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colour-science/colour | 38782ac059e8ddd91939f3432bf06811c16667f0 | colour/io/image.py | python | read_image | (path, bit_depth='float32', method='OpenImageIO', **kwargs) | return function(path, bit_depth, **kwargs) | Reads the image at given path using given method.
Parameters
----------
path : str
Image path.
bit_depth : str, optional
**{'float32', 'uint8', 'uint16', 'float16'}**,
Returned image bit depth, for the *Imageio* method, the image data is
converted with :func:`colour.io.convert_bit_depth` definition after
reading the image, for the *OpenImageIO* method, the bit depth
conversion behaviour is driven directly by the library, this definition
only converts to the relevant data type after reading.
method : str, optional
**{'OpenImageIO', 'Imageio'}**,
Read method, i.e. the image library used for reading images.
Other Parameters
----------------
attributes : bool, optional
{:func:`colour.io.read_image_OpenImageIO`},
Whether to return the image attributes.
Returns
-------
ndarray
Image as a ndarray.
Notes
-----
- If the given method is *OpenImageIO* but the library is not available
writing will be performed by *Imageio*.
- If the given method is *Imageio*, ``kwargs`` is passed directly to the
wrapped definition.
- For convenience, single channel images are squeezed to 2d arrays.
Examples
--------
>>> import os
>>> import colour
>>> path = os.path.join(colour.__path__[0], 'io', 'tests', 'resources',
... 'CMS_Test_Pattern.exr')
>>> image = read_image(path)
>>> image.shape # doctest: +SKIP
(1267, 1274, 3)
>>> image.dtype
dtype('float32') | Reads the image at given path using given method. | [
"Reads",
"the",
"image",
"at",
"given",
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"using",
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"method",
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] | def read_image(path, bit_depth='float32', method='OpenImageIO', **kwargs):
"""
Reads the image at given path using given method.
Parameters
----------
path : str
Image path.
bit_depth : str, optional
**{'float32', 'uint8', 'uint16', 'float16'}**,
Returned image bit depth, for the *Imageio* method, the image data is
converted with :func:`colour.io.convert_bit_depth` definition after
reading the image, for the *OpenImageIO* method, the bit depth
conversion behaviour is driven directly by the library, this definition
only converts to the relevant data type after reading.
method : str, optional
**{'OpenImageIO', 'Imageio'}**,
Read method, i.e. the image library used for reading images.
Other Parameters
----------------
attributes : bool, optional
{:func:`colour.io.read_image_OpenImageIO`},
Whether to return the image attributes.
Returns
-------
ndarray
Image as a ndarray.
Notes
-----
- If the given method is *OpenImageIO* but the library is not available
writing will be performed by *Imageio*.
- If the given method is *Imageio*, ``kwargs`` is passed directly to the
wrapped definition.
- For convenience, single channel images are squeezed to 2d arrays.
Examples
--------
>>> import os
>>> import colour
>>> path = os.path.join(colour.__path__[0], 'io', 'tests', 'resources',
... 'CMS_Test_Pattern.exr')
>>> image = read_image(path)
>>> image.shape # doctest: +SKIP
(1267, 1274, 3)
>>> image.dtype
dtype('float32')
"""
method = validate_method(method, READ_IMAGE_METHODS)
if method == 'openimageio': # pragma: no cover
if not is_openimageio_installed():
usage_warning(
'"OpenImageIO" related API features are not available, '
'switching to "Imageio"!')
method = 'Imageio'
function = READ_IMAGE_METHODS[method]
if method == 'openimageio': # pragma: no cover
kwargs = filter_kwargs(function, **kwargs)
return function(path, bit_depth, **kwargs) | [
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pallets/werkzeug | 9efe8c00dcb2b6fc086961ba304729db01912652 | src/werkzeug/security.py | python | check_password_hash | (pwhash: str, password: str) | return hmac.compare_digest(_hash_internal(method, salt, password)[0], hashval) | Check a password against a given salted and hashed password value.
In order to support unsalted legacy passwords this method supports
plain text passwords, md5 and sha1 hashes (both salted and unsalted).
Returns `True` if the password matched, `False` otherwise.
:param pwhash: a hashed string like returned by
:func:`generate_password_hash`.
:param password: the plaintext password to compare against the hash. | Check a password against a given salted and hashed password value.
In order to support unsalted legacy passwords this method supports
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In order to support unsalted legacy passwords this method supports
plain text passwords, md5 and sha1 hashes (both salted and unsalted).
Returns `True` if the password matched, `False` otherwise.
:param pwhash: a hashed string like returned by
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"""
if pwhash.count("$") < 2:
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/heatmap/_colorbar.py | python | ColorBar.tickvals | (self) | return self["tickvals"] | Sets the values at which ticks on this axis appear. Only has an
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The 'tickvals' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
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numpy.ndarray | Sets the values at which ticks on this axis appear. Only has an
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The 'tickvals' property is an array that may be specified as a tuple,
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The 'tickvals' property is an array that may be specified as a tuple,
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Returns
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plaid/plaid-python | 8c60fca608e426f3ff30da8857775946d29e122c | plaid/model/transfer_cancel_request.py | python | TransferCancelRequest.__init__ | (self, transfer_id, *args, **kwargs) | TransferCancelRequest - a model defined in OpenAPI
Args:
transfer_id (str): Plaid’s unique identifier for a transfer.
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,)
client_id (str): Your Plaid API `client_id`. The `client_id` is required and may be provided either in the `PLAID-CLIENT-ID` header or as part of a request body.. [optional] # noqa: E501
secret (str): Your Plaid API `secret`. The `secret` is required and may be provided either in the `PLAID-SECRET` header or as part of a request body.. [optional] # noqa: E501 | TransferCancelRequest - a model defined in OpenAPI | [
"TransferCancelRequest",
"-",
"a",
"model",
"defined",
"in",
"OpenAPI"
] | def __init__(self, transfer_id, *args, **kwargs): # noqa: E501
"""TransferCancelRequest - a model defined in OpenAPI
Args:
transfer_id (str): Plaid’s unique identifier for a transfer.
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
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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
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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,)
client_id (str): Your Plaid API `client_id`. The `client_id` is required and may be provided either in the `PLAID-CLIENT-ID` header or as part of a request body.. [optional] # noqa: E501
secret (str): Your Plaid API `secret`. The `secret` is required and may be provided either in the `PLAID-SECRET` header or as part of a request body.. [optional] # noqa: E501
"""
_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.transfer_id = transfer_id
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:
# discard variable.
continue
setattr(self, var_name, var_value) | [
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coleifer/walrus | 2583ac8ba81d3f6aa43fccbe28c0c13b99a1fa9d | walrus/containers.py | python | Hash.values | (self) | return self.database.hvals(self.key) | Return the values stored in the hash. | Return the values stored in the hash. | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/queue.py | python | Queue.join | (self) | Blocks until all items in the Queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the
queue. The count goes down whenever a consumer thread calls task_done()
to indicate the item was retrieved and all work on it is complete.
When the count of unfinished tasks drops to zero, join() unblocks. | Blocks until all items in the Queue have been gotten and processed. | [
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'''Blocks until all items in the Queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the
queue. The count goes down whenever a consumer thread calls task_done()
to indicate the item was retrieved and all work on it is complete.
When the count of unfinished tasks drops to zero, join() unblocks.
'''
with self.all_tasks_done:
while self.unfinished_tasks:
self.all_tasks_done.wait() | [
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zhoubear/open-paperless | b42d4e3bc88343ca5803c613321327406eabca1a | mayan/apps/converter/classes.py | python | ConverterBase.soffice | (self) | Executes LibreOffice as a subprocess | Executes LibreOffice as a subprocess | [
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] | def soffice(self):
"""
Executes LibreOffice as a subprocess
"""
if not LIBREOFFICE:
raise OfficeConversionError(
_('LibreOffice not installed or not found.')
)
new_file_object, input_filepath = mkstemp()
self.file_object.seek(0)
os.write(new_file_object, self.file_object.read())
self.file_object.seek(0)
os.lseek(new_file_object, 0, os.SEEK_SET)
os.close(new_file_object)
libreoffice_filter = None
if self.mime_type == 'text/plain':
libreoffice_filter = 'Text (encoded):UTF8,LF,,,'
libreoffice_home_directory = mkdtemp()
args = (
input_filepath, '--outdir', setting_temporary_directory.value,
'-env:UserInstallation=file://{}'.format(
os.path.join(
libreoffice_home_directory, 'LibreOffice_Conversion'
)
),
)
kwargs = {'_env': {'HOME': libreoffice_home_directory}}
if libreoffice_filter:
kwargs.update({'infilter': libreoffice_filter})
try:
LIBREOFFICE(*args, **kwargs)
except sh.ErrorReturnCode as exception:
raise OfficeConversionError(exception)
except Exception as exception:
logger.error('Exception launching Libre Office; %s', exception)
raise
finally:
fs_cleanup(input_filepath)
fs_cleanup(libreoffice_home_directory)
filename, extension = os.path.splitext(
os.path.basename(input_filepath)
)
logger.debug('filename: %s', filename)
logger.debug('extension: %s', extension)
converted_output = os.path.join(
setting_temporary_directory.value, os.path.extsep.join(
(filename, 'pdf')
)
)
logger.debug('converted_output: %s', converted_output)
with open(converted_output) as converted_file_object:
while True:
data = converted_file_object.read(CHUNK_SIZE)
if not data:
break
yield data
fs_cleanup(input_filepath)
fs_cleanup(converted_output) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/turtle.py | python | RawTurtle.undobufferentries | (self) | return self.undobuffer.nr_of_items() | Return count of entries in the undobuffer.
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Example (for a Turtle instance named turtle):
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if self.undobuffer is None:
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loli/medpy | 39131b94f0ab5328ab14a874229320efc2f74d98 | medpy/graphcut/generate.py | python | __boundary_term_label | (graph, label_image, boundary_term_args) | return {} | Fake regional_term function with the appropriate signature. | Fake regional_term function with the appropriate signature. | [
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ni/nidaqmx-python | 62fc6b48cbbb330fe1bcc9aedadc86610a1269b6 | nidaqmx/_task_modules/export_signals.py | python | ExportSignals.adv_cmplt_event_delay | (self, val) | [] | def adv_cmplt_event_delay(self, val):
cfunc = lib_importer.windll.DAQmxSetExportedAdvCmpltEventDelayVal
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_double]
error_code = cfunc(
self._handle, val)
check_for_error(error_code) | [
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Antergos/Cnchi | 13ac2209da9432d453e0097cf48a107640b563a9 | src/widgets/zfs_treeview.py | python | ZFSTreeview.get_num_drives | (self) | return num_drives | Returns number of active drives that will conform the zfs pool | Returns number of active drives that will conform the zfs pool | [
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num_drives = 0
for row in self.device_list_store:
if row[ZFSTreeview.COL_USE_ACTIVE]:
num_drives += 1
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] | https://github.com/Antergos/Cnchi/blob/13ac2209da9432d453e0097cf48a107640b563a9/src/widgets/zfs_treeview.py#L175-L181 | |
IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv/lib/python2.7/site-packages/setuptools/py26compat.py | python | strip_fragment | (url) | return url | In `Python 8280 <http://bugs.python.org/issue8280>`_, Python 2.7 and
later was patched to disregard the fragment when making URL requests.
Do the same for Python 2.6 and earlier. | In `Python 8280 <http://bugs.python.org/issue8280>`_, Python 2.7 and
later was patched to disregard the fragment when making URL requests.
Do the same for Python 2.6 and earlier. | [
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"""
In `Python 8280 <http://bugs.python.org/issue8280>`_, Python 2.7 and
later was patched to disregard the fragment when making URL requests.
Do the same for Python 2.6 and earlier.
"""
url, fragment = splittag(url)
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | lms/djangoapps/teams/api.py | python | is_instructor_managed_team | (team) | return is_instructor_managed_topic(team.course_id, team.topic_id) | Return true if the team is managed by instructors. | Return true if the team is managed by instructors. | [
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] | def is_instructor_managed_team(team):
"""
Return true if the team is managed by instructors.
