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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/platform/tf_logging.py | python | log_if | (level, msg, condition, *args) | Log 'msg % args' at level 'level' only if condition is fulfilled. | Log 'msg % args' at level 'level' only if condition is fulfilled. | [
"Log",
"msg",
"%",
"args",
"at",
"level",
"level",
"only",
"if",
"condition",
"is",
"fulfilled",
"."
] | def log_if(level, msg, condition, *args):
"""Log 'msg % args' at level 'level' only if condition is fulfilled."""
if condition:
vlog(level, msg, *args) | [
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||
bumptop/BumpTop | 466d23597a07ae738f4265262fa01087fc6e257c | trunk/win/Source/bin/jinja2/visitor.py | python | NodeVisitor.generic_visit | (self, node, *args, **kwargs) | Called if no explicit visitor function exists for a node. | Called if no explicit visitor function exists for a node. | [
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] | def generic_visit(self, node, *args, **kwargs):
"""Called if no explicit visitor function exists for a node."""
for node in node.iter_child_nodes():
self.visit(node, *args, **kwargs) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/Paste/paste/url.py | python | URLResource.setvars | (self, **kw) | return self.__class__(self.url, vars=kw.items(),
attrs=self.attrs,
params=self.original_params) | Creates a copy of this URL, but with all the variables set/reset
(like .setvar(), except clears past variables at the same time) | Creates a copy of this URL, but with all the variables set/reset
(like .setvar(), except clears past variables at the same time) | [
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] | def setvars(self, **kw):
"""
Creates a copy of this URL, but with all the variables set/reset
(like .setvar(), except clears past variables at the same time)
"""
return self.__class__(self.url, vars=kw.items(),
attrs=self.attrs,
params=self.original_params) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/html.py | python | HtmlHelpController.FindTopLevelWindow | (*args, **kwargs) | return _html.HtmlHelpController_FindTopLevelWindow(*args, **kwargs) | FindTopLevelWindow(self) -> Window | FindTopLevelWindow(self) -> Window | [
"FindTopLevelWindow",
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")",
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">",
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] | def FindTopLevelWindow(*args, **kwargs):
"""FindTopLevelWindow(self) -> Window"""
return _html.HtmlHelpController_FindTopLevelWindow(*args, **kwargs) | [
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|
arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/gyp/lib/ninja_syntax.py | python | Writer._line | (self, text, indent=0) | Write 'text' word-wrapped at self.width characters. | Write 'text' word-wrapped at self.width characters. | [
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"-",
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] | def _line(self, text, indent=0):
"""Write 'text' word-wrapped at self.width characters."""
leading_space = ' ' * indent
while len(leading_space) + len(text) > self.width:
# The text is too wide; wrap if possible.
# Find the rightmost space that would obey our width constraint and
# that's not an escaped space.
available_space = self.width - len(leading_space) - len(' $')
space = available_space
while True:
space = text.rfind(' ', 0, space)
if space < 0 or \
self._count_dollars_before_index(text, space) % 2 == 0:
break
if space < 0:
# No such space; just use the first unescaped space we can find.
space = available_space - 1
while True:
space = text.find(' ', space + 1)
if space < 0 or \
self._count_dollars_before_index(text, space) % 2 == 0:
break
if space < 0:
# Give up on breaking.
break
self.output.write(leading_space + text[0:space] + ' $\n')
text = text[space+1:]
# Subsequent lines are continuations, so indent them.
leading_space = ' ' * (indent+2)
self.output.write(leading_space + text + '\n') | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_basewin.py | python | EDBaseFileTree.OnDestroy | (self, event) | Cleanup message handlers | Cleanup message handlers | [
"Cleanup",
"message",
"handlers"
] | def OnDestroy(self, event):
"""Cleanup message handlers"""
if self:
ed_msg.Unsubscribe(self.OnActivateMsg)
self.DoOnDestroy()
event.Skip() | [
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||
PrincetonUniversity/athena-public-version | 9c266692b9423743d8e23509b3ab266a232a92d2 | tst/style/cpplint.py | python | CheckForBadCharacters | (filename, lines, error) | Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found. | Logs an error for each line containing bad characters. | [
"Logs",
"an",
"error",
"for",
"each",
"line",
"containing",
"bad",
"characters",
"."
] | def CheckForBadCharacters(filename, lines, error):
"""Logs an error for each line containing bad characters.
Two kinds of bad characters:
1. Unicode replacement characters: These indicate that either the file
contained invalid UTF-8 (likely) or Unicode replacement characters (which
it shouldn't). Note that it's possible for this to throw off line
numbering if the invalid UTF-8 occurred adjacent to a newline.
2. NUL bytes. These are problematic for some tools.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
for linenum, line in enumerate(lines):
if unicode_escape_decode('\ufffd') in line:
error(filename, linenum, 'readability/utf8', 5,
'Line contains invalid UTF-8 (or Unicode replacement character).')
if '\0' in line:
error(filename, linenum, 'readability/nul', 5, 'Line contains NUL byte.') | [
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||
ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/orchestrator/module.py | python | OrchestratorCli._rgw_add | (self,
svc_id: str,
placement: Optional[str] = None,
_end_positional_: int = 0,
port: Optional[int] = None,
ssl: bool = False,
inbuf: Optional[str] = None) | return self._daemon_add_misc(spec) | Start RGW daemon(s) | Start RGW daemon(s) | [
"Start",
"RGW",
"daemon",
"(",
"s",
")"
] | def _rgw_add(self,
svc_id: str,
placement: Optional[str] = None,
_end_positional_: int = 0,
port: Optional[int] = None,
ssl: bool = False,
inbuf: Optional[str] = None) -> HandleCommandResult:
"""Start RGW daemon(s)"""
if inbuf:
raise OrchestratorValidationError('unrecognized command -i; -h or --help for usage')
spec = RGWSpec(
service_id=svc_id,
rgw_frontend_port=port,
ssl=ssl,
placement=PlacementSpec.from_string(placement),
)
return self._daemon_add_misc(spec) | [
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|
GJDuck/LowFat | ecf6a0f0fa1b73a27a626cf493cc39e477b6faea | llvm-4.0.0.src/tools/clang/utils/check_cfc/check_cfc.py | python | WrapperCheck.__init__ | (self, output_file_a) | Record the base output file that will be compared against. | Record the base output file that will be compared against. | [
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"file",
"that",
"will",
"be",
"compared",
"against",
"."
] | def __init__(self, output_file_a):
"""Record the base output file that will be compared against."""
self._output_file_a = output_file_a | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/mailbox.py | python | _create_carefully | (path) | Create a file if it doesn't exist and open for reading and writing. | Create a file if it doesn't exist and open for reading and writing. | [
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] | def _create_carefully(path):
"""Create a file if it doesn't exist and open for reading and writing."""
fd = os.open(path, os.O_CREAT | os.O_EXCL | os.O_RDWR, 0666)
try:
return open(path, 'rb+')
finally:
os.close(fd) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/docview.py | python | Document.GetPrintableName | (self) | Copies a suitable document name into the supplied name buffer.
The default function uses the title, or if there is no title, uses the
filename; or if no filename, the string 'Untitled'. | Copies a suitable document name into the supplied name buffer.
The default function uses the title, or if there is no title, uses the
filename; or if no filename, the string 'Untitled'. | [
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"""
Copies a suitable document name into the supplied name buffer.
The default function uses the title, or if there is no title, uses the
filename; or if no filename, the string 'Untitled'.
"""
if self._documentTitle:
return self._documentTitle
elif self._documentFile:
return FileNameFromPath(self._documentFile)
else:
return _("Untitled") | [
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google-coral/edgetpu | 5020de9386ff370dcc1f63291a2d0f98eeb98adb | benchmarks/imprinting_benchmarks.py | python | run_benchmark | (model) | return training_time | Measures training time for given model with random data.
Args:
model: string, file name of the input model.
Returns:
float, training time. | Measures training time for given model with random data. | [
"Measures",
"training",
"time",
"for",
"given",
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"with",
"random",
"data",
"."
] | def run_benchmark(model):
"""Measures training time for given model with random data.
Args:
model: string, file name of the input model.
Returns:
float, training time.
"""
input_size = input_tensor_size(model)
engine = ImprintingEngine(test_utils.test_data_path(model), keep_classes=False)
np.random.seed(12345)
data_by_category = {}
# 10 Categories, each has 20 images.
for i in range(0, 10):
data_by_category[i] = []
for j in range(0, 20):
data_by_category[i].append(np.random.randint(0, 255, input_size))
start = time.perf_counter()
for class_id, tensors in enumerate(data_by_category.values()):
engine.train(tensors, class_id)
with tempfile.NamedTemporaryFile() as f:
engine.save_model(f.name)
training_time = time.perf_counter() - start
print('Model: %s' % model)
print('Training time: %.2fs' % training_time)
return training_time | [
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|
pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/inspector_protocol/jinja2/runtime.py | python | LoopContextBase.cycle | (self, *args) | return args[self.index0 % len(args)] | Cycles among the arguments with the current loop index. | Cycles among the arguments with the current loop index. | [
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] | def cycle(self, *args):
"""Cycles among the arguments with the current loop index."""
if not args:
raise TypeError('no items for cycling given')
return args[self.index0 % len(args)] | [
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|
SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | uCSIsSpecials | (code) | return ret | Check whether the character is part of Specials UCS Block | Check whether the character is part of Specials UCS Block | [
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] | def uCSIsSpecials(code):
"""Check whether the character is part of Specials UCS Block """
ret = libxml2mod.xmlUCSIsSpecials(code)
return ret | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pkg_resources/__init__.py | python | Environment.__iadd__ | (self, other) | return self | In-place addition of a distribution or environment | In-place addition of a distribution or environment | [
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"""In-place addition of a distribution or environment"""
if isinstance(other, Distribution):
self.add(other)
elif isinstance(other, Environment):
for project in other:
for dist in other[project]:
self.add(dist)
else:
raise TypeError("Can't add %r to environment" % (other,))
return self | [
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|
TGAC/KAT | e8870331de2b4bb0a1b3b91c6afb8fb9d59e9216 | deps/boost/tools/build/src/build/feature.py | python | set_default | (feature, value) | Sets the default value of the given feature, overriding any previous default.
