations unchanged.")
return
rich.print("Python configurations updated.")
rich.print("Changes:")
rich.print()
toml_relpath = toml_file_path.relative_to(DIR).as_posix()
diff_lines = difflib.unified_diff(
original_toml.splitlines(keepends=True),
result_toml.splitlines(keepends=True),
fromfile=toml_relpath,
tofile=toml_relpath,
)
rich.print(Syntax("".join(diff_lines), "diff", theme="ansi_light"))
rich.print()
if force:
toml_file_path.write_text(result_toml)
rich.print("[green]TOML file updated.")
else:
rich.print("[yellow]File left unchanged. Use --force flag to update.")
if __name__ == "__main__":
update_pythons()
|
import logging
from binascii import hexlify
from pprint import pformat
from lntenna.bitcoin import AuthServiceProxy, SATOSHIS, make_service_url
try:
from lntenna.server.bitcoind_password import BITCOIND_PW
except ModuleNotFoundError:
pass
from lntenna.database import (
mesh_add_verify_quote,
orders_get_network,
mesh_get_refund_addr,
)
from lntenna.gotenna.utilities import log
from lntenna.lightning.lnaddr import lndecode
from lntenna.server.config import CONFIG
from lntenna.swap.verify_redeemscript import verify_redeem_script
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG, format=CONFIG["logging"]["FORMAT"])
def auto_swap_verify_quote(message, cli=False):
result = {}
if cli:
log("\n---------------------------------------\n\n", cli)
log(f"Your lntenna UUID for this order is: {message["u"]}", cli)
log(
f"You can use this to re-send swap_tx message to GATEWAY and to query "
f"status of interrupted swaps.",
cli,
)
log("\n\n---------------------------------------\n", cli)
# decode the invoice, raise value error if signature mismatch
decoded_inv = lndecode(message["i"])
log(f"Decoded invoice: {decoded_inv}", cli)
log(f"Redeem script: {message["rs"]}", cli)
# Check the Pubkey from the invoice matches hardcoded keys
log("Checking decoded pubkey matches known blockstream pubkeys...", cli)
pubkey = hexlify(decoded_inv.pubkey.serialize()).decode("utf-8")
if pubkey in CONFIG["blocksat_pubkeys"].values():
log(
f"Pubkey {pubkey} successfully matched to hardcoded keys in config.ini!",
cli,
)
else:
log(f"Pubkey {pubkey} not matched to hardcoded keys in config.ini!", cli)
return False
# check the redeem_script matches the invoice payment_hash and P2SH address
log("Checking swap redeem script matches lightning invoice payment hash...", cli)
payment_hash = decoded_inv.paymenthash.hex()
# get refund address from db
refund_addr = mesh_get_refund_addr(message["u"])
if verify_redeem_script(payment_hash, message["rs"], message["ad"], refund_addr):
log(
"Redeem script verified and matched to P2SH address provided by swap server",
cli,
)
else:
log(
"Redeem script NOT verified and matched to P2SH address provided by swap server",
cli,
)
return False
# lookup network using UUID from db
network = orders_get_network(message["u"])
# calculate amount the bitcoin transaction and require user confirmation
amount = f'{message['am'] / SATOSHIS:.8f}'
if cli:
log(
f"\nAre you happy to proceed with creating the below transaction to fulfill"
f" swap request:\n"
f"\tNETWORK: {network}\n"
f"\tAMOUNT: {amount}\n",
cli,
)
res = input("Enter 'y' to continue\t") or "y"
if res.lower() != "y":
log("satellite message payment cancelled", cli)
return
# setup the transaction
proxy = AuthServiceProxy(service_url=make_service_url(network))
try:
result["tx_hash"] = proxy.sendtoaddress(message["ad"], amount)
except Exception as e1:
if BITCOIND_PW:
try:
proxy.walletpassphrase(BITCOIND_PW, 60)
result["tx_hash"] = proxy.sendtoaddress(message["ad"], amount)
proxy.walletlock()
except Exception as e2:
log(
f"raised errors during transaction construction: \n {e1}\n {e2}",
cli,
)
tx_hash = proxy.gettransaction(result["tx_hash"])
result["tx_hex"] = tx_hash["hex"]
# TODO: for separate machines should change to getrawtransaction as per below
# result["tx_hex"] = proxy.getrawtransaction(result["tx_hash"])
result["uuid"] = message["u"]
# write to db as we don't have it on our side yet.:
mesh_add_verify_quote(
message["u"],
message["i"],
message["am"],
message["ad"],
message["rs"],
pubkey,
payment_hash,
tx_hash["txid"],
tx_hash["hex"],
)
log(f"Returning swap tx to GATEWAY:\n{pformat(result)}", cli)
return {"swap_tx": result}
| import logging
from binascii import hexlify
from pprint import pformat
from lntenna.bitcoin import AuthServiceProxy, SATOSHIS, make_service_url
try:
from lntenna.server.bitcoind_password import BITCOIND_PW
except ModuleNotFoundError:
pass
from lntenna.database import (
mesh_add_verify_quote,
orders_get_network,
mesh_get_refund_addr,
)
from lntenna.gotenna.utilities import log
from lntenna.lightning.lnaddr import lndecode
from lntenna.server.config import CONFIG
from lntenna.swap.verify_redeemscript import verify_redeem_script
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG, format=CONFIG["logging"]["FORMAT"])
def auto_swap_verify_quote(message, cli=False):
result = {}
if cli:
log("\n---------------------------------------\n\n", cli)
log(f"Your lntenna UUID for this order is: {message['u']}", cli)
log(
f"You can use this to re-send swap_tx message to GATEWAY and to query "
f"status of interrupted swaps.",
cli,
)
log("\n\n---------------------------------------\n", cli)
# decode the invoice, raise value error if signature mismatch
decoded_inv = lndecode(message["i"])
log(f"Decoded invoice: {decoded_inv}", cli)
log(f"Redeem script: {message['rs']}", cli)
# Check the Pubkey from the invoice matches hardcoded keys
log("Checking decoded pubkey matches known blockstream pubkeys...", cli)
pubkey = hexlify(decoded_inv.pubkey.serialize()).decode("utf-8")
if pubkey in CONFIG["blocksat_pubkeys"].values():
log(
f"Pubkey {pubkey} successfully matched to hardcoded keys in config.ini!",
cli,
)
else:
log(f"Pubkey {pubkey} not matched to hardcoded keys in config.ini!", cli)
return False
# check the redeem_script matches the invoice payment_hash and P2SH address
log("Checking swap redeem script matches lightning invoice payment hash...", cli)
payment_hash = decoded_inv.paymenthash.hex()
# get refund address from db
refund_addr = mesh_get_refund_addr(message["u"])
if verify_redeem_script(payment_hash, message["rs"], message["ad"], refund_addr):
log(
"Redeem script verified and matched to P2SH address provided by swap server",
cli,
)
else:
log(
"Redeem script NOT verified and matched to P2SH address provided by swap server",
cli,
)
return False
# lookup network using UUID from db
network = orders_get_network(message["u"])
# calculate amount the bitcoin transaction and require user confirmation
amount = f'{message["am"] / SATOSHIS:.8f}'
if cli:
log(
f"\nAre you happy to proceed with creating the below transaction to fulfill"
f" swap request:\n"
f"\tNETWORK: {network}\n"
f"\tAMOUNT: {amount}\n",
cli,
)
res = input("Enter 'y' to continue\t") or "y"
if res.lower() != "y":
log("satellite message payment cancelled", cli)
return
# setup the transaction
proxy = AuthServiceProxy(service_url=make_service_url(network))
try:
result["tx_hash"] = proxy.sendtoaddress(message["ad"], amount)
except Exception as e1:
if BITCOIND_PW:
try:
proxy.walletpassphrase(BITCOIND_PW, 60)
result["tx_hash"] = proxy.sendtoaddress(message["ad"], amount)
proxy.walletlock()
except Exception as e2:
log(
f"raised errors during transaction construction: \n {e1}\n {e2}",
cli,
)
tx_hash = proxy.gettransaction(result["tx_hash"])
result["tx_hex"] = tx_hash["hex"]
# TODO: for separate machines should change to getrawtransaction as per below
# result["tx_hex"] = proxy.getrawtransaction(result["tx_hash"])
result["uuid"] = message["u"]
# write to db as we don't have it on our side yet.:
mesh_add_verify_quote(
message["u"],
message["i"],
message["am"],
message["ad"],
message["rs"],
pubkey,
payment_hash,
tx_hash["txid"],
tx_hash["hex"],
)
log(f"Returning swap tx to GATEWAY:\n{pformat(result)}", cli)
return {"swap_tx": result}
|
#!/usr/bin/env python
# Copyright 2022 The IREE Authors
#
# Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Creates a new branch that bumps the llvm-project commit.
# Typical usage (from the iree/ repository):
# /path/to/here/bump_llvm.py
#
# In the default configuration, it will create a new branch
# "bump-llvm-YYYYMMDD"
# This will fail if the branch already exists, in which case, you can:
# * Specify an explicit branch name with --branch-name=my-integrate
# * Pass "--reuse-branch" if you are sure that you want to lose the current
# branch state. This is largely meant for developing this script or YOLO
# use.
#
# In order to not interfere with your personal preferences, a remote named
# 'UPSTREAM_AUTOMATION' is used (and setup if needed). Sorry if you use that
# name for your daily work.
#
# This then reverts any changes to the llvm-project submodule setup (i.e.
# resets it to llvm-project's repository and disables branch tracking) and
# resets the submodule to the curren HEAD commit, generating a nice commit
# message.
#
# The branch is then pushed to the main repository. GitHub will print the usual
# message to create a pull request, which you should do to kick off pre-merge
# checks. You should land changes to this branch until green (or get other
# people to do so).
#
# When satisfied, Squash and Merge, opting to delete the branch to keep things
# tidy.
import argparse
from datetime import date
import os
import sys
import iree_modules
import iree_utils
def main(args):
if not args.disable_setup_remote:
iree_utils.git_setup_remote(args.upstream_remote,
args.upstream_repository)
iree_utils.git_check_porcelain()
print(f"Fetching remote repository: {args.upstream_remote}")
iree_utils.git_fetch(repository=args.upstream_remote)
# If re-using a branch, make sure we are not on that branch.
if args.reuse_branch:
iree_utils.git_checkout("main")
# Create branch.
branch_name = args.branch_name
if not branch_name:
branch_name = f"bump-llvm-{date.today().strftime("%Y%m%d")}"
print(f"Creating branch {branch_name} (override with --branch-name=)")
iree_utils.git_create_branch(branch_name,
checkout=True,
ref=f"{args.upstream_remote}/main",
force=args.reuse_branch)
# Reset the llvm-project submodule to track upstream.
# This will discard any cherrypicks that may have been committed locally,
# but the assumption is that if doing a main llvm version bump, the
# cherrypicks will be incorporated at the new commit. If not, well, ymmv
# and you will find out.
iree_utils.git_submodule_set_origin(
"third_party/llvm-project",
url="https://github.com/llvm/llvm-project.git",
branch="--default")
# Remove the branch pin file, reverting us to pure upstream.
branch_pin_file = os.path.join(
iree_utils.get_repo_root(),
iree_modules.MODULE_INFOS["llvm-project"].branch_pin_file)
if os.path.exists(branch_pin_file):
os.remove(branch_pin_file)
# Update the LLVM submodule.
llvm_commit = args.llvm_commit
print(f"Updating LLVM submodule to {llvm_commit}")
llvm_root = iree_utils.get_submodule_root("llvm-project")
iree_utils.git_fetch(repo_dir=llvm_root)
if llvm_commit == "HEAD":
llvm_commit = "origin/main"
iree_utils.git_reset(llvm_commit, repo_dir=llvm_root)
llvm_commit, llvm_summary = iree_utils.git_current_commit(
repo_dir=llvm_root)
print(f"LLVM submodule reset to:\n {llvm_summary}\n")
# Create a commit.
print("Create commit...")
iree_utils.git_create_commit(
message=(f"Integrate llvm-project at {llvm_commit}\n\n"
f"* Reset third_party/llvm-project: {llvm_summary}"),
add_all=True)
# Push.
print("Pushing...")
iree_utils.git_push_branch(args.upstream_remote, branch_name)
def parse_arguments(argv):
parser = argparse.ArgumentParser(description="IREE LLVM-bump-inator")
parser.add_argument("--upstream-remote",
help="Upstream remote",
default="UPSTREAM_AUTOMATION")
parser.add_argument("--upstream-repository",
help="Upstream repository URL",
default="git@github.com:google/iree.git")
parser.add_argument("--disable-setup-remote",
help="Disable remote setup",
action="store_true",
default=False)
parser.add_argument("--llvm-commit",
help="LLVM commit sha",
default="HEAD")
parser.add_argument("--branch-name",
help="Integrate branch to create",
default=None)
parser.add_argument("--reuse-branch",
help="Allow re-use of an existing branch",
action="store_true",
default=False)
args = parser.parse_args(argv)
return args
if __name__ == "__main__":
main(parse_arguments(sys.argv[1:]))
| #!/usr/bin/env python
# Copyright 2022 The IREE Authors
#
# Licensed under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Creates a new branch that bumps the llvm-project commit.
# Typical usage (from the iree/ repository):
# /path/to/here/bump_llvm.py
#
# In the default configuration, it will create a new branch
# "bump-llvm-YYYYMMDD"
# This will fail if the branch already exists, in which case, you can:
# * Specify an explicit branch name with --branch-name=my-integrate
# * Pass "--reuse-branch" if you are sure that you want to lose the current
# branch state. This is largely meant for developing this script or YOLO
# use.
#
# In order to not interfere with your personal preferences, a remote named
# 'UPSTREAM_AUTOMATION' is used (and setup if needed). Sorry if you use that
# name for your daily work.
#
# This then reverts any changes to the llvm-project submodule setup (i.e.
# resets it to llvm-project's repository and disables branch tracking) and
# resets the submodule to the curren HEAD commit, generating a nice commit
# message.
#
# The branch is then pushed to the main repository. GitHub will print the usual
# message to create a pull request, which you should do to kick off pre-merge
# checks. You should land changes to this branch until green (or get other
# people to do so).
#
# When satisfied, Squash and Merge, opting to delete the branch to keep things
# tidy.
import argparse
from datetime import date
import os
import sys
import iree_modules
import iree_utils
def main(args):
if not args.disable_setup_remote:
iree_utils.git_setup_remote(args.upstream_remote,
args.upstream_repository)
iree_utils.git_check_porcelain()
print(f"Fetching remote repository: {args.upstream_remote}")
iree_utils.git_fetch(repository=args.upstream_remote)
# If re-using a branch, make sure we are not on that branch.
if args.reuse_branch:
iree_utils.git_checkout("main")
# Create branch.
branch_name = args.branch_name
if not branch_name:
branch_name = f"bump-llvm-{date.today().strftime('%Y%m%d')}"
print(f"Creating branch {branch_name} (override with --branch-name=)")
iree_utils.git_create_branch(branch_name,
checkout=True,
ref=f"{args.upstream_remote}/main",
force=args.reuse_branch)
# Reset the llvm-project submodule to track upstream.
# This will discard any cherrypicks that may have been committed locally,
# but the assumption is that if doing a main llvm version bump, the
# cherrypicks will be incorporated at the new commit. If not, well, ymmv
# and you will find out.
iree_utils.git_submodule_set_origin(
"third_party/llvm-project",
url="https://github.com/llvm/llvm-project.git",
branch="--default")
# Remove the branch pin file, reverting us to pure upstream.
branch_pin_file = os.path.join(
iree_utils.get_repo_root(),
iree_modules.MODULE_INFOS["llvm-project"].branch_pin_file)
if os.path.exists(branch_pin_file):
os.remove(branch_pin_file)
# Update the LLVM submodule.
llvm_commit = args.llvm_commit
print(f"Updating LLVM submodule to {llvm_commit}")
llvm_root = iree_utils.get_submodule_root("llvm-project")
iree_utils.git_fetch(repo_dir=llvm_root)
if llvm_commit == "HEAD":
llvm_commit = "origin/main"
iree_utils.git_reset(llvm_commit, repo_dir=llvm_root)
llvm_commit, llvm_summary = iree_utils.git_current_commit(
repo_dir=llvm_root)
print(f"LLVM submodule reset to:\n {llvm_summary}\n")
# Create a commit.
print("Create commit...")
iree_utils.git_create_commit(
message=(f"Integrate llvm-project at {llvm_commit}\n\n"
f"* Reset third_party/llvm-project: {llvm_summary}"),
add_all=True)
# Push.
print("Pushing...")
iree_utils.git_push_branch(args.upstream_remote, branch_name)
def parse_arguments(argv):
parser = argparse.ArgumentParser(description="IREE LLVM-bump-inator")
parser.add_argument("--upstream-remote",
help="Upstream remote",
default="UPSTREAM_AUTOMATION")
parser.add_argument("--upstream-repository",
help="Upstream repository URL",
default="git@github.com:google/iree.git")
parser.add_argument("--disable-setup-remote",
help="Disable remote setup",
action="store_true",
default=False)
parser.add_argument("--llvm-commit",
help="LLVM commit sha",
default="HEAD")
parser.add_argument("--branch-name",
help="Integrate branch to create",
default=None)
parser.add_argument("--reuse-branch",
help="Allow re-use of an existing branch",
action="store_true",
default=False)
args = parser.parse_args(argv)
return args
if __name__ == "__main__":
main(parse_arguments(sys.argv[1:]))
|
from pandac.PandaModules import Point3, VBase3, Vec4, Vec3
objectStruct = {'Objects': {'1156371286.47dzzz0': {'Type': 'Building Interior','Name': '','Instanced': False,'Objects': {'1165344228.45kmuller': {'Type': 'Furniture','DisableCollision': False,'Hpr': VBase3(2.145, 0.0, 0.0),'Pos': Point3(-1.873, -5.288, -0.154),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.76, 0.63, 0.63, 1.0),'Model': 'models/props/table_shanty_2'}},'1165344324.52kmuller': {'Type': 'Furniture','DisableCollision': False,'Hpr': VBase3(-45.131, 3.722, -3.619),'Pos': Point3(-1.364, 0.471, -0.116),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (1.0, 0.8500000238418579, 0.8899999856948853, 1.0),'Model': 'models/props/chair_shanty'}},'1165344556.34kmuller': {'Type': 'Furniture','DisableCollision': False,'Hpr': VBase3(88.434, 0.0, 0.0),'Objects': {'1257796843.25caoconno': {'Type': 'Holiday','DisableCollision': False,'Holiday': 'WinterFestival','Hpr': VBase3(0.746, 0.0, 41.101),'Pos': Point3(5.953, 0.347, 7.885),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/pir_m_prp_hol_candycane_winter09'}},'1257797277.72caoconno': {'Type': 'Holiday','DisableCollision': False,'Holiday': 'WinterFestival','Hpr': VBase3(2.188, 2.043, 40.716),'Pos': Point3(5.951, 0.341, 7.887),'Scale': VBase3(1.0, 1.0, 1.0),'VisSize': '','Visual': {'Model': 'models/props/pir_m_prp_hol_candycane_winter09'}}},'Pos': Point3(-18.325, -5.523, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.800000011920929, 0.6899999976158142, 0.6200000047683716, 1.0),'Model': 'models/props/bench_shanty_1'}},'1165344792.45kmuller': {'Type': 'Furniture','DisableCollision': False,'Hpr': VBase3(87.026, 0.0, 0.0),'Pos': Point3(3.702, -5.822, -0.059),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (1.0, 0.85, 0.89, 1.0),'Model': 'models/props/bench_shanty_2'}},'1166055838.34kmuller': {'Type': 'Wall_Hangings','DisableCollision': False,'Hpr': VBase3(89.83, 0.0, 0.0),'Pos': Point3(-19.983, -6.616, 8.019),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/Map_01_unframed'}},'1166130817.64kmuller': {'Type': 'Furniture','DisableCollision': False,'Hpr': VBase3(87.026, 0.0, 0.0),'Pos': Point3(-4.754, -5.382, -0.154),'Scale': VBase3(1.001, 1.001, 1.001),'Visual': {'Color': (0.85, 0.74, 0.79, 1.0),'Model': 'models/props/bench_shanty_2'}},'1166130858.42kmuller': {'Type': 'Furniture','DisableCollision': False,'Hpr': VBase3(96.22, 0.235, 0.509),'Pos': Point3(-0.609, -10.763, 0.332),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.7599999904632568, 0.7599999904632568, 0.699999988079071, 1.0),'Model': 'models/props/chair_shanty'}},'1166130963.4kmuller': {'Type': 'LaundryRope','DisableCollision': False,'Hpr': VBase3(-24.584, 0.0, 0.0),'Pos': Point3(12.688, 24.692, -5.477),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/LaundryRope'}},'1166131071.71kmuller': {'Type': 'Bucket','DisableCollision': False,'Hpr': VBase3(35.24, 3.466, -2.446),'Pos': Point3(18.515, 20.409, 0.0),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/washtub'}},'1166131830.51kmuller': {'Type': 'Tools','DisableCollision': False,'Hpr': VBase3(-0.376, -4.662, 4.742),'Pos': Point3(-6.011, 11.734, 0.168),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Color': (0.7699999809265137, 0.75, 0.7300000190734863, 1.0),'Model': 'models/props/broom'}},'1166132516.06kmuller': {'Type': 'Baskets','DisableCollision': True,'Hpr': VBase3(-43.987, 0.0, 0.0),'Pos': Point3(19.041, -5.375, 0.0),'Scale': VBase3(1.775, 1.775, 1.775),'Visual': {'Model': 'models/props/crab_pot'}},'1166132600.56kmuller': {'Type': 'Bucket','DisableCollision': True,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(8.542, -25.98, 0.093),'Scale': VBase3(0.676, 0.676, 0.676),'Visual': {'Model': 'models/props/bucket_handles'}},'1166132865.18kmuller': {'Type': 'ChickenCage','DisableCollision': True,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(-17.673, -13.339, 0.034),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/ChickenCage'}},'1166132969.78kmuller': {'Type': 'Animal','Hpr': VBase3(0.0, 0.0, 0.0),'Patrol Radius': '1.0000','Pos': Point3(-17.559, -13.569, 0.406),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Chicken','Start State': 'Idle','StartFrame': '0'},'1166133856.57kmuller': {'Type': 'Prop_Groups','DisableCollision': False,'Hpr': VBase3(89.351, 0.0, 0.0),'Pos': Point3(-15.962, -20.852, 0.106),'Scale': VBase3(0.855, 0.855, 0.855),'Visual': {'Model': 'models/props/prop_group_B'}},'1166135474.96kmuller': {'Type': 'Tools','DisableCollision': False,'Hpr': VBase3(180.0, 6.596, 171.649),'Pos': Point3(19.924, -2.215, 4.423),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/rake'}},'1166135589.98kmuller': {'Type': 'Ship_Props','DisableCollision': False,'Hpr': VBase3(88.476, 0.0, 0.0),'Pos': Point3(20.013, -8.183, 10.185),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/wheel_wallprop'}},'1166135667.84kmuller': {'Type': 'Trunks','DisableCollision': True,'Hpr': 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'["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797342.74caoconno"]','1257797342.75caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797342.75caoconno"]'}}
extraInfo = {'camPos': Point3(38.4972, 15.2174, 22.107),'camHpr': VBase3(119.723, -14.2124, 0),'focalLength': 0.657999992371,'skyState': -1,'fog': 0} | from pandac.PandaModules import Point3, VBase3, Vec4, Vec3
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'["Objects"]["1156371286.47dzzz0"]["Objects"]["1165344556.34kmuller"]["Objects"]["1257796843.25caoconno"]','1257797083.57caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797083.57caoconno"]','1257797129.54caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797129.54caoconno"]','1257797143.12caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797143.12caoconno"]','1257797208.82caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797208.82caoconno"]','1257797277.72caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1165344556.34kmuller"]["Objects"]["1257797277.72caoconno"]','1257797277.76caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797277.76caoconno"]','1257797277.77caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797277.77caoconno"]','1257797342.72caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797342.72caoconno"]','1257797342.74caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797342.74caoconno"]','1257797342.75caoconno': '["Objects"]["1156371286.47dzzz0"]["Objects"]["1257797342.75caoconno"]'}}
extraInfo = {'camPos': Point3(38.4972, 15.2174, 22.107),'camHpr': VBase3(119.723, -14.2124, 0),'focalLength': 0.657999992371,'skyState': -1,'fog': 0} |
"""
Copyright (c) 2022 Huawei Technologies Co.,Ltd.
openGauss is licensed under Mulan PSL v2.
You can use this software according to the terms and conditions of the Mulan PSL v2.
You may obtain a copy of Mulan PSL v2 at:
http://license.coscl.org.cn/MulanPSL2
THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
See the Mulan PSL v2 for more details.
"""
"""
Case Type : security_sm3
Case Name : 创建用户时的加密算法sm3，认证方式MD5，非初始用户错误的密码通过JDBC连接数据库
Description :
1.修改password_encryption_type=3
2.pg_hba.conf文件中修改认证方式为MD5
3.非初始用户错误的密码通过JDBC登录数据库
Expect :
1-2.参数设置成功
3.数据库连接失败
History :
"""
import os
import unittest
from yat.test import Node
from yat.test import macro
from testcase.utils.Common import Common
from testcase.utils.CommonSH import CommonSH
from testcase.utils.Logger import Logger
class Security(unittest.TestCase):
def setUp(self):
self.logger = Logger()
self.logger.info('--Opengauss_Function_Security_sm3_Case0045 start--')
self.userNode = Node('PrimaryDbUser')
self.primary_root = Node('PrimaryRoot')
self.DB_ENV_PATH = macro.DB_ENV_PATH
self.DB_INSTANCE_PATH = macro.DB_INSTANCE_PATH
self.sh_primy = CommonSH('PrimaryDbUser')
self.common = Common()
self.user = 'u_security_sm3_0045'
self.targetpath = "/home/jdbc_test"
self.properties = os.path.join(self.targetpath, "jdbc_connect.conf")
self.java_name = "jdbc_drop_schema_case0001"
self.script_name = 'bcprov-jdk15on-1.68'
self.config = os.path.join(self.DB_INSTANCE_PATH, 'pg_hba.conf')
self.confignew = os.path.join(self.DB_INSTANCE_PATH, 'pg_hba_bak.conf')
self.logger.info('--------获取参数默认值--------')
self.default_msg_list = ''
check_default = 'show password_encryption_type;'
default_msg = self.sh_primy.execut_db_sql(check_default)
self.logger.info(default_msg)
self.default_msg_list = default_msg.splitlines()[2].strip()
self.logger.info(self.default_msg_list)
self.logger.info('--------备份白名单文件---------')
cp_cmd = f"cp {self.config} {self.confignew}"
self.userNode.sh(cp_cmd).result()
def test_encrypted(self):
text = '---step1:修改password_encryption_type=3;expect:成功---'
self.logger.info(text)
exe_cmd1 = f'source {self.DB_ENV_PATH};' \
f'gs_guc reload -D {self.DB_INSTANCE_PATH} -c ' \
'"password_encryption_type=3"'
msg1 = self.userNode.sh(exe_cmd1).result()
self.logger.info(msg1)
check_cmd = 'show password_encryption_type;'
check_msg = self.sh_primy.execut_db_sql(check_cmd)
self.logger.info(check_msg)
self.common.equal_sql_mdg(check_msg, 'password_encryption_type', '3',
'(1 row)', flag='1')
text = '---step2:pg_hba.conf文件中增加认证方式为md5;expect:成功---'
self.logger.info(text)
exe_cmd2 = f'grep "IPv4 local connections:" {self.config}'
msg2 = self.userNode.sh(exe_cmd2).result()
self.logger.info(msg2)
insert_messages = f"host {self.userNode.db_name} {self.user} " \
f"{self.userNode.db_host}/32 md5"
exe_cmd3 = f'sed -i "/{msg2}/a\{insert_messages}" {self.config}'
self.logger.info(exe_cmd3)
msg3 = self.userNode.sh(exe_cmd3).result()
self.logger.info(msg3)
restart_cmd = f'source {macro.DB_ENV_PATH};' \
f'gs_ctl restart -D {macro.DB_INSTANCE_PATH} -M primary'
restart_msg = self.userNode.sh(restart_cmd).result()
self.logger.info(restart_msg)
text = '---step3:创建用户1;expect:成功---'
self.logger.info(text)
sql_cmd4 = f'create user {self.user} with password \'' \
f'{macro.COMMON_PASSWD}\';'
msg4 = self.sh_primy.execut_db_sql(sql_cmd4)
self.logger.info(msg4)
self.assertIn('CREATE ROLE', msg4, '执行失败:' + text)
text = '---step4.1:写入配置文件，用户1设置错误的密码;expect:成功---'
self.logger.info(text)
self.common.scp_file(self.primary_root,
f"{self.java_name}.java", self.targetpath)
self.common.scp_file(self.primary_root,
f"{self.script_name}.jar", self.targetpath)
result = self.primary_root.sh(
f"touch {self.properties}").result()
self.logger.info(result)
error_passwd = macro.COMMON_PASSWD + '_error'
config = f'echo "password={error_passwd}"> {self.properties}'
self.primary_root.sh(config)
config = f'echo "port={self.userNode.db_port}">> ' \
f'{self.properties}'
self.primary_root.sh(config)
config = f'echo "hostname={self.userNode.db_host}">> ' \
f'{self.properties}'
self.primary_root.sh(config)
config = f'echo "user={self.user}">> {self.properties}'
self.primary_root.sh(config)
config = f'echo "dbname={self.userNode.db_name}">> ' \
f'{self.properties}'
self.primary_root.sh(config)
config = f'cat {self.properties}'
result = self.primary_root.sh(config).result()
assert1 = "password=" in result and "port=" in result and \
"hostname=" in result and "user=" in result and \
"dbname=" in result
self.assertTrue(assert1, '执行失败:' + text)
text = '---step4.2:编译java脚本;expect:成功---'
self.logger.info(text)
scp_cmd = self.primary_root.scp_put(macro.JDBC_PATH,
f"{self.targetpath}/postgresql.jar")
self.logger.info(scp_cmd)
cmd = f"javac -encoding utf-8 -cp " \
f"{os.path.join(self.targetpath, "postgresql.jar")} " \
f"{os.path.join(self.targetpath, f"{self.java_name}.java")}"
self.logger.info(cmd)
result = self.primary_root.sh(cmd).result()
self.logger.info(result)
text = '---step4.3:运行java脚本，数据库连接成功;expect:成功---'
self.logger.info(text)
cmd = f"java -cp {os.path.join(self.targetpath, "postgresql.jar")}:" \
f"{os.path.join(self.targetpath, f"{self.script_name}.jar")}:" \
f"{self.targetpath} {self.java_name} -F" \
f" {self.properties}"
self.logger.info(cmd)
result = self.primary_root.sh(cmd).result()
self.logger.info(result)
self.assertIn('连接失败', result, '执行失败:' + text)
def tearDown(self):
self.logger.info('-------1.恢复配置文件中的信息------')
check_cmd = f'if [ -f {self.config} ];then mv {self.confignew} ' \
f'{self.config};rm -rf {self.targetpath};fi'
self.logger.info(check_cmd)
self.primary_root.sh(check_cmd).result()
restart_cmd = f'source {macro.DB_ENV_PATH};' \
f'gs_ctl restart -D {macro.DB_INSTANCE_PATH} -M primary'
restart_msg = self.userNode.sh(restart_cmd).result()
self.logger.info(restart_msg)
self.logger.info('-------2.恢复加密方式配置------')
exe_cmd2 = f'source {self.DB_ENV_PATH};' \
f'gs_guc reload -D {self.DB_INSTANCE_PATH} -c ' \
f'"password_encryption_type={self.default_msg_list}"'
msg2 = self.userNode.sh(exe_cmd2).result()
self.logger.info(msg2)
sql_cmd3 = 'show password_encryption_type;'
msg3 = self.sh_primy.execut_db_sql(sql_cmd3)
self.logger.info(msg3)
self.logger.info('-------3.删除用户-------')
sql_cmd4 = f'drop user {self.user}'
msg4 = self.sh_primy.execut_db_sql(sql_cmd4)
self.logger.info(msg4)
self.logger.info(
'----Opengauss_Function_Security_sm3_Case0045 finish----')
| """
Copyright (c) 2022 Huawei Technologies Co.,Ltd.
openGauss is licensed under Mulan PSL v2.
You can use this software according to the terms and conditions of the Mulan PSL v2.
You may obtain a copy of Mulan PSL v2 at:
http://license.coscl.org.cn/MulanPSL2
THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
See the Mulan PSL v2 for more details.
"""
"""
Case Type : security_sm3
Case Name : 创建用户时的加密算法sm3，认证方式MD5，非初始用户错误的密码通过JDBC连接数据库
Description :
1.修改password_encryption_type=3
2.pg_hba.conf文件中修改认证方式为MD5
3.非初始用户错误的密码通过JDBC登录数据库
Expect :
1-2.参数设置成功
3.数据库连接失败
History :
"""
import os
import unittest
from yat.test import Node
from yat.test import macro
from testcase.utils.Common import Common
from testcase.utils.CommonSH import CommonSH
from testcase.utils.Logger import Logger
class Security(unittest.TestCase):
def setUp(self):
self.logger = Logger()
self.logger.info('--Opengauss_Function_Security_sm3_Case0045 start--')
self.userNode = Node('PrimaryDbUser')
self.primary_root = Node('PrimaryRoot')
self.DB_ENV_PATH = macro.DB_ENV_PATH
self.DB_INSTANCE_PATH = macro.DB_INSTANCE_PATH
self.sh_primy = CommonSH('PrimaryDbUser')
self.common = Common()
self.user = 'u_security_sm3_0045'
self.targetpath = "/home/jdbc_test"
self.properties = os.path.join(self.targetpath, "jdbc_connect.conf")
self.java_name = "jdbc_drop_schema_case0001"
self.script_name = 'bcprov-jdk15on-1.68'
self.config = os.path.join(self.DB_INSTANCE_PATH, 'pg_hba.conf')
self.confignew = os.path.join(self.DB_INSTANCE_PATH, 'pg_hba_bak.conf')
self.logger.info('--------获取参数默认值--------')
self.default_msg_list = ''
check_default = 'show password_encryption_type;'
default_msg = self.sh_primy.execut_db_sql(check_default)
self.logger.info(default_msg)
self.default_msg_list = default_msg.splitlines()[2].strip()
self.logger.info(self.default_msg_list)
self.logger.info('--------备份白名单文件---------')
cp_cmd = f"cp {self.config} {self.confignew}"
self.userNode.sh(cp_cmd).result()
def test_encrypted(self):
text = '---step1:修改password_encryption_type=3;expect:成功---'
self.logger.info(text)
exe_cmd1 = f'source {self.DB_ENV_PATH};' \
f'gs_guc reload -D {self.DB_INSTANCE_PATH} -c ' \
'"password_encryption_type=3"'
msg1 = self.userNode.sh(exe_cmd1).result()
self.logger.info(msg1)
check_cmd = 'show password_encryption_type;'
check_msg = self.sh_primy.execut_db_sql(check_cmd)
self.logger.info(check_msg)
self.common.equal_sql_mdg(check_msg, 'password_encryption_type', '3',
'(1 row)', flag='1')
text = '---step2:pg_hba.conf文件中增加认证方式为md5;expect:成功---'
self.logger.info(text)
exe_cmd2 = f'grep "IPv4 local connections:" {self.config}'
msg2 = self.userNode.sh(exe_cmd2).result()
self.logger.info(msg2)
insert_messages = f"host {self.userNode.db_name} {self.user} " \
f"{self.userNode.db_host}/32 md5"
exe_cmd3 = f'sed -i "/{msg2}/a\{insert_messages}" {self.config}'
self.logger.info(exe_cmd3)
msg3 = self.userNode.sh(exe_cmd3).result()
self.logger.info(msg3)
restart_cmd = f'source {macro.DB_ENV_PATH};' \
f'gs_ctl restart -D {macro.DB_INSTANCE_PATH} -M primary'
restart_msg = self.userNode.sh(restart_cmd).result()
self.logger.info(restart_msg)
text = '---step3:创建用户1;expect:成功---'
self.logger.info(text)
sql_cmd4 = f'create user {self.user} with password \'' \
f'{macro.COMMON_PASSWD}\';'
msg4 = self.sh_primy.execut_db_sql(sql_cmd4)
self.logger.info(msg4)
self.assertIn('CREATE ROLE', msg4, '执行失败:' + text)
text = '---step4.1:写入配置文件，用户1设置错误的密码;expect:成功---'
self.logger.info(text)
self.common.scp_file(self.primary_root,
f"{self.java_name}.java", self.targetpath)
self.common.scp_file(self.primary_root,
f"{self.script_name}.jar", self.targetpath)
result = self.primary_root.sh(
f"touch {self.properties}").result()
self.logger.info(result)
error_passwd = macro.COMMON_PASSWD + '_error'
config = f'echo "password={error_passwd}"> {self.properties}'
self.primary_root.sh(config)
config = f'echo "port={self.userNode.db_port}">> ' \
f'{self.properties}'
self.primary_root.sh(config)
config = f'echo "hostname={self.userNode.db_host}">> ' \
f'{self.properties}'
self.primary_root.sh(config)
config = f'echo "user={self.user}">> {self.properties}'
self.primary_root.sh(config)
config = f'echo "dbname={self.userNode.db_name}">> ' \
f'{self.properties}'
self.primary_root.sh(config)
config = f'cat {self.properties}'
result = self.primary_root.sh(config).result()
assert1 = "password=" in result and "port=" in result and \
"hostname=" in result and "user=" in result and \
"dbname=" in result
self.assertTrue(assert1, '执行失败:' + text)
text = '---step4.2:编译java脚本;expect:成功---'
self.logger.info(text)
scp_cmd = self.primary_root.scp_put(macro.JDBC_PATH,
f"{self.targetpath}/postgresql.jar")
self.logger.info(scp_cmd)
cmd = f"javac -encoding utf-8 -cp " \
f"{os.path.join(self.targetpath, 'postgresql.jar')} " \
f"{os.path.join(self.targetpath, f'{self.java_name}.java')}"
self.logger.info(cmd)
result = self.primary_root.sh(cmd).result()
self.logger.info(result)
text = '---step4.3:运行java脚本，数据库连接成功;expect:成功---'
self.logger.info(text)
cmd = f"java -cp {os.path.join(self.targetpath, 'postgresql.jar')}:" \
f"{os.path.join(self.targetpath, f'{self.script_name}.jar')}:" \
f"{self.targetpath} {self.java_name} -F" \
f" {self.properties}"
self.logger.info(cmd)
result = self.primary_root.sh(cmd).result()
self.logger.info(result)
self.assertIn('连接失败', result, '执行失败:' + text)
def tearDown(self):
self.logger.info('-------1.恢复配置文件中的信息------')
check_cmd = f'if [ -f {self.config} ];then mv {self.confignew} ' \
f'{self.config};rm -rf {self.targetpath};fi'
self.logger.info(check_cmd)
self.primary_root.sh(check_cmd).result()
restart_cmd = f'source {macro.DB_ENV_PATH};' \
f'gs_ctl restart -D {macro.DB_INSTANCE_PATH} -M primary'
restart_msg = self.userNode.sh(restart_cmd).result()
self.logger.info(restart_msg)
self.logger.info('-------2.恢复加密方式配置------')
exe_cmd2 = f'source {self.DB_ENV_PATH};' \
f'gs_guc reload -D {self.DB_INSTANCE_PATH} -c ' \
f'"password_encryption_type={self.default_msg_list}"'
msg2 = self.userNode.sh(exe_cmd2).result()
self.logger.info(msg2)
sql_cmd3 = 'show password_encryption_type;'
msg3 = self.sh_primy.execut_db_sql(sql_cmd3)
self.logger.info(msg3)
self.logger.info('-------3.删除用户-------')
sql_cmd4 = f'drop user {self.user}'
msg4 = self.sh_primy.execut_db_sql(sql_cmd4)
self.logger.info(msg4)
self.logger.info(
'----Opengauss_Function_Security_sm3_Case0045 finish----')
|
import logging
import os
from pathlib import Path
from typing import Text, Optional, Dict, List, Union
import rasa.shared.data
import rasa.shared.utils.io
from rasa.shared.core.domain import Domain
from rasa.shared.core.training_data.story_reader.markdown_story_reader import (
MarkdownStoryReader,
)
from rasa.shared.core.training_data.story_reader.story_reader import StoryReader
from rasa.shared.core.training_data.story_reader.yaml_story_reader import (
YAMLStoryReader,
)
from rasa.shared.core.training_data.structures import StoryStep
from rasa.shared.data import YAML_FILE_EXTENSIONS, MARKDOWN_FILE_EXTENSIONS
logger = logging.getLogger(__name__)
def _get_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if rasa.shared.data.is_likely_markdown_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
elif rasa.shared.data.is_likely_yaml_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
else:
# This is a use case for uploading the story over REST API.
# The source file has a random name.
return _guess_reader(filename, domain, template_variables, use_e2e)
def _guess_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if YAMLStoryReader.is_stories_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
elif MarkdownStoryReader.is_stories_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
raise ValueError(
f"Failed to find a reader class for the story file `{filename}`. "
f"Supported formats are "
f"{", ".join(MARKDOWN_FILE_EXTENSIONS + YAML_FILE_EXTENSIONS)}."
)
async def load_data_from_resource(
resource: Union[Text, Path],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified folder.
Args:
resource: Folder/File with core training data files.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies if the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
if not os.path.exists(resource):
raise ValueError(f"Resource '{resource}' does not exist.")
return await load_data_from_files(
rasa.shared.utils.io.list_files(resource),
domain,
template_variables,
use_e2e,
exclusion_percentage,
)
async def load_data_from_files(
story_files: List[Text],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified files.
Args:
story_files: List of files with training data in it.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies whether the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
story_steps = []
for story_file in story_files:
reader = _get_reader(story_file, domain, template_variables, use_e2e)
steps = reader.read_from_file(story_file)
story_steps.extend(steps)
if exclusion_percentage and exclusion_percentage != 100:
import random
idx = int(round(exclusion_percentage / 100.0 * len(story_steps)))
random.shuffle(story_steps)
story_steps = story_steps[:-idx]
return story_steps
| import logging
import os
from pathlib import Path
from typing import Text, Optional, Dict, List, Union
import rasa.shared.data
import rasa.shared.utils.io
from rasa.shared.core.domain import Domain
from rasa.shared.core.training_data.story_reader.markdown_story_reader import (
MarkdownStoryReader,
)
from rasa.shared.core.training_data.story_reader.story_reader import StoryReader
from rasa.shared.core.training_data.story_reader.yaml_story_reader import (
YAMLStoryReader,
)
from rasa.shared.core.training_data.structures import StoryStep
from rasa.shared.data import YAML_FILE_EXTENSIONS, MARKDOWN_FILE_EXTENSIONS
logger = logging.getLogger(__name__)
def _get_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if rasa.shared.data.is_likely_markdown_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
elif rasa.shared.data.is_likely_yaml_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
else:
# This is a use case for uploading the story over REST API.
# The source file has a random name.
return _guess_reader(filename, domain, template_variables, use_e2e)
def _guess_reader(
filename: Text,
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
) -> StoryReader:
if YAMLStoryReader.is_stories_file(filename):
return YAMLStoryReader(domain, template_variables, use_e2e, filename)
elif MarkdownStoryReader.is_stories_file(filename):
return MarkdownStoryReader(domain, template_variables, use_e2e, filename)
raise ValueError(
f"Failed to find a reader class for the story file `{filename}`. "
f"Supported formats are "
f"{', '.join(MARKDOWN_FILE_EXTENSIONS + YAML_FILE_EXTENSIONS)}."
)
async def load_data_from_resource(
resource: Union[Text, Path],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified folder.
Args:
resource: Folder/File with core training data files.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies if the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
if not os.path.exists(resource):
raise ValueError(f"Resource '{resource}' does not exist.")
return await load_data_from_files(
rasa.shared.utils.io.list_files(resource),
domain,
template_variables,
use_e2e,
exclusion_percentage,
)
async def load_data_from_files(
story_files: List[Text],
domain: Domain,
template_variables: Optional[Dict] = None,
use_e2e: bool = False,
exclusion_percentage: Optional[int] = None,
) -> List["StoryStep"]:
"""Loads core training data from the specified files.
Args:
story_files: List of files with training data in it.
domain: Domain object.
template_variables: Variables that have to be replaced in the training data.
use_e2e: Identifies whether the e2e reader should be used.
exclusion_percentage: Identifies the percentage of training data that
should be excluded from the training.
Returns:
Story steps from the training data.
"""
story_steps = []
for story_file in story_files:
reader = _get_reader(story_file, domain, template_variables, use_e2e)
steps = reader.read_from_file(story_file)
story_steps.extend(steps)
if exclusion_percentage and exclusion_percentage != 100:
import random
idx = int(round(exclusion_percentage / 100.0 * len(story_steps)))
random.shuffle(story_steps)
story_steps = story_steps[:-idx]
return story_steps
|
import base64
import pathlib
import sys
import threading
def read(path: pathlib.Path, stdout_lock: threading.Lock):
with path.open(mode='rb') as file, stdout_lock:
sys.stdout.write(
f'{{'contents':'{base64.b64encode(file.read()).decode('utf-8')}"}}\n',
)
sys.stdout.flush()
| import base64
import pathlib
import sys
import threading
def read(path: pathlib.Path, stdout_lock: threading.Lock):
with path.open(mode='rb') as file, stdout_lock:
sys.stdout.write(
f'{{"contents":"{base64.b64encode(file.read()).decode("utf-8")}"}}\n',
)
sys.stdout.flush()
|
# coding: utf-8
"""JupyterLab command handler"""
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
import contextlib
import errno
import hashlib
import itertools
import json
import logging
import os
import os.path as osp
import re
import shutil
import stat
import site
import subprocess
import sys
import tarfile
import warnings
from copy import deepcopy
from glob import glob
from pathlib import Path
from tempfile import TemporaryDirectory
from threading import Event
from urllib.error import URLError
from urllib.request import Request, quote, urljoin, urlopen
from jupyter_core.paths import jupyter_config_path
from jupyter_server.extension.serverextension import GREEN_ENABLED, GREEN_OK, RED_DISABLED, RED_X
from jupyterlab_server.config import (LabConfig, get_federated_extensions,
get_package_url, get_page_config,
get_static_page_config,
write_page_config)
from jupyterlab_server.process import Process, WatchHelper, list2cmdline, which
from packaging.version import Version
from traitlets import Bool, Dict, HasTraits, Instance, List, Unicode, default
from jupyterlab.coreconfig import CoreConfig
from jupyterlab.jlpmapp import HERE, YARN_PATH
from jupyterlab.semver import Range, gt, gte, lt, lte, make_semver
from jupyterlab._version import __version__
# The regex for expecting the webpack output.
WEBPACK_EXPECT = re.compile(r'.*theme-light-extension/style/index.css')
# The repo root directory
REPO_ROOT = osp.abspath(osp.join(HERE, '..'))
# The dev mode directory.
DEV_DIR = osp.join(REPO_ROOT, 'dev_mode')
# If we are pinning the package, rename it `pin@<alias>`
PIN_PREFIX = 'pin@'
# Default Yarn registry used in default yarn.lock
YARN_DEFAULT_REGISTRY = 'https://registry.yarnpkg.com'
class ProgressProcess(Process):
def __init__(self, cmd, logger=None, cwd=None, kill_event=None,
env=None):
"""Start a subprocess that can be run asynchronously.
Parameters
----------
cmd: list
The command to run.
logger: :class:`~logger.Logger`, optional
The logger instance.
cwd: string, optional
The cwd of the process.
kill_event: :class:`~threading.Event`, optional
An event used to kill the process operation.
env: dict, optional
The environment for the process.
"""
if not isinstance(cmd, (list, tuple)):
raise ValueError('Command must be given as a list')
if kill_event and kill_event.is_set():
raise ValueError('Process aborted')
self.logger = _ensure_logger(logger)
self._last_line = ''
self.cmd = cmd
self.logger.debug('> ' + list2cmdline(cmd))
self.proc = self._create_process(
cwd=cwd,
env=env,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
universal_newlines=True,
encoding='utf-8',
)
self._kill_event = kill_event or Event()
Process._procs.add(self)
def wait(self):
cache = []
proc = self.proc
kill_event = self._kill_event
spinner = itertools.cycle(['-', '\\', '|', '/'])
while proc.poll() is None:
sys.stdout.write(next(spinner)) # write the next character
sys.stdout.flush() # flush stdout buffer (actual character display)
sys.stdout.write('\b')
if kill_event.is_set():
self.terminate()
raise ValueError('Process was aborted')
try:
out, _ = proc.communicate(timeout=.1)
cache.append(out)
except subprocess.TimeoutExpired:
continue
self.logger.debug('\n'.join(cache))
sys.stdout.flush()
return self.terminate()
def pjoin(*args):
"""Join paths to create a real path.
"""
return osp.abspath(osp.join(*args))
def get_user_settings_dir():
"""Get the configured JupyterLab user settings directory.
"""
settings_dir = os.environ.get('JUPYTERLAB_SETTINGS_DIR')
settings_dir = settings_dir or pjoin(
jupyter_config_path()[0], 'lab', 'user-settings'
)
return osp.abspath(settings_dir)
def get_workspaces_dir():
"""Get the configured JupyterLab workspaces directory.
"""
workspaces_dir = os.environ.get('JUPYTERLAB_WORKSPACES_DIR')
workspaces_dir = workspaces_dir or pjoin(
jupyter_config_path()[0], 'lab', 'workspaces'
)
return osp.abspath(workspaces_dir)
def get_app_dir():
"""Get the configured JupyterLab app directory.
"""
# Default to the override environment variable.
if os.environ.get('JUPYTERLAB_DIR'):
# We must resolve the path to get the canonical case of the path for
# case-sensitive systems
return str(Path(os.environ['JUPYTERLAB_DIR']).resolve())
# Use the default locations for data_files.
app_dir = pjoin(sys.prefix, 'share', 'jupyter', 'lab')
# Check for a user level install.
# Ensure that USER_BASE is defined
if hasattr(site, 'getuserbase'):
site.getuserbase()
userbase = getattr(site, 'USER_BASE', None)
if HERE.startswith(userbase) and not app_dir.startswith(userbase):
app_dir = pjoin(userbase, 'share', 'jupyter', 'lab')
# Check for a system install in '/usr/local/share'.
elif (sys.prefix.startswith('/usr') and not
osp.exists(app_dir) and
osp.exists('/usr/local/share/jupyter/lab')):
app_dir = '/usr/local/share/jupyter/lab'
# We must resolve the path to get the canonical case of the path for
# case-sensitive systems
return str(Path(app_dir).resolve())
def dedupe_yarn(path, logger=None):
""" `yarn-deduplicate` with the `fewer` strategy to minimize total
packages installed in a given staging directory
This means a extension (or dependency) _could_ cause a downgrade of an
version expected at publication time, but core should aggressively set
pins above, for example, known-bad versions
"""
had_dupes = ProgressProcess(
['node', YARN_PATH, 'yarn-deduplicate', '-s', 'fewer', '--fail'],
cwd=path, logger=logger
).wait() != 0
if had_dupes:
yarn_proc = ProgressProcess(['node', YARN_PATH], cwd=path, logger=logger)
yarn_proc.wait()
def ensure_node_modules(cwd, logger=None):
"""Ensure that node_modules is up to date.
Returns true if the node_modules was updated.
"""
logger = _ensure_logger(logger)
yarn_proc = ProgressProcess(['node', YARN_PATH, 'check', '--verify-tree'], cwd=cwd, logger=logger)
ret = yarn_proc.wait()
# Update node_modules if needed.
if ret != 0:
yarn_proc = ProgressProcess(['node', YARN_PATH], cwd=cwd, logger=logger)
yarn_proc.wait()
dedupe_yarn(REPO_ROOT, logger)
return ret != 0
def ensure_dev(logger=None):
"""Ensure that the dev assets are available.
"""
logger = _ensure_logger(logger)
target = pjoin(DEV_DIR, 'static')
# Determine whether to build.
if ensure_node_modules(REPO_ROOT, logger) or not osp.exists(target):
yarn_proc = ProgressProcess(['node', YARN_PATH, 'build'], cwd=REPO_ROOT,
logger=logger)
yarn_proc.wait()
def ensure_core(logger=None):
"""Ensure that the core assets are available.
"""
staging = pjoin(HERE, 'staging')
logger = _ensure_logger(logger)
# Determine whether to build.
target = pjoin(HERE, 'static', 'index.html')
if not osp.exists(target):
ensure_node_modules(staging, logger)
yarn_proc = ProgressProcess(['node', YARN_PATH, 'build'], cwd=staging,
logger=logger)
yarn_proc.wait()
def ensure_app(app_dir):
"""Ensure that an application directory is available.
If it does not exist, return a list of messages to prompt the user.
"""
if osp.exists(pjoin(app_dir, 'static', 'index.html')):
return
msgs = ['JupyterLab application assets not found in "%s"' % app_dir,
'Please run `jupyter lab build` or use a different app directory']
return msgs
def watch_packages(logger=None):
"""Run watch mode for the source packages.
Parameters
----------
logger: :class:`~logger.Logger`, optional
The logger instance.
Returns
-------
A list of `WatchHelper` objects.
"""
logger = _ensure_logger(logger)
ensure_node_modules(REPO_ROOT, logger)
ts_dir = osp.abspath(osp.join(REPO_ROOT, 'packages', 'metapackage'))
# Run typescript watch and wait for the string indicating it is done.
ts_regex = r'.* Found 0 errors\. Watching for file changes\.'
ts_proc = WatchHelper(['node', YARN_PATH, 'run', 'watch'],
cwd=ts_dir, logger=logger, startup_regex=ts_regex)
return [ts_proc]
def watch_dev(logger=None):
"""Run watch mode in a given directory.
Parameters
----------
logger: :class:`~logger.Logger`, optional
The logger instance.
Returns
-------
A list of `WatchHelper` objects.
"""
logger = _ensure_logger(logger)
package_procs = watch_packages(logger)
# Run webpack watch and wait for compilation.
wp_proc = WatchHelper(['node', YARN_PATH, 'run', 'watch'],
cwd=DEV_DIR, logger=logger,
startup_regex=WEBPACK_EXPECT)
return package_procs + [wp_proc]
class AppOptions(HasTraits):
"""Options object for build system"""
def __init__(self, logger=None, core_config=None, **kwargs):
if core_config is not None:
kwargs['core_config'] = core_config
if logger is not None:
kwargs['logger'] = logger
# use the default if app_dir is empty
if 'app_dir' in kwargs and not kwargs['app_dir']:
kwargs.pop('app_dir')
super(AppOptions, self).__init__(**kwargs)
app_dir = Unicode(help='The application directory')
use_sys_dir = Bool(
True,
help=('Whether to shadow the default app_dir if that is set to a '
'non-default value'))
logger = Instance(logging.Logger, help='The logger to use')
core_config = Instance(CoreConfig, help='Configuration for core data')
kill_event = Instance(Event, args=(), help='Event for aborting call')
labextensions_path = List(Unicode(), help='The paths to look in for prebuilt JupyterLab extensions')
registry = Unicode(help="NPM packages registry URL")
splice_source = Bool(False, help="Splice source packages into app directory.")
@default('logger')
def _default_logger(self):
return logging.getLogger('jupyterlab')
# These defaults need to be federated to pick up
# any changes to env vars:
@default('app_dir')
def _default_app_dir(self):
return get_app_dir()
@default('core_config')
def _default_core_config(self):
return CoreConfig()
@default('registry')
def _default_registry(self):
config = _yarn_config(self.logger)["yarn config"]
return config.get("registry", YARN_DEFAULT_REGISTRY)
def _ensure_options(options):
"""Helper to use deprecated kwargs for AppOption"""
if options is None:
return AppOptions()
elif issubclass(options.__class__, AppOptions):
return options
else:
return AppOptions(**options)
def watch(app_options=None):
"""Watch the application.
Parameters
----------
app_options: :class:`AppOptions`, optional
The application options.
Returns
-------
A list of processes to run asynchronously.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
if app_options.splice_source:
package_procs = watch_packages(app_options.logger)
else:
package_procs = []
return package_procs + handler.watch()
def install_extension(extension, app_options=None, pin=None):
"""Install an extension package into JupyterLab.
The extension is first validated.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
return handler.install_extension(extension, pin=pin)
def uninstall_extension(name=None, app_options=None, all_=False):
"""Uninstall an extension by name or path.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
if all_ is True:
return handler.uninstall_all_extensions()
return handler.uninstall_extension(name)
def update_extension(name=None, all_=False, app_dir=None, app_options=None):
"""Update an extension by name, or all extensions.
Either `name` must be given as a string, or `all_` must be `True`.
If `all_` is `True`, the value of `name` is ignored.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
if all_ is True:
return handler.update_all_extensions()
return handler.update_extension(name)
def clean(app_options=None):
"""Clean the JupyterLab application directory."""
app_options = _ensure_options(app_options)
handler = _AppHandler(app_options)
logger = app_options.logger
app_dir = app_options.app_dir
logger.info('Cleaning %s...', app_dir)
if app_dir == pjoin(HERE, 'dev'):
raise ValueError('Cannot clean the dev app')
if app_dir == pjoin(HERE, 'core'):
raise ValueError('Cannot clean the core app')
if getattr(app_options, 'all', False):
logger.info('Removing everything in %s...', app_dir)
_rmtree_star(app_dir, logger)
else:
possibleTargets = ['extensions', 'settings', 'staging', 'static']
targets = [t for t in possibleTargets if getattr(app_options, t)]
for name in targets:
target = pjoin(app_dir, name)
if osp.exists(target):
logger.info('Removing %s...', name)
_rmtree(target, logger)
else:
logger.info('%s not present, skipping...', name)
logger.info('Success!')
if getattr(app_options, 'all', False) or getattr(app_options, 'extensions', False):
logger.info('All of your extensions have been removed, and will need to be reinstalled')
def build(name=None, version=None, static_url=None,
kill_event=None,
clean_staging=False, app_options=None, production=True, minimize=True):
"""Build the JupyterLab application.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
return handler.build(name=name, version=version, static_url=static_url,
production=production, minimize=minimize, clean_staging=clean_staging)
def get_app_info(app_options=None):
"""Get a dictionary of information about the app.
"""
handler = _AppHandler(app_options)
handler._ensure_disabled_info()
return handler.info
def enable_extension(extension, app_options=None, level='sys_prefix'):
"""Enable a JupyterLab extension.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.toggle_extension(extension, False, level=level)
def disable_extension(extension, app_options=None, level='sys_prefix'):
"""Disable a JupyterLab package.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.toggle_extension(extension, True, level=level)
def check_extension(extension, installed=False, app_options=None):
"""Check if a JupyterLab extension is enabled or disabled.
"""
handler = _AppHandler(app_options)
return handler.check_extension(extension, installed)
def build_check(app_options=None):
"""Determine whether JupyterLab should be built.
Returns a list of messages.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
return handler.build_check()
def list_extensions(app_options=None):
"""List the extensions.
"""
handler = _AppHandler(app_options)
return handler.list_extensions()
def link_package(path, app_options=None):
"""Link a package against the JupyterLab build.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.link_package(path)
def unlink_package(package, app_options=None):
"""Unlink a package from JupyterLab by path or name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.unlink_package(package)
def get_app_version(app_options=None):
"""Get the application version."""
handler = _AppHandler(app_options)
return handler.info['version']
def get_latest_compatible_package_versions(names, app_options=None):
"""Get the latest compatible version of a list of packages.
"""
handler = _AppHandler(app_options)
return handler.latest_compatible_package_versions(names)
def read_package(target):
"""Read the package data in a given target tarball.
"""
tar = tarfile.open(target, "r")
f = tar.extractfile('package/package.json')
data = json.loads(f.read().decode('utf8'))
data['jupyterlab_extracted_files'] = [
f.path[len('package/'):] for f in tar.getmembers()
]
tar.close()
return data
# ----------------------------------------------------------------------
# Implementation details
# ----------------------------------------------------------------------
class _AppHandler(object):
def __init__(self, options):
"""Create a new _AppHandler object
"""
options = _ensure_options(options)
self._options = options
self.app_dir = options.app_dir
self.sys_dir = get_app_dir() if options.use_sys_dir else self.app_dir
self.logger = options.logger
# Make a deep copy of the core data so we don't influence the original copy
self.core_data = deepcopy(options.core_config._data)
self.labextensions_path = options.labextensions_path
self.kill_event = options.kill_event
self.registry = options.registry
# Do this last since it relies on other attributes
self.info = self._get_app_info()
def install_extension(self, extension, existing=None, pin=None):
"""Install an extension package into JupyterLab.
The extension is first validated.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
extension = _normalize_path(extension)
extensions = self.info['extensions']
# Check for a core extensions.
if extension in self.info['core_extensions']:
config = self._read_build_config()
uninstalled = config.get('uninstalled_core_extensions', [])
if extension in uninstalled:
self.logger.info('Installing core extension %s' % extension)
uninstalled.remove(extension)
config['uninstalled_core_extensions'] = uninstalled
self._write_build_config(config)
return True
return False
# Create the app dirs if needed.
self._ensure_app_dirs()
# Install the package using a temporary directory.
with TemporaryDirectory() as tempdir:
info = self._install_extension(extension, tempdir, pin=pin)
name = info['name']
# Local directories get name mangled and stored in metadata.
if info['is_dir']:
config = self._read_build_config()
local = config.setdefault('local_extensions', dict())
local[name] = info['source']
self._write_build_config(config)
# Remove an existing extension with the same name and different path
if name in extensions:
other = extensions[name]
if other['path'] != info['path'] and other['location'] == 'app':
os.remove(other['path'])
return True
def build(self, name=None, version=None, static_url=None,
clean_staging=False, production=True, minimize=True):
"""Build the application.
"""
if production is None:
production = not (self.info['linked_packages'] or self.info['local_extensions'])
if not production:
minimize = False
# If splicing, make sure the source packages are built
if self._options.splice_source:
ensure_node_modules(REPO_ROOT, logger=self.logger)
self._run(['node', YARN_PATH, 'build:packages'], cwd=REPO_ROOT)
info = ['production' if production else 'development']
if production:
info.append('minimized' if minimize else 'not minimized')
self.logger.info(f'Building jupyterlab assets ({', '.join(info)})')
# Set up the build directory.
app_dir = self.app_dir
self._populate_staging(
name=name, version=version, static_url=static_url,
clean=clean_staging
)
staging = pjoin(app_dir, 'staging')
# Make sure packages are installed.
ret = self._run(['node', YARN_PATH, 'install', '--non-interactive'], cwd=staging)
if ret != 0:
msg = 'npm dependencies failed to install'
self.logger.debug(msg)
raise RuntimeError(msg)
# Build the app.
dedupe_yarn(staging, self.logger)
command = f'build:{'prod' if production else 'dev'}{':minimize' if minimize else ''}'
ret = self._run(['node', YARN_PATH, 'run', command], cwd=staging)
if ret != 0:
msg = 'JupyterLab failed to build'
self.logger.debug(msg)
raise RuntimeError(msg)
def watch(self):
"""Start the application watcher and then run the watch in
the background.
"""
staging = pjoin(self.app_dir, 'staging')
self._populate_staging()
# Make sure packages are installed.
self._run(['node', YARN_PATH, 'install'], cwd=staging)
dedupe_yarn(staging, self.logger)
proc = WatchHelper(['node', YARN_PATH, 'run', 'watch'],
cwd=pjoin(self.app_dir, 'staging'),
startup_regex=WEBPACK_EXPECT,
logger=self.logger)
return [proc]
def list_extensions(self):
"""Print an output of the extensions.
"""
self._ensure_disabled_info()
logger = self.logger
info = self.info
logger.info('JupyterLab v%s' % info['version'])
if info['federated_extensions'] or info['extensions']:
info['compat_errors'] = self._get_extension_compat()
if info['federated_extensions']:
self._list_federated_extensions()
if info['extensions']:
logger.info('Other labextensions (built into JupyterLab)')
self._list_extensions(info, 'app')
self._list_extensions(info, 'sys')
local = info['local_extensions']
if local:
logger.info('\n local extensions:')
for name in sorted(local):
logger.info(' %s: %s' % (name, local[name]))
linked_packages = info['linked_packages']
if linked_packages:
logger.info('\n linked packages:')
for key in sorted(linked_packages):
source = linked_packages[key]['source']
logger.info(' %s: %s' % (key, source))
uninstalled_core = info['uninstalled_core']
if uninstalled_core:
logger.info('\nUninstalled core extensions:')
[logger.info(' %s' % item) for item in sorted(uninstalled_core)]
all_exts = list(info['federated_extensions']) + list(info['extensions']) + list(info['core_extensions'])
# Ignore disabled extensions that are not installed
disabled = [i for i in info['disabled'] if i.partition(':')[0] in all_exts]
if disabled:
logger.info('\nDisabled extensions:')
for item in sorted(disabled):
# Show that all plugins will be disabled if the whole extension matches
if item in all_exts:
item += ' (all plugins)'
logger.info(' %s' % item)
# Here check if modules are improperly shadowed
improper_shadowed = []
for ext_name in self.info['shadowed_exts']:
source_version = self.info['extensions'][ext_name]['version']
prebuilt_version = self.info['federated_extensions'][ext_name]['version']
if not gte(prebuilt_version, source_version, True):
improper_shadowed.append(ext_name)
if improper_shadowed:
logger.info('\nThe following source extensions are overshadowed by older prebuilt extensions:')
[logger.info(' %s' % name) for name in sorted(improper_shadowed)]
messages = self.build_check(fast=True)
if messages:
logger.info('\nBuild recommended, please run `jupyter lab build`:')
[logger.info(' %s' % item) for item in messages]
def build_check(self, fast=False):
"""Determine whether JupyterLab should be built.
Returns a list of messages.
"""
app_dir = self.app_dir
local = self.info['local_extensions']
linked = self.info['linked_packages']
messages = []
# Check for no application.
pkg_path = pjoin(app_dir, 'static', 'package.json')
if not osp.exists(pkg_path):
return ['No built application']
static_data = self.info['static_data']
old_jlab = static_data['jupyterlab']
old_deps = static_data.get('dependencies', dict())
# Look for mismatched version.
static_version = old_jlab.get('version', '')
if not static_version.endswith('-spliced'):
core_version = old_jlab['version']
if Version(static_version) != Version(core_version):
msg = 'Version mismatch: %s (built), %s (current)'
return [msg % (static_version, core_version)]
shadowed_exts = self.info['shadowed_exts']
# Look for mismatched extensions.
new_package = self._get_package_template(silent=fast)
new_jlab = new_package['jupyterlab']
new_deps = new_package.get('dependencies', dict())
for ext_type in ['extensions', 'mimeExtensions']:
# Extensions that were added.
for ext in new_jlab[ext_type]:
if ext in shadowed_exts:
continue
if ext not in old_jlab[ext_type]:
messages.append('%s needs to be included in build' % ext)
# Extensions that were removed.
for ext in old_jlab[ext_type]:
if ext in shadowed_exts:
continue
if ext not in new_jlab[ext_type]:
messages.append('%s needs to be removed from build' % ext)
# Look for mismatched dependencies
src_pkg_dir = pjoin(REPO_ROOT, 'packages')
for (pkg, dep) in new_deps.items():
if old_deps.get(pkg, '').startswith(src_pkg_dir):
continue
if pkg not in old_deps:
continue
# Skip local and linked since we pick them up separately.
if pkg in local or pkg in linked:
continue
if old_deps[pkg] != dep:
msg = '%s changed from %s to %s'
messages.append(msg % (pkg, old_deps[pkg], new_deps[pkg]))
# Look for updated local extensions.
for (name, source) in local.items():
if fast or name in shadowed_exts:
continue
dname = pjoin(app_dir, 'extensions')
if self._check_local(name, source, dname):
messages.append('%s content changed' % name)
# Look for updated linked packages.
for (name, item) in linked.items():
if fast or name in shadowed_exts:
continue
dname = pjoin(app_dir, 'staging', 'linked_packages')
if self._check_local(name, item['source'], dname):
messages.append('%s content changed' % name)
return messages
def uninstall_extension(self, name):
"""Uninstall an extension by name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
info = self.info
logger = self.logger
if name in info['federated_extensions']:
if info['federated_extensions'][name].get('install', dict()).get('uninstallInstructions', None):
logger.error('JupyterLab cannot uninstall this extension. %s' % info['federated_extensions'][name]['install']['uninstallInstructions'])
else:
logger.error('JupyterLab cannot uninstall %s since it was installed outside of JupyterLab. Use the same method used to install this extension to uninstall this extension.' % name)
return False
# Allow for uninstalled core extensions.
if name in info['core_extensions']:
config = self._read_build_config()
uninstalled = config.get('uninstalled_core_extensions', [])
if name not in uninstalled:
logger.info('Uninstalling core extension %s' % name)
uninstalled.append(name)
config['uninstalled_core_extensions'] = uninstalled
self._write_build_config(config)
return True
return False
local = info['local_extensions']
for (extname, data) in info['extensions'].items():
path = data['path']
if extname == name:
msg = 'Uninstalling %s from %s' % (name, osp.dirname(path))
logger.info(msg)
os.remove(path)
# Handle local extensions.
if extname in local:
config = self._read_build_config()
data = config.setdefault('local_extensions', dict())
del data[extname]
self._write_build_config(config)
return True
logger.warn('No labextension named "%s" installed' % name)
return False
def uninstall_all_extensions(self):
"""Uninstalls all extensions
Returns `True` if a rebuild is recommended, `False` otherwise
"""
should_rebuild = False
for (extname, _) in self.info['extensions'].items():
uninstalled = self.uninstall_extension(extname)
should_rebuild = should_rebuild or uninstalled
return should_rebuild
def update_all_extensions(self):
"""Update all non-local extensions.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
should_rebuild = False
for (extname, _) in self.info['extensions'].items():
if extname in self.info['local_extensions']:
continue
updated = self._update_extension(extname)
# Rebuild if at least one update happens:
should_rebuild = should_rebuild or updated
return should_rebuild
def update_extension(self, name):
"""Update an extension by name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
if name not in self.info['extensions']:
self.logger.warning('No labextension named "%s" installed' % name)
return False
return self._update_extension(name)
def _update_extension(self, name):
"""Update an extension by name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
data = self.info['extensions'][name]
if data["alias_package_source"]:
self.logger.warn("Skipping updating pinned extension '%s'." % name)
return False
try:
latest = self._latest_compatible_package_version(name)
except URLError:
return False
if latest is None:
self.logger.warn('No compatible version found for %s!' % (name,))
return False
if latest == data['version']:
self.logger.info('Extension %r already up to date' % name)
return False
self.logger.info('Updating %s to version %s' % (name, latest))
return self.install_extension('%s@%s' % (name, latest))
def link_package(self, path):
"""Link a package at the given path.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
path = _normalize_path(path)
if not osp.exists(path) or not osp.isdir(path):
msg = 'Cannot install "%s" only link local directories'
raise ValueError(msg % path)
with TemporaryDirectory() as tempdir:
info = self._extract_package(path, tempdir)
messages = _validate_extension(info['data'])
if not messages:
return self.install_extension(path)
# Warn that it is a linked package.
self.logger.warning('Installing %s as a linked package because it does not have extension metadata:', path)
[self.logger.warning(' %s' % m) for m in messages]
# Add to metadata.
config = self._read_build_config()
linked = config.setdefault('linked_packages', dict())
linked[info['name']] = info['source']
self._write_build_config(config)
return True
def unlink_package(self, path):
"""Unlink a package by name or at the given path.
A ValueError is raised if the path is not an unlinkable package.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
path = _normalize_path(path)
config = self._read_build_config()
linked = config.setdefault('linked_packages', dict())
found = None
for (name, source) in linked.items():
if name == path or source == path:
found = name
if found:
del linked[found]
else:
local = config.setdefault('local_extensions', dict())
for (name, source) in local.items():
if name == path or source == path:
found = name
if found:
del local[found]
path = self.info['extensions'][found]['path']
os.remove(path)
if not found:
raise ValueError('No linked package for %s' % path)
self._write_build_config(config)
return True
def toggle_extension(self, extension, value, level='sys_prefix'):
"""Enable or disable a lab extension.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
lab_config = LabConfig()
app_settings_dir = osp.join(self.app_dir, 'settings')
page_config = get_static_page_config(app_settings_dir=app_settings_dir, logger=self.logger, level=level)
disabled = page_config.get('disabledExtensions', {})
did_something = False
is_disabled = disabled.get(extension, False)
if value and not is_disabled:
disabled[extension] = True
did_something = True
elif not value and is_disabled:
disabled[extension] = False
did_something = True
if did_something:
page_config['disabledExtensions'] = disabled
write_page_config(page_config, level=level)
return did_something
def check_extension(self, extension, check_installed_only=False):
"""Check if a lab extension is enabled or disabled
"""
self._ensure_disabled_info()
info = self.info
if extension in info["core_extensions"]:
return self._check_core_extension(
extension, info, check_installed_only)
if extension in info["linked_packages"]:
self.logger.info('%s:%s' % (extension, GREEN_ENABLED))
return True
return self._check_common_extension(
extension, info, check_installed_only)
def _check_core_extension(self, extension, info, check_installed_only):
"""Check if a core extension is enabled or disabled
"""
if extension in info['uninstalled_core']:
self.logger.info('%s:%s' % (extension, RED_X))
return False
if check_installed_only:
self.logger.info('%s: %s' % (extension, GREEN_OK))
return True
if extension in info['disabled_core']:
self.logger.info('%s: %s' % (extension, RED_DISABLED))
return False
self.logger.info('%s:%s' % (extension, GREEN_ENABLED))
return True
def _check_common_extension(self, extension, info, check_installed_only):
"""Check if a common (non-core) extension is enabled or disabled
"""
if extension not in info['extensions']:
self.logger.info('%s:%s' % (extension, RED_X))
return False
errors = self._get_extension_compat()[extension]
if errors:
self.logger.info('%s:%s (compatibility errors)' %
(extension, RED_X))
return False
if check_installed_only:
self.logger.info('%s: %s' % (extension, GREEN_OK))
return True
if _is_disabled(extension, info['disabled']):
self.logger.info('%s: %s' % (extension, RED_DISABLED))
return False
self.logger.info('%s:%s' % (extension, GREEN_ENABLED))
return True
def _get_app_info(self):
"""Get information about the app.
"""
info = dict()
info['core_data'] = core_data = self.core_data
info['extensions'] = extensions = self._get_extensions(core_data)
info['local_extensions'] = self._get_local_extensions()
info['linked_packages'] = self._get_linked_packages()
info['app_extensions'] = app = []
info['sys_extensions'] = sys = []
for (name, data) in extensions.items():
data['is_local'] = name in info['local_extensions']
if data['location'] == 'app':
app.append(name)
else:
sys.append(name)
info['uninstalled_core'] = self._get_uninstalled_core_extensions()
info['static_data'] = _get_static_data(self.app_dir)
app_data = info['static_data'] or core_data
info['version'] = app_data['jupyterlab']['version']
info['staticUrl'] = app_data['jupyterlab'].get('staticUrl', '')
info['sys_dir'] = self.sys_dir
info['app_dir'] = self.app_dir
info['core_extensions'] = _get_core_extensions(self.core_data)
info['federated_extensions'] = get_federated_extensions(self.labextensions_path)
info['shadowed_exts'] = [ext for ext in info['extensions'] if ext in info['federated_extensions']]
return info
def _ensure_disabled_info(self):
info = self.info
if 'disabled' in info:
return
labextensions_path = self.labextensions_path
app_settings_dir = osp.join(self.app_dir, 'settings')
page_config = get_page_config(labextensions_path, app_settings_dir=app_settings_dir, logger=self.logger)
info['disabled'] = page_config.get('disabledExtensions', [])
disabled_core = []
for key in info['core_extensions']:
if key in info['disabled']:
disabled_core.append(key)
info['disabled_core'] = disabled_core
def _populate_staging(self, name=None, version=None, static_url=None,
clean=False):
"""Set up the assets in the staging directory.
"""
app_dir = self.app_dir
staging = pjoin(app_dir, 'staging')
if clean and osp.exists(staging):
self.logger.info("Cleaning %s", staging)
_rmtree(staging, self.logger)
self._ensure_app_dirs()
if not version:
version = self.info['core_data']['jupyterlab']['version']
splice_source = self._options.splice_source
if splice_source:
self.logger.debug('Splicing dev packages into app directory.')
source_dir = DEV_DIR
version = __version__ + '-spliced'
else:
source_dir = pjoin(HERE, 'staging')
# Look for mismatched version.
pkg_path = pjoin(staging, 'package.json')
if osp.exists(pkg_path):
with open(pkg_path) as fid:
data = json.load(fid)
if data['jupyterlab'].get('version', '') != version:
_rmtree(staging, self.logger)
os.makedirs(staging)
for fname in ['index.js', 'bootstrap.js', 'publicpath.js',
'webpack.config.js',
'webpack.prod.config.js',
'webpack.prod.minimize.config.js']:
target = pjoin(staging, fname)
shutil.copy(pjoin(source_dir, fname), target)
for fname in ['.yarnrc', 'yarn.js']:
target = pjoin(staging, fname)
shutil.copy(pjoin(HERE, 'staging', fname), target)
# Ensure a clean templates directory
templates = pjoin(staging, 'templates')
if osp.exists(templates):
_rmtree(templates, self.logger)
try:
shutil.copytree(pjoin(source_dir, 'templates'), templates)
except shutil.Error as error:
# `copytree` throws an error if copying to + from NFS even though
# the copy is successful (see https://bugs.python.org/issue24564
# and https://github.com/jupyterlab/jupyterlab/issues/5233)
real_error = '[Errno 22]' not in str(error) and '[Errno 5]' not in str(error)
if real_error or not osp.exists(templates):
raise
# Ensure a clean linked packages directory.
linked_dir = pjoin(staging, 'linked_packages')
if osp.exists(linked_dir):
_rmtree(linked_dir, self.logger)
os.makedirs(linked_dir)
# Template the package.json file.
# Update the local extensions.
extensions = self.info['extensions']
removed = False
for (key, source) in self.info['local_extensions'].items():
# Handle a local extension that was removed.
if key not in extensions:
config = self._read_build_config()
data = config.setdefault('local_extensions', dict())
del data[key]
self._write_build_config(config)
removed = True
continue
dname = pjoin(app_dir, 'extensions')
self._update_local(key, source, dname, extensions[key],
'local_extensions')
# Update the list of local extensions if any were removed.
if removed:
self.info['local_extensions'] = self._get_local_extensions()
# Update the linked packages.
linked = self.info['linked_packages']
for (key, item) in linked.items():
dname = pjoin(staging, 'linked_packages')
self._update_local(key, item['source'], dname, item,
'linked_packages')
# Then get the package template.
data = self._get_package_template()
jlab = data['jupyterlab']
if version:
jlab['version'] = version
if name:
jlab['name'] = name
if static_url:
jlab['staticUrl'] = static_url
# Handle splicing of packages
if splice_source:
# Splice workspace tree as linked dependencies
for path in glob(pjoin(REPO_ROOT, 'packages', '*', 'package.json')):
local_path = osp.dirname(osp.abspath(path))
pkg_data = json.loads(Path(path).read_text(encoding='utf-8'))
name = pkg_data['name']
if name in data['dependencies']:
data['dependencies'][name] = local_path
jlab['linkedPackages'][name] = local_path
if name in data['resolutions']:
data['resolutions'][name] = local_path
# splice the builder as well
local_path = osp.abspath(pjoin(REPO_ROOT, 'builder'))
data['devDependencies']['@jupyterlab/builder'] = local_path
target = osp.join(staging, 'node_modules', '@jupyterlab', 'builder')
# Remove node_modules so it gets re-populated
node_modules = pjoin(staging, 'node_modules')
if osp.exists(node_modules):
shutil.rmtree(node_modules, ignore_errors=True)
# Write the package file
pkg_path = pjoin(staging, 'package.json')
with open(pkg_path, 'w') as fid:
json.dump(data, fid, indent=4)
# copy known-good yarn.lock if missing
lock_path = pjoin(staging, 'yarn.lock')
lock_template = pjoin(HERE, 'staging', 'yarn.lock')
if self.registry != YARN_DEFAULT_REGISTRY: # Replace on the fly the yarn repository see #3658
with open(lock_template, encoding='utf-8') as f:
template = f.read()
template = template.replace(YARN_DEFAULT_REGISTRY, self.registry.strip("/"))
with open(lock_path, 'w', encoding='utf-8') as f:
f.write(template)
elif not osp.exists(lock_path):
shutil.copy(lock_template, lock_path)
os.chmod(lock_path, stat.S_IWRITE | stat.S_IREAD)
def _get_package_template(self, silent=False):
"""Get the template the for staging package.json file.
"""
logger = self.logger
# make a deep copy of the data so we don't influence the core data
data = deepcopy(self.info['core_data'])
local = self.info['local_extensions']
linked = self.info['linked_packages']
extensions = self.info['extensions']
shadowed_exts = self.info['shadowed_exts']
jlab = data['jupyterlab']
def format_path(path):
path = osp.relpath(path, pjoin(self.app_dir, 'staging'))
path = 'file:' + path.replace(os.sep, '/')
if os.name == 'nt':
path = path.lower()
return path
jlab['linkedPackages'] = dict()
# Handle local extensions.
for (key, source) in local.items():
if key in shadowed_exts:
continue
jlab['linkedPackages'][key] = source
data['resolutions'][key] = 'file:' + self.info['extensions'][key]['path']
# Handle linked packages.
for (key, item) in linked.items():
if key in shadowed_exts:
continue
path = pjoin(self.app_dir, 'staging', 'linked_packages')
path = pjoin(path, item['filename'])
data['dependencies'][key] = format_path(path)
jlab['linkedPackages'][key] = item['source']
data['resolutions'][key] = format_path(path)
data['jupyterlab']['extensionMetadata'] = dict()
# Handle extensions
compat_errors = self._get_extension_compat()
for (key, value) in extensions.items():
# Reject incompatible extensions with a message.
errors = compat_errors[key]
if errors:
if not silent:
_log_single_compat_errors(
logger, key, value['version'], errors
)
continue
data['dependencies'][key] = format_path(value['path'])
jlab_data = value['jupyterlab']
for item in ['extension', 'mimeExtension']:
ext = jlab_data.get(item, False)
if not ext:
continue
if ext is True:
ext = ''
jlab[item + 's'][key] = ext
# Add metadata for the extension
data['jupyterlab']['extensionMetadata'][key] = jlab_data
# Handle uninstalled core extensions.
for item in self.info['uninstalled_core']:
if item in jlab['extensions']:
data['jupyterlab']['extensions'].pop(item)
elif item in jlab['mimeExtensions']:
data['jupyterlab']['mimeExtensions'].pop(item)
# Remove from dependencies as well.
if item in data['dependencies']:
data['dependencies'].pop(item)
return data
def _check_local(self, name, source, dname):
"""Check if a local package has changed.
`dname` is the directory name of existing package tar archives.
"""
# Extract the package in a temporary directory.
with TemporaryDirectory() as tempdir:
info = self._extract_package(source, tempdir)
# Test if the file content has changed.
# This relies on `_extract_package` adding the hashsum
# to the filename, allowing a simple exist check to
# compare the hash to the "cache" in dname.
target = pjoin(dname, info['filename'])
return not osp.exists(target)
def _update_local(self, name, source, dname, data, dtype):
"""Update a local dependency. Return `True` if changed.
"""
# Extract the package in a temporary directory.
existing = data['filename']
if not osp.exists(pjoin(dname, existing)):
existing = ''
with TemporaryDirectory() as tempdir:
info = self._extract_package(source, tempdir)
# Bail if the file content has not changed.
if info['filename'] == existing:
return existing
shutil.move(info['path'], pjoin(dname, info['filename']))
# Remove the previous tarball and return the new file name.
if existing:
os.remove(pjoin(dname, existing))
data['filename'] = info['filename']
data['path'] = pjoin(data['tar_dir'], data['filename'])
return info['filename']
def _get_extensions(self, core_data):
"""Get the extensions for the application.
"""
app_dir = self.app_dir
extensions = dict()
# Get system level packages.
sys_path = pjoin(self.sys_dir, 'extensions')
app_path = pjoin(self.app_dir, 'extensions')
extensions = self._get_extensions_in_dir(self.sys_dir, core_data)
# Look in app_dir if different.
app_path = pjoin(app_dir, 'extensions')
if app_path == sys_path or not osp.exists(app_path):
return extensions
extensions.update(self._get_extensions_in_dir(app_dir, core_data))
return extensions
def _get_extensions_in_dir(self, dname, core_data):
"""Get the extensions in a given directory.
"""
extensions = dict()
location = 'app' if dname == self.app_dir else 'sys'
for target in glob(pjoin(dname, 'extensions', '*.tgz')):
data = read_package(target)
deps = data.get('dependencies', dict())
name = data['name']
jlab = data.get('jupyterlab', dict())
path = osp.abspath(target)
filename = osp.basename(target)
if filename.startswith(PIN_PREFIX):
alias = filename[len(PIN_PREFIX):-len(".tgz")]
else:
alias = None
url = get_package_url(data)
extensions[alias or name] = dict(path=path,
filename=osp.basename(path),
url=url,
version=data['version'],
# Only save the package name if the extension name is an alias
alias_package_source=name if alias else None,
jupyterlab=jlab,
dependencies=deps,
tar_dir=osp.dirname(path),
location=location)
return extensions
def _get_extension_compat(self):
"""Get the extension compatibility info.
"""
compat = dict()
core_data = self.info['core_data']
seen = set()
for (name, data) in self.info['federated_extensions'].items():
deps = data['dependencies']
compat[name] = _validate_compatibility(name, deps, core_data)
seen.add(name)
for (name, data) in self.info['extensions'].items():
if name in seen:
continue
deps = data['dependencies']
compat[name] = _validate_compatibility(name, deps, core_data)
return compat
def _get_local_extensions(self):
"""Get the locally installed extensions.
"""
return self._get_local_data('local_extensions')
def _get_linked_packages(self):
"""Get the linked packages.
"""
info = self._get_local_data('linked_packages')
dname = pjoin(self.app_dir, 'staging', 'linked_packages')
for (name, source) in info.items():
info[name] = dict(source=source, filename='', tar_dir=dname)
if not osp.exists(dname):
return info
for path in glob(pjoin(dname, '*.tgz')):
path = osp.abspath(path)
data = read_package(path)
name = data['name']
if name not in info:
self.logger.warn('Removing orphaned linked package %s' % name)
os.remove(path)
continue
item = info[name]
item['filename'] = osp.basename(path)
item['path'] = path
item['version'] = data['version']
item['data'] = data
return info
def _get_uninstalled_core_extensions(self):
"""Get the uninstalled core extensions.
"""
config = self._read_build_config()
return config.get('uninstalled_core_extensions', [])
def _ensure_app_dirs(self):
"""Ensure that the application directories exist"""
dirs = ['extensions', 'settings', 'staging', 'schemas', 'themes']
for dname in dirs:
path = pjoin(self.app_dir, dname)
if not osp.exists(path):
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
def _list_extensions(self, info, ext_type):
"""List the extensions of a given type.
"""
self._ensure_disabled_info()
logger = self.logger
names = info['%s_extensions' % ext_type]
if not names:
return
dname = info['%s_dir' % ext_type]
error_accumulator = {}
logger.info(' %s dir: %s' % (ext_type, dname))
for name in sorted(names):
if name in info['federated_extensions']:
continue
data = info['extensions'][name]
version = data['version']
errors = info['compat_errors'][name]
extra = ''
if _is_disabled(name, info['disabled']):
extra += ' %s' % RED_DISABLED
else:
extra += ' %s' % GREEN_ENABLED
if errors:
extra += ' %s' % RED_X
else:
extra += ' %s' % GREEN_OK
if data['is_local']:
extra += '*'
# If we have the package name in the data, this means this extension's name is the alias name
alias_package_source = data['alias_package_source']
if alias_package_source:
logger.info(' %s %s v%s%s' % (name, alias_package_source, version, extra))
else:
logger.info(' %s v%s%s' % (name, version, extra))
if errors:
error_accumulator[name] = (version, errors)
# Write all errors at end:
_log_multiple_compat_errors(logger, error_accumulator)
# Write a blank line separator
logger.info('')
def _list_federated_extensions(self):
self._ensure_disabled_info()
info = self.info
logger = self.logger
error_accumulator = {}
ext_dirs = dict((p, False) for p in self.labextensions_path)
for value in info['federated_extensions'].values():
ext_dirs[value['ext_dir']] = True
for ext_dir, has_exts in ext_dirs.items():
if not has_exts:
continue
logger.info(ext_dir)
for name in info['federated_extensions']:
data = info['federated_extensions'][name]
if data['ext_dir'] != ext_dir:
continue
version = data['version']
errors = info['compat_errors'][name]
extra = ''
if _is_disabled(name, info['disabled']):
extra += ' %s' % RED_DISABLED
else:
extra += ' %s' % GREEN_ENABLED
if errors:
extra += ' %s' % RED_X
else:
extra += ' %s' % GREEN_OK
if data['is_local']:
extra += '*'
install = data.get('install')
if install:
extra += ' (%s, %s)' % (
install['packageManager'],
install['packageName']
)
logger.info(' %s v%s%s' % (name, version, extra))
if errors:
error_accumulator[name] = (version, errors)
# Add a spacer line after
logger.info('')
# Write all errors at end:
_log_multiple_compat_errors(logger, error_accumulator)
def _read_build_config(self):
"""Get the build config data for the app dir.
"""
target = pjoin(self.app_dir, 'settings', 'build_config.json')
if not osp.exists(target):
return {}
else:
with open(target) as fid:
return json.load(fid)
def _write_build_config(self, config):
"""Write the build config to the app dir.
"""
self._ensure_app_dirs()
target = pjoin(self.app_dir, 'settings', 'build_config.json')
with open(target, 'w') as fid:
json.dump(config, fid, indent=4)
def _get_local_data(self, source):
"""Get the local data for extensions or linked packages.
"""
config = self._read_build_config()
data = config.setdefault(source, dict())
dead = []
for (name, source) in data.items():
if not osp.exists(source):
dead.append(name)
for name in dead:
link_type = source.replace('_', ' ')
msg = '**Note: Removing dead %s "%s"' % (link_type, name)
self.logger.warn(msg)
del data[name]
if dead:
self._write_build_config(config)
return data
def _install_extension(self, extension, tempdir, pin=None):
"""Install an extension with validation and return the name and path.
"""
info = self._extract_package(extension, tempdir, pin=pin)
data = info['data']
# Check for compatible version unless:
# - A specific version was requested (@ in name,
# but after first char to allow for scope marker).
# - Package is locally installed.
allow_fallback = '@' not in extension[1:] and not info['is_dir']
name = info['name']
# Verify that the package is an extension.
messages = _validate_extension(data)
if messages:
msg = '"%s" is not a valid extension:\n%s'
msg = msg % (extension, '\n'.join(messages))
if allow_fallback:
try:
version = self._latest_compatible_package_version(name)
except URLError:
raise ValueError(msg)
else:
raise ValueError(msg)
# Verify package compatibility.
deps = data.get('dependencies', dict())
errors = _validate_compatibility(extension, deps, self.core_data)
if errors:
msg = _format_compatibility_errors(
data['name'], data['version'], errors
)
if allow_fallback:
try:
version = self._latest_compatible_package_version(name)
except URLError:
# We cannot add any additional information to error message
raise ValueError(msg)
if version and name:
self.logger.debug('Incompatible extension:\n%s', name)
self.logger.debug('Found compatible version: %s', version)
with TemporaryDirectory() as tempdir2:
return self._install_extension(
'%s@%s' % (name, version), tempdir2)
# Extend message to better guide the user what to do:
conflicts = '\n'.join(msg.splitlines()[2:])
msg = ''.join((
self._format_no_compatible_package_version(name),
"\n\n",
conflicts))
raise ValueError(msg)
# Move the file to the app directory.
target = pjoin(self.app_dir, 'extensions', info['filename'])
if osp.exists(target):
os.remove(target)
shutil.move(info['path'], target)
info['path'] = target
return info
def _extract_package(self, source, tempdir, pin=None):
"""Call `npm pack` for an extension.
The pack command will download the package tar if `source` is
a package name, or run `npm pack` locally if `source` is a
directory.
"""
is_dir = osp.exists(source) and osp.isdir(source)
if is_dir and not osp.exists(pjoin(source, 'node_modules')):
self._run(['node', YARN_PATH, 'install'], cwd=source)
info = dict(source=source, is_dir=is_dir)
ret = self._run([which('npm'), 'pack', source], cwd=tempdir)
if ret != 0:
msg = '"%s" is not a valid npm package'
raise ValueError(msg % source)
path = glob(pjoin(tempdir, '*.tgz'))[0]
info['data'] = read_package(path)
if is_dir:
info['sha'] = sha = _tarsum(path)
target = path.replace('.tgz', '-%s.tgz' % sha)
shutil.move(path, target)
info['path'] = target
else:
info['path'] = path
if pin:
old_path = info['path']
new_path = pjoin(osp.dirname(old_path), '{}{}.tgz'.format(PIN_PREFIX, pin))
shutil.move(old_path, new_path)
info['path'] = new_path
info['filename'] = osp.basename(info['path'])
info['name'] = info['data']['name']
info['version'] = info['data']['version']
return info
def _latest_compatible_package_version(self, name):
"""Get the latest compatible version of a package"""
core_data = self.info['core_data']
try:
metadata = _fetch_package_metadata(self.registry, name, self.logger)
except URLError:
return
versions = metadata.get('versions', {})
# Sort pre-release first, as we will reverse the sort:
def sort_key(key_value):
return _semver_key(key_value[0], prerelease_first=True)
for version, data in sorted(versions.items(),
key=sort_key,
reverse=True):
deps = data.get('dependencies', {})
errors = _validate_compatibility(name, deps, core_data)
if not errors:
# Found a compatible version
# skip deprecated versions
if 'deprecated' in data:
self.logger.debug(
'Disregarding compatible version of package as it is deprecated: %s@%s'
% (name, version)
)
continue
# Verify that the version is a valid extension.
with TemporaryDirectory() as tempdir:
info = self._extract_package(
'%s@%s' % (name, version), tempdir)
if _validate_extension(info['data']):
# Invalid, do not consider other versions
return
# Valid
return version
def latest_compatible_package_versions(self, names):
"""Get the latest compatible versions of several packages
Like _latest_compatible_package_version, but optimized for
retrieving the latest version for several packages in one go.