"""
if not team:
return False
return is_instructor_managed_topic(team.course_id, team.topic_id) | [
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caktux/pytrader | b45b216dab3db78d6028d85e9a6f80419c22cea0 | api.py | python | Api._on_order_not_found | (self, msg) | this means we have sent order/cancel with non-existing oid | this means we have sent order/cancel with non-existing oid | [
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"""this means we have sent order/cancel with non-existing oid"""
parts = msg["id"].split(":")
oid = parts[1]
self.debug("### got 'Order not found' for", oid)
# we are now going to fake a user_order message (the one we
# obviously missed earlier) that will have the effect of
# removing the order cleanly.
fakemsg = {"user_order": {"oid": oid, "reason": "requested"}}
self._on_op_private_user_order(fakemsg) | [
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microsoft/unilm | 65f15af2a307ebb64cfb25adf54375b002e6fe8d | xtune/src/transformers/tokenization_gpt2.py | python | GPT2Tokenizer.convert_tokens_to_string | (self, tokens) | return text | Converts a sequence of tokens (string) in a single string. | Converts a sequence of tokens (string) in a single string. | [
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] | def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/encodings/cp875.py | python | Codec.decode | (self,input,errors='strict') | return codecs.charmap_decode(input,errors,decoding_table) | [] | def decode(self,input,errors='strict'):
return codecs.charmap_decode(input,errors,decoding_table) | [
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barseghyanartur/django-fobi | a998feae007d7fe3637429a80e42952ec7cda79f | src/fobi/base.py | python | get_registered_form_callbacks | (stage=None) | return form_callback_registry.get_callbacks(stage=stage) | Get registered form callbacks for the stage given. | Get registered form callbacks for the stage given. | [
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] | def get_registered_form_callbacks(stage=None):
"""Get registered form callbacks for the stage given."""
return form_callback_registry.get_callbacks(stage=stage) | [
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DonnchaC/shadowbrokers-exploits | 42d8265db860b634717da4faa668b2670457cf7e | windows/fuzzbunch/pyreadline/modes/notemacs.py | python | NotEmacsMode.beginning_of_line | (self, e) | Move to the start of the current line. | Move to the start of the current line. | [
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'''Move to the start of the current line. '''
self.l_buffer.beginning_of_line() | [
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openid/python-openid | afa6adacbe1a41d8f614c8bce2264dfbe9e76489 | openid/extensions/draft/pape2.py | python | Request.parseExtensionArgs | (self, args) | Set the state of this request to be that expressed in these
PAPE arguments
@param args: The PAPE arguments without a namespace
@rtype: None
@raises ValueError: When the max_auth_age is not parseable as
an integer | Set the state of this request to be that expressed in these
PAPE arguments | [
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"""Set the state of this request to be that expressed in these
PAPE arguments
@param args: The PAPE arguments without a namespace
@rtype: None
@raises ValueError: When the max_auth_age is not parseable as
an integer
"""
# preferred_auth_policies is a space-separated list of policy URIs
self.preferred_auth_policies = []
policies_str = args.get('preferred_auth_policies')
if policies_str:
for uri in policies_str.split(' '):
if uri not in self.preferred_auth_policies:
self.preferred_auth_policies.append(uri)
# max_auth_age is base-10 integer number of seconds
max_auth_age_str = args.get('max_auth_age')
self.max_auth_age = None
if max_auth_age_str:
try:
self.max_auth_age = int(max_auth_age_str)
except ValueError:
pass | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/Django/django/db/backends/mysql/base.py | python | DatabaseWrapper.check_constraints | (self, table_names=None) | Checks each table name in `table_names` for rows with invalid foreign key references. This method is
intended to be used in conjunction with `disable_constraint_checking()` and `enable_constraint_checking()`, to
determine if rows with invalid references were entered while constraint checks were off.
Raises an IntegrityError on the first invalid foreign key reference encountered (if any) and provides
detailed information about the invalid reference in the error message.
Backends can override this method if they can more directly apply constraint checking (e.g. via "SET CONSTRAINTS
ALL IMMEDIATE") | Checks each table name in `table_names` for rows with invalid foreign key references. This method is
intended to be used in conjunction with `disable_constraint_checking()` and `enable_constraint_checking()`, to
determine if rows with invalid references were entered while constraint checks were off. | [
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"""
Checks each table name in `table_names` for rows with invalid foreign key references. This method is
intended to be used in conjunction with `disable_constraint_checking()` and `enable_constraint_checking()`, to
determine if rows with invalid references were entered while constraint checks were off.
Raises an IntegrityError on the first invalid foreign key reference encountered (if any) and provides
detailed information about the invalid reference in the error message.
Backends can override this method if they can more directly apply constraint checking (e.g. via "SET CONSTRAINTS
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"""
cursor = self.cursor()
if table_names is None:
table_names = self.introspection.table_names(cursor)
for table_name in table_names:
primary_key_column_name = self.introspection.get_primary_key_column(cursor, table_name)
if not primary_key_column_name:
continue
key_columns = self.introspection.get_key_columns(cursor, table_name)
for column_name, referenced_table_name, referenced_column_name in key_columns:
cursor.execute("""
SELECT REFERRING.`%s`, REFERRING.`%s` FROM `%s` as REFERRING
LEFT JOIN `%s` as REFERRED
ON (REFERRING.`%s` = REFERRED.`%s`)
WHERE REFERRING.`%s` IS NOT NULL AND REFERRED.`%s` IS NULL"""
% (primary_key_column_name, column_name, table_name, referenced_table_name,
column_name, referenced_column_name, column_name, referenced_column_name))
for bad_row in cursor.fetchall():
raise utils.IntegrityError("The row in table '%s' with primary key '%s' has an invalid "
"foreign key: %s.%s contains a value '%s' that does not have a corresponding value in %s.%s."
% (table_name, bad_row[0],
table_name, column_name, bad_row[1],
referenced_table_name, referenced_column_name)) | [
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titu1994/progressive-neural-architecture-search | b180b63fe0c79a65400c3f64888257845889ee8f | manager.py | python | NetworkManager.get_rewards | (self, model_fn, actions, display_model_summary=True) | return reward | Creates a subnetwork given the actions predicted by the controller RNN,
trains it on the provided dataset, and then returns a reward.
# Args:
model_fn: a function which accepts one argument, a list of
parsed actions, obtained via an inverse mapping from the
StateSpace.
actions: a list of parsed actions obtained via an inverse mapping
from the StateSpace. It is in a specific order as given below:
Consider 4 states were added to the StateSpace via the `add_state`
method. Then the `actions` array will be of length 4, with the
values of those states in the order that they were added.
If number of layers is greater than one, then the `actions` array
will be of length `4 * number of layers` (in the above scenario).
The index from [0:4] will be for layer 0, from [4:8] for layer 1,
etc for the number of layers.
These action values are for direct use in the construction of models.
display_model_summary: Display the child model summary at the end of training.
# Returns:
a reward for training a model with the given actions | Creates a subnetwork given the actions predicted by the controller RNN,
trains it on the provided dataset, and then returns a reward. | [
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] | def get_rewards(self, model_fn, actions, display_model_summary=True):
'''
Creates a subnetwork given the actions predicted by the controller RNN,
trains it on the provided dataset, and then returns a reward.
# Args:
model_fn: a function which accepts one argument, a list of
parsed actions, obtained via an inverse mapping from the
StateSpace.
actions: a list of parsed actions obtained via an inverse mapping
from the StateSpace. It is in a specific order as given below:
Consider 4 states were added to the StateSpace via the `add_state`
method. Then the `actions` array will be of length 4, with the
values of those states in the order that they were added.
If number of layers is greater than one, then the `actions` array
will be of length `4 * number of layers` (in the above scenario).
The index from [0:4] will be for layer 0, from [4:8] for layer 1,
etc for the number of layers.
These action values are for direct use in the construction of models.
display_model_summary: Display the child model summary at the end of training.
# Returns:
a reward for training a model with the given actions
'''
if tf.test.is_gpu_available():
device = '/gpu:0'
else:
device = '/cpu:0'
tf.keras.backend.reset_uids()
# generate a submodel given predicted actions
with tf.device(device):
model = model_fn(actions) # type: Model
# build model shapes
X_train, y_train, X_val, y_val = self.dataset
# generate the dataset for training
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_dataset = train_dataset.apply(tf.data.experimental.shuffle_and_repeat(10000, seed=0))
train_dataset = train_dataset.batch(self.batchsize)
train_dataset = train_dataset.apply(tf.data.experimental.prefetch_to_device(device))
# generate the dataset for evaluation
val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
val_dataset = val_dataset.batch(self.batchsize)
val_dataset = val_dataset.apply(tf.data.experimental.prefetch_to_device(device))
num_train_batches = X_train.shape[0] // self.batchsize + 1
global_step = tf.train.get_or_create_global_step()
lr = tf.train.cosine_decay(self.lr, global_step, decay_steps=num_train_batches * self.epochs, alpha=0.1)
# construct the optimizer and saver of the child model
optimizer = tf.train.AdamOptimizer(lr)
saver = tf.train.Checkpoint(model=model, optimizer=optimizer, global_step=global_step)
best_val_acc = 0.0
for epoch in range(self.epochs):
# train child model
with tqdm.tqdm(train_dataset,
desc='Train Epoch (%d / %d): ' % (epoch + 1, self.epochs),
total=num_train_batches) as iterator:
for i, (x, y) in enumerate(iterator):
# get gradients
with tf.GradientTape() as tape:
preds = model(x, training=True)
loss = tf.keras.losses.categorical_crossentropy(y, preds)
grad = tape.gradient(loss, model.variables)
grad_vars = zip(grad, model.variables)
# update weights of the child model
optimizer.apply_gradients(grad_vars, global_step)
if (i + 1) >= num_train_batches:
break
print()
# evaluate child model
acc = tfe.metrics.CategoricalAccuracy()
for j, (x, y) in enumerate(val_dataset):
preds = model(x, training=False)
acc(y, preds)
acc = acc.result().numpy()
print("Epoch %d: Val accuracy = %0.6f" % (epoch + 1, acc))
# if acc improved, save the weights
if acc > best_val_acc:
print("Val accuracy improved from %0.6f to %0.6f. Saving weights !" % (
best_val_acc, acc))
best_val_acc = acc
saver.save('temp_weights/temp_network')
print()
# load best weights of the child model
path = tf.train.latest_checkpoint('temp_weights/')
saver.restore(path)
# display the structure of the child model
if display_model_summary:
model.summary()
# evaluate the best weights of the child model
acc = tfe.metrics.CategoricalAccuracy()
for j, (x, y) in enumerate(val_dataset):
preds = model(x, training=False)
acc(y, preds)
acc = acc.result().numpy()
# compute the reward (validation accuracy)
reward = acc
print()
print("Manager: Accuracy = ", reward)
# clean up resources and GPU memory
del model
del optimizer
del global_step
return reward | [
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ctxis/CAPE | dae9fa6a254ecdbabeb7eb0d2389fa63722c1e82 | lib/cuckoo/common/abstracts.py | python | Machinery.set_status | (self, label, status) | Set status for a virtual machine.
@param label: virtual machine label
@param status: new virtual machine status | Set status for a virtual machine. | [
"Set",
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"virtual",
"machine",
"."
] | def set_status(self, label, status):
"""Set status for a virtual machine.
@param label: virtual machine label
@param status: new virtual machine status
"""
self.db.set_machine_status(label, status) | [
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biocore/qiime | 76d633c0389671e93febbe1338b5ded658eba31f | qiime/workflow/downstream.py | python | run_alpha_rarefaction | (otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
tree_fp=None,
num_steps=10,
parallel=False,
logger=None,
min_rare_depth=10,
max_rare_depth=None,
suppress_md5=False,
status_update_callback=print_to_stdout,
plot_stderr_and_stddev=False,
retain_intermediate_files=True) | Run the data preparation steps of Qiime
The steps performed by this function are:
1) Generate rarefied OTU tables;
2) Compute alpha diversity metrics for each rarefied OTU table;
3) Collate alpha diversity results;
4) Generate alpha rarefaction plots. | Run the data preparation steps of Qiime | [
"Run",
"the",
"data",
"preparation",
"steps",
"of",
"Qiime"
] | def run_alpha_rarefaction(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
tree_fp=None,
num_steps=10,
parallel=False,
logger=None,
min_rare_depth=10,
max_rare_depth=None,
suppress_md5=False,
status_update_callback=print_to_stdout,
plot_stderr_and_stddev=False,
retain_intermediate_files=True):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Generate rarefied OTU tables;
2) Compute alpha diversity metrics for each rarefied OTU table;
3) Collate alpha diversity results;
4) Generate alpha rarefaction plots.