feature: the name of the feature
value: the default value to assign | Sets the default value of the given feature, overriding any previous default.
feature: the name of the feature
value: the default value to assign | [
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""" Sets the default value of the given feature, overriding any previous default.
feature: the name of the feature
value: the default value to assign
"""
f = __all_features[feature]
bad_attribute = None
if f.free:
bad_attribute = "free"
elif f.optional:
bad_attribute = "optional"
if bad_attribute:
raise InvalidValue ("%s property %s cannot have a default" % (bad_attribute, f.name))
if value not in f.values:
raise InvalidValue ("The specified default value, '%s' is invalid.\n" % value + "allowed values are: %s" % f.values)
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||
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/vis/visualization.py | python | VisualizationScene.listItems | (self,root=None,indent=0) | Prints out all items in the visualization world. | Prints out all items in the visualization world. | [
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"items",
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"the",
"visualization",
"world",
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] | def listItems(self,root=None,indent=0):
"""Prints out all items in the visualization world."""
if root is None:
for name,value in self.items.items():
self.listItems(value,indent)
else:
if isinstance(root,str):
root = self.getItem(root)
if indent > 0:
print(" "*(indent-1), end=' ')
print(root.name)
for n,v in root.subAppearances.items():
self.listItems(v,indent+2) | [
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||
PX4/PX4-Autopilot | 0b9f60a0370be53d683352c63fd92db3d6586e18 | Tools/ecl_ekf/plotting/data_plots.py | python | DataPlot.close | (self) | closes the figure.
:return: | closes the figure.
:return: | [
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] | def close(self) -> None:
"""
closes the figure.
:return:
"""
plt.close(self._fig) | [
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||
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/layers/python/layers/feature_column.py | python | _BucketizedColumn.to_weighted_sum | (self,
input_tensor,
num_outputs=1,
weight_collections=None,
trainable=True) | return _create_embedding_lookup(
input_tensor=self.to_sparse_tensor(input_tensor),
weight_tensor=None,
vocab_size=self.length * self.source_column.dimension,
dimension=num_outputs,
weight_collections=_add_variable_collection(weight_collections),
initializer=init_ops.zeros_initializer,
combiner="sum",
trainable=trainable,
name=self.name) | Returns a Tensor as linear predictions and a list of created Variable. | Returns a Tensor as linear predictions and a list of created Variable. | [
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] | def to_weighted_sum(self,
input_tensor,
num_outputs=1,
weight_collections=None,
trainable=True):
"""Returns a Tensor as linear predictions and a list of created Variable."""
return _create_embedding_lookup(
input_tensor=self.to_sparse_tensor(input_tensor),
weight_tensor=None,
vocab_size=self.length * self.source_column.dimension,
dimension=num_outputs,
weight_collections=_add_variable_collection(weight_collections),
initializer=init_ops.zeros_initializer,
combiner="sum",
trainable=trainable,
name=self.name) | [
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py | python | StateSpaceModel._filtering_step | (self, current_times, current_values, state, predictions) | return (filtered_state, predictions) | Compute posteriors and accumulate one-step-ahead predictions.
Args:
current_times: A [batch size] Tensor for times for each observation.
current_values: A [batch size] Tensor of values for each observaiton.
state: A tuple of (mean, covariance, previous_times) having shapes
mean; [batch size x state dimension]
covariance; [batch size x state dimension x state dimension]
previous_times; [batch size]
predictions: A dictionary containing mean and covariance Tensors, the
output of _prediction_step.
Returns:
A tuple of (posteriors, outputs):
posteriors: Model state updated to take `current_values` into account.
outputs: The `predictions` dictionary updated to include "loss" and
"log_likelihood" entries (loss simply being negative log
likelihood). | Compute posteriors and accumulate one-step-ahead predictions. | [
"Compute",
"posteriors",
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"accumulate",
"one",
"-",
"step",
"-",
"ahead",
"predictions",
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] | def _filtering_step(self, current_times, current_values, state, predictions):
"""Compute posteriors and accumulate one-step-ahead predictions.
Args:
current_times: A [batch size] Tensor for times for each observation.
current_values: A [batch size] Tensor of values for each observaiton.
state: A tuple of (mean, covariance, previous_times) having shapes
mean; [batch size x state dimension]
covariance; [batch size x state dimension x state dimension]
previous_times; [batch size]
predictions: A dictionary containing mean and covariance Tensors, the
output of _prediction_step.
Returns:
A tuple of (posteriors, outputs):
posteriors: Model state updated to take `current_values` into account.
outputs: The `predictions` dictionary updated to include "loss" and
"log_likelihood" entries (loss simply being negative log
likelihood).
"""
estimated_state, estimated_state_covariance, previous_times = state
observation_model = self.get_broadcasted_observation_model(current_times)
imputed_to_current_step_assert = control_flow_ops.Assert(
math_ops.reduce_all(math_ops.equal(current_times, previous_times)),
["Attempted to perform filtering without imputation/prediction"])
with ops.control_dependencies([imputed_to_current_step_assert]):
estimated_state_covariance = math_utils.clip_covariance(
estimated_state_covariance,
self._configuration.filtering_maximum_posterior_variance_ratio,
self._configuration.filtering_minimum_posterior_variance)
(filtered_state, filtered_state_covariance,
log_prob) = self._kalman_filter.do_filter(
estimated_state=estimated_state,
estimated_state_covariance=estimated_state_covariance,
predicted_observation=predictions["mean"],
predicted_observation_covariance=predictions["covariance"],
observation=current_values,
observation_model=observation_model,
observation_noise=self._observation_noise_covariance)
filtered_state = (filtered_state, filtered_state_covariance, current_times)
log_prob.set_shape(current_times.get_shape())
predictions["loss"] = -log_prob
predictions["log_likelihood"] = log_prob
if self._configuration.filtering_postprocessor is not None:
return self._configuration.filtering_postprocessor.process_filtering_step(
current_times=current_times,
current_values=current_values,
predicted_state=state,
filtered_state=filtered_state,
outputs=predictions)
return (filtered_state, predictions) | [
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|
wenwei202/caffe | f54a74abaf6951d8485cbdcfa1d74a4c37839466 | scripts/cpp_lint.py | python | _CppLintState.SetFilters | (self, filters) | Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter" | Sets the error-message filters. | [
"Sets",
"the",
"error",
"-",
"message",
"filters",
"."
] | def SetFilters(self, filters):
"""Sets the error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "+whitespace/indent").
Each filter should start with + or -; else we die.
Raises:
ValueError: The comma-separated filters did not all start with '+' or '-'.
E.g. "-,+whitespace,-whitespace/indent,whitespace/badfilter"
"""
# Default filters always have less priority than the flag ones.
self.filters = _DEFAULT_FILTERS[:]
for filt in filters.split(','):
clean_filt = filt.strip()
if clean_filt:
self.filters.append(clean_filt)
for filt in self.filters:
if not (filt.startswith('+') or filt.startswith('-')):
raise ValueError('Every filter in --filters must start with + or -'
' (%s does not)' % filt) | [
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||
thomaskeck/FastBDT | e67f71525612020acc78721031fca681d173c144 | examples/ugboost.py | python | calculate_cdf_and_pdf | (X) | return numpy.hstack([0.0, cdf, 1.0]), numpy.hstack([0.0, pdf, 0.0]), bins | Calculates cdf and pdf of given sample and adds under/overflow bins
@param X 1-d numpy.array | Calculates cdf and pdf of given sample and adds under/overflow bins | [
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] | def calculate_cdf_and_pdf(X):
"""
Calculates cdf and pdf of given sample and adds under/overflow bins
@param X 1-d numpy.array
"""
pdf, bins = numpy.histogram(X, bins=100, density=True)
cdf = numpy.cumsum(pdf * (bins - numpy.roll(bins, 1))[1:])
return numpy.hstack([0.0, cdf, 1.0]), numpy.hstack([0.0, pdf, 0.0]), bins | [
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|
pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/msvs.py | python | GenerateOutput | (target_list, target_dicts, data, params) | Generate .sln and .vcproj files.
This is the entry point for this generator.
Arguments:
target_list: List of target pairs: 'base/base.gyp:base'.
target_dicts: Dict of target properties keyed on target pair.
data: Dictionary containing per .gyp data. | Generate .sln and .vcproj files. | [
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"""Generate .sln and .vcproj files.
This is the entry point for this generator.
Arguments:
target_list: List of target pairs: 'base/base.gyp:base'.
target_dicts: Dict of target properties keyed on target pair.
data: Dictionary containing per .gyp data.
"""
global fixpath_prefix
options = params['options']
# Get the project file format version back out of where we stashed it in
# GeneratorCalculatedVariables.
msvs_version = params['msvs_version']
generator_flags = params.get('generator_flags', {})
# Optionally shard targets marked with 'msvs_shard': SHARD_COUNT.
(target_list, target_dicts) = MSVSUtil.ShardTargets(target_list, target_dicts)
# Optionally use the large PDB workaround for targets marked with
# 'msvs_large_pdb': 1.
(target_list, target_dicts) = MSVSUtil.InsertLargePdbShims(
target_list, target_dicts, generator_default_variables)
# Optionally configure each spec to use ninja as the external builder.
if params.get('flavor') == 'ninja':
_InitNinjaFlavor(params, target_list, target_dicts)
# Prepare the set of configurations.
configs = set()
for qualified_target in target_list:
spec = target_dicts[qualified_target]
for config_name, config in spec['configurations'].items():
configs.add(_ConfigFullName(config_name, config))
configs = list(configs)
# Figure out all the projects that will be generated and their guids
project_objects = _CreateProjectObjects(target_list, target_dicts, options,
msvs_version)
# Generate each project.
missing_sources = []
for project in project_objects.values():
fixpath_prefix = project.fixpath_prefix
missing_sources.extend(_GenerateProject(project, options, msvs_version,
generator_flags))
fixpath_prefix = None
for build_file in data:
# Validate build_file extension
if not build_file.endswith('.gyp'):
continue
sln_path = os.path.splitext(build_file)[0] + options.suffix + '.sln'
if options.generator_output:
sln_path = os.path.join(options.generator_output, sln_path)
# Get projects in the solution, and their dependents.
sln_projects = gyp.common.BuildFileTargets(target_list, build_file)
sln_projects += gyp.common.DeepDependencyTargets(target_dicts, sln_projects)
# Create folder hierarchy.
root_entries = _GatherSolutionFolders(
sln_projects, project_objects, flat=msvs_version.FlatSolution())
# Create solution.
sln = MSVSNew.MSVSSolution(sln_path,
entries=root_entries,
variants=configs,
websiteProperties=False,
version=msvs_version)
sln.Write()
if missing_sources:
error_message = "Missing input files:\n" + \
'\n'.join(set(missing_sources))
if generator_flags.get('msvs_error_on_missing_sources', False):
raise GypError(error_message)
else:
print("Warning: " + error_message, file=sys.stdout) | [
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||
ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/ceph-volume/ceph_volume/util/__init__.py | python | prompt_bool | (question, input_=None) | Interface to prompt a boolean (or boolean-like) response from a user.