"""
core_data = self.info['core_data']
keys = []
for name in names:
try:
metadata = _fetch_package_metadata(self.registry, name, self.logger)
except URLError:
continue
versions = metadata.get('versions', {})
# Sort pre-release first, as we will reverse the sort:
def sort_key(key_value):
return _semver_key(key_value[0], prerelease_first=True)
for version, data in sorted(versions.items(),
key=sort_key,
reverse=True):
# skip deprecated versions
if 'deprecated' in data:
continue
deps = data.get('dependencies', {})
errors = _validate_compatibility(name, deps, core_data)
if not errors:
# Found a compatible version
keys.append('%s@%s' % (name, version))
break # break inner for
versions = {}
if not keys:
return versions
with TemporaryDirectory() as tempdir:
ret = self._run([which('npm'), 'pack'] + keys, cwd=tempdir)
if ret != 0:
msg = '"%s" is not a valid npm package'
raise ValueError(msg % keys)
for key in keys:
fname = key[0].replace('@', '') + key[1:].replace('@', '-').replace('/', '-') + '.tgz'
data = read_package(osp.join(tempdir, fname))
# Verify that the version is a valid extension.
if not _validate_extension(data):
# Valid
versions[data['name']] = data['version']
return versions
def _format_no_compatible_package_version(self, name):
"""Get the latest compatible version of a package"""
core_data = self.info['core_data']
# Whether lab version is too new:
lab_newer_than_latest = False
# Whether the latest version of the extension depend on a "future" version
# of a singleton package (from the perspective of current lab version):
latest_newer_than_lab = False
try:
metadata = _fetch_package_metadata(self.registry, name, self.logger)
except URLError:
pass
else:
versions = metadata.get('versions', {})
# Sort pre-release first, as we will reverse the sort:
def sort_key(key_value):
return _semver_key(key_value[0], prerelease_first=True)
store = tuple(sorted(versions.items(), key=sort_key, reverse=True))
latest_deps = store[0][1].get('dependencies', {})
core_deps = core_data['resolutions']
singletons = core_data['jupyterlab']['singletonPackages']
for (key, value) in latest_deps.items():
if key in singletons:
# Drop prereleases in comparisons to allow extension authors
# to not have to update their versions for each
# Jupyterlab prerelease version.
c = _compare_ranges(core_deps[key], value, drop_prerelease1=True)
lab_newer_than_latest = lab_newer_than_latest or c < 0
latest_newer_than_lab = latest_newer_than_lab or c > 0
if lab_newer_than_latest:
# All singleton deps in current version of lab are newer than those
# in the latest version of the extension
return ("The extension \"%s\" does not yet support the current version of "
"JupyterLab.\n" % name)
parts = ["No version of {extension} could be found that is compatible with "
"the current version of JupyterLab."]
if latest_newer_than_lab:
parts.extend(("However, it seems to support a new version of JupyterLab.",
"Consider upgrading JupyterLab."))
return " ".join(parts).format(extension=name)
def _run(self, cmd, **kwargs):
"""Run the command using our logger and abort callback.
Returns the exit code.
"""
if self.kill_event.is_set():
raise ValueError('Command was killed')
kwargs['logger'] = self.logger
kwargs['kill_event'] = self.kill_event
proc = ProgressProcess(cmd, **kwargs)
return proc.wait()
def _node_check(logger):
"""Check for the existence of nodejs with the correct version.
"""
node = which('node')
try:
output = subprocess.check_output([node, 'node-version-check.js'], cwd=HERE)
logger.debug(output.decode('utf-8'))
except Exception:
data = CoreConfig()._data
ver = data['engines']['node']
msg = 'Please install nodejs %s before continuing. nodejs may be installed using conda or directly from the nodejs website.' % ver
raise ValueError(msg)
def _yarn_config(logger):
"""Get the yarn configuration.
Returns
-------
{"yarn config": dict, "npm config": dict} if unsuccessfull the subdictionary are empty
"""
configuration = {"yarn config": {}, "npm config": {}}
try:
node = which('node')
except ValueError: # Node not found == user with no need for building jupyterlab
logger.debug("NodeJS was not found. Yarn user configuration is ignored.")
return configuration
try:
output_binary = subprocess.check_output([node, YARN_PATH, 'config', 'list', '--json'], stderr=subprocess.PIPE, cwd=HERE)
output = output_binary.decode('utf-8')
lines = iter(output.splitlines())
try:
for line in lines:
info = json.loads(line)
if info["type"] == "info":
key = info["data"]
inspect = json.loads(next(lines))
if inspect["type"] == "inspect":
configuration[key] = inspect["data"]
except StopIteration:
pass
logger.debug("Yarn configuration loaded.")
except subprocess.CalledProcessError as e:
logger.error("Fail to get yarn configuration. {!s}{!s}".format(e.stderr.decode('utf-8'), e.output.decode('utf-8')))
except Exception as e:
logger.error("Fail to get yarn configuration. {!s}".format(e))
finally:
return configuration
def _ensure_logger(logger=None):
"""Ensure that we have a logger"""
return logger or logging.getLogger('jupyterlab')
def _normalize_path(extension):
"""Normalize a given extension if it is a path.
"""
extension = osp.expanduser(extension)
if osp.exists(extension):
extension = osp.abspath(extension)
return extension
def _rmtree(path, logger):
"""Remove a tree, logging errors"""
def onerror(*exc_info):
logger.debug('Error in shutil.rmtree', exc_info=exc_info)
shutil.rmtree(path, onerror=onerror)
def _unlink(path, logger):
"""Remove a file, logging errors"""
try:
os.unlink(path)
except Exception:
logger.debug('Error in os.unlink', exc_info=sys.exc_info())
def _rmtree_star(path, logger):
"""Remove all files/trees within a dir, logging errors"""
for filename in os.listdir(path):
file_path = osp.join(path, filename)
if osp.isfile(file_path) or osp.islink(file_path):
_unlink(file_path, logger)
elif osp.isdir(file_path):
_rmtree(file_path, logger)
def _validate_extension(data):
"""Detect if a package is an extension using its metadata.
Returns any problems it finds.
"""
jlab = data.get('jupyterlab', None)
if jlab is None:
return ['No `jupyterlab` key']
if not isinstance(jlab, dict):
return ['The `jupyterlab` key must be a JSON object']
extension = jlab.get('extension', False)
mime_extension = jlab.get('mimeExtension', False)
themePath = jlab.get('themePath', '')
schemaDir = jlab.get('schemaDir', '')
messages = []
if not extension and not mime_extension:
messages.append('No `extension` or `mimeExtension` key present')
if extension == mime_extension:
msg = '`mimeExtension` and `extension` must point to different modules'
messages.append(msg)
files = data['jupyterlab_extracted_files']
main = data.get('main', 'index.js')
if not main.endswith('.js'):
main += '.js'
if extension is True:
extension = main
elif extension and not extension.endswith('.js'):
extension += '.js'
if mime_extension is True:
mime_extension = main
elif mime_extension and not mime_extension.endswith('.js'):
mime_extension += '.js'
if extension and extension not in files:
messages.append('Missing extension module "%s"' % extension)
if mime_extension and mime_extension not in files:
messages.append('Missing mimeExtension module "%s"' % mime_extension)
if themePath and not any(f.startswith(themePath) for f in files):
messages.append('themePath is empty: "%s"' % themePath)
if schemaDir and not any(f.startswith(schemaDir) for f in files):
messages.append('schemaDir is empty: "%s"' % schemaDir)
return messages
def _tarsum(input_file):
"""
Compute the recursive sha sum of a tar file.
"""
tar = tarfile.open(input_file, "r")
chunk_size = 100 * 1024
h = hashlib.new("sha1")
for member in tar:
if not member.isfile():
continue
f = tar.extractfile(member)
data = f.read(chunk_size)
while data:
h.update(data)
data = f.read(chunk_size)
return h.hexdigest()
def _get_static_data(app_dir):
"""Get the data for the app static dir.
"""
target = pjoin(app_dir, 'static', 'package.json')
if osp.exists(target):
with open(target) as fid:
return json.load(fid)
else:
return None
def _validate_compatibility(extension, deps, core_data):
"""Validate the compatibility of an extension.
"""
core_deps = core_data['resolutions']
singletons = core_data['jupyterlab']['singletonPackages']
errors = []
for (key, value) in deps.items():
if key in singletons:
# Drop prereleases in comparisons to allow extension authors
# to not have to update their versions for each
# Jupyterlab prerelease version.
overlap = _test_overlap(core_deps[key], value, drop_prerelease1=True)
if overlap is False:
errors.append((key, core_deps[key], value))
return errors
def _test_overlap(spec1, spec2, drop_prerelease1=False, drop_prerelease2=False):
"""Test whether two version specs overlap.
Returns `None` if we cannot determine compatibility,
otherwise whether there is an overlap
"""
cmp = _compare_ranges(spec1, spec2, drop_prerelease1=drop_prerelease1,
drop_prerelease2=drop_prerelease2)
if cmp is None:
return
return cmp == 0
def _compare_ranges(spec1, spec2, drop_prerelease1=False, drop_prerelease2=False):
"""Test whether two version specs overlap.
Returns `None` if we cannot determine compatibility,
otherwise return 0 if there is an overlap, 1 if
spec1 is lower/older than spec2, and -1 if spec1
is higher/newer than spec2.
"""
# Test for overlapping semver ranges.
r1 = Range(spec1, True)
r2 = Range(spec2, True)
# If either range is empty, we cannot verify.
if not r1.range or not r2.range:
return
# Set return_value to a sentinel value
return_value = False
# r1.set may be a list of ranges if the range involved an ||, so we need to test for overlaps between each pair.
for r1set, r2set in itertools.product(r1.set, r2.set):
x1 = r1set[0].semver
x2 = r1set[-1].semver
y1 = r2set[0].semver
y2 = r2set[-1].semver
if x1.prerelease and drop_prerelease1:
x1 = x1.inc('patch')
if y1.prerelease and drop_prerelease2:
y1 = y1.inc('patch')
o1 = r1set[0].operator
o2 = r2set[0].operator
# We do not handle (<) specifiers.
if (o1.startswith('<') or o2.startswith('<')):
continue
# Handle single value specifiers.
lx = lte if x1 == x2 else lt
ly = lte if y1 == y2 else lt
gx = gte if x1 == x2 else gt
gy = gte if x1 == x2 else gt
# Handle unbounded (>) specifiers.
def noop(x, y, z):
return True
if x1 == x2 and o1.startswith('>'):
lx = noop
if y1 == y2 and o2.startswith('>'):
ly = noop
# Check for overlap.
if (gte(x1, y1, True) and ly(x1, y2, True) or
gy(x2, y1, True) and ly(x2, y2, True) or
gte(y1, x1, True) and lx(y1, x2, True) or
gx(y2, x1, True) and lx(y2, x2, True)
):
# if we ever find an overlap, we can return immediately
return 0
if gte(y1, x2, True):
if return_value is False:
# We can possibly return 1
return_value = 1
elif return_value == -1:
# conflicting information, so we must return None
return_value = None
continue
if gte(x1, y2, True):
if return_value is False:
return_value = -1
elif return_value == 1:
# conflicting information, so we must return None
return_value = None
continue
raise AssertionError('Unexpected case comparing version ranges')
if return_value is False:
return_value = None
return return_value
def _is_disabled(name, disabled=[]):
"""Test whether the package is disabled.
"""
for pattern in disabled:
if name == pattern:
return True
if re.compile(pattern).match(name) is not None:
return True
return False
def _format_compatibility_errors(name, version, errors):
"""Format a message for compatibility errors.
"""
msgs = []
l0 = 10
l1 = 10
for error in errors:
pkg, jlab, ext = error
jlab = str(Range(jlab, True))
ext = str(Range(ext, True))
msgs.append((pkg, jlab, ext))
l0 = max(l0, len(pkg) + 1)
l1 = max(l1, len(jlab) + 1)
msg = '\n"%s@%s" is not compatible with the current JupyterLab'
msg = msg % (name, version)
msg += '\nConflicting Dependencies:\n'
msg += 'JupyterLab'.ljust(l0)
msg += 'Extension'.ljust(l1)
msg += 'Package\n'
for (pkg, jlab, ext) in msgs:
msg += jlab.ljust(l0) + ext.ljust(l1) + pkg + '\n'
return msg
def _log_multiple_compat_errors(logger, errors_map):
"""Log compatibility errors for multiple extensions at once"""
outdated = []
others = []
for name, (version, errors) in errors_map.items():
age = _compat_error_age(errors)
if age > 0:
outdated.append(name)
else:
others.append(name)
if outdated:
logger.warn('\n '.join(
['\n The following extension are outdated:'] +
outdated +
['\n Consider running "jupyter labextension update --all" '
'to check for updates.\n']
))
for name in others:
version, errors = errors_map[name]
msg = _format_compatibility_errors(name, version, errors)
logger.warn(msg + '\n')
def _log_single_compat_errors(logger, name, version, errors):
"""Log compatability errors for a single extension"""
age = _compat_error_age(errors)
if age > 0:
logger.warn('The extension "%s" is outdated.\n', name)
else:
msg = _format_compatibility_errors(name, version, errors)
logger.warn(msg + '\n')
def _compat_error_age(errors):
"""Compare all incompatabilites for an extension.
Returns a number > 0 if all extensions are older than that supported by lab.
Returns a number < 0 if all extensions are newer than that supported by lab.
Returns 0 otherwise (i.e. a mix).
"""
# Do any extensions depend on too old lab packages?
any_older = False
# Do any extensions depend on too new lab packages?
any_newer = False
for _, jlab, ext in errors:
# Drop prereleases in comparisons to allow extension authors
# to not have to update their versions for each
# Jupyterlab prerelease version.
c = _compare_ranges(ext, jlab, drop_prerelease1=True)
any_newer = any_newer or c < 0
any_older = any_older or c > 0
if any_older and not any_newer:
return 1
elif any_newer and not any_older:
return -1
return 0
def _get_core_extensions(core_data):
"""Get the core extensions.
"""
data = core_data['jupyterlab']
return list(data['extensions']) + list(data['mimeExtensions'])
def _semver_prerelease_key(prerelease):
"""Sort key for prereleases.
Precedence for two pre-release versions with the same
major, minor, and patch version MUST be determined by
comparing each dot separated identifier from left to
right until a difference is found as follows:
identifiers consisting of only digits are compare
numerically and identifiers with letters or hyphens
are compared lexically in ASCII sort order. Numeric
identifiers always have lower precedence than non-
numeric identifiers. A larger set of pre-release
fields has a higher precedence than a smaller set,
if all of the preceding identifiers are equal.
"""
for entry in prerelease:
if isinstance(entry, int):
# Assure numerics always sort before string
yield ('', entry)
else:
# Use ASCII compare:
yield (entry,)
def _semver_key(version, prerelease_first=False):
"""A sort key-function for sorting semver version string.
The default sorting order is ascending (0.x -> 1.x -> 2.x).
If `prerelease_first`, pre-releases will come before
ALL other semver keys (not just those with same version).
I.e (1.0-pre, 2.0-pre -> 0.x -> 1.x -> 2.x).
Otherwise it will sort in the standard way that it simply
comes before any release with shared version string
(0.x -> 1.0-pre -> 1.x -> 2.0-pre -> 2.x).
"""
v = make_semver(version, True)
if prerelease_first:
key = (0,) if v.prerelease else (1,)
else:
key = ()
key = key + (v.major, v.minor, v.patch)
if not prerelease_first:
# NOT having a prerelease is > having one
key = key + (0,) if v.prerelease else (1,)
if v.prerelease:
key = key + tuple(_semver_prerelease_key(
v.prerelease))
return key
def _fetch_package_metadata(registry, name, logger):
"""Fetch the metadata for a package from the npm registry"""
req = Request(
urljoin(registry, quote(name, safe='@')),
headers={
'Accept': ('application/vnd.npm.install-v1+json;'
' q=1.0, application/json; q=0.8, */*')
}
)
try:
logger.debug('Fetching URL: %s' % (req.full_url))
except AttributeError:
logger.debug('Fetching URL: %s' % (req.get_full_url()))
try:
with contextlib.closing(urlopen(req)) as response:
return json.loads(response.read().decode('utf-8'))
except URLError as exc:
logger.warning(
'Failed to fetch package metadata for %r: %r',
name, exc)
raise
if __name__ == '__main__':
watch_dev(HERE)
| # coding: utf-8
"""JupyterLab command handler"""
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
import contextlib
import errno
import hashlib
import itertools
import json
import logging
import os
import os.path as osp
import re
import shutil
import stat
import site
import subprocess
import sys
import tarfile
import warnings
from copy import deepcopy
from glob import glob
from pathlib import Path
from tempfile import TemporaryDirectory
from threading import Event
from urllib.error import URLError
from urllib.request import Request, quote, urljoin, urlopen
from jupyter_core.paths import jupyter_config_path
from jupyter_server.extension.serverextension import GREEN_ENABLED, GREEN_OK, RED_DISABLED, RED_X
from jupyterlab_server.config import (LabConfig, get_federated_extensions,
get_package_url, get_page_config,
get_static_page_config,
write_page_config)
from jupyterlab_server.process import Process, WatchHelper, list2cmdline, which
from packaging.version import Version
from traitlets import Bool, Dict, HasTraits, Instance, List, Unicode, default
from jupyterlab.coreconfig import CoreConfig
from jupyterlab.jlpmapp import HERE, YARN_PATH
from jupyterlab.semver import Range, gt, gte, lt, lte, make_semver
from jupyterlab._version import __version__
# The regex for expecting the webpack output.
WEBPACK_EXPECT = re.compile(r'.*theme-light-extension/style/index.css')
# The repo root directory
REPO_ROOT = osp.abspath(osp.join(HERE, '..'))
# The dev mode directory.
DEV_DIR = osp.join(REPO_ROOT, 'dev_mode')
# If we are pinning the package, rename it `pin@<alias>`
PIN_PREFIX = 'pin@'
# Default Yarn registry used in default yarn.lock
YARN_DEFAULT_REGISTRY = 'https://registry.yarnpkg.com'
class ProgressProcess(Process):
def __init__(self, cmd, logger=None, cwd=None, kill_event=None,
env=None):
"""Start a subprocess that can be run asynchronously.
Parameters
----------
cmd: list
The command to run.
logger: :class:`~logger.Logger`, optional
The logger instance.
cwd: string, optional
The cwd of the process.
kill_event: :class:`~threading.Event`, optional
An event used to kill the process operation.
env: dict, optional
The environment for the process.
"""
if not isinstance(cmd, (list, tuple)):
raise ValueError('Command must be given as a list')
if kill_event and kill_event.is_set():
raise ValueError('Process aborted')
self.logger = _ensure_logger(logger)
self._last_line = ''
self.cmd = cmd
self.logger.debug('> ' + list2cmdline(cmd))
self.proc = self._create_process(
cwd=cwd,
env=env,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
universal_newlines=True,
encoding='utf-8',
)
self._kill_event = kill_event or Event()
Process._procs.add(self)
def wait(self):
cache = []
proc = self.proc
kill_event = self._kill_event
spinner = itertools.cycle(['-', '\\', '|', '/'])
while proc.poll() is None:
sys.stdout.write(next(spinner)) # write the next character
sys.stdout.flush() # flush stdout buffer (actual character display)
sys.stdout.write('\b')
if kill_event.is_set():
self.terminate()
raise ValueError('Process was aborted')
try:
out, _ = proc.communicate(timeout=.1)
cache.append(out)
except subprocess.TimeoutExpired:
continue
self.logger.debug('\n'.join(cache))
sys.stdout.flush()
return self.terminate()
def pjoin(*args):
"""Join paths to create a real path.
"""
return osp.abspath(osp.join(*args))
def get_user_settings_dir():
"""Get the configured JupyterLab user settings directory.
"""
settings_dir = os.environ.get('JUPYTERLAB_SETTINGS_DIR')
settings_dir = settings_dir or pjoin(
jupyter_config_path()[0], 'lab', 'user-settings'
)
return osp.abspath(settings_dir)
def get_workspaces_dir():
"""Get the configured JupyterLab workspaces directory.
"""
workspaces_dir = os.environ.get('JUPYTERLAB_WORKSPACES_DIR')
workspaces_dir = workspaces_dir or pjoin(
jupyter_config_path()[0], 'lab', 'workspaces'
)
return osp.abspath(workspaces_dir)
def get_app_dir():
"""Get the configured JupyterLab app directory.
"""
# Default to the override environment variable.
if os.environ.get('JUPYTERLAB_DIR'):
# We must resolve the path to get the canonical case of the path for
# case-sensitive systems
return str(Path(os.environ['JUPYTERLAB_DIR']).resolve())
# Use the default locations for data_files.
app_dir = pjoin(sys.prefix, 'share', 'jupyter', 'lab')
# Check for a user level install.
# Ensure that USER_BASE is defined
if hasattr(site, 'getuserbase'):
site.getuserbase()
userbase = getattr(site, 'USER_BASE', None)
if HERE.startswith(userbase) and not app_dir.startswith(userbase):
app_dir = pjoin(userbase, 'share', 'jupyter', 'lab')
# Check for a system install in '/usr/local/share'.
elif (sys.prefix.startswith('/usr') and not
osp.exists(app_dir) and
osp.exists('/usr/local/share/jupyter/lab')):
app_dir = '/usr/local/share/jupyter/lab'
# We must resolve the path to get the canonical case of the path for
# case-sensitive systems
return str(Path(app_dir).resolve())
def dedupe_yarn(path, logger=None):
""" `yarn-deduplicate` with the `fewer` strategy to minimize total
packages installed in a given staging directory
This means a extension (or dependency) _could_ cause a downgrade of an
version expected at publication time, but core should aggressively set
pins above, for example, known-bad versions
"""
had_dupes = ProgressProcess(
['node', YARN_PATH, 'yarn-deduplicate', '-s', 'fewer', '--fail'],
cwd=path, logger=logger
).wait() != 0
if had_dupes:
yarn_proc = ProgressProcess(['node', YARN_PATH], cwd=path, logger=logger)
yarn_proc.wait()
def ensure_node_modules(cwd, logger=None):
"""Ensure that node_modules is up to date.
Returns true if the node_modules was updated.
"""
logger = _ensure_logger(logger)
yarn_proc = ProgressProcess(['node', YARN_PATH, 'check', '--verify-tree'], cwd=cwd, logger=logger)
ret = yarn_proc.wait()
# Update node_modules if needed.
if ret != 0:
yarn_proc = ProgressProcess(['node', YARN_PATH], cwd=cwd, logger=logger)
yarn_proc.wait()
dedupe_yarn(REPO_ROOT, logger)
return ret != 0
def ensure_dev(logger=None):
"""Ensure that the dev assets are available.
"""
logger = _ensure_logger(logger)
target = pjoin(DEV_DIR, 'static')
# Determine whether to build.
if ensure_node_modules(REPO_ROOT, logger) or not osp.exists(target):
yarn_proc = ProgressProcess(['node', YARN_PATH, 'build'], cwd=REPO_ROOT,
logger=logger)
yarn_proc.wait()
def ensure_core(logger=None):
"""Ensure that the core assets are available.
"""
staging = pjoin(HERE, 'staging')
logger = _ensure_logger(logger)
# Determine whether to build.
target = pjoin(HERE, 'static', 'index.html')
if not osp.exists(target):
ensure_node_modules(staging, logger)
yarn_proc = ProgressProcess(['node', YARN_PATH, 'build'], cwd=staging,
logger=logger)
yarn_proc.wait()
def ensure_app(app_dir):
"""Ensure that an application directory is available.
If it does not exist, return a list of messages to prompt the user.
"""
if osp.exists(pjoin(app_dir, 'static', 'index.html')):
return
msgs = ['JupyterLab application assets not found in "%s"' % app_dir,
'Please run `jupyter lab build` or use a different app directory']
return msgs
def watch_packages(logger=None):
"""Run watch mode for the source packages.
Parameters
----------
logger: :class:`~logger.Logger`, optional
The logger instance.
Returns
-------
A list of `WatchHelper` objects.
"""
logger = _ensure_logger(logger)
ensure_node_modules(REPO_ROOT, logger)
ts_dir = osp.abspath(osp.join(REPO_ROOT, 'packages', 'metapackage'))
# Run typescript watch and wait for the string indicating it is done.
ts_regex = r'.* Found 0 errors\. Watching for file changes\.'
ts_proc = WatchHelper(['node', YARN_PATH, 'run', 'watch'],
cwd=ts_dir, logger=logger, startup_regex=ts_regex)
return [ts_proc]
def watch_dev(logger=None):
"""Run watch mode in a given directory.
Parameters
----------
logger: :class:`~logger.Logger`, optional
The logger instance.
Returns
-------
A list of `WatchHelper` objects.
"""
logger = _ensure_logger(logger)
package_procs = watch_packages(logger)
# Run webpack watch and wait for compilation.
wp_proc = WatchHelper(['node', YARN_PATH, 'run', 'watch'],
cwd=DEV_DIR, logger=logger,
startup_regex=WEBPACK_EXPECT)
return package_procs + [wp_proc]
class AppOptions(HasTraits):
"""Options object for build system"""
def __init__(self, logger=None, core_config=None, **kwargs):
if core_config is not None:
kwargs['core_config'] = core_config
if logger is not None:
kwargs['logger'] = logger
# use the default if app_dir is empty
if 'app_dir' in kwargs and not kwargs['app_dir']:
kwargs.pop('app_dir')
super(AppOptions, self).__init__(**kwargs)
app_dir = Unicode(help='The application directory')
use_sys_dir = Bool(
True,
help=('Whether to shadow the default app_dir if that is set to a '
'non-default value'))
logger = Instance(logging.Logger, help='The logger to use')
core_config = Instance(CoreConfig, help='Configuration for core data')
kill_event = Instance(Event, args=(), help='Event for aborting call')
labextensions_path = List(Unicode(), help='The paths to look in for prebuilt JupyterLab extensions')
registry = Unicode(help="NPM packages registry URL")
splice_source = Bool(False, help="Splice source packages into app directory.")
@default('logger')
def _default_logger(self):
return logging.getLogger('jupyterlab')
# These defaults need to be federated to pick up
# any changes to env vars:
@default('app_dir')
def _default_app_dir(self):
return get_app_dir()
@default('core_config')
def _default_core_config(self):
return CoreConfig()
@default('registry')
def _default_registry(self):
config = _yarn_config(self.logger)["yarn config"]
return config.get("registry", YARN_DEFAULT_REGISTRY)
def _ensure_options(options):
"""Helper to use deprecated kwargs for AppOption"""
if options is None:
return AppOptions()
elif issubclass(options.__class__, AppOptions):
return options
else:
return AppOptions(**options)
def watch(app_options=None):
"""Watch the application.
Parameters
----------
app_options: :class:`AppOptions`, optional
The application options.
Returns
-------
A list of processes to run asynchronously.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
if app_options.splice_source:
package_procs = watch_packages(app_options.logger)
else:
package_procs = []
return package_procs + handler.watch()
def install_extension(extension, app_options=None, pin=None):
"""Install an extension package into JupyterLab.
The extension is first validated.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
return handler.install_extension(extension, pin=pin)
def uninstall_extension(name=None, app_options=None, all_=False):
"""Uninstall an extension by name or path.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
if all_ is True:
return handler.uninstall_all_extensions()
return handler.uninstall_extension(name)
def update_extension(name=None, all_=False, app_dir=None, app_options=None):
"""Update an extension by name, or all extensions.
Either `name` must be given as a string, or `all_` must be `True`.
If `all_` is `True`, the value of `name` is ignored.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
if all_ is True:
return handler.update_all_extensions()
return handler.update_extension(name)
def clean(app_options=None):
"""Clean the JupyterLab application directory."""
app_options = _ensure_options(app_options)
handler = _AppHandler(app_options)
logger = app_options.logger
app_dir = app_options.app_dir
logger.info('Cleaning %s...', app_dir)
if app_dir == pjoin(HERE, 'dev'):
raise ValueError('Cannot clean the dev app')
if app_dir == pjoin(HERE, 'core'):
raise ValueError('Cannot clean the core app')
if getattr(app_options, 'all', False):
logger.info('Removing everything in %s...', app_dir)
_rmtree_star(app_dir, logger)
else:
possibleTargets = ['extensions', 'settings', 'staging', 'static']
targets = [t for t in possibleTargets if getattr(app_options, t)]
for name in targets:
target = pjoin(app_dir, name)
if osp.exists(target):
logger.info('Removing %s...', name)
_rmtree(target, logger)
else:
logger.info('%s not present, skipping...', name)
logger.info('Success!')
if getattr(app_options, 'all', False) or getattr(app_options, 'extensions', False):
logger.info('All of your extensions have been removed, and will need to be reinstalled')
def build(name=None, version=None, static_url=None,
kill_event=None,
clean_staging=False, app_options=None, production=True, minimize=True):
"""Build the JupyterLab application.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
return handler.build(name=name, version=version, static_url=static_url,
production=production, minimize=minimize, clean_staging=clean_staging)
def get_app_info(app_options=None):
"""Get a dictionary of information about the app.
"""
handler = _AppHandler(app_options)
handler._ensure_disabled_info()
return handler.info
def enable_extension(extension, app_options=None, level='sys_prefix'):
"""Enable a JupyterLab extension.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.toggle_extension(extension, False, level=level)
def disable_extension(extension, app_options=None, level='sys_prefix'):
"""Disable a JupyterLab package.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.toggle_extension(extension, True, level=level)
def check_extension(extension, installed=False, app_options=None):
"""Check if a JupyterLab extension is enabled or disabled.
"""
handler = _AppHandler(app_options)
return handler.check_extension(extension, installed)
def build_check(app_options=None):
"""Determine whether JupyterLab should be built.
Returns a list of messages.
"""
app_options = _ensure_options(app_options)
_node_check(app_options.logger)
handler = _AppHandler(app_options)
return handler.build_check()
def list_extensions(app_options=None):
"""List the extensions.
"""
handler = _AppHandler(app_options)
return handler.list_extensions()
def link_package(path, app_options=None):
"""Link a package against the JupyterLab build.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.link_package(path)
def unlink_package(package, app_options=None):
"""Unlink a package from JupyterLab by path or name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
handler = _AppHandler(app_options)
return handler.unlink_package(package)
def get_app_version(app_options=None):
"""Get the application version."""
handler = _AppHandler(app_options)
return handler.info['version']
def get_latest_compatible_package_versions(names, app_options=None):
"""Get the latest compatible version of a list of packages.
"""
handler = _AppHandler(app_options)
return handler.latest_compatible_package_versions(names)
def read_package(target):
"""Read the package data in a given target tarball.
"""
tar = tarfile.open(target, "r")
f = tar.extractfile('package/package.json')
data = json.loads(f.read().decode('utf8'))
data['jupyterlab_extracted_files'] = [
f.path[len('package/'):] for f in tar.getmembers()
]
tar.close()
return data
# ----------------------------------------------------------------------
# Implementation details
# ----------------------------------------------------------------------
class _AppHandler(object):
def __init__(self, options):
"""Create a new _AppHandler object
"""
options = _ensure_options(options)
self._options = options
self.app_dir = options.app_dir
self.sys_dir = get_app_dir() if options.use_sys_dir else self.app_dir
self.logger = options.logger
# Make a deep copy of the core data so we don't influence the original copy
self.core_data = deepcopy(options.core_config._data)
self.labextensions_path = options.labextensions_path
self.kill_event = options.kill_event
self.registry = options.registry
# Do this last since it relies on other attributes
self.info = self._get_app_info()
def install_extension(self, extension, existing=None, pin=None):
"""Install an extension package into JupyterLab.
The extension is first validated.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
extension = _normalize_path(extension)
extensions = self.info['extensions']
# Check for a core extensions.
if extension in self.info['core_extensions']:
config = self._read_build_config()
uninstalled = config.get('uninstalled_core_extensions', [])
if extension in uninstalled:
self.logger.info('Installing core extension %s' % extension)
uninstalled.remove(extension)
config['uninstalled_core_extensions'] = uninstalled
self._write_build_config(config)
return True
return False
# Create the app dirs if needed.
self._ensure_app_dirs()
# Install the package using a temporary directory.
with TemporaryDirectory() as tempdir:
info = self._install_extension(extension, tempdir, pin=pin)
name = info['name']
# Local directories get name mangled and stored in metadata.
if info['is_dir']:
config = self._read_build_config()
local = config.setdefault('local_extensions', dict())
local[name] = info['source']
self._write_build_config(config)
# Remove an existing extension with the same name and different path
if name in extensions:
other = extensions[name]
if other['path'] != info['path'] and other['location'] == 'app':
os.remove(other['path'])
return True
def build(self, name=None, version=None, static_url=None,
clean_staging=False, production=True, minimize=True):
"""Build the application.
"""
if production is None:
production = not (self.info['linked_packages'] or self.info['local_extensions'])
if not production:
minimize = False
# If splicing, make sure the source packages are built
if self._options.splice_source:
ensure_node_modules(REPO_ROOT, logger=self.logger)
self._run(['node', YARN_PATH, 'build:packages'], cwd=REPO_ROOT)
info = ['production' if production else 'development']
if production:
info.append('minimized' if minimize else 'not minimized')
self.logger.info(f'Building jupyterlab assets ({", ".join(info)})')
# Set up the build directory.
app_dir = self.app_dir
self._populate_staging(
name=name, version=version, static_url=static_url,
clean=clean_staging
)
staging = pjoin(app_dir, 'staging')
# Make sure packages are installed.
ret = self._run(['node', YARN_PATH, 'install', '--non-interactive'], cwd=staging)
if ret != 0:
msg = 'npm dependencies failed to install'
self.logger.debug(msg)
raise RuntimeError(msg)
# Build the app.
dedupe_yarn(staging, self.logger)
command = f'build:{"prod" if production else "dev"}{":minimize" if minimize else ""}'
ret = self._run(['node', YARN_PATH, 'run', command], cwd=staging)
if ret != 0:
msg = 'JupyterLab failed to build'
self.logger.debug(msg)
raise RuntimeError(msg)
def watch(self):
"""Start the application watcher and then run the watch in
the background.
"""
staging = pjoin(self.app_dir, 'staging')
self._populate_staging()
# Make sure packages are installed.
self._run(['node', YARN_PATH, 'install'], cwd=staging)
dedupe_yarn(staging, self.logger)
proc = WatchHelper(['node', YARN_PATH, 'run', 'watch'],
cwd=pjoin(self.app_dir, 'staging'),
startup_regex=WEBPACK_EXPECT,
logger=self.logger)
return [proc]
def list_extensions(self):
"""Print an output of the extensions.
"""
self._ensure_disabled_info()
logger = self.logger
info = self.info
logger.info('JupyterLab v%s' % info['version'])
if info['federated_extensions'] or info['extensions']:
info['compat_errors'] = self._get_extension_compat()
if info['federated_extensions']:
self._list_federated_extensions()
if info['extensions']:
logger.info('Other labextensions (built into JupyterLab)')
self._list_extensions(info, 'app')
self._list_extensions(info, 'sys')
local = info['local_extensions']
if local:
logger.info('\n local extensions:')
for name in sorted(local):
logger.info(' %s: %s' % (name, local[name]))
linked_packages = info['linked_packages']
if linked_packages:
logger.info('\n linked packages:')
for key in sorted(linked_packages):
source = linked_packages[key]['source']
logger.info(' %s: %s' % (key, source))
uninstalled_core = info['uninstalled_core']
if uninstalled_core:
logger.info('\nUninstalled core extensions:')
[logger.info(' %s' % item) for item in sorted(uninstalled_core)]
all_exts = list(info['federated_extensions']) + list(info['extensions']) + list(info['core_extensions'])
# Ignore disabled extensions that are not installed
disabled = [i for i in info['disabled'] if i.partition(':')[0] in all_exts]
if disabled:
logger.info('\nDisabled extensions:')
for item in sorted(disabled):
# Show that all plugins will be disabled if the whole extension matches
if item in all_exts:
item += ' (all plugins)'
logger.info(' %s' % item)
# Here check if modules are improperly shadowed
improper_shadowed = []
for ext_name in self.info['shadowed_exts']:
source_version = self.info['extensions'][ext_name]['version']
prebuilt_version = self.info['federated_extensions'][ext_name]['version']
if not gte(prebuilt_version, source_version, True):
improper_shadowed.append(ext_name)
if improper_shadowed:
logger.info('\nThe following source extensions are overshadowed by older prebuilt extensions:')
[logger.info(' %s' % name) for name in sorted(improper_shadowed)]
messages = self.build_check(fast=True)
if messages:
logger.info('\nBuild recommended, please run `jupyter lab build`:')
[logger.info(' %s' % item) for item in messages]
def build_check(self, fast=False):
"""Determine whether JupyterLab should be built.
Returns a list of messages.
"""
app_dir = self.app_dir
local = self.info['local_extensions']
linked = self.info['linked_packages']
messages = []
# Check for no application.
pkg_path = pjoin(app_dir, 'static', 'package.json')
if not osp.exists(pkg_path):
return ['No built application']
static_data = self.info['static_data']
old_jlab = static_data['jupyterlab']
old_deps = static_data.get('dependencies', dict())
# Look for mismatched version.
static_version = old_jlab.get('version', '')
if not static_version.endswith('-spliced'):
core_version = old_jlab['version']
if Version(static_version) != Version(core_version):
msg = 'Version mismatch: %s (built), %s (current)'
return [msg % (static_version, core_version)]
shadowed_exts = self.info['shadowed_exts']
# Look for mismatched extensions.
new_package = self._get_package_template(silent=fast)
new_jlab = new_package['jupyterlab']
new_deps = new_package.get('dependencies', dict())
for ext_type in ['extensions', 'mimeExtensions']:
# Extensions that were added.
for ext in new_jlab[ext_type]:
if ext in shadowed_exts:
continue
if ext not in old_jlab[ext_type]:
messages.append('%s needs to be included in build' % ext)
# Extensions that were removed.
for ext in old_jlab[ext_type]:
if ext in shadowed_exts:
continue
if ext not in new_jlab[ext_type]:
messages.append('%s needs to be removed from build' % ext)
# Look for mismatched dependencies
src_pkg_dir = pjoin(REPO_ROOT, 'packages')
for (pkg, dep) in new_deps.items():
if old_deps.get(pkg, '').startswith(src_pkg_dir):
continue
if pkg not in old_deps:
continue
# Skip local and linked since we pick them up separately.
if pkg in local or pkg in linked:
continue
if old_deps[pkg] != dep:
msg = '%s changed from %s to %s'
messages.append(msg % (pkg, old_deps[pkg], new_deps[pkg]))
# Look for updated local extensions.
for (name, source) in local.items():
if fast or name in shadowed_exts:
continue
dname = pjoin(app_dir, 'extensions')
if self._check_local(name, source, dname):
messages.append('%s content changed' % name)
# Look for updated linked packages.
for (name, item) in linked.items():
if fast or name in shadowed_exts:
continue
dname = pjoin(app_dir, 'staging', 'linked_packages')
if self._check_local(name, item['source'], dname):
messages.append('%s content changed' % name)
return messages
def uninstall_extension(self, name):
"""Uninstall an extension by name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
info = self.info
logger = self.logger
if name in info['federated_extensions']:
if info['federated_extensions'][name].get('install', dict()).get('uninstallInstructions', None):
logger.error('JupyterLab cannot uninstall this extension. %s' % info['federated_extensions'][name]['install']['uninstallInstructions'])
else:
logger.error('JupyterLab cannot uninstall %s since it was installed outside of JupyterLab. Use the same method used to install this extension to uninstall this extension.' % name)
return False
# Allow for uninstalled core extensions.
if name in info['core_extensions']:
config = self._read_build_config()
uninstalled = config.get('uninstalled_core_extensions', [])
if name not in uninstalled:
logger.info('Uninstalling core extension %s' % name)
uninstalled.append(name)
config['uninstalled_core_extensions'] = uninstalled
self._write_build_config(config)
return True
return False
local = info['local_extensions']
for (extname, data) in info['extensions'].items():
path = data['path']
if extname == name:
msg = 'Uninstalling %s from %s' % (name, osp.dirname(path))
logger.info(msg)
os.remove(path)
# Handle local extensions.
if extname in local:
config = self._read_build_config()
data = config.setdefault('local_extensions', dict())
del data[extname]
self._write_build_config(config)
return True
logger.warn('No labextension named "%s" installed' % name)
return False
def uninstall_all_extensions(self):
"""Uninstalls all extensions
Returns `True` if a rebuild is recommended, `False` otherwise
"""
should_rebuild = False
for (extname, _) in self.info['extensions'].items():
uninstalled = self.uninstall_extension(extname)
should_rebuild = should_rebuild or uninstalled
return should_rebuild
def update_all_extensions(self):
"""Update all non-local extensions.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
should_rebuild = False
for (extname, _) in self.info['extensions'].items():
if extname in self.info['local_extensions']:
continue
updated = self._update_extension(extname)
# Rebuild if at least one update happens:
should_rebuild = should_rebuild or updated
return should_rebuild
def update_extension(self, name):
"""Update an extension by name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
if name not in self.info['extensions']:
self.logger.warning('No labextension named "%s" installed' % name)
return False
return self._update_extension(name)
def _update_extension(self, name):
"""Update an extension by name.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
data = self.info['extensions'][name]
if data["alias_package_source"]:
self.logger.warn("Skipping updating pinned extension '%s'." % name)
return False
try:
latest = self._latest_compatible_package_version(name)
except URLError:
return False
if latest is None:
self.logger.warn('No compatible version found for %s!' % (name,))
return False
if latest == data['version']:
self.logger.info('Extension %r already up to date' % name)
return False
self.logger.info('Updating %s to version %s' % (name, latest))
return self.install_extension('%s@%s' % (name, latest))
def link_package(self, path):
"""Link a package at the given path.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
path = _normalize_path(path)
if not osp.exists(path) or not osp.isdir(path):
msg = 'Cannot install "%s" only link local directories'
raise ValueError(msg % path)
with TemporaryDirectory() as tempdir:
info = self._extract_package(path, tempdir)
messages = _validate_extension(info['data'])
if not messages:
return self.install_extension(path)
# Warn that it is a linked package.
self.logger.warning('Installing %s as a linked package because it does not have extension metadata:', path)
[self.logger.warning(' %s' % m) for m in messages]
# Add to metadata.
config = self._read_build_config()
linked = config.setdefault('linked_packages', dict())
linked[info['name']] = info['source']
self._write_build_config(config)
return True
def unlink_package(self, path):
"""Unlink a package by name or at the given path.
A ValueError is raised if the path is not an unlinkable package.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
path = _normalize_path(path)
config = self._read_build_config()
linked = config.setdefault('linked_packages', dict())
found = None
for (name, source) in linked.items():
if name == path or source == path:
found = name
if found:
del linked[found]
else:
local = config.setdefault('local_extensions', dict())
for (name, source) in local.items():
if name == path or source == path:
found = name
if found:
del local[found]
path = self.info['extensions'][found]['path']
os.remove(path)
if not found:
raise ValueError('No linked package for %s' % path)
self._write_build_config(config)
return True
def toggle_extension(self, extension, value, level='sys_prefix'):
"""Enable or disable a lab extension.
Returns `True` if a rebuild is recommended, `False` otherwise.
"""
lab_config = LabConfig()
app_settings_dir = osp.join(self.app_dir, 'settings')
page_config = get_static_page_config(app_settings_dir=app_settings_dir, logger=self.logger, level=level)
disabled = page_config.get('disabledExtensions', {})
did_something = False
is_disabled = disabled.get(extension, False)
if value and not is_disabled:
disabled[extension] = True
did_something = True
elif not value and is_disabled:
disabled[extension] = False
did_something = True
if did_something:
page_config['disabledExtensions'] = disabled
write_page_config(page_config, level=level)
return did_something
def check_extension(self, extension, check_installed_only=False):
"""Check if a lab extension is enabled or disabled
"""
self._ensure_disabled_info()
info = self.info
if extension in info["core_extensions"]:
return self._check_core_extension(
extension, info, check_installed_only)
if extension in info["linked_packages"]:
self.logger.info('%s:%s' % (extension, GREEN_ENABLED))
return True
return self._check_common_extension(
extension, info, check_installed_only)
def _check_core_extension(self, extension, info, check_installed_only):
"""Check if a core extension is enabled or disabled
"""
if extension in info['uninstalled_core']:
self.logger.info('%s:%s' % (extension, RED_X))
return False
if check_installed_only:
self.logger.info('%s: %s' % (extension, GREEN_OK))
return True
if extension in info['disabled_core']:
self.logger.info('%s: %s' % (extension, RED_DISABLED))
return False
self.logger.info('%s:%s' % (extension, GREEN_ENABLED))
return True
def _check_common_extension(self, extension, info, check_installed_only):
"""Check if a common (non-core) extension is enabled or disabled
"""
if extension not in info['extensions']:
self.logger.info('%s:%s' % (extension, RED_X))
return False
errors = self._get_extension_compat()[extension]
if errors:
self.logger.info('%s:%s (compatibility errors)' %
(extension, RED_X))
return False
if check_installed_only:
self.logger.info('%s: %s' % (extension, GREEN_OK))
return True
if _is_disabled(extension, info['disabled']):
self.logger.info('%s: %s' % (extension, RED_DISABLED))
return False
self.logger.info('%s:%s' % (extension, GREEN_ENABLED))
return True
def _get_app_info(self):
"""Get information about the app.
"""
info = dict()
info['core_data'] = core_data = self.core_data
info['extensions'] = extensions = self._get_extensions(core_data)
info['local_extensions'] = self._get_local_extensions()
info['linked_packages'] = self._get_linked_packages()
info['app_extensions'] = app = []
info['sys_extensions'] = sys = []
for (name, data) in extensions.items():
data['is_local'] = name in info['local_extensions']
if data['location'] == 'app':
app.append(name)
else:
sys.append(name)
info['uninstalled_core'] = self._get_uninstalled_core_extensions()
info['static_data'] = _get_static_data(self.app_dir)
app_data = info['static_data'] or core_data
info['version'] = app_data['jupyterlab']['version']
info['staticUrl'] = app_data['jupyterlab'].get('staticUrl', '')
info['sys_dir'] = self.sys_dir
info['app_dir'] = self.app_dir
info['core_extensions'] = _get_core_extensions(self.core_data)
info['federated_extensions'] = get_federated_extensions(self.labextensions_path)
info['shadowed_exts'] = [ext for ext in info['extensions'] if ext in info['federated_extensions']]
return info
def _ensure_disabled_info(self):
info = self.info
if 'disabled' in info:
return
labextensions_path = self.labextensions_path
app_settings_dir = osp.join(self.app_dir, 'settings')
page_config = get_page_config(labextensions_path, app_settings_dir=app_settings_dir, logger=self.logger)
info['disabled'] = page_config.get('disabledExtensions', [])
disabled_core = []
for key in info['core_extensions']:
if key in info['disabled']:
disabled_core.append(key)
info['disabled_core'] = disabled_core
def _populate_staging(self, name=None, version=None, static_url=None,
clean=False):
"""Set up the assets in the staging directory.
"""
app_dir = self.app_dir
staging = pjoin(app_dir, 'staging')
if clean and osp.exists(staging):
self.logger.info("Cleaning %s", staging)
_rmtree(staging, self.logger)
self._ensure_app_dirs()
if not version:
version = self.info['core_data']['jupyterlab']['version']
splice_source = self._options.splice_source
if splice_source:
self.logger.debug('Splicing dev packages into app directory.')
source_dir = DEV_DIR
version = __version__ + '-spliced'
else:
source_dir = pjoin(HERE, 'staging')
# Look for mismatched version.
pkg_path = pjoin(staging, 'package.json')
if osp.exists(pkg_path):
with open(pkg_path) as fid:
data = json.load(fid)
if data['jupyterlab'].get('version', '') != version:
_rmtree(staging, self.logger)
os.makedirs(staging)
for fname in ['index.js', 'bootstrap.js', 'publicpath.js',
'webpack.config.js',
'webpack.prod.config.js',
'webpack.prod.minimize.config.js']:
target = pjoin(staging, fname)
shutil.copy(pjoin(source_dir, fname), target)
for fname in ['.yarnrc', 'yarn.js']:
target = pjoin(staging, fname)
shutil.copy(pjoin(HERE, 'staging', fname), target)
# Ensure a clean templates directory
templates = pjoin(staging, 'templates')
if osp.exists(templates):
_rmtree(templates, self.logger)
try:
shutil.copytree(pjoin(source_dir, 'templates'), templates)
except shutil.Error as error:
# `copytree` throws an error if copying to + from NFS even though
# the copy is successful (see https://bugs.python.org/issue24564
# and https://github.com/jupyterlab/jupyterlab/issues/5233)
real_error = '[Errno 22]' not in str(error) and '[Errno 5]' not in str(error)
if real_error or not osp.exists(templates):
raise
# Ensure a clean linked packages directory.
linked_dir = pjoin(staging, 'linked_packages')
if osp.exists(linked_dir):
_rmtree(linked_dir, self.logger)
os.makedirs(linked_dir)
# Template the package.json file.
# Update the local extensions.
extensions = self.info['extensions']
removed = False
for (key, source) in self.info['local_extensions'].items():
# Handle a local extension that was removed.
if key not in extensions:
config = self._read_build_config()
data = config.setdefault('local_extensions', dict())
del data[key]
self._write_build_config(config)
removed = True
continue
dname = pjoin(app_dir, 'extensions')
self._update_local(key, source, dname, extensions[key],
'local_extensions')
# Update the list of local extensions if any were removed.
if removed:
self.info['local_extensions'] = self._get_local_extensions()
# Update the linked packages.
linked = self.info['linked_packages']
for (key, item) in linked.items():
dname = pjoin(staging, 'linked_packages')
self._update_local(key, item['source'], dname, item,
'linked_packages')
# Then get the package template.
data = self._get_package_template()
jlab = data['jupyterlab']
if version:
jlab['version'] = version
if name:
jlab['name'] = name
if static_url:
jlab['staticUrl'] = static_url
# Handle splicing of packages
if splice_source:
# Splice workspace tree as linked dependencies
for path in glob(pjoin(REPO_ROOT, 'packages', '*', 'package.json')):
local_path = osp.dirname(osp.abspath(path))
pkg_data = json.loads(Path(path).read_text(encoding='utf-8'))
name = pkg_data['name']
if name in data['dependencies']:
data['dependencies'][name] = local_path
jlab['linkedPackages'][name] = local_path
if name in data['resolutions']:
data['resolutions'][name] = local_path
# splice the builder as well
local_path = osp.abspath(pjoin(REPO_ROOT, 'builder'))
data['devDependencies']['@jupyterlab/builder'] = local_path
target = osp.join(staging, 'node_modules', '@jupyterlab', 'builder')
# Remove node_modules so it gets re-populated
node_modules = pjoin(staging, 'node_modules')
if osp.exists(node_modules):
shutil.rmtree(node_modules, ignore_errors=True)
# Write the package file
pkg_path = pjoin(staging, 'package.json')
with open(pkg_path, 'w') as fid:
json.dump(data, fid, indent=4)
# copy known-good yarn.lock if missing
lock_path = pjoin(staging, 'yarn.lock')
lock_template = pjoin(HERE, 'staging', 'yarn.lock')
if self.registry != YARN_DEFAULT_REGISTRY: # Replace on the fly the yarn repository see #3658
with open(lock_template, encoding='utf-8') as f:
template = f.read()
template = template.replace(YARN_DEFAULT_REGISTRY, self.registry.strip("/"))
with open(lock_path, 'w', encoding='utf-8') as f:
f.write(template)
elif not osp.exists(lock_path):
shutil.copy(lock_template, lock_path)
os.chmod(lock_path, stat.S_IWRITE | stat.S_IREAD)
def _get_package_template(self, silent=False):
"""Get the template the for staging package.json file.
"""
logger = self.logger
# make a deep copy of the data so we don't influence the core data
data = deepcopy(self.info['core_data'])
local = self.info['local_extensions']
linked = self.info['linked_packages']
extensions = self.info['extensions']
shadowed_exts = self.info['shadowed_exts']
jlab = data['jupyterlab']
def format_path(path):
path = osp.relpath(path, pjoin(self.app_dir, 'staging'))
path = 'file:' + path.replace(os.sep, '/')
if os.name == 'nt':
path = path.lower()
return path
jlab['linkedPackages'] = dict()
# Handle local extensions.
for (key, source) in local.items():
if key in shadowed_exts:
continue
jlab['linkedPackages'][key] = source
data['resolutions'][key] = 'file:' + self.info['extensions'][key]['path']
# Handle linked packages.
for (key, item) in linked.items():
if key in shadowed_exts:
continue
path = pjoin(self.app_dir, 'staging', 'linked_packages')
path = pjoin(path, item['filename'])
data['dependencies'][key] = format_path(path)
jlab['linkedPackages'][key] = item['source']
data['resolutions'][key] = format_path(path)
data['jupyterlab']['extensionMetadata'] = dict()
# Handle extensions
compat_errors = self._get_extension_compat()
for (key, value) in extensions.items():
# Reject incompatible extensions with a message.
errors = compat_errors[key]
if errors:
if not silent:
_log_single_compat_errors(
logger, key, value['version'], errors
)
continue
data['dependencies'][key] = format_path(value['path'])
jlab_data = value['jupyterlab']
for item in ['extension', 'mimeExtension']:
ext = jlab_data.get(item, False)
if not ext:
continue
if ext is True:
ext = ''
jlab[item + 's'][key] = ext
# Add metadata for the extension
data['jupyterlab']['extensionMetadata'][key] = jlab_data
# Handle uninstalled core extensions.
for item in self.info['uninstalled_core']:
if item in jlab['extensions']:
data['jupyterlab']['extensions'].pop(item)
elif item in jlab['mimeExtensions']:
data['jupyterlab']['mimeExtensions'].pop(item)
# Remove from dependencies as well.
if item in data['dependencies']:
data['dependencies'].pop(item)
return data
def _check_local(self, name, source, dname):
"""Check if a local package has changed.
`dname` is the directory name of existing package tar archives.
"""
# Extract the package in a temporary directory.
with TemporaryDirectory() as tempdir:
info = self._extract_package(source, tempdir)
# Test if the file content has changed.
# This relies on `_extract_package` adding the hashsum
# to the filename, allowing a simple exist check to
# compare the hash to the "cache" in dname.
target = pjoin(dname, info['filename'])
return not osp.exists(target)
def _update_local(self, name, source, dname, data, dtype):
"""Update a local dependency. Return `True` if changed.
"""
# Extract the package in a temporary directory.
existing = data['filename']
if not osp.exists(pjoin(dname, existing)):
existing = ''
with TemporaryDirectory() as tempdir:
info = self._extract_package(source, tempdir)
# Bail if the file content has not changed.
if info['filename'] == existing:
return existing
shutil.move(info['path'], pjoin(dname, info['filename']))
# Remove the previous tarball and return the new file name.
if existing:
os.remove(pjoin(dname, existing))
data['filename'] = info['filename']
data['path'] = pjoin(data['tar_dir'], data['filename'])
return info['filename']
def _get_extensions(self, core_data):
"""Get the extensions for the application.
"""
app_dir = self.app_dir
extensions = dict()
# Get system level packages.
sys_path = pjoin(self.sys_dir, 'extensions')
app_path = pjoin(self.app_dir, 'extensions')
extensions = self._get_extensions_in_dir(self.sys_dir, core_data)
# Look in app_dir if different.
app_path = pjoin(app_dir, 'extensions')
if app_path == sys_path or not osp.exists(app_path):
return extensions
extensions.update(self._get_extensions_in_dir(app_dir, core_data))
return extensions
def _get_extensions_in_dir(self, dname, core_data):
"""Get the extensions in a given directory.
"""
extensions = dict()
location = 'app' if dname == self.app_dir else 'sys'
for target in glob(pjoin(dname, 'extensions', '*.tgz')):
data = read_package(target)
deps = data.get('dependencies', dict())
name = data['name']
jlab = data.get('jupyterlab', dict())
path = osp.abspath(target)
filename = osp.basename(target)
if filename.startswith(PIN_PREFIX):
alias = filename[len(PIN_PREFIX):-len(".tgz")]
else:
alias = None
url = get_package_url(data)
extensions[alias or name] = dict(path=path,
filename=osp.basename(path),
url=url,
version=data['version'],
# Only save the package name if the extension name is an alias
alias_package_source=name if alias else None,
jupyterlab=jlab,
dependencies=deps,
tar_dir=osp.dirname(path),
location=location)
return extensions
def _get_extension_compat(self):
"""Get the extension compatibility info.
"""
compat = dict()
core_data = self.info['core_data']
seen = set()
for (name, data) in self.info['federated_extensions'].items():
deps = data['dependencies']
compat[name] = _validate_compatibility(name, deps, core_data)
seen.add(name)
for (name, data) in self.info['extensions'].items():
if name in seen:
continue
deps = data['dependencies']
compat[name] = _validate_compatibility(name, deps, core_data)
return compat
def _get_local_extensions(self):
"""Get the locally installed extensions.
"""
return self._get_local_data('local_extensions')
def _get_linked_packages(self):
"""Get the linked packages.
"""
info = self._get_local_data('linked_packages')
dname = pjoin(self.app_dir, 'staging', 'linked_packages')
for (name, source) in info.items():
info[name] = dict(source=source, filename='', tar_dir=dname)
if not osp.exists(dname):
return info
for path in glob(pjoin(dname, '*.tgz')):
path = osp.abspath(path)
data = read_package(path)
name = data['name']
if name not in info:
self.logger.warn('Removing orphaned linked package %s' % name)
os.remove(path)
continue
item = info[name]
item['filename'] = osp.basename(path)
item['path'] = path
item['version'] = data['version']
item['data'] = data
return info
def _get_uninstalled_core_extensions(self):
"""Get the uninstalled core extensions.
"""
config = self._read_build_config()
return config.get('uninstalled_core_extensions', [])
def _ensure_app_dirs(self):
"""Ensure that the application directories exist"""
dirs = ['extensions', 'settings', 'staging', 'schemas', 'themes']
for dname in dirs:
path = pjoin(self.app_dir, dname)
if not osp.exists(path):
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
def _list_extensions(self, info, ext_type):
"""List the extensions of a given type.
"""
self._ensure_disabled_info()
logger = self.logger
names = info['%s_extensions' % ext_type]
if not names:
return
dname = info['%s_dir' % ext_type]
error_accumulator = {}
logger.info(' %s dir: %s' % (ext_type, dname))
for name in sorted(names):
if name in info['federated_extensions']:
continue
data = info['extensions'][name]
version = data['version']
errors = info['compat_errors'][name]
extra = ''
if _is_disabled(name, info['disabled']):
extra += ' %s' % RED_DISABLED
else:
extra += ' %s' % GREEN_ENABLED
if errors:
extra += ' %s' % RED_X
else:
extra += ' %s' % GREEN_OK
if data['is_local']:
extra += '*'
# If we have the package name in the data, this means this extension's name is the alias name
alias_package_source = data['alias_package_source']
if alias_package_source:
logger.info(' %s %s v%s%s' % (name, alias_package_source, version, extra))
else:
logger.info(' %s v%s%s' % (name, version, extra))
if errors:
error_accumulator[name] = (version, errors)
# Write all errors at end:
_log_multiple_compat_errors(logger, error_accumulator)
# Write a blank line separator
logger.info('')
def _list_federated_extensions(self):
self._ensure_disabled_info()
info = self.info
logger = self.logger
error_accumulator = {}
ext_dirs = dict((p, False) for p in self.labextensions_path)
for value in info['federated_extensions'].values():
ext_dirs[value['ext_dir']] = True
for ext_dir, has_exts in ext_dirs.items():
if not has_exts:
continue
logger.info(ext_dir)
for name in info['federated_extensions']:
data = info['federated_extensions'][name]
if data['ext_dir'] != ext_dir:
continue
version = data['version']
errors = info['compat_errors'][name]
extra = ''
if _is_disabled(name, info['disabled']):
extra += ' %s' % RED_DISABLED
else:
extra += ' %s' % GREEN_ENABLED
if errors:
extra += ' %s' % RED_X
else:
extra += ' %s' % GREEN_OK
if data['is_local']:
extra += '*'
install = data.get('install')
if install:
extra += ' (%s, %s)' % (
install['packageManager'],
install['packageName']
)
logger.info(' %s v%s%s' % (name, version, extra))
if errors:
error_accumulator[name] = (version, errors)
# Add a spacer line after
logger.info('')
# Write all errors at end:
_log_multiple_compat_errors(logger, error_accumulator)
def _read_build_config(self):
"""Get the build config data for the app dir.
"""
target = pjoin(self.app_dir, 'settings', 'build_config.json')
if not osp.exists(target):
return {}
else:
with open(target) as fid:
return json.load(fid)
def _write_build_config(self, config):
"""Write the build config to the app dir.
"""
self._ensure_app_dirs()
target = pjoin(self.app_dir, 'settings', 'build_config.json')
with open(target, 'w') as fid:
json.dump(config, fid, indent=4)
def _get_local_data(self, source):
"""Get the local data for extensions or linked packages.
"""
config = self._read_build_config()
data = config.setdefault(source, dict())
dead = []
for (name, source) in data.items():
if not osp.exists(source):
dead.append(name)
for name in dead:
link_type = source.replace('_', ' ')
msg = '**Note: Removing dead %s "%s"' % (link_type, name)
self.logger.warn(msg)
del data[name]
if dead:
self._write_build_config(config)
return data
def _install_extension(self, extension, tempdir, pin=None):
"""Install an extension with validation and return the name and path.
"""
info = self._extract_package(extension, tempdir, pin=pin)
data = info['data']
# Check for compatible version unless:
# - A specific version was requested (@ in name,
# but after first char to allow for scope marker).
# - Package is locally installed.
allow_fallback = '@' not in extension[1:] and not info['is_dir']
name = info['name']
# Verify that the package is an extension.
messages = _validate_extension(data)
if messages:
msg = '"%s" is not a valid extension:\n%s'
msg = msg % (extension, '\n'.join(messages))
if allow_fallback:
try:
version = self._latest_compatible_package_version(name)
except URLError:
raise ValueError(msg)
else:
raise ValueError(msg)
# Verify package compatibility.
deps = data.get('dependencies', dict())
errors = _validate_compatibility(extension, deps, self.core_data)
if errors:
msg = _format_compatibility_errors(
data['name'], data['version'], errors
)
if allow_fallback:
try:
version = self._latest_compatible_package_version(name)
except URLError:
# We cannot add any additional information to error message
raise ValueError(msg)
if version and name:
self.logger.debug('Incompatible extension:\n%s', name)
self.logger.debug('Found compatible version: %s', version)
with TemporaryDirectory() as tempdir2:
return self._install_extension(
'%s@%s' % (name, version), tempdir2)
# Extend message to better guide the user what to do:
conflicts = '\n'.join(msg.splitlines()[2:])
msg = ''.join((
self._format_no_compatible_package_version(name),
"\n\n",
conflicts))
raise ValueError(msg)
# Move the file to the app directory.
target = pjoin(self.app_dir, 'extensions', info['filename'])
if osp.exists(target):
os.remove(target)
shutil.move(info['path'], target)
info['path'] = target
return info
def _extract_package(self, source, tempdir, pin=None):
"""Call `npm pack` for an extension.
The pack command will download the package tar if `source` is
a package name, or run `npm pack` locally if `source` is a
directory.
"""
is_dir = osp.exists(source) and osp.isdir(source)
if is_dir and not osp.exists(pjoin(source, 'node_modules')):
self._run(['node', YARN_PATH, 'install'], cwd=source)
info = dict(source=source, is_dir=is_dir)
ret = self._run([which('npm'), 'pack', source], cwd=tempdir)
if ret != 0:
msg = '"%s" is not a valid npm package'
raise ValueError(msg % source)
path = glob(pjoin(tempdir, '*.tgz'))[0]
info['data'] = read_package(path)
if is_dir:
info['sha'] = sha = _tarsum(path)
target = path.replace('.tgz', '-%s.tgz' % sha)
shutil.move(path, target)
info['path'] = target
else:
info['path'] = path
if pin:
old_path = info['path']
new_path = pjoin(osp.dirname(old_path), '{}{}.tgz'.format(PIN_PREFIX, pin))
shutil.move(old_path, new_path)
info['path'] = new_path
info['filename'] = osp.basename(info['path'])
info['name'] = info['data']['name']
info['version'] = info['data']['version']
return info
def _latest_compatible_package_version(self, name):
"""Get the latest compatible version of a package"""
core_data = self.info['core_data']
try:
metadata = _fetch_package_metadata(self.registry, name, self.logger)
except URLError:
return
versions = metadata.get('versions', {})
# Sort pre-release first, as we will reverse the sort:
def sort_key(key_value):
return _semver_key(key_value[0], prerelease_first=True)
for version, data in sorted(versions.items(),
key=sort_key,
reverse=True):
deps = data.get('dependencies', {})
errors = _validate_compatibility(name, deps, core_data)
if not errors:
# Found a compatible version
# skip deprecated versions
if 'deprecated' in data:
self.logger.debug(
'Disregarding compatible version of package as it is deprecated: %s@%s'
% (name, version)
)
continue
# Verify that the version is a valid extension.
with TemporaryDirectory() as tempdir:
info = self._extract_package(
'%s@%s' % (name, version), tempdir)
if _validate_extension(info['data']):
# Invalid, do not consider other versions
return
# Valid
return version
def latest_compatible_package_versions(self, names):
"""Get the latest compatible versions of several packages
Like _latest_compatible_package_version, but optimized for
retrieving the latest version for several packages in one go.
"""
core_data = self.info['core_data']
keys = []
for name in names:
try:
metadata = _fetch_package_metadata(self.registry, name, self.logger)
except URLError:
continue
versions = metadata.get('versions', {})
# Sort pre-release first, as we will reverse the sort:
def sort_key(key_value):
return _semver_key(key_value[0], prerelease_first=True)
for version, data in sorted(versions.items(),
key=sort_key,
reverse=True):
# skip deprecated versions
if 'deprecated' in data:
continue
deps = data.get('dependencies', {})
errors = _validate_compatibility(name, deps, core_data)
if not errors:
# Found a compatible version
keys.append('%s@%s' % (name, version))
break # break inner for
versions = {}
if not keys:
return versions
with TemporaryDirectory() as tempdir:
ret = self._run([which('npm'), 'pack'] + keys, cwd=tempdir)
if ret != 0:
msg = '"%s" is not a valid npm package'
raise ValueError(msg % keys)
for key in keys:
fname = key[0].replace('@', '') + key[1:].replace('@', '-').replace('/', '-') + '.tgz'
data = read_package(osp.join(tempdir, fname))
# Verify that the version is a valid extension.
if not _validate_extension(data):
# Valid
versions[data['name']] = data['version']
return versions
def _format_no_compatible_package_version(self, name):
"""Get the latest compatible version of a package"""
core_data = self.info['core_data']
# Whether lab version is too new:
lab_newer_than_latest = False
# Whether the latest version of the extension depend on a "future" version
# of a singleton package (from the perspective of current lab version):
latest_newer_than_lab = False
try:
metadata = _fetch_package_metadata(self.registry, name, self.logger)
except URLError:
pass
else:
versions = metadata.get('versions', {})
# Sort pre-release first, as we will reverse the sort:
def sort_key(key_value):
return _semver_key(key_value[0], prerelease_first=True)
store = tuple(sorted(versions.items(), key=sort_key, reverse=True))
latest_deps = store[0][1].get('dependencies', {})
core_deps = core_data['resolutions']
singletons = core_data['jupyterlab']['singletonPackages']
for (key, value) in latest_deps.items():
if key in singletons:
# Drop prereleases in comparisons to allow extension authors
# to not have to update their versions for each
# Jupyterlab prerelease version.
c = _compare_ranges(core_deps[key], value, drop_prerelease1=True)
lab_newer_than_latest = lab_newer_than_latest or c < 0
latest_newer_than_lab = latest_newer_than_lab or c > 0
if lab_newer_than_latest:
# All singleton deps in current version of lab are newer than those
# in the latest version of the extension
return ("The extension \"%s\" does not yet support the current version of "
"JupyterLab.\n" % name)
parts = ["No version of {extension} could be found that is compatible with "
"the current version of JupyterLab."]
if latest_newer_than_lab:
parts.extend(("However, it seems to support a new version of JupyterLab.",
"Consider upgrading JupyterLab."))
return " ".join(parts).format(extension=name)
def _run(self, cmd, **kwargs):
"""Run the command using our logger and abort callback.
Returns the exit code.
"""
if self.kill_event.is_set():
raise ValueError('Command was killed')
kwargs['logger'] = self.logger
kwargs['kill_event'] = self.kill_event
proc = ProgressProcess(cmd, **kwargs)
return proc.wait()
def _node_check(logger):
"""Check for the existence of nodejs with the correct version.
"""
node = which('node')
try:
output = subprocess.check_output([node, 'node-version-check.js'], cwd=HERE)
logger.debug(output.decode('utf-8'))
except Exception:
data = CoreConfig()._data
ver = data['engines']['node']
msg = 'Please install nodejs %s before continuing. nodejs may be installed using conda or directly from the nodejs website.' % ver
raise ValueError(msg)
def _yarn_config(logger):
"""Get the yarn configuration.
Returns
-------
{"yarn config": dict, "npm config": dict} if unsuccessfull the subdictionary are empty
"""
configuration = {"yarn config": {}, "npm config": {}}
try:
node = which('node')
except ValueError: # Node not found == user with no need for building jupyterlab
logger.debug("NodeJS was not found. Yarn user configuration is ignored.")
return configuration
try:
output_binary = subprocess.check_output([node, YARN_PATH, 'config', 'list', '--json'], stderr=subprocess.PIPE, cwd=HERE)
output = output_binary.decode('utf-8')
lines = iter(output.splitlines())
try:
for line in lines:
info = json.loads(line)
if info["type"] == "info":
key = info["data"]
inspect = json.loads(next(lines))
if inspect["type"] == "inspect":
configuration[key] = inspect["data"]
except StopIteration:
pass
logger.debug("Yarn configuration loaded.")
except subprocess.CalledProcessError as e:
logger.error("Fail to get yarn configuration. {!s}{!s}".format(e.stderr.decode('utf-8'), e.output.decode('utf-8')))
except Exception as e:
logger.error("Fail to get yarn configuration. {!s}".format(e))
finally:
return configuration
def _ensure_logger(logger=None):
"""Ensure that we have a logger"""
return logger or logging.getLogger('jupyterlab')
def _normalize_path(extension):
"""Normalize a given extension if it is a path.
"""
extension = osp.expanduser(extension)
if osp.exists(extension):
extension = osp.abspath(extension)
return extension
def _rmtree(path, logger):
"""Remove a tree, logging errors"""
def onerror(*exc_info):
logger.debug('Error in shutil.rmtree', exc_info=exc_info)
shutil.rmtree(path, onerror=onerror)
def _unlink(path, logger):
"""Remove a file, logging errors"""
try:
os.unlink(path)
except Exception:
logger.debug('Error in os.unlink', exc_info=sys.exc_info())
def _rmtree_star(path, logger):
"""Remove all files/trees within a dir, logging errors"""
for filename in os.listdir(path):
file_path = osp.join(path, filename)
if osp.isfile(file_path) or osp.islink(file_path):
_unlink(file_path, logger)
elif osp.isdir(file_path):
_rmtree(file_path, logger)
def _validate_extension(data):
"""Detect if a package is an extension using its metadata.
Returns any problems it finds.
"""
jlab = data.get('jupyterlab', None)
if jlab is None:
return ['No `jupyterlab` key']
if not isinstance(jlab, dict):
return ['The `jupyterlab` key must be a JSON object']
extension = jlab.get('extension', False)
mime_extension = jlab.get('mimeExtension', False)
themePath = jlab.get('themePath', '')
schemaDir = jlab.get('schemaDir', '')
messages = []
if not extension and not mime_extension:
messages.append('No `extension` or `mimeExtension` key present')
if extension == mime_extension:
msg = '`mimeExtension` and `extension` must point to different modules'
messages.append(msg)
files = data['jupyterlab_extracted_files']
main = data.get('main', 'index.js')
if not main.endswith('.js'):
main += '.js'
if extension is True:
extension = main
elif extension and not extension.endswith('.js'):
extension += '.js'
if mime_extension is True:
mime_extension = main
elif mime_extension and not mime_extension.endswith('.js'):
mime_extension += '.js'
if extension and extension not in files:
messages.append('Missing extension module "%s"' % extension)
if mime_extension and mime_extension not in files:
messages.append('Missing mimeExtension module "%s"' % mime_extension)
if themePath and not any(f.startswith(themePath) for f in files):
messages.append('themePath is empty: "%s"' % themePath)
if schemaDir and not any(f.startswith(schemaDir) for f in files):
messages.append('schemaDir is empty: "%s"' % schemaDir)
return messages
def _tarsum(input_file):
"""
Compute the recursive sha sum of a tar file.
"""
tar = tarfile.open(input_file, "r")
chunk_size = 100 * 1024
h = hashlib.new("sha1")
for member in tar:
if not member.isfile():
continue
f = tar.extractfile(member)
data = f.read(chunk_size)
while data:
h.update(data)
data = f.read(chunk_size)
return h.hexdigest()
def _get_static_data(app_dir):
"""Get the data for the app static dir.
"""
target = pjoin(app_dir, 'static', 'package.json')
if osp.exists(target):
with open(target) as fid:
return json.load(fid)
else:
return None
def _validate_compatibility(extension, deps, core_data):
"""Validate the compatibility of an extension.
"""
core_deps = core_data['resolutions']
singletons = core_data['jupyterlab']['singletonPackages']
errors = []
for (key, value) in deps.items():
if key in singletons:
# Drop prereleases in comparisons to allow extension authors
# to not have to update their versions for each
# Jupyterlab prerelease version.
overlap = _test_overlap(core_deps[key], value, drop_prerelease1=True)
if overlap is False:
errors.append((key, core_deps[key], value))
return errors
def _test_overlap(spec1, spec2, drop_prerelease1=False, drop_prerelease2=False):
"""Test whether two version specs overlap.
Returns `None` if we cannot determine compatibility,
otherwise whether there is an overlap
"""
cmp = _compare_ranges(spec1, spec2, drop_prerelease1=drop_prerelease1,
drop_prerelease2=drop_prerelease2)
if cmp is None:
return
return cmp == 0
def _compare_ranges(spec1, spec2, drop_prerelease1=False, drop_prerelease2=False):
"""Test whether two version specs overlap.
Returns `None` if we cannot determine compatibility,
otherwise return 0 if there is an overlap, 1 if
spec1 is lower/older than spec2, and -1 if spec1
is higher/newer than spec2.
"""
# Test for overlapping semver ranges.
r1 = Range(spec1, True)
r2 = Range(spec2, True)
# If either range is empty, we cannot verify.
if not r1.range or not r2.range:
return
# Set return_value to a sentinel value
return_value = False
# r1.set may be a list of ranges if the range involved an ||, so we need to test for overlaps between each pair.
for r1set, r2set in itertools.product(r1.set, r2.set):
x1 = r1set[0].semver
x2 = r1set[-1].semver
y1 = r2set[0].semver
y2 = r2set[-1].semver
if x1.prerelease and drop_prerelease1:
x1 = x1.inc('patch')
if y1.prerelease and drop_prerelease2:
y1 = y1.inc('patch')
o1 = r1set[0].operator
o2 = r2set[0].operator
# We do not handle (<) specifiers.
if (o1.startswith('<') or o2.startswith('<')):
continue
# Handle single value specifiers.
lx = lte if x1 == x2 else lt
ly = lte if y1 == y2 else lt
gx = gte if x1 == x2 else gt
gy = gte if x1 == x2 else gt
# Handle unbounded (>) specifiers.
def noop(x, y, z):
return True
if x1 == x2 and o1.startswith('>'):
lx = noop
if y1 == y2 and o2.startswith('>'):
ly = noop
# Check for overlap.
if (gte(x1, y1, True) and ly(x1, y2, True) or
gy(x2, y1, True) and ly(x2, y2, True) or
gte(y1, x1, True) and lx(y1, x2, True) or
gx(y2, x1, True) and lx(y2, x2, True)
):
# if we ever find an overlap, we can return immediately
return 0
if gte(y1, x2, True):
if return_value is False:
# We can possibly return 1
return_value = 1
elif return_value == -1:
# conflicting information, so we must return None
return_value = None
continue
if gte(x1, y2, True):
if return_value is False:
return_value = -1
elif return_value == 1:
# conflicting information, so we must return None
return_value = None
continue
raise AssertionError('Unexpected case comparing version ranges')
if return_value is False:
return_value = None
return return_value
def _is_disabled(name, disabled=[]):
"""Test whether the package is disabled.
"""
for pattern in disabled:
if name == pattern:
return True
if re.compile(pattern).match(name) is not None:
return True
return False
def _format_compatibility_errors(name, version, errors):
"""Format a message for compatibility errors.
"""
msgs = []
l0 = 10
l1 = 10
for error in errors:
pkg, jlab, ext = error
jlab = str(Range(jlab, True))
ext = str(Range(ext, True))
msgs.append((pkg, jlab, ext))
l0 = max(l0, len(pkg) + 1)
l1 = max(l1, len(jlab) + 1)
msg = '\n"%s@%s" is not compatible with the current JupyterLab'
msg = msg % (name, version)
msg += '\nConflicting Dependencies:\n'
msg += 'JupyterLab'.ljust(l0)
msg += 'Extension'.ljust(l1)
msg += 'Package\n'
for (pkg, jlab, ext) in msgs:
msg += jlab.ljust(l0) + ext.ljust(l1) + pkg + '\n'
return msg
def _log_multiple_compat_errors(logger, errors_map):
"""Log compatibility errors for multiple extensions at once"""
outdated = []
others = []
for name, (version, errors) in errors_map.items():
age = _compat_error_age(errors)
if age > 0:
outdated.append(name)
else:
others.append(name)
if outdated:
logger.warn('\n '.join(
['\n The following extension are outdated:'] +
outdated +
['\n Consider running "jupyter labextension update --all" '
'to check for updates.\n']
))
for name in others:
version, errors = errors_map[name]
msg = _format_compatibility_errors(name, version, errors)
logger.warn(msg + '\n')
def _log_single_compat_errors(logger, name, version, errors):
"""Log compatability errors for a single extension"""
age = _compat_error_age(errors)
if age > 0:
logger.warn('The extension "%s" is outdated.\n', name)
else:
msg = _format_compatibility_errors(name, version, errors)
logger.warn(msg + '\n')
def _compat_error_age(errors):
"""Compare all incompatabilites for an extension.
Returns a number > 0 if all extensions are older than that supported by lab.
Returns a number < 0 if all extensions are newer than that supported by lab.
Returns 0 otherwise (i.e. a mix).
"""
# Do any extensions depend on too old lab packages?
any_older = False
# Do any extensions depend on too new lab packages?
any_newer = False
for _, jlab, ext in errors:
# Drop prereleases in comparisons to allow extension authors
# to not have to update their versions for each
# Jupyterlab prerelease version.
c = _compare_ranges(ext, jlab, drop_prerelease1=True)
any_newer = any_newer or c < 0
any_older = any_older or c > 0
if any_older and not any_newer:
return 1
elif any_newer and not any_older:
return -1
return 0
def _get_core_extensions(core_data):
"""Get the core extensions.
"""
data = core_data['jupyterlab']
return list(data['extensions']) + list(data['mimeExtensions'])
def _semver_prerelease_key(prerelease):
"""Sort key for prereleases.
Precedence for two pre-release versions with the same
major, minor, and patch version MUST be determined by
comparing each dot separated identifier from left to
right until a difference is found as follows:
identifiers consisting of only digits are compare
numerically and identifiers with letters or hyphens
are compared lexically in ASCII sort order. Numeric
identifiers always have lower precedence than non-
numeric identifiers. A larger set of pre-release
fields has a higher precedence than a smaller set,
if all of the preceding identifiers are equal.
"""
for entry in prerelease:
if isinstance(entry, int):
# Assure numerics always sort before string
yield ('', entry)
else:
# Use ASCII compare:
yield (entry,)
def _semver_key(version, prerelease_first=False):
"""A sort key-function for sorting semver version string.
The default sorting order is ascending (0.x -> 1.x -> 2.x).
If `prerelease_first`, pre-releases will come before
ALL other semver keys (not just those with same version).
I.e (1.0-pre, 2.0-pre -> 0.x -> 1.x -> 2.x).
Otherwise it will sort in the standard way that it simply
comes before any release with shared version string
(0.x -> 1.0-pre -> 1.x -> 2.0-pre -> 2.x).
"""
v = make_semver(version, True)
if prerelease_first:
key = (0,) if v.prerelease else (1,)
else:
key = ()
key = key + (v.major, v.minor, v.patch)
if not prerelease_first:
# NOT having a prerelease is > having one
key = key + (0,) if v.prerelease else (1,)
if v.prerelease:
key = key + tuple(_semver_prerelease_key(
v.prerelease))
return key
def _fetch_package_metadata(registry, name, logger):
"""Fetch the metadata for a package from the npm registry"""
req = Request(
urljoin(registry, quote(name, safe='@')),
headers={
'Accept': ('application/vnd.npm.install-v1+json;'
' q=1.0, application/json; q=0.8, */*')
}
)
try:
logger.debug('Fetching URL: %s' % (req.full_url))
except AttributeError:
logger.debug('Fetching URL: %s' % (req.get_full_url()))
try:
with contextlib.closing(urlopen(req)) as response:
return json.loads(response.read().decode('utf-8'))
except URLError as exc:
logger.warning(
'Failed to fetch package metadata for %r: %r',
name, exc)
raise
if __name__ == '__main__':
watch_dev(HERE)
|
"""This script is used to measure output dispersion score of synthetic datasets
"""
import os
import sys
import numpy as np
import torch
import random
import tqdm
import time
from pathlib import Path
from os.path import join
from model.model import EncoderDecoder
sys.path.append(join(os.path.dirname(os.path.abspath(__file__)), "../"))
from dataset.toy_dataset.toydataset import ToyDataset
from auxiliary.my_utils import plant_seeds
from auxiliary.metric_parser import parser
from model.pseudo_network import Generator
from eval.metric import ChamferDistanceL2, compute_ptcloud_dismatrix_batch, cluster_eval
from eval.eval_utils import get_logger, CountFrequency, dic_to_array, mean_std
import auxiliary.ChamferDistancePytorch.chamfer3D.dist_chamfer_3D as dist_chamfer_3D
opt = parser()
###Mkdir and logger
opt.device = torch.device("cuda")
res_path = join(opt.dir_name, opt.res_folder)
Path(res_path).mkdir(parents=True, exist_ok=True)
proc_logger = get_logger("process", res_path, "process.log")
res_logger = get_logger("results", res_path, "score.log")
opt.logger = proc_logger
print(opt.trained_exp_dir)
nviews_dic = {"train":opt.nviews_train, "test":opt.nviews_test}
num_seed = max(len(opt.seed_list), 1)
score_collect = {}
eval_label_list = set()
for seed_idx in range(num_seed):
if opt.seed_list:
opt.seed = opt.seed_list[seed_idx]
score_collect.update({str(opt.seed):{}})
plant_seeds(opt.seed)
##Loading Data and Network
if opt.split == 'pred':
eval_loss = ChamferDistanceL2().to(opt.device)
distChamfer = dist_chamfer_3D.chamfer_3DDist()
if opt.network=='atlasnet':
network = EncoderDecoder(opt)
opt.logger.info(f"Reloading Network Weights from {opt.reload_model_path}...")
network.load_state_dict(torch.load(opt.reload_model_path)['model_state_dict'])
network.to(opt.device)
if opt.split == "train":
dataset = ToyDataset(data_base_dir=opt.data_base_dir,
json_file=opt.train_json_file,
num_points=opt.number_points,
train=True,
normalization=opt.normalization,
logger=opt.logger)
elif opt.split == "test" or opt.split == "pred":
dataset = ToyDataset(data_base_dir=opt.data_base_dir,
json_file=opt.test_json_file,
num_points=opt.number_points,
train=False,
normalization=opt.normalization,
logger=opt.logger)
else:
raise NotImplementedError()
loader = torch.utils.data.DataLoader(dataset,
batch_size=opt.pred_batch_size,
shuffle=False, num_workers=8)
if opt.rsample == 1:
sample_num = len(dataset)
opt.nsample = len(dataset)
else:
if opt.rsample != -1:
opt.nsample = int(opt.rsample * len(dataset))
subset_index = random.sample(range(len(dataset)), opt.nsample)
dataset = torch.utils.data.Subset(dataset, subset_index)
sample_num = len(subset_index)
data = None
pred_loss = 0.0
with torch.set_grad_enabled(False):
for batch in tqdm.tqdm(loader, desc=f"loading {opt.split} {opt.type} data"):
if opt.split == 'pred':
input_img = batch['image'].to(opt.device)
pred_points = network(input_img, train=False)
pred_points = pred_points.transpose(2, 3).contiguous()
B = pred_points.shape[0]
pred_points = pred_points.view(B, -1, 3)
gt_points = batch['points'].to(opt.device)
assert gt_points.shape[0] == B, f'gt {gt_points.shape[0]}, while pred {B}'
if data is None:
data = pred_points
else:
data = torch.cat((data, pred_points), dim=0)
pred_loss += eval_loss(gt_points, pred_points).item()
dist1, dist2, idx1, idx2 = distChamfer(gt_points, pred_points)
opt.type = 'points'
pred_loss /= len(loader)
proc_logger.info(f"Pred Chamfer Loss: {pred_loss:4f}")
start_time = time.time()
if opt.type == 'points':
data = data.to(opt.device)
metric = ChamferDistanceL2().to(opt.device)
distance_matrix = compute_ptcloud_dismatrix_batch(data, data, metric,
opt.pred_batch_size, opt.device, proc_logger)
else:
raise NotImplementedError()
elasp_time = (time.time() - start_time) / 60
distance_matrix = distance_matrix.cpu().numpy()
score_collect[str(opt.seed)].update({"dm": distance_matrix})
score_collect[str(opt.seed)].update({"pred_chamfer": pred_loss})
n_evals = len(opt.perf_pc_list)
for index in range(n_evals):
c_method, e_method, n_cluster, perf_pc = opt.c_method[index], opt.e_method[index], opt.cluster_k[index], opt.perf_pc_list[index]
score, part_label = cluster_eval(c_method=c_method, e_method=e_method, distance_matrix=distance_matrix,
seed=opt.seed, n_cluster=n_cluster, pc=perf_pc)
label_stat_verbose = ""
freq = CountFrequency(part_label)
for key, value in freq.items():
label_stat_verbose += "% d :% d | "%(key, value)
proc_logger.info(f"{opt.type} mode: {opt.mode}, split: {opt.split} " +
f"nviews: train {opt.nviews_train}, test {opt.nviews_test}, sample num:{sample_num} " +
f"seed{opt.seed}, metric{opt.metric} perf{perf_pc}% " +
f"samp{distance_matrix.shape[0]}, Pred Chamfer: {pred_loss:.4f}, score: {score:.4f} DM" +
f"{distance_matrix.shape[0]}, compute time {elasp_time:2f} min")
eval_label = f"{c_method}_{e_method}_k{n_cluster}p{perf_pc}"
score_collect[str(opt.seed)].update({eval_label: {}})
eval_label_list.add(eval_label)
score_collect[str(opt.seed)][eval_label].update({"score": score})
score_collect[str(opt.seed)][eval_label].update({"label": np.array(part_label)}) # cluster label
score_collect[str(opt.seed)][eval_label].update({"perf_percent": perf_pc})
score_collect[str(opt.seed)][eval_label].update({"label_stats": dic_to_array(freq)})
eval_label_list = list(eval_label_list)
eval_label_list.sort()
ss_list = {}
for eval_label in eval_label_list:
ss_list.update({eval_label:[]})
pred_list = []
for seed in score_collect:
pred_list.append(score_collect[seed]['pred_chamfer'])
for eval_label in eval_label_list:
ss_list[eval_label].append(score_collect[seed][eval_label]["score"])
for eval_label in eval_label_list:
avg_score_lst = [score/sample_num for score in ss_list[eval_label]]
ss_mean, ss_std = mean_std(ss_list[eval_label])
avg_ss_mean, avg_ss_std = mean_std(avg_score_lst)
score_collect.update({f'{eval_label}': np.array([ss_mean, ss_std])})
score_collect.update({f'avg_{eval_label}': np.array([avg_ss_mean, avg_ss_std])})
pred_loss_mean, pred_loss_std = mean_std(pred_list)
score_collect.update({'split': opt.split})
score_collect.update({'type': opt.type})
score_collect.update({'mode': opt.mode})
score_collect.update({'sample_num': sample_num})
score_collect.update({'chamfer_stats': np.array([pred_loss_mean, pred_loss_std])})
score_collect.update({'trainnv': np.array([opt.nviews_train])})
score_collect.update({'testnv': np.array([opt.nviews_test])})
for eval_label in eval_label_list:
ss_mean, ss_std = score_collect[f'{eval_label}'][0], score_collect[f'{eval_label}'][1]
avg_ss_mean, avg_ss_std = score_collect[f'avg_{eval_label}'][0], score_collect[f'avg_{eval_label}'][1]
res_logger.info(f"{opt.network} {opt.type} mode: {opt.mode}, split: {opt.split}, " +
f"nviews: train {opt.nviews_train}, test {opt.nviews_test}, sample num: {sample_num} " +
f"seed_list {opt.seed_list}, metric {opt.metric} perf: {perf_pc} % {opt.metric} {opt.trained_exp_dir} {eval_label} " +
f"Sum of Score: (mean: {ss_mean:.4f}|std: {ss_std:.4f}) "+
f"Average Score: (mean: {avg_ss_mean:.4f}|std: {avg_ss_std:.4f}) "+
f"Pred Chamfer: (mean:{pred_loss_mean:.4f}|std: {pred_loss_std:.4f}) " +
f"DM compute time {elasp_time:.2f} min")
np.savez_compressed(os.path.join(res_path,
f"{opt.network}_{opt.mode}_{opt.split}_{opt.type}_{sample_num}_{opt.trained_exp_dir.split("/")[-1]}.npz"), **score_collect)
res_logger.info(f"###############END OF {opt.type} {opt.network} {opt.trained_exp_dir} PIPELINE#################")
| """This script is used to measure output dispersion score of synthetic datasets
"""
import os
import sys
import numpy as np
import torch
import random
import tqdm
import time
from pathlib import Path
from os.path import join
from model.model import EncoderDecoder
sys.path.append(join(os.path.dirname(os.path.abspath(__file__)), "../"))
from dataset.toy_dataset.toydataset import ToyDataset
from auxiliary.my_utils import plant_seeds
from auxiliary.metric_parser import parser
from model.pseudo_network import Generator
from eval.metric import ChamferDistanceL2, compute_ptcloud_dismatrix_batch, cluster_eval
from eval.eval_utils import get_logger, CountFrequency, dic_to_array, mean_std
import auxiliary.ChamferDistancePytorch.chamfer3D.dist_chamfer_3D as dist_chamfer_3D
opt = parser()
###Mkdir and logger
opt.device = torch.device("cuda")
res_path = join(opt.dir_name, opt.res_folder)
Path(res_path).mkdir(parents=True, exist_ok=True)
proc_logger = get_logger("process", res_path, "process.log")
res_logger = get_logger("results", res_path, "score.log")
opt.logger = proc_logger
print(opt.trained_exp_dir)
nviews_dic = {"train":opt.nviews_train, "test":opt.nviews_test}
num_seed = max(len(opt.seed_list), 1)
score_collect = {}
eval_label_list = set()
for seed_idx in range(num_seed):
if opt.seed_list:
opt.seed = opt.seed_list[seed_idx]
score_collect.update({str(opt.seed):{}})
plant_seeds(opt.seed)
##Loading Data and Network
if opt.split == 'pred':
eval_loss = ChamferDistanceL2().to(opt.device)
distChamfer = dist_chamfer_3D.chamfer_3DDist()
if opt.network=='atlasnet':
network = EncoderDecoder(opt)
opt.logger.info(f"Reloading Network Weights from {opt.reload_model_path}...")
network.load_state_dict(torch.load(opt.reload_model_path)['model_state_dict'])
network.to(opt.device)
if opt.split == "train":
dataset = ToyDataset(data_base_dir=opt.data_base_dir,
json_file=opt.train_json_file,
num_points=opt.number_points,
train=True,
normalization=opt.normalization,
logger=opt.logger)
elif opt.split == "test" or opt.split == "pred":
dataset = ToyDataset(data_base_dir=opt.data_base_dir,
json_file=opt.test_json_file,
num_points=opt.number_points,
train=False,
normalization=opt.normalization,
logger=opt.logger)
else:
raise NotImplementedError()
loader = torch.utils.data.DataLoader(dataset,
batch_size=opt.pred_batch_size,
shuffle=False, num_workers=8)
if opt.rsample == 1:
sample_num = len(dataset)
opt.nsample = len(dataset)
else:
if opt.rsample != -1:
opt.nsample = int(opt.rsample * len(dataset))
subset_index = random.sample(range(len(dataset)), opt.nsample)
dataset = torch.utils.data.Subset(dataset, subset_index)
sample_num = len(subset_index)
data = None
pred_loss = 0.0
with torch.set_grad_enabled(False):
for batch in tqdm.tqdm(loader, desc=f"loading {opt.split} {opt.type} data"):
if opt.split == 'pred':
input_img = batch['image'].to(opt.device)
pred_points = network(input_img, train=False)
pred_points = pred_points.transpose(2, 3).contiguous()
B = pred_points.shape[0]
pred_points = pred_points.view(B, -1, 3)
gt_points = batch['points'].to(opt.device)
assert gt_points.shape[0] == B, f'gt {gt_points.shape[0]}, while pred {B}'
if data is None:
data = pred_points
else:
data = torch.cat((data, pred_points), dim=0)
pred_loss += eval_loss(gt_points, pred_points).item()
dist1, dist2, idx1, idx2 = distChamfer(gt_points, pred_points)
opt.type = 'points'
pred_loss /= len(loader)
proc_logger.info(f"Pred Chamfer Loss: {pred_loss:4f}")
start_time = time.time()
if opt.type == 'points':
data = data.to(opt.device)
metric = ChamferDistanceL2().to(opt.device)
distance_matrix = compute_ptcloud_dismatrix_batch(data, data, metric,
opt.pred_batch_size, opt.device, proc_logger)
else:
raise NotImplementedError()
elasp_time = (time.time() - start_time) / 60
distance_matrix = distance_matrix.cpu().numpy()
score_collect[str(opt.seed)].update({"dm": distance_matrix})
score_collect[str(opt.seed)].update({"pred_chamfer": pred_loss})
n_evals = len(opt.perf_pc_list)
for index in range(n_evals):
c_method, e_method, n_cluster, perf_pc = opt.c_method[index], opt.e_method[index], opt.cluster_k[index], opt.perf_pc_list[index]
score, part_label = cluster_eval(c_method=c_method, e_method=e_method, distance_matrix=distance_matrix,
seed=opt.seed, n_cluster=n_cluster, pc=perf_pc)
label_stat_verbose = ""
freq = CountFrequency(part_label)
for key, value in freq.items():
label_stat_verbose += "% d :% d | "%(key, value)
proc_logger.info(f"{opt.type} mode: {opt.mode}, split: {opt.split} " +
f"nviews: train {opt.nviews_train}, test {opt.nviews_test}, sample num:{sample_num} " +
f"seed{opt.seed}, metric{opt.metric} perf{perf_pc}% " +
f"samp{distance_matrix.shape[0]}, Pred Chamfer: {pred_loss:.4f}, score: {score:.4f} DM" +
f"{distance_matrix.shape[0]}, compute time {elasp_time:2f} min")
eval_label = f"{c_method}_{e_method}_k{n_cluster}p{perf_pc}"
score_collect[str(opt.seed)].update({eval_label: {}})
eval_label_list.add(eval_label)
score_collect[str(opt.seed)][eval_label].update({"score": score})
score_collect[str(opt.seed)][eval_label].update({"label": np.array(part_label)}) # cluster label
score_collect[str(opt.seed)][eval_label].update({"perf_percent": perf_pc})
score_collect[str(opt.seed)][eval_label].update({"label_stats": dic_to_array(freq)})
eval_label_list = list(eval_label_list)
eval_label_list.sort()
ss_list = {}
for eval_label in eval_label_list:
ss_list.update({eval_label:[]})
pred_list = []
for seed in score_collect:
pred_list.append(score_collect[seed]['pred_chamfer'])
for eval_label in eval_label_list:
ss_list[eval_label].append(score_collect[seed][eval_label]["score"])
for eval_label in eval_label_list:
avg_score_lst = [score/sample_num for score in ss_list[eval_label]]
ss_mean, ss_std = mean_std(ss_list[eval_label])
avg_ss_mean, avg_ss_std = mean_std(avg_score_lst)
score_collect.update({f'{eval_label}': np.array([ss_mean, ss_std])})
score_collect.update({f'avg_{eval_label}': np.array([avg_ss_mean, avg_ss_std])})
pred_loss_mean, pred_loss_std = mean_std(pred_list)
score_collect.update({'split': opt.split})
score_collect.update({'type': opt.type})
score_collect.update({'mode': opt.mode})
score_collect.update({'sample_num': sample_num})
score_collect.update({'chamfer_stats': np.array([pred_loss_mean, pred_loss_std])})
score_collect.update({'trainnv': np.array([opt.nviews_train])})
score_collect.update({'testnv': np.array([opt.nviews_test])})
for eval_label in eval_label_list:
ss_mean, ss_std = score_collect[f'{eval_label}'][0], score_collect[f'{eval_label}'][1]
avg_ss_mean, avg_ss_std = score_collect[f'avg_{eval_label}'][0], score_collect[f'avg_{eval_label}'][1]
res_logger.info(f"{opt.network} {opt.type} mode: {opt.mode}, split: {opt.split}, " +
f"nviews: train {opt.nviews_train}, test {opt.nviews_test}, sample num: {sample_num} " +
f"seed_list {opt.seed_list}, metric {opt.metric} perf: {perf_pc} % {opt.metric} {opt.trained_exp_dir} {eval_label} " +
f"Sum of Score: (mean: {ss_mean:.4f}|std: {ss_std:.4f}) "+
f"Average Score: (mean: {avg_ss_mean:.4f}|std: {avg_ss_std:.4f}) "+
f"Pred Chamfer: (mean:{pred_loss_mean:.4f}|std: {pred_loss_std:.4f}) " +
f"DM compute time {elasp_time:.2f} min")
np.savez_compressed(os.path.join(res_path,
f"{opt.network}_{opt.mode}_{opt.split}_{opt.type}_{sample_num}_{opt.trained_exp_dir.split('/')[-1]}.npz"), **score_collect)
res_logger.info(f"###############END OF {opt.type} {opt.network} {opt.trained_exp_dir} PIPELINE#################")
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a network across multiple GPUs.
"""
import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.distributed import utils as distributed_utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics
from fairseq.nan_detector import NanDetector
from fairseq.optim import lr_scheduler
from omegaconf import OmegaConf
logger = logging.getLogger(__name__)
class Trainer(object):
"""Main class for data parallel training.
This class supports synchronous distributed data parallel training,
where multiple workers each have a full model replica and gradients
are accumulated across workers before each update. We use
:class:`~torch.nn.parallel.DistributedDataParallel` to handle
communication of the gradients across workers.
"""
def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None):
if isinstance(cfg, Namespace):
logger.warning(
"argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf"
)
cfg = convert_namespace_to_omegaconf(cfg)
self.cfg = cfg
self.task = task
# catalog shared parameters
shared_params = _catalog_shared_params(model)
self.tpu = cfg.common.tpu
self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu
if self.cuda:
self.device = torch.device("cuda")
elif self.tpu:
self.device = utils.get_tpu_device()
else:
self.device = torch.device("cpu")
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
if self.cfg.common.bf16:
raise ValueError(
"FullyShardedDataParallel is not compatible with --bf16 or "
"--memory-efficient-bf16"
)
if self.cfg.distributed_training.zero_sharding != "none":
raise ValueError(
"FullyShardedDataParallel is not compatible with --zero-sharding "
"option (it's already built in)"
)
else:
if self.cfg.distributed_training.cpu_offload:
raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded")
# copy model and criterion to current device/dtype
self._criterion = criterion
self._model = model
if cfg.distributed_training.ddp_backend != "fully_sharded":
if cfg.common.fp16:
self._criterion = self._criterion.half()
self._model = self._model.half()
elif cfg.common.bf16:
self._criterion = self._criterion.to(dtype=torch.bfloat16)
self._model = self._model.to(dtype=torch.bfloat16)
if (
not cfg.distributed_training.pipeline_model_parallel
# the DistributedFairseqModel wrapper will handle moving to device,
# so only handle cases which don't use the wrapper
and not self.use_distributed_wrapper
):
self._criterion = self._criterion.to(device=self.device)
self._model = self._model.to(device=self.device)
self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel
self.last_device = None
if self.cuda and self.pipeline_model_parallel:
self.last_device = torch.device(
cfg.distributed_training.pipeline_devices[-1]
)
# check that shared parameters are preserved after device transfer
for shared_param in shared_params:
ref = _get_module_by_path(self._model, shared_param[0])
for path in shared_param[1:]:
logger.info(
"detected shared parameter: {} <- {}".format(shared_param[0], path)
)
_set_module_by_path(self._model, path, ref)
self._dummy_batch = None # indicates we don't have a dummy batch at first
self._lr_scheduler = None
self._num_updates = 0
self._num_xla_compiles = 0 # for TPUs
self._optim_history = None
self._optimizer = None
self._warn_once = set()
self._wrapped_criterion = None
self._wrapped_model = None
# TODO(myleott): support tpu
if self.cuda and self.data_parallel_world_size > 1:
self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size)
else:
self._grad_norm_buf = None
self.quantizer = quantizer
if self.quantizer is not None:
self.quantizer.set_trainer(self)
# get detailed cuda environment
if self.cuda:
self.cuda_env = utils.CudaEnvironment()
if self.data_parallel_world_size > 1:
self.cuda_env_arr = distributed_utils.all_gather_list(
self.cuda_env, group=distributed_utils.get_global_group()
)
else:
self.cuda_env_arr = [self.cuda_env]
if self.data_parallel_rank == 0:
utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr)
else:
self.cuda_env = None
self.cuda_env_arr = None
metrics.log_start_time("wall", priority=790, round=0)
self._start_time = time.time()
self._previous_training_time = 0
self._cumulative_training_time = None
def reinitialize(self):
"""Reinitialize the Trainer, typically after model params change."""
self._lr_scheduler = None
self._optimizer = None
self._wrapped_criterion = None
self._wrapped_model = None
@property
def data_parallel_world_size(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 1
return distributed_utils.get_data_parallel_world_size()
@property
def data_parallel_process_group(self):
return distributed_utils.get_data_parallel_group()
@property
def data_parallel_rank(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 0
return distributed_utils.get_data_parallel_rank()
@property
def is_data_parallel_master(self):
# NOTE: this returns true for all model parallel replicas with data
# parallel rank 0
return self.data_parallel_rank == 0
@property
def use_distributed_wrapper(self) -> bool:
return (
self.data_parallel_world_size > 1
and not self.cfg.optimization.use_bmuf
) or (
self.cfg.distributed_training.ddp_backend == "fully_sharded"
and self.cfg.distributed_training.cpu_offload
)
@property
def should_save_checkpoint_on_current_rank(self) -> bool:
"""Indicates whether to save checkpoints on the current DDP rank."""
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
return True
else:
return self.is_data_parallel_master
@property
def checkpoint_suffix(self) -> str:
"""Suffix to add to the checkpoint file name."""
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format(self.data_parallel_rank)
else:
return self.cfg.checkpoint.checkpoint_suffix or ""
@property
def criterion(self):
if self._wrapped_criterion is None:
if (
utils.has_parameters(self._criterion)
and self.use_distributed_wrapper
):
self._wrapped_criterion = models.DistributedFairseqModel(
self.cfg.distributed_training,
self._criterion,
process_group=self.data_parallel_process_group,
device=self.device,
)
else:
self._wrapped_criterion = self._criterion
return self._wrapped_criterion
@property
def model(self):
if self._wrapped_model is None:
if self.use_distributed_wrapper:
self._wrapped_model = models.DistributedFairseqModel(
self.cfg.distributed_training,
self._model,
process_group=self.data_parallel_process_group,
device=self.device,
)
else:
self._wrapped_model = self._model
return self._wrapped_model
@property
def optimizer(self):
if self._optimizer is None:
self._build_optimizer()
return self._optimizer
@property
def lr_scheduler(self):
if self._lr_scheduler is None:
self._build_optimizer() # this will initialize self._lr_scheduler
return self._lr_scheduler
def _build_optimizer(self):
params = list(
filter(
lambda p: p.requires_grad,
chain(self.model.parameters(), self.criterion.parameters()),
)
)
if (
self.cfg.distributed_training.ddp_backend == "fully_sharded"
and self.cfg.common.fp16
):
# FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper,
# mostly for the grad scaling. But if we don't have the
# --memory-efficient-fp16 flag set, then we're effectively doing
# regular --fp16 and can allow the use of optimizers that would
# otherwise be unsupported by MemoryEfficientFP16Optimizer.
allow_unsupported = not self.cfg.common.memory_efficient_fp16
self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer(
self.cfg, params, allow_unsupported=allow_unsupported
)
elif self.cfg.common.fp16 or self.cfg.common.bf16:
if self.cuda and torch.cuda.get_device_capability(0)[0] < 7:
logger.info(
"NOTE: your device does NOT support faster training with --fp16, "
"please switch to FP32 which is likely to be faster"
)
if (
self.cfg.common.memory_efficient_fp16
or self.cfg.common.memory_efficient_bf16
):
self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer(
self.cfg, params
)
else:
self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params)
else:
if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7:
logger.info("NOTE: your device may support faster training with --fp16")
self._optimizer = optim.build_optimizer(self.cfg.optimizer, params)
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
assert not self.cfg.optimization.use_bmuf, \
"--ddp-backend=fully_sharded is not compatible with BMUF"
assert self._optimizer.supports_flat_params, (
"--ddp-backend=fully_sharded is only compatible with pointwise "
"optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). "
"However, the sharding will result in slightly different results when "
"using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)"
)
if self.cfg.optimization.use_bmuf:
self._optimizer = optim.FairseqBMUF(
self.cfg.bmuf,
self._optimizer,
)
if self.cfg.distributed_training.zero_sharding == "os":
if (
self.cfg.common.fp16
and not self.cfg.common.memory_efficient_fp16
and not self.cfg.common.memory_efficient_bf16
) and not self.cfg.common.fp16_no_flatten_grads:
raise ValueError(
"ZeRO is incomptabile with fp16 and flattened grads. "
"Please use --fp16-no-flatten-grads"
)
else:
optim.shard_(self._optimizer, self.data_parallel_process_group)
# We should initialize the learning rate scheduler immediately after
# building the optimizer, so that the initial learning rate is set.
self._lr_scheduler = lr_scheduler.build_lr_scheduler(
self.cfg.lr_scheduler,
self.optimizer,
)
self._lr_scheduler.step_update(0)
def consolidate_optimizer(self):
"""For OSS, we need to consolidate the state dict."""
if hasattr(self.optimizer.optimizer, "consolidate_state_dict"):
self.optimizer.optimizer.consolidate_state_dict()
def state_dict(self):
state_dict = {
"args": None, # legacy
"cfg": (
OmegaConf.to_container(self.cfg)
if OmegaConf.is_config(self.cfg) else self.cfg
),
"model": self.model.state_dict(),
"criterion": (
self.criterion.state_dict()
if utils.has_parameters(self.criterion) else None
),
"optimizer_history": (self._optim_history or [])
+ [
{
"criterion_name": self.get_criterion().__class__.__name__,
"optimizer_name": self.optimizer.__class__.__name__,
"lr_scheduler_state": self.lr_scheduler.state_dict(),
"num_updates": self.get_num_updates(),
}
],
"task_state": self.task.state_dict() if self.task is not None else {},
"extra_state": {
"metrics": metrics.state_dict(),
"previous_training_time": self.cumulative_training_time(),
}
}
if not self.cfg.checkpoint.no_save_optimizer_state:
state_dict["last_optimizer_state"] = self.optimizer.state_dict()
return state_dict
def save_checkpoint(self, filename, extra_state):
"""Save all training state in a checkpoint file."""
logger.info(f"Saving checkpoint to {filename}")
# call state_dict on all ranks in case it needs internal communication
state_dict = utils.move_to_cpu(self.state_dict())
state_dict["extra_state"].update(extra_state)
if self.should_save_checkpoint_on_current_rank:
checkpoint_utils.torch_persistent_save(
state_dict,
filename,
async_write=self.cfg.checkpoint.write_checkpoints_asynchronously,
)
logger.info(f"Finished saving checkpoint to {filename}")
def load_checkpoint(
self,
filename,
reset_optimizer=False,
reset_lr_scheduler=False,
optimizer_overrides=None,
reset_meters=False,
):
"""
Load all training state from a checkpoint file.
rank = 0 will load the checkpoint, and then broadcast it to all
other ranks.
"""
extra_state, self._optim_history, last_optim_state = None, [], None
logger.info(f"Preparing to load checkpoint {filename}")
is_distributed = self.data_parallel_world_size > 1
bexists = PathManager.isfile(filename)
if bexists:
load_on_all_ranks = (
self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks
# TPUs don't support broadcast yet, so load checkpoints
# on every worker for now
or self.tpu
# FSDP requires loading checkpoint shards on all ranks
or self.cfg.distributed_training.ddp_backend == "fully_sharded"
)
if load_on_all_ranks or self.data_parallel_rank == 0:
state = checkpoint_utils.load_checkpoint_to_cpu(
filename, load_on_all_ranks=load_on_all_ranks
)
last_optim_state = state.get("last_optimizer_state", None)
# If doing zero_sharding, do not broadcast global optimizer
# state. Later we will broadcast sharded states to each rank
# to avoid memory from exploding.
if (
not load_on_all_ranks
and self.cfg.distributed_training.zero_sharding == "os"
and "last_optimizer_state" in state
and is_distributed
):
state["last_optimizer_state"] = "SHARDED"
else:
last_optim_state = None
state = None
if is_distributed and not load_on_all_ranks:
state = distributed_utils.broadcast_object(
state,
src_rank=0,
group=self.data_parallel_process_group,
dist_device=self.device,
)
if self.data_parallel_rank > 0:
last_optim_state = state.get("last_optimizer_state", None)
# load model parameters
try:
self.model.load_state_dict(
state["model"], strict=True, model_cfg=self.cfg.model
)
# save memory for later steps
del state["model"]
if utils.has_parameters(self.get_criterion()):
self.get_criterion().load_state_dict(
state["criterion"], strict=True
)
del state["criterion"]
except Exception:
raise Exception(
"Cannot load model parameters from checkpoint {}; "
"please ensure that the architectures match.".format(filename)
)
extra_state = state["extra_state"]
self._optim_history = state["optimizer_history"]
if last_optim_state is not None and not reset_optimizer:
# rebuild optimizer after loading model, since params may have changed
self._build_optimizer()
# only reload optimizer and lr_scheduler if they match
last_optim = self._optim_history[-1]
assert (
last_optim["criterion_name"] == self.get_criterion().__class__.__name__
), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim["criterion_name"]} vs {self.get_criterion().__class__.__name__}"
assert (
last_optim["optimizer_name"] == self.optimizer.__class__.__name__
), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim["optimizer_name"]} vs {self.optimizer.__class__.__name__}"
if not reset_lr_scheduler:
self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"])
if not load_on_all_ranks and is_distributed:
last_optim_state = self.optimizer.broadcast_global_state_dict(
last_optim_state
)
self.optimizer.load_state_dict(last_optim_state, optimizer_overrides)
self.set_num_updates(last_optim["num_updates"])
if extra_state is not None:
itr_state = extra_state["train_iterator"]
epoch = itr_state["epoch"]
if "previous_training_time" in extra_state:
self._previous_training_time = extra_state["previous_training_time"]
self._start_time = time.time()
self.lr_step(epoch)
if itr_state.get("version", 1) >= 2 and itr_state["iterations_in_epoch"] == 0:
# reset meters at start of epoch
reset_meters = True
if "metrics" in extra_state and not reset_meters:
metrics.load_state_dict(extra_state["metrics"])
# reset TimeMeters, since their start times don't make sense anymore
for meter in metrics.get_meters("default"):
if isinstance(meter, meters.TimeMeter):
meter.reset()
logger.info(
"Loaded checkpoint {} (epoch {} @ {} updates)".format(
filename, epoch, self.get_num_updates()
)
)
else:
logger.info("No existing checkpoint found {}".format(filename))
return extra_state
def get_train_iterator(
self,
epoch,
combine=True,
load_dataset=True,
data_selector=None,
shard_batch_itr=True,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over the training set for a given epoch."""
if load_dataset:
logger.info("loading train data for epoch {}".format(epoch))
self.task.load_dataset(
self.cfg.dataset.train_subset,
epoch=epoch,
combine=combine,
data_selector=data_selector,
tpu=self.tpu,
)
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.dataset(self.cfg.dataset.train_subset),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
self.cfg.dataset.max_tokens,
),
ignore_invalid_inputs=True,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size if shard_batch_itr else 1,
shard_id=self.data_parallel_rank if shard_batch_itr else 0,
num_workers=self.cfg.dataset.num_workers,
epoch=epoch,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
)
self.reset_dummy_batch(batch_iterator.first_batch)
return batch_iterator
def get_valid_iterator(
self,
subset,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over given validation subset for a given epoch."""
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.dataset(subset),
max_tokens=self.cfg.dataset.max_tokens_valid,
max_sentences=self.cfg.dataset.batch_size_valid,
max_positions=utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
),
ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size,
shard_id=self.data_parallel_rank,
num_workers=self.cfg.dataset.num_workers,
# always pass a fixed "epoch" to keep validation data consistent
# across training epochs
epoch=1,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
)
self.reset_dummy_batch(batch_iterator.first_batch)
return batch_iterator
def begin_epoch(self, epoch):
"""Called at the beginning of each epoch."""
logger.info("begin training epoch {}".format(epoch))
self.lr_step_begin_epoch(epoch)
if self.quantizer is not None:
self.quantizer.begin_epoch(epoch)
# task specific setup per epoch
self.task.begin_epoch(epoch, self.get_model())
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("begin_epoch") # wait for all workers
xm.mark_step()
def begin_valid_epoch(self, epoch):
"""Called at the beginning of each validation epoch."""
# task specific setup per validation epoch
self.task.begin_valid_epoch(epoch, self.get_model())
def reset_dummy_batch(self, batch):
self._dummy_batch = batch
@metrics.aggregate("train")
def train_step(self, samples, raise_oom=False):
"""Do forward, backward and parameter update."""
self._set_seed()
self.model.train()
self.criterion.train()
self.zero_grad()
metrics.log_start_time("train_wall", priority=800, round=0)
# forward and backward pass
logging_outputs, sample_size, ooms = [], 0, 0
for i, sample in enumerate(samples): # delayed update loop
sample, is_dummy_batch = self._prepare_sample(sample)
def maybe_no_sync():
"""
Whenever *samples* contains more than one mini-batch, we
want to accumulate gradients locally and only call
all-reduce in the last backwards pass.
"""
if (
self.data_parallel_world_size > 1
and hasattr(self.model, "no_sync")
and i < len(samples) - 1
):
return self.model.no_sync()
else:
return contextlib.ExitStack() # dummy contextmanager
try:
with maybe_no_sync():
# forward and backward
loss, sample_size_i, logging_output = self.task.train_step(
sample=sample,
model=self.model,
criterion=self.criterion,
optimizer=self.optimizer,
update_num=self.get_num_updates(),
ignore_grad=is_dummy_batch,
)
del loss
logging_outputs.append(logging_output)
sample_size += sample_size_i
# emptying the CUDA cache after the first step can
# reduce the chance of OOM
if self.cuda and self.get_num_updates() == 0:
torch.cuda.empty_cache()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if raise_oom:
raise e
logger.warning(
"attempting to recover from OOM in forward/backward pass"
)
ooms += 1
self.zero_grad()
if self.cuda:
torch.cuda.empty_cache()
if self.cfg.distributed_training.distributed_world_size == 1:
return None
else:
raise e
if self.tpu and i < len(samples) - 1:
# tpu-comment: every XLA operation before marking step is
# appended to the IR graph, and processing too many batches
# before marking step can lead to OOM errors.
# To handle gradient accumulation use case, we explicitly
# mark step here for every forward pass without a backward pass
self._xla_markstep_and_send_to_cpu()
if is_dummy_batch:
if torch.is_tensor(sample_size):
sample_size.zero_()
else:
sample_size *= 0.0
if torch.is_tensor(sample_size):
sample_size = sample_size.float()
else:
sample_size = float(sample_size)
# gather logging outputs from all replicas
if self._sync_stats():
train_time = self._local_cumulative_training_time()
logging_outputs, (
sample_size,
ooms,
total_train_time,
) = self._aggregate_logging_outputs(
logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch
)
self._cumulative_training_time = (
total_train_time / self.data_parallel_world_size
)
overflow = False
try:
with torch.autograd.profiler.record_function("reduce-grads"):
# reduce gradients across workers
self.optimizer.all_reduce_grads(self.model)
if utils.has_parameters(self.criterion):
self.optimizer.all_reduce_grads(self.criterion)
with torch.autograd.profiler.record_function("multiply-grads"):
# multiply gradients by (data_parallel_size / sample_size) since
# DDP normalizes by the number of data parallel workers for
# improved fp16 precision.
# Thus we get (sum_of_gradients / sample_size) at the end.
# In case of fp16, this step also undoes loss scaling.
# (Debugging note: Some optimizers perform this scaling on the
# fly, so inspecting model.parameters() or optimizer.params may
# still show the original, unscaled gradients.)
numer = (
self.data_parallel_world_size
if not self.cfg.optimization.use_bmuf or self._sync_stats()
else 1
)
self.optimizer.multiply_grads(numer / (sample_size or 1.0))
# Note: (sample_size or 1.0) handles the case of a zero gradient, in a
# way that avoids CPU/device transfers in case sample_size is a GPU or
# TPU object. The assumption is that the gradient itself is also 0.
with torch.autograd.profiler.record_function("clip-grads"):
# clip grads
grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm)
# check that grad norms are consistent across workers
# on tpu check tensor is slow
if not self.tpu:
if (
not self.cfg.optimization.use_bmuf
and self.cfg.distributed_training.ddp_backend != "slow_mo"
):
self._check_grad_norms(grad_norm)
if not torch.isfinite(grad_norm).all():
# check local gradnorm single GPU case, trigger NanDetector
raise FloatingPointError("gradients are Nan/Inf")
with torch.autograd.profiler.record_function("optimizer"):
# take an optimization step
self.task.optimizer_step(
self.optimizer, model=self.model, update_num=self.get_num_updates()
)
except FloatingPointError:
# re-run the forward and backward pass with hooks attached to print
# out where it fails
self.zero_grad()
with NanDetector(self.get_model()):
for _, sample in enumerate(samples):
sample, _ = self._prepare_sample(sample)
self.task.train_step(
sample,
self.model,
self.criterion,
self.optimizer,
self.get_num_updates(),
ignore_grad=False,
)
raise
except OverflowError as e:
overflow = True
logger.info(f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}")
grad_norm = torch.tensor(0.0).cuda()
self.zero_grad()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
logger.error("OOM during optimization, irrecoverable")
raise e
# Some distributed wrappers (e.g., SlowMo) need access to the optimizer
# after the step
if hasattr(self.model, "perform_additional_optimizer_actions"):
if hasattr(self.optimizer, "fp32_params"):
self.model.perform_additional_optimizer_actions(
self.optimizer.optimizer, self.optimizer.fp32_params
)
else:
self.model.perform_additional_optimizer_actions(
self.optimizer.optimizer
)
logging_output = None
if not overflow or self.cfg.distributed_training.ddp_backend == "slow_mo":
self.set_num_updates(self.get_num_updates() + 1)
if self.tpu:
import torch_xla.core.xla_model as xm
# mark step on TPUs
self._xla_markstep_and_send_to_cpu()
# only log stats every log_interval steps
# this causes wps to be misreported when log_interval > 1
logging_output = {}
if self.get_num_updates() % self.cfg.common.log_interval == 0:
# log memory usage
mem_info = xm.get_memory_info(self.device)
gb_free = mem_info["kb_free"] / 1024 / 1024
gb_total = mem_info["kb_total"] / 1024 / 1024
metrics.log_scalar(
"gb_free", gb_free, priority=1500, round=1, weight=0
)
metrics.log_scalar(
"gb_total", gb_total, priority=1600, round=1, weight=0
)
logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs)
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm
)
# log whenever there's an XLA compilation, since these
# slow down training and may indicate opportunities for
# optimization
self._check_xla_compilation()
else:
if self.cuda and self.cuda_env is not None:
# log minimum free memory over the iteration
gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024
torch.cuda.reset_peak_memory_stats()
gb_free = self.cuda_env.total_memory_in_GB - gb_used
metrics.log_scalar(
"gb_free", gb_free, priority=1500, round=1, weight=0
)
# log stats
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm
)
# clear CUDA cache to reduce memory fragmentation
if (
self.cuda
and self.cfg.common.empty_cache_freq > 0
and (
(self.get_num_updates() + self.cfg.common.empty_cache_freq - 1)
% self.cfg.common.empty_cache_freq
)
== 0
):
torch.cuda.empty_cache()
if self.cfg.common.fp16:
metrics.log_scalar(
"loss_scale",
self.optimizer.scaler.loss_scale,
priority=700,
round=4,
weight=0,
)
metrics.log_stop_time("train_wall")
return logging_output
@metrics.aggregate("valid")
def valid_step(self, sample, raise_oom=False):
"""Do forward pass in evaluation mode."""
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("valid_step") # wait for all workers
with torch.no_grad():
self.model.eval()
self.criterion.eval()
sample, is_dummy_batch = self._prepare_sample(sample)
try:
_loss, sample_size, logging_output = self.task.valid_step(
sample, self.model, self.criterion
)
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if not raise_oom:
logger.warning(
"ran out of memory in validation step, retrying batch"
)
for p in self.model.parameters():
if p.grad is not None:
p.grad = None # free some memory
if self.cuda:
torch.cuda.empty_cache()
return self.valid_step(sample, raise_oom=True)
raise e
logging_outputs = [logging_output]
if is_dummy_batch:
if torch.is_tensor(sample_size):
sample_size.zero_()
else:
sample_size *= 0.0
# gather logging outputs from all replicas
if self.data_parallel_world_size > 1:
logging_outputs, (sample_size,) = self._aggregate_logging_outputs(
logging_outputs,
sample_size,
ignore=is_dummy_batch,
)
# log validation stats
if self.tpu:
logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs)
logging_output = self._reduce_and_log_stats(logging_outputs, sample_size)
return logging_output
def zero_grad(self):
self.optimizer.zero_grad()
def lr_step_begin_epoch(self, epoch):
"""Adjust the learning rate at the beginning of the epoch."""
self.lr_scheduler.step_begin_epoch(epoch)
# prefer updating the LR based on the number of steps
return self.lr_step_update()
def lr_step(self, epoch, val_loss=None):
"""Adjust the learning rate at the end of the epoch."""
self.lr_scheduler.step(epoch, val_loss)
# prefer updating the LR based on the number of steps
return self.lr_step_update()
def lr_step_update(self):
"""Update the learning rate after each update."""
new_lr = self.lr_scheduler.step_update(self.get_num_updates())
if isinstance(new_lr, dict):
for k, v in new_lr.items():
metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300)
new_lr = new_lr.get("default", next(iter(new_lr.values())))
else:
metrics.log_scalar("lr", new_lr, weight=0, priority=300)
return new_lr
def get_lr(self):
"""Get the current learning rate."""
return self.optimizer.get_lr()
def get_model(self):
"""Get the (non-wrapped) model instance."""
return self._model
def get_criterion(self):
"""Get the (non-wrapped) criterion instance."""
return self._criterion
def get_meter(self, name):
"""[deprecated] Get a specific meter by name."""
from fairseq import meters
if "get_meter" not in self._warn_once:
self._warn_once.add("get_meter")
utils.deprecation_warning(
"Trainer.get_meter is deprecated. Please use fairseq.metrics instead."
)
train_meters = metrics.get_meters("train")
if train_meters is None:
train_meters = {}
if name == "train_loss" and "loss" in train_meters:
return train_meters["loss"]
elif name == "train_nll_loss":
# support for legacy train.py, which assumed this meter is
# always initialized
m = train_meters.get("nll_loss", None)
return m or meters.AverageMeter()
elif name == "wall":
# support for legacy train.py, which assumed this meter is
# always initialized
m = metrics.get_meter("default", "wall")
return m or meters.TimeMeter()
elif name == "wps":
m = metrics.get_meter("train", "wps")
return m or meters.TimeMeter()
elif name in {"valid_loss", "valid_nll_loss"}:
# support for legacy train.py, which assumed these meters
# are always initialized
k = name[len("valid_") :]
m = metrics.get_meter("valid", k)
return m or meters.AverageMeter()
elif name == "oom":
return meters.AverageMeter()
elif name in train_meters:
return train_meters[name]
return None
def get_num_updates(self):
"""Get the number of parameters updates."""
return self._num_updates
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
self._num_updates = num_updates
self.lr_step_update()
if self.quantizer:
self.quantizer.step_update(self._num_updates)
metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200)
def clip_grad_norm(self, clip_norm):
def agg_norm_fn(total_norm):
total_norm = total_norm.cuda().float() ** 2
total_norm = distributed_utils.all_reduce(
total_norm, group=self.data_parallel_process_group
)
return total_norm ** 0.5
should_agg_norm = (
self.cfg.distributed_training.ddp_backend == "fully_sharded"
and (
self.data_parallel_process_group is not None
or torch.distributed.is_initialized()
)
)
return self.optimizer.clip_grad_norm(
clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None
)
def cumulative_training_time(self):
if self._cumulative_training_time is None:
# single GPU
return self._local_cumulative_training_time()
else:
return self._cumulative_training_time
def _local_cumulative_training_time(self):
"""Aggregate training time in seconds."""
return time.time() - self._start_time + self._previous_training_time
def _prepare_sample(self, sample, is_dummy=False):
if sample == "DUMMY":
raise Exception(
"Trying to use an uninitialized 'dummy' batch. This usually indicates "
"that the total number of batches is smaller than the number of "
"participating GPUs. Try reducing the batch size or using fewer GPUs."
)
if sample is None or len(sample) == 0:
assert (
self._dummy_batch is not None and len(self._dummy_batch) > 0
), "Invalid dummy batch: {}".format(self._dummy_batch)
sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True)
return sample, True
if self.cuda:
if self.pipeline_model_parallel:
if "target" in sample:
sample["target"] = utils.move_to_cuda(
sample["target"], device=self.last_device
)
else:
sample = utils.move_to_cuda(sample)
elif self.tpu and is_dummy:
# the dummy batch may not be on the appropriate device
sample = utils.move_to_cuda(sample, device=self.device)
def apply_half(t):
if t.dtype is torch.float32:
return t.half()
return t
def apply_bfloat16(t):
if t.dtype is torch.float32:
return t.to(dtype=torch.bfloat16)
return t
if self.cfg.common.fp16:
sample = utils.apply_to_sample(apply_half, sample)
if self.cfg.common.bf16:
sample = utils.apply_to_sample(apply_bfloat16, sample)
if self._dummy_batch == "DUMMY":
self._dummy_batch = sample
return sample, False
def _set_seed(self):
# Set seed based on args.seed and the update number so that we get
# reproducible results when resuming from checkpoints
seed = self.cfg.common.seed + self.get_num_updates()
utils.set_torch_seed(seed)
def _sync_stats(self):
# Return True if it's using multiple GPUs and DDP or multiple GPUs with
# BMUF and it's a bmuf sync with warmup iterations completed before.
if self.data_parallel_world_size == 1:
return False
elif self.cfg.optimization.use_bmuf:
return (
self.get_num_updates() + 1
) % self.cfg.bmuf.global_sync_iter == 0 and (
self.get_num_updates() + 1
) > self.cfg.bmuf.warmup_iterations
else:
return True
def _log_oom(self, exc):
msg = "OOM: Ran out of memory with exception: {}".format(exc)
logger.warning(msg)
if torch.cuda.is_available() and hasattr(torch.cuda, "memory_summary"):
for device_idx in range(torch.cuda.device_count()):
logger.warning(torch.cuda.memory_summary(device=device_idx))
sys.stderr.flush()
def _aggregate_logging_outputs(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
if self.task.__class__.logging_outputs_can_be_summed(self.get_criterion()):
return self._fast_stat_sync_sum(
logging_outputs, *extra_stats_to_sum, ignore=ignore
)
else:
return self._all_gather_list_sync(
logging_outputs, *extra_stats_to_sum, ignore=ignore
)
def _all_gather_list_sync(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
"""
Sync logging outputs across workers. all_gather_list_sync is
suitable when logging outputs are complex types.
"""
if self.tpu:
raise NotImplementedError
if ignore:
logging_outputs = []
results = list(
zip(
*distributed_utils.all_gather_list(
[logging_outputs] + list(extra_stats_to_sum),
max_size=getattr(self.cfg.common, "all_gather_list_size", 16384),
group=self.data_parallel_process_group,
)
)
)
logging_outputs, extra_stats_to_sum = results[0], results[1:]
logging_outputs = list(chain.from_iterable(logging_outputs))
extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum]
return logging_outputs, extra_stats_to_sum
def _fast_stat_sync_sum(
self, logging_outputs: List[Dict[str, Any]], *extra_stats_to_sum, ignore=False,
):
"""
Sync logging outputs across workers. fast_stat_sync_sum is
faster than all_gather_list_sync, but is only suitable when
logging outputs are scalars and can be summed. Note that
*logging_outputs* cannot contain any nested dicts/lists.
"""
data = {}
for i, stat in enumerate(extra_stats_to_sum):
data["extra_stats_" + str(i)] = stat
if len(logging_outputs) > 0:
log_keys = list(logging_outputs[0].keys())
for k in log_keys:
if not ignore:
v = sum(log[k] for log in logging_outputs if k in log)
else:
v = logging_outputs[0][k]
v = torch.zeros_like(v) if torch.is_tensor(v) else 0
data["logging_outputs_" + k] = v
else:
log_keys = None
data = distributed_utils.all_reduce_dict(
data, device=self.device, group=self.data_parallel_process_group
)
extra_stats_to_sum = [
data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum))
]
if log_keys is not None:
logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}]
else:
logging_outputs = []
return logging_outputs, extra_stats_to_sum
def _check_grad_norms(self, grad_norm):
"""Check that grad norms are consistent across workers."""
if self._grad_norm_buf is not None:
self._grad_norm_buf.zero_()
self._grad_norm_buf[self.data_parallel_rank] = grad_norm
distributed_utils.all_reduce(
self._grad_norm_buf, group=self.data_parallel_process_group
)
def is_consistent(tensor):
max_abs_diff = torch.max(torch.abs(tensor - tensor[0]))
return (
torch.isfinite(tensor).all()
and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all()
)
if not is_consistent(self._grad_norm_buf):
pretty_detail = "\n".join(
"rank {:3d} = {:.8f}".format(r, n)
for r, n in enumerate(self._grad_norm_buf.tolist())
)
error_detail = "grad_norm across the workers:\n{}\n".format(
pretty_detail
)
# use FloatingPointError to trigger NanDetector
raise FloatingPointError(
"Fatal error: gradients are inconsistent between workers. "
"Try --ddp-backend=legacy_ddp. "
"Or are you mixing up different generation of GPUs in training?"
+ "\n"
+ "-" * 80
+ "\n{}\n".format(error_detail)
+ "-" * 80
)
def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None):
if grad_norm is not None and (
not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm)
):
metrics.log_speed("ups", 1.0, priority=100, round=2)
metrics.log_scalar("gnorm", grad_norm, priority=400, round=3)
if self.cfg.optimization.clip_norm > 0:
metrics.log_scalar(
"clip",
torch.where(
grad_norm > self.cfg.optimization.clip_norm,
grad_norm.new_tensor(100),
grad_norm.new_tensor(0),
),
priority=500,
round=1,
)
with metrics.aggregate() as agg:
if logging_outputs is not None:
self.task.reduce_metrics(logging_outputs, self.get_criterion())
del logging_outputs
# extra warning for criterions that don't properly log a loss value
if "loss" not in agg:
if "loss" not in self._warn_once:
self._warn_once.add("loss")
logger.warning(
"Criterion.reduce_metrics did not log a 'loss' value, "
"which may break some functionality"
)
metrics.log_scalar("loss", -1)
# support legacy interface
if self.tpu:
logging_output = {}
else:
logging_output = agg.get_smoothed_values()
logging_output["sample_size"] = sample_size
for key_to_delete in ["ppl", "wps", "wpb", "bsz"]:
if key_to_delete in logging_output:
del logging_output[key_to_delete]
return logging_output
def _check_xla_compilation(self):
import torch_xla.debug.metrics as met
compile_stats = met.metric_data("CompileTime")
if compile_stats is None:
return
num_xla_compiles = compile_stats[0]
if num_xla_compiles > self._num_xla_compiles:
logger.warning(
"XLA compilation detected on device #{}; too many of these can lead "
"to slow training, but we expect a few in the beginning".format(
self.cfg.distributed_training.distributed_rank
)
)
self._num_xla_compiles = num_xla_compiles
def _xla_markstep_and_send_to_cpu(self, data=None):
import torch_xla.core.xla_model as xm
xm.mark_step()
if data is not None:
from fairseq.utils import xla_device_to_cpu
return xla_device_to_cpu(data)
def _catalog_shared_params(module, memo=None, prefix=""):
if memo is None:
first_call = True
memo = {}
else:
first_call = False
for name, param in module._parameters.items():
param_prefix = prefix + ("." if prefix else "") + name
if param not in memo:
memo[param] = []
memo[param].append(param_prefix)
for name, m in module._modules.items():
if m is None:
continue
submodule_prefix = prefix + ("." if prefix else "") + name
_catalog_shared_params(m, memo, submodule_prefix)
if first_call:
return [x for x in memo.values() if len(x) > 1]
def _get_module_by_path(module, path):
path = path.split(".")
for name in path:
module = getattr(module, name)
return module
def _set_module_by_path(module, path, value):
path = path.split(".")
for name in path[:-1]:
module = getattr(module, name)
setattr(module, path[-1], value)
| # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a network across multiple GPUs.
"""
import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.distributed import utils as distributed_utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics
from fairseq.nan_detector import NanDetector
from fairseq.optim import lr_scheduler
from omegaconf import OmegaConf
logger = logging.getLogger(__name__)
class Trainer(object):
"""Main class for data parallel training.
This class supports synchronous distributed data parallel training,
where multiple workers each have a full model replica and gradients
are accumulated across workers before each update. We use
:class:`~torch.nn.parallel.DistributedDataParallel` to handle
communication of the gradients across workers.
"""
def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None):
if isinstance(cfg, Namespace):
logger.warning(
"argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf"
)
cfg = convert_namespace_to_omegaconf(cfg)
self.cfg = cfg
self.task = task
# catalog shared parameters
shared_params = _catalog_shared_params(model)
self.tpu = cfg.common.tpu
self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu
if self.cuda:
self.device = torch.device("cuda")
elif self.tpu:
self.device = utils.get_tpu_device()
else:
self.device = torch.device("cpu")
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
if self.cfg.common.bf16:
raise ValueError(
"FullyShardedDataParallel is not compatible with --bf16 or "
"--memory-efficient-bf16"
)
if self.cfg.distributed_training.zero_sharding != "none":
raise ValueError(
"FullyShardedDataParallel is not compatible with --zero-sharding "
"option (it's already built in)"
)
else:
if self.cfg.distributed_training.cpu_offload:
raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded")
# copy model and criterion to current device/dtype
self._criterion = criterion
self._model = model
if cfg.distributed_training.ddp_backend != "fully_sharded":
if cfg.common.fp16:
self._criterion = self._criterion.half()
self._model = self._model.half()
elif cfg.common.bf16:
self._criterion = self._criterion.to(dtype=torch.bfloat16)
self._model = self._model.to(dtype=torch.bfloat16)
if (
not cfg.distributed_training.pipeline_model_parallel
# the DistributedFairseqModel wrapper will handle moving to device,
# so only handle cases which don't use the wrapper
and not self.use_distributed_wrapper
):
self._criterion = self._criterion.to(device=self.device)
self._model = self._model.to(device=self.device)
self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel
self.last_device = None
if self.cuda and self.pipeline_model_parallel:
self.last_device = torch.device(
cfg.distributed_training.pipeline_devices[-1]
)
# check that shared parameters are preserved after device transfer
for shared_param in shared_params:
ref = _get_module_by_path(self._model, shared_param[0])
for path in shared_param[1:]:
logger.info(
"detected shared parameter: {} <- {}".format(shared_param[0], path)
)
_set_module_by_path(self._model, path, ref)
self._dummy_batch = None # indicates we don't have a dummy batch at first
self._lr_scheduler = None
self._num_updates = 0
self._num_xla_compiles = 0 # for TPUs
self._optim_history = None
self._optimizer = None
self._warn_once = set()
self._wrapped_criterion = None
self._wrapped_model = None
# TODO(myleott): support tpu
if self.cuda and self.data_parallel_world_size > 1:
self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size)
else:
self._grad_norm_buf = None
self.quantizer = quantizer
if self.quantizer is not None:
self.quantizer.set_trainer(self)
# get detailed cuda environment
if self.cuda:
self.cuda_env = utils.CudaEnvironment()
if self.data_parallel_world_size > 1:
self.cuda_env_arr = distributed_utils.all_gather_list(
self.cuda_env, group=distributed_utils.get_global_group()
)
else:
self.cuda_env_arr = [self.cuda_env]
if self.data_parallel_rank == 0:
utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr)
else:
self.cuda_env = None
self.cuda_env_arr = None
metrics.log_start_time("wall", priority=790, round=0)
self._start_time = time.time()
self._previous_training_time = 0
self._cumulative_training_time = None
def reinitialize(self):
"""Reinitialize the Trainer, typically after model params change."""
self._lr_scheduler = None
self._optimizer = None
self._wrapped_criterion = None
self._wrapped_model = None
@property
def data_parallel_world_size(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 1
return distributed_utils.get_data_parallel_world_size()
@property
def data_parallel_process_group(self):
return distributed_utils.get_data_parallel_group()
@property
def data_parallel_rank(self):
if self.cfg.distributed_training.distributed_world_size == 1:
return 0
return distributed_utils.get_data_parallel_rank()
@property
def is_data_parallel_master(self):
# NOTE: this returns true for all model parallel replicas with data
# parallel rank 0
return self.data_parallel_rank == 0
@property
def use_distributed_wrapper(self) -> bool:
return (
self.data_parallel_world_size > 1
and not self.cfg.optimization.use_bmuf
) or (
self.cfg.distributed_training.ddp_backend == "fully_sharded"
and self.cfg.distributed_training.cpu_offload
)
@property
def should_save_checkpoint_on_current_rank(self) -> bool:
"""Indicates whether to save checkpoints on the current DDP rank."""
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
return True
else:
return self.is_data_parallel_master
@property
def checkpoint_suffix(self) -> str:
"""Suffix to add to the checkpoint file name."""
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format(self.data_parallel_rank)
else:
return self.cfg.checkpoint.checkpoint_suffix or ""
@property
def criterion(self):
if self._wrapped_criterion is None:
if (
utils.has_parameters(self._criterion)
and self.use_distributed_wrapper
):
self._wrapped_criterion = models.DistributedFairseqModel(
self.cfg.distributed_training,
self._criterion,
process_group=self.data_parallel_process_group,
device=self.device,
)
else:
self._wrapped_criterion = self._criterion
return self._wrapped_criterion
@property
def model(self):
if self._wrapped_model is None:
if self.use_distributed_wrapper:
self._wrapped_model = models.DistributedFairseqModel(
self.cfg.distributed_training,
self._model,
process_group=self.data_parallel_process_group,
device=self.device,
)
else:
self._wrapped_model = self._model
return self._wrapped_model
@property
def optimizer(self):
if self._optimizer is None:
self._build_optimizer()
return self._optimizer
@property
def lr_scheduler(self):
if self._lr_scheduler is None:
self._build_optimizer() # this will initialize self._lr_scheduler
return self._lr_scheduler
def _build_optimizer(self):
params = list(
filter(
lambda p: p.requires_grad,
chain(self.model.parameters(), self.criterion.parameters()),
)
)
if (
self.cfg.distributed_training.ddp_backend == "fully_sharded"
and self.cfg.common.fp16
):
# FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper,
# mostly for the grad scaling. But if we don't have the
# --memory-efficient-fp16 flag set, then we're effectively doing
# regular --fp16 and can allow the use of optimizers that would
# otherwise be unsupported by MemoryEfficientFP16Optimizer.
allow_unsupported = not self.cfg.common.memory_efficient_fp16
self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer(
self.cfg, params, allow_unsupported=allow_unsupported
)
elif self.cfg.common.fp16 or self.cfg.common.bf16:
if self.cuda and torch.cuda.get_device_capability(0)[0] < 7:
logger.info(
"NOTE: your device does NOT support faster training with --fp16, "
"please switch to FP32 which is likely to be faster"
)
if (
self.cfg.common.memory_efficient_fp16
or self.cfg.common.memory_efficient_bf16
):
self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer(
self.cfg, params
)
else:
self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params)
else:
if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7:
logger.info("NOTE: your device may support faster training with --fp16")
self._optimizer = optim.build_optimizer(self.cfg.optimizer, params)
if self.cfg.distributed_training.ddp_backend == "fully_sharded":
assert not self.cfg.optimization.use_bmuf, \
"--ddp-backend=fully_sharded is not compatible with BMUF"
assert self._optimizer.supports_flat_params, (
"--ddp-backend=fully_sharded is only compatible with pointwise "
"optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). "
"However, the sharding will result in slightly different results when "
"using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)"
)
if self.cfg.optimization.use_bmuf:
self._optimizer = optim.FairseqBMUF(
self.cfg.bmuf,
self._optimizer,
)
if self.cfg.distributed_training.zero_sharding == "os":
if (
self.cfg.common.fp16
and not self.cfg.common.memory_efficient_fp16
and not self.cfg.common.memory_efficient_bf16
) and not self.cfg.common.fp16_no_flatten_grads:
raise ValueError(
"ZeRO is incomptabile with fp16 and flattened grads. "
"Please use --fp16-no-flatten-grads"
)
else:
optim.shard_(self._optimizer, self.data_parallel_process_group)
# We should initialize the learning rate scheduler immediately after
# building the optimizer, so that the initial learning rate is set.
self._lr_scheduler = lr_scheduler.build_lr_scheduler(
self.cfg.lr_scheduler,
self.optimizer,
)
self._lr_scheduler.step_update(0)
def consolidate_optimizer(self):
"""For OSS, we need to consolidate the state dict."""
if hasattr(self.optimizer.optimizer, "consolidate_state_dict"):
self.optimizer.optimizer.consolidate_state_dict()
def state_dict(self):
state_dict = {
"args": None, # legacy
"cfg": (
OmegaConf.to_container(self.cfg)
if OmegaConf.is_config(self.cfg) else self.cfg
),
"model": self.model.state_dict(),
"criterion": (
self.criterion.state_dict()
if utils.has_parameters(self.criterion) else None
),
"optimizer_history": (self._optim_history or [])
+ [
{
"criterion_name": self.get_criterion().__class__.__name__,
"optimizer_name": self.optimizer.__class__.__name__,
"lr_scheduler_state": self.lr_scheduler.state_dict(),
"num_updates": self.get_num_updates(),
}
],
"task_state": self.task.state_dict() if self.task is not None else {},
"extra_state": {
"metrics": metrics.state_dict(),
"previous_training_time": self.cumulative_training_time(),
}
}
if not self.cfg.checkpoint.no_save_optimizer_state:
state_dict["last_optimizer_state"] = self.optimizer.state_dict()
return state_dict
def save_checkpoint(self, filename, extra_state):
"""Save all training state in a checkpoint file."""
logger.info(f"Saving checkpoint to {filename}")
# call state_dict on all ranks in case it needs internal communication
state_dict = utils.move_to_cpu(self.state_dict())
state_dict["extra_state"].update(extra_state)
if self.should_save_checkpoint_on_current_rank:
checkpoint_utils.torch_persistent_save(
state_dict,
filename,
async_write=self.cfg.checkpoint.write_checkpoints_asynchronously,
)
logger.info(f"Finished saving checkpoint to {filename}")
def load_checkpoint(
self,
filename,
reset_optimizer=False,
reset_lr_scheduler=False,
optimizer_overrides=None,
reset_meters=False,
):
"""
Load all training state from a checkpoint file.
rank = 0 will load the checkpoint, and then broadcast it to all
other ranks.
"""
extra_state, self._optim_history, last_optim_state = None, [], None
logger.info(f"Preparing to load checkpoint {filename}")
is_distributed = self.data_parallel_world_size > 1
bexists = PathManager.isfile(filename)
if bexists:
load_on_all_ranks = (
self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks
# TPUs don't support broadcast yet, so load checkpoints
# on every worker for now
or self.tpu
# FSDP requires loading checkpoint shards on all ranks
or self.cfg.distributed_training.ddp_backend == "fully_sharded"
)
if load_on_all_ranks or self.data_parallel_rank == 0:
state = checkpoint_utils.load_checkpoint_to_cpu(
filename, load_on_all_ranks=load_on_all_ranks
)
last_optim_state = state.get("last_optimizer_state", None)
# If doing zero_sharding, do not broadcast global optimizer
# state. Later we will broadcast sharded states to each rank
# to avoid memory from exploding.
if (
not load_on_all_ranks
and self.cfg.distributed_training.zero_sharding == "os"
and "last_optimizer_state" in state
and is_distributed
):
state["last_optimizer_state"] = "SHARDED"
else:
last_optim_state = None
state = None
if is_distributed and not load_on_all_ranks:
state = distributed_utils.broadcast_object(
state,
src_rank=0,
group=self.data_parallel_process_group,
dist_device=self.device,
)
if self.data_parallel_rank > 0:
last_optim_state = state.get("last_optimizer_state", None)
# load model parameters
try:
self.model.load_state_dict(
state["model"], strict=True, model_cfg=self.cfg.model
)
# save memory for later steps
del state["model"]
if utils.has_parameters(self.get_criterion()):
self.get_criterion().load_state_dict(
state["criterion"], strict=True
)
del state["criterion"]
except Exception:
raise Exception(
"Cannot load model parameters from checkpoint {}; "
"please ensure that the architectures match.".format(filename)
)
extra_state = state["extra_state"]
self._optim_history = state["optimizer_history"]
if last_optim_state is not None and not reset_optimizer:
# rebuild optimizer after loading model, since params may have changed
self._build_optimizer()
# only reload optimizer and lr_scheduler if they match
last_optim = self._optim_history[-1]
assert (
last_optim["criterion_name"] == self.get_criterion().__class__.__name__
), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim['criterion_name']} vs {self.get_criterion().__class__.__name__}"
assert (
last_optim["optimizer_name"] == self.optimizer.__class__.__name__
), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim['optimizer_name']} vs {self.optimizer.__class__.__name__}"
if not reset_lr_scheduler:
self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"])
if not load_on_all_ranks and is_distributed:
last_optim_state = self.optimizer.broadcast_global_state_dict(
last_optim_state
)
self.optimizer.load_state_dict(last_optim_state, optimizer_overrides)
self.set_num_updates(last_optim["num_updates"])
if extra_state is not None:
itr_state = extra_state["train_iterator"]
epoch = itr_state["epoch"]
if "previous_training_time" in extra_state:
self._previous_training_time = extra_state["previous_training_time"]
self._start_time = time.time()
self.lr_step(epoch)
if itr_state.get("version", 1) >= 2 and itr_state["iterations_in_epoch"] == 0:
# reset meters at start of epoch
reset_meters = True
if "metrics" in extra_state and not reset_meters:
metrics.load_state_dict(extra_state["metrics"])
# reset TimeMeters, since their start times don't make sense anymore
for meter in metrics.get_meters("default"):
if isinstance(meter, meters.TimeMeter):
meter.reset()
logger.info(
"Loaded checkpoint {} (epoch {} @ {} updates)".format(
filename, epoch, self.get_num_updates()
)
)
else:
logger.info("No existing checkpoint found {}".format(filename))
return extra_state
def get_train_iterator(
self,
epoch,
combine=True,
load_dataset=True,
data_selector=None,
shard_batch_itr=True,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over the training set for a given epoch."""
if load_dataset:
logger.info("loading train data for epoch {}".format(epoch))
self.task.load_dataset(
self.cfg.dataset.train_subset,
epoch=epoch,
combine=combine,
data_selector=data_selector,
tpu=self.tpu,
)
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.dataset(self.cfg.dataset.train_subset),
max_tokens=self.cfg.dataset.max_tokens,
max_sentences=self.cfg.dataset.batch_size,
max_positions=utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
self.cfg.dataset.max_tokens,
),
ignore_invalid_inputs=True,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size if shard_batch_itr else 1,
shard_id=self.data_parallel_rank if shard_batch_itr else 0,
num_workers=self.cfg.dataset.num_workers,
epoch=epoch,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
)
self.reset_dummy_batch(batch_iterator.first_batch)
return batch_iterator
def get_valid_iterator(
self,
subset,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over given validation subset for a given epoch."""
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.dataset(subset),
max_tokens=self.cfg.dataset.max_tokens_valid,
max_sentences=self.cfg.dataset.batch_size_valid,
max_positions=utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
),
ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple,
seed=self.cfg.common.seed,
num_shards=self.data_parallel_world_size,
shard_id=self.data_parallel_rank,
num_workers=self.cfg.dataset.num_workers,
# always pass a fixed "epoch" to keep validation data consistent
# across training epochs
epoch=1,
data_buffer_size=self.cfg.dataset.data_buffer_size,
disable_iterator_cache=disable_iterator_cache,
)
self.reset_dummy_batch(batch_iterator.first_batch)
return batch_iterator
def begin_epoch(self, epoch):
"""Called at the beginning of each epoch."""
logger.info("begin training epoch {}".format(epoch))
self.lr_step_begin_epoch(epoch)
if self.quantizer is not None:
self.quantizer.begin_epoch(epoch)
# task specific setup per epoch
self.task.begin_epoch(epoch, self.get_model())
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("begin_epoch") # wait for all workers
xm.mark_step()
def begin_valid_epoch(self, epoch):
"""Called at the beginning of each validation epoch."""
# task specific setup per validation epoch
self.task.begin_valid_epoch(epoch, self.get_model())
def reset_dummy_batch(self, batch):
self._dummy_batch = batch
@metrics.aggregate("train")
def train_step(self, samples, raise_oom=False):
"""Do forward, backward and parameter update."""
self._set_seed()
self.model.train()
self.criterion.train()
self.zero_grad()
metrics.log_start_time("train_wall", priority=800, round=0)
# forward and backward pass
logging_outputs, sample_size, ooms = [], 0, 0
for i, sample in enumerate(samples): # delayed update loop
sample, is_dummy_batch = self._prepare_sample(sample)
def maybe_no_sync():
"""
Whenever *samples* contains more than one mini-batch, we
want to accumulate gradients locally and only call
all-reduce in the last backwards pass.
"""
if (
self.data_parallel_world_size > 1
and hasattr(self.model, "no_sync")
and i < len(samples) - 1
):
return self.model.no_sync()
else:
return contextlib.ExitStack() # dummy contextmanager
try:
with maybe_no_sync():
# forward and backward
loss, sample_size_i, logging_output = self.task.train_step(
sample=sample,
model=self.model,
criterion=self.criterion,
optimizer=self.optimizer,
update_num=self.get_num_updates(),
ignore_grad=is_dummy_batch,
)
del loss
logging_outputs.append(logging_output)
sample_size += sample_size_i
# emptying the CUDA cache after the first step can
# reduce the chance of OOM
if self.cuda and self.get_num_updates() == 0:
torch.cuda.empty_cache()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if raise_oom:
raise e
logger.warning(
"attempting to recover from OOM in forward/backward pass"
)
ooms += 1
self.zero_grad()
if self.cuda:
torch.cuda.empty_cache()
if self.cfg.distributed_training.distributed_world_size == 1:
return None
else:
raise e
if self.tpu and i < len(samples) - 1:
# tpu-comment: every XLA operation before marking step is
# appended to the IR graph, and processing too many batches
# before marking step can lead to OOM errors.
# To handle gradient accumulation use case, we explicitly
# mark step here for every forward pass without a backward pass
self._xla_markstep_and_send_to_cpu()
if is_dummy_batch:
if torch.is_tensor(sample_size):
sample_size.zero_()
else:
sample_size *= 0.0
if torch.is_tensor(sample_size):
sample_size = sample_size.float()
else:
sample_size = float(sample_size)
# gather logging outputs from all replicas
if self._sync_stats():
train_time = self._local_cumulative_training_time()
logging_outputs, (
sample_size,
ooms,
total_train_time,
) = self._aggregate_logging_outputs(
logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch
)
self._cumulative_training_time = (
total_train_time / self.data_parallel_world_size
)
overflow = False
try:
with torch.autograd.profiler.record_function("reduce-grads"):
# reduce gradients across workers
self.optimizer.all_reduce_grads(self.model)
if utils.has_parameters(self.criterion):
self.optimizer.all_reduce_grads(self.criterion)
with torch.autograd.profiler.record_function("multiply-grads"):
# multiply gradients by (data_parallel_size / sample_size) since
# DDP normalizes by the number of data parallel workers for
# improved fp16 precision.
# Thus we get (sum_of_gradients / sample_size) at the end.
# In case of fp16, this step also undoes loss scaling.
# (Debugging note: Some optimizers perform this scaling on the
# fly, so inspecting model.parameters() or optimizer.params may
# still show the original, unscaled gradients.)
numer = (
self.data_parallel_world_size
if not self.cfg.optimization.use_bmuf or self._sync_stats()
else 1
)
self.optimizer.multiply_grads(numer / (sample_size or 1.0))
# Note: (sample_size or 1.0) handles the case of a zero gradient, in a
# way that avoids CPU/device transfers in case sample_size is a GPU or
# TPU object. The assumption is that the gradient itself is also 0.
with torch.autograd.profiler.record_function("clip-grads"):
# clip grads
grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm)
# check that grad norms are consistent across workers
# on tpu check tensor is slow
if not self.tpu:
if (
not self.cfg.optimization.use_bmuf
and self.cfg.distributed_training.ddp_backend != "slow_mo"
):
self._check_grad_norms(grad_norm)
if not torch.isfinite(grad_norm).all():
# check local gradnorm single GPU case, trigger NanDetector
raise FloatingPointError("gradients are Nan/Inf")
with torch.autograd.profiler.record_function("optimizer"):
# take an optimization step
self.task.optimizer_step(
self.optimizer, model=self.model, update_num=self.get_num_updates()
)
except FloatingPointError:
# re-run the forward and backward pass with hooks attached to print
# out where it fails
self.zero_grad()
with NanDetector(self.get_model()):
for _, sample in enumerate(samples):
sample, _ = self._prepare_sample(sample)
self.task.train_step(
sample,
self.model,
self.criterion,
self.optimizer,
self.get_num_updates(),
ignore_grad=False,
)
raise
except OverflowError as e:
overflow = True
logger.info(f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}")
grad_norm = torch.tensor(0.0).cuda()
self.zero_grad()
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
logger.error("OOM during optimization, irrecoverable")
raise e
# Some distributed wrappers (e.g., SlowMo) need access to the optimizer
# after the step
if hasattr(self.model, "perform_additional_optimizer_actions"):
if hasattr(self.optimizer, "fp32_params"):
self.model.perform_additional_optimizer_actions(
self.optimizer.optimizer, self.optimizer.fp32_params
)
else:
self.model.perform_additional_optimizer_actions(
self.optimizer.optimizer
)
logging_output = None
if not overflow or self.cfg.distributed_training.ddp_backend == "slow_mo":
self.set_num_updates(self.get_num_updates() + 1)
if self.tpu:
import torch_xla.core.xla_model as xm
# mark step on TPUs
self._xla_markstep_and_send_to_cpu()
# only log stats every log_interval steps
# this causes wps to be misreported when log_interval > 1
logging_output = {}
if self.get_num_updates() % self.cfg.common.log_interval == 0:
# log memory usage
mem_info = xm.get_memory_info(self.device)
gb_free = mem_info["kb_free"] / 1024 / 1024
gb_total = mem_info["kb_total"] / 1024 / 1024
metrics.log_scalar(
"gb_free", gb_free, priority=1500, round=1, weight=0
)
metrics.log_scalar(
"gb_total", gb_total, priority=1600, round=1, weight=0
)
logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs)
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm
)
# log whenever there's an XLA compilation, since these
# slow down training and may indicate opportunities for
# optimization
self._check_xla_compilation()
else:
if self.cuda and self.cuda_env is not None:
# log minimum free memory over the iteration
gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024
torch.cuda.reset_peak_memory_stats()
gb_free = self.cuda_env.total_memory_in_GB - gb_used
metrics.log_scalar(
"gb_free", gb_free, priority=1500, round=1, weight=0
)
# log stats
logging_output = self._reduce_and_log_stats(
logging_outputs, sample_size, grad_norm
)
# clear CUDA cache to reduce memory fragmentation
if (
self.cuda
and self.cfg.common.empty_cache_freq > 0
and (
(self.get_num_updates() + self.cfg.common.empty_cache_freq - 1)
% self.cfg.common.empty_cache_freq
)
== 0
):
torch.cuda.empty_cache()
if self.cfg.common.fp16:
metrics.log_scalar(
"loss_scale",
self.optimizer.scaler.loss_scale,
priority=700,
round=4,
weight=0,
)
metrics.log_stop_time("train_wall")
return logging_output
@metrics.aggregate("valid")
def valid_step(self, sample, raise_oom=False):
"""Do forward pass in evaluation mode."""
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("valid_step") # wait for all workers
with torch.no_grad():
self.model.eval()
self.criterion.eval()
sample, is_dummy_batch = self._prepare_sample(sample)
try:
_loss, sample_size, logging_output = self.task.valid_step(
sample, self.model, self.criterion
)
except RuntimeError as e:
if "out of memory" in str(e):
self._log_oom(e)
if not raise_oom:
logger.warning(
"ran out of memory in validation step, retrying batch"
)
for p in self.model.parameters():
if p.grad is not None:
p.grad = None # free some memory
if self.cuda:
torch.cuda.empty_cache()
return self.valid_step(sample, raise_oom=True)
raise e
logging_outputs = [logging_output]
if is_dummy_batch:
if torch.is_tensor(sample_size):
sample_size.zero_()
else:
sample_size *= 0.0
# gather logging outputs from all replicas
if self.data_parallel_world_size > 1:
logging_outputs, (sample_size,) = self._aggregate_logging_outputs(
logging_outputs,
sample_size,
ignore=is_dummy_batch,
)
# log validation stats
if self.tpu:
logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs)
logging_output = self._reduce_and_log_stats(logging_outputs, sample_size)
return logging_output
def zero_grad(self):
self.optimizer.zero_grad()
def lr_step_begin_epoch(self, epoch):
"""Adjust the learning rate at the beginning of the epoch."""
self.lr_scheduler.step_begin_epoch(epoch)
# prefer updating the LR based on the number of steps
return self.lr_step_update()
def lr_step(self, epoch, val_loss=None):
"""Adjust the learning rate at the end of the epoch."""
self.lr_scheduler.step(epoch, val_loss)
# prefer updating the LR based on the number of steps
return self.lr_step_update()
def lr_step_update(self):
"""Update the learning rate after each update."""
new_lr = self.lr_scheduler.step_update(self.get_num_updates())
if isinstance(new_lr, dict):
for k, v in new_lr.items():
metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300)
new_lr = new_lr.get("default", next(iter(new_lr.values())))
else:
metrics.log_scalar("lr", new_lr, weight=0, priority=300)
return new_lr
def get_lr(self):
"""Get the current learning rate."""
return self.optimizer.get_lr()
def get_model(self):
"""Get the (non-wrapped) model instance."""
return self._model
def get_criterion(self):
"""Get the (non-wrapped) criterion instance."""
return self._criterion
def get_meter(self, name):
"""[deprecated] Get a specific meter by name."""
from fairseq import meters
if "get_meter" not in self._warn_once:
self._warn_once.add("get_meter")
utils.deprecation_warning(
"Trainer.get_meter is deprecated. Please use fairseq.metrics instead."
)
train_meters = metrics.get_meters("train")
if train_meters is None:
train_meters = {}
if name == "train_loss" and "loss" in train_meters:
return train_meters["loss"]
elif name == "train_nll_loss":
# support for legacy train.py, which assumed this meter is
# always initialized
m = train_meters.get("nll_loss", None)
return m or meters.AverageMeter()
elif name == "wall":
# support for legacy train.py, which assumed this meter is
# always initialized
m = metrics.get_meter("default", "wall")
return m or meters.TimeMeter()
elif name == "wps":
m = metrics.get_meter("train", "wps")
return m or meters.TimeMeter()
elif name in {"valid_loss", "valid_nll_loss"}:
# support for legacy train.py, which assumed these meters
# are always initialized
k = name[len("valid_") :]
m = metrics.get_meter("valid", k)
return m or meters.AverageMeter()
elif name == "oom":
return meters.AverageMeter()
elif name in train_meters:
return train_meters[name]
return None
def get_num_updates(self):
"""Get the number of parameters updates."""
return self._num_updates
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
self._num_updates = num_updates
self.lr_step_update()
if self.quantizer:
self.quantizer.step_update(self._num_updates)
metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200)
def clip_grad_norm(self, clip_norm):
def agg_norm_fn(total_norm):
total_norm = total_norm.cuda().float() ** 2
total_norm = distributed_utils.all_reduce(
total_norm, group=self.data_parallel_process_group
)
return total_norm ** 0.5
should_agg_norm = (
self.cfg.distributed_training.ddp_backend == "fully_sharded"
and (
self.data_parallel_process_group is not None
or torch.distributed.is_initialized()
)
)
return self.optimizer.clip_grad_norm(
clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None
)
def cumulative_training_time(self):
if self._cumulative_training_time is None:
# single GPU
return self._local_cumulative_training_time()
else:
return self._cumulative_training_time
def _local_cumulative_training_time(self):
"""Aggregate training time in seconds."""
return time.time() - self._start_time + self._previous_training_time
def _prepare_sample(self, sample, is_dummy=False):
if sample == "DUMMY":
raise Exception(
"Trying to use an uninitialized 'dummy' batch. This usually indicates "
"that the total number of batches is smaller than the number of "
"participating GPUs. Try reducing the batch size or using fewer GPUs."
)
if sample is None or len(sample) == 0:
assert (
self._dummy_batch is not None and len(self._dummy_batch) > 0
), "Invalid dummy batch: {}".format(self._dummy_batch)
sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True)
return sample, True
if self.cuda:
if self.pipeline_model_parallel:
if "target" in sample:
sample["target"] = utils.move_to_cuda(
sample["target"], device=self.last_device
)
else:
sample = utils.move_to_cuda(sample)
elif self.tpu and is_dummy:
# the dummy batch may not be on the appropriate device
sample = utils.move_to_cuda(sample, device=self.device)
def apply_half(t):
if t.dtype is torch.float32:
return t.half()
return t
def apply_bfloat16(t):
if t.dtype is torch.float32:
return t.to(dtype=torch.bfloat16)
return t
if self.cfg.common.fp16:
sample = utils.apply_to_sample(apply_half, sample)
if self.cfg.common.bf16:
sample = utils.apply_to_sample(apply_bfloat16, sample)
if self._dummy_batch == "DUMMY":
self._dummy_batch = sample
return sample, False
def _set_seed(self):
# Set seed based on args.seed and the update number so that we get
# reproducible results when resuming from checkpoints
seed = self.cfg.common.seed + self.get_num_updates()
utils.set_torch_seed(seed)
def _sync_stats(self):
# Return True if it's using multiple GPUs and DDP or multiple GPUs with
# BMUF and it's a bmuf sync with warmup iterations completed before.
if self.data_parallel_world_size == 1:
return False
elif self.cfg.optimization.use_bmuf:
return (
self.get_num_updates() + 1
) % self.cfg.bmuf.global_sync_iter == 0 and (
self.get_num_updates() + 1
) > self.cfg.bmuf.warmup_iterations
else:
return True
def _log_oom(self, exc):
msg = "OOM: Ran out of memory with exception: {}".format(exc)
logger.warning(msg)
if torch.cuda.is_available() and hasattr(torch.cuda, "memory_summary"):
for device_idx in range(torch.cuda.device_count()):
logger.warning(torch.cuda.memory_summary(device=device_idx))
sys.stderr.flush()
def _aggregate_logging_outputs(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
if self.task.__class__.logging_outputs_can_be_summed(self.get_criterion()):
return self._fast_stat_sync_sum(
logging_outputs, *extra_stats_to_sum, ignore=ignore
)
else:
return self._all_gather_list_sync(
logging_outputs, *extra_stats_to_sum, ignore=ignore
)
def _all_gather_list_sync(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
"""
Sync logging outputs across workers. all_gather_list_sync is
suitable when logging outputs are complex types.
"""
if self.tpu:
raise NotImplementedError
if ignore:
logging_outputs = []
results = list(
zip(
*distributed_utils.all_gather_list(
[logging_outputs] + list(extra_stats_to_sum),
max_size=getattr(self.cfg.common, "all_gather_list_size", 16384),
group=self.data_parallel_process_group,
)
)
)
logging_outputs, extra_stats_to_sum = results[0], results[1:]
logging_outputs = list(chain.from_iterable(logging_outputs))
extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum]
return logging_outputs, extra_stats_to_sum
def _fast_stat_sync_sum(
self, logging_outputs: List[Dict[str, Any]], *extra_stats_to_sum, ignore=False,
):
"""
Sync logging outputs across workers. fast_stat_sync_sum is
faster than all_gather_list_sync, but is only suitable when
logging outputs are scalars and can be summed. Note that
*logging_outputs* cannot contain any nested dicts/lists.
"""
data = {}
for i, stat in enumerate(extra_stats_to_sum):
data["extra_stats_" + str(i)] = stat
if len(logging_outputs) > 0:
log_keys = list(logging_outputs[0].keys())
for k in log_keys:
if not ignore:
v = sum(log[k] for log in logging_outputs if k in log)
else:
v = logging_outputs[0][k]
v = torch.zeros_like(v) if torch.is_tensor(v) else 0
data["logging_outputs_" + k] = v
else:
log_keys = None
data = distributed_utils.all_reduce_dict(
data, device=self.device, group=self.data_parallel_process_group
)
extra_stats_to_sum = [
data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum))
]
if log_keys is not None:
logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}]
else:
logging_outputs = []
return logging_outputs, extra_stats_to_sum
def _check_grad_norms(self, grad_norm):
"""Check that grad norms are consistent across workers."""
if self._grad_norm_buf is not None:
self._grad_norm_buf.zero_()
self._grad_norm_buf[self.data_parallel_rank] = grad_norm
distributed_utils.all_reduce(
self._grad_norm_buf, group=self.data_parallel_process_group
)
def is_consistent(tensor):
max_abs_diff = torch.max(torch.abs(tensor - tensor[0]))
return (
torch.isfinite(tensor).all()
and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all()
)
if not is_consistent(self._grad_norm_buf):
pretty_detail = "\n".join(
"rank {:3d} = {:.8f}".format(r, n)
for r, n in enumerate(self._grad_norm_buf.tolist())
)
error_detail = "grad_norm across the workers:\n{}\n".format(
pretty_detail
)
# use FloatingPointError to trigger NanDetector
raise FloatingPointError(
"Fatal error: gradients are inconsistent between workers. "
"Try --ddp-backend=legacy_ddp. "
"Or are you mixing up different generation of GPUs in training?"
+ "\n"
+ "-" * 80
+ "\n{}\n".format(error_detail)
+ "-" * 80
)
def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None):
if grad_norm is not None and (
not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm)
):
metrics.log_speed("ups", 1.0, priority=100, round=2)
metrics.log_scalar("gnorm", grad_norm, priority=400, round=3)
if self.cfg.optimization.clip_norm > 0:
metrics.log_scalar(
"clip",
torch.where(
grad_norm > self.cfg.optimization.clip_norm,
grad_norm.new_tensor(100),
grad_norm.new_tensor(0),
),
priority=500,
round=1,
)
with metrics.aggregate() as agg:
if logging_outputs is not None:
self.task.reduce_metrics(logging_outputs, self.get_criterion())
del logging_outputs
# extra warning for criterions that don't properly log a loss value
if "loss" not in agg:
if "loss" not in self._warn_once:
self._warn_once.add("loss")
logger.warning(
"Criterion.reduce_metrics did not log a 'loss' value, "
"which may break some functionality"
)
metrics.log_scalar("loss", -1)
# support legacy interface
if self.tpu:
logging_output = {}
else:
logging_output = agg.get_smoothed_values()
logging_output["sample_size"] = sample_size
for key_to_delete in ["ppl", "wps", "wpb", "bsz"]:
if key_to_delete in logging_output:
del logging_output[key_to_delete]
return logging_output
def _check_xla_compilation(self):
import torch_xla.debug.metrics as met
compile_stats = met.metric_data("CompileTime")
if compile_stats is None:
return
num_xla_compiles = compile_stats[0]
if num_xla_compiles > self._num_xla_compiles:
logger.warning(
"XLA compilation detected on device #{}; too many of these can lead "
"to slow training, but we expect a few in the beginning".format(
self.cfg.distributed_training.distributed_rank
)
)
self._num_xla_compiles = num_xla_compiles
def _xla_markstep_and_send_to_cpu(self, data=None):
import torch_xla.core.xla_model as xm
xm.mark_step()
if data is not None:
from fairseq.utils import xla_device_to_cpu
return xla_device_to_cpu(data)
def _catalog_shared_params(module, memo=None, prefix=""):
if memo is None:
first_call = True
memo = {}
else:
first_call = False
for name, param in module._parameters.items():
param_prefix = prefix + ("." if prefix else "") + name
if param not in memo:
memo[param] = []
memo[param].append(param_prefix)
for name, m in module._modules.items():
if m is None:
continue
submodule_prefix = prefix + ("." if prefix else "") + name
_catalog_shared_params(m, memo, submodule_prefix)
if first_call:
return [x for x in memo.values() if len(x) > 1]
def _get_module_by_path(module, path):
path = path.split(".")
for name in path:
module = getattr(module, name)
return module
def _set_module_by_path(module, path, value):
path = path.split(".")
for name in path[:-1]:
module = getattr(module, name)
setattr(module, path[-1], value)
|
import argparse
import time
import csv
import yaml
import os
import logging
from pathlib import Path
import numpy as np
from tqdm import tqdm
from tensorboardX import SummaryWriter
import cv2
import matplotlib.pyplot as plt
class plot_results(object):
def __init__(self, frame_list=[100], mode='base'):
# frame_list = [0, 100, 200, 300]
# frame_list = [100, 700, 1200]
# frame_list = [100]
self.frame_list = frame_list
print(f"mode = {mode}")
self.get_image_names(mode=mode)
pass
def get_image_names(self, mode='base'):
frame_list = self.frame_list
plot_folder = "plots/"
image_name = None
if mode == 'base':
prefix = ["Si-Df-k", "Sp-Df-fp-end-k"]
plot_name = "mask_conf_" # 'corr_all_'
# image_name = [f"{plot_folder}{plot_name}{prefix}{i:06}_{(i+1):06}.png" for i in frame_list]
elif mode == 'good' or mode == 'bad':
prefix = [f"Si-Df-fp-k_{mode}", f"Sp-Df-fp-end-k_{mode}"]
plot_name = "mask_conf_" # "mask_conf_" # 'corr_all_'
elif mode == 'freeze':
print(f"freeze!")
iter_list = [0, 400, 1000]
prefix_base = "Sp-Df-f-end-k-freezeDf"
plot_name = 'corr_all_random_' # 'corr_all_', "mask_conf_" "epi_dist_all_" "corr_all_random_"
print(f"plot_name: {plot_name}")
# prefix = [f'{prefix_base}_{iter/1000}k_' for iter in iter_list] # 'Sp-Df-fp-end-k'
prefix = [f'{prefix_base}_s{frame_list[0]}_{iter/1000}k' for iter in iter_list] # 'Sp-Df-fp-end-k'
image_name = [f"{plot_folder}{plot_name}{p}.png" for p in prefix]
# prefix = f'Sp-Df-f-end-k-freezeDf_s{j}_{iter/1000}k'
# image_name = [
# f"{plot_folder}{plot_name}{pre}{i:06}_{(i+1):06}.png"
# for i in frame_list
# for pre in prefix
# ]
if image_name is None:
image_name = [
f"{plot_folder}{plot_name}{pre}_{i}.png"
for i in frame_list
for pre in prefix
]
self.prefix = prefix
self.image_name = image_name
self.image_data = []
self.plot_name = plot_name
print(image_name)
def __len__(self):
return len(self.image_name)
def read_images(self):
image_data = []
image_name = self.image_name
for i, file in enumerate(image_name):
img = cv2.imread(file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
image_data.append(img)
print(f"read {i}: {file}")
# plt.imshow(img)
# plt.show()
self.image_data = image_data
pass
def plot_images(
self, row=2, col=2, col_labels=["Baseline - Si-Df-fp", "Ours - Sp-Df-fp-end"],
save=True,
figsize=(48,12),
ext='pdf'
):
## create subgraph for combinations
# row, col = 2, 2
img_num = row * col
assert self.__len__() >= img_num
image_data = self.image_data
f, axarr = plt.subplots(row, col, figsize=figsize)
# f, axarr = plt.subplots(row, col, figsize=(48, 12))
axarr = axarr.reshape(-1, col)
for i in range(img_num):
print(f"axarr: {axarr.shape}, i= {i}")
axarr[int(i / col), int(i % col)].imshow(image_data[i])
axarr[int(i / col), int(i % col)].axis("off")
# axarr[i/2,i%2].imshow(imaget(_datas[1])
# axarr[1,0].imshow(image_datas[2])
# axarr[1,1].imshow(image_datas[3])
for ax, col_name in zip(axarr[0], col_labels):
ax.set_title(col_name, fontsize=figsize[0])
f.tight_layout()
# f.suptitle(f'{self.prefix}', fontsize=12)
savefile = f"{self.plot_name}_{str("_").join(self.prefix)}_{str("_").join([str(f) for f in self.frame_list])}"
if save:
if ext == 'pdf':
file = f"plots/{savefile}.pdf"
plt.savefig(file, bbox_inches="tight")
else:
file = f"plots/{savefile}.png"
plt.savefig(file, dpi=300, bbox_inches="tight")
logging.info(f"save image: {savefile}")
print(f"save image: {file}")
else:
print(f"not saved!!")
# logging.info(f"save image: {file}")
plt.show()
if __name__ == "__main__":
plot_helper = plot_class()
plot_helper.read_images()
# plot_helper.plot_images(row=3,col=2)
plot_helper.plot_images(row=1,col=2)
# class plot_class(object):
# def __init__(self):
# # frame_list = [0, 100, 200, 300]
# frame_list = [100, 700, 1200]
# # frame_list = [100]
# prefix = ['Si-Df-k', 'Sp-Df-fp-end-k']
# plot_folder = 'plots/'
# plot_name = 'mask_conf_' # 'corr_all_'
# # image_name = [f"{plot_folder}{plot_name}{prefix}{i:06}_{(i+1):06}.png" for i in frame_list]
# image_name = [f"{plot_folder}{plot_name}{pre}{i:06}_{(i+1):06}.png" for i in frame_list for pre in prefix ]
# self.frame_list = frame_list
# self.prefix = prefix
# self.image_name = image_name
# self.image_data = []
# print(image_name)
# pass
# def __len__(self):
# return len(self.image_name)
# def read_images(self):
# image_data = []
# image_name = self.image_name
# for i, file in enumerate(image_name):
# img = cv2.imread(file)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# image_data.append(img)
# print(f"read {i}: {file}")
# # plt.imshow(img)
# # plt.show()
# self.image_data = image_data
# pass
# def plot_images(self, row=2, col=2, col_labels=['Baseline - Si-Df-fp', 'Ours - Sp-Df-fp-end']):
# ## create subgraph for combinations
# # row, col = 2, 2
# img_num = row*col
# assert self.__len__() >= img_num
# image_data = self.image_data
# f, axarr = plt.subplots(row, col, figsize=(48, 12))
# # f, axarr = plt.subplots(row, col, figsize=(48, 12))
# axarr = axarr.reshape(-1, col)
# for i in range(img_num):
# print(f'axarr: {axarr.shape}, i= {i}')
# axarr[int(i/col),int(i%col)].imshow(image_data[i])
# axarr[int(i/col),int(i%col)].axis('off')
# # axarr[i/2,i%2].imshow(imaget(_datas[1])
# # axarr[1,0].imshow(image_datas[2])
# # axarr[1,1].imshow(image_datas[3])
# for ax, col_name in zip(axarr[0], col_labels):
# ax.set_title(col_name)
# f.tight_layout()
# # f.suptitle(f'{self.prefix}', fontsize=12)
# savefile = f"{str("_").join(self.prefix)}_{str("_").join([str(f) for f in self.frame_list])}"
# file = f"plots/{savefile}.png"
# # logging.info(f"save image: {file}")
# print(f"save image: {file}")
# plt.show()
# def plot_imgs(imgs, titles=None, cmap='brg', ylabel='', normalize=False, ax=None, dpi=100):
# n = len(imgs)
# if not isinstance(cmap, list):
# cmap = [cmap]*n
# if ax is None:
# fig, ax = plt.subplots(1, n, figsize=(6*n, 6), dpi=dpi)
# if n == 1:
# ax = [ax]
# else:
# if not isinstance(ax, list):
# ax = [ax]
# assert len(ax) == len(imgs)
# for i in range(n):
# if imgs[i].shape[-1] == 3:
# imgs[i] = imgs[i][..., ::-1] # BGR to RGB
# ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmap[i]),
# vmin=None if normalize else 0,
# vmax=None if normalize else 1)
# if titles:
# ax[i].set_title(titles[i])
# ax[i].get_yaxis().set_ticks([])
# ax[i].get_xaxis().set_ticks([])
# for spine in ax[i].spines.values(): # remove frame
# spine.set_visible(False)
# ax[0].set_ylabel(ylabel)
# plt.tight_layout()
# # from utils.draw import img_overlap
# def img_overlap(img_r, img_g, img_gray): # img_b repeat
# img = np.concatenate((img_gray, img_gray, img_gray), axis=0)
# img[0, :, :] += img_r[0, :, :]
# img[1, :, :] += img_g[0, :, :]
# img[img > 1] = 1
# img[img < 0] = 0
# return img
# def draw_keypoints(img, corners, color=(0, 255, 0), radius=3, s=3):
# '''
# :param img:
# image:
# numpy [H, W]
# :param corners:
# Points
# numpy [N, 2]
# :param color:
# :param radius:
# :param s:
# :return:
# overlaying image
# numpy [H, W]
# '''
# img = np.repeat(cv2.resize(img, None, fx=s, fy=s)[..., np.newaxis], 3, -1)
# for c in np.stack(corners).T:
# # cv2.circle(img, tuple(s * np.flip(c, 0)), radius, color, thickness=-1)
# cv2.circle(img, tuple((s * c[:2]).astype(int)), radius, color, thickness=-1)
# return img
# # def draw_keypoints(img, corners, color=(0, 255, 0), radius=3, s=3):
# # '''
# # :param img:
# # np (H, W)
# # :param corners:
# # np (3, N)
# # :param color:
# # :param radius:
# # :param s:
# # :return:
# # '''
# # img = np.repeat(cv2.resize(img, None, fx=s, fy=s)[..., np.newaxis], 3, -1)
# # for c in np.stack(corners).T:
# # # cv2.circle(img, tuple(s * np.flip(c, 0)), radius, color, thickness=-1)
# # cv2.circle(img, tuple((s*c[:2]).astype(int)), radius, color, thickness=-1)
# # return img
# def draw_matches(rgb1, rgb2, match_pairs, filename='matches.png', show=False):
# '''
# :param rgb1:
# image1
# numpy (H, W)
# :param rgb2:
# image2
# numpy (H, W)
# :param match_pairs:
# numpy (keypoiny1 x, keypoint1 y, keypoint2 x, keypoint 2 y)
# :return:
# None
# '''
# from matplotlib import pyplot as plt
# h1, w1 = rgb1.shape[:2]
# h2, w2 = rgb2.shape[:2]
# canvas = np.zeros((max(h1, h2), w1 + w2, 3), dtype=rgb1.dtype)
# canvas[:h1, :w1] = rgb1[:,:,np.newaxis]
# canvas[:h2, w1:] = rgb2[:,:,np.newaxis]
# # fig = plt.figure(frameon=False)
# fig = plt.imshow(canvas)
# xs = match_pairs[:, [0, 2]]
# xs[:, 1] += w1
# ys = match_pairs[:, [1, 3]]
# alpha = 1
# sf = 5
# lw = 0.5
# # markersize = 1
# markersize = 2
# plt.plot(
# xs.T, ys.T,
# alpha=alpha,
# linestyle="-",
# linewidth=lw,
# aa=False,
# marker='o',
# markersize=markersize,
# fillstyle='none',
# color=[0.0, 0.8, 0.0],
# );
# plt.tight_layout()
# plt.savefig(filename, dpi=300, bbox_inches='tight')
# print('#Matches = {}'.format(len(match_pairs)))
# if show:
# plt.show()
# # from utils.draw import draw_matches_cv
# def draw_matches_cv(data):
# keypoints1 = [cv2.KeyPoint(p[1], p[0], 1) for p in data['keypoints1']]
# keypoints2 = [cv2.KeyPoint(p[1], p[0], 1) for p in data['keypoints2']]
# inliers = data['inliers'].astype(bool)
# matches = np.array(data['matches'])[inliers].tolist()
# def to3dim(img):
# if img.ndim == 2:
# img = img[:, :, np.newaxis]
# return img
# img1 = to3dim(data['image1'])
# img2 = to3dim(data['image2'])
# img1 = np.concatenate([img1, img1, img1], axis=2)
# img2 = np.concatenate([img2, img2, img2], axis=2)
# return cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches,
# None, matchColor=(0,255,0), singlePointColor=(0, 0, 255))
# def drawBox(points, img, offset=np.array([0,0]), color=(0,255,0)):
# # print("origin", points)
# offset = offset[::-1]
# points = points + offset
# points = points.astype(int)
# for i in range(len(points)):
# img = img + cv2.line(np.zeros_like(img),tuple(points[-1+i]), tuple(points[i]), color,5)
# return img
| import argparse
import time
import csv
import yaml
import os
import logging
from pathlib import Path
import numpy as np
from tqdm import tqdm
from tensorboardX import SummaryWriter
import cv2
import matplotlib.pyplot as plt
class plot_results(object):
def __init__(self, frame_list=[100], mode='base'):
# frame_list = [0, 100, 200, 300]
# frame_list = [100, 700, 1200]
# frame_list = [100]
self.frame_list = frame_list
print(f"mode = {mode}")
self.get_image_names(mode=mode)
pass
def get_image_names(self, mode='base'):
frame_list = self.frame_list
plot_folder = "plots/"
image_name = None
if mode == 'base':
prefix = ["Si-Df-k", "Sp-Df-fp-end-k"]
plot_name = "mask_conf_" # 'corr_all_'
# image_name = [f"{plot_folder}{plot_name}{prefix}{i:06}_{(i+1):06}.png" for i in frame_list]
elif mode == 'good' or mode == 'bad':
prefix = [f"Si-Df-fp-k_{mode}", f"Sp-Df-fp-end-k_{mode}"]
plot_name = "mask_conf_" # "mask_conf_" # 'corr_all_'
elif mode == 'freeze':
print(f"freeze!")
iter_list = [0, 400, 1000]
prefix_base = "Sp-Df-f-end-k-freezeDf"
plot_name = 'corr_all_random_' # 'corr_all_', "mask_conf_" "epi_dist_all_" "corr_all_random_"
print(f"plot_name: {plot_name}")
# prefix = [f'{prefix_base}_{iter/1000}k_' for iter in iter_list] # 'Sp-Df-fp-end-k'
prefix = [f'{prefix_base}_s{frame_list[0]}_{iter/1000}k' for iter in iter_list] # 'Sp-Df-fp-end-k'
image_name = [f"{plot_folder}{plot_name}{p}.png" for p in prefix]
# prefix = f'Sp-Df-f-end-k-freezeDf_s{j}_{iter/1000}k'
# image_name = [
# f"{plot_folder}{plot_name}{pre}{i:06}_{(i+1):06}.png"
# for i in frame_list
# for pre in prefix
# ]
if image_name is None:
image_name = [
f"{plot_folder}{plot_name}{pre}_{i}.png"
for i in frame_list
for pre in prefix
]
self.prefix = prefix
self.image_name = image_name
self.image_data = []
self.plot_name = plot_name
print(image_name)
def __len__(self):
return len(self.image_name)
def read_images(self):
image_data = []
image_name = self.image_name
for i, file in enumerate(image_name):
img = cv2.imread(file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
image_data.append(img)
print(f"read {i}: {file}")
# plt.imshow(img)
# plt.show()
self.image_data = image_data
pass
def plot_images(
self, row=2, col=2, col_labels=["Baseline - Si-Df-fp", "Ours - Sp-Df-fp-end"],
save=True,
figsize=(48,12),
ext='pdf'
):
## create subgraph for combinations
# row, col = 2, 2
img_num = row * col
assert self.__len__() >= img_num
image_data = self.image_data
f, axarr = plt.subplots(row, col, figsize=figsize)
# f, axarr = plt.subplots(row, col, figsize=(48, 12))
axarr = axarr.reshape(-1, col)
for i in range(img_num):
print(f"axarr: {axarr.shape}, i= {i}")
axarr[int(i / col), int(i % col)].imshow(image_data[i])
axarr[int(i / col), int(i % col)].axis("off")
# axarr[i/2,i%2].imshow(imaget(_datas[1])
# axarr[1,0].imshow(image_datas[2])
# axarr[1,1].imshow(image_datas[3])
for ax, col_name in zip(axarr[0], col_labels):
ax.set_title(col_name, fontsize=figsize[0])
f.tight_layout()
# f.suptitle(f'{self.prefix}', fontsize=12)
savefile = f"{self.plot_name}_{str('_').join(self.prefix)}_{str('_').join([str(f) for f in self.frame_list])}"
if save:
if ext == 'pdf':
file = f"plots/{savefile}.pdf"
plt.savefig(file, bbox_inches="tight")
else:
file = f"plots/{savefile}.png"
plt.savefig(file, dpi=300, bbox_inches="tight")
logging.info(f"save image: {savefile}")
print(f"save image: {file}")
else:
print(f"not saved!!")
# logging.info(f"save image: {file}")
plt.show()
if __name__ == "__main__":
plot_helper = plot_class()
plot_helper.read_images()
# plot_helper.plot_images(row=3,col=2)
plot_helper.plot_images(row=1,col=2)
# class plot_class(object):
# def __init__(self):
# # frame_list = [0, 100, 200, 300]
# frame_list = [100, 700, 1200]
# # frame_list = [100]
# prefix = ['Si-Df-k', 'Sp-Df-fp-end-k']
# plot_folder = 'plots/'
# plot_name = 'mask_conf_' # 'corr_all_'
# # image_name = [f"{plot_folder}{plot_name}{prefix}{i:06}_{(i+1):06}.png" for i in frame_list]
# image_name = [f"{plot_folder}{plot_name}{pre}{i:06}_{(i+1):06}.png" for i in frame_list for pre in prefix ]
# self.frame_list = frame_list
# self.prefix = prefix
# self.image_name = image_name
# self.image_data = []
# print(image_name)
# pass
# def __len__(self):
# return len(self.image_name)
# def read_images(self):
# image_data = []
# image_name = self.image_name
# for i, file in enumerate(image_name):
# img = cv2.imread(file)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# image_data.append(img)
# print(f"read {i}: {file}")
# # plt.imshow(img)
# # plt.show()
# self.image_data = image_data
# pass
# def plot_images(self, row=2, col=2, col_labels=['Baseline - Si-Df-fp', 'Ours - Sp-Df-fp-end']):
# ## create subgraph for combinations
# # row, col = 2, 2
# img_num = row*col
# assert self.__len__() >= img_num
# image_data = self.image_data
# f, axarr = plt.subplots(row, col, figsize=(48, 12))
# # f, axarr = plt.subplots(row, col, figsize=(48, 12))
# axarr = axarr.reshape(-1, col)
# for i in range(img_num):
# print(f'axarr: {axarr.shape}, i= {i}')
# axarr[int(i/col),int(i%col)].imshow(image_data[i])
# axarr[int(i/col),int(i%col)].axis('off')
# # axarr[i/2,i%2].imshow(imaget(_datas[1])
# # axarr[1,0].imshow(image_datas[2])
# # axarr[1,1].imshow(image_datas[3])
# for ax, col_name in zip(axarr[0], col_labels):
# ax.set_title(col_name)
# f.tight_layout()
# # f.suptitle(f'{self.prefix}', fontsize=12)
# savefile = f"{str('_').join(self.prefix)}_{str('_').join([str(f) for f in self.frame_list])}"
# file = f"plots/{savefile}.png"
# # logging.info(f"save image: {file}")
# print(f"save image: {file}")
# plt.show()
# def plot_imgs(imgs, titles=None, cmap='brg', ylabel='', normalize=False, ax=None, dpi=100):
# n = len(imgs)
# if not isinstance(cmap, list):
# cmap = [cmap]*n
# if ax is None:
# fig, ax = plt.subplots(1, n, figsize=(6*n, 6), dpi=dpi)
# if n == 1:
# ax = [ax]
# else:
# if not isinstance(ax, list):
# ax = [ax]
# assert len(ax) == len(imgs)
# for i in range(n):
# if imgs[i].shape[-1] == 3:
# imgs[i] = imgs[i][..., ::-1] # BGR to RGB
# ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmap[i]),
# vmin=None if normalize else 0,
# vmax=None if normalize else 1)
# if titles:
# ax[i].set_title(titles[i])
# ax[i].get_yaxis().set_ticks([])
# ax[i].get_xaxis().set_ticks([])
# for spine in ax[i].spines.values(): # remove frame
# spine.set_visible(False)
# ax[0].set_ylabel(ylabel)
# plt.tight_layout()
# # from utils.draw import img_overlap
# def img_overlap(img_r, img_g, img_gray): # img_b repeat
# img = np.concatenate((img_gray, img_gray, img_gray), axis=0)
# img[0, :, :] += img_r[0, :, :]
# img[1, :, :] += img_g[0, :, :]
# img[img > 1] = 1
# img[img < 0] = 0
# return img
# def draw_keypoints(img, corners, color=(0, 255, 0), radius=3, s=3):
# '''
# :param img:
# image:
# numpy [H, W]
# :param corners:
# Points
# numpy [N, 2]
# :param color:
# :param radius:
# :param s:
# :return:
# overlaying image
# numpy [H, W]
# '''
# img = np.repeat(cv2.resize(img, None, fx=s, fy=s)[..., np.newaxis], 3, -1)
# for c in np.stack(corners).T:
# # cv2.circle(img, tuple(s * np.flip(c, 0)), radius, color, thickness=-1)
# cv2.circle(img, tuple((s * c[:2]).astype(int)), radius, color, thickness=-1)
# return img
# # def draw_keypoints(img, corners, color=(0, 255, 0), radius=3, s=3):
# # '''
# # :param img:
# # np (H, W)
# # :param corners:
# # np (3, N)
# # :param color:
# # :param radius:
# # :param s:
# # :return:
# # '''
# # img = np.repeat(cv2.resize(img, None, fx=s, fy=s)[..., np.newaxis], 3, -1)
# # for c in np.stack(corners).T:
# # # cv2.circle(img, tuple(s * np.flip(c, 0)), radius, color, thickness=-1)
# # cv2.circle(img, tuple((s*c[:2]).astype(int)), radius, color, thickness=-1)
# # return img
# def draw_matches(rgb1, rgb2, match_pairs, filename='matches.png', show=False):
# '''
# :param rgb1:
# image1
# numpy (H, W)
# :param rgb2:
# image2
# numpy (H, W)
# :param match_pairs:
# numpy (keypoiny1 x, keypoint1 y, keypoint2 x, keypoint 2 y)
# :return:
# None
# '''
# from matplotlib import pyplot as plt
# h1, w1 = rgb1.shape[:2]
# h2, w2 = rgb2.shape[:2]
# canvas = np.zeros((max(h1, h2), w1 + w2, 3), dtype=rgb1.dtype)
# canvas[:h1, :w1] = rgb1[:,:,np.newaxis]
# canvas[:h2, w1:] = rgb2[:,:,np.newaxis]
# # fig = plt.figure(frameon=False)
# fig = plt.imshow(canvas)
# xs = match_pairs[:, [0, 2]]
# xs[:, 1] += w1
# ys = match_pairs[:, [1, 3]]
# alpha = 1
# sf = 5
# lw = 0.5
# # markersize = 1
# markersize = 2
# plt.plot(
# xs.T, ys.T,
# alpha=alpha,
# linestyle="-",
# linewidth=lw,
# aa=False,
# marker='o',
# markersize=markersize,
# fillstyle='none',
# color=[0.0, 0.8, 0.0],
# );
# plt.tight_layout()
# plt.savefig(filename, dpi=300, bbox_inches='tight')
# print('#Matches = {}'.format(len(match_pairs)))
# if show:
# plt.show()
# # from utils.draw import draw_matches_cv
# def draw_matches_cv(data):
# keypoints1 = [cv2.KeyPoint(p[1], p[0], 1) for p in data['keypoints1']]
# keypoints2 = [cv2.KeyPoint(p[1], p[0], 1) for p in data['keypoints2']]
# inliers = data['inliers'].astype(bool)
# matches = np.array(data['matches'])[inliers].tolist()
# def to3dim(img):
# if img.ndim == 2:
# img = img[:, :, np.newaxis]
# return img
# img1 = to3dim(data['image1'])
# img2 = to3dim(data['image2'])
# img1 = np.concatenate([img1, img1, img1], axis=2)
# img2 = np.concatenate([img2, img2, img2], axis=2)
# return cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches,
# None, matchColor=(0,255,0), singlePointColor=(0, 0, 255))
# def drawBox(points, img, offset=np.array([0,0]), color=(0,255,0)):
# # print("origin", points)
# offset = offset[::-1]
# points = points + offset
# points = points.astype(int)
# for i in range(len(points)):
# img = img + cv2.line(np.zeros_like(img),tuple(points[-1+i]), tuple(points[i]), color,5)
# return img
|
from pyrogram import Client as LuciferMoringstar_Robot, filters as Worker
from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton
from pyrogram.errors import UserIsBlocked, PeerIdInvalid
from LuciferMoringstar_Robot.database.autofilter_db import is_subscribed, get_file_details
from LuciferMoringstar_Robot.database._utils import get_size
from translation import LuciferMoringstar
from config import BUTTONS, FORCES_SUB, CUSTOM_FILE_CAPTION, START_MSG, DEV_NAME, bot_info, ADMINS
@LuciferMoringstar_Robot.on_callback_query()
async def cb_handler(client: LuciferMoringstar_Robot, query):
clicked = query.from_user.id
try:
typed = query.message.reply_to_message.from_user.id
except:
typed = query.from_user.id
if (clicked == typed):
# # ---------- 🔘 [ | 𝗚𝗥𝗢𝗨𝗣 𝗙𝗜𝗟𝗧𝗘𝗥𝗦 | ] 🔘 ---------- # #
if query.data.startswith("nextgroup"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == int(data["total"]) - 2:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backgroup_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backgroup_{int(index)+1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextgroup_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
elif query.data.startswith("backgroup"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == 1:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("Next Page ➡", callback_data=f"nextgroup_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backgroup_{int(index)-1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextgroup_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
# # ---------- 🔘 [ | 𝗕𝗢𝗧 𝗣𝗠 𝗙𝗜𝗟𝗧𝗘𝗥𝗦 | ] 🔘 ---------- # #
elif query.data.startswith("nextbot"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == int(data["total"]) - 2:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backbot_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backbot_{int(index)+1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextbot_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
elif query.data.startswith("backbot"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == 1:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("Next Page ➡", callback_data=f"nextbot_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backbot_{int(index)-1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextbot_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data["total"]}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
# ---------- 📁 [ | 𝗚𝗘𝗧 𝗙𝗜𝗟𝗘𝗦 | ] 📁 ---------- #
elif query.data.startswith("lucifermoringstar_robot"):
ident, file_id = query.data.split("#")
files_ = await get_file_details(file_id)
if not files_:
return await query.answer('No such file exist.')
files = files_[0]
title = files.file_name
size=get_size(files.file_size)
f_caption=files.caption
if CUSTOM_FILE_CAPTION:
try:
f_caption=CUSTOM_FILE_CAPTION.format(mention=query.from_user.mention, file_name=title, file_size=size, file_caption=f_caption)
except Exception as e:
print(e)
f_caption=f_caption
if f_caption is None:
f_caption = LuciferMoringstar.FILE_CAPTIONS.format(mention=query.from_user.mention, title=title, size=size)
try:
if FORCES_SUB and not await is_subscribed(client, query):
await query.answer(url=f"https://t.me/{bot_info.BOT_USERNAME}?start=subscribe")
return
else:
await client.send_cached_media(
chat_id=query.from_user.id,
file_id=file_id,
caption=f_caption
)
await query.answer('🤖 Check PM, I have Sent Files In Pm 🤖',show_alert = True)
except UserIsBlocked:
await query.answer('Unblock the bot mahn !',show_alert = True)
except PeerIdInvalid:
await query.answer(url=f"https://t.me/{bot_info.BOT_USERNAME}?start=subscribe")
except Exception as e:
await query.answer(url=f"https://t.me/{bot_info.BOT_USERNAME}?start=subscribe")
# ---------- 📁 [ | 𝗣𝗠 𝗙𝗜𝗟𝗘𝗦 | ] 📁 ---------- #
elif query.data.startswith("pmfile"):
if FORCES_SUB and not await is_subscribed(client, query):
await query.answer("I Like Your Smartness, But Don't Be Oversmart 😒",show_alert=True)
return
ident, file_id = query.data.split("#")
filedetails = await get_file_details(file_id)
for files in filedetails:
title = files.file_name
size=get_size(files.file_size)
f_caption=files.caption
if CUSTOM_FILE_CAPTION:
try:
f_caption=CUSTOM_FILE_CAPTION.format(mention=query.from_user.mention, title=title, file_size=size, file_caption=f_caption)
except Exception as e:
print(e)
f_caption=f_caption
if f_caption is None:
f_caption = LuciferMoringstar.FILE_CAPTIONS
buttons = [[
InlineKeyboardButton('🧑💻 Main Channel 🧑💻', url='https://t.me/Moviemadh')
]]
await query.answer()
await client.send_cached_media(
chat_id=query.from_user.id,
file_id=file_id,
caption=f_caption,
reply_markup=InlineKeyboardMarkup(buttons)
)
# ---------- 📁 [ | 𝗠𝗢𝗗𝗨𝗟𝗘𝗦 | ] 📁 ---------- #
elif query.data == "start":
if query.from_user.id not in ADMINS:
buttons = [[
InlineKeyboardButton("➕️ Add me to Your Chat ➕️", url=f"http://t.me/{bot_info.BOT_USERNAME}?startgroup=true")
],[
InlineKeyboardButton("ℹ️ Help", callback_data="help"),
InlineKeyboardButton("😎 About", callback_data="about")
],[
InlineKeyboardButton("Main Channel", url="https://t.me/Moviemadh"),
InlineKeyboardButton("Request Here", url="https://t.me/MM_Request1")
]]
else:
buttons = [[
InlineKeyboardButton("➕️ Add me to Your Chat ➕️", url=f"http://t.me/{bot_info.BOT_USERNAME}?startgroup=true")
],[
InlineKeyboardButton("ℹ️ Help", callback_data="bot_owner"),
InlineKeyboardButton("😎 About", callback_data="about")
],[
InlineKeyboardButton("Main Channel", url="https://t.me/Moviemadh"),
InlineKeyboardButton("Request Here", url="https://t.me/MM_Request1")
]]
await query.message.edit(text=START_MSG.format(mention=query.from_user.mention, bot_name=bot_info.BOT_NAME, bot_username=bot_info.BOT_USERNAME), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "help":
buttons = [[
InlineKeyboardButton("🏠 Home", callback_data="start"),
InlineKeyboardButton("About 😎", callback_data="about")
]]
await query.message.edit(text=LuciferMoringstar.HELP_MSG.format(mention=query.from_user.mention), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "about":
buttons = [[
InlineKeyboardButton("🏠 Home", callback_data="start"),
InlineKeyboardButton("Close 🗑️", callback_data="close")
]]
await query.message.edit(text=LuciferMoringstar.ABOUT_MSG.format(mention=query.from_user.mention, bot_name=bot_info.BOT_NAME, bot_username=bot_info.BOT_USERNAME, dev_name=DEV_NAME), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "bot_owner":
buttons = [[
InlineKeyboardButton('🏠 Home', callback_data="start"),
InlineKeyboardButton('About 😎', callback_data="about")
]]
await query.message.edit(text=LuciferMoringstar.PR0FESS0R_99.format(mention=query.from_user.mention), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "pages":
await query.answer()
else:
await query.answer("Please Request",show_alert=True)
| from pyrogram import Client as LuciferMoringstar_Robot, filters as Worker
from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton
from pyrogram.errors import UserIsBlocked, PeerIdInvalid
from LuciferMoringstar_Robot.database.autofilter_db import is_subscribed, get_file_details
from LuciferMoringstar_Robot.database._utils import get_size
from translation import LuciferMoringstar
from config import BUTTONS, FORCES_SUB, CUSTOM_FILE_CAPTION, START_MSG, DEV_NAME, bot_info, ADMINS
@LuciferMoringstar_Robot.on_callback_query()
async def cb_handler(client: LuciferMoringstar_Robot, query):
clicked = query.from_user.id
try:
typed = query.message.reply_to_message.from_user.id
except:
typed = query.from_user.id
if (clicked == typed):
# # ---------- 🔘 [ | 𝗚𝗥𝗢𝗨𝗣 𝗙𝗜𝗟𝗧𝗘𝗥𝗦 | ] 🔘 ---------- # #
if query.data.startswith("nextgroup"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == int(data["total"]) - 2:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backgroup_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backgroup_{int(index)+1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextgroup_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
elif query.data.startswith("backgroup"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == 1:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("Next Page ➡", callback_data=f"nextgroup_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backgroup_{int(index)-1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextgroup_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
buttons.append(
[InlineKeyboardButton(text="🤖 CHECK MY PM 🤖", url=f"https://telegram.dog/{bot_info.BOT_USERNAME}")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
# # ---------- 🔘 [ | 𝗕𝗢𝗧 𝗣𝗠 𝗙𝗜𝗟𝗧𝗘𝗥𝗦 | ] 🔘 ---------- # #
elif query.data.startswith("nextbot"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == int(data["total"]) - 2:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backbot_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)+1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backbot_{int(index)+1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextbot_{int(index)+1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
elif query.data.startswith("backbot"):
ident, index, keyword = query.data.split("_")
try:
data = BUTTONS[keyword]
except KeyError:
await query.answer("This Is My Old Message So Please Request Again 🙏",show_alert=True)
return
if int(index) == 1:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("Next Page ➡", callback_data=f"nextbot_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
else:
buttons = data['buttons'][int(index)-1].copy()
buttons.append(
[InlineKeyboardButton("🔙 Back Page", callback_data=f"backbot_{int(index)-1}_{keyword}"),InlineKeyboardButton("Next Page ➡", callback_data=f"nextbot_{int(index)-1}_{keyword}")]
)
buttons.append(
[InlineKeyboardButton(f"📃 Pages {int(index)}/{data['total']}", callback_data="pages"),
InlineKeyboardButton("Close 🗑️", callback_data="close")]
)
await query.edit_message_reply_markup(
reply_markup=InlineKeyboardMarkup(buttons)
)
return
# ---------- 📁 [ | 𝗚𝗘𝗧 𝗙𝗜𝗟𝗘𝗦 | ] 📁 ---------- #
elif query.data.startswith("lucifermoringstar_robot"):
ident, file_id = query.data.split("#")
files_ = await get_file_details(file_id)
if not files_:
return await query.answer('No such file exist.')
files = files_[0]
title = files.file_name
size=get_size(files.file_size)
f_caption=files.caption
if CUSTOM_FILE_CAPTION:
try:
f_caption=CUSTOM_FILE_CAPTION.format(mention=query.from_user.mention, file_name=title, file_size=size, file_caption=f_caption)
except Exception as e:
print(e)
f_caption=f_caption
if f_caption is None:
f_caption = LuciferMoringstar.FILE_CAPTIONS.format(mention=query.from_user.mention, title=title, size=size)
try:
if FORCES_SUB and not await is_subscribed(client, query):
await query.answer(url=f"https://t.me/{bot_info.BOT_USERNAME}?start=subscribe")
return
else:
await client.send_cached_media(
chat_id=query.from_user.id,
file_id=file_id,
caption=f_caption
)
await query.answer('🤖 Check PM, I have Sent Files In Pm 🤖',show_alert = True)
except UserIsBlocked:
await query.answer('Unblock the bot mahn !',show_alert = True)
except PeerIdInvalid:
await query.answer(url=f"https://t.me/{bot_info.BOT_USERNAME}?start=subscribe")
except Exception as e:
await query.answer(url=f"https://t.me/{bot_info.BOT_USERNAME}?start=subscribe")
# ---------- 📁 [ | 𝗣𝗠 𝗙𝗜𝗟𝗘𝗦 | ] 📁 ---------- #
elif query.data.startswith("pmfile"):
if FORCES_SUB and not await is_subscribed(client, query):
await query.answer("I Like Your Smartness, But Don't Be Oversmart 😒",show_alert=True)
return
ident, file_id = query.data.split("#")
filedetails = await get_file_details(file_id)
for files in filedetails:
title = files.file_name
size=get_size(files.file_size)
f_caption=files.caption
if CUSTOM_FILE_CAPTION:
try:
f_caption=CUSTOM_FILE_CAPTION.format(mention=query.from_user.mention, title=title, file_size=size, file_caption=f_caption)
except Exception as e:
print(e)
f_caption=f_caption
if f_caption is None:
f_caption = LuciferMoringstar.FILE_CAPTIONS
buttons = [[
InlineKeyboardButton('🧑💻 Main Channel 🧑💻', url='https://t.me/Moviemadh')
]]
await query.answer()
await client.send_cached_media(
chat_id=query.from_user.id,
file_id=file_id,
caption=f_caption,
reply_markup=InlineKeyboardMarkup(buttons)
)
# ---------- 📁 [ | 𝗠𝗢𝗗𝗨𝗟𝗘𝗦 | ] 📁 ---------- #
elif query.data == "start":
if query.from_user.id not in ADMINS:
buttons = [[
InlineKeyboardButton("➕️ Add me to Your Chat ➕️", url=f"http://t.me/{bot_info.BOT_USERNAME}?startgroup=true")
],[
InlineKeyboardButton("ℹ️ Help", callback_data="help"),
InlineKeyboardButton("😎 About", callback_data="about")
],[
InlineKeyboardButton("Main Channel", url="https://t.me/Moviemadh"),
InlineKeyboardButton("Request Here", url="https://t.me/MM_Request1")
]]
else:
buttons = [[
InlineKeyboardButton("➕️ Add me to Your Chat ➕️", url=f"http://t.me/{bot_info.BOT_USERNAME}?startgroup=true")
],[
InlineKeyboardButton("ℹ️ Help", callback_data="bot_owner"),
InlineKeyboardButton("😎 About", callback_data="about")
],[
InlineKeyboardButton("Main Channel", url="https://t.me/Moviemadh"),
InlineKeyboardButton("Request Here", url="https://t.me/MM_Request1")
]]
await query.message.edit(text=START_MSG.format(mention=query.from_user.mention, bot_name=bot_info.BOT_NAME, bot_username=bot_info.BOT_USERNAME), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "help":
buttons = [[
InlineKeyboardButton("🏠 Home", callback_data="start"),
InlineKeyboardButton("About 😎", callback_data="about")
]]
await query.message.edit(text=LuciferMoringstar.HELP_MSG.format(mention=query.from_user.mention), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "about":
buttons = [[
InlineKeyboardButton("🏠 Home", callback_data="start"),
InlineKeyboardButton("Close 🗑️", callback_data="close")
]]
await query.message.edit(text=LuciferMoringstar.ABOUT_MSG.format(mention=query.from_user.mention, bot_name=bot_info.BOT_NAME, bot_username=bot_info.BOT_USERNAME, dev_name=DEV_NAME), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "bot_owner":
buttons = [[
InlineKeyboardButton('🏠 Home', callback_data="start"),
InlineKeyboardButton('About 😎', callback_data="about")
]]
await query.message.edit(text=LuciferMoringstar.PR0FESS0R_99.format(mention=query.from_user.mention), reply_markup=InlineKeyboardMarkup(buttons), disable_web_page_preview=True)
elif query.data == "pages":
await query.answer()
else:
await query.answer("Please Request",show_alert=True)
|
import speech_recognition as sr
from pydub import AudioSegment
import os
from datetime import date
import sounddevice as sd
from scipy.io.wavfile import write
from random import choice, randint
import pyttsx3
import time
import webbrowser
from playsound import playsound
# Commands
hello = ["hi", "Hi", "hello", "Hello", "wsg", "Wsg", "WSG", "sup", "Sup", "hey", "Hey", "hi!", "Hi!", "hello!",
"Hello!", "wsg!", "Wsg!", "WSG!", "sup!", "Sup!", "hey!", "Hey!", "hi :)", "Hi :)", "hello :)", "Hello :)",
"wsg :)", "Wsg :)", "WSG :)", "sup :)", "Sup :)", "hey :)", "Hey :)", "hi! :)", "Hi! :)", "hello! :)",
"Hello! :)", "wsg! :)", "Wsg! :)", "WSG! :)", "sup! :)", "Sup! :)", "hey! :)", "Hey! :)", "Ello", "ello",
"'Ello", "'ello"]
bye = ["bye", "Bye", "goodbye", "Goodbye", "good bye", "Good Bye", "see you", "See you", "later", "Later", "byee",
"Byee", "byeee", "Byeee"]
insult = ["fucktard", "idot", "idiot", "dumbass", "motherfucker", "stupid", "gay", "fucker", "Fucktard", "Idot",
"Idiot", "Dumbass", "Motherfucker", "Stupid", "Gay", "Fucker" "ur fat", "Ur fat", "your fat", "Your fat",
"youre fat", "youre fat", "faggot", "retard", "bitch", "whore", "thot", "fat", "fatty", "ur gay", "Ur gay",
"your gay", "youre gay", "Youre gay", "Fag", "fag", "Loser", "loser"]
compliment = ["gg", "good job", "nice", "great", "awesome", "good", "your hot", "ur hot", "youre hot", "youre awesome",
"youre cool", "Nice"]
hi = ["Sup", "Hello", "Hi", "good morning", "Good morning", "Good afternoon", "good afternoon", "good evening",
"Good evening"]
hi2 = ["Sup", "Hello", "Hi"]
gn = ["Good night", "good night"]
yes = ["yes", "Sure!", "sure", "of course", "yeah"]
no = ["yeah no", "no", "heck no"]
thankYou = ["thank you", "Thank you", "Thanks", "thanks", "Thank you", "thank you", "thx!", "Thx!", "Ty!", "ty!",
"Thanks!", "thanks!", "Thank u", "thank u"]
startTimer = ["Can you start a timer", "Can you start a timer?", "can you start a timer", "can you start a timer?",
"please start a timer", "Please start a timer", "timer start", "Timer start", "start timer",
"Start timer", "can you please start a timer?", "can you start a timer please",
"Can you start a timer please", "can you start a timer please?", "Can you start a timer please?"]
endTimer = ["End the timer please", "end the timer please", "please end the timer", "Please end the timer", "timer end",
"Timer end", "End timer", "end timer", "Stop the timer please", "stop the timer please",
"please stop the timer", "Please stop the timer", "timer stop", "Timer stop", "Stop timer", "stop timer"]
howMany = ["How many", "how many", "how many?", "How many?"]
canIJoin = ["can i join", "Can i join", "Can i join?", "can i join?", "can I join", "Can I join", "Can I join?",
"can I join?"]
howAreYou = ["How are you", "how are you", "How are you?", "how are you?", "How are you doing", "how are you doing",
"how are you doing?", "How are you doing?", "How are u", "how are u", "How are u?", "how are u?"]
howImDoing = ["Ok so far", "Pretty good", "Good", "Great"]
wyd = ["What are you doing", "what are you doing", "Wyd", "wyd", "WYD", "What are you doing?", "what are you doing?",
"Wyd?", "wyd?", "WYD?"]
wid = ["Smoking crack", "Coding", "Talking to people", "Nothing right now", "Playing piano", "Invading poland",
"Making tacos"]
invpoland = ["wanna go invade poland", "Wanna go invade poland", "Wanna go invade poland?", "wanna go invade poland?",
"want to go invade poland"]
ily = ["i love you", "I love you", "ily", "Ily", "ILY", "i <3 you", "I <3 you", "i <3 u", "i love u", "I love u"]
isFren = ["Are you a friend", "are you a friend", "Are you a friend?", "are you a friend?", "Are you fren",
"are you fren", "Are you a fren?", "are you a fren?", "Are you a fren", "are you a fren", "Are you a fren?",
"are you a fren?", "Are you fren?", "are you fren?", "are you fren", "Are you fren"]
whatCanYouDo = ["What can you do", "what can you do", "what can you do?", "What can you do?", "What do you do?",
"what do you do?", "cmd use", "Cmd use", "!use"]
theDate = ["What is the date", "what is the date", "what is today", "What is today", "can you please tell me the date",
"Can you please tell me the date", "what is the date today", "What is the date today", "What is the date?",
"what is the date?", "what is today?", "What is today?", "can you please tell me the date?",
"Can you please tell me the date?", "what is the date today?", "What is the date today?"]
enable_speech = ["enable speech", "speech enable", "speech on"]
disable_speech = ["disable speech", "speech disable", "speech off"]
enable_man = ["enable manual", "manual enable", "manual on"]
disable_man = ["disable manual", "manual disable", "manual off"]
openSite = ["Open site", "open site", "website", "site", "site open"]
engine = pyttsx3.init()
fs = 44100
seconds = 3
strtTime = 0
endtime = 0
manual = False
speech = True
bot_name = ['ivan', 'hey ivan', 'boot ivan', 'help ivan', 'Yo ivan wake up']
toSay = ''
count = 0
window = Tk()
try:
os.remove('output.wav', 'transcript.wav')
except:
pass
print("Started!")
def main():
global count
while count < 3:
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
sd.wait()
write('output.wav', fs, myrecording) # Save as WAV file
sound = AudioSegment.from_wav('output.wav')
sound.export('transcript.wav', format="wav")
AUDIO_FILE = "transcript.wav"
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
global speech
global manual
global strtTime
global endtime
global toSay
audio = r.record(source)
try:
transcribed = r.recognize_google(audio)
except:
transcribed = "Sorry, i did not understand"
engine.say(transcribed)
engine.runAndWait()
if manual == True:
transcribed = input("Manual Command> ")
try:
print("Transcription: " + transcribed)
text = transcribed.lower()
if text in theDate:
toSay = (date.today())
elif text in openSite:
engine.say("What site do you want to open?")
engine.runAndWait()
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
sd.wait()
write('output.wav', fs, myrecording) # Save as WAV file
AUDIO_FILE = "output.wav"
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source)
speech = True
try:
transcribed = r.recognize_google(audio)
except:
transcribed = "I couldn't understand what you said"
engine.say(transcribed)
engine.runAndWait()
print(transcribed)
engine.say("Opening site.")
engine.runAndWait()
if transcribed != "I couldn't understand what you said":
url = f'https://www.{transcribed}.org'
webbrowser.open(url)
if transcribed.lower() != 'python':
url = f'https://www.{transcribed}.com'
webbrowser.open(url)
elif text in compliment:
toSay = choice(thankYou)
elif text in whatCanYouDo:
toSay = f"I am {bot_name}. I can answer questions and run commands as you wish! Just remember i was made by a thirteen year old and a twelve year old"
elif text in isFren:
toSay = "Of course, im always here to help"
elif text in canIJoin:
toSay = 'Sure'
elif text in insult:
toSay = "You do know i don't get offended, right?"
elif text in enable_man:
manual = True
elif text in disable_man:
manual = False
elif text in ily:
playsound('yugay.wav')
elif text in wyd:
toSay = choice(wid)
elif text in thankYou:
toSay = "You're welcome"
elif text in howMany:
toSay = str(randint(1, 50))
elif text in howAreYou:
toSay = choice(howImDoing)
elif text in invpoland:
toSay = "Sure"
elif text in hi:
toSay = choice(hi2)
elif text in hello:
toSay = choice(hi2)
elif text in bye:
toSay = choice(bye)
elif text in startTimer:
strtTime == time.time()
toSay = 'Ok'
elif text in endTimer:
endtime == time.time()
toSay = (f'Ok, Time is {str(endtime - strtTime)}')
elif text in enable_speech:
global speech
speech = True
toSay = "Ok"
elif text in disable_speech:
global speech
speech = False
toSay = "Ok"
elif text == 'what is the time':
t = time.localtime()
current_time = time.strftime("%H:%M:%S", t)
print(current_time)
else:
toSay = "Unknown command"
print(toSay)
if speech == True:
engine.say(toSay)
engine.runAndWait()
else:
count += 1
pass
input("")
except:
pass
# input("Continue? ")
count = 0
while True:
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
sd.wait()
write('output.wav', fs, myrecording)
sound = AudioSegment.from_wav("output.wav")
sound.export("transcript.wav", format="wav")
AUDIO_FILE = "transcript.wav"
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source)
speech = True
try:
transcribed = r.recognize_google(audio)
except:
pass
try:
if transcribed.lower() in bot_name and transcribed:
print("Voice Acivated")
engine.say(f"Hello {os.getenv("USERNAME")}, how may i help")
engine.runAndWait()
main()
except:
pass
| import speech_recognition as sr
from pydub import AudioSegment
import os
from datetime import date
import sounddevice as sd
from scipy.io.wavfile import write
from random import choice, randint
import pyttsx3
import time
import webbrowser
from playsound import playsound
# Commands
hello = ["hi", "Hi", "hello", "Hello", "wsg", "Wsg", "WSG", "sup", "Sup", "hey", "Hey", "hi!", "Hi!", "hello!",
"Hello!", "wsg!", "Wsg!", "WSG!", "sup!", "Sup!", "hey!", "Hey!", "hi :)", "Hi :)", "hello :)", "Hello :)",
"wsg :)", "Wsg :)", "WSG :)", "sup :)", "Sup :)", "hey :)", "Hey :)", "hi! :)", "Hi! :)", "hello! :)",
"Hello! :)", "wsg! :)", "Wsg! :)", "WSG! :)", "sup! :)", "Sup! :)", "hey! :)", "Hey! :)", "Ello", "ello",
"'Ello", "'ello"]
bye = ["bye", "Bye", "goodbye", "Goodbye", "good bye", "Good Bye", "see you", "See you", "later", "Later", "byee",
"Byee", "byeee", "Byeee"]
insult = ["fucktard", "idot", "idiot", "dumbass", "motherfucker", "stupid", "gay", "fucker", "Fucktard", "Idot",
"Idiot", "Dumbass", "Motherfucker", "Stupid", "Gay", "Fucker" "ur fat", "Ur fat", "your fat", "Your fat",
"youre fat", "youre fat", "faggot", "retard", "bitch", "whore", "thot", "fat", "fatty", "ur gay", "Ur gay",
"your gay", "youre gay", "Youre gay", "Fag", "fag", "Loser", "loser"]
compliment = ["gg", "good job", "nice", "great", "awesome", "good", "your hot", "ur hot", "youre hot", "youre awesome",
"youre cool", "Nice"]
hi = ["Sup", "Hello", "Hi", "good morning", "Good morning", "Good afternoon", "good afternoon", "good evening",
"Good evening"]
hi2 = ["Sup", "Hello", "Hi"]
gn = ["Good night", "good night"]
yes = ["yes", "Sure!", "sure", "of course", "yeah"]
no = ["yeah no", "no", "heck no"]
thankYou = ["thank you", "Thank you", "Thanks", "thanks", "Thank you", "thank you", "thx!", "Thx!", "Ty!", "ty!",
"Thanks!", "thanks!", "Thank u", "thank u"]
startTimer = ["Can you start a timer", "Can you start a timer?", "can you start a timer", "can you start a timer?",
"please start a timer", "Please start a timer", "timer start", "Timer start", "start timer",
"Start timer", "can you please start a timer?", "can you start a timer please",
"Can you start a timer please", "can you start a timer please?", "Can you start a timer please?"]
endTimer = ["End the timer please", "end the timer please", "please end the timer", "Please end the timer", "timer end",
"Timer end", "End timer", "end timer", "Stop the timer please", "stop the timer please",
"please stop the timer", "Please stop the timer", "timer stop", "Timer stop", "Stop timer", "stop timer"]
howMany = ["How many", "how many", "how many?", "How many?"]
canIJoin = ["can i join", "Can i join", "Can i join?", "can i join?", "can I join", "Can I join", "Can I join?",
"can I join?"]
howAreYou = ["How are you", "how are you", "How are you?", "how are you?", "How are you doing", "how are you doing",
"how are you doing?", "How are you doing?", "How are u", "how are u", "How are u?", "how are u?"]
howImDoing = ["Ok so far", "Pretty good", "Good", "Great"]
wyd = ["What are you doing", "what are you doing", "Wyd", "wyd", "WYD", "What are you doing?", "what are you doing?",
"Wyd?", "wyd?", "WYD?"]
wid = ["Smoking crack", "Coding", "Talking to people", "Nothing right now", "Playing piano", "Invading poland",
"Making tacos"]
invpoland = ["wanna go invade poland", "Wanna go invade poland", "Wanna go invade poland?", "wanna go invade poland?",
"want to go invade poland"]
ily = ["i love you", "I love you", "ily", "Ily", "ILY", "i <3 you", "I <3 you", "i <3 u", "i love u", "I love u"]
isFren = ["Are you a friend", "are you a friend", "Are you a friend?", "are you a friend?", "Are you fren",
"are you fren", "Are you a fren?", "are you a fren?", "Are you a fren", "are you a fren", "Are you a fren?",
"are you a fren?", "Are you fren?", "are you fren?", "are you fren", "Are you fren"]
whatCanYouDo = ["What can you do", "what can you do", "what can you do?", "What can you do?", "What do you do?",
"what do you do?", "cmd use", "Cmd use", "!use"]
theDate = ["What is the date", "what is the date", "what is today", "What is today", "can you please tell me the date",
"Can you please tell me the date", "what is the date today", "What is the date today", "What is the date?",
"what is the date?", "what is today?", "What is today?", "can you please tell me the date?",
"Can you please tell me the date?", "what is the date today?", "What is the date today?"]
enable_speech = ["enable speech", "speech enable", "speech on"]
disable_speech = ["disable speech", "speech disable", "speech off"]
enable_man = ["enable manual", "manual enable", "manual on"]
disable_man = ["disable manual", "manual disable", "manual off"]
openSite = ["Open site", "open site", "website", "site", "site open"]
engine = pyttsx3.init()
fs = 44100
seconds = 3
strtTime = 0
endtime = 0
manual = False
speech = True
bot_name = ['ivan', 'hey ivan', 'boot ivan', 'help ivan', 'Yo ivan wake up']
toSay = ''
count = 0
window = Tk()
try:
os.remove('output.wav', 'transcript.wav')
except:
pass
print("Started!")
def main():
global count
while count < 3:
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
sd.wait()
write('output.wav', fs, myrecording) # Save as WAV file
sound = AudioSegment.from_wav('output.wav')
sound.export('transcript.wav', format="wav")
AUDIO_FILE = "transcript.wav"
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
global speech
global manual
global strtTime
global endtime
global toSay
audio = r.record(source)
try:
transcribed = r.recognize_google(audio)
except:
transcribed = "Sorry, i did not understand"
engine.say(transcribed)
engine.runAndWait()
if manual == True:
transcribed = input("Manual Command> ")
try:
print("Transcription: " + transcribed)
text = transcribed.lower()
if text in theDate:
toSay = (date.today())
elif text in openSite:
engine.say("What site do you want to open?")
engine.runAndWait()
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
sd.wait()
write('output.wav', fs, myrecording) # Save as WAV file
AUDIO_FILE = "output.wav"
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source)
speech = True
try:
transcribed = r.recognize_google(audio)
except:
transcribed = "I couldn't understand what you said"
engine.say(transcribed)
engine.runAndWait()
print(transcribed)
engine.say("Opening site.")
engine.runAndWait()
if transcribed != "I couldn't understand what you said":
url = f'https://www.{transcribed}.org'
webbrowser.open(url)
if transcribed.lower() != 'python':
url = f'https://www.{transcribed}.com'
webbrowser.open(url)
elif text in compliment:
toSay = choice(thankYou)
elif text in whatCanYouDo:
toSay = f"I am {bot_name}. I can answer questions and run commands as you wish! Just remember i was made by a thirteen year old and a twelve year old"
elif text in isFren:
toSay = "Of course, im always here to help"
elif text in canIJoin:
toSay = 'Sure'
elif text in insult:
toSay = "You do know i don't get offended, right?"
elif text in enable_man:
manual = True
elif text in disable_man:
manual = False
elif text in ily:
playsound('yugay.wav')
elif text in wyd:
toSay = choice(wid)
elif text in thankYou:
toSay = "You're welcome"
elif text in howMany:
toSay = str(randint(1, 50))
elif text in howAreYou:
toSay = choice(howImDoing)
elif text in invpoland:
toSay = "Sure"
elif text in hi:
toSay = choice(hi2)
elif text in hello:
toSay = choice(hi2)
elif text in bye:
toSay = choice(bye)
elif text in startTimer:
strtTime == time.time()
toSay = 'Ok'
elif text in endTimer:
endtime == time.time()
toSay = (f'Ok, Time is {str(endtime - strtTime)}')
elif text in enable_speech:
global speech
speech = True
toSay = "Ok"
elif text in disable_speech:
global speech
speech = False
toSay = "Ok"
elif text == 'what is the time':
t = time.localtime()
current_time = time.strftime("%H:%M:%S", t)
print(current_time)
else:
toSay = "Unknown command"
print(toSay)
if speech == True:
engine.say(toSay)
engine.runAndWait()
else:
count += 1
pass
input("")
except:
pass
# input("Continue? ")
count = 0
while True:
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
sd.wait()
write('output.wav', fs, myrecording)
sound = AudioSegment.from_wav("output.wav")
sound.export("transcript.wav", format="wav")
AUDIO_FILE = "transcript.wav"
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source)
speech = True
try:
transcribed = r.recognize_google(audio)
except:
pass
try:
if transcribed.lower() in bot_name and transcribed:
print("Voice Acivated")
engine.say(f"Hello {os.getenv('USERNAME')}, how may i help")
engine.runAndWait()
main()
except:
pass
|
#! /usr/bin/env python3
"""
Bishbot - https://github.com/ldgregory/bishbot
Leif Gregory <leif@devtek.org>
space.py v0.1
Tested to Python v3.7.3
Description:
Bot commands for the Space channel
Changelog:
20200603 - Initial code
Copyright 2020 Leif Gregory
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import json
import requests
from discord.ext import commands
class Space(commands.Cog):
def __init__(self, bot):
self.bot = bot
# Events
@commands.Cog.listener()
async def on_ready(self):
print('- Space Cog loaded')
@commands.command(name='launches',
description='Show the next five launches',
help='Show the next five launches',
ignore_extra=True,
hidden=False,
enabled=True)
async def launches(self, ctx):
response = requests.get('https://fdo.rocketlaunch.live/json/launches/next/5')
data = json.loads(response.text)
launches = '**Here are the next five launches**\n\n'
for result in data['result']:
launches += f"- {result["quicktext"]}\n"
await ctx.channel.send(launches)
def setup(bot):
bot.add_cog(Space(bot))
| #! /usr/bin/env python3
"""
Bishbot - https://github.com/ldgregory/bishbot
Leif Gregory <leif@devtek.org>
space.py v0.1
Tested to Python v3.7.3
Description:
Bot commands for the Space channel
Changelog:
20200603 - Initial code
Copyright 2020 Leif Gregory
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import json
import requests
from discord.ext import commands
class Space(commands.Cog):
def __init__(self, bot):
self.bot = bot
# Events
@commands.Cog.listener()
async def on_ready(self):
print('- Space Cog loaded')
@commands.command(name='launches',
description='Show the next five launches',
help='Show the next five launches',
ignore_extra=True,
hidden=False,
enabled=True)
async def launches(self, ctx):
response = requests.get('https://fdo.rocketlaunch.live/json/launches/next/5')
data = json.loads(response.text)
launches = '**Here are the next five launches**\n\n'
for result in data['result']:
launches += f"- {result['quicktext']}\n"
await ctx.channel.send(launches)
def setup(bot):
bot.add_cog(Space(bot))
|
# Calcular o IMC de uma pessoa
cores = {
'limpo':'\033[m',
'verde':'\033[32m',
'amarelo':'\033[33m',
}
linha = f'{cores['amarelo']}-=' * 26 + f'{cores['limpo']}'
print(linha)
print('Vamos calcular seu IMC!')
print(linha)
peso_kg = str(input('Preciso saber seu peso, em Quilogramas: ')).strip()
alt_metros = str(input('Agora, preciso saber sua altura, em metros: ')).strip()
print(linha)
peso_kg = float(peso_kg.split()[0])
alt_metros = float(alt_metros.split()[0])
imc_pessoa = peso_kg / (alt_metros ** 2)
if imc_pessoa >= 0 and imc_pessoa < 18.5:
imc_class = 'Peso Baixo'
elif imc_pessoa >= 18.5 and imc_pessoa <= 24.9:
imc_class = 'Peso Normal'
elif imc_pessoa >= 25 and imc_pessoa <= 29.9:
imc_class = 'Sobre Peso'
elif imc_pessoa >= 30 and imc_pessoa <= 34.9:
imc_class = 'Obesidade (Grau I)'
elif imc_pessoa >= 35 and imc_pessoa <= 39.9:
imc_class = 'Obesidade Severa (Grau II)'
else:
imc_class = 'Obesidade Mórbida (Grau III)'
print(f'Seu {cores['verde']}IMC é {imc_pessoa:.2f}{cores['limpo']} e sua {cores['verde']}classificação é {imc_class}{cores['limpo']}')
print(linha)
| # Calcular o IMC de uma pessoa
cores = {
'limpo':'\033[m',
'verde':'\033[32m',
'amarelo':'\033[33m',
}
linha = f'{cores["amarelo"]}-=' * 26 + f'{cores["limpo"]}'
print(linha)
print('Vamos calcular seu IMC!')
print(linha)
peso_kg = str(input('Preciso saber seu peso, em Quilogramas: ')).strip()
alt_metros = str(input('Agora, preciso saber sua altura, em metros: ')).strip()
print(linha)
peso_kg = float(peso_kg.split()[0])
alt_metros = float(alt_metros.split()[0])
imc_pessoa = peso_kg / (alt_metros ** 2)
if imc_pessoa >= 0 and imc_pessoa < 18.5:
imc_class = 'Peso Baixo'
elif imc_pessoa >= 18.5 and imc_pessoa <= 24.9:
imc_class = 'Peso Normal'
elif imc_pessoa >= 25 and imc_pessoa <= 29.9:
imc_class = 'Sobre Peso'
elif imc_pessoa >= 30 and imc_pessoa <= 34.9:
imc_class = 'Obesidade (Grau I)'
elif imc_pessoa >= 35 and imc_pessoa <= 39.9:
imc_class = 'Obesidade Severa (Grau II)'
else:
imc_class = 'Obesidade Mórbida (Grau III)'
print(f'Seu {cores["verde"]}IMC é {imc_pessoa:.2f}{cores["limpo"]} e sua {cores["verde"]}classificação é {imc_class}{cores["limpo"]}')
print(linha)
|
"""
/*
* Copyright (c) 2021, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
*/
"""
from utils.GlobalVars import *
from recbole.config import Config, EvalSetting
from recbole.sampler import Sampler, RepeatableSampler, KGSampler
from recbole.utils import ModelType, init_logger, get_model, get_trainer, init_seed, InputType
from recbole.utils.utils import set_color
from recbole.data.utils import get_data_loader
from recbole.data import save_split_dataloaders
from RobustnessGymDataset import RobustnessGymDataset
from logging import getLogger, shutdown
import importlib
import pprint as pprint
import pickle
def create_dataset(config):
"""
Initializes RobustnessGymDataset for each recommendation system type in RecBole.
Args:
config (Config): Config file indicating MODEL_TYPE and model.
Returns:
RobustnessGymDataset instance.
"""
dataset_module = importlib.import_module('recbole.data.dataset')
if hasattr(dataset_module, config['model'] + 'Dataset'):
return getattr(dataset_module, config['model'] + 'Dataset')(config)
else:
model_type = config['MODEL_TYPE']
if model_type == ModelType.SEQUENTIAL:
from recbole.data.dataset import SequentialDataset
SequentialDataset.__bases__ = (RobustnessGymDataset,)
return SequentialDataset(config)
elif model_type == ModelType.KNOWLEDGE:
from recbole.data.dataset import KnowledgeBasedDataset
KnowledgeBasedDataset.__bases__ = (RobustnessGymDataset,)
return KnowledgeBasedDataset(config)
elif model_type == ModelType.SOCIAL:
from recbole.data.dataset import SocialDataset
SocialDataset.__bases__ = (RobustnessGymDataset,)
return SocialDataset(config)
elif model_type == ModelType.DECISIONTREE:
from recbole.data.dataset import DecisionTreeDataset
DecisionTreeDataset.__bases__ = (RobustnessGymDataset,)
return DecisionTreeDataset(config)
else:
return RobustnessGymDataset(config)
def get_transformed_train(config, train_kwargs, train_dataloader, robustness_testing_datasets):
"""
Converts training data set created by transformations into dataloader object. Uses same config
settings as original training data.
Args:
train_kwargs (dict): Training dataset config
train_dataloader (Dataloader): Training dataloader
config (Config): General config
robustness_testing_datasets (dict): Modified datasets resulting from robustness tests
Returns:
transformed_train (Dataloader)
"""
transformed_train = None
if "transformation_train" in robustness_testing_datasets:
transformation_kwargs = {
'config': config,
'dataset': robustness_testing_datasets['transformation_train'],
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
try:
transformation_kwargs['sampler'] = train_kwargs['sampler']
transformation_kwargs['neg_sample_args'] = train_kwargs['neg_sample_args']
transformed_train = train_dataloader(**transformation_kwargs)
except:
transformed_train = train_dataloader(**transformation_kwargs)
return transformed_train
def get_sparsity_train(config, train_kwargs, train_dataloader, robustness_testing_datasets):
"""
Converts training data set created by sparsity into dataloader object. Uses same config
settings as original training data.
Args:
train_kwargs (dict): Training dataset config
train_dataloader (Dataloader): Training dataloader
config (Config): General config
robustness_testing_datasets (dict): Modified datasets resulting from robustness tests
Returns:
sparsity_train (Dataloader)
"""
sparsity_train = None
if "sparsity" in robustness_testing_datasets:
sparsity_kwargs = {
'config': config,
'dataset': robustness_testing_datasets['sparsity'],
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
try:
sparsity_kwargs['sampler'] = train_kwargs['sampler']
sparsity_kwargs['neg_sample_args'] = train_kwargs['neg_sample_args']
sparsity_train = train_dataloader(**sparsity_kwargs)
except:
sparsity_train = train_dataloader(**sparsity_kwargs)
return sparsity_train
def get_distributional_slice_test(eval_kwargs, test_kwargs, test_dataloader, robustness_testing_datasets):
"""
Args:
test_dataloader:
test_kwargs:
eval_kwargs (dict):
test_dataloader (Dataloader):
robustness_testing_datasets (dict):
Returns:
"""
slice_test = None
if 'distributional_slice' in robustness_testing_datasets:
slice_kwargs = {'dataset': robustness_testing_datasets['distributional_slice']}
if 'sampler' in test_kwargs:
slice_kwargs['sampler'] = test_kwargs['sampler']
slice_kwargs.update(eval_kwargs)
slice_test = test_dataloader(**slice_kwargs)
return slice_test
def get_slice_test(eval_kwargs, test_kwargs, test_dataloader, robustness_testing_datasets):
"""
Args:
test_dataloader:
test_kwargs:
eval_kwargs (dict):
test_dataloader (Dataloader):
robustness_testing_datasets (dict):
Returns:
"""
slice_test = None
if 'slice' in robustness_testing_datasets:
slice_kwargs = {'dataset': robustness_testing_datasets['slice']}
if 'sampler' in test_kwargs:
slice_kwargs['sampler'] = test_kwargs['sampler']
slice_kwargs.update(eval_kwargs)
slice_test = test_dataloader(**slice_kwargs)
return slice_test
def get_transformation_test(eval_kwargs, test_kwargs, test_dataloader, robustness_testing_datasets):
"""
Args:
test_dataloader:
test_kwargs:
eval_kwargs (dict):
test_dataloader (Dataloader):
robustness_testing_datasets (dict):
Returns:
"""
transformation_test = None
if 'transformation' in robustness_testing_datasets:
transformation_kwargs = {'dataset': robustness_testing_datasets['transformation']}
if 'sampler' in test_kwargs:
transformation_kwargs['sampler'] = test_kwargs['sampler']
transformation_kwargs.update(eval_kwargs)
transformation_test = test_dataloader(**transformation_kwargs)
return transformation_test
def data_preparation(config, dataset, save=False):
"""
Builds datasets, including datasets built by applying robustness tests, configures train, validation, test
sets, converts to tensors. Overloads RecBole data_preparation - we include the preparation of the robustness test
train/test/valid sets here.
Args:
config (Config):
dataset (RobustnessGymDataset):
save (bool):
Returns:
"""
model_type = config['MODEL_TYPE']
model = config['model']
es = EvalSetting(config)
original_datasets, robustness_testing_datasets = dataset.build(es)
train_dataset, valid_dataset, test_dataset = original_datasets
phases = ['train', 'valid', 'test']
sampler = None
logger = getLogger()
train_neg_sample_args = config['train_neg_sample_args']
eval_neg_sample_args = es.neg_sample_args
# Training
train_kwargs = {
'config': config,
'dataset': train_dataset,
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
if train_neg_sample_args['strategy'] != 'none':
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'`training_neg_sample_num` should be 0 '
f'if inter_feat have label_field [{dataset.label_field}].'
)
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, original_datasets, train_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset, train_neg_sample_args['distribution'])
if model not in ["MultiVAE", "MultiDAE", "MacridVAE", "CDAE", "ENMF", "RaCT", "RecVAE"]:
train_kwargs['sampler'] = sampler.set_phase('train')
train_kwargs['neg_sample_args'] = train_neg_sample_args
if model_type == ModelType.KNOWLEDGE:
kg_sampler = KGSampler(dataset, train_neg_sample_args['distribution'])
train_kwargs['kg_sampler'] = kg_sampler
dataloader = get_data_loader('train', config, train_neg_sample_args)
logger.info(
set_color('Build', 'pink') + set_color(f' [{dataloader.__name__}]', 'yellow') + ' for ' +
set_color('[train]', 'yellow') + ' with format ' + set_color(f'[{train_kwargs['dl_format']}]', 'yellow')
)
if train_neg_sample_args['strategy'] != 'none':
logger.info(
set_color('[train]', 'pink') + set_color(' Negative Sampling', 'blue') + f': {train_neg_sample_args}'
)
else:
logger.info(set_color('[train]', 'pink') + set_color(' No Negative Sampling', 'yellow'))
logger.info(
set_color('[train]', 'pink') + set_color(' batch_size', 'cyan') + ' = ' +
set_color(f'[{train_kwargs['batch_size']}]', 'yellow') + ', ' + set_color('shuffle', 'cyan') + ' = ' +
set_color(f'[{train_kwargs['shuffle']}]\n', 'yellow')
)
train_data = dataloader(**train_kwargs)
transformed_train = get_transformed_train(config, train_kwargs, dataloader, robustness_testing_datasets)
sparsity_train = get_sparsity_train(config, train_kwargs, dataloader, robustness_testing_datasets)
# Evaluation
eval_kwargs = {
'config': config,
'batch_size': config['eval_batch_size'],
'dl_format': InputType.POINTWISE,
'shuffle': False,
}
valid_kwargs = {'dataset': valid_dataset}
test_kwargs = {'dataset': test_dataset}
if eval_neg_sample_args['strategy'] != 'none':
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'It can not validate with `{es.es_str[1]}` '
f'when inter_feat have label_field [{dataset.label_field}].'
)
if sampler is None:
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, original_datasets, eval_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset, eval_neg_sample_args['distribution'])
else:
sampler.set_distribution(eval_neg_sample_args['distribution'])
eval_kwargs['neg_sample_args'] = eval_neg_sample_args
valid_kwargs['sampler'] = sampler.set_phase('valid')
test_kwargs['sampler'] = sampler.set_phase('test')
valid_kwargs.update(eval_kwargs)
test_kwargs.update(eval_kwargs)
dataloader = get_data_loader('evaluation', config, eval_neg_sample_args)
logger.info(
set_color('Build', 'pink') + set_color(f' [{dataloader.__name__}]', 'yellow') + ' for ' +
set_color('[evaluation]', 'yellow') + ' with format ' + set_color(f'[{eval_kwargs['dl_format']}]', 'yellow')
)
logger.info(es)
logger.info(
set_color('[evaluation]', 'pink') + set_color(' batch_size', 'cyan') + ' = ' +
set_color(f'[{eval_kwargs['batch_size']}]', 'yellow') + ', ' + set_color('shuffle', 'cyan') + ' = ' +
set_color(f'[{eval_kwargs['shuffle']}]\n', 'yellow')
)
valid_data = dataloader(**valid_kwargs)
test_data = dataloader(**test_kwargs)
transformed_test = None
if 'transformation_test' in robustness_testing_datasets:
transformed_test_kwargs = test_kwargs
transformed_test_kwargs['dataset'] = robustness_testing_datasets['transformation_test']
transformed_test = dataloader(**transformed_test_kwargs)
slice_test = get_slice_test(eval_kwargs, test_kwargs, dataloader, robustness_testing_datasets)
distributional_slice_test = get_distributional_slice_test(eval_kwargs, test_kwargs, dataloader,
robustness_testing_datasets)
if save:
save_split_dataloaders(config, dataloaders=(train_data, valid_data, test_data))
robustness_testing_data = {'slice': slice_test,
'distributional_slice': distributional_slice_test,
'transformation_train': transformed_train,
'transformation_test': transformed_test,
'sparsity': sparsity_train}
return train_data, valid_data, test_data, robustness_testing_data
def get_config_dict(robustness_tests, base_config_dict):
"""
Combines robustness_test and train_config_dict into a single config_dict.
Args:
robustness_tests (dict): robustness test config dict
base_config_dict (dict): train/data/eval/model/hyperparam config dict
Returns:
config_dict (dict): config dict
"""
config_dict = {}
if robustness_tests is not None:
if base_config_dict is not None:
config_dict = {**robustness_tests, **base_config_dict}
else:
config_dict = robustness_tests
else:
if base_config_dict is not None:
config_dict = base_config_dict
return config_dict
def train_and_test(model, dataset, robustness_tests=None, base_config_dict=None, save_model=True):
"""
Train a recommendation model and run robustness tests.
Args:
model (str): Name of model to be trained.
dataset (str): Dataset name; must match the dataset's folder name located in 'data_path' path.
base_config_dict: Configuration dictionary. If no config passed, takes default values.
save_model (bool): Determines whether or not to externally save the model after training.
robustness_tests (dict): Configuration dictionary for robustness tests.
Returns:
"""
config_dict = get_config_dict(robustness_tests, base_config_dict)
config = Config(model=model, dataset=dataset, config_dict=config_dict)
init_seed(config['seed'], config['reproducibility'])
logger = getLogger()
if len(logger.handlers) != 0:
logger.removeHandler(logger.handlers[1])
init_logger(config)
logger.info(config)
# dataset filtering
dataset = create_dataset(config)
logger.info(dataset)
# dataset splitting
train_data, valid_data, test_data, robustness_testing_data = data_preparation(config, dataset, save=True)
for robustness_test in robustness_testing_data:
if robustness_testing_data[robustness_test] is not None:
logger.info(set_color('Robustness Test', 'yellow') + f': {robustness_test}')
# model loading and initialization
model = get_model(config['model'])(config, train_data).to(config['device'])
logger.info(model)
# trainer loading and initialization
trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model)
# model training
best_valid_score, best_valid_result = trainer.fit(
train_data, valid_data, saved=save_model, show_progress=config['show_progress']
)
# model evaluation
test_result = trainer.evaluate(test_data, load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('best valid ', 'yellow') + f': {best_valid_result}')
logger.info(set_color('test result', 'yellow') + f': {test_result}')
test_result_transformation, test_result_sparsity, \
test_result_slice, test_result_distributional_slice = None, None, None, None
if robustness_testing_data['slice'] is not None:
test_result_slice = trainer.evaluate(robustness_testing_data['slice'], load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('test result for slice', 'yellow') + f': {test_result_slice}')
if robustness_testing_data['distributional_slice'] is not None:
test_result_distributional_slice = trainer.evaluate(robustness_testing_data['distributional_slice'],
load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('test result for distributional slice', 'yellow') + f': '
f'{test_result_distributional_slice}')
if robustness_testing_data['transformation_test'] is not None:
test_result_transformation = trainer.evaluate(robustness_testing_data['transformation_test'],
load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('test result for transformation on test', 'yellow') + f': {test_result_transformation}')
if robustness_testing_data['transformation_train'] is not None:
transformation_model = get_model(config['model'])(config, robustness_testing_data['transformation_train']).to(
config['device'])
logger.info(transformation_model)
transformation_trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, transformation_model)
best_valid_score_transformation, best_valid_result_transformation = transformation_trainer.fit(
robustness_testing_data['transformation_train'], valid_data, saved=save_model,
show_progress=config['show_progress'])
test_result_transformation = transformation_trainer.evaluate(test_data, load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(
set_color('best valid for transformed training set', 'yellow') + f': {best_valid_result_transformation}')
logger.info(set_color('test result for transformed training set', 'yellow') + f': {test_result_transformation}')
if robustness_testing_data['sparsity'] is not None:
sparsity_model = get_model(config['model'])(config, robustness_testing_data['sparsity']).to(config['device'])
logger.info(sparsity_model)
sparsity_trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, sparsity_model)
best_valid_score_sparsity, best_valid_result_sparsity = sparsity_trainer.fit(
robustness_testing_data['sparsity'], valid_data, saved=save_model,
show_progress=config['show_progress'])
test_result_sparsity = sparsity_trainer.evaluate(test_data, load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('best valid for sparsified training set', 'yellow') + f': {best_valid_result_sparsity}')
logger.info(set_color('test result for sparsified training set', 'yellow') + f': {test_result_sparsity}')
logger.handlers.clear()
shutdown()
del logger
return {
'test_result': test_result,
'distributional_test_result': test_result_distributional_slice,
'transformation_test_result': test_result_transformation,
'sparsity_test_result': test_result_sparsity,
'slice_test_result': test_result_slice
}
def test(model, dataset, model_path, dataloader_path=None, robustness_tests=None, base_config_dict=None):
"""
Test a pre-trained model from file path. Note that the only robustness test applicable here
is slicing.
Args:
model (str): Name of model.
dataset (str): Name of dataset.
model_path (str): Path to saved model.
robustness_tests (dict): Configuration dictionary for robustness tests.
base_config_dict (dict): Configuration dictionary for data/model/training/evaluation.
Returns:
"""
config_dict = get_config_dict(robustness_tests, base_config_dict)
config = Config(model=model, dataset=dataset, config_dict=config_dict)
init_seed(config['seed'], config['reproducibility'])
# logger initialization
logger = getLogger()
if len(logger.handlers) != 0:
logger.removeHandler(logger.handlers[1])
init_logger(config)
# dataset filtering
dataset = create_dataset(config)
logger.info(dataset)
# dataset splitting
if dataloader_path is None:
train_data, _, test_data, robustness_testing_data = data_preparation(config, dataset, save=False)
else:
train_data, valid_data, test_data = pickle.load(open(SAVED_DIR + dataloader_path, "rb"))
robustness_testing_data = {"slice": None, "transformation": None, "sparsity": None}
# model loading and initialization
model = get_model(config['model'])(config, train_data).to(config['device'])
logger.info(model)
# trainer loading and initialization
trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model)
# model evaluation
test_result = trainer.evaluate(test_data, load_best_model=True, model_file=model_path,
show_progress=config['show_progress'])
logger.info(set_color('test result', 'yellow') + f': {test_result}')
test_result_slice = None
if robustness_testing_data['slice'] is not None:
test_result_slice = trainer.evaluate(robustness_testing_data['slice'], load_best_model=True,
model_file=model_path,
show_progress=config['show_progress'])
logger.info(set_color('test result for slice', 'yellow') + f': {test_result_slice}')
return {
'test_result': test_result,
'slice_test_result': test_result_slice
}
if __name__ == '__main__':
all_results = {}
for model in ["BPR"]:
dataset = "ml-100k"
base_config_dict = {
'data_path': DATASETS_DIR,
'show_progress': False,
'save_dataset': True,
'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'],
'user': ['user_id', 'age', 'gender', 'occupation'],
'item': ['item_id', 'release_year', 'class']}
}
# robustness_dict = {
# uncomment and add robustness test specifications here
# }
results = train_and_test(model=model, dataset=dataset, robustness_tests=robustness_dict,
base_config_dict=base_config_dict)
| """
/*
* Copyright (c) 2021, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
*/
"""
from utils.GlobalVars import *
from recbole.config import Config, EvalSetting
from recbole.sampler import Sampler, RepeatableSampler, KGSampler
from recbole.utils import ModelType, init_logger, get_model, get_trainer, init_seed, InputType
from recbole.utils.utils import set_color
from recbole.data.utils import get_data_loader
from recbole.data import save_split_dataloaders
from RobustnessGymDataset import RobustnessGymDataset
from logging import getLogger, shutdown
import importlib
import pprint as pprint
import pickle
def create_dataset(config):
"""
Initializes RobustnessGymDataset for each recommendation system type in RecBole.
Args:
config (Config): Config file indicating MODEL_TYPE and model.
Returns:
RobustnessGymDataset instance.
"""
dataset_module = importlib.import_module('recbole.data.dataset')
if hasattr(dataset_module, config['model'] + 'Dataset'):
return getattr(dataset_module, config['model'] + 'Dataset')(config)
else:
model_type = config['MODEL_TYPE']
if model_type == ModelType.SEQUENTIAL:
from recbole.data.dataset import SequentialDataset
SequentialDataset.__bases__ = (RobustnessGymDataset,)
return SequentialDataset(config)
elif model_type == ModelType.KNOWLEDGE:
from recbole.data.dataset import KnowledgeBasedDataset
KnowledgeBasedDataset.__bases__ = (RobustnessGymDataset,)
return KnowledgeBasedDataset(config)
elif model_type == ModelType.SOCIAL:
from recbole.data.dataset import SocialDataset
SocialDataset.__bases__ = (RobustnessGymDataset,)
return SocialDataset(config)
elif model_type == ModelType.DECISIONTREE:
from recbole.data.dataset import DecisionTreeDataset
DecisionTreeDataset.__bases__ = (RobustnessGymDataset,)
return DecisionTreeDataset(config)
else:
return RobustnessGymDataset(config)
def get_transformed_train(config, train_kwargs, train_dataloader, robustness_testing_datasets):
"""
Converts training data set created by transformations into dataloader object. Uses same config
settings as original training data.
Args:
train_kwargs (dict): Training dataset config
train_dataloader (Dataloader): Training dataloader
config (Config): General config
robustness_testing_datasets (dict): Modified datasets resulting from robustness tests
Returns:
transformed_train (Dataloader)
"""
transformed_train = None
if "transformation_train" in robustness_testing_datasets:
transformation_kwargs = {
'config': config,
'dataset': robustness_testing_datasets['transformation_train'],
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
try:
transformation_kwargs['sampler'] = train_kwargs['sampler']
transformation_kwargs['neg_sample_args'] = train_kwargs['neg_sample_args']
transformed_train = train_dataloader(**transformation_kwargs)
except:
transformed_train = train_dataloader(**transformation_kwargs)
return transformed_train
def get_sparsity_train(config, train_kwargs, train_dataloader, robustness_testing_datasets):
"""
Converts training data set created by sparsity into dataloader object. Uses same config
settings as original training data.
Args:
train_kwargs (dict): Training dataset config
train_dataloader (Dataloader): Training dataloader
config (Config): General config
robustness_testing_datasets (dict): Modified datasets resulting from robustness tests
Returns:
sparsity_train (Dataloader)
"""
sparsity_train = None
if "sparsity" in robustness_testing_datasets:
sparsity_kwargs = {
'config': config,
'dataset': robustness_testing_datasets['sparsity'],
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
try:
sparsity_kwargs['sampler'] = train_kwargs['sampler']
sparsity_kwargs['neg_sample_args'] = train_kwargs['neg_sample_args']
sparsity_train = train_dataloader(**sparsity_kwargs)
except:
sparsity_train = train_dataloader(**sparsity_kwargs)
return sparsity_train
def get_distributional_slice_test(eval_kwargs, test_kwargs, test_dataloader, robustness_testing_datasets):
"""
Args:
test_dataloader:
test_kwargs:
eval_kwargs (dict):
test_dataloader (Dataloader):
robustness_testing_datasets (dict):
Returns:
"""
slice_test = None
if 'distributional_slice' in robustness_testing_datasets:
slice_kwargs = {'dataset': robustness_testing_datasets['distributional_slice']}
if 'sampler' in test_kwargs:
slice_kwargs['sampler'] = test_kwargs['sampler']
slice_kwargs.update(eval_kwargs)
slice_test = test_dataloader(**slice_kwargs)
return slice_test
def get_slice_test(eval_kwargs, test_kwargs, test_dataloader, robustness_testing_datasets):
"""
Args:
test_dataloader:
test_kwargs:
eval_kwargs (dict):
test_dataloader (Dataloader):
robustness_testing_datasets (dict):
Returns:
"""
slice_test = None
if 'slice' in robustness_testing_datasets:
slice_kwargs = {'dataset': robustness_testing_datasets['slice']}
if 'sampler' in test_kwargs:
slice_kwargs['sampler'] = test_kwargs['sampler']
slice_kwargs.update(eval_kwargs)
slice_test = test_dataloader(**slice_kwargs)
return slice_test
def get_transformation_test(eval_kwargs, test_kwargs, test_dataloader, robustness_testing_datasets):
"""
Args:
test_dataloader:
test_kwargs:
eval_kwargs (dict):
test_dataloader (Dataloader):
robustness_testing_datasets (dict):
Returns:
"""
transformation_test = None
if 'transformation' in robustness_testing_datasets:
transformation_kwargs = {'dataset': robustness_testing_datasets['transformation']}
if 'sampler' in test_kwargs:
transformation_kwargs['sampler'] = test_kwargs['sampler']
transformation_kwargs.update(eval_kwargs)
transformation_test = test_dataloader(**transformation_kwargs)
return transformation_test
def data_preparation(config, dataset, save=False):
"""
Builds datasets, including datasets built by applying robustness tests, configures train, validation, test
sets, converts to tensors. Overloads RecBole data_preparation - we include the preparation of the robustness test
train/test/valid sets here.
Args:
config (Config):
dataset (RobustnessGymDataset):
save (bool):
Returns:
"""
model_type = config['MODEL_TYPE']
model = config['model']
es = EvalSetting(config)
original_datasets, robustness_testing_datasets = dataset.build(es)
train_dataset, valid_dataset, test_dataset = original_datasets
phases = ['train', 'valid', 'test']
sampler = None
logger = getLogger()
train_neg_sample_args = config['train_neg_sample_args']
eval_neg_sample_args = es.neg_sample_args
# Training
train_kwargs = {
'config': config,
'dataset': train_dataset,
'batch_size': config['train_batch_size'],
'dl_format': config['MODEL_INPUT_TYPE'],
'shuffle': True,
}
if train_neg_sample_args['strategy'] != 'none':
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'`training_neg_sample_num` should be 0 '
f'if inter_feat have label_field [{dataset.label_field}].'
)
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, original_datasets, train_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset, train_neg_sample_args['distribution'])
if model not in ["MultiVAE", "MultiDAE", "MacridVAE", "CDAE", "ENMF", "RaCT", "RecVAE"]:
train_kwargs['sampler'] = sampler.set_phase('train')
train_kwargs['neg_sample_args'] = train_neg_sample_args
if model_type == ModelType.KNOWLEDGE:
kg_sampler = KGSampler(dataset, train_neg_sample_args['distribution'])
train_kwargs['kg_sampler'] = kg_sampler
dataloader = get_data_loader('train', config, train_neg_sample_args)
logger.info(
set_color('Build', 'pink') + set_color(f' [{dataloader.__name__}]', 'yellow') + ' for ' +
set_color('[train]', 'yellow') + ' with format ' + set_color(f'[{train_kwargs["dl_format"]}]', 'yellow')
)
if train_neg_sample_args['strategy'] != 'none':
logger.info(
set_color('[train]', 'pink') + set_color(' Negative Sampling', 'blue') + f': {train_neg_sample_args}'
)
else:
logger.info(set_color('[train]', 'pink') + set_color(' No Negative Sampling', 'yellow'))
logger.info(
set_color('[train]', 'pink') + set_color(' batch_size', 'cyan') + ' = ' +
set_color(f'[{train_kwargs["batch_size"]}]', 'yellow') + ', ' + set_color('shuffle', 'cyan') + ' = ' +
set_color(f'[{train_kwargs["shuffle"]}]\n', 'yellow')
)
train_data = dataloader(**train_kwargs)
transformed_train = get_transformed_train(config, train_kwargs, dataloader, robustness_testing_datasets)
sparsity_train = get_sparsity_train(config, train_kwargs, dataloader, robustness_testing_datasets)
# Evaluation
eval_kwargs = {
'config': config,
'batch_size': config['eval_batch_size'],
'dl_format': InputType.POINTWISE,
'shuffle': False,
}
valid_kwargs = {'dataset': valid_dataset}
test_kwargs = {'dataset': test_dataset}
if eval_neg_sample_args['strategy'] != 'none':
if dataset.label_field in dataset.inter_feat:
raise ValueError(
f'It can not validate with `{es.es_str[1]}` '
f'when inter_feat have label_field [{dataset.label_field}].'
)
if sampler is None:
if model_type != ModelType.SEQUENTIAL:
sampler = Sampler(phases, original_datasets, eval_neg_sample_args['distribution'])
else:
sampler = RepeatableSampler(phases, dataset, eval_neg_sample_args['distribution'])
else:
sampler.set_distribution(eval_neg_sample_args['distribution'])
eval_kwargs['neg_sample_args'] = eval_neg_sample_args
valid_kwargs['sampler'] = sampler.set_phase('valid')
test_kwargs['sampler'] = sampler.set_phase('test')
valid_kwargs.update(eval_kwargs)
test_kwargs.update(eval_kwargs)
dataloader = get_data_loader('evaluation', config, eval_neg_sample_args)
logger.info(
set_color('Build', 'pink') + set_color(f' [{dataloader.__name__}]', 'yellow') + ' for ' +
set_color('[evaluation]', 'yellow') + ' with format ' + set_color(f'[{eval_kwargs["dl_format"]}]', 'yellow')
)
logger.info(es)
logger.info(
set_color('[evaluation]', 'pink') + set_color(' batch_size', 'cyan') + ' = ' +
set_color(f'[{eval_kwargs["batch_size"]}]', 'yellow') + ', ' + set_color('shuffle', 'cyan') + ' = ' +
set_color(f'[{eval_kwargs["shuffle"]}]\n', 'yellow')
)
valid_data = dataloader(**valid_kwargs)
test_data = dataloader(**test_kwargs)
transformed_test = None
if 'transformation_test' in robustness_testing_datasets:
transformed_test_kwargs = test_kwargs
transformed_test_kwargs['dataset'] = robustness_testing_datasets['transformation_test']
transformed_test = dataloader(**transformed_test_kwargs)
slice_test = get_slice_test(eval_kwargs, test_kwargs, dataloader, robustness_testing_datasets)
distributional_slice_test = get_distributional_slice_test(eval_kwargs, test_kwargs, dataloader,
robustness_testing_datasets)
if save:
save_split_dataloaders(config, dataloaders=(train_data, valid_data, test_data))
robustness_testing_data = {'slice': slice_test,
'distributional_slice': distributional_slice_test,
'transformation_train': transformed_train,
'transformation_test': transformed_test,
'sparsity': sparsity_train}
return train_data, valid_data, test_data, robustness_testing_data
def get_config_dict(robustness_tests, base_config_dict):
"""
Combines robustness_test and train_config_dict into a single config_dict.
Args:
robustness_tests (dict): robustness test config dict
base_config_dict (dict): train/data/eval/model/hyperparam config dict
Returns:
config_dict (dict): config dict
"""
config_dict = {}
if robustness_tests is not None:
if base_config_dict is not None:
config_dict = {**robustness_tests, **base_config_dict}
else:
config_dict = robustness_tests
else:
if base_config_dict is not None:
config_dict = base_config_dict
return config_dict
def train_and_test(model, dataset, robustness_tests=None, base_config_dict=None, save_model=True):
"""
Train a recommendation model and run robustness tests.
Args:
model (str): Name of model to be trained.
dataset (str): Dataset name; must match the dataset's folder name located in 'data_path' path.
base_config_dict: Configuration dictionary. If no config passed, takes default values.
save_model (bool): Determines whether or not to externally save the model after training.
robustness_tests (dict): Configuration dictionary for robustness tests.
Returns:
"""
config_dict = get_config_dict(robustness_tests, base_config_dict)
config = Config(model=model, dataset=dataset, config_dict=config_dict)
init_seed(config['seed'], config['reproducibility'])
logger = getLogger()
if len(logger.handlers) != 0:
logger.removeHandler(logger.handlers[1])
init_logger(config)
logger.info(config)
# dataset filtering
dataset = create_dataset(config)
logger.info(dataset)
# dataset splitting
train_data, valid_data, test_data, robustness_testing_data = data_preparation(config, dataset, save=True)
for robustness_test in robustness_testing_data:
if robustness_testing_data[robustness_test] is not None:
logger.info(set_color('Robustness Test', 'yellow') + f': {robustness_test}')
# model loading and initialization
model = get_model(config['model'])(config, train_data).to(config['device'])
logger.info(model)
# trainer loading and initialization
trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model)
# model training
best_valid_score, best_valid_result = trainer.fit(
train_data, valid_data, saved=save_model, show_progress=config['show_progress']
)
# model evaluation
test_result = trainer.evaluate(test_data, load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('best valid ', 'yellow') + f': {best_valid_result}')
logger.info(set_color('test result', 'yellow') + f': {test_result}')
test_result_transformation, test_result_sparsity, \
test_result_slice, test_result_distributional_slice = None, None, None, None
if robustness_testing_data['slice'] is not None:
test_result_slice = trainer.evaluate(robustness_testing_data['slice'], load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('test result for slice', 'yellow') + f': {test_result_slice}')
if robustness_testing_data['distributional_slice'] is not None:
test_result_distributional_slice = trainer.evaluate(robustness_testing_data['distributional_slice'],
load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('test result for distributional slice', 'yellow') + f': '
f'{test_result_distributional_slice}')
if robustness_testing_data['transformation_test'] is not None:
test_result_transformation = trainer.evaluate(robustness_testing_data['transformation_test'],
load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('test result for transformation on test', 'yellow') + f': {test_result_transformation}')
if robustness_testing_data['transformation_train'] is not None:
transformation_model = get_model(config['model'])(config, robustness_testing_data['transformation_train']).to(
config['device'])
logger.info(transformation_model)
transformation_trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, transformation_model)
best_valid_score_transformation, best_valid_result_transformation = transformation_trainer.fit(
robustness_testing_data['transformation_train'], valid_data, saved=save_model,
show_progress=config['show_progress'])
test_result_transformation = transformation_trainer.evaluate(test_data, load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(
set_color('best valid for transformed training set', 'yellow') + f': {best_valid_result_transformation}')
logger.info(set_color('test result for transformed training set', 'yellow') + f': {test_result_transformation}')
if robustness_testing_data['sparsity'] is not None:
sparsity_model = get_model(config['model'])(config, robustness_testing_data['sparsity']).to(config['device'])
logger.info(sparsity_model)
sparsity_trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, sparsity_model)
best_valid_score_sparsity, best_valid_result_sparsity = sparsity_trainer.fit(
robustness_testing_data['sparsity'], valid_data, saved=save_model,
show_progress=config['show_progress'])
test_result_sparsity = sparsity_trainer.evaluate(test_data, load_best_model=save_model,
show_progress=config['show_progress'])
logger.info(set_color('best valid for sparsified training set', 'yellow') + f': {best_valid_result_sparsity}')
logger.info(set_color('test result for sparsified training set', 'yellow') + f': {test_result_sparsity}')
logger.handlers.clear()
shutdown()
del logger
return {
'test_result': test_result,
'distributional_test_result': test_result_distributional_slice,
'transformation_test_result': test_result_transformation,
'sparsity_test_result': test_result_sparsity,
'slice_test_result': test_result_slice
}
def test(model, dataset, model_path, dataloader_path=None, robustness_tests=None, base_config_dict=None):
"""
Test a pre-trained model from file path. Note that the only robustness test applicable here
is slicing.
Args:
model (str): Name of model.
dataset (str): Name of dataset.
model_path (str): Path to saved model.
robustness_tests (dict): Configuration dictionary for robustness tests.
base_config_dict (dict): Configuration dictionary for data/model/training/evaluation.
Returns:
"""
config_dict = get_config_dict(robustness_tests, base_config_dict)
config = Config(model=model, dataset=dataset, config_dict=config_dict)
init_seed(config['seed'], config['reproducibility'])
# logger initialization
logger = getLogger()
if len(logger.handlers) != 0:
logger.removeHandler(logger.handlers[1])
init_logger(config)
# dataset filtering
dataset = create_dataset(config)
logger.info(dataset)
# dataset splitting
if dataloader_path is None:
train_data, _, test_data, robustness_testing_data = data_preparation(config, dataset, save=False)
else:
train_data, valid_data, test_data = pickle.load(open(SAVED_DIR + dataloader_path, "rb"))
robustness_testing_data = {"slice": None, "transformation": None, "sparsity": None}
# model loading and initialization
model = get_model(config['model'])(config, train_data).to(config['device'])
logger.info(model)
# trainer loading and initialization
trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model)
# model evaluation
test_result = trainer.evaluate(test_data, load_best_model=True, model_file=model_path,
show_progress=config['show_progress'])
logger.info(set_color('test result', 'yellow') + f': {test_result}')
test_result_slice = None
if robustness_testing_data['slice'] is not None:
test_result_slice = trainer.evaluate(robustness_testing_data['slice'], load_best_model=True,
model_file=model_path,
show_progress=config['show_progress'])
logger.info(set_color('test result for slice', 'yellow') + f': {test_result_slice}')
return {
'test_result': test_result,
'slice_test_result': test_result_slice
}
if __name__ == '__main__':
all_results = {}
for model in ["BPR"]:
dataset = "ml-100k"
base_config_dict = {
'data_path': DATASETS_DIR,
'show_progress': False,
'save_dataset': True,
'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'],
'user': ['user_id', 'age', 'gender', 'occupation'],
'item': ['item_id', 'release_year', 'class']}
}
# robustness_dict = {
# uncomment and add robustness test specifications here
# }
results = train_and_test(model=model, dataset=dataset, robustness_tests=robustness_dict,
base_config_dict=base_config_dict)
|
from typing import Iterable, Optional
from app.integrations.mailchimp import exceptions
from app.integrations.mailchimp.http import MailchimpHTTP
from app.integrations.mailchimp.member import MailchimpMember
from users.models import User
class AppMailchimp:
def __init__(self):
self.http = MailchimpHTTP()
def subscribe_django_user(self, list_id: str, user: User, tags: Optional[Iterable] = None):
member = MailchimpMember.from_django_user(user)
self.mass_subscribe(
list_id=list_id,
members=[member],
)
if tags is not None:
self.set_tags(
list_id=list_id,
member=member,
tags=tags,
)
def mass_subscribe(self, list_id: str, members: Iterable[MailchimpMember]):
member_list = list()
for member in members:
member_list.append({
**member.to_mailchimp(),
'status': 'subscribed',
})
response = self.http.post(
url=f'lists/{list_id}',
payload={
'members': member_list,
'update_existing': True,
},
)
if len(response['errors']):
raise exceptions.MailchimpSubscriptionFailed(', '.join([f'{err['email_address']}: {err['error']} ({err['error_code']})' for err in response['errors']]))
def set_tags(self, list_id: str, member: MailchimpMember, tags: Iterable[str]):
self.http.post(
url=f'/lists/{list_id}/members/{member.subscriber_hash}/tags',
payload={
'tags': [{'name': tag, 'status': 'active'} for tag in tags],
},
expected_status_code=204,
)
__all__ = [
AppMailchimp,
]
| from typing import Iterable, Optional
from app.integrations.mailchimp import exceptions
from app.integrations.mailchimp.http import MailchimpHTTP
from app.integrations.mailchimp.member import MailchimpMember
from users.models import User
class AppMailchimp:
def __init__(self):
self.http = MailchimpHTTP()
def subscribe_django_user(self, list_id: str, user: User, tags: Optional[Iterable] = None):
member = MailchimpMember.from_django_user(user)
self.mass_subscribe(
list_id=list_id,
members=[member],
)
if tags is not None:
self.set_tags(
list_id=list_id,
member=member,
tags=tags,
)
def mass_subscribe(self, list_id: str, members: Iterable[MailchimpMember]):
member_list = list()
for member in members:
member_list.append({
**member.to_mailchimp(),
'status': 'subscribed',
})
response = self.http.post(
url=f'lists/{list_id}',
payload={
'members': member_list,
'update_existing': True,
},
)
if len(response['errors']):
raise exceptions.MailchimpSubscriptionFailed(', '.join([f'{err["email_address"]}: {err["error"]} ({err["error_code"]})' for err in response['errors']]))
def set_tags(self, list_id: str, member: MailchimpMember, tags: Iterable[str]):
self.http.post(
url=f'/lists/{list_id}/members/{member.subscriber_hash}/tags',
payload={
'tags': [{'name': tag, 'status': 'active'} for tag in tags],
},
expected_status_code=204,
)
__all__ = [
AppMailchimp,
]
|
#!/usr/bin/env python
# coding: utf-8
from __future__ import absolute_import, division, print_function
import json
import logging
import math
import os
import random
import warnings
from multiprocessing import cpu_count
import numpy as np
from scipy.stats import mode, pearsonr
from sklearn.metrics import (
confusion_matrix,
label_ranking_average_precision_score,
matthews_corrcoef,
mean_squared_error,
)
from tqdm.auto import tqdm, trange
import pandas as pd
import torch
from simpletransformers.classification.classification_utils import InputExample, convert_examples_to_features
from simpletransformers.classification.transformer_models.albert_model import AlbertForSequenceClassification
from simpletransformers.classification.transformer_models.bert_model import BertForSequenceClassification
from simpletransformers.classification.transformer_models.camembert_model import CamembertForSequenceClassification
from simpletransformers.classification.transformer_models.distilbert_model import DistilBertForSequenceClassification
from simpletransformers.classification.transformer_models.flaubert_model import FlaubertForSequenceClassification
from simpletransformers.classification.transformer_models.roberta_model import RobertaForSequenceClassification
from simpletransformers.classification.transformer_models.xlm_model import XLMForSequenceClassification
from simpletransformers.classification.transformer_models.xlm_roberta_model import XLMRobertaForSequenceClassification
from simpletransformers.classification.transformer_models.xlnet_model import XLNetForSequenceClassification
from simpletransformers.config.global_args import global_args
from simpletransformers.custom_models.models import ElectraForSequenceClassification
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from transformers import (
WEIGHTS_NAME,
AdamW,
AlbertConfig,
AlbertTokenizer,
BertConfig,
BertTokenizer,
CamembertConfig,
CamembertTokenizer,
DistilBertConfig,
DistilBertTokenizer,
ElectraConfig,
ElectraTokenizer,
FlaubertConfig,
FlaubertTokenizer,
RobertaConfig,
RobertaTokenizer,
XLMConfig,
XLMRobertaConfig,
XLMRobertaTokenizer,
XLMTokenizer,
XLNetConfig,
XLNetTokenizer,
get_linear_schedule_with_warmup,
)
try:
import wandb
wandb_available = True
except ImportError:
wandb_available = False
logger = logging.getLogger(__name__)
class ClassificationModel:
def __init__(
self, model_type, model_name, num_labels=None, weight=None, args=None, use_cuda=True, cuda_device=-1, **kwargs,
):
"""
Initializes a ClassificationModel model.
Args:
model_type: The type of model (bert, xlnet, xlm, roberta, distilbert)
model_name: The exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.
num_labels (optional): The number of labels or classes in the dataset.
weight (optional): A list of length num_labels containing the weights to assign to each label for loss calculation.
args (optional): Default args will be used if this parameter is not provided. If provided, it should be a dict containing the args that should be changed in the default args.
use_cuda (optional): Use GPU if available. Setting to False will force model to use CPU only.
cuda_device (optional): Specific GPU that should be used. Will use the first available GPU by default.
**kwargs (optional): For providing proxies, force_download, resume_download, cache_dir and other options specific to the 'from_pretrained' implementation where this will be supplied.
""" # noqa: ignore flake8"
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
"xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
"xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
"roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
"distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
"albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
"camembert": (CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer),
"xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
"flaubert": (FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer),
"electra": (ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer),
}
if args and "manual_seed" in args:
random.seed(args["manual_seed"])
np.random.seed(args["manual_seed"])
torch.manual_seed(args["manual_seed"])
if "n_gpu" in args and args["n_gpu"] > 0:
torch.cuda.manual_seed_all(args["manual_seed"])
self.args = {
"sliding_window": False,
"tie_value": 1,
"stride": 0.8,
"regression": False,
}
self.args.update(global_args)
saved_model_args = self._load_model_args(model_name)
if saved_model_args:
self.args.update(saved_model_args)
if args:
self.args.update(args)
config_class, model_class, tokenizer_class = MODEL_CLASSES[model_type]
if num_labels:
self.config = config_class.from_pretrained(model_name, num_labels=num_labels, **self.args["config"])
self.num_labels = num_labels
else:
self.config = config_class.from_pretrained(model_name, **self.args["config"])
self.num_labels = self.config.num_labels
self.weight = weight
if use_cuda:
if torch.cuda.is_available():
if cuda_device == -1:
self.device = torch.device("cuda")
else:
self.device = torch.device(f"cuda:{cuda_device}")
else:
raise ValueError(
"'use_cuda' set to True when cuda is unavailable."
" Make sure CUDA is available or set use_cuda=False."
)
else:
self.device = "cpu"
if self.weight:
self.model = model_class.from_pretrained(
model_name, config=self.config, weight=torch.Tensor(self.weight).to(self.device), **kwargs,
)
else:
self.model = model_class.from_pretrained(model_name, config=self.config, **kwargs)
self.results = {}
if not use_cuda:
self.args["fp16"] = False
self.tokenizer = tokenizer_class.from_pretrained(
model_name, do_lower_case=self.args["do_lower_case"], **kwargs
)
self.args["model_name"] = model_name
self.args["model_type"] = model_type
if model_type in ["camembert", "xlmroberta"]:
warnings.warn(
f"use_multiprocessing automatically disabled as {model_type}"
" fails when using multiprocessing for feature conversion."
)
self.args["use_multiprocessing"] = False
if self.args["wandb_project"] and not wandb_available:
warnings.warn("wandb_project specified but wandb is not available. Wandb disabled.")
self.args["wandb_project"] = None
def train_model(
self,
train_df,
multi_label=False,
output_dir=None,
show_running_loss=True,
args=None,
eval_df=None,
verbose=True,
**kwargs,
):
"""
Trains the model using 'train_df'
Args:
train_df: Pandas Dataframe containing at least two columns. If the Dataframe has a header, it should contain a 'text' and a 'labels' column. If no header is present,
the Dataframe should contain at least two columns, with the first column containing the text, and the second column containing the label. The model will be trained on this Dataframe.
output_dir: The directory where model files will be saved. If not given, self.args['output_dir'] will be used.
show_running_loss (optional): Set to False to prevent running loss from being printed to console. Defaults to True.
args (optional): Optional changes to the args dict of the model. Any changes made will persist for the model.
eval_df (optional): A DataFrame against which evaluation will be performed when evaluate_during_training is enabled. Is required if evaluate_during_training is enabled.
**kwargs: Additional metrics that should be used. Pass in the metrics as keyword arguments (name of metric: function to use). E.g. f1=sklearn.metrics.f1_score.
A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
Returns:
None
""" # noqa: ignore flake8"
if args:
self.args.update(args)
if self.args["silent"]:
show_running_loss = False
if self.args["evaluate_during_training"] and eval_df is None:
raise ValueError(
"evaluate_during_training is enabled but eval_df is not specified."
" Pass eval_df to model.train_model() if using evaluate_during_training."
)
if not output_dir:
output_dir = self.args["output_dir"]
if os.path.exists(output_dir) and os.listdir(output_dir) and not self.args["overwrite_output_dir"]:
raise ValueError(
"Output directory ({}) already exists and is not empty."
" Use --overwrite_output_dir to overcome.".format(output_dir)
)
self._move_model_to_device()
if "text" in train_df.columns and "labels" in train_df.columns:
train_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(train_df["text"], train_df["labels"]))
]
elif "text_a" in train_df.columns and "text_b" in train_df.columns:
train_examples = [
InputExample(i, text_a, text_b, label)
for i, (text_a, text_b, label) in enumerate(
zip(train_df["text_a"], train_df["text_b"], train_df["labels"])
)
]
else:
warnings.warn(
"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels."
)
train_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(train_df.iloc[:, 0], train_df.iloc[:, 1]))
]
train_dataset = self.load_and_cache_examples(train_examples, verbose=verbose)
os.makedirs(output_dir, exist_ok=True)
global_step, tr_loss = self.train(
train_dataset,
output_dir,
multi_label=multi_label,
show_running_loss=show_running_loss,
eval_df=eval_df,
verbose=verbose,
**kwargs,
)
# model_to_save = self.model.module if hasattr(self.model, "module") else self.model
# model_to_save.save_pretrained(output_dir)
# self.tokenizer.save_pretrained(output_dir)
# torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
self._save_model()
if verbose:
logger.info(" Training of {} model complete. Saved to {}.".format(self.args["model_type"], output_dir))
def train(
self,
train_dataset,
output_dir,
multi_label=False,
show_running_loss=True,
eval_df=None,
verbose=True,
**kwargs,
):
"""
Trains the model on train_dataset.
Utility function to be used by the train_model() method. Not intended to be used directly.
"""
device = self.device
model = self.model
args = self.args
tb_writer = SummaryWriter(logdir=args["tensorboard_dir"])
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args["train_batch_size"])
t_total = len(train_dataloader) // args["gradient_accumulation_steps"] * args["num_train_epochs"]
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args["weight_decay"],
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
warmup_steps = math.ceil(t_total * args["warmup_ratio"])
args["warmup_steps"] = warmup_steps if args["warmup_steps"] == 0 else args["warmup_steps"]
optimizer = AdamW(optimizer_grouped_parameters, lr=args["learning_rate"], eps=args["adam_epsilon"])
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args["warmup_steps"], num_training_steps=t_total
)
if args["fp16"]:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args["fp16_opt_level"])
if args["n_gpu"] > 1:
model = torch.nn.DataParallel(model)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args["num_train_epochs"]), desc="Epoch", disable=args["silent"], mininterval=0)
epoch_number = 0
best_eval_metric = None
early_stopping_counter = 0
steps_trained_in_current_epoch = 0
epochs_trained = 0
if args["model_name"] and os.path.exists(args["model_name"]):
try:
# set global_step to gobal_step of last saved checkpoint from model path
checkpoint_suffix = args["model_name"].split("/")[-1].split("-")
if len(checkpoint_suffix) > 2:
checkpoint_suffix = checkpoint_suffix[1]
else:
checkpoint_suffix = checkpoint_suffix[-1]
global_step = int(checkpoint_suffix)
epochs_trained = global_step // (len(train_dataloader) // args["gradient_accumulation_steps"])
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) // args["gradient_accumulation_steps"]
)
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the current epoch", steps_trained_in_current_epoch)
except ValueError:
logger.info(" Starting fine-tuning.")
if args["evaluate_during_training"]:
training_progress_scores = self._create_training_progress_scores(multi_label, **kwargs)
if args["wandb_project"]:
wandb.init(project=args["wandb_project"], config={**args}, **args["wandb_kwargs"])
wandb.watch(self.model)
model.train()
for _ in train_iterator:
if epochs_trained > 0:
epochs_trained -= 1
continue
# epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(tqdm(train_dataloader, desc="Current iteration", disable=args["silent"])):
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
batch = tuple(t.to(device) for t in batch)
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
# model outputs are always tuple in pytorch-transformers (see doc)
loss = outputs[0]
if args["n_gpu"] > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
current_loss = loss.item()
if show_running_loss:
print("\rRunning loss: %f" % loss, end="")
if args["gradient_accumulation_steps"] > 1:
loss = loss / args["gradient_accumulation_steps"]
if args["fp16"]:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
# torch.nn.utils.clip_grad_norm_(
# amp.master_params(optimizer), args["max_grad_norm"]
# )
else:
loss.backward()
# torch.nn.utils.clip_grad_norm_(
# model.parameters(), args["max_grad_norm"]
# )
tr_loss += loss.item()
if (step + 1) % args["gradient_accumulation_steps"] == 0:
if args["fp16"]:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args["max_grad_norm"])
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args["max_grad_norm"])
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args["logging_steps"] > 0 and global_step % args["logging_steps"] == 0:
# Log metrics
tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args["logging_steps"], global_step)
logging_loss = tr_loss
if args["wandb_project"]:
wandb.log(
{
"Training loss": current_loss,
"lr": scheduler.get_lr()[0],
"global_step": global_step,
}
)
if args["save_steps"] > 0 and global_step % args["save_steps"] == 0:
# Save model checkpoint
output_dir_current = os.path.join(output_dir, "checkpoint-{}".format(global_step))
self._save_model(output_dir_current, optimizer, scheduler, model=model)
if args["evaluate_during_training"] and (
args["evaluate_during_training_steps"] > 0
and global_step % args["evaluate_during_training_steps"] == 0
):
# Only evaluate when single GPU otherwise metrics may not average well
results, _, _ = self.eval_model(
eval_df,
verbose=verbose and args["evaluate_during_training_verbose"],
silent=True,
**kwargs,
)
for key, value in results.items():
tb_writer.add_scalar("eval_{}".format(key), value, global_step)
output_dir_current = os.path.join(output_dir, "checkpoint-{}".format(global_step))
if args["save_eval_checkpoints"]:
self._save_model(output_dir_current, optimizer, scheduler, model=model, results=results)
training_progress_scores["global_step"].append(global_step)
training_progress_scores["train_loss"].append(current_loss)
for key in results:
training_progress_scores[key].append(results[key])
report = pd.DataFrame(training_progress_scores)
report.to_csv(
os.path.join(args["output_dir"], "training_progress_scores.csv"), index=False,
)
if args["wandb_project"]:
wandb.log(self._get_last_metrics(training_progress_scores))
if not best_eval_metric:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(
args["best_model_dir"], optimizer, scheduler, model=model, results=results
)
if best_eval_metric and args["early_stopping_metric_minimize"]:
if (
results[args["early_stopping_metric"]] - best_eval_metric
< args["early_stopping_delta"]
):
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(
args["best_model_dir"], optimizer, scheduler, model=model, results=results
)
early_stopping_counter = 0
else:
if args["use_early_stopping"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args["early_stopping_metric"]}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args["early_stopping_patience"]}")
else:
if verbose:
logger.info(
f" Patience of {args["early_stopping_patience"]} steps reached"
)
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
else:
if (
results[args["early_stopping_metric"]] - best_eval_metric
> args["early_stopping_delta"]
):
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(
args["best_model_dir"], optimizer, scheduler, model=model, results=results
)
early_stopping_counter = 0
else:
if args["use_early_stopping"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args["early_stopping_metric"]}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args["early_stopping_patience"]}")
else:
if verbose:
logger.info(
f" Patience of {args["early_stopping_patience"]} steps reached"
)
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
epoch_number += 1
output_dir_current = os.path.join(output_dir, "checkpoint-{}-epoch-{}".format(global_step, epoch_number))
if args["save_model_every_epoch"] or args["evaluate_during_training"]:
os.makedirs(output_dir_current, exist_ok=True)
if args["save_model_every_epoch"]:
self._save_model(output_dir_current, optimizer, scheduler, model=model)
if args["evaluate_during_training"]:
results, _, _ = self.eval_model(
eval_df, verbose=verbose and args["evaluate_during_training_verbose"], silent=True, **kwargs
)
self._save_model(output_dir_current, optimizer, scheduler, results=results)
training_progress_scores["global_step"].append(global_step)
training_progress_scores["train_loss"].append(current_loss)
for key in results:
training_progress_scores[key].append(results[key])
report = pd.DataFrame(training_progress_scores)
report.to_csv(os.path.join(args["output_dir"], "training_progress_scores.csv"), index=False)
if args["wandb_project"]:
wandb.log(self._get_last_metrics(training_progress_scores))
if not best_eval_metric:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(args["best_model_dir"], optimizer, scheduler, model=model, results=results)
if best_eval_metric and args["early_stopping_metric_minimize"]:
if results[args["early_stopping_metric"]] - best_eval_metric < args["early_stopping_delta"]:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(args["best_model_dir"], optimizer, scheduler, model=model, results=results)
early_stopping_counter = 0
else:
if args["use_early_stopping"] and args["early_stopping_consider_epochs"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args["early_stopping_metric"]}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args["early_stopping_patience"]}")
else:
if verbose:
logger.info(f" Patience of {args["early_stopping_patience"]} steps reached")
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
else:
if results[args["early_stopping_metric"]] - best_eval_metric > args["early_stopping_delta"]:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(args["best_model_dir"], optimizer, scheduler, model=model, results=results)
early_stopping_counter = 0
else:
if args["use_early_stopping"] and args["early_stopping_consider_epochs"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args["early_stopping_metric"]}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args["early_stopping_patience"]}")
else:
if verbose:
logger.info(f" Patience of {args["early_stopping_patience"]} steps reached")
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
return global_step, tr_loss / global_step
def eval_model(self, eval_df, multi_label=False, output_dir=None, verbose=True, silent=False, **kwargs):
"""
Evaluates the model on eval_df. Saves results to output_dir.
Args:
eval_df: Pandas Dataframe containing at least two columns. If the Dataframe has a header, it should contain a 'text' and a 'labels' column. If no header is present,
the Dataframe should contain at least two columns, with the first column containing the text, and the second column containing the label. The model will be evaluated on this Dataframe.
output_dir: The directory where model files will be saved. If not given, self.args['output_dir'] will be used.
verbose: If verbose, results will be printed to the console on completion of evaluation.
silent: If silent, tqdm progress bars will be hidden.
**kwargs: Additional metrics that should be used. Pass in the metrics as keyword arguments (name of metric: function to use). E.g. f1=sklearn.metrics.f1_score.
A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
Returns:
result: Dictionary containing evaluation results.
model_outputs: List of model outputs for each row in eval_df
wrong_preds: List of InputExample objects corresponding to each incorrect prediction by the model
""" # noqa: ignore flake8"
if not output_dir:
output_dir = self.args["output_dir"]
self._move_model_to_device()
result, model_outputs, wrong_preds = self.evaluate(
eval_df, output_dir, multi_label=multi_label, verbose=verbose, silent=silent, **kwargs
)
self.results.update(result)
if verbose:
logger.info(self.results)
return result, model_outputs, wrong_preds
def evaluate(self, eval_df, output_dir, multi_label=False, prefix="", verbose=True, silent=False, **kwargs):
"""
Evaluates the model on eval_df.
Utility function to be used by the eval_model() method. Not intended to be used directly.
"""
device = self.device
model = self.model
args = self.args
eval_output_dir = output_dir
results = {}
if "text" in eval_df.columns and "labels" in eval_df.columns:
eval_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(eval_df["text"], eval_df["labels"]))
]
elif "text_a" in eval_df.columns and "text_b" in eval_df.columns:
eval_examples = [
InputExample(i, text_a, text_b, label)
for i, (text_a, text_b, label) in enumerate(
zip(eval_df["text_a"], eval_df["text_b"], eval_df["labels"])
)
]
else:
warnings.warn(
"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels."
)
eval_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(eval_df.iloc[:, 0], eval_df.iloc[:, 1]))
]
if args["sliding_window"]:
eval_dataset, window_counts = self.load_and_cache_examples(
eval_examples, evaluate=True, verbose=verbose, silent=silent
)
else:
eval_dataset = self.load_and_cache_examples(eval_examples, evaluate=True, verbose=verbose, silent=silent)
os.makedirs(eval_output_dir, exist_ok=True)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args["eval_batch_size"])
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
model.eval()
for batch in tqdm(eval_dataloader, disable=args["silent"] or silent):
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
if multi_label:
logits = logits.sigmoid()
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args["sliding_window"]:
count = 0
window_ranges = []
for n_windows in window_counts:
window_ranges.append([count, count + n_windows])
count += n_windows
preds = [preds[window_range[0] : window_range[1]] for window_range in window_ranges]
out_label_ids = [
out_label_ids[i] for i in range(len(out_label_ids)) if i in [window[0] for window in window_ranges]
]
model_outputs = preds
preds = [np.argmax(pred, axis=1) for pred in preds]
final_preds = []
for pred_row in preds:
mode_pred, counts = mode(pred_row)
if len(counts) > 1 and counts[0] == counts[1]:
final_preds.append(args["tie_value"])
else:
final_preds.append(mode_pred[0])
preds = np.array(final_preds)
elif not multi_label and args["regression"] is True:
preds = np.squeeze(preds)
model_outputs = preds
else:
model_outputs = preds
if not multi_label:
preds = np.argmax(preds, axis=1)
result, wrong = self.compute_metrics(preds, out_label_ids, eval_examples, **kwargs)
result["eval_loss"] = eval_loss
results.update(result)
output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(result.keys()):
writer.write("{} = {}\n".format(key, str(result[key])))
return results, model_outputs, wrong
def load_and_cache_examples(
self, examples, evaluate=False, no_cache=False, multi_label=False, verbose=True, silent=False
):
"""
Converts a list of InputExample objects to a TensorDataset containing InputFeatures. Caches the InputFeatures.
Utility function for train() and eval() methods. Not intended to be used directly.
"""
process_count = self.args["process_count"]
tokenizer = self.tokenizer
args = self.args
if not no_cache:
no_cache = args["no_cache"]
if not multi_label and args["regression"]:
output_mode = "regression"
else:
output_mode = "classification"
os.makedirs(self.args["cache_dir"], exist_ok=True)
mode = "dev" if evaluate else "train"
cached_features_file = os.path.join(
args["cache_dir"],
"cached_{}_{}_{}_{}_{}".format(
mode, args["model_type"], args["max_seq_length"], self.num_labels, len(examples),
),
)
if os.path.exists(cached_features_file) and (
(not args["reprocess_input_data"] and not no_cache)
or (mode == "dev" and args["use_cached_eval_features"] and not no_cache)
):
features = torch.load(cached_features_file)
if verbose:
logger.info(f" Features loaded from cache at {cached_features_file}")
else:
if verbose:
logger.info(f" Converting to features started. Cache is not used.")
if args["sliding_window"]:
logger.info(" Sliding window enabled")
features = convert_examples_to_features(
examples,
args["max_seq_length"],
tokenizer,
output_mode,
# XLNet has a CLS token at the end
cls_token_at_end=bool(args["model_type"] in ["xlnet"]),
cls_token=tokenizer.cls_token,
cls_token_segment_id=2 if args["model_type"] in ["xlnet"] else 0,
sep_token=tokenizer.sep_token,
# RoBERTa uses an extra separator b/w pairs of sentences,
# cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
sep_token_extra=bool(args["model_type"] in ["roberta", "camembert", "xlmroberta"]),
# PAD on the left for XLNet
pad_on_left=bool(args["model_type"] in ["xlnet"]),
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=4 if args["model_type"] in ["xlnet"] else 0,
process_count=process_count,
multi_label=multi_label,
silent=args["silent"] or silent,
use_multiprocessing=args["use_multiprocessing"],
sliding_window=args["sliding_window"],
flatten=not evaluate,
stride=args["stride"],
)
if verbose and args["sliding_window"]:
logger.info(f" {len(features)} features created from {len(examples)} samples.")
if not no_cache:
torch.save(features, cached_features_file)
if args["sliding_window"] and evaluate:
window_counts = [len(sample) for sample in features]
features = [feature for feature_set in features for feature in feature_set]
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
if args["sliding_window"] and evaluate:
return dataset, window_counts
else:
return dataset
def compute_metrics(self, preds, labels, eval_examples, multi_label=False, **kwargs):
"""
Computes the evaluation metrics for the model predictions.
Args:
preds: Model predictions
labels: Ground truth labels
eval_examples: List of examples on which evaluation was performed
**kwargs: Additional metrics that should be used. Pass in the metrics as keyword arguments (name of metric: function to use). E.g. f1=sklearn.metrics.f1_score.
A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
Returns:
result: Dictionary containing evaluation results. (Matthews correlation coefficient, tp, tn, fp, fn)
wrong: List of InputExample objects corresponding to each incorrect prediction by the model
""" # noqa: ignore flake8"
assert len(preds) == len(labels)
extra_metrics = {}
for metric, func in kwargs.items():
extra_metrics[metric] = func(labels, preds)
mismatched = labels != preds
wrong = [i for (i, v) in zip(eval_examples, mismatched) if v.any()]
if multi_label:
label_ranking_score = label_ranking_average_precision_score(labels, preds)
return {**{"LRAP": label_ranking_score}, **extra_metrics}, wrong
elif self.args["regression"]:
return {**extra_metrics}, wrong
mcc = matthews_corrcoef(labels, preds)
if self.model.num_labels == 2:
tn, fp, fn, tp = confusion_matrix(labels, preds, labels=[0, 1]).ravel()
return (
{**{"mcc": mcc, "tp": tp, "tn": tn, "fp": fp, "fn": fn}, **extra_metrics},
wrong,
)
else:
return {**{"mcc": mcc}, **extra_metrics}, wrong
def predict(self, to_predict, multi_label=False):
"""
Performs predictions on a list of text.
Args:
to_predict: A python list of text (str) to be sent to the model for prediction.
Returns:
preds: A python list of the predictions (0 or 1) for each text.
model_outputs: A python list of the raw model outputs for each text.
"""
device = self.device
model = self.model
args = self.args
self._move_model_to_device()
if multi_label:
eval_examples = [
InputExample(i, text, None, [0 for i in range(self.num_labels)]) for i, text in enumerate(to_predict)
]
else:
if isinstance(to_predict[0], list):
eval_examples = [InputExample(i, text[0], text[1], 0) for i, text in enumerate(to_predict)]
else:
eval_examples = [InputExample(i, text, None, 0) for i, text in enumerate(to_predict)]
if args["sliding_window"]:
eval_dataset, window_counts = self.load_and_cache_examples(eval_examples, evaluate=True, no_cache=True)
else:
eval_dataset = self.load_and_cache_examples(
eval_examples, evaluate=True, multi_label=multi_label, no_cache=True
)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args["eval_batch_size"])
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
if self.config.output_hidden_states:
for batch in tqdm(eval_dataloader, disable=args["silent"]):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
embedding_outputs, layer_hidden_states = outputs[2][0], outputs[2][1:]
if multi_label:
logits = logits.sigmoid()
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
all_layer_hidden_states = [state.detach().cpu().numpy() for state in layer_hidden_states]
all_embedding_outputs = embedding_outputs.detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
all_layer_hidden_states = np.append(
[state.detach().cpu().numpy() for state in layer_hidden_states], axis=0
)
all_embedding_outputs = np.append(embedding_outputs.detach().cpu().numpy(), axis=0)
else:
for batch in tqdm(eval_dataloader, disable=args["silent"]):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
if multi_label:
logits = logits.sigmoid()
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args["sliding_window"]:
count = 0
window_ranges = []
for n_windows in window_counts:
window_ranges.append([count, count + n_windows])
count += n_windows
preds = [preds[window_range[0] : window_range[1]] for window_range in window_ranges]
model_outputs = preds
preds = [np.argmax(pred, axis=1) for pred in preds]
final_preds = []
for pred_row in preds:
mode_pred, counts = mode(pred_row)
if len(counts) > 1 and counts[0] == counts[1]:
final_preds.append(args["tie_value"])
else:
final_preds.append(mode_pred[0])
preds = np.array(final_preds)
elif not multi_label and args["regression"] is True:
preds = np.squeeze(preds)
model_outputs = preds
else:
model_outputs = preds
if multi_label:
if isinstance(args["threshold"], list):
threshold_values = args["threshold"]
preds = [
[self._threshold(pred, threshold_values[i]) for i, pred in enumerate(example)]
for example in preds
]
else:
preds = [[self._threshold(pred, args["threshold"]) for pred in example] for example in preds]
else:
preds = np.argmax(preds, axis=1)
if self.config.output_hidden_states:
return preds, model_outputs, all_embedding_outputs, all_layer_hidden_states
else:
return preds, model_outputs
def _threshold(self, x, threshold):
if x >= threshold:
return 1
return 0
def _move_model_to_device(self):
self.model.to(self.device)
def _get_inputs_dict(self, batch):
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
# XLM, DistilBERT and RoBERTa don't use segment_ids
if self.args["model_type"] != "distilbert":
inputs["token_type_ids"] = batch[2] if self.args["model_type"] in ["bert", "xlnet", "albert"] else None
return inputs
def _get_last_metrics(self, metric_values):
return {metric: values[-1] for metric, values in metric_values.items()}
def _create_training_progress_scores(self, multi_label, **kwargs):
extra_metrics = {key: [] for key in kwargs}
if multi_label:
training_progress_scores = {
"global_step": [],
"LRAP": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
else:
if self.model.num_labels == 2:
training_progress_scores = {
"global_step": [],
"tp": [],
"tn": [],
"fp": [],
"fn": [],
"mcc": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
elif self.model.num_labels == 1:
training_progress_scores = {
"global_step": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
else:
training_progress_scores = {
"global_step": [],
"mcc": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
return training_progress_scores
def _save_model(self, output_dir=None, optimizer=None, scheduler=None, model=None, results=None):
if not output_dir:
output_dir = self.args["output_dir"]
os.makedirs(output_dir, exist_ok=True)
if model and not self.args["no_save"]:
# Take care of distributed/parallel training
model_to_save = model.module if hasattr(model, "module") else model
model_to_save.save_pretrained(output_dir)
self.tokenizer.save_pretrained(output_dir)
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
if optimizer and scheduler:
torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
self._save_model_args(output_dir)
if results:
output_eval_file = os.path.join(output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
def _save_model_args(self, output_dir):
os.makedirs(output_dir, exist_ok=True)
with open(os.path.join(output_dir, "model_args.json"), "w") as f:
json.dump(self.args, f)
def _load_model_args(self, input_dir):
model_args_file = os.path.join(input_dir, "model_args.json")
if os.path.isfile(model_args_file):
with open(model_args_file, "r") as f:
model_args = json.load(f)
return model_args
| #!/usr/bin/env python
# coding: utf-8
from __future__ import absolute_import, division, print_function
import json
import logging
import math
import os
import random
import warnings
from multiprocessing import cpu_count
import numpy as np
from scipy.stats import mode, pearsonr
from sklearn.metrics import (
confusion_matrix,
label_ranking_average_precision_score,
matthews_corrcoef,
mean_squared_error,
)
from tqdm.auto import tqdm, trange
import pandas as pd
import torch
from simpletransformers.classification.classification_utils import InputExample, convert_examples_to_features
from simpletransformers.classification.transformer_models.albert_model import AlbertForSequenceClassification
from simpletransformers.classification.transformer_models.bert_model import BertForSequenceClassification
from simpletransformers.classification.transformer_models.camembert_model import CamembertForSequenceClassification
from simpletransformers.classification.transformer_models.distilbert_model import DistilBertForSequenceClassification
from simpletransformers.classification.transformer_models.flaubert_model import FlaubertForSequenceClassification
from simpletransformers.classification.transformer_models.roberta_model import RobertaForSequenceClassification
from simpletransformers.classification.transformer_models.xlm_model import XLMForSequenceClassification
from simpletransformers.classification.transformer_models.xlm_roberta_model import XLMRobertaForSequenceClassification
from simpletransformers.classification.transformer_models.xlnet_model import XLNetForSequenceClassification
from simpletransformers.config.global_args import global_args
from simpletransformers.custom_models.models import ElectraForSequenceClassification
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from transformers import (
WEIGHTS_NAME,
AdamW,
AlbertConfig,
AlbertTokenizer,
BertConfig,
BertTokenizer,
CamembertConfig,
CamembertTokenizer,
DistilBertConfig,
DistilBertTokenizer,
ElectraConfig,
ElectraTokenizer,
FlaubertConfig,
FlaubertTokenizer,
RobertaConfig,
RobertaTokenizer,
XLMConfig,
XLMRobertaConfig,
XLMRobertaTokenizer,
XLMTokenizer,
XLNetConfig,
XLNetTokenizer,
get_linear_schedule_with_warmup,
)
try:
import wandb
wandb_available = True
except ImportError:
wandb_available = False
logger = logging.getLogger(__name__)
class ClassificationModel:
def __init__(
self, model_type, model_name, num_labels=None, weight=None, args=None, use_cuda=True, cuda_device=-1, **kwargs,
):
"""
Initializes a ClassificationModel model.
Args:
model_type: The type of model (bert, xlnet, xlm, roberta, distilbert)
model_name: The exact architecture and trained weights to use. This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files.
num_labels (optional): The number of labels or classes in the dataset.
weight (optional): A list of length num_labels containing the weights to assign to each label for loss calculation.
args (optional): Default args will be used if this parameter is not provided. If provided, it should be a dict containing the args that should be changed in the default args.
use_cuda (optional): Use GPU if available. Setting to False will force model to use CPU only.
cuda_device (optional): Specific GPU that should be used. Will use the first available GPU by default.
**kwargs (optional): For providing proxies, force_download, resume_download, cache_dir and other options specific to the 'from_pretrained' implementation where this will be supplied.
""" # noqa: ignore flake8"
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
"xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
"xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
"roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
"distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
"albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
"camembert": (CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer),
"xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
"flaubert": (FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer),
"electra": (ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer),
}
if args and "manual_seed" in args:
random.seed(args["manual_seed"])
np.random.seed(args["manual_seed"])
torch.manual_seed(args["manual_seed"])
if "n_gpu" in args and args["n_gpu"] > 0:
torch.cuda.manual_seed_all(args["manual_seed"])
self.args = {
"sliding_window": False,
"tie_value": 1,
"stride": 0.8,
"regression": False,
}
self.args.update(global_args)
saved_model_args = self._load_model_args(model_name)
if saved_model_args:
self.args.update(saved_model_args)
if args:
self.args.update(args)
config_class, model_class, tokenizer_class = MODEL_CLASSES[model_type]
if num_labels:
self.config = config_class.from_pretrained(model_name, num_labels=num_labels, **self.args["config"])
self.num_labels = num_labels
else:
self.config = config_class.from_pretrained(model_name, **self.args["config"])
self.num_labels = self.config.num_labels
self.weight = weight
if use_cuda:
if torch.cuda.is_available():
if cuda_device == -1:
self.device = torch.device("cuda")
else:
self.device = torch.device(f"cuda:{cuda_device}")
else:
raise ValueError(
"'use_cuda' set to True when cuda is unavailable."
" Make sure CUDA is available or set use_cuda=False."
)
else:
self.device = "cpu"
if self.weight:
self.model = model_class.from_pretrained(
model_name, config=self.config, weight=torch.Tensor(self.weight).to(self.device), **kwargs,
)
else:
self.model = model_class.from_pretrained(model_name, config=self.config, **kwargs)
self.results = {}
if not use_cuda:
self.args["fp16"] = False
self.tokenizer = tokenizer_class.from_pretrained(
model_name, do_lower_case=self.args["do_lower_case"], **kwargs
)
self.args["model_name"] = model_name
self.args["model_type"] = model_type
if model_type in ["camembert", "xlmroberta"]:
warnings.warn(
f"use_multiprocessing automatically disabled as {model_type}"
" fails when using multiprocessing for feature conversion."
)
self.args["use_multiprocessing"] = False
if self.args["wandb_project"] and not wandb_available:
warnings.warn("wandb_project specified but wandb is not available. Wandb disabled.")
self.args["wandb_project"] = None
def train_model(
self,
train_df,
multi_label=False,
output_dir=None,
show_running_loss=True,
args=None,
eval_df=None,
verbose=True,
**kwargs,
):
"""
Trains the model using 'train_df'
Args:
train_df: Pandas Dataframe containing at least two columns. If the Dataframe has a header, it should contain a 'text' and a 'labels' column. If no header is present,
the Dataframe should contain at least two columns, with the first column containing the text, and the second column containing the label. The model will be trained on this Dataframe.
output_dir: The directory where model files will be saved. If not given, self.args['output_dir'] will be used.
show_running_loss (optional): Set to False to prevent running loss from being printed to console. Defaults to True.
args (optional): Optional changes to the args dict of the model. Any changes made will persist for the model.
eval_df (optional): A DataFrame against which evaluation will be performed when evaluate_during_training is enabled. Is required if evaluate_during_training is enabled.
**kwargs: Additional metrics that should be used. Pass in the metrics as keyword arguments (name of metric: function to use). E.g. f1=sklearn.metrics.f1_score.
A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
Returns:
None
""" # noqa: ignore flake8"
if args:
self.args.update(args)
if self.args["silent"]:
show_running_loss = False
if self.args["evaluate_during_training"] and eval_df is None:
raise ValueError(
"evaluate_during_training is enabled but eval_df is not specified."
" Pass eval_df to model.train_model() if using evaluate_during_training."
)
if not output_dir:
output_dir = self.args["output_dir"]
if os.path.exists(output_dir) and os.listdir(output_dir) and not self.args["overwrite_output_dir"]:
raise ValueError(
"Output directory ({}) already exists and is not empty."
" Use --overwrite_output_dir to overcome.".format(output_dir)
)
self._move_model_to_device()
if "text" in train_df.columns and "labels" in train_df.columns:
train_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(train_df["text"], train_df["labels"]))
]
elif "text_a" in train_df.columns and "text_b" in train_df.columns:
train_examples = [
InputExample(i, text_a, text_b, label)
for i, (text_a, text_b, label) in enumerate(
zip(train_df["text_a"], train_df["text_b"], train_df["labels"])
)
]
else:
warnings.warn(
"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels."
)
train_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(train_df.iloc[:, 0], train_df.iloc[:, 1]))
]
train_dataset = self.load_and_cache_examples(train_examples, verbose=verbose)
os.makedirs(output_dir, exist_ok=True)
global_step, tr_loss = self.train(
train_dataset,
output_dir,
multi_label=multi_label,
show_running_loss=show_running_loss,
eval_df=eval_df,
verbose=verbose,
**kwargs,
)
# model_to_save = self.model.module if hasattr(self.model, "module") else self.model
# model_to_save.save_pretrained(output_dir)
# self.tokenizer.save_pretrained(output_dir)
# torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
self._save_model()
if verbose:
logger.info(" Training of {} model complete. Saved to {}.".format(self.args["model_type"], output_dir))
def train(
self,
train_dataset,
output_dir,
multi_label=False,
show_running_loss=True,
eval_df=None,
verbose=True,
**kwargs,
):
"""
Trains the model on train_dataset.
Utility function to be used by the train_model() method. Not intended to be used directly.
"""
device = self.device
model = self.model
args = self.args
tb_writer = SummaryWriter(logdir=args["tensorboard_dir"])
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args["train_batch_size"])
t_total = len(train_dataloader) // args["gradient_accumulation_steps"] * args["num_train_epochs"]
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args["weight_decay"],
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
warmup_steps = math.ceil(t_total * args["warmup_ratio"])
args["warmup_steps"] = warmup_steps if args["warmup_steps"] == 0 else args["warmup_steps"]
optimizer = AdamW(optimizer_grouped_parameters, lr=args["learning_rate"], eps=args["adam_epsilon"])
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args["warmup_steps"], num_training_steps=t_total
)
if args["fp16"]:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args["fp16_opt_level"])
if args["n_gpu"] > 1:
model = torch.nn.DataParallel(model)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args["num_train_epochs"]), desc="Epoch", disable=args["silent"], mininterval=0)
epoch_number = 0
best_eval_metric = None
early_stopping_counter = 0
steps_trained_in_current_epoch = 0
epochs_trained = 0
if args["model_name"] and os.path.exists(args["model_name"]):
try:
# set global_step to gobal_step of last saved checkpoint from model path
checkpoint_suffix = args["model_name"].split("/")[-1].split("-")
if len(checkpoint_suffix) > 2:
checkpoint_suffix = checkpoint_suffix[1]
else:
checkpoint_suffix = checkpoint_suffix[-1]
global_step = int(checkpoint_suffix)
epochs_trained = global_step // (len(train_dataloader) // args["gradient_accumulation_steps"])
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) // args["gradient_accumulation_steps"]
)
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the current epoch", steps_trained_in_current_epoch)
except ValueError:
logger.info(" Starting fine-tuning.")
if args["evaluate_during_training"]:
training_progress_scores = self._create_training_progress_scores(multi_label, **kwargs)
if args["wandb_project"]:
wandb.init(project=args["wandb_project"], config={**args}, **args["wandb_kwargs"])
wandb.watch(self.model)
model.train()
for _ in train_iterator:
if epochs_trained > 0:
epochs_trained -= 1
continue
# epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(tqdm(train_dataloader, desc="Current iteration", disable=args["silent"])):
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
batch = tuple(t.to(device) for t in batch)
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
# model outputs are always tuple in pytorch-transformers (see doc)
loss = outputs[0]
if args["n_gpu"] > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
current_loss = loss.item()
if show_running_loss:
print("\rRunning loss: %f" % loss, end="")
if args["gradient_accumulation_steps"] > 1:
loss = loss / args["gradient_accumulation_steps"]
if args["fp16"]:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
# torch.nn.utils.clip_grad_norm_(
# amp.master_params(optimizer), args["max_grad_norm"]
# )
else:
loss.backward()
# torch.nn.utils.clip_grad_norm_(
# model.parameters(), args["max_grad_norm"]
# )
tr_loss += loss.item()
if (step + 1) % args["gradient_accumulation_steps"] == 0:
if args["fp16"]:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args["max_grad_norm"])
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args["max_grad_norm"])
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args["logging_steps"] > 0 and global_step % args["logging_steps"] == 0:
# Log metrics
tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args["logging_steps"], global_step)
logging_loss = tr_loss
if args["wandb_project"]:
wandb.log(
{
"Training loss": current_loss,
"lr": scheduler.get_lr()[0],
"global_step": global_step,
}
)
if args["save_steps"] > 0 and global_step % args["save_steps"] == 0:
# Save model checkpoint
output_dir_current = os.path.join(output_dir, "checkpoint-{}".format(global_step))
self._save_model(output_dir_current, optimizer, scheduler, model=model)
if args["evaluate_during_training"] and (
args["evaluate_during_training_steps"] > 0
and global_step % args["evaluate_during_training_steps"] == 0
):
# Only evaluate when single GPU otherwise metrics may not average well
results, _, _ = self.eval_model(
eval_df,
verbose=verbose and args["evaluate_during_training_verbose"],
silent=True,
**kwargs,
)
for key, value in results.items():
tb_writer.add_scalar("eval_{}".format(key), value, global_step)
output_dir_current = os.path.join(output_dir, "checkpoint-{}".format(global_step))
if args["save_eval_checkpoints"]:
self._save_model(output_dir_current, optimizer, scheduler, model=model, results=results)
training_progress_scores["global_step"].append(global_step)
training_progress_scores["train_loss"].append(current_loss)
for key in results:
training_progress_scores[key].append(results[key])
report = pd.DataFrame(training_progress_scores)
report.to_csv(
os.path.join(args["output_dir"], "training_progress_scores.csv"), index=False,
)
if args["wandb_project"]:
wandb.log(self._get_last_metrics(training_progress_scores))
if not best_eval_metric:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(
args["best_model_dir"], optimizer, scheduler, model=model, results=results
)
if best_eval_metric and args["early_stopping_metric_minimize"]:
if (
results[args["early_stopping_metric"]] - best_eval_metric
< args["early_stopping_delta"]
):
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(
args["best_model_dir"], optimizer, scheduler, model=model, results=results
)
early_stopping_counter = 0
else:
if args["use_early_stopping"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args['early_stopping_metric']}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args['early_stopping_patience']}")
else:
if verbose:
logger.info(
f" Patience of {args['early_stopping_patience']} steps reached"
)
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
else:
if (
results[args["early_stopping_metric"]] - best_eval_metric
> args["early_stopping_delta"]
):
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(
args["best_model_dir"], optimizer, scheduler, model=model, results=results
)
early_stopping_counter = 0
else:
if args["use_early_stopping"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args['early_stopping_metric']}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args['early_stopping_patience']}")
else:
if verbose:
logger.info(
f" Patience of {args['early_stopping_patience']} steps reached"
)
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
epoch_number += 1
output_dir_current = os.path.join(output_dir, "checkpoint-{}-epoch-{}".format(global_step, epoch_number))
if args["save_model_every_epoch"] or args["evaluate_during_training"]:
os.makedirs(output_dir_current, exist_ok=True)
if args["save_model_every_epoch"]:
self._save_model(output_dir_current, optimizer, scheduler, model=model)
if args["evaluate_during_training"]:
results, _, _ = self.eval_model(
eval_df, verbose=verbose and args["evaluate_during_training_verbose"], silent=True, **kwargs
)
self._save_model(output_dir_current, optimizer, scheduler, results=results)
training_progress_scores["global_step"].append(global_step)
training_progress_scores["train_loss"].append(current_loss)
for key in results:
training_progress_scores[key].append(results[key])
report = pd.DataFrame(training_progress_scores)
report.to_csv(os.path.join(args["output_dir"], "training_progress_scores.csv"), index=False)
if args["wandb_project"]:
wandb.log(self._get_last_metrics(training_progress_scores))
if not best_eval_metric:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(args["best_model_dir"], optimizer, scheduler, model=model, results=results)
if best_eval_metric and args["early_stopping_metric_minimize"]:
if results[args["early_stopping_metric"]] - best_eval_metric < args["early_stopping_delta"]:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(args["best_model_dir"], optimizer, scheduler, model=model, results=results)
early_stopping_counter = 0
else:
if args["use_early_stopping"] and args["early_stopping_consider_epochs"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args['early_stopping_metric']}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args['early_stopping_patience']}")
else:
if verbose:
logger.info(f" Patience of {args['early_stopping_patience']} steps reached")
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
else:
if results[args["early_stopping_metric"]] - best_eval_metric > args["early_stopping_delta"]:
best_eval_metric = results[args["early_stopping_metric"]]
self._save_model(args["best_model_dir"], optimizer, scheduler, model=model, results=results)
early_stopping_counter = 0
else:
if args["use_early_stopping"] and args["early_stopping_consider_epochs"]:
if early_stopping_counter < args["early_stopping_patience"]:
early_stopping_counter += 1
if verbose:
logger.info(f" No improvement in {args['early_stopping_metric']}")
logger.info(f" Current step: {early_stopping_counter}")
logger.info(f" Early stopping patience: {args['early_stopping_patience']}")
else:
if verbose:
logger.info(f" Patience of {args['early_stopping_patience']} steps reached")
logger.info(" Training terminated.")
train_iterator.close()
return global_step, tr_loss / global_step
return global_step, tr_loss / global_step
def eval_model(self, eval_df, multi_label=False, output_dir=None, verbose=True, silent=False, **kwargs):
"""
Evaluates the model on eval_df. Saves results to output_dir.
Args:
eval_df: Pandas Dataframe containing at least two columns. If the Dataframe has a header, it should contain a 'text' and a 'labels' column. If no header is present,
the Dataframe should contain at least two columns, with the first column containing the text, and the second column containing the label. The model will be evaluated on this Dataframe.
output_dir: The directory where model files will be saved. If not given, self.args['output_dir'] will be used.
verbose: If verbose, results will be printed to the console on completion of evaluation.
silent: If silent, tqdm progress bars will be hidden.
**kwargs: Additional metrics that should be used. Pass in the metrics as keyword arguments (name of metric: function to use). E.g. f1=sklearn.metrics.f1_score.
A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
Returns:
result: Dictionary containing evaluation results.
model_outputs: List of model outputs for each row in eval_df
wrong_preds: List of InputExample objects corresponding to each incorrect prediction by the model
""" # noqa: ignore flake8"
if not output_dir:
output_dir = self.args["output_dir"]
self._move_model_to_device()
result, model_outputs, wrong_preds = self.evaluate(
eval_df, output_dir, multi_label=multi_label, verbose=verbose, silent=silent, **kwargs
)
self.results.update(result)
if verbose:
logger.info(self.results)
return result, model_outputs, wrong_preds
def evaluate(self, eval_df, output_dir, multi_label=False, prefix="", verbose=True, silent=False, **kwargs):
"""
Evaluates the model on eval_df.
Utility function to be used by the eval_model() method. Not intended to be used directly.
"""
device = self.device
model = self.model
args = self.args
eval_output_dir = output_dir
results = {}
if "text" in eval_df.columns and "labels" in eval_df.columns:
eval_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(eval_df["text"], eval_df["labels"]))
]
elif "text_a" in eval_df.columns and "text_b" in eval_df.columns:
eval_examples = [
InputExample(i, text_a, text_b, label)
for i, (text_a, text_b, label) in enumerate(
zip(eval_df["text_a"], eval_df["text_b"], eval_df["labels"])
)
]
else:
warnings.warn(
"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels."
)
eval_examples = [
InputExample(i, text, None, label)
for i, (text, label) in enumerate(zip(eval_df.iloc[:, 0], eval_df.iloc[:, 1]))
]
if args["sliding_window"]:
eval_dataset, window_counts = self.load_and_cache_examples(
eval_examples, evaluate=True, verbose=verbose, silent=silent
)
else:
eval_dataset = self.load_and_cache_examples(eval_examples, evaluate=True, verbose=verbose, silent=silent)
os.makedirs(eval_output_dir, exist_ok=True)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args["eval_batch_size"])
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
model.eval()
for batch in tqdm(eval_dataloader, disable=args["silent"] or silent):
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
if multi_label:
logits = logits.sigmoid()
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args["sliding_window"]:
count = 0
window_ranges = []
for n_windows in window_counts:
window_ranges.append([count, count + n_windows])
count += n_windows
preds = [preds[window_range[0] : window_range[1]] for window_range in window_ranges]
out_label_ids = [
out_label_ids[i] for i in range(len(out_label_ids)) if i in [window[0] for window in window_ranges]
]
model_outputs = preds
preds = [np.argmax(pred, axis=1) for pred in preds]
final_preds = []
for pred_row in preds:
mode_pred, counts = mode(pred_row)
if len(counts) > 1 and counts[0] == counts[1]:
final_preds.append(args["tie_value"])
else:
final_preds.append(mode_pred[0])
preds = np.array(final_preds)
elif not multi_label and args["regression"] is True:
preds = np.squeeze(preds)
model_outputs = preds
else:
model_outputs = preds
if not multi_label:
preds = np.argmax(preds, axis=1)
result, wrong = self.compute_metrics(preds, out_label_ids, eval_examples, **kwargs)
result["eval_loss"] = eval_loss
results.update(result)
output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(result.keys()):
writer.write("{} = {}\n".format(key, str(result[key])))
return results, model_outputs, wrong
def load_and_cache_examples(
self, examples, evaluate=False, no_cache=False, multi_label=False, verbose=True, silent=False
):
"""
Converts a list of InputExample objects to a TensorDataset containing InputFeatures. Caches the InputFeatures.
Utility function for train() and eval() methods. Not intended to be used directly.
"""
process_count = self.args["process_count"]
tokenizer = self.tokenizer
args = self.args
if not no_cache:
no_cache = args["no_cache"]
if not multi_label and args["regression"]:
output_mode = "regression"
else:
output_mode = "classification"
os.makedirs(self.args["cache_dir"], exist_ok=True)
mode = "dev" if evaluate else "train"
cached_features_file = os.path.join(
args["cache_dir"],
"cached_{}_{}_{}_{}_{}".format(
mode, args["model_type"], args["max_seq_length"], self.num_labels, len(examples),
),
)
if os.path.exists(cached_features_file) and (
(not args["reprocess_input_data"] and not no_cache)
or (mode == "dev" and args["use_cached_eval_features"] and not no_cache)
):
features = torch.load(cached_features_file)
if verbose:
logger.info(f" Features loaded from cache at {cached_features_file}")
else:
if verbose:
logger.info(f" Converting to features started. Cache is not used.")
if args["sliding_window"]:
logger.info(" Sliding window enabled")
features = convert_examples_to_features(
examples,
args["max_seq_length"],
tokenizer,
output_mode,
# XLNet has a CLS token at the end
cls_token_at_end=bool(args["model_type"] in ["xlnet"]),
cls_token=tokenizer.cls_token,
cls_token_segment_id=2 if args["model_type"] in ["xlnet"] else 0,
sep_token=tokenizer.sep_token,
# RoBERTa uses an extra separator b/w pairs of sentences,
# cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
sep_token_extra=bool(args["model_type"] in ["roberta", "camembert", "xlmroberta"]),
# PAD on the left for XLNet
pad_on_left=bool(args["model_type"] in ["xlnet"]),
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=4 if args["model_type"] in ["xlnet"] else 0,
process_count=process_count,
multi_label=multi_label,
silent=args["silent"] or silent,
use_multiprocessing=args["use_multiprocessing"],
sliding_window=args["sliding_window"],
flatten=not evaluate,
stride=args["stride"],
)
if verbose and args["sliding_window"]:
logger.info(f" {len(features)} features created from {len(examples)} samples.")
if not no_cache:
torch.save(features, cached_features_file)
if args["sliding_window"] and evaluate:
window_counts = [len(sample) for sample in features]
features = [feature for feature_set in features for feature in feature_set]
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
if args["sliding_window"] and evaluate:
return dataset, window_counts
else:
return dataset
def compute_metrics(self, preds, labels, eval_examples, multi_label=False, **kwargs):
"""
Computes the evaluation metrics for the model predictions.
Args:
preds: Model predictions
labels: Ground truth labels
eval_examples: List of examples on which evaluation was performed
**kwargs: Additional metrics that should be used. Pass in the metrics as keyword arguments (name of metric: function to use). E.g. f1=sklearn.metrics.f1_score.
A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
Returns:
result: Dictionary containing evaluation results. (Matthews correlation coefficient, tp, tn, fp, fn)
wrong: List of InputExample objects corresponding to each incorrect prediction by the model
""" # noqa: ignore flake8"
assert len(preds) == len(labels)
extra_metrics = {}
for metric, func in kwargs.items():
extra_metrics[metric] = func(labels, preds)
mismatched = labels != preds
wrong = [i for (i, v) in zip(eval_examples, mismatched) if v.any()]
if multi_label:
label_ranking_score = label_ranking_average_precision_score(labels, preds)
return {**{"LRAP": label_ranking_score}, **extra_metrics}, wrong
elif self.args["regression"]:
return {**extra_metrics}, wrong
mcc = matthews_corrcoef(labels, preds)
if self.model.num_labels == 2:
tn, fp, fn, tp = confusion_matrix(labels, preds, labels=[0, 1]).ravel()
return (
{**{"mcc": mcc, "tp": tp, "tn": tn, "fp": fp, "fn": fn}, **extra_metrics},
wrong,
)
else:
return {**{"mcc": mcc}, **extra_metrics}, wrong
def predict(self, to_predict, multi_label=False):
"""
Performs predictions on a list of text.
Args:
to_predict: A python list of text (str) to be sent to the model for prediction.
Returns:
preds: A python list of the predictions (0 or 1) for each text.
model_outputs: A python list of the raw model outputs for each text.
"""
device = self.device
model = self.model
args = self.args
self._move_model_to_device()
if multi_label:
eval_examples = [
InputExample(i, text, None, [0 for i in range(self.num_labels)]) for i, text in enumerate(to_predict)
]
else:
if isinstance(to_predict[0], list):
eval_examples = [InputExample(i, text[0], text[1], 0) for i, text in enumerate(to_predict)]
else:
eval_examples = [InputExample(i, text, None, 0) for i, text in enumerate(to_predict)]
if args["sliding_window"]:
eval_dataset, window_counts = self.load_and_cache_examples(eval_examples, evaluate=True, no_cache=True)
else:
eval_dataset = self.load_and_cache_examples(
eval_examples, evaluate=True, multi_label=multi_label, no_cache=True
)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args["eval_batch_size"])
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
if self.config.output_hidden_states:
for batch in tqdm(eval_dataloader, disable=args["silent"]):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
embedding_outputs, layer_hidden_states = outputs[2][0], outputs[2][1:]
if multi_label:
logits = logits.sigmoid()
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
all_layer_hidden_states = [state.detach().cpu().numpy() for state in layer_hidden_states]
all_embedding_outputs = embedding_outputs.detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
all_layer_hidden_states = np.append(
[state.detach().cpu().numpy() for state in layer_hidden_states], axis=0
)
all_embedding_outputs = np.append(embedding_outputs.detach().cpu().numpy(), axis=0)
else:
for batch in tqdm(eval_dataloader, disable=args["silent"]):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = self._get_inputs_dict(batch)
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
if multi_label:
logits = logits.sigmoid()
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args["sliding_window"]:
count = 0
window_ranges = []
for n_windows in window_counts:
window_ranges.append([count, count + n_windows])
count += n_windows
preds = [preds[window_range[0] : window_range[1]] for window_range in window_ranges]
model_outputs = preds
preds = [np.argmax(pred, axis=1) for pred in preds]
final_preds = []
for pred_row in preds:
mode_pred, counts = mode(pred_row)
if len(counts) > 1 and counts[0] == counts[1]:
final_preds.append(args["tie_value"])
else:
final_preds.append(mode_pred[0])
preds = np.array(final_preds)
elif not multi_label and args["regression"] is True:
preds = np.squeeze(preds)
model_outputs = preds
else:
model_outputs = preds
if multi_label:
if isinstance(args["threshold"], list):
threshold_values = args["threshold"]
preds = [
[self._threshold(pred, threshold_values[i]) for i, pred in enumerate(example)]
for example in preds
]
else:
preds = [[self._threshold(pred, args["threshold"]) for pred in example] for example in preds]
else:
preds = np.argmax(preds, axis=1)
if self.config.output_hidden_states:
return preds, model_outputs, all_embedding_outputs, all_layer_hidden_states
else:
return preds, model_outputs
def _threshold(self, x, threshold):
if x >= threshold:
return 1
return 0
def _move_model_to_device(self):
self.model.to(self.device)
def _get_inputs_dict(self, batch):
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
# XLM, DistilBERT and RoBERTa don't use segment_ids
if self.args["model_type"] != "distilbert":
inputs["token_type_ids"] = batch[2] if self.args["model_type"] in ["bert", "xlnet", "albert"] else None
return inputs
def _get_last_metrics(self, metric_values):
return {metric: values[-1] for metric, values in metric_values.items()}
def _create_training_progress_scores(self, multi_label, **kwargs):
extra_metrics = {key: [] for key in kwargs}
if multi_label:
training_progress_scores = {
"global_step": [],
"LRAP": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
else:
if self.model.num_labels == 2:
training_progress_scores = {
"global_step": [],
"tp": [],
"tn": [],
"fp": [],
"fn": [],
"mcc": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
elif self.model.num_labels == 1:
training_progress_scores = {
"global_step": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
else:
training_progress_scores = {
"global_step": [],
"mcc": [],
"train_loss": [],
"eval_loss": [],
**extra_metrics,
}
return training_progress_scores
def _save_model(self, output_dir=None, optimizer=None, scheduler=None, model=None, results=None):
if not output_dir:
output_dir = self.args["output_dir"]
os.makedirs(output_dir, exist_ok=True)
if model and not self.args["no_save"]:
# Take care of distributed/parallel training
model_to_save = model.module if hasattr(model, "module") else model
model_to_save.save_pretrained(output_dir)
self.tokenizer.save_pretrained(output_dir)
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
if optimizer and scheduler:
torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
self._save_model_args(output_dir)
if results:
output_eval_file = os.path.join(output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
def _save_model_args(self, output_dir):
os.makedirs(output_dir, exist_ok=True)
with open(os.path.join(output_dir, "model_args.json"), "w") as f:
json.dump(self.args, f)
def _load_model_args(self, input_dir):
model_args_file = os.path.join(input_dir, "model_args.json")
if os.path.isfile(model_args_file):
with open(model_args_file, "r") as f:
model_args = json.load(f)
return model_args
|
# Constellation Control Server
# A centralized server for controlling museum exhibit components
# Written by Morgan Rehnberg, Fort Worth Museum of Science and History
# Released under the MIT license
# Standard modules
from http.server import HTTPServer, SimpleHTTPRequestHandler
from socketserver import ThreadingMixIn
import logging
import datetime
import configparser
import json
import os
import mimetypes
import cgi
import signal
import socket
import sys
import shutil
import traceback
import threading
import pickle
import urllib.request
import time
import re
# Non-standard modules
import dateutil.parser
# Constellation modules
import config
import constellation_exhibit as c_exhibit
import constellation_issues as c_issues
import constellation_maintenance as c_maint
import constellation_projector as c_proj
import constellation_schedule as c_sched
import constellation_tracker as c_track
class ThreadedHTTPServer(ThreadingMixIn, HTTPServer):
"""Stub which triggers dispatch of requests into individual threads."""
daemon_threads = True
class RequestHandler(SimpleHTTPRequestHandler):
"""Handle incoming requests to the control server"""
def send_current_configuration(self, id_):
"""Function to respond to a POST with a dictionary defining the current exhibit configuration"""
json_string = json.dumps(c_exhibit.get_exhibit_component(id_).config)
if len(c_exhibit.get_exhibit_component(id_).config["commands"]) > 0:
# Clear the command list now that we have sent
c_exhibit.get_exhibit_component(id_).config["commands"] = []
self.wfile.write(bytes(json_string, encoding="UTF-8"))
def send_webpage_update(self):
"""Function to collect the current exhibit status, format it, and send it back to the web client to update the page"""
component_dict_list = []
for item in config.componentList:
temp = {"id": item.id,
"type": item.type}
if "content" in item.config:
temp["content"] = item.config["content"]
if "error" in item.config:
temp["error"] = item.config["error"]
if "allowed_actions" in item.config:
temp["allowed_actions"] = item.config["allowed_actions"]
if "description" in item.config:
temp["description"] = item.config["description"]
if "AnyDeskID" in item.config:
temp["AnyDeskID"] = item.config["AnyDeskID"]
temp["class"] = "exhibitComponent"
temp["status"] = item.current_status()
temp["ip_address"] = item.ip
temp["helperPort"] = item.helperPort
temp["helperAddress"] = item.helperAddress
component_dict_list.append(temp)
for item in config.projectorList:
temp = {"id": item.id,
"type": 'PROJECTOR',
"ip_address": item.ip}
if "allowed_actions" in item.config:
temp["allowed_actions"] = item.config["allowed_actions"]
if "description" in item.config:
temp["description"] = item.config["description"]
temp["class"] = "exhibitComponent"
temp["status"] = item.state["status"]
component_dict_list.append(temp)
for item in config.wakeOnLANList:
temp = {"id": item.id,
"type": 'WAKE_ON_LAN',
"ip_address": item.ip}
if "allowed_actions" in item.config:
temp["allowed_actions"] = item.config["allowed_actions"]
if "description" in item.config:
temp["description"] = item.config["description"]
temp["class"] = "exhibitComponent"
temp["status"] = item.state["status"]
component_dict_list.append(temp)
# Also include an object with the status of the overall gallery
temp = {"class": "gallery",
"currentExhibit": config.currentExhibit,
"availableExhibits": config.exhibit_list,
"galleryName": gallery_name,
"updateAvailable": str(software_update_available).lower()}
component_dict_list.append(temp)
# Also include an object containing the current issues
temp = {"class": "issues",
"issueList": [x.details for x in config.issueList],
"lastUpdateDate": config.issueList_last_update_date,
"assignable_staff": config.assignable_staff}
component_dict_list.append(temp)
# Also include an object containing the current schedule
with config.scheduleLock:
temp = {"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
component_dict_list.append(temp)
json_string = json.dumps(component_dict_list, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
def log_request(self, code='-', size='-'):
# Override to suppress the automatic logging
pass
def copy_byte_range(self, infile, start=None, stop=None, bufsize=16 * 1024):
"""Like shutil.copyfileobj, but only copy a range of the streams.
Both start and stop are inclusive.
"""
if start is not None:
infile.seek(start)
while 1:
to_read = min(bufsize, stop + 1 - infile.tell() if stop else bufsize)
buf = infile.read(to_read)
if not buf:
break
self.wfile.write(buf)
def handle_range_request(self, f):
"""Handle a GET request using a byte range.
Inspired by https://github.com/danvk/RangeHTTPServer
"""
try:
self.range = parse_byte_range(self.headers['Range'])
except ValueError:
self.send_error(400, 'Invalid byte range')
return
first, last = self.range
fs = os.fstat(f.fileno())
file_len = fs[6]
if first >= file_len:
self.send_error(416, 'Requested Range Not Satisfiable')
return None
ctype = self.guess_type(self.translate_path(self.path))
if last is None or last >= file_len:
last = file_len - 1
response_length = last - first + 1
try:
self.send_response(206)
self.send_header('Content-type', ctype)
self.send_header('Accept-Ranges', 'bytes')
self.send_header('Content-Range',
'bytes %s-%s/%s' % (first, last, file_len))
self.send_header('Content-Length', str(response_length))
self.send_header('Last-Modified', self.date_time_string(fs.st_mtime))
self.end_headers()
self.copy_byte_range(f)
except IOError as e:
print(e)
def do_GET(self):
# Receive a GET request and respond with a console webpage
# print("+++++++++++++++")
# print("BEGIN GET")
print(f" Active threads: {threading.active_count()} ", end="\r", flush=True)
# print(f" path = {self.path}")
# Strip out any options from the query string
self.path = self.path.split("?")[0]
if self.path.lower().endswith("html") or self.path == "/":
if self.path == "/":
file_path = os.path.join(config.APP_PATH, "webpage.html")
if not os.path.isfile(file_path):
# Handle the case of a Pyinstaller --onefile binary
file_path = os.path.join(config.EXEC_PATH, "webpage.html")
f = open(file_path, "r", encoding='UTF-8')
else:
if self.path.startswith("/"):
self.path = self.path[1:]
file_path = os.path.join(config.APP_PATH, self.path)
if not os.path.isfile(file_path):
# Handle the case of a Pyinstaller --onefile binary
file_path = os.path.join(config.EXEC_PATH, self.path)
f = open(file_path, "r", encoding='UTF-8')
page = str(f.read())
# Build the address that the webpage should contact to reach this server
address_to_insert = "'http://" + str(ip_address) + ":" + str(server_port) + "'"
# Then, insert that into the document
page = page.replace("INSERT_SERVERIP_HERE", address_to_insert)
self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
self.wfile.write(bytes(page, encoding="UTF-8"))
f.close()
# print("END GET")
# print("+++++++++++++++")
return
else:
# Open the file requested and send it
mimetype = mimetypes.guess_type(self.path, strict=False)[0]
if self.path[0] == '/':
# Strip out leading /, as it screws up os.path.join
self.path = self.path[1:]
try:
file_path = os.path.join(config.APP_PATH, self.path)
if not os.path.isfile(file_path):
# Handle the case of a Pyinstaller --onefile binary
file_path = os.path.join(config.EXEC_PATH, self.path)
with open(file_path, 'rb') as f:
if "Range" in self.headers:
self.handle_range_request(f)
else:
try:
self.send_response(200)
self.send_header('Content-type', mimetype)
self.end_headers()
# print(f" Writing data to client")
self.wfile.write(f.read())
except BrokenPipeError:
print("Connection closed prematurely")
# print("END GET")
# print("+++++++++++++++")
return
except IOError:
self.send_error(404, f"File Not Found: {self.path}")
with config.logLock:
logging.error("GET for unexpected file %s", self.path)
# print("END GET")
# print("+++++++++++++++")
def do_OPTIONS(self):
"""Respond to an OPTIONS request"""
# print("---------------")
# print("BEGIN OPTIONS")
self.send_response(200, "OK")
self.send_header("Access-Control-Allow-Origin", "*")
self.send_header('Access-Control-Allow-Headers', 'Content-Type,Authorization')
self.send_header('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
self.send_header('Access-Control-Allow-Credentials', 'true')
self.end_headers()
# print("END OPTIONS")
# print("---------------")
def do_POST(self):
"""Receives pings from client devices and respond with any updated information"""
# print("===============")
# print("BEGIN POST")
print(f" Active threads: {threading.active_count()} ", end="\r", flush=True)
self.send_response(200, "OK")
self.send_header("Access-Control-Allow-Origin", "*")
self.send_header('Access-Control-Allow-Headers', 'Content-Type,Authorization')
self.send_header('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
self.send_header('Access-Control-Allow-Credentials', 'true')
self.end_headers()
# Get the data from the request
try:
ctype, pdict = cgi.parse_header(self.headers.get('content-type'))
except:
print("DO_POST: Error: Are we missing the Content-Type header?")
with config.logLock:
logging.warning("POST received without content-type header")
print(self.headers)
return
if ctype == "multipart/form-data": # File upload
try:
pdict['boundary'] = bytes(pdict['boundary'], "utf-8")
content_len = int(self.headers.get('Content-length'))
pdict['CONTENT-LENGTH'] = content_len
fields = cgi.parse_multipart(self.rfile, pdict)
file = fields.get('file')[0]
action = fields.get("action")[0]
if action == "uploadIssueMedia":
content_path = os.path.join(config.APP_PATH, "issues", "media")
_, extension = os.path.splitext(fields.get("filename")[0])
# Create a new filename so we never have collisions
new_filename = str(time.time()).replace(".", "") + extension
filepath = os.path.join(content_path, new_filename)
print(f"Saving uploaded file to {filepath}")
with config.issueMediaLock:
with open(filepath, "wb") as f:
f.write(file)
else:
print("Unknown file upload action:", action)
return
json_string = json.dumps({"success": True, "filename": new_filename})
except:
json_string = json.dumps({"success": False})
try:
self.wfile.write(bytes(json_string, encoding="UTF-8"))
except BrokenPipeError:
pass
elif ctype == "application/json":
# print(" application/json")
# Unpack the data
length = int(self.headers['Content-length'])
data_str = self.rfile.read(length).decode("utf-8")
try: # JSON
data = json.loads(data_str)
except json.decoder.JSONDecodeError: # not JSON
data = {}
split = data_str.split("&")
for seg in split:
split2 = seg.split("=")
data[split2[0]] = split2[1]
try:
ping_class = data["class"]
except KeyError:
print("Error: ping received without class field")
response = {"success": False,
"reason": "Request missing 'class' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
# print(f" class = {ping_class}")
if ping_class == "webpage":
try:
action = data["action"]
except KeyError:
print("Error: webpage ping received without action field")
# print("END POST")
# print("===============")
response = {"success": True,
"reason": "Missing required field 'action'."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
# print(f" action = {action}")
if action == "fetchUpdate":
self.send_webpage_update()
elif action == "fetchProjectorUpdate":
if "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id'.",
"status": None}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
proj = c_proj.get_projector(data["id"])
if proj is not None:
response_dict = {"success": True,
"state": proj.state}
else:
response_dict = {"success": False,
"reason": f"Projector {data["id"]} does not exist",
"status": "DELETE"}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "reloadConfiguration":
load_default_configuration()
json_string = json.dumps({"success": True})
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "queueCommand":
if "command" not in data or "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id' or 'command'."}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
c_exhibit.get_exhibit_component(data["id"]).queue_command(data["command"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "queueProjectorCommand":
if "command" not in data or "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id' or 'command'."}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
c_proj.get_projector(data["id"]).queue_command(data["command"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "queueWOLCommand":
if "command" not in data or "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id' or 'command'."}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
c_exhibit.get_wake_on_LAN_component(data["id"]).queue_command(data["command"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "updateSchedule":
# This command handles both adding a new scheduled action
# and editing an existing action
if "name" not in data or "timeToSet" not in data or "actionToSet" not in data or "targetToSet" not in data or "isAddition" not in data:
response_dict = {"success": False,
"reason": "Missing one or more required keys"}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
return
try:
time_to_set = dateutil.parser.parse(data['timeToSet']).time()
except dateutil.parser._parser.ParserError:
response_dict = {"success": False,
"reason": "Unknown date format"}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
return
error = False
error_message = ""
line_to_set = f"{data["timeToSet"]} = {data["actionToSet"]}"
if data["targetToSet"] is None:
line_to_set += "\n"
else:
line_to_set += f", {data["targetToSet"]}\n"
sched_dir = os.path.join(config.APP_PATH, "schedules")
path = os.path.join(sched_dir, data["name"] + ".ini")
if data["isAddition"]:
# Check if this time already exists
error = c_sched.check_if_schedule_time_exists(path, time_to_set)
if not error:
with config.scheduleLock:
with open(path, 'a', encoding="UTF-8") as f:
f.write(line_to_set)
else:
error_message = "An action with this time already exists"
elif "timeToReplace" in data:
output_text = ""
time_to_replace = dateutil.parser.parse(data['timeToReplace']).time()
print("replacing schedule",
time_to_replace, time_to_set,
c_sched.check_if_schedule_time_exists(path, time_to_set))
# We need to make sure we are not editing this entry to have
# the same time as another entry
if time_to_set == time_to_replace:
okay_to_edit = True
else:
okay_to_edit = not c_sched.check_if_schedule_time_exists(path, time_to_set)
if okay_to_edit:
with config.scheduleLock:
# Iterate the file to replace the line we are changing
with open(path, 'r', encoding='UTF-8') as f:
for line in f.readlines():
split = line.split("=")
if len(split) == 2:
# We have a valid ini line
if dateutil.parser.parse(split[0]).time() != time_to_replace:
# This line doesn't match, so keep it as is
output_text += line
else:
output_text += line_to_set
else:
output_text += line
with open(path, 'w', encoding='UTF-8') as f:
f.write(output_text)
else:
error = True
error_message = "An action with this time already exists"
response_dict = {}
if not error:
# Reload the schedule from disk
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict["class"] = "schedule"
response_dict["updateTime"] = config.scheduleUpdateTime
response_dict["schedule"] = config.scheduleList
response_dict["nextEvent"] = config.nextEvent
response_dict["success"] = True
else:
response_dict["success"] = False
response_dict["reason"] = error_message
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == 'refreshSchedule':
# This command reloads the schedule from disk. Normal schedule
# changes are passed during fetchUpdate
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "convertSchedule":
if "date" not in data or "from" not in data:
response = {"success": False,
"reason": "Request missing 'date' or 'from' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
sched_dir = os.path.join(config.APP_PATH, "schedules")
with config.scheduleLock:
shutil.copy(os.path.join(sched_dir, data["from"].lower() + ".ini"),
os.path.join(sched_dir, data["date"] + ".ini"))
# Reload the schedule from disk
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "deleteSchedule":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
with config.scheduleLock:
sched_dir = os.path.join(config.APP_PATH, "schedules")
os.remove(os.path.join(sched_dir, data["name"] + ".ini"))
# Reload the schedule from disk
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "deleteScheduleAction":
if "from" not in data or "time" not in data:
response = {"success": False,
"reason": "Request missing 'from' or 'time' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
c_sched.delete_schedule_action(data["from"], data["time"])
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "setExhibit":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
print("Changing exhibit to:", data["name"])
c_exhibit.read_exhibit_configuration(data["name"], update_default=True)
# Update the components that the configuration has changed
for component in config.componentList:
component.update_configuration()
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "createExhibit":
if "name" not in data or data["name"] == "":
response = {"success": False,
"reason": "Request missing 'name' field or name is blank."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
clone = None
if "cloneFrom" in data and data["cloneFrom"] != "":
clone = data["cloneFrom"]
c_exhibit.create_new_exhibit(data["name"], clone)
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "deleteExhibit":
if "name" not in data or data["name"] == "":
response = {"success": False,
"reason": "Request missing 'name' field or name is empty."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
c_exhibit.delete_exhibit(data["name"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "setComponentContent":
if "id" not in data or "content" not in data:
response = {"success": False,
"reason": "Request missing 'id' or 'content' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
content_to_set = data["content"]
print(f"Changing content for {data["id"]}:", content_to_set)
if not isinstance(content_to_set, list):
content_to_set = [data["content"]]
c_exhibit.set_component_content(data['id'], content_to_set)
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "getHelpText":
try:
readme_path = os.path.join(config.APP_PATH,
"README.md")
with open(readme_path, 'r', encoding='UTF-8') as f:
text = f.read()
self.wfile.write(bytes(text, encoding="UTF-8"))
except FileNotFoundError:
with config.logLock:
logging.error("Unable to read README.md")
elif action == "createIssue":
if "details" in data:
with config.issueLock:
new_issue = c_issues.Issue(data["details"])
config.issueList.append(new_issue)
c_issues.save_issueList()
response_dict = {"success": True}
else:
response_dict = {"success": False,
"reason": "Must include field 'details'"}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "editIssue":
if "details" in data and "id" in data["details"]:
c_issues.edit_issue(data["details"])
c_issues.save_issueList()
response_dict = {"success": True}
else:
response_dict = {
"success": False,
"reason": "Must include field 'details' with property 'id'"
}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "deleteIssue":
if "id" in data:
c_issues.remove_issue(data["id"])
c_issues.save_issueList()
response_dict = {"success": True, "reason": ""}
else:
response_dict = {"success": False, "reason": "Must include field 'id'"}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "getIssueList":
response = {
"success": True,
"issueList": [x.details for x in config.issueList]
}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "issueMediaDelete":
if "filename" not in data:
response = {"success": False,
"reason": "Request missing 'filename' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
this_id = None
if "id" in data:
this_id = data["id"]
c_issues.delete_issue_media_file(data["filename"], owner=this_id)
response = {"success": True}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == 'updateMaintenanceStatus':
if "id" not in data or "status" not in data or "notes" not in data:
response = {"success": False,
"reason": "Request missing 'id', 'status', or 'notes' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
file_path = os.path.join(config.APP_PATH, "maintenance-logs", data["id"] + ".txt")
record = {"id": data["id"],
"date": datetime.datetime.now().isoformat(),
"status": data['status'],
"notes": data["notes"]}
with config.maintenanceLock:
try:
with open(file_path, 'a', encoding='UTF-8') as f:
f.write(json.dumps(record) + "\n")
success = True
reason = ""
except FileNotFoundError:
success = False
reason = f"File path {file_path} does not exist"
except PermissionError:
success = False
reason = f"You do not have write permission for the file {file_path}"
response_dict = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == 'deleteMaintenanceRecord':
if "id" not in data:
response = {"success": False,
"reason": "Request missing 'id' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
else:
file_path = os.path.join(config.APP_PATH,
"maintenance-logs", data["id"] + ".txt")
with config.maintenanceLock:
response = delete_file(file_path)
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == 'getMaintenanceStatus':
if "id" not in data:
response = {"success": False,
"reason": "Request missing 'id' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
file_path = os.path.join(config.APP_PATH,
"maintenance-logs", data["id"] + ".txt")
with config.maintenanceLock:
response_dict = c_maint.get_maintenance_report(file_path)
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "getAllMaintenanceStatuses":
record_list = []
maintenance_path = os.path.join(config.APP_PATH,
"maintenance-logs")
for file in os.listdir(maintenance_path):
if file.lower().endswith(".txt"):
with config.maintenanceLock:
file_path = os.path.join(maintenance_path, file)
record_list.append(c_maint.get_maintenance_report(file_path))
response_dict = {"success": True,
"records": record_list}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
else:
print(f"Error: Unknown webpage command received: {action}")
with config.logLock:
logging.error(f"Unknown webpage command received: {action}")
elif ping_class == "exhibitComponent":
if "action" in data: # not a ping
action = data["action"]
# if "id" in data:
# print(f" id = {data["id"]}")
# print(f" action = {action}")
if action == "getUploadedFile":
if "id" not in data:
response = {"success": False,
"reason": "Request missing 'id' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
component = c_exhibit.get_exhibit_component(data["id"])
if len(component.dataToUpload) > 0:
upload = component.dataToUpload.pop(0)
# json_string = json.dumps(upload)
# self.wfile.write(bytes(json_string, encoding="UTF-8"))
self.wfile.write(upload)
elif action == "beginSynchronization":
if "synchronizeWith" in data:
c_exhibit.update_synchronization_list(data["id"], data["synchronizeWith"])
else: # it's a ping
try:
id = data["id"]
# type = data["type"]
if id == "UNKNOWN":
print(f"Warning: exhibitComponent ping with id=UNKNOWN coming from {self.address_string()}")
self.wfile.write(bytes(json.dumps({}), encoding='UTF-8'))
# print("END POST")
# print("===============")
return
except KeyError:
print("Error: exhibitComponent ping received without id or type field")
# print("END POST")
# print("===============")
return # No id or type, so bail out
# print(f" id = {id}")
# print(" action = ping")
c_exhibit.update_exhibit_component_status(data, self.address_string())
self.send_current_configuration(id)
elif ping_class == "tracker":
if "action" not in data:
response = {"success": False,
"reason": "Request missing 'action' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
action = data["action"]
if action == "getLayoutDefinition":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
layout_definition, success, reason = c_track.get_layout_definition(data["name"] + ".ini",
kind=kind)
response = {"success": success,
"reason": reason,
"layout": layout_definition}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "submitData":
if "data" not in data or "name" not in data:
response = {"success": False,
"reason": "Request missing 'data' or 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
file_path = os.path.join(config.APP_PATH, kind, "data", data["name"] + ".txt")
success, reason = c_track.write_JSON(data["data"], file_path)
response = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "submitRawText":
if "text" not in data or "name" not in data:
response = {"success": False,
"reason": "Request missing 'text' or 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
success, reason = c_track.write_raw_text(data["text"], data["name"] + ".txt", kind)
response = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "retrieveRawText":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
result, success, reason = c_track.get_raw_text(data["name"] + ".txt", kind)
response = {"success": success, "reason": reason, "text": result}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "submitAnalytics":
if "data" not in data or 'name' not in data:
response = {"success": False,
"reason": "Request missing 'data' or 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
file_path = os.path.join(config.APP_PATH, "analytics", data["name"] + ".txt")
success, reason = c_track.write_JSON(data["data"], file_path)
response = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "getAvailableDefinitions":
kind = data.get("kind", "flexible-tracker")
definition_list = []
template_path = os.path.join(config.APP_PATH, kind, "templates")
for file in os.listdir(template_path):
if file.lower().endswith(".ini"):
definition_list.append(file)
self.wfile.write(bytes(json.dumps(definition_list), encoding="UTF-8"))
elif action == "downloadTrackerData":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
name = data["name"]
if not name.lower().endswith(".txt"):
name += ".txt"
data_path = os.path.join(config.APP_PATH, kind, "data", name)
result = c_track.create_CSV(data_path)
response = {"success": True,
"csv": result}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "clearTrackerData":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
name = data["name"]
if not name.lower().endswith(".txt"):
name += ".txt"
data_path = os.path.join(config.APP_PATH, kind, "data", name)
success = True
reason = ""
with config.trackingDataWriteLock:
try:
os.remove(data_path)
except PermissionError:
success = False
reason = f"You do not have write permission for the file {data_path}"
except FileNotFoundError:
success = True # This error results in the user's desired action!
reason = f"File does not exist: {data_path}"
if reason != "":
print(reason)
response = {"success": success,
"reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "createTemplate":
if "name" not in data or "template" not in data:
response = {"success": False,
"reason": "Request missing 'name' or 'template' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
name = data["name"]
if not name.lower().endswith(".ini"):
name += ".ini"
file_path = os.path.join(config.APP_PATH, kind, "templates", name)
success = c_track.create_template(file_path, data["template"])
response = {"success": success}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "deleteTemplate":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
file_path = os.path.join(config.APP_PATH, kind, "templates", data["name"] + ".ini")
with config.trackerTemplateWriteLock:
response = delete_file(file_path)
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "checkConnection":
self.wfile.write(bytes(json.dumps({"success": True}), encoding="UTF-8"))
else:
print(f"Error: ping with unknown class '{ping_class}' received")
response = {"success": False,
"reason": f"Unknown class {ping_class}"}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
# print("END POST")
# print("===============")
return
# print("END POST")
# print("===============")
def delete_file(file_path) -> dict:
"""Delete the specified file and return a dictionary with the result"""
response = {"success": False}
try:
os.remove(file_path)
response["success"] = True
except FileNotFoundError:
response["reason"] = f"File {file_path} does not exist"
except PermissionError:
response["reason"] = f"You do not have permission for the file f{file_path}"
return response
def parse_byte_range(byte_range):
"""Returns the two numbers in 'bytes=123-456' or throws ValueError.
The last number or both numbers may be None.
"""
BYTE_RANGE_RE = re.compile(r'bytes=(\d+)-(\d+)?$')
if byte_range.strip() == '':
return None, None
m = BYTE_RANGE_RE.match(byte_range)
if not m:
raise ValueError(f'Invalid byte range {byte_range}')
first, last = [x and int(x) for x in m.groups()]
if last and last < first:
raise ValueError(f'Invalid byte range {byte_range}')
return first, last
def clear_terminal():
"""Clear the terminal"""
os.system('cls' if os.name == 'nt' else 'clear')
def command_line_setup():
"""Prompt the user for several pieces of information on first-time setup"""
settings_dict = {}
clear_terminal()
print("##########################################################")
print("Welcome to Constellation Control Server!")
print("")
print("This appears to be your first time running Control Server.")
print("In order to set up your configuration, you will be asked")
print("a few questions. If you don't know the answer, or wish to")
print("accept the default, just press the enter key.")
print("")
gallery_name = input("Enter a name for the gallery (default: Constellation): ").strip()
if gallery_name == "":
gallery_name = "Constellation"
settings_dict["gallery_name"] = gallery_name
default_ip = socket.gethostbyname(socket.gethostname())
ip_address = input(f"Enter this computer's static IP address (default: {default_ip}): ").strip()
if ip_address == "":
ip_address = default_ip
settings_dict["ip_address"] = ip_address
default_port = 8082
while True:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
if s.connect_ex((ip_address, default_port)) != 0:
# Port is free
break
else:
default_port += 1
port = input(f"Enter the desired port (default: {default_port}): ").strip()
if port == "":
port = default_port
else:
port = int(port)
settings_dict["server_port"] = port
settings_dict["current_exhibit"] = "default.exhibit"
return {"CURRENT": settings_dict}
def load_default_configuration():
"""Read the current exhibit configuration from file and initialize it"""
global server_port
global ip_address
global gallery_name
# First, retrieve the config filename that defines the desired gallery
config_reader = configparser.ConfigParser(delimiters="=")
config_reader.optionxform = str # Override default, which is case in-sensitive
gal_path = os.path.join(config.APP_PATH, "galleryConfiguration.ini")
with config.galleryConfigurationLock:
config_reader.read(gal_path)
try:
current = config_reader["CURRENT"]
except KeyError:
# We don't have a config file, so let's get info from the user to create one
settings_dict = command_line_setup()
config_reader.read_dict(settings_dict)
with open(os.path.join(config.APP_PATH, "galleryConfiguration.ini"), "w", encoding="UTF-8") as f:
config_reader.write(f)
current = config_reader["CURRENT"]
server_port = current.getint("server_port", 8080)
ip_address = current.get("server_ip_address", socket.gethostbyname(socket.gethostname()))
gallery_name = current.get("gallery_name", "Constellation")
staff_list = current.get("assignable_staff", [])
if len(staff_list) > 0:
config.assignable_staff = [x.strip() for x in staff_list.split(",")]
c_sched.retrieve_schedule()
config.projectorList = []
# Load the component descriptions. Do this first, so they are available when
# creating the various components
try:
print("Reading component descriptions...", end="", flush=True)
config.componentDescriptions = dict(config_reader["COMPONENT_DESCRIPTIONS"])
print(" done")
except KeyError:
print("None found")
config.componentDescriptions = {}
# Parse list of PJLink projectors
try:
pjlink_projectors = config_reader["PJLINK_PROJECTORS"]
print("Connecting to PJLink projectors...", end="\r", flush=True)
except KeyError:
print("No PJLink projectors specified")
pjlink_projectors = []
n_proj = len(pjlink_projectors)
cur_proj = 0
for key in pjlink_projectors:
cur_proj += 1
print(f"Connecting to PJLink projectors... {cur_proj}/{n_proj}", end="\r", flush=True)
if c_proj.get_projector(key) is None:
# Try to split on a comma. If we get two elements back, that means
# we have the form "ip, password"
split = pjlink_projectors[key].split(",")
if len(split) == 2:
# We have an IP address and a password
ip = split[0].strip()
password = split[1].strip()
if password == "":
password = None
new_proj = c_proj.Projector(key, ip, "pjlink", password=password)
elif len(split) == 1:
# We have an IP address only
new_proj = c_proj.Projector(key, pjlink_projectors[key], "pjlink")
else:
print("Invalid PJLink projector entry:", pjlink_projectors[key])
break
config.projectorList.append(new_proj)
print("Connecting to PJLink projectors... done ")
# Parse list of serial projectors
try:
serial_projectors = config_reader["SERIAL_PROJECTORS"]
print("Connecting to serial projectors...", end="\r", flush=True)
except KeyError:
print("No serial projectors specified")
serial_projectors = []
n_proj = len(serial_projectors)
cur_proj = 0
for key in serial_projectors:
cur_proj += 1
print(f"Connecting to serial projectors... {cur_proj}/{n_proj}", end="\r", flush=True)
if c_proj.get_projector(key) is None:
# Try to split on a comma. If we get two elements back, that means
# we have the form "ip, password"
split = serial_projectors[key].split(",")
if len(split) == 2:
# We have an IP address and a make
ip = split[0].strip()
make = split[1].strip()
if make == "":
make = None
new_proj = c_proj.Projector(key, ip, "serial", make=make)
elif len(split) == 1:
# We have an IP address only
new_proj = c_proj.Projector(key, serial_projectors[key], "serial")
else:
print("Invalid serial projector entry:", serial_projectors[key])
break
config.projectorList.append(new_proj)
print("Connecting to serial projectors... done ")
# Parse list of Wake on LAN devices
try:
wol = config_reader["WAKE_ON_LAN"]
print("Collecting Wake on LAN devices...", end="", flush=True)
for key in wol:
if c_exhibit.get_exhibit_component(key) is None:
# If 'get_exhibit_component' is not None, this key corresponds
# to a WoL device with a matching exhibit component ID and
# we have already loaded that component from the pickle file
value_split = wol[key].split(",")
if len(value_split) == 2:
# We have been given a MAC address and IP address
device = c_exhibit.WakeOnLANDevice(key,
value_split[0].strip(),
ip_address=value_split[1].strip())
elif len(value_split) == 1:
# We have been given only a MAC address
device = c_exhibit.WakeOnLANDevice(key, value_split[0].strip())
else:
print(f"Wake on LAN device specified with unknown format: {wol[key]}")
continue
config.wakeOnLANList.append(device)
print(" done")
except KeyError:
print("No wake on LAN devices specified")
config.wakeOnLANList = []
# Build any existing issues
try:
issue_file = os.path.join(config.APP_PATH, "issues", "issues.json")
with open(issue_file, "r", encoding="UTF-8") as file_object:
issues = json.load(file_object)
print("Reading stored issues...", end="", flush=True)
for issue in issues:
new_issue = c_issues.Issue(issue)
config.issueList.append(new_issue)
print(" done")
except FileNotFoundError:
print("No stored issues to read")
# Parse list of static components
try:
static_components = config_reader["STATIC_COMPONENTS"]
print("Adding static components... ", end="\r", flush=True)
for this_type in static_components:
split = static_components[this_type].split(",")
for this_id in split:
c_exhibit.add_exhibit_component(this_id.strip(), this_type, category="static")
print("done")
except KeyError:
print("none specified")
# Parse the reboot_time if necessary
if "reboot_time" in current:
reboot_time = dateutil.parser.parse(current["reboot_time"])
if reboot_time < datetime.datetime.now():
reboot_time += datetime.timedelta(days=1)
config.serverRebootTime = reboot_time
print("Server will reboot at:", config.serverRebootTime.isoformat())
# Then, load the configuration for that exhibit
c_exhibit.read_exhibit_configuration(current["current_exhibit"])
# Update the components that the configuration has changed
for component in config.componentList:
component.update_configuration()
def check_file_structure():
"""Check to make sure we have the appropriate file structure set up"""
schedules_dir = os.path.join(config.APP_PATH, "schedules")
exhibits_dir = os.path.join(config.APP_PATH, "exhibits")
misc_dirs = {"analytics": os.path.join(config.APP_PATH, "analytics"),
"flexible-tracker": os.path.join(config.APP_PATH, "flexible-tracker"),
"flexible-tracker/data": os.path.join(config.APP_PATH, "flexible-tracker", "data"),
"flexible-tracker/templates": os.path.join(config.APP_PATH, "flexible-tracker", "templates"),
"flexible-voter": os.path.join(config.APP_PATH, "flexible-voter"),
"flexible-voter/data": os.path.join(config.APP_PATH, "flexible-voter", "data"),
"flexible-voter/templates": os.path.join(config.APP_PATH, "flexible-voter", "templates"),
"issues": os.path.join(config.APP_PATH, "issues"),
"issues/media": os.path.join(config.APP_PATH, "issues", "media"),
"maintenance-logs": os.path.join(config.APP_PATH, "maintenance-logs")}
try:
os.listdir(schedules_dir)
except FileNotFoundError:
print("Missing schedules directory. Creating now...")
try:
os.mkdir(schedules_dir)
default_schedule_list = ["monday.ini", "tuesday.ini",
"wednesday.ini", "thursday.ini",
"friday.ini", "saturday.ini",
"sunday.ini"]
for file in default_schedule_list:
with open(os.path.join(schedules_dir, file), 'w', encoding="UTF-8") as f:
f.write("[SCHEDULE]\n")
except PermissionError:
print("Error: unable to create 'schedules' directory. Do you have write permission?")
try:
os.listdir(exhibits_dir)
except FileNotFoundError:
print("Missing exhibits directory. Creating now...")
try:
os.mkdir(exhibits_dir)
with open(os.path.join(exhibits_dir, "default.exhibit"), 'w', encoding="UTF-8") as f:
f.write("")
except PermissionError:
print("Error: unable to create 'exhibits' directory. Do you have write permission?")
for key in misc_dirs:
try:
os.listdir(misc_dirs[key])
except FileNotFoundError:
print(f"Missing {key} directory. Creating now...")
try:
os.mkdir(misc_dirs[key])
except PermissionError:
print(f"Error: unable to create '{key}' directory. Do you have write permission?")
def quit_handler(*args):
"""Handle cleanly shutting down the server"""
try:
if config.rebooting is True:
exit_code = 1
print("\nRebooting server...")
else:
exit_code = 0
print('\nKeyboard interrupt detected. Cleaning up and shutting down...')
except RuntimeError:
exit_code = 0
# Save the current component lists to a pickle file so that
# we can resume from the current state
path_to_write = os.path.join(config.APP_PATH, "current_state.dat")
with open(path_to_write, 'wb') as f:
pickle.dump(config.componentList, f)
for key in config.polling_thread_dict:
config.polling_thread_dict[key].cancel()
with config.logLock:
logging.info("Server shutdown")
with config.galleryConfigurationLock:
with config.scheduleLock:
with config.trackingDataWriteLock:
sys.exit(exit_code)
def error_handler(*exc_info):
"""Catch errors and log them to file"""
text = "".join(traceback.format_exception(*exc_info)).replace('"', "'").replace("\n", "<newline>")
with config.logLock:
logging.error(f'"{text}"')
print(f"Error: see control_server.log for more details ({datetime.datetime.now()})")
def check_for_software_update():
"""Download the version.txt file from GitHub and check if there is an update"""
global software_update_available
print("Checking for update... ", end="")
try:
for line in urllib.request.urlopen(
"https://raw.githubusercontent.com/Cosmic-Chatter/Constellation/main/control_server/version.txt"):
if float(line.decode('utf-8')) > SOFTWARE_VERSION:
software_update_available = True
break
except urllib.error.HTTPError:
print("cannot connect to update server")
return
if software_update_available:
print("update available!")
else:
print("the server is up to date.")
# Check whether we have packaged with Pyinstaller and set the appropriate root path.
config.EXEC_PATH = os.path.dirname(os.path.abspath(__file__))
if getattr(sys, 'frozen', False):
# If the application is run as a --onefile bundle, the PyInstaller bootloader
# extends the sys module by a flag frozen=True and sets the app
# path into variable sys.executable.
config.APP_PATH = os.path.dirname(sys.executable)
else:
config.APP_PATH = config.EXEC_PATH
server_port: int = 8080 # Default; should be set in currentExhibitConfiguration.ini
ip_address: str = socket.gethostbyname(socket.gethostname()) # Default; should be set in galleryConfiguration.ini
ADDR: str = "" # Accept connections from all interfaces
gallery_name: str = ""
SOFTWARE_VERSION = 1.0
software_update_available: bool = False
# Set up log file
log_path: str = os.path.join(config.APP_PATH, "control_server.log")
logging.basicConfig(datefmt='%Y-%m-%d %H:%M:%S',
filename=log_path,
format='%(levelname)s, %(asctime)s, %(message)s',
level=logging.DEBUG)
signal.signal(signal.SIGINT, quit_handler)
sys.excepthook = error_handler
with config.logLock:
logging.info("Server started")
# Try to reload the previous state from the pickle file current_state.dat
try:
state_path = os.path.join(config.APP_PATH, "current_state.dat")
with open(state_path, "rb") as previous_state:
config.componentList = pickle.load(previous_state)
print("Previous server state loaded")
except (FileNotFoundError, EOFError):
print("Could not load previous server state")
check_file_structure()
c_exhibit.check_available_exhibits()
load_default_configuration()
c_sched.poll_event_schedule()
c_proj.poll_projectors()
c_exhibit.poll_wake_on_LAN_devices()
check_for_software_update()
httpd = ThreadedHTTPServer((ADDR, server_port), RequestHandler)
httpd.serve_forever()
| # Constellation Control Server
# A centralized server for controlling museum exhibit components
# Written by Morgan Rehnberg, Fort Worth Museum of Science and History
# Released under the MIT license
# Standard modules
from http.server import HTTPServer, SimpleHTTPRequestHandler
from socketserver import ThreadingMixIn
import logging
import datetime
import configparser
import json
import os
import mimetypes
import cgi
import signal
import socket
import sys
import shutil
import traceback
import threading
import pickle
import urllib.request
import time
import re
# Non-standard modules
import dateutil.parser
# Constellation modules
import config
import constellation_exhibit as c_exhibit
import constellation_issues as c_issues
import constellation_maintenance as c_maint
import constellation_projector as c_proj
import constellation_schedule as c_sched
import constellation_tracker as c_track
class ThreadedHTTPServer(ThreadingMixIn, HTTPServer):
"""Stub which triggers dispatch of requests into individual threads."""
daemon_threads = True
class RequestHandler(SimpleHTTPRequestHandler):
"""Handle incoming requests to the control server"""
def send_current_configuration(self, id_):
"""Function to respond to a POST with a dictionary defining the current exhibit configuration"""
json_string = json.dumps(c_exhibit.get_exhibit_component(id_).config)
if len(c_exhibit.get_exhibit_component(id_).config["commands"]) > 0:
# Clear the command list now that we have sent
c_exhibit.get_exhibit_component(id_).config["commands"] = []
self.wfile.write(bytes(json_string, encoding="UTF-8"))
def send_webpage_update(self):
"""Function to collect the current exhibit status, format it, and send it back to the web client to update the page"""
component_dict_list = []
for item in config.componentList:
temp = {"id": item.id,
"type": item.type}
if "content" in item.config:
temp["content"] = item.config["content"]
if "error" in item.config:
temp["error"] = item.config["error"]
if "allowed_actions" in item.config:
temp["allowed_actions"] = item.config["allowed_actions"]
if "description" in item.config:
temp["description"] = item.config["description"]
if "AnyDeskID" in item.config:
temp["AnyDeskID"] = item.config["AnyDeskID"]
temp["class"] = "exhibitComponent"
temp["status"] = item.current_status()
temp["ip_address"] = item.ip
temp["helperPort"] = item.helperPort
temp["helperAddress"] = item.helperAddress
component_dict_list.append(temp)
for item in config.projectorList:
temp = {"id": item.id,
"type": 'PROJECTOR',
"ip_address": item.ip}
if "allowed_actions" in item.config:
temp["allowed_actions"] = item.config["allowed_actions"]
if "description" in item.config:
temp["description"] = item.config["description"]
temp["class"] = "exhibitComponent"
temp["status"] = item.state["status"]
component_dict_list.append(temp)
for item in config.wakeOnLANList:
temp = {"id": item.id,
"type": 'WAKE_ON_LAN',
"ip_address": item.ip}
if "allowed_actions" in item.config:
temp["allowed_actions"] = item.config["allowed_actions"]
if "description" in item.config:
temp["description"] = item.config["description"]
temp["class"] = "exhibitComponent"
temp["status"] = item.state["status"]
component_dict_list.append(temp)
# Also include an object with the status of the overall gallery
temp = {"class": "gallery",
"currentExhibit": config.currentExhibit,
"availableExhibits": config.exhibit_list,
"galleryName": gallery_name,
"updateAvailable": str(software_update_available).lower()}
component_dict_list.append(temp)
# Also include an object containing the current issues
temp = {"class": "issues",
"issueList": [x.details for x in config.issueList],
"lastUpdateDate": config.issueList_last_update_date,
"assignable_staff": config.assignable_staff}
component_dict_list.append(temp)
# Also include an object containing the current schedule
with config.scheduleLock:
temp = {"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
component_dict_list.append(temp)
json_string = json.dumps(component_dict_list, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
def log_request(self, code='-', size='-'):
# Override to suppress the automatic logging
pass
def copy_byte_range(self, infile, start=None, stop=None, bufsize=16 * 1024):
"""Like shutil.copyfileobj, but only copy a range of the streams.
Both start and stop are inclusive.
"""
if start is not None:
infile.seek(start)
while 1:
to_read = min(bufsize, stop + 1 - infile.tell() if stop else bufsize)
buf = infile.read(to_read)
if not buf:
break
self.wfile.write(buf)
def handle_range_request(self, f):
"""Handle a GET request using a byte range.
Inspired by https://github.com/danvk/RangeHTTPServer
"""
try:
self.range = parse_byte_range(self.headers['Range'])
except ValueError:
self.send_error(400, 'Invalid byte range')
return
first, last = self.range
fs = os.fstat(f.fileno())
file_len = fs[6]
if first >= file_len:
self.send_error(416, 'Requested Range Not Satisfiable')
return None
ctype = self.guess_type(self.translate_path(self.path))
if last is None or last >= file_len:
last = file_len - 1
response_length = last - first + 1
try:
self.send_response(206)
self.send_header('Content-type', ctype)
self.send_header('Accept-Ranges', 'bytes')
self.send_header('Content-Range',
'bytes %s-%s/%s' % (first, last, file_len))
self.send_header('Content-Length', str(response_length))
self.send_header('Last-Modified', self.date_time_string(fs.st_mtime))
self.end_headers()
self.copy_byte_range(f)
except IOError as e:
print(e)
def do_GET(self):
# Receive a GET request and respond with a console webpage
# print("+++++++++++++++")
# print("BEGIN GET")
print(f" Active threads: {threading.active_count()} ", end="\r", flush=True)
# print(f" path = {self.path}")
# Strip out any options from the query string
self.path = self.path.split("?")[0]
if self.path.lower().endswith("html") or self.path == "/":
if self.path == "/":
file_path = os.path.join(config.APP_PATH, "webpage.html")
if not os.path.isfile(file_path):
# Handle the case of a Pyinstaller --onefile binary
file_path = os.path.join(config.EXEC_PATH, "webpage.html")
f = open(file_path, "r", encoding='UTF-8')
else:
if self.path.startswith("/"):
self.path = self.path[1:]
file_path = os.path.join(config.APP_PATH, self.path)
if not os.path.isfile(file_path):
# Handle the case of a Pyinstaller --onefile binary
file_path = os.path.join(config.EXEC_PATH, self.path)
f = open(file_path, "r", encoding='UTF-8')
page = str(f.read())
# Build the address that the webpage should contact to reach this server
address_to_insert = "'http://" + str(ip_address) + ":" + str(server_port) + "'"
# Then, insert that into the document
page = page.replace("INSERT_SERVERIP_HERE", address_to_insert)
self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
self.wfile.write(bytes(page, encoding="UTF-8"))
f.close()
# print("END GET")
# print("+++++++++++++++")
return
else:
# Open the file requested and send it
mimetype = mimetypes.guess_type(self.path, strict=False)[0]
if self.path[0] == '/':
# Strip out leading /, as it screws up os.path.join
self.path = self.path[1:]
try:
file_path = os.path.join(config.APP_PATH, self.path)
if not os.path.isfile(file_path):
# Handle the case of a Pyinstaller --onefile binary
file_path = os.path.join(config.EXEC_PATH, self.path)
with open(file_path, 'rb') as f:
if "Range" in self.headers:
self.handle_range_request(f)
else:
try:
self.send_response(200)
self.send_header('Content-type', mimetype)
self.end_headers()
# print(f" Writing data to client")
self.wfile.write(f.read())
except BrokenPipeError:
print("Connection closed prematurely")
# print("END GET")
# print("+++++++++++++++")
return
except IOError:
self.send_error(404, f"File Not Found: {self.path}")
with config.logLock:
logging.error("GET for unexpected file %s", self.path)
# print("END GET")
# print("+++++++++++++++")
def do_OPTIONS(self):
"""Respond to an OPTIONS request"""
# print("---------------")
# print("BEGIN OPTIONS")
self.send_response(200, "OK")
self.send_header("Access-Control-Allow-Origin", "*")
self.send_header('Access-Control-Allow-Headers', 'Content-Type,Authorization')
self.send_header('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
self.send_header('Access-Control-Allow-Credentials', 'true')
self.end_headers()
# print("END OPTIONS")
# print("---------------")
def do_POST(self):
"""Receives pings from client devices and respond with any updated information"""
# print("===============")
# print("BEGIN POST")
print(f" Active threads: {threading.active_count()} ", end="\r", flush=True)
self.send_response(200, "OK")
self.send_header("Access-Control-Allow-Origin", "*")
self.send_header('Access-Control-Allow-Headers', 'Content-Type,Authorization')
self.send_header('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
self.send_header('Access-Control-Allow-Credentials', 'true')
self.end_headers()
# Get the data from the request
try:
ctype, pdict = cgi.parse_header(self.headers.get('content-type'))
except:
print("DO_POST: Error: Are we missing the Content-Type header?")
with config.logLock:
logging.warning("POST received without content-type header")
print(self.headers)
return
if ctype == "multipart/form-data": # File upload
try:
pdict['boundary'] = bytes(pdict['boundary'], "utf-8")
content_len = int(self.headers.get('Content-length'))
pdict['CONTENT-LENGTH'] = content_len
fields = cgi.parse_multipart(self.rfile, pdict)
file = fields.get('file')[0]
action = fields.get("action")[0]
if action == "uploadIssueMedia":
content_path = os.path.join(config.APP_PATH, "issues", "media")
_, extension = os.path.splitext(fields.get("filename")[0])
# Create a new filename so we never have collisions
new_filename = str(time.time()).replace(".", "") + extension
filepath = os.path.join(content_path, new_filename)
print(f"Saving uploaded file to {filepath}")
with config.issueMediaLock:
with open(filepath, "wb") as f:
f.write(file)
else:
print("Unknown file upload action:", action)
return
json_string = json.dumps({"success": True, "filename": new_filename})
except:
json_string = json.dumps({"success": False})
try:
self.wfile.write(bytes(json_string, encoding="UTF-8"))
except BrokenPipeError:
pass
elif ctype == "application/json":
# print(" application/json")
# Unpack the data
length = int(self.headers['Content-length'])
data_str = self.rfile.read(length).decode("utf-8")
try: # JSON
data = json.loads(data_str)
except json.decoder.JSONDecodeError: # not JSON
data = {}
split = data_str.split("&")
for seg in split:
split2 = seg.split("=")
data[split2[0]] = split2[1]
try:
ping_class = data["class"]
except KeyError:
print("Error: ping received without class field")
response = {"success": False,
"reason": "Request missing 'class' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
# print(f" class = {ping_class}")
if ping_class == "webpage":
try:
action = data["action"]
except KeyError:
print("Error: webpage ping received without action field")
# print("END POST")
# print("===============")
response = {"success": True,
"reason": "Missing required field 'action'."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
# print(f" action = {action}")
if action == "fetchUpdate":
self.send_webpage_update()
elif action == "fetchProjectorUpdate":
if "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id'.",
"status": None}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
proj = c_proj.get_projector(data["id"])
if proj is not None:
response_dict = {"success": True,
"state": proj.state}
else:
response_dict = {"success": False,
"reason": f"Projector {data['id']} does not exist",
"status": "DELETE"}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "reloadConfiguration":
load_default_configuration()
json_string = json.dumps({"success": True})
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "queueCommand":
if "command" not in data or "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id' or 'command'."}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
c_exhibit.get_exhibit_component(data["id"]).queue_command(data["command"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "queueProjectorCommand":
if "command" not in data or "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id' or 'command'."}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
c_proj.get_projector(data["id"]).queue_command(data["command"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "queueWOLCommand":
if "command" not in data or "id" not in data:
response_dict = {"success": False,
"reason": "Missing required field 'id' or 'command'."}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
return
c_exhibit.get_wake_on_LAN_component(data["id"]).queue_command(data["command"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "updateSchedule":
# This command handles both adding a new scheduled action
# and editing an existing action
if "name" not in data or "timeToSet" not in data or "actionToSet" not in data or "targetToSet" not in data or "isAddition" not in data:
response_dict = {"success": False,
"reason": "Missing one or more required keys"}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
return
try:
time_to_set = dateutil.parser.parse(data['timeToSet']).time()
except dateutil.parser._parser.ParserError:
response_dict = {"success": False,
"reason": "Unknown date format"}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
return
error = False
error_message = ""
line_to_set = f"{data['timeToSet']} = {data['actionToSet']}"
if data["targetToSet"] is None:
line_to_set += "\n"
else:
line_to_set += f", {data['targetToSet']}\n"
sched_dir = os.path.join(config.APP_PATH, "schedules")
path = os.path.join(sched_dir, data["name"] + ".ini")
if data["isAddition"]:
# Check if this time already exists
error = c_sched.check_if_schedule_time_exists(path, time_to_set)
if not error:
with config.scheduleLock:
with open(path, 'a', encoding="UTF-8") as f:
f.write(line_to_set)
else:
error_message = "An action with this time already exists"
elif "timeToReplace" in data:
output_text = ""
time_to_replace = dateutil.parser.parse(data['timeToReplace']).time()
print("replacing schedule",
time_to_replace, time_to_set,
c_sched.check_if_schedule_time_exists(path, time_to_set))
# We need to make sure we are not editing this entry to have
# the same time as another entry
if time_to_set == time_to_replace:
okay_to_edit = True
else:
okay_to_edit = not c_sched.check_if_schedule_time_exists(path, time_to_set)
if okay_to_edit:
with config.scheduleLock:
# Iterate the file to replace the line we are changing
with open(path, 'r', encoding='UTF-8') as f:
for line in f.readlines():
split = line.split("=")
if len(split) == 2:
# We have a valid ini line
if dateutil.parser.parse(split[0]).time() != time_to_replace:
# This line doesn't match, so keep it as is
output_text += line
else:
output_text += line_to_set
else:
output_text += line
with open(path, 'w', encoding='UTF-8') as f:
f.write(output_text)
else:
error = True
error_message = "An action with this time already exists"
response_dict = {}
if not error:
# Reload the schedule from disk
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict["class"] = "schedule"
response_dict["updateTime"] = config.scheduleUpdateTime
response_dict["schedule"] = config.scheduleList
response_dict["nextEvent"] = config.nextEvent
response_dict["success"] = True
else:
response_dict["success"] = False
response_dict["reason"] = error_message
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == 'refreshSchedule':
# This command reloads the schedule from disk. Normal schedule
# changes are passed during fetchUpdate
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "convertSchedule":
if "date" not in data or "from" not in data:
response = {"success": False,
"reason": "Request missing 'date' or 'from' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
sched_dir = os.path.join(config.APP_PATH, "schedules")
with config.scheduleLock:
shutil.copy(os.path.join(sched_dir, data["from"].lower() + ".ini"),
os.path.join(sched_dir, data["date"] + ".ini"))
# Reload the schedule from disk
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "deleteSchedule":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
with config.scheduleLock:
sched_dir = os.path.join(config.APP_PATH, "schedules")
os.remove(os.path.join(sched_dir, data["name"] + ".ini"))
# Reload the schedule from disk
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "deleteScheduleAction":
if "from" not in data or "time" not in data:
response = {"success": False,
"reason": "Request missing 'from' or 'time' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
c_sched.delete_schedule_action(data["from"], data["time"])
c_sched.retrieve_schedule()
# Send the updated schedule back
with config.scheduleLock:
response_dict = {"success": True,
"class": "schedule",
"updateTime": config.scheduleUpdateTime,
"schedule": config.scheduleList,
"nextEvent": config.nextEvent}
json_string = json.dumps(response_dict, default=str)
self.wfile.write(bytes(json_string, encoding="UTF-8"))
elif action == "setExhibit":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
print("Changing exhibit to:", data["name"])
c_exhibit.read_exhibit_configuration(data["name"], update_default=True)
# Update the components that the configuration has changed
for component in config.componentList:
component.update_configuration()
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "createExhibit":
if "name" not in data or data["name"] == "":
response = {"success": False,
"reason": "Request missing 'name' field or name is blank."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
clone = None
if "cloneFrom" in data and data["cloneFrom"] != "":
clone = data["cloneFrom"]
c_exhibit.create_new_exhibit(data["name"], clone)
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "deleteExhibit":
if "name" not in data or data["name"] == "":
response = {"success": False,
"reason": "Request missing 'name' field or name is empty."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
c_exhibit.delete_exhibit(data["name"])
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "setComponentContent":
if "id" not in data or "content" not in data:
response = {"success": False,
"reason": "Request missing 'id' or 'content' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
content_to_set = data["content"]
print(f"Changing content for {data['id']}:", content_to_set)
if not isinstance(content_to_set, list):
content_to_set = [data["content"]]
c_exhibit.set_component_content(data['id'], content_to_set)
response = {"success": True, "reason": ""}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "getHelpText":
try:
readme_path = os.path.join(config.APP_PATH,
"README.md")
with open(readme_path, 'r', encoding='UTF-8') as f:
text = f.read()
self.wfile.write(bytes(text, encoding="UTF-8"))
except FileNotFoundError:
with config.logLock:
logging.error("Unable to read README.md")
elif action == "createIssue":
if "details" in data:
with config.issueLock:
new_issue = c_issues.Issue(data["details"])
config.issueList.append(new_issue)
c_issues.save_issueList()
response_dict = {"success": True}
else:
response_dict = {"success": False,
"reason": "Must include field 'details'"}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "editIssue":
if "details" in data and "id" in data["details"]:
c_issues.edit_issue(data["details"])
c_issues.save_issueList()
response_dict = {"success": True}
else:
response_dict = {
"success": False,
"reason": "Must include field 'details' with property 'id'"
}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "deleteIssue":
if "id" in data:
c_issues.remove_issue(data["id"])
c_issues.save_issueList()
response_dict = {"success": True, "reason": ""}
else:
response_dict = {"success": False, "reason": "Must include field 'id'"}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "getIssueList":
response = {
"success": True,
"issueList": [x.details for x in config.issueList]
}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "issueMediaDelete":
if "filename" not in data:
response = {"success": False,
"reason": "Request missing 'filename' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
this_id = None
if "id" in data:
this_id = data["id"]
c_issues.delete_issue_media_file(data["filename"], owner=this_id)
response = {"success": True}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == 'updateMaintenanceStatus':
if "id" not in data or "status" not in data or "notes" not in data:
response = {"success": False,
"reason": "Request missing 'id', 'status', or 'notes' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
file_path = os.path.join(config.APP_PATH, "maintenance-logs", data["id"] + ".txt")
record = {"id": data["id"],
"date": datetime.datetime.now().isoformat(),
"status": data['status'],
"notes": data["notes"]}
with config.maintenanceLock:
try:
with open(file_path, 'a', encoding='UTF-8') as f:
f.write(json.dumps(record) + "\n")
success = True
reason = ""
except FileNotFoundError:
success = False
reason = f"File path {file_path} does not exist"
except PermissionError:
success = False
reason = f"You do not have write permission for the file {file_path}"
response_dict = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == 'deleteMaintenanceRecord':
if "id" not in data:
response = {"success": False,
"reason": "Request missing 'id' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
else:
file_path = os.path.join(config.APP_PATH,
"maintenance-logs", data["id"] + ".txt")
with config.maintenanceLock:
response = delete_file(file_path)
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == 'getMaintenanceStatus':
if "id" not in data:
response = {"success": False,
"reason": "Request missing 'id' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
file_path = os.path.join(config.APP_PATH,
"maintenance-logs", data["id"] + ".txt")
with config.maintenanceLock:
response_dict = c_maint.get_maintenance_report(file_path)
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
elif action == "getAllMaintenanceStatuses":
record_list = []
maintenance_path = os.path.join(config.APP_PATH,
"maintenance-logs")
for file in os.listdir(maintenance_path):
if file.lower().endswith(".txt"):
with config.maintenanceLock:
file_path = os.path.join(maintenance_path, file)
record_list.append(c_maint.get_maintenance_report(file_path))
response_dict = {"success": True,
"records": record_list}
self.wfile.write(bytes(json.dumps(response_dict), encoding="UTF-8"))
else:
print(f"Error: Unknown webpage command received: {action}")
with config.logLock:
logging.error(f"Unknown webpage command received: {action}")
elif ping_class == "exhibitComponent":
if "action" in data: # not a ping
action = data["action"]
# if "id" in data:
# print(f" id = {data['id']}")
# print(f" action = {action}")
if action == "getUploadedFile":
if "id" not in data:
response = {"success": False,
"reason": "Request missing 'id' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
component = c_exhibit.get_exhibit_component(data["id"])
if len(component.dataToUpload) > 0:
upload = component.dataToUpload.pop(0)
# json_string = json.dumps(upload)
# self.wfile.write(bytes(json_string, encoding="UTF-8"))
self.wfile.write(upload)
elif action == "beginSynchronization":
if "synchronizeWith" in data:
c_exhibit.update_synchronization_list(data["id"], data["synchronizeWith"])
else: # it's a ping
try:
id = data["id"]
# type = data["type"]
if id == "UNKNOWN":
print(f"Warning: exhibitComponent ping with id=UNKNOWN coming from {self.address_string()}")
self.wfile.write(bytes(json.dumps({}), encoding='UTF-8'))
# print("END POST")
# print("===============")
return
except KeyError:
print("Error: exhibitComponent ping received without id or type field")
# print("END POST")
# print("===============")
return # No id or type, so bail out
# print(f" id = {id}")
# print(" action = ping")
c_exhibit.update_exhibit_component_status(data, self.address_string())
self.send_current_configuration(id)
elif ping_class == "tracker":
if "action" not in data:
response = {"success": False,
"reason": "Request missing 'action' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
action = data["action"]
if action == "getLayoutDefinition":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
layout_definition, success, reason = c_track.get_layout_definition(data["name"] + ".ini",
kind=kind)
response = {"success": success,
"reason": reason,
"layout": layout_definition}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "submitData":
if "data" not in data or "name" not in data:
response = {"success": False,
"reason": "Request missing 'data' or 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
file_path = os.path.join(config.APP_PATH, kind, "data", data["name"] + ".txt")
success, reason = c_track.write_JSON(data["data"], file_path)
response = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "submitRawText":
if "text" not in data or "name" not in data:
response = {"success": False,
"reason": "Request missing 'text' or 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
success, reason = c_track.write_raw_text(data["text"], data["name"] + ".txt", kind)
response = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "retrieveRawText":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
result, success, reason = c_track.get_raw_text(data["name"] + ".txt", kind)
response = {"success": success, "reason": reason, "text": result}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "submitAnalytics":
if "data" not in data or 'name' not in data:
response = {"success": False,
"reason": "Request missing 'data' or 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
file_path = os.path.join(config.APP_PATH, "analytics", data["name"] + ".txt")
success, reason = c_track.write_JSON(data["data"], file_path)
response = {"success": success, "reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "getAvailableDefinitions":
kind = data.get("kind", "flexible-tracker")
definition_list = []
template_path = os.path.join(config.APP_PATH, kind, "templates")
for file in os.listdir(template_path):
if file.lower().endswith(".ini"):
definition_list.append(file)
self.wfile.write(bytes(json.dumps(definition_list), encoding="UTF-8"))
elif action == "downloadTrackerData":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
name = data["name"]
if not name.lower().endswith(".txt"):
name += ".txt"
data_path = os.path.join(config.APP_PATH, kind, "data", name)
result = c_track.create_CSV(data_path)
response = {"success": True,
"csv": result}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "clearTrackerData":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
name = data["name"]
if not name.lower().endswith(".txt"):
name += ".txt"
data_path = os.path.join(config.APP_PATH, kind, "data", name)
success = True
reason = ""
with config.trackingDataWriteLock:
try:
os.remove(data_path)
except PermissionError:
success = False
reason = f"You do not have write permission for the file {data_path}"
except FileNotFoundError:
success = True # This error results in the user's desired action!
reason = f"File does not exist: {data_path}"
if reason != "":
print(reason)
response = {"success": success,
"reason": reason}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "createTemplate":
if "name" not in data or "template" not in data:
response = {"success": False,
"reason": "Request missing 'name' or 'template' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
name = data["name"]
if not name.lower().endswith(".ini"):
name += ".ini"
file_path = os.path.join(config.APP_PATH, kind, "templates", name)
success = c_track.create_template(file_path, data["template"])
response = {"success": success}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "deleteTemplate":
if "name" not in data:
response = {"success": False,
"reason": "Request missing 'name' field."}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
return
kind = data.get("kind", "flexible-tracker")
file_path = os.path.join(config.APP_PATH, kind, "templates", data["name"] + ".ini")
with config.trackerTemplateWriteLock:
response = delete_file(file_path)
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
elif action == "checkConnection":
self.wfile.write(bytes(json.dumps({"success": True}), encoding="UTF-8"))
else:
print(f"Error: ping with unknown class '{ping_class}' received")
response = {"success": False,
"reason": f"Unknown class {ping_class}"}
self.wfile.write(bytes(json.dumps(response), encoding="UTF-8"))
# print("END POST")
# print("===============")
return
# print("END POST")
# print("===============")
def delete_file(file_path) -> dict:
"""Delete the specified file and return a dictionary with the result"""
response = {"success": False}
try:
os.remove(file_path)
response["success"] = True
except FileNotFoundError:
response["reason"] = f"File {file_path} does not exist"
except PermissionError:
response["reason"] = f"You do not have permission for the file f{file_path}"
return response
def parse_byte_range(byte_range):
"""Returns the two numbers in 'bytes=123-456' or throws ValueError.
The last number or both numbers may be None.
"""
BYTE_RANGE_RE = re.compile(r'bytes=(\d+)-(\d+)?$')
if byte_range.strip() == '':
return None, None
m = BYTE_RANGE_RE.match(byte_range)
if not m:
raise ValueError(f'Invalid byte range {byte_range}')
first, last = [x and int(x) for x in m.groups()]
if last and last < first:
raise ValueError(f'Invalid byte range {byte_range}')
return first, last
def clear_terminal():
"""Clear the terminal"""
os.system('cls' if os.name == 'nt' else 'clear')
def command_line_setup():
"""Prompt the user for several pieces of information on first-time setup"""
settings_dict = {}
clear_terminal()
print("##########################################################")
print("Welcome to Constellation Control Server!")
print("")
print("This appears to be your first time running Control Server.")
print("In order to set up your configuration, you will be asked")
print("a few questions. If you don't know the answer, or wish to")
print("accept the default, just press the enter key.")
print("")
gallery_name = input("Enter a name for the gallery (default: Constellation): ").strip()
if gallery_name == "":
gallery_name = "Constellation"
settings_dict["gallery_name"] = gallery_name
default_ip = socket.gethostbyname(socket.gethostname())
ip_address = input(f"Enter this computer's static IP address (default: {default_ip}): ").strip()
if ip_address == "":
ip_address = default_ip
settings_dict["ip_address"] = ip_address
default_port = 8082
while True:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
if s.connect_ex((ip_address, default_port)) != 0:
# Port is free
break
else:
default_port += 1
port = input(f"Enter the desired port (default: {default_port}): ").strip()
if port == "":
port = default_port
else:
port = int(port)
settings_dict["server_port"] = port
settings_dict["current_exhibit"] = "default.exhibit"
return {"CURRENT": settings_dict}
def load_default_configuration():
"""Read the current exhibit configuration from file and initialize it"""
global server_port
global ip_address
global gallery_name
# First, retrieve the config filename that defines the desired gallery
config_reader = configparser.ConfigParser(delimiters="=")
config_reader.optionxform = str # Override default, which is case in-sensitive
gal_path = os.path.join(config.APP_PATH, "galleryConfiguration.ini")
with config.galleryConfigurationLock:
config_reader.read(gal_path)
try:
current = config_reader["CURRENT"]
except KeyError:
# We don't have a config file, so let's get info from the user to create one
settings_dict = command_line_setup()
config_reader.read_dict(settings_dict)
with open(os.path.join(config.APP_PATH, "galleryConfiguration.ini"), "w", encoding="UTF-8") as f:
config_reader.write(f)
current = config_reader["CURRENT"]
server_port = current.getint("server_port", 8080)
ip_address = current.get("server_ip_address", socket.gethostbyname(socket.gethostname()))
gallery_name = current.get("gallery_name", "Constellation")
staff_list = current.get("assignable_staff", [])
if len(staff_list) > 0:
config.assignable_staff = [x.strip() for x in staff_list.split(",")]
c_sched.retrieve_schedule()
config.projectorList = []
# Load the component descriptions. Do this first, so they are available when
# creating the various components
try:
print("Reading component descriptions...", end="", flush=True)
config.componentDescriptions = dict(config_reader["COMPONENT_DESCRIPTIONS"])
print(" done")
except KeyError:
print("None found")
config.componentDescriptions = {}
# Parse list of PJLink projectors
try:
pjlink_projectors = config_reader["PJLINK_PROJECTORS"]
print("Connecting to PJLink projectors...", end="\r", flush=True)
except KeyError:
print("No PJLink projectors specified")
pjlink_projectors = []
n_proj = len(pjlink_projectors)
cur_proj = 0
for key in pjlink_projectors:
cur_proj += 1
print(f"Connecting to PJLink projectors... {cur_proj}/{n_proj}", end="\r", flush=True)
if c_proj.get_projector(key) is None:
# Try to split on a comma. If we get two elements back, that means
# we have the form "ip, password"
split = pjlink_projectors[key].split(",")
if len(split) == 2:
# We have an IP address and a password
ip = split[0].strip()
password = split[1].strip()
if password == "":
password = None
new_proj = c_proj.Projector(key, ip, "pjlink", password=password)
elif len(split) == 1:
# We have an IP address only
new_proj = c_proj.Projector(key, pjlink_projectors[key], "pjlink")
else:
print("Invalid PJLink projector entry:", pjlink_projectors[key])
break
config.projectorList.append(new_proj)
print("Connecting to PJLink projectors... done ")
# Parse list of serial projectors
try:
serial_projectors = config_reader["SERIAL_PROJECTORS"]
print("Connecting to serial projectors...", end="\r", flush=True)
except KeyError:
print("No serial projectors specified")
serial_projectors = []
n_proj = len(serial_projectors)
cur_proj = 0
for key in serial_projectors:
cur_proj += 1
print(f"Connecting to serial projectors... {cur_proj}/{n_proj}", end="\r", flush=True)
if c_proj.get_projector(key) is None:
# Try to split on a comma. If we get two elements back, that means
# we have the form "ip, password"
split = serial_projectors[key].split(",")
if len(split) == 2:
# We have an IP address and a make
ip = split[0].strip()
make = split[1].strip()
if make == "":
make = None
new_proj = c_proj.Projector(key, ip, "serial", make=make)
elif len(split) == 1:
# We have an IP address only
new_proj = c_proj.Projector(key, serial_projectors[key], "serial")
else:
print("Invalid serial projector entry:", serial_projectors[key])
break
config.projectorList.append(new_proj)
print("Connecting to serial projectors... done ")
# Parse list of Wake on LAN devices
try:
wol = config_reader["WAKE_ON_LAN"]
print("Collecting Wake on LAN devices...", end="", flush=True)
for key in wol:
if c_exhibit.get_exhibit_component(key) is None:
# If 'get_exhibit_component' is not None, this key corresponds
# to a WoL device with a matching exhibit component ID and
# we have already loaded that component from the pickle file
value_split = wol[key].split(",")
if len(value_split) == 2:
# We have been given a MAC address and IP address
device = c_exhibit.WakeOnLANDevice(key,
value_split[0].strip(),
ip_address=value_split[1].strip())
elif len(value_split) == 1:
# We have been given only a MAC address
device = c_exhibit.WakeOnLANDevice(key, value_split[0].strip())
else:
print(f"Wake on LAN device specified with unknown format: {wol[key]}")
continue
config.wakeOnLANList.append(device)
print(" done")
except KeyError:
print("No wake on LAN devices specified")
config.wakeOnLANList = []
# Build any existing issues
try:
issue_file = os.path.join(config.APP_PATH, "issues", "issues.json")
with open(issue_file, "r", encoding="UTF-8") as file_object:
issues = json.load(file_object)
print("Reading stored issues...", end="", flush=True)
for issue in issues:
new_issue = c_issues.Issue(issue)
config.issueList.append(new_issue)
print(" done")
except FileNotFoundError:
print("No stored issues to read")
# Parse list of static components
try:
static_components = config_reader["STATIC_COMPONENTS"]
print("Adding static components... ", end="\r", flush=True)
for this_type in static_components:
split = static_components[this_type].split(",")
for this_id in split:
c_exhibit.add_exhibit_component(this_id.strip(), this_type, category="static")
print("done")
except KeyError:
print("none specified")
# Parse the reboot_time if necessary
if "reboot_time" in current:
reboot_time = dateutil.parser.parse(current["reboot_time"])
if reboot_time < datetime.datetime.now():
reboot_time += datetime.timedelta(days=1)
config.serverRebootTime = reboot_time
print("Server will reboot at:", config.serverRebootTime.isoformat())
# Then, load the configuration for that exhibit
c_exhibit.read_exhibit_configuration(current["current_exhibit"])
# Update the components that the configuration has changed
for component in config.componentList:
component.update_configuration()
def check_file_structure():
"""Check to make sure we have the appropriate file structure set up"""
schedules_dir = os.path.join(config.APP_PATH, "schedules")
exhibits_dir = os.path.join(config.APP_PATH, "exhibits")
misc_dirs = {"analytics": os.path.join(config.APP_PATH, "analytics"),
"flexible-tracker": os.path.join(config.APP_PATH, "flexible-tracker"),
"flexible-tracker/data": os.path.join(config.APP_PATH, "flexible-tracker", "data"),
"flexible-tracker/templates": os.path.join(config.APP_PATH, "flexible-tracker", "templates"),
"flexible-voter": os.path.join(config.APP_PATH, "flexible-voter"),
"flexible-voter/data": os.path.join(config.APP_PATH, "flexible-voter", "data"),
"flexible-voter/templates": os.path.join(config.APP_PATH, "flexible-voter", "templates"),
"issues": os.path.join(config.APP_PATH, "issues"),
"issues/media": os.path.join(config.APP_PATH, "issues", "media"),
"maintenance-logs": os.path.join(config.APP_PATH, "maintenance-logs")}
try:
os.listdir(schedules_dir)
except FileNotFoundError:
print("Missing schedules directory. Creating now...")
try:
os.mkdir(schedules_dir)
default_schedule_list = ["monday.ini", "tuesday.ini",
"wednesday.ini", "thursday.ini",
"friday.ini", "saturday.ini",
"sunday.ini"]
for file in default_schedule_list:
with open(os.path.join(schedules_dir, file), 'w', encoding="UTF-8") as f:
f.write("[SCHEDULE]\n")
except PermissionError:
print("Error: unable to create 'schedules' directory. Do you have write permission?")
try:
os.listdir(exhibits_dir)
except FileNotFoundError:
print("Missing exhibits directory. Creating now...")
try:
os.mkdir(exhibits_dir)
with open(os.path.join(exhibits_dir, "default.exhibit"), 'w', encoding="UTF-8") as f:
f.write("")
except PermissionError:
print("Error: unable to create 'exhibits' directory. Do you have write permission?")
for key in misc_dirs:
try:
os.listdir(misc_dirs[key])
except FileNotFoundError:
print(f"Missing {key} directory. Creating now...")
try:
os.mkdir(misc_dirs[key])
except PermissionError:
print(f"Error: unable to create '{key}' directory. Do you have write permission?")
def quit_handler(*args):
"""Handle cleanly shutting down the server"""
try:
if config.rebooting is True:
exit_code = 1
print("\nRebooting server...")
else:
exit_code = 0
print('\nKeyboard interrupt detected. Cleaning up and shutting down...')
except RuntimeError:
exit_code = 0
# Save the current component lists to a pickle file so that
# we can resume from the current state
path_to_write = os.path.join(config.APP_PATH, "current_state.dat")
with open(path_to_write, 'wb') as f:
pickle.dump(config.componentList, f)
for key in config.polling_thread_dict:
config.polling_thread_dict[key].cancel()
with config.logLock:
logging.info("Server shutdown")
with config.galleryConfigurationLock:
with config.scheduleLock:
with config.trackingDataWriteLock:
sys.exit(exit_code)
def error_handler(*exc_info):
"""Catch errors and log them to file"""
text = "".join(traceback.format_exception(*exc_info)).replace('"', "'").replace("\n", "<newline>")
with config.logLock:
logging.error(f'"{text}"')
print(f"Error: see control_server.log for more details ({datetime.datetime.now()})")
def check_for_software_update():
"""Download the version.txt file from GitHub and check if there is an update"""
global software_update_available
print("Checking for update... ", end="")
try:
for line in urllib.request.urlopen(
"https://raw.githubusercontent.com/Cosmic-Chatter/Constellation/main/control_server/version.txt"):
if float(line.decode('utf-8')) > SOFTWARE_VERSION:
software_update_available = True
break
except urllib.error.HTTPError:
print("cannot connect to update server")
return
if software_update_available:
print("update available!")
else:
print("the server is up to date.")
# Check whether we have packaged with Pyinstaller and set the appropriate root path.
config.EXEC_PATH = os.path.dirname(os.path.abspath(__file__))
if getattr(sys, 'frozen', False):
# If the application is run as a --onefile bundle, the PyInstaller bootloader
# extends the sys module by a flag frozen=True and sets the app
# path into variable sys.executable.
config.APP_PATH = os.path.dirname(sys.executable)
else:
config.APP_PATH = config.EXEC_PATH
server_port: int = 8080 # Default; should be set in currentExhibitConfiguration.ini
ip_address: str = socket.gethostbyname(socket.gethostname()) # Default; should be set in galleryConfiguration.ini
ADDR: str = "" # Accept connections from all interfaces
gallery_name: str = ""
SOFTWARE_VERSION = 1.0
software_update_available: bool = False
# Set up log file
log_path: str = os.path.join(config.APP_PATH, "control_server.log")
logging.basicConfig(datefmt='%Y-%m-%d %H:%M:%S',
filename=log_path,
format='%(levelname)s, %(asctime)s, %(message)s',
level=logging.DEBUG)
signal.signal(signal.SIGINT, quit_handler)
sys.excepthook = error_handler
with config.logLock:
logging.info("Server started")
# Try to reload the previous state from the pickle file current_state.dat
try:
state_path = os.path.join(config.APP_PATH, "current_state.dat")
with open(state_path, "rb") as previous_state:
config.componentList = pickle.load(previous_state)
print("Previous server state loaded")
except (FileNotFoundError, EOFError):
print("Could not load previous server state")
check_file_structure()
c_exhibit.check_available_exhibits()
load_default_configuration()
c_sched.poll_event_schedule()
c_proj.poll_projectors()
c_exhibit.poll_wake_on_LAN_devices()
check_for_software_update()
httpd = ThreadedHTTPServer((ADDR, server_port), RequestHandler)
httpd.serve_forever()
|
import json
from flask import Flask, render_template , request
from flask.wrappers import Response
from get_ocr import get_ocr
import os
import datetime
from werkzeug.utils import secure_filename
import csv
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route("/upload",methods=['POST','GET'])
def upload():
if request.method == 'POST':
img = request.files.get('imagefile', '')
img.save(os.path.join(os.getcwd() , "static" , secure_filename('img_test' + '.jpg')))
img_path = 'static/img_test.jpg'
data , text , textnew , newpath= get_ocr(img_path)
return render_template('edit_details.html' , data= data , text=text , img = newpath , textnew=textnew )
current_uid = {}
current_uid['id'] = "None"
@app.route("/submit",methods=['POST','GET'])
def sub():
if request.method == 'POST':
global updated_data
updated_data = {}
updated_data['DOC_type'] = request.form.get('doctype')
updated_data['Name'] = request.form.get('name')
updated_data['Gender'] = request.form.get('gender')
updated_data['Birth year'] = request.form.get('byear')
updated_data['Uid'] = request.form.get('uid')
current_uid['id'] = updated_data['Uid']
return render_template('jsonify.html' , data = updated_data)
@app.route("/insert",methods=['POST','GET'])
def insert():
date = str(datetime.datetime.now())
f = open("records.csv", "a")
f.write(f"{updated_data["DOC_type"]},{updated_data["Uid"]},{updated_data["Name"]},{updated_data["Birth year"]},{updated_data["Gender"]}, {date[0:10]},{date[11:19]}\n")
f.close()
return render_template('jsonify.html' , data = updated_data , msg=1)
@app.route("/downlaod",methods=['POST','GET'])
def download():
uid = current_uid['id']
json_file_path = f"static/document/{uid}.json"
with open(json_file_path, 'r') as fp:
json_data = json.load(fp)
return Response(
json_data,
mimetype="text/json",
headers={"Content-disposition":
f"attachment; filename={uid}.json"})
@app.route("/record", methods=['POST','GET']) # page to view record of all users
def rec():
val = []
with open("records.csv", "r") as f:
reader = csv.reader(f)
for i in reader:
val.append(i)
return render_template('/records.html' , val = val)
if __name__ == '__main__':
app.run(debug=True)
# app.run(host='0.0.0.0')
| import json
from flask import Flask, render_template , request
from flask.wrappers import Response
from get_ocr import get_ocr
import os
import datetime
from werkzeug.utils import secure_filename
import csv
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
@app.route("/upload",methods=['POST','GET'])
def upload():
if request.method == 'POST':
img = request.files.get('imagefile', '')
img.save(os.path.join(os.getcwd() , "static" , secure_filename('img_test' + '.jpg')))
img_path = 'static/img_test.jpg'
data , text , textnew , newpath= get_ocr(img_path)
return render_template('edit_details.html' , data= data , text=text , img = newpath , textnew=textnew )
current_uid = {}
current_uid['id'] = "None"
@app.route("/submit",methods=['POST','GET'])
def sub():
if request.method == 'POST':
global updated_data
updated_data = {}
updated_data['DOC_type'] = request.form.get('doctype')
updated_data['Name'] = request.form.get('name')
updated_data['Gender'] = request.form.get('gender')
updated_data['Birth year'] = request.form.get('byear')
updated_data['Uid'] = request.form.get('uid')
current_uid['id'] = updated_data['Uid']
return render_template('jsonify.html' , data = updated_data)
@app.route("/insert",methods=['POST','GET'])
def insert():
date = str(datetime.datetime.now())
f = open("records.csv", "a")
f.write(f"{updated_data['DOC_type']},{updated_data['Uid']},{updated_data['Name']},{updated_data['Birth year']},{updated_data['Gender']}, {date[0:10]},{date[11:19]}\n")
f.close()
return render_template('jsonify.html' , data = updated_data , msg=1)
@app.route("/downlaod",methods=['POST','GET'])
def download():
uid = current_uid['id']
json_file_path = f"static/document/{uid}.json"
with open(json_file_path, 'r') as fp:
json_data = json.load(fp)
return Response(
json_data,
mimetype="text/json",
headers={"Content-disposition":
f"attachment; filename={uid}.json"})
@app.route("/record", methods=['POST','GET']) # page to view record of all users
def rec():
val = []
with open("records.csv", "r") as f:
reader = csv.reader(f)
for i in reader:
val.append(i)
return render_template('/records.html' , val = val)
if __name__ == '__main__':
app.run(debug=True)
# app.run(host='0.0.0.0')
|
import pytest
from r2d7.meta import Metawing
import json
list_printer_tests = (
('{"position": 5, "id": 2085, "name": "DBS Swarm", "faction": "Separatist Alliance", "ships": [{"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 62, "name": "Hyena-class Droid Bomber", "xws": "hyenaclassdroidbomber", "link": "https://meta.listfortress.com/ships/62.json"}], "squadron_count": 1, "tournaments_count": 1, "average_percentile": 81.25, "weight": 0.803933897124467}',
'''DBS Swarm :vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::hyenaclassdroidbomber:
Average: 81%, Weighted: 80%'''),
('{"position": 1, "id": 2336, "name": null, "faction": "Resistance", "link": "https://meta.listfortress.com/ship_combos/2336.json", "ships": [{"id": 50, "name": "T-70 X-wing", "xws": "t70xwing", "link": "https://meta.listfortress.com/ships/50.json"}, {"id": 50, "name": "T-70 X-wing", "xws": "t70xwing", "link": "https://meta.listfortress.com/ships/50.json"}, {"id": 50, "name": "T-70 X-wing", "xws": "t70xwing", "link": "https://meta.listfortress.com/ships/50.json"}, {"id": 49, "name": "RZ-2 A-wing", "xws": "rz2awing", "link": "https://meta.listfortress.com/ships/49.json"}, {"id": 61, "name": "Resistance Transport Pod", "xws": "resistancetransportpod", "link": "https://meta.listfortress.com/ships/61.json"}], "squadron_count": 2, "tournaments_count": 2, "average_percentile": 98.68, "weight": 1.16857595629635}',
'''<https://meta.listfortress.com/ship_combos/2336|(unnamed)> :t70xwing::t70xwing::t70xwing::rz2awing::resistancetransportpod:
Average: 99%, Weighted: 117%'''),
('{"position": 2, "id": 191, "name": "Double Firespray", "faction": "Scum and Villainy", "link": "https://meta.listfortress.com/ship_combos/191.json", "ships": [{"id": 22, "name": "Firespray-class Patrol Craft", "xws": "firesprayclasspatrolcraft", "link": "https://meta.listfortress.com/ships/22.json"}, {"id": 22, "name": "Firespray-class Patrol Craft", "xws": "firesprayclasspatrolcraft", "link": "https://meta.listfortress.com/ships/22.json"}], "squadron_count": 13, "tournaments_count": 8, "average_percentile": 38.81, "weight": 1.01919215760887}',
'''<https://meta.listfortress.com/ship_combos/191|Double Firespray> :firesprayclasspatrolcraft::firesprayclasspatrolcraft:
Average: 39%, Weighted: 102%'''),
('{"position": 3, "id": 2801, "name": "The Baron and the Bros", "faction": "First Order", "link": "https://meta.listfortress.com/ship_combos/2801.json", "ships": [{"id": 51, "name": "TIE/fo Fighter", "xws": "tiefofighter", "link": "https://meta.listfortress.com/ships/51.json"}, {"id": 67, "name": "TIE/ba Interceptor", "xws": "tiebainterceptor", "link": "https://meta.listfortress.com/ships/67.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}], "squadron_count": 1, "tournaments_count": 1, "average_percentile": 95.45, "weight": 0.944481781237137}',
'''<https://meta.listfortress.com/ship_combos/2801|The Baron and the Bros> :tiefofighter::tiebainterceptor::tiesffighter::tiesffighter::tiesffighter:
Average: 95%, Weighted: 94%'''),
('{"position": 4, "id": 2324, "name": null, "faction": "First Order", "link": "https://meta.listfortress.com/ship_combos/2324.json", "ships": [{"id": 51, "name": "TIE/fo Fighter", "xws": "tiefofighter", "link": "https://meta.listfortress.com/ships/51.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 53, "name": "TIE/vn Silencer", "xws": "tievnsilencer", "link": "https://meta.listfortress.com/ships/53.json"}], "squadron_count": 1, "tournaments_count": 1, "average_percentile": 91.47, "weight": 0.905128373685589}',
'''<https://meta.listfortress.com/ship_combos/2324|(unnamed)> :tiefofighter::tiesffighter::tiesffighter::tiesffighter::tievnsilencer:
Average: 91%, Weighted: 91%'''),
)
@pytest.mark.parametrize('message, expected', list_printer_tests)
def test_list_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.list_printer(j) == expected
ship_printer_tests = (
('{"position": 1, "id": 21, "xws": "fangfighter", "name": "Fang Fighter", "link": "https://meta.listfortress.com/ships/21.json", "pilots": [{"id": 99, "name": "Old Teroch", "link": "https://meta.listfortress.com/pilots/99.json", "image": "https://meta.listfortress.com/pilots/99/image.png"}, {"id": 96, "name": "Fenn Rau", "link": "https://meta.listfortress.com/pilots/96.json", "image": "https://meta.listfortress.com/pilots/96/image.png"}, {"id": 97, "name": "Joy Rekkoff", "link": "https://meta.listfortress.com/pilots/97.json", "image": "https://meta.listfortress.com/pilots/97/image.png"}, {"id": 98, "name": "Kad Solus", "link": "https://meta.listfortress.com/pilots/98.json", "image": "https://meta.listfortress.com/pilots/98/image.png"}, {"id": 100, "name": "Skull Squadron Pilot", "link": "https://meta.listfortress.com/pilots/100.json", "image": "https://meta.listfortress.com/pilots/100/image.png"}, {"id": 101, "name": "Zealous Recruit", "link": "https://meta.listfortress.com/pilots/101.json", "image": "https://meta.listfortress.com/pilots/101/image.png"}], "squadron_count": 18, "tournaments_count": 7, "average_percentile": 34.75, "weight": 1.24538930744309, "faction": "Scum and Villainy"}',
'''<https://meta.listfortress.com/ships/21|Fang Fighter>:fangfighter:
Average: 35%, Weighted: 125%'''),
('{"position": 2, "id": 28, "xws": "m3ainterceptor", "name": "M3-A Interceptor", "link": "https://meta.listfortress.com/ships/28.json", "pilots": [{"id": 132, "name": "Cartel Spacer", "link": "https://meta.listfortress.com/pilots/132.json", "image": "https://meta.listfortress.com/pilots/132/image.png"}, {"id": 134, "name": "Inaldra", "link": "https://meta.listfortress.com/pilots/134.json", "image": "https://meta.listfortress.com/pilots/134/image.png"}, {"id": 135, "name": "Laetin A\'shera", "link": "https://meta.listfortress.com/pilots/135.json", "image": "https://meta.listfortress.com/pilots/135/image.png"}, {"id": 136, "name": "Quinn Jast", "link": "https://meta.listfortress.com/pilots/136.json", "image": "https://meta.listfortress.com/pilots/136/image.png"}, {"id": 137, "name": "Serissu", "link": "https://meta.listfortress.com/pilots/137.json", "image": "https://meta.listfortress.com/pilots/137/image.png"}, {"id": 138, "name": "Sunny Bounder", "link": "https://meta.listfortress.com/pilots/138.json", "image": "https://meta.listfortress.com/pilots/138/image.png"}, {"id": 139, "name": "Tansarii Point Veteran", "link": "https://meta.listfortress.com/pilots/139.json", "image": "https://meta.listfortress.com/pilots/139/image.png"}, {"id": 133, "name": "Genesis Red", "link": "https://meta.listfortress.com/pilots/133.json", "image": "https://meta.listfortress.com/pilots/133/image.png"}, {"id": 369, "name": "G4R-G0R V/M", "link": "https://meta.listfortress.com/pilots/369.json", "image": "https://meta.listfortress.com/pilots/369/image.png"}], "squadron_count": 17, "tournaments_count": 8, "average_percentile": 36.67, "weight": 1.23087941090141, "faction": "Scum and Villainy"}',
'''<https://meta.listfortress.com/ships/28|M3-A Interceptor>:m3ainterceptor:
Average: 37%, Weighted: 123%'''),
('{"position": 4, "id": 61, "xws": "resistancetransportpod", "name": "Resistance Transport Pod", "link": "https://meta.listfortress.com/ships/61.json", "pilots": [{"id": 341, "name": "BB-8", "link": "https://meta.listfortress.com/pilots/341.json", "image": "https://meta.listfortress.com/pilots/341/image.png"}, {"id": 342, "name": "Rose Tico", "link": "https://meta.listfortress.com/pilots/342.json", "image": "https://meta.listfortress.com/pilots/342/image.png"}, {"id": 343, "name": "Vi Moradi", "link": "https://meta.listfortress.com/pilots/343.json", "image": "https://meta.listfortress.com/pilots/343/image.png"}, {"id": 344, "name": "Finn", "link": "https://meta.listfortress.com/pilots/344.json", "image": "https://meta.listfortress.com/pilots/344/image.png"}], "squadron_count": 8, "tournaments_count": 7, "average_percentile": 48.6, "weight": 0, "faction": "Resistance"}',
'''<https://meta.listfortress.com/ships/61|Resistance Transport Pod>:resistancetransportpod:
Average: 49%, Weighted: 0%'''),
)
@pytest.mark.parametrize('message, expected', ship_printer_tests)
def test_ship_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.ship_printer(j) == expected
pilot_printer_tests = (
('{"position": 1, "id": 271, "xws": "tn3465", "name": "TN-3465", "link": "https://meta.listfortress.com/pilots/271.json", "image": "https://meta.listfortress.com/pilots/271/image.png", "ship": {"id": 51, "name": "TIE/fo Fighter", "link": "https://meta.listfortress.com/ships/51.json"}, "squadron_count": 2, "tournaments_count": 1, "average_percentile": 93.46, "weight": 1.46578136825278, "faction": "First Order"}',
'''<https://meta.listfortress.com/pilots/271|TN-3465> :tiefofighter:
Average: 93%, Weighted: 147%'''),
('{"position": 3, "id": 102, "xws": "bobafett", "name": "Boba Fett", "link": "https://meta.listfortress.com/pilots/102.json", "image": "https://meta.listfortress.com/pilots/102/image.png", "ship": {"id": 22, "name": "Firespray-class Patrol Craft", "link": "https://meta.listfortress.com/ships/22.json"}, "squadron_count": 21, "tournaments_count": 9, "average_percentile": 36.35, "weight": 1.16356864601878, "faction": "Scum and Villainy"}',
'''<https://meta.listfortress.com/pilots/102|Boba Fett> :firesprayclasspatrolcraft:
Average: 36%, Weighted: 116%'''),
('{"position": 5, "id": 321, "xws": "plokoon", "name": "Plo Koon", "link": "https://meta.listfortress.com/pilots/321.json", "image": "https://meta.listfortress.com/pilots/321/image.png", "ship": {"id": 58, "name": "Delta-7 Aethersprite", "link": "https://meta.listfortress.com/ships/58.json"}, "squadron_count": 6, "tournaments_count": 4, "average_percentile": 48.14, "weight": 1.12182792544073, "faction": "Galactic Republic"}',
'''<https://meta.listfortress.com/pilots/321|Plo Koon> :delta7aethersprite:
Average: 48%, Weighted: 112%'''),
)
@pytest.mark.parametrize('message, expected', pilot_printer_tests)
def test_pilot_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.pilot_printer(j) == expected
upgrade_printer_tests = (
('{"position": 1, "id": 160, "xws": "heroic", "name": "Heroic", "link": "https://meta.listfortress.com/upgrades/160.json", "image": "https://meta.listfortress.com/upgrades/160/image.png", "squadron_count": 18, "tournaments_count": 11, "average_percentile": 43.71, "weight": 1.37863771336788}',
'''<https://meta.listfortress.com/upgrades/160|Heroic>
Average: 44%, Weighted: 138%'''),
('{"position": 4, "id": 92, "xws": "protonbombs", "name": "Proton Bombs", "link": "https://meta.listfortress.com/upgrades/92.json", "image": "https://meta.listfortress.com/upgrades/92/image.png", "squadron_count": 28, "tournaments_count": 12, "average_percentile": 34.47, "weight": 1.22427215793812}',
'''<https://meta.listfortress.com/upgrades/92|Proton Bombs>
Average: 34%, Weighted: 122%'''),
('{"position": 5, "id": 227, "xws": "autoblasters", "name": "Autoblasters", "link": "https://meta.listfortress.com/upgrades/227.json", "image": "https://meta.listfortress.com/upgrades/227/image.png", "squadron_count": 27, "tournaments_count": 12, "average_percentile": 6, "weight": 0.055}',
'''<https://meta.listfortress.com/upgrades/227|Autoblasters>
Average: 6%, Weighted: 6%'''),
)
@pytest.mark.parametrize('message, expected', upgrade_printer_tests)
def test_upgrade_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.upgrade_printer(j) == expected
| import pytest
from r2d7.meta import Metawing
import json
list_printer_tests = (
('{"position": 5, "id": 2085, "name": "DBS Swarm", "faction": "Separatist Alliance", "ships": [{"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 55, "name": "Vulture-class Droid Fighter", "xws": "vultureclassdroidfighter", "link": "https://meta.listfortress.com/ships/55.json"}, {"id": 62, "name": "Hyena-class Droid Bomber", "xws": "hyenaclassdroidbomber", "link": "https://meta.listfortress.com/ships/62.json"}], "squadron_count": 1, "tournaments_count": 1, "average_percentile": 81.25, "weight": 0.803933897124467}',
'''DBS Swarm :vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::vultureclassdroidfighter::hyenaclassdroidbomber:
Average: 81%, Weighted: 80%'''),
('{"position": 1, "id": 2336, "name": null, "faction": "Resistance", "link": "https://meta.listfortress.com/ship_combos/2336.json", "ships": [{"id": 50, "name": "T-70 X-wing", "xws": "t70xwing", "link": "https://meta.listfortress.com/ships/50.json"}, {"id": 50, "name": "T-70 X-wing", "xws": "t70xwing", "link": "https://meta.listfortress.com/ships/50.json"}, {"id": 50, "name": "T-70 X-wing", "xws": "t70xwing", "link": "https://meta.listfortress.com/ships/50.json"}, {"id": 49, "name": "RZ-2 A-wing", "xws": "rz2awing", "link": "https://meta.listfortress.com/ships/49.json"}, {"id": 61, "name": "Resistance Transport Pod", "xws": "resistancetransportpod", "link": "https://meta.listfortress.com/ships/61.json"}], "squadron_count": 2, "tournaments_count": 2, "average_percentile": 98.68, "weight": 1.16857595629635}',
'''<https://meta.listfortress.com/ship_combos/2336|(unnamed)> :t70xwing::t70xwing::t70xwing::rz2awing::resistancetransportpod:
Average: 99%, Weighted: 117%'''),
('{"position": 2, "id": 191, "name": "Double Firespray", "faction": "Scum and Villainy", "link": "https://meta.listfortress.com/ship_combos/191.json", "ships": [{"id": 22, "name": "Firespray-class Patrol Craft", "xws": "firesprayclasspatrolcraft", "link": "https://meta.listfortress.com/ships/22.json"}, {"id": 22, "name": "Firespray-class Patrol Craft", "xws": "firesprayclasspatrolcraft", "link": "https://meta.listfortress.com/ships/22.json"}], "squadron_count": 13, "tournaments_count": 8, "average_percentile": 38.81, "weight": 1.01919215760887}',
'''<https://meta.listfortress.com/ship_combos/191|Double Firespray> :firesprayclasspatrolcraft::firesprayclasspatrolcraft:
Average: 39%, Weighted: 102%'''),
('{"position": 3, "id": 2801, "name": "The Baron and the Bros", "faction": "First Order", "link": "https://meta.listfortress.com/ship_combos/2801.json", "ships": [{"id": 51, "name": "TIE/fo Fighter", "xws": "tiefofighter", "link": "https://meta.listfortress.com/ships/51.json"}, {"id": 67, "name": "TIE/ba Interceptor", "xws": "tiebainterceptor", "link": "https://meta.listfortress.com/ships/67.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}], "squadron_count": 1, "tournaments_count": 1, "average_percentile": 95.45, "weight": 0.944481781237137}',
'''<https://meta.listfortress.com/ship_combos/2801|The Baron and the Bros> :tiefofighter::tiebainterceptor::tiesffighter::tiesffighter::tiesffighter:
Average: 95%, Weighted: 94%'''),
('{"position": 4, "id": 2324, "name": null, "faction": "First Order", "link": "https://meta.listfortress.com/ship_combos/2324.json", "ships": [{"id": 51, "name": "TIE/fo Fighter", "xws": "tiefofighter", "link": "https://meta.listfortress.com/ships/51.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 52, "name": "TIE/sf Fighter", "xws": "tiesffighter", "link": "https://meta.listfortress.com/ships/52.json"}, {"id": 53, "name": "TIE/vn Silencer", "xws": "tievnsilencer", "link": "https://meta.listfortress.com/ships/53.json"}], "squadron_count": 1, "tournaments_count": 1, "average_percentile": 91.47, "weight": 0.905128373685589}',
'''<https://meta.listfortress.com/ship_combos/2324|(unnamed)> :tiefofighter::tiesffighter::tiesffighter::tiesffighter::tievnsilencer:
Average: 91%, Weighted: 91%'''),
)
@pytest.mark.parametrize('message, expected', list_printer_tests)
def test_list_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.list_printer(j) == expected
ship_printer_tests = (
('{"position": 1, "id": 21, "xws": "fangfighter", "name": "Fang Fighter", "link": "https://meta.listfortress.com/ships/21.json", "pilots": [{"id": 99, "name": "Old Teroch", "link": "https://meta.listfortress.com/pilots/99.json", "image": "https://meta.listfortress.com/pilots/99/image.png"}, {"id": 96, "name": "Fenn Rau", "link": "https://meta.listfortress.com/pilots/96.json", "image": "https://meta.listfortress.com/pilots/96/image.png"}, {"id": 97, "name": "Joy Rekkoff", "link": "https://meta.listfortress.com/pilots/97.json", "image": "https://meta.listfortress.com/pilots/97/image.png"}, {"id": 98, "name": "Kad Solus", "link": "https://meta.listfortress.com/pilots/98.json", "image": "https://meta.listfortress.com/pilots/98/image.png"}, {"id": 100, "name": "Skull Squadron Pilot", "link": "https://meta.listfortress.com/pilots/100.json", "image": "https://meta.listfortress.com/pilots/100/image.png"}, {"id": 101, "name": "Zealous Recruit", "link": "https://meta.listfortress.com/pilots/101.json", "image": "https://meta.listfortress.com/pilots/101/image.png"}], "squadron_count": 18, "tournaments_count": 7, "average_percentile": 34.75, "weight": 1.24538930744309, "faction": "Scum and Villainy"}',
'''<https://meta.listfortress.com/ships/21|Fang Fighter>:fangfighter:
Average: 35%, Weighted: 125%'''),
('{"position": 2, "id": 28, "xws": "m3ainterceptor", "name": "M3-A Interceptor", "link": "https://meta.listfortress.com/ships/28.json", "pilots": [{"id": 132, "name": "Cartel Spacer", "link": "https://meta.listfortress.com/pilots/132.json", "image": "https://meta.listfortress.com/pilots/132/image.png"}, {"id": 134, "name": "Inaldra", "link": "https://meta.listfortress.com/pilots/134.json", "image": "https://meta.listfortress.com/pilots/134/image.png"}, {"id": 135, "name": "Laetin A\'shera", "link": "https://meta.listfortress.com/pilots/135.json", "image": "https://meta.listfortress.com/pilots/135/image.png"}, {"id": 136, "name": "Quinn Jast", "link": "https://meta.listfortress.com/pilots/136.json", "image": "https://meta.listfortress.com/pilots/136/image.png"}, {"id": 137, "name": "Serissu", "link": "https://meta.listfortress.com/pilots/137.json", "image": "https://meta.listfortress.com/pilots/137/image.png"}, {"id": 138, "name": "Sunny Bounder", "link": "https://meta.listfortress.com/pilots/138.json", "image": "https://meta.listfortress.com/pilots/138/image.png"}, {"id": 139, "name": "Tansarii Point Veteran", "link": "https://meta.listfortress.com/pilots/139.json", "image": "https://meta.listfortress.com/pilots/139/image.png"}, {"id": 133, "name": "Genesis Red", "link": "https://meta.listfortress.com/pilots/133.json", "image": "https://meta.listfortress.com/pilots/133/image.png"}, {"id": 369, "name": "G4R-G0R V/M", "link": "https://meta.listfortress.com/pilots/369.json", "image": "https://meta.listfortress.com/pilots/369/image.png"}], "squadron_count": 17, "tournaments_count": 8, "average_percentile": 36.67, "weight": 1.23087941090141, "faction": "Scum and Villainy"}',
'''<https://meta.listfortress.com/ships/28|M3-A Interceptor>:m3ainterceptor:
Average: 37%, Weighted: 123%'''),
('{"position": 4, "id": 61, "xws": "resistancetransportpod", "name": "Resistance Transport Pod", "link": "https://meta.listfortress.com/ships/61.json", "pilots": [{"id": 341, "name": "BB-8", "link": "https://meta.listfortress.com/pilots/341.json", "image": "https://meta.listfortress.com/pilots/341/image.png"}, {"id": 342, "name": "Rose Tico", "link": "https://meta.listfortress.com/pilots/342.json", "image": "https://meta.listfortress.com/pilots/342/image.png"}, {"id": 343, "name": "Vi Moradi", "link": "https://meta.listfortress.com/pilots/343.json", "image": "https://meta.listfortress.com/pilots/343/image.png"}, {"id": 344, "name": "Finn", "link": "https://meta.listfortress.com/pilots/344.json", "image": "https://meta.listfortress.com/pilots/344/image.png"}], "squadron_count": 8, "tournaments_count": 7, "average_percentile": 48.6, "weight": 0, "faction": "Resistance"}',
'''<https://meta.listfortress.com/ships/61|Resistance Transport Pod>:resistancetransportpod:
Average: 49%, Weighted: 0%'''),
)
@pytest.mark.parametrize('message, expected', ship_printer_tests)
def test_ship_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.ship_printer(j) == expected
pilot_printer_tests = (
('{"position": 1, "id": 271, "xws": "tn3465", "name": "TN-3465", "link": "https://meta.listfortress.com/pilots/271.json", "image": "https://meta.listfortress.com/pilots/271/image.png", "ship": {"id": 51, "name": "TIE/fo Fighter", "link": "https://meta.listfortress.com/ships/51.json"}, "squadron_count": 2, "tournaments_count": 1, "average_percentile": 93.46, "weight": 1.46578136825278, "faction": "First Order"}',
'''<https://meta.listfortress.com/pilots/271|TN-3465> :tiefofighter:
Average: 93%, Weighted: 147%'''),
('{"position": 3, "id": 102, "xws": "bobafett", "name": "Boba Fett", "link": "https://meta.listfortress.com/pilots/102.json", "image": "https://meta.listfortress.com/pilots/102/image.png", "ship": {"id": 22, "name": "Firespray-class Patrol Craft", "link": "https://meta.listfortress.com/ships/22.json"}, "squadron_count": 21, "tournaments_count": 9, "average_percentile": 36.35, "weight": 1.16356864601878, "faction": "Scum and Villainy"}',
'''<https://meta.listfortress.com/pilots/102|Boba Fett> :firesprayclasspatrolcraft:
Average: 36%, Weighted: 116%'''),
('{"position": 5, "id": 321, "xws": "plokoon", "name": "Plo Koon", "link": "https://meta.listfortress.com/pilots/321.json", "image": "https://meta.listfortress.com/pilots/321/image.png", "ship": {"id": 58, "name": "Delta-7 Aethersprite", "link": "https://meta.listfortress.com/ships/58.json"}, "squadron_count": 6, "tournaments_count": 4, "average_percentile": 48.14, "weight": 1.12182792544073, "faction": "Galactic Republic"}',
'''<https://meta.listfortress.com/pilots/321|Plo Koon> :delta7aethersprite:
Average: 48%, Weighted: 112%'''),
)
@pytest.mark.parametrize('message, expected', pilot_printer_tests)
def test_pilot_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.pilot_printer(j) == expected
upgrade_printer_tests = (
('{"position": 1, "id": 160, "xws": "heroic", "name": "Heroic", "link": "https://meta.listfortress.com/upgrades/160.json", "image": "https://meta.listfortress.com/upgrades/160/image.png", "squadron_count": 18, "tournaments_count": 11, "average_percentile": 43.71, "weight": 1.37863771336788}',
'''<https://meta.listfortress.com/upgrades/160|Heroic>
Average: 44%, Weighted: 138%'''),
('{"position": 4, "id": 92, "xws": "protonbombs", "name": "Proton Bombs", "link": "https://meta.listfortress.com/upgrades/92.json", "image": "https://meta.listfortress.com/upgrades/92/image.png", "squadron_count": 28, "tournaments_count": 12, "average_percentile": 34.47, "weight": 1.22427215793812}',
'''<https://meta.listfortress.com/upgrades/92|Proton Bombs>
Average: 34%, Weighted: 122%'''),
('{"position": 5, "id": 227, "xws": "autoblasters", "name": "Autoblasters", "link": "https://meta.listfortress.com/upgrades/227.json", "image": "https://meta.listfortress.com/upgrades/227/image.png", "squadron_count": 27, "tournaments_count": 12, "average_percentile": 6, "weight": 0.055}',
'''<https://meta.listfortress.com/upgrades/227|Autoblasters>
Average: 6%, Weighted: 6%'''),
)
@pytest.mark.parametrize('message, expected', upgrade_printer_tests)
def test_upgrade_printer(testbot, message, expected):
j = json.loads(message)
assert testbot.upgrade_printer(j) == expected
|
import json
from landsatxplore.api import API
from pprint import pprint
# Initialize a new API instance and get an access key
username = "batuhang"
password = "SLCH6i5k9L.."
api = API(username, password)
# Search for Landsat TM scenes
scenes = api.search(
dataset='landsat_8_c1',
latitude=28.85,
longitude=41.35,
start_date='2019-01-01',
end_date='2021-10-01',
max_cloud_cover=50,
min_cloud_cover=20
)
print(f"{len(scenes)} scenes found.")
# Process the result
for scene in scenes:
# print(scene['acquisition_date'].strftime('%Y-%m-%d'))
# # Write scene footprints to disk
# fname = f"{scene["landsat_product_id"]}.geojson"
# with open(fname, "w") as f:
# json.dump(scene['spatial_coverage'].__geo_interface__, f)
pprint(scene['entity_id'])
api.logout()
from landsatxplore.earthexplorer import EarthExplorer
ee = EarthExplorer(username, password)
ee.download(scenes[0]['entity_id'], output_dir='./data') | import json
from landsatxplore.api import API
from pprint import pprint
# Initialize a new API instance and get an access key
username = "batuhang"
password = "SLCH6i5k9L.."
api = API(username, password)
# Search for Landsat TM scenes
scenes = api.search(
dataset='landsat_8_c1',
latitude=28.85,
longitude=41.35,
start_date='2019-01-01',
end_date='2021-10-01',
max_cloud_cover=50,
min_cloud_cover=20
)
print(f"{len(scenes)} scenes found.")
# Process the result
for scene in scenes:
# print(scene['acquisition_date'].strftime('%Y-%m-%d'))
# # Write scene footprints to disk
# fname = f"{scene['landsat_product_id']}.geojson"
# with open(fname, "w") as f:
# json.dump(scene['spatial_coverage'].__geo_interface__, f)
pprint(scene['entity_id'])
api.logout()
from landsatxplore.earthexplorer import EarthExplorer
ee = EarthExplorer(username, password)
ee.download(scenes[0]['entity_id'], output_dir='./data') |
_E='replace'
_D=False
_C='\n'
_B='\r\n'
_A=None
import contextlib,io,os,shlex,shutil,sys,tempfile
from . import formatting,termui,utils
from ._compat import _find_binary_reader
class EchoingStdin:
def __init__(A,input,output):A._input=input;A._output=output
def __getattr__(A,x):return getattr(A._input,x)
def _echo(A,rv):A._output.write(rv);return rv
def read(A,n=-1):return A._echo(A._input.read(n))
def readline(A,n=-1):return A._echo(A._input.readline(n))
def readlines(A):return[A._echo(B)for B in A._input.readlines()]
def __iter__(A):return iter((A._echo(B)for B in A._input))
def __repr__(A):return repr(A._input)
def make_input_stream(input,charset):
if hasattr(input,'read'):
A=_find_binary_reader(input)
if A is not _A:return A
raise TypeError('Could not find binary reader for input stream.')
if input is _A:input=b''
elif not isinstance(input,bytes):input=input.encode(charset)
return io.BytesIO(input)
class Result:
def __init__(A,runner,stdout_bytes,stderr_bytes,exit_code,exception,exc_info=_A):A.runner=runner;A.stdout_bytes=stdout_bytes;A.stderr_bytes=stderr_bytes;A.exit_code=exit_code;A.exception=exception;A.exc_info=exc_info
@property
def output(self):return self.stdout
@property
def stdout(self):return self.stdout_bytes.decode(self.runner.charset,_E).replace(_B,_C)
@property
def stderr(self):
A=self
if A.stderr_bytes is _A:raise ValueError('stderr not separately captured')
return A.stderr_bytes.decode(A.runner.charset,_E).replace(_B,_C)
def __repr__(A):B=repr(A.exception)if A.exception else'okay';return f"<{type(A).__name__} {B}>"
class CliRunner:
def __init__(A,charset='utf-8',env=_A,echo_stdin=_D,mix_stderr=True):A.charset=charset;A.env=env or{};A.echo_stdin=echo_stdin;A.mix_stderr=mix_stderr
def get_default_prog_name(A,cli):return cli.name or'root'
def make_env(C,overrides=_A):
A=overrides;B=dict(C.env)
if A:B.update(A)
return B
@contextlib.contextmanager
def isolation(self,input=_A,env=_A,color=_D):
D=env;A=self;input=make_input_stream(input,A.charset);H=sys.stdin;I=sys.stdout;J=sys.stderr;K=formatting.FORCED_WIDTH;formatting.FORCED_WIDTH=80;D=A.make_env(D);E=io.BytesIO()
if A.echo_stdin:input=EchoingStdin(input,E)
input=io.TextIOWrapper(input,encoding=A.charset);sys.stdout=io.TextIOWrapper(E,encoding=A.charset)
if not A.mix_stderr:F=io.BytesIO();sys.stderr=io.TextIOWrapper(F,encoding=A.charset)
if A.mix_stderr:sys.stderr=sys.stdout
sys.stdin=input
def L(prompt=_A):sys.stdout.write(prompt or'');A=input.readline().rstrip(_B);sys.stdout.write(f"{A}\n");sys.stdout.flush();return A
def M(prompt=_A):sys.stdout.write(f"{prompt or""}\n");sys.stdout.flush();return input.readline().rstrip(_B)
def N(echo):
A=sys.stdin.read(1)
if echo:sys.stdout.write(A);sys.stdout.flush()
return A
O=color
def P(stream=_A,color=_A):
A=color
if A is _A:return not O
return not A
Q=termui.visible_prompt_func;R=termui.hidden_prompt_func;S=termui._getchar;T=utils.should_strip_ansi;termui.visible_prompt_func=L;termui.hidden_prompt_func=M;termui._getchar=N;utils.should_strip_ansi=P;G={}
try:
for (B,C) in D.items():
G[B]=os.environ.get(B)
if C is _A:
try:del os.environ[B]
except Exception:pass
else:os.environ[B]=C
yield(E,not A.mix_stderr and F)
finally:
for (B,C) in G.items():
if C is _A:
try:del os.environ[B]
except Exception:pass
else:os.environ[B]=C
sys.stdout=I;sys.stderr=J;sys.stdin=H;termui.visible_prompt_func=Q;termui.hidden_prompt_func=R;termui._getchar=S;utils.should_strip_ansi=T;formatting.FORCED_WIDTH=K
def invoke(B,cli,args=_A,input=_A,env=_A,catch_exceptions=True,color=_D,**G):
C=args;E=_A
with B.isolation(input=input,env=env,color=color)as H:
F=_A;A=0
if isinstance(C,str):C=shlex.split(C)
try:I=G.pop('prog_name')
except KeyError:I=B.get_default_prog_name(cli)
try:cli.main(args=C or(),prog_name=I,**G)
except SystemExit as D:
E=sys.exc_info();A=D.code
if A is _A:A=0
if A!=0:F=D
if not isinstance(A,int):sys.stdout.write(str(A));sys.stdout.write(_C);A=1
except Exception as D:
if not catch_exceptions:raise
F=D;A=1;E=sys.exc_info()
finally:
sys.stdout.flush();K=H[0].getvalue()
if B.mix_stderr:J=_A
else:J=H[1].getvalue()
return Result(runner=B,stdout_bytes=K,stderr_bytes=J,exit_code=A,exception=F,exc_info=E)
@contextlib.contextmanager
def isolated_filesystem(self):
B=os.getcwd();A=tempfile.mkdtemp();os.chdir(A)
try:yield A
finally:
os.chdir(B)
try:shutil.rmtree(A)
except OSError:pass | _E='replace'
_D=False
_C='\n'
_B='\r\n'
_A=None
import contextlib,io,os,shlex,shutil,sys,tempfile
from . import formatting,termui,utils
from ._compat import _find_binary_reader
class EchoingStdin:
def __init__(A,input,output):A._input=input;A._output=output
def __getattr__(A,x):return getattr(A._input,x)
def _echo(A,rv):A._output.write(rv);return rv
def read(A,n=-1):return A._echo(A._input.read(n))
def readline(A,n=-1):return A._echo(A._input.readline(n))
def readlines(A):return[A._echo(B)for B in A._input.readlines()]
def __iter__(A):return iter((A._echo(B)for B in A._input))
def __repr__(A):return repr(A._input)
def make_input_stream(input,charset):
if hasattr(input,'read'):
A=_find_binary_reader(input)
if A is not _A:return A
raise TypeError('Could not find binary reader for input stream.')
if input is _A:input=b''
elif not isinstance(input,bytes):input=input.encode(charset)
return io.BytesIO(input)
class Result:
def __init__(A,runner,stdout_bytes,stderr_bytes,exit_code,exception,exc_info=_A):A.runner=runner;A.stdout_bytes=stdout_bytes;A.stderr_bytes=stderr_bytes;A.exit_code=exit_code;A.exception=exception;A.exc_info=exc_info
@property
def output(self):return self.stdout
@property
def stdout(self):return self.stdout_bytes.decode(self.runner.charset,_E).replace(_B,_C)
@property
def stderr(self):
A=self
if A.stderr_bytes is _A:raise ValueError('stderr not separately captured')
return A.stderr_bytes.decode(A.runner.charset,_E).replace(_B,_C)
def __repr__(A):B=repr(A.exception)if A.exception else'okay';return f"<{type(A).__name__} {B}>"
class CliRunner:
def __init__(A,charset='utf-8',env=_A,echo_stdin=_D,mix_stderr=True):A.charset=charset;A.env=env or{};A.echo_stdin=echo_stdin;A.mix_stderr=mix_stderr
def get_default_prog_name(A,cli):return cli.name or'root'
def make_env(C,overrides=_A):
A=overrides;B=dict(C.env)
if A:B.update(A)
return B
@contextlib.contextmanager
def isolation(self,input=_A,env=_A,color=_D):
D=env;A=self;input=make_input_stream(input,A.charset);H=sys.stdin;I=sys.stdout;J=sys.stderr;K=formatting.FORCED_WIDTH;formatting.FORCED_WIDTH=80;D=A.make_env(D);E=io.BytesIO()
if A.echo_stdin:input=EchoingStdin(input,E)
input=io.TextIOWrapper(input,encoding=A.charset);sys.stdout=io.TextIOWrapper(E,encoding=A.charset)
if not A.mix_stderr:F=io.BytesIO();sys.stderr=io.TextIOWrapper(F,encoding=A.charset)
if A.mix_stderr:sys.stderr=sys.stdout
sys.stdin=input
def L(prompt=_A):sys.stdout.write(prompt or'');A=input.readline().rstrip(_B);sys.stdout.write(f"{A}\n");sys.stdout.flush();return A
def M(prompt=_A):sys.stdout.write(f"{prompt or''}\n");sys.stdout.flush();return input.readline().rstrip(_B)
def N(echo):
A=sys.stdin.read(1)
if echo:sys.stdout.write(A);sys.stdout.flush()
return A
O=color
def P(stream=_A,color=_A):
A=color
if A is _A:return not O
return not A
Q=termui.visible_prompt_func;R=termui.hidden_prompt_func;S=termui._getchar;T=utils.should_strip_ansi;termui.visible_prompt_func=L;termui.hidden_prompt_func=M;termui._getchar=N;utils.should_strip_ansi=P;G={}
try:
for (B,C) in D.items():
G[B]=os.environ.get(B)
if C is _A:
try:del os.environ[B]
except Exception:pass
else:os.environ[B]=C
yield(E,not A.mix_stderr and F)
finally:
for (B,C) in G.items():
if C is _A:
try:del os.environ[B]
except Exception:pass
else:os.environ[B]=C
sys.stdout=I;sys.stderr=J;sys.stdin=H;termui.visible_prompt_func=Q;termui.hidden_prompt_func=R;termui._getchar=S;utils.should_strip_ansi=T;formatting.FORCED_WIDTH=K
def invoke(B,cli,args=_A,input=_A,env=_A,catch_exceptions=True,color=_D,**G):
C=args;E=_A
with B.isolation(input=input,env=env,color=color)as H:
F=_A;A=0
if isinstance(C,str):C=shlex.split(C)
try:I=G.pop('prog_name')
except KeyError:I=B.get_default_prog_name(cli)
try:cli.main(args=C or(),prog_name=I,**G)
except SystemExit as D:
E=sys.exc_info();A=D.code
if A is _A:A=0
if A!=0:F=D
if not isinstance(A,int):sys.stdout.write(str(A));sys.stdout.write(_C);A=1
except Exception as D:
if not catch_exceptions:raise
F=D;A=1;E=sys.exc_info()
finally:
sys.stdout.flush();K=H[0].getvalue()
if B.mix_stderr:J=_A
else:J=H[1].getvalue()
return Result(runner=B,stdout_bytes=K,stderr_bytes=J,exit_code=A,exception=F,exc_info=E)
@contextlib.contextmanager
def isolated_filesystem(self):
B=os.getcwd();A=tempfile.mkdtemp();os.chdir(A)
try:yield A
finally:
os.chdir(B)
try:shutil.rmtree(A)
except OSError:pass |
# Copyright 2019, The Emissions API Developers
# https://emissions-api.org
# This software is available under the terms of an MIT license.
# See LICENSE fore more information.
class RESTParamError(ValueError):
"""User-specific exception, used in :func:`~emissionsapi.utils.polygon_to_wkt`.
"""
pass
def bounding_box_to_wkt(lon1, lat1, lon2, lat2):
"""Convert a bounding box specified by its top-left and bottom-right
coordinates to a wkt string defining a polygon.
"""
return f'POLYGON(({lon1} {lat1},{lon1} {lat2},{lon2} {lat2},'\
f'{lon2} {lat1},{lon1} {lat1}))'
def polygon_to_wkt(polygon):
"""Converts a list of points to a WKT string defining a polygon.
:param polygon: List of values with every pair of values representing a
consecutive vertex of the polygon.
:type polygon: list
:return: WKT defining the polygon.
:rtype: str
"""
# check if element number is even
if len(polygon) % 2 != 0:
raise RESTParamError('Number of elements has to be even')
# check if polygon is closed
if polygon[-2:] != polygon[:2]:
# close polygon by adding the first lon/lat pair at the end of the list
polygon.extend(polygon[0:2])
# check if we have at least 3 (+1 to close the polygon) coordinate points
if len(polygon) < 8:
raise RESTParamError('At least 4 points are needed to define a '
'polygon')
# create list with x-y points as strings
points = []
for index in range(0, len(polygon), 2):
points.append(f'{polygon[index]} {polygon[index+1]}')
# return string with points, joined by ','
return f'POLYGON(({','.join(points)}))'
| # Copyright 2019, The Emissions API Developers
# https://emissions-api.org
# This software is available under the terms of an MIT license.
# See LICENSE fore more information.
class RESTParamError(ValueError):
"""User-specific exception, used in :func:`~emissionsapi.utils.polygon_to_wkt`.
"""
pass
def bounding_box_to_wkt(lon1, lat1, lon2, lat2):
"""Convert a bounding box specified by its top-left and bottom-right
coordinates to a wkt string defining a polygon.
"""
return f'POLYGON(({lon1} {lat1},{lon1} {lat2},{lon2} {lat2},'\
f'{lon2} {lat1},{lon1} {lat1}))'
def polygon_to_wkt(polygon):
"""Converts a list of points to a WKT string defining a polygon.
:param polygon: List of values with every pair of values representing a
consecutive vertex of the polygon.
:type polygon: list
:return: WKT defining the polygon.
:rtype: str
"""
# check if element number is even
if len(polygon) % 2 != 0:
raise RESTParamError('Number of elements has to be even')
# check if polygon is closed
if polygon[-2:] != polygon[:2]:
# close polygon by adding the first lon/lat pair at the end of the list
polygon.extend(polygon[0:2])
# check if we have at least 3 (+1 to close the polygon) coordinate points
if len(polygon) < 8:
raise RESTParamError('At least 4 points are needed to define a '
'polygon')
# create list with x-y points as strings
points = []
for index in range(0, len(polygon), 2):
points.append(f'{polygon[index]} {polygon[index+1]}')
# return string with points, joined by ','
return f'POLYGON(({",".join(points)}))'
|
from falcon.testing import Result, TestClient
from sustainerds.api.entities.user.model import UserDbModel
###############################################################################
# model tests
###############################################################################
def test_something():
d = UserDbModel()
d.email = "tim@elbart.com"
d.password = "secret123"
###############################################################################
# endpoint tests
###############################################################################
def test_register_user(test_client: TestClient):
doc = {"email": "tim@elbart.com", "password": "bla123"}
result: Result = test_client.simulate_post("/user", json=doc)
assert result.status_code == 200
data = result.json
result2: Result = test_client.simulate_get(f"/user/{data["id"]}")
assert result2.status_code == 200
assert data == result2.json
| from falcon.testing import Result, TestClient
from sustainerds.api.entities.user.model import UserDbModel
###############################################################################
# model tests
###############################################################################
def test_something():
d = UserDbModel()
d.email = "tim@elbart.com"
d.password = "secret123"
###############################################################################
# endpoint tests
###############################################################################
def test_register_user(test_client: TestClient):
doc = {"email": "tim@elbart.com", "password": "bla123"}
result: Result = test_client.simulate_post("/user", json=doc)
assert result.status_code == 200
data = result.json
result2: Result = test_client.simulate_get(f"/user/{data['id']}")
assert result2.status_code == 200
assert data == result2.json
|
from inspect import trace
from fastapi import APIRouter, Depends, status, Response
from typing import Optional
from pydantic import BaseModel
from core.config import (
ALLOWED_HOSTS,
PROJECT_NAME,
PROJECT_VERSION,
API_PORT,
DATABASE_NAME,
NER_LABEL_COLLECTION,
Feedback_Template_Collection,
Feedback_Suggestion_Collection,
LABEL_COLLECTION,
LABEL_TRAIN_JOB_COLLECTION,
)
from db.mongodb import AsyncIOMotorClient, get_database
from bson.objectid import ObjectId
import asyncio
from typing import Any, Dict, AnyStr, List, Union
from datetime import datetime
from db.utils import convert_mongo_id
from utils.trainer_communicate import asyncio_update_db_last_modify_time, set_trainer_restart_required
import re
JSONObject = Dict[AnyStr, Any]
JSONArray = List[Any]
JSONStructure = Union[JSONArray, JSONObject]
router = APIRouter()
LABEL_API_TAGS = ["Label"]
example_text = "Dan Will be deemed to have completed its delivery obligations before 2021-7-5 if in Niall's opinion, the Jeep Car satisfies the Acceptance Criteria, and Niall notifies Dan in writing that it is accepting the Jeep Car."
class create_new_label_body(BaseModel):
user: str = "example@gmail.com"
label_name: str = "Party"
inherit: list = ["B-per", "I-per", "B-org", "I-org"]
alias_as: list = ["String"]
comment: str = """This is the example cased."""
tags: list = []
@router.post("/labels", tags = LABEL_API_TAGS, status_code=status.HTTP_200_OK)
async def define_new_label(response: Response, data: create_new_label_body):
result = re.findall(r'[;|(|)]',data.label_name)
if len(result) != 0:
response.status_code = status.HTTP_406_NOT_ACCEPTABLE
return {
"message": "Label Name must not contain \"" + '\", \"'.join(result) + '"'
}
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][LABEL_COLLECTION]
result = col.find({"label_name": data.label_name})
result = await result.to_list(None)
if len(result) != 0:
result[0]["id"] = str(result[0]["_id"])
del result[0]["_id"]
return {
"message": f"Failed, Already have {data.label_name}",
"label": result[0]
}
else:
dataToStore = {
"user": data.user,
"label_name": data.label_name,
"inherit": data.inherit,
"alias_as": data.alias_as,
"comment": data.comment,
"tags": data.tags,
"adapter": {
"current_filename": "",
"training_status": "",
"history": [],
"update_time": datetime.now(),
},
"create_time": datetime.now(),
}
try:
result = await col.insert_one(dataToStore)
response.status_code = status.HTTP_201_CREATED
return {
"message": f"Success, new label {data.label_name} added."
}
except Exception as e:
return {
"message": f"Fail, please check the Error Message",
"error_msg": str(e)
}
@router.get("/labels", tags = LABEL_API_TAGS)
async def get_all_label():
mongo_client = await get_database()
label_define_col = mongo_client[DATABASE_NAME][LABEL_COLLECTION]
labels = await label_define_col.find({}, {"_id": False}).to_list(None)
return labels
@router.get("/labels/{label_name}", tags = LABEL_API_TAGS)
async def get_label_by_name(label_name, response: Response):
mongo_client = await get_database()
label_define_col = mongo_client[DATABASE_NAME][LABEL_COLLECTION]
label_data_col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
label = await label_define_col.find_one({"label_name": label_name})
if label == None:
response.status_code = status.HTTP_404_NOT_FOUND
return {
"message": "Failed, Can't find this label name"
}
label = convert_mongo_id(label)
texts = label_data_col.find(
{"text_and_labels.labels": {"$in": [label_name] + label["inherit"]}},
{"text_and_labels": False}
)
texts = await texts.to_list(None)
texts_count = len(texts)
label["data_count"] = texts_count
label["description(auto_generated)"] = f"""Label "{label["label_name"]}" is to label out {label["label_name"]} in concerto contract. {label["label_name"]} also all the {label["inherit"]} labels in current and future dataset, and when training {label["alias_as"]}, text which labeled as Party will also be labeled positively. Currently, we have {label["data_count"]} datas contain this label in training dataset."""
return label
class update_data_body(BaseModel):
user: str = "example@gmail.com"
tags: Optional[list] = [""]
texts: JSONArray = [
{
"text": "Eason",
"labels": ["Party", "String"]
},
{
"text": "will",
"labels": ["O"]
},
{
"text": "meet",
"labels": ["O"]
},
{
"text": "Dan",
"labels": ["Party", "String"]
},
{
"text": "at",
"labels": ["O"]
},
{
"text": "2021-08-04 18:00",
"labels": ["TemporalUnit"]
},
{
"text": ".",
"labels": ["O"]
},
]
from utils.tokenizer import tokenizer as roberta_tokenizer
@router.post("/data/labeledText", tags = LABEL_API_TAGS, status_code=status.HTTP_200_OK)
async def update_labeled_data(response: Response,
data: update_data_body,
refreash_trainer: bool = False):
sentences = []
current_sentence = []
# Split the texts by dot.
# So avoid CUDA Out of Memory at training by too long texts.
# This won't affect NER model's performance.
for text in data.texts:
current_sentence.append(text)
if text == {'text': '.', 'labels': ['O']}:
# New Sentence, New data.
sentences.append(current_sentence)
current_sentence = []
sentences.append(current_sentence)
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
insert_ids = []
for sentence in sentences:
token_and_labels = []
last_word_index = len(sentence)-1
for i, text in enumerate(sentence):
if i != 0 and i != last_word_index:
text_to_do = " " + text["text"]
else:
text_to_do = text["text"]
tokens = roberta_tokenizer.tokenize(text_to_do)
for j, token in enumerate(tokens):
token_and_labels.append({
"token": token,
"labels": text["labels"]
})
dataToStore = {
"user": data.user,
"tags": data.tags,
"text_and_labels": sentence,
"token_and_labels": token_and_labels,
"TimeStamp": datetime.now(),
}
# todo: Check the label in text all included.
result = await col.insert_one(dataToStore)
insert_ids.append(str(result.inserted_id))
await asyncio_update_db_last_modify_time(NER_LABEL_COLLECTION)
if refreash_trainer:
await set_trainer_restart_required(True)
return {
"message": "Add Success",
"insert_ids": insert_ids
}
@router.get("/data/labeledText", tags = LABEL_API_TAGS)
async def get_labeled_data(response: Response,
label_name: str = None,
detail: bool = False,
start: int = 0,
end: int = 10):
if end == -1 and start == -1: end = None
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = col.find({"text_and_labels.labels": {"$in": [label_name]}},
{"text_and_labels": detail,
"token_and_labels": detail})
result = await result.to_list(end)
result = result[start:end]
result = list(map(convert_mongo_id,result))
response.status_code = status.HTTP_200_OK
return {
"message": "Success",
"data": result
}
class custom_filter(BaseModel):
mongo_filter: dict = {"user": "example@gmail.com"}
@router.post("/data/labeledText/find:by:mongo:filter", tags = LABEL_API_TAGS)
async def get_labeled_data_by_custom_filter(response: Response,
data: custom_filter,
detail: bool = False,
start: int = 0,
end: int = 10):
if end == -1 and start == -1: end = None
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = col.find(data.mongo_filter,
{"text_and_labels": detail,
"token_and_labels": detail})
result = await result.to_list(end)
result = result[start:end]
result = list(map(convert_mongo_id,result))
response.status_code = status.HTTP_200_OK
return {
"message": "Success",
"data": result
}
@router.get("/data/labeledText/{_id}", tags = LABEL_API_TAGS)
async def get_labeled_data_by_id(response: Response,
_id,
detail: bool = True):
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = await col.find_one({"_id": ObjectId(_id)},
{"text_and_labels": detail,
"token_and_labels": detail})
if result:
result["_id"] = str(result["_id"])
response.status_code = status.HTTP_200_OK
return {
"message": "Success",
"data": result
}
else:
response.status_code = status.HTTP_404_NOT_FOUND
return {
"message": f"_id {_id} Not Found."
}
@router.delete("/data/labeledText/{_id}", tags = LABEL_API_TAGS)
async def delete_labeled_data_by_id(response: Response, _id):
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = await col.delete_one({"_id": ObjectId(_id)})
if result.deleted_count:
response.status_code = status.HTTP_204_NO_CONTENT
return response
else:
response.status_code = status.HTTP_404_NOT_FOUND
response.status_code = status.HTTP_200_OK
return {
"message": f"Failed, {_id} Not Found."
} | from inspect import trace
from fastapi import APIRouter, Depends, status, Response
from typing import Optional
from pydantic import BaseModel
from core.config import (
ALLOWED_HOSTS,
PROJECT_NAME,
PROJECT_VERSION,
API_PORT,
DATABASE_NAME,
NER_LABEL_COLLECTION,
Feedback_Template_Collection,
Feedback_Suggestion_Collection,
LABEL_COLLECTION,
LABEL_TRAIN_JOB_COLLECTION,
)
from db.mongodb import AsyncIOMotorClient, get_database
from bson.objectid import ObjectId
import asyncio
from typing import Any, Dict, AnyStr, List, Union
from datetime import datetime
from db.utils import convert_mongo_id
from utils.trainer_communicate import asyncio_update_db_last_modify_time, set_trainer_restart_required
import re
JSONObject = Dict[AnyStr, Any]
JSONArray = List[Any]
JSONStructure = Union[JSONArray, JSONObject]
router = APIRouter()
LABEL_API_TAGS = ["Label"]
example_text = "Dan Will be deemed to have completed its delivery obligations before 2021-7-5 if in Niall's opinion, the Jeep Car satisfies the Acceptance Criteria, and Niall notifies Dan in writing that it is accepting the Jeep Car."
class create_new_label_body(BaseModel):
user: str = "example@gmail.com"
label_name: str = "Party"
inherit: list = ["B-per", "I-per", "B-org", "I-org"]
alias_as: list = ["String"]
comment: str = """This is the example cased."""
tags: list = []
@router.post("/labels", tags = LABEL_API_TAGS, status_code=status.HTTP_200_OK)
async def define_new_label(response: Response, data: create_new_label_body):
result = re.findall(r'[;|(|)]',data.label_name)
if len(result) != 0:
response.status_code = status.HTTP_406_NOT_ACCEPTABLE
return {
"message": "Label Name must not contain \"" + '\", \"'.join(result) + '"'
}
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][LABEL_COLLECTION]
result = col.find({"label_name": data.label_name})
result = await result.to_list(None)
if len(result) != 0:
result[0]["id"] = str(result[0]["_id"])
del result[0]["_id"]
return {
"message": f"Failed, Already have {data.label_name}",
"label": result[0]
}
else:
dataToStore = {
"user": data.user,
"label_name": data.label_name,
"inherit": data.inherit,
"alias_as": data.alias_as,
"comment": data.comment,
"tags": data.tags,
"adapter": {
"current_filename": "",
"training_status": "",
"history": [],
"update_time": datetime.now(),
},
"create_time": datetime.now(),
}
try:
result = await col.insert_one(dataToStore)
response.status_code = status.HTTP_201_CREATED
return {
"message": f"Success, new label {data.label_name} added."
}
except Exception as e:
return {
"message": f"Fail, please check the Error Message",
"error_msg": str(e)
}
@router.get("/labels", tags = LABEL_API_TAGS)
async def get_all_label():
mongo_client = await get_database()
label_define_col = mongo_client[DATABASE_NAME][LABEL_COLLECTION]
labels = await label_define_col.find({}, {"_id": False}).to_list(None)
return labels
@router.get("/labels/{label_name}", tags = LABEL_API_TAGS)
async def get_label_by_name(label_name, response: Response):
mongo_client = await get_database()
label_define_col = mongo_client[DATABASE_NAME][LABEL_COLLECTION]
label_data_col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
label = await label_define_col.find_one({"label_name": label_name})
if label == None:
response.status_code = status.HTTP_404_NOT_FOUND
return {
"message": "Failed, Can't find this label name"
}
label = convert_mongo_id(label)
texts = label_data_col.find(
{"text_and_labels.labels": {"$in": [label_name] + label["inherit"]}},
{"text_and_labels": False}
)
texts = await texts.to_list(None)
texts_count = len(texts)
label["data_count"] = texts_count
label["description(auto_generated)"] = f"""Label "{label["label_name"]}" is to label out {label["label_name"]} in concerto contract. {label["label_name"]} also all the {label["inherit"]} labels in current and future dataset, and when training {label["alias_as"]}, text which labeled as Party will also be labeled positively. Currently, we have {label["data_count"]} datas contain this label in training dataset."""
return label
class update_data_body(BaseModel):
user: str = "example@gmail.com"
tags: Optional[list] = [""]
texts: JSONArray = [
{
"text": "Eason",
"labels": ["Party", "String"]
},
{
"text": "will",
"labels": ["O"]
},
{
"text": "meet",
"labels": ["O"]
},
{
"text": "Dan",
"labels": ["Party", "String"]
},
{
"text": "at",
"labels": ["O"]
},
{
"text": "2021-08-04 18:00",
"labels": ["TemporalUnit"]
},
{
"text": ".",
"labels": ["O"]
},
]
from utils.tokenizer import tokenizer as roberta_tokenizer
@router.post("/data/labeledText", tags = LABEL_API_TAGS, status_code=status.HTTP_200_OK)
async def update_labeled_data(response: Response,
data: update_data_body,
refreash_trainer: bool = False):
sentences = []
current_sentence = []
# Split the texts by dot.
# So avoid CUDA Out of Memory at training by too long texts.
# This won't affect NER model's performance.
for text in data.texts:
current_sentence.append(text)
if text == {'text': '.', 'labels': ['O']}:
# New Sentence, New data.
sentences.append(current_sentence)
current_sentence = []
sentences.append(current_sentence)
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
insert_ids = []
for sentence in sentences:
token_and_labels = []
last_word_index = len(sentence)-1
for i, text in enumerate(sentence):
if i != 0 and i != last_word_index:
text_to_do = " " + text["text"]
else:
text_to_do = text["text"]
tokens = roberta_tokenizer.tokenize(text_to_do)
for j, token in enumerate(tokens):
token_and_labels.append({
"token": token,
"labels": text["labels"]
})
dataToStore = {
"user": data.user,
"tags": data.tags,
"text_and_labels": sentence,
"token_and_labels": token_and_labels,
"TimeStamp": datetime.now(),
}
# todo: Check the label in text all included.
result = await col.insert_one(dataToStore)
insert_ids.append(str(result.inserted_id))
await asyncio_update_db_last_modify_time(NER_LABEL_COLLECTION)
if refreash_trainer:
await set_trainer_restart_required(True)
return {
"message": "Add Success",
"insert_ids": insert_ids
}
@router.get("/data/labeledText", tags = LABEL_API_TAGS)
async def get_labeled_data(response: Response,
label_name: str = None,
detail: bool = False,
start: int = 0,
end: int = 10):
if end == -1 and start == -1: end = None
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = col.find({"text_and_labels.labels": {"$in": [label_name]}},
{"text_and_labels": detail,
"token_and_labels": detail})
result = await result.to_list(end)
result = result[start:end]
result = list(map(convert_mongo_id,result))
response.status_code = status.HTTP_200_OK
return {
"message": "Success",
"data": result
}
class custom_filter(BaseModel):
mongo_filter: dict = {"user": "example@gmail.com"}
@router.post("/data/labeledText/find:by:mongo:filter", tags = LABEL_API_TAGS)
async def get_labeled_data_by_custom_filter(response: Response,
data: custom_filter,
detail: bool = False,
start: int = 0,
end: int = 10):
if end == -1 and start == -1: end = None
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = col.find(data.mongo_filter,
{"text_and_labels": detail,
"token_and_labels": detail})
result = await result.to_list(end)
result = result[start:end]
result = list(map(convert_mongo_id,result))
response.status_code = status.HTTP_200_OK
return {
"message": "Success",
"data": result
}
@router.get("/data/labeledText/{_id}", tags = LABEL_API_TAGS)
async def get_labeled_data_by_id(response: Response,
_id,
detail: bool = True):
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = await col.find_one({"_id": ObjectId(_id)},
{"text_and_labels": detail,
"token_and_labels": detail})
if result:
result["_id"] = str(result["_id"])
response.status_code = status.HTTP_200_OK
return {
"message": "Success",
"data": result
}
else:
response.status_code = status.HTTP_404_NOT_FOUND
return {
"message": f"_id {_id} Not Found."
}
@router.delete("/data/labeledText/{_id}", tags = LABEL_API_TAGS)
async def delete_labeled_data_by_id(response: Response, _id):
mongo_client = await get_database()
col = mongo_client[DATABASE_NAME][NER_LABEL_COLLECTION]
result = await col.delete_one({"_id": ObjectId(_id)})
if result.deleted_count:
response.status_code = status.HTTP_204_NO_CONTENT
return response
else:
response.status_code = status.HTTP_404_NOT_FOUND
response.status_code = status.HTTP_200_OK
return {
"message": f"Failed, {_id} Not Found."
} |
#!/usr/bin/env python3
import argparse
import copy
from datetime import datetime
from distutils.util import strtobool
from distutils.version import LooseVersion
import functools
import os
import pathlib
import shutil
import signal
import subprocess
import sys
import tempfile
import torch
from torch.utils import cpp_extension
from torch.testing._internal.common_utils import (
FILE_SCHEMA,
IS_IN_CI,
TEST_WITH_ROCM,
shell,
set_cwd,
parser as common_parser,
)
import torch.distributed as dist
from typing import Dict, Optional, List
REPO_ROOT = pathlib.Path(__file__).resolve().parent.parent
try:
# using tools/ to optimize test run.
sys.path.append(str(REPO_ROOT))
from tools.testing.test_selections import (
export_S3_test_times,
get_shard_based_on_S3,
# NS: Disable target determination
# get_slow_tests_based_on_S3,
get_specified_test_cases,
get_reordered_tests,
get_test_case_configs,
)
# NS: Disable target determination
# from tools.testing.modulefinder_determinator import (
# should_run_test,
# TARGET_DET_LIST,
# )
HAVE_TEST_SELECTION_TOOLS = True
except ImportError:
HAVE_TEST_SELECTION_TOOLS = False
print(
"Unable to import test_selections from tools/testing. Running without test selection stats..."
)
def discover_tests(
base_dir: Optional[pathlib.Path] = None,
blocklisted_patterns: Optional[List[str]] = None,
blocklisted_tests: Optional[List[str]] = None,
extra_tests: Optional[List[str]] = None) -> List[str]:
"""
Searches for all python files starting with test_ excluding one specified by patterns
"""
def skip_test_p(name: str) -> bool:
rc = False
if blocklisted_patterns is not None:
rc |= any(name.startswith(pattern) for pattern in blocklisted_patterns)
if blocklisted_tests is not None:
rc |= name in blocklisted_tests
return rc
cwd = pathlib.Path(__file__).resolve().parent if base_dir is None else base_dir
all_py_files = list(cwd.glob('**/test_*.py'))
rc = [str(fname.relative_to(cwd))[:-3] for fname in all_py_files]
# Invert slashes on Windows
if sys.platform == "win32":
rc = [name.replace('\\', '/') for name in rc]
rc = [test for test in rc if not skip_test_p(test)]
if extra_tests is not None:
rc += extra_tests
return sorted(rc)
TESTS = discover_tests(
blocklisted_patterns=[
'ao',
'bottleneck_test',
'custom_backend',
'custom_operator',
'fx', # executed by test_fx.py
'jit', # executed by test_jit.py
'mobile',
'onnx',
'package', # executed by test_package.py
'quantization', # executed by test_quantization.py
'autograd', # executed by test_autograd.py
],
blocklisted_tests=[
'test_bundled_images',
'test_cpp_extensions_aot',
'test_determination',
'test_jit_fuser',
'test_jit_simple',
'test_jit_string',
'test_kernel_launch_checks',
'test_metal',
'test_nnapi',
'test_segment_reductions',
'test_static_runtime',
'test_throughput_benchmark',
'test_typing',
"distributed/algorithms/ddp_comm_hooks/test_ddp_hooks",
"distributed/algorithms/quantization/test_quantization",
"distributed/bin/test_script",
"distributed/elastic/multiprocessing/bin/test_script",
"distributed/launcher/bin/test_script",
"distributed/launcher/bin/test_script_init_method",
"distributed/launcher/bin/test_script_is_torchelastic_launched",
"distributed/launcher/bin/test_script_local_rank",
"distributed/test_c10d_spawn",
'distributions/test_transforms',
'distributions/test_utils',
],
extra_tests=[
"test_cpp_extensions_aot_ninja",
"test_cpp_extensions_aot_no_ninja",
"distributed/elastic/timer/api_test",
"distributed/elastic/timer/local_timer_example",
"distributed/elastic/timer/local_timer_test",
"distributed/elastic/events/lib_test",
"distributed/elastic/metrics/api_test",
"distributed/elastic/utils/logging_test",
"distributed/elastic/utils/util_test",
"distributed/elastic/utils/distributed_test",
"distributed/elastic/multiprocessing/api_test",
"test_deploy",
]
)
FSDP_TEST = [test for test in TESTS if test.startswith("distributed/fsdp")]
# Tests need to be run with pytest.
USE_PYTEST_LIST = [
"distributed/pipeline/sync/skip/test_api",
"distributed/pipeline/sync/skip/test_gpipe",
"distributed/pipeline/sync/skip/test_inspect_skip_layout",
"distributed/pipeline/sync/skip/test_leak",
"distributed/pipeline/sync/skip/test_portal",
"distributed/pipeline/sync/skip/test_stash_pop",
"distributed/pipeline/sync/skip/test_tracker",
"distributed/pipeline/sync/skip/test_verify_skippables",
"distributed/pipeline/sync/test_balance",
"distributed/pipeline/sync/test_bugs",
"distributed/pipeline/sync/test_checkpoint",
"distributed/pipeline/sync/test_copy",
"distributed/pipeline/sync/test_deferred_batch_norm",
"distributed/pipeline/sync/test_dependency",
"distributed/pipeline/sync/test_inplace",
"distributed/pipeline/sync/test_microbatch",
"distributed/pipeline/sync/test_phony",
"distributed/pipeline/sync/test_pipe",
"distributed/pipeline/sync/test_pipeline",
"distributed/pipeline/sync/test_stream",
"distributed/pipeline/sync/test_transparency",
"distributed/pipeline/sync/test_worker",
"distributions/test_constraints",
"distributions/test_transforms",
"distributions/test_utils",
"test_typing",
"distributed/elastic/events/lib_test",
"distributed/elastic/agent/server/test/api_test",
"test_deploy",
]
WINDOWS_BLOCKLIST = [
"distributed/nn/jit/test_instantiator",
"distributed/rpc/test_faulty_agent",
"distributed/rpc/test_tensorpipe_agent",
"distributed/rpc/test_share_memory",
"distributed/rpc/cuda/test_tensorpipe_agent",
"distributed/pipeline/sync/skip/test_api",
"distributed/pipeline/sync/skip/test_gpipe",
"distributed/pipeline/sync/skip/test_inspect_skip_layout",
"distributed/pipeline/sync/skip/test_leak",
"distributed/pipeline/sync/skip/test_portal",
"distributed/pipeline/sync/skip/test_stash_pop",
"distributed/pipeline/sync/skip/test_tracker",
"distributed/pipeline/sync/skip/test_verify_skippables",
"distributed/pipeline/sync/test_balance",
"distributed/pipeline/sync/test_bugs",
"distributed/pipeline/sync/test_checkpoint",
"distributed/pipeline/sync/test_copy",
"distributed/pipeline/sync/test_deferred_batch_norm",
"distributed/pipeline/sync/test_dependency",
"distributed/pipeline/sync/test_inplace",
"distributed/pipeline/sync/test_microbatch",
"distributed/pipeline/sync/test_phony",
"distributed/pipeline/sync/test_pipe",
"distributed/pipeline/sync/test_pipeline",
"distributed/pipeline/sync/test_stream",
"distributed/pipeline/sync/test_transparency",
"distributed/pipeline/sync/test_worker",
"distributed/elastic/agent/server/test/api_test",
"distributed/elastic/multiprocessing/api_test",
"distributed/_shard/checkpoint/test_checkpoint"
"distributed/_shard/checkpoint/test_file_system_checkpoint"
"distributed/_shard/sharding_spec/test_sharding_spec",
"distributed/_shard/sharding_plan/test_sharding_plan",
"distributed/_shard/sharded_tensor/test_megatron_prototype",
"distributed/_shard/sharded_tensor/test_sharded_tensor",
"distributed/_shard/sharded_tensor/test_sharded_tensor_reshard",
"distributed/_shard/sharded_tensor/ops/test_chunk",
"distributed/_shard/sharded_tensor/ops/test_elementwise_ops",
"distributed/_shard/sharded_tensor/ops/test_embedding",
"distributed/_shard/sharded_tensor/ops/test_embedding_bag",
"distributed/_shard/sharded_tensor/ops/test_binary_cmp",
"distributed/_shard/sharded_tensor/ops/test_init",
"distributed/_shard/sharded_tensor/ops/test_linear",
"distributed/_shard/sharded_tensor/ops/test_math_ops",
"distributed/_shard/sharded_tensor/ops/test_matrix_ops",
"distributed/_shard/sharded_tensor/ops/test_softmax",
"distributed/_shard/sharded_optim/test_sharded_optim",
"distributed/_shard/test_partial_tensor",
"distributed/_shard/test_replicated_tensor",
] + FSDP_TEST
ROCM_BLOCKLIST = [
"distributed/nn/jit/test_instantiator",
"distributed/rpc/test_faulty_agent",
"distributed/rpc/test_tensorpipe_agent",
"distributed/rpc/test_share_memory",
"distributed/rpc/cuda/test_tensorpipe_agent",
"distributed/_shard/checkpoint/test_checkpoint"
"distributed/_shard/checkpoint/test_file_system_checkpoint"
"distributed/_shard/sharding_spec/test_sharding_spec",
"distributed/_shard/sharding_plan/test_sharding_plan",
"distributed/_shard/sharded_tensor/test_megatron_prototype",
"distributed/_shard/sharded_tensor/test_sharded_tensor",
"distributed/_shard/sharded_tensor/test_sharded_tensor_reshard",
"distributed/_shard/sharded_tensor/ops/test_chunk",
"distributed/_shard/sharded_tensor/ops/test_elementwise_ops",
"distributed/_shard/sharded_tensor/ops/test_embedding",
"distributed/_shard/sharded_tensor/ops/test_embedding_bag",
"distributed/_shard/sharded_tensor/ops/test_binary_cmp",
"distributed/_shard/sharded_tensor/ops/test_init",
"distributed/_shard/sharded_tensor/ops/test_linear",
"distributed/_shard/sharded_tensor/ops/test_math_ops",
"distributed/_shard/sharded_tensor/ops/test_matrix_ops",
"distributed/_shard/sharded_tensor/ops/test_softmax",
"distributed/_shard/sharded_optim/test_sharded_optim",
"distributed/_shard/test_partial_tensor",
"distributed/_shard/test_replicated_tensor",
"test_determination",
"test_jit_legacy",
"test_type_hints",
"test_openmp",
]
RUN_PARALLEL_BLOCKLIST = [
"test_cpp_extensions_jit",
"test_jit_disabled",
"test_mobile_optimizer",
"test_multiprocessing",
"test_multiprocessing_spawn",
"test_namedtuple_return_api",
"test_overrides",
"test_show_pickle",
"test_tensorexpr",
"test_cuda_primary_ctx",
] + FSDP_TEST
WINDOWS_COVERAGE_BLOCKLIST = []
# A subset of our TEST list that validates PyTorch's ops, modules, and autograd function as expected
CORE_TEST_LIST = [
"test_autograd",
"test_modules",
"test_nn",
"test_ops",
"test_ops_gradients",
"test_ops_jit",
"test_torch"
]
# the JSON file to store the S3 test stats
TEST_TIMES_FILE = ".pytorch-test-times.json"
# if a test file takes longer than 5 min, we add it to TARGET_DET_LIST
SLOW_TEST_THRESHOLD = 300
DISTRIBUTED_TESTS_CONFIG = {}
if dist.is_available():
DISTRIBUTED_TESTS_CONFIG["test"] = {"WORLD_SIZE": "1"}
if not TEST_WITH_ROCM and dist.is_mpi_available():
DISTRIBUTED_TESTS_CONFIG["mpi"] = {
"WORLD_SIZE": "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-mpi",
}
if dist.is_nccl_available():
DISTRIBUTED_TESTS_CONFIG["nccl"] = {
"WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-nccl",
}
if dist.is_gloo_available():
DISTRIBUTED_TESTS_CONFIG["gloo"] = {
"WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-gloo",
}
# https://stackoverflow.com/questions/2549939/get-signal-names-from-numbers-in-python
SIGNALS_TO_NAMES_DICT = {
getattr(signal, n): n for n in dir(signal) if n.startswith("SIG") and "_" not in n
}
CPP_EXTENSIONS_ERROR = """
Ninja (https://ninja-build.org) is required for some of the C++ extensions
tests, but it could not be found. Install ninja with `pip install ninja`
or `conda install ninja`. Alternatively, disable said tests with
`run_test.py --exclude test_cpp_extensions_aot_ninja test_cpp_extensions_jit`.
"""
PYTORCH_COLLECT_COVERAGE = bool(os.environ.get("PYTORCH_COLLECT_COVERAGE"))
ENABLE_PR_HISTORY_REORDERING = bool(
os.environ.get("ENABLE_PR_HISTORY_REORDERING", "0") == "1"
)
JIT_EXECUTOR_TESTS = [
"test_jit_profiling",
"test_jit_legacy",
"test_jit_fuser_legacy",
]
DISTRIBUTED_TESTS = [test for test in TESTS if test.startswith("distributed")]
TESTS_REQUIRING_LAPACK = [
"distributions/test_constraints",
"distributions/test_distributions",
]
# Dictionary matching test modules (in TESTS) to lists of test cases (within that test_module) that would be run when
# options.run_specified_test_cases is enabled.
# For example:
# {
# "test_nn": ["test_doubletensor_avg_pool3d", "test_share_memory", "test_hook_requires_grad"],
# ...
# }
# then for test_nn.py, we would ONLY run test_doubletensor_avg_pool3d, test_share_memory, and test_hook_requires_grad.
SPECIFIED_TEST_CASES_DICT: Dict[str, List[str]] = {}
# The file from which the SPECIFIED_TEST_CASES_DICT will be filled, a CSV of test cases that would be run when
# options.run_specified_test_cases is enabled.
SPECIFIED_TEST_CASES_FILE: str = ".pytorch_specified_test_cases.csv"
def print_to_stderr(message):
print(message, file=sys.stderr)
def get_test_case_args(test_module, using_pytest) -> List[str]:
args = []
# if test_module not specified or specified with '__all__' then run all tests
if (
test_module not in SPECIFIED_TEST_CASES_DICT
or "__all__" in SPECIFIED_TEST_CASES_DICT[test_module]
):
return args
if using_pytest:
args.append("-k")
args.append(" or ".join(SPECIFIED_TEST_CASES_DICT[test_module]))
else:
for test in SPECIFIED_TEST_CASES_DICT[test_module]:
args.append("-k")
args.append(test)
return args
def get_executable_command(options, allow_pytest, disable_coverage=False):
if options.coverage and not disable_coverage:
executable = ["coverage", "run", "--parallel-mode", "--source=torch"]
else:
executable = [sys.executable]
if options.pytest:
if allow_pytest:
executable += ["-m", "pytest"]
else:
print_to_stderr(
"Pytest cannot be used for this test. Falling back to unittest."
)
return executable
def run_test(
test_module, test_directory, options, launcher_cmd=None, extra_unittest_args=None
):
unittest_args = options.additional_unittest_args.copy()
if options.verbose:
unittest_args.append(f'-{'v'*options.verbose}') # in case of pytest
if test_module in RUN_PARALLEL_BLOCKLIST:
unittest_args = [
arg for arg in unittest_args if not arg.startswith("--run-parallel")
]
if extra_unittest_args:
assert isinstance(extra_unittest_args, list)
unittest_args.extend(extra_unittest_args)
# If using pytest, replace -f with equivalent -x
if options.pytest:
unittest_args = [arg if arg != "-f" else "-x" for arg in unittest_args]
elif IS_IN_CI:
# use the downloaded test cases configuration, not supported in pytest
unittest_args.extend(["--import-slow-tests", "--import-disabled-tests"])
# Multiprocessing related tests cannot run with coverage.
# Tracking issue: https://github.com/pytorch/pytorch/issues/50661
disable_coverage = (
sys.platform == "win32" and test_module in WINDOWS_COVERAGE_BLOCKLIST
)
# Extra arguments are not supported with pytest
executable = get_executable_command(
options, allow_pytest=not extra_unittest_args, disable_coverage=disable_coverage
)
# TODO: move this logic into common_utils.py instead of passing in "-k" individually
# The following logic for running specified tests will only run for non-distributed tests, as those are dispatched
# to test_distributed and not run_test (this function)
if options.run_specified_test_cases:
unittest_args.extend(get_test_case_args(test_module, "pytest" in executable))
# Can't call `python -m unittest test_*` here because it doesn't run code
# in `if __name__ == '__main__': `. So call `python test_*.py` instead.
argv = [test_module + ".py"] + unittest_args
command = (launcher_cmd or []) + executable + argv
print_to_stderr("Executing {} ... [{}]".format(command, datetime.now()))
return shell(command, test_directory)
def test_cuda_primary_ctx(test_module, test_directory, options):
return run_test(
test_module, test_directory, options, extra_unittest_args=["--subprocess"]
)
run_test_with_subprocess = functools.partial(run_test, extra_unittest_args=["--subprocess"])
def get_run_test_with_subprocess_fn():
return lambda test_module, test_directory, options: run_test_with_subprocess(test_module, test_directory, options)
def _test_cpp_extensions_aot(test_directory, options, use_ninja):
if use_ninja:
try:
cpp_extension.verify_ninja_availability()
except RuntimeError:
print(CPP_EXTENSIONS_ERROR)
return 1
# Wipe the build folder, if it exists already
cpp_extensions_test_dir = os.path.join(test_directory, "cpp_extensions")
cpp_extensions_test_build_dir = os.path.join(cpp_extensions_test_dir, "build")
if os.path.exists(cpp_extensions_test_build_dir):
shutil.rmtree(cpp_extensions_test_build_dir)
# Build the test cpp extensions modules
shell_env = os.environ.copy()
shell_env["USE_NINJA"] = str(1 if use_ninja else 0)
cmd = [sys.executable, "setup.py", "install", "--root", "./install"]
return_code = shell(cmd, cwd=cpp_extensions_test_dir, env=shell_env)
if return_code != 0:
return return_code
if sys.platform != "win32":
return_code = shell(
cmd,
cwd=os.path.join(cpp_extensions_test_dir, "no_python_abi_suffix_test"),
env=shell_env,
)
if return_code != 0:
return return_code
# "install" the test modules and run tests
python_path = os.environ.get("PYTHONPATH", "")
from shutil import copyfile
test_module = "test_cpp_extensions_aot" + ("_ninja" if use_ninja else "_no_ninja")
copyfile(
test_directory + "/test_cpp_extensions_aot.py",
test_directory + "/" + test_module + ".py",
)
try:
cpp_extensions = os.path.join(test_directory, "cpp_extensions")
install_directory = ""
# install directory is the one that is named site-packages
for root, directories, _ in os.walk(os.path.join(cpp_extensions, "install")):
for directory in directories:
if "-packages" in directory:
install_directory = os.path.join(root, directory)
assert install_directory, "install_directory must not be empty"
os.environ["PYTHONPATH"] = os.pathsep.join([install_directory, python_path])
return run_test(test_module, test_directory, options)
finally:
os.environ["PYTHONPATH"] = python_path
if os.path.exists(test_directory + "/" + test_module + ".py"):
os.remove(test_directory + "/" + test_module + ".py")
def test_cpp_extensions_aot_ninja(test_module, test_directory, options):
return _test_cpp_extensions_aot(test_directory, options, use_ninja=True)
def test_cpp_extensions_aot_no_ninja(test_module, test_directory, options):
return _test_cpp_extensions_aot(test_directory, options, use_ninja=False)
def test_distributed(test_module, test_directory, options):
# MPI tests are broken with Python-3.9
mpi_available = subprocess.call(
"command -v mpiexec", shell=True
) == 0 and sys.version_info < (3, 9)
if options.verbose and not mpi_available:
print_to_stderr("MPI not available -- MPI backend tests will be skipped")
config = DISTRIBUTED_TESTS_CONFIG
for backend, env_vars in config.items():
if sys.platform == "win32" and backend != "gloo":
continue
if backend == "mpi" and not mpi_available:
continue
for with_init_file in {True, False}:
if sys.platform == "win32" and not with_init_file:
continue
tmp_dir = tempfile.mkdtemp()
if options.verbose:
init_str = "with {} init_method"
with_init = init_str.format("file" if with_init_file else "env")
print_to_stderr(
"Running distributed tests for the {} backend {}".format(
backend, with_init
)
)
old_environ = dict(os.environ)
os.environ["TEMP_DIR"] = tmp_dir
os.environ["BACKEND"] = backend
os.environ["INIT_METHOD"] = "env://"
os.environ.update(env_vars)
if with_init_file:
if test_module == "test_distributed_spawn":
init_method = f"{FILE_SCHEMA}{tmp_dir}/"
else:
init_method = f"{FILE_SCHEMA}{tmp_dir}/shared_init_file"
os.environ["INIT_METHOD"] = init_method
try:
os.mkdir(os.path.join(tmp_dir, "barrier"))
os.mkdir(os.path.join(tmp_dir, "test_dir"))
if backend == "mpi":
# test mpiexec for --noprefix option
with open(os.devnull, "w") as devnull:
allowrunasroot_opt = (
"--allow-run-as-root"
if subprocess.call(
'mpiexec --allow-run-as-root -n 1 bash -c ""',
shell=True,
stdout=devnull,
stderr=subprocess.STDOUT,
)
== 0
else ""
)
noprefix_opt = (
"--noprefix"
if subprocess.call(
f'mpiexec {allowrunasroot_opt} -n 1 --noprefix bash -c ""',
shell=True,
stdout=devnull,
stderr=subprocess.STDOUT,
)
== 0
else ""
)
mpiexec = ["mpiexec", "-n", "3", noprefix_opt, allowrunasroot_opt]
return_code = run_test(
test_module, test_directory, options, launcher_cmd=mpiexec
)
else:
return_code = run_test(test_module, test_directory, options, extra_unittest_args=["--subprocess"])
if return_code != 0:
return return_code
finally:
shutil.rmtree(tmp_dir)
os.environ.clear()
os.environ.update(old_environ)
return 0
CUSTOM_HANDLERS = {
"test_cuda_primary_ctx": test_cuda_primary_ctx,
"test_cpp_extensions_aot_no_ninja": test_cpp_extensions_aot_no_ninja,
"test_cpp_extensions_aot_ninja": test_cpp_extensions_aot_ninja,
"distributed/test_distributed_spawn": test_distributed,
"distributed/test_c10d_nccl": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_gloo": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_common": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_spawn_gloo": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_spawn_nccl": get_run_test_with_subprocess_fn(),
"distributed/test_store": get_run_test_with_subprocess_fn(),
"distributed/test_pg_wrapper": get_run_test_with_subprocess_fn(),
"distributed/rpc/test_faulty_agent": get_run_test_with_subprocess_fn(),
"distributed/rpc/test_tensorpipe_agent": get_run_test_with_subprocess_fn(),
"distributed/rpc/test_share_memory": get_run_test_with_subprocess_fn(),
"distributed/rpc/cuda/test_tensorpipe_agent": get_run_test_with_subprocess_fn(),
}
def parse_test_module(test):
return test.split(".")[0]
class TestChoices(list):
def __init__(self, *args, **kwargs):
super(TestChoices, self).__init__(args[0])
def __contains__(self, item):
return list.__contains__(self, parse_test_module(item))
def parse_args():
parser = argparse.ArgumentParser(
description="Run the PyTorch unit test suite",
epilog="where TESTS is any of: {}".format(", ".join(TESTS)),
formatter_class=argparse.RawTextHelpFormatter,
parents=[common_parser]
)
parser.add_argument(
"-v",
"--verbose",
action="count",
default=0,
help="print verbose information and test-by-test results",
)
parser.add_argument("--jit", "--jit", action="store_true", help="run all jit tests")
parser.add_argument(
"--distributed-tests",
"--distributed-tests",
action="store_true",
help="run all distributed tests",
)
parser.add_argument(
"-core",
"--core",
action="store_true",
help="Only run core tests, or tests that validate PyTorch's ops, modules,"
"and autograd. They are defined by CORE_TEST_LIST."
)
parser.add_argument(
"-pt",
"--pytest",
action="store_true",
help="If true, use `pytest` to execute the tests. E.g., this runs "
"TestTorch with pytest in verbose and coverage mode: "
"python run_test.py -vci torch -pt",
)
parser.add_argument(
"-c",
"--coverage",
action="store_true",
help="enable coverage",
default=PYTORCH_COLLECT_COVERAGE,
)
parser.add_argument(
"-i",
"--include",
nargs="+",
choices=TestChoices(TESTS),
default=TESTS,
metavar="TESTS",
help="select a set of tests to include (defaults to ALL tests)."
" tests must be a part of the TESTS list defined in run_test.py",
)
parser.add_argument(
"-x",
"--exclude",
nargs="+",
choices=TESTS,
metavar="TESTS",
default=[],
help="select a set of tests to exclude",
)
parser.add_argument(
"-f",
"--first",
choices=TESTS,
metavar="TESTS",
help="select the test to start from (excludes previous tests)",
)
parser.add_argument(
"-l",
"--last",
choices=TESTS,
metavar="TESTS",
help="select the last test to run (excludes following tests)",
)
parser.add_argument(
"--bring-to-front",
nargs="+",
choices=TestChoices(TESTS),
default=[],
metavar="TESTS",
help="select a set of tests to run first. This can be used in situations"
" where you want to run all tests, but care more about some set, "
"e.g. after making a change to a specific component",
)
parser.add_argument(
"--ignore-win-blocklist",
action="store_true",
help="always run blocklisted windows tests",
)
# NS: Disable target determination until it can be made more reliable
# parser.add_argument(
# "--determine-from",
# help="File of affected source filenames to determine which tests to run.",
# )
parser.add_argument(
"--continue-through-error",
action="store_true",
help="Runs the full test suite despite one of the tests failing",
default=strtobool(os.environ.get("CONTINUE_THROUGH_ERROR", "False")),
)
parser.add_argument(
"additional_unittest_args",
nargs="*",
help="additional arguments passed through to unittest, e.g., "
"python run_test.py -i sparse -- TestSparse.test_factory_size_check",
)
parser.add_argument(
"--export-past-test-times",
nargs="?",
type=str,
const=TEST_TIMES_FILE,
help="dumps test times from previous S3 stats into a file, format JSON",
)
parser.add_argument(
"--shard",
nargs=2,
type=int,
help="runs a shard of the tests (taking into account other selections), e.g., "
"--shard 2 3 will break up the selected tests into 3 shards and run the tests "
"in the 2nd shard (the first number should not exceed the second)",
)
parser.add_argument(
"--exclude-jit-executor",
action="store_true",
help="exclude tests that are run for a specific jit config",
)
parser.add_argument(
"--exclude-distributed-tests",
action="store_true",
help="exclude distributed tests",
)
parser.add_argument(
"--run-specified-test-cases",
nargs="?",
type=str,
const=SPECIFIED_TEST_CASES_FILE,
help="load specified test cases file dumped from previous OSS CI stats, format CSV. "
" If all test cases should run for a <test_module> please add a single row: \n"
" test_filename,test_case_name\n"
" ...\n"
" <test_module>,__all__\n"
" ...\n"
'how we use the stats will be based on option "--use-specified-test-cases-by".',
)
parser.add_argument(
"--use-specified-test-cases-by",
type=str,
choices=["include", "bring-to-front"],
default="include",
help='used together with option "--run-specified-test-cases". When specified test case '
"file is set, this option allows the user to control whether to only run the specified test "
"modules or to simply bring the specified modules to front and also run the remaining "
"modules. Note: regardless of this option, we will only run the specified test cases "
" within a specified test module. For unspecified test modules with the bring-to-front "
"option, all test cases will be run, as one may expect.",
)
parser.add_argument(
"--dry-run",
action="store_true",
help="Only list the test that will run.",
)
return parser.parse_args()
def find_test_index(test, selected_tests, find_last_index=False):
"""Find the index of the first or last occurrence of a given test/test module in the list of selected tests.
This function is used to determine the indices when slicing the list of selected tests when
``options.first``(:attr:`find_last_index`=False) and/or ``options.last``(:attr:`find_last_index`=True) are used.
:attr:`selected_tests` can be a list that contains multiple consequent occurrences of tests
as part of the same test module, e.g.:
```
selected_tests = ['autograd', 'cuda', **'torch.TestTorch.test_acos',
'torch.TestTorch.test_tan', 'torch.TestTorch.test_add'**, 'utils']
```
If :attr:`test`='torch' and :attr:`find_last_index`=False, result should be **2**.
If :attr:`test`='torch' and :attr:`find_last_index`=True, result should be **4**.
Args:
test (str): Name of test to lookup
selected_tests (list): List of tests
find_last_index (bool, optional): should we lookup the index of first or last
occurrence (first is default)
Returns:
index of the first or last occurrence of the given test
"""
idx = 0
found_idx = -1
for t in selected_tests:
if t.startswith(test):
found_idx = idx
if not find_last_index:
break
idx += 1
return found_idx
def exclude_tests(exclude_list, selected_tests, exclude_message=None):
for exclude_test in exclude_list:
tests_copy = selected_tests[:]
for test in tests_copy:
if test.startswith(exclude_test):
if exclude_message is not None:
print_to_stderr("Excluding {} {}".format(test, exclude_message))
selected_tests.remove(test)
return selected_tests
def get_selected_tests(options):
# First make sure run specific test cases options are processed.
if options.run_specified_test_cases:
if options.use_specified_test_cases_by == "include":
options.include = list(SPECIFIED_TEST_CASES_DICT.keys())
elif options.use_specified_test_cases_by == "bring-to-front":
options.bring_to_front = list(SPECIFIED_TEST_CASES_DICT.keys())
selected_tests = options.include
# filter if there's JIT only and distributed only test options
if options.jit:
selected_tests = list(
filter(lambda test_name: "jit" in test_name, selected_tests)
)
if options.distributed_tests:
selected_tests = list(
filter(lambda test_name: test_name in DISTRIBUTED_TESTS, selected_tests)
)
# Filter to only run core tests when --core option is specified
if options.core:
selected_tests = list(
filter(lambda test_name: test_name in CORE_TEST_LIST, selected_tests)
)
# process reordering
if options.bring_to_front:
to_front = set(options.bring_to_front)
selected_tests = options.bring_to_front + list(
filter(lambda name: name not in to_front, selected_tests)
)
if options.first:
first_index = find_test_index(options.first, selected_tests)
selected_tests = selected_tests[first_index:]
if options.last:
last_index = find_test_index(options.last, selected_tests, find_last_index=True)
selected_tests = selected_tests[: last_index + 1]
# process exclusion
if options.exclude_jit_executor:
options.exclude.extend(JIT_EXECUTOR_TESTS)
if options.exclude_distributed_tests:
options.exclude.extend(DISTRIBUTED_TESTS)
# these tests failing in CUDA 11.6 temporary disabling. issue https://github.com/pytorch/pytorch/issues/75375
if torch.version.cuda is not None and LooseVersion(torch.version.cuda) == "11.6":
options.exclude.extend(["distributions/test_constraints"])
selected_tests = exclude_tests(options.exclude, selected_tests)
if sys.platform == "win32" and not options.ignore_win_blocklist:
target_arch = os.environ.get("VSCMD_ARG_TGT_ARCH")
if target_arch != "x64":
WINDOWS_BLOCKLIST.append("cpp_extensions_aot_no_ninja")
WINDOWS_BLOCKLIST.append("cpp_extensions_aot_ninja")
WINDOWS_BLOCKLIST.append("cpp_extensions_jit")
WINDOWS_BLOCKLIST.append("jit")
WINDOWS_BLOCKLIST.append("jit_fuser")
# This is exception that's caused by this issue https://github.com/pytorch/pytorch/issues/69460
# This below code should be removed once this issue is solved
if torch.version.cuda is not None and LooseVersion(torch.version.cuda) >= "11.5":
WINDOWS_BLOCKLIST.append("test_cpp_extensions_aot")
WINDOWS_BLOCKLIST.append("test_cpp_extensions_aot_ninja")
WINDOWS_BLOCKLIST.append("test_cpp_extensions_aot_no_ninja")
selected_tests = exclude_tests(WINDOWS_BLOCKLIST, selected_tests, "on Windows")
elif TEST_WITH_ROCM:
selected_tests = exclude_tests(ROCM_BLOCKLIST, selected_tests, "on ROCm")
# sharding
if options.shard:
assert len(options.shard) == 2, "Unexpected shard format"
assert min(options.shard) > 0, "Shards must be positive numbers"
which_shard, num_shards = options.shard
assert (
which_shard <= num_shards
), "Selected shard must be less than or equal to total number of shards"
assert num_shards <= len(
selected_tests
), f"Number of shards must be less than {len(selected_tests)}"
# TODO: fix this to use test_times_filename, but currently this is not working
# because setting the export arg immeidately halts the test execution.
selected_tests = get_shard_based_on_S3(
which_shard, num_shards, selected_tests, TEST_TIMES_FILE
)
# skip all distributed tests if distributed package is not available.
if not dist.is_available():
selected_tests = exclude_tests(DISTRIBUTED_TESTS, selected_tests,
"PyTorch is built without distributed support.")
# skip tests that require LAPACK when it's not available
if not torch._C.has_lapack:
selected_tests = exclude_tests(TESTS_REQUIRING_LAPACK, selected_tests,
"PyTorch is built without LAPACK support.")
return selected_tests
def run_test_module(test: str, test_directory: str, options) -> Optional[str]:
test_module = parse_test_module(test)
# Printing the date here can help diagnose which tests are slow
print_to_stderr("Running {} ... [{}]".format(test, datetime.now()))
handler = CUSTOM_HANDLERS.get(test_module, run_test)
return_code = handler(test_module, test_directory, options)
assert isinstance(return_code, int) and not isinstance(
return_code, bool
), "Return code should be an integer"
if return_code == 0:
return None
message = f"{test} failed!"
if return_code < 0:
# subprocess.Popen returns the child process' exit signal as
# return code -N, where N is the signal number.
signal_name = SIGNALS_TO_NAMES_DICT[-return_code]
message += f" Received signal: {signal_name}"
return message
def main():
options = parse_args()
# TODO: move this export & download function in tools/ folder
test_times_filename = options.export_past_test_times
if test_times_filename:
print(
f"Exporting past test times from S3 to {test_times_filename}, no tests will be run."
)
export_S3_test_times(test_times_filename)
return
specified_test_cases_filename = options.run_specified_test_cases
if specified_test_cases_filename:
print(
f"Loading specified test cases to run from {specified_test_cases_filename}."
)
global SPECIFIED_TEST_CASES_DICT
SPECIFIED_TEST_CASES_DICT = get_specified_test_cases(
specified_test_cases_filename, TESTS
)
test_directory = str(REPO_ROOT / "test")
selected_tests = get_selected_tests(options)
if options.verbose:
print_to_stderr("Selected tests:\n {}".format("\n ".join(selected_tests)))
if options.dry_run:
return
if options.coverage and not PYTORCH_COLLECT_COVERAGE:
shell(["coverage", "erase"])
# NS: Disable target determination until it can be made more reliable
# if options.determine_from is not None and os.path.exists(options.determine_from):
# slow_tests = get_slow_tests_based_on_S3(
# TESTS, TARGET_DET_LIST, SLOW_TEST_THRESHOLD
# )
# print_to_stderr(
# "Added the following tests to target_det tests as calculated based on S3:"
# )
# print_to_stderr(slow_tests)
# with open(options.determine_from, "r") as fh:
# touched_files = [
# os.path.normpath(name.strip())
# for name in fh.read().split("\n")
# if len(name.strip()) > 0
# ]
# # HACK: Ensure the 'test' paths can be traversed by Modulefinder
# sys.path.append(test_directory)
# selected_tests = [
# test
# for test in selected_tests
# if should_run_test(
# TARGET_DET_LIST + slow_tests, test, touched_files, options
# )
# ]
# sys.path.remove(test_directory)
if IS_IN_CI:
selected_tests = get_reordered_tests(
selected_tests, ENABLE_PR_HISTORY_REORDERING
)
# downloading test cases configuration to local environment
get_test_case_configs(dirpath=test_directory)
has_failed = False
failure_messages = []
try:
for test in selected_tests:
options_clone = copy.deepcopy(options)
if test in USE_PYTEST_LIST:
options_clone.pytest = True
err_message = run_test_module(test, test_directory, options_clone)
if err_message is None:
continue
has_failed = True
failure_messages.append(err_message)
if not options_clone.continue_through_error:
raise RuntimeError(err_message)
print_to_stderr(err_message)
finally:
if options.coverage:
from coverage import Coverage
with set_cwd(test_directory):
cov = Coverage()
if PYTORCH_COLLECT_COVERAGE:
cov.load()
cov.combine(strict=False)
cov.save()
if not PYTORCH_COLLECT_COVERAGE:
cov.html_report()
if options.continue_through_error and has_failed:
for err in failure_messages:
print_to_stderr(err)
sys.exit(1)
if __name__ == "__main__":
main()
| #!/usr/bin/env python3
import argparse
import copy
from datetime import datetime
from distutils.util import strtobool
from distutils.version import LooseVersion
import functools
import os
import pathlib
import shutil
import signal
import subprocess
import sys
import tempfile
import torch
from torch.utils import cpp_extension
from torch.testing._internal.common_utils import (
FILE_SCHEMA,
IS_IN_CI,
TEST_WITH_ROCM,
shell,
set_cwd,
parser as common_parser,
)
import torch.distributed as dist
from typing import Dict, Optional, List
REPO_ROOT = pathlib.Path(__file__).resolve().parent.parent
try:
# using tools/ to optimize test run.
sys.path.append(str(REPO_ROOT))
from tools.testing.test_selections import (
export_S3_test_times,
get_shard_based_on_S3,
# NS: Disable target determination
# get_slow_tests_based_on_S3,
get_specified_test_cases,
get_reordered_tests,
get_test_case_configs,
)
# NS: Disable target determination
# from tools.testing.modulefinder_determinator import (
# should_run_test,
# TARGET_DET_LIST,
# )
HAVE_TEST_SELECTION_TOOLS = True
except ImportError:
HAVE_TEST_SELECTION_TOOLS = False
print(
"Unable to import test_selections from tools/testing. Running without test selection stats..."
)
def discover_tests(
base_dir: Optional[pathlib.Path] = None,
blocklisted_patterns: Optional[List[str]] = None,
blocklisted_tests: Optional[List[str]] = None,
extra_tests: Optional[List[str]] = None) -> List[str]:
"""
Searches for all python files starting with test_ excluding one specified by patterns
"""
def skip_test_p(name: str) -> bool:
rc = False
if blocklisted_patterns is not None:
rc |= any(name.startswith(pattern) for pattern in blocklisted_patterns)
if blocklisted_tests is not None:
rc |= name in blocklisted_tests
return rc
cwd = pathlib.Path(__file__).resolve().parent if base_dir is None else base_dir
all_py_files = list(cwd.glob('**/test_*.py'))
rc = [str(fname.relative_to(cwd))[:-3] for fname in all_py_files]
# Invert slashes on Windows
if sys.platform == "win32":
rc = [name.replace('\\', '/') for name in rc]
rc = [test for test in rc if not skip_test_p(test)]
if extra_tests is not None:
rc += extra_tests
return sorted(rc)
TESTS = discover_tests(
blocklisted_patterns=[
'ao',
'bottleneck_test',
'custom_backend',
'custom_operator',
'fx', # executed by test_fx.py
'jit', # executed by test_jit.py
'mobile',
'onnx',
'package', # executed by test_package.py
'quantization', # executed by test_quantization.py
'autograd', # executed by test_autograd.py
],
blocklisted_tests=[
'test_bundled_images',
'test_cpp_extensions_aot',
'test_determination',
'test_jit_fuser',
'test_jit_simple',
'test_jit_string',
'test_kernel_launch_checks',
'test_metal',
'test_nnapi',
'test_segment_reductions',
'test_static_runtime',
'test_throughput_benchmark',
'test_typing',
"distributed/algorithms/ddp_comm_hooks/test_ddp_hooks",
"distributed/algorithms/quantization/test_quantization",
"distributed/bin/test_script",
"distributed/elastic/multiprocessing/bin/test_script",
"distributed/launcher/bin/test_script",
"distributed/launcher/bin/test_script_init_method",
"distributed/launcher/bin/test_script_is_torchelastic_launched",
"distributed/launcher/bin/test_script_local_rank",
"distributed/test_c10d_spawn",
'distributions/test_transforms',
'distributions/test_utils',
],
extra_tests=[
"test_cpp_extensions_aot_ninja",
"test_cpp_extensions_aot_no_ninja",
"distributed/elastic/timer/api_test",
"distributed/elastic/timer/local_timer_example",
"distributed/elastic/timer/local_timer_test",
"distributed/elastic/events/lib_test",
"distributed/elastic/metrics/api_test",
"distributed/elastic/utils/logging_test",
"distributed/elastic/utils/util_test",
"distributed/elastic/utils/distributed_test",
"distributed/elastic/multiprocessing/api_test",
"test_deploy",
]
)
FSDP_TEST = [test for test in TESTS if test.startswith("distributed/fsdp")]
# Tests need to be run with pytest.
USE_PYTEST_LIST = [
"distributed/pipeline/sync/skip/test_api",
"distributed/pipeline/sync/skip/test_gpipe",
"distributed/pipeline/sync/skip/test_inspect_skip_layout",
"distributed/pipeline/sync/skip/test_leak",
"distributed/pipeline/sync/skip/test_portal",
"distributed/pipeline/sync/skip/test_stash_pop",
"distributed/pipeline/sync/skip/test_tracker",
"distributed/pipeline/sync/skip/test_verify_skippables",
"distributed/pipeline/sync/test_balance",
"distributed/pipeline/sync/test_bugs",
"distributed/pipeline/sync/test_checkpoint",
"distributed/pipeline/sync/test_copy",
"distributed/pipeline/sync/test_deferred_batch_norm",
"distributed/pipeline/sync/test_dependency",
"distributed/pipeline/sync/test_inplace",
"distributed/pipeline/sync/test_microbatch",
"distributed/pipeline/sync/test_phony",
"distributed/pipeline/sync/test_pipe",
"distributed/pipeline/sync/test_pipeline",
"distributed/pipeline/sync/test_stream",
"distributed/pipeline/sync/test_transparency",
"distributed/pipeline/sync/test_worker",
"distributions/test_constraints",
"distributions/test_transforms",
"distributions/test_utils",
"test_typing",
"distributed/elastic/events/lib_test",
"distributed/elastic/agent/server/test/api_test",
"test_deploy",
]
WINDOWS_BLOCKLIST = [
"distributed/nn/jit/test_instantiator",
"distributed/rpc/test_faulty_agent",
"distributed/rpc/test_tensorpipe_agent",
"distributed/rpc/test_share_memory",
"distributed/rpc/cuda/test_tensorpipe_agent",
"distributed/pipeline/sync/skip/test_api",
"distributed/pipeline/sync/skip/test_gpipe",
"distributed/pipeline/sync/skip/test_inspect_skip_layout",
"distributed/pipeline/sync/skip/test_leak",
"distributed/pipeline/sync/skip/test_portal",
"distributed/pipeline/sync/skip/test_stash_pop",
"distributed/pipeline/sync/skip/test_tracker",
"distributed/pipeline/sync/skip/test_verify_skippables",
"distributed/pipeline/sync/test_balance",
"distributed/pipeline/sync/test_bugs",
"distributed/pipeline/sync/test_checkpoint",
"distributed/pipeline/sync/test_copy",
"distributed/pipeline/sync/test_deferred_batch_norm",
"distributed/pipeline/sync/test_dependency",
"distributed/pipeline/sync/test_inplace",
"distributed/pipeline/sync/test_microbatch",
"distributed/pipeline/sync/test_phony",
"distributed/pipeline/sync/test_pipe",
"distributed/pipeline/sync/test_pipeline",
"distributed/pipeline/sync/test_stream",
"distributed/pipeline/sync/test_transparency",
"distributed/pipeline/sync/test_worker",
"distributed/elastic/agent/server/test/api_test",
"distributed/elastic/multiprocessing/api_test",
"distributed/_shard/checkpoint/test_checkpoint"
"distributed/_shard/checkpoint/test_file_system_checkpoint"
"distributed/_shard/sharding_spec/test_sharding_spec",
"distributed/_shard/sharding_plan/test_sharding_plan",
"distributed/_shard/sharded_tensor/test_megatron_prototype",
"distributed/_shard/sharded_tensor/test_sharded_tensor",
"distributed/_shard/sharded_tensor/test_sharded_tensor_reshard",
"distributed/_shard/sharded_tensor/ops/test_chunk",
"distributed/_shard/sharded_tensor/ops/test_elementwise_ops",
"distributed/_shard/sharded_tensor/ops/test_embedding",
"distributed/_shard/sharded_tensor/ops/test_embedding_bag",
"distributed/_shard/sharded_tensor/ops/test_binary_cmp",
"distributed/_shard/sharded_tensor/ops/test_init",
"distributed/_shard/sharded_tensor/ops/test_linear",
"distributed/_shard/sharded_tensor/ops/test_math_ops",
"distributed/_shard/sharded_tensor/ops/test_matrix_ops",
"distributed/_shard/sharded_tensor/ops/test_softmax",
"distributed/_shard/sharded_optim/test_sharded_optim",
"distributed/_shard/test_partial_tensor",
"distributed/_shard/test_replicated_tensor",
] + FSDP_TEST
ROCM_BLOCKLIST = [
"distributed/nn/jit/test_instantiator",
"distributed/rpc/test_faulty_agent",
"distributed/rpc/test_tensorpipe_agent",
"distributed/rpc/test_share_memory",
"distributed/rpc/cuda/test_tensorpipe_agent",
"distributed/_shard/checkpoint/test_checkpoint"
"distributed/_shard/checkpoint/test_file_system_checkpoint"
"distributed/_shard/sharding_spec/test_sharding_spec",
"distributed/_shard/sharding_plan/test_sharding_plan",
"distributed/_shard/sharded_tensor/test_megatron_prototype",
"distributed/_shard/sharded_tensor/test_sharded_tensor",
"distributed/_shard/sharded_tensor/test_sharded_tensor_reshard",
"distributed/_shard/sharded_tensor/ops/test_chunk",
"distributed/_shard/sharded_tensor/ops/test_elementwise_ops",
"distributed/_shard/sharded_tensor/ops/test_embedding",
"distributed/_shard/sharded_tensor/ops/test_embedding_bag",
"distributed/_shard/sharded_tensor/ops/test_binary_cmp",
"distributed/_shard/sharded_tensor/ops/test_init",
"distributed/_shard/sharded_tensor/ops/test_linear",
"distributed/_shard/sharded_tensor/ops/test_math_ops",
"distributed/_shard/sharded_tensor/ops/test_matrix_ops",
"distributed/_shard/sharded_tensor/ops/test_softmax",
"distributed/_shard/sharded_optim/test_sharded_optim",
"distributed/_shard/test_partial_tensor",
"distributed/_shard/test_replicated_tensor",
"test_determination",
"test_jit_legacy",
"test_type_hints",
"test_openmp",
]
RUN_PARALLEL_BLOCKLIST = [
"test_cpp_extensions_jit",
"test_jit_disabled",
"test_mobile_optimizer",
"test_multiprocessing",
"test_multiprocessing_spawn",
"test_namedtuple_return_api",
"test_overrides",
"test_show_pickle",
"test_tensorexpr",
"test_cuda_primary_ctx",
] + FSDP_TEST
WINDOWS_COVERAGE_BLOCKLIST = []
# A subset of our TEST list that validates PyTorch's ops, modules, and autograd function as expected
CORE_TEST_LIST = [
"test_autograd",
"test_modules",
"test_nn",
"test_ops",
"test_ops_gradients",
"test_ops_jit",
"test_torch"
]
# the JSON file to store the S3 test stats
TEST_TIMES_FILE = ".pytorch-test-times.json"
# if a test file takes longer than 5 min, we add it to TARGET_DET_LIST
SLOW_TEST_THRESHOLD = 300
DISTRIBUTED_TESTS_CONFIG = {}
if dist.is_available():
DISTRIBUTED_TESTS_CONFIG["test"] = {"WORLD_SIZE": "1"}
if not TEST_WITH_ROCM and dist.is_mpi_available():
DISTRIBUTED_TESTS_CONFIG["mpi"] = {
"WORLD_SIZE": "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-mpi",
}
if dist.is_nccl_available():
DISTRIBUTED_TESTS_CONFIG["nccl"] = {
"WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-nccl",
}
if dist.is_gloo_available():
DISTRIBUTED_TESTS_CONFIG["gloo"] = {
"WORLD_SIZE": "2" if torch.cuda.device_count() == 2 else "3",
"TEST_REPORT_SOURCE_OVERRIDE": "dist-gloo",
}
# https://stackoverflow.com/questions/2549939/get-signal-names-from-numbers-in-python
SIGNALS_TO_NAMES_DICT = {
getattr(signal, n): n for n in dir(signal) if n.startswith("SIG") and "_" not in n
}
CPP_EXTENSIONS_ERROR = """
Ninja (https://ninja-build.org) is required for some of the C++ extensions
tests, but it could not be found. Install ninja with `pip install ninja`
or `conda install ninja`. Alternatively, disable said tests with
`run_test.py --exclude test_cpp_extensions_aot_ninja test_cpp_extensions_jit`.
"""
PYTORCH_COLLECT_COVERAGE = bool(os.environ.get("PYTORCH_COLLECT_COVERAGE"))
ENABLE_PR_HISTORY_REORDERING = bool(
os.environ.get("ENABLE_PR_HISTORY_REORDERING", "0") == "1"
)
JIT_EXECUTOR_TESTS = [
"test_jit_profiling",
"test_jit_legacy",
"test_jit_fuser_legacy",
]
DISTRIBUTED_TESTS = [test for test in TESTS if test.startswith("distributed")]
TESTS_REQUIRING_LAPACK = [
"distributions/test_constraints",
"distributions/test_distributions",
]
# Dictionary matching test modules (in TESTS) to lists of test cases (within that test_module) that would be run when
# options.run_specified_test_cases is enabled.
# For example:
# {
# "test_nn": ["test_doubletensor_avg_pool3d", "test_share_memory", "test_hook_requires_grad"],
# ...
# }
# then for test_nn.py, we would ONLY run test_doubletensor_avg_pool3d, test_share_memory, and test_hook_requires_grad.
SPECIFIED_TEST_CASES_DICT: Dict[str, List[str]] = {}
# The file from which the SPECIFIED_TEST_CASES_DICT will be filled, a CSV of test cases that would be run when
# options.run_specified_test_cases is enabled.
SPECIFIED_TEST_CASES_FILE: str = ".pytorch_specified_test_cases.csv"
def print_to_stderr(message):
print(message, file=sys.stderr)
def get_test_case_args(test_module, using_pytest) -> List[str]:
args = []
# if test_module not specified or specified with '__all__' then run all tests
if (
test_module not in SPECIFIED_TEST_CASES_DICT
or "__all__" in SPECIFIED_TEST_CASES_DICT[test_module]
):
return args
if using_pytest:
args.append("-k")
args.append(" or ".join(SPECIFIED_TEST_CASES_DICT[test_module]))
else:
for test in SPECIFIED_TEST_CASES_DICT[test_module]:
args.append("-k")
args.append(test)
return args
def get_executable_command(options, allow_pytest, disable_coverage=False):
if options.coverage and not disable_coverage:
executable = ["coverage", "run", "--parallel-mode", "--source=torch"]
else:
executable = [sys.executable]
if options.pytest:
if allow_pytest:
executable += ["-m", "pytest"]
else:
print_to_stderr(
"Pytest cannot be used for this test. Falling back to unittest."
)
return executable
def run_test(
test_module, test_directory, options, launcher_cmd=None, extra_unittest_args=None
):
unittest_args = options.additional_unittest_args.copy()
if options.verbose:
unittest_args.append(f'-{"v"*options.verbose}') # in case of pytest
if test_module in RUN_PARALLEL_BLOCKLIST:
unittest_args = [
arg for arg in unittest_args if not arg.startswith("--run-parallel")
]
if extra_unittest_args:
assert isinstance(extra_unittest_args, list)
unittest_args.extend(extra_unittest_args)
# If using pytest, replace -f with equivalent -x
if options.pytest:
unittest_args = [arg if arg != "-f" else "-x" for arg in unittest_args]
elif IS_IN_CI:
# use the downloaded test cases configuration, not supported in pytest
unittest_args.extend(["--import-slow-tests", "--import-disabled-tests"])
# Multiprocessing related tests cannot run with coverage.
# Tracking issue: https://github.com/pytorch/pytorch/issues/50661
disable_coverage = (
sys.platform == "win32" and test_module in WINDOWS_COVERAGE_BLOCKLIST
)
# Extra arguments are not supported with pytest
executable = get_executable_command(
options, allow_pytest=not extra_unittest_args, disable_coverage=disable_coverage
)
# TODO: move this logic into common_utils.py instead of passing in "-k" individually
# The following logic for running specified tests will only run for non-distributed tests, as those are dispatched
# to test_distributed and not run_test (this function)
if options.run_specified_test_cases:
unittest_args.extend(get_test_case_args(test_module, "pytest" in executable))
# Can't call `python -m unittest test_*` here because it doesn't run code
# in `if __name__ == '__main__': `. So call `python test_*.py` instead.
argv = [test_module + ".py"] + unittest_args
command = (launcher_cmd or []) + executable + argv
print_to_stderr("Executing {} ... [{}]".format(command, datetime.now()))
return shell(command, test_directory)
def test_cuda_primary_ctx(test_module, test_directory, options):
return run_test(
test_module, test_directory, options, extra_unittest_args=["--subprocess"]
)
run_test_with_subprocess = functools.partial(run_test, extra_unittest_args=["--subprocess"])
def get_run_test_with_subprocess_fn():
return lambda test_module, test_directory, options: run_test_with_subprocess(test_module, test_directory, options)
def _test_cpp_extensions_aot(test_directory, options, use_ninja):
if use_ninja:
try:
cpp_extension.verify_ninja_availability()
except RuntimeError:
print(CPP_EXTENSIONS_ERROR)
return 1
# Wipe the build folder, if it exists already
cpp_extensions_test_dir = os.path.join(test_directory, "cpp_extensions")
cpp_extensions_test_build_dir = os.path.join(cpp_extensions_test_dir, "build")
if os.path.exists(cpp_extensions_test_build_dir):
shutil.rmtree(cpp_extensions_test_build_dir)
# Build the test cpp extensions modules
shell_env = os.environ.copy()
shell_env["USE_NINJA"] = str(1 if use_ninja else 0)
cmd = [sys.executable, "setup.py", "install", "--root", "./install"]
return_code = shell(cmd, cwd=cpp_extensions_test_dir, env=shell_env)
if return_code != 0:
return return_code
if sys.platform != "win32":
return_code = shell(
cmd,
cwd=os.path.join(cpp_extensions_test_dir, "no_python_abi_suffix_test"),
env=shell_env,
)
if return_code != 0:
return return_code
# "install" the test modules and run tests
python_path = os.environ.get("PYTHONPATH", "")
from shutil import copyfile
test_module = "test_cpp_extensions_aot" + ("_ninja" if use_ninja else "_no_ninja")
copyfile(
test_directory + "/test_cpp_extensions_aot.py",
test_directory + "/" + test_module + ".py",
)
try:
cpp_extensions = os.path.join(test_directory, "cpp_extensions")
install_directory = ""
# install directory is the one that is named site-packages
for root, directories, _ in os.walk(os.path.join(cpp_extensions, "install")):
for directory in directories:
if "-packages" in directory:
install_directory = os.path.join(root, directory)
assert install_directory, "install_directory must not be empty"
os.environ["PYTHONPATH"] = os.pathsep.join([install_directory, python_path])
return run_test(test_module, test_directory, options)
finally:
os.environ["PYTHONPATH"] = python_path
if os.path.exists(test_directory + "/" + test_module + ".py"):
os.remove(test_directory + "/" + test_module + ".py")
def test_cpp_extensions_aot_ninja(test_module, test_directory, options):
return _test_cpp_extensions_aot(test_directory, options, use_ninja=True)
def test_cpp_extensions_aot_no_ninja(test_module, test_directory, options):
return _test_cpp_extensions_aot(test_directory, options, use_ninja=False)
def test_distributed(test_module, test_directory, options):
# MPI tests are broken with Python-3.9
mpi_available = subprocess.call(
"command -v mpiexec", shell=True
) == 0 and sys.version_info < (3, 9)
if options.verbose and not mpi_available:
print_to_stderr("MPI not available -- MPI backend tests will be skipped")
config = DISTRIBUTED_TESTS_CONFIG
for backend, env_vars in config.items():
if sys.platform == "win32" and backend != "gloo":
continue
if backend == "mpi" and not mpi_available:
continue
for with_init_file in {True, False}:
if sys.platform == "win32" and not with_init_file:
continue
tmp_dir = tempfile.mkdtemp()
if options.verbose:
init_str = "with {} init_method"
with_init = init_str.format("file" if with_init_file else "env")
print_to_stderr(
"Running distributed tests for the {} backend {}".format(
backend, with_init
)
)
old_environ = dict(os.environ)
os.environ["TEMP_DIR"] = tmp_dir
os.environ["BACKEND"] = backend
os.environ["INIT_METHOD"] = "env://"
os.environ.update(env_vars)
if with_init_file:
if test_module == "test_distributed_spawn":
init_method = f"{FILE_SCHEMA}{tmp_dir}/"
else:
init_method = f"{FILE_SCHEMA}{tmp_dir}/shared_init_file"
os.environ["INIT_METHOD"] = init_method
try:
os.mkdir(os.path.join(tmp_dir, "barrier"))
os.mkdir(os.path.join(tmp_dir, "test_dir"))
if backend == "mpi":
# test mpiexec for --noprefix option
with open(os.devnull, "w") as devnull:
allowrunasroot_opt = (
"--allow-run-as-root"
if subprocess.call(
'mpiexec --allow-run-as-root -n 1 bash -c ""',
shell=True,
stdout=devnull,
stderr=subprocess.STDOUT,
)
== 0
else ""
)
noprefix_opt = (
"--noprefix"
if subprocess.call(
f'mpiexec {allowrunasroot_opt} -n 1 --noprefix bash -c ""',
shell=True,
stdout=devnull,
stderr=subprocess.STDOUT,
)
== 0
else ""
)
mpiexec = ["mpiexec", "-n", "3", noprefix_opt, allowrunasroot_opt]
return_code = run_test(
test_module, test_directory, options, launcher_cmd=mpiexec
)
else:
return_code = run_test(test_module, test_directory, options, extra_unittest_args=["--subprocess"])
if return_code != 0:
return return_code
finally:
shutil.rmtree(tmp_dir)
os.environ.clear()
os.environ.update(old_environ)
return 0
CUSTOM_HANDLERS = {
"test_cuda_primary_ctx": test_cuda_primary_ctx,
"test_cpp_extensions_aot_no_ninja": test_cpp_extensions_aot_no_ninja,
"test_cpp_extensions_aot_ninja": test_cpp_extensions_aot_ninja,
"distributed/test_distributed_spawn": test_distributed,
"distributed/test_c10d_nccl": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_gloo": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_common": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_spawn_gloo": get_run_test_with_subprocess_fn(),
"distributed/test_c10d_spawn_nccl": get_run_test_with_subprocess_fn(),
"distributed/test_store": get_run_test_with_subprocess_fn(),
"distributed/test_pg_wrapper": get_run_test_with_subprocess_fn(),
"distributed/rpc/test_faulty_agent": get_run_test_with_subprocess_fn(),
"distributed/rpc/test_tensorpipe_agent": get_run_test_with_subprocess_fn(),
"distributed/rpc/test_share_memory": get_run_test_with_subprocess_fn(),
"distributed/rpc/cuda/test_tensorpipe_agent": get_run_test_with_subprocess_fn(),
}
def parse_test_module(test):
return test.split(".")[0]
class TestChoices(list):
def __init__(self, *args, **kwargs):
super(TestChoices, self).__init__(args[0])
def __contains__(self, item):
return list.__contains__(self, parse_test_module(item))
def parse_args():
parser = argparse.ArgumentParser(
description="Run the PyTorch unit test suite",
epilog="where TESTS is any of: {}".format(", ".join(TESTS)),
formatter_class=argparse.RawTextHelpFormatter,
parents=[common_parser]
)
parser.add_argument(
"-v",
"--verbose",
action="count",
default=0,
help="print verbose information and test-by-test results",
)
parser.add_argument("--jit", "--jit", action="store_true", help="run all jit tests")
parser.add_argument(
"--distributed-tests",
"--distributed-tests",
action="store_true",
help="run all distributed tests",
)
parser.add_argument(
"-core",
"--core",
action="store_true",
help="Only run core tests, or tests that validate PyTorch's ops, modules,"
"and autograd. They are defined by CORE_TEST_LIST."
)
parser.add_argument(
"-pt",
"--pytest",
action="store_true",
help="If true, use `pytest` to execute the tests. E.g., this runs "
"TestTorch with pytest in verbose and coverage mode: "
"python run_test.py -vci torch -pt",
)
parser.add_argument(
"-c",
"--coverage",
action="store_true",
help="enable coverage",
default=PYTORCH_COLLECT_COVERAGE,
)
parser.add_argument(
"-i",
"--include",
nargs="+",
choices=TestChoices(TESTS),
default=TESTS,
metavar="TESTS",
help="select a set of tests to include (defaults to ALL tests)."
" tests must be a part of the TESTS list defined in run_test.py",
)
parser.add_argument(
"-x",
"--exclude",
nargs="+",
choices=TESTS,
metavar="TESTS",
default=[],
help="select a set of tests to exclude",
)
parser.add_argument(
"-f",
"--first",
choices=TESTS,
metavar="TESTS",
help="select the test to start from (excludes previous tests)",
)
parser.add_argument(
"-l",
"--last",
choices=TESTS,
metavar="TESTS",
help="select the last test to run (excludes following tests)",
)
parser.add_argument(
"--bring-to-front",
nargs="+",
choices=TestChoices(TESTS),
default=[],
metavar="TESTS",
help="select a set of tests to run first. This can be used in situations"
" where you want to run all tests, but care more about some set, "
"e.g. after making a change to a specific component",
)
parser.add_argument(
"--ignore-win-blocklist",
action="store_true",
help="always run blocklisted windows tests",
)
# NS: Disable target determination until it can be made more reliable
# parser.add_argument(
# "--determine-from",
# help="File of affected source filenames to determine which tests to run.",
# )
parser.add_argument(
"--continue-through-error",
action="store_true",
help="Runs the full test suite despite one of the tests failing",
default=strtobool(os.environ.get("CONTINUE_THROUGH_ERROR", "False")),
)
parser.add_argument(
"additional_unittest_args",
nargs="*",
help="additional arguments passed through to unittest, e.g., "
"python run_test.py -i sparse -- TestSparse.test_factory_size_check",
)
parser.add_argument(
"--export-past-test-times",
nargs="?",
type=str,
const=TEST_TIMES_FILE,
help="dumps test times from previous S3 stats into a file, format JSON",
)
parser.add_argument(
"--shard",
nargs=2,
type=int,
help="runs a shard of the tests (taking into account other selections), e.g., "
"--shard 2 3 will break up the selected tests into 3 shards and run the tests "
"in the 2nd shard (the first number should not exceed the second)",
)
parser.add_argument(
"--exclude-jit-executor",
action="store_true",
help="exclude tests that are run for a specific jit config",
)
parser.add_argument(
"--exclude-distributed-tests",
action="store_true",
help="exclude distributed tests",
)
parser.add_argument(
"--run-specified-test-cases",
nargs="?",
type=str,
const=SPECIFIED_TEST_CASES_FILE,
help="load specified test cases file dumped from previous OSS CI stats, format CSV. "
" If all test cases should run for a <test_module> please add a single row: \n"
" test_filename,test_case_name\n"
" ...\n"
" <test_module>,__all__\n"
" ...\n"
'how we use the stats will be based on option "--use-specified-test-cases-by".',
)
parser.add_argument(
"--use-specified-test-cases-by",
type=str,
choices=["include", "bring-to-front"],
default="include",
help='used together with option "--run-specified-test-cases". When specified test case '
"file is set, this option allows the user to control whether to only run the specified test "
"modules or to simply bring the specified modules to front and also run the remaining "
"modules. Note: regardless of this option, we will only run the specified test cases "
" within a specified test module. For unspecified test modules with the bring-to-front "
"option, all test cases will be run, as one may expect.",
)
parser.add_argument(
"--dry-run",
action="store_true",
help="Only list the test that will run.",
)
return parser.parse_args()
def find_test_index(test, selected_tests, find_last_index=False):
"""Find the index of the first or last occurrence of a given test/test module in the list of selected tests.
This function is used to determine the indices when slicing the list of selected tests when
``options.first``(:attr:`find_last_index`=False) and/or ``options.last``(:attr:`find_last_index`=True) are used.
:attr:`selected_tests` can be a list that contains multiple consequent occurrences of tests
as part of the same test module, e.g.:
```
selected_tests = ['autograd', 'cuda', **'torch.TestTorch.test_acos',
'torch.TestTorch.test_tan', 'torch.TestTorch.test_add'**, 'utils']
```
If :attr:`test`='torch' and :attr:`find_last_index`=False, result should be **2**.
If :attr:`test`='torch' and :attr:`find_last_index`=True, result should be **4**.
Args:
test (str): Name of test to lookup
selected_tests (list): List of tests
find_last_index (bool, optional): should we lookup the index of first or last
occurrence (first is default)
Returns:
index of the first or last occurrence of the given test
"""
idx = 0
found_idx = -1
for t in selected_tests:
if t.startswith(test):
found_idx = idx
if not find_last_index:
break
idx += 1
return found_idx
def exclude_tests(exclude_list, selected_tests, exclude_message=None):
for exclude_test in exclude_list:
tests_copy = selected_tests[:]
for test in tests_copy:
if test.startswith(exclude_test):
if exclude_message is not None:
print_to_stderr("Excluding {} {}".format(test, exclude_message))
selected_tests.remove(test)
return selected_tests
def get_selected_tests(options):
# First make sure run specific test cases options are processed.
if options.run_specified_test_cases:
if options.use_specified_test_cases_by == "include":
options.include = list(SPECIFIED_TEST_CASES_DICT.keys())
elif options.use_specified_test_cases_by == "bring-to-front":
options.bring_to_front = list(SPECIFIED_TEST_CASES_DICT.keys())
selected_tests = options.include
# filter if there's JIT only and distributed only test options
if options.jit:
selected_tests = list(
filter(lambda test_name: "jit" in test_name, selected_tests)
)
if options.distributed_tests:
selected_tests = list(
filter(lambda test_name: test_name in DISTRIBUTED_TESTS, selected_tests)
)
# Filter to only run core tests when --core option is specified
if options.core:
selected_tests = list(
filter(lambda test_name: test_name in CORE_TEST_LIST, selected_tests)
)
# process reordering
if options.bring_to_front:
to_front = set(options.bring_to_front)
selected_tests = options.bring_to_front + list(
filter(lambda name: name not in to_front, selected_tests)
)
if options.first:
first_index = find_test_index(options.first, selected_tests)
selected_tests = selected_tests[first_index:]
if options.last:
last_index = find_test_index(options.last, selected_tests, find_last_index=True)
selected_tests = selected_tests[: last_index + 1]
# process exclusion
if options.exclude_jit_executor:
options.exclude.extend(JIT_EXECUTOR_TESTS)
if options.exclude_distributed_tests:
options.exclude.extend(DISTRIBUTED_TESTS)
# these tests failing in CUDA 11.6 temporary disabling. issue https://github.com/pytorch/pytorch/issues/75375
if torch.version.cuda is not None and LooseVersion(torch.version.cuda) == "11.6":
options.exclude.extend(["distributions/test_constraints"])
selected_tests = exclude_tests(options.exclude, selected_tests)
if sys.platform == "win32" and not options.ignore_win_blocklist:
target_arch = os.environ.get("VSCMD_ARG_TGT_ARCH")
if target_arch != "x64":
WINDOWS_BLOCKLIST.append("cpp_extensions_aot_no_ninja")
WINDOWS_BLOCKLIST.append("cpp_extensions_aot_ninja")
WINDOWS_BLOCKLIST.append("cpp_extensions_jit")
WINDOWS_BLOCKLIST.append("jit")
WINDOWS_BLOCKLIST.append("jit_fuser")
# This is exception that's caused by this issue https://github.com/pytorch/pytorch/issues/69460
# This below code should be removed once this issue is solved
if torch.version.cuda is not None and LooseVersion(torch.version.cuda) >= "11.5":
WINDOWS_BLOCKLIST.append("test_cpp_extensions_aot")
WINDOWS_BLOCKLIST.append("test_cpp_extensions_aot_ninja")
WINDOWS_BLOCKLIST.append("test_cpp_extensions_aot_no_ninja")
selected_tests = exclude_tests(WINDOWS_BLOCKLIST, selected_tests, "on Windows")
elif TEST_WITH_ROCM:
selected_tests = exclude_tests(ROCM_BLOCKLIST, selected_tests, "on ROCm")
# sharding
if options.shard:
assert len(options.shard) == 2, "Unexpected shard format"
assert min(options.shard) > 0, "Shards must be positive numbers"
which_shard, num_shards = options.shard
assert (
which_shard <= num_shards
), "Selected shard must be less than or equal to total number of shards"
assert num_shards <= len(
selected_tests
), f"Number of shards must be less than {len(selected_tests)}"
# TODO: fix this to use test_times_filename, but currently this is not working
# because setting the export arg immeidately halts the test execution.
selected_tests = get_shard_based_on_S3(
which_shard, num_shards, selected_tests, TEST_TIMES_FILE
)
# skip all distributed tests if distributed package is not available.
if not dist.is_available():
selected_tests = exclude_tests(DISTRIBUTED_TESTS, selected_tests,
"PyTorch is built without distributed support.")
# skip tests that require LAPACK when it's not available
if not torch._C.has_lapack:
selected_tests = exclude_tests(TESTS_REQUIRING_LAPACK, selected_tests,
"PyTorch is built without LAPACK support.")
return selected_tests
def run_test_module(test: str, test_directory: str, options) -> Optional[str]:
test_module = parse_test_module(test)
# Printing the date here can help diagnose which tests are slow
print_to_stderr("Running {} ... [{}]".format(test, datetime.now()))
handler = CUSTOM_HANDLERS.get(test_module, run_test)
return_code = handler(test_module, test_directory, options)
assert isinstance(return_code, int) and not isinstance(
return_code, bool
), "Return code should be an integer"
if return_code == 0:
return None
message = f"{test} failed!"
if return_code < 0:
# subprocess.Popen returns the child process' exit signal as
# return code -N, where N is the signal number.
signal_name = SIGNALS_TO_NAMES_DICT[-return_code]
message += f" Received signal: {signal_name}"
return message
def main():
options = parse_args()
# TODO: move this export & download function in tools/ folder
test_times_filename = options.export_past_test_times
if test_times_filename:
print(
f"Exporting past test times from S3 to {test_times_filename}, no tests will be run."
)
export_S3_test_times(test_times_filename)
return
specified_test_cases_filename = options.run_specified_test_cases
if specified_test_cases_filename:
print(
f"Loading specified test cases to run from {specified_test_cases_filename}."
)
global SPECIFIED_TEST_CASES_DICT
SPECIFIED_TEST_CASES_DICT = get_specified_test_cases(
specified_test_cases_filename, TESTS
)
test_directory = str(REPO_ROOT / "test")
selected_tests = get_selected_tests(options)
if options.verbose:
print_to_stderr("Selected tests:\n {}".format("\n ".join(selected_tests)))
if options.dry_run:
return
if options.coverage and not PYTORCH_COLLECT_COVERAGE:
shell(["coverage", "erase"])
# NS: Disable target determination until it can be made more reliable
# if options.determine_from is not None and os.path.exists(options.determine_from):
# slow_tests = get_slow_tests_based_on_S3(
# TESTS, TARGET_DET_LIST, SLOW_TEST_THRESHOLD
# )
# print_to_stderr(
# "Added the following tests to target_det tests as calculated based on S3:"
# )
# print_to_stderr(slow_tests)
# with open(options.determine_from, "r") as fh:
# touched_files = [
# os.path.normpath(name.strip())
# for name in fh.read().split("\n")
# if len(name.strip()) > 0
# ]
# # HACK: Ensure the 'test' paths can be traversed by Modulefinder
# sys.path.append(test_directory)
# selected_tests = [
# test
# for test in selected_tests
# if should_run_test(
# TARGET_DET_LIST + slow_tests, test, touched_files, options
# )
# ]
# sys.path.remove(test_directory)
if IS_IN_CI:
selected_tests = get_reordered_tests(
selected_tests, ENABLE_PR_HISTORY_REORDERING
)
# downloading test cases configuration to local environment
get_test_case_configs(dirpath=test_directory)
has_failed = False
failure_messages = []
try:
for test in selected_tests:
options_clone = copy.deepcopy(options)
if test in USE_PYTEST_LIST:
options_clone.pytest = True
err_message = run_test_module(test, test_directory, options_clone)
if err_message is None:
continue
has_failed = True
failure_messages.append(err_message)
if not options_clone.continue_through_error:
raise RuntimeError(err_message)
print_to_stderr(err_message)
finally:
if options.coverage:
from coverage import Coverage
with set_cwd(test_directory):
cov = Coverage()
if PYTORCH_COLLECT_COVERAGE:
cov.load()
cov.combine(strict=False)
cov.save()
if not PYTORCH_COLLECT_COVERAGE:
cov.html_report()
if options.continue_through_error and has_failed:
for err in failure_messages:
print_to_stderr(err)
sys.exit(1)
if __name__ == "__main__":
main()
|
#
# This file is part of pretix (Community Edition).
#
# Copyright (C) 2014-2020 Raphael Michel and contributors
# Copyright (C) 2020-2021 rami.io GmbH and contributors
#
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General
# Public License as published by the Free Software Foundation in version 3 of the License.
#
# ADDITIONAL TERMS APPLY: Pursuant to Section 7 of the GNU Affero General Public License, additional terms are
# applicable granting you additional permissions and placing additional restrictions on your usage of this software.
# Please refer to the pretix LICENSE file to obtain the full terms applicable to this work. If you did not receive
# this file, see <https://pretix.eu/about/en/license>.
#
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License along with this program. If not, see
# <https://www.gnu.org/licenses/>.
#
# This file is based on an earlier version of pretix which was released under the Apache License 2.0. The full text of
# the Apache License 2.0 can be obtained at <http://www.apache.org/licenses/LICENSE-2.0>.
#
# This file may have since been changed and any changes are released under the terms of AGPLv3 as described above. A
# full history of changes and contributors is available at <https://github.com/pretix/pretix>.
#
# This file contains Apache-licensed contributions copyrighted by: Jakob Schnell, Tobias Kunze
#
# Unless required by applicable law or agreed to in writing, software distributed under the Apache License 2.0 is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under the License.
from datetime import datetime, time, timedelta
from decimal import Decimal
from urllib.parse import urlencode
from django import forms
from django.apps import apps
from django.conf import settings
from django.db.models import (
Count, Exists, F, Max, Model, OrderBy, OuterRef, Q, QuerySet,
)
from django.db.models.functions import Coalesce, ExtractWeekDay, Upper
from django.urls import reverse, reverse_lazy
from django.utils.formats import date_format, localize
from django.utils.functional import cached_property
from django.utils.timezone import get_current_timezone, make_aware, now
from django.utils.translation import gettext, gettext_lazy as _, pgettext_lazy
from django_scopes.forms import SafeModelChoiceField
from pretix.base.channels import get_all_sales_channels
from pretix.base.forms.widgets import (
DatePickerWidget, SplitDateTimePickerWidget, TimePickerWidget,
)
from pretix.base.models import (
Checkin, CheckinList, Device, Event, EventMetaProperty, EventMetaValue,
Gate, Invoice, InvoiceAddress, Item, Order, OrderPayment, OrderPosition,
OrderRefund, Organizer, Question, QuestionAnswer, SubEvent, Team,
TeamAPIToken, TeamInvite,
)
from pretix.base.signals import register_payment_providers
from pretix.control.forms.widgets import Select2
from pretix.control.signals import order_search_filter_q
from pretix.helpers.countries import CachedCountries
from pretix.helpers.database import rolledback_transaction
from pretix.helpers.dicts import move_to_end
from pretix.helpers.i18n import i18ncomp
PAYMENT_PROVIDERS = []
def get_all_payment_providers():
global PAYMENT_PROVIDERS
if PAYMENT_PROVIDERS:
return PAYMENT_PROVIDERS
with rolledback_transaction():
event = Event.objects.create(
plugins=",".join([app.name for app in apps.get_app_configs()]),
name="INTERNAL",
date_from=now(),
organizer=Organizer.objects.create(name="INTERNAL")
)
provs = register_payment_providers.send(
sender=event
)
choices = []
for recv, prov in provs:
if isinstance(prov, list):
for p in prov:
p = p(event)
if not p.is_meta:
choices.append((p.identifier, p.verbose_name))
else:
prov = prov(event)
if not prov.is_meta:
choices.append((prov.identifier, prov.verbose_name))
PAYMENT_PROVIDERS = choices
return choices
class FilterForm(forms.Form):
orders = {}
def filter_qs(self, qs):
return qs
@property
def filtered(self):
return self.is_valid() and any(self.cleaned_data.values())
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['ordering'] = forms.ChoiceField(
choices=sum([
[(a, a), ('-' + a, '-' + a)]
for a in self.orders.keys()
], []),
required=False
)
def get_order_by(self):
o = self.cleaned_data.get('ordering')
if o.startswith('-') and o not in self.orders:
return '-' + self.orders[o[1:]]
else:
return self.orders[o]
def filter_to_strings(self):
string = []
for k, f in self.fields.items():
v = self.cleaned_data.get(k)
if v is None or (isinstance(v, (list, str, QuerySet)) and len(v) == 0):
continue
if k == "saveas":
continue
if isinstance(v, bool):
val = _('Yes') if v else _('No')
elif isinstance(v, QuerySet):
q = ['"' + str(m) + '"' for m in v]
if not q:
continue
val = ' or '.join(q)
elif isinstance(v, Model):
val = '"' + str(v) + '"'
elif isinstance(f, forms.MultipleChoiceField):
valdict = dict(f.choices)
val = ' or '.join([str(valdict.get(m)) for m in v])
elif isinstance(f, forms.ChoiceField):
val = str(dict(f.choices).get(v))
elif isinstance(v, datetime):
val = date_format(v, 'SHORT_DATETIME_FORMAT')
elif isinstance(v, Decimal):
val = localize(v)
else:
val = v
string.append('{}: {}'.format(f.label, val))
return string
class OrderFilterForm(FilterForm):
query = forms.CharField(
label=_('Search for…'),
widget=forms.TextInput(attrs={
'placeholder': _('Search for…'),
'autofocus': 'autofocus'
}),
required=False
)
provider = forms.ChoiceField(
label=_('Payment provider'),
choices=[
('', _('All payment providers')),
],
required=False,
)
status = forms.ChoiceField(
label=_('Order status'),
choices=(
('', _('All orders')),
(_('Valid orders'), (
(Order.STATUS_PAID, _('Paid (or canceled with paid fee)')),
(Order.STATUS_PENDING, _('Pending')),
(Order.STATUS_PENDING + Order.STATUS_PAID, _('Pending or paid')),
)),
(_('Cancellations'), (
(Order.STATUS_CANCELED, _('Canceled (fully)')),
('cp', _('Canceled (fully or with paid fee)')),
('rc', _('Cancellation requested')),
('cni', _('Fully canceled but invoice not canceled')),
)),
(_('Payment process'), (
(Order.STATUS_EXPIRED, _('Expired')),
(Order.STATUS_PENDING + Order.STATUS_EXPIRED, _('Pending or expired')),
('o', _('Pending (overdue)')),
('overpaid', _('Overpaid')),
('partially_paid', _('Partially paid')),
('underpaid', _('Underpaid (but confirmed)')),
('pendingpaid', _('Pending (but fully paid)')),
)),
(_('Approval process'), (
('na', _('Approved, payment pending')),
('pa', _('Approval pending')),
)),
(_('Follow-up date'), (
('custom_followup_at', _('Follow-up configured')),
('custom_followup_due', _('Follow-up due')),
)),
('testmode', _('Test mode')),
),
required=False,
)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
u = fdata.get('query')
if "-" in u:
code = (Q(event__slug__icontains=u.rsplit("-", 1)[0])
& Q(code__icontains=Order.normalize_code(u.rsplit("-", 1)[1])))
else:
code = Q(code__icontains=Order.normalize_code(u))
matching_invoices = Invoice.objects.filter(
Q(invoice_no__iexact=u)
| Q(invoice_no__iexact=u.zfill(5))
| Q(full_invoice_no__iexact=u)
).values_list('order_id', flat=True)
matching_positions = OrderPosition.objects.filter(
Q(
Q(attendee_name_cached__icontains=u) | Q(attendee_email__icontains=u)
| Q(secret__istartswith=u)
| Q(pseudonymization_id__istartswith=u)
)
).values_list('order_id', flat=True)
matching_invoice_addresses = InvoiceAddress.objects.filter(
Q(
Q(name_cached__icontains=u) | Q(company__icontains=u)
)
).values_list('order_id', flat=True)
matching_orders = Order.objects.filter(
code
| Q(email__icontains=u)
| Q(comment__icontains=u)
).values_list('id', flat=True)
mainq = (
Q(pk__in=matching_orders)
| Q(pk__in=matching_invoices)
| Q(pk__in=matching_positions)
| Q(pk__in=matching_invoice_addresses)
| Q(pk__in=matching_invoices)
)
for recv, q in order_search_filter_q.send(sender=getattr(self, 'event', None), query=u):
mainq = mainq | q
qs = qs.filter(
mainq
)
if fdata.get('status'):
s = fdata.get('status')
if s == 'o':
qs = qs.filter(status=Order.STATUS_PENDING, expires__lt=now().replace(hour=0, minute=0, second=0))
elif s == 'np':
qs = qs.filter(status__in=[Order.STATUS_PENDING, Order.STATUS_PAID])
elif s == 'ne':
qs = qs.filter(status__in=[Order.STATUS_PENDING, Order.STATUS_EXPIRED])
elif s in ('p', 'n', 'e', 'c', 'r'):
qs = qs.filter(status=s)
elif s == 'overpaid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
Q(~Q(status=Order.STATUS_CANCELED) & Q(pending_sum_t__lt=0))
| Q(Q(status=Order.STATUS_CANCELED) & Q(pending_sum_rc__lt=0))
)
elif s == 'rc':
qs = qs.filter(
cancellation_requests__isnull=False
).annotate(
cancellation_request_time=Max('cancellation_requests__created')
).order_by(
'-cancellation_request_time'
)
elif s == 'pendingpaid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
Q(status__in=(Order.STATUS_EXPIRED, Order.STATUS_PENDING)) & Q(pending_sum_t__lte=0)
& Q(require_approval=False)
)
elif s == 'partially_paid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
computed_payment_refund_sum__lt=F('total'),
computed_payment_refund_sum__gt=Decimal('0.00')
).exclude(
status=Order.STATUS_CANCELED
)
elif s == 'underpaid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
Q(status=Order.STATUS_PAID, pending_sum_t__gt=0) |
Q(status=Order.STATUS_CANCELED, pending_sum_rc__gt=0)
)
elif s == 'cni':
i = Invoice.objects.filter(
order=OuterRef('pk'),
is_cancellation=False,
refered__isnull=True,
).order_by().values('order').annotate(k=Count('id')).values('k')
qs = qs.annotate(
icnt=i
).filter(
icnt__gt=0,
status=Order.STATUS_CANCELED,
)
elif s == 'pa':
qs = qs.filter(
status=Order.STATUS_PENDING,
require_approval=True
)
elif s == 'na':
qs = qs.filter(
status=Order.STATUS_PENDING,
require_approval=False
)
elif s == 'custom_followup_at':
qs = qs.filter(
custom_followup_at__isnull=False
)
elif s == 'custom_followup_due':
qs = qs.filter(
custom_followup_at__lte=now().astimezone(get_current_timezone()).date()
)
elif s == 'testmode':
qs = qs.filter(
testmode=True
)
elif s == 'cp':
s = OrderPosition.objects.filter(
order=OuterRef('pk')
)
qs = qs.annotate(
has_pc=Exists(s)
).filter(
Q(status=Order.STATUS_PAID, has_pc=False) | Q(status=Order.STATUS_CANCELED)
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
if fdata.get('provider'):
qs = qs.annotate(
has_payment_with_provider=Exists(
OrderPayment.objects.filter(
Q(order=OuterRef('pk')) & Q(provider=fdata.get('provider'))
)
)
)
qs = qs.filter(has_payment_with_provider=1)
return qs
class EventOrderFilterForm(OrderFilterForm):
orders = {'code': 'code', 'email': 'email', 'total': 'total',
'datetime': 'datetime', 'status': 'status'}
item = forms.ChoiceField(
label=_('Products'),
required=False,
)
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
question = forms.ModelChoiceField(
queryset=Question.objects.none(),
required=False,
)
answer = forms.CharField(
required=False
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.fields['item'].queryset = self.event.items.all()
self.fields['question'].queryset = self.event.questions.all()
self.fields['provider'].choices += [(k, v.verbose_name) for k, v
in self.event.get_payment_providers().items()]
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
choices = [('', _('All products'))]
for i in self.event.items.prefetch_related('variations').all():
variations = list(i.variations.all())
if variations:
choices.append((str(i.pk), _('{product} – Any variation').format(product=str(i))))
for v in variations:
choices.append(('%d-%d' % (i.pk, v.pk), '%s – %s' % (str(i), v.value)))
else:
choices.append((str(i.pk), str(i)))
self.fields['item'].choices = choices
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
item = fdata.get('item')
if item:
if '-' in item:
var = item.split('-')[1]
qs = qs.filter(all_positions__variation_id=var, all_positions__canceled=False).distinct()
else:
qs = qs.filter(all_positions__item_id=fdata.get('item'), all_positions__canceled=False).distinct()
if fdata.get('subevent'):
qs = qs.filter(all_positions__subevent=fdata.get('subevent'), all_positions__canceled=False).distinct()
if fdata.get('question') and fdata.get('answer') is not None:
q = fdata.get('question')
if q.type == Question.TYPE_FILE:
answers = QuestionAnswer.objects.filter(
orderposition__order_id=OuterRef('pk'),
question_id=q.pk,
file__isnull=False
)
qs = qs.annotate(has_answer=Exists(answers)).filter(has_answer=True)
elif q.type in (Question.TYPE_CHOICE, Question.TYPE_CHOICE_MULTIPLE):
answers = QuestionAnswer.objects.filter(
question_id=q.pk,
orderposition__order_id=OuterRef('pk'),
options__pk=fdata.get('answer')
)
qs = qs.annotate(has_answer=Exists(answers)).filter(has_answer=True)
else:
answers = QuestionAnswer.objects.filter(
question_id=q.pk,
orderposition__order_id=OuterRef('pk'),
answer__exact=fdata.get('answer')
)
qs = qs.annotate(has_answer=Exists(answers)).filter(has_answer=True)
return qs
class FilterNullBooleanSelect(forms.NullBooleanSelect):
def __init__(self, attrs=None):
choices = (
('unknown', _('All')),
('true', _('Yes')),
('false', _('No')),
)
super(forms.NullBooleanSelect, self).__init__(attrs, choices)
class EventOrderExpertFilterForm(EventOrderFilterForm):
subevents_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'All dates starting at or after'),
required=False,
)
subevents_to = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'All dates starting before'),
required=False,
)
created_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=_('Order placed at or after'),
required=False,
)
created_to = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=_('Order placed before'),
required=False,
)
email = forms.CharField(
required=False,
label=_('E-mail address')
)
comment = forms.CharField(
required=False,
label=_('Comment')
)
locale = forms.ChoiceField(
required=False,
label=_('Locale'),
choices=settings.LANGUAGES
)
email_known_to_work = forms.NullBooleanField(
required=False,
widget=FilterNullBooleanSelect,
label=_('E-mail address verified'),
)
total = forms.DecimalField(
localize=True,
required=False,
label=_('Total amount'),
)
payment_sum_min = forms.DecimalField(
localize=True,
required=False,
label=_('Minimal sum of payments and refunds'),
)
payment_sum_max = forms.DecimalField(
localize=True,
required=False,
label=_('Maximal sum of payments and refunds'),
)
sales_channel = forms.ChoiceField(
label=_('Sales channel'),
required=False,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
del self.fields['query']
del self.fields['question']
del self.fields['answer']
del self.fields['ordering']
if not self.event.has_subevents:
del self.fields['subevents_from']
del self.fields['subevents_to']
self.fields['sales_channel'].choices = [('', '')] + [
(k, v.verbose_name) for k, v in get_all_sales_channels().items()
]
locale_names = dict(settings.LANGUAGES)
self.fields['locale'].choices = [('', '')] + [(a, locale_names[a]) for a in self.event.settings.locales]
move_to_end(self.fields, 'item')
move_to_end(self.fields, 'provider')
self.fields['invoice_address_company'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Company')
)
self.fields['invoice_address_name'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Name')
)
self.fields['invoice_address_street'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Address')
)
self.fields['invoice_address_zipcode'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('ZIP code'),
help_text=_('Exact matches only')
)
self.fields['invoice_address_city'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('City'),
help_text=_('Exact matches only')
)
self.fields['invoice_address_country'] = forms.ChoiceField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Country'),
choices=[('', '')] + list(CachedCountries())
)
self.fields['attendee_name'] = forms.CharField(
required=False,
label=_('Attendee name')
)
self.fields['attendee_email'] = forms.CharField(
required=False,
label=_('Attendee e-mail address')
)
self.fields['attendee_address_company'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('Company')
)
self.fields['attendee_address_street'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('Address')
)
self.fields['attendee_address_zipcode'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('ZIP code'),
help_text=_('Exact matches only')
)
self.fields['attendee_address_city'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('City'),
help_text=_('Exact matches only')
)
self.fields['attendee_address_country'] = forms.ChoiceField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('Country'),
choices=[('', '')] + list(CachedCountries())
)
self.fields['ticket_secret'] = forms.CharField(
label=_('Ticket secret'),
required=False
)
for q in self.event.questions.all():
self.fields['question_{}'.format(q.pk)] = forms.CharField(
label=q.question,
required=False,
help_text=_('Exact matches only')
)
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
if fdata.get('subevents_from'):
qs = qs.filter(
all_positions__subevent__date_from__gte=fdata.get('subevents_from'),
all_positions__canceled=False
).distinct()
if fdata.get('subevents_to'):
qs = qs.filter(
all_positions__subevent__date_from__lt=fdata.get('subevents_to'),
all_positions__canceled=False
).distinct()
if fdata.get('email'):
qs = qs.filter(
email__icontains=fdata.get('email')
)
if fdata.get('created_from'):
qs = qs.filter(datetime__gte=fdata.get('created_from'))
if fdata.get('created_to'):
qs = qs.filter(datetime__lte=fdata.get('created_to'))
if fdata.get('comment'):
qs = qs.filter(comment__icontains=fdata.get('comment'))
if fdata.get('sales_channel'):
qs = qs.filter(sales_channel=fdata.get('sales_channel'))
if fdata.get('total'):
qs = qs.filter(total=fdata.get('total'))
if fdata.get('email_known_to_work') is not None:
qs = qs.filter(email_known_to_work=fdata.get('email_known_to_work'))
if fdata.get('locale'):
qs = qs.filter(locale=fdata.get('locale'))
if fdata.get('payment_sum_min') is not None:
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
computed_payment_refund_sum__gte=fdata['payment_sum_min'],
)
if fdata.get('payment_sum_max') is not None:
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
computed_payment_refund_sum__lte=fdata['payment_sum_max'],
)
if fdata.get('invoice_address_company'):
qs = qs.filter(invoice_address__company__icontains=fdata.get('invoice_address_company'))
if fdata.get('invoice_address_name'):
qs = qs.filter(invoice_address__name_cached__icontains=fdata.get('invoice_address_name'))
if fdata.get('invoice_address_street'):
qs = qs.filter(invoice_address__street__icontains=fdata.get('invoice_address_street'))
if fdata.get('invoice_address_zipcode'):
qs = qs.filter(invoice_address__zipcode__iexact=fdata.get('invoice_address_zipcode'))
if fdata.get('invoice_address_city'):
qs = qs.filter(invoice_address__city__iexact=fdata.get('invoice_address_city'))
if fdata.get('invoice_address_country'):
qs = qs.filter(invoice_address__country=fdata.get('invoice_address_country'))
if fdata.get('attendee_name'):
qs = qs.filter(
all_positions__attendee_name_cached__icontains=fdata.get('attendee_name')
)
if fdata.get('attendee_address_company'):
qs = qs.filter(
all_positions__company__icontains=fdata.get('attendee_address_company')
).distinct()
if fdata.get('attendee_address_street'):
qs = qs.filter(
all_positions__street__icontains=fdata.get('attendee_address_street')
).distinct()
if fdata.get('attendee_address_city'):
qs = qs.filter(
all_positions__city__iexact=fdata.get('attendee_address_city')
).distinct()
if fdata.get('attendee_address_country'):
qs = qs.filter(
all_positions__country=fdata.get('attendee_address_country')
).distinct()
if fdata.get('ticket_secret'):
qs = qs.filter(
all_positions__secret__icontains=fdata.get('ticket_secret')
).distinct()
for q in self.event.questions.all():
if fdata.get(f'question_{q.pk}'):
answers = QuestionAnswer.objects.filter(
question_id=q.pk,
orderposition__order_id=OuterRef('pk'),
answer__iexact=fdata.get(f'question_{q.pk}')
)
qs = qs.annotate(**{f'q_{q.pk}': Exists(answers)}).filter(**{f'q_{q.pk}': True})
return qs
class OrderSearchFilterForm(OrderFilterForm):
orders = {'code': 'code', 'email': 'email', 'total': 'total',
'datetime': 'datetime', 'status': 'status',
'event': 'event'}
organizer = forms.ModelChoiceField(
label=_('Organizer'),
queryset=Organizer.objects.none(),
required=False,
empty_label=_('All organizers'),
widget=Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse_lazy('control:organizers.select2'),
'data-placeholder': _('All organizers')
}
)
)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request')
super().__init__(*args, **kwargs)
if self.request.user.has_active_staff_session(self.request.session.session_key):
self.fields['organizer'].queryset = Organizer.objects.all()
else:
self.fields['organizer'].queryset = Organizer.objects.filter(
pk__in=self.request.user.teams.values_list('organizer', flat=True)
)
self.fields['provider'].choices += get_all_payment_providers()
seen = set()
for p in self.meta_properties.all():
if p.name in seen:
continue
seen.add(p.name)
self.fields['meta_{}'.format(p.name)] = forms.CharField(
label=p.name,
required=False,
widget=forms.TextInput(
attrs={
'data-typeahead-url': reverse('control:events.meta.typeahead') + '?' + urlencode({
'property': p.name,
'organizer': ''
})
}
)
)
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
if fdata.get('organizer'):
qs = qs.filter(event__organizer=fdata.get('organizer'))
filters_by_property_name = {}
for i, p in enumerate(self.meta_properties):
d = fdata.get('meta_{}'.format(p.name))
if d:
emv_with_value = EventMetaValue.objects.filter(
event=OuterRef('event_id'),
property__pk=p.pk,
value=d
)
emv_with_any_value = EventMetaValue.objects.filter(
event=OuterRef('event_id'),
property__pk=p.pk,
)
qs = qs.annotate(**{'attr_{}'.format(i): Exists(emv_with_value)})
if p.name in filters_by_property_name:
filters_by_property_name[p.name] |= Q(**{'attr_{}'.format(i): True})
else:
filters_by_property_name[p.name] = Q(**{'attr_{}'.format(i): True})
if p.default == d:
qs = qs.annotate(**{'attr_{}_any'.format(i): Exists(emv_with_any_value)})
filters_by_property_name[p.name] |= Q(**{
'attr_{}_any'.format(i): False, 'event__organizer_id': p.organizer_id
})
for f in filters_by_property_name.values():
qs = qs.filter(f)
return qs
@cached_property
def meta_properties(self):
# We ignore superuser permissions here. This is intentional – we do not want to show super
# users a form with all meta properties ever assigned.
return EventMetaProperty.objects.filter(
organizer_id__in=self.request.user.teams.values_list('organizer', flat=True)
)
class OrderPaymentSearchFilterForm(forms.Form):
orders = {'id': 'id', 'local_id': 'local_id', 'state': 'state', 'amount': 'amount', 'order': 'order',
'created': 'created', 'payment_date': 'payment_date', 'provider': 'provider', 'info': 'info',
'fee': 'fee'}
query = forms.CharField(
label=_('Search for…'),
widget=forms.TextInput(attrs={
'placeholder': _('Search for…'),
'autofocus': 'autofocus'
}),
required=False,
)
event = forms.ModelChoiceField(
label=_('Event'),
queryset=Event.objects.none(),
required=False,
widget=Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse_lazy('control:events.typeahead'),
'data-placeholder': _('All events')
}
)
)
organizer = forms.ModelChoiceField(
label=_('Organizer'),
queryset=Organizer.objects.none(),
required=False,
empty_label=_('All organizers'),
widget=Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse_lazy('control:organizers.select2'),
'data-placeholder': _('All organizers')
}
),
)
state = forms.ChoiceField(
label=_('Status'),
required=False,
choices=[('', _('All payments'))] + list(OrderPayment.PAYMENT_STATES),
)
provider = forms.ChoiceField(
label=_('Payment provider'),
choices=[
('', _('All payment providers')),
],
required=False,
)
created_from = forms.DateField(
label=_('Payment created from'),
required=False,
widget=DatePickerWidget,
)
created_until = forms.DateField(
label=_('Payment created until'),
required=False,
widget=DatePickerWidget,
)
completed_from = forms.DateField(
label=_('Paid from'),
required=False,
widget=DatePickerWidget,
)
completed_until = forms.DateField(
label=_('Paid until'),
required=False,
widget=DatePickerWidget,
)
amount = forms.CharField(
label=_('Amount'),
required=False,
widget=forms.NumberInput(attrs={
'placeholder': _('Amount'),
}),
)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request')
super().__init__(*args, **kwargs)
self.fields['ordering'] = forms.ChoiceField(
choices=sum([
[(a, a), ('-' + a, '-' + a)]
for a in self.orders.keys()
], []),
required=False
)
if self.request.user.has_active_staff_session(self.request.session.session_key):
self.fields['organizer'].queryset = Organizer.objects.all()
self.fields['event'].queryset = Event.objects.all()
else:
self.fields['organizer'].queryset = Organizer.objects.filter(
pk__in=self.request.user.teams.values_list('organizer', flat=True)
)
self.fields['event'].queryset = self.request.user.get_events_with_permission('can_view_orders')
self.fields['provider'].choices += get_all_payment_providers()
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('created_from'):
date_start = make_aware(datetime.combine(
fdata.get('created_from'),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(created__gte=date_start)
if fdata.get('created_until'):
date_end = make_aware(datetime.combine(
fdata.get('created_until') + timedelta(days=1),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(created__lt=date_end)
if fdata.get('completed_from'):
date_start = make_aware(datetime.combine(
fdata.get('completed_from'),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(payment_date__gte=date_start)
if fdata.get('completed_until'):
date_end = make_aware(datetime.combine(
fdata.get('completed_until') + timedelta(days=1),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(payment_date__lt=date_end)
if fdata.get('event'):
qs = qs.filter(order__event=fdata.get('event'))
if fdata.get('organizer'):
qs = qs.filter(order__event__organizer=fdata.get('organizer'))
if fdata.get('state'):
qs = qs.filter(state=fdata.get('state'))
if fdata.get('provider'):
qs = qs.filter(provider=fdata.get('provider'))
if fdata.get('query'):
u = fdata.get('query')
matching_invoices = Invoice.objects.filter(
Q(invoice_no__iexact=u)
| Q(invoice_no__iexact=u.zfill(5))
| Q(full_invoice_no__iexact=u)
).values_list('order_id', flat=True)
matching_invoice_addresses = InvoiceAddress.objects.filter(
Q(
Q(name_cached__icontains=u) | Q(company__icontains=u)
)
).values_list('order_id', flat=True)
if "-" in u:
code = (Q(event__slug__icontains=u.rsplit("-", 1)[0])
& Q(code__icontains=Order.normalize_code(u.rsplit("-", 1)[1])))
else:
code = Q(code__icontains=Order.normalize_code(u))
matching_orders = Order.objects.filter(
Q(
code
| Q(email__icontains=u)
| Q(comment__icontains=u)
)
).values_list('id', flat=True)
mainq = (
Q(order__id__in=matching_invoices)
| Q(order__id__in=matching_invoice_addresses)
| Q(order__id__in=matching_orders)
)
qs = qs.filter(mainq)
if fdata.get('amount'):
amount = fdata.get('amount')
def is_decimal(value):
result = True
parts = value.split('.', maxsplit=1)
for part in parts:
result = result & part.isdecimal()
return result
if is_decimal(amount):
qs = qs.filter(amount=Decimal(amount))
if fdata.get('ordering'):
p = self.cleaned_data.get('ordering')
if p.startswith('-') and p not in self.orders:
qs = qs.order_by('-' + self.orders[p[1:]])
else:
qs = qs.order_by(self.orders[p])
else:
qs = qs.order_by('-created')
return qs
class SubEventFilterForm(FilterForm):
orders = {
'date_from': 'date_from',
'active': 'active',
'sum_quota_available': 'sum_quota_available'
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('active', _('Active')),
('running', _('Shop live and presale running')),
('inactive', _('Inactive')),
('future', _('Presale not started')),
('past', _('Presale over')),
),
required=False
)
date_from = forms.DateField(
label=_('Date from'),
required=False,
widget=DatePickerWidget({
'placeholder': _('Date from'),
}),
)
date_until = forms.DateField(
label=_('Date until'),
required=False,
widget=DatePickerWidget({
'placeholder': _('Date until'),
}),
)
time_from = forms.TimeField(
label=_('Start time from'),
required=False,
widget=TimePickerWidget({}),
)
time_until = forms.TimeField(
label=_('Start time until'),
required=False,
widget=TimePickerWidget({}),
)
weekday = forms.MultipleChoiceField(
label=_('Weekday'),
choices=(
('2', _('Monday')),
('3', _('Tuesday')),
('4', _('Wednesday')),
('5', _('Thursday')),
('6', _('Friday')),
('7', _('Saturday')),
('1', _('Sunday')),
),
widget=forms.CheckboxSelectMultiple,
required=False
)
query = forms.CharField(
label=_('Event name'),
widget=forms.TextInput(attrs={
'placeholder': _('Event name'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['date_from'].widget = DatePickerWidget()
self.fields['date_until'].widget = DatePickerWidget()
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status') == 'active':
qs = qs.filter(active=True)
elif fdata.get('status') == 'running':
qs = qs.filter(
active=True
).filter(
Q(presale_start__isnull=True) | Q(presale_start__lte=now())
).filter(
Q(Q(presale_end__isnull=True) & Q(
Q(date_to__gte=now()) |
Q(date_to__isnull=True, date_from__gte=now())
)) |
Q(presale_end__gte=now())
)
elif fdata.get('status') == 'inactive':
qs = qs.filter(active=False)
elif fdata.get('status') == 'future':
qs = qs.filter(presale_start__gte=now())
elif fdata.get('status') == 'past':
qs = qs.filter(
Q(presale_end__lte=now()) | Q(
Q(presale_end__isnull=True) & Q(
Q(date_to__lte=now()) |
Q(date_to__isnull=True, date_from__gte=now())
)
)
)
if fdata.get('weekday'):
qs = qs.annotate(wday=ExtractWeekDay('date_from')).filter(wday__in=fdata.get('weekday'))
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=i18ncomp(query)) | Q(location__icontains=query)
)
if fdata.get('date_until'):
date_end = make_aware(datetime.combine(
fdata.get('date_until') + timedelta(days=1),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(
Q(date_to__isnull=True, date_from__lt=date_end) |
Q(date_to__isnull=False, date_to__lt=date_end)
)
if fdata.get('date_from'):
date_start = make_aware(datetime.combine(
fdata.get('date_from'),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(date_from__gte=date_start)
if fdata.get('time_until'):
qs = qs.filter(date_from__time__lte=fdata.get('time_until'))
if fdata.get('time_from'):
qs = qs.filter(date_from__time__gte=fdata.get('time_from'))
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-date_from')
return qs
class OrganizerFilterForm(FilterForm):
orders = {
'slug': 'slug',
'name': 'name',
}
query = forms.CharField(
label=_('Organizer name'),
widget=forms.TextInput(attrs={
'placeholder': _('Organizer name'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=query) | Q(slug__icontains=query)
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
return qs
class GiftCardFilterForm(FilterForm):
orders = {
'issuance': 'issuance',
'expires': F('expires').asc(nulls_last=True),
'-expires': F('expires').desc(nulls_first=True),
'secret': 'secret',
'value': 'cached_value',
}
testmode = forms.ChoiceField(
label=_('Test mode'),
choices=(
('', _('All')),
('yes', _('Test mode')),
('no', _('Live')),
),
required=False
)
state = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('empty', _('Empty')),
('valid_value', _('Valid and with value')),
('expired_value', _('Expired and with value')),
('expired', _('Expired')),
),
required=False
)
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(secret__icontains=query)
| Q(transactions__text__icontains=query)
| Q(transactions__order__code__icontains=query)
)
if fdata.get('testmode') == 'yes':
qs = qs.filter(testmode=True)
elif fdata.get('testmode') == 'no':
qs = qs.filter(testmode=False)
if fdata.get('state') == 'empty':
qs = qs.filter(cached_value=0)
elif fdata.get('state') == 'valid_value':
qs = qs.exclude(cached_value=0).filter(Q(expires__isnull=True) | Q(expires__gte=now()))
elif fdata.get('state') == 'expired_value':
qs = qs.exclude(cached_value=0).filter(expires__lt=now())
elif fdata.get('state') == 'expired':
qs = qs.filter(expires__lt=now())
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-issuance')
return qs.distinct()
class CustomerFilterForm(FilterForm):
orders = {
'email': 'email',
'identifier': 'identifier',
'name_cached': 'name_cached',
}
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
status = forms.ChoiceField(
label=_('Status'),
required=False,
choices=(
('', _('All')),
('active', _('active')),
('disabled', _('disabled')),
('unverified', _('not yet activated')),
)
)
memberships = forms.ChoiceField(
label=_('Memberships'),
required=False,
choices=(
('', _('All')),
('no', _('Has no memberships')),
('any', _('Has any membership')),
('valid', _('Has valid membership')),
)
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(email__icontains=query)
| Q(name_cached__icontains=query)
| Q(identifier__istartswith=query)
)
if fdata.get('status') == 'active':
qs = qs.filter(is_active=True, is_verified=True)
elif fdata.get('status') == 'disabled':
qs = qs.filter(is_active=False)
elif fdata.get('status') == 'unverified':
qs = qs.filter(is_verified=False)
if fdata.get('memberships') == 'no':
qs = qs.filter(memberships__isnull=True)
elif fdata.get('memberships') == 'any':
qs = qs.filter(memberships__isnull=False)
elif fdata.get('memberships') == 'valid':
qs = qs.filter(memberships__date_start__lt=now(), memberships__date_end__gt=now(), memberships__canceled=False)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-email')
return qs.distinct()
class TeamFilterForm(FilterForm):
orders = {
'name': 'name',
}
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(Exists(
Team.members.through.objects.filter(
Q(user__email__icontains=query) | Q(user__fullname__icontains=query),
team_id=OuterRef('pk'),
)
))
| Q(Exists(
TeamInvite.objects.filter(
email__icontains=query,
team_id=OuterRef('pk'),
)
))
| Q(Exists(
TeamAPIToken.objects.filter(
name__icontains=query,
team_id=OuterRef('pk'),
)
))
| Q(name__icontains=query)
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('name')
return qs.distinct()
class EventFilterForm(FilterForm):
orders = {
'slug': 'slug',
'organizer': 'organizer__name',
'date_from': 'order_from',
'date_to': 'order_to',
'live': 'live',
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All events')),
('live', _('Shop live')),
('running', _('Shop live and presale running')),
('notlive', _('Shop not live')),
('future', _('Presale not started')),
('past', _('Presale over')),
('date_future', _('Single event running or in the future')),
('date_past', _('Single event in the past')),
('series', _('Event series')),
),
required=False
)
organizer = forms.ModelChoiceField(
label=_('Organizer'),
queryset=Organizer.objects.none(),
required=False,
empty_label=_('All organizers'),
widget=Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse_lazy('control:organizers.select2'),
'data-placeholder': _('All organizers')
}
)
)
query = forms.CharField(
label=_('Event name'),
widget=forms.TextInput(attrs={
'placeholder': _('Event name'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request')
self.organizer = kwargs.pop('organizer', None)
super().__init__(*args, **kwargs)
seen = set()
for p in self.meta_properties.all():
if p.name in seen:
continue
seen.add(p.name)
self.fields['meta_{}'.format(p.name)] = forms.CharField(
label=p.name,
required=False,
widget=forms.TextInput(
attrs={
'data-typeahead-url': reverse('control:events.meta.typeahead') + '?' + urlencode({
'property': p.name,
'organizer': self.organizer.slug if self.organizer else ''
})
}
)
)
if self.organizer:
del self.fields['organizer']
else:
if self.request.user.has_active_staff_session(self.request.session.session_key):
self.fields['organizer'].queryset = Organizer.objects.all()
else:
self.fields['organizer'].queryset = Organizer.objects.filter(
pk__in=self.request.user.teams.values_list('organizer', flat=True)
)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status') == 'live':
qs = qs.filter(live=True)
elif fdata.get('status') == 'running':
qs = qs.filter(
live=True
).annotate(
p_end=Coalesce(F('presale_end'), F('date_to'), F('date_from'))
).filter(
Q(presale_start__isnull=True) | Q(presale_start__lte=now())
).filter(
Q(p_end__gte=now())
)
elif fdata.get('status') == 'notlive':
qs = qs.filter(live=False)
elif fdata.get('status') == 'future':
qs = qs.filter(presale_start__gte=now())
elif fdata.get('status') == 'past':
qs = qs.filter(presale_end__lte=now())
elif fdata.get('status') == 'date_future':
qs = qs.filter(
Q(has_subevents=False) &
Q(
Q(Q(date_to__isnull=True) & Q(date_from__gte=now()))
| Q(Q(date_to__isnull=False) & Q(date_to__gte=now()))
)
)
elif fdata.get('status') == 'date_past':
qs = qs.filter(
Q(has_subevents=False) &
Q(
Q(Q(date_to__isnull=True) & Q(date_from__lt=now()))
| Q(Q(date_to__isnull=False) & Q(date_to__lt=now()))
)
)
elif fdata.get('status') == 'series':
qs = qs.filter(has_subevents=True)
if fdata.get('organizer'):
qs = qs.filter(organizer=fdata.get('organizer'))
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=i18ncomp(query)) | Q(slug__icontains=query)
)
filters_by_property_name = {}
for i, p in enumerate(self.meta_properties):
d = fdata.get('meta_{}'.format(p.name))
if d:
emv_with_value = EventMetaValue.objects.filter(
event=OuterRef('pk'),
property__pk=p.pk,
value=d
)
emv_with_any_value = EventMetaValue.objects.filter(
event=OuterRef('pk'),
property__pk=p.pk,
)
qs = qs.annotate(**{'attr_{}'.format(i): Exists(emv_with_value)})
if p.name in filters_by_property_name:
filters_by_property_name[p.name] |= Q(**{'attr_{}'.format(i): True})
else:
filters_by_property_name[p.name] = Q(**{'attr_{}'.format(i): True})
if p.default == d:
qs = qs.annotate(**{'attr_{}_any'.format(i): Exists(emv_with_any_value)})
filters_by_property_name[p.name] |= Q(**{'attr_{}_any'.format(i): False, 'organizer_id': p.organizer_id})
for f in filters_by_property_name.values():
qs = qs.filter(f)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
return qs
@cached_property
def meta_properties(self):
if self.organizer:
return self.organizer.meta_properties.all()
else:
# We ignore superuser permissions here. This is intentional – we do not want to show super
# users a form with all meta properties ever assigned.
return EventMetaProperty.objects.filter(
organizer_id__in=self.request.user.teams.values_list('organizer', flat=True)
)
class CheckinListAttendeeFilterForm(FilterForm):
orders = {
'code': ('order__code', 'item__name'),
'-code': ('-order__code', '-item__name'),
'email': ('order__email', 'item__name'),
'-email': ('-order__email', '-item__name'),
'status': (OrderBy(F('last_entry'), nulls_first=True, descending=True), 'order__code'),
'-status': (OrderBy(F('last_entry'), nulls_last=True), '-order__code'),
'timestamp': (OrderBy(F('last_entry'), nulls_first=True), 'order__code'),
'-timestamp': (OrderBy(F('last_entry'), nulls_last=True, descending=True), '-order__code'),
'item': ('item__name', 'variation__value', 'order__code'),
'-item': ('-item__name', '-variation__value', '-order__code'),
'seat': ('seat__sorting_rank', 'seat__guid'),
'-seat': ('-seat__sorting_rank', '-seat__guid'),
'date': ('subevent__date_from', 'subevent__id', 'order__code'),
'-date': ('-subevent__date_from', 'subevent__id', '-order__code'),
'name': {'_order': F('display_name').asc(nulls_first=True),
'display_name': Coalesce('attendee_name_cached', 'addon_to__attendee_name_cached')},
'-name': {'_order': F('display_name').desc(nulls_last=True),
'display_name': Coalesce('attendee_name_cached', 'addon_to__attendee_name_cached')},
}
user = forms.CharField(
label=_('Search attendee…'),
widget=forms.TextInput(attrs={
'placeholder': _('Search attendee…'),
'autofocus': 'autofocus'
}),
required=False
)
status = forms.ChoiceField(
label=_('Check-in status'),
choices=(
('', _('All attendees')),
('3', pgettext_lazy('checkin state', 'Checked in but left')),
('2', pgettext_lazy('checkin state', 'Present')),
('1', _('Checked in')),
('0', _('Not checked in')),
),
required=False,
)
item = forms.ModelChoiceField(
label=_('Products'),
queryset=Item.objects.none(),
required=False,
empty_label=_('All products')
)
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
subevent_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'Date start from'),
required=False,
)
subevent_until = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'Date start until'),
required=False,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
self.list = kwargs.pop('list')
super().__init__(*args, **kwargs)
if self.list.all_products:
self.fields['item'].queryset = self.event.items.all()
else:
self.fields['item'].queryset = self.list.limit_products.all()
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
else:
del self.fields['subevent']
del self.fields['subevent_from']
del self.fields['subevent_until']
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('user'):
u = fdata.get('user')
qs = qs.filter(
Q(order__code__istartswith=u)
| Q(secret__istartswith=u)
| Q(pseudonymization_id__istartswith=u)
| Q(order__email__icontains=u)
| Q(attendee_name_cached__icontains=u)
| Q(attendee_email__icontains=u)
| Q(voucher__code__istartswith=u)
| Q(order__invoice_address__name_cached__icontains=u)
| Q(order__invoice_address__company__icontains=u)
)
if fdata.get('status'):
s = fdata.get('status')
if s == '1':
qs = qs.filter(last_entry__isnull=False)
elif s == '2':
qs = qs.filter(last_entry__isnull=False).filter(
Q(last_exit__isnull=True) | Q(last_exit__lt=F('last_entry'))
)
elif s == '3':
qs = qs.filter(last_entry__isnull=False).filter(
Q(last_exit__isnull=False) & Q(last_exit__gte=F('last_entry'))
)
elif s == '0':
qs = qs.filter(last_entry__isnull=True)
if fdata.get('ordering'):
ob = self.orders[fdata.get('ordering')]
if isinstance(ob, dict):
ob = dict(ob)
o = ob.pop('_order')
qs = qs.annotate(**ob).order_by(o)
elif isinstance(ob, (list, tuple)):
qs = qs.order_by(*ob)
else:
qs = qs.order_by(ob)
if fdata.get('item'):
qs = qs.filter(item=fdata.get('item'))
if fdata.get('subevent'):
qs = qs.filter(subevent_id=fdata.get('subevent').pk)
if fdata.get('subevent_from'):
qs = qs.filter(subevent__date_from__gte=fdata.get('subevent_from'))
if fdata.get('subevent_until'):
qs = qs.filter(subevent__date_from__lte=fdata.get('subevent_until'))
return qs
class UserFilterForm(FilterForm):
orders = {
'fullname': 'fullname',
'email': 'email',
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('active', _('Active')),
('inactive', _('Inactive')),
),
required=False
)
superuser = forms.ChoiceField(
label=_('Administrator'),
choices=(
('', _('All')),
('yes', _('Administrator')),
('no', _('No administrator')),
),
required=False
)
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status') == 'active':
qs = qs.filter(is_active=True)
elif fdata.get('status') == 'inactive':
qs = qs.filter(is_active=False)
if fdata.get('superuser') == 'yes':
qs = qs.filter(is_staff=True)
elif fdata.get('superuser') == 'no':
qs = qs.filter(is_staff=False)
if fdata.get('query'):
qs = qs.filter(
Q(email__icontains=fdata.get('query'))
| Q(fullname__icontains=fdata.get('query'))
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
return qs
class VoucherFilterForm(FilterForm):
orders = {
'code': 'code',
'-code': '-code',
'redeemed': 'redeemed',
'-redeemed': '-redeemed',
'valid_until': 'valid_until',
'-valid_until': '-valid_until',
'tag': 'tag',
'-tag': '-tag',
'item': (
'seat__sorting_rank',
'item__category__position',
'item__category',
'item__position',
'item__variation__position',
'quota__name',
),
'subevent': 'subevent__date_from',
'-subevent': '-subevent__date_from',
'-item': (
'-seat__sorting_rank',
'-item__category__position',
'-item__category',
'-item__position',
'-item__variation__position',
'-quota__name',
)
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('v', _('Valid')),
('u', _('Unredeemed')),
('r', _('Redeemed at least once')),
('f', _('Fully redeemed')),
('e', _('Expired')),
('c', _('Redeemed and checked in with ticket')),
),
required=False
)
qm = forms.ChoiceField(
label=_('Quota handling'),
choices=(
('', _('All')),
('b', _('Reserve ticket from quota')),
('i', _('Allow to ignore quota')),
),
required=False
)
tag = forms.CharField(
label=_('Filter by tag'),
widget=forms.TextInput(attrs={
'placeholder': _('Filter by tag'),
}),
required=False
)
search = forms.CharField(
label=_('Search voucher'),
widget=forms.TextInput(attrs={
'placeholder': _('Search voucher'),
'autofocus': 'autofocus'
}),
required=False
)
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
itemvar = forms.ChoiceField(
label=_("Product"),
required=False
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
choices = [('', _('All products'))]
for i in self.event.items.prefetch_related('variations').all():
variations = list(i.variations.all())
if variations:
choices.append((str(i.pk), _('{product} – Any variation').format(product=i.name)))
for v in variations:
choices.append(('%d-%d' % (i.pk, v.pk), '%s – %s' % (i.name, v.value)))
else:
choices.append((str(i.pk), i.name))
for q in self.event.quotas.all():
choices.append(('q-%d' % q.pk, _('Any product in quota "{quota}"').format(quota=q)))
self.fields['itemvar'].choices = choices
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('search'):
s = fdata.get('search').strip()
qs = qs.filter(Q(code__icontains=s) | Q(tag__icontains=s) | Q(comment__icontains=s))
if fdata.get('tag'):
s = fdata.get('tag').strip()
if s == '<>':
qs = qs.filter(Q(tag__isnull=True) | Q(tag=''))
elif s[0] == '"' and s[-1] == '"':
qs = qs.filter(tag__exact=s[1:-1])
else:
qs = qs.filter(tag__icontains=s)
if fdata.get('qm'):
s = fdata.get('qm')
if s == 'b':
qs = qs.filter(block_quota=True)
elif s == 'i':
qs = qs.filter(allow_ignore_quota=True)
if fdata.get('status'):
s = fdata.get('status')
if s == 'v':
qs = qs.filter(Q(valid_until__isnull=True) | Q(valid_until__gt=now())).filter(redeemed__lt=F('max_usages'))
elif s == 'r':
qs = qs.filter(redeemed__gt=0)
elif s == 'u':
qs = qs.filter(redeemed=0)
elif s == 'f':
qs = qs.filter(redeemed__gte=F('max_usages'))
elif s == 'e':
qs = qs.filter(Q(valid_until__isnull=False) & Q(valid_until__lt=now())).filter(redeemed=0)
elif s == 'c':
checkins = Checkin.objects.filter(
position__voucher=OuterRef('pk')
)
qs = qs.annotate(has_checkin=Exists(checkins)).filter(
redeemed__gt=0, has_checkin=True
)
if fdata.get('itemvar'):
if fdata.get('itemvar').startswith('q-'):
qs = qs.filter(quota_id=fdata.get('itemvar').split('-')[1])
elif '-' in fdata.get('itemvar'):
qs = qs.filter(item_id=fdata.get('itemvar').split('-')[0],
variation_id=fdata.get('itemvar').split('-')[1])
else:
qs = qs.filter(item_id=fdata.get('itemvar'))
if fdata.get('subevent'):
qs = qs.filter(subevent_id=fdata.get('subevent').pk)
if fdata.get('ordering'):
ob = self.orders[fdata.get('ordering')]
if isinstance(ob, dict):
ob = dict(ob)
o = ob.pop('_order')
qs = qs.annotate(**ob).order_by(o)
elif isinstance(ob, (list, tuple)):
qs = qs.order_by(*ob)
else:
qs = qs.order_by(ob)
return qs
class VoucherTagFilterForm(FilterForm):
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('subevent'):
qs = qs.filter(subevent_id=fdata.get('subevent').pk)
return qs
class RefundFilterForm(FilterForm):
orders = {'provider': 'provider', 'state': 'state', 'order': 'order__code',
'source': 'source', 'amount': 'amount', 'created': 'created'}
provider = forms.ChoiceField(
label=_('Payment provider'),
choices=[
('', _('All payment providers')),
],
required=False,
)
status = forms.ChoiceField(
label=_('Refund status'),
choices=(
('', _('All open refunds')),
('all', _('All refunds')),
) + OrderRefund.REFUND_STATES,
required=False,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.fields['provider'].choices += [(k, v.verbose_name) for k, v
in self.event.get_payment_providers().items()]
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
if fdata.get('provider'):
qs = qs.filter(provider=fdata.get('provider'))
if fdata.get('status'):
if fdata.get('status') != 'all':
qs = qs.filter(state=fdata.get('status'))
else:
qs = qs.filter(state__in=[OrderRefund.REFUND_STATE_CREATED, OrderRefund.REFUND_STATE_TRANSIT,
OrderRefund.REFUND_STATE_EXTERNAL])
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-created')
return qs
class OverviewFilterForm(FilterForm):
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
date_axis = forms.ChoiceField(
label=_('Date filter'),
choices=(
('', _('Filter by…')),
('order_date', _('Order date')),
('last_payment_date', _('Date of last successful payment')),
),
required=False,
)
date_from = forms.DateField(
label=_('Date from'),
required=False,
widget=DatePickerWidget,
)
date_until = forms.DateField(
label=_('Date until'),
required=False,
widget=DatePickerWidget,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
class CheckinFilterForm(FilterForm):
status = forms.ChoiceField(
label=_('Status'),
choices=[
('', _('All check-ins')),
('successful', _('Successful check-ins')),
('unsuccessful', _('Unsuccessful check-ins')),
] + list(Checkin.REASONS),
required=False
)
type = forms.ChoiceField(
label=_('Scan type'),
choices=[
('', _('All directions')),
] + list(Checkin.CHECKIN_TYPES),
required=False
)
itemvar = forms.ChoiceField(
label=_("Product"),
required=False
)
device = SafeModelChoiceField(
label=_('Device'),
empty_label=_('All devices'),
queryset=Device.objects.none(),
required=False
)
gate = SafeModelChoiceField(
label=_('Gate'),
empty_label=_('All gates'),
queryset=Gate.objects.none(),
required=False
)
checkin_list = SafeModelChoiceField(queryset=CheckinList.objects.none(), required=False) # overridden later
datetime_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('filter', 'Start date'),
required=False,
)
datetime_until = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('filter', 'End date'),
required=False,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.fields['device'].queryset = self.event.organizer.devices.all()
self.fields['gate'].queryset = self.event.organizer.gates.all()
self.fields['checkin_list'].queryset = self.event.checkin_lists.all()
self.fields['checkin_list'].widget = Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse('control:event.orders.checkinlists.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': _('Check-in list'),
}
)
self.fields['checkin_list'].widget.choices = self.fields['checkin_list'].choices
self.fields['checkin_list'].label = _('Check-in list')
choices = [('', _('All products'))]
for i in self.event.items.prefetch_related('variations').all():
variations = list(i.variations.all())
if variations:
choices.append((str(i.pk), _('{product} – Any variation').format(product=i.name)))
for v in variations:
choices.append(('%d-%d' % (i.pk, v.pk), '%s – %s' % (i.name, v.value)))
else:
choices.append((str(i.pk), i.name))
self.fields['itemvar'].choices = choices
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status'):
s = fdata.get('status')
if s == 'successful':
qs = qs.filter(successful=True)
elif s == 'unsuccessful':
qs = qs.filter(successful=False)
elif s:
qs = qs.filter(successful=False, error_reason=s)
if fdata.get('type'):
qs = qs.filter(type=fdata.get('type'))
if fdata.get('itemvar'):
if '-' in fdata.get('itemvar'):
qs = qs.alias(
item_id=Coalesce('raw_item_id', 'position__item_id'),
variation_id=Coalesce('raw_variation_id', 'position__variation_id'),
).filter(
item_id=fdata.get('itemvar').split('-')[0],
variation_id=fdata.get('itemvar').split('-')[1]
)
else:
qs = qs.alias(
item_id=Coalesce('raw_item_id', 'position__item_id'),
).filter(item_id=fdata.get('itemvar'))
if fdata.get('device'):
qs = qs.filter(device_id=fdata.get('device').pk)
if fdata.get('gate'):
qs = qs.filter(gate_id=fdata.get('gate').pk)
if fdata.get('checkin_list'):
qs = qs.filter(list_id=fdata.get('checkin_list').pk)
if fdata.get('datetime_from'):
qs = qs.filter(datetime__gte=fdata.get('datetime_from'))
if fdata.get('datetime_until'):
qs = qs.filter(datetime__lte=fdata.get('datetime_until'))
return qs
class DeviceFilterForm(FilterForm):
orders = {
'name': Upper('name'),
'-name': Upper('name').desc(),
'device_id': 'device_id',
'initialized': F('initialized').asc(nulls_last=True),
'-initialized': F('initialized').desc(nulls_first=True),
}
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
gate = forms.ModelChoiceField(
queryset=Gate.objects.none(),
label=_('Gate'),
empty_label=_('All gates'),
required=False,
)
software_brand = forms.ChoiceField(
label=_('Software'),
choices=[
('', _('All')),
],
required=False,
)
state = forms.ChoiceField(
label=_('Device status'),
choices=[
('', _('All devices')),
('active', _('Active devices')),
('revoked', _('Revoked devices'))
],
required=False
)
def __init__(self, *args, **kwargs):
request = kwargs.pop('request')
super().__init__(*args, **kwargs)
self.fields['gate'].queryset = request.organizer.gates.all()
self.fields['software_brand'].choices = [
('', _('All')),
] + [
(f['software_brand'], f['software_brand']) for f in
request.organizer.devices.order_by().values('software_brand').annotate(c=Count('*'))
if f['software_brand']
]
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=query)
| Q(unique_serial__icontains=query)
| Q(hardware_brand__icontains=query)
| Q(hardware_model__icontains=query)
| Q(software_brand__icontains=query)
)
if fdata.get('gate'):
qs = qs.filter(gate=fdata['gate'])
if fdata.get('state') == 'active':
qs = qs.filter(revoked=False)
elif fdata.get('state') == 'revoked':
qs = qs.filter(revoked=True)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-device_id')
return qs
| #
# This file is part of pretix (Community Edition).
#
# Copyright (C) 2014-2020 Raphael Michel and contributors
# Copyright (C) 2020-2021 rami.io GmbH and contributors
#
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General
# Public License as published by the Free Software Foundation in version 3 of the License.
#
# ADDITIONAL TERMS APPLY: Pursuant to Section 7 of the GNU Affero General Public License, additional terms are
# applicable granting you additional permissions and placing additional restrictions on your usage of this software.
# Please refer to the pretix LICENSE file to obtain the full terms applicable to this work. If you did not receive
# this file, see <https://pretix.eu/about/en/license>.
#
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License along with this program. If not, see
# <https://www.gnu.org/licenses/>.
#
# This file is based on an earlier version of pretix which was released under the Apache License 2.0. The full text of
# the Apache License 2.0 can be obtained at <http://www.apache.org/licenses/LICENSE-2.0>.
#
# This file may have since been changed and any changes are released under the terms of AGPLv3 as described above. A
# full history of changes and contributors is available at <https://github.com/pretix/pretix>.
#
# This file contains Apache-licensed contributions copyrighted by: Jakob Schnell, Tobias Kunze
#
# Unless required by applicable law or agreed to in writing, software distributed under the Apache License 2.0 is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under the License.
from datetime import datetime, time, timedelta
from decimal import Decimal
from urllib.parse import urlencode
from django import forms
from django.apps import apps
from django.conf import settings
from django.db.models import (
Count, Exists, F, Max, Model, OrderBy, OuterRef, Q, QuerySet,
)
from django.db.models.functions import Coalesce, ExtractWeekDay, Upper
from django.urls import reverse, reverse_lazy
from django.utils.formats import date_format, localize
from django.utils.functional import cached_property
from django.utils.timezone import get_current_timezone, make_aware, now
from django.utils.translation import gettext, gettext_lazy as _, pgettext_lazy
from django_scopes.forms import SafeModelChoiceField
from pretix.base.channels import get_all_sales_channels
from pretix.base.forms.widgets import (
DatePickerWidget, SplitDateTimePickerWidget, TimePickerWidget,
)
from pretix.base.models import (
Checkin, CheckinList, Device, Event, EventMetaProperty, EventMetaValue,
Gate, Invoice, InvoiceAddress, Item, Order, OrderPayment, OrderPosition,
OrderRefund, Organizer, Question, QuestionAnswer, SubEvent, Team,
TeamAPIToken, TeamInvite,
)
from pretix.base.signals import register_payment_providers
from pretix.control.forms.widgets import Select2
from pretix.control.signals import order_search_filter_q
from pretix.helpers.countries import CachedCountries
from pretix.helpers.database import rolledback_transaction
from pretix.helpers.dicts import move_to_end
from pretix.helpers.i18n import i18ncomp
PAYMENT_PROVIDERS = []
def get_all_payment_providers():
global PAYMENT_PROVIDERS
if PAYMENT_PROVIDERS:
return PAYMENT_PROVIDERS
with rolledback_transaction():
event = Event.objects.create(
plugins=",".join([app.name for app in apps.get_app_configs()]),
name="INTERNAL",
date_from=now(),
organizer=Organizer.objects.create(name="INTERNAL")
)
provs = register_payment_providers.send(
sender=event
)
choices = []
for recv, prov in provs:
if isinstance(prov, list):
for p in prov:
p = p(event)
if not p.is_meta:
choices.append((p.identifier, p.verbose_name))
else:
prov = prov(event)
if not prov.is_meta:
choices.append((prov.identifier, prov.verbose_name))
PAYMENT_PROVIDERS = choices
return choices
class FilterForm(forms.Form):
orders = {}
def filter_qs(self, qs):
return qs
@property
def filtered(self):
return self.is_valid() and any(self.cleaned_data.values())
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['ordering'] = forms.ChoiceField(
choices=sum([
[(a, a), ('-' + a, '-' + a)]
for a in self.orders.keys()
], []),
required=False
)
def get_order_by(self):
o = self.cleaned_data.get('ordering')
if o.startswith('-') and o not in self.orders:
return '-' + self.orders[o[1:]]
else:
return self.orders[o]
def filter_to_strings(self):
string = []
for k, f in self.fields.items():
v = self.cleaned_data.get(k)
if v is None or (isinstance(v, (list, str, QuerySet)) and len(v) == 0):
continue
if k == "saveas":
continue
if isinstance(v, bool):
val = _('Yes') if v else _('No')
elif isinstance(v, QuerySet):
q = ['"' + str(m) + '"' for m in v]
if not q:
continue
val = ' or '.join(q)
elif isinstance(v, Model):
val = '"' + str(v) + '"'
elif isinstance(f, forms.MultipleChoiceField):
valdict = dict(f.choices)
val = ' or '.join([str(valdict.get(m)) for m in v])
elif isinstance(f, forms.ChoiceField):
val = str(dict(f.choices).get(v))
elif isinstance(v, datetime):
val = date_format(v, 'SHORT_DATETIME_FORMAT')
elif isinstance(v, Decimal):
val = localize(v)
else:
val = v
string.append('{}: {}'.format(f.label, val))
return string
class OrderFilterForm(FilterForm):
query = forms.CharField(
label=_('Search for…'),
widget=forms.TextInput(attrs={
'placeholder': _('Search for…'),
'autofocus': 'autofocus'
}),
required=False
)
provider = forms.ChoiceField(
label=_('Payment provider'),
choices=[
('', _('All payment providers')),
],
required=False,
)
status = forms.ChoiceField(
label=_('Order status'),
choices=(
('', _('All orders')),
(_('Valid orders'), (
(Order.STATUS_PAID, _('Paid (or canceled with paid fee)')),
(Order.STATUS_PENDING, _('Pending')),
(Order.STATUS_PENDING + Order.STATUS_PAID, _('Pending or paid')),
)),
(_('Cancellations'), (
(Order.STATUS_CANCELED, _('Canceled (fully)')),
('cp', _('Canceled (fully or with paid fee)')),
('rc', _('Cancellation requested')),
('cni', _('Fully canceled but invoice not canceled')),
)),
(_('Payment process'), (
(Order.STATUS_EXPIRED, _('Expired')),
(Order.STATUS_PENDING + Order.STATUS_EXPIRED, _('Pending or expired')),
('o', _('Pending (overdue)')),
('overpaid', _('Overpaid')),
('partially_paid', _('Partially paid')),
('underpaid', _('Underpaid (but confirmed)')),
('pendingpaid', _('Pending (but fully paid)')),
)),
(_('Approval process'), (
('na', _('Approved, payment pending')),
('pa', _('Approval pending')),
)),
(_('Follow-up date'), (
('custom_followup_at', _('Follow-up configured')),
('custom_followup_due', _('Follow-up due')),
)),
('testmode', _('Test mode')),
),
required=False,
)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
u = fdata.get('query')
if "-" in u:
code = (Q(event__slug__icontains=u.rsplit("-", 1)[0])
& Q(code__icontains=Order.normalize_code(u.rsplit("-", 1)[1])))
else:
code = Q(code__icontains=Order.normalize_code(u))
matching_invoices = Invoice.objects.filter(
Q(invoice_no__iexact=u)
| Q(invoice_no__iexact=u.zfill(5))
| Q(full_invoice_no__iexact=u)
).values_list('order_id', flat=True)
matching_positions = OrderPosition.objects.filter(
Q(
Q(attendee_name_cached__icontains=u) | Q(attendee_email__icontains=u)
| Q(secret__istartswith=u)
| Q(pseudonymization_id__istartswith=u)
)
).values_list('order_id', flat=True)
matching_invoice_addresses = InvoiceAddress.objects.filter(
Q(
Q(name_cached__icontains=u) | Q(company__icontains=u)
)
).values_list('order_id', flat=True)
matching_orders = Order.objects.filter(
code
| Q(email__icontains=u)
| Q(comment__icontains=u)
).values_list('id', flat=True)
mainq = (
Q(pk__in=matching_orders)
| Q(pk__in=matching_invoices)
| Q(pk__in=matching_positions)
| Q(pk__in=matching_invoice_addresses)
| Q(pk__in=matching_invoices)
)
for recv, q in order_search_filter_q.send(sender=getattr(self, 'event', None), query=u):
mainq = mainq | q
qs = qs.filter(
mainq
)
if fdata.get('status'):
s = fdata.get('status')
if s == 'o':
qs = qs.filter(status=Order.STATUS_PENDING, expires__lt=now().replace(hour=0, minute=0, second=0))
elif s == 'np':
qs = qs.filter(status__in=[Order.STATUS_PENDING, Order.STATUS_PAID])
elif s == 'ne':
qs = qs.filter(status__in=[Order.STATUS_PENDING, Order.STATUS_EXPIRED])
elif s in ('p', 'n', 'e', 'c', 'r'):
qs = qs.filter(status=s)
elif s == 'overpaid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
Q(~Q(status=Order.STATUS_CANCELED) & Q(pending_sum_t__lt=0))
| Q(Q(status=Order.STATUS_CANCELED) & Q(pending_sum_rc__lt=0))
)
elif s == 'rc':
qs = qs.filter(
cancellation_requests__isnull=False
).annotate(
cancellation_request_time=Max('cancellation_requests__created')
).order_by(
'-cancellation_request_time'
)
elif s == 'pendingpaid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
Q(status__in=(Order.STATUS_EXPIRED, Order.STATUS_PENDING)) & Q(pending_sum_t__lte=0)
& Q(require_approval=False)
)
elif s == 'partially_paid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
computed_payment_refund_sum__lt=F('total'),
computed_payment_refund_sum__gt=Decimal('0.00')
).exclude(
status=Order.STATUS_CANCELED
)
elif s == 'underpaid':
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
Q(status=Order.STATUS_PAID, pending_sum_t__gt=0) |
Q(status=Order.STATUS_CANCELED, pending_sum_rc__gt=0)
)
elif s == 'cni':
i = Invoice.objects.filter(
order=OuterRef('pk'),
is_cancellation=False,
refered__isnull=True,
).order_by().values('order').annotate(k=Count('id')).values('k')
qs = qs.annotate(
icnt=i
).filter(
icnt__gt=0,
status=Order.STATUS_CANCELED,
)
elif s == 'pa':
qs = qs.filter(
status=Order.STATUS_PENDING,
require_approval=True
)
elif s == 'na':
qs = qs.filter(
status=Order.STATUS_PENDING,
require_approval=False
)
elif s == 'custom_followup_at':
qs = qs.filter(
custom_followup_at__isnull=False
)
elif s == 'custom_followup_due':
qs = qs.filter(
custom_followup_at__lte=now().astimezone(get_current_timezone()).date()
)
elif s == 'testmode':
qs = qs.filter(
testmode=True
)
elif s == 'cp':
s = OrderPosition.objects.filter(
order=OuterRef('pk')
)
qs = qs.annotate(
has_pc=Exists(s)
).filter(
Q(status=Order.STATUS_PAID, has_pc=False) | Q(status=Order.STATUS_CANCELED)
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
if fdata.get('provider'):
qs = qs.annotate(
has_payment_with_provider=Exists(
OrderPayment.objects.filter(
Q(order=OuterRef('pk')) & Q(provider=fdata.get('provider'))
)
)
)
qs = qs.filter(has_payment_with_provider=1)
return qs
class EventOrderFilterForm(OrderFilterForm):
orders = {'code': 'code', 'email': 'email', 'total': 'total',
'datetime': 'datetime', 'status': 'status'}
item = forms.ChoiceField(
label=_('Products'),
required=False,
)
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
question = forms.ModelChoiceField(
queryset=Question.objects.none(),
required=False,
)
answer = forms.CharField(
required=False
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.fields['item'].queryset = self.event.items.all()
self.fields['question'].queryset = self.event.questions.all()
self.fields['provider'].choices += [(k, v.verbose_name) for k, v
in self.event.get_payment_providers().items()]
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
choices = [('', _('All products'))]
for i in self.event.items.prefetch_related('variations').all():
variations = list(i.variations.all())
if variations:
choices.append((str(i.pk), _('{product} – Any variation').format(product=str(i))))
for v in variations:
choices.append(('%d-%d' % (i.pk, v.pk), '%s – %s' % (str(i), v.value)))
else:
choices.append((str(i.pk), str(i)))
self.fields['item'].choices = choices
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
item = fdata.get('item')
if item:
if '-' in item:
var = item.split('-')[1]
qs = qs.filter(all_positions__variation_id=var, all_positions__canceled=False).distinct()
else:
qs = qs.filter(all_positions__item_id=fdata.get('item'), all_positions__canceled=False).distinct()
if fdata.get('subevent'):
qs = qs.filter(all_positions__subevent=fdata.get('subevent'), all_positions__canceled=False).distinct()
if fdata.get('question') and fdata.get('answer') is not None:
q = fdata.get('question')
if q.type == Question.TYPE_FILE:
answers = QuestionAnswer.objects.filter(
orderposition__order_id=OuterRef('pk'),
question_id=q.pk,
file__isnull=False
)
qs = qs.annotate(has_answer=Exists(answers)).filter(has_answer=True)
elif q.type in (Question.TYPE_CHOICE, Question.TYPE_CHOICE_MULTIPLE):
answers = QuestionAnswer.objects.filter(
question_id=q.pk,
orderposition__order_id=OuterRef('pk'),
options__pk=fdata.get('answer')
)
qs = qs.annotate(has_answer=Exists(answers)).filter(has_answer=True)
else:
answers = QuestionAnswer.objects.filter(
question_id=q.pk,
orderposition__order_id=OuterRef('pk'),
answer__exact=fdata.get('answer')
)
qs = qs.annotate(has_answer=Exists(answers)).filter(has_answer=True)
return qs
class FilterNullBooleanSelect(forms.NullBooleanSelect):
def __init__(self, attrs=None):
choices = (
('unknown', _('All')),
('true', _('Yes')),
('false', _('No')),
)
super(forms.NullBooleanSelect, self).__init__(attrs, choices)
class EventOrderExpertFilterForm(EventOrderFilterForm):
subevents_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'All dates starting at or after'),
required=False,
)
subevents_to = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'All dates starting before'),
required=False,
)
created_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=_('Order placed at or after'),
required=False,
)
created_to = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=_('Order placed before'),
required=False,
)
email = forms.CharField(
required=False,
label=_('E-mail address')
)
comment = forms.CharField(
required=False,
label=_('Comment')
)
locale = forms.ChoiceField(
required=False,
label=_('Locale'),
choices=settings.LANGUAGES
)
email_known_to_work = forms.NullBooleanField(
required=False,
widget=FilterNullBooleanSelect,
label=_('E-mail address verified'),
)
total = forms.DecimalField(
localize=True,
required=False,
label=_('Total amount'),
)
payment_sum_min = forms.DecimalField(
localize=True,
required=False,
label=_('Minimal sum of payments and refunds'),
)
payment_sum_max = forms.DecimalField(
localize=True,
required=False,
label=_('Maximal sum of payments and refunds'),
)
sales_channel = forms.ChoiceField(
label=_('Sales channel'),
required=False,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
del self.fields['query']
del self.fields['question']
del self.fields['answer']
del self.fields['ordering']
if not self.event.has_subevents:
del self.fields['subevents_from']
del self.fields['subevents_to']
self.fields['sales_channel'].choices = [('', '')] + [
(k, v.verbose_name) for k, v in get_all_sales_channels().items()
]
locale_names = dict(settings.LANGUAGES)
self.fields['locale'].choices = [('', '')] + [(a, locale_names[a]) for a in self.event.settings.locales]
move_to_end(self.fields, 'item')
move_to_end(self.fields, 'provider')
self.fields['invoice_address_company'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Company')
)
self.fields['invoice_address_name'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Name')
)
self.fields['invoice_address_street'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Address')
)
self.fields['invoice_address_zipcode'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('ZIP code'),
help_text=_('Exact matches only')
)
self.fields['invoice_address_city'] = forms.CharField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('City'),
help_text=_('Exact matches only')
)
self.fields['invoice_address_country'] = forms.ChoiceField(
required=False,
label=gettext('Invoice address') + ': ' + gettext('Country'),
choices=[('', '')] + list(CachedCountries())
)
self.fields['attendee_name'] = forms.CharField(
required=False,
label=_('Attendee name')
)
self.fields['attendee_email'] = forms.CharField(
required=False,
label=_('Attendee e-mail address')
)
self.fields['attendee_address_company'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('Company')
)
self.fields['attendee_address_street'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('Address')
)
self.fields['attendee_address_zipcode'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('ZIP code'),
help_text=_('Exact matches only')
)
self.fields['attendee_address_city'] = forms.CharField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('City'),
help_text=_('Exact matches only')
)
self.fields['attendee_address_country'] = forms.ChoiceField(
required=False,
label=gettext('Attendee address') + ': ' + gettext('Country'),
choices=[('', '')] + list(CachedCountries())
)
self.fields['ticket_secret'] = forms.CharField(
label=_('Ticket secret'),
required=False
)
for q in self.event.questions.all():
self.fields['question_{}'.format(q.pk)] = forms.CharField(
label=q.question,
required=False,
help_text=_('Exact matches only')
)
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
if fdata.get('subevents_from'):
qs = qs.filter(
all_positions__subevent__date_from__gte=fdata.get('subevents_from'),
all_positions__canceled=False
).distinct()
if fdata.get('subevents_to'):
qs = qs.filter(
all_positions__subevent__date_from__lt=fdata.get('subevents_to'),
all_positions__canceled=False
).distinct()
if fdata.get('email'):
qs = qs.filter(
email__icontains=fdata.get('email')
)
if fdata.get('created_from'):
qs = qs.filter(datetime__gte=fdata.get('created_from'))
if fdata.get('created_to'):
qs = qs.filter(datetime__lte=fdata.get('created_to'))
if fdata.get('comment'):
qs = qs.filter(comment__icontains=fdata.get('comment'))
if fdata.get('sales_channel'):
qs = qs.filter(sales_channel=fdata.get('sales_channel'))
if fdata.get('total'):
qs = qs.filter(total=fdata.get('total'))
if fdata.get('email_known_to_work') is not None:
qs = qs.filter(email_known_to_work=fdata.get('email_known_to_work'))
if fdata.get('locale'):
qs = qs.filter(locale=fdata.get('locale'))
if fdata.get('payment_sum_min') is not None:
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
computed_payment_refund_sum__gte=fdata['payment_sum_min'],
)
if fdata.get('payment_sum_max') is not None:
qs = Order.annotate_overpayments(qs, refunds=False, results=False, sums=True)
qs = qs.filter(
computed_payment_refund_sum__lte=fdata['payment_sum_max'],
)
if fdata.get('invoice_address_company'):
qs = qs.filter(invoice_address__company__icontains=fdata.get('invoice_address_company'))
if fdata.get('invoice_address_name'):
qs = qs.filter(invoice_address__name_cached__icontains=fdata.get('invoice_address_name'))
if fdata.get('invoice_address_street'):
qs = qs.filter(invoice_address__street__icontains=fdata.get('invoice_address_street'))
if fdata.get('invoice_address_zipcode'):
qs = qs.filter(invoice_address__zipcode__iexact=fdata.get('invoice_address_zipcode'))
if fdata.get('invoice_address_city'):
qs = qs.filter(invoice_address__city__iexact=fdata.get('invoice_address_city'))
if fdata.get('invoice_address_country'):
qs = qs.filter(invoice_address__country=fdata.get('invoice_address_country'))
if fdata.get('attendee_name'):
qs = qs.filter(
all_positions__attendee_name_cached__icontains=fdata.get('attendee_name')
)
if fdata.get('attendee_address_company'):
qs = qs.filter(
all_positions__company__icontains=fdata.get('attendee_address_company')
).distinct()
if fdata.get('attendee_address_street'):
qs = qs.filter(
all_positions__street__icontains=fdata.get('attendee_address_street')
).distinct()
if fdata.get('attendee_address_city'):
qs = qs.filter(
all_positions__city__iexact=fdata.get('attendee_address_city')
).distinct()
if fdata.get('attendee_address_country'):
qs = qs.filter(
all_positions__country=fdata.get('attendee_address_country')
).distinct()
if fdata.get('ticket_secret'):
qs = qs.filter(
all_positions__secret__icontains=fdata.get('ticket_secret')
).distinct()
for q in self.event.questions.all():
if fdata.get(f'question_{q.pk}'):
answers = QuestionAnswer.objects.filter(
question_id=q.pk,
orderposition__order_id=OuterRef('pk'),
answer__iexact=fdata.get(f'question_{q.pk}')
)
qs = qs.annotate(**{f'q_{q.pk}': Exists(answers)}).filter(**{f'q_{q.pk}': True})
return qs
class OrderSearchFilterForm(OrderFilterForm):
orders = {'code': 'code', 'email': 'email', 'total': 'total',
'datetime': 'datetime', 'status': 'status',
'event': 'event'}
organizer = forms.ModelChoiceField(
label=_('Organizer'),
queryset=Organizer.objects.none(),
required=False,
empty_label=_('All organizers'),
widget=Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse_lazy('control:organizers.select2'),
'data-placeholder': _('All organizers')
}
)
)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request')
super().__init__(*args, **kwargs)
if self.request.user.has_active_staff_session(self.request.session.session_key):
self.fields['organizer'].queryset = Organizer.objects.all()
else:
self.fields['organizer'].queryset = Organizer.objects.filter(
pk__in=self.request.user.teams.values_list('organizer', flat=True)
)
self.fields['provider'].choices += get_all_payment_providers()
seen = set()
for p in self.meta_properties.all():
if p.name in seen:
continue
seen.add(p.name)
self.fields['meta_{}'.format(p.name)] = forms.CharField(
label=p.name,
required=False,
widget=forms.TextInput(
attrs={
'data-typeahead-url': reverse('control:events.meta.typeahead') + '?' + urlencode({
'property': p.name,
'organizer': ''
})
}
)
)
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
if fdata.get('organizer'):
qs = qs.filter(event__organizer=fdata.get('organizer'))
filters_by_property_name = {}
for i, p in enumerate(self.meta_properties):
d = fdata.get('meta_{}'.format(p.name))
if d:
emv_with_value = EventMetaValue.objects.filter(
event=OuterRef('event_id'),
property__pk=p.pk,
value=d
)
emv_with_any_value = EventMetaValue.objects.filter(
event=OuterRef('event_id'),
property__pk=p.pk,
)
qs = qs.annotate(**{'attr_{}'.format(i): Exists(emv_with_value)})
if p.name in filters_by_property_name:
filters_by_property_name[p.name] |= Q(**{'attr_{}'.format(i): True})
else:
filters_by_property_name[p.name] = Q(**{'attr_{}'.format(i): True})
if p.default == d:
qs = qs.annotate(**{'attr_{}_any'.format(i): Exists(emv_with_any_value)})
filters_by_property_name[p.name] |= Q(**{
'attr_{}_any'.format(i): False, 'event__organizer_id': p.organizer_id
})
for f in filters_by_property_name.values():
qs = qs.filter(f)
return qs
@cached_property
def meta_properties(self):
# We ignore superuser permissions here. This is intentional – we do not want to show super
# users a form with all meta properties ever assigned.
return EventMetaProperty.objects.filter(
organizer_id__in=self.request.user.teams.values_list('organizer', flat=True)
)
class OrderPaymentSearchFilterForm(forms.Form):
orders = {'id': 'id', 'local_id': 'local_id', 'state': 'state', 'amount': 'amount', 'order': 'order',
'created': 'created', 'payment_date': 'payment_date', 'provider': 'provider', 'info': 'info',
'fee': 'fee'}
query = forms.CharField(
label=_('Search for…'),
widget=forms.TextInput(attrs={
'placeholder': _('Search for…'),
'autofocus': 'autofocus'
}),
required=False,
)
event = forms.ModelChoiceField(
label=_('Event'),
queryset=Event.objects.none(),
required=False,
widget=Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse_lazy('control:events.typeahead'),
'data-placeholder': _('All events')
}
)
)
organizer = forms.ModelChoiceField(
label=_('Organizer'),
queryset=Organizer.objects.none(),
required=False,
empty_label=_('All organizers'),
widget=Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse_lazy('control:organizers.select2'),
'data-placeholder': _('All organizers')
}
),
)
state = forms.ChoiceField(
label=_('Status'),
required=False,
choices=[('', _('All payments'))] + list(OrderPayment.PAYMENT_STATES),
)
provider = forms.ChoiceField(
label=_('Payment provider'),
choices=[
('', _('All payment providers')),
],
required=False,
)
created_from = forms.DateField(
label=_('Payment created from'),
required=False,
widget=DatePickerWidget,
)
created_until = forms.DateField(
label=_('Payment created until'),
required=False,
widget=DatePickerWidget,
)
completed_from = forms.DateField(
label=_('Paid from'),
required=False,
widget=DatePickerWidget,
)
completed_until = forms.DateField(
label=_('Paid until'),
required=False,
widget=DatePickerWidget,
)
amount = forms.CharField(
label=_('Amount'),
required=False,
widget=forms.NumberInput(attrs={
'placeholder': _('Amount'),
}),
)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request')
super().__init__(*args, **kwargs)
self.fields['ordering'] = forms.ChoiceField(
choices=sum([
[(a, a), ('-' + a, '-' + a)]
for a in self.orders.keys()
], []),
required=False
)
if self.request.user.has_active_staff_session(self.request.session.session_key):
self.fields['organizer'].queryset = Organizer.objects.all()
self.fields['event'].queryset = Event.objects.all()
else:
self.fields['organizer'].queryset = Organizer.objects.filter(
pk__in=self.request.user.teams.values_list('organizer', flat=True)
)
self.fields['event'].queryset = self.request.user.get_events_with_permission('can_view_orders')
self.fields['provider'].choices += get_all_payment_providers()
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('created_from'):
date_start = make_aware(datetime.combine(
fdata.get('created_from'),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(created__gte=date_start)
if fdata.get('created_until'):
date_end = make_aware(datetime.combine(
fdata.get('created_until') + timedelta(days=1),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(created__lt=date_end)
if fdata.get('completed_from'):
date_start = make_aware(datetime.combine(
fdata.get('completed_from'),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(payment_date__gte=date_start)
if fdata.get('completed_until'):
date_end = make_aware(datetime.combine(
fdata.get('completed_until') + timedelta(days=1),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(payment_date__lt=date_end)
if fdata.get('event'):
qs = qs.filter(order__event=fdata.get('event'))
if fdata.get('organizer'):
qs = qs.filter(order__event__organizer=fdata.get('organizer'))
if fdata.get('state'):
qs = qs.filter(state=fdata.get('state'))
if fdata.get('provider'):
qs = qs.filter(provider=fdata.get('provider'))
if fdata.get('query'):
u = fdata.get('query')
matching_invoices = Invoice.objects.filter(
Q(invoice_no__iexact=u)
| Q(invoice_no__iexact=u.zfill(5))
| Q(full_invoice_no__iexact=u)
).values_list('order_id', flat=True)
matching_invoice_addresses = InvoiceAddress.objects.filter(
Q(
Q(name_cached__icontains=u) | Q(company__icontains=u)
)
).values_list('order_id', flat=True)
if "-" in u:
code = (Q(event__slug__icontains=u.rsplit("-", 1)[0])
& Q(code__icontains=Order.normalize_code(u.rsplit("-", 1)[1])))
else:
code = Q(code__icontains=Order.normalize_code(u))
matching_orders = Order.objects.filter(
Q(
code
| Q(email__icontains=u)
| Q(comment__icontains=u)
)
).values_list('id', flat=True)
mainq = (
Q(order__id__in=matching_invoices)
| Q(order__id__in=matching_invoice_addresses)
| Q(order__id__in=matching_orders)
)
qs = qs.filter(mainq)
if fdata.get('amount'):
amount = fdata.get('amount')
def is_decimal(value):
result = True
parts = value.split('.', maxsplit=1)
for part in parts:
result = result & part.isdecimal()
return result
if is_decimal(amount):
qs = qs.filter(amount=Decimal(amount))
if fdata.get('ordering'):
p = self.cleaned_data.get('ordering')
if p.startswith('-') and p not in self.orders:
qs = qs.order_by('-' + self.orders[p[1:]])
else:
qs = qs.order_by(self.orders[p])
else:
qs = qs.order_by('-created')
return qs
class SubEventFilterForm(FilterForm):
orders = {
'date_from': 'date_from',
'active': 'active',
'sum_quota_available': 'sum_quota_available'
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('active', _('Active')),
('running', _('Shop live and presale running')),
('inactive', _('Inactive')),
('future', _('Presale not started')),
('past', _('Presale over')),
),
required=False
)
date_from = forms.DateField(
label=_('Date from'),
required=False,
widget=DatePickerWidget({
'placeholder': _('Date from'),
}),
)
date_until = forms.DateField(
label=_('Date until'),
required=False,
widget=DatePickerWidget({
'placeholder': _('Date until'),
}),
)
time_from = forms.TimeField(
label=_('Start time from'),
required=False,
widget=TimePickerWidget({}),
)
time_until = forms.TimeField(
label=_('Start time until'),
required=False,
widget=TimePickerWidget({}),
)
weekday = forms.MultipleChoiceField(
label=_('Weekday'),
choices=(
('2', _('Monday')),
('3', _('Tuesday')),
('4', _('Wednesday')),
('5', _('Thursday')),
('6', _('Friday')),
('7', _('Saturday')),
('1', _('Sunday')),
),
widget=forms.CheckboxSelectMultiple,
required=False
)
query = forms.CharField(
label=_('Event name'),
widget=forms.TextInput(attrs={
'placeholder': _('Event name'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields['date_from'].widget = DatePickerWidget()
self.fields['date_until'].widget = DatePickerWidget()
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status') == 'active':
qs = qs.filter(active=True)
elif fdata.get('status') == 'running':
qs = qs.filter(
active=True
).filter(
Q(presale_start__isnull=True) | Q(presale_start__lte=now())
).filter(
Q(Q(presale_end__isnull=True) & Q(
Q(date_to__gte=now()) |
Q(date_to__isnull=True, date_from__gte=now())
)) |
Q(presale_end__gte=now())
)
elif fdata.get('status') == 'inactive':
qs = qs.filter(active=False)
elif fdata.get('status') == 'future':
qs = qs.filter(presale_start__gte=now())
elif fdata.get('status') == 'past':
qs = qs.filter(
Q(presale_end__lte=now()) | Q(
Q(presale_end__isnull=True) & Q(
Q(date_to__lte=now()) |
Q(date_to__isnull=True, date_from__gte=now())
)
)
)
if fdata.get('weekday'):
qs = qs.annotate(wday=ExtractWeekDay('date_from')).filter(wday__in=fdata.get('weekday'))
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=i18ncomp(query)) | Q(location__icontains=query)
)
if fdata.get('date_until'):
date_end = make_aware(datetime.combine(
fdata.get('date_until') + timedelta(days=1),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(
Q(date_to__isnull=True, date_from__lt=date_end) |
Q(date_to__isnull=False, date_to__lt=date_end)
)
if fdata.get('date_from'):
date_start = make_aware(datetime.combine(
fdata.get('date_from'),
time(hour=0, minute=0, second=0, microsecond=0)
), get_current_timezone())
qs = qs.filter(date_from__gte=date_start)
if fdata.get('time_until'):
qs = qs.filter(date_from__time__lte=fdata.get('time_until'))
if fdata.get('time_from'):
qs = qs.filter(date_from__time__gte=fdata.get('time_from'))
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-date_from')
return qs
class OrganizerFilterForm(FilterForm):
orders = {
'slug': 'slug',
'name': 'name',
}
query = forms.CharField(
label=_('Organizer name'),
widget=forms.TextInput(attrs={
'placeholder': _('Organizer name'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=query) | Q(slug__icontains=query)
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
return qs
class GiftCardFilterForm(FilterForm):
orders = {
'issuance': 'issuance',
'expires': F('expires').asc(nulls_last=True),
'-expires': F('expires').desc(nulls_first=True),
'secret': 'secret',
'value': 'cached_value',
}
testmode = forms.ChoiceField(
label=_('Test mode'),
choices=(
('', _('All')),
('yes', _('Test mode')),
('no', _('Live')),
),
required=False
)
state = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('empty', _('Empty')),
('valid_value', _('Valid and with value')),
('expired_value', _('Expired and with value')),
('expired', _('Expired')),
),
required=False
)
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(secret__icontains=query)
| Q(transactions__text__icontains=query)
| Q(transactions__order__code__icontains=query)
)
if fdata.get('testmode') == 'yes':
qs = qs.filter(testmode=True)
elif fdata.get('testmode') == 'no':
qs = qs.filter(testmode=False)
if fdata.get('state') == 'empty':
qs = qs.filter(cached_value=0)
elif fdata.get('state') == 'valid_value':
qs = qs.exclude(cached_value=0).filter(Q(expires__isnull=True) | Q(expires__gte=now()))
elif fdata.get('state') == 'expired_value':
qs = qs.exclude(cached_value=0).filter(expires__lt=now())
elif fdata.get('state') == 'expired':
qs = qs.filter(expires__lt=now())
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-issuance')
return qs.distinct()
class CustomerFilterForm(FilterForm):
orders = {
'email': 'email',
'identifier': 'identifier',
'name_cached': 'name_cached',
}
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
status = forms.ChoiceField(
label=_('Status'),
required=False,
choices=(
('', _('All')),
('active', _('active')),
('disabled', _('disabled')),
('unverified', _('not yet activated')),
)
)
memberships = forms.ChoiceField(
label=_('Memberships'),
required=False,
choices=(
('', _('All')),
('no', _('Has no memberships')),
('any', _('Has any membership')),
('valid', _('Has valid membership')),
)
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(email__icontains=query)
| Q(name_cached__icontains=query)
| Q(identifier__istartswith=query)
)
if fdata.get('status') == 'active':
qs = qs.filter(is_active=True, is_verified=True)
elif fdata.get('status') == 'disabled':
qs = qs.filter(is_active=False)
elif fdata.get('status') == 'unverified':
qs = qs.filter(is_verified=False)
if fdata.get('memberships') == 'no':
qs = qs.filter(memberships__isnull=True)
elif fdata.get('memberships') == 'any':
qs = qs.filter(memberships__isnull=False)
elif fdata.get('memberships') == 'valid':
qs = qs.filter(memberships__date_start__lt=now(), memberships__date_end__gt=now(), memberships__canceled=False)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-email')
return qs.distinct()
class TeamFilterForm(FilterForm):
orders = {
'name': 'name',
}
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
kwargs.pop('request')
super().__init__(*args, **kwargs)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(Exists(
Team.members.through.objects.filter(
Q(user__email__icontains=query) | Q(user__fullname__icontains=query),
team_id=OuterRef('pk'),
)
))
| Q(Exists(
TeamInvite.objects.filter(
email__icontains=query,
team_id=OuterRef('pk'),
)
))
| Q(Exists(
TeamAPIToken.objects.filter(
name__icontains=query,
team_id=OuterRef('pk'),
)
))
| Q(name__icontains=query)
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('name')
return qs.distinct()
class EventFilterForm(FilterForm):
orders = {
'slug': 'slug',
'organizer': 'organizer__name',
'date_from': 'order_from',
'date_to': 'order_to',
'live': 'live',
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All events')),
('live', _('Shop live')),
('running', _('Shop live and presale running')),
('notlive', _('Shop not live')),
('future', _('Presale not started')),
('past', _('Presale over')),
('date_future', _('Single event running or in the future')),
('date_past', _('Single event in the past')),
('series', _('Event series')),
),
required=False
)
organizer = forms.ModelChoiceField(
label=_('Organizer'),
queryset=Organizer.objects.none(),
required=False,
empty_label=_('All organizers'),
widget=Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse_lazy('control:organizers.select2'),
'data-placeholder': _('All organizers')
}
)
)
query = forms.CharField(
label=_('Event name'),
widget=forms.TextInput(attrs={
'placeholder': _('Event name'),
'autofocus': 'autofocus'
}),
required=False
)
def __init__(self, *args, **kwargs):
self.request = kwargs.pop('request')
self.organizer = kwargs.pop('organizer', None)
super().__init__(*args, **kwargs)
seen = set()
for p in self.meta_properties.all():
if p.name in seen:
continue
seen.add(p.name)
self.fields['meta_{}'.format(p.name)] = forms.CharField(
label=p.name,
required=False,
widget=forms.TextInput(
attrs={
'data-typeahead-url': reverse('control:events.meta.typeahead') + '?' + urlencode({
'property': p.name,
'organizer': self.organizer.slug if self.organizer else ''
})
}
)
)
if self.organizer:
del self.fields['organizer']
else:
if self.request.user.has_active_staff_session(self.request.session.session_key):
self.fields['organizer'].queryset = Organizer.objects.all()
else:
self.fields['organizer'].queryset = Organizer.objects.filter(
pk__in=self.request.user.teams.values_list('organizer', flat=True)
)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status') == 'live':
qs = qs.filter(live=True)
elif fdata.get('status') == 'running':
qs = qs.filter(
live=True
).annotate(
p_end=Coalesce(F('presale_end'), F('date_to'), F('date_from'))
).filter(
Q(presale_start__isnull=True) | Q(presale_start__lte=now())
).filter(
Q(p_end__gte=now())
)
elif fdata.get('status') == 'notlive':
qs = qs.filter(live=False)
elif fdata.get('status') == 'future':
qs = qs.filter(presale_start__gte=now())
elif fdata.get('status') == 'past':
qs = qs.filter(presale_end__lte=now())
elif fdata.get('status') == 'date_future':
qs = qs.filter(
Q(has_subevents=False) &
Q(
Q(Q(date_to__isnull=True) & Q(date_from__gte=now()))
| Q(Q(date_to__isnull=False) & Q(date_to__gte=now()))
)
)
elif fdata.get('status') == 'date_past':
qs = qs.filter(
Q(has_subevents=False) &
Q(
Q(Q(date_to__isnull=True) & Q(date_from__lt=now()))
| Q(Q(date_to__isnull=False) & Q(date_to__lt=now()))
)
)
elif fdata.get('status') == 'series':
qs = qs.filter(has_subevents=True)
if fdata.get('organizer'):
qs = qs.filter(organizer=fdata.get('organizer'))
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=i18ncomp(query)) | Q(slug__icontains=query)
)
filters_by_property_name = {}
for i, p in enumerate(self.meta_properties):
d = fdata.get('meta_{}'.format(p.name))
if d:
emv_with_value = EventMetaValue.objects.filter(
event=OuterRef('pk'),
property__pk=p.pk,
value=d
)
emv_with_any_value = EventMetaValue.objects.filter(
event=OuterRef('pk'),
property__pk=p.pk,
)
qs = qs.annotate(**{'attr_{}'.format(i): Exists(emv_with_value)})
if p.name in filters_by_property_name:
filters_by_property_name[p.name] |= Q(**{'attr_{}'.format(i): True})
else:
filters_by_property_name[p.name] = Q(**{'attr_{}'.format(i): True})
if p.default == d:
qs = qs.annotate(**{'attr_{}_any'.format(i): Exists(emv_with_any_value)})
filters_by_property_name[p.name] |= Q(**{'attr_{}_any'.format(i): False, 'organizer_id': p.organizer_id})
for f in filters_by_property_name.values():
qs = qs.filter(f)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
return qs
@cached_property
def meta_properties(self):
if self.organizer:
return self.organizer.meta_properties.all()
else:
# We ignore superuser permissions here. This is intentional – we do not want to show super
# users a form with all meta properties ever assigned.
return EventMetaProperty.objects.filter(
organizer_id__in=self.request.user.teams.values_list('organizer', flat=True)
)
class CheckinListAttendeeFilterForm(FilterForm):
orders = {
'code': ('order__code', 'item__name'),
'-code': ('-order__code', '-item__name'),
'email': ('order__email', 'item__name'),
'-email': ('-order__email', '-item__name'),
'status': (OrderBy(F('last_entry'), nulls_first=True, descending=True), 'order__code'),
'-status': (OrderBy(F('last_entry'), nulls_last=True), '-order__code'),
'timestamp': (OrderBy(F('last_entry'), nulls_first=True), 'order__code'),
'-timestamp': (OrderBy(F('last_entry'), nulls_last=True, descending=True), '-order__code'),
'item': ('item__name', 'variation__value', 'order__code'),
'-item': ('-item__name', '-variation__value', '-order__code'),
'seat': ('seat__sorting_rank', 'seat__guid'),
'-seat': ('-seat__sorting_rank', '-seat__guid'),
'date': ('subevent__date_from', 'subevent__id', 'order__code'),
'-date': ('-subevent__date_from', 'subevent__id', '-order__code'),
'name': {'_order': F('display_name').asc(nulls_first=True),
'display_name': Coalesce('attendee_name_cached', 'addon_to__attendee_name_cached')},
'-name': {'_order': F('display_name').desc(nulls_last=True),
'display_name': Coalesce('attendee_name_cached', 'addon_to__attendee_name_cached')},
}
user = forms.CharField(
label=_('Search attendee…'),
widget=forms.TextInput(attrs={
'placeholder': _('Search attendee…'),
'autofocus': 'autofocus'
}),
required=False
)
status = forms.ChoiceField(
label=_('Check-in status'),
choices=(
('', _('All attendees')),
('3', pgettext_lazy('checkin state', 'Checked in but left')),
('2', pgettext_lazy('checkin state', 'Present')),
('1', _('Checked in')),
('0', _('Not checked in')),
),
required=False,
)
item = forms.ModelChoiceField(
label=_('Products'),
queryset=Item.objects.none(),
required=False,
empty_label=_('All products')
)
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
subevent_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'Date start from'),
required=False,
)
subevent_until = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('subevent', 'Date start until'),
required=False,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
self.list = kwargs.pop('list')
super().__init__(*args, **kwargs)
if self.list.all_products:
self.fields['item'].queryset = self.event.items.all()
else:
self.fields['item'].queryset = self.list.limit_products.all()
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
else:
del self.fields['subevent']
del self.fields['subevent_from']
del self.fields['subevent_until']
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('user'):
u = fdata.get('user')
qs = qs.filter(
Q(order__code__istartswith=u)
| Q(secret__istartswith=u)
| Q(pseudonymization_id__istartswith=u)
| Q(order__email__icontains=u)
| Q(attendee_name_cached__icontains=u)
| Q(attendee_email__icontains=u)
| Q(voucher__code__istartswith=u)
| Q(order__invoice_address__name_cached__icontains=u)
| Q(order__invoice_address__company__icontains=u)
)
if fdata.get('status'):
s = fdata.get('status')
if s == '1':
qs = qs.filter(last_entry__isnull=False)
elif s == '2':
qs = qs.filter(last_entry__isnull=False).filter(
Q(last_exit__isnull=True) | Q(last_exit__lt=F('last_entry'))
)
elif s == '3':
qs = qs.filter(last_entry__isnull=False).filter(
Q(last_exit__isnull=False) & Q(last_exit__gte=F('last_entry'))
)
elif s == '0':
qs = qs.filter(last_entry__isnull=True)
if fdata.get('ordering'):
ob = self.orders[fdata.get('ordering')]
if isinstance(ob, dict):
ob = dict(ob)
o = ob.pop('_order')
qs = qs.annotate(**ob).order_by(o)
elif isinstance(ob, (list, tuple)):
qs = qs.order_by(*ob)
else:
qs = qs.order_by(ob)
if fdata.get('item'):
qs = qs.filter(item=fdata.get('item'))
if fdata.get('subevent'):
qs = qs.filter(subevent_id=fdata.get('subevent').pk)
if fdata.get('subevent_from'):
qs = qs.filter(subevent__date_from__gte=fdata.get('subevent_from'))
if fdata.get('subevent_until'):
qs = qs.filter(subevent__date_from__lte=fdata.get('subevent_until'))
return qs
class UserFilterForm(FilterForm):
orders = {
'fullname': 'fullname',
'email': 'email',
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('active', _('Active')),
('inactive', _('Inactive')),
),
required=False
)
superuser = forms.ChoiceField(
label=_('Administrator'),
choices=(
('', _('All')),
('yes', _('Administrator')),
('no', _('No administrator')),
),
required=False
)
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status') == 'active':
qs = qs.filter(is_active=True)
elif fdata.get('status') == 'inactive':
qs = qs.filter(is_active=False)
if fdata.get('superuser') == 'yes':
qs = qs.filter(is_staff=True)
elif fdata.get('superuser') == 'no':
qs = qs.filter(is_staff=False)
if fdata.get('query'):
qs = qs.filter(
Q(email__icontains=fdata.get('query'))
| Q(fullname__icontains=fdata.get('query'))
)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
return qs
class VoucherFilterForm(FilterForm):
orders = {
'code': 'code',
'-code': '-code',
'redeemed': 'redeemed',
'-redeemed': '-redeemed',
'valid_until': 'valid_until',
'-valid_until': '-valid_until',
'tag': 'tag',
'-tag': '-tag',
'item': (
'seat__sorting_rank',
'item__category__position',
'item__category',
'item__position',
'item__variation__position',
'quota__name',
),
'subevent': 'subevent__date_from',
'-subevent': '-subevent__date_from',
'-item': (
'-seat__sorting_rank',
'-item__category__position',
'-item__category',
'-item__position',
'-item__variation__position',
'-quota__name',
)
}
status = forms.ChoiceField(
label=_('Status'),
choices=(
('', _('All')),
('v', _('Valid')),
('u', _('Unredeemed')),
('r', _('Redeemed at least once')),
('f', _('Fully redeemed')),
('e', _('Expired')),
('c', _('Redeemed and checked in with ticket')),
),
required=False
)
qm = forms.ChoiceField(
label=_('Quota handling'),
choices=(
('', _('All')),
('b', _('Reserve ticket from quota')),
('i', _('Allow to ignore quota')),
),
required=False
)
tag = forms.CharField(
label=_('Filter by tag'),
widget=forms.TextInput(attrs={
'placeholder': _('Filter by tag'),
}),
required=False
)
search = forms.CharField(
label=_('Search voucher'),
widget=forms.TextInput(attrs={
'placeholder': _('Search voucher'),
'autofocus': 'autofocus'
}),
required=False
)
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
itemvar = forms.ChoiceField(
label=_("Product"),
required=False
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
choices = [('', _('All products'))]
for i in self.event.items.prefetch_related('variations').all():
variations = list(i.variations.all())
if variations:
choices.append((str(i.pk), _('{product} – Any variation').format(product=i.name)))
for v in variations:
choices.append(('%d-%d' % (i.pk, v.pk), '%s – %s' % (i.name, v.value)))
else:
choices.append((str(i.pk), i.name))
for q in self.event.quotas.all():
choices.append(('q-%d' % q.pk, _('Any product in quota "{quota}"').format(quota=q)))
self.fields['itemvar'].choices = choices
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('search'):
s = fdata.get('search').strip()
qs = qs.filter(Q(code__icontains=s) | Q(tag__icontains=s) | Q(comment__icontains=s))
if fdata.get('tag'):
s = fdata.get('tag').strip()
if s == '<>':
qs = qs.filter(Q(tag__isnull=True) | Q(tag=''))
elif s[0] == '"' and s[-1] == '"':
qs = qs.filter(tag__exact=s[1:-1])
else:
qs = qs.filter(tag__icontains=s)
if fdata.get('qm'):
s = fdata.get('qm')
if s == 'b':
qs = qs.filter(block_quota=True)
elif s == 'i':
qs = qs.filter(allow_ignore_quota=True)
if fdata.get('status'):
s = fdata.get('status')
if s == 'v':
qs = qs.filter(Q(valid_until__isnull=True) | Q(valid_until__gt=now())).filter(redeemed__lt=F('max_usages'))
elif s == 'r':
qs = qs.filter(redeemed__gt=0)
elif s == 'u':
qs = qs.filter(redeemed=0)
elif s == 'f':
qs = qs.filter(redeemed__gte=F('max_usages'))
elif s == 'e':
qs = qs.filter(Q(valid_until__isnull=False) & Q(valid_until__lt=now())).filter(redeemed=0)
elif s == 'c':
checkins = Checkin.objects.filter(
position__voucher=OuterRef('pk')
)
qs = qs.annotate(has_checkin=Exists(checkins)).filter(
redeemed__gt=0, has_checkin=True
)
if fdata.get('itemvar'):
if fdata.get('itemvar').startswith('q-'):
qs = qs.filter(quota_id=fdata.get('itemvar').split('-')[1])
elif '-' in fdata.get('itemvar'):
qs = qs.filter(item_id=fdata.get('itemvar').split('-')[0],
variation_id=fdata.get('itemvar').split('-')[1])
else:
qs = qs.filter(item_id=fdata.get('itemvar'))
if fdata.get('subevent'):
qs = qs.filter(subevent_id=fdata.get('subevent').pk)
if fdata.get('ordering'):
ob = self.orders[fdata.get('ordering')]
if isinstance(ob, dict):
ob = dict(ob)
o = ob.pop('_order')
qs = qs.annotate(**ob).order_by(o)
elif isinstance(ob, (list, tuple)):
qs = qs.order_by(*ob)
else:
qs = qs.order_by(ob)
return qs
class VoucherTagFilterForm(FilterForm):
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('subevent'):
qs = qs.filter(subevent_id=fdata.get('subevent').pk)
return qs
class RefundFilterForm(FilterForm):
orders = {'provider': 'provider', 'state': 'state', 'order': 'order__code',
'source': 'source', 'amount': 'amount', 'created': 'created'}
provider = forms.ChoiceField(
label=_('Payment provider'),
choices=[
('', _('All payment providers')),
],
required=False,
)
status = forms.ChoiceField(
label=_('Refund status'),
choices=(
('', _('All open refunds')),
('all', _('All refunds')),
) + OrderRefund.REFUND_STATES,
required=False,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.fields['provider'].choices += [(k, v.verbose_name) for k, v
in self.event.get_payment_providers().items()]
def filter_qs(self, qs):
fdata = self.cleaned_data
qs = super().filter_qs(qs)
if fdata.get('provider'):
qs = qs.filter(provider=fdata.get('provider'))
if fdata.get('status'):
if fdata.get('status') != 'all':
qs = qs.filter(state=fdata.get('status'))
else:
qs = qs.filter(state__in=[OrderRefund.REFUND_STATE_CREATED, OrderRefund.REFUND_STATE_TRANSIT,
OrderRefund.REFUND_STATE_EXTERNAL])
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-created')
return qs
class OverviewFilterForm(FilterForm):
subevent = forms.ModelChoiceField(
label=pgettext_lazy('subevent', 'Date'),
queryset=SubEvent.objects.none(),
required=False,
empty_label=pgettext_lazy('subevent', 'All dates')
)
date_axis = forms.ChoiceField(
label=_('Date filter'),
choices=(
('', _('Filter by…')),
('order_date', _('Order date')),
('last_payment_date', _('Date of last successful payment')),
),
required=False,
)
date_from = forms.DateField(
label=_('Date from'),
required=False,
widget=DatePickerWidget,
)
date_until = forms.DateField(
label=_('Date until'),
required=False,
widget=DatePickerWidget,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
if self.event.has_subevents:
self.fields['subevent'].queryset = self.event.subevents.all()
self.fields['subevent'].widget = Select2(
attrs={
'data-model-select2': 'event',
'data-select2-url': reverse('control:event.subevents.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': pgettext_lazy('subevent', 'All dates')
}
)
self.fields['subevent'].widget.choices = self.fields['subevent'].choices
elif 'subevent':
del self.fields['subevent']
class CheckinFilterForm(FilterForm):
status = forms.ChoiceField(
label=_('Status'),
choices=[
('', _('All check-ins')),
('successful', _('Successful check-ins')),
('unsuccessful', _('Unsuccessful check-ins')),
] + list(Checkin.REASONS),
required=False
)
type = forms.ChoiceField(
label=_('Scan type'),
choices=[
('', _('All directions')),
] + list(Checkin.CHECKIN_TYPES),
required=False
)
itemvar = forms.ChoiceField(
label=_("Product"),
required=False
)
device = SafeModelChoiceField(
label=_('Device'),
empty_label=_('All devices'),
queryset=Device.objects.none(),
required=False
)
gate = SafeModelChoiceField(
label=_('Gate'),
empty_label=_('All gates'),
queryset=Gate.objects.none(),
required=False
)
checkin_list = SafeModelChoiceField(queryset=CheckinList.objects.none(), required=False) # overridden later
datetime_from = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('filter', 'Start date'),
required=False,
)
datetime_until = forms.SplitDateTimeField(
widget=SplitDateTimePickerWidget(attrs={
}),
label=pgettext_lazy('filter', 'End date'),
required=False,
)
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.fields['device'].queryset = self.event.organizer.devices.all()
self.fields['gate'].queryset = self.event.organizer.gates.all()
self.fields['checkin_list'].queryset = self.event.checkin_lists.all()
self.fields['checkin_list'].widget = Select2(
attrs={
'data-model-select2': 'generic',
'data-select2-url': reverse('control:event.orders.checkinlists.select2', kwargs={
'event': self.event.slug,
'organizer': self.event.organizer.slug,
}),
'data-placeholder': _('Check-in list'),
}
)
self.fields['checkin_list'].widget.choices = self.fields['checkin_list'].choices
self.fields['checkin_list'].label = _('Check-in list')
choices = [('', _('All products'))]
for i in self.event.items.prefetch_related('variations').all():
variations = list(i.variations.all())
if variations:
choices.append((str(i.pk), _('{product} – Any variation').format(product=i.name)))
for v in variations:
choices.append(('%d-%d' % (i.pk, v.pk), '%s – %s' % (i.name, v.value)))
else:
choices.append((str(i.pk), i.name))
self.fields['itemvar'].choices = choices
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('status'):
s = fdata.get('status')
if s == 'successful':
qs = qs.filter(successful=True)
elif s == 'unsuccessful':
qs = qs.filter(successful=False)
elif s:
qs = qs.filter(successful=False, error_reason=s)
if fdata.get('type'):
qs = qs.filter(type=fdata.get('type'))
if fdata.get('itemvar'):
if '-' in fdata.get('itemvar'):
qs = qs.alias(
item_id=Coalesce('raw_item_id', 'position__item_id'),
variation_id=Coalesce('raw_variation_id', 'position__variation_id'),
).filter(
item_id=fdata.get('itemvar').split('-')[0],
variation_id=fdata.get('itemvar').split('-')[1]
)
else:
qs = qs.alias(
item_id=Coalesce('raw_item_id', 'position__item_id'),
).filter(item_id=fdata.get('itemvar'))
if fdata.get('device'):
qs = qs.filter(device_id=fdata.get('device').pk)
if fdata.get('gate'):
qs = qs.filter(gate_id=fdata.get('gate').pk)
if fdata.get('checkin_list'):
qs = qs.filter(list_id=fdata.get('checkin_list').pk)
if fdata.get('datetime_from'):
qs = qs.filter(datetime__gte=fdata.get('datetime_from'))
if fdata.get('datetime_until'):
qs = qs.filter(datetime__lte=fdata.get('datetime_until'))
return qs
class DeviceFilterForm(FilterForm):
orders = {
'name': Upper('name'),
'-name': Upper('name').desc(),
'device_id': 'device_id',
'initialized': F('initialized').asc(nulls_last=True),
'-initialized': F('initialized').desc(nulls_first=True),
}
query = forms.CharField(
label=_('Search query'),
widget=forms.TextInput(attrs={
'placeholder': _('Search query'),
'autofocus': 'autofocus'
}),
required=False
)
gate = forms.ModelChoiceField(
queryset=Gate.objects.none(),
label=_('Gate'),
empty_label=_('All gates'),
required=False,
)
software_brand = forms.ChoiceField(
label=_('Software'),
choices=[
('', _('All')),
],
required=False,
)
state = forms.ChoiceField(
label=_('Device status'),
choices=[
('', _('All devices')),
('active', _('Active devices')),
('revoked', _('Revoked devices'))
],
required=False
)
def __init__(self, *args, **kwargs):
request = kwargs.pop('request')
super().__init__(*args, **kwargs)
self.fields['gate'].queryset = request.organizer.gates.all()
self.fields['software_brand'].choices = [
('', _('All')),
] + [
(f['software_brand'], f['software_brand']) for f in
request.organizer.devices.order_by().values('software_brand').annotate(c=Count('*'))
if f['software_brand']
]
def filter_qs(self, qs):
fdata = self.cleaned_data
if fdata.get('query'):
query = fdata.get('query')
qs = qs.filter(
Q(name__icontains=query)
| Q(unique_serial__icontains=query)
| Q(hardware_brand__icontains=query)
| Q(hardware_model__icontains=query)
| Q(software_brand__icontains=query)
)
if fdata.get('gate'):
qs = qs.filter(gate=fdata['gate'])
if fdata.get('state') == 'active':
qs = qs.filter(revoked=False)
elif fdata.get('state') == 'revoked':
qs = qs.filter(revoked=True)
if fdata.get('ordering'):
qs = qs.order_by(self.get_order_by())
else:
qs = qs.order_by('-device_id')
return qs
|
import torch
import pandas as pd
from io import BytesIO
from subprocess import check_output
from . import writing
import time
def memory(device=0):
total_mem = torch.cuda.get_device_properties(f'cuda:{device}').total_memory
writing.max(f'gpu-memory/cache/{device}', torch.cuda.max_memory_cached(device)/total_mem)
torch.cuda.reset_max_memory_cached()
writing.max(f'gpu-memory/alloc/{device}', torch.cuda.max_memory_allocated(device)/total_mem)
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_max_memory_cached()
def dataframe():
"""Use `nvidia-smi --help-query-gpu` to get a list of query params"""
params = {
'device': 'index',
'compute': 'utilization.gpu', 'access': 'utilization.memory',
'memused': 'memory.used', 'memtotal': 'memory.total',
'fan': 'fan.speed', 'power': 'power.draw', 'temp': 'temperature.gpu'}
command = f"""nvidia-smi --format=csv,nounits,noheader --query-gpu={",".join(params.values())}"""
df = pd.read_csv(BytesIO(check_output(command, shell=True)), header=None)
df.columns = list(params.keys())
df = df.set_index('device')
df = df.apply(pd.to_numeric, errors='coerce')
return df
_last = -1
def vitals(device=None, throttle=0):
# This is a fairly expensive op, so let's avoid doing it too often
global _last
if time.time() - _last < throttle:
return
_last = time.time()
df = dataframe()
if device is None:
pass
elif isinstance(device, int):
df = df.loc[[device]]
else:
df = df.loc[device]
fields = ['compute', 'access', 'fan', 'power', 'temp']
for (device, field), value in df[fields].stack().iteritems():
writing.mean(f'gpu/{field}/{device}', value)
for device in df.index:
writing.mean(f'gpu/memory/{device}', 100*df.loc[device, 'memused']/df.loc[device, 'memtotal']) | import torch
import pandas as pd
from io import BytesIO
from subprocess import check_output
from . import writing
import time
def memory(device=0):
total_mem = torch.cuda.get_device_properties(f'cuda:{device}').total_memory
writing.max(f'gpu-memory/cache/{device}', torch.cuda.max_memory_cached(device)/total_mem)
torch.cuda.reset_max_memory_cached()
writing.max(f'gpu-memory/alloc/{device}', torch.cuda.max_memory_allocated(device)/total_mem)
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_max_memory_cached()
def dataframe():
"""Use `nvidia-smi --help-query-gpu` to get a list of query params"""
params = {
'device': 'index',
'compute': 'utilization.gpu', 'access': 'utilization.memory',
'memused': 'memory.used', 'memtotal': 'memory.total',
'fan': 'fan.speed', 'power': 'power.draw', 'temp': 'temperature.gpu'}
command = f"""nvidia-smi --format=csv,nounits,noheader --query-gpu={','.join(params.values())}"""
df = pd.read_csv(BytesIO(check_output(command, shell=True)), header=None)
df.columns = list(params.keys())
df = df.set_index('device')
df = df.apply(pd.to_numeric, errors='coerce')
return df
_last = -1
def vitals(device=None, throttle=0):
# This is a fairly expensive op, so let's avoid doing it too often
global _last
if time.time() - _last < throttle:
return
_last = time.time()
df = dataframe()
if device is None:
pass
elif isinstance(device, int):
df = df.loc[[device]]
else:
df = df.loc[device]
fields = ['compute', 'access', 'fan', 'power', 'temp']
for (device, field), value in df[fields].stack().iteritems():
writing.mean(f'gpu/{field}/{device}', value)
for device in df.index:
writing.mean(f'gpu/memory/{device}', 100*df.loc[device, 'memused']/df.loc[device, 'memtotal']) |
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