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3709ed8950af21a0bed749254f870b3a03f82b764aaddf9674f5428632f8ca2d
|
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'data': ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), 'name': (str,)}
|
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
|
clients/client/python/ory_client/model/schema_patch.py
|
openapi_types
|
ory/sdk
| 77 |
python
|
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'data': ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), 'name': (str,)}
|
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
return {'data': ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},), 'name': (str,)}<|docstring|>This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.<|endoftext|>
|
7416d08f34534575efe630dd31ab1d3530ae39f530261da0b319ab97c4146bb6
|
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, data, name, *args, **kwargs):
'SchemaPatch - a model defined in OpenAPI\n\n Args:\n data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema\n name (str): The user defined schema name\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.data = data
self.name = name
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self
|
SchemaPatch - a model defined in OpenAPI
Args:
data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema
name (str): The user defined schema name
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
|
clients/client/python/ory_client/model/schema_patch.py
|
_from_openapi_data
|
ory/sdk
| 77 |
python
|
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, data, name, *args, **kwargs):
'SchemaPatch - a model defined in OpenAPI\n\n Args:\n data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema\n name (str): The user defined schema name\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.data = data
self.name = name
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self
|
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, data, name, *args, **kwargs):
'SchemaPatch - a model defined in OpenAPI\n\n Args:\n data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema\n name (str): The user defined schema name\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.data = data
self.name = name
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self<|docstring|>SchemaPatch - a model defined in OpenAPI
Args:
data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema
name (str): The user defined schema name
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)<|endoftext|>
|
cb2567eeb3ce0c408b95ce7a9e138a690e11c4e6a78e70a4eb20129f03ec1cf7
|
@convert_js_args_to_python_args
def __init__(self, data, name, *args, **kwargs):
'SchemaPatch - a model defined in OpenAPI\n\n Args:\n data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema\n name (str): The user defined schema name\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.data = data
self.name = name
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.')
|
SchemaPatch - a model defined in OpenAPI
Args:
data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema
name (str): The user defined schema name
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
|
clients/client/python/ory_client/model/schema_patch.py
|
__init__
|
ory/sdk
| 77 |
python
|
@convert_js_args_to_python_args
def __init__(self, data, name, *args, **kwargs):
'SchemaPatch - a model defined in OpenAPI\n\n Args:\n data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema\n name (str): The user defined schema name\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.data = data
self.name = name
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.')
|
@convert_js_args_to_python_args
def __init__(self, data, name, *args, **kwargs):
'SchemaPatch - a model defined in OpenAPI\n\n Args:\n data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema\n name (str): The user defined schema name\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.data = data
self.name = name
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.')<|docstring|>SchemaPatch - a model defined in OpenAPI
Args:
data ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The json schema
name (str): The user defined schema name
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)<|endoftext|>
|
82cae52bd5a4555b36ab1487725ebee1e498b03e715ada7162bfd773db9ec2a6
|
@task
def info(ctx):
'Show information about the current Python and environment.'
version = get_version()
suffix = get_suffix()
print(f'Python being used: {PYTHONBIN}')
print(f'Python extension suffix: {suffix}')
print(f'Package version: {version}')
venv = get_virtualenv()
if venv:
print(f'Virtual environment:', venv)
|
Show information about the current Python and environment.
|
tasks.py
|
info
|
pycopia/devtest
| 0 |
python
|
@task
def info(ctx):
version = get_version()
suffix = get_suffix()
print(f'Python being used: {PYTHONBIN}')
print(f'Python extension suffix: {suffix}')
print(f'Package version: {version}')
venv = get_virtualenv()
if venv:
print(f'Virtual environment:', venv)
|
@task
def info(ctx):
version = get_version()
suffix = get_suffix()
print(f'Python being used: {PYTHONBIN}')
print(f'Python extension suffix: {suffix}')
print(f'Package version: {version}')
venv = get_virtualenv()
if venv:
print(f'Virtual environment:', venv)<|docstring|>Show information about the current Python and environment.<|endoftext|>
|
565aefb526a63ca7d432dcf9c3ecc251a68cd33ce8d8775bb4826e1b0cbced52
|
@task
def flake8(ctx, pathname='devtest'):
'Run flake8 linter on the package.'
ctx.run(f'{PYTHONBIN} -m flake8 {pathname}')
|
Run flake8 linter on the package.
|
tasks.py
|
flake8
|
pycopia/devtest
| 0 |
python
|
@task
def flake8(ctx, pathname='devtest'):
ctx.run(f'{PYTHONBIN} -m flake8 {pathname}')
|
@task
def flake8(ctx, pathname='devtest'):
ctx.run(f'{PYTHONBIN} -m flake8 {pathname}')<|docstring|>Run flake8 linter on the package.<|endoftext|>
|
9b03ee9b57c2a7c8dd26bdfcfcde06138d9d368d8e1f6519c4744d1162143a47
|
@task
def format(ctx, pathname='devtest', check=False):
'Run yapf formatter on the specified file, or recurse into directory.'
option = ('-d' if check else '-i')
recurse = ('--recursive' if os.path.isdir(pathname) else '')
ctx.run(f'{PYTHONBIN} -m yapf --style setup.cfg {option} {recurse} {pathname}')
|
Run yapf formatter on the specified file, or recurse into directory.
|
tasks.py
|
format
|
pycopia/devtest
| 0 |
python
|
@task
def format(ctx, pathname='devtest', check=False):
option = ('-d' if check else '-i')
recurse = ('--recursive' if os.path.isdir(pathname) else )
ctx.run(f'{PYTHONBIN} -m yapf --style setup.cfg {option} {recurse} {pathname}')
|
@task
def format(ctx, pathname='devtest', check=False):
option = ('-d' if check else '-i')
recurse = ('--recursive' if os.path.isdir(pathname) else )
ctx.run(f'{PYTHONBIN} -m yapf --style setup.cfg {option} {recurse} {pathname}')<|docstring|>Run yapf formatter on the specified file, or recurse into directory.<|endoftext|>
|
af1cfe32ee47cf866382a397e1b5c385a67a21c47d3393d75c569207a757d3f8
|
@task
def format_changed(ctx, check=False, untracked=False):
'Run yapf formatter on currently modified python files.\n\n If check option given then just show the diff.\n '
option = ('-d' if check else '-i')
files = get_modified_files(untracked)
if files:
ctx.run(f"{PYTHONBIN} -m yapf --style setup.cfg {option} {' '.join(files)}")
else:
print('No changed python files.')
|
Run yapf formatter on currently modified python files.
If check option given then just show the diff.
|
tasks.py
|
format_changed
|
pycopia/devtest
| 0 |
python
|
@task
def format_changed(ctx, check=False, untracked=False):
'Run yapf formatter on currently modified python files.\n\n If check option given then just show the diff.\n '
option = ('-d' if check else '-i')
files = get_modified_files(untracked)
if files:
ctx.run(f"{PYTHONBIN} -m yapf --style setup.cfg {option} {' '.join(files)}")
else:
print('No changed python files.')
|
@task
def format_changed(ctx, check=False, untracked=False):
'Run yapf formatter on currently modified python files.\n\n If check option given then just show the diff.\n '
option = ('-d' if check else '-i')
files = get_modified_files(untracked)
if files:
ctx.run(f"{PYTHONBIN} -m yapf --style setup.cfg {option} {' '.join(files)}")
else:
print('No changed python files.')<|docstring|>Run yapf formatter on currently modified python files.
If check option given then just show the diff.<|endoftext|>
|
ab3e53487efaf5404a6b74f57965edf69201edce83bfb1ddd6f989490e871231
|
@task
def set_pypi_token(ctx):
'Set the token in the local key ring.\n '
pw = getpass.getpass(f'Enter pypi token? ')
if pw:
keyring.set_password(PYPI_HOST, PYPI_USER, pw)
else:
raise Exit('No password entered.', 3)
|
Set the token in the local key ring.
|
tasks.py
|
set_pypi_token
|
pycopia/devtest
| 0 |
python
|
@task
def set_pypi_token(ctx):
'\n '
pw = getpass.getpass(f'Enter pypi token? ')
if pw:
keyring.set_password(PYPI_HOST, PYPI_USER, pw)
else:
raise Exit('No password entered.', 3)
|
@task
def set_pypi_token(ctx):
'\n '
pw = getpass.getpass(f'Enter pypi token? ')
if pw:
keyring.set_password(PYPI_HOST, PYPI_USER, pw)
else:
raise Exit('No password entered.', 3)<|docstring|>Set the token in the local key ring.<|endoftext|>
|
b3bf68f6e6482bc1654de606d90b2d4e7bf10e4e4e1fc7f24ed3f780e7fbf086
|
@task
def build(ctx):
'Build the intermediate package components.'
ctx.run(f'{PYTHONBIN} setup.py build')
|
Build the intermediate package components.
|
tasks.py
|
build
|
pycopia/devtest
| 0 |
python
|
@task
def build(ctx):
ctx.run(f'{PYTHONBIN} setup.py build')
|
@task
def build(ctx):
ctx.run(f'{PYTHONBIN} setup.py build')<|docstring|>Build the intermediate package components.<|endoftext|>
|
551028788eb7bec2e92b4b1fc6552175982aa23ee373870fb2ce2d04cc4e6775
|
@task
def dev_requirements(ctx):
'Install development requirements.'
ctx.run(f'{PYTHONBIN} -m pip install --index-url {PYPI_INDEX} --trusted-host {PYPI_HOST} -r dev-requirements.txt --user')
|
Install development requirements.
|
tasks.py
|
dev_requirements
|
pycopia/devtest
| 0 |
python
|
@task
def dev_requirements(ctx):
ctx.run(f'{PYTHONBIN} -m pip install --index-url {PYPI_INDEX} --trusted-host {PYPI_HOST} -r dev-requirements.txt --user')
|
@task
def dev_requirements(ctx):
ctx.run(f'{PYTHONBIN} -m pip install --index-url {PYPI_INDEX} --trusted-host {PYPI_HOST} -r dev-requirements.txt --user')<|docstring|>Install development requirements.<|endoftext|>
|
b4459ba89724486dd41d0c604babc447b63da6df38a4ca66acf40950ea7c279f
|
@task(pre=[dev_requirements])
def develop(ctx, uninstall=False):
'Start developing in developer mode.'
if uninstall:
ctx.run(f'{PYTHONBIN} setup.py develop --uninstall --user')
else:
ctx.run(f'{PYTHONBIN} setup.py develop --index-url "{PYPI_INDEX}" --user')
|
Start developing in developer mode.
|
tasks.py
|
develop
|
pycopia/devtest
| 0 |
python
|
@task(pre=[dev_requirements])
def develop(ctx, uninstall=False):
if uninstall:
ctx.run(f'{PYTHONBIN} setup.py develop --uninstall --user')
else:
ctx.run(f'{PYTHONBIN} setup.py develop --index-url "{PYPI_INDEX}" --user')
|
@task(pre=[dev_requirements])
def develop(ctx, uninstall=False):
if uninstall:
ctx.run(f'{PYTHONBIN} setup.py develop --uninstall --user')
else:
ctx.run(f'{PYTHONBIN} setup.py develop --index-url "{PYPI_INDEX}" --user')<|docstring|>Start developing in developer mode.<|endoftext|>
|
d06c9f2b0c5357b854dc4e99240aa215bce4e7e61103aa6a2c54cf73ba83a195
|
@task
def clean(ctx):
'Clean out build and cache files. Remove extension modules.'
ctx.run(f'{PYTHONBIN} setup.py clean')
ctx.run('find . -depth -type d -name __pycache__ -exec rm -rf {} \\;')
ctx.run('find devtest -name "*.so" -delete')
with ctx.cd('docs'):
ctx.run('rm -f modules/devtest.*.rst')
ctx.run(f'{PYTHONBIN} -m sphinx.cmd.build -M clean . _build')
|
Clean out build and cache files. Remove extension modules.
|
tasks.py
|
clean
|
pycopia/devtest
| 0 |
python
|
@task
def clean(ctx):
ctx.run(f'{PYTHONBIN} setup.py clean')
ctx.run('find . -depth -type d -name __pycache__ -exec rm -rf {} \\;')
ctx.run('find devtest -name "*.so" -delete')
with ctx.cd('docs'):
ctx.run('rm -f modules/devtest.*.rst')
ctx.run(f'{PYTHONBIN} -m sphinx.cmd.build -M clean . _build')
|
@task
def clean(ctx):
ctx.run(f'{PYTHONBIN} setup.py clean')
ctx.run('find . -depth -type d -name __pycache__ -exec rm -rf {} \\;')
ctx.run('find devtest -name "*.so" -delete')
with ctx.cd('docs'):
ctx.run('rm -f modules/devtest.*.rst')
ctx.run(f'{PYTHONBIN} -m sphinx.cmd.build -M clean . _build')<|docstring|>Clean out build and cache files. Remove extension modules.<|endoftext|>
|
ac7ee43985404f8137b657aa209ee5867cde46276b5e1c887dda1b2c19dc8b5e
|
@task
def cleandist(ctx):
'Clean out dist subdirectory.'
if os.path.isdir('dist'):
shutil.rmtree('dist', ignore_errors=True)
os.mkdir('dist')
|
Clean out dist subdirectory.
|
tasks.py
|
cleandist
|
pycopia/devtest
| 0 |
python
|
@task
def cleandist(ctx):
if os.path.isdir('dist'):
shutil.rmtree('dist', ignore_errors=True)
os.mkdir('dist')
|
@task
def cleandist(ctx):
if os.path.isdir('dist'):
shutil.rmtree('dist', ignore_errors=True)
os.mkdir('dist')<|docstring|>Clean out dist subdirectory.<|endoftext|>
|
4b8443fb09f31162237469a1a461b42627b407aec35106bd64e52679dcaf257b
|
@task
def test(ctx, testfile=None, ls=False):
'Run unit tests. Use ls option to only list them.'
if ls:
ctx.run(f'{PYTHONBIN} -m pytest --collect-only -qq tests')
elif testfile:
ctx.run(f'{PYTHONBIN} -m pytest -s {testfile}')
else:
ctx.run(f'{PYTHONBIN} -m pytest tests', hide=False, in_stream=False)
|
Run unit tests. Use ls option to only list them.
|
tasks.py
|
test
|
pycopia/devtest
| 0 |
python
|
@task
def test(ctx, testfile=None, ls=False):
if ls:
ctx.run(f'{PYTHONBIN} -m pytest --collect-only -qq tests')
elif testfile:
ctx.run(f'{PYTHONBIN} -m pytest -s {testfile}')
else:
ctx.run(f'{PYTHONBIN} -m pytest tests', hide=False, in_stream=False)
|
@task
def test(ctx, testfile=None, ls=False):
if ls:
ctx.run(f'{PYTHONBIN} -m pytest --collect-only -qq tests')
elif testfile:
ctx.run(f'{PYTHONBIN} -m pytest -s {testfile}')
else:
ctx.run(f'{PYTHONBIN} -m pytest tests', hide=False, in_stream=False)<|docstring|>Run unit tests. Use ls option to only list them.<|endoftext|>
|
c516f5044d0753ecc92429544b9dfbaf42add48890a2d81e0c8ae40501d22c5b
|
@task
def tag(ctx, tag=None, major=False, minor=False, patch=False):
"Tag or bump release with a semver tag. Makes a signed tag if you're a signer."
latest = None
if (tag is None):
tags = get_tags()
if (not tags):
latest = semver.VersionInfo(0, 0, 0)
else:
latest = tags[(- 1)]
if patch:
nextver = latest.bump_patch()
elif minor:
nextver = latest.bump_minor()
elif major:
nextver = latest.bump_major()
else:
nextver = latest.bump_patch()
else:
if tag.startswith('v'):
tag = tag[1:]
try:
nextver = semver.parse_version_info(tag)
except ValueError:
raise Exit('Invalid semver tag.', 2)
print(latest, '->', nextver)
tagopt = ('-s' if (CURRENT_USER in SIGNERS) else '-a')
ctx.run(f'git tag {tagopt} -m "Release v{nextver}" v{nextver}')
|
Tag or bump release with a semver tag. Makes a signed tag if you're a signer.
|
tasks.py
|
tag
|
pycopia/devtest
| 0 |
python
|
@task
def tag(ctx, tag=None, major=False, minor=False, patch=False):
latest = None
if (tag is None):
tags = get_tags()
if (not tags):
latest = semver.VersionInfo(0, 0, 0)
else:
latest = tags[(- 1)]
if patch:
nextver = latest.bump_patch()
elif minor:
nextver = latest.bump_minor()
elif major:
nextver = latest.bump_major()
else:
nextver = latest.bump_patch()
else:
if tag.startswith('v'):
tag = tag[1:]
try:
nextver = semver.parse_version_info(tag)
except ValueError:
raise Exit('Invalid semver tag.', 2)
print(latest, '->', nextver)
tagopt = ('-s' if (CURRENT_USER in SIGNERS) else '-a')
ctx.run(f'git tag {tagopt} -m "Release v{nextver}" v{nextver}')
|
@task
def tag(ctx, tag=None, major=False, minor=False, patch=False):
latest = None
if (tag is None):
tags = get_tags()
if (not tags):
latest = semver.VersionInfo(0, 0, 0)
else:
latest = tags[(- 1)]
if patch:
nextver = latest.bump_patch()
elif minor:
nextver = latest.bump_minor()
elif major:
nextver = latest.bump_major()
else:
nextver = latest.bump_patch()
else:
if tag.startswith('v'):
tag = tag[1:]
try:
nextver = semver.parse_version_info(tag)
except ValueError:
raise Exit('Invalid semver tag.', 2)
print(latest, '->', nextver)
tagopt = ('-s' if (CURRENT_USER in SIGNERS) else '-a')
ctx.run(f'git tag {tagopt} -m "Release v{nextver}" v{nextver}')<|docstring|>Tag or bump release with a semver tag. Makes a signed tag if you're a signer.<|endoftext|>
|
267be847d3f4869e6bbcc9f4e973bec74c50db04c49a378839d18a103054e685
|
@task
def tag_delete(ctx, tag=None):
'Delete a tag, both local and remote.'
if tag:
ctx.run(f'git tag -d {tag}')
ctx.run(f'git push origin :refs/tags/{tag}')
|
Delete a tag, both local and remote.
|
tasks.py
|
tag_delete
|
pycopia/devtest
| 0 |
python
|
@task
def tag_delete(ctx, tag=None):
if tag:
ctx.run(f'git tag -d {tag}')
ctx.run(f'git push origin :refs/tags/{tag}')
|
@task
def tag_delete(ctx, tag=None):
if tag:
ctx.run(f'git tag -d {tag}')
ctx.run(f'git push origin :refs/tags/{tag}')<|docstring|>Delete a tag, both local and remote.<|endoftext|>
|
8d377d27cf01c46a73a512ecaec87cdc8ccdd34dc04fd171888ebacfe5d59126
|
@task(cleandist)
def sdist(ctx):
'Build source distribution.'
ctx.run(f'{PYTHONBIN} setup.py sdist')
|
Build source distribution.
|
tasks.py
|
sdist
|
pycopia/devtest
| 0 |
python
|
@task(cleandist)
def sdist(ctx):
ctx.run(f'{PYTHONBIN} setup.py sdist')
|
@task(cleandist)
def sdist(ctx):
ctx.run(f'{PYTHONBIN} setup.py sdist')<|docstring|>Build source distribution.<|endoftext|>
|
12e2819d5de105538746c0c305e1d07e861d2d9e7398bdee12ad3c729c139b14
|
@task
def build_ext(ctx):
'Build compiled extension modules, in place.'
ctx.run(f'{PYTHONBIN} setup.py build_ext --inplace')
|
Build compiled extension modules, in place.
|
tasks.py
|
build_ext
|
pycopia/devtest
| 0 |
python
|
@task
def build_ext(ctx):
ctx.run(f'{PYTHONBIN} setup.py build_ext --inplace')
|
@task
def build_ext(ctx):
ctx.run(f'{PYTHONBIN} setup.py build_ext --inplace')<|docstring|>Build compiled extension modules, in place.<|endoftext|>
|
385866f764584632fd2a4dcd98beac5d6707174f3fe2971bfe65955ac662d244
|
@task(sdist)
def bdist(ctx):
'Build a standard wheel file, an installable format.'
ctx.run(f'{PYTHONBIN} setup.py bdist_wheel')
|
Build a standard wheel file, an installable format.
|
tasks.py
|
bdist
|
pycopia/devtest
| 0 |
python
|
@task(sdist)
def bdist(ctx):
ctx.run(f'{PYTHONBIN} setup.py bdist_wheel')
|
@task(sdist)
def bdist(ctx):
ctx.run(f'{PYTHONBIN} setup.py bdist_wheel')<|docstring|>Build a standard wheel file, an installable format.<|endoftext|>
|
9d3b23d0e840370a19776691ca12e11eb917e77ec0f83c6842d8c50734e7f89e
|
@task(bdist)
def sign(ctx):
'Cryptographically sign dist with your default GPG key.'
if (CURRENT_USER in SIGNERS):
ctx.run(f'{GPG} --detach-sign -a dist/devtest-*.whl')
ctx.run(f'{GPG} --detach-sign -a dist/devtest-*.tar.gz')
else:
print('Not signing.')
|
Cryptographically sign dist with your default GPG key.
|
tasks.py
|
sign
|
pycopia/devtest
| 0 |
python
|
@task(bdist)
def sign(ctx):
if (CURRENT_USER in SIGNERS):
ctx.run(f'{GPG} --detach-sign -a dist/devtest-*.whl')
ctx.run(f'{GPG} --detach-sign -a dist/devtest-*.tar.gz')
else:
print('Not signing.')
|
@task(bdist)
def sign(ctx):
if (CURRENT_USER in SIGNERS):
ctx.run(f'{GPG} --detach-sign -a dist/devtest-*.whl')
ctx.run(f'{GPG} --detach-sign -a dist/devtest-*.tar.gz')
else:
print('Not signing.')<|docstring|>Cryptographically sign dist with your default GPG key.<|endoftext|>
|
2db5575244c1496800df39877039884e98e358d66c456b979c0ccc8c10a495e9
|
@task(pre=[sign])
def publish(ctx):
'Publish built wheel file to package repo.'
token = get_pypi_token()
distfiles = glob('dist/*.whl')
distfiles.extend(glob('dist/*.tar.gz'))
if (not distfiles):
raise Exit('Nothing in dist folder!')
distfiles = ' '.join(distfiles)
ctx.run(f'{PYTHONBIN} -m twine upload --repository-url "{PYPI_URL}" --username {PYPI_USER} --password {token} {distfiles}')
|
Publish built wheel file to package repo.
|
tasks.py
|
publish
|
pycopia/devtest
| 0 |
python
|
@task(pre=[sign])
def publish(ctx):
token = get_pypi_token()
distfiles = glob('dist/*.whl')
distfiles.extend(glob('dist/*.tar.gz'))
if (not distfiles):
raise Exit('Nothing in dist folder!')
distfiles = ' '.join(distfiles)
ctx.run(f'{PYTHONBIN} -m twine upload --repository-url "{PYPI_URL}" --username {PYPI_USER} --password {token} {distfiles}')
|
@task(pre=[sign])
def publish(ctx):
token = get_pypi_token()
distfiles = glob('dist/*.whl')
distfiles.extend(glob('dist/*.tar.gz'))
if (not distfiles):
raise Exit('Nothing in dist folder!')
distfiles = ' '.join(distfiles)
ctx.run(f'{PYTHONBIN} -m twine upload --repository-url "{PYPI_URL}" --username {PYPI_USER} --password {token} {distfiles}')<|docstring|>Publish built wheel file to package repo.<|endoftext|>
|
b2b6e597dc45f72118abdeb8d661a8c80aff4851b7f4e46981e0d96fe43547a5
|
@task
def docs(ctx):
'Build the HTML documentation.'
ctx.run('rm docs/modules/devtest.*.rst', warn=True)
ctx.run(f'{PYTHONBIN} -m sphinx.ext.apidoc --force --separate --no-toc --output-dir docs/modules devtest')
with ctx.cd('docs'):
ctx.run(f'{PYTHONBIN} -m sphinx.cmd.build -M html . _build')
if os.environ.get('DISPLAY'):
ctx.run('xdg-open docs/_build/html/index.html')
|
Build the HTML documentation.
|
tasks.py
|
docs
|
pycopia/devtest
| 0 |
python
|
@task
def docs(ctx):
ctx.run('rm docs/modules/devtest.*.rst', warn=True)
ctx.run(f'{PYTHONBIN} -m sphinx.ext.apidoc --force --separate --no-toc --output-dir docs/modules devtest')
with ctx.cd('docs'):
ctx.run(f'{PYTHONBIN} -m sphinx.cmd.build -M html . _build')
if os.environ.get('DISPLAY'):
ctx.run('xdg-open docs/_build/html/index.html')
|
@task
def docs(ctx):
ctx.run('rm docs/modules/devtest.*.rst', warn=True)
ctx.run(f'{PYTHONBIN} -m sphinx.ext.apidoc --force --separate --no-toc --output-dir docs/modules devtest')
with ctx.cd('docs'):
ctx.run(f'{PYTHONBIN} -m sphinx.cmd.build -M html . _build')
if os.environ.get('DISPLAY'):
ctx.run('xdg-open docs/_build/html/index.html')<|docstring|>Build the HTML documentation.<|endoftext|>
|
ccbca7478a38f768bf6994d233815c23b8fc53ee769ded37ca6c25e6157b9522
|
@task
def branch(ctx, name=None):
'start a new branch, both local and remote tracking.'
if name:
ctx.run(f'git checkout -b {name}')
ctx.run(f'git push -u origin {name}')
else:
ctx.run('git --no-pager branch')
|
start a new branch, both local and remote tracking.
|
tasks.py
|
branch
|
pycopia/devtest
| 0 |
python
|
@task
def branch(ctx, name=None):
if name:
ctx.run(f'git checkout -b {name}')
ctx.run(f'git push -u origin {name}')
else:
ctx.run('git --no-pager branch')
|
@task
def branch(ctx, name=None):
if name:
ctx.run(f'git checkout -b {name}')
ctx.run(f'git push -u origin {name}')
else:
ctx.run('git --no-pager branch')<|docstring|>start a new branch, both local and remote tracking.<|endoftext|>
|
fefe75b8f4321e551d47f25427f8e6fe7c6b579d2a4d6d6b53b8597221d7a896
|
@task
def branch_delete(ctx, name=None):
'Delete local, remote and tracking branch by name.'
if name:
ctx.run(f'git branch -d {name}', warn=True)
ctx.run(f'git branch -d -r {name}', warn=True)
ctx.run(f'git push origin --delete {name}', warn=True)
else:
print('Supply a branch name: --name <name>')
|
Delete local, remote and tracking branch by name.
|
tasks.py
|
branch_delete
|
pycopia/devtest
| 0 |
python
|
@task
def branch_delete(ctx, name=None):
if name:
ctx.run(f'git branch -d {name}', warn=True)
ctx.run(f'git branch -d -r {name}', warn=True)
ctx.run(f'git push origin --delete {name}', warn=True)
else:
print('Supply a branch name: --name <name>')
|
@task
def branch_delete(ctx, name=None):
if name:
ctx.run(f'git branch -d {name}', warn=True)
ctx.run(f'git branch -d -r {name}', warn=True)
ctx.run(f'git push origin --delete {name}', warn=True)
else:
print('Supply a branch name: --name <name>')<|docstring|>Delete local, remote and tracking branch by name.<|endoftext|>
|
2e3258bed2ffbe7866b6fe9344662480676cee0ee67a4b98527ce3487c0bd9b2
|
@task(pre=[sdist])
def docker_build(ctx):
'Build docker image.'
version = get_version()
if (not version):
raise Exit('Need to tag a version first.', 2)
environ = {'PYVER': '{}.{}'.format(sys.version_info.major, sys.version_info.minor), 'VERSION': version, 'PYPI_REPO': PYPI_INDEX, 'PYPI_HOST': PYPI_HOST}
ctx.run(f'docker build --build-arg PYVER --build-arg VERSION --build-arg PYPI_REPO --build-arg PYPI_HOST -t devtest:{version} .', env=environ)
print(f'''Done. To run it:
docker run -it devtest:{version}''')
|
Build docker image.
|
tasks.py
|
docker_build
|
pycopia/devtest
| 0 |
python
|
@task(pre=[sdist])
def docker_build(ctx):
version = get_version()
if (not version):
raise Exit('Need to tag a version first.', 2)
environ = {'PYVER': '{}.{}'.format(sys.version_info.major, sys.version_info.minor), 'VERSION': version, 'PYPI_REPO': PYPI_INDEX, 'PYPI_HOST': PYPI_HOST}
ctx.run(f'docker build --build-arg PYVER --build-arg VERSION --build-arg PYPI_REPO --build-arg PYPI_HOST -t devtest:{version} .', env=environ)
print(f'Done. To run it:
docker run -it devtest:{version}')
|
@task(pre=[sdist])
def docker_build(ctx):
version = get_version()
if (not version):
raise Exit('Need to tag a version first.', 2)
environ = {'PYVER': '{}.{}'.format(sys.version_info.major, sys.version_info.minor), 'VERSION': version, 'PYPI_REPO': PYPI_INDEX, 'PYPI_HOST': PYPI_HOST}
ctx.run(f'docker build --build-arg PYVER --build-arg VERSION --build-arg PYPI_REPO --build-arg PYPI_HOST -t devtest:{version} .', env=environ)
print(f'Done. To run it:
docker run -it devtest:{version}')<|docstring|>Build docker image.<|endoftext|>
|
196e88fae02e34628a6559774d237dc85c1f8ccd7e510809fceb3f1ecf8940b8
|
@task
def logfile(ctx, name='devtester'):
'Dump the system log file with optional name filter.'
if WINDOWS:
ctx.run(f'wevtutil.exe qe Application /query:"*[System[Provider[@Name={name!r}]]]" /f:text')
elif LINUX:
ctx.run(f'journalctl --identifier={name!r} --no-pager --priority=debug')
elif DARWIN:
ctx.run(f"""log stream --predicate 'senderImagePath contains "Python"' --level debug""")
|
Dump the system log file with optional name filter.
|
tasks.py
|
logfile
|
pycopia/devtest
| 0 |
python
|
@task
def logfile(ctx, name='devtester'):
if WINDOWS:
ctx.run(f'wevtutil.exe qe Application /query:"*[System[Provider[@Name={name!r}]]]" /f:text')
elif LINUX:
ctx.run(f'journalctl --identifier={name!r} --no-pager --priority=debug')
elif DARWIN:
ctx.run(f"log stream --predicate 'senderImagePath contains "Python"' --level debug")
|
@task
def logfile(ctx, name='devtester'):
if WINDOWS:
ctx.run(f'wevtutil.exe qe Application /query:"*[System[Provider[@Name={name!r}]]]" /f:text')
elif LINUX:
ctx.run(f'journalctl --identifier={name!r} --no-pager --priority=debug')
elif DARWIN:
ctx.run(f"log stream --predicate 'senderImagePath contains "Python"' --level debug")<|docstring|>Dump the system log file with optional name filter.<|endoftext|>
|
8e43f24fcbfc1fef0646b177097b8f947ccc2139df12b1c6916a275a407f9f6c
|
def find_git_base():
'Find the base directory of this git repo.\n\n The git status output is always relative to this directory.\n '
start = Path.cwd().resolve()
while start:
if (start / '.git').exists():
return start
start = start.parent
raise Exit('Not able to find git repo base.')
|
Find the base directory of this git repo.