Usually a confirmation. | Interface to prompt a boolean (or boolean-like) response from a user.
Usually a confirmation. | [
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] | def prompt_bool(question, input_=None):
"""
Interface to prompt a boolean (or boolean-like) response from a user.
Usually a confirmation.
"""
input_prompt = input_ or input
prompt_format = '--> {question} '.format(question=question)
response = input_prompt(prompt_format)
try:
return str_to_bool(response)
except ValueError:
terminal.error('Valid true responses are: y, yes, <Enter>')
terminal.error('Valid false responses are: n, no')
terminal.error('That response was invalid, please try again')
return prompt_bool(question, input_=input_prompt) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/json_schema_compiler/js_util.py | python | JsUtil.GetLicense | (self) | return (LICENSE % datetime.now().year) | Returns the license text for JS extern and interface files. | Returns the license text for JS extern and interface files. | [
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"""Returns the license text for JS extern and interface files.
"""
return (LICENSE % datetime.now().year) | [
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|
wang-bin/QtAV | 3b937991afce248648836ae811324d4051b31def | python/configure.py | python | inform | (msg) | Display an information message. msg is the text of the error message. | Display an information message. msg is the text of the error message. | [
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""" Display an information message. msg is the text of the error message.
"""
sys.stdout.write(_format(msg) + "\n") | [
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||
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/estimators/head.py | python | _logits | (logits_input, logits, logits_dimension) | return logits | Validate logits args, and create `logits` if necessary.
Exactly one of `logits_input` and `logits` must be provided.
Args:
logits_input: `Tensor` input to `logits`.
logits: `Tensor` output.
logits_dimension: Integer, last dimension of `logits`. This is used to
create `logits` from `logits_input` if `logits` is `None`; otherwise, it's
used to validate `logits`.
Returns:
`logits` `Tensor`.
Raises:
ValueError: if neither or both of `logits` and `logits_input` are supplied. | Validate logits args, and create `logits` if necessary. | [
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] | def _logits(logits_input, logits, logits_dimension):
"""Validate logits args, and create `logits` if necessary.
Exactly one of `logits_input` and `logits` must be provided.
Args:
logits_input: `Tensor` input to `logits`.
logits: `Tensor` output.
logits_dimension: Integer, last dimension of `logits`. This is used to
create `logits` from `logits_input` if `logits` is `None`; otherwise, it's
used to validate `logits`.
Returns:
`logits` `Tensor`.
Raises:
ValueError: if neither or both of `logits` and `logits_input` are supplied.
"""
if (logits_dimension is None) or (logits_dimension < 1):
raise ValueError("Invalid logits_dimension %s." % logits_dimension)
# If not provided, create logits.
if logits is None:
if logits_input is None:
raise ValueError("Neither logits nor logits_input supplied.")
return layers_lib.linear(logits_input, logits_dimension, scope="logits")
if logits_input is not None:
raise ValueError("Both logits and logits_input supplied.")
logits = ops.convert_to_tensor(logits, name="logits")
logits_dims = logits.get_shape().dims
if logits_dims is not None:
logits_dims[-1].assert_is_compatible_with(logits_dimension)
return logits | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/metrics/_plot/precision_recall_curve.py | python | plot_precision_recall_curve | (estimator, X, y,
sample_weight=None, response_method="auto",
name=None, ax=None, **kwargs) | return viz.plot(ax=ax, name=name, **kwargs) | Plot Precision Recall Curve for binary classifiers.
Extra keyword arguments will be passed to matplotlib's `plot`.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
estimator : estimator instance
Trained classifier.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
y : array-like of shape (n_samples,)
Binary target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
response_method : {'predict_proba', 'decision_function', 'auto'}, \
default='auto'
Specifies whether to use :term:`predict_proba` or
:term:`decision_function` as the target response. If set to 'auto',
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.
name : str, default=None
Name for labeling curve. If `None`, the name of the
estimator is used.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is created.
**kwargs : dict
Keyword arguments to be passed to matplotlib's `plot`.
Returns
-------
display : :class:`~sklearn.metrics.PrecisionRecallDisplay`
Object that stores computed values. | Plot Precision Recall Curve for binary classifiers. | [
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"Recall",
"Curve",
"for",
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"classifiers",
"."
] | def plot_precision_recall_curve(estimator, X, y,
sample_weight=None, response_method="auto",
name=None, ax=None, **kwargs):
"""Plot Precision Recall Curve for binary classifiers.
Extra keyword arguments will be passed to matplotlib's `plot`.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
estimator : estimator instance
Trained classifier.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
y : array-like of shape (n_samples,)
Binary target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
response_method : {'predict_proba', 'decision_function', 'auto'}, \
default='auto'
Specifies whether to use :term:`predict_proba` or
:term:`decision_function` as the target response. If set to 'auto',
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.
name : str, default=None
Name for labeling curve. If `None`, the name of the
estimator is used.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is created.
**kwargs : dict
Keyword arguments to be passed to matplotlib's `plot`.
Returns
-------
display : :class:`~sklearn.metrics.PrecisionRecallDisplay`
Object that stores computed values.
"""
check_matplotlib_support("plot_precision_recall_curve")
classification_error = ("{} should be a binary classifier".format(
estimator.__class__.__name__))
if not is_classifier(estimator):
raise ValueError(classification_error)
prediction_method = _check_classifer_response_method(estimator,
response_method)
y_pred = prediction_method(X)
if y_pred.ndim != 1:
if y_pred.shape[1] != 2:
raise ValueError(classification_error)
else:
y_pred = y_pred[:, 1]
pos_label = estimator.classes_[1]
precision, recall, _ = precision_recall_curve(y, y_pred,
pos_label=pos_label,
sample_weight=sample_weight)
average_precision = average_precision_score(y, y_pred,
pos_label=pos_label,
sample_weight=sample_weight)
name = name if name is not None else estimator.__class__.__name__
viz = PrecisionRecallDisplay(
precision=precision, recall=recall,
average_precision=average_precision, estimator_name=name
)
return viz.plot(ax=ax, name=name, **kwargs) | [
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|
NREL/EnergyPlus | fadc5973b85c70e8cc923efb69c144e808a26078 | src/EnergyPlus/api/datatransfer.py | python | DataExchange.today_weather_outdoor_dry_bulb_at_time | (self, state: c_void_p, hour: int, time_step_number: int) | return self.api.todayWeatherOutDryBulbAtTime(state, hour, time_step_number) | Gets the specified weather data at the specified hour and time step index within that hour
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param hour: Integer hour of day (0 to 23)
:param time_step_number: Time step index in hour, from 1 to the number of zone time steps per hour
:return: Value of the weather condition at the specified time | Gets the specified weather data at the specified hour and time step index within that hour | [
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"""
Gets the specified weather data at the specified hour and time step index within that hour
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param hour: Integer hour of day (0 to 23)
:param time_step_number: Time step index in hour, from 1 to the number of zone time steps per hour
:return: Value of the weather condition at the specified time
"""
return self.api.todayWeatherOutDryBulbAtTime(state, hour, time_step_number) | [
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|
zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | scripts/cpp_lint.py | python | _IncludeState.IsInAlphabeticalOrder | (self, clean_lines, linenum, header_path) | return True | Check if a header is in alphabetical order with the previous header.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
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Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
header_path: Canonicalized header to be checked.
Returns:
Returns true if the header is in alphabetical order.
"""
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#
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|
tensorflow/io | 92b44e180674a8af0e12e405530f7343e3e693e4 | tensorflow_io/python/ops/hdf5_io_tensor_ops.py | python | BaseHDF5GraphIOTensor.to_tensor | (self) | return core_ops.io_hdf5_readable_read(
input=self._filename,
shared=self._filename,
component=self._component,
shape=self._shape,
start=0,
stop=-1,
dtype=self._dtype,
container="HDF5IOTensor",
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Args:
name: A name prefix for the returned tensors (optional).
Returns:
A `Tensor` with value obtained from this `IOTensor`. | Converts this `IOTensor` into a `tf.Tensor`. | [
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"""Converts this `IOTensor` into a `tf.Tensor`.
Args:
name: A name prefix for the returned tensors (optional).
Returns:
A `Tensor` with value obtained from this `IOTensor`.
"""
return core_ops.io_hdf5_readable_read(
input=self._filename,
shared=self._filename,
component=self._component,
shape=self._shape,
start=0,
stop=-1,
dtype=self._dtype,
container="HDF5IOTensor",
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | _flatten | (seq) | return res | Internal function. | Internal function. | [
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"""Internal function."""
res = ()
for item in seq:
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elif item is not None:
res = res + (item,)
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|
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/package/file_structure_representation.py | python | Directory.has_file | (self, filename: str) | return False | Checks if a file is present in a :class:`Directory`.
Args:
filename (str): Path of file to search for.
Returns:
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"""Checks if a file is present in a :class:`Directory`.
