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quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/service.py | python | Service.GetResponseClass | (self, method_descriptor) | Returns the class of the response message for the specified method.
This method isn't really needed, as the RpcChannel's CallMethod constructs
the response protocol message. It's provided anyway in case it is useful
for the caller to know the response type in advance. | Returns the class of the response message for the specified method. | [
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] | def GetResponseClass(self, method_descriptor):
"""Returns the class of the response message for the specified method.
This method isn't really needed, as the RpcChannel's CallMethod constructs
the response protocol message. It's provided anyway in case it is useful
for the caller to know the response type in advance.
"""
raise NotImplementedError | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextEvent.GetListType | (*args, **kwargs) | return _stc.StyledTextEvent_GetListType(*args, **kwargs) | GetListType(self) -> int | GetListType(self) -> int | [
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"""GetListType(self) -> int"""
return _stc.StyledTextEvent_GetListType(*args, **kwargs) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/ftplib.py | python | FTP.storlines | (self, cmd, fp, callback=None) | return self.voidresp() | Store a file in line mode. A new port is created for you.
Args:
cmd: A STOR command.
fp: A file-like object with a readline() method.
callback: An optional single parameter callable that is called on
on each line after it is sent. [default: None]
Returns:
The response code. | Store a file in line mode. A new port is created for you. | [
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"""Store a file in line mode. A new port is created for you.
Args:
cmd: A STOR command.
fp: A file-like object with a readline() method.
callback: An optional single parameter callable that is called on
on each line after it is sent. [default: None]
Returns:
The response code.
"""
self.voidcmd('TYPE A')
conn = self.transfercmd(cmd)
while 1:
buf = fp.readline()
if not buf: break
if buf[-2:] != CRLF:
if buf[-1] in CRLF: buf = buf[:-1]
buf = buf + CRLF
conn.sendall(buf)
if callback: callback(buf)
conn.close()
return self.voidresp() | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/skia/tools/skp/webpages_playback.py | python | SkPicturePlayback._RenameSkpFiles | (self, page_set) | Rename generated SKP files into more descriptive names.
Look into the subdirectory of TMP_SKP_DIR and find the most interesting
.skp in there to be this page_set's representative .skp. | Rename generated SKP files into more descriptive names. | [
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"names",
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] | def _RenameSkpFiles(self, page_set):
"""Rename generated SKP files into more descriptive names.
Look into the subdirectory of TMP_SKP_DIR and find the most interesting
.skp in there to be this page_set's representative .skp.
"""
subdirs = glob.glob(os.path.join(TMP_SKP_DIR, '*'))
for site in subdirs:
if self._IsChromiumPageSet(page_set):
filename = self._GetChromiumSkpFileName(page_set, site)
else:
filename = self._GetSkiaSkpFileName(page_set)
filename = filename.lower()
if self._skp_prefix:
filename = '%s%s' % (self._skp_prefix, filename)
# We choose the largest .skp as the most likely to be interesting.
largest_skp = max(glob.glob(os.path.join(site, '*.skp')),
key=lambda path: os.stat(path).st_size)
dest = os.path.join(self._local_skp_dir, filename)
print 'Moving', largest_skp, 'to', dest
shutil.move(largest_skp, dest)
self._skp_files.append(filename)
shutil.rmtree(site) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | SAXCallback.reference | (self, name) | called when an entity reference has been found | called when an entity reference has been found | [
"called",
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"entity",
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"has",
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] | def reference(self, name):
"""called when an entity reference has been found"""
pass | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/buffer.py | python | Buffer.auto_down | (self, count=1, go_to_start_of_line_if_history_changes=False) | If we're not on the last line (of a multiline input) go a line down,
otherwise go forward in history. (If nothing is selected.) | If we're not on the last line (of a multiline input) go a line down,
otherwise go forward in history. (If nothing is selected.) | [
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] | def auto_down(self, count=1, go_to_start_of_line_if_history_changes=False):
"""
If we're not on the last line (of a multiline input) go a line down,
otherwise go forward in history. (If nothing is selected.)
"""
if self.complete_state:
self.complete_next(count=count)
elif self.document.cursor_position_row < self.document.line_count - 1:
self.cursor_down(count=count)
elif not self.selection_state:
self.history_forward(count=count)
# Go to the start of the line?
if go_to_start_of_line_if_history_changes:
self.cursor_position += self.document.get_start_of_line_position() | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/Input/BlockInfo.py | python | BlockInfo.addParameter | (self, param) | Adds a parameter.
Input:
param[ParameterInfo]: New parameter to be added | Adds a parameter.
Input:
param[ParameterInfo]: New parameter to be added | [
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"""
Adds a parameter.
Input:
param[ParameterInfo]: New parameter to be added
"""
param.parent = self
self.parameters[param.name] = param
if param not in self.parameters_list:
self.parameters_list.append(param.name) | [
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trailofbits/llvm-sanitizer-tutorial | d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99 | llvm/tools/clang/tools/scan-build-py/libscanbuild/clang.py | python | get_active_checkers | (clang, plugins) | return frozenset(result) | Get the active checker list.
:param clang: the compiler we are using
:param plugins: list of plugins which was requested by the user
:return: list of checker names which are active
To get the default checkers we execute Clang to print how this
compilation would be called. And take out the enabled checker from the
arguments. For input file we specify stdin and pass only language
information. | Get the active checker list. | [
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] | def get_active_checkers(clang, plugins):
""" Get the active checker list.
:param clang: the compiler we are using
:param plugins: list of plugins which was requested by the user
:return: list of checker names which are active
To get the default checkers we execute Clang to print how this
compilation would be called. And take out the enabled checker from the
arguments. For input file we specify stdin and pass only language
information. """
def get_active_checkers_for(language):
""" Returns a list of active checkers for the given language. """
load_args = [arg
for plugin in plugins
for arg in ['-Xclang', '-load', '-Xclang', plugin]]
cmd = [clang, '--analyze'] + load_args + ['-x', language, '-']
return [ACTIVE_CHECKER_PATTERN.match(arg).group(1)
for arg in get_arguments(cmd, '.')
if ACTIVE_CHECKER_PATTERN.match(arg)]
result = set()
for language in ['c', 'c++', 'objective-c', 'objective-c++']:
result.update(get_active_checkers_for(language))
return frozenset(result) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/linear_model/_bayes.py | python | BayesianRidge._update_coef_ | (self, X, y, n_samples, n_features, XT_y, U, Vh,
eigen_vals_, alpha_, lambda_) | return coef_, rmse_ | Update posterior mean and compute corresponding rmse.
Posterior mean is given by coef_ = scaled_sigma_ * X.T * y where
scaled_sigma_ = (lambda_/alpha_ * np.eye(n_features)
+ np.dot(X.T, X))^-1 | Update posterior mean and compute corresponding rmse. | [
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"rmse",
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] | def _update_coef_(self, X, y, n_samples, n_features, XT_y, U, Vh,
eigen_vals_, alpha_, lambda_):
"""Update posterior mean and compute corresponding rmse.
Posterior mean is given by coef_ = scaled_sigma_ * X.T * y where
scaled_sigma_ = (lambda_/alpha_ * np.eye(n_features)
+ np.dot(X.T, X))^-1
"""
if n_samples > n_features:
coef_ = np.dot(Vh.T,
Vh / (eigen_vals_ +
lambda_ / alpha_)[:, np.newaxis])
coef_ = np.dot(coef_, XT_y)
else:
coef_ = np.dot(X.T, np.dot(
U / (eigen_vals_ + lambda_ / alpha_)[None, :], U.T))
coef_ = np.dot(coef_, y)
rmse_ = np.sum((y - np.dot(X, coef_)) ** 2)
return coef_, rmse_ | [
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/google/protobuf/service_reflection.py | python | _ServiceStubBuilder.BuildServiceStub | (self, cls) | Constructs the stub class.
Args:
cls: The class that will be constructed. | Constructs the stub class. | [
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"."
] | def BuildServiceStub(self, cls):
"""Constructs the stub class.
Args:
cls: The class that will be constructed.
"""
def _ServiceStubInit(stub, rpc_channel):
stub.rpc_channel = rpc_channel
self.cls = cls
cls.__init__ = _ServiceStubInit
for method in self.descriptor.methods:
setattr(cls, method.name, self._GenerateStubMethod(method)) | [
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/api/execution_api.py | python | ExecutionApi.execution_get_with_http_info | (self, **kwargs) | return self.api_client.call_api(
'/execution', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[Execution]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats) | Get all raw executions for your account. # noqa: E501
This returns all raw transactions, which includes order opening and cancelation, and order status changes. It can be quite noisy. More focused information is available at `/execution/tradeHistory`. You may also use the `filter` param to target your query. Specify an array as a filter value, such as `{\"execType\": [\"Settlement\", \"Trade\"]}` to filter on multiple values. See [the FIX Spec](http://www.onixs.biz/fix-dictionary/5.0.SP2/msgType_8_8.html) for explanations of these fields. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.execution_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str symbol: Instrument symbol. Send a bare series (e.g. XBT) to get data for the nearest expiring contract in that series. You can also send a timeframe, e.g. `XBT:quarterly`. Timeframes are `nearest`, `daily`, `weekly`, `monthly`, `quarterly`, `biquarterly`, and `perpetual`.
:param str filter: Generic table filter. Send JSON key/value pairs, such as `{\"key\": \"value\"}`. You can key on individual fields, and do more advanced querying on timestamps. See the [Timestamp Docs](https://www.bitmex.com/app/restAPI#Timestamp-Filters) for more details.
:param str columns: Array of column names to fetch. If omitted, will return all columns. Note that this method will always return item keys, even when not specified, so you may receive more columns that you expect.
:param float count: Number of results to fetch.
:param float start: Starting point for results.
:param bool reverse: If true, will sort results newest first.
:param datetime start_time: Starting date filter for results.
:param datetime end_time: Ending date filter for results.
:return: list[Execution]
If the method is called asynchronously,
returns the request thread. | Get all raw executions for your account. # noqa: E501 | [
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] | def execution_get_with_http_info(self, **kwargs): # noqa: E501
"""Get all raw executions for your account. # noqa: E501
This returns all raw transactions, which includes order opening and cancelation, and order status changes. It can be quite noisy. More focused information is available at `/execution/tradeHistory`. You may also use the `filter` param to target your query. Specify an array as a filter value, such as `{\"execType\": [\"Settlement\", \"Trade\"]}` to filter on multiple values. See [the FIX Spec](http://www.onixs.biz/fix-dictionary/5.0.SP2/msgType_8_8.html) for explanations of these fields. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.execution_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str symbol: Instrument symbol. Send a bare series (e.g. XBT) to get data for the nearest expiring contract in that series. You can also send a timeframe, e.g. `XBT:quarterly`. Timeframes are `nearest`, `daily`, `weekly`, `monthly`, `quarterly`, `biquarterly`, and `perpetual`.
:param str filter: Generic table filter. Send JSON key/value pairs, such as `{\"key\": \"value\"}`. You can key on individual fields, and do more advanced querying on timestamps. See the [Timestamp Docs](https://www.bitmex.com/app/restAPI#Timestamp-Filters) for more details.
:param str columns: Array of column names to fetch. If omitted, will return all columns. Note that this method will always return item keys, even when not specified, so you may receive more columns that you expect.
:param float count: Number of results to fetch.
:param float start: Starting point for results.