The git status output is always relative to this directory.
|
tasks.py
|
find_git_base
|
pycopia/devtest
| 0 |
python
|
def find_git_base():
'Find the base directory of this git repo.\n\n The git status output is always relative to this directory.\n '
start = Path.cwd().resolve()
while start:
if (start / '.git').exists():
return start
start = start.parent
raise Exit('Not able to find git repo base.')
|
def find_git_base():
'Find the base directory of this git repo.\n\n The git status output is always relative to this directory.\n '
start = Path.cwd().resolve()
while start:
if (start / '.git').exists():
return start
start = start.parent
raise Exit('Not able to find git repo base.')<|docstring|>Find the base directory of this git repo.
The git status output is always relative to this directory.<|endoftext|>
|
cc31a770f782739dab0797c3926c2177e99c075f6909cf6fd6d3b33182910af3
|
def get_modified_files(untracked):
'Find the list of modified and, optionally, untracked Python files.\n\n If `untracked` is True, also include untracked Python files.\n '
filelist = []
gitbase = find_git_base()
gitout = run('git status --porcelain=1 -z', hide=True)
for line in gitout.stdout.split('\x00'):
if line:
if (not line.endswith('.py')):
continue
if (line[0:2] == ' M'):
filelist.append(resolve_path(gitbase, line[3:]))
if (untracked and (line[0:2] == '??')):
filelist.append(resolve_path(gitbase, line[3:]))
return filelist
|
Find the list of modified and, optionally, untracked Python files.
If `untracked` is True, also include untracked Python files.
|
tasks.py
|
get_modified_files
|
pycopia/devtest
| 0 |
python
|
def get_modified_files(untracked):
'Find the list of modified and, optionally, untracked Python files.\n\n If `untracked` is True, also include untracked Python files.\n '
filelist = []
gitbase = find_git_base()
gitout = run('git status --porcelain=1 -z', hide=True)
for line in gitout.stdout.split('\x00'):
if line:
if (not line.endswith('.py')):
continue
if (line[0:2] == ' M'):
filelist.append(resolve_path(gitbase, line[3:]))
if (untracked and (line[0:2] == '??')):
filelist.append(resolve_path(gitbase, line[3:]))
return filelist
|
def get_modified_files(untracked):
'Find the list of modified and, optionally, untracked Python files.\n\n If `untracked` is True, also include untracked Python files.\n '
filelist = []
gitbase = find_git_base()
gitout = run('git status --porcelain=1 -z', hide=True)
for line in gitout.stdout.split('\x00'):
if line:
if (not line.endswith('.py')):
continue
if (line[0:2] == ' M'):
filelist.append(resolve_path(gitbase, line[3:]))
if (untracked and (line[0:2] == '??')):
filelist.append(resolve_path(gitbase, line[3:]))
return filelist<|docstring|>Find the list of modified and, optionally, untracked Python files.
If `untracked` is True, also include untracked Python files.<|endoftext|>
|
6c1797e18f6c9c8799febe2923b2c71f3c306128e4f0dd2fab9d79e56b073f0f
|
def __init__(self, temboo_session):
'\n Create a new instance of the UploadInstallationData Choreo. A TembooSession object, containing a valid\n set of Temboo credentials, must be supplied.\n '
super(UploadInstallationData, self).__init__(temboo_session, '/Library/Parse/PushNotifications/UploadInstallationData')
|
Create a new instance of the UploadInstallationData Choreo. A TembooSession object, containing a valid
set of Temboo credentials, must be supplied.
|
temboo/core/Library/Parse/PushNotifications/UploadInstallationData.py
|
__init__
|
jordanemedlock/psychtruths
| 7 |
python
|
def __init__(self, temboo_session):
'\n Create a new instance of the UploadInstallationData Choreo. A TembooSession object, containing a valid\n set of Temboo credentials, must be supplied.\n '
super(UploadInstallationData, self).__init__(temboo_session, '/Library/Parse/PushNotifications/UploadInstallationData')
|
def __init__(self, temboo_session):
'\n Create a new instance of the UploadInstallationData Choreo. A TembooSession object, containing a valid\n set of Temboo credentials, must be supplied.\n '
super(UploadInstallationData, self).__init__(temboo_session, '/Library/Parse/PushNotifications/UploadInstallationData')<|docstring|>Create a new instance of the UploadInstallationData Choreo. A TembooSession object, containing a valid
set of Temboo credentials, must be supplied.<|endoftext|>
|
e9974be4942f6ca77ae754cc6892d23e9472a4136d82a6da0b2e76aa77d9c3bb
|
def set_Installation(self, value):
'\n Set the value of the Installation input for this Choreo. ((required, json) A JSON string containing the installation data. See documentation for syntax examples.)\n '
super(UploadInstallationDataInputSet, self)._set_input('Installation', value)
|
Set the value of the Installation input for this Choreo. ((required, json) A JSON string containing the installation data. See documentation for syntax examples.)
|
temboo/core/Library/Parse/PushNotifications/UploadInstallationData.py
|
set_Installation
|
jordanemedlock/psychtruths
| 7 |
python
|
def set_Installation(self, value):
'\n \n '
super(UploadInstallationDataInputSet, self)._set_input('Installation', value)
|
def set_Installation(self, value):
'\n \n '
super(UploadInstallationDataInputSet, self)._set_input('Installation', value)<|docstring|>Set the value of the Installation input for this Choreo. ((required, json) A JSON string containing the installation data. See documentation for syntax examples.)<|endoftext|>
|
976ce64dd71143306b6a77c55ecf6557296d438f352ed11ce0c59f5b065b7fc3
|
def set_ApplicationID(self, value):
'\n Set the value of the ApplicationID input for this Choreo. ((required, string) The Application ID provided by Parse.)\n '
super(UploadInstallationDataInputSet, self)._set_input('ApplicationID', value)
|
Set the value of the ApplicationID input for this Choreo. ((required, string) The Application ID provided by Parse.)
|
temboo/core/Library/Parse/PushNotifications/UploadInstallationData.py
|
set_ApplicationID
|
jordanemedlock/psychtruths
| 7 |
python
|
def set_ApplicationID(self, value):
'\n \n '
super(UploadInstallationDataInputSet, self)._set_input('ApplicationID', value)
|
def set_ApplicationID(self, value):
'\n \n '
super(UploadInstallationDataInputSet, self)._set_input('ApplicationID', value)<|docstring|>Set the value of the ApplicationID input for this Choreo. ((required, string) The Application ID provided by Parse.)<|endoftext|>
|
3cffe567936c6464dbe3ef68d624ce199873bbbd876a7b02ad18f32d82a71858
|
def set_RESTAPIKey(self, value):
'\n Set the value of the RESTAPIKey input for this Choreo. ((required, string) The REST API Key provided by Parse.)\n '
super(UploadInstallationDataInputSet, self)._set_input('RESTAPIKey', value)
|
Set the value of the RESTAPIKey input for this Choreo. ((required, string) The REST API Key provided by Parse.)
|
temboo/core/Library/Parse/PushNotifications/UploadInstallationData.py
|
set_RESTAPIKey
|
jordanemedlock/psychtruths
| 7 |
python
|
def set_RESTAPIKey(self, value):
'\n \n '
super(UploadInstallationDataInputSet, self)._set_input('RESTAPIKey', value)
|
def set_RESTAPIKey(self, value):
'\n \n '
super(UploadInstallationDataInputSet, self)._set_input('RESTAPIKey', value)<|docstring|>Set the value of the RESTAPIKey input for this Choreo. ((required, string) The REST API Key provided by Parse.)<|endoftext|>
|
43cfb02d7da32b267644600875c1e5ec4c3ef9d4a3416d2c412e17cfd9b362ea
|
def get_Response(self):
'\n Retrieve the value for the "Response" output from this Choreo execution. ((json) The response from Parse.)\n '
return self._output.get('Response', None)
|
Retrieve the value for the "Response" output from this Choreo execution. ((json) The response from Parse.)
|
temboo/core/Library/Parse/PushNotifications/UploadInstallationData.py
|
get_Response
|
jordanemedlock/psychtruths
| 7 |
python
|
def get_Response(self):
'\n \n '
return self._output.get('Response', None)
|
def get_Response(self):
'\n \n '
return self._output.get('Response', None)<|docstring|>Retrieve the value for the "Response" output from this Choreo execution. ((json) The response from Parse.)<|endoftext|>
|
abd9972410a57877ee3fdeaf28492b55254fd84e89a316fedc95b9eec5799887
|
@staticmethod
def print_move(move):
'issue an order'
stdout.write('{}\n'.format(move))
stdout.flush()
|
issue an order
|
bot/game.py
|
print_move
|
thequeenofspades/AlphaGOLADZero
| 1 |
python
|
@staticmethod
def print_move(move):
stdout.write('{}\n'.format(move))
stdout.flush()
|
@staticmethod
def print_move(move):
stdout.write('{}\n'.format(move))
stdout.flush()<|docstring|>issue an order<|endoftext|>
|
c57aad9d46510899ecd3121bf938061c185496b6515d825c725350f695a08cca
|
def run(self, bot):
'parse input, update game state and call the bot classes do_turn method'
not_finished = True
data = ''
while ((not stdin.closed) and not_finished):
try:
current_line = stdin.readline().rstrip('\r\n')
if (len(current_line) <= 0):
time.sleep(1)
continue
data += (current_line + '\n')
if current_line.lower().startswith('action'):
self.update(data)
move = bot.make_move(self)
self.print_move(move)
data = ''
elif current_line.lower().startswith('quit'):
not_finished = False
except EOFError:
break
except KeyboardInterrupt:
raise
except:
traceback.print_exc(file=stderr)
stderr.flush()
|
parse input, update game state and call the bot classes do_turn method
|
bot/game.py
|
run
|
thequeenofspades/AlphaGOLADZero
| 1 |
python
|
def run(self, bot):
not_finished = True
data =
while ((not stdin.closed) and not_finished):
try:
current_line = stdin.readline().rstrip('\r\n')
if (len(current_line) <= 0):
time.sleep(1)
continue
data += (current_line + '\n')
if current_line.lower().startswith('action'):
self.update(data)
move = bot.make_move(self)
self.print_move(move)
data =
elif current_line.lower().startswith('quit'):
not_finished = False
except EOFError:
break
except KeyboardInterrupt:
raise
except:
traceback.print_exc(file=stderr)
stderr.flush()
|
def run(self, bot):
not_finished = True
data =
while ((not stdin.closed) and not_finished):
try:
current_line = stdin.readline().rstrip('\r\n')
if (len(current_line) <= 0):
time.sleep(1)
continue
data += (current_line + '\n')
if current_line.lower().startswith('action'):
self.update(data)
move = bot.make_move(self)
self.print_move(move)
data =
elif current_line.lower().startswith('quit'):
not_finished = False
except EOFError:
break
except KeyboardInterrupt:
raise
except:
traceback.print_exc(file=stderr)
stderr.flush()<|docstring|>parse input, update game state and call the bot classes do_turn method<|endoftext|>
|
289dda90419607c202636066fa233488336c3eacf4589d9a8b0be7da70e7fa10
|
def build_model(cfg: dict=None, src_vocab: Vocabulary=None, trg_vocab: Vocabulary=None, trv_vocab: Vocabulary=None, canonizer=None) -> Model:
'\n Build and initialize the model according to the configuration.\n\n :param cfg: dictionary configuration containing model specifications\n :param src_vocab: source vocabulary\n :param trg_vocab: target vocabulary\n :param trv_vocab: kb true value lookup vocabulary\n :return: built and initialized model\n '
src_padding_idx = src_vocab.stoi[PAD_TOKEN]
trg_padding_idx = trg_vocab.stoi[PAD_TOKEN]
if ('embedding_files' in cfg.keys()):
assert (not cfg.get('tied_embeddings', False)), 'TODO implement tied embeddings along with pretrained initialization'
raise NotImplementedError('TODO implement kbsrc embed loading for embedding files')
weight_tensors = []
for weight_file in cfg['embedding_files']:
with open(weight_file, 'r') as f:
weight = []
for line in f.readlines():
line = line.split()
line = [float(x) for x in line]
weight.append(line)
weight = FloatTensor(weight)
weight_tensors.append(weight)
src_embed = Embeddings(int(weight_tensors[0][0].shape[0]), False, len(weight_tensors[0]))
src_embed.lut.weight.data = weight_tensors[0]
trg_embed = Embeddings(int(weight_tensors[1][0].shape[0]), False, len(weight_tensors[1]))
trg_embed.lut.weight.data = weight_tensors[1]
else:
src_embed = Embeddings(**cfg['encoder']['embeddings'], vocab_size=len(src_vocab), padding_idx=src_padding_idx)
if cfg.get('kb_embed_separate', False):
kbsrc_embed = Embeddings(**cfg['encoder']['embeddings'], vocab_size=len(src_vocab), padding_idx=src_padding_idx)
else:
kbsrc_embed = src_embed
if cfg.get('tied_embeddings', False):
if (src_vocab.itos == trg_vocab.itos):
trg_embed = src_embed
else:
raise ConfigurationError('Embedding cannot be tied since vocabularies differ.')
else:
trg_embed = Embeddings(**cfg['decoder']['embeddings'], vocab_size=len(trg_vocab), padding_idx=trg_padding_idx)
enc_dropout = cfg['encoder'].get('dropout', 0.0)
enc_emb_dropout = cfg['encoder']['embeddings'].get('dropout', enc_dropout)
if (cfg['encoder'].get('type', 'recurrent') == 'transformer'):
assert (cfg['encoder']['embeddings']['embedding_dim'] == cfg['encoder']['hidden_size']), 'for transformer, emb_size must be hidden_size'
encoder = TransformerEncoder(**cfg['encoder'], emb_size=src_embed.embedding_dim, emb_dropout=enc_emb_dropout)
else:
encoder = RecurrentEncoder(**cfg['encoder'], emb_size=src_embed.embedding_dim, emb_dropout=enc_emb_dropout)
kb_task = bool(cfg.get('kb', False))
k_hops = int(cfg.get('k_hops', 1))
same_module_for_all_hops = bool(cfg.get('same_module_for_all_hops', False))
do_postproc = bool(cfg.get('do_postproc', True))
copy_from_source = bool(cfg.get('copy_from_source', True))
canonization_func = (None if (canonizer is None) else canonizer(copy_from_source=copy_from_source))
kb_input_feeding = bool(cfg.get('kb_input_feeding', True))
kb_feed_rnn = bool(cfg.get('kb_feed_rnn', True))
kb_multihead_feed = bool(cfg.get('kb_multihead_feed', False))
posEncKBkeys = cfg.get('posEncdKBkeys', False)
tfstyletf = cfg.get('tfstyletf', True)
infeedkb = bool(cfg.get('infeedkb', False))
outfeedkb = bool(cfg.get('outfeedkb', False))
add_kb_biases_to_output = bool(cfg.get('add_kb_biases_to_output', True))
kb_max_dims = cfg.get('kb_max_dims', (16, 32))
double_decoder = cfg.get('double_decoder', False)
tied_side_softmax = cfg.get('tied_side_softmax', False)
do_pad_kb_keys = cfg.get('pad_kb_keys', True)
if hasattr(kb_max_dims, '__iter__'):
kb_max_dims = tuple(kb_max_dims)
else:
assert (type(kb_max_dims) == int), kb_max_dims
kb_max_dims = (kb_max_dims,)
assert cfg['decoder']['hidden_size']
dec_dropout = cfg['decoder'].get('dropout', 0.0)
dec_emb_dropout = cfg['decoder']['embeddings'].get('dropout', dec_dropout)
if (cfg['decoder'].get('type', 'recurrent') == 'transformer'):
if tfstyletf:
decoder = TransformerDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, kb_task=kb_task, kb_key_emb_size=kbsrc_embed.embedding_dim, feed_kb_hidden=kb_input_feeding, infeedkb=infeedkb, outfeedkb=outfeedkb, double_decoder=double_decoder)
else:
decoder = TransformerKBrnnDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, kb_task=kb_task, k_hops=k_hops, kb_max=kb_max_dims, same_module_for_all_hops=same_module_for_all_hops, kb_key_emb_size=kbsrc_embed.embedding_dim, kb_input_feeding=kb_input_feeding, kb_feed_rnn=kb_feed_rnn, kb_multihead_feed=kb_multihead_feed)
elif (not kb_task):
decoder = RecurrentDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout)
else:
decoder = KeyValRetRNNDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, k_hops=k_hops, kb_max=kb_max_dims, same_module_for_all_hops=same_module_for_all_hops, kb_key_emb_size=kbsrc_embed.embedding_dim, kb_input_feeding=kb_input_feeding, kb_feed_rnn=kb_feed_rnn, kb_multihead_feed=kb_multihead_feed, do_pad_kb_keys=do_pad_kb_keys)
generator = Generator(dec_hidden_size=cfg['decoder']['hidden_size'], vocab_size=len(trg_vocab), add_kb_biases_to_output=add_kb_biases_to_output, double_decoder=double_decoder)
model = Model(encoder=encoder, decoder=decoder, generator=generator, src_embed=src_embed, trg_embed=trg_embed, src_vocab=src_vocab, trg_vocab=trg_vocab, kb_key_embed=kbsrc_embed, trv_vocab=trv_vocab, k_hops=k_hops, do_postproc=do_postproc, canonize=canonization_func, kb_att_dims=len(kb_max_dims), posEncKBkeys=posEncKBkeys)
if cfg.get('tied_softmax', False):
if (trg_embed.lut.weight.shape == model.generator.output_layer.weight.shape):
model.generator.output_layer.weight = trg_embed.lut.weight
if model.generator.double_decoder:
assert hasattr(model.generator, 'side_output_layer')
if tied_side_softmax:
model.generator.side_output_layer.weight = trg_embed.lut.weight
else:
raise ConfigurationError('For tied_softmax, the decoder embedding_dim and decoder hidden_size must be the same.The decoder must be a Transformer.')
initialize_model(model, cfg, src_padding_idx, trg_padding_idx)
return model
|
Build and initialize the model according to the configuration.
:param cfg: dictionary configuration containing model specifications
:param src_vocab: source vocabulary
:param trg_vocab: target vocabulary
:param trv_vocab: kb true value lookup vocabulary
:return: built and initialized model
|
joeynmt/model.py
|
build_model
|
marvosyntactical/joeynmt
| 3 |
python
|
def build_model(cfg: dict=None, src_vocab: Vocabulary=None, trg_vocab: Vocabulary=None, trv_vocab: Vocabulary=None, canonizer=None) -> Model:
'\n Build and initialize the model according to the configuration.\n\n :param cfg: dictionary configuration containing model specifications\n :param src_vocab: source vocabulary\n :param trg_vocab: target vocabulary\n :param trv_vocab: kb true value lookup vocabulary\n :return: built and initialized model\n '
src_padding_idx = src_vocab.stoi[PAD_TOKEN]
trg_padding_idx = trg_vocab.stoi[PAD_TOKEN]
if ('embedding_files' in cfg.keys()):
assert (not cfg.get('tied_embeddings', False)), 'TODO implement tied embeddings along with pretrained initialization'
raise NotImplementedError('TODO implement kbsrc embed loading for embedding files')
weight_tensors = []
for weight_file in cfg['embedding_files']:
with open(weight_file, 'r') as f:
weight = []
for line in f.readlines():
line = line.split()
line = [float(x) for x in line]
weight.append(line)
weight = FloatTensor(weight)
weight_tensors.append(weight)
src_embed = Embeddings(int(weight_tensors[0][0].shape[0]), False, len(weight_tensors[0]))
src_embed.lut.weight.data = weight_tensors[0]
trg_embed = Embeddings(int(weight_tensors[1][0].shape[0]), False, len(weight_tensors[1]))
trg_embed.lut.weight.data = weight_tensors[1]
else:
src_embed = Embeddings(**cfg['encoder']['embeddings'], vocab_size=len(src_vocab), padding_idx=src_padding_idx)
if cfg.get('kb_embed_separate', False):
kbsrc_embed = Embeddings(**cfg['encoder']['embeddings'], vocab_size=len(src_vocab), padding_idx=src_padding_idx)
else:
kbsrc_embed = src_embed
if cfg.get('tied_embeddings', False):
if (src_vocab.itos == trg_vocab.itos):
trg_embed = src_embed
else:
raise ConfigurationError('Embedding cannot be tied since vocabularies differ.')
else:
trg_embed = Embeddings(**cfg['decoder']['embeddings'], vocab_size=len(trg_vocab), padding_idx=trg_padding_idx)
enc_dropout = cfg['encoder'].get('dropout', 0.0)
enc_emb_dropout = cfg['encoder']['embeddings'].get('dropout', enc_dropout)
if (cfg['encoder'].get('type', 'recurrent') == 'transformer'):
assert (cfg['encoder']['embeddings']['embedding_dim'] == cfg['encoder']['hidden_size']), 'for transformer, emb_size must be hidden_size'
encoder = TransformerEncoder(**cfg['encoder'], emb_size=src_embed.embedding_dim, emb_dropout=enc_emb_dropout)
else:
encoder = RecurrentEncoder(**cfg['encoder'], emb_size=src_embed.embedding_dim, emb_dropout=enc_emb_dropout)
kb_task = bool(cfg.get('kb', False))
k_hops = int(cfg.get('k_hops', 1))
same_module_for_all_hops = bool(cfg.get('same_module_for_all_hops', False))
do_postproc = bool(cfg.get('do_postproc', True))
copy_from_source = bool(cfg.get('copy_from_source', True))
canonization_func = (None if (canonizer is None) else canonizer(copy_from_source=copy_from_source))
kb_input_feeding = bool(cfg.get('kb_input_feeding', True))
kb_feed_rnn = bool(cfg.get('kb_feed_rnn', True))
kb_multihead_feed = bool(cfg.get('kb_multihead_feed', False))
posEncKBkeys = cfg.get('posEncdKBkeys', False)
tfstyletf = cfg.get('tfstyletf', True)
infeedkb = bool(cfg.get('infeedkb', False))
outfeedkb = bool(cfg.get('outfeedkb', False))
add_kb_biases_to_output = bool(cfg.get('add_kb_biases_to_output', True))
kb_max_dims = cfg.get('kb_max_dims', (16, 32))
double_decoder = cfg.get('double_decoder', False)
tied_side_softmax = cfg.get('tied_side_softmax', False)
do_pad_kb_keys = cfg.get('pad_kb_keys', True)
if hasattr(kb_max_dims, '__iter__'):
kb_max_dims = tuple(kb_max_dims)
else:
assert (type(kb_max_dims) == int), kb_max_dims
kb_max_dims = (kb_max_dims,)
assert cfg['decoder']['hidden_size']
dec_dropout = cfg['decoder'].get('dropout', 0.0)
dec_emb_dropout = cfg['decoder']['embeddings'].get('dropout', dec_dropout)
if (cfg['decoder'].get('type', 'recurrent') == 'transformer'):
if tfstyletf:
decoder = TransformerDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, kb_task=kb_task, kb_key_emb_size=kbsrc_embed.embedding_dim, feed_kb_hidden=kb_input_feeding, infeedkb=infeedkb, outfeedkb=outfeedkb, double_decoder=double_decoder)
else:
decoder = TransformerKBrnnDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, kb_task=kb_task, k_hops=k_hops, kb_max=kb_max_dims, same_module_for_all_hops=same_module_for_all_hops, kb_key_emb_size=kbsrc_embed.embedding_dim, kb_input_feeding=kb_input_feeding, kb_feed_rnn=kb_feed_rnn, kb_multihead_feed=kb_multihead_feed)
elif (not kb_task):
decoder = RecurrentDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout)
else:
decoder = KeyValRetRNNDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, k_hops=k_hops, kb_max=kb_max_dims, same_module_for_all_hops=same_module_for_all_hops, kb_key_emb_size=kbsrc_embed.embedding_dim, kb_input_feeding=kb_input_feeding, kb_feed_rnn=kb_feed_rnn, kb_multihead_feed=kb_multihead_feed, do_pad_kb_keys=do_pad_kb_keys)
generator = Generator(dec_hidden_size=cfg['decoder']['hidden_size'], vocab_size=len(trg_vocab), add_kb_biases_to_output=add_kb_biases_to_output, double_decoder=double_decoder)
model = Model(encoder=encoder, decoder=decoder, generator=generator, src_embed=src_embed, trg_embed=trg_embed, src_vocab=src_vocab, trg_vocab=trg_vocab, kb_key_embed=kbsrc_embed, trv_vocab=trv_vocab, k_hops=k_hops, do_postproc=do_postproc, canonize=canonization_func, kb_att_dims=len(kb_max_dims), posEncKBkeys=posEncKBkeys)
if cfg.get('tied_softmax', False):
if (trg_embed.lut.weight.shape == model.generator.output_layer.weight.shape):
model.generator.output_layer.weight = trg_embed.lut.weight
if model.generator.double_decoder:
assert hasattr(model.generator, 'side_output_layer')
if tied_side_softmax:
model.generator.side_output_layer.weight = trg_embed.lut.weight
else:
raise ConfigurationError('For tied_softmax, the decoder embedding_dim and decoder hidden_size must be the same.The decoder must be a Transformer.')
initialize_model(model, cfg, src_padding_idx, trg_padding_idx)
return model
|
def build_model(cfg: dict=None, src_vocab: Vocabulary=None, trg_vocab: Vocabulary=None, trv_vocab: Vocabulary=None, canonizer=None) -> Model:
'\n Build and initialize the model according to the configuration.\n\n :param cfg: dictionary configuration containing model specifications\n :param src_vocab: source vocabulary\n :param trg_vocab: target vocabulary\n :param trv_vocab: kb true value lookup vocabulary\n :return: built and initialized model\n '
src_padding_idx = src_vocab.stoi[PAD_TOKEN]
trg_padding_idx = trg_vocab.stoi[PAD_TOKEN]
if ('embedding_files' in cfg.keys()):
assert (not cfg.get('tied_embeddings', False)), 'TODO implement tied embeddings along with pretrained initialization'
raise NotImplementedError('TODO implement kbsrc embed loading for embedding files')
weight_tensors = []
for weight_file in cfg['embedding_files']:
with open(weight_file, 'r') as f:
weight = []
for line in f.readlines():
line = line.split()
line = [float(x) for x in line]
weight.append(line)
weight = FloatTensor(weight)
weight_tensors.append(weight)
src_embed = Embeddings(int(weight_tensors[0][0].shape[0]), False, len(weight_tensors[0]))
src_embed.lut.weight.data = weight_tensors[0]
trg_embed = Embeddings(int(weight_tensors[1][0].shape[0]), False, len(weight_tensors[1]))
trg_embed.lut.weight.data = weight_tensors[1]
else:
src_embed = Embeddings(**cfg['encoder']['embeddings'], vocab_size=len(src_vocab), padding_idx=src_padding_idx)
if cfg.get('kb_embed_separate', False):
kbsrc_embed = Embeddings(**cfg['encoder']['embeddings'], vocab_size=len(src_vocab), padding_idx=src_padding_idx)
else:
kbsrc_embed = src_embed
if cfg.get('tied_embeddings', False):
if (src_vocab.itos == trg_vocab.itos):
trg_embed = src_embed
else:
raise ConfigurationError('Embedding cannot be tied since vocabularies differ.')
else:
trg_embed = Embeddings(**cfg['decoder']['embeddings'], vocab_size=len(trg_vocab), padding_idx=trg_padding_idx)
enc_dropout = cfg['encoder'].get('dropout', 0.0)
enc_emb_dropout = cfg['encoder']['embeddings'].get('dropout', enc_dropout)
if (cfg['encoder'].get('type', 'recurrent') == 'transformer'):
assert (cfg['encoder']['embeddings']['embedding_dim'] == cfg['encoder']['hidden_size']), 'for transformer, emb_size must be hidden_size'
encoder = TransformerEncoder(**cfg['encoder'], emb_size=src_embed.embedding_dim, emb_dropout=enc_emb_dropout)
else:
encoder = RecurrentEncoder(**cfg['encoder'], emb_size=src_embed.embedding_dim, emb_dropout=enc_emb_dropout)
kb_task = bool(cfg.get('kb', False))
k_hops = int(cfg.get('k_hops', 1))
same_module_for_all_hops = bool(cfg.get('same_module_for_all_hops', False))
do_postproc = bool(cfg.get('do_postproc', True))
copy_from_source = bool(cfg.get('copy_from_source', True))
canonization_func = (None if (canonizer is None) else canonizer(copy_from_source=copy_from_source))
kb_input_feeding = bool(cfg.get('kb_input_feeding', True))
kb_feed_rnn = bool(cfg.get('kb_feed_rnn', True))
kb_multihead_feed = bool(cfg.get('kb_multihead_feed', False))
posEncKBkeys = cfg.get('posEncdKBkeys', False)
tfstyletf = cfg.get('tfstyletf', True)
infeedkb = bool(cfg.get('infeedkb', False))
outfeedkb = bool(cfg.get('outfeedkb', False))
add_kb_biases_to_output = bool(cfg.get('add_kb_biases_to_output', True))
kb_max_dims = cfg.get('kb_max_dims', (16, 32))
double_decoder = cfg.get('double_decoder', False)
tied_side_softmax = cfg.get('tied_side_softmax', False)
do_pad_kb_keys = cfg.get('pad_kb_keys', True)
if hasattr(kb_max_dims, '__iter__'):
kb_max_dims = tuple(kb_max_dims)
else:
assert (type(kb_max_dims) == int), kb_max_dims
kb_max_dims = (kb_max_dims,)
assert cfg['decoder']['hidden_size']
dec_dropout = cfg['decoder'].get('dropout', 0.0)
dec_emb_dropout = cfg['decoder']['embeddings'].get('dropout', dec_dropout)
if (cfg['decoder'].get('type', 'recurrent') == 'transformer'):
if tfstyletf:
decoder = TransformerDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, kb_task=kb_task, kb_key_emb_size=kbsrc_embed.embedding_dim, feed_kb_hidden=kb_input_feeding, infeedkb=infeedkb, outfeedkb=outfeedkb, double_decoder=double_decoder)
else:
decoder = TransformerKBrnnDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, kb_task=kb_task, k_hops=k_hops, kb_max=kb_max_dims, same_module_for_all_hops=same_module_for_all_hops, kb_key_emb_size=kbsrc_embed.embedding_dim, kb_input_feeding=kb_input_feeding, kb_feed_rnn=kb_feed_rnn, kb_multihead_feed=kb_multihead_feed)
elif (not kb_task):
decoder = RecurrentDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout)
else:
decoder = KeyValRetRNNDecoder(**cfg['decoder'], encoder=encoder, vocab_size=len(trg_vocab), emb_size=trg_embed.embedding_dim, emb_dropout=dec_emb_dropout, k_hops=k_hops, kb_max=kb_max_dims, same_module_for_all_hops=same_module_for_all_hops, kb_key_emb_size=kbsrc_embed.embedding_dim, kb_input_feeding=kb_input_feeding, kb_feed_rnn=kb_feed_rnn, kb_multihead_feed=kb_multihead_feed, do_pad_kb_keys=do_pad_kb_keys)
generator = Generator(dec_hidden_size=cfg['decoder']['hidden_size'], vocab_size=len(trg_vocab), add_kb_biases_to_output=add_kb_biases_to_output, double_decoder=double_decoder)
model = Model(encoder=encoder, decoder=decoder, generator=generator, src_embed=src_embed, trg_embed=trg_embed, src_vocab=src_vocab, trg_vocab=trg_vocab, kb_key_embed=kbsrc_embed, trv_vocab=trv_vocab, k_hops=k_hops, do_postproc=do_postproc, canonize=canonization_func, kb_att_dims=len(kb_max_dims), posEncKBkeys=posEncKBkeys)
if cfg.get('tied_softmax', False):
if (trg_embed.lut.weight.shape == model.generator.output_layer.weight.shape):
model.generator.output_layer.weight = trg_embed.lut.weight
if model.generator.double_decoder:
assert hasattr(model.generator, 'side_output_layer')
if tied_side_softmax:
model.generator.side_output_layer.weight = trg_embed.lut.weight
else:
raise ConfigurationError('For tied_softmax, the decoder embedding_dim and decoder hidden_size must be the same.The decoder must be a Transformer.')
initialize_model(model, cfg, src_padding_idx, trg_padding_idx)
return model<|docstring|>Build and initialize the model according to the configuration.