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
if logger is None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
if not suppress_md5:
log_input_md5s(logger, [otu_table_fp, mapping_fp, tree_fp])
if max_rare_depth is None:
min_count, max_count, median_count, mean_count, counts_per_sample =\
compute_counts_per_sample_stats(
load_table(otu_table_fp))
max_rare_depth = median_count
step = int((max_rare_depth - min_rare_depth) / num_steps) or 1
max_rare_depth = int(max_rare_depth)
rarefaction_dir = '%s/rarefaction/' % output_dir
create_dir(rarefaction_dir)
try:
params_str = get_params_str(params['multiple_rarefactions'])
except KeyError:
params_str = ''
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the rarefaction command
rarefaction_cmd = \
'parallel_multiple_rarefactions.py -T -i %s -m %s -x %s -s %s -o %s %s' %\
(otu_table_fp, min_rare_depth, max_rare_depth, step,
rarefaction_dir, params_str)
else:
# Build the rarefaction command
rarefaction_cmd = \
'multiple_rarefactions.py -i %s -m %s -x %s -s %s -o %s %s' %\
(otu_table_fp, min_rare_depth, max_rare_depth, step,
rarefaction_dir, params_str)
commands.append([('Alpha rarefaction', rarefaction_cmd)])
# Prep the alpha diversity command
alpha_diversity_dir = '%s/alpha_div/' % output_dir
create_dir(alpha_diversity_dir)
try:
params_str = get_params_str(params['alpha_diversity'])
except KeyError:
params_str = ''
if tree_fp:
params_str += ' -t %s' % tree_fp
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the alpha diversity command
alpha_diversity_cmd = \
"parallel_alpha_diversity.py -T -i %s -o %s %s" %\
(rarefaction_dir, alpha_diversity_dir, params_str)
else:
# Build the alpha diversity command
alpha_diversity_cmd = \
"alpha_diversity.py -i %s -o %s %s" %\
(rarefaction_dir, alpha_diversity_dir, params_str)
commands.append(
[('Alpha diversity on rarefied OTU tables', alpha_diversity_cmd)])
# Prep the alpha diversity collation command
alpha_collated_dir = '%s/alpha_div_collated/' % output_dir
create_dir(alpha_collated_dir)
try:
params_str = get_params_str(params['collate_alpha'])
except KeyError:
params_str = ''
# Build the alpha diversity collation command
alpha_collated_cmd = 'collate_alpha.py -i %s -o %s %s' %\
(alpha_diversity_dir, alpha_collated_dir, params_str)
commands.append([('Collate alpha', alpha_collated_cmd)])
if not retain_intermediate_files:
commands.append([('Removing intermediate files',
'rm -r %s %s' % (rarefaction_dir, alpha_diversity_dir))])
else:
commands.append([('Skipping removal of intermediate files.', '')])
# Prep the make rarefaction plot command(s)
try:
params_str = get_params_str(params['make_rarefaction_plots'])
except KeyError:
params_str = ''
if 'std_type' in params['make_rarefaction_plots'] or not plot_stderr_and_stddev:
rarefaction_plot_dir = '%s/alpha_rarefaction_plots/' % output_dir
create_dir(rarefaction_plot_dir)
# Build the make rarefaction plot command(s)
# for metric in alpha_diversity_metrics:
make_rarefaction_plot_cmd =\
'make_rarefaction_plots.py -i %s -m %s -o %s %s' %\
(alpha_collated_dir, mapping_fp, rarefaction_plot_dir, params_str)
commands.append(
[('Rarefaction plot: %s' % 'All metrics', make_rarefaction_plot_cmd)])
else:
rarefaction_plot_dir_stddev = '%s/alpha_rarefaction_plots_stddev/' % output_dir
rarefaction_plot_dir_stderr = '%s/alpha_rarefaction_plots_stderr/' % output_dir
create_dir(rarefaction_plot_dir_stddev)
create_dir(rarefaction_plot_dir_stderr)
# Build the make rarefaction plot command(s)
# for metric in alpha_diversity_metrics:
make_rarefaction_plot_cmd =\
'make_rarefaction_plots.py -i %s -m %s -o %s %s --std_type stddev' %\
(alpha_collated_dir, mapping_fp, rarefaction_plot_dir_stddev,
params_str)
commands.append(
[('Rarefaction plot: %s' % 'All metrics', make_rarefaction_plot_cmd)])
make_rarefaction_plot_cmd =\
'make_rarefaction_plots.py -i %s -m %s -o %s %s --std_type stderr' %\
(alpha_collated_dir, mapping_fp, rarefaction_plot_dir_stderr,
params_str)
commands.append(
[('Rarefaction plot: %s' % 'All metrics', make_rarefaction_plot_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=close_logger_on_success) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/docutils-0.14/docutils/utils/error_reporting.py | python | ErrorOutput.write | (self, data) | Write `data` to self.stream. Ignore, if self.stream is False.
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/glance/glance/openstack/common/notifier/api.py | python | notify | (context, publisher_id, event_type, priority, payload) | Sends a notification using the specified driver
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a UUID representing the id for this notification
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the GMT timestamp the notification was sent at
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Message example::
{'message_id': str(uuid.uuid4()),
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'timestamp': timeutils.utcnow(),
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"""Sends a notification using the specified driver
:param publisher_id: the source worker_type.host of the message
:param event_type: the literal type of event (ex. Instance Creation)
:param priority: patterned after the enumeration of Python logging
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:param payload: A python dictionary of attributes
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message_id
a UUID representing the id for this notification
timestamp
the GMT timestamp the notification was sent at
The composite message will be constructed as a dictionary of the above
attributes, which will then be sent via the transport mechanism defined
by the driver.
Message example::
{'message_id': str(uuid.uuid4()),
'publisher_id': 'compute.host1',
'timestamp': timeutils.utcnow(),
'priority': 'WARN',
'event_type': 'compute.create_instance',
'payload': {'instance_id': 12, ... }}
"""
if priority not in log_levels:
raise BadPriorityException(
_('%s not in valid priorities') % priority)
# Ensure everything is JSON serializable.
payload = jsonutils.to_primitive(payload, convert_instances=True)
msg = dict(message_id=str(uuid.uuid4()),
publisher_id=publisher_id,
event_type=event_type,
priority=priority,
payload=payload,
timestamp=str(timeutils.utcnow()))
for driver in _get_drivers():
try:
driver.notify(context, msg)
except Exception as e:
LOG.exception(_("Problem '%(e)s' attempting to "
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baidu/knowledge-driven-dialogue | ba85518a1ac2a57988188fc5f2b8fe42e1facf64 | generative_pt/source/utils/metrics.py | python | EmbeddingMetrics.__init__ | (self, field) | [] | def __init__(self, field):
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DataIntegrationAlliance/data_integration_celery | 6775292030213dd1fa33a1ec0f542d5d2d2e612a | tasks/wind/index.py | python | import_index_info | (wind_codes, chain_param=None) | 导入指数信息
:param wind_codes:
:return: | 导入指数信息
:param wind_codes:
:return: | [
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"""
导入指数信息
:param wind_codes:
:return:
"""
table_name = 'wind_index_info'
has_table = engine_md.has_table(table_name)
col_name_param_list = [
('LAUNCHDATE', Date),
('BASEDATE', Date),
('BASEVALUE', DOUBLE),
('COUNTRY', String(20)),
('CRM_ISSUER', String(20)),
('SEC_NAME', String(20)),
]
col_name_param = ",".join([key.lower() for key, _ in col_name_param_list])
col_name_param_dic = {col_name.upper(): col_name.lower() for col_name, _ in col_name_param_list}
# 设置dtype类型
dtype = {key.lower(): val for key, val in col_name_param_list}
dtype['wind_code'] = String(20)
info_df = invoker.wss(wind_codes, col_name_param)
if info_df is None or info_df.shape[0] == 0:
logger.warning("没有数据可导入")
return
info_df.rename(columns=col_name_param_dic, inplace=True)
info_df.index.rename("wind_code", inplace=True)
info_df.reset_index(inplace=True)
bunch_insert_on_duplicate_update(info_df, table_name, engine_md, dtype=dtype)
# info_df.to_sql(table_name, engine_md, if_exists='append', index=True,
# dtype={
# 'wind_code': String(20),
# 'null': Date,
# 'basedate': Date,
# 'basevalue': DOUBLE,
# 'country': String(20),
# 'crm_issuer': String(20),
# 'sec_name': String(20),
# })
logger.info('%d 条指数信息导入成功\n%s', info_df.shape[0], info_df)
if not has_table and engine_md.has_table(table_name):
alter_table_2_myisam(engine_md, [table_name])
build_primary_key([table_name])
# 更新 code_mapping 表
update_from_info_table(table_name) | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/datasets/ogb_mag.py | python | OGB_MAG.download | (self) | [] | def download(self):
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fortharris/Pcode | 147962d160a834c219e12cb456abc130826468e4 | rope/refactor/patchedast.py | python | _PatchingASTWalker._Assert | (self, node) | [] | def _Assert(self, node):
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JetBrains/python-skeletons | 95ad24b666e475998e5d1cc02ed53a2188036167 | os/__init__.py | python | execle | (path, *args) | Execute a new program, replacing the current process.
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joerick/pyinstrument | d3c45164a385021f366c1081baec18a1a226a573 | metrics/multi_overhead.py | python | time_base | (function, repeats) | return timer.repeat(number=repeats) | [] | def time_base(function, repeats):
timer = Timer(stmt=function)
return timer.repeat(number=repeats) | [
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tdamdouni/Pythonista | 3e082d53b6b9b501a3c8cf3251a8ad4c8be9c2ad | installer/pexif.py | python | IfdData.isifd | (self, other) | return issubclass(other.__class__, IfdData) | Return true if other is an IFD | Return true if other is an IFD | [
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h2oai/h2o4gpu | aaf7795d3c3be430129eacfd99d598da0cc838e7 | src/interface_py/h2o4gpu/solvers/truncated_svd.py | python | TruncatedSVDH2O.transform | (self, X) | return X_new | Perform dimensionality reduction on X.
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Training data.
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biopython/biopython | 2dd97e71762af7b046d7f7f8a4f1e38db6b06c86 | Bio/HMM/MarkovModel.py | python | HiddenMarkovModel.get_blank_transitions | (self) | return self._transition_pseudo | Get the default transitions for the model.
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project-alice-assistant/ProjectAlice | 9e0ba019643bdae0a5535c9fa4180739231dc361 | core/base/SkillManager.py | python | SkillManager.getSkillRepository | (self, skillName: str, directory: str = None) | Returns a Git object for the given skill
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:param directory: where to look for that skill, if not standard directory
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:param skillName:
:param directory: where to look for that skill, if not standard directory
:return:
"""
if not directory:
directory = self.getSkillDirectory(skillName=skillName)
try:
return Repository(directory=directory)
except:
raise | [
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tdamdouni/Pythonista | 3e082d53b6b9b501a3c8cf3251a8ad4c8be9c2ad | weather/WeatherAnywhereScene-coomlata.py | python | SceneViewer.alerts | (self, sender) | Kill scene to cut memory overhead, textview then loads much faster into ui view. Note too that whene textview is loaded via .present() without killing the scene, the scene will lock up after closing the textview window. | Kill scene to cut memory overhead, textview then loads much faster into ui view. Note too that whene textview is loaded via .present() without killing the scene, the scene will lock up after closing the textview window. | [
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# Retrieve alerts
alerts = wa.get_alerts(json_w, json_f)
'''
Kill scene to cut memory overhead, textview then loads much faster into ui view. Note too that whene textview is loaded via .present() without killing the scene, the scene will lock up after closing the textview window.