Args:
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Returns:
bool: If a :class:`Directory` contains the specified file.
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|
OAID/Caffe-HRT | aae71e498ab842c6f92bcc23fc668423615a4d65 | scripts/cpp_lint.py | python | IsBlankLine | (line) | return not line or line.isspace() | Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
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"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
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Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
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|
google-coral/edgetpu | 5020de9386ff370dcc1f63291a2d0f98eeb98adb | edgetpu/learn/backprop/softmax_regression.py | python | SoftmaxRegression.save_as_tflite_model | (self, in_model_path, out_model_path) | Appends learned weights to your TensorFlow Lite model and saves it as a copy.
Beware that learned weights and biases are quantized from float32 to uint8.
Args:
in_model_path (str): Path to the embedding extractor model (``.tflite`` file).
out_model_path (str): Path where you'd like to save the new model with learned weights
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/html.py | python | HtmlContainerCell.SetBackgroundColour | (*args, **kwargs) | return _html.HtmlContainerCell_SetBackgroundColour(*args, **kwargs) | SetBackgroundColour(self, Colour clr) | SetBackgroundColour(self, Colour clr) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/format.py | python | reformat_comment | (data, limit, comment_header) | return '\n'.join(comment_header+line for line in newdata) + block_suffix | Return data reformatted to specified width with comment header. | Return data reformatted to specified width with comment header. | [
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|
envoyproxy/envoy | 65541accdafe255e72310b4298d646e091da2d80 | tools/protodoc/protodoc.py | python | format_proto_as_block_comment | (proto) | return '\n\nproto::\n\n' + map_lines(functools.partial(indent, 2), str(proto)) + '\n' | Format a proto as a RST block comment.
Useful in debugging, not usually referenced. | Format a proto as a RST block comment. | [
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] | def format_proto_as_block_comment(proto):
"""Format a proto as a RST block comment.
Useful in debugging, not usually referenced.
"""
return '\n\nproto::\n\n' + map_lines(functools.partial(indent, 2), str(proto)) + '\n' | [
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|
CaoWGG/TensorRT-CenterNet | f949252e37b51e60f873808f46d3683f15735e79 | onnx-tensorrt/third_party/onnx/onnx/helper.py | python | make_tensor | (
name, # type: Text
data_type, # type: int
dims, # type: Sequence[int]
vals, # type: Any
raw=False # type: bool
) | return tensor | Make a TensorProto with specified arguments. If raw is False, this
function will choose the corresponding proto field to store the
values based on data_type. If raw is True, use "raw_data" proto
field to store the values, and values should be of type bytes in
this case. | Make a TensorProto with specified arguments. If raw is False, this
function will choose the corresponding proto field to store the
values based on data_type. If raw is True, use "raw_data" proto
field to store the values, and values should be of type bytes in
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name, # type: Text
data_type, # type: int
dims, # type: Sequence[int]
vals, # type: Any
raw=False # type: bool
): # type: (...) -> TensorProto
'''
Make a TensorProto with specified arguments. If raw is False, this
function will choose the corresponding proto field to store the
values based on data_type. If raw is True, use "raw_data" proto
field to store the values, and values should be of type bytes in
this case.
'''
tensor = TensorProto()
tensor.data_type = data_type
tensor.name = name
if data_type == TensorProto.STRING:
assert not raw, "Can not use raw_data to store string type"
if (data_type == TensorProto.COMPLEX64
or data_type == TensorProto.COMPLEX128):
vals = split_complex_to_pairs(vals)
if raw:
tensor.raw_data = vals
else:
field = mapping.STORAGE_TENSOR_TYPE_TO_FIELD[
mapping.TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE[data_type]]
getattr(tensor, field).extend(vals)
tensor.dims.extend(dims)
return tensor | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/fsspec/spec.py | python | AbstractBufferedFile.readlines | (self) | Return all data, split by the newline character | Return all data, split by the newline character | [
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"data",
"split",
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] | def readlines(self):
"""Return all data, split by the newline character"""
data = self.read()
lines = data.split(b"\n")
out = [l + b"\n" for l in lines[:-1]]
if data.endswith(b"\n"):
return out
else:
return out + [lines[-1]] | [
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||
SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/processor/conversion/de1/processor.py | python | DE1Processor.convert | (cls, gamespec, args, string_resources, existing_graphics) | return modpacks | Input game specification and media here and get a set of
modpacks back.
:param gamespec: Gamedata from empires.dat read in by the
reader functions.
:type gamespec: class: ...dataformat.value_members.ArrayMember
:returns: A list of modpacks.
:rtype: list | Input game specification and media here and get a set of
modpacks back. | [
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] | def convert(cls, gamespec, args, string_resources, existing_graphics):
"""
Input game specification and media here and get a set of
modpacks back.
:param gamespec: Gamedata from empires.dat read in by the
reader functions.
:type gamespec: class: ...dataformat.value_members.ArrayMember
:returns: A list of modpacks.
:rtype: list
"""
info("Starting conversion...")
# Create a new container for the conversion process
dataset = cls._pre_processor(
gamespec,
args.game_version,
string_resources,
existing_graphics
)
debug_converter_objects(args.debugdir, args.debug_info, dataset)
# Create the custom openage formats (nyan, sprite, terrain)
dataset = cls._processor(gamespec, dataset)
debug_converter_object_groups(args.debugdir, args.debug_info, dataset)
# Create modpack definitions
modpacks = cls._post_processor(dataset)
return modpacks | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | GraphicsContext.ConcatTransform | (*args, **kwargs) | return _gdi_.GraphicsContext_ConcatTransform(*args, **kwargs) | ConcatTransform(self, GraphicsMatrix matrix)
Concatenates the passed in transform with the current transform of
this context. | ConcatTransform(self, GraphicsMatrix matrix) | [
"ConcatTransform",
"(",
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"GraphicsMatrix",
"matrix",
")"
] | def ConcatTransform(*args, **kwargs):
"""
ConcatTransform(self, GraphicsMatrix matrix)
Concatenates the passed in transform with the current transform of
this context.
"""
return _gdi_.GraphicsContext_ConcatTransform(*args, **kwargs) | [
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|
swift/swift | 12d031cf8177fdec0137f9aa7e2912fa23c4416b | 3rdParty/SCons/scons-3.0.1/engine/SCons/Util.py | python | unique | (s) | return u | Return a list of the elements in s, but without duplicates.
For example, unique([1,2,3,1,2,3]) is some permutation of [1,2,3],
unique("abcabc") some permutation of ["a", "b", "c"], and
unique(([1, 2], [2, 3], [1, 2])) some permutation of
[[2, 3], [1, 2]].
For best speed, all sequence elements should be hashable. Then
unique() will usually work in linear time.
If not possible, the sequence elements should enjoy a total
ordering, and if list(s).sort() doesn't raise TypeError it's
assumed that they do enjoy a total ordering. Then unique() will
usually work in O(N*log2(N)) time.
If that's not possible either, the sequence elements must support
equality-testing. Then unique() will usually work in quadratic
time. | Return a list of the elements in s, but without duplicates. | [
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] | def unique(s):
"""Return a list of the elements in s, but without duplicates.
For example, unique([1,2,3,1,2,3]) is some permutation of [1,2,3],
unique("abcabc") some permutation of ["a", "b", "c"], and
unique(([1, 2], [2, 3], [1, 2])) some permutation of
[[2, 3], [1, 2]].
For best speed, all sequence elements should be hashable. Then
unique() will usually work in linear time.
If not possible, the sequence elements should enjoy a total
ordering, and if list(s).sort() doesn't raise TypeError it's
assumed that they do enjoy a total ordering. Then unique() will
usually work in O(N*log2(N)) time.
If that's not possible either, the sequence elements must support
equality-testing. Then unique() will usually work in quadratic
time.
"""
n = len(s)
if n == 0:
return []
# Try using a dict first, as that's the fastest and will usually
# work. If it doesn't work, it will usually fail quickly, so it
# usually doesn't cost much to *try* it. It requires that all the
# sequence elements be hashable, and support equality comparison.
u = {}
try:
for x in s:
u[x] = 1
except TypeError:
pass # move on to the next method
else:
return list(u.keys())
del u
# We can't hash all the elements. Second fastest is to sort,
# which brings the equal elements together; then duplicates are
# easy to weed out in a single pass.
# NOTE: Python's list.sort() was designed to be efficient in the
# presence of many duplicate elements. This isn't true of all
# sort functions in all languages or libraries, so this approach
# is more effective in Python than it may be elsewhere.
try:
t = sorted(s)
except TypeError:
pass # move on to the next method
else:
assert n > 0
last = t[0]
lasti = i = 1
while i < n:
if t[i] != last:
t[lasti] = last = t[i]
lasti = lasti + 1
i = i + 1
return t[:lasti]
del t
# Brute force is all that's left.
u = []
for x in s:
if x not in u:
u.append(x)
return u | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/_vendor/pyparsing.py | python | ParseBaseException.markInputline | ( self, markerString = ">!<" ) | return line_str.strip() | Extracts the exception line from the input string, and marks
the location of the exception with a special symbol. | Extracts the exception line from the input string, and marks
the location of the exception with a special symbol. | [
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"symbol",
"."
] | def markInputline( self, markerString = ">!<" ):
"""Extracts the exception line from the input string, and marks
the location of the exception with a special symbol.
"""
line_str = self.line
line_column = self.column - 1
if markerString:
line_str = "".join((line_str[:line_column],
markerString, line_str[line_column:]))
return line_str.strip() | [
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/nccl_ops.py | python | _apply_reduce | (reduction, tensors) | return result | Helper function for reduce_* functions. | Helper function for reduce_* functions. | [
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"."
] | def _apply_reduce(reduction, tensors):
"""Helper function for reduce_* functions."""
if not tensors:
raise ValueError('Must pass >0 tensors to reduce operations')
for t in tensors:
_check_device(t)
result = gen_nccl_ops.nccl_reduce(input=tensors, reduction=reduction)
try:
next(t for t in tensors if t.device == result.device)
except StopIteration:
raise ValueError('One input tensor must be assigned to current device')
return result | [
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|
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/python/google/path_utils.py | python | FindAncestor | (start_dir, ancestor) | Finds an ancestor dir in a path.