:param bool reverse: If true, will sort results newest first.
:param datetime start_time: Starting date filter for results.
:param datetime end_time: Ending date filter for results.
:return: list[Execution]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['symbol', 'filter', 'columns', 'count', 'start', 'reverse', 'start_time', 'end_time'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method execution_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'symbol' in params:
query_params.append(('symbol', params['symbol'])) # noqa: E501
if 'filter' in params:
query_params.append(('filter', params['filter'])) # noqa: E501
if 'columns' in params:
query_params.append(('columns', params['columns'])) # noqa: E501
if 'count' in params:
query_params.append(('count', params['count'])) # noqa: E501
if 'start' in params:
query_params.append(('start', params['start'])) # noqa: E501
if 'reverse' in params:
query_params.append(('reverse', params['reverse'])) # noqa: E501
if 'start_time' in params:
query_params.append(('startTime', params['start_time'])) # noqa: E501
if 'end_time' in params:
query_params.append(('endTime', params['end_time'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'application/xml', 'text/xml', 'application/javascript', 'text/javascript']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded']) # noqa: E501
# Authentication setting
auth_settings = ['apiExpires', 'apiKey', 'apiSignature'] # noqa: E501
return self.api_client.call_api(
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path_params,
query_params,
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body=body_params,
post_params=form_params,
files=local_var_files,
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xenia-project/xenia | 9b1fdac98665ac091b9660a5d0fbb259ed79e578 | third_party/google-styleguide/cpplint/cpplint.py | python | ProcessLine | (filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions=[]) | Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error | Processes a single line in the file. | [
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] | def ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions=[]):
"""Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
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line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
nesting_state: A NestingState instance which maintains information about
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error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
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"""
raw_lines = clean_lines.raw_lines
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nesting_state.Update(filename, clean_lines, line, error)
if nesting_state.InAsmBlock(): return
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CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
CheckStyle(filename, clean_lines, line, file_extension, nesting_state, error)
CheckLanguage(filename, clean_lines, line, file_extension, include_state,
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CheckForNonConstReference(filename, clean_lines, line, nesting_state, error)
CheckForNonStandardConstructs(filename, clean_lines, line,
nesting_state, error)
CheckVlogArguments(filename, clean_lines, line, error)
CheckPosixThreading(filename, clean_lines, line, error)
CheckInvalidIncrement(filename, clean_lines, line, error)
CheckMakePairUsesDeduction(filename, clean_lines, line, error)
CheckDefaultLambdaCaptures(filename, clean_lines, line, error)
for check_fn in extra_check_functions:
check_fn(filename, clean_lines, line, error) | [
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rootm0s/Protectors | 5b3f4d11687a5955caf9c3af30666c4bfc2c19ab | OWASP-ZSC/module/readline_windows/pyreadline/modes/notemacs.py | python | NotEmacsMode.backward_char | (self, e) | Move back a character. | Move back a character. | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/dataset/engine/datasets.py | python | Dataset.is_sharded | (self) | return False | Returns True if the dataset or its children is sharded. | Returns True if the dataset or its children is sharded. | [
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keyboardio/Kaleidoscope | d59604e98b2439d108647f15be52984a6837d360 | bin/cpplint.py | python | _CppLintState.AddFilters | (self, filters) | Adds more filters to the existing list of error-message filters. | Adds more filters to the existing list of error-message filters. | [
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"filt",
")"
] | https://github.com/keyboardio/Kaleidoscope/blob/d59604e98b2439d108647f15be52984a6837d360/bin/cpplint.py#L1059-L1068 | ||
google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/symbol_database.py | python | SymbolDatabase.RegisterFileDescriptor | (self, file_descriptor) | Registers the given file descriptor in the local database.
Args:
file_descriptor: a descriptor.FileDescriptor.
Returns:
The provided descriptor. | Registers the given file descriptor in the local database. | [
"Registers",
"the",
"given",
"file",
"descriptor",
"in",
"the",
"local",
"database",
"."
] | def RegisterFileDescriptor(self, file_descriptor):
"""Registers the given file descriptor in the local database.
Args:
file_descriptor: a descriptor.FileDescriptor.
Returns:
The provided descriptor.
"""
self.pool.AddFileDescriptor(file_descriptor) | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/make.py | python | MakefileWriter.ComputeOutputBasename | (self, spec) | return target_prefix + target + target_ext | Return the 'output basename' of a gyp spec.
E.g., the loadable module 'foobar' in directory 'baz' will produce
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] | def ComputeOutputBasename(self, spec):
"""Return the 'output basename' of a gyp spec.
E.g., the loadable module 'foobar' in directory 'baz' will produce
'libfoobar.so'
"""
assert not self.is_mac_bundle
if self.flavor == 'mac' and self.type in (
'static_library', 'executable', 'shared_library', 'loadable_module'):
return self.xcode_settings.GetExecutablePath()
target = spec['target_name']
target_prefix = ''
target_ext = ''
if self.type == 'static_library':
if target[:3] == 'lib':
target = target[3:]
target_prefix = 'lib'
target_ext = '.a'
elif self.type in ('loadable_module', 'shared_library'):
if target[:3] == 'lib':
target = target[3:]
target_prefix = 'lib'
if self.flavor == 'aix':
target_ext = '.a'
else:
target_ext = '.so'
elif self.type == 'none':
target = '%s.stamp' % target
elif self.type != 'executable':
print ("ERROR: What output file should be generated?",
"type", self.type, "target", target)
target_prefix = spec.get('product_prefix', target_prefix)
target = spec.get('product_name', target)
product_ext = spec.get('product_extension')
if product_ext:
target_ext = '.' + product_ext
return target_prefix + target + target_ext | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/share/doc/python3.7/examples/Tools/iobench/iobench.py | python | modify_small_chunks | (f, source) | modify 20 units at a time | modify 20 units at a time | [
"modify",
"20",
"units",
"at",
"a",
"time"
] | def modify_small_chunks(f, source):
""" modify 20 units at a time """
f.seek(0)
for i in xrange(0, len(source), 20):
f.write(source[i:i+20]) | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/value_object/read/media/datfile/unit.py | python | UnitCommand.get_data_format_members | (cls, game_version) | return data_format | Return the members in this struct. | Return the members in this struct. | [
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] | def get_data_format_members(cls, game_version):
"""
Return the members in this struct.
"""
data_format = [
# Type (0 = Generic, 1 = Tribe)
(READ_GEN, "command_used", StorageType.INT_MEMBER, "int16_t"),
(READ_GEN, "command_id", StorageType.ID_MEMBER, "int16_t"),
(SKIP, "is_default", StorageType.BOOLEAN_MEMBER, "int8_t"),
(READ_GEN, "type", StorageType.ID_MEMBER, EnumLookupMember(
raw_type="int16_t",
type_name="command_ability",
lookup_dict=COMMAND_ABILITY
)),
(READ_GEN, "class_id", StorageType.ID_MEMBER, "int16_t"),
(READ_GEN, "unit_id", StorageType.ID_MEMBER, "int16_t"),
(READ_GEN, "terrain_id", StorageType.ID_MEMBER, "int16_t"),
(READ_GEN, "resource_in", StorageType.INT_MEMBER, "int16_t"), # carry resource
# resource that multiplies the amount you can gather
(READ_GEN, "resource_multiplier", StorageType.INT_MEMBER, "int16_t"),
(READ_GEN, "resource_out", StorageType.INT_MEMBER, "int16_t"), # drop resource
(SKIP, "unused_resource", StorageType.INT_MEMBER, "int16_t"),
(READ_GEN, "work_value1", StorageType.FLOAT_MEMBER, "float"), # quantity
(READ_GEN, "work_value2", StorageType.FLOAT_MEMBER, "float"), # execution radius?
(READ_GEN, "work_range", StorageType.FLOAT_MEMBER, "float"),
(READ_GEN, "search_mode", StorageType.BOOLEAN_MEMBER, "int8_t"),
(READ_GEN, "search_time", StorageType.FLOAT_MEMBER, "float"),
(READ_GEN, "enable_targeting", StorageType.BOOLEAN_MEMBER, "int8_t"),
(READ_GEN, "combat_level_flag", StorageType.ID_MEMBER, "int8_t"),
(READ_GEN, "gather_type", StorageType.INT_MEMBER, "int16_t"),
(READ, "work_mode2", StorageType.INT_MEMBER, "int16_t"),
(READ_GEN, "owner_type", StorageType.ID_MEMBER, EnumLookupMember(
# what can be selected as a target for the unit command?
raw_type="int8_t",
type_name="selection_type",
lookup_dict=OWNER_TYPE
)),
# checks if the targeted unit has > 0 resources
(READ_GEN, "carry_check", StorageType.BOOLEAN_MEMBER, "int8_t"),
(READ_GEN, "state_build", StorageType.BOOLEAN_MEMBER, "int8_t"),
# walking with tool but no resource
(READ_GEN, "move_sprite_id", StorageType.ID_MEMBER, "int16_t"),
# proceeding resource gathering or attack
(READ_GEN, "proceed_sprite_id", StorageType.ID_MEMBER, "int16_t"),
# actual execution or transformation graphic
(READ_GEN, "work_sprite_id", StorageType.ID_MEMBER, "int16_t"),
# display resources in hands
(READ_GEN, "carry_sprite_id", StorageType.ID_MEMBER, "int16_t"),
# sound to play when execution starts
(READ_GEN, "resource_gather_sound_id", StorageType.ID_MEMBER, "int16_t"),
# sound to play on resource drop
(READ_GEN, "resource_deposit_sound_id", StorageType.ID_MEMBER, "int16_t"),
]
if game_version[0].game_id == "AOE2DE":
data_format.extend([
(READ_GEN, "wwise_resource_gather_sound_id", StorageType.ID_MEMBER, "uint32_t"),
# sound to play on resource drop
(READ_GEN, "wwise_resource_deposit_sound_id", StorageType.ID_MEMBER, "uint32_t"),
])
return data_format | [
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] | https://github.com/SFTtech/openage/blob/d6a08c53c48dc1e157807471df92197f6ca9e04d/openage/convert/value_object/read/media/datfile/unit.py#L24-L85 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/linalg/interpolative.py | python | rand | (*shape) | return backend.id_srand(np.prod(shape)).reshape(shape) | Generate standard uniform pseudorandom numbers via a very efficient lagged
Fibonacci method.
This routine is used for all random number generation in this package and
can affect ID and SVD results.
Parameters
----------
shape
Shape of output array | Generate standard uniform pseudorandom numbers via a very efficient lagged
Fibonacci method. | [
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"pseudorandom",
"numbers",
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"."
] | def rand(*shape):
"""
Generate standard uniform pseudorandom numbers via a very efficient lagged
Fibonacci method.
This routine is used for all random number generation in this package and
can affect ID and SVD results.
Parameters
----------
shape
Shape of output array
"""
# For details, see :func:`backend.id_srand`, and :func:`backend.id_srando`.
return backend.id_srand(np.prod(shape)).reshape(shape) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Node/__init__.py | python | Node.add_wkid | (self, wkid) | Add a node to the list of kids waiting to be evaluated | Add a node to the list of kids waiting to be evaluated | [
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] | def add_wkid(self, wkid):
"""Add a node to the list of kids waiting to be evaluated"""
if self.wkids is not None:
self.wkids.append(wkid) | [
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sailing-pmls/bosen | 06cb58902d011fbea5f9428f10ce30e621492204 | style_script/cpplint.py | python | FileInfo.NoExtension | (self) | return '/'.join(self.Split()[0:2]) | File has no source file extension. | File has no source file extension. | [
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] | def NoExtension(self):
"""File has no source file extension."""
return '/'.join(self.Split()[0:2]) | [
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msftguy/ssh-rd | a5f3a79daeac5844edebf01916c9613563f1c390 | _3rd/boost_1_48_0/tools/build/v2/build/feature.py | python | Feature.parent | (self) | return self._parent | For subfeatures, return pair of (parent_feature, value).