:param cfg: dictionary configuration containing model specifications
:param src_vocab: source vocabulary
:param trg_vocab: target vocabulary
:param trv_vocab: kb true value lookup vocabulary
:return: built and initialized model<|endoftext|>
|
a4342208f732e02d68eb54d73044ef236d6d6875284344dbdea4226e17248b43
|
def __init__(self, encoder: Encoder, decoder: Decoder, generator: nn.Module, src_embed: Embeddings, trg_embed: Embeddings, src_vocab: Vocabulary, trg_vocab: Vocabulary, kb_key_embed: Embeddings, trv_vocab: Vocabulary=None, k_hops: int=1, do_postproc: bool=True, canonize=None, kb_att_dims: int=1, posEncKBkeys: bool=False) -> None:
'\n Create a new encoder-decoder model\n\n :param encoder: encoder\n :param decoder: decoder\n :param src_embed: source embedding\n :param trg_embed: target embedding\n :param src_vocab: source vocabulary\n :param trg_vocab: target vocabulary\n :param trv_vocab: kb true value lookup vocabulary\n :param k_hops: number of kvr attention forward passes to do\n :param do_postproc: do postprocessing (decode canonical tokens) in KVR task?\n :param canonize: callable canonization object to try to create KB on the fly if none exists; not used by model but piggybacks off it\n :param kb_att_dims: number of dimensions of KB \n :param posEncdKBkeys: apply positional encoding to KB keys?\n '
super(Model, self).__init__()
self.src_embed = src_embed
self.trg_embed = trg_embed
self.encoder = encoder
self.decoder = decoder
self.generator = generator
self.src_vocab = src_vocab
self.trg_vocab = trg_vocab
self.bos_index = self.trg_vocab.stoi[BOS_TOKEN]
self.pad_index = self.trg_vocab.stoi[PAD_TOKEN]
self.eos_index = self.trg_vocab.stoi[EOS_TOKEN]
self.kbsrc_embed = (kb_key_embed if (kb_key_embed is not None) else self.src_embed)
if (trv_vocab != None):
self.trv_vocab = trv_vocab
self.pad_idx_kbsrc = self.src_vocab.stoi[PAD_TOKEN]
self.eos_idx_src = self.src_vocab.stoi[EOS_TOKEN]
self.k_hops = k_hops
self.do_postproc = do_postproc
self.canonize = canonize
self.kb_att_dims = kb_att_dims
if posEncKBkeys:
try:
decoder_hidden_size = self.decoder.hidden_size
except AttributeError:
decoder_hidden_size = self.decoder._hidden_size
self.posEnc = PositionalEncoding(decoder_hidden_size, e=2)
self.Timer = Timer()
|
Create a new encoder-decoder model
:param encoder: encoder
:param decoder: decoder
:param src_embed: source embedding
:param trg_embed: target embedding
:param src_vocab: source vocabulary
:param trg_vocab: target vocabulary
:param trv_vocab: kb true value lookup vocabulary
:param k_hops: number of kvr attention forward passes to do
:param do_postproc: do postprocessing (decode canonical tokens) in KVR task?
:param canonize: callable canonization object to try to create KB on the fly if none exists; not used by model but piggybacks off it
:param kb_att_dims: number of dimensions of KB
:param posEncdKBkeys: apply positional encoding to KB keys?
|
joeynmt/model.py
|
__init__
|
marvosyntactical/joeynmt
| 3 |
python
|
def __init__(self, encoder: Encoder, decoder: Decoder, generator: nn.Module, src_embed: Embeddings, trg_embed: Embeddings, src_vocab: Vocabulary, trg_vocab: Vocabulary, kb_key_embed: Embeddings, trv_vocab: Vocabulary=None, k_hops: int=1, do_postproc: bool=True, canonize=None, kb_att_dims: int=1, posEncKBkeys: bool=False) -> None:
'\n Create a new encoder-decoder model\n\n :param encoder: encoder\n :param decoder: decoder\n :param src_embed: source embedding\n :param trg_embed: target embedding\n :param src_vocab: source vocabulary\n :param trg_vocab: target vocabulary\n :param trv_vocab: kb true value lookup vocabulary\n :param k_hops: number of kvr attention forward passes to do\n :param do_postproc: do postprocessing (decode canonical tokens) in KVR task?\n :param canonize: callable canonization object to try to create KB on the fly if none exists; not used by model but piggybacks off it\n :param kb_att_dims: number of dimensions of KB \n :param posEncdKBkeys: apply positional encoding to KB keys?\n '
super(Model, self).__init__()
self.src_embed = src_embed
self.trg_embed = trg_embed
self.encoder = encoder
self.decoder = decoder
self.generator = generator
self.src_vocab = src_vocab
self.trg_vocab = trg_vocab
self.bos_index = self.trg_vocab.stoi[BOS_TOKEN]
self.pad_index = self.trg_vocab.stoi[PAD_TOKEN]
self.eos_index = self.trg_vocab.stoi[EOS_TOKEN]
self.kbsrc_embed = (kb_key_embed if (kb_key_embed is not None) else self.src_embed)
if (trv_vocab != None):
self.trv_vocab = trv_vocab
self.pad_idx_kbsrc = self.src_vocab.stoi[PAD_TOKEN]
self.eos_idx_src = self.src_vocab.stoi[EOS_TOKEN]
self.k_hops = k_hops
self.do_postproc = do_postproc
self.canonize = canonize
self.kb_att_dims = kb_att_dims
if posEncKBkeys:
try:
decoder_hidden_size = self.decoder.hidden_size
except AttributeError:
decoder_hidden_size = self.decoder._hidden_size
self.posEnc = PositionalEncoding(decoder_hidden_size, e=2)
self.Timer = Timer()
|
def __init__(self, encoder: Encoder, decoder: Decoder, generator: nn.Module, src_embed: Embeddings, trg_embed: Embeddings, src_vocab: Vocabulary, trg_vocab: Vocabulary, kb_key_embed: Embeddings, trv_vocab: Vocabulary=None, k_hops: int=1, do_postproc: bool=True, canonize=None, kb_att_dims: int=1, posEncKBkeys: bool=False) -> None:
'\n Create a new encoder-decoder model\n\n :param encoder: encoder\n :param decoder: decoder\n :param src_embed: source embedding\n :param trg_embed: target embedding\n :param src_vocab: source vocabulary\n :param trg_vocab: target vocabulary\n :param trv_vocab: kb true value lookup vocabulary\n :param k_hops: number of kvr attention forward passes to do\n :param do_postproc: do postprocessing (decode canonical tokens) in KVR task?\n :param canonize: callable canonization object to try to create KB on the fly if none exists; not used by model but piggybacks off it\n :param kb_att_dims: number of dimensions of KB \n :param posEncdKBkeys: apply positional encoding to KB keys?\n '
super(Model, self).__init__()
self.src_embed = src_embed
self.trg_embed = trg_embed
self.encoder = encoder
self.decoder = decoder
self.generator = generator
self.src_vocab = src_vocab
self.trg_vocab = trg_vocab
self.bos_index = self.trg_vocab.stoi[BOS_TOKEN]
self.pad_index = self.trg_vocab.stoi[PAD_TOKEN]
self.eos_index = self.trg_vocab.stoi[EOS_TOKEN]
self.kbsrc_embed = (kb_key_embed if (kb_key_embed is not None) else self.src_embed)
if (trv_vocab != None):
self.trv_vocab = trv_vocab
self.pad_idx_kbsrc = self.src_vocab.stoi[PAD_TOKEN]
self.eos_idx_src = self.src_vocab.stoi[EOS_TOKEN]
self.k_hops = k_hops
self.do_postproc = do_postproc
self.canonize = canonize
self.kb_att_dims = kb_att_dims
if posEncKBkeys:
try:
decoder_hidden_size = self.decoder.hidden_size
except AttributeError:
decoder_hidden_size = self.decoder._hidden_size
self.posEnc = PositionalEncoding(decoder_hidden_size, e=2)
self.Timer = Timer()<|docstring|>Create a new encoder-decoder model
:param encoder: encoder
:param decoder: decoder
:param src_embed: source embedding
:param trg_embed: target embedding
:param src_vocab: source vocabulary
:param trg_vocab: target vocabulary
:param trv_vocab: kb true value lookup vocabulary
:param k_hops: number of kvr attention forward passes to do
:param do_postproc: do postprocessing (decode canonical tokens) in KVR task?
:param canonize: callable canonization object to try to create KB on the fly if none exists; not used by model but piggybacks off it
:param kb_att_dims: number of dimensions of KB
:param posEncdKBkeys: apply positional encoding to KB keys?<|endoftext|>
|
8ba7b8f264666ffa95abcabd8f9dd4a63b430f14909a4b64be71c08477b4322d
|
def forward(self, src: Tensor, trg_input: Tensor, src_mask: Tensor, src_lengths: Tensor, trg_mask: Tensor=None, kb_keys: Tensor=None, kb_mask=None, kb_values_embed=None) -> (Tensor, Tensor, Tensor, Tensor, Tensor):
'\n First encodes the source sentence.\n Then produces the target one word at a time.\n\n :param src: source input\n :param trg_input: target input\n :param src_mask: source mask\n :param src_lengths: length of source inputs\n :param trg_mask: target mask\n :return: decoder outputs\n '
(encoder_output, encoder_hidden) = self.encode(src=src, src_length=src_lengths, src_mask=src_mask)
unroll_steps = trg_input.size(1)
return self.decode(encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=src_mask, trg_input=trg_input, unroll_steps=unroll_steps, trg_mask=trg_mask, kb_keys=kb_keys, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
|
First encodes the source sentence.
Then produces the target one word at a time.
:param src: source input
:param trg_input: target input
:param src_mask: source mask
:param src_lengths: length of source inputs
:param trg_mask: target mask
:return: decoder outputs
|
joeynmt/model.py
|
forward
|
marvosyntactical/joeynmt
| 3 |
python
|
def forward(self, src: Tensor, trg_input: Tensor, src_mask: Tensor, src_lengths: Tensor, trg_mask: Tensor=None, kb_keys: Tensor=None, kb_mask=None, kb_values_embed=None) -> (Tensor, Tensor, Tensor, Tensor, Tensor):
'\n First encodes the source sentence.\n Then produces the target one word at a time.\n\n :param src: source input\n :param trg_input: target input\n :param src_mask: source mask\n :param src_lengths: length of source inputs\n :param trg_mask: target mask\n :return: decoder outputs\n '
(encoder_output, encoder_hidden) = self.encode(src=src, src_length=src_lengths, src_mask=src_mask)
unroll_steps = trg_input.size(1)
return self.decode(encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=src_mask, trg_input=trg_input, unroll_steps=unroll_steps, trg_mask=trg_mask, kb_keys=kb_keys, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
|
def forward(self, src: Tensor, trg_input: Tensor, src_mask: Tensor, src_lengths: Tensor, trg_mask: Tensor=None, kb_keys: Tensor=None, kb_mask=None, kb_values_embed=None) -> (Tensor, Tensor, Tensor, Tensor, Tensor):
'\n First encodes the source sentence.\n Then produces the target one word at a time.\n\n :param src: source input\n :param trg_input: target input\n :param src_mask: source mask\n :param src_lengths: length of source inputs\n :param trg_mask: target mask\n :return: decoder outputs\n '
(encoder_output, encoder_hidden) = self.encode(src=src, src_length=src_lengths, src_mask=src_mask)
unroll_steps = trg_input.size(1)
return self.decode(encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=src_mask, trg_input=trg_input, unroll_steps=unroll_steps, trg_mask=trg_mask, kb_keys=kb_keys, kb_mask=kb_mask, kb_values_embed=kb_values_embed)<|docstring|>First encodes the source sentence.
Then produces the target one word at a time.
:param src: source input
:param trg_input: target input
:param src_mask: source mask
:param src_lengths: length of source inputs
:param trg_mask: target mask
:return: decoder outputs<|endoftext|>
|
9b18ce5e182446fe2ce8a2e7bf03c52d5fcf35f421deeeea73b4332c23caa0c0
|
def encode(self, src: Tensor, src_length: Tensor, src_mask: Tensor) -> (Tensor, Tensor):
'\n Encodes the source sentence.\n\n :param src:\n :param src_length:\n :param src_mask:\n :return: encoder outputs (output, hidden_concat)\n '
return self.encoder(self.src_embed(src), src_length, src_mask)
|
Encodes the source sentence.
:param src:
:param src_length:
:param src_mask:
:return: encoder outputs (output, hidden_concat)
|
joeynmt/model.py
|
encode
|
marvosyntactical/joeynmt
| 3 |
python
|
def encode(self, src: Tensor, src_length: Tensor, src_mask: Tensor) -> (Tensor, Tensor):
'\n Encodes the source sentence.\n\n :param src:\n :param src_length:\n :param src_mask:\n :return: encoder outputs (output, hidden_concat)\n '
return self.encoder(self.src_embed(src), src_length, src_mask)
|
def encode(self, src: Tensor, src_length: Tensor, src_mask: Tensor) -> (Tensor, Tensor):
'\n Encodes the source sentence.\n\n :param src:\n :param src_length:\n :param src_mask:\n :return: encoder outputs (output, hidden_concat)\n '
return self.encoder(self.src_embed(src), src_length, src_mask)<|docstring|>Encodes the source sentence.
:param src:
:param src_length:
:param src_mask:
:return: encoder outputs (output, hidden_concat)<|endoftext|>
|
7da7690cb9dfaeaf68aca79a9be7aaf2df3e8a6273ba69b626b04ebeb8f5b8f5
|
def decode(self, encoder_output: Tensor, encoder_hidden: Tensor, src_mask: Tensor, trg_input: Tensor, unroll_steps: int, decoder_hidden: Tensor=None, trg_mask: Tensor=None, kb_keys: Tensor=None, kb_mask: Tensor=None, kb_values_embed=None) -> (Tensor, Tensor, Tensor, Tensor):
'\n Decode, given an encoded source sentence.\n\n :param encoder_output: encoder states for attention computation\n :param encoder_hidden: last encoder state for decoder initialization\n :param src_mask: source mask, 1 at valid tokens\n :param trg_input: target inputs\n :param unroll_steps: number of steps to unrol the decoder for\n :param decoder_hidden: decoder hidden state (optional)\n :param trg_mask: mask for target steps\n :return: decoder outputs (outputs, hidden, att_probs, att_vectors)\n '
return self.decoder(trg_embed=self.trg_embed(trg_input), encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=src_mask, unroll_steps=unroll_steps, hidden=decoder_hidden, trg_mask=trg_mask, kb_keys=kb_keys, k_hops=self.k_hops, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
|
Decode, given an encoded source sentence.
:param encoder_output: encoder states for attention computation
:param encoder_hidden: last encoder state for decoder initialization
:param src_mask: source mask, 1 at valid tokens
:param trg_input: target inputs
:param unroll_steps: number of steps to unrol the decoder for
:param decoder_hidden: decoder hidden state (optional)
:param trg_mask: mask for target steps
:return: decoder outputs (outputs, hidden, att_probs, att_vectors)
|
joeynmt/model.py
|
decode
|
marvosyntactical/joeynmt
| 3 |
python
|
def decode(self, encoder_output: Tensor, encoder_hidden: Tensor, src_mask: Tensor, trg_input: Tensor, unroll_steps: int, decoder_hidden: Tensor=None, trg_mask: Tensor=None, kb_keys: Tensor=None, kb_mask: Tensor=None, kb_values_embed=None) -> (Tensor, Tensor, Tensor, Tensor):
'\n Decode, given an encoded source sentence.\n\n :param encoder_output: encoder states for attention computation\n :param encoder_hidden: last encoder state for decoder initialization\n :param src_mask: source mask, 1 at valid tokens\n :param trg_input: target inputs\n :param unroll_steps: number of steps to unrol the decoder for\n :param decoder_hidden: decoder hidden state (optional)\n :param trg_mask: mask for target steps\n :return: decoder outputs (outputs, hidden, att_probs, att_vectors)\n '
return self.decoder(trg_embed=self.trg_embed(trg_input), encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=src_mask, unroll_steps=unroll_steps, hidden=decoder_hidden, trg_mask=trg_mask, kb_keys=kb_keys, k_hops=self.k_hops, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
|
def decode(self, encoder_output: Tensor, encoder_hidden: Tensor, src_mask: Tensor, trg_input: Tensor, unroll_steps: int, decoder_hidden: Tensor=None, trg_mask: Tensor=None, kb_keys: Tensor=None, kb_mask: Tensor=None, kb_values_embed=None) -> (Tensor, Tensor, Tensor, Tensor):
'\n Decode, given an encoded source sentence.\n\n :param encoder_output: encoder states for attention computation\n :param encoder_hidden: last encoder state for decoder initialization\n :param src_mask: source mask, 1 at valid tokens\n :param trg_input: target inputs\n :param unroll_steps: number of steps to unrol the decoder for\n :param decoder_hidden: decoder hidden state (optional)\n :param trg_mask: mask for target steps\n :return: decoder outputs (outputs, hidden, att_probs, att_vectors)\n '
return self.decoder(trg_embed=self.trg_embed(trg_input), encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=src_mask, unroll_steps=unroll_steps, hidden=decoder_hidden, trg_mask=trg_mask, kb_keys=kb_keys, k_hops=self.k_hops, kb_mask=kb_mask, kb_values_embed=kb_values_embed)<|docstring|>Decode, given an encoded source sentence.
:param encoder_output: encoder states for attention computation
:param encoder_hidden: last encoder state for decoder initialization
:param src_mask: source mask, 1 at valid tokens
:param trg_input: target inputs
:param unroll_steps: number of steps to unrol the decoder for
:param decoder_hidden: decoder hidden state (optional)
:param trg_mask: mask for target steps
:return: decoder outputs (outputs, hidden, att_probs, att_vectors)<|endoftext|>
|
f9bf959f42f324c00ff145bbdcab6655b83a1c541dc4ee6a06c8ed1996f50c55
|
def get_loss_for_batch(self, batch: Batch, loss_function: nn.Module, max_output_length: int=None, e_i: float=1.0, greedy_threshold: float=0.9) -> Tensor:
'\n Compute non-normalized loss and number of tokens for a batch\n\n :param batch: batch to compute loss for\n :param loss_function: loss function, computes for input and target\n a scalar loss for the complete batch\n :param max_output_length: maximum length of hypotheses\n :param e_i: scheduled sampling probability of taking true label vs model generation at every decoding step\n (https://arxiv.org/abs/1506.03099 Section 2.4)\n :param greedy_threshold: only actually do greedy search once e_i is below this threshold\n :return: batch_loss: sum of losses over non-pad elements in the batch\n '
print(f'''
{('-' * 10)}GET LOSS FWD PASS: START current batch{('-' * 10)}
''')
assert (0.0 <= e_i <= 1.0), f'e_i={e_i} should be a probability'
do_teacher_force = (e_i >= greedy_threshold)
(trg, trg_input, trg_mask) = (batch.trg, batch.trg_input, batch.trg_mask)
batch_size = trg.size(0)
if hasattr(batch, 'kbsrc'):
(kb_keys, kb_values, kb_values_embed, _, kb_mask) = self.preprocess_batch_kb(batch, kbattdims=self.kb_att_dims)
else:
kb_keys = None
log_probs = None
if (kb_keys is not None):
assert (batch.kbsrc != None), batch.kbsrc
if hasattr(batch, 'trgcanon'):
assert (batch.trgcanon.shape[0] == batch.trg.shape[0]), [t.shape for t in [batch.trg, batch.trgcanon]]
(trg, trg_input, trg_mask) = (batch.trgcanon, batch.trgcanon_input, batch.trgcanon_mask)
if (not do_teacher_force):
with self.Timer('model training: KB Task: do greedy search'):
(encoder_output, encoder_hidden) = self.encode(batch.src, batch.src_lengths, batch.src_mask)
if (max_output_length is None):
max_output_length = int((max(batch.src_lengths.cpu().numpy()) * 1.5))
print(f'in model.glfb; kb_keys are {kb_keys}')
(stacked_output, stacked_attention_scores, stacked_kb_att_scores, log_probs) = greedy(encoder_hidden=encoder_hidden, encoder_output=encoder_output, src_mask=batch.src_mask, embed=self.trg_embed, bos_index=self.bos_index, decoder=self.decoder, generator=self.generator, max_output_length=trg.size((- 1)), knowledgebase=(kb_keys, kb_values, kb_values_embed, kb_mask), trg_input=trg_input, e_i=e_i)
else:
with self.Timer('model training: KB Task: model fwd pass'):
(hidden, att_probs, out, kb_probs, _, _) = self.forward(src=batch.src, trg_input=trg_input, src_mask=batch.src_mask, src_lengths=batch.src_lengths, trg_mask=trg_mask, kb_keys=kb_keys, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
else:
if (not do_teacher_force):
raise NotImplementedError('scheduled sampling only works for KB task atm')
(hidden, att_probs, out, kb_probs, _, _) = self.forward(src=batch.src, trg_input=trg_input, src_mask=batch.src_mask, src_lengths=batch.src_lengths, trg_mask=trg_mask)
kb_values = None
if (log_probs is None):
log_probs = self.generator(out, kb_probs=kb_probs, kb_values=kb_values)
if hasattr(batch, 'trgcanon'):
assert (not log_probs.requires_grad), "using batch.trgcanon shouldnt happen / be done during training (canonized data is used in the 'trg' field there)"
assert (log_probs.size((- 1)) == self.generator.output_size), (log_probs.shape, self.generator.output_size)
try:
batch_loss = loss_function(log_probs, trg)
except Exception as e:
print(f'batch_size: {batch_size}')
print(f'log_probs= {log_probs.shape}')
print(f'trg = {trg.shape}')
print(f'')
print(f'')
raise e
with self.Timer('debugging: greedy hypothesis:'):
mle_tokens = argmax(log_probs, dim=(- 1))
mle_tokens = mle_tokens.cpu().numpy()
print(f'proc_batch: Hypothesis: {self.trg_vocab.arrays_to_sentences(mle_tokens)[(- 1)]}')
print(f'''
{('-' * 10)}GET LOSS FWD PASS: END current batch{('-' * 10)}
''')
return batch_loss
|
Compute non-normalized loss and number of tokens for a batch
:param batch: batch to compute loss for
:param loss_function: loss function, computes for input and target
a scalar loss for the complete batch
:param max_output_length: maximum length of hypotheses
:param e_i: scheduled sampling probability of taking true label vs model generation at every decoding step
(https://arxiv.org/abs/1506.03099 Section 2.4)
:param greedy_threshold: only actually do greedy search once e_i is below this threshold
:return: batch_loss: sum of losses over non-pad elements in the batch
|
joeynmt/model.py
|
get_loss_for_batch
|
marvosyntactical/joeynmt
| 3 |
python
|
def get_loss_for_batch(self, batch: Batch, loss_function: nn.Module, max_output_length: int=None, e_i: float=1.0, greedy_threshold: float=0.9) -> Tensor:
'\n Compute non-normalized loss and number of tokens for a batch\n\n :param batch: batch to compute loss for\n :param loss_function: loss function, computes for input and target\n a scalar loss for the complete batch\n :param max_output_length: maximum length of hypotheses\n :param e_i: scheduled sampling probability of taking true label vs model generation at every decoding step\n (https://arxiv.org/abs/1506.03099 Section 2.4)\n :param greedy_threshold: only actually do greedy search once e_i is below this threshold\n :return: batch_loss: sum of losses over non-pad elements in the batch\n '
print(f'
{('-' * 10)}GET LOSS FWD PASS: START current batch{('-' * 10)}
')
assert (0.0 <= e_i <= 1.0), f'e_i={e_i} should be a probability'
do_teacher_force = (e_i >= greedy_threshold)
(trg, trg_input, trg_mask) = (batch.trg, batch.trg_input, batch.trg_mask)
batch_size = trg.size(0)
if hasattr(batch, 'kbsrc'):
(kb_keys, kb_values, kb_values_embed, _, kb_mask) = self.preprocess_batch_kb(batch, kbattdims=self.kb_att_dims)
else:
kb_keys = None
log_probs = None
if (kb_keys is not None):
assert (batch.kbsrc != None), batch.kbsrc
if hasattr(batch, 'trgcanon'):
assert (batch.trgcanon.shape[0] == batch.trg.shape[0]), [t.shape for t in [batch.trg, batch.trgcanon]]
(trg, trg_input, trg_mask) = (batch.trgcanon, batch.trgcanon_input, batch.trgcanon_mask)
if (not do_teacher_force):
with self.Timer('model training: KB Task: do greedy search'):
(encoder_output, encoder_hidden) = self.encode(batch.src, batch.src_lengths, batch.src_mask)
if (max_output_length is None):
max_output_length = int((max(batch.src_lengths.cpu().numpy()) * 1.5))
print(f'in model.glfb; kb_keys are {kb_keys}')
(stacked_output, stacked_attention_scores, stacked_kb_att_scores, log_probs) = greedy(encoder_hidden=encoder_hidden, encoder_output=encoder_output, src_mask=batch.src_mask, embed=self.trg_embed, bos_index=self.bos_index, decoder=self.decoder, generator=self.generator, max_output_length=trg.size((- 1)), knowledgebase=(kb_keys, kb_values, kb_values_embed, kb_mask), trg_input=trg_input, e_i=e_i)
else:
with self.Timer('model training: KB Task: model fwd pass'):
(hidden, att_probs, out, kb_probs, _, _) = self.forward(src=batch.src, trg_input=trg_input, src_mask=batch.src_mask, src_lengths=batch.src_lengths, trg_mask=trg_mask, kb_keys=kb_keys, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
else:
if (not do_teacher_force):
raise NotImplementedError('scheduled sampling only works for KB task atm')
(hidden, att_probs, out, kb_probs, _, _) = self.forward(src=batch.src, trg_input=trg_input, src_mask=batch.src_mask, src_lengths=batch.src_lengths, trg_mask=trg_mask)
kb_values = None
if (log_probs is None):
log_probs = self.generator(out, kb_probs=kb_probs, kb_values=kb_values)
if hasattr(batch, 'trgcanon'):
assert (not log_probs.requires_grad), "using batch.trgcanon shouldnt happen / be done during training (canonized data is used in the 'trg' field there)"
assert (log_probs.size((- 1)) == self.generator.output_size), (log_probs.shape, self.generator.output_size)
try:
batch_loss = loss_function(log_probs, trg)
except Exception as e:
print(f'batch_size: {batch_size}')
print(f'log_probs= {log_probs.shape}')
print(f'trg = {trg.shape}')
print(f)
print(f)
raise e
with self.Timer('debugging: greedy hypothesis:'):
mle_tokens = argmax(log_probs, dim=(- 1))
mle_tokens = mle_tokens.cpu().numpy()
print(f'proc_batch: Hypothesis: {self.trg_vocab.arrays_to_sentences(mle_tokens)[(- 1)]}')
print(f'
{('-' * 10)}GET LOSS FWD PASS: END current batch{('-' * 10)}
')
return batch_loss
|
def get_loss_for_batch(self, batch: Batch, loss_function: nn.Module, max_output_length: int=None, e_i: float=1.0, greedy_threshold: float=0.9) -> Tensor:
'\n Compute non-normalized loss and number of tokens for a batch\n\n :param batch: batch to compute loss for\n :param loss_function: loss function, computes for input and target\n a scalar loss for the complete batch\n :param max_output_length: maximum length of hypotheses\n :param e_i: scheduled sampling probability of taking true label vs model generation at every decoding step\n (https://arxiv.org/abs/1506.03099 Section 2.4)\n :param greedy_threshold: only actually do greedy search once e_i is below this threshold\n :return: batch_loss: sum of losses over non-pad elements in the batch\n '
print(f'
{('-' * 10)}GET LOSS FWD PASS: START current batch{('-' * 10)}
')
assert (0.0 <= e_i <= 1.0), f'e_i={e_i} should be a probability'
do_teacher_force = (e_i >= greedy_threshold)
(trg, trg_input, trg_mask) = (batch.trg, batch.trg_input, batch.trg_mask)
batch_size = trg.size(0)
if hasattr(batch, 'kbsrc'):
(kb_keys, kb_values, kb_values_embed, _, kb_mask) = self.preprocess_batch_kb(batch, kbattdims=self.kb_att_dims)
else:
kb_keys = None
log_probs = None
if (kb_keys is not None):
assert (batch.kbsrc != None), batch.kbsrc
if hasattr(batch, 'trgcanon'):
assert (batch.trgcanon.shape[0] == batch.trg.shape[0]), [t.shape for t in [batch.trg, batch.trgcanon]]
(trg, trg_input, trg_mask) = (batch.trgcanon, batch.trgcanon_input, batch.trgcanon_mask)
if (not do_teacher_force):
with self.Timer('model training: KB Task: do greedy search'):
(encoder_output, encoder_hidden) = self.encode(batch.src, batch.src_lengths, batch.src_mask)
if (max_output_length is None):
max_output_length = int((max(batch.src_lengths.cpu().numpy()) * 1.5))
print(f'in model.glfb; kb_keys are {kb_keys}')
(stacked_output, stacked_attention_scores, stacked_kb_att_scores, log_probs) = greedy(encoder_hidden=encoder_hidden, encoder_output=encoder_output, src_mask=batch.src_mask, embed=self.trg_embed, bos_index=self.bos_index, decoder=self.decoder, generator=self.generator, max_output_length=trg.size((- 1)), knowledgebase=(kb_keys, kb_values, kb_values_embed, kb_mask), trg_input=trg_input, e_i=e_i)
else:
with self.Timer('model training: KB Task: model fwd pass'):
(hidden, att_probs, out, kb_probs, _, _) = self.forward(src=batch.src, trg_input=trg_input, src_mask=batch.src_mask, src_lengths=batch.src_lengths, trg_mask=trg_mask, kb_keys=kb_keys, kb_mask=kb_mask, kb_values_embed=kb_values_embed)
else:
if (not do_teacher_force):
raise NotImplementedError('scheduled sampling only works for KB task atm')
(hidden, att_probs, out, kb_probs, _, _) = self.forward(src=batch.src, trg_input=trg_input, src_mask=batch.src_mask, src_lengths=batch.src_lengths, trg_mask=trg_mask)
kb_values = None
if (log_probs is None):
log_probs = self.generator(out, kb_probs=kb_probs, kb_values=kb_values)
if hasattr(batch, 'trgcanon'):
assert (not log_probs.requires_grad), "using batch.trgcanon shouldnt happen / be done during training (canonized data is used in the 'trg' field there)"
assert (log_probs.size((- 1)) == self.generator.output_size), (log_probs.shape, self.generator.output_size)
try:
batch_loss = loss_function(log_probs, trg)
except Exception as e:
print(f'batch_size: {batch_size}')
print(f'log_probs= {log_probs.shape}')
print(f'trg = {trg.shape}')
print(f)
print(f)
raise e
with self.Timer('debugging: greedy hypothesis:'):
mle_tokens = argmax(log_probs, dim=(- 1))
mle_tokens = mle_tokens.cpu().numpy()
print(f'proc_batch: Hypothesis: {self.trg_vocab.arrays_to_sentences(mle_tokens)[(- 1)]}')
print(f'
{('-' * 10)}GET LOSS FWD PASS: END current batch{('-' * 10)}
')
return batch_loss<|docstring|>Compute non-normalized loss and number of tokens for a batch
:param batch: batch to compute loss for
:param loss_function: loss function, computes for input and target
a scalar loss for the complete batch
:param max_output_length: maximum length of hypotheses
:param e_i: scheduled sampling probability of taking true label vs model generation at every decoding step
(https://arxiv.org/abs/1506.03099 Section 2.4)
:param greedy_threshold: only actually do greedy search once e_i is below this threshold
:return: batch_loss: sum of losses over non-pad elements in the batch<|endoftext|>