For example, FindAncestor('c:\foo\bar\baz', 'bar') would return
'c:\foo\bar'. Unlike FindUpward*, this only looks at direct path ancestors. | Finds an ancestor dir in a path. | [
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] | def FindAncestor(start_dir, ancestor):
"""Finds an ancestor dir in a path.
For example, FindAncestor('c:\foo\bar\baz', 'bar') would return
'c:\foo\bar'. Unlike FindUpward*, this only looks at direct path ancestors.
"""
start_dir = os.path.abspath(start_dir)
path = start_dir
while True:
(parent, tail) = os.path.split(path)
if tail == ancestor:
return path
if not tail:
break
path = parent
raise PathNotFound("Unable to find ancestor %s in %s" % (ancestor, start_dir)) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/pipeline/pipeline/pipeline.py | python | After._thread_init | (cls) | Ensure thread local is initialized. | Ensure thread local is initialized. | [
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] | def _thread_init(cls):
"""Ensure thread local is initialized."""
if not hasattr(cls._local, '_after_all_futures'):
cls._local._after_all_futures = [] | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/_custom_op/fused_abs_max1_impl.py | python | shape1 | (tik_instance, input_x_shape, ori_shape, input_x, res) | return tik_instance, res | shape1 | shape1 | [
"shape1"
] | def shape1(tik_instance, input_x_shape, ori_shape, input_x, res):
"""shape1"""
if ori_shape == (147, 147):
phase_1 = 16384
blocks = 32
each_block_element = phase_1 // blocks + 64
with tik_instance.for_range(0, blocks, block_num=blocks) as block_index:
input_x_ub = tik_instance.Tensor("float32", (each_block_element,), name="input_x_ub",
scope=tik.scope_ubuf)
broadcast_0_local_ub = tik_instance.Tensor("float32", (4096,), name="broadcast_0_local_ub",
scope=tik.scope_ubuf)
tik_instance.data_move(input_x_ub, input_x[512 * block_index], 0, 1, 512 // 8, 0, 0)
line_id = block_index % 19
tik_instance.data_move(input_x_ub[512], input_x[16384 + 128 * line_id], 0, 1, 8, 0, 0)
repeat_time = each_block_element // 64
tik_instance.vabs(64, input_x_ub, input_x_ub, repeat_time, 1, 1, 8, 8)
tik_instance.vmax(19, input_x_ub, input_x_ub, input_x_ub[512], 1, 1, 1, 1, 8, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[256], 4, 1, 1, 1, 8, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[128], 2, 1, 1, 1, 8, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[64], 1, 1, 1, 1, 8, 8, 8)
tik_instance, res = _update_tik(tik_instance, input_x_ub, broadcast_0_local_ub, block_index, res)
elif ori_shape in ((256, 256), None, (-1, -1)):
total_elements1 = 1
for val in input_x_shape:
total_elements1 *= val
blocks = 32
each_block_element = total_elements1 // blocks
with tik_instance.for_range(0, blocks, block_num=blocks) as block_index:
input_x_ub = tik_instance.Tensor("float32", (each_block_element,), name="input_x_ub",
scope=tik.scope_ubuf)
broadcast_0_local_ub = tik_instance.Tensor("float32", (4096,), name="broadcast_0_local_ub",
scope=tik.scope_ubuf)
tik_instance.data_move(input_x_ub, input_x[each_block_element * block_index], 0, 1,
each_block_element // 8, 0, 0)
repeat_time = each_block_element // 64
tik_instance.vabs(64, input_x_ub, input_x_ub, repeat_time, 1, 1, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[512], 8, 1, 1, 1, 8, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[256], 4, 1, 1, 1, 8, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[128], 2, 1, 1, 1, 8, 8, 8)
tik_instance.vmax(64, input_x_ub, input_x_ub, input_x_ub[64], 1, 1, 1, 1, 8, 8, 8)
tik_instance, res = _update_tik(tik_instance, input_x_ub, broadcast_0_local_ub, block_index, res)
else:
raise RuntimeError("origin shape %s is not supported" % str(ori_shape))
return tik_instance, res | [
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] | https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/_op_impl/_custom_op/fused_abs_max1_impl.py#L100-L143 |
|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/debug/cli/debugger_cli_common.py | python | RichTextLines.append | (self, line, font_attr_segs=None) | Append a single line of text.
Args:
line: (str) The text to be added to the end.
font_attr_segs: (list of tuples) Font attribute segments of the appended
line. | Append a single line of text. | [
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"a",
"single",
"line",
"of",
"text",
"."
] | def append(self, line, font_attr_segs=None):
"""Append a single line of text.
Args:
line: (str) The text to be added to the end.
font_attr_segs: (list of tuples) Font attribute segments of the appended
line.
"""
self._lines.append(line)
if font_attr_segs:
self._font_attr_segs[len(self._lines) - 1] = font_attr_segs | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/valgrind/suppressions.py | python | ValgrindStyleSuppression.__str__ | (self) | return "{\n %s\n}\n" % "\n ".join(lines) | Stringify. | Stringify. | [
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"."
] | def __str__(self):
"""Stringify."""
lines = [self.description, self.type] + self.stack
return "{\n %s\n}\n" % "\n ".join(lines) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/polynomial/chebyshev.py | python | chebsub | (c1, c2) | return pu.trimseq(ret) | Subtract one Chebyshev series from another.
Returns the difference of two Chebyshev series `c1` - `c2`. The
sequences of coefficients are from lowest order term to highest, i.e.,
[1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Chebyshev series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Of Chebyshev series coefficients representing their difference.
See Also
--------
chebadd, chebmulx, chebmul, chebdiv, chebpow
Notes
-----
Unlike multiplication, division, etc., the difference of two Chebyshev
series is a Chebyshev series (without having to "reproject" the result
onto the basis set) so subtraction, just like that of "standard"
polynomials, is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> C.chebsub(c1,c2)
array([-2., 0., 2.])
>>> C.chebsub(c2,c1) # -C.chebsub(c1,c2)
array([ 2., 0., -2.]) | Subtract one Chebyshev series from another. | [
"Subtract",
"one",
"Chebyshev",
"series",
"from",
"another",
"."
] | def chebsub(c1, c2):
"""
Subtract one Chebyshev series from another.
Returns the difference of two Chebyshev series `c1` - `c2`. The
sequences of coefficients are from lowest order term to highest, i.e.,
[1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Chebyshev series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Of Chebyshev series coefficients representing their difference.
See Also
--------
chebadd, chebmulx, chebmul, chebdiv, chebpow
Notes
-----
Unlike multiplication, division, etc., the difference of two Chebyshev
series is a Chebyshev series (without having to "reproject" the result
onto the basis set) so subtraction, just like that of "standard"
polynomials, is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> C.chebsub(c1,c2)
array([-2., 0., 2.])
>>> C.chebsub(c2,c1) # -C.chebsub(c1,c2)
array([ 2., 0., -2.])
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if len(c1) > len(c2):
c1[:c2.size] -= c2
ret = c1
else:
c2 = -c2
c2[:c1.size] += c1
ret = c2
return pu.trimseq(ret) | [
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|
KhronosGroup/SPIRV-LLVM | 1eb85593f3fe2c39379b9a9b088d51eda4f42b8b | utils/llvm-build/llvmbuild/main.py | python | cmake_quote_string | (value) | return value | cmake_quote_string(value) -> str
Return a quoted form of the given value that is suitable for use in CMake
language files. | cmake_quote_string(value) -> str | [
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"""
cmake_quote_string(value) -> str
Return a quoted form of the given value that is suitable for use in CMake
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"""
# Currently, we only handle escaping backslashes.
value = value.replace("\\", "\\\\")
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OpenXRay/xray-15 | 1390dfb08ed20997d7e8c95147ea8e8cb71f5e86 | cs/sdk/3d_sdk/maya/ver-2008/devkit/plug-ins/scripted/motionTraceCmd.py | python | motionTrace.__jMakeCurve | (self, cvs) | Make a degree 1 curve from the given CVs.
Note that in Python, a double underscore in front of a member name
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Note that in Python, a double underscore in front of a member name
make the method "private" to the class through name-mangling | [
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"""
Make a degree 1 curve from the given CVs.
Note that in Python, a double underscore in front of a member name
make the method "private" to the class through name-mangling
"""
deg = 1
knots = OpenMaya.MDoubleArray()
for i in range(cvs.length()):
knots.append(i)
# Now create the curve
nullObj = OpenMaya.MObject()
curveFn = OpenMaya.MFnNurbsCurve()
curveFn.create(cvs, knots, deg, OpenMaya.MFnNurbsCurve.kOpen, False, False, nullObj) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/pkg_resources/_vendor/pyparsing.py | python | ParserElement.setResultsName | ( self, name, listAllMatches=False ) | return newself | Define name for referencing matching tokens as a nested attribute
of the returned parse results.
NOTE: this returns a *copy* of the original C{ParserElement} object;
this is so that the client can define a basic element, such as an
integer, and reference it in multiple places with different names.
You can also set results names using the abbreviated syntax,
C{expr("name")} in place of C{expr.setResultsName("name")} -
see L{I{__call__}<__call__>}.
Example::
date_str = (integer.setResultsName("year") + '/'
+ integer.setResultsName("month") + '/'
+ integer.setResultsName("day"))
# equivalent form:
date_str = integer("year") + '/' + integer("month") + '/' + integer("day") | Define name for referencing matching tokens as a nested attribute
of the returned parse results.
NOTE: this returns a *copy* of the original C{ParserElement} object;
this is so that the client can define a basic element, such as an
integer, and reference it in multiple places with different names. | [
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"""
newself = self.copy()
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|
BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/error.py | python | Error.__init__ | (self, error=None) | Error - a model defined in Swagger | Error - a model defined in Swagger | [
"Error",
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"""Error - a model defined in Swagger""" # noqa: E501
self._error = None
self.discriminator = None
self.error = error | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/linalg/linear_operator.py | python | LinearOperator.log_abs_determinant | (self, name="log_abs_det") | Log absolute value of determinant for every batch member.