Value may be None if this subfeature is not specific to any
value of the parent feature. | For subfeatures, return pair of (parent_feature, value). | [
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"of",
"(",
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] | def parent(self):
"""For subfeatures, return pair of (parent_feature, value).
Value may be None if this subfeature is not specific to any
value of the parent feature.
"""
return self._parent | [
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dmlc/treelite | df56babb6a4a2d7c29d719c28ce53acfa7dbab3c | python/treelite/sklearn/gbm_classifier.py | python | SKLGBMClassifierMixin.process_model | (cls, sklearn_model) | return builder.commit() | Process a GradientBoostingClassifier (binary classifier) to convert it into a
Treelite model | Process a GradientBoostingClassifier (binary classifier) to convert it into a
Treelite model | [
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] | def process_model(cls, sklearn_model):
"""Process a GradientBoostingClassifier (binary classifier) to convert it into a
Treelite model"""
# Check for init='zero'
if sklearn_model.init != 'zero':
raise treelite.TreeliteError("Gradient boosted trees must be trained with "
"the option init='zero'")
# Initialize Treelite model builder
# Set average_tree_output=False for gradient boosted trees
# Set pred_transform='sigmoid' to obtain probability predictions
builder = treelite.ModelBuilder(
num_feature=sklearn_model.n_features_, average_tree_output=False,
pred_transform='sigmoid', threshold_type='float64', leaf_output_type='float64')
for i in range(sklearn_model.n_estimators):
# Process i-th tree and add to the builder
builder.append(cls.process_tree(sklearn_model.estimators_[i][0].tree_,
sklearn_model))
return builder.commit() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | Dialog.SetLayoutAdaptationLevel | (*args, **kwargs) | return _windows_.Dialog_SetLayoutAdaptationLevel(*args, **kwargs) | SetLayoutAdaptationLevel(self, int level) | SetLayoutAdaptationLevel(self, int level) | [
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"(",
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"int",
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] | def SetLayoutAdaptationLevel(*args, **kwargs):
"""SetLayoutAdaptationLevel(self, int level)"""
return _windows_.Dialog_SetLayoutAdaptationLevel(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cuda/cudadrv/driver.py | python | Driver.reset | (self) | Reset all devices | Reset all devices | [
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"all",
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] | def reset(self):
"""Reset all devices
"""
for dev in self.devices.values():
dev.reset() | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/build/linux_setup_msr.py | python | _CheckMsrKernelModule | () | return True | Return whether the 'msr' kernel module is loaded. | Return whether the 'msr' kernel module is loaded. | [
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"""Return whether the 'msr' kernel module is loaded."""
proc = subprocess.Popen('/sbin/lsmod', stdout=subprocess.PIPE)
stdout = proc.communicate()[0]
ret = proc.wait()
if ret != 0:
raise OSError('lsmod failed')
if not any([line.startswith('msr ') for line in stdout.splitlines()]):
print 'Error: MSR module not loaded.'
return False
return True | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_vim.py | python | EditraCommander.FindTillNextChar | (self, char, repeat) | Similar to FindNextChar, but stop one character short | Similar to FindNextChar, but stop one character short | [
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] | def FindTillNextChar(self, char, repeat):
"""Similar to FindNextChar, but stop one character short"""
self.stc.FindChar(char, repeat, extra_offset=-1) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/indexes/base.py | python | ensure_index_from_sequences | (sequences, names=None) | Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> ensure_index_from_sequences([[1, 2, 3]], names=['name'])
Int64Index([1, 2, 3], dtype='int64', name='name')
>>> ensure_index_from_sequences([['a', 'a'], ['a', 'b']],
names=['L1', 'L2'])
MultiIndex(levels=[['a'], ['a', 'b']],
codes=[[0, 0], [0, 1]],
names=['L1', 'L2'])
See Also
--------
ensure_index | Construct an index from sequences of data. | [
"Construct",
"an",
"index",
"from",
"sequences",
"of",
"data",
"."
] | def ensure_index_from_sequences(sequences, names=None):
"""
Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
Returns
-------
index : Index or MultiIndex
Examples
--------
>>> ensure_index_from_sequences([[1, 2, 3]], names=['name'])
Int64Index([1, 2, 3], dtype='int64', name='name')
>>> ensure_index_from_sequences([['a', 'a'], ['a', 'b']],
names=['L1', 'L2'])
MultiIndex(levels=[['a'], ['a', 'b']],
codes=[[0, 0], [0, 1]],
names=['L1', 'L2'])
See Also
--------
ensure_index
"""
from .multi import MultiIndex
if len(sequences) == 1:
if names is not None:
names = names[0]
return Index(sequences[0], name=names)
else:
return MultiIndex.from_arrays(sequences, names=names) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_selectplane.py | python | Draft_SelectPlane.onSetExtension | (self, i) | Execute when setting grid extension. | Execute when setting grid extension. | [
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] | def onSetExtension(self, i):
"""Execute when setting grid extension."""
if i > 1:
self.param.SetInt("gridSize", i)
if hasattr(FreeCADGui, "Snapper"):
FreeCADGui.Snapper.setGrid() | [
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/utils/path.py | python | DPPath.__truediv__ | (self, key: str) | Used for / operator. | Used for / operator. | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/ops/nn.py | python | separable_conv2d | (input, depthwise_filter, pointwise_filter, strides,
padding,
name=None) | 2-D convolution with separable filters.
Performs a depthwise convolution that acts separately on channels followed by
a pointwise convolution that mixes channels. Note that this is separability
between dimensions `[1, 2]` and `3`, not spatial separability between
dimensions `1` and `2`.
In detail,
output[b, i, j, k] = sum_{di, dj, q, r]
input[b, strides[1] * i + di, strides[2] * j + dj, q] *
depthwise_filter[di, dj, q, r] *
pointwise_filter[0, 0, q * channel_multiplier + r, k]
`strides` controls the strides for the depthwise convolution only, since
the pointwise convolution has implicit strides of `[1, 1, 1, 1]`. Must have
`strides[0] = strides[3] = 1`. For the most common case of the same
horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
Args:
input: 4-D `Tensor` with shape `[batch, in_height, in_width, in_channels]`.
depthwise_filter: 4-D `Tensor` with shape
`[filter_height, filter_width, in_channels, channel_multiplier]`.
Contains `in_channels` convolutional filters of depth 1.
pointwise_filter: 4-D `Tensor` with shape
`[1, 1, channel_multiplier * in_channels, out_channels]`. Pointwise
filter to mix channels after `depthwise_filter` has convolved spatially.
strides: 1-D of size 4. The strides for the depthwise convolution for
each dimension of `input`.
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
See the [comment
here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
name: A name for this operation (optional).
Returns:
A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
Raises:
ValueError: If channel_multiplier * in_channels > out_channels,
which means that the separable convolution is overparameterized. | 2-D convolution with separable filters. | [
"2",
"-",
"D",
"convolution",
"with",
"separable",
"filters",
"."
] | def separable_conv2d(input, depthwise_filter, pointwise_filter, strides,
padding,
name=None):
"""2-D convolution with separable filters.
Performs a depthwise convolution that acts separately on channels followed by
a pointwise convolution that mixes channels. Note that this is separability
between dimensions `[1, 2]` and `3`, not spatial separability between
dimensions `1` and `2`.
In detail,
output[b, i, j, k] = sum_{di, dj, q, r]
input[b, strides[1] * i + di, strides[2] * j + dj, q] *
depthwise_filter[di, dj, q, r] *
pointwise_filter[0, 0, q * channel_multiplier + r, k]
`strides` controls the strides for the depthwise convolution only, since
the pointwise convolution has implicit strides of `[1, 1, 1, 1]`. Must have
`strides[0] = strides[3] = 1`. For the most common case of the same
horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
Args:
input: 4-D `Tensor` with shape `[batch, in_height, in_width, in_channels]`.
depthwise_filter: 4-D `Tensor` with shape
`[filter_height, filter_width, in_channels, channel_multiplier]`.
Contains `in_channels` convolutional filters of depth 1.
pointwise_filter: 4-D `Tensor` with shape
`[1, 1, channel_multiplier * in_channels, out_channels]`. Pointwise
filter to mix channels after `depthwise_filter` has convolved spatially.
strides: 1-D of size 4. The strides for the depthwise convolution for
each dimension of `input`.
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
See the [comment
here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
name: A name for this operation (optional).
Returns:
A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
Raises:
ValueError: If channel_multiplier * in_channels > out_channels,
which means that the separable convolution is overparameterized.
"""
with ops.name_scope(name, "separable_conv2d",
[input, depthwise_filter, pointwise_filter]) as name:
input = ops.convert_to_tensor(input, name="tensor_in")
depthwise_filter = ops.convert_to_tensor(
depthwise_filter, name="depthwise_filter")
pointwise_filter = ops.convert_to_tensor(
pointwise_filter, name="pointwise_filter")
pointwise_filter_shape = pointwise_filter.get_shape().with_rank(4)
pointwise_filter_shape[0].assert_is_compatible_with(1)
pointwise_filter_shape[1].assert_is_compatible_with(1)
channel_multiplier = depthwise_filter.get_shape().with_rank(4)[3]
in_channels = input.get_shape().with_rank(4)[3]
out_channels = pointwise_filter_shape[3]
# If any of channel numbers is unknown, then the comparison below returns
# None. See TensorShape.__gt__().
if channel_multiplier * in_channels > out_channels:
raise ValueError(
"Refusing to perform an overparameterized separable "
"convolution: channel_multiplier * in_channels = "
"%d * %d = %d > %d = out_channels" %
(channel_multiplier, in_channels,
channel_multiplier * in_channels, out_channels))
# The layout of the ops in the graph are expected to be as follows:
# depthwise_conv2d // Conv2D op corresponding to native deptwise conv.
# separable_conv2d // Conv2D op corresponding to the pointwise conv.
depthwise = nn_ops.depthwise_conv2d_native(
input, depthwise_filter, strides, padding, name="depthwise")
return nn_ops.conv2d(
depthwise, pointwise_filter, [1, 1, 1, 1], padding="VALID", name=name) | [
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] | https://github.com/hughperkins/tf-coriander/blob/970d3df6c11400ad68405f22b0c42a52374e94ca/tensorflow/python/ops/nn.py#L646-L722 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/tools/datetimes.py | python | should_cache | (
arg: ArrayConvertible, unique_share: float = 0.7, check_count: Optional[int] = None
) | return do_caching | Decides whether to do caching.
If the percent of unique elements among `check_count` elements less
than `unique_share * 100` then we can do caching.
Parameters
----------
arg: listlike, tuple, 1-d array, Series
unique_share: float, default=0.7, optional
0 < unique_share < 1
check_count: int, optional
0 <= check_count <= len(arg)
Returns
-------
do_caching: bool
Notes
-----
By default for a sequence of less than 50 items in size, we don't do
caching; for the number of elements less than 5000, we take ten percent of
all elements to check for a uniqueness share; if the sequence size is more
than 5000, then we check only the first 500 elements.