|
a7e7930bdc91a7d858a476af8509cad37c07f8631c62efe682ed8e3d2bb5ac78
|
def run_batch(self, batch: Batch, max_output_length: int, beam_size: int, beam_alpha: float) -> (np.array, np.array):
'\n Get outputs and attentions scores for a given batch\n\n :param batch: batch to generate hypotheses for\n :param max_output_length: maximum length of hypotheses\n :param beam_size: size of the beam for beam search, if 0 use greedy\n :param beam_alpha: alpha value for beam search\n :return: \n stacked_output: hypotheses for batch,\n stacked_attention_scores: attention scores for batch\n '
(encoder_output, encoder_hidden) = self.encode(batch.src, batch.src_lengths, batch.src_mask)
if (max_output_length is None):
max_output_length = int((max(batch.src_lengths.cpu().numpy()) * 1.5))
if hasattr(batch, 'kbsrc'):
(kb_keys, kb_values, kb_values_embed, kb_trv, kb_mask) = self.preprocess_batch_kb(batch, kbattdims=self.kb_att_dims)
if (kb_keys is None):
knowledgebase = None
else:
knowledgebase = (kb_keys, kb_values, kb_values_embed, kb_mask)
else:
knowledgebase = None
if (beam_size == 0):
(stacked_output, stacked_attention_scores, stacked_kb_att_scores, _) = greedy(encoder_hidden=encoder_hidden, encoder_output=encoder_output, src_mask=batch.src_mask, embed=self.trg_embed, bos_index=self.bos_index, decoder=self.decoder, generator=self.generator, max_output_length=max_output_length, knowledgebase=knowledgebase)
else:
(stacked_output, stacked_attention_scores, stacked_kb_att_scores) = beam_search(decoder=self.decoder, generator=self.generator, size=beam_size, encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=batch.src_mask, embed=self.trg_embed, max_output_length=max_output_length, alpha=beam_alpha, eos_index=self.eos_index, pad_index=self.pad_index, bos_index=self.bos_index, knowledgebase=knowledgebase)
if ((knowledgebase != None) and self.do_postproc):
with self.Timer('postprocessing hypotheses'):
stacked_output = self.postprocess_batch_hypotheses(stacked_output, stacked_kb_att_scores, kb_values, kb_trv)
print(f'proc_batch: Hypotheses: {self.trv_vocab.arrays_to_sentences(stacked_output)}')
else:
print(f'proc_batch: Hypotheses: {self.trg_vocab.arrays_to_sentences(stacked_output)}')
return (stacked_output, stacked_attention_scores, stacked_kb_att_scores)
|
Get outputs and attentions scores for a given batch
:param batch: batch to generate hypotheses for
:param max_output_length: maximum length of hypotheses
:param beam_size: size of the beam for beam search, if 0 use greedy
:param beam_alpha: alpha value for beam search
:return:
stacked_output: hypotheses for batch,
stacked_attention_scores: attention scores for batch
|
joeynmt/model.py
|
run_batch
|
marvosyntactical/joeynmt
| 3 |
python
|
def run_batch(self, batch: Batch, max_output_length: int, beam_size: int, beam_alpha: float) -> (np.array, np.array):
'\n Get outputs and attentions scores for a given batch\n\n :param batch: batch to generate hypotheses for\n :param max_output_length: maximum length of hypotheses\n :param beam_size: size of the beam for beam search, if 0 use greedy\n :param beam_alpha: alpha value for beam search\n :return: \n stacked_output: hypotheses for batch,\n stacked_attention_scores: attention scores for batch\n '
(encoder_output, encoder_hidden) = self.encode(batch.src, batch.src_lengths, batch.src_mask)
if (max_output_length is None):
max_output_length = int((max(batch.src_lengths.cpu().numpy()) * 1.5))
if hasattr(batch, 'kbsrc'):
(kb_keys, kb_values, kb_values_embed, kb_trv, kb_mask) = self.preprocess_batch_kb(batch, kbattdims=self.kb_att_dims)
if (kb_keys is None):
knowledgebase = None
else:
knowledgebase = (kb_keys, kb_values, kb_values_embed, kb_mask)
else:
knowledgebase = None
if (beam_size == 0):
(stacked_output, stacked_attention_scores, stacked_kb_att_scores, _) = greedy(encoder_hidden=encoder_hidden, encoder_output=encoder_output, src_mask=batch.src_mask, embed=self.trg_embed, bos_index=self.bos_index, decoder=self.decoder, generator=self.generator, max_output_length=max_output_length, knowledgebase=knowledgebase)
else:
(stacked_output, stacked_attention_scores, stacked_kb_att_scores) = beam_search(decoder=self.decoder, generator=self.generator, size=beam_size, encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=batch.src_mask, embed=self.trg_embed, max_output_length=max_output_length, alpha=beam_alpha, eos_index=self.eos_index, pad_index=self.pad_index, bos_index=self.bos_index, knowledgebase=knowledgebase)
if ((knowledgebase != None) and self.do_postproc):
with self.Timer('postprocessing hypotheses'):
stacked_output = self.postprocess_batch_hypotheses(stacked_output, stacked_kb_att_scores, kb_values, kb_trv)
print(f'proc_batch: Hypotheses: {self.trv_vocab.arrays_to_sentences(stacked_output)}')
else:
print(f'proc_batch: Hypotheses: {self.trg_vocab.arrays_to_sentences(stacked_output)}')
return (stacked_output, stacked_attention_scores, stacked_kb_att_scores)
|
def run_batch(self, batch: Batch, max_output_length: int, beam_size: int, beam_alpha: float) -> (np.array, np.array):
'\n Get outputs and attentions scores for a given batch\n\n :param batch: batch to generate hypotheses for\n :param max_output_length: maximum length of hypotheses\n :param beam_size: size of the beam for beam search, if 0 use greedy\n :param beam_alpha: alpha value for beam search\n :return: \n stacked_output: hypotheses for batch,\n stacked_attention_scores: attention scores for batch\n '
(encoder_output, encoder_hidden) = self.encode(batch.src, batch.src_lengths, batch.src_mask)
if (max_output_length is None):
max_output_length = int((max(batch.src_lengths.cpu().numpy()) * 1.5))
if hasattr(batch, 'kbsrc'):
(kb_keys, kb_values, kb_values_embed, kb_trv, kb_mask) = self.preprocess_batch_kb(batch, kbattdims=self.kb_att_dims)
if (kb_keys is None):
knowledgebase = None
else:
knowledgebase = (kb_keys, kb_values, kb_values_embed, kb_mask)
else:
knowledgebase = None
if (beam_size == 0):
(stacked_output, stacked_attention_scores, stacked_kb_att_scores, _) = greedy(encoder_hidden=encoder_hidden, encoder_output=encoder_output, src_mask=batch.src_mask, embed=self.trg_embed, bos_index=self.bos_index, decoder=self.decoder, generator=self.generator, max_output_length=max_output_length, knowledgebase=knowledgebase)
else:
(stacked_output, stacked_attention_scores, stacked_kb_att_scores) = beam_search(decoder=self.decoder, generator=self.generator, size=beam_size, encoder_output=encoder_output, encoder_hidden=encoder_hidden, src_mask=batch.src_mask, embed=self.trg_embed, max_output_length=max_output_length, alpha=beam_alpha, eos_index=self.eos_index, pad_index=self.pad_index, bos_index=self.bos_index, knowledgebase=knowledgebase)
if ((knowledgebase != None) and self.do_postproc):
with self.Timer('postprocessing hypotheses'):
stacked_output = self.postprocess_batch_hypotheses(stacked_output, stacked_kb_att_scores, kb_values, kb_trv)
print(f'proc_batch: Hypotheses: {self.trv_vocab.arrays_to_sentences(stacked_output)}')
else:
print(f'proc_batch: Hypotheses: {self.trg_vocab.arrays_to_sentences(stacked_output)}')
return (stacked_output, stacked_attention_scores, stacked_kb_att_scores)<|docstring|>Get outputs and attentions scores for a given batch
:param batch: batch to generate hypotheses for
:param max_output_length: maximum length of hypotheses
:param beam_size: size of the beam for beam search, if 0 use greedy
:param beam_alpha: alpha value for beam search
:return:
stacked_output: hypotheses for batch,
stacked_attention_scores: attention scores for batch<|endoftext|>
|
e8230c1dfb51b996f5844d0a5a32f9062fd50a52952d8c135a5f56a2e87237f6
|
def postprocess_batch_hypotheses(self, stacked_output, stacked_kb_att_scores, kb_values, kb_truval, pad_value=float('-inf')) -> np.array:
'\n called in self.run_batch() during knowledgebase task\n\n postprocesses batch hypotheses\n replaces kb value tokens such as @meeting_time with 7pm\n\n Arguments:\n :param stacked_output: array\n :param stacked_kb_att_scores: array\n :param kb_values: Tensor\n :param kb_truval: Tensor\n :param attention_pad_value: float indicating what parts of each attention matrix in the stacked_kb_att_score array to cut off in case of beam search\n :return: post_proc_stacked_output\n '
print(stacked_kb_att_scores.shape, kb_values.shape, kb_truval.shape)
kb_trv = kb_truval.cpu().numpy()[(0, :)]
kb_val = kb_values.cpu().numpy()[(0, :)]
kb_att = stacked_kb_att_scores
print('[[[[[[[[[[[[[[ START POSTPROC VALID/TEST BATCH ]]]]]]]]]]]]]]')
trvSent = self.trv_vocab.array_to_sentence
post_proc_stacked_output = []
outputs = stacked_output.tolist()
for (i, hyp) in enumerate(outputs):
post_proc_hyp = []
for (step, token) in enumerate(hyp):
if (token == self.eos_index):
break
if (token >= self.trg_vocab.canon_onwards):
kb_att_i = kb_att[i]
'\n if (kb_att_i == pad_value).any():\n # try to remove padded columns\n\n idx_first_pad_ = (kb_att_i==pad_val).non_zero(as_tuple)[:,0].min().item()\n kb_att_i = kb_att_i[:idx_first_pad_,:]\n '
str_tok = trvSent([token])
hypotSent = self.trg_vocab.array_to_sentence(hyp)
print(f'''
pp: {('=' * 10)} DECIDING REPLACEMENT FOR CANONICAL: {str_tok} {('=' * 10)}
''')
print(f'''pp: while deciding for hypothesis:
{hypotSent}''')
print(f'''pp: decoded hypothesis thus far:
{trvSent(post_proc_hyp)}''')
assert (str_tok[0] in hypotSent), (hyp, str_tok, hypotSent)
matching_trv_candidates = np.where((kb_val == token), kb_trv, (- 1))
print(f'''pp: matching_trv_candidates tokens (should belong to same canonical):
{trvSent(matching_trv_candidates[(matching_trv_candidates != (- 1))].tolist())}''')
if matching_trv_candidates[(matching_trv_candidates != (- 1))].shape[0]:
print(f'pp: SUCCESS! Found matches for canonical: {str_tok}')
try:
matching_scores = np.where((matching_trv_candidates != (- 1)), kb_att_i[(step, :)], float('-inf'))
except Exception as e:
print(stacked_kb_att_scores.shape)
raise e
print(f'''pp: matching_scores (should have no '-1's):
{matching_scores}''')
top_matching = np.argsort(matching_scores)[::(- 1)].copy()
top_match_candids = matching_trv_candidates[top_matching]
print(f'''pp: matching_trv_candidates in descending order of attention:
{trvSent(top_match_candids[(top_match_candids != (- 1))].tolist())}''')
top1_match = matching_trv_candidates[top_matching[0]]
print(f'''pp: top1_match:
{trvSent([top1_match])}''')
assert (top1_match != (- 1)), 'somehow selected true value with non matching canonical category, shouldnt happen'
post_proc_hyp.append(int(top1_match))
else:
print(f'pp: FAILURE! Found no matches for canonical: {str_tok}')
scores = kb_att_i[(step, :)]
hi_scores = np.argsort(scores)[::(- 1)].copy()
print(f'''pp: failure debug: highest attended tokens overall:
{trvSent(kb_trv[hi_scores].tolist())}''')
print(f'pp: CURRENT POLICY: REPLACING FOUND CANONICAL {str_tok} WITH NON-MATCHING HIGHEST ATTENDED')
top1_but_not_matching = kb_trv[hi_scores[0]]
post_proc_hyp.append(top1_but_not_matching)
print(f'''
pp: {('+' * 10)} DECIDED REPLACEMENT FOR CANONICAL: {str_tok}: {trvSent([post_proc_hyp[(- 1)]])} {('+' * 10)}
''')
else:
post_proc_hyp.append(token)
print(f'pp: finished hyp: {trvSent(post_proc_hyp)}, hyp past first <EOS> would be: {trvSent(post_proc_hyp, cut_at_eos=False)}')
post_proc_stacked_output.append(post_proc_hyp)
print()
print(f'''pp: raw hyps:
{self.trg_vocab.arrays_to_sentences(outputs)}''')
print()
print(f'''pp: post processed hyps:
{self.trv_vocab.arrays_to_sentences(post_proc_stacked_output)}''')
print()
print(f'pp: knowledgebase: {trvSent(kb_trv.tolist()[:40])}')
print()
print('[[[[[[[[[[[[[[ END POSTPROC VALID/TEST BATCH ]]]]]]]]]]]]]]')
post_proc_stacked_output = np.array(post_proc_stacked_output)
return post_proc_stacked_output
|
called in self.run_batch() during knowledgebase task
postprocesses batch hypotheses
replaces kb value tokens such as @meeting_time with 7pm
Arguments:
:param stacked_output: array
:param stacked_kb_att_scores: array
:param kb_values: Tensor
:param kb_truval: Tensor
:param attention_pad_value: float indicating what parts of each attention matrix in the stacked_kb_att_score array to cut off in case of beam search
:return: post_proc_stacked_output
|
joeynmt/model.py
|
postprocess_batch_hypotheses
|
marvosyntactical/joeynmt
| 3 |
python
|
def postprocess_batch_hypotheses(self, stacked_output, stacked_kb_att_scores, kb_values, kb_truval, pad_value=float('-inf')) -> np.array:
'\n called in self.run_batch() during knowledgebase task\n\n postprocesses batch hypotheses\n replaces kb value tokens such as @meeting_time with 7pm\n\n Arguments:\n :param stacked_output: array\n :param stacked_kb_att_scores: array\n :param kb_values: Tensor\n :param kb_truval: Tensor\n :param attention_pad_value: float indicating what parts of each attention matrix in the stacked_kb_att_score array to cut off in case of beam search\n :return: post_proc_stacked_output\n '
print(stacked_kb_att_scores.shape, kb_values.shape, kb_truval.shape)
kb_trv = kb_truval.cpu().numpy()[(0, :)]
kb_val = kb_values.cpu().numpy()[(0, :)]
kb_att = stacked_kb_att_scores
print('[[[[[[[[[[[[[[ START POSTPROC VALID/TEST BATCH ]]]]]]]]]]]]]]')
trvSent = self.trv_vocab.array_to_sentence
post_proc_stacked_output = []
outputs = stacked_output.tolist()
for (i, hyp) in enumerate(outputs):
post_proc_hyp = []
for (step, token) in enumerate(hyp):
if (token == self.eos_index):
break
if (token >= self.trg_vocab.canon_onwards):
kb_att_i = kb_att[i]
'\n if (kb_att_i == pad_value).any():\n # try to remove padded columns\n\n idx_first_pad_ = (kb_att_i==pad_val).non_zero(as_tuple)[:,0].min().item()\n kb_att_i = kb_att_i[:idx_first_pad_,:]\n '
str_tok = trvSent([token])
hypotSent = self.trg_vocab.array_to_sentence(hyp)
print(f'
pp: {('=' * 10)} DECIDING REPLACEMENT FOR CANONICAL: {str_tok} {('=' * 10)}
')
print(f'pp: while deciding for hypothesis:
{hypotSent}')
print(f'pp: decoded hypothesis thus far:
{trvSent(post_proc_hyp)}')
assert (str_tok[0] in hypotSent), (hyp, str_tok, hypotSent)
matching_trv_candidates = np.where((kb_val == token), kb_trv, (- 1))
print(f'pp: matching_trv_candidates tokens (should belong to same canonical):
{trvSent(matching_trv_candidates[(matching_trv_candidates != (- 1))].tolist())}')
if matching_trv_candidates[(matching_trv_candidates != (- 1))].shape[0]:
print(f'pp: SUCCESS! Found matches for canonical: {str_tok}')
try:
matching_scores = np.where((matching_trv_candidates != (- 1)), kb_att_i[(step, :)], float('-inf'))
except Exception as e:
print(stacked_kb_att_scores.shape)
raise e
print(f'pp: matching_scores (should have no '-1's):
{matching_scores}')
top_matching = np.argsort(matching_scores)[::(- 1)].copy()
top_match_candids = matching_trv_candidates[top_matching]
print(f'pp: matching_trv_candidates in descending order of attention:
{trvSent(top_match_candids[(top_match_candids != (- 1))].tolist())}')
top1_match = matching_trv_candidates[top_matching[0]]
print(f'pp: top1_match:
{trvSent([top1_match])}')
assert (top1_match != (- 1)), 'somehow selected true value with non matching canonical category, shouldnt happen'
post_proc_hyp.append(int(top1_match))
else:
print(f'pp: FAILURE! Found no matches for canonical: {str_tok}')
scores = kb_att_i[(step, :)]
hi_scores = np.argsort(scores)[::(- 1)].copy()
print(f'pp: failure debug: highest attended tokens overall:
{trvSent(kb_trv[hi_scores].tolist())}')
print(f'pp: CURRENT POLICY: REPLACING FOUND CANONICAL {str_tok} WITH NON-MATCHING HIGHEST ATTENDED')
top1_but_not_matching = kb_trv[hi_scores[0]]
post_proc_hyp.append(top1_but_not_matching)
print(f'
pp: {('+' * 10)} DECIDED REPLACEMENT FOR CANONICAL: {str_tok}: {trvSent([post_proc_hyp[(- 1)]])} {('+' * 10)}
')
else:
post_proc_hyp.append(token)
print(f'pp: finished hyp: {trvSent(post_proc_hyp)}, hyp past first <EOS> would be: {trvSent(post_proc_hyp, cut_at_eos=False)}')
post_proc_stacked_output.append(post_proc_hyp)
print()
print(f'pp: raw hyps:
{self.trg_vocab.arrays_to_sentences(outputs)}')
print()
print(f'pp: post processed hyps:
{self.trv_vocab.arrays_to_sentences(post_proc_stacked_output)}')
print()
print(f'pp: knowledgebase: {trvSent(kb_trv.tolist()[:40])}')
print()
print('[[[[[[[[[[[[[[ END POSTPROC VALID/TEST BATCH ]]]]]]]]]]]]]]')
post_proc_stacked_output = np.array(post_proc_stacked_output)
return post_proc_stacked_output
|
def postprocess_batch_hypotheses(self, stacked_output, stacked_kb_att_scores, kb_values, kb_truval, pad_value=float('-inf')) -> np.array:
'\n called in self.run_batch() during knowledgebase task\n\n postprocesses batch hypotheses\n replaces kb value tokens such as @meeting_time with 7pm\n\n Arguments:\n :param stacked_output: array\n :param stacked_kb_att_scores: array\n :param kb_values: Tensor\n :param kb_truval: Tensor\n :param attention_pad_value: float indicating what parts of each attention matrix in the stacked_kb_att_score array to cut off in case of beam search\n :return: post_proc_stacked_output\n '
print(stacked_kb_att_scores.shape, kb_values.shape, kb_truval.shape)
kb_trv = kb_truval.cpu().numpy()[(0, :)]
kb_val = kb_values.cpu().numpy()[(0, :)]
kb_att = stacked_kb_att_scores
print('[[[[[[[[[[[[[[ START POSTPROC VALID/TEST BATCH ]]]]]]]]]]]]]]')
trvSent = self.trv_vocab.array_to_sentence
post_proc_stacked_output = []
outputs = stacked_output.tolist()
for (i, hyp) in enumerate(outputs):
post_proc_hyp = []
for (step, token) in enumerate(hyp):
if (token == self.eos_index):
break
if (token >= self.trg_vocab.canon_onwards):
kb_att_i = kb_att[i]
'\n if (kb_att_i == pad_value).any():\n # try to remove padded columns\n\n idx_first_pad_ = (kb_att_i==pad_val).non_zero(as_tuple)[:,0].min().item()\n kb_att_i = kb_att_i[:idx_first_pad_,:]\n '
str_tok = trvSent([token])
hypotSent = self.trg_vocab.array_to_sentence(hyp)
print(f'
pp: {('=' * 10)} DECIDING REPLACEMENT FOR CANONICAL: {str_tok} {('=' * 10)}
')
print(f'pp: while deciding for hypothesis:
{hypotSent}')
print(f'pp: decoded hypothesis thus far:
{trvSent(post_proc_hyp)}')
assert (str_tok[0] in hypotSent), (hyp, str_tok, hypotSent)
matching_trv_candidates = np.where((kb_val == token), kb_trv, (- 1))
print(f'pp: matching_trv_candidates tokens (should belong to same canonical):
{trvSent(matching_trv_candidates[(matching_trv_candidates != (- 1))].tolist())}')
if matching_trv_candidates[(matching_trv_candidates != (- 1))].shape[0]:
print(f'pp: SUCCESS! Found matches for canonical: {str_tok}')
try:
matching_scores = np.where((matching_trv_candidates != (- 1)), kb_att_i[(step, :)], float('-inf'))
except Exception as e:
print(stacked_kb_att_scores.shape)
raise e
print(f'pp: matching_scores (should have no '-1's):
{matching_scores}')
top_matching = np.argsort(matching_scores)[::(- 1)].copy()
top_match_candids = matching_trv_candidates[top_matching]
print(f'pp: matching_trv_candidates in descending order of attention:
{trvSent(top_match_candids[(top_match_candids != (- 1))].tolist())}')
top1_match = matching_trv_candidates[top_matching[0]]
print(f'pp: top1_match:
{trvSent([top1_match])}')
assert (top1_match != (- 1)), 'somehow selected true value with non matching canonical category, shouldnt happen'
post_proc_hyp.append(int(top1_match))
else:
print(f'pp: FAILURE! Found no matches for canonical: {str_tok}')
scores = kb_att_i[(step, :)]
hi_scores = np.argsort(scores)[::(- 1)].copy()
print(f'pp: failure debug: highest attended tokens overall:
{trvSent(kb_trv[hi_scores].tolist())}')
print(f'pp: CURRENT POLICY: REPLACING FOUND CANONICAL {str_tok} WITH NON-MATCHING HIGHEST ATTENDED')
top1_but_not_matching = kb_trv[hi_scores[0]]
post_proc_hyp.append(top1_but_not_matching)
print(f'
pp: {('+' * 10)} DECIDED REPLACEMENT FOR CANONICAL: {str_tok}: {trvSent([post_proc_hyp[(- 1)]])} {('+' * 10)}
')
else:
post_proc_hyp.append(token)
print(f'pp: finished hyp: {trvSent(post_proc_hyp)}, hyp past first <EOS> would be: {trvSent(post_proc_hyp, cut_at_eos=False)}')
post_proc_stacked_output.append(post_proc_hyp)
print()
print(f'pp: raw hyps:
{self.trg_vocab.arrays_to_sentences(outputs)}')
print()
print(f'pp: post processed hyps:
{self.trv_vocab.arrays_to_sentences(post_proc_stacked_output)}')
print()
print(f'pp: knowledgebase: {trvSent(kb_trv.tolist()[:40])}')
print()
print('[[[[[[[[[[[[[[ END POSTPROC VALID/TEST BATCH ]]]]]]]]]]]]]]')
post_proc_stacked_output = np.array(post_proc_stacked_output)
return post_proc_stacked_output<|docstring|>called in self.run_batch() during knowledgebase task
postprocesses batch hypotheses
replaces kb value tokens such as @meeting_time with 7pm
Arguments:
:param stacked_output: array
:param stacked_kb_att_scores: array
:param kb_values: Tensor
:param kb_truval: Tensor
:param attention_pad_value: float indicating what parts of each attention matrix in the stacked_kb_att_score array to cut off in case of beam search
:return: post_proc_stacked_output<|endoftext|>
|
f8f84c47fbd654c028d7fa592970da3ed85315cd4bbcbe3eb31d134e78ec5677
|
def __repr__(self) -> str:
'\n String representation: a description of encoder, decoder and embeddings\n\n :return: string representation\n '
return ('%s(\n\tencoder=%s,\n\tdecoder=%s,\n\tsrc_embed=%s,\n\ttrg_embed=%s)' % (self.__class__.__name__, self.encoder, self.decoder, self.src_embed, self.trg_embed))
|
String representation: a description of encoder, decoder and embeddings
:return: string representation
|
joeynmt/model.py
|
__repr__
|
marvosyntactical/joeynmt
| 3 |
python
|
def __repr__(self) -> str:
'\n String representation: a description of encoder, decoder and embeddings\n\n :return: string representation\n '
return ('%s(\n\tencoder=%s,\n\tdecoder=%s,\n\tsrc_embed=%s,\n\ttrg_embed=%s)' % (self.__class__.__name__, self.encoder, self.decoder, self.src_embed, self.trg_embed))
|
def __repr__(self) -> str:
'\n String representation: a description of encoder, decoder and embeddings\n\n :return: string representation\n '
return ('%s(\n\tencoder=%s,\n\tdecoder=%s,\n\tsrc_embed=%s,\n\ttrg_embed=%s)' % (self.__class__.__name__, self.encoder, self.decoder, self.src_embed, self.trg_embed))<|docstring|>String representation: a description of encoder, decoder and embeddings
:return: string representation<|endoftext|>
|
cce786519a4d3f03af9aeb092282ab48e9a3bf094288f4d17bfbb0ab614c6a15
|
def add_message(request, msg, type='info'):
' Add status message\n\n Predefined message types\n\n * info\n\n * success\n\n * warning\n\n * error\n\n '
if (':' not in type):
type = ('message:%s' % type)
request.session.flash(render(request, type, msg), 'status')
|
Add status message
Predefined message types
* info
* success
* warning
* error
|
venv/Lib/site-packages/player/message.py
|
add_message
|
mandamg/Exercicios-de-Python-do-Curso-em-Video
| 0 |
python
|
def add_message(request, msg, type='info'):
' Add status message\n\n Predefined message types\n\n * info\n\n * success\n\n * warning\n\n * error\n\n '
if (':' not in type):
type = ('message:%s' % type)
request.session.flash(render(request, type, msg), 'status')
|
def add_message(request, msg, type='info'):
' Add status message\n\n Predefined message types\n\n * info\n\n * success\n\n * warning\n\n * error\n\n '
if (':' not in type):
type = ('message:%s' % type)
request.session.flash(render(request, type, msg), 'status')<|docstring|>Add status message
Predefined message types
* info
* success
* warning
* error<|endoftext|>
|
2eab19907938c4790a9087cc776b7aeb1ff3d68479b2c20103440e1229150771
|
def render_messages(request):
' Render previously added messages '
return ''.join(request.session.pop_flash('status'))
|
Render previously added messages
|
venv/Lib/site-packages/player/message.py
|
render_messages
|
mandamg/Exercicios-de-Python-do-Curso-em-Video
| 0 |
python
|
def render_messages(request):
' '
return .join(request.session.pop_flash('status'))
|
def render_messages(request):
' '
return .join(request.session.pop_flash('status'))<|docstring|>Render previously added messages<|endoftext|>
|
b3666e37bdd7c1c082daa42cc836b31d9be744b9160b560241aeac0dff49f428
|
@tmpl_filter('message:error')
def error_message(context, request):
' Error message filter '
if (not isinstance(context, (set, list, tuple))):
context = (context,)
errors = []
for err in context:
if isinstance(err, Exception):
err = ('%s: %s' % (err.__class__.__name__, escape(str(err), True)))
errors.append(err)
return {'errors': errors}
|
Error message filter
|
venv/Lib/site-packages/player/message.py
|
error_message
|
mandamg/Exercicios-de-Python-do-Curso-em-Video
| 0 |
python
|
@tmpl_filter('message:error')
def error_message(context, request):
' '
if (not isinstance(context, (set, list, tuple))):
context = (context,)
errors = []
for err in context:
if isinstance(err, Exception):
err = ('%s: %s' % (err.__class__.__name__, escape(str(err), True)))
errors.append(err)
return {'errors': errors}
|
@tmpl_filter('message:error')
def error_message(context, request):
' '
if (not isinstance(context, (set, list, tuple))):
context = (context,)
errors = []
for err in context:
if isinstance(err, Exception):
err = ('%s: %s' % (err.__class__.__name__, escape(str(err), True)))
errors.append(err)
return {'errors': errors}<|docstring|>Error message filter<|endoftext|>
|
d5e93d53d2ad6717a8b1d05f6a2dd96883669af9cdc8dd7dfc2b596ed95f64a5
|
def activate(self, kalman_filter, frame_id):
'Start a new tracklet'
self.kalman_filter = kalman_filter
self.track_id = self.next_id()
(self.mean, self.covariance) = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
self.tracklet_len = 0
self.state = TrackState.Tracked
self.frame_id = frame_id
self.start_frame = frame_id
|
Start a new tracklet
|
trackers/tracker_api.py
|
activate
|
jjandnn/AlphaPose
| 6,306 |
python
|
def activate(self, kalman_filter, frame_id):
self.kalman_filter = kalman_filter
self.track_id = self.next_id()
(self.mean, self.covariance) = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
self.tracklet_len = 0
self.state = TrackState.Tracked
self.frame_id = frame_id
self.start_frame = frame_id
|
def activate(self, kalman_filter, frame_id):
self.kalman_filter = kalman_filter
self.track_id = self.next_id()
(self.mean, self.covariance) = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
self.tracklet_len = 0
self.state = TrackState.Tracked
self.frame_id = frame_id
self.start_frame = frame_id<|docstring|>Start a new tracklet<|endoftext|>
|
9effb05ca1893440f78d0273d0dc6d05e09ec4e9b67498ce96a95fd4d7012931
|
def update(self, new_track, frame_id, update_feature=True):
'\n Update a matched track\n :type new_track: STrack\n :type frame_id: int\n :type update_feature: bool\n :return:\n '
self.frame_id = frame_id
self.tracklet_len += 1
self.pose = new_track.pose
self.detscore = new_track.detscore
self.crop_box = new_track.crop_box
self.file_name = new_track.file_name
new_tlwh = new_track.tlwh
(self.mean, self.covariance) = self.kalman_filter.update(self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
self.state = TrackState.Tracked
self.is_activated = True
self.score = new_track.score
if update_feature:
self.update_features(new_track.curr_feat)
|
Update a matched track
:type new_track: STrack
:type frame_id: int
:type update_feature: bool
:return:
|
trackers/tracker_api.py
|
update
|
jjandnn/AlphaPose
| 6,306 |
python
|
def update(self, new_track, frame_id, update_feature=True):
'\n Update a matched track\n :type new_track: STrack\n :type frame_id: int\n :type update_feature: bool\n :return:\n '
self.frame_id = frame_id
self.tracklet_len += 1
self.pose = new_track.pose
self.detscore = new_track.detscore
self.crop_box = new_track.crop_box
self.file_name = new_track.file_name
new_tlwh = new_track.tlwh
(self.mean, self.covariance) = self.kalman_filter.update(self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
self.state = TrackState.Tracked
self.is_activated = True
self.score = new_track.score
if update_feature:
self.update_features(new_track.curr_feat)
|
def update(self, new_track, frame_id, update_feature=True):
'\n Update a matched track\n :type new_track: STrack\n :type frame_id: int\n :type update_feature: bool\n :return:\n '
self.frame_id = frame_id
self.tracklet_len += 1
self.pose = new_track.pose
self.detscore = new_track.detscore
self.crop_box = new_track.crop_box
self.file_name = new_track.file_name
new_tlwh = new_track.tlwh
(self.mean, self.covariance) = self.kalman_filter.update(self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
self.state = TrackState.Tracked
self.is_activated = True
self.score = new_track.score
if update_feature:
self.update_features(new_track.curr_feat)<|docstring|>Update a matched track
:type new_track: STrack
:type frame_id: int
:type update_feature: bool
:return:<|endoftext|>
|
0eaa46df82d38b1c1b6a31fb41f8d026f5e70725602bda05c00aa65f5f91ca4b
|
@property
def tlwh(self):
'Get current position in bounding box format `(top left x, top left y,\n width, height)`.\n '
if (self.mean is None):
return self._tlwh.copy()
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= (ret[2:] / 2)
return ret
|
Get current position in bounding box format `(top left x, top left y,
width, height)`.