'''
self.remove_subview(self.scene_view)
tv = ui.TextView()
#w, h = ui.get_screen_size()
#tv.frame = (0, 20, w, h / 1.2)
tv.frame = self.bounds
tv.border_color='grey'
tv.border_width = 3
tv.background_color = ('white')
tv.text = alerts
tv.editable = False
tv.selectable = False
if py_ver == '2' and is_P6:
tv.font = ('<system>', 12)
else:
tv.font = ('<system>', 9)
self.closebutton(tv)
self.add_subview(tv) | [
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jacoxu/encoder_decoder | 6829c3bc42105aedfd1b8a81c81be19df4326c0a | keras/old_models.py | python | Model.to_json | (self, **kwargs) | return json.dumps(config, default=get_json_type, **kwargs) | Return a JSON string containing the model configuration.
To load a model from a JSON save file, use
`keras.models.from_json(json_string, custom_objects={})`. | Return a JSON string containing the model configuration. | [
"Return",
"a",
"JSON",
"string",
"containing",
"the",
"model",
"configuration",
"."
] | def to_json(self, **kwargs):
'''Return a JSON string containing the model configuration.
To load a model from a JSON save file, use
`keras.models.from_json(json_string, custom_objects={})`.
'''
import json
def get_json_type(obj):
# if obj is any numpy type
if type(obj).__module__ == np.__name__:
return obj.item()
# if obj is a python 'type'
if type(obj).__name__ == type.__name__:
return obj.__name__
raise TypeError('Not JSON Serializable')
config = self.get_config()
return json.dumps(config, default=get_json_type, **kwargs) | [
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... | https://github.com/jacoxu/encoder_decoder/blob/6829c3bc42105aedfd1b8a81c81be19df4326c0a/keras/old_models.py#L440-L460 | |
ragulpr/wtte-rnn | 162f5c17f21db79a316d563b60835d178142fd69 | python/wtte/wtte.py | python | loglik_continuous_conditional_correction | (y, u, a, b, epsilon=K.epsilon()) | return loglikelihoods | Integrated conditional excess loss.
Explanation TODO | Integrated conditional excess loss.
Explanation TODO | [
"Integrated",
"conditional",
"excess",
"loss",
".",
"Explanation",
"TODO"
] | def loglik_continuous_conditional_correction(y, u, a, b, epsilon=K.epsilon()):
"""Integrated conditional excess loss.
Explanation TODO
"""
ya = (y + epsilon) / a
loglikelihoods = y * \
(u * (K.log(b) + b * K.log(ya)) - (b / (b + 1.)) * K.pow(ya, b))
return loglikelihoods | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/http/client.py | python | HTTPResponse.close | (self) | [] | def close(self):
if self.fp:
self.fp.close()
self.fp = None | [
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appium/python-client | 72f942d2152ee1b0d7539f3bf2705b50d01133f7 | appium/webdriver/extensions/remote_fs.py | python | RemoteFS.push_file | (
self: T, destination_path: str, base64data: Optional[str] = None, source_path: Optional[str] = None
) | return self | Puts the data from the file at `source_path`, encoded as Base64, in the file specified as `path`.
Specify either `base64data` or `source_path`, if both specified default to `source_path`
Args:
destination_path: the location on the device/simulator where the local file contents should be saved
base64data: file contents, encoded as Base64, to be written
to the file on the device/simulator
source_path: local file path for the file to be loaded on device
Returns:
Union['WebDriver', 'RemoteFS']: Self instance | Puts the data from the file at `source_path`, encoded as Base64, in the file specified as `path`. | [
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"encoded",
"as",
"Base64",
"in",
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"specified",
"as",
"path",
"."
] | def push_file(
self: T, destination_path: str, base64data: Optional[str] = None, source_path: Optional[str] = None
) -> T:
"""Puts the data from the file at `source_path`, encoded as Base64, in the file specified as `path`.
Specify either `base64data` or `source_path`, if both specified default to `source_path`
Args:
destination_path: the location on the device/simulator where the local file contents should be saved
base64data: file contents, encoded as Base64, to be written
to the file on the device/simulator
source_path: local file path for the file to be loaded on device
Returns:
Union['WebDriver', 'RemoteFS']: Self instance
"""
if source_path is None and base64data is None:
raise InvalidArgumentException('Must either pass base64 data or a local file path')
if source_path is not None:
try:
with open(source_path, 'rb') as f:
file_data = f.read()
except IOError as e:
message = f'source_path "{source_path}" could not be found. Are you sure the file exists?'
raise InvalidArgumentException(message) from e
base64data = base64.b64encode(file_data).decode('utf-8')
data = {
'path': destination_path,
'data': base64data,
}
self.execute(Command.PUSH_FILE, data)
return self | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/satel_integra/__init__.py | python | async_setup | (hass: HomeAssistant, config: ConfigType) | return True | Set up the Satel Integra component. | Set up the Satel Integra component. | [
"Set",
"up",
"the",
"Satel",
"Integra",
"component",
"."
] | async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
"""Set up the Satel Integra component."""
conf = config[DOMAIN]
zones = conf.get(CONF_ZONES)
outputs = conf.get(CONF_OUTPUTS)
switchable_outputs = conf.get(CONF_SWITCHABLE_OUTPUTS)
host = conf.get(CONF_HOST)
port = conf.get(CONF_PORT)
partitions = conf.get(CONF_DEVICE_PARTITIONS)
monitored_outputs = collections.OrderedDict(
list(outputs.items()) + list(switchable_outputs.items())
)
controller = AsyncSatel(host, port, hass.loop, zones, monitored_outputs, partitions)
hass.data[DATA_SATEL] = controller
result = await controller.connect()
if not result:
return False
@callback
def _close(*_):
controller.close()
hass.bus.async_listen_once(EVENT_HOMEASSISTANT_STOP, _close)
_LOGGER.debug("Arm home config: %s, mode: %s ", conf, conf.get(CONF_ARM_HOME_MODE))
hass.async_create_task(
async_load_platform(hass, Platform.ALARM_CONTROL_PANEL, DOMAIN, conf, config)
)
hass.async_create_task(
async_load_platform(
hass,
Platform.BINARY_SENSOR,
DOMAIN,
{CONF_ZONES: zones, CONF_OUTPUTS: outputs},
config,
)
)
hass.async_create_task(
async_load_platform(
hass,
Platform.SWITCH,
DOMAIN,
{
CONF_SWITCHABLE_OUTPUTS: switchable_outputs,
CONF_DEVICE_CODE: conf.get(CONF_DEVICE_CODE),
},
config,
)
)
@callback
def alarm_status_update_callback():
"""Send status update received from alarm to Home Assistant."""
_LOGGER.debug("Sending request to update panel state")
async_dispatcher_send(hass, SIGNAL_PANEL_MESSAGE)
@callback
def zones_update_callback(status):
"""Update zone objects as per notification from the alarm."""
_LOGGER.debug("Zones callback, status: %s", status)
async_dispatcher_send(hass, SIGNAL_ZONES_UPDATED, status[ZONES])
@callback
def outputs_update_callback(status):
"""Update zone objects as per notification from the alarm."""
_LOGGER.debug("Outputs updated callback , status: %s", status)
async_dispatcher_send(hass, SIGNAL_OUTPUTS_UPDATED, status["outputs"])
# Create a task instead of adding a tracking job, since this task will
# run until the connection to satel_integra is closed.
hass.loop.create_task(controller.keep_alive())
hass.loop.create_task(
controller.monitor_status(
alarm_status_update_callback, zones_update_callback, outputs_update_callback
)
)
return True | [
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/sqlalchemy/engine/base.py | python | ResultProxy.lastrowid | (self) | return self._saved_cursor.lastrowid | return the 'lastrowid' accessor on the DBAPI cursor.
This is a DBAPI specific method and is only functional
for those backends which support it, for statements
where it is appropriate. It's behavior is not
consistent across backends.
Usage of this method is normally unnecessary; the
:attr:`~ResultProxy.inserted_primary_key` attribute provides a
tuple of primary key values for a newly inserted row,
regardless of database backend. | return the 'lastrowid' accessor on the DBAPI cursor.
This is a DBAPI specific method and is only functional
for those backends which support it, for statements
where it is appropriate. It's behavior is not
consistent across backends.
Usage of this method is normally unnecessary; the
:attr:`~ResultProxy.inserted_primary_key` attribute provides a
tuple of primary key values for a newly inserted row,
regardless of database backend. | [
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"""return the 'lastrowid' accessor on the DBAPI cursor.
This is a DBAPI specific method and is only functional
for those backends which support it, for statements
where it is appropriate. It's behavior is not
consistent across backends.
Usage of this method is normally unnecessary; the
:attr:`~ResultProxy.inserted_primary_key` attribute provides a
tuple of primary key values for a newly inserted row,
regardless of database backend.
"""
return self._saved_cursor.lastrowid | [
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OCA/stock-logistics-warehouse | 185c1b0cb9e31e3746a89ec269b4bc09c69b2411 | stock_packaging_calculator/models/product.py | python | Product._product_qty_by_packaging | (self, pkg_by_qty, qty, with_contained=False) | return res | Produce a list of dictionaries of packaging info. | Produce a list of dictionaries of packaging info. | [
"Produce",
"a",
"list",
"of",
"dictionaries",
"of",
"packaging",
"info",
"."
] | def _product_qty_by_packaging(self, pkg_by_qty, qty, with_contained=False):
"""Produce a list of dictionaries of packaging info."""
# TODO: refactor to handle fractional quantities (eg: 0.5 Kg)
res = []
prepare_values = self.env.context.get(
"_packaging_values_handler", self._prepare_qty_by_packaging_values
)
for pkg in pkg_by_qty:
qty_per_pkg, qty = self._qty_by_pkg(pkg.qty, qty)
if qty_per_pkg:
value = prepare_values(pkg, qty_per_pkg)
if with_contained:
contained = None
if not pkg.is_unit:
mapping = self.packaging_contained_mapping
# integer keys are serialized as strings :/
contained = mapping.get(str(pkg.id))
value["contained"] = contained
res.append(value)
if not qty:
break
return res | [
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VITA-Group/FasterSeg | 478b0265eb9ab626cfbe503ad16d2452878b38cc | latency/operations.py | python | ConvNorm.forward_latency | (self, size) | return latency, (c_out, h_out, w_out) | [] | def forward_latency(self, size):
c_in, h_in, w_in = size
if self.slimmable:
assert c_in == int(self.C_in * self.ratio[0]), "c_in %d, self.C_in * self.ratio[0] %d"%(c_in, self.C_in * self.ratio[0])
c_out = int(self.C_out * self.ratio[1])
else:
assert c_in == self.C_in, "c_in %d, self.C_in %d"%(c_in, self.C_in)
c_out = self.C_out
if self.stride == 1:
h_out = h_in; w_out = w_in
else:
h_out = h_in // 2; w_out = w_in // 2
name = "ConvNorm_H%d_W%d_Cin%d_Cout%d_kernel%d_stride%d"%(h_in, w_in, c_in, c_out, self.kernel_size, self.stride)
if name in latency_lookup_table:
latency = latency_lookup_table[name]
else:
print("not found in latency_lookup_table:", name)
latency = ConvNorm._latency(h_in, w_in, c_in, c_out, self.kernel_size, self.stride, self.padding, self.dilation, self.groups, self.bias)
latency_lookup_table[name] = latency
np.save(table_file_name, latency_lookup_table)
return latency, (c_out, h_out, w_out) | [
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HKUST-Aerial-Robotics/Stereo-RCNN | 63c6ab98b7a5e36c7bcfdec4529804fc940ee900 | lib/model/utils/blob.py | python | im_list_to_blob | (imgs_left, imgs_right) | return blob_left, blob_right | Convert a list of images into a network input.
Assumes images are already prepared (means subtracted, BGR order, ...). | Convert a list of images into a network input. | [
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"."
] | def im_list_to_blob(imgs_left, imgs_right):
"""Convert a list of images into a network input.
Assumes images are already prepared (means subtracted, BGR order, ...).