All constants were chosen empirically by. | Decides whether to do caching. | [
"Decides",
"whether",
"to",
"do",
"caching",
"."
] | def should_cache(
arg: ArrayConvertible, unique_share: float = 0.7, check_count: Optional[int] = None
) -> bool:
"""
Decides whether to do caching.
If the percent of unique elements among `check_count` elements less
than `unique_share * 100` then we can do caching.
Parameters
----------
arg: listlike, tuple, 1-d array, Series
unique_share: float, default=0.7, optional
0 < unique_share < 1
check_count: int, optional
0 <= check_count <= len(arg)
Returns
-------
do_caching: bool
Notes
-----
By default for a sequence of less than 50 items in size, we don't do
caching; for the number of elements less than 5000, we take ten percent of
all elements to check for a uniqueness share; if the sequence size is more
than 5000, then we check only the first 500 elements.
All constants were chosen empirically by.
"""
do_caching = True
# default realization
if check_count is None:
# in this case, the gain from caching is negligible
if len(arg) <= 50:
return False
if len(arg) <= 5000:
check_count = int(len(arg) * 0.1)
else:
check_count = 500
else:
assert (
0 <= check_count <= len(arg)
), "check_count must be in next bounds: [0; len(arg)]"
if check_count == 0:
return False
assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
unique_elements = set(islice(arg, check_count))
if len(unique_elements) > check_count * unique_share:
do_caching = False
return do_caching | [
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apiaryio/snowcrash | b5b39faa85f88ee17459edf39fdc6fe4fc70d2e3 | tools/gyp/pylib/gyp/xcodeproj_file.py | python | XCConfigurationList.AppendBuildSetting | (self, key, value) | Appends value to the build setting for key, which is treated as a list,
in all child XCBuildConfiguration objects. | Appends value to the build setting for key, which is treated as a list,
in all child XCBuildConfiguration objects. | [
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] | def AppendBuildSetting(self, key, value):
"""Appends value to the build setting for key, which is treated as a list,
in all child XCBuildConfiguration objects.
"""
for configuration in self._properties['buildConfigurations']:
configuration.AppendBuildSetting(key, value) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/traitlets/py3/traitlets/traitlets.py | python | _add_all | () | add all trait types to `__all__`
do in a function to avoid iterating through globals while defining local variables | add all trait types to `__all__` | [
"add",
"all",
"trait",
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"to",
"__all__"
] | def _add_all():
"""add all trait types to `__all__`
do in a function to avoid iterating through globals while defining local variables
"""
for _name, _value in globals().items():
if not _name.startswith('_') and isinstance(_value, type) and issubclass(_value, TraitType):
__all__.append(_name) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/layers/python/layers/feature_column.py | python | _OneHotColumn._to_dnn_input_layer | (self,
transformed_input_tensor,
unused_weight_collections=None,
unused_trainable=False,
output_rank=2) | return math_ops.reduce_sum(
one_hot_id_tensor, reduction_indices=[output_rank - 1]) | Returns a Tensor as an input to the first layer of neural network.
Args:
transformed_input_tensor: A tensor that has undergone the transformations
in `insert_transformed_feature`. Rank should be >= `output_rank`.
unused_weight_collections: Unused. One hot encodings are not variable.
unused_trainable: Unused. One hot encodings are not trainable.
output_rank: the desired rank of the output `Tensor`.
Returns:
A multi-hot Tensor to be fed into the first layer of neural network.
Raises:
ValueError: When using one_hot_column with weighted_sparse_column.
This is not yet supported. | Returns a Tensor as an input to the first layer of neural network. | [
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] | def _to_dnn_input_layer(self,
transformed_input_tensor,
unused_weight_collections=None,
unused_trainable=False,
output_rank=2):
"""Returns a Tensor as an input to the first layer of neural network.
Args:
transformed_input_tensor: A tensor that has undergone the transformations
in `insert_transformed_feature`. Rank should be >= `output_rank`.
unused_weight_collections: Unused. One hot encodings are not variable.
unused_trainable: Unused. One hot encodings are not trainable.
output_rank: the desired rank of the output `Tensor`.
Returns:
A multi-hot Tensor to be fed into the first layer of neural network.
Raises:
ValueError: When using one_hot_column with weighted_sparse_column.
This is not yet supported.
"""
# Reshape ID column to `output_rank`.
sparse_id_column = self.sparse_id_column.id_tensor(transformed_input_tensor)
# pylint: disable=protected-access
sparse_id_column = layers._inner_flatten(sparse_id_column, output_rank)
weight_tensor = self.sparse_id_column.weight_tensor(
transformed_input_tensor)
if weight_tensor is not None:
weighted_column = sparse_ops.sparse_merge(sp_ids=sparse_id_column,
sp_values=weight_tensor,
vocab_size=self.length)
return sparse_ops.sparse_tensor_to_dense(weighted_column)
dense_id_tensor = sparse_ops.sparse_tensor_to_dense(sparse_id_column,
default_value=-1)
# One hot must be float for tf.concat reasons since all other inputs to
# input_layer are float32.
one_hot_id_tensor = array_ops.one_hot(
dense_id_tensor, depth=self.length, on_value=1.0, off_value=0.0)
# Reduce to get a multi-hot per example.
return math_ops.reduce_sum(
one_hot_id_tensor, reduction_indices=[output_rank - 1]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/ntpath.py | python | relpath | (path, start=None) | Return a relative version of a path | Return a relative version of a path | [
"Return",
"a",
"relative",
"version",
"of",
"a",
"path"
] | def relpath(path, start=None):
"""Return a relative version of a path"""
path = os.fspath(path)
if isinstance(path, bytes):
sep = b'\\'
curdir = b'.'
pardir = b'..'
else:
sep = '\\'
curdir = '.'
pardir = '..'
if start is None:
start = curdir
if not path:
raise ValueError("no path specified")
start = os.fspath(start)
try:
start_abs = abspath(normpath(start))
path_abs = abspath(normpath(path))
start_drive, start_rest = splitdrive(start_abs)
path_drive, path_rest = splitdrive(path_abs)
if normcase(start_drive) != normcase(path_drive):
raise ValueError("path is on mount %r, start on mount %r" % (
path_drive, start_drive))
start_list = [x for x in start_rest.split(sep) if x]
path_list = [x for x in path_rest.split(sep) if x]
# Work out how much of the filepath is shared by start and path.
i = 0
for e1, e2 in zip(start_list, path_list):
if normcase(e1) != normcase(e2):
break
i += 1
rel_list = [pardir] * (len(start_list)-i) + path_list[i:]
if not rel_list:
return curdir
return join(*rel_list)
except (TypeError, ValueError, AttributeError, BytesWarning, DeprecationWarning):
genericpath._check_arg_types('relpath', path, start)
raise | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | build/android/emulator.py | python | Emulator.__init__ | (self, fast_and_loose=False) | Init an Emulator.
Args:
fast_and_loose: Loosen up the rules for reliable running for speed.
Intended for quick testing or re-testing. | Init an Emulator. | [
"Init",
"an",
"Emulator",
"."
] | def __init__(self, fast_and_loose=False):
"""Init an Emulator.
Args:
fast_and_loose: Loosen up the rules for reliable running for speed.
Intended for quick testing or re-testing.
"""
try:
android_sdk_root = os.environ['ANDROID_SDK_ROOT']
except KeyError:
logging.critical('The ANDROID_SDK_ROOT must be set to run the test on '
'emulator.')
raise
self.emulator = os.path.join(android_sdk_root, 'tools', 'emulator')
self.popen = None
self.device = None
self.fast_and_loose = fast_and_loose | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/llvmlite/ir/builder.py | python | IRBuilder.fadd | (self, lhs, rhs, name='') | Floating-point addition:
name = lhs + rhs | Floating-point addition:
name = lhs + rhs | [
"Floating",
"-",
"point",
"addition",
":",
"name",
"=",
"lhs",
"+",
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] | def fadd(self, lhs, rhs, name=''):
"""
Floating-point addition:
name = lhs + rhs
""" | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/pytables.py | python | Table.indexables | (self) | return _indexables | create/cache the indexables if they don't exist | create/cache the indexables if they don't exist | [
"create",
"/",
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"the",
"indexables",
"if",
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"exist"
] | def indexables(self):
"""create/cache the indexables if they don't exist"""
_indexables = []
desc = self.description
table_attrs = self.table.attrs
# Note: each of the `name` kwargs below are str, ensured
# by the definition in index_cols.
# index columns
for i, (axis, name) in enumerate(self.attrs.index_cols):
atom = getattr(desc, name)
md = self.read_metadata(name)
meta = "category" if md is not None else None
kind_attr = f"{name}_kind"
kind = getattr(table_attrs, kind_attr, None)
index_col = IndexCol(
name=name,
axis=axis,
pos=i,
kind=kind,
typ=atom,
table=self.table,
meta=meta,
metadata=md,
)
_indexables.append(index_col)
# values columns
dc = set(self.data_columns)
base_pos = len(_indexables)
def f(i, c):
assert isinstance(c, str)
klass = DataCol
if c in dc:
klass = DataIndexableCol
atom = getattr(desc, c)
adj_name = _maybe_adjust_name(c, self.version)
# TODO: why kind_attr here?
values = getattr(table_attrs, f"{adj_name}_kind", None)
dtype = getattr(table_attrs, f"{adj_name}_dtype", None)
kind = _dtype_to_kind(dtype)
md = self.read_metadata(c)
# TODO: figure out why these two versions of `meta` dont always match.
# meta = "category" if md is not None else None
meta = getattr(table_attrs, f"{adj_name}_meta", None)
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cname=c,
values=values,
kind=kind,
pos=base_pos + i,
typ=atom,
table=self.table,
meta=meta,
metadata=md,
dtype=dtype,
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return obj
# Note: the definition of `values_cols` ensures that each
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exomerge3.py | python | ExodusModel.create_node_field | (self, node_field_name, value='auto') | Create a node field and assign it a default value.
A default value can be passed. If no default value is given, 0.0 will
be used if the element field appears to be a displacement field.
Otherwise, NaN will be used.
Example:
>>> model.create_node_field('temperature', 298.15) | Create a node field and assign it a default value. | [
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"""
Create a node field and assign it a default value.
A default value can be passed. If no default value is given, 0.0 will
be used if the element field appears to be a displacement field.
Otherwise, NaN will be used.
Example:
>>> model.create_node_field('temperature', 298.15)
"""
# issue warning if no timesteps exist
if not self.get_timesteps():
self._empty_field_warning()
# if it exists, no need to do anything
if self.node_field_exists(node_field_name):
self._exists_warning(node_field_name, 'node field')
return
# get the value
if value == 'auto':
value = self._get_default_field_value(node_field_name)
# create the new field
new_field_values = []
for _ in range(len(self.timesteps)):
new_field_values.append([value] * len(self.nodes))
self.node_fields[node_field_name] = new_field_values | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/importlib/_bootstrap.py | python | _wrap | (new, old) | Simple substitute for functools.update_wrapper. | Simple substitute for functools.update_wrapper. | [
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".",
"update_wrapper",
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] | def _wrap(new, old):
"""Simple substitute for functools.update_wrapper."""
for replace in ['__module__', '__name__', '__qualname__', '__doc__']:
if hasattr(old, replace):
setattr(new, replace, getattr(old, replace))
new.__dict__.update(old.__dict__) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TNEGraph.Reserve | (self, *args) | return _snap.TNEGraph_Reserve(self, *args) | Reserve(TNEGraph self, int const & Nodes, int const & Edges)
Parameters:
Nodes: int const &
Edges: int const & | Reserve(TNEGraph self, int const & Nodes, int const & Edges) | [
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"""
Reserve(TNEGraph self, int const & Nodes, int const & Edges)
Parameters:
Nodes: int const &
Edges: int const &
"""
return _snap.TNEGraph_Reserve(self, *args) | [
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alibaba/weex_js_engine | 2bdf4b6f020c1fc99c63f649718f6faf7e27fdde | jni/v8core/v8/build/gyp/pylib/gyp/xcode_emulation.py | python | XcodeSettings.GetFullProductName | (self) | Returns FULL_PRODUCT_NAME. | Returns FULL_PRODUCT_NAME. | [
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"""Returns FULL_PRODUCT_NAME."""
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return self.GetWrapperName()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | AboutDialogInfo.GetLicence | (*args, **kwargs) | return _misc_.AboutDialogInfo_GetLicence(*args, **kwargs) | GetLicence(self) -> String
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"""
GetLicence(self) -> String
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return _misc_.AboutDialogInfo_GetLicence(*args, **kwargs) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/builder.py | python | NeuralNetworkBuilder.set_optional_input | (self, input_idx, value=None, format="float") | Marks given input as optional input.
Optionally, sets default value for optional input if value is not None
Parameters
----------
input_idx: int
Index of input to be marked and fill with default value
value: int/double/float/None
Value to be fill as default value
format: str
Format of default value
Must be one of 'float', 'double' or 'int' | Marks given input as optional input.
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Marks given input as optional input.
Optionally, sets default value for optional input if value is not None
Parameters
----------
input_idx: int
Index of input to be marked and fill with default value
value: int/double/float/None
Value to be fill as default value
format: str
Format of default value
Must be one of 'float', 'double' or 'int'
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if input_idx >= len(self.spec.description.input):
msg = (
str(input_idx)
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raise ValueError("Setting invalid input as optional! {}".format(msg))
self.spec.description.input[input_idx].type.isOptional = True
if value is None:
return
# Default value is supported from CoreML 4 onwards.
self.spec.specificationVersion = max(
self.spec.specificationVersion, _SPECIFICATION_VERSION_IOS_14
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format = format.lower()
if format == "float":
self.spec.description.input[
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/ndlstm/python/misc.py | python | one_hot_mask | (labels, num_classes, scope=None) | Compute 1-hot encodings for masks.
Given a label image, this computes the one hot encoding at
each pixel.
Args:
labels: (batch_size, width, height, 1) tensor containing labels.
num_classes: number of classes
scope: optional scope name
Returns:
Tensor of shape (batch_size, width, height, num_classes) with
a 1-hot encoding. | Compute 1-hot encodings for masks. | [
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] | def one_hot_mask(labels, num_classes, scope=None):
"""Compute 1-hot encodings for masks.
Given a label image, this computes the one hot encoding at
each pixel.
Args:
labels: (batch_size, width, height, 1) tensor containing labels.
num_classes: number of classes
scope: optional scope name
Returns:
Tensor of shape (batch_size, width, height, num_classes) with
a 1-hot encoding.
"""
with ops.name_scope(scope, "OneHotMask", [labels]):
height, width, depth = _shape(labels)
assert depth == 1
sparse_labels = math_ops.to_int32(array_ops.reshape(labels, [-1, 1]))
sparse_size, _ = _shape(sparse_labels)
indices = array_ops.reshape(math_ops.range(0, sparse_size, 1), [-1, 1])
concated = array_ops.concat([indices, sparse_labels], 1)
dense_result = sparse_ops.sparse_to_dense(concated,
[sparse_size, num_classes], 1.0,
0.0)
result = array_ops.reshape(dense_result, [height, width, num_classes])
return result | [
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | Pythonwin/pywin/framework/interact.py | python | CreateMDIInteractiveWindow | (makeDoc=None, makeFrame=None) | Create a standard (non-docked) interactive window unconditionally | Create a standard (non-docked) interactive window unconditionally | [
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"""Create a standard (non-docked) interactive window unconditionally"""
global edit
if makeDoc is None:
makeDoc = InteractiveDocument
if makeFrame is None:
makeFrame = InteractiveFrame
edit = CInteractivePython(makeDoc=makeDoc, makeFrame=makeFrame) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/spatial/distance.py | python | cityblock | (u, v, w=None) | return l1_diff.sum() | Compute the City Block (Manhattan) distance.
Computes the Manhattan distance between two 1-D arrays `u` and `v`,
which is defined as
.. math::
\\sum_i {\\left| u_i - v_i \\right|}.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
w : (N,) array_like, optional
The weights for each value in `u` and `v`. Default is None,
which gives each value a weight of 1.0
Returns
-------
cityblock : double
The City Block (Manhattan) distance between vectors `u` and `v`.
Examples
--------
>>> from scipy.spatial import distance
>>> distance.cityblock([1, 0, 0], [0, 1, 0])
2
>>> distance.cityblock([1, 0, 0], [0, 2, 0])
3
>>> distance.cityblock([1, 0, 0], [1, 1, 0])
1 | Compute the City Block (Manhattan) distance. | [
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] | def cityblock(u, v, w=None):
"""
Compute the City Block (Manhattan) distance.
Computes the Manhattan distance between two 1-D arrays `u` and `v`,
which is defined as
.. math::
\\sum_i {\\left| u_i - v_i \\right|}.
Parameters
----------
u : (N,) array_like
Input array.
v : (N,) array_like
Input array.
w : (N,) array_like, optional
The weights for each value in `u` and `v`. Default is None,
which gives each value a weight of 1.0
Returns
-------
cityblock : double
The City Block (Manhattan) distance between vectors `u` and `v`.
Examples
--------
>>> from scipy.spatial import distance
>>> distance.cityblock([1, 0, 0], [0, 1, 0])
2
>>> distance.cityblock([1, 0, 0], [0, 2, 0])
3
>>> distance.cityblock([1, 0, 0], [1, 1, 0])
1
"""
u = _validate_vector(u)
v = _validate_vector(v)
l1_diff = abs(u - v)
if w is not None:
w = _validate_weights(w)
l1_diff = w * l1_diff
return l1_diff.sum() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | GenericDirCtrl.SelectPath | (*args, **kwargs) | return _controls_.GenericDirCtrl_SelectPath(*args, **kwargs) | SelectPath(self, String path, bool select=True) | SelectPath(self, String path, bool select=True) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/summary/impl/gcs.py | python | CheckIsSupported | () | Raises an OSError if the system isn't set up for Google Cloud Storage.
Raises:
OSError: If the system hasn't been set up so that TensorBoard can access
Google Cloud Storage. The error's message contains installation
instructions. | Raises an OSError if the system isn't set up for Google Cloud Storage. | [
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"""Raises an OSError if the system isn't set up for Google Cloud Storage.
Raises:
OSError: If the system hasn't been set up so that TensorBoard can access
Google Cloud Storage. The error's message contains installation
instructions.
"""
try:
subprocess.check_output(['gsutil', 'version'])
except OSError as e:
logging.error('Error while checking for gsutil: %s', e)
raise OSError(
'Unable to execute the gsutil binary, which is required for Google '
'Cloud Storage support. You can find installation instructions at '
'https://goo.gl/sST520') | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/ndarray/ndarray.py | python | NDArray.expm1 | (self, *args, **kwargs) | return op.expm1(self, *args, **kwargs) | Convenience fluent method for :py:func:`expm1`.
The arguments are the same as for :py:func:`expm1`, with
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"""Convenience fluent method for :py:func:`expm1`.
The arguments are the same as for :py:func:`expm1`, with
this array as data.
"""
return op.expm1(self, *args, **kwargs) | [
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nyuwireless-unipd/ns3-mmwave | 4ff9e87e8079764e04cbeccd8e85bff15ae16fb3 | src/visualizer/visualizer/core.py | python | Node.set_position | (self, x, y) | !
Set position function.
@param self: class object.
@param x: x position
@param y: y position
@return none | !
Set position function. | [
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] | def set_position(self, x, y):
"""!
Set position function.
@param self: class object.
@param x: x position
@param y: y position
@return none
"""
self.canvas_item.set_property("center_x", x)
self.canvas_item.set_property("center_y", y)
if self.svg_item is not None:
self._update_svg_position(x, y)
for link in self.links:
link.update_points()
if self._label_canvas_item is not None:
self._label_canvas_item.set_properties(x=x, y=(y+self._size*3))
# If the location of the point is now beyond the bounds of the
# canvas then those bounds now need to be increased
try:
bounds = self.visualizer.canvas.get_bounds()
(min_x, min_y, max_x, max_y) = bounds
min_x = min(x, min_x)
min_y = min(y, min_y)
max_x = max(x, max_x)
max_y = max(y, max_y)
new_bounds = (min_x, min_y, max_x, max_y)
if new_bounds != bounds:
self.visualizer.canvas.set_bounds(*new_bounds)
except TypeError:
# bug 2969: GooCanvas.Canvas.get_bounds() inconsistency
pass | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/ValidationKit/bootsectors/bs3-cpu-generated-1-data.py | python | Bs3Cg1EncodedTests.bytesToLines | (sPrefix, asBytes) | return asRet | Formats a series of bytes into one or more lines.
A byte ending with a newline indicates that we should start a new line,
and prefix it by len(sPrefix) spaces.
Returns list of lines. | Formats a series of bytes into one or more lines.
A byte ending with a newline indicates that we should start a new line,
and prefix it by len(sPrefix) spaces. | [
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"""
Formats a series of bytes into one or more lines.
A byte ending with a newline indicates that we should start a new line,
and prefix it by len(sPrefix) spaces.
Returns list of lines.
"""
asRet = [];
sLine = sPrefix;
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sLine = ' ' * len(sPrefix);
sLine += sByte + ', ';
if len(sLine) > len(sPrefix):
asRet.append(sLine);
return asRet; | [
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google/nucleus | 68d3947fafba1337f294c0668a6e1c7f3f1273e3 | nucleus/io/tfrecord.py | python | read_shard_sorted_tfrecords | (path,
key,
proto=None,
max_records=None,
compression_type=None) | Yields the parsed records in a TFRecord file path in sorted order.
The input TFRecord file must have each shard already in sorted order when
using the key function for comparison (but elements can be interleaved across
shards). Under those constraints, the elements will be yielded in a global
sorted order.
Args:
path: String. A path to a TFRecord-formatted file containing protos.
key: Callable. A function that takes as input a single instance of the proto
class and returns a value on which the comparison for sorted ordering is
performed.
proto: A proto class. proto.FromString() will be called on each serialized
record in path to parse it.
max_records: int >= 0 or None. Maximum number of records to read from path.
If None, the default, all records will be read.
compression_type: 'GZIP', 'ZLIB', '' (uncompressed), or None to autodetect
based on file extension.
Yields:
proto.FromString() values on each record in path in sorted order. | Yields the parsed records in a TFRecord file path in sorted order. | [
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] | def read_shard_sorted_tfrecords(path,
key,
proto=None,
max_records=None,
compression_type=None):
"""Yields the parsed records in a TFRecord file path in sorted order.
The input TFRecord file must have each shard already in sorted order when
using the key function for comparison (but elements can be interleaved across
shards). Under those constraints, the elements will be yielded in a global
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Args:
path: String. A path to a TFRecord-formatted file containing protos.
key: Callable. A function that takes as input a single instance of the proto
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proto: A proto class. proto.FromString() will be called on each serialized
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max_records: int >= 0 or None. Maximum number of records to read from path.
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"""
if sharded_file_utils.is_sharded_file_spec(path):
paths = sharded_file_utils.generate_sharded_filenames(path)
else:
paths = [path]
keyed_iterables = []
for path in paths:
protos = Reader(path, proto, compression_type=compression_type).iterate()
keyed_iterables.append(((key(elem), elem) for elem in protos))
for i, (_, value) in enumerate(heapq.merge(*keyed_iterables)):
if max_records is not None and i >= max_records:
return
yield value | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/design-circular-deque.py | python | MyCircularDeque.getFront | (self) | return -1 if self.isEmpty() else self.__buffer[self.__start] | Get the front item from the deque.
:rtype: int | Get the front item from the deque.
:rtype: int | [
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"""
Get the front item from the deque.
:rtype: int
"""
return -1 if self.isEmpty() else self.__buffer[self.__start] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/Tkinter.py | python | Misc.winfo_vrootwidth | (self) | return getint(
self.tk.call('winfo', 'vrootwidth', self._w)) | Return the width of the virtual root window associated with this
widget in pixel. If there is no virtual root window return the
width of the screen. | Return the width of the virtual root window associated with this
widget in pixel. If there is no virtual root window return the
width of the screen. | [
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"""Return the width of the virtual root window associated with this
widget in pixel. If there is no virtual root window return the
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return getint(
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mingchen/protobuf-ios | 0958df34558cd54cb7b6e6ca5c8855bf3d475046 | compiler/python/google/protobuf/reflection.py | python | _AddMergeFromStringMethod | (message_descriptor, cls) | Helper for _AddMessageMethods(). | Helper for _AddMessageMethods(). | [
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byte_count = 0
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break
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return byte_count
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fifengine/fifengine | 4b62c42e85bec19893cef8e63e6855927cff2c47 | engine/python/fife/extensions/pychan/internal.py | python | Manager.addWidget | (self, widget) | Adds Widget to the manager. So the manager "owns" the Widget.
Note: As long as the wiget is in self.allWidgets the Python
GC can not free it. | Adds Widget to the manager. So the manager "owns" the Widget.
Note: As long as the wiget is in self.allWidgets the Python
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"""
if not widget._added:
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/win32com/client/gencache.py | python | SplitGeneratedFileName | (fname) | return tuple(fname.split('x',4)) | Reverse of GetGeneratedFileName() | Reverse of GetGeneratedFileName() | [
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"""Reverse of GetGeneratedFileName()
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/dir_util.py | python | _build_cmdtuple | (path, cmdtuples) | Helper for remove_tree(). | Helper for remove_tree(). | [
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] | def _build_cmdtuple(path, cmdtuples):
"""Helper for remove_tree()."""
for f in os.listdir(path):
real_f = os.path.join(path,f)
if os.path.isdir(real_f) and not os.path.islink(real_f):
_build_cmdtuple(real_f, cmdtuples)
else:
cmdtuples.append((os.remove, real_f))
cmdtuples.append((os.rmdir, path)) | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/devstack.py | python | create_volume | (devstack_node, ceph_node, vol_name, size) | return vol_info['id'] | :param size: The size of the volume, in GB | :param size: The size of the volume, in GB | [
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"""
:param size: The size of the volume, in GB
"""
size = str(size)
log.info("Creating a {size}GB volume named {name}...".format(
name=vol_name,
size=size))
args = ['source', 'devstack/openrc', run.Raw('&&'), 'cinder', 'create',
'--display-name', vol_name, size]
cinder_create = devstack_node.sh(args, wait=True)
vol_info = parse_os_table(cinder_create)
log.debug("Volume info: %s", str(vol_info))
try:
rbd_output = ceph_node.sh("rbd --id cinder ls -l volumes", wait=True)
except run.CommandFailedError:
log.debug("Original rbd call failed; retrying without '--id cinder'")
rbd_output = ceph_node.sh("rbd ls -l volumes", wait=True)
assert vol_info['id'] in rbd_output, \
"Volume not found on Ceph cluster"
assert vol_info['size'] == size, \
"Volume size on Ceph cluster is different than specified"
return vol_info['id'] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/functools.py | python | update_wrapper | (wrapper,
wrapped,
assigned = WRAPPER_ASSIGNMENTS,
updated = WRAPPER_UPDATES) | return wrapper | Update a wrapper function to look like the wrapped function
wrapper is the function to be updated
wrapped is the original function
assigned is a tuple naming the attributes assigned directly
from the wrapped function to the wrapper function (defaults to
functools.WRAPPER_ASSIGNMENTS)
updated is a tuple naming the attributes of the wrapper that
are updated with the corresponding attribute from the wrapped
function (defaults to functools.WRAPPER_UPDATES) | Update a wrapper function to look like the wrapped function | [
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"function",
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"look",
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"the",
"wrapped",
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] | def update_wrapper(wrapper,
wrapped,
assigned = WRAPPER_ASSIGNMENTS,
updated = WRAPPER_UPDATES):
"""Update a wrapper function to look like the wrapped function
wrapper is the function to be updated
wrapped is the original function
assigned is a tuple naming the attributes assigned directly
from the wrapped function to the wrapper function (defaults to
functools.WRAPPER_ASSIGNMENTS)
updated is a tuple naming the attributes of the wrapper that
are updated with the corresponding attribute from the wrapped
function (defaults to functools.WRAPPER_UPDATES)
"""
for attr in assigned:
try:
value = getattr(wrapped, attr)
except AttributeError:
pass
else:
setattr(wrapper, attr, value)
for attr in updated:
getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
# Issue #17482: set __wrapped__ last so we don't inadvertently copy it
# from the wrapped function when updating __dict__
wrapper.__wrapped__ = wrapped
# Return the wrapper so this can be used as a decorator via partial()
return wrapper | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | external/tools/build/v2/build/targets.py | python | AbstractTarget.__init__ | (self, name, project, manager = None) | manager: the Manager object
name: name of the target
project: the project target to which this one belongs
manager:the manager object. If none, uses project.manager () | manager: the Manager object
name: name of the target
project: the project target to which this one belongs
manager:the manager object. If none, uses project.manager () | [
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manager:the manager object. If none, uses project.manager ()
"""
assert (isinstance (project, ProjectTarget))
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/compiler/xla/xla.py | python | check_function_argument_count | (func, input_arity, infeed_queue) | return None | Validate the number of input arguments to an XLA function.
Args:
func: the Python function that will be called to generate the body of an XLA
computation graph.
input_arity: the number of explicit arguments supplied by the caller.
infeed_queue: if not None, the infeed queue that will supply
additional arguments to the function.
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Args:
func: the Python function that will be called to generate the body of an XLA
computation graph.
input_arity: the number of explicit arguments supplied by the caller.
infeed_queue: if not None, the infeed queue that will supply
additional arguments to the function.
Returns:
None if function can be called with the supplied number of
arguments, or an error string if it cannot.
"""
def format_error(complaint, quantity):
return '%s %d argument%s' % (complaint, quantity, ''
if quantity == 1 else 's')
num_args_supplied = input_arity
if infeed_queue is not None:
num_args_supplied += infeed_queue.number_of_tuple_elements
arg_spec = tf_inspect.getargspec(func)
num_func_args = len(arg_spec.args)
if arg_spec.defaults is None:
num_func_defaults = 0
else:
num_func_defaults = len(arg_spec.defaults)
min_func_args = num_func_args - num_func_defaults
if num_args_supplied < min_func_args:
# The required number of arguments is not enough to call the function.
if num_func_defaults == 0 and arg_spec.varargs is None:
return format_error('exactly', num_func_args)
else:
return format_error('at least', min_func_args)
if arg_spec.varargs is None and num_args_supplied > num_func_args:
# The required number of arguments is too many to call the function.
if num_func_defaults == 0:
return format_error('exactly', num_func_args)
else:
return format_error('at most', num_func_args)
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# 2) Number of supplied arguments falls in range of acceptable argument count
# of func.
return None | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/symsrc/pefile.py | python | PE.parse_import_directory | (self, rva, size) | return import_descs | Walk and parse the import directory. | Walk and parse the import directory. | [
"Walk",
"and",
"parse",
"the",
"import",
"directory",
"."
] | def parse_import_directory(self, rva, size):
"""Walk and parse the import directory."""
import_descs = []
while True:
try:
# If the RVA is invalid all would blow up. Some EXEs seem to be
# specially nasty and have an invalid RVA.
data = self.get_data(rva)
except PEFormatError, e:
self.__warnings.append(
'Error parsing the Import directory at RVA: 0x%x' % ( rva ) )
break
import_desc = self.__unpack_data__(
self.__IMAGE_IMPORT_DESCRIPTOR_format__,
data, file_offset = self.get_offset_from_rva(rva) )
# If the structure is all zeores, we reached the end of the list
if not import_desc or import_desc.all_zeroes():
break
rva += import_desc.sizeof()
try:
import_data = self.parse_imports(
import_desc.OriginalFirstThunk,
import_desc.FirstThunk,
import_desc.ForwarderChain)
except PEFormatError, excp:
self.__warnings.append(
'Error parsing the Import directory. ' +
'Invalid Import data at RVA: 0x%x' % ( rva ) )
break
#raise excp
if not import_data:
continue
dll = self.get_string_at_rva(import_desc.Name)
if dll:
import_descs.append(
ImportDescData(
struct = import_desc,
imports = import_data,
dll = dll))
return import_descs | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/tools/compatibility/ast_edits.py | python | _PastaEditVisitor.visit_Attribute | (self, node) | Handle bare Attributes i.e. [tf.foo, tf.bar]. | Handle bare Attributes i.e. [tf.foo, tf.bar]. | [
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"e",
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] | def visit_Attribute(self, node): # pylint: disable=invalid-name
"""Handle bare Attributes i.e. [tf.foo, tf.bar]."""
assert self._stack[-1] is node
full_name = self._get_full_name(node)
if full_name:
parent = self._stack[-2]
# Make sure the warning comes first, otherwise the name may have changed
self._maybe_add_warning(node, full_name)
# Once we did a modification, node is invalid and not worth inspecting
# further. Also, we only perform modifications for simple nodes, so
# There'd be no point in descending further.
if self._maybe_rename(parent, node, full_name):
return
if self._maybe_change_to_function_call(parent, node, full_name):
return
# The isinstance check is enough -- a bare Attribute is never root.
i = 2
while isinstance(self._stack[-i], ast.Attribute):
i += 1
whole_name = pasta.dump(self._stack[-(i-1)])
self._maybe_add_module_deprecation_warning(node, full_name, whole_name)
self.generic_visit(node) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/closure_linter/closure_linter/statetracker.py | python | StateTracker.IsBlockClose | (self) | return self._is_block_close | Returns true if the current token is a block close.
Returns:
True if the current token is a block close. | Returns true if the current token is a block close. | [
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"current",
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] | def IsBlockClose(self):
"""Returns true if the current token is a block close.
Returns:
True if the current token is a block close.
"""
return self._is_block_close | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/re.py | python | purge | () | Clear the regular expression caches | Clear the regular expression caches | [
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] | def purge():
"Clear the regular expression caches"
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/tpu/tpu_feed.py | python | InfeedQueue.split_inputs_and_generate_enqueue_ops | (self,
inputs,
device_assignment=None,
placement_function=None,
tpu_ordinal_function=None) | return [
self._generate_enqueue_op(
shard,
name_prefix,
index,
device=placement_function(index),
tpu_ordinal=tpu_ordinal_function(index))
for (shard, index) in zip(sharded_inputs, range(self.number_of_shards))
] | POORLY-PERFORMING ON MULTI-HOST SYSTEMS.
Generates the host-side Ops to enqueue a tuple.
This method performs poorly because it takes an entire input on a single
host, splits it, and distributes it to all of the cores. It is present only
to simplify tutorial examples.
inputs is a list of Tensors to use to feed the queue. Each input is split
into self.number_of_shards shards. Returns an Op for each shard to enqueue
the shard. The Op for shard i is placed on device placement_function(i).
Implicitly freezes the queue configuration if it is not already
frozen. If the configuration has already been frozen, and is not
compatible with the types and shapes of inputs, an error
will be raised.
Args:
inputs: a list of Tensors which indicates the types and shapes of the
queue tuple.
device_assignment: if not `None`, a TPU `DeviceAssignment`. If
device_assignment is not `None`, but `placement_function` and
`ordinal_function` are None, then `device_assignment` will be used to
place infeeds on the first k TPU shards, where k is the number of shards
in the queue. If all three are `None`, then default placement and
ordinal functions are used.
placement_function: if not None, a function that takes the shard
index as input and returns a device string indicating which
device the shard's infeed should be placed on. If placement_function
and tpu_ordinal_function are None, inputs are sharded round-robin
across the devices in the system.
tpu_ordinal_function: if not None, a function that takes the
shard index as input and returns the ordinal of the TPU device
the shard's infeed should be placed on. If placement_function
and tpu_ordinal_function are None, inputs are sharded round-robin
across the devices in the system.
Returns:
A list of host-side Ops, one for each shard, that when executed together
will enqueue a full-size element of infeed.
Raises:
ValueError: if the queue configuration has previously been frozen and the
shapes of the elements of inputs are not compatible with the frozen
configuration.
TypeError: if the queue configuration has previously been frozen and the
types of the elements of inputs are not compatible with the frozen
configuration. | POORLY-PERFORMING ON MULTI-HOST SYSTEMS. | [
"POORLY",
"-",
"PERFORMING",
"ON",
"MULTI",
"-",
"HOST",
"SYSTEMS",
"."
] | def split_inputs_and_generate_enqueue_ops(self,
inputs,
device_assignment=None,
placement_function=None,
tpu_ordinal_function=None):
"""POORLY-PERFORMING ON MULTI-HOST SYSTEMS.
Generates the host-side Ops to enqueue a tuple.
This method performs poorly because it takes an entire input on a single
host, splits it, and distributes it to all of the cores. It is present only
to simplify tutorial examples.
inputs is a list of Tensors to use to feed the queue. Each input is split
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the shard. The Op for shard i is placed on device placement_function(i).
Implicitly freezes the queue configuration if it is not already
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device_assignment: if not `None`, a TPU `DeviceAssignment`. If
device_assignment is not `None`, but `placement_function` and
`ordinal_function` are None, then `device_assignment` will be used to
place infeeds on the first k TPU shards, where k is the number of shards
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ordinal functions are used.
placement_function: if not None, a function that takes the shard
index as input and returns a device string indicating which
device the shard's infeed should be placed on. If placement_function
and tpu_ordinal_function are None, inputs are sharded round-robin
across the devices in the system.
tpu_ordinal_function: if not None, a function that takes the
shard index as input and returns the ordinal of the TPU device
the shard's infeed should be placed on. If placement_function
and tpu_ordinal_function are None, inputs are sharded round-robin
across the devices in the system.
Returns:
A list of host-side Ops, one for each shard, that when executed together
will enqueue a full-size element of infeed.
Raises:
ValueError: if the queue configuration has previously been frozen and the
shapes of the elements of inputs are not compatible with the frozen
configuration.
TypeError: if the queue configuration has previously been frozen and the
types of the elements of inputs are not compatible with the frozen
configuration.
"""
if device_assignment is None:
if placement_function is None:
placement_function = self._default_placement_function
if tpu_ordinal_function is None:
tpu_ordinal_function = self._default_ordinal_function
else:
def _placement_function_from_map(index):
return device_assignment.host_device(replica=index)
def _ordinal_function_from_map(index):
return device_assignment.tpu_ordinal(replica=index)
if placement_function is None:
placement_function = _placement_function_from_map
if tpu_ordinal_function is None:
tpu_ordinal_function = _ordinal_function_from_map
self.set_configuration_from_input_tensors(inputs)
self.freeze()
if self._generated_enqueue_ops and not ops.inside_function():
raise ValueError("Can't generate two enqueue Ops from the same queue")
self._generated_enqueue_ops = True
split_name_prefix = "%s/split" % self._name
if self.number_of_shards == 1:
transposed_sharded_inputs = [[inp] for inp in inputs]
else:
def split_fn(inp, num_shards, axis, name):
with ops.colocate_with(inp):
return array_ops.split(inp, num_shards, axis=axis, name=name)
transposed_sharded_inputs = [
split_fn(
inp,
self.number_of_shards,
axis=policy.shard_dimension,
name="%s/%d" % (split_name_prefix, index))
for (inp, policy, index) in zip(inputs, self._sharding_policies,
range(self.number_of_tuple_elements))
]
sharded_inputs = [[shard[i]
for shard in transposed_sharded_inputs]
for i in range(self.number_of_shards)]
name_prefix = "%s/enqueue" % self._name
return [
self._generate_enqueue_op(
shard,
name_prefix,
index,
device=placement_function(index),
tpu_ordinal=tpu_ordinal_function(index))
for (shard, index) in zip(sharded_inputs, range(self.number_of_shards))
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/protorpc/protorpc/messages.py | python | Enum.def_enum | (dct, name) | return type(name, (Enum,), dct) | Define enum class from dictionary.
Args:
dct: Dictionary of enumerated values for type.
name: Name of enum. | Define enum class from dictionary. | [
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Args:
dct: Dictionary of enumerated values for type.
name: Name of enum.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/contrib/telnet/server.py | python | TelnetConnection.erase_screen | (self) | Erase the screen and move the cursor to the top. | Erase the screen and move the cursor to the top. | [
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] | def erase_screen(self) -> None:
"""
Erase the screen and move the cursor to the top.
"""
if self.vt100_output is None:
return
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self.vt100_output.cursor_goto(0, 0)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/py/py/_path/local.py | python | LocalPath.dirpath | (self, *args, **kwargs) | return super(LocalPath, self).dirpath(*args, **kwargs) | return the directory path joined with any given path arguments. | return the directory path joined with any given path arguments. | [
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path.strpath = dirname(self.strpath)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/util.py | python | IntValidator.Validate | (self, win) | return val.isdigit() | Validate an window value
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epiqc/ScaffCC | 66a79944ee4cd116b27bc1a69137276885461db8 | clang/tools/scan-build-py/libscanbuild/report.py | python | encode_value | (container, key, encode) | Run 'encode' on 'container[key]' value and update it. | Run 'encode' on 'container[key]' value and update it. | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Misc.winfo_pointery | (self) | return getint(
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/StringIO.py | python | StringIO.read | (self, n = -1) | return r | Read at most size bytes from the file
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/minimum-possible-integer-after-at-most-k-adjacent-swaps-on-digits.py | python | Solution.minInteger | (self, num, k) | return "".join(map(str, result)) | :type num: str
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"""
:type num: str
:type k: int
:rtype: str
"""
lookup = collections.defaultdict(list)
bit = BIT(len(num)+1)
for i in reversed(xrange(len(num))):
bit.add(i+1, 1)
lookup[int(num[i])].append(i+1)
result = []
for _ in xrange(len(num)):
for d in xrange(10):
if lookup[d] and bit.sum(lookup[d][-1]-1) <= k:
k -= bit.sum(lookup[d][-1]-1)
bit.add(lookup[d].pop(), -1)
result.append(d)
break
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/index.py | python | DatetimeIndex.dayofyear | (self) | return self._get_dt_field("day_of_year") | The day of the year, from 1-365 in non-leap years and
from 1-366 in leap years.
Examples
--------
>>> import pandas as pd
>>> import cudf
>>> datetime_index = cudf.Index(pd.date_range("2016-12-31",
... "2017-01-08", freq="D"))
>>> datetime_index
DatetimeIndex(['2016-12-31', '2017-01-01', '2017-01-02', '2017-01-03',
'2017-01-04', '2017-01-05', '2017-01-06', '2017-01-07',
'2017-01-08'],
dtype='datetime64[ns]')
>>> datetime_index.dayofyear
Int16Index([366, 1, 2, 3, 4, 5, 6, 7, 8], dtype='int16') | The day of the year, from 1-365 in non-leap years and
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>>> import pandas as pd
>>> import cudf
>>> datetime_index = cudf.Index(pd.date_range("2016-12-31",
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>>> datetime_index
DatetimeIndex(['2016-12-31', '2017-01-01', '2017-01-02', '2017-01-03',
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lukasmonk/lucaschess | 13e2e5cb13b38a720ccf897af649054a64bcb914 | Code/SQL/DBF.py | python | DBF.skip | (self, num=1) | return self.goto(num + self.recno) | Salta un registro. | Salta un registro. | [
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envoyproxy/envoy-wasm | ab5d9381fdf92a1efa0b87cff80036b5b3e81198 | source/extensions/filters/network/kafka/protocol/generator.py | python | StatefulProcessor.parse_type | (self, type_name, field_spec, highest_possible_version) | Parse a given type element - returns an array type, primitive (e.g. uint32_t) or complex one. | Parse a given type element - returns an array type, primitive (e.g. uint32_t) or complex one. | [
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"""
if (type_name.startswith('[]')):
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underlying_type = self.parse_type(type_name[2:], field_spec, highest_possible_version)
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/multiprocessing/process.py | python | Process.authkey | (self, authkey) | Set authorization key of process | Set authorization key of process | [
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'''
Set authorization key of process
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/oldnumeric/ma.py | python | getmask | (a) | Mask of values in a; could be nomask.
Returns nomask if a is not a masked array.
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Returns nomask if a is not a masked array.
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/ops/io_ops.py | python | ReaderBase.restore_state | (self, state, name=None) | return gen_io_ops._reader_restore_state(self._reader_ref, state, name=name) | Restore a reader to a previously saved state.
Not all Readers support being restored, so this can produce an
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Args:
state: A string Tensor.
Result of a SerializeState of a Reader with matching type.
name: A name for the operation (optional).
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Args:
state: A string Tensor.
Result of a SerializeState of a Reader with matching type.
name: A name for the operation (optional).
Returns:
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return gen_io_ops._reader_restore_state(self._reader_ref, state, name=name) | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/filter/set_.py | python | _ParticleFilterSetOperations._cpp_cls_name | (self) | The name of the C++ class in the `_hoomd` module.
Used for Python class's inheritance. | The name of the C++ class in the `_hoomd` module. | [
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"""The name of the C++ class in the `_hoomd` module.
Used for Python class's inheritance.
"""
raise NotImplementedError | [
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neoml-lib/neoml | a0d370fba05269a1b2258cef126f77bbd2054a3e | NeoML/Python/neoml/Dnn/Loss.py | python | Loss.train_labels | (self, train) | Specifies if gradients should also be calculated for the second input,
which contains the class labels. | Specifies if gradients should also be calculated for the second input,
which contains the class labels. | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/symbol/numpy/_symbol.py | python | tri | (N, M=None, k=0, dtype=None, ctx=None) | return _npi.tri(N, M, k, dtype, ctx) | r"""
An array with ones at and below the given diagonal and zeros elsewhere.
Parameters
----------
N : int
Number of rows in the array.
M : int, optional
Number of columns in the array.
By default, `M` is taken equal to `N`.
k : int, optional
The sub-diagonal at and below which the array is filled.
`k` = 0 is the main diagonal, while `k` < 0 is below it,
and `k` > 0 is above. The default is 0.
dtype : dtype, optional
Data type of the returned array. The default is float.
Returns
-------
tri : Symbol of shape (N, M)
Array with its lower triangle filled with ones and zero elsewhere;
in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise. | r"""
An array with ones at and below the given diagonal and zeros elsewhere. | [
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r"""
An array with ones at and below the given diagonal and zeros elsewhere.
Parameters
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N : int
Number of rows in the array.
M : int, optional
Number of columns in the array.
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if dtype is None:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/oauthlib/oauth2/rfc6749/endpoints/resource.py | python | ResourceEndpoint.verify_request | (self, uri, http_method='GET', body=None, headers=None,
scopes=None) | return token_type_handler.validate_request(request), request | Validate client, code etc, return body + headers | Validate client, code etc, return body + headers | [
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scopes=None):
"""Validate client, code etc, return body + headers"""
request = Request(uri, http_method, body, headers)
request.token_type = self.find_token_type(request)
request.scopes = scopes
token_type_handler = self.tokens.get(request.token_type,
self.default_token_type_handler)
log.debug('Dispatching token_type %s request to %r.',
request.token_type, token_type_handler)
return token_type_handler.validate_request(request), request | [
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gnina/gnina | b9ae032f52fc7a8153987bde09c0efa3620d8bb6 | caffe/scripts/cpp_lint.py | python | CheckCaffeDataLayerSetUp | (filename, clean_lines, linenum, error) | Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | [
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"""Except the base classes, Caffe DataLayer should define DataLayerSetUp
instead of LayerSetUp.
The base DataLayers define common SetUp steps, the subclasses should
not override them.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
ix = line.find('DataLayer<Dtype>::LayerSetUp')
if ix >= 0 and (
line.find('void DataLayer<Dtype>::LayerSetUp') != -1 or
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line.find('void MemoryDataLayer<Dtype>::LayerSetUp') != -1 or
line.find('void WindowDataLayer<Dtype>::LayerSetUp') != -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.')
ix = line.find('DataLayer<Dtype>::DataLayerSetUp')
if ix >= 0 and (
line.find('void Base') == -1 and
line.find('void DataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void ImageDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void MemoryDataLayer<Dtype>::DataLayerSetUp') == -1 and
line.find('void WindowDataLayer<Dtype>::DataLayerSetUp') == -1):
error(filename, linenum, 'caffe/data_layer_setup', 2,
'Except the base classes, Caffe DataLayer should define'
+ ' DataLayerSetUp instead of LayerSetUp. The base DataLayers'
+ ' define common SetUp steps, the subclasses should'
+ ' not override them.') | [
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mkeeter/antimony | ee525bbdad34ae94879fd055821f92bcef74e83f | py/fab/shapes.py | python | rounded_rectangle | (xmin, xmax, ymin, ymax, r) | return (
rectangle(xmin, xmax, ymin+r, ymax-r) |
rectangle(xmin+r, xmax-r, ymin, ymax) |
circle(xmin+r, ymin+r, r) |
circle(xmin+r, ymax-r, r) |
circle(xmax-r, ymin+r, r) |
circle(xmax-r, ymax-r, r)
) | Returns a rectangle with rounded corners.
r is a roundedness fraction between 0 (not rounded)
and 1 (completely rounded) | Returns a rectangle with rounded corners.
r is a roundedness fraction between 0 (not rounded)
and 1 (completely rounded) | [
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] | def rounded_rectangle(xmin, xmax, ymin, ymax, r):
""" Returns a rectangle with rounded corners.
r is a roundedness fraction between 0 (not rounded)
and 1 (completely rounded)
"""
r *= min(xmax - xmin, ymax - ymin)/2
return (
rectangle(xmin, xmax, ymin+r, ymax-r) |
rectangle(xmin+r, xmax-r, ymin, ymax) |
circle(xmin+r, ymin+r, r) |
circle(xmin+r, ymax-r, r) |
circle(xmax-r, ymin+r, r) |
circle(xmax-r, ymax-r, r)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Tools/vala.py | python | configure | (self) | Use the following to enforce minimum vala version::
def configure(conf):
conf.load('vala', funs='')
conf.check_vala(min_version=(0,10,0)) | Use the following to enforce minimum vala version:: | [
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"minimum",
"vala",
"version",
"::"
] | def configure(self):
"""
Use the following to enforce minimum vala version::
def configure(conf):
conf.load('vala', funs='')
conf.check_vala(min_version=(0,10,0))
"""
self.load('gnu_dirs')
self.check_vala_deps()
self.check_vala()
self.env.VALAFLAGS = ['-C', '--quiet'] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/doodle/doodle.py | python | DoodleWindow.OnLeftDown | (self, event) | called when the left mouse button is pressed | called when the left mouse button is pressed | [
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"left",
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"button",
"is",
"pressed"
] | def OnLeftDown(self, event):
"""called when the left mouse button is pressed"""
self.curLine = []
self.pos = event.GetPosition()
self.CaptureMouse() | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/ntupleDataFormat.py | python | Event.stripHits | (self) | return StripHits(self._tree) | Returns StripHits object. | Returns StripHits object. | [
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return StripHits(self._tree) | [
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Jittor/jittor | e9aca0444c2bdc8e2389d99122954cd0903eec46 | python/jittor/misc.py | python | gather | (x, dim, index) | return x.getitem(tuple(indexes)) | if x is a 3-D array, reindex x like:
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
Parameters::
* x (jt.Var) – the source array
* dim (int) – the axis along which to index
* index (jt.Var) – the indices of elements to gather
Example::
t = jt.array([[1, 2], [3, 4]])
data = t.gather(1, jt.array([[0, 0], [1, 0]]))
assert (data.data == [[ 1, 1], [ 4, 3]]).all()
data = t.gather(0, jt.array([[0, 0], [1, 0]]))
assert (data.data == [[ 1, 2], [ 3, 2]]).all() | if x is a 3-D array, reindex x like: | [
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] | def gather(x, dim, index):
''' if x is a 3-D array, reindex x like:
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
Parameters::
* x (jt.Var) – the source array
* dim (int) – the axis along which to index
* index (jt.Var) – the indices of elements to gather
Example::
t = jt.array([[1, 2], [3, 4]])
data = t.gather(1, jt.array([[0, 0], [1, 0]]))
assert (data.data == [[ 1, 1], [ 4, 3]]).all()
data = t.gather(0, jt.array([[0, 0], [1, 0]]))
assert (data.data == [[ 1, 2], [ 3, 2]]).all()
'''
shape = index.shape
indexes = [ f'i{i}' for i in range(len(shape)) ]
indexes[dim] = index
return x.getitem(tuple(indexes)) | [
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limbo018/DREAMPlace | 146c3b9fd003d1acd52c96d9fd02e3f0a05154e4 | dreamplace/EvalMetrics.py | python | EvalMetrics.__repr__ | (self) | return self.__str__() | @brief print | [] | def __repr__(self):
"""
@brief print
"""
return self.__str__() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/dateutil/parser/isoparser.py | python | isoparser.__init__ | (self, sep=None) | :param sep:
A single character that separates date and time portions. If
``None``, the parser will accept any single character.
For strict ISO-8601 adherence, pass ``'T'``. | :param sep:
A single character that separates date and time portions. If
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"""
if sep is not None:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | GetTextFromUser | (*args, **kwargs) | return _misc_.GetTextFromUser(*args, **kwargs) | GetTextFromUser(String message, String caption=EmptyString, String default_value=EmptyString,
Window parent=None,
int x=-1, int y=-1, bool centre=True) -> String | GetTextFromUser(String message, String caption=EmptyString, String default_value=EmptyString,
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int x=-1, int y=-1, bool centre=True) -> String | [
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return _misc_.GetTextFromUser(*args, **kwargs) | [
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facebookincubator/mvfst | 034a40c797485113d00127852d4df3c5bb44b3ed | build/fbcode_builder/getdeps/fetcher.py | python | ChangeStatus.__init__ | (self, all_changed=False) | Construct a ChangeStatus object. The default is to create
a status that indicates no changes, but passing all_changed=True
will create one that indicates that everything changed | Construct a ChangeStatus object. The default is to create
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"""Construct a ChangeStatus object. The default is to create
a status that indicates no changes, but passing all_changed=True
will create one that indicates that everything changed"""
if all_changed:
self.source_files = 1
self.make_files = 1
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self.source_files = 0
self.make_files = 0 | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/init_ops_v2.py | python | VarianceScaling.__call__ | (self, shape, dtype=dtypes.float32) | Returns a tensor object initialized as specified by the initializer.
Args:
shape: Shape of the tensor.
dtype: Optional dtype of the tensor. Only floating point types are
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Raises:
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"""Returns a tensor object initialized as specified by the initializer.
Args:
shape: Shape of the tensor.
dtype: Optional dtype of the tensor. Only floating point types are
supported.
Raises:
ValueError: If the dtype is not floating point
"""
partition_info = None # Keeps logic so can be readded later if necessary
dtype = _assert_float_dtype(dtype)
scale = self.scale
scale_shape = shape
if partition_info is not None:
scale_shape = partition_info.full_shape
fan_in, fan_out = _compute_fans(scale_shape)
if self.mode == "fan_in":
scale /= max(1., fan_in)
elif self.mode == "fan_out":
scale /= max(1., fan_out)
else:
scale /= max(1., (fan_in + fan_out) / 2.)
if self.distribution == "truncated_normal":
# constant from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
stddev = math.sqrt(scale) / .87962566103423978
return self._random_generator.truncated_normal(shape, 0.0, stddev, dtype)
elif self.distribution == "untruncated_normal":
stddev = math.sqrt(scale)
return self._random_generator.random_normal(shape, 0.0, stddev, dtype)
else:
limit = math.sqrt(3.0 * scale)
return self._random_generator.random_uniform(shape, -limit, limit, dtype) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/dataview.py | python | DataViewListCtrl.GetToggleValue | (*args, **kwargs) | return _dataview.DataViewListCtrl_GetToggleValue(*args, **kwargs) | GetToggleValue(self, unsigned int row, unsigned int col) -> bool | GetToggleValue(self, unsigned int row, unsigned int col) -> bool | [
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"""GetToggleValue(self, unsigned int row, unsigned int col) -> bool"""
return _dataview.DataViewListCtrl_GetToggleValue(*args, **kwargs) | [
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