|
trackers/tracker_api.py
|
tlwh
|
jjandnn/AlphaPose
| 6,306 |
python
|
@property
def tlwh(self):
'Get current position in bounding box format `(top left x, top left y,\n width, height)`.\n '
if (self.mean is None):
return self._tlwh.copy()
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= (ret[2:] / 2)
return ret
|
@property
def tlwh(self):
'Get current position in bounding box format `(top left x, top left y,\n width, height)`.\n '
if (self.mean is None):
return self._tlwh.copy()
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= (ret[2:] / 2)
return ret<|docstring|>Get current position in bounding box format `(top left x, top left y,
width, height)`.<|endoftext|>
|
e3273e24b56241b12ef1783840c1a1e7ca455535f0a6ada7d7ab743b983040ea
|
@property
def tlbr(self):
'Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n `(top left, bottom right)`.\n '
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret
|
Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
|
trackers/tracker_api.py
|
tlbr
|
jjandnn/AlphaPose
| 6,306 |
python
|
@property
def tlbr(self):
'Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n `(top left, bottom right)`.\n '
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret
|
@property
def tlbr(self):
'Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n `(top left, bottom right)`.\n '
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret<|docstring|>Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.<|endoftext|>
|
27823033c4fc5a97a08046241da17d9a07bf7a105f3d485b2f1fff55e451f5a5
|
@staticmethod
def tlwh_to_xyah(tlwh):
'Convert bounding box to format `(center x, center y, aspect ratio,\n height)`, where the aspect ratio is `width / height`.\n '
ret = np.asarray(tlwh).copy()
ret[:2] += (ret[2:] / 2)
ret[2] /= ret[3]
return ret
|
Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
|
trackers/tracker_api.py
|
tlwh_to_xyah
|
jjandnn/AlphaPose
| 6,306 |
python
|
@staticmethod
def tlwh_to_xyah(tlwh):
'Convert bounding box to format `(center x, center y, aspect ratio,\n height)`, where the aspect ratio is `width / height`.\n '
ret = np.asarray(tlwh).copy()
ret[:2] += (ret[2:] / 2)
ret[2] /= ret[3]
return ret
|
@staticmethod
def tlwh_to_xyah(tlwh):
'Convert bounding box to format `(center x, center y, aspect ratio,\n height)`, where the aspect ratio is `width / height`.\n '
ret = np.asarray(tlwh).copy()
ret[:2] += (ret[2:] / 2)
ret[2] /= ret[3]
return ret<|docstring|>Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.<|endoftext|>
|
fac41b10474139c6b3836cab025ec20ff66fadbab8c9d3e04f76428082bd9ac6
|
def fetch_texture1D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (W,) or (W,C,) or (B,W,C,)\n uv : Tensor\n the input parameter tensor with shape (N,) or (B,N,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the (N,C,) or (B,N,C,) fetched data from the input texture\n "
return torch.t(torch.reshape(grid_sample(torch.reshape(torch.t(texture), (1, 3, (- 1), 1)), torch.reshape(torch.cat((torch.zeros(numel(uv), 1, dtype=torch.float, device=texture.device), to_column(((uv * 2) - 1))), dim=1), (1, 1, (- 1), 2)), mode=mode, padding_mode='border'), (3, numel(uv))))
|
Fetches an input texture using the given UVs in range [0,1]
Parameters
----------
texture : Tensor
the input texture tensor with shape (W,) or (W,C,) or (B,W,C,)
uv : Tensor
the input parameter tensor with shape (N,) or (B,N,)
mode : str (optional)
interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.
Returns
-------
Tensor
the (N,C,) or (B,N,C,) fetched data from the input texture
|
ACME/color/fetch_texture.py
|
fetch_texture1D
|
mauriziokovacic/ACME
| 3 |
python
|
def fetch_texture1D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (W,) or (W,C,) or (B,W,C,)\n uv : Tensor\n the input parameter tensor with shape (N,) or (B,N,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the (N,C,) or (B,N,C,) fetched data from the input texture\n "
return torch.t(torch.reshape(grid_sample(torch.reshape(torch.t(texture), (1, 3, (- 1), 1)), torch.reshape(torch.cat((torch.zeros(numel(uv), 1, dtype=torch.float, device=texture.device), to_column(((uv * 2) - 1))), dim=1), (1, 1, (- 1), 2)), mode=mode, padding_mode='border'), (3, numel(uv))))
|
def fetch_texture1D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (W,) or (W,C,) or (B,W,C,)\n uv : Tensor\n the input parameter tensor with shape (N,) or (B,N,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the (N,C,) or (B,N,C,) fetched data from the input texture\n "
return torch.t(torch.reshape(grid_sample(torch.reshape(torch.t(texture), (1, 3, (- 1), 1)), torch.reshape(torch.cat((torch.zeros(numel(uv), 1, dtype=torch.float, device=texture.device), to_column(((uv * 2) - 1))), dim=1), (1, 1, (- 1), 2)), mode=mode, padding_mode='border'), (3, numel(uv))))<|docstring|>Fetches an input texture using the given UVs in range [0,1]
Parameters
----------
texture : Tensor
the input texture tensor with shape (W,) or (W,C,) or (B,W,C,)
uv : Tensor
the input parameter tensor with shape (N,) or (B,N,)
mode : str (optional)
interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.
Returns
-------
Tensor
the (N,C,) or (B,N,C,) fetched data from the input texture<|endoftext|>
|
da4b43d1b0e984d494dc55472c5f7db340930c48068dc62d6323892ed9081273
|
def fetch_texture2D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (W,H,) or (C,W,H,) or (B,C,W,H,)\n uv : Tensor\n the input UV tensor with shape (N,2,) or (B,N,2,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the (N,C,) or (B,N,C,) fetched data from the input texture\n "
t = texture
if (t.ndimension() < 3):
t = t.view(1, *t.shape)
if (t.ndimension() < 4):
t = t.view(1, *t.shape)
c = ((uv * 2) - 1)
if (c.ndimension() < 3):
c = c.expand(t.shape[0], *c.shape)
if (c.ndimension() < 4):
c = c.view(c.shape[0], 1, *c.shape[1:])
if (t.shape[0] < c.shape[0]):
t = t.expand(c.shape[0], *t.shape[1:])
return torch.transpose(grid_sample(t, c, mode=mode, padding_mode='border').squeeze(), (- 1), (- 2))
|
Fetches an input texture using the given UVs in range [0,1]
Parameters
----------
texture : Tensor
the input texture tensor with shape (W,H,) or (C,W,H,) or (B,C,W,H,)
uv : Tensor
the input UV tensor with shape (N,2,) or (B,N,2,)
mode : str (optional)
interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.
Returns
-------
Tensor
the (N,C,) or (B,N,C,) fetched data from the input texture
|
ACME/color/fetch_texture.py
|
fetch_texture2D
|
mauriziokovacic/ACME
| 3 |
python
|
def fetch_texture2D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (W,H,) or (C,W,H,) or (B,C,W,H,)\n uv : Tensor\n the input UV tensor with shape (N,2,) or (B,N,2,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the (N,C,) or (B,N,C,) fetched data from the input texture\n "
t = texture
if (t.ndimension() < 3):
t = t.view(1, *t.shape)
if (t.ndimension() < 4):
t = t.view(1, *t.shape)
c = ((uv * 2) - 1)
if (c.ndimension() < 3):
c = c.expand(t.shape[0], *c.shape)
if (c.ndimension() < 4):
c = c.view(c.shape[0], 1, *c.shape[1:])
if (t.shape[0] < c.shape[0]):
t = t.expand(c.shape[0], *t.shape[1:])
return torch.transpose(grid_sample(t, c, mode=mode, padding_mode='border').squeeze(), (- 1), (- 2))
|
def fetch_texture2D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (W,H,) or (C,W,H,) or (B,C,W,H,)\n uv : Tensor\n the input UV tensor with shape (N,2,) or (B,N,2,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the (N,C,) or (B,N,C,) fetched data from the input texture\n "
t = texture
if (t.ndimension() < 3):
t = t.view(1, *t.shape)
if (t.ndimension() < 4):
t = t.view(1, *t.shape)
c = ((uv * 2) - 1)
if (c.ndimension() < 3):
c = c.expand(t.shape[0], *c.shape)
if (c.ndimension() < 4):
c = c.view(c.shape[0], 1, *c.shape[1:])
if (t.shape[0] < c.shape[0]):
t = t.expand(c.shape[0], *t.shape[1:])
return torch.transpose(grid_sample(t, c, mode=mode, padding_mode='border').squeeze(), (- 1), (- 2))<|docstring|>Fetches an input texture using the given UVs in range [0,1]
Parameters
----------
texture : Tensor
the input texture tensor with shape (W,H,) or (C,W,H,) or (B,C,W,H,)
uv : Tensor
the input UV tensor with shape (N,2,) or (B,N,2,)
mode : str (optional)
interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.
Returns
-------
Tensor
the (N,C,) or (B,N,C,) fetched data from the input texture<|endoftext|>
|
2853ce3f7533a65488e13b1f755bc5bdc6224f18f4345edd3b7704a03f452ecb
|
def fetch_texture3D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (C,W,H,D)\n uv : Tensor\n the input UV tensor with shape (N,3,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the fetched data from the input texture\n "
return torch.reshape(grid_sample(texture.unsqueeze(0), torch.reshape(((uv * 2) - 1), (1, 1, 1, (- 1), 3)), mode=mode, padding_mode='border').squeeze(0), ((- 1), texture.shape[0]))
|
Fetches an input texture using the given UVs in range [0,1]
Parameters
----------
texture : Tensor
the input texture tensor with shape (C,W,H,D)
uv : Tensor
the input UV tensor with shape (N,3,)
mode : str (optional)
interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.
Returns
-------
Tensor
the fetched data from the input texture
|
ACME/color/fetch_texture.py
|
fetch_texture3D
|
mauriziokovacic/ACME
| 3 |
python
|
def fetch_texture3D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (C,W,H,D)\n uv : Tensor\n the input UV tensor with shape (N,3,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the fetched data from the input texture\n "
return torch.reshape(grid_sample(texture.unsqueeze(0), torch.reshape(((uv * 2) - 1), (1, 1, 1, (- 1), 3)), mode=mode, padding_mode='border').squeeze(0), ((- 1), texture.shape[0]))
|
def fetch_texture3D(texture, uv, mode='bilinear'):
"\n Fetches an input texture using the given UVs in range [0,1]\n\n Parameters\n ----------\n texture : Tensor\n the input texture tensor with shape (C,W,H,D)\n uv : Tensor\n the input UV tensor with shape (N,3,)\n mode : str (optional)\n interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.\n\n Returns\n -------\n Tensor\n the fetched data from the input texture\n "
return torch.reshape(grid_sample(texture.unsqueeze(0), torch.reshape(((uv * 2) - 1), (1, 1, 1, (- 1), 3)), mode=mode, padding_mode='border').squeeze(0), ((- 1), texture.shape[0]))<|docstring|>Fetches an input texture using the given UVs in range [0,1]
Parameters
----------
texture : Tensor
the input texture tensor with shape (C,W,H,D)
uv : Tensor
the input UV tensor with shape (N,3,)
mode : str (optional)
interpolation method. Only 'bilinear' or 'nearest' are accepted. Only 'bilinear' retains the gradient.
Returns
-------
Tensor
the fetched data from the input texture<|endoftext|>
|
b4c63ff939b5e44596dac9c60f2d03a96486886793dcdd5ae07e27c16773d2ec
|
def tree(self, path):
'\n .. versionadded:: 2014.7.0\n\n Recurse through etcd and return all values\n '
ret = {}
try:
items = self.read(path)
except (etcd.EtcdKeyNotFound, ValueError):
return None
except etcd.EtcdConnectionFailed:
log.error("etcd: failed to perform 'tree' operation on path %s due to connection error", path)
return None
for item in items.children:
comps = str(item.key).split('/')
if (item.dir is True):
if (item.key == path):
continue
ret[comps[(- 1)]] = self.tree(item.key)
else:
ret[comps[(- 1)]] = item.value
return ret
|
.. versionadded:: 2014.7.0
Recurse through etcd and return all values
|
salt/utils/etcd_util.py
|
tree
|
Flowdalic/salt
| 9,425 |
python
|
def tree(self, path):
'\n .. versionadded:: 2014.7.0\n\n Recurse through etcd and return all values\n '
ret = {}
try:
items = self.read(path)
except (etcd.EtcdKeyNotFound, ValueError):
return None
except etcd.EtcdConnectionFailed:
log.error("etcd: failed to perform 'tree' operation on path %s due to connection error", path)
return None
for item in items.children:
comps = str(item.key).split('/')
if (item.dir is True):
if (item.key == path):
continue
ret[comps[(- 1)]] = self.tree(item.key)
else:
ret[comps[(- 1)]] = item.value
return ret
|
def tree(self, path):
'\n .. versionadded:: 2014.7.0\n\n Recurse through etcd and return all values\n '
ret = {}
try:
items = self.read(path)
except (etcd.EtcdKeyNotFound, ValueError):
return None
except etcd.EtcdConnectionFailed:
log.error("etcd: failed to perform 'tree' operation on path %s due to connection error", path)
return None
for item in items.children:
comps = str(item.key).split('/')
if (item.dir is True):
if (item.key == path):
continue
ret[comps[(- 1)]] = self.tree(item.key)
else:
ret[comps[(- 1)]] = item.value
return ret<|docstring|>.. versionadded:: 2014.7.0
Recurse through etcd and return all values<|endoftext|>
|
a93ee4fa2613e4f046e25066c681e4e186eee9bab78c8e58031d8f914838705e
|
def save_file(notif_file, data):
'\n Overwrite the file with the new information\n '
notif_file.seek(0)
header = data[0].keys()
writer = csv.DictWriter(notif_file, fieldnames=header)
writer.writeheader()
writer.writerows(data)
notif_file.flush()
|
Overwrite the file with the new information
|
Chapter10/send_notifications.py
|
save_file
|
PacktPublishing/Python-Automation-Cookbook--Second-Edition
| 155 |
python
|
def save_file(notif_file, data):
'\n \n '
notif_file.seek(0)
header = data[0].keys()
writer = csv.DictWriter(notif_file, fieldnames=header)
writer.writeheader()
writer.writerows(data)
notif_file.flush()
|
def save_file(notif_file, data):
'\n \n '
notif_file.seek(0)
header = data[0].keys()
writer = csv.DictWriter(notif_file, fieldnames=header)
writer.writeheader()
writer.writerows(data)
notif_file.flush()<|docstring|>Overwrite the file with the new information<|endoftext|>
|
5a4193f00c1bdfc356ae3094f8aaab3f0f15c42a19d9245111bd0d1e6aae8358
|
def int_ceil(x: int, y: int) -> int:
'\n equivalent to math.ceil(x / y)\n :param x:\n :param y:\n :return:\n '
(q, r) = divmod(x, y)
if r:
q += 1
return q
|
equivalent to math.ceil(x / y)
:param x:
:param y:
:return:
|
paginatify/__init__.py
|
int_ceil
|
coint-hub/paginatify-sqlalchemy
| 0 |
python
|
def int_ceil(x: int, y: int) -> int:
'\n equivalent to math.ceil(x / y)\n :param x:\n :param y:\n :return:\n '
(q, r) = divmod(x, y)
if r:
q += 1
return q
|
def int_ceil(x: int, y: int) -> int:
'\n equivalent to math.ceil(x / y)\n :param x:\n :param y:\n :return:\n '
(q, r) = divmod(x, y)
if r:
q += 1
return q<|docstring|>equivalent to math.ceil(x / y)
:param x:
:param y:
:return:<|endoftext|>
|
ff51d48df9b9839a9deaaa34091a17a67ebba05e8ddf5272ac8916e27cae9e9e
|
def fetch_resolved_tokens(path, pattern, default_expression=DEFAULT_EXPRESSION, match_start=True, match_end=True):
'Return resolved tokens from *path* and *pattern* if compatible.\n\n For instance::\n\n >>> fetch_resolved_tokens(\n ... "/path/my_project/ep002/sh003/scripts",\n ... "/path/{project}/{episode:ep\\d+}/{shot:sh\\d+}/scripts"\n ... )\n {\n "project": "my_project",\n "episode": "ep002",\n "shot": "sh003"\n }\n\n If the *path* and *pattern* are compatible, but the *pattern* does not\n specify any tokens, an empty mapping will be returned.\n\n >>> fetch_resolved_tokens(\n ... "/path/project/scripts",\n ... "/path/project/scripts"\n ... )\n {}\n\n If the *path* and *pattern* are not compatible, None is returned.\n\n >>> fetch_resolved_tokens(\n ... "/path/my_project/build/character/scripts",\n ... "/path/{project}/{episode:ep\\d+}/{shot:sh\\d+}/scripts"\n ... )\n None\n\n :param path: Path to compare template pattern to.\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :param default_expression: Regular expression pattern to use for tokens\n when no expression is specified. Default is\n :data:`nomanclator.symbol.DEFAULT_EXPRESSION`.\n\n :param match_start: Indicate whether the *path* should match against the\n start of the *pattern*. Default is True.\n\n :param match_end: Indicate whether the *path* should match against the\n end of the *pattern*. Default is True.\n\n :return: Mapping regrouping resolved token value associated with\n their name, or None if the *path* and *pattern* are not compatible.\n\n '
regex = construct_regexp(pattern, default_expression=default_expression, match_start=match_start, match_end=match_end)
match = regex.search(path)
if match:
return match.groupdict()
|
Return resolved tokens from *path* and *pattern* if compatible.
For instance::
>>> fetch_resolved_tokens(
... "/path/my_project/ep002/sh003/scripts",
... "/path/{project}/{episode:ep\d+}/{shot:sh\d+}/scripts"
... )
{
"project": "my_project",
"episode": "ep002",
"shot": "sh003"
}
If the *path* and *pattern* are compatible, but the *pattern* does not
specify any tokens, an empty mapping will be returned.
>>> fetch_resolved_tokens(
... "/path/project/scripts",
... "/path/project/scripts"
... )
{}
If the *path* and *pattern* are not compatible, None is returned.
>>> fetch_resolved_tokens(
... "/path/my_project/build/character/scripts",
... "/path/{project}/{episode:ep\d+}/{shot:sh\d+}/scripts"
... )
None
:param path: Path to compare template pattern to.
:param pattern: String representing a template pattern path,
with or without tokens.
:param default_expression: Regular expression pattern to use for tokens
when no expression is specified. Default is
:data:`nomanclator.symbol.DEFAULT_EXPRESSION`.
:param match_start: Indicate whether the *path* should match against the
start of the *pattern*. Default is True.
:param match_end: Indicate whether the *path* should match against the
end of the *pattern*. Default is True.
:return: Mapping regrouping resolved token value associated with
their name, or None if the *path* and *pattern* are not compatible.
|
source/nomenclator/template.py
|
fetch_resolved_tokens
|
buddly27/nomenclator-nuke
| 11 |
python
|
def fetch_resolved_tokens(path, pattern, default_expression=DEFAULT_EXPRESSION, match_start=True, match_end=True):
'Return resolved tokens from *path* and *pattern* if compatible.\n\n For instance::\n\n >>> fetch_resolved_tokens(\n ... "/path/my_project/ep002/sh003/scripts",\n ... "/path/{project}/{episode:ep\\d+}/{shot:sh\\d+}/scripts"\n ... )\n {\n "project": "my_project",\n "episode": "ep002",\n "shot": "sh003"\n }\n\n If the *path* and *pattern* are compatible, but the *pattern* does not\n specify any tokens, an empty mapping will be returned.\n\n >>> fetch_resolved_tokens(\n ... "/path/project/scripts",\n ... "/path/project/scripts"\n ... )\n {}\n\n If the *path* and *pattern* are not compatible, None is returned.\n\n >>> fetch_resolved_tokens(\n ... "/path/my_project/build/character/scripts",\n ... "/path/{project}/{episode:ep\\d+}/{shot:sh\\d+}/scripts"\n ... )\n None\n\n :param path: Path to compare template pattern to.\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :param default_expression: Regular expression pattern to use for tokens\n when no expression is specified. Default is\n :data:`nomanclator.symbol.DEFAULT_EXPRESSION`.\n\n :param match_start: Indicate whether the *path* should match against the\n start of the *pattern*. Default is True.\n\n :param match_end: Indicate whether the *path* should match against the\n end of the *pattern*. Default is True.\n\n :return: Mapping regrouping resolved token value associated with\n their name, or None if the *path* and *pattern* are not compatible.\n\n '
regex = construct_regexp(pattern, default_expression=default_expression, match_start=match_start, match_end=match_end)
match = regex.search(path)
if match:
return match.groupdict()
|
def fetch_resolved_tokens(path, pattern, default_expression=DEFAULT_EXPRESSION, match_start=True, match_end=True):
'Return resolved tokens from *path* and *pattern* if compatible.\n\n For instance::\n\n >>> fetch_resolved_tokens(\n ... "/path/my_project/ep002/sh003/scripts",\n ... "/path/{project}/{episode:ep\\d+}/{shot:sh\\d+}/scripts"\n ... )\n {\n "project": "my_project",\n "episode": "ep002",\n "shot": "sh003"\n }\n\n If the *path* and *pattern* are compatible, but the *pattern* does not\n specify any tokens, an empty mapping will be returned.\n\n >>> fetch_resolved_tokens(\n ... "/path/project/scripts",\n ... "/path/project/scripts"\n ... )\n {}\n\n If the *path* and *pattern* are not compatible, None is returned.\n\n >>> fetch_resolved_tokens(\n ... "/path/my_project/build/character/scripts",\n ... "/path/{project}/{episode:ep\\d+}/{shot:sh\\d+}/scripts"\n ... )\n None\n\n :param path: Path to compare template pattern to.\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :param default_expression: Regular expression pattern to use for tokens\n when no expression is specified. Default is\n :data:`nomanclator.symbol.DEFAULT_EXPRESSION`.\n\n :param match_start: Indicate whether the *path* should match against the\n start of the *pattern*. Default is True.\n\n :param match_end: Indicate whether the *path* should match against the\n end of the *pattern*. Default is True.\n\n :return: Mapping regrouping resolved token value associated with\n their name, or None if the *path* and *pattern* are not compatible.\n\n '
regex = construct_regexp(pattern, default_expression=default_expression, match_start=match_start, match_end=match_end)
match = regex.search(path)
if match:
return match.groupdict()<|docstring|>Return resolved tokens from *path* and *pattern* if compatible.
For instance::
>>> fetch_resolved_tokens(
... "/path/my_project/ep002/sh003/scripts",
... "/path/{project}/{episode:ep\d+}/{shot:sh\d+}/scripts"
... )
{
"project": "my_project",
"episode": "ep002",
"shot": "sh003"
}
If the *path* and *pattern* are compatible, but the *pattern* does not
specify any tokens, an empty mapping will be returned.
>>> fetch_resolved_tokens(
... "/path/project/scripts",
... "/path/project/scripts"
... )
{}
If the *path* and *pattern* are not compatible, None is returned.
>>> fetch_resolved_tokens(
... "/path/my_project/build/character/scripts",
... "/path/{project}/{episode:ep\d+}/{shot:sh\d+}/scripts"
... )
None
:param path: Path to compare template pattern to.
:param pattern: String representing a template pattern path,
with or without tokens.
:param default_expression: Regular expression pattern to use for tokens
when no expression is specified. Default is
:data:`nomanclator.symbol.DEFAULT_EXPRESSION`.
:param match_start: Indicate whether the *path* should match against the
start of the *pattern*. Default is True.
:param match_end: Indicate whether the *path* should match against the
end of the *pattern*. Default is True.
:return: Mapping regrouping resolved token value associated with
their name, or None if the *path* and *pattern* are not compatible.<|endoftext|>
|
796697e69b6cb57a40fa75ac482a1a0cf1f84a7ec6207b8a48930fd6b09dcaa0
|
def construct_regexp(pattern, default_expression=DEFAULT_EXPRESSION, match_start=True, match_end=True):
'Return template pattern converted into a regular expression.\n\n For instance::\n\n >>> construct_regexp("/path/{project}/{episode:ep\\d+}")\n re.compile(r"^/path/(?P<project>[\\w_.-]+)/(?P<episode>ep\\d+)$")\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :param default_expression: Regular expression pattern to use for tokens\n when no expression is specified. Default is\n :data:`nomanclator.symbol.DEFAULT_TOKEN_EXPRESSION`.\n\n :param match_start: Indicate whether the regular expression returned\n should match against the start of an input. Default is True.\n\n :param match_end: Indicate whether the regular expression returned\n should match against the end of an input. Default is True.\n\n :return: Compiled regular expression.\n\n '
pattern = sanitize_pattern(pattern)
def _convert(match):
'Return corresponding regular expression.'
name = match.group('name')
expression = (match.group('expression') or default_expression)
return '(?P<{0}>{1})'.format(name, expression)
sub_pattern = '{(?P<name>.+?)(:(?P<expression>.+?))?}'
pattern = re.sub(sub_pattern, _convert, pattern)
if match_start:
pattern = ('^' + pattern)
if match_end:
pattern += '$'
return re.compile(pattern)
|
Return template pattern converted into a regular expression.
For instance::
>>> construct_regexp("/path/{project}/{episode:ep\d+}")
re.compile(r"^/path/(?P<project>[\w_.-]+)/(?P<episode>ep\d+)$")
:param pattern: String representing a template pattern path,
with or without tokens.
:param default_expression: Regular expression pattern to use for tokens
when no expression is specified. Default is
:data:`nomanclator.symbol.DEFAULT_TOKEN_EXPRESSION`.
:param match_start: Indicate whether the regular expression returned
should match against the start of an input. Default is True.
:param match_end: Indicate whether the regular expression returned
should match against the end of an input. Default is True.
:return: Compiled regular expression.
|
source/nomenclator/template.py
|
construct_regexp
|
buddly27/nomenclator-nuke
| 11 |
python
|
def construct_regexp(pattern, default_expression=DEFAULT_EXPRESSION, match_start=True, match_end=True):
'Return template pattern converted into a regular expression.\n\n For instance::\n\n >>> construct_regexp("/path/{project}/{episode:ep\\d+}")\n re.compile(r"^/path/(?P<project>[\\w_.-]+)/(?P<episode>ep\\d+)$")\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :param default_expression: Regular expression pattern to use for tokens\n when no expression is specified. Default is\n :data:`nomanclator.symbol.DEFAULT_TOKEN_EXPRESSION`.\n\n :param match_start: Indicate whether the regular expression returned\n should match against the start of an input. Default is True.\n\n :param match_end: Indicate whether the regular expression returned\n should match against the end of an input. Default is True.\n\n :return: Compiled regular expression.\n\n '
pattern = sanitize_pattern(pattern)
def _convert(match):
'Return corresponding regular expression.'
name = match.group('name')
expression = (match.group('expression') or default_expression)
return '(?P<{0}>{1})'.format(name, expression)
sub_pattern = '{(?P<name>.+?)(:(?P<expression>.+?))?}'
pattern = re.sub(sub_pattern, _convert, pattern)
if match_start:
pattern = ('^' + pattern)
if match_end:
pattern += '$'
return re.compile(pattern)
|
def construct_regexp(pattern, default_expression=DEFAULT_EXPRESSION, match_start=True, match_end=True):
'Return template pattern converted into a regular expression.\n\n For instance::\n\n >>> construct_regexp("/path/{project}/{episode:ep\\d+}")\n re.compile(r"^/path/(?P<project>[\\w_.-]+)/(?P<episode>ep\\d+)$")\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :param default_expression: Regular expression pattern to use for tokens\n when no expression is specified. Default is\n :data:`nomanclator.symbol.DEFAULT_TOKEN_EXPRESSION`.\n\n :param match_start: Indicate whether the regular expression returned\n should match against the start of an input. Default is True.\n\n :param match_end: Indicate whether the regular expression returned\n should match against the end of an input. Default is True.\n\n :return: Compiled regular expression.\n\n '
pattern = sanitize_pattern(pattern)
def _convert(match):
'Return corresponding regular expression.'
name = match.group('name')
expression = (match.group('expression') or default_expression)
return '(?P<{0}>{1})'.format(name, expression)
sub_pattern = '{(?P<name>.+?)(:(?P<expression>.+?))?}'
pattern = re.sub(sub_pattern, _convert, pattern)
if match_start:
pattern = ('^' + pattern)
if match_end:
pattern += '$'
return re.compile(pattern)<|docstring|>Return template pattern converted into a regular expression.
For instance::
>>> construct_regexp("/path/{project}/{episode:ep\d+}")
re.compile(r"^/path/(?P<project>[\w_.-]+)/(?P<episode>ep\d+)$")
:param pattern: String representing a template pattern path,
with or without tokens.
:param default_expression: Regular expression pattern to use for tokens
when no expression is specified. Default is
:data:`nomanclator.symbol.DEFAULT_TOKEN_EXPRESSION`.
:param match_start: Indicate whether the regular expression returned
should match against the start of an input. Default is True.
:param match_end: Indicate whether the regular expression returned
should match against the end of an input. Default is True.