"""
max_shape = np.array([im.shape for im in imgs_left]).max(axis=0)
num_images = len(imgs_left)
blob_left = np.zeros((num_images, max_shape[0], max_shape[1], 3),
dtype=np.float32)
blob_right = np.zeros((num_images, max_shape[0], max_shape[1], 3),
dtype=np.float32)
for i in xrange(num_images):
im_l = imgs_left[i]
im_r = imgs_right[i]
blob_left[i, 0:im_l.shape[0], 0:im_l.shape[1], :] = im_l
blob_right[i, 0:im_r.shape[0], 0:im_r.shape[1], :] = im_r
return blob_left, blob_right | [
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quantopian/zipline | 014f1fc339dc8b7671d29be2d85ce57d3daec343 | zipline/pipeline/loaders/events.py | python | required_event_fields | (next_value_columns, previous_value_columns) | return {
TS_FIELD_NAME,
SID_FIELD_NAME,
EVENT_DATE_FIELD_NAME,
}.union(
# We also expect any of the field names that our loadable columns
# are mapped to.
viewvalues(next_value_columns),
viewvalues(previous_value_columns),
) | Compute the set of resource columns required to serve
``next_value_columns`` and ``previous_value_columns``. | Compute the set of resource columns required to serve
``next_value_columns`` and ``previous_value_columns``. | [
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"."
] | def required_event_fields(next_value_columns, previous_value_columns):
"""
Compute the set of resource columns required to serve
``next_value_columns`` and ``previous_value_columns``.
"""
# These metadata columns are used to align event indexers.
return {
TS_FIELD_NAME,
SID_FIELD_NAME,
EVENT_DATE_FIELD_NAME,
}.union(
# We also expect any of the field names that our loadable columns
# are mapped to.
viewvalues(next_value_columns),
viewvalues(previous_value_columns),
) | [
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openstack/heat | ea6633c35b04bb49c4a2858edc9df0a82d039478 | heat/engine/resources/openstack/heat/software_component.py | python | SoftwareComponent.validate | (self) | Validate SoftwareComponent properties consistency. | Validate SoftwareComponent properties consistency. | [
"Validate",
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] | def validate(self):
"""Validate SoftwareComponent properties consistency."""
super(SoftwareComponent, self).validate()
# One lifecycle action (e.g. CREATE) can only be associated with one
# config; otherwise a way to define ordering would be required.
configs = self.properties[self.CONFIGS]
config_actions = set()
for config in configs:
actions = config.get(self.CONFIG_ACTIONS)
if any(action in config_actions for action in actions):
msg = _('Defining more than one configuration for the same '
'action in SoftwareComponent "%s" is not allowed.'
) % self.name
raise exception.StackValidationFailed(message=msg)
config_actions.update(actions) | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/site-packages/Crypto/Hash/HMAC.py | python | HMAC.copy | (self) | return other | Return a copy ("clone") of the MAC object.
The copy will have the same internal state as the original MAC
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This can be used to efficiently compute the MAC of strings that
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:Returns: An `HMAC` object
"""
other = HMAC(b(""))
other.digestmod = self.digestmod
other.inner = self.inner.copy()
other.outer = self.outer.copy()
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/setuptools/_vendor/pyparsing.py | python | ParseExpression.__str__ | ( self ) | return self.strRepr | [] | def __str__( self ):
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mardix/flask-cloudy | cf9f6f074e92af1d98f99fdf6ca458e29099e4b1 | flask_cloudy.py | python | get_file_extension_type | (filename) | return "OTHER" | Return the group associated to the file
:param filename:
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pgmpy/pgmpy | 24279929a28082ea994c52f3d165ca63fc56b02b | pgmpy/models/SEM.py | python | SEMAlg.generate_samples | (self, n_samples=100) | return pd.DataFrame(samples, columns=observed) | Generates random samples from the model.
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n_samples: int
The number of samples to generate.
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-------
pd.DataFrame: The genrated samples. | Generates random samples from the model. | [
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"""
Generates random samples from the model.
Parameters
----------
n_samples: int
The number of samples to generate.
Returns
-------
pd.DataFrame: The genrated samples.
"""
if (self.B_fixed_mask is None) or (self.zeta_fixed_mask is None):
raise ValueError("Parameters for the model has not been specified.")
B_inv = np.linalg.inv(np.eye(self.B_fixed_mask.shape[0]) - self.B_fixed_mask)
implied_cov = (
self.wedge_y @ B_inv @ self.zeta_fixed_mask @ B_inv.T @ self.wedge_y.T
)
# Check if implied covariance matrix is positive definite.
if not np.all(np.linalg.eigvals(implied_cov) > 0):
raise ValueError(
"The implied covariance matrix is not positive definite."
+ "Please check model parameters."
)
# Get the order of observed variables
x_index, y_index = np.nonzero(self.wedge_y)
observed = [self.eta[i] for i in y_index]
# Generate samples and return a dataframe.
samples = np.random.multivariate_normal(
mean=[0 for i in range(implied_cov.shape[0])],
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/mako/ext/babelplugin.py | python | BabelMakoExtractor.process_python | (self, code, code_lineno, translator_strings) | [] | def process_python(self, code, code_lineno, translator_strings):
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/compiler/pycodegen.py | python | CodeGenerator._implicitNameOp | (self, prefix, name) | Emit name ops for names generated implicitly by for loops
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Xyntax/DirBrute | 84a54013f57a4588add9c2032c7c6c0902e6f504 | libs/requests/models.py | python | Response.json | (self, **kwargs) | return json.loads(self.text, **kwargs) | Returns the json-encoded content of a response, if any.
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# No encoding set. JSON RFC 4627 section 3 states we should expect
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# a best guess).
encoding = guess_json_utf(self.content)
if encoding is not None:
try:
return json.loads(self.content.decode(encoding), **kwargs)
except UnicodeDecodeError:
# Wrong UTF codec detected; usually because it's not UTF-8
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hyde/hyde | 7f415402cc3e007a746eb2b5bc102281fdb415bd | hyde/ext/plugins/structure.py | python | FlattenerPlugin.begin_site | (self) | Finds all the folders that need flattening and changes the
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prakhar1989/Algorithms | 82e64ce9d451b33c1bce64a63276d6341a1f13b0 | sorting and basics/karatsuba.py | python | karatsuba | (x, y, b=10) | return (bm**2)*z1 + bm*z2 + z3 | returns product of x, y. Uses base b
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>>> karatsuba(1234223123412323, 1234534213423333123)
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/jupyter_contrib_core-0.3.3-py3.7.egg/jupyter_contrib_core/notebook_compat/_compat/nbextensions.py | python | _nbextension_dirs | () | return [
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ansible/ansible-modules-core | 00911a75ad6635834b6d28eef41f197b2f73c381 | cloud/openstack/os_port.py | python | _needs_update | (module, port, cloud) | return False | Check for differences in the updatable values.
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"""
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compare_list = ['security_groups']
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for key in compare_list:
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tacnetsol/ghidra_scripts | 5c4d24bc7166f672015003572daeeb04d2e1f30e | MipsRopSystemChain.py | python | find_epilogue | (rop_finder, controlled_registers) | return function_epilogue | Find epilogues that grant control of each register. Ideal will return nine
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if all(reg in controlled_registers for reg in control_registers):
continue
epilogue_gadget = rop_finder.find_instructions(
[epilogue], controllable_calls=False,
overwrite_register=control_registers,
preserve_register=mipsropchain.REGISTERS[i + 1:])
if epilogue_gadget.gadgets:
function_epilogue.append(epilogue_gadget.gadgets[0])
return function_epilogue | [
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cronyo/cronyo | cd5abab0871b68bf31b18aac934303928130a441 | cronyo/vendor/urllib3/response.py | python | HTTPResponse.flush | (self) | [] | def flush(self):
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ZhenYangIACAS/NMT_GAN | e27f9468112b117509f065f0f8f298f5b80b5602 | model.py | python | shift_right | (input, pad=2) | return tf.concat((tf.ones_like(input[:, :1]) * pad, input[:, :-1]), 1) | Shift input tensor right to create decoder input. '2' denotes <S> | Shift input tensor right to create decoder input. '2' denotes <S> | [
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google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/server/grr_response_server/gui/api_call_router.py | python | ApiCallRouterStub.GetFileDetails | (self, args, context=None) | Get details of a VFS file on a given client. | Get details of a VFS file on a given client. | [
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] | def GetFileDetails(self, args, context=None):
"""Get details of a VFS file on a given client."""
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mitre/caldera | 46429594584f3852d1f9353402407fdb7c1a8302 | app/api/v2/responses.py | python | json_request_validation_middleware | (request, handler) | Middleware that converts json decoding and marshmallow validation
errors into 400 responses w/ json bodies. | Middleware that converts json decoding and marshmallow validation
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] | async def json_request_validation_middleware(request, handler):
"""Middleware that converts json decoding and marshmallow validation
errors into 400 responses w/ json bodies.
"""
try:
return await handler(request)
except errors.DataValidationError as ex:
raise JsonHttpBadRequest(
error=str(ex),
details={ex.name: ex.value}
)
except errors.RequestValidationError as ex:
raise JsonHttpBadRequest('Received invalid json', details=ex.errors)
except errors.RequestUnparsableJsonError:
raise JsonHttpBadRequest('Unexpected error occurred while parsing json') | [
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getsentry/rb | 6f96a68dca2d77e9ac1d8bdd7a21e2575af65a20 | rb/clients.py | python | CommandBuffer.fileno | (self) | return self.connection._sock.fileno() | Returns the file number of the underlying connection's socket
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"""Returns the file number of the underlying connection's socket
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"""
assert_open(self)
return self.connection._sock.fileno() | [
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WeblateOrg/weblate | 8126f3dda9d24f2846b755955132a8b8410866c8 | weblate/trans/models/unit.py | python | Unit.update_source_units | (self, previous_source, user, author) | Update source for units withing same component.
This is needed when editing template translation for monolingual formats. | Update source for units withing same component. | [
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] | def update_source_units(self, previous_source, user, author):
"""Update source for units withing same component.
This is needed when editing template translation for monolingual formats.
"""
# Find relevant units
for unit in self.unit_set.exclude(id=self.id).prefetch().prefetch_bulk():
# Update source and number of words
unit.source = self.target
unit.num_words = self.num_words
# Find reverted units
if unit.state == STATE_FUZZY and unit.previous_source == self.target:
# Unset fuzzy on reverted
unit.original_state = unit.state = STATE_TRANSLATED
unit.previous_source = ""
elif (
unit.original_state == STATE_FUZZY
and unit.previous_source == self.target
):
# Unset fuzzy on reverted
unit.original_state = STATE_TRANSLATED
unit.previous_source = ""
elif unit.state >= STATE_TRANSLATED:
# Set fuzzy on changed
unit.original_state = STATE_FUZZY
if unit.state < STATE_READONLY:
unit.state = STATE_FUZZY
unit.previous_source = previous_source
# Save unit and change
unit.save()
Change.objects.create(
unit=unit,
action=Change.ACTION_SOURCE_CHANGE,
user=user,
author=author,
old=previous_source,
target=self.target,
)
# Invalidate stats
unit.translation.invalidate_cache() | [
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pyRiemann/pyRiemann | 30c2cd7204d19f1a60d3b7945dfd8ee3c46a8df8 | pyriemann/utils/utils.py | python | check_version | (library, min_version) | return ok | Check minimum library version required
Parameters
----------
library : str
The library name to import. Must have a ``__version__`` property.
min_version : str
The minimum version string. Anything that matches
``'(\\d+ | [a-z]+ | \\.)'``
Returns
-------
ok : bool
True if the library exists with at least the specified version.
Adapted from MNE-Python: http://github.com/mne-tools/mne-python | Check minimum library version required | [
"Check",
"minimum",
"library",
"version",
"required"
] | def check_version(library, min_version):
"""Check minimum library version required
Parameters
----------
library : str
The library name to import. Must have a ``__version__`` property.
min_version : str
The minimum version string. Anything that matches
``'(\\d+ | [a-z]+ | \\.)'``
Returns
-------
ok : bool
True if the library exists with at least the specified version.
Adapted from MNE-Python: http://github.com/mne-tools/mne-python
"""
from distutils.version import LooseVersion
ok = True
try:
library = __import__(library)
except ImportError:
ok = False
else:
this_version = LooseVersion(library.__version__)
if this_version < min_version:
ok = False
return ok | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/patched/notpip/_vendor/pkg_resources/__init__.py | python | _call_aside | (f, *args, **kwargs) | return f | [] | def _call_aside(f, *args, **kwargs):
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/inspect.py | python | Signature.from_function | (cls, func) | return _signature_from_function(cls, func) | Constructs Signature for the given python function.
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] | def from_function(cls, func):
"""Constructs Signature for the given python function.
Deprecated since Python 3.5, use `Signature.from_callable()`.
"""
warnings.warn("inspect.Signature.from_function() is deprecated since "
"Python 3.5, use Signature.from_callable()",
DeprecationWarning, stacklevel=2)
return _signature_from_function(cls, func) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/lib-tk/Tkinter.py | python | Misc.send | (self, interp, cmd, *args) | return self.tk.call(('send', interp, cmd) + args) | Send Tcl command CMD to different interpreter INTERP to be executed. | Send Tcl command CMD to different interpreter INTERP to be executed. | [
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"""Send Tcl command CMD to different interpreter INTERP to be executed."""
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/rings/asymptotic/misc.py | python | parent_to_repr_short | (P) | return s | r"""
Helper method which generates a short(er) representation string
out of a parent.
INPUT:
- ``P`` -- a parent.
OUTPUT:
A string.
EXAMPLES::
sage: from sage.rings.asymptotic.misc import parent_to_repr_short
sage: parent_to_repr_short(ZZ)
'ZZ'
sage: parent_to_repr_short(QQ)
'QQ'
sage: parent_to_repr_short(SR)
'SR'
sage: parent_to_repr_short(RR)
'RR'
sage: parent_to_repr_short(CC)
'CC'
sage: parent_to_repr_short(ZZ['x'])
'ZZ[x]'
sage: parent_to_repr_short(QQ['d, k'])
'QQ[d, k]'
sage: parent_to_repr_short(QQ['e'])
'QQ[e]'
sage: parent_to_repr_short(SR[['a, r']])
'SR[[a, r]]'
sage: parent_to_repr_short(Zmod(3))
'Ring of integers modulo 3'
sage: parent_to_repr_short(Zmod(3)['g'])
'Univariate Polynomial Ring in g over Ring of integers modulo 3' | r"""
Helper method which generates a short(er) representation string
out of a parent. | [
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] | def parent_to_repr_short(P):
r"""
Helper method which generates a short(er) representation string
out of a parent.
INPUT:
- ``P`` -- a parent.
OUTPUT:
A string.
EXAMPLES::
sage: from sage.rings.asymptotic.misc import parent_to_repr_short
sage: parent_to_repr_short(ZZ)
'ZZ'
sage: parent_to_repr_short(QQ)
'QQ'
sage: parent_to_repr_short(SR)
'SR'
sage: parent_to_repr_short(RR)
'RR'
sage: parent_to_repr_short(CC)
'CC'
sage: parent_to_repr_short(ZZ['x'])
'ZZ[x]'
sage: parent_to_repr_short(QQ['d, k'])
'QQ[d, k]'
sage: parent_to_repr_short(QQ['e'])
'QQ[e]'
sage: parent_to_repr_short(SR[['a, r']])
'SR[[a, r]]'
sage: parent_to_repr_short(Zmod(3))
'Ring of integers modulo 3'
sage: parent_to_repr_short(Zmod(3)['g'])
'Univariate Polynomial Ring in g over Ring of integers modulo 3'
"""
from sage.rings.all import RR, CC, RIF, CIF, RBF, CBF
from sage.rings.integer_ring import ZZ
from sage.rings.rational_field import QQ
from sage.symbolic.ring import SR
from sage.rings.polynomial.polynomial_ring import is_PolynomialRing
from sage.rings.polynomial.multi_polynomial_ring_base import is_MPolynomialRing
from sage.rings.power_series_ring import is_PowerSeriesRing
def abbreviate(P):
try:
return P._repr_short_()
except AttributeError:
pass
abbreviations = {ZZ: 'ZZ', QQ: 'QQ', SR: 'SR',
RR: 'RR', CC: 'CC',
RIF: 'RIF', CIF: 'CIF',
RBF: 'RBF', CBF: 'CBF'}
try:
return abbreviations[P]
except KeyError:
pass
raise ValueError('Cannot abbreviate %s.' % (P,))
poly = is_PolynomialRing(P) or is_MPolynomialRing(P)
from sage.rings import multi_power_series_ring
power = is_PowerSeriesRing(P) or \
multi_power_series_ring.is_MPowerSeriesRing(P)
if poly or power:
if poly:
op, cl = ('[', ']')
else:
op, cl = ('[[', ']]')
try:
s = abbreviate(P.base_ring()) + op + ', '.join(P.variable_names()) + cl
except ValueError:
s = str(P)
else:
try:
s = abbreviate(P)
except ValueError:
s = str(P)
return s | [
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avidLearnerInProgress/python-automation-scripts | 859cbbf72571673500cfc0fbcf493beaed48b7c5 | hackernews-scraper/json_to_csv.py | python | reduce_item | (key, value) | [] | def reduce_item(key, value):
global reduced_item
#Reduction Condition 1
if type(value) is list:
i=0
for sub_item in value:
reduce_item(key+'_'+to_string(i), sub_item)
i=i+1
#Reduction Condition 2
elif type(value) is dict:
sub_keys = value.keys()
for sub_key in sub_keys:
reduce_item(key+'_'+to_string(sub_key), value[sub_key])
#Base Condition
else:
reduced_item[to_string(key)] = to_string(value) | [
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CityOfZion/neo-python | 99783bc8310982a5380081ec41a6ee07ba843f3f | neo/Core/TX/Transaction.py | python | Transaction.GetTransactionResults | (self) | return realresults | Get the execution results of the transaction.
Returns:
None: if the transaction has no references.
list: of TransactionResult objects. | Get the execution results of the transaction. | [
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] | def GetTransactionResults(self):
"""
Get the execution results of the transaction.
Returns:
None: if the transaction has no references.
list: of TransactionResult objects.
"""
if self.References is None:
return None
results = []
realresults = []
for ref_output in self.References.values():
results.append(TransactionResult(ref_output.AssetId, ref_output.Value))
for output in self.outputs:
results.append(TransactionResult(output.AssetId, output.Value * Fixed8(-1)))
for key, group in groupby(results, lambda x: x.AssetId):
sum = Fixed8(0)
for item in group:
sum = sum + item.Amount
if sum != Fixed8.Zero():
realresults.append(TransactionResult(key, sum))
return realresults | [
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | tutorials/models/3_generative_model/5_dgmg.py | python | DGMGSkeleton.__init__ | (self, v_max) | Parameters
----------
v_max: int
Max number of nodes considered | Parameters
----------
v_max: int
Max number of nodes considered | [
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] | def __init__(self, v_max):
"""
Parameters
----------
v_max: int
Max number of nodes considered
"""
super(DGMGSkeleton, self).__init__()
# Graph configuration
self.v_max = v_max | [
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] | https://github.com/dmlc/dgl/blob/8d14a739bc9e446d6c92ef83eafe5782398118de/tutorials/models/3_generative_model/5_dgmg.py#L246-L256 | ||
saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/vmctl.py | python | create_disk | (name, size) | return ret | Create a VMM disk with the specified `name` and `size`.
size:
Size in megabytes, or use a specifier such as M, G, T.
CLI Example:
.. code-block:: bash
salt '*' vmctl.create_disk /path/to/disk.img size=10G | Create a VMM disk with the specified `name` and `size`. | [
"Create",
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] | def create_disk(name, size):
"""
Create a VMM disk with the specified `name` and `size`.
size:
Size in megabytes, or use a specifier such as M, G, T.
CLI Example:
.. code-block:: bash
salt '*' vmctl.create_disk /path/to/disk.img size=10G
"""
ret = False
cmd = "vmctl create {} -s {}".format(name, size)
result = __salt__["cmd.run_all"](cmd, output_loglevel="trace", python_shell=False)
if result["retcode"] == 0:
ret = True
else:
raise CommandExecutionError(
"Problem encountered creating disk image",
info={"errors": [result["stderr"]], "changes": ret},
)
return ret | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/integrals/prde.py | python | prde_spde | (a, b, Q, n, DE) | return (A, B, Qq, R, n1) | Special Polynomial Differential Equation algorithm: Parametric Version.
Given a derivation D on k[t], an integer n, and a, b, q1, ..., qm in k[t]
with deg(a) > 0 and gcd(a, b) == 1, return (A, B, Q, R, n1), with
Qq = [q1, ..., qm] and R = [r1, ..., rm], such that for any solution
c1, ..., cm in Const(k) and q in k[t] of degree at most n of
a*Dq + b*q == Sum(ci*gi, (i, 1, m)), p = (q - Sum(ci*ri, (i, 1, m)))/a has
degree at most n1 and satisfies A*Dp + B*p == Sum(ci*qi, (i, 1, m)) | Special Polynomial Differential Equation algorithm: Parametric Version. | [
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"."
] | def prde_spde(a, b, Q, n, DE):
"""
Special Polynomial Differential Equation algorithm: Parametric Version.
Given a derivation D on k[t], an integer n, and a, b, q1, ..., qm in k[t]
with deg(a) > 0 and gcd(a, b) == 1, return (A, B, Q, R, n1), with
Qq = [q1, ..., qm] and R = [r1, ..., rm], such that for any solution
c1, ..., cm in Const(k) and q in k[t] of degree at most n of
a*Dq + b*q == Sum(ci*gi, (i, 1, m)), p = (q - Sum(ci*ri, (i, 1, m)))/a has
degree at most n1 and satisfies A*Dp + B*p == Sum(ci*qi, (i, 1, m))
"""
R, Z = list(zip(*[gcdex_diophantine(b, a, qi) for qi in Q]))
A = a
B = b + derivation(a, DE)
Qq = [zi - derivation(ri, DE) for ri, zi in zip(R, Z)]
R = list(R)
n1 = n - a.degree(DE.t)
return (A, B, Qq, R, n1) | [
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netaddr/netaddr | e84688f7034b7a88ac00a676359be57eb7a78184 | netaddr/ip/__init__.py | python | BaseIP.__lt__ | (self, other) | :param other: an `IPAddress` or `IPNetwork` object.
:return: ``True`` if this `IPAddress` or `IPNetwork` object is
less than ``other``, ``False`` otherwise. | :param other: an `IPAddress` or `IPNetwork` object. | [
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"""
:param other: an `IPAddress` or `IPNetwork` object.
:return: ``True`` if this `IPAddress` or `IPNetwork` object is
less than ``other``, ``False`` otherwise.
"""
try:
return self.sort_key() < other.sort_key()
except (AttributeError, TypeError):
return NotImplemented | [
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deepgram/sidomo | b70825d9017eb0c2c6b6389345cccbcbd52cf669 | sidomo/sidomo.py | python | dodo | (do, image, sharedir, display) | dodo (like sudo but for docker) runs argument in a docker image.
do is the command to run in the image.
image taken from (1) command-line, (2) "DODOIMAGE" environment variable, or (3) first built image.
sharedir (e.g., to pass data to command) is mounted (default: current directory). empty string does no mounting.
display is environment variable to set in docker image that allows X11 forwarding. | dodo (like sudo but for docker) runs argument in a docker image. | [
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] | def dodo(do, image, sharedir, display):
""" dodo (like sudo but for docker) runs argument in a docker image.
do is the command to run in the image.
image taken from (1) command-line, (2) "DODOIMAGE" environment variable, or (3) first built image.
sharedir (e.g., to pass data to command) is mounted (default: current directory). empty string does no mounting.
display is environment variable to set in docker image that allows X11 forwarding.
"""
# try to set image three ways
if not image:
if 'DODOIMAGE' in os.environ:
image = os.environ['DODOIMAGE']
else:
ims = client.images()
if len(ims) >= 1:
image = [im['RepoTags'][0] for im in client.images()][0]
assert image, 'No image given or found locally.'
# get image if not available locally
imnames = [im['RepoTags'][0] for im in client.images()]
if (not any([image in imname for imname in imnames])) and client.search(image):
print('Image {} not found locally. Pulling from docker hub.'.format(image))
client.pull(image)
# mount directory in docker
if sharedir:
volumes = ['{}:/home'.format(sharedir)]
else:
volumes = []
# set docker environment to display X11 locally
if display:
environment = ['DISPLAY={}'.format(display)]
elif 'DODODISPLAY' in os.environ:
environment = ['DISPLAY={}'.format(os.environ['DODODISPLAY'])]
else:
environment = []
with Container(image, volumes=volumes, cleanup=True, environment=environment) as c:
for output_line in c.run(do):
print('{}:\t {}'.format(image, output_line.decode('utf-8'))) | [
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"... | https://github.com/deepgram/sidomo/blob/b70825d9017eb0c2c6b6389345cccbcbd52cf669/sidomo/sidomo.py#L79-L121 | ||
home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/rtsp_to_webrtc/__init__.py | python | async_setup_entry | (hass: HomeAssistant, entry: ConfigEntry) | return True | Set up RTSPtoWebRTC from a config entry. | Set up RTSPtoWebRTC from a config entry. | [
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] | async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Set up RTSPtoWebRTC from a config entry."""
hass.data.setdefault(DOMAIN, {})
client: WebRTCClientInterface
try:
async with async_timeout.timeout(TIMEOUT):
client = await get_adaptive_client(
async_get_clientsession(hass), entry.data[DATA_SERVER_URL]
)
except ResponseError as err:
raise ConfigEntryNotReady from err
except (TimeoutError, ClientError) as err:
raise ConfigEntryNotReady from err
async def async_offer_for_stream_source(
stream_source: str,
offer_sdp: str,
stream_id: str,
) -> str:
"""Handle the signal path for a WebRTC stream.
This signal path is used to route the offer created by the client to the
proxy server that translates a stream to WebRTC. The communication for
the stream itself happens directly between the client and proxy.
"""
try:
async with async_timeout.timeout(TIMEOUT):
return await client.offer_stream_id(offer_sdp, stream_source, stream_id)
except TimeoutError as err:
raise HomeAssistantError("Timeout talking to RTSPtoWebRTC server") from err
except ClientError as err:
raise HomeAssistantError(str(err)) from err
entry.async_on_unload(
camera.async_register_rtsp_to_web_rtc_provider(
hass, DOMAIN, async_offer_for_stream_source
)
)
return True | [
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mukulhase/WebWhatsapp-Wrapper | d2ea6e821cf754ba4e16a6d5ebc141c3f65da339 | sample/flask/webapi.py | python | init_client | (client_id) | return drivers[client_id] | Initialse a driver for client and store for future reference
@param client_id: ID of client user
@return whebwhatsapi object | Initialse a driver for client and store for future reference | [
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] | def init_client(client_id):
"""Initialse a driver for client and store for future reference
@param client_id: ID of client user
@return whebwhatsapi object
"""
if client_id not in drivers:
drivers[client_id] = init_driver(client_id)
return drivers[client_id] | [
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behave/behave | e6364fe3d62c2befe34bc56471cfb317a218cd01 | behave4cmd0/log/steps.py | python | step_command_output_should_not_contain_log_records_from_categories | (context) | Verifies that the command output contains not log records from
the provided log categories (in any order).
.. code-block: gherkin
Given I define the log record schema:
| category | level | message |
| root | ERROR | __LOG_MESSAGE__ |
Then the command output should not contain log records from categories:
| category |
| bar | | Verifies that the command output contains not log records from
the provided log categories (in any order). | [
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] | def step_command_output_should_not_contain_log_records_from_categories(context):
"""
Verifies that the command output contains not log records from
the provided log categories (in any order).
.. code-block: gherkin
Given I define the log record schema:
| category | level | message |
| root | ERROR | __LOG_MESSAGE__ |
Then the command output should not contain log records from categories:
| category |
| bar |
"""
assert context.table, "REQUIRE: context.table"
context.table.require_column("category")
record_schema = context.log_record_row_schema
LogRecordTable.annotate_with_row_schema(context.table, record_schema)
step_command_output_should_not_contain_log_records(context)
context.table.remove_columns(["level", "message"]) | [
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iclavera/learning_to_adapt | bd7d99ba402521c96631e7d09714128f549db0f1 | learning_to_adapt/mujoco_py/mjtypes.py | python | MjvOptionWrapper.ptr | (self) | return self._wrapped | [] | def ptr(self):
return self._wrapped | [
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/pkg_resources/__init__.py | python | WorkingSet.resolve | (self, requirements, env=None, installer=None,
replace_conflicting=False, extras=None) | return to_activate | List all distributions needed to (recursively) meet `requirements`
`requirements` must be a sequence of ``Requirement`` objects. `env`,
if supplied, should be an ``Environment`` instance. If
not supplied, it defaults to all distributions available within any
entry or distribution in the working set. `installer`, if supplied,
will be invoked with each requirement that cannot be met by an
already-installed distribution; it should return a ``Distribution`` or
``None``.
Unless `replace_conflicting=True`, raises a VersionConflict exception
if
any requirements are found on the path that have the correct name but
the wrong version. Otherwise, if an `installer` is supplied it will be
invoked to obtain the correct version of the requirement and activate
it.
`extras` is a list of the extras to be used with these requirements.
This is important because extra requirements may look like `my_req;
extra = "my_extra"`, which would otherwise be interpreted as a purely
optional requirement. Instead, we want to be able to assert that these
requirements are truly required. | List all distributions needed to (recursively) meet `requirements` | [
"List",
"all",
"distributions",
"needed",
"to",
"(",
"recursively",
")",
"meet",
"requirements"
] | def resolve(self, requirements, env=None, installer=None,
replace_conflicting=False, extras=None):
"""List all distributions needed to (recursively) meet `requirements`
`requirements` must be a sequence of ``Requirement`` objects. `env`,
if supplied, should be an ``Environment`` instance. If
not supplied, it defaults to all distributions available within any
entry or distribution in the working set. `installer`, if supplied,
will be invoked with each requirement that cannot be met by an
already-installed distribution; it should return a ``Distribution`` or
``None``.
Unless `replace_conflicting=True`, raises a VersionConflict exception
if
any requirements are found on the path that have the correct name but
the wrong version. Otherwise, if an `installer` is supplied it will be
invoked to obtain the correct version of the requirement and activate
it.
`extras` is a list of the extras to be used with these requirements.
This is important because extra requirements may look like `my_req;
extra = "my_extra"`, which would otherwise be interpreted as a purely
optional requirement. Instead, we want to be able to assert that these
requirements are truly required.
"""
# set up the stack
requirements = list(requirements)[::-1]
# set of processed requirements
processed = {}
# key -> dist
best = {}
to_activate = []
req_extras = _ReqExtras()
# Mapping of requirement to set of distributions that required it;
# useful for reporting info about conflicts.
required_by = collections.defaultdict(set)
while requirements:
# process dependencies breadth-first
req = requirements.pop(0)
if req in processed:
# Ignore cyclic or redundant dependencies
continue
if not req_extras.markers_pass(req, extras):
continue
dist = best.get(req.key)
if dist is None:
# Find the best distribution and add it to the map
dist = self.by_key.get(req.key)
if dist is None or (dist not in req and replace_conflicting):
ws = self
if env is None:
if dist is None:
env = Environment(self.entries)
else:
# Use an empty environment and workingset to avoid
# any further conflicts with the conflicting
# distribution
env = Environment([])
ws = WorkingSet([])
dist = best[req.key] = env.best_match(
req, ws, installer,
replace_conflicting=replace_conflicting
)
if dist is None:
requirers = required_by.get(req, None)
raise DistributionNotFound(req, requirers)
to_activate.append(dist)
if dist not in req:
# Oops, the "best" so far conflicts with a dependency
dependent_req = required_by[req]
raise VersionConflict(dist, req).with_context(dependent_req)
# push the new requirements onto the stack
new_requirements = dist.requires(req.extras)[::-1]
requirements.extend(new_requirements)
# Register the new requirements needed by req
for new_requirement in new_requirements:
required_by[new_requirement].add(req.project_name)
req_extras[new_requirement] = req.extras
processed[req] = True
# return list of distros to activate
return to_activate | [
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Map-A-Droid/MAD | 81375b5c9ccc5ca3161eb487aa81469d40ded221 | mapadroid/madmin/routes/config.py | python | MADminConfig.settings | (self) | return redirect(url_for('settings_devices'), code=302) | [] | def settings(self):
return redirect(url_for('settings_devices'), code=302) | [
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lykops/lykops | ed7e35d0c1abb1eacf7ab365e041347d0862c0a7 | library/security/encryption/AES256/api.py | python | Using_AES256._split_header | (self, data) | return (b_header, b_ciphertext) | 对加密数据进行分割，分为加密头和加密数据
:parm
data ：需要分割的加密数据 | 对加密数据进行分割，分为加密头和加密数据
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data ：需要分割的加密数据 | [
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'''
对加密数据进行分割，分为加密头和加密数据
:parm
data ：需要分割的加密数据
'''
result = obj2bytes(data)
if result[0] :
data = result[1]
else :
data = string2bytes(data)
b_data = data.split(b'\n')
b_header = b_data[0].strip()
b_ciphertext = b''.join(b_data[1:])
return (b_header, b_ciphertext) | [
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PyQt5/PyQt | d2bbfc9fd6f3fdb5ed5404983cdd0848aa654cf0 | QScrollArea/QQSettingPanel.py | python | Window.onItemClicked | (self, item) | 左侧item | 左侧item | [
"左侧item"
] | def onItemClicked(self, item):
"""左侧item"""
row = self.listWidget.row(item) # 获取点击的item的索引
# 由于右侧的widget是按照命名widget_0 widget_1这样比较规范的方法,可以通过getattr找到
widget = getattr(self, 'widget_%d' % row, None)
if not widget:
return
# 定位右侧位置并滚动
self._blockSignals = True
self.scrollArea.verticalScrollBar().setSliderPosition(widget.pos().y())
self._blockSignals = False | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pandas-0.24.2-py3.7-macosx-10.9-x86_64.egg/pandas/core/internals/blocks.py | python | Block.is_datelike | (self) | return self.is_datetime or self.is_timedelta | return True if I am a non-datelike | return True if I am a non-datelike | [
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""" return True if I am a non-datelike """
return self.is_datetime or self.is_timedelta | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/rings/asymptotic/growth_group.py | python | MonomialGrowthGroup.factory | (cls,
base, var,
extend_by_non_growth_group=False,
return_factors=False,
**kwds) | r"""
Create a monomial growth group.
INPUT:
- ``base``, ``var``, keywords -- use in the initialization of the
exponential growth group; see :class:`MonomialGrowthGroup`
for details.
- ``extend_by_non_growth_group`` -- a boolean (default ``False``). If set, then
the growth group consists of two parts, one part dealing with
the absolute values of the bases and one for their arguments.
- ``return_factors`` -- a boolean (default: ``False``). If set,
then a tuple of the (cartesian) factors of this growth group
is returned.
OUTPUT:
A growth group or tuple of growth groups.
EXAMPLES::
sage: from sage.rings.asymptotic.growth_group import MonomialGrowthGroup
sage: from sage.groups.misc_gps.imaginary_groups import ImaginaryGroup
sage: MonomialGrowthGroup.factory(ZZ, 'n')
Growth Group n^ZZ
sage: MonomialGrowthGroup.factory(ImaginaryGroup(ZZ), 'n')
Growth Group n^(ZZ*I)
TESTS::
sage: MonomialGrowthGroup.factory(ZZ, 'n', return_factors=True)
(Growth Group n^ZZ,)
sage: MonomialGrowthGroup.factory(ZZ, 'n', extend_by_non_growth_group=True)
Growth Group n^ZZ * n^(ZZ*I)
sage: MonomialGrowthGroup.factory(ZZ, 'n', return_factors=True,
....: extend_by_non_growth_group=True)
(Growth Group n^ZZ, Growth Group n^(ZZ*I)) | r"""
Create a monomial growth group. | [
"r",
"Create",
"a",
"monomial",
"growth",
"group",
"."
] | def factory(cls,
base, var,
extend_by_non_growth_group=False,
return_factors=False,
**kwds):
r"""
Create a monomial growth group.
INPUT:
- ``base``, ``var``, keywords -- use in the initialization of the
exponential growth group; see :class:`MonomialGrowthGroup`
for details.
- ``extend_by_non_growth_group`` -- a boolean (default ``False``). If set, then
the growth group consists of two parts, one part dealing with
the absolute values of the bases and one for their arguments.
- ``return_factors`` -- a boolean (default: ``False``). If set,
then a tuple of the (cartesian) factors of this growth group
is returned.
OUTPUT:
A growth group or tuple of growth groups.
EXAMPLES::
sage: from sage.rings.asymptotic.growth_group import MonomialGrowthGroup
sage: from sage.groups.misc_gps.imaginary_groups import ImaginaryGroup
sage: MonomialGrowthGroup.factory(ZZ, 'n')
Growth Group n^ZZ
sage: MonomialGrowthGroup.factory(ImaginaryGroup(ZZ), 'n')
Growth Group n^(ZZ*I)
TESTS::
sage: MonomialGrowthGroup.factory(ZZ, 'n', return_factors=True)
(Growth Group n^ZZ,)
sage: MonomialGrowthGroup.factory(ZZ, 'n', extend_by_non_growth_group=True)
Growth Group n^ZZ * n^(ZZ*I)
sage: MonomialGrowthGroup.factory(ZZ, 'n', return_factors=True,
....: extend_by_non_growth_group=True)
(Growth Group n^ZZ, Growth Group n^(ZZ*I))
"""
from sage.categories.cartesian_product import cartesian_product
from sage.groups.misc_gps.imaginary_groups import ImaginaryGroup
if isinstance(base, ImaginaryGroup):
groups = (cls._non_growth_group_class_(base, var, **kwds),)
elif extend_by_non_growth_group:
M = cls(base, var, **kwds)
groups = (M, M.non_growth_group())
else:
groups = (cls(base, var, **kwds),)
if return_factors:
return tuple(groups)
else:
return cartesian_product(groups) | [
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jilljenn/tryalgo | 46da31aea9a0ff58819e360c987f9506b21f6e9d | tryalgo/laser_mirrors.py | python | laser_mirrors | (rows, cols, mir) | return None | Orienting mirrors to allow reachability by laser beam
:param int rows:
:param int cols: rows and cols are the dimension of the grid
:param mir: list of mirror coordinates, except
mir[0]= laser entrance,
mir[-1]= laser exit.
:complexity: :math:`O(2^n)` | Orienting mirrors to allow reachability by laser beam | [
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:param int rows:
:param int cols: rows and cols are the dimension of the grid
:param mir: list of mirror coordinates, except
mir[0]= laser entrance,
mir[-1]= laser exit.
:complexity: :math:`O(2^n)`
"""
# build structures
n = len(mir)
orien = [None] * (n + 2)
orien[n] = 0 # arbitrary orientations
orien[n + 1] = 0
succ = [[None for direc in range(4)] for i in range(n + 2)]
L = [(mir[i][0], mir[i][1], i) for i in range(n)]
L.append((0, -1, n)) # enter
L.append((0, cols, n + 1)) # exit
last_r, last_i = None, None
for (r, c, i) in sorted(L): # sweep by row
if last_r == r:
succ[i][LEFT] = last_i
succ[last_i][RIGHT] = i
last_r, last_i = r, i
last_c = None
for (r, c, i) in sorted(L, key=lambda rci: (rci[1], rci[0])):
if last_c == c: # sweep by column
succ[i][UP] = last_i
succ[last_i][DOWN] = i
last_c, last_i = c, i
if solve(succ, orien, n, RIGHT): # exploration
return orien[:n]
return None | [
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Abjad/abjad | d0646dfbe83db3dc5ab268f76a0950712b87b7fd | abjad/parsers/parser.py | python | GuileProxy.skip | (self, duration) | return leaf | r"""
Handles LilyPond ``\skip`` command. | r"""
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r"""
Handles LilyPond ``\skip`` command.
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leaf = Skip(duration.duration)
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attach(duration.multiplier, leaf)
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metabrainz/picard | 535bf8c7d9363ffc7abb3f69418ec11823c38118 | picard/script/functions.py | python | func_eq | (parser, x, y) | [] | def func_eq(parser, x, y):
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/layout/_title.py | python | Title.x | (self) | return self["x"] | Sets the x position with respect to `xref` in normalized
coordinates from 0 (left) to 1 (right).
The 'x' property is a number and may be specified as:
- An int or float in the interval [0, 1]
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-------
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tobiasfshr/MOTSFusion | 32fa6d7d3282c5046e45842370d225fa5a06603e | external/BB2SegNet/refinement_net/network/deeplab/core/preprocess_utils.py | python | resolve_shape | (tensor, rank=None, scope=None) | Fully resolves the shape of a Tensor.
Use as much as possible the shape components already known during graph
creation and resolve the remaining ones during runtime.
Args:
tensor: Input tensor whose shape we query.
rank: The rank of the tensor, provided that we know it.
scope: Optional name scope.
Returns:
shape: The full shape of the tensor. | Fully resolves the shape of a Tensor. | [
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"""Fully resolves the shape of a Tensor.
Use as much as possible the shape components already known during graph
creation and resolve the remaining ones during runtime.
Args:
tensor: Input tensor whose shape we query.
rank: The rank of the tensor, provided that we know it.
scope: Optional name scope.
Returns:
shape: The full shape of the tensor.
"""
with tf.name_scope(scope, 'resolve_shape', [tensor]):
if rank is not None:
shape = tensor.get_shape().with_rank(rank).as_list()
else:
shape = tensor.get_shape().as_list()
if None in shape:
shape_dynamic = tf.shape(tensor)
for i in range(len(shape)):
if shape[i] is None:
shape[i] = shape_dynamic[i]
return shape | [
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openstack/barbican | a9d2b133c8dc3307974f119f9a2b23a4ba82e8ce | barbican/common/config.py | python | list_opts | () | [] | def list_opts():
yield None, context_opts
yield None, common_opts
yield None, host_opts
yield None, db_opts
yield None, _options.eventlet_backdoor_opts
yield retry_opt_group, retry_opts
yield queue_opt_group, queue_opts
yield ks_queue_opt_group, ks_queue_opts
yield quota_opt_group, quota_opts | [
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textX/textX | 452b684d434c557a63ff7ae2b014770c29669e4c | textx/registration.py | python | register_generator | (generator_desc_or_language, target=None, description='',
generator=None) | Programmatically register a generator.
Args:
generator_desc_or_language (GeneratorDesc or str): If GeneratorDesc is
given other parameters are not used.
For other parameters see `GeneratorDesc`. | Programmatically register a generator. | [
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] | def register_generator(generator_desc_or_language, target=None, description='',
generator=None):
"""
Programmatically register a generator.
Args:
generator_desc_or_language (GeneratorDesc or str): If GeneratorDesc is
given other parameters are not used.
For other parameters see `GeneratorDesc`.
"""
global generators
if generators is None:
generator_descriptions()
if not isinstance(generator_desc_or_language, GeneratorDesc):
generator_desc = GeneratorDesc(
language=generator_desc_or_language,
target=target,
description=description,
generator=generator
)
else:
generator_desc = generator_desc_or_language
lang_gens = generators.setdefault(generator_desc.language.lower(), {})
if generator_desc.target.lower() in lang_gens:
raise TextXRegistrationError(
'Generator "{}->{}" already registered.'.format(
generator_desc.language, generator_desc.target))
lang_gens[generator_desc.target.lower()] = generator_desc | [
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jtriley/StarCluster | bc7c950e73f193eac9aab986b6764939cfdad978 | starcluster/cluster.py | python | ClusterValidator.validate | (self) | Checks that all cluster template settings are valid and raises an
exception.ClusterValidationError exception if not. | Checks that all cluster template settings are valid and raises an
exception.ClusterValidationError exception if not. | [
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"""
Checks that all cluster template settings are valid and raises an
exception.ClusterValidationError exception if not.
"""
log.info("Validating cluster template settings...")
try:
self.validate_required_settings()
self.validate_vpc()
self.validate_dns_prefix()
self.validate_spot_bid()
self.validate_cluster_size()
self.validate_cluster_user()
self.validate_shell_setting()
self.validate_permission_settings()
self.validate_credentials()
self.validate_keypair()
self.validate_zone()
self.validate_ebs_settings()
self.validate_ebs_aws_settings()
self.validate_image_settings()
self.validate_instance_types()
self.validate_userdata()
log.info('Cluster template settings are valid')
return True
except exception.ClusterValidationError as e:
e.msg = 'Cluster settings are not valid:\n%s' % e.msg
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googledatalab/pydatalab | 1c86e26a0d24e3bc8097895ddeab4d0607be4c40 | datalab/bigquery/_table.py | python | Table.to_query | (self, fields=None) | return _query.Query('SELECT %s FROM %s' % (fields, self._repr_sql_()), context=self._context) | Return a Query for this Table.
Args:
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Args:
fields: the fields to return. If None, all fields will be returned. This can be a string
which will be injected into the Query after SELECT, or a list of field names.
Returns:
A Query object that will return the specified fields from the records in the Table.
"""
# Do import here to avoid top-level circular dependencies.
from . import _query
if fields is None:
fields = '*'
elif isinstance(fields, list):
fields = ','.join(fields)
return _query.Query('SELECT %s FROM %s' % (fields, self._repr_sql_()), context=self._context) | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_volume_error.py | python | V1VolumeError.message | (self) | return self._message | Gets the message of this V1VolumeError. # noqa: E501
String detailing the error encountered during Attach or Detach operation. This string may be logged, so it should not contain sensitive information. # noqa: E501
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mcfletch/pyopengl | 02d11dad9ff18e50db10e975c4756e17bf198464 | OpenGL/GL/INTEL/fragment_shader_ordering.py | python | glInitFragmentShaderOrderingINTEL | () | return extensions.hasGLExtension( _EXTENSION_NAME ) | Return boolean indicating whether this extension is available | Return boolean indicating whether this extension is available | [
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'''Return boolean indicating whether this extension is available'''
from OpenGL import extensions
return extensions.hasGLExtension( _EXTENSION_NAME ) | [
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sendgrid/sendgrid-python | df13b78b0cdcb410b4516f6761c4d3138edd4b2d | sendgrid/helpers/mail/batch_id.py | python | BatchId.get | (self) | return self.batch_id | Get a JSON-ready representation of this BatchId object.
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"self",
")",
":",
"return",
"self",
".",
"batch_id"
] | https://github.com/sendgrid/sendgrid-python/blob/df13b78b0cdcb410b4516f6761c4d3138edd4b2d/sendgrid/helpers/mail/batch_id.py#L43-L50 | |
pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/dparse/parser.py | python | RequirementsTXTParser.parse | (self) | Parses a requirements.txt-like file | Parses a requirements.txt-like file | [
"Parses",
"a",
"requirements",
".",
"txt",
"-",
"like",
"file"
] | def parse(self):
"""
Parses a requirements.txt-like file
"""
index_server = None
for num, line in enumerate(self.iter_lines()):
line = line.rstrip()
if not line:
continue
if line.startswith('#'):
# comments are lines that start with # only
continue
if line.startswith('-i') or \
line.startswith('--index-url') or \
line.startswith('--extra-index-url'):
# this file is using a private index server, try to parse it
index_server = self.parse_index_server(line)
continue
elif self.obj.path and (line.startswith('-r') or line.startswith('--requirement')):
self.obj.resolved_files.append(self.resolve_file(self.obj.path, line))
elif line.startswith('-f') or line.startswith('--find-links') or \
line.startswith('--no-index') or line.startswith('--allow-external') or \
line.startswith('--allow-unverified') or line.startswith('-Z') or \
line.startswith('--always-unzip'):
continue
elif self.is_marked_line(line):
continue
else:
try:
parseable_line = line
# multiline requirements are not parseable
if "\\" in line:
parseable_line = line.replace("\\", "")
for next_line in self.iter_lines(num + 1):
parseable_line += next_line.strip().replace("\\", "")
line += "\n" + next_line
if "\\" in next_line:
continue
break
# ignore multiline requirements if they are marked
if self.is_marked_line(parseable_line):
continue
hashes = []
if "--hash" in parseable_line:
parseable_line, hashes = Parser.parse_hashes(parseable_line)
req = RequirementsTXTLineParser.parse(parseable_line)
if req:
req.hashes = hashes
req.index_server = index_server
# replace the requirements line with the 'real' line
req.line = line
self.obj.dependencies.append(req)
except ValueError:
continue | [
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"(",
")",
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"continue"... | https://github.com/pypa/pipenv/blob/b21baade71a86ab3ee1429f71fbc14d4f95fb75d/pipenv/vendor/dparse/parser.py#L207-L264 |
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