Args:
name: A name for this `Op.
Returns:
`Tensor` with shape `self.batch_shape` and same `dtype` as `self`.
Raises:
NotImplementedError: If `self.is_square` is `False`. | Log absolute value of determinant for every batch member. | [
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"""Log absolute value of determinant for every batch member.
Args:
name: A name for this `Op.
Returns:
`Tensor` with shape `self.batch_shape` and same `dtype` as `self`.
Raises:
NotImplementedError: If `self.is_square` is `False`.
"""
if self.is_square is False:
raise NotImplementedError(
"Determinant not implemented for an operator that is expected to "
"not be square.")
with self._name_scope(name):
return self._log_abs_determinant() | [
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PX4/PX4-Autopilot | 0b9f60a0370be53d683352c63fd92db3d6586e18 | src/lib/mixer/MultirotorMixer/geometries/tools/px_generate_mixers.py | python | geometry_to_thrust_matrix | (geometry) | return At | Compute thrust matrix At from geometry dictionnary
At is a 3xN matrix where N is the number of rotors
Each column is the thrust generated by one rotor | Compute thrust matrix At from geometry dictionnary
At is a 3xN matrix where N is the number of rotors
Each column is the thrust generated by one rotor | [
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'''
Compute thrust matrix At from geometry dictionnary
At is a 3xN matrix where N is the number of rotors
Each column is the thrust generated by one rotor
'''
At = thrust_matrix(axis=np.array([rotor['axis'] for rotor in geometry['rotors']]),
Ct=np.array([[rotor['Ct']] for rotor in geometry['rotors']])).T
return At | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_style.py | python | StyleItem.GetSize | (self) | return self.size | Returns the value of the size attribute as a string
@return: style items font size attribute | Returns the value of the size attribute as a string
@return: style items font size attribute | [
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"""Returns the value of the size attribute as a string
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | PropertyGrid.IsEditorFocused | (*args, **kwargs) | return _propgrid.PropertyGrid_IsEditorFocused(*args, **kwargs) | IsEditorFocused(self) -> bool | IsEditorFocused(self) -> bool | [
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"""IsEditorFocused(self) -> bool"""
return _propgrid.PropertyGrid_IsEditorFocused(*args, **kwargs) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pyclbr.py | python | _readmodule | (module, path, inpackage=None) | return _create_tree(fullmodule, path, fname, source, tree, inpackage) | Do the hard work for readmodule[_ex].
If inpackage is given, it must be the dotted name of the package in
which we are searching for a submodule, and then PATH must be the
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] | def _readmodule(module, path, inpackage=None):
"""Do the hard work for readmodule[_ex].
If inpackage is given, it must be the dotted name of the package in
which we are searching for a submodule, and then PATH must be the
package search path; otherwise, we are searching for a top-level
module, and path is combined with sys.path.
"""
# Compute the full module name (prepending inpackage if set).
if inpackage is not None:
fullmodule = "%s.%s" % (inpackage, module)
else:
fullmodule = module
# Check in the cache.
if fullmodule in _modules:
return _modules[fullmodule]
# Initialize the dict for this module's contents.
tree = {}
# Check if it is a built-in module; we don't do much for these.
if module in sys.builtin_module_names and inpackage is None:
_modules[module] = tree
return tree
# Check for a dotted module name.
i = module.rfind('.')
if i >= 0:
package = module[:i]
submodule = module[i+1:]
parent = _readmodule(package, path, inpackage)
if inpackage is not None:
package = "%s.%s" % (inpackage, package)
if not '__path__' in parent:
raise ImportError('No package named {}'.format(package))
return _readmodule(submodule, parent['__path__'], package)
# Search the path for the module.
f = None
if inpackage is not None:
search_path = path
else:
search_path = path + sys.path
spec = importlib.util._find_spec_from_path(fullmodule, search_path)
_modules[fullmodule] = tree
# Is module a package?
if spec.submodule_search_locations is not None:
tree['__path__'] = spec.submodule_search_locations
try:
source = spec.loader.get_source(fullmodule)
if source is None:
return tree
except (AttributeError, ImportError):
# If module is not Python source, we cannot do anything.
return tree
fname = spec.loader.get_filename(fullmodule)
return _create_tree(fullmodule, path, fname, source, tree, inpackage) | [
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|
polyworld/polyworld | eb7e6bbc82fe77ba79e3bc48c3da2ad8c8238c26 | scripts/agent/reader.py | python | BackwardsReader.__init__ | (self, file, blksize=4096) | initialize the internal structures | initialize the internal structures | [
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"internal",
"structures"
] | def __init__(self, file, blksize=4096):
"""initialize the internal structures"""
# get the file size
self.size = os.stat(file)[6]
# how big of a block to read from the file...
self.blksize = blksize
# how many blocks we've read
self.blkcount = 1
self.f = open(file, 'rb')
# if the file is smaller than the blocksize, read a block,
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self.f.seek(-self.blksize * self.blkcount, 2) # read from end of file
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# strip the last item if it's empty... a byproduct of the last line having
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/magics/execution.py | python | TimeitTemplateFiller.visit_FunctionDef | (self, node) | return node | Fill in the setup statement | Fill in the setup statement | [
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] | def visit_FunctionDef(self, node):
"Fill in the setup statement"
self.generic_visit(node)
if node.name == "inner":
node.body[:1] = self.ast_setup.body
return node | [
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|
scribusproject/scribus | 41ec7c775a060912cf251682a8b1437f753f80f4 | scribus/plugins/scriptplugin/scripts/Ligatursatz.py | python | GermanLigatureSupport.simple_case_fold_for_lookup | (self, my_unicode_string) | return my_unicode_string.lower().replace("ſ", "s") | Before applying the hyphenation algorithm to a string, some
“folding” has to be done. The german word “auffallend” has the
ligature ["auf", "fallend"]. If it is the first
word of a sentence, than it is written with capital letter
“Auffallend”. The “case” (the
fact that a letter is a small letter or a capital
letter) does not matter. You can read
more about this topic in the Unicode standard:
3.13 Default Case Algorithms → Caseless matching
The pattern uses lower case. So we have to map all
upper case letters in a string to
lower case letters before applying the
hyphenation algorithm. Unicode describes
“full case folding” and “simple case folding”.
“full case folding” converts for example
both lowercase ß and uppercase ẞ to ss: it
maps one original character to two
substitution characters. “simple case folding”
leaves lowercase ß as is, and converts
uppercase ẞ to lowercase ß. I think the
only relevant application of this “one-to-many
mapping” for the german language is the sharp
s. As the patter is generated for
both (normal german with ß and swiss
german without ß but with ss), this “one-to-many
folding” is not necessary. A simple
toLowercase() with additional mapping
of the lowercase long s (ſ) to the lowercase
normal s should be enough.
Preconditions: my_unicode_string is of type “unicode”.
Postconditions: Returns a “unicode” value
that corresponds to my_unicode_string, but has
mapped uppercase characters to lowercase
characters – or at least these that are important
for our patterns. The mapping is guaranteed
to be a one-to-one mapping of Unicode Scalar
Values. That means that one Unicode Scalar
Value is replaced by exactly one other
Unicode Scalar Value. So the count of
Unicode Scalar Values of the return value is equal
to the count of Unicode Scalar Values of
my_unicode_string. (Note that the count of code
units might change between input and output
if you do not use UTF32.)
WARNING This function must be kept
in synch with isWordCharacter(). | Before applying the hyphenation algorithm to a string, some
“folding” has to be done. The german word “auffallend” has the
ligature ["auf", "fallend"]. If it is the first
word of a sentence, than it is written with capital letter
“Auffallend”. The “case” (the
fact that a letter is a small letter or a capital
letter) does not matter. You can read
more about this topic in the Unicode standard:
3.13 Default Case Algorithms → Caseless matching
The pattern uses lower case. So we have to map all
upper case letters in a string to
lower case letters before applying the
hyphenation algorithm. Unicode describes
“full case folding” and “simple case folding”.
“full case folding” converts for example
both lowercase ß and uppercase ẞ to ss: it
maps one original character to two
substitution characters. “simple case folding”
leaves lowercase ß as is, and converts
uppercase ẞ to lowercase ß. I think the
only relevant application of this “one-to-many
mapping” for the german language is the sharp
s. As the patter is generated for
both (normal german with ß and swiss
german without ß but with ss), this “one-to-many
folding” is not necessary. A simple
toLowercase() with additional mapping
of the lowercase long s (ſ) to the lowercase
normal s should be enough. | [
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"""Before applying the hyphenation algorithm to a string, some
“folding” has to be done. The german word “auffallend” has the
ligature ["auf", "fallend"]. If it is the first
word of a sentence, than it is written with capital letter
“Auffallend”. The “case” (the
fact that a letter is a small letter or a capital
letter) does not matter. You can read
more about this topic in the Unicode standard:
3.13 Default Case Algorithms → Caseless matching
The pattern uses lower case. So we have to map all
upper case letters in a string to
lower case letters before applying the
hyphenation algorithm. Unicode describes
“full case folding” and “simple case folding”.
“full case folding” converts for example
both lowercase ß and uppercase ẞ to ss: it
maps one original character to two
substitution characters. “simple case folding”
leaves lowercase ß as is, and converts
uppercase ẞ to lowercase ß. I think the
only relevant application of this “one-to-many
mapping” for the german language is the sharp
s. As the patter is generated for
both (normal german with ß and swiss
german without ß but with ss), this “one-to-many
folding” is not necessary. A simple
toLowercase() with additional mapping
of the lowercase long s (ſ) to the lowercase
normal s should be enough.
Preconditions: my_unicode_string is of type “unicode”.
Postconditions: Returns a “unicode” value
that corresponds to my_unicode_string, but has
mapped uppercase characters to lowercase
characters – or at least these that are important
for our patterns. The mapping is guaranteed
to be a one-to-one mapping of Unicode Scalar
Values. That means that one Unicode Scalar
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Unicode Scalar Value. So the count of
Unicode Scalar Values of the return value is equal
to the count of Unicode Scalar Values of
my_unicode_string. (Note that the count of code
units might change between input and output
if you do not use UTF32.)