:return: Compiled regular expression.<|endoftext|>
|
082e0e67cfd3a9d3e8f127a42c6ce15297c153e721b765d90f3436163cfc7ad2
|
def sanitize_pattern(pattern):
'Return template pattern with all special characters escaped.\n\n Tokens name and expressions are returned unchanged.\n\n For instance::\n\n >>> sanitize_pattern("/path*/{job:J_.*}")\n "/path\\*/{job:J_.*}"\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :return: Sanitized string template pattern.\n\n '
def _escape(match):
"Escape 'other' group value if required."
groups = match.groupdict()
if (groups['other'] is not None):
return re.escape(groups['other'])
return groups['token']
sub_pattern = '(?P<token>{(.+?)(:.+?)?})|(?P<other>.+?)'
return re.sub(sub_pattern, _escape, pattern)
|
Return template pattern with all special characters escaped.
Tokens name and expressions are returned unchanged.
For instance::
>>> sanitize_pattern("/path*/{job:J_.*}")
"/path\*/{job:J_.*}"
:param pattern: String representing a template pattern path,
with or without tokens.
:return: Sanitized string template pattern.
|
source/nomenclator/template.py
|
sanitize_pattern
|
buddly27/nomenclator-nuke
| 11 |
python
|
def sanitize_pattern(pattern):
'Return template pattern with all special characters escaped.\n\n Tokens name and expressions are returned unchanged.\n\n For instance::\n\n >>> sanitize_pattern("/path*/{job:J_.*}")\n "/path\\*/{job:J_.*}"\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :return: Sanitized string template pattern.\n\n '
def _escape(match):
"Escape 'other' group value if required."
groups = match.groupdict()
if (groups['other'] is not None):
return re.escape(groups['other'])
return groups['token']
sub_pattern = '(?P<token>{(.+?)(:.+?)?})|(?P<other>.+?)'
return re.sub(sub_pattern, _escape, pattern)
|
def sanitize_pattern(pattern):
'Return template pattern with all special characters escaped.\n\n Tokens name and expressions are returned unchanged.\n\n For instance::\n\n >>> sanitize_pattern("/path*/{job:J_.*}")\n "/path\\*/{job:J_.*}"\n\n :param pattern: String representing a template pattern path,\n with or without tokens.\n\n :return: Sanitized string template pattern.\n\n '
def _escape(match):
"Escape 'other' group value if required."
groups = match.groupdict()
if (groups['other'] is not None):
return re.escape(groups['other'])
return groups['token']
sub_pattern = '(?P<token>{(.+?)(:.+?)?})|(?P<other>.+?)'
return re.sub(sub_pattern, _escape, pattern)<|docstring|>Return template pattern with all special characters escaped.
Tokens name and expressions are returned unchanged.
For instance::
>>> sanitize_pattern("/path*/{job:J_.*}")
"/path\*/{job:J_.*}"
:param pattern: String representing a template pattern path,
with or without tokens.
:return: Sanitized string template pattern.<|endoftext|>
|
9e458897c5aa8afc4e6a1fe0b2da3c2996b543884044d02153df9d231a89e69d
|
def generate_scene_name(pattern, suffix, append_username=False, token_mapping=None):
'Generate scene name from *pattern* using a mapping of resolved tokens.\n\n :param pattern: String representing a template base,\n with or without tokens.\n\n :param suffix: Suffix to apply for the generated name (e.g. "nk" or "hrox").\n\n :param append_username: Indicate whether username should be appended to base name.\n Default is False.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name. Default is None.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
if append_username:
pattern += '_{username}'
pattern += '.{}'.format(suffix)
return resolve(pattern, (token_mapping or {}))
|
Generate scene name from *pattern* using a mapping of resolved tokens.
:param pattern: String representing a template base,
with or without tokens.
:param suffix: Suffix to apply for the generated name (e.g. "nk" or "hrox").
:param append_username: Indicate whether username should be appended to base name.
Default is False.
:param token_mapping: Mapping regrouping resolved token values associated
with their name. Default is None.
:return: String name.
:raise: exc:`ValueError` if a token within the *pattern* does not have any
value within the token map.
|
source/nomenclator/template.py
|
generate_scene_name
|
buddly27/nomenclator-nuke
| 11 |
python
|
def generate_scene_name(pattern, suffix, append_username=False, token_mapping=None):
'Generate scene name from *pattern* using a mapping of resolved tokens.\n\n :param pattern: String representing a template base,\n with or without tokens.\n\n :param suffix: Suffix to apply for the generated name (e.g. "nk" or "hrox").\n\n :param append_username: Indicate whether username should be appended to base name.\n Default is False.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name. Default is None.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
if append_username:
pattern += '_{username}'
pattern += '.{}'.format(suffix)
return resolve(pattern, (token_mapping or {}))
|
def generate_scene_name(pattern, suffix, append_username=False, token_mapping=None):
'Generate scene name from *pattern* using a mapping of resolved tokens.\n\n :param pattern: String representing a template base,\n with or without tokens.\n\n :param suffix: Suffix to apply for the generated name (e.g. "nk" or "hrox").\n\n :param append_username: Indicate whether username should be appended to base name.\n Default is False.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name. Default is None.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
if append_username:
pattern += '_{username}'
pattern += '.{}'.format(suffix)
return resolve(pattern, (token_mapping or {}))<|docstring|>Generate scene name from *pattern* using a mapping of resolved tokens.
:param pattern: String representing a template base,
with or without tokens.
:param suffix: Suffix to apply for the generated name (e.g. "nk" or "hrox").
:param append_username: Indicate whether username should be appended to base name.
Default is False.
:param token_mapping: Mapping regrouping resolved token values associated
with their name. Default is None.
:return: String name.
:raise: exc:`ValueError` if a token within the *pattern* does not have any
value within the token map.<|endoftext|>
|
f4d35876bbef025cc4c1e4f80c6e014820f640abff743682e6fa936a1e1adeec
|
def generate_output_name(pattern, suffix, append_passname_to_subfolder=False, append_passname=False, append_colorspace=False, append_username=False, multi_views=False, token_mapping=None):
'Generate output name from *pattern* using a mapping of resolved tokens.\n\n :param pattern: String representing a template base,\n with or without tokens.\n\n :param suffix: Suffix to apply for the generated name (e.g. "exr").\n\n :param append_passname_to_subfolder: Indicate whether passname should be appended to sub-folder.\n Default is False.\n\n :param append_passname: Indicate whether passname should be appended to base name.\n Default is False.\n\n :param append_colorspace: Indicate whether colorspace should be appended to base name.\n Default is False.\n\n :param append_username: Indicate whether username should be appended to base name.\n Default is False.\n\n :param multi_views: Indicate whether the view should be appended to base name\n with the pattern \'%V\'. Default is False.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name. Default is None.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
elements = pattern.split(os.sep)
if ((len(elements) > 1) and append_passname_to_subfolder):
elements[(- 2)] += '_{passname}'
if append_colorspace:
elements[(- 1)] += '_{colorspace}'
if append_username:
elements[(- 1)] += '_{username}'
if append_passname:
elements[(- 1)] += '_{passname}'
if multi_views:
elements[(- 1)] += '_%V'
if (suffix not in VIDEO_TYPES):
elements[(- 1)] += '.{padding}'
elements[(- 1)] += '.{}'.format(suffix)
return resolve(os.sep.join(elements), (token_mapping or {}))
|
Generate output name from *pattern* using a mapping of resolved tokens.
:param pattern: String representing a template base,
with or without tokens.
:param suffix: Suffix to apply for the generated name (e.g. "exr").
:param append_passname_to_subfolder: Indicate whether passname should be appended to sub-folder.
Default is False.
:param append_passname: Indicate whether passname should be appended to base name.
Default is False.
:param append_colorspace: Indicate whether colorspace should be appended to base name.
Default is False.
:param append_username: Indicate whether username should be appended to base name.
Default is False.
:param multi_views: Indicate whether the view should be appended to base name
with the pattern '%V'. Default is False.
:param token_mapping: Mapping regrouping resolved token values associated
with their name. Default is None.
:return: String name.
:raise: exc:`ValueError` if a token within the *pattern* does not have any
value within the token map.
|
source/nomenclator/template.py
|
generate_output_name
|
buddly27/nomenclator-nuke
| 11 |
python
|
def generate_output_name(pattern, suffix, append_passname_to_subfolder=False, append_passname=False, append_colorspace=False, append_username=False, multi_views=False, token_mapping=None):
'Generate output name from *pattern* using a mapping of resolved tokens.\n\n :param pattern: String representing a template base,\n with or without tokens.\n\n :param suffix: Suffix to apply for the generated name (e.g. "exr").\n\n :param append_passname_to_subfolder: Indicate whether passname should be appended to sub-folder.\n Default is False.\n\n :param append_passname: Indicate whether passname should be appended to base name.\n Default is False.\n\n :param append_colorspace: Indicate whether colorspace should be appended to base name.\n Default is False.\n\n :param append_username: Indicate whether username should be appended to base name.\n Default is False.\n\n :param multi_views: Indicate whether the view should be appended to base name\n with the pattern \'%V\'. Default is False.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name. Default is None.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
elements = pattern.split(os.sep)
if ((len(elements) > 1) and append_passname_to_subfolder):
elements[(- 2)] += '_{passname}'
if append_colorspace:
elements[(- 1)] += '_{colorspace}'
if append_username:
elements[(- 1)] += '_{username}'
if append_passname:
elements[(- 1)] += '_{passname}'
if multi_views:
elements[(- 1)] += '_%V'
if (suffix not in VIDEO_TYPES):
elements[(- 1)] += '.{padding}'
elements[(- 1)] += '.{}'.format(suffix)
return resolve(os.sep.join(elements), (token_mapping or {}))
|
def generate_output_name(pattern, suffix, append_passname_to_subfolder=False, append_passname=False, append_colorspace=False, append_username=False, multi_views=False, token_mapping=None):
'Generate output name from *pattern* using a mapping of resolved tokens.\n\n :param pattern: String representing a template base,\n with or without tokens.\n\n :param suffix: Suffix to apply for the generated name (e.g. "exr").\n\n :param append_passname_to_subfolder: Indicate whether passname should be appended to sub-folder.\n Default is False.\n\n :param append_passname: Indicate whether passname should be appended to base name.\n Default is False.\n\n :param append_colorspace: Indicate whether colorspace should be appended to base name.\n Default is False.\n\n :param append_username: Indicate whether username should be appended to base name.\n Default is False.\n\n :param multi_views: Indicate whether the view should be appended to base name\n with the pattern \'%V\'. Default is False.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name. Default is None.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
elements = pattern.split(os.sep)
if ((len(elements) > 1) and append_passname_to_subfolder):
elements[(- 2)] += '_{passname}'
if append_colorspace:
elements[(- 1)] += '_{colorspace}'
if append_username:
elements[(- 1)] += '_{username}'
if append_passname:
elements[(- 1)] += '_{passname}'
if multi_views:
elements[(- 1)] += '_%V'
if (suffix not in VIDEO_TYPES):
elements[(- 1)] += '.{padding}'
elements[(- 1)] += '.{}'.format(suffix)
return resolve(os.sep.join(elements), (token_mapping or {}))<|docstring|>Generate output name from *pattern* using a mapping of resolved tokens.
:param pattern: String representing a template base,
with or without tokens.
:param suffix: Suffix to apply for the generated name (e.g. "exr").
:param append_passname_to_subfolder: Indicate whether passname should be appended to sub-folder.
Default is False.
:param append_passname: Indicate whether passname should be appended to base name.
Default is False.
:param append_colorspace: Indicate whether colorspace should be appended to base name.
Default is False.
:param append_username: Indicate whether username should be appended to base name.
Default is False.
:param multi_views: Indicate whether the view should be appended to base name
with the pattern '%V'. Default is False.
:param token_mapping: Mapping regrouping resolved token values associated
with their name. Default is None.
:return: String name.
:raise: exc:`ValueError` if a token within the *pattern* does not have any
value within the token map.<|endoftext|>
|
092de97fa268c2164569d1a8a75fa70c779cb552c96df8d034edc52c8d455286
|
def resolve(pattern, token_mapping):
'Return the resolved name for *pattern*.\n\n :param pattern: String representing a template pattern,\n with or without tokens.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
def _remove_expression(match):
'Return corresponding pattern without expression.'
return '{{{0}}}'.format(match.group('name').split(':', 1)[0])
sub_pattern = '{(?P<name>.+?)}'
pattern = re.sub(sub_pattern, _remove_expression, pattern)
try:
return pattern.format(**token_mapping)
except KeyError as error:
raise ValueError('Missing token value: {}'.format(error))
|
Return the resolved name for *pattern*.
:param pattern: String representing a template pattern,
with or without tokens.
:param token_mapping: Mapping regrouping resolved token values associated
with their name.
:return: String name.
:raise: exc:`ValueError` if a token within the *pattern* does not have any
value within the token map.
|
source/nomenclator/template.py
|
resolve
|
buddly27/nomenclator-nuke
| 11 |
python
|
def resolve(pattern, token_mapping):
'Return the resolved name for *pattern*.\n\n :param pattern: String representing a template pattern,\n with or without tokens.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
def _remove_expression(match):
'Return corresponding pattern without expression.'
return '{{{0}}}'.format(match.group('name').split(':', 1)[0])
sub_pattern = '{(?P<name>.+?)}'
pattern = re.sub(sub_pattern, _remove_expression, pattern)
try:
return pattern.format(**token_mapping)
except KeyError as error:
raise ValueError('Missing token value: {}'.format(error))
|
def resolve(pattern, token_mapping):
'Return the resolved name for *pattern*.\n\n :param pattern: String representing a template pattern,\n with or without tokens.\n\n :param token_mapping: Mapping regrouping resolved token values associated\n with their name.\n\n :return: String name.\n\n :raise: exc:`ValueError` if a token within the *pattern* does not have any\n value within the token map.\n\n '
def _remove_expression(match):
'Return corresponding pattern without expression.'
return '{{{0}}}'.format(match.group('name').split(':', 1)[0])
sub_pattern = '{(?P<name>.+?)}'
pattern = re.sub(sub_pattern, _remove_expression, pattern)
try:
return pattern.format(**token_mapping)
except KeyError as error:
raise ValueError('Missing token value: {}'.format(error))<|docstring|>Return the resolved name for *pattern*.
:param pattern: String representing a template pattern,
with or without tokens.
:param token_mapping: Mapping regrouping resolved token values associated
with their name.
:return: String name.
:raise: exc:`ValueError` if a token within the *pattern* does not have any
value within the token map.<|endoftext|>
|
d7acdca05e5bbe8a5716fe3a92f52c58c42ebe43f48b8e92fab2df316485ec89
|
def _convert(match):
'Return corresponding regular expression.'
name = match.group('name')
expression = (match.group('expression') or default_expression)
return '(?P<{0}>{1})'.format(name, expression)
|
Return corresponding regular expression.
|
source/nomenclator/template.py
|
_convert
|
buddly27/nomenclator-nuke
| 11 |
python
|
def _convert(match):
name = match.group('name')
expression = (match.group('expression') or default_expression)
return '(?P<{0}>{1})'.format(name, expression)
|
def _convert(match):
name = match.group('name')
expression = (match.group('expression') or default_expression)
return '(?P<{0}>{1})'.format(name, expression)<|docstring|>Return corresponding regular expression.<|endoftext|>
|
a712f850e283448872c07f9c4e4a49050181985be81937ee48adb52f32346f38
|
def _escape(match):
"Escape 'other' group value if required."
groups = match.groupdict()
if (groups['other'] is not None):
return re.escape(groups['other'])
return groups['token']
|
Escape 'other' group value if required.
|
source/nomenclator/template.py
|
_escape
|
buddly27/nomenclator-nuke
| 11 |
python
|
def _escape(match):
groups = match.groupdict()
if (groups['other'] is not None):
return re.escape(groups['other'])
return groups['token']
|
def _escape(match):
groups = match.groupdict()
if (groups['other'] is not None):
return re.escape(groups['other'])
return groups['token']<|docstring|>Escape 'other' group value if required.<|endoftext|>
|
ac3c727abcfaf0e9666c2bb668b0288aa596c8eaaf3fb69844626b581d4f889e
|
def _remove_expression(match):
'Return corresponding pattern without expression.'
return '{{{0}}}'.format(match.group('name').split(':', 1)[0])
|
Return corresponding pattern without expression.
|
source/nomenclator/template.py
|
_remove_expression
|
buddly27/nomenclator-nuke
| 11 |
python
|
def _remove_expression(match):
return '{{{0}}}'.format(match.group('name').split(':', 1)[0])
|
def _remove_expression(match):
return '{{{0}}}'.format(match.group('name').split(':', 1)[0])<|docstring|>Return corresponding pattern without expression.<|endoftext|>
|
72813f49de6950a9568ad2751f65fecd8d525f78321a22012914cb9fdc1c7f39
|
def __init__(self, name, price=10, weight=20, flammability=0.5, identifier=random.randint(1000000, 9999999)):
'\n Part 1: Constructor initializes Product class\n '
self.name = str(name)
self.price = int(price)
self.weight = int(weight)
self.flammability = float(flammability)
self.identifier = int(identifier)
|
Part 1: Constructor initializes Product class
|
acme.py
|
__init__
|
andrewlee977/lambdata
| 0 |
python
|
def __init__(self, name, price=10, weight=20, flammability=0.5, identifier=random.randint(1000000, 9999999)):
'\n \n '
self.name = str(name)
self.price = int(price)
self.weight = int(weight)
self.flammability = float(flammability)
self.identifier = int(identifier)
|
def __init__(self, name, price=10, weight=20, flammability=0.5, identifier=random.randint(1000000, 9999999)):
'\n \n '
self.name = str(name)
self.price = int(price)
self.weight = int(weight)
self.flammability = float(flammability)
self.identifier = int(identifier)<|docstring|>Part 1: Constructor initializes Product class<|endoftext|>
|
b01eb46be0ad74880356e1deb8052d077b70be1f57c6970815355f3a50f38b1a
|
def stealability(self):
'\n Part 2: function calculates stealability of an instance\n '
ppw = (self.price / self.weight)
if (ppw < 0.5):
return 'Not so stealable...'
elif ((ppw >= 0.5) and (ppw < 1.0)):
return 'Kinda stealable...'
else:
return 'Very stealable!'
|
Part 2: function calculates stealability of an instance
|
acme.py
|
stealability
|
andrewlee977/lambdata
| 0 |
python
|
def stealability(self):
'\n \n '
ppw = (self.price / self.weight)
if (ppw < 0.5):
return 'Not so stealable...'
elif ((ppw >= 0.5) and (ppw < 1.0)):
return 'Kinda stealable...'
else:
return 'Very stealable!'
|
def stealability(self):
'\n \n '
ppw = (self.price / self.weight)
if (ppw < 0.5):
return 'Not so stealable...'
elif ((ppw >= 0.5) and (ppw < 1.0)):
return 'Kinda stealable...'
else:
return 'Very stealable!'<|docstring|>Part 2: function calculates stealability of an instance<|endoftext|>
|
e1524837a446a9240ecb173e6573d121cd0b6976dc60da3df3cee6f8fffb1dc8
|
def explode(self):
'\n Part 2: function calculates explodability of an instance\n '
ftw = (self.flammability * self.weight)
if (ftw < 10):
return '...fizzle'
elif ((ftw >= 10) and (ftw < 50)):
return '...boom!'
else:
return '...BABOOM!!'
|
Part 2: function calculates explodability of an instance
|
acme.py
|
explode
|
andrewlee977/lambdata
| 0 |
python
|
def explode(self):
'\n \n '
ftw = (self.flammability * self.weight)
if (ftw < 10):
return '...fizzle'
elif ((ftw >= 10) and (ftw < 50)):
return '...boom!'
else:
return '...BABOOM!!'
|
def explode(self):
'\n \n '
ftw = (self.flammability * self.weight)
if (ftw < 10):
return '...fizzle'
elif ((ftw >= 10) and (ftw < 50)):
return '...boom!'
else:
return '...BABOOM!!'<|docstring|>Part 2: function calculates explodability of an instance<|endoftext|>
|
6909ff8c8050772b5bf0d7602a3b83523365c101713043206b0c17293314485b
|
def __init___(self, name, price, flammability, identifier, weight=10):
'\n Part 3: Constructor initializes BoxingGlove class\n '
super().__init__(name, price, weight, flammability, identifier)
|
Part 3: Constructor initializes BoxingGlove class
|
acme.py
|
__init___
|
andrewlee977/lambdata
| 0 |
python
|
def __init___(self, name, price, flammability, identifier, weight=10):
'\n \n '
super().__init__(name, price, weight, flammability, identifier)
|
def __init___(self, name, price, flammability, identifier, weight=10):
'\n \n '
super().__init__(name, price, weight, flammability, identifier)<|docstring|>Part 3: Constructor initializes BoxingGlove class<|endoftext|>
|
1dc80e1fd6386190ff8f53d8ea5baa98bcb19323f610373b6f89e06b0d3c671f
|
def explode(self):
'\n Part 3: function calculates explodability of an instance\n '
return "...it's a glove."
|
Part 3: function calculates explodability of an instance
|
acme.py
|
explode
|
andrewlee977/lambdata
| 0 |
python
|
def explode(self):
'\n \n '
return "...it's a glove."
|
def explode(self):
'\n \n '
return "...it's a glove."<|docstring|>Part 3: function calculates explodability of an instance<|endoftext|>
|
d94c0ea2cb56757610577b6cd948c1949518e44818bf4a91445e2c37c2580850
|
def add_char(store, char):
'add a character to the count store'
already_there = False
for x in store:
if (x[0] == char):
x[1] = (x[1] + 1)
already_there = True
if (not already_there):
store.append([char, 1])
|
add a character to the count store
|
code/char_histo_vert.py
|
add_char
|
tumuum/prog-book
| 0 |
python
|
def add_char(store, char):
already_there = False
for x in store:
if (x[0] == char):
x[1] = (x[1] + 1)
already_there = True
if (not already_there):
store.append([char, 1])
|
def add_char(store, char):
already_there = False
for x in store:
if (x[0] == char):
x[1] = (x[1] + 1)
already_there = True
if (not already_there):
store.append([char, 1])<|docstring|>add a character to the count store<|endoftext|>
|
5b80f7fdd96dec5c8e3e0bb7c7cc7d27e3c8821f72acd8b3dc986c6a3aab5fdc
|
def mk_py(names):
'\n Generate .py file with a dict of default (english) values\n '
log('> Generating __maps.py with default (english) values')
py_file = codecs.open(PY_PATH, 'w', ' utf8')
py_file.write('# -*- coding: utf-8 -*-\n\n# AUTOMATICALLY GENERATED FILE, DO NOT EDIT')
def write(key):
py_file.write(('\n\n%s = {\n' % key))
for (k, v) in names[key].items():
py_file.write((u" '%s': u'%s',\n" % (k, v.replace(u"'", u"\\'"))))
py_file.write('}')
for name in ITEMS:
write(name)
py_file.close()
|
Generate .py file with a dict of default (english) values
|
maker/l18n_maker/mktrans.py
|
mk_py
|
tkhyn/l18n
| 3 |
python
|
def mk_py(names):
'\n \n '
log('> Generating __maps.py with default (english) values')
py_file = codecs.open(PY_PATH, 'w', ' utf8')
py_file.write('# -*- coding: utf-8 -*-\n\n# AUTOMATICALLY GENERATED FILE, DO NOT EDIT')
def write(key):
py_file.write(('\n\n%s = {\n' % key))
for (k, v) in names[key].items():
py_file.write((u" '%s': u'%s',\n" % (k, v.replace(u"'", u"\\'"))))
py_file.write('}')
for name in ITEMS:
write(name)
py_file.close()
|
def mk_py(names):
'\n \n '
log('> Generating __maps.py with default (english) values')
py_file = codecs.open(PY_PATH, 'w', ' utf8')
py_file.write('# -*- coding: utf-8 -*-\n\n# AUTOMATICALLY GENERATED FILE, DO NOT EDIT')
def write(key):
py_file.write(('\n\n%s = {\n' % key))
for (k, v) in names[key].items():
py_file.write((u" '%s': u'%s',\n" % (k, v.replace(u"'", u"\\'"))))
py_file.write('}')
for name in ITEMS:
write(name)
py_file.close()<|docstring|>Generate .py file with a dict of default (english) values<|endoftext|>
|
6bf3d4e98b0c0ad1811d52fc513f8090cc7b3688816d721f5e6485bd3bdad83d
|
def mk_po(loc, root_names, trans):
'\n Generate a .po file for locale loc\n '
header = (u'# PYTZ TIMEZONE CITIES AND TERRITORIES TRANSLATION FILE\n\nmsgid ""\nmsgstr ""\n\n"Project-Id-Version: l18n\\n"\n"Report-Msgid-Bugs-To: \\n"\n"POT-Creation-Date: %(date)s\\n"\n"PO-Revision-Date: \\n"\n"Last-Translator: l18n maker\\n"\n"Language-Team: \\n"\n"MIME-Version: 1.0\\n"\n"Content-Type: text/plain; charset=UTF-8\\n"\n"Content-Transfer-Encoding: 8bit\\n"\n"Plural-Forms: nplurals=2; plural=(n > 1)\\n"\n"X-Poedit-SourceCharset: utf-8\\n"\n"Language: ' % {'date': datetime.now(pytz.utc).replace(microsecond=0)})
log(('> Generating .po file for locale ' + loc))
po_path = (PO_PATH % loc)
try:
os.makedirs(os.path.dirname(po_path))
except OSError:
pass
po_file = codecs.open(po_path, 'w', ' utf8')
po_file.write(((header + loc) + u'\\n"\n\n'))
def write(key):
for (k, v) in trans[key].items():
try:
root_name = root_names[key][k]
except KeyError:
if (key == 'tz_locations'):
root_name = root_names['tz_cities'][k]
else:
raise
po_file.write((u'msgid "%s"\nmsgstr "%s"\n\n' % (root_name, v)))
for name in ITEMS:
write(name)
po_file.close()
return po_path
|
Generate a .po file for locale loc
|
maker/l18n_maker/mktrans.py
|
mk_po
|
tkhyn/l18n
| 3 |
python
|
def mk_po(loc, root_names, trans):
'\n \n '
header = (u'# PYTZ TIMEZONE CITIES AND TERRITORIES TRANSLATION FILE\n\nmsgid \nmsgstr \n\n"Project-Id-Version: l18n\\n"\n"Report-Msgid-Bugs-To: \\n"\n"POT-Creation-Date: %(date)s\\n"\n"PO-Revision-Date: \\n"\n"Last-Translator: l18n maker\\n"\n"Language-Team: \\n"\n"MIME-Version: 1.0\\n"\n"Content-Type: text/plain; charset=UTF-8\\n"\n"Content-Transfer-Encoding: 8bit\\n"\n"Plural-Forms: nplurals=2; plural=(n > 1)\\n"\n"X-Poedit-SourceCharset: utf-8\\n"\n"Language: ' % {'date': datetime.now(pytz.utc).replace(microsecond=0)})
log(('> Generating .po file for locale ' + loc))
po_path = (PO_PATH % loc)
try:
os.makedirs(os.path.dirname(po_path))
except OSError:
pass
po_file = codecs.open(po_path, 'w', ' utf8')
po_file.write(((header + loc) + u'\\n"\n\n'))
def write(key):
for (k, v) in trans[key].items():
try:
root_name = root_names[key][k]
except KeyError:
if (key == 'tz_locations'):
root_name = root_names['tz_cities'][k]
else:
raise
po_file.write((u'msgid "%s"\nmsgstr "%s"\n\n' % (root_name, v)))
for name in ITEMS:
write(name)
po_file.close()
return po_path
|
def mk_po(loc, root_names, trans):
'\n \n '
header = (u'# PYTZ TIMEZONE CITIES AND TERRITORIES TRANSLATION FILE\n\nmsgid \nmsgstr \n\n"Project-Id-Version: l18n\\n"\n"Report-Msgid-Bugs-To: \\n"\n"POT-Creation-Date: %(date)s\\n"\n"PO-Revision-Date: \\n"\n"Last-Translator: l18n maker\\n"\n"Language-Team: \\n"\n"MIME-Version: 1.0\\n"\n"Content-Type: text/plain; charset=UTF-8\\n"\n"Content-Transfer-Encoding: 8bit\\n"\n"Plural-Forms: nplurals=2; plural=(n > 1)\\n"\n"X-Poedit-SourceCharset: utf-8\\n"\n"Language: ' % {'date': datetime.now(pytz.utc).replace(microsecond=0)})
log(('> Generating .po file for locale ' + loc))
po_path = (PO_PATH % loc)
try:
os.makedirs(os.path.dirname(po_path))
except OSError:
pass
po_file = codecs.open(po_path, 'w', ' utf8')
po_file.write(((header + loc) + u'\\n"\n\n'))
def write(key):
for (k, v) in trans[key].items():
try:
root_name = root_names[key][k]
except KeyError:
if (key == 'tz_locations'):
root_name = root_names['tz_cities'][k]
else:
raise
po_file.write((u'msgid "%s"\nmsgstr "%s"\n\n' % (root_name, v)))
for name in ITEMS:
write(name)
po_file.close()
return po_path<|docstring|>Generate a .po file for locale loc<|endoftext|>
|
bc83df16ffd32fe2561777d3de3b86e0fecbf680f7f5b52cd71cc3eb3a01f9e0
|
def forward(self, encodings: Dict[(str, Any)], sent_lengths: torch.Tensor) -> Tuple[(torch.Tensor, torch.Tensor, torch.Tensor)]:
'Predict POS, heads and deprel scores.\n\n ## Outputs\n\n `tag_scores, arc_scores, lab_scores` with shapes\n\n - `tag_score`: $`batch_size×max_sent_length×num_pos_tags`$\n - `arc_scores`: $`batch_size×n_deps×n_possible_heads`$\n - `label_scores`: $`batch_size×n_deps×n_possible_heads×num_deprels`$\n '
embeddings = [self.lexers[lexer_name](encodings[lexer_name]) for lexer_name in self.lexers_order]
inpt = torch.cat(embeddings, dim=(- 1))
packed_inpt = pack_padded_sequence(inpt, sent_lengths, batch_first=True, enforce_sorted=False)
(packed_dep_embeddings, _) = self.dep_rnn(packed_inpt)
(dep_embeddings, _) = pad_packed_sequence(packed_dep_embeddings, batch_first=True)
tag_scores = self.pos_tagger(dep_embeddings)
arc_h = self.arc_mlp_h(dep_embeddings)
arc_d = self.arc_mlp_d(dep_embeddings)
lab_h = self.lab_mlp_h(dep_embeddings)
lab_d = self.lab_mlp_d(dep_embeddings)
arc_scores = self.arc_biaffine(arc_d, arc_h).squeeze((- 1))
lab_scores = self.lab_biaffine(lab_d, lab_h)
return (tag_scores, arc_scores, lab_scores)
|
Predict POS, heads and deprel scores.
## Outputs
`tag_scores, arc_scores, lab_scores` with shapes
- `tag_score`: $`batch_size×max_sent_length×num_pos_tags`$
- `arc_scores`: $`batch_size×n_deps×n_possible_heads`$
- `label_scores`: $`batch_size×n_deps×n_possible_heads×num_deprels`$
|
hopsparser/parser.py
|
forward
|
hopsparser/npdependency
| 0 |
python
|
def forward(self, encodings: Dict[(str, Any)], sent_lengths: torch.Tensor) -> Tuple[(torch.Tensor, torch.Tensor, torch.Tensor)]:
'Predict POS, heads and deprel scores.\n\n ## Outputs\n\n `tag_scores, arc_scores, lab_scores` with shapes\n\n - `tag_score`: $`batch_size×max_sent_length×num_pos_tags`$\n - `arc_scores`: $`batch_size×n_deps×n_possible_heads`$\n - `label_scores`: $`batch_size×n_deps×n_possible_heads×num_deprels`$\n '
embeddings = [self.lexers[lexer_name](encodings[lexer_name]) for lexer_name in self.lexers_order]
inpt = torch.cat(embeddings, dim=(- 1))
packed_inpt = pack_padded_sequence(inpt, sent_lengths, batch_first=True, enforce_sorted=False)
(packed_dep_embeddings, _) = self.dep_rnn(packed_inpt)
(dep_embeddings, _) = pad_packed_sequence(packed_dep_embeddings, batch_first=True)
tag_scores = self.pos_tagger(dep_embeddings)
arc_h = self.arc_mlp_h(dep_embeddings)
arc_d = self.arc_mlp_d(dep_embeddings)
lab_h = self.lab_mlp_h(dep_embeddings)
lab_d = self.lab_mlp_d(dep_embeddings)
arc_scores = self.arc_biaffine(arc_d, arc_h).squeeze((- 1))
lab_scores = self.lab_biaffine(lab_d, lab_h)
return (tag_scores, arc_scores, lab_scores)
|
def forward(self, encodings: Dict[(str, Any)], sent_lengths: torch.Tensor) -> Tuple[(torch.Tensor, torch.Tensor, torch.Tensor)]:
'Predict POS, heads and deprel scores.\n\n ## Outputs\n\n `tag_scores, arc_scores, lab_scores` with shapes\n\n - `tag_score`: $`batch_size×max_sent_length×num_pos_tags`$\n - `arc_scores`: $`batch_size×n_deps×n_possible_heads`$\n - `label_scores`: $`batch_size×n_deps×n_possible_heads×num_deprels`$\n '
embeddings = [self.lexers[lexer_name](encodings[lexer_name]) for lexer_name in self.lexers_order]
inpt = torch.cat(embeddings, dim=(- 1))
packed_inpt = pack_padded_sequence(inpt, sent_lengths, batch_first=True, enforce_sorted=False)
(packed_dep_embeddings, _) = self.dep_rnn(packed_inpt)
(dep_embeddings, _) = pad_packed_sequence(packed_dep_embeddings, batch_first=True)
tag_scores = self.pos_tagger(dep_embeddings)
arc_h = self.arc_mlp_h(dep_embeddings)
arc_d = self.arc_mlp_d(dep_embeddings)
lab_h = self.lab_mlp_h(dep_embeddings)
lab_d = self.lab_mlp_d(dep_embeddings)
arc_scores = self.arc_biaffine(arc_d, arc_h).squeeze((- 1))
lab_scores = self.lab_biaffine(lab_d, lab_h)
return (tag_scores, arc_scores, lab_scores)<|docstring|>Predict POS, heads and deprel scores.
## Outputs
`tag_scores, arc_scores, lab_scores` with shapes
- `tag_score`: $`batch_size×max_sent_length×num_pos_tags`$
- `arc_scores`: $`batch_size×n_deps×n_possible_heads`$
- `label_scores`: $`batch_size×n_deps×n_possible_heads×num_deprels`$<|endoftext|>
|
bb5aef842639ce35d23d1d76422b9640dc74515c5b33dfcf49ce4ca4c80126d0
|
def get_version(filename):
'\n Trivial parser to extract a __version__ variable from a source file.\n\n :param str filename: the file to extract __version__ from\n :returns str: the version string for the package\n '
version_re = re.compile('(\\d\\.\\d(\\.\\d+)?)')
with io.open(filename, 'r') as source:
for (line_num, line) in enumerate(source):
if line.startswith('__version__'):
match = version_re.search(line)
if (not match):
raise Exception(('Invalid __version__ string found on line %d of %s' % ((line_num + 1), filename)))
return match.group(1)
raise Exception(('No __version__ line found in %s' % filename))
|
Trivial parser to extract a __version__ variable from a source file.
:param str filename: the file to extract __version__ from
:returns str: the version string for the package
|
utils.py
|
get_version
|
waveform80/lars
| 14 |
python
|
def get_version(filename):
'\n Trivial parser to extract a __version__ variable from a source file.\n\n :param str filename: the file to extract __version__ from\n :returns str: the version string for the package\n '
version_re = re.compile('(\\d\\.\\d(\\.\\d+)?)')
with io.open(filename, 'r') as source:
for (line_num, line) in enumerate(source):
if line.startswith('__version__'):
match = version_re.search(line)
if (not match):
raise Exception(('Invalid __version__ string found on line %d of %s' % ((line_num + 1), filename)))
return match.group(1)
raise Exception(('No __version__ line found in %s' % filename))
|
def get_version(filename):
'\n Trivial parser to extract a __version__ variable from a source file.\n\n :param str filename: the file to extract __version__ from\n :returns str: the version string for the package\n '
version_re = re.compile('(\\d\\.\\d(\\.\\d+)?)')
with io.open(filename, 'r') as source:
for (line_num, line) in enumerate(source):
if line.startswith('__version__'):
match = version_re.search(line)
if (not match):
raise Exception(('Invalid __version__ string found on line %d of %s' % ((line_num + 1), filename)))
return match.group(1)
raise Exception(('No __version__ line found in %s' % filename))<|docstring|>Trivial parser to extract a __version__ variable from a source file.
:param str filename: the file to extract __version__ from
:returns str: the version string for the package<|endoftext|>
|
40bff2d6f96270fbfe2962f1e83e066aa2b15882607bc75e31c1b52e4a4e411d
|
def description(filename):
'\n Returns the first non-heading paragraph from a ReStructuredText file.\n\n :param str filename: the file to extract the description from\n :returns str: the description of the package\n '
state = 'before_header'
result = []
with io.open(filename, 'r') as rst_file:
for line in rst_file:
line = line.rstrip()
if (line == ''):
if (state == 'in_para'):
state = 'after_para'
elif (line == ('=' * len(line))):
if (state == 'before_header'):
state = 'in_header'
elif (state == 'in_header'):
state = 'before_para'
elif (state == 'before_para'):
state = 'in_para'
if (state == 'in_para'):
result.append(line)
elif (state == 'after_para'):
break
return ' '.join((line.strip() for line in result))
|
Returns the first non-heading paragraph from a ReStructuredText file.
:param str filename: the file to extract the description from
:returns str: the description of the package
|
utils.py
|
description
|
waveform80/lars
| 14 |
python
|
def description(filename):
'\n Returns the first non-heading paragraph from a ReStructuredText file.\n\n :param str filename: the file to extract the description from\n :returns str: the description of the package\n '
state = 'before_header'
result = []
with io.open(filename, 'r') as rst_file:
for line in rst_file:
line = line.rstrip()
if (line == ):
if (state == 'in_para'):
state = 'after_para'
elif (line == ('=' * len(line))):
if (state == 'before_header'):
state = 'in_header'
elif (state == 'in_header'):
state = 'before_para'
elif (state == 'before_para'):
state = 'in_para'
if (state == 'in_para'):
result.append(line)
elif (state == 'after_para'):
break
return ' '.join((line.strip() for line in result))
|
def description(filename):
'\n Returns the first non-heading paragraph from a ReStructuredText file.\n\n :param str filename: the file to extract the description from\n :returns str: the description of the package\n '
state = 'before_header'
result = []
with io.open(filename, 'r') as rst_file:
for line in rst_file:
line = line.rstrip()
if (line == ):
if (state == 'in_para'):
state = 'after_para'
elif (line == ('=' * len(line))):
if (state == 'before_header'):
state = 'in_header'
elif (state == 'in_header'):
state = 'before_para'
elif (state == 'before_para'):
state = 'in_para'
if (state == 'in_para'):
result.append(line)
elif (state == 'after_para'):
break
return ' '.join((line.strip() for line in result))<|docstring|>Returns the first non-heading paragraph from a ReStructuredText file.
:param str filename: the file to extract the description from
:returns str: the description of the package<|endoftext|>
|
0fdbd41d54fe7cedae6636988d42f8776c97586de1305d1cac3a10582197315a
|
def configure_session(intra_threads=32, inter_threads=2, kmp_blocktime=None, kmp_affinity=None, omp_num_threads=None, gpu=None):
'Sets the thread knobs in the TF backend'
if (kmp_blocktime is not None):
os.environ['KMP_BLOCKTIME'] = str(kmp_blocktime)
if (kmp_affinity is not None):
os.environ['KMP_AFFINITY'] = kmp_affinity
if (omp_num_threads is not None):
os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
if (dist.rank() == 0):
logging.info('KMP_BLOCKTIME %s', os.environ.get('KMP_BLOCKTIME', ''))
logging.info('KMP_AFFINITY %s', os.environ.get('KMP_AFFINITY', ''))
logging.info('OMP_NUM_THREADS %s', os.environ.get('OMP_NUM_THREADS', ''))
logging.info('INTRA_THREADS %i', intra_threads)
logging.info('INTER_THREADS %i', inter_threads)
config = tf.ConfigProto(inter_op_parallelism_threads=inter_threads, intra_op_parallelism_threads=intra_threads)
if (gpu is not None):
config.gpu_options.visible_device_list = str(gpu)
tf.keras.backend.set_session(tf.Session(config=config))
|
Sets the thread knobs in the TF backend
|
utils/device.py
|
configure_session
|
lukasgd/cosmoflow-benchmark
| 0 |
python
|
def configure_session(intra_threads=32, inter_threads=2, kmp_blocktime=None, kmp_affinity=None, omp_num_threads=None, gpu=None):
if (kmp_blocktime is not None):
os.environ['KMP_BLOCKTIME'] = str(kmp_blocktime)
if (kmp_affinity is not None):
os.environ['KMP_AFFINITY'] = kmp_affinity
if (omp_num_threads is not None):
os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
if (dist.rank() == 0):
logging.info('KMP_BLOCKTIME %s', os.environ.get('KMP_BLOCKTIME', ))
logging.info('KMP_AFFINITY %s', os.environ.get('KMP_AFFINITY', ))
logging.info('OMP_NUM_THREADS %s', os.environ.get('OMP_NUM_THREADS', ))
logging.info('INTRA_THREADS %i', intra_threads)
logging.info('INTER_THREADS %i', inter_threads)
config = tf.ConfigProto(inter_op_parallelism_threads=inter_threads, intra_op_parallelism_threads=intra_threads)
if (gpu is not None):
config.gpu_options.visible_device_list = str(gpu)
tf.keras.backend.set_session(tf.Session(config=config))
|
def configure_session(intra_threads=32, inter_threads=2, kmp_blocktime=None, kmp_affinity=None, omp_num_threads=None, gpu=None):
if (kmp_blocktime is not None):
os.environ['KMP_BLOCKTIME'] = str(kmp_blocktime)
if (kmp_affinity is not None):
os.environ['KMP_AFFINITY'] = kmp_affinity
if (omp_num_threads is not None):
os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
if (dist.rank() == 0):
logging.info('KMP_BLOCKTIME %s', os.environ.get('KMP_BLOCKTIME', ))
logging.info('KMP_AFFINITY %s', os.environ.get('KMP_AFFINITY', ))
logging.info('OMP_NUM_THREADS %s', os.environ.get('OMP_NUM_THREADS', ))
logging.info('INTRA_THREADS %i', intra_threads)
logging.info('INTER_THREADS %i', inter_threads)
config = tf.ConfigProto(inter_op_parallelism_threads=inter_threads, intra_op_parallelism_threads=intra_threads)
if (gpu is not None):
config.gpu_options.visible_device_list = str(gpu)
tf.keras.backend.set_session(tf.Session(config=config))<|docstring|>Sets the thread knobs in the TF backend<|endoftext|>
|
fa0ff45cedea8a0c17de6539a7f7fbaf3f7fedbde4edb56f9f982e488f0bde22
|
@click.group()
def datastore():
u'Perform commands to set up the datastore.\n '
|
Perform commands to set up the datastore.
|
ckanext/datastore/cli.py
|
datastore
|
gvarela1981/ckan
| 58 |
python
|
@click.group()
def datastore():
u'\n '
|
@click.group()
def datastore():
u'\n '<|docstring|>Perform commands to set up the datastore.<|endoftext|>
|
ac43d477aee532855d1ad748d35dbb0a28da506b72a5597848997ebb9bdfdeb7
|
@datastore.command(u'set-permissions', short_help=u'Generate SQL for permission configuration.')
def set_permissions():
u'Emit an SQL script that will set the permissions for the datastore\n users as configured in your configuration file.'
write_url = _parse_db_config(u'ckan.datastore.write_url')
read_url = _parse_db_config(u'ckan.datastore.read_url')
db_url = _parse_db_config(u'sqlalchemy.url')
if (write_url[u'db_name'] != read_url[u'db_name']):
click.secho(u'The datastore write_url and read_url must refer to the same database!', fg=u'red', bold=True)
raise click.Abort()
sql = permissions_sql(maindb=db_url[u'db_name'], datastoredb=write_url[u'db_name'], mainuser=db_url[u'db_user'], writeuser=write_url[u'db_user'], readuser=read_url[u'db_user'])
click.echo(sql)
|
Emit an SQL script that will set the permissions for the datastore
users as configured in your configuration file.
|
ckanext/datastore/cli.py
|
set_permissions
|
gvarela1981/ckan
| 58 |
python
|
@datastore.command(u'set-permissions', short_help=u'Generate SQL for permission configuration.')
def set_permissions():
u'Emit an SQL script that will set the permissions for the datastore\n users as configured in your configuration file.'
write_url = _parse_db_config(u'ckan.datastore.write_url')
read_url = _parse_db_config(u'ckan.datastore.read_url')
db_url = _parse_db_config(u'sqlalchemy.url')
if (write_url[u'db_name'] != read_url[u'db_name']):
click.secho(u'The datastore write_url and read_url must refer to the same database!', fg=u'red', bold=True)
raise click.Abort()
sql = permissions_sql(maindb=db_url[u'db_name'], datastoredb=write_url[u'db_name'], mainuser=db_url[u'db_user'], writeuser=write_url[u'db_user'], readuser=read_url[u'db_user'])
click.echo(sql)
|
@datastore.command(u'set-permissions', short_help=u'Generate SQL for permission configuration.')
def set_permissions():
u'Emit an SQL script that will set the permissions for the datastore\n users as configured in your configuration file.'
write_url = _parse_db_config(u'ckan.datastore.write_url')
read_url = _parse_db_config(u'ckan.datastore.read_url')
db_url = _parse_db_config(u'sqlalchemy.url')
if (write_url[u'db_name'] != read_url[u'db_name']):
click.secho(u'The datastore write_url and read_url must refer to the same database!', fg=u'red', bold=True)
raise click.Abort()
sql = permissions_sql(maindb=db_url[u'db_name'], datastoredb=write_url[u'db_name'], mainuser=db_url[u'db_user'], writeuser=write_url[u'db_user'], readuser=read_url[u'db_user'])
click.echo(sql)<|docstring|>Emit an SQL script that will set the permissions for the datastore
users as configured in your configuration file.<|endoftext|>
|
5f5fca688c9937bd3275bfcb61de16e8c54d0a4ced370d376f596313406bb2a7
|
@datastore.command()
@click.argument(u'resource-id', nargs=1)
@click.argument(u'output-file', type=click.File(u'wb'), default=click.get_binary_stream(u'stdout'))
@click.option(u'--format', default=u'csv', type=click.Choice(DUMP_FORMATS))
@click.option(u'--offset', type=click.IntRange(0, None), default=0)
@click.option(u'--limit', type=click.IntRange(0))
@click.option(u'--bom', is_flag=True)
@click.pass_context
def dump(ctx, resource_id, output_file, format, offset, limit, bom):
u'Dump a datastore resource.\n '
flask_app = ctx.meta['flask_app']
with flask_app.test_request_context():
dump_to(resource_id, output_file, fmt=format, offset=offset, limit=limit, options={u'bom': bom}, sort=u'_id', search_params={})
|
Dump a datastore resource.
|
ckanext/datastore/cli.py
|
dump
|
gvarela1981/ckan
| 58 |
python
|
@datastore.command()
@click.argument(u'resource-id', nargs=1)
@click.argument(u'output-file', type=click.File(u'wb'), default=click.get_binary_stream(u'stdout'))
@click.option(u'--format', default=u'csv', type=click.Choice(DUMP_FORMATS))
@click.option(u'--offset', type=click.IntRange(0, None), default=0)
@click.option(u'--limit', type=click.IntRange(0))
@click.option(u'--bom', is_flag=True)
@click.pass_context
def dump(ctx, resource_id, output_file, format, offset, limit, bom):
u'\n '
flask_app = ctx.meta['flask_app']
with flask_app.test_request_context():
dump_to(resource_id, output_file, fmt=format, offset=offset, limit=limit, options={u'bom': bom}, sort=u'_id', search_params={})
|
@datastore.command()
@click.argument(u'resource-id', nargs=1)
@click.argument(u'output-file', type=click.File(u'wb'), default=click.get_binary_stream(u'stdout'))
@click.option(u'--format', default=u'csv', type=click.Choice(DUMP_FORMATS))
@click.option(u'--offset', type=click.IntRange(0, None), default=0)
@click.option(u'--limit', type=click.IntRange(0))
@click.option(u'--bom', is_flag=True)
@click.pass_context
def dump(ctx, resource_id, output_file, format, offset, limit, bom):
u'\n '
flask_app = ctx.meta['flask_app']
with flask_app.test_request_context():
dump_to(resource_id, output_file, fmt=format, offset=offset, limit=limit, options={u'bom': bom}, sort=u'_id', search_params={})<|docstring|>Dump a datastore resource.<|endoftext|>
|
c5390e1e8494b001e286b7d6833542c30343f759e3915ac918ea1abd98df83d4
|
def __init__(self, collections=[], query_bands=[], bbox=(), start_date=None, end_date=None, limit=30):
'Build DataCube object with config parameters including access token, STAC url and earth observation service url.'
if (len(config.ACCESS_TOKEN) == 0):
config.ACCESS_TOKEN = input('Please insert a valid user token from BDC Auth: ')
if (len(config.EOCUBE_URL) == 0):
config.EOCUBE_URL = input('Please insert a valid url for EO Service: ')
if (len(config.STAC_URL) == 0):
config.STAC_URL = input('Please insert a valid url for STAC Service: ')
self.utils = Utils()
self.stac_client = stac.STAC(config.STAC_URL, access_token=config.ACCESS_TOKEN)
if (not collections):
raise AttributeError('Please insert a list of available collections!')
else:
self.collections = collections
if (not query_bands):
raise AttributeError('Please insert a list of available bands with query_bands!')
else:
self.query_bands = query_bands
if (not bbox):
raise AttributeError('Please insert a bounding box parameter!')
else:
valid = self.utils._validateBBOX(bbox)
if valid:
self.bbox = bbox
try:
_start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')
_end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')
if (_end_date <= _start_date):
raise ValueError('Start date is greater than end date!')
else:
self.start_date = start_date
self.end_date = end_date
except:
raise AttributeError('Dates are not correctly formatted, should be %Y-%m-%d')
self.timeline = []
self.data_images = {}
self.data_array = None
items = None
try:
items = self.stac_client.search({'collections': self.collections, 'bbox': self.bbox, 'datetime': f'{self.start_date}/{self.end_date}', 'limit': limit})
except:
raise RuntimeError('Connection refused!')
images = []
if items:
for item in items.features:
bands = {}
available_bands = item.get('properties').get('eo:bands')
for band in available_bands:
band_common_name = str(band.get('common_name', ''))
band_name = str(band.get('name'))
if (band_common_name in self.query_bands):
bands[band_common_name] = band.get('name')
elif (band_name in self.query_bands):
bands[band_name] = band_name
images.append(Image(item=item, bands=bands, bbox=self.bbox))
if (len(images) == 0):
raise ValueError('No data cube created!')
x_data = {}
for image in images:
date = image.time
self.data_images[date] = image
x_data[date] = []
for band in self.query_bands:
data = delayed(image.getBand)(band)
x_data[date].append({str(band): data})
self.timeline = sorted(list(x_data.keys()))
data_timeline = {}
for i in range(len(list(self.query_bands))):
data_timeline[self.query_bands[i]] = []
for time in self.timeline:
data_timeline[self.query_bands[i]].append(x_data[time][i][self.query_bands[i]])
time_series = []
for band in self.query_bands:
time_series.append(data_timeline[band])
self.description = {}
for collection in self.collections:
response = self.stac_client.collections[collection]
self.description[str(response['id'])] = str(response['title'])
self.data_array = xr.DataArray(np.array(time_series), coords=[self.query_bands, self.timeline], dims=['band', 'time'], name=['DataCube'])
self.data_array.attrs = self.description
|
Build DataCube object with config parameters including access token, STAC url and earth observation service url.
|
eocube/eocube.py
|
__init__
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def __init__(self, collections=[], query_bands=[], bbox=(), start_date=None, end_date=None, limit=30):
if (len(config.ACCESS_TOKEN) == 0):
config.ACCESS_TOKEN = input('Please insert a valid user token from BDC Auth: ')
if (len(config.EOCUBE_URL) == 0):
config.EOCUBE_URL = input('Please insert a valid url for EO Service: ')
if (len(config.STAC_URL) == 0):
config.STAC_URL = input('Please insert a valid url for STAC Service: ')
self.utils = Utils()
self.stac_client = stac.STAC(config.STAC_URL, access_token=config.ACCESS_TOKEN)
if (not collections):
raise AttributeError('Please insert a list of available collections!')
else:
self.collections = collections
if (not query_bands):
raise AttributeError('Please insert a list of available bands with query_bands!')
else:
self.query_bands = query_bands
if (not bbox):
raise AttributeError('Please insert a bounding box parameter!')
else:
valid = self.utils._validateBBOX(bbox)
if valid:
self.bbox = bbox
try:
_start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')
_end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')
if (_end_date <= _start_date):
raise ValueError('Start date is greater than end date!')
else:
self.start_date = start_date
self.end_date = end_date
except:
raise AttributeError('Dates are not correctly formatted, should be %Y-%m-%d')
self.timeline = []
self.data_images = {}
self.data_array = None
items = None
try:
items = self.stac_client.search({'collections': self.collections, 'bbox': self.bbox, 'datetime': f'{self.start_date}/{self.end_date}', 'limit': limit})
except:
raise RuntimeError('Connection refused!')
images = []
if items:
for item in items.features:
bands = {}
available_bands = item.get('properties').get('eo:bands')
for band in available_bands:
band_common_name = str(band.get('common_name', ))
band_name = str(band.get('name'))
if (band_common_name in self.query_bands):
bands[band_common_name] = band.get('name')
elif (band_name in self.query_bands):
bands[band_name] = band_name
images.append(Image(item=item, bands=bands, bbox=self.bbox))
if (len(images) == 0):
raise ValueError('No data cube created!')
x_data = {}
for image in images:
date = image.time
self.data_images[date] = image
x_data[date] = []
for band in self.query_bands:
data = delayed(image.getBand)(band)
x_data[date].append({str(band): data})
self.timeline = sorted(list(x_data.keys()))
data_timeline = {}
for i in range(len(list(self.query_bands))):
data_timeline[self.query_bands[i]] = []
for time in self.timeline:
data_timeline[self.query_bands[i]].append(x_data[time][i][self.query_bands[i]])
time_series = []
for band in self.query_bands:
time_series.append(data_timeline[band])
self.description = {}
for collection in self.collections:
response = self.stac_client.collections[collection]
self.description[str(response['id'])] = str(response['title'])
self.data_array = xr.DataArray(np.array(time_series), coords=[self.query_bands, self.timeline], dims=['band', 'time'], name=['DataCube'])
self.data_array.attrs = self.description
|
def __init__(self, collections=[], query_bands=[], bbox=(), start_date=None, end_date=None, limit=30):
if (len(config.ACCESS_TOKEN) == 0):
config.ACCESS_TOKEN = input('Please insert a valid user token from BDC Auth: ')
if (len(config.EOCUBE_URL) == 0):
config.EOCUBE_URL = input('Please insert a valid url for EO Service: ')
if (len(config.STAC_URL) == 0):
config.STAC_URL = input('Please insert a valid url for STAC Service: ')
self.utils = Utils()
self.stac_client = stac.STAC(config.STAC_URL, access_token=config.ACCESS_TOKEN)
if (not collections):
raise AttributeError('Please insert a list of available collections!')
else:
self.collections = collections
if (not query_bands):
raise AttributeError('Please insert a list of available bands with query_bands!')
else:
self.query_bands = query_bands
if (not bbox):
raise AttributeError('Please insert a bounding box parameter!')
else:
valid = self.utils._validateBBOX(bbox)
if valid:
self.bbox = bbox
try:
_start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')
_end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d')
if (_end_date <= _start_date):
raise ValueError('Start date is greater than end date!')
else:
self.start_date = start_date
self.end_date = end_date
except:
raise AttributeError('Dates are not correctly formatted, should be %Y-%m-%d')
self.timeline = []
self.data_images = {}
self.data_array = None
items = None
try:
items = self.stac_client.search({'collections': self.collections, 'bbox': self.bbox, 'datetime': f'{self.start_date}/{self.end_date}', 'limit': limit})
except:
raise RuntimeError('Connection refused!')
images = []
if items:
for item in items.features:
bands = {}
available_bands = item.get('properties').get('eo:bands')
for band in available_bands:
band_common_name = str(band.get('common_name', ))
band_name = str(band.get('name'))
if (band_common_name in self.query_bands):
bands[band_common_name] = band.get('name')
elif (band_name in self.query_bands):
bands[band_name] = band_name
images.append(Image(item=item, bands=bands, bbox=self.bbox))
if (len(images) == 0):
raise ValueError('No data cube created!')
x_data = {}
for image in images:
date = image.time
self.data_images[date] = image
x_data[date] = []
for band in self.query_bands:
data = delayed(image.getBand)(band)
x_data[date].append({str(band): data})
self.timeline = sorted(list(x_data.keys()))
data_timeline = {}
for i in range(len(list(self.query_bands))):
data_timeline[self.query_bands[i]] = []
for time in self.timeline:
data_timeline[self.query_bands[i]].append(x_data[time][i][self.query_bands[i]])
time_series = []
for band in self.query_bands:
time_series.append(data_timeline[band])
self.description = {}
for collection in self.collections:
response = self.stac_client.collections[collection]
self.description[str(response['id'])] = str(response['title'])
self.data_array = xr.DataArray(np.array(time_series), coords=[self.query_bands, self.timeline], dims=['band', 'time'], name=['DataCube'])
self.data_array.attrs = self.description<|docstring|>Build DataCube object with config parameters including access token, STAC url and earth observation service url.<|endoftext|>
|
c9c346176dd85163385cf4defbfd4ef3e3e5bd13811dcadcf5cf336db6909a50
|
def nearTime(self, time):
'Search in all dataset a date time near to given time.\n Return a date time from dataset timeline.\n\n Parameters\n\n - time <datetime, required>: the given date time to search formated "yyyy-mm-dd".\n\n Raise\n\n - ValueError: If time is not correctly formatted.\n '
_date = self.data_array.sel(time=time, method='nearest').time.values
_date = datetime.datetime.utcfromtimestamp((_date.tolist() / 1000000000.0))
return _date
|
Search in all dataset a date time near to given time.
Return a date time from dataset timeline.
Parameters
- time <datetime, required>: the given date time to search formated "yyyy-mm-dd".
Raise
- ValueError: If time is not correctly formatted.
|
eocube/eocube.py
|
nearTime
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def nearTime(self, time):
'Search in all dataset a date time near to given time.\n Return a date time from dataset timeline.\n\n Parameters\n\n - time <datetime, required>: the given date time to search formated "yyyy-mm-dd".\n\n Raise\n\n - ValueError: If time is not correctly formatted.\n '
_date = self.data_array.sel(time=time, method='nearest').time.values
_date = datetime.datetime.utcfromtimestamp((_date.tolist() / 1000000000.0))
return _date
|
def nearTime(self, time):
'Search in all dataset a date time near to given time.\n Return a date time from dataset timeline.\n\n Parameters\n\n - time <datetime, required>: the given date time to search formated "yyyy-mm-dd".\n\n Raise\n\n - ValueError: If time is not correctly formatted.\n '
_date = self.data_array.sel(time=time, method='nearest').time.values
_date = datetime.datetime.utcfromtimestamp((_date.tolist() / 1000000000.0))
return _date<|docstring|>Search in all dataset a date time near to given time.
Return a date time from dataset timeline.
Parameters
- time <datetime, required>: the given date time to search formated "yyyy-mm-dd".
Raise
- ValueError: If time is not correctly formatted.<|endoftext|>
|
94c60fca90f9ec961cefb2d84de22127beb07f5cd5198e9d19752a0007b25ff0
|
def select(self, band=None, time=None, start_date=None, end_date=None):
'Select method to retrieve data from delayed dataset and return all dataset for black params but takes longer.\n\n Parameters:\n\n - band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - time <string, optional>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n - start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
result = None
if (start_date and end_date):
_start_date = self.nearTime(start_date)
_end_date = self.nearTime(end_date)
else:
_start_date = self.nearTime(self.start_date)
_end_date = self.nearTime(self.end_date)
if band:
if time:
_date = self.nearTime(time)
_timeline = [_date]
_data = self.data_array.loc[(band, _date)].values.reshape(1)
else:
_data = self.data_array.loc[(band, _start_date:_end_date)]
_timeline = _data.time.values
_data = _data.values
_result = []
for raster in _data:
value = raster.compute()
_result.append(value)
_x = list(range(0, value.shape[1]))
_y = list(range(0, value.shape[0]))
result = xr.DataArray(np.array(_result), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=[f'ResultSearch_{band}'])
else:
_bands = self.query_bands
_timeline = self.timeline
_data = []
for band in _bands:
d = self.data_array.loc[band].values
values = []
for i in range(len(d)):
raster = d[i].compute()
values.append(raster)
_x = list(range(0, raster.shape[1]))
_y = list(range(0, raster.shape[0]))
_data.append(values)
result = xr.DataArray(np.array(_data), coords=[_bands, _timeline, _y, _x], dims=['band', 'time', 'y', 'x'], name=['DataCube'])
result.attrs = self.description
return result
|
Select method to retrieve data from delayed dataset and return all dataset for black params but takes longer.
Parameters:
- band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).
- time <string, optional>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
- start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.
- end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.
Raise:
- KeyError: If the given parameter not exists.
|
eocube/eocube.py
|
select
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def select(self, band=None, time=None, start_date=None, end_date=None):
'Select method to retrieve data from delayed dataset and return all dataset for black params but takes longer.\n\n Parameters:\n\n - band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - time <string, optional>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n - start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
result = None
if (start_date and end_date):
_start_date = self.nearTime(start_date)
_end_date = self.nearTime(end_date)
else:
_start_date = self.nearTime(self.start_date)
_end_date = self.nearTime(self.end_date)
if band:
if time:
_date = self.nearTime(time)
_timeline = [_date]
_data = self.data_array.loc[(band, _date)].values.reshape(1)
else:
_data = self.data_array.loc[(band, _start_date:_end_date)]
_timeline = _data.time.values
_data = _data.values
_result = []
for raster in _data:
value = raster.compute()
_result.append(value)
_x = list(range(0, value.shape[1]))
_y = list(range(0, value.shape[0]))
result = xr.DataArray(np.array(_result), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=[f'ResultSearch_{band}'])
else:
_bands = self.query_bands
_timeline = self.timeline
_data = []
for band in _bands:
d = self.data_array.loc[band].values
values = []
for i in range(len(d)):
raster = d[i].compute()
values.append(raster)
_x = list(range(0, raster.shape[1]))
_y = list(range(0, raster.shape[0]))
_data.append(values)
result = xr.DataArray(np.array(_data), coords=[_bands, _timeline, _y, _x], dims=['band', 'time', 'y', 'x'], name=['DataCube'])
result.attrs = self.description
return result
|
def select(self, band=None, time=None, start_date=None, end_date=None):
'Select method to retrieve data from delayed dataset and return all dataset for black params but takes longer.\n\n Parameters:\n\n - band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - time <string, optional>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n - start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
result = None
if (start_date and end_date):
_start_date = self.nearTime(start_date)
_end_date = self.nearTime(end_date)
else:
_start_date = self.nearTime(self.start_date)
_end_date = self.nearTime(self.end_date)
if band:
if time:
_date = self.nearTime(time)
_timeline = [_date]
_data = self.data_array.loc[(band, _date)].values.reshape(1)
else:
_data = self.data_array.loc[(band, _start_date:_end_date)]
_timeline = _data.time.values
_data = _data.values
_result = []
for raster in _data:
value = raster.compute()
_result.append(value)
_x = list(range(0, value.shape[1]))
_y = list(range(0, value.shape[0]))
result = xr.DataArray(np.array(_result), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=[f'ResultSearch_{band}'])
else:
_bands = self.query_bands
_timeline = self.timeline
_data = []
for band in _bands:
d = self.data_array.loc[band].values
values = []
for i in range(len(d)):
raster = d[i].compute()
values.append(raster)
_x = list(range(0, raster.shape[1]))
_y = list(range(0, raster.shape[0]))
_data.append(values)
result = xr.DataArray(np.array(_data), coords=[_bands, _timeline, _y, _x], dims=['band', 'time', 'y', 'x'], name=['DataCube'])
result.attrs = self.description
return result<|docstring|>Select method to retrieve data from delayed dataset and return all dataset for black params but takes longer.
Parameters:
- band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).
- time <string, optional>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
- start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.
- end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.
Raise:
- KeyError: If the given parameter not exists.<|endoftext|>
|
39ba0af42e93598442bf55c2226bc469b991977cac03f4ea4cd8fc3269611f83
|
def getTimeSeries(self, band=None, lon=None, lat=None, start_date=None, end_date=None):
'Get time series band values from a given point and timeline.\n\n Parameters:\n\n - band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - lon <float, optional>: The given longitude of point (EPSG:4326).\n\n - lat <float, optional>: The given latitude of point (EPSG:4326).\n\n - start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
_image = None
if (start_date and end_date):
start = start_date
end = end_date
else:
start = self.start_date
end = self.end_date
_start_date = datetime.datetime.strptime(start, '%Y-%m-%d')
_end_date = datetime.datetime.strptime(end, '%Y-%m-%d')
for time in self.timeline:
if ((time.year == _start_date.year) and (time.month == _start_date.month)):
_image = self.data_images[time]
break
if (not _image):
_image = self.data_images[self.timeline[0]]
point = _image._afimCoordsToPoint(lon, lat, band)
_data = self.data_array.loc[(band, _start_date:_end_date)]
sample = _data.values[0].compute()
if ((point[0] > sample.shape[0]) and (point[1] > sample.shape[1])):
raise ValueError(f'Given point is out of bounding box {self.bbox}')
result = []
for raster in _data.values:
result.append(raster.compute()[point[0]][point[1]])
_result = xr.DataArray(np.array(result), coords=[_data.time], dims=['time'], name=[f'TimeSeries_{band.upper()}'])
_result.attrs = {'longitude': lon, 'latitude': lat}
return _result
|
Get time series band values from a given point and timeline.
Parameters:
- band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).
- lon <float, optional>: The given longitude of point (EPSG:4326).
- lat <float, optional>: The given latitude of point (EPSG:4326).
- start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.
- end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.
Raise:
- KeyError: If the given parameter not exists.
|
eocube/eocube.py
|
getTimeSeries
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def getTimeSeries(self, band=None, lon=None, lat=None, start_date=None, end_date=None):
'Get time series band values from a given point and timeline.\n\n Parameters:\n\n - band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - lon <float, optional>: The given longitude of point (EPSG:4326).\n\n - lat <float, optional>: The given latitude of point (EPSG:4326).\n\n - start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
_image = None
if (start_date and end_date):
start = start_date
end = end_date
else:
start = self.start_date
end = self.end_date
_start_date = datetime.datetime.strptime(start, '%Y-%m-%d')
_end_date = datetime.datetime.strptime(end, '%Y-%m-%d')
for time in self.timeline:
if ((time.year == _start_date.year) and (time.month == _start_date.month)):
_image = self.data_images[time]
break
if (not _image):
_image = self.data_images[self.timeline[0]]
point = _image._afimCoordsToPoint(lon, lat, band)
_data = self.data_array.loc[(band, _start_date:_end_date)]
sample = _data.values[0].compute()
if ((point[0] > sample.shape[0]) and (point[1] > sample.shape[1])):
raise ValueError(f'Given point is out of bounding box {self.bbox}')
result = []
for raster in _data.values:
result.append(raster.compute()[point[0]][point[1]])
_result = xr.DataArray(np.array(result), coords=[_data.time], dims=['time'], name=[f'TimeSeries_{band.upper()}'])
_result.attrs = {'longitude': lon, 'latitude': lat}
return _result
|
def getTimeSeries(self, band=None, lon=None, lat=None, start_date=None, end_date=None):
'Get time series band values from a given point and timeline.\n\n Parameters:\n\n - band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - lon <float, optional>: The given longitude of point (EPSG:4326).\n\n - lat <float, optional>: The given latitude of point (EPSG:4326).\n\n - start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
_image = None
if (start_date and end_date):
start = start_date
end = end_date
else:
start = self.start_date
end = self.end_date
_start_date = datetime.datetime.strptime(start, '%Y-%m-%d')
_end_date = datetime.datetime.strptime(end, '%Y-%m-%d')
for time in self.timeline:
if ((time.year == _start_date.year) and (time.month == _start_date.month)):
_image = self.data_images[time]
break
if (not _image):
_image = self.data_images[self.timeline[0]]
point = _image._afimCoordsToPoint(lon, lat, band)
_data = self.data_array.loc[(band, _start_date:_end_date)]
sample = _data.values[0].compute()
if ((point[0] > sample.shape[0]) and (point[1] > sample.shape[1])):
raise ValueError(f'Given point is out of bounding box {self.bbox}')
result = []
for raster in _data.values:
result.append(raster.compute()[point[0]][point[1]])
_result = xr.DataArray(np.array(result), coords=[_data.time], dims=['time'], name=[f'TimeSeries_{band.upper()}'])
_result.attrs = {'longitude': lon, 'latitude': lat}
return _result<|docstring|>Get time series band values from a given point and timeline.
Parameters:
- band <string, optional>: The commom name of band (nir, ndvi, red, ... see info.collections).
- lon <float, optional>: The given longitude of point (EPSG:4326).
- lat <float, optional>: The given latitude of point (EPSG:4326).
- start_date <string, optional>: The string start date formated "yyyy-mm-dd" to complete the interval.
- end_date <string, optional>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.
Raise:
- KeyError: If the given parameter not exists.<|endoftext|>
|
9648d5dd0889224297022d49eb7b5486cc6b720cfdb88f5ae1c98636f71e0b29
|
def calculateNDVI(self, time):
'Calculate the Normalized Difference Vegetation Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDVI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDVI'])
result.attrs = self.description
return result
|
Calculate the Normalized Difference Vegetation Index - NDVI of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.
|
eocube/eocube.py
|
calculateNDVI
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def calculateNDVI(self, time):
'Calculate the Normalized Difference Vegetation Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDVI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDVI'])
result.attrs = self.description
return result
|
def calculateNDVI(self, time):
'Calculate the Normalized Difference Vegetation Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDVI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDVI'])
result.attrs = self.description
return result<|docstring|>Calculate the Normalized Difference Vegetation Index - NDVI of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.<|endoftext|>
|
a979b4189263243eb1f05b3cbfae5852c525bbaa275e6275eef8af28e93ef7f1
|
def calculateNDWI(self, time):
'Calculate the Normalized Difference Water Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDWI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDWI'])
result.attrs = self.description
return result
|
Calculate the Normalized Difference Water Index - NDVI of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.
|
eocube/eocube.py
|
calculateNDWI
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def calculateNDWI(self, time):
'Calculate the Normalized Difference Water Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDWI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDWI'])
result.attrs = self.description
return result
|
def calculateNDWI(self, time):
'Calculate the Normalized Difference Water Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDWI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDWI'])
result.attrs = self.description
return result<|docstring|>Calculate the Normalized Difference Water Index - NDVI of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.<|endoftext|>
|
2a141ea202a760440d8d5d97f62f460b7686f306f3e6422ef11894546afcbd53
|
def calculateNDBI(self, time):
'Calculate the Normalized Difference Built-up Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDBI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDBI'])
result.attrs = self.description
return result
|
Calculate the Normalized Difference Built-up Index - NDVI of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.
|
eocube/eocube.py
|
calculateNDBI
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def calculateNDBI(self, time):
'Calculate the Normalized Difference Built-up Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDBI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDBI'])
result.attrs = self.description
return result
|
def calculateNDBI(self, time):
'Calculate the Normalized Difference Built-up Index - NDVI of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getNDBI()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ImageNDBI'])
result.attrs = self.description
return result<|docstring|>Calculate the Normalized Difference Built-up Index - NDVI of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.<|endoftext|>
|
034208a4c3c56254802b7921ae63960e398fe65b4ec43ba9f98886b7e97aef66
|
def calculateColorComposition(self, time):
'Calculate the color composition RGB of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getRGB()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
_rgb = ['red', 'green', 'blue']
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x, _rgb], dims=['time', 'y', 'x', 'rgb'], name=['ColorComposition'])
result.attrs = self.description
return result
|
Calculate the color composition RGB of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.
|
eocube/eocube.py
|
calculateColorComposition
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def calculateColorComposition(self, time):
'Calculate the color composition RGB of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getRGB()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
_rgb = ['red', 'green', 'blue']
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x, _rgb], dims=['time', 'y', 'x', 'rgb'], name=['ColorComposition'])
result.attrs = self.description
return result
|
def calculateColorComposition(self, time):
'Calculate the color composition RGB of a given period.\n\n Parameters\n\n - time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".\n\n Raise:\n\n - KeyError: No data for given date time selected.\n '
_date = self.nearTime(time)
_data = self.data_images[_date].getRGB()
_timeline = [_date]
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
_rgb = ['red', 'green', 'blue']
result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x, _rgb], dims=['time', 'y', 'x', 'rgb'], name=['ColorComposition'])
result.attrs = self.description
return result<|docstring|>Calculate the color composition RGB of a given period.
Parameters
- time <string, required>: The given time to retrieve a sigle image formated "yyyy-mm-dd".
Raise:
- KeyError: No data for given date time selected.<|endoftext|>
|
fb1753f5ec4917d128dba5cd59ee8639c8a056b08f6895166e2af4bf07739036
|
def classifyDifference(self, band, start_date, end_date, limiar_min=0, limiar_max=0):
'Classify two different images with start and end date based on limiar mim and max.\n\n Parameters:\n\n - band <string, required>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - start_date <string, required>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, required>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n - limiar_min <float, required>: The minimum value classified to difference.\n\n - limiar_max <float, required>: The maximum value classified to difference to complete the interval.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
time_1 = self.nearTime(start_date)
data_1 = self.data_images[time_1].getBand(band)
time_2 = self.nearTime(end_date)
data_2 = self.data_images[time_2].getBand(band)
spectral = Spectral()
data_1 = spectral._format(data_1)
data_2 = spectral._format(data_2)
_data = None
if spectral._validate_shape(data_1, data_2):
diff = spectral._matrix_diff(data_1, data_2)
_data = spectral._classify_diff(diff, limiar_min=limiar_min, limiar_max=limiar_max)
else:
raise ValueError('Time 1 and 2 has different shapes!')
_timeline = [f'{time_1} - {time_2}']
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
_result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ClassifyDifference'])
return _result
|
Classify two different images with start and end date based on limiar mim and max.
Parameters:
- band <string, required>: The commom name of band (nir, ndvi, red, ... see info.collections).
- start_date <string, required>: The string start date formated "yyyy-mm-dd" to complete the interval.
- end_date <string, required>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.
- limiar_min <float, required>: The minimum value classified to difference.
- limiar_max <float, required>: The maximum value classified to difference to complete the interval.
Raise:
- KeyError: If the given parameter not exists.
|
eocube/eocube.py
|
classifyDifference
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def classifyDifference(self, band, start_date, end_date, limiar_min=0, limiar_max=0):
'Classify two different images with start and end date based on limiar mim and max.\n\n Parameters:\n\n - band <string, required>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - start_date <string, required>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, required>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n - limiar_min <float, required>: The minimum value classified to difference.\n\n - limiar_max <float, required>: The maximum value classified to difference to complete the interval.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
time_1 = self.nearTime(start_date)
data_1 = self.data_images[time_1].getBand(band)
time_2 = self.nearTime(end_date)
data_2 = self.data_images[time_2].getBand(band)
spectral = Spectral()
data_1 = spectral._format(data_1)
data_2 = spectral._format(data_2)
_data = None
if spectral._validate_shape(data_1, data_2):
diff = spectral._matrix_diff(data_1, data_2)
_data = spectral._classify_diff(diff, limiar_min=limiar_min, limiar_max=limiar_max)
else:
raise ValueError('Time 1 and 2 has different shapes!')
_timeline = [f'{time_1} - {time_2}']
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
_result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ClassifyDifference'])
return _result
|
def classifyDifference(self, band, start_date, end_date, limiar_min=0, limiar_max=0):
'Classify two different images with start and end date based on limiar mim and max.\n\n Parameters:\n\n - band <string, required>: The commom name of band (nir, ndvi, red, ... see info.collections).\n\n - start_date <string, required>: The string start date formated "yyyy-mm-dd" to complete the interval.\n\n - end_date <string, required>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.\n\n - limiar_min <float, required>: The minimum value classified to difference.\n\n - limiar_max <float, required>: The maximum value classified to difference to complete the interval.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
time_1 = self.nearTime(start_date)
data_1 = self.data_images[time_1].getBand(band)
time_2 = self.nearTime(end_date)
data_2 = self.data_images[time_2].getBand(band)
spectral = Spectral()
data_1 = spectral._format(data_1)
data_2 = spectral._format(data_2)
_data = None
if spectral._validate_shape(data_1, data_2):
diff = spectral._matrix_diff(data_1, data_2)
_data = spectral._classify_diff(diff, limiar_min=limiar_min, limiar_max=limiar_max)
else:
raise ValueError('Time 1 and 2 has different shapes!')
_timeline = [f'{time_1} - {time_2}']
_x = list(range(0, _data.shape[1]))
_y = list(range(0, _data.shape[0]))
_result = xr.DataArray(np.array([_data]), coords=[_timeline, _y, _x], dims=['time', 'y', 'x'], name=['ClassifyDifference'])
return _result<|docstring|>Classify two different images with start and end date based on limiar mim and max.
Parameters:
- band <string, required>: The commom name of band (nir, ndvi, red, ... see info.collections).
- start_date <string, required>: The string start date formated "yyyy-mm-dd" to complete the interval.
- end_date <string, required>: The string end date formated "yyyy-mm-dd" to complete the interval and retrieve a dataset.
- limiar_min <float, required>: The minimum value classified to difference.
- limiar_max <float, required>: The maximum value classified to difference to complete the interval.
Raise:
- KeyError: If the given parameter not exists.<|endoftext|>
|
45de1528c29f59147b3d7d8227178e1f72a56bd8f3e41fb9a0b6360a2d8c179a
|
def interactPlot(self, method):
'Return all dataset with a interactive plot date time slider.\n\n Parameters:\n\n - method <string, required>: The method like rgb, ndvi, ndwi, ndbi, ... or any of selected bands.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
@interact(date=self.timeline)
def sliderplot(date):
plt.clf()
plt.figure(figsize=(25, 8))
if (method == 'rgb'):
plt.imshow(self.data_images[date].getRGB())
plt.title(f'''
Composição Colorida Verdadeira {date}
''')
elif ((method == 'ndvi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Greens', 1000)
plt.imshow(self.data_images[date].getNDVI(), cmap=colormap)
plt.title(f'''
NDVI - Normalized Difference Vegetation Index {date}
''')
plt.colorbar()
elif ((method == 'ndwi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Blues', 1000)
plt.imshow(self.data_images[date].getNDWI(), cmap=colormap)
plt.title(f'''
NDWI - Normalized Difference Water Index {date}
''')
plt.colorbar()
elif ((method == 'ndbi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Greys', 1000)
plt.imshow(self.data_images[date].getNDBI(), cmap=colormap)
plt.title(f'''
NDBI - Normalized Difference Built-up Index {date}
''')
plt.colorbar()
elif (method in self.query_bands):
colormap = plt.get_cmap('Greys', 1000)
if (method in ['red', 'green', 'blue']):
colormap = plt.get_cmap('Greys', 255).reversed()
plt.imshow(self.data_images[date].getBand(method), cmap=colormap)
plt.title(f'''
Composição da Banda {method.upper()} {date}
''')
plt.colorbar()
else:
raise ValueError('Please insert a valid method rgb, ndvi, ndwi, ndbi, ... or any of selected bands!')
plt.tight_layout()
plt.show()
|
Return all dataset with a interactive plot date time slider.
Parameters:
- method <string, required>: The method like rgb, ndvi, ndwi, ndbi, ... or any of selected bands.
Raise:
- KeyError: If the given parameter not exists.
|
eocube/eocube.py
|
interactPlot
|
AbnerErnaniADSFatec/eocubes-dev
| 0 |
python
|
def interactPlot(self, method):
'Return all dataset with a interactive plot date time slider.\n\n Parameters:\n\n - method <string, required>: The method like rgb, ndvi, ndwi, ndbi, ... or any of selected bands.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
@interact(date=self.timeline)
def sliderplot(date):
plt.clf()
plt.figure(figsize=(25, 8))
if (method == 'rgb'):
plt.imshow(self.data_images[date].getRGB())
plt.title(f'
Composição Colorida Verdadeira {date}
')
elif ((method == 'ndvi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Greens', 1000)
plt.imshow(self.data_images[date].getNDVI(), cmap=colormap)
plt.title(f'
NDVI - Normalized Difference Vegetation Index {date}
')
plt.colorbar()
elif ((method == 'ndwi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Blues', 1000)
plt.imshow(self.data_images[date].getNDWI(), cmap=colormap)
plt.title(f'
NDWI - Normalized Difference Water Index {date}
')
plt.colorbar()
elif ((method == 'ndbi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Greys', 1000)
plt.imshow(self.data_images[date].getNDBI(), cmap=colormap)
plt.title(f'
NDBI - Normalized Difference Built-up Index {date}
')
plt.colorbar()
elif (method in self.query_bands):
colormap = plt.get_cmap('Greys', 1000)
if (method in ['red', 'green', 'blue']):
colormap = plt.get_cmap('Greys', 255).reversed()
plt.imshow(self.data_images[date].getBand(method), cmap=colormap)
plt.title(f'
Composição da Banda {method.upper()} {date}
')
plt.colorbar()
else:
raise ValueError('Please insert a valid method rgb, ndvi, ndwi, ndbi, ... or any of selected bands!')
plt.tight_layout()
plt.show()
|
def interactPlot(self, method):
'Return all dataset with a interactive plot date time slider.\n\n Parameters:\n\n - method <string, required>: The method like rgb, ndvi, ndwi, ndbi, ... or any of selected bands.\n\n Raise:\n\n - KeyError: If the given parameter not exists.\n '
@interact(date=self.timeline)
def sliderplot(date):
plt.clf()
plt.figure(figsize=(25, 8))
if (method == 'rgb'):
plt.imshow(self.data_images[date].getRGB())
plt.title(f'
Composição Colorida Verdadeira {date}
')
elif ((method == 'ndvi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Greens', 1000)
plt.imshow(self.data_images[date].getNDVI(), cmap=colormap)
plt.title(f'
NDVI - Normalized Difference Vegetation Index {date}
')
plt.colorbar()
elif ((method == 'ndwi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Blues', 1000)
plt.imshow(self.data_images[date].getNDWI(), cmap=colormap)
plt.title(f'
NDWI - Normalized Difference Water Index {date}
')
plt.colorbar()
elif ((method == 'ndbi') and (not (method in self.query_bands))):
colormap = plt.get_cmap('Greys', 1000)
plt.imshow(self.data_images[date].getNDBI(), cmap=colormap)
plt.title(f'
NDBI - Normalized Difference Built-up Index {date}
')
plt.colorbar()
elif (method in self.query_bands):
colormap = plt.get_cmap('Greys', 1000)
if (method in ['red', 'green', 'blue']):
colormap = plt.get_cmap('Greys', 255).reversed()
plt.imshow(self.data_images[date].getBand(method), cmap=colormap)
plt.title(f'
Composição da Banda {method.upper()} {date}
')
plt.colorbar()
else:
raise ValueError('Please insert a valid method rgb, ndvi, ndwi, ndbi, ... or any of selected bands!')
plt.tight_layout()
plt.show()<|docstring|>Return all dataset with a interactive plot date time slider.
Parameters:
- method <string, required>: The method like rgb, ndvi, ndwi, ndbi, ... or any of selected bands.
Raise:
- KeyError: If the given parameter not exists.<|endoftext|>
|
e5a876b0dbead667c3d77ef6fb0ec9e55c1bf8866ff8b78215fbda9d434b35c0
|
@commands.command(name='help')
async def help(self, context):
'\n List all commands from every Cog the bot has loaded.\n '
prefix = config['bot_prefix']
if (not isinstance(prefix, str)):
prefix = prefix[0]
embed = discord.Embed(title='Help', description='List of available commands:', color=config['success'])
for i in self.bot.cogs:
cog = self.bot.get_cog(i.lower())
print(i)
if (i not in ['owner', 'template', 'moderation']):
commands = cog.get_commands()
command_list = [command.name for command in commands]
command_description = [command.help for command in commands]
help_text = '\n'.join((f'{prefix}{n} - {h}' for (n, h) in zip(command_list, command_description)))
embed.add_field(name=i.capitalize(), value=f'```{help_text}```', inline=False)
(await context.send(embed=embed))
|
List all commands from every Cog the bot has loaded.
|
cogs/help.py
|
help
|
Nanosplitter/DadBotV2.0
| 2 |
python
|
@commands.command(name='help')
async def help(self, context):
'\n \n '
prefix = config['bot_prefix']
if (not isinstance(prefix, str)):
prefix = prefix[0]
embed = discord.Embed(title='Help', description='List of available commands:', color=config['success'])
for i in self.bot.cogs:
cog = self.bot.get_cog(i.lower())
print(i)
if (i not in ['owner', 'template', 'moderation']):
commands = cog.get_commands()
command_list = [command.name for command in commands]
command_description = [command.help for command in commands]
help_text = '\n'.join((f'{prefix}{n} - {h}' for (n, h) in zip(command_list, command_description)))
embed.add_field(name=i.capitalize(), value=f'```{help_text}```', inline=False)
(await context.send(embed=embed))
|
@commands.command(name='help')
async def help(self, context):
'\n \n '
prefix = config['bot_prefix']
if (not isinstance(prefix, str)):
prefix = prefix[0]
embed = discord.Embed(title='Help', description='List of available commands:', color=config['success'])
for i in self.bot.cogs:
cog = self.bot.get_cog(i.lower())
print(i)
if (i not in ['owner', 'template', 'moderation']):
commands = cog.get_commands()
command_list = [command.name for command in commands]
command_description = [command.help for command in commands]
help_text = '\n'.join((f'{prefix}{n} - {h}' for (n, h) in zip(command_list, command_description)))
embed.add_field(name=i.capitalize(), value=f'```{help_text}```', inline=False)
(await context.send(embed=embed))<|docstring|>List all commands from every Cog the bot has loaded.<|endoftext|>
|
4ce8cc981c8ac6ff9c13af7410d26694a0de292b2bc53c8969f0c9ebdcc76c9e
|
def test_logging():
'Test the logging function'
differ.utils.setup_logging('INFO')
differ.utils.setup_logging('DEBUG')
differ.utils.setup_logging('ERROR')
|
Test the logging function
|
tests/test_utils.py
|
test_logging
|
MadRussian/differ
| 1 |
python
|
def test_logging():
differ.utils.setup_logging('INFO')
differ.utils.setup_logging('DEBUG')
differ.utils.setup_logging('ERROR')
|
def test_logging():
differ.utils.setup_logging('INFO')
differ.utils.setup_logging('DEBUG')
differ.utils.setup_logging('ERROR')<|docstring|>Test the logging function<|endoftext|>
|
70af37fcea824d1a602212426b3adc996483b62ded964230f46f6e28bb923d0c
|
def test_get_file_type():
'Test get_file_type'
assert (differ.utils.get_file_type(None) == None)
assert (differ.utils.get_file_type("Doesn't exist") == None)
|
Test get_file_type
|
tests/test_utils.py
|
test_get_file_type
|
MadRussian/differ
| 1 |
python
|
def test_get_file_type():
assert (differ.utils.get_file_type(None) == None)
assert (differ.utils.get_file_type("Doesn't exist") == None)
|
def test_get_file_type():
assert (differ.utils.get_file_type(None) == None)
assert (differ.utils.get_file_type("Doesn't exist") == None)<|docstring|>Test get_file_type<|endoftext|>
|
79e534f8ad3a19dad760e0244cf1106ab36d5f658c407980baa5193e9c18eec7
|
def update(self, item: T, new_item: T) -> None:
'Updates given item value in heap if present.'
if (item not in self.elem_to_index):
raise KeyError('Item not found')
index = self.elem_to_index[item]
self._heap[index] = new_item
self.elem_to_index[new_item] = index
self._heapify_up(index)
self._heapify_down(index)
|
Updates given item value in heap if present.
|
cs/structures/heap/binary_heap.py
|
update
|
TylerYep/workshop
| 1 |
python
|
def update(self, item: T, new_item: T) -> None:
if (item not in self.elem_to_index):
raise KeyError('Item not found')
index = self.elem_to_index[item]
self._heap[index] = new_item
self.elem_to_index[new_item] = index
self._heapify_up(index)
self._heapify_down(index)
|
def update(self, item: T, new_item: T) -> None:
if (item not in self.elem_to_index):
raise KeyError('Item not found')
index = self.elem_to_index[item]
self._heap[index] = new_item
self.elem_to_index[new_item] = index
self._heapify_up(index)
self._heapify_down(index)<|docstring|>Updates given item value in heap if present.<|endoftext|>
|
7fba11e89cebc02de4105f2b77e2ad593f18fb61ca409ca158044d29d4adcb37
|
def dequeue(self, item: T) -> None:
'Deletes given item from heap if present.'
if (item not in self.elem_to_index):
raise KeyError('Item not found')
index = self.elem_to_index[item]
del self.elem_to_index[item]
self._heap[index] = self._heap[(self.size - 1)]
self.elem_to_index[self._heap[(self.size - 1)]] = index
self.size -= 1
if (self.size > index):
self._heapify_up(index)
self._heapify_down(index)
|
Deletes given item from heap if present.
|
cs/structures/heap/binary_heap.py
|
dequeue
|
TylerYep/workshop
| 1 |
python
|
def dequeue(self, item: T) -> None:
if (item not in self.elem_to_index):
raise KeyError('Item not found')
index = self.elem_to_index[item]
del self.elem_to_index[item]
self._heap[index] = self._heap[(self.size - 1)]
self.elem_to_index[self._heap[(self.size - 1)]] = index
self.size -= 1
if (self.size > index):
self._heapify_up(index)
self._heapify_down(index)
|
def dequeue(self, item: T) -> None:
if (item not in self.elem_to_index):
raise KeyError('Item not found')
index = self.elem_to_index[item]
del self.elem_to_index[item]
self._heap[index] = self._heap[(self.size - 1)]
self.elem_to_index[self._heap[(self.size - 1)]] = index
self.size -= 1
if (self.size > index):
self._heapify_up(index)
self._heapify_down(index)<|docstring|>Deletes given item from heap if present.<|endoftext|>
|
ad81412c35c7f807661c46caf0d20fe64b9aeedb2c0034a9906e8d0ba706f6de
|
def enqueue(self, item: T) -> None:
'Inserts given item with given value in heap.'
new_node = item
if (len(self._heap) == self.size):
self._heap.append(new_node)
else:
self._heap[self.size] = new_node
self.elem_to_index[item] = self.size
self.size += 1
self._heapify_up((self.size - 1))
|
Inserts given item with given value in heap.
|
cs/structures/heap/binary_heap.py
|
enqueue
|
TylerYep/workshop
| 1 |
python
|
def enqueue(self, item: T) -> None:
new_node = item
if (len(self._heap) == self.size):
self._heap.append(new_node)
else:
self._heap[self.size] = new_node
self.elem_to_index[item] = self.size
self.size += 1
self._heapify_up((self.size - 1))
|
def enqueue(self, item: T) -> None:
new_node = item
if (len(self._heap) == self.size):
self._heap.append(new_node)
else:
self._heap[self.size] = new_node
self.elem_to_index[item] = self.size
self.size += 1
self._heapify_up((self.size - 1))<|docstring|>Inserts given item with given value in heap.<|endoftext|>
|
a75074403d3524d66e61f7470e791754dabccfe2f78b94da8a771c2491bd81e1
|
def peek(self) -> T:
'Returns top item tuple (Calculated value, item) from heap if present.'
if (self.size == 0):
raise ValueError('Heap is empty.')
return self._heap[0]
|
Returns top item tuple (Calculated value, item) from heap if present.
|
cs/structures/heap/binary_heap.py
|
peek
|
TylerYep/workshop
| 1 |
python
|
def peek(self) -> T:
if (self.size == 0):
raise ValueError('Heap is empty.')
return self._heap[0]
|
def peek(self) -> T:
if (self.size == 0):
raise ValueError('Heap is empty.')
return self._heap[0]<|docstring|>Returns top item tuple (Calculated value, item) from heap if present.<|endoftext|>
|
d8ac5852d3627c4638f2416bfe0ccb477855c9f50395fca9fab5b0c23b4f1e8c
|
def pop(self) -> T:
'\n Return top item tuple (Calculated value, item) from heap and removes it as well\n if present.\n '
top_item = self.peek()
self.dequeue(top_item)
return top_item
|
Return top item tuple (Calculated value, item) from heap and removes it as well
if present.
|
cs/structures/heap/binary_heap.py
|
pop
|
TylerYep/workshop
| 1 |
python
|
def pop(self) -> T:
'\n Return top item tuple (Calculated value, item) from heap and removes it as well\n if present.\n '
top_item = self.peek()
self.dequeue(top_item)
return top_item
|
def pop(self) -> T:
'\n Return top item tuple (Calculated value, item) from heap and removes it as well\n if present.\n '
top_item = self.peek()
self.dequeue(top_item)
return top_item<|docstring|>Return top item tuple (Calculated value, item) from heap and removes it as well
if present.<|endoftext|>
|
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