WARNING This function must be kept
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"""
if type(my_unicode_string) is not str:
raise TypeError("The “my_unicode_string” parameter must be of "
"type “unicode”, but it isn’t.")
return my_unicode_string.lower().replace("ſ", "s") | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Start/StartPage/StartPage.py | python | postStart | () | executes needed operations after loading a file | executes needed operations after loading a file | [
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"executes needed operations after loading a file"
param = FreeCAD.ParamGet("User parameter:BaseApp/Preferences/Mod/Start")
# switch workbench
wb = param.GetString("AutoloadModule","")
if "$LastModule" == wb:
wb = FreeCAD.ParamGet("User parameter:BaseApp/Preferences/General").GetString("LastModule","")
if wb:
# don't switch workbenches if we are not in Start anymore
if FreeCADGui.activeWorkbench() and (FreeCADGui.activeWorkbench().name() == "StartWorkbench"):
FreeCADGui.activateWorkbench(wb)
# close start tab
cl = param.GetBool("closeStart",False)
if cl:
title = QtGui.QApplication.translate("Workbench","Start page")
mw = FreeCADGui.getMainWindow()
if mw:
mdi = mw.findChild(QtGui.QMdiArea)
if mdi:
for mdichild in mdi.children():
for subw in mdichild.findChildren(QtGui.QMdiSubWindow):
if subw.windowTitle() == title:
subw.close() | [
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||
baidu/AnyQ | d94d450d2aaa5f7ed73424b10aa4539835b97527 | tools/simnet/train/paddle/util/data_reader.py | python | get_reader | (conf_dict, is_infer, samples_file) | Get Reader | Get Reader | [
"Get",
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] | def get_reader(conf_dict, is_infer, samples_file):
"""
Get Reader
"""
def reader_with_pairwise():
"""
Reader with Pairwise
"""
if is_infer:
with open(conf_dict["test_file_path"]) as file:
for line in file:
if not utils.pattern_match(r"(\d+)\t(\d+)\t((\d+ )*\d+)\t((\d+ )*\d+)\n", line):
logging.warning("line not match format in test file")
continue
items = line.strip("\n").split("\t")
query = [int(id) for id in items[2].split(" ")]
title = [int(id) for id in items[3].split(" ")]
if samples_file:
samples_file.write(line)
yield [query, title]
else:
with open(conf_dict["train_file_path"]) as file:
for line in file:
if not utils.pattern_match(r"((\d+ )*\d+)\t((\d+ )*\d+)\t((\d+ )*\d+)\n", line):
logging.warning("line not match format in train file")
continue
items = line.strip("\n").split("\t")
query = [int(id) for id in items[0].split(" ")]
pos_title = [int(id) for id in items[1].split(" ")]
neg_title = [int(id) for id in items[2].split(" ")]
if samples_file:
samples_file.write(line)
yield [query, pos_title, neg_title]
def reader_with_pointwise():
"""
Reader with Pointwise
"""
if is_infer:
with open(conf_dict["test_file_path"]) as file:
for line in file:
if not utils.pattern_match(r"((\d+ )*\d+)\t((\d+ )*\d+)\t(\d+)\n", line):
logging.warning("line not match format in test file")
continue
items = line.strip("\n").split("\t")
query = [int(id) for id in items[0].split(" ")]
title = [int(id) for id in items[1].split(" ")]
if samples_file:
samples_file.write(line)
yield [query, title]
else:
with open(conf_dict["train_file_path"]) as file:
for line in file:
if not utils.pattern_match(r"((\d+ )*\d+)\t((\d+ )*\d+)\t(\d+)\n", line):
logging.warning("line not match format in train file: %s" % line)
continue
items = line.strip("\n").split("\t")
query = [int(id) for id in items[0].split(" ")]
title = [int(id) for id in items[1].split(" ")]
label = int(items[2])
if samples_file:
samples_file.write(line)
yield [query, title, label]
if conf_dict["task_mode"] == "pairwise":
return reader_with_pairwise
else:
return reader_with_pointwise | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py2/jinja2/ext.py | python | InternationalizationExtension.parse | (self, parser) | Parse a translatable tag. | Parse a translatable tag. | [
"Parse",
"a",
"translatable",
"tag",
"."
] | def parse(self, parser):
"""Parse a translatable tag."""
lineno = next(parser.stream).lineno
num_called_num = False
# find all the variables referenced. Additionally a variable can be
# defined in the body of the trans block too, but this is checked at
# a later state.
plural_expr = None
plural_expr_assignment = None
variables = {}
trimmed = None
while parser.stream.current.type != "block_end":
if variables:
parser.stream.expect("comma")
# skip colon for python compatibility
if parser.stream.skip_if("colon"):
break
name = parser.stream.expect("name")
if name.value in variables:
parser.fail(
"translatable variable %r defined twice." % name.value,
name.lineno,
exc=TemplateAssertionError,
)
# expressions
if parser.stream.current.type == "assign":
next(parser.stream)
variables[name.value] = var = parser.parse_expression()
elif trimmed is None and name.value in ("trimmed", "notrimmed"):
trimmed = name.value == "trimmed"
continue
else:
variables[name.value] = var = nodes.Name(name.value, "load")
if plural_expr is None:
if isinstance(var, nodes.Call):
plural_expr = nodes.Name("_trans", "load")
variables[name.value] = plural_expr
plural_expr_assignment = nodes.Assign(
nodes.Name("_trans", "store"), var
)
else:
plural_expr = var
num_called_num = name.value == "num"
parser.stream.expect("block_end")
plural = None
have_plural = False
referenced = set()
# now parse until endtrans or pluralize
singular_names, singular = self._parse_block(parser, True)
if singular_names:
referenced.update(singular_names)
if plural_expr is None:
plural_expr = nodes.Name(singular_names[0], "load")
num_called_num = singular_names[0] == "num"
# if we have a pluralize block, we parse that too
if parser.stream.current.test("name:pluralize"):
have_plural = True
next(parser.stream)
if parser.stream.current.type != "block_end":
name = parser.stream.expect("name")
if name.value not in variables:
parser.fail(
"unknown variable %r for pluralization" % name.value,
name.lineno,
exc=TemplateAssertionError,
)
plural_expr = variables[name.value]
num_called_num = name.value == "num"
parser.stream.expect("block_end")
plural_names, plural = self._parse_block(parser, False)
next(parser.stream)
referenced.update(plural_names)
else:
next(parser.stream)
# register free names as simple name expressions
for var in referenced:
if var not in variables:
variables[var] = nodes.Name(var, "load")
if not have_plural:
plural_expr = None
elif plural_expr is None:
parser.fail("pluralize without variables", lineno)
if trimmed is None:
trimmed = self.environment.policies["ext.i18n.trimmed"]
if trimmed:
singular = self._trim_whitespace(singular)
if plural:
plural = self._trim_whitespace(plural)
node = self._make_node(
singular,
plural,
variables,
plural_expr,
bool(referenced),
num_called_num and have_plural,
)
node.set_lineno(lineno)
if plural_expr_assignment is not None:
return [plural_expr_assignment, node]
else:
return node | [
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] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/Jinja2/py2/jinja2/ext.py#L229-L342 |
||
okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/urllib3/contrib/pyopenssl.py | python | extract_from_urllib3 | () | Undo monkey-patching by :func:`inject_into_urllib3`. | Undo monkey-patching by :func:`inject_into_urllib3`. | [
"Undo",
"monkey",
"-",
"patching",
"by",
":",
"func",
":",
"inject_into_urllib3",
"."
] | def extract_from_urllib3():
'Undo monkey-patching by :func:`inject_into_urllib3`.'
util.ssl_.SSLContext = orig_util_SSLContext
util.HAS_SNI = orig_util_HAS_SNI
util.ssl_.HAS_SNI = orig_util_HAS_SNI
util.IS_PYOPENSSL = False
util.ssl_.IS_PYOPENSSL = False | [
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||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/boosted_trees/python/utils/losses.py | python | per_example_exp_loss | (labels, weights, predictions, name=None, eps=0.1) | return unweighted_loss * weights, control_flow_ops.no_op() | Trimmed exponential loss given labels, example weights and predictions.
Note that this is only for binary classification.
If logistic loss tries to make sure that the classifier is certain of its
predictions, exp loss says: "as long as it got it correct, even barely, i
don't care". Can be used on noisy data, or when you don't care about getting
the actual probabilities from the model, just the correct label.
The loss returns is exp(-targets*modified_predictions), where
modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive
class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in
the interval 0.5-eps, 0.5+eps, where a = 1/eps, b=1/(2eps).
Args:
labels: Rank 2 (N, D) tensor of per-example labels.
weights: Rank 2 (N, 1) tensor of per-example weights.
predictions: Rank 2 (N, D) tensor of per-example predictions.
name: A name for the operation (optional).
eps: For the range (0.5-eps, 0.5+eps) we set the predictions to be ax+b.
Returns:
loss: A Rank 2 (N, 1) tensor of per-example exp loss
update_op: An update operation to update the loss's internal state. | Trimmed exponential loss given labels, example weights and predictions. | [
"Trimmed",
"exponential",
"loss",
"given",
"labels",
"example",
"weights",
"and",
"predictions",
"."
] | def per_example_exp_loss(labels, weights, predictions, name=None, eps=0.1):
"""Trimmed exponential loss given labels, example weights and predictions.
Note that this is only for binary classification.
If logistic loss tries to make sure that the classifier is certain of its
predictions, exp loss says: "as long as it got it correct, even barely, i
don't care". Can be used on noisy data, or when you don't care about getting
the actual probabilities from the model, just the correct label.
The loss returns is exp(-targets*modified_predictions), where
modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive
class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in
the interval 0.5-eps, 0.5+eps, where a = 1/eps, b=1/(2eps).
Args:
labels: Rank 2 (N, D) tensor of per-example labels.
weights: Rank 2 (N, 1) tensor of per-example weights.
predictions: Rank 2 (N, D) tensor of per-example predictions.
name: A name for the operation (optional).
eps: For the range (0.5-eps, 0.5+eps) we set the predictions to be ax+b.
Returns:
loss: A Rank 2 (N, 1) tensor of per-example exp loss
update_op: An update operation to update the loss's internal state.
"""
def exp_with_logits(name, eps, labels=None, logits=None):
"""Computes exponential loss given `logits`.
The loss returns is exp(-targets*modified_predictions), where
modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive
class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in
the interval 0.5-eps, 0.5+eps, where a = 1/eps, b=1/(2eps).
Args:
name: A name for the operation (optional).
eps: For the range (0.5-eps, 0.5+eps) we set the predictions to be ax+b.
labels: A `Tensor` of the same type and shape as `logits`.
logits: A `Tensor` of type `float32` or `float64`.
Returns:
A `Tensor` of the same shape as `logits` with the componentwise
exponential losses.
Raises:
ValueError: If `logits` and `labels` do not have the same shape.
"""
with ops.name_scope(name, "exp_loss", [logits, labels]) as name:
logits = ops.convert_to_tensor(logits, name="logits")
labels = ops.convert_to_tensor(labels, name="labels")
try:
labels.get_shape().merge_with(logits.get_shape())
except ValueError:
raise ValueError("logits and labels must have the same shape (%s vs %s)"
% (logits.get_shape(), labels.get_shape()))
# Default threshold to switch between classes
zeros = array_ops.zeros_like(logits, dtype=logits.dtype)
ones = array_ops.ones_like(logits, dtype=logits.dtype)
neg_ones = -array_ops.ones_like(logits, dtype=logits.dtype)
# Convert labels to 1 and -1
cond_labels = (labels > zeros)
labels_converted = array_ops.where(cond_labels, ones, neg_ones)
# Convert predictions to 1 and -1
# The loss we build is min(1, max(-1,ax+b))
# where a=1/eps, b=-1/2eps.
a = 1.0 / eps
b = -1.0 / 2 / eps
probs = math_ops.sigmoid(logits)
y = a * probs + b
# Build max(-1, ax+b)
cond = (y < -1)
max_res = array_ops.where(cond, neg_ones, y)
# Build min part
cond = (max_res > 1)
min_res = array_ops.where(cond, ones, max_res)
preds_converted = min_res
return math_ops.exp(-preds_converted * labels_converted)
labels = math_ops.cast(labels, dtypes.float32)
unweighted_loss = exp_with_logits(
name=name, eps=eps, labels=labels, logits=predictions)
return unweighted_loss * weights, control_flow_ops.no_op() | [
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|
ultralight-ux/WebCore | 8a83d7f3a7517b75ae7dc18183b4ff9ce898419b | Source/JavaScriptCore/Scripts/jsmin.py | python | jsmin | (js) | return outs.getvalue() | returns a minified version of the javascript string | returns a minified version of the javascript string | [
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] | def jsmin(js):
"""
returns a minified version of the javascript string
"""
if not is_3:
if cStringIO and not isinstance(js, unicode):
# strings can use cStringIO for a 3x performance
# improvement, but unicode (in python2) cannot
klass = cStringIO.StringIO
else:
klass = StringIO.StringIO
else:
klass = io.StringIO
ins = klass(js)
outs = klass()
JavascriptMinify(ins, outs).minify()
return outs.getvalue() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | NavigationKeyEvent.SetFlags | (*args, **kwargs) | return _core_.NavigationKeyEvent_SetFlags(*args, **kwargs) | SetFlags(self, long flags)
Set the navigation flags to a combination of the following:
* wx.NavigationKeyEvent.IsBackward
* wx.NavigationKeyEvent.IsForward
* wx.NavigationKeyEvent.WinChange
* wx.NavigationKeyEvent.FromTab | SetFlags(self, long flags) | [
"SetFlags",
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] | def SetFlags(*args, **kwargs):
"""
SetFlags(self, long flags)
Set the navigation flags to a combination of the following:
* wx.NavigationKeyEvent.IsBackward
* wx.NavigationKeyEvent.IsForward
* wx.NavigationKeyEvent.WinChange
* wx.NavigationKeyEvent.FromTab
"""
return _core_.NavigationKeyEvent_SetFlags(*args, **kwargs) | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/libraries/ioss/src/visualization/catalyst/phactori/Operation/PhactoriExtractStructuredMultiBlock.py | python | FigureBlockIndicesFromBlockListOneBlock | (includeIndexList, includeBlockList,
inMetaData, ioFlatIndexCounter, inCsdata, inForceSetting) | determine if this one block should have it's flat index tripped on for
the extract block filter (leaf item of recursion) | determine if this one block should have it's flat index tripped on for
the extract block filter (leaf item of recursion) | [
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inMetaData, ioFlatIndexCounter, inCsdata, inForceSetting):
"""determine if this one block should have it's flat index tripped on for
the extract block filter (leaf item of recursion)"""
if PhactoriDbg(100):
myDebugPrint3("FigureBlockIndicesFromBlockListOneBlock entered\n"
"ioFlatIndexCounter " + str(ioFlatIndexCounter,) + " inForceSetting " + str(inForceSetting) + "\n"
"2 inMetaData: " + str(inMetaData) + "\n")
if inMetaData == None:
thisBlockName = None
else:
thisBlockName = inMetaData.Get(vtk.vtkCompositeDataSet.NAME())
if (thisBlockName == None) and (inForceSetting != 1):
if PhactoriDbg(100):
myDebugPrint3("block with no name " + \
" not in include list, not + to mBlockIndices (flat index " + \
str(ioFlatIndexCounter[0] - 1) + ")\n")
elif (inForceSetting == 1) or (thisBlockName in includeBlockList):
includeIndexList.append(int(ioFlatIndexCounter[0]) - 1)
blockClassName = inCsdata.GetClassName()
if blockClassName == "vtkStructuredGrid":
global gStructuredGridFound
gStructuredGridFound = inCsdata
global gStructuredGridFoundExtent
gStructuredGridFoundExtent = inMetaData.Get(vtk.vtkStreamingDemandDrivenPipeline.WHOLE_EXTENT())
if PhactoriDbg(100):
myDebugPrint3("this leaf is structured grid: " + str(thisBlockName) + "\n"
"vtk.vtkStreamingDemandDrivenPipeline.WHOLE_EXTENT(): " + str(gStructuredGridFoundExtent) + "\n"
"A inCsdata.GetExtent(): " + str(inCsdata.GetExtent()) + "\n")
#global gDuplicateNameCounter
#if thisBlockName in gDuplicateNameCounter:
# oldCount = gDuplicateNameCounter[thisBlockName]
# gDuplicateNameCounter[thisBlockName] = oldCount+1
#else:
# gDuplicateNameCounter[thisBlockName] = 1
if PhactoriDbg(100):
myDebugPrint3("block " + str(thisBlockName) + \
" in include list, + to mBlockIndices (flat index " + \
str(ioFlatIndexCounter[0] - 1) + ")\n")
else:
if PhactoriDbg(100):
myDebugPrint3("block " + str(thisBlockName) + \
" not in include list, not + to mBlockIndices (flat index " + \
str(ioFlatIndexCounter[0] - 1) + ")\n")
if PhactoriDbg(100):
myDebugPrint3("FigureBlockIndicesFromBlockListOneBlock returning\n") | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pathlib2/pathlib2/__init__.py | python | PurePath.with_name | (self, name) | return self._from_parsed_parts(self._drv, self._root,
self._parts[:-1] + parts[-1:]) | Return a new path with the file name changed. | Return a new path with the file name changed. | [
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] | def with_name(self, name):
"""Return a new path with the file name changed."""
if not self.name:
raise ValueError("%r has an empty name" % (self,))
drv, root, parts = self._flavour.parse_parts((name,))
if (not name or name[-1] in [self._flavour.sep, self._flavour.altsep]
or drv or root or len(parts) != 1):
raise ValueError("Invalid name %r" % (name))
return self._from_parsed_parts(self._drv, self._root,
self._parts[:-1] + parts[-1:]) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/py/shell.py | python | Shell.redirectStdout | (self, redirect=True) | If redirect is true then sys.stdout will go to the shell. | If redirect is true then sys.stdout will go to the shell. | [
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] | def redirectStdout(self, redirect=True):
"""If redirect is true then sys.stdout will go to the shell."""
if redirect:
sys.stdout = PseudoFileOut(self.writeOut)
else:
sys.stdout = self.stdout | [
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||
tfwu/FaceDetection-ConvNet-3D | f9251c48eb40c5aec8fba7455115c355466555be | python/build/lib.linux-x86_64-2.7/mxnet/kvstore.py | python | KVStore._set_updater | (self, updater) | Set a push updater into the store.
This function only changes the local store. Use set_optimizer for
multi-machines.
Parameters
----------
updater : function
the updater function
Examples
--------
>>> def update(key, input, stored):
... print "update on key: %d" % key
... stored += input * 2
>>> kv._set_updater(update)
>>> kv.pull(3, out=a)
>>> print a.asnumpy()
[[ 4. 4. 4.]
[ 4. 4. 4.]]
>>> kv.push(3, mx.nd.ones(shape))
update on key: 3
>>> kv.pull(3, out=a)
>>> print a.asnumpy()
[[ 6. 6. 6.]
[ 6. 6. 6.]] | Set a push updater into the store. | [
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] | def _set_updater(self, updater):
"""Set a push updater into the store.
This function only changes the local store. Use set_optimizer for
multi-machines.
Parameters
----------
updater : function
the updater function
Examples
--------
>>> def update(key, input, stored):
... print "update on key: %d" % key
... stored += input * 2
>>> kv._set_updater(update)
>>> kv.pull(3, out=a)
>>> print a.asnumpy()
[[ 4. 4. 4.]
[ 4. 4. 4.]]
>>> kv.push(3, mx.nd.ones(shape))
update on key: 3
>>> kv.pull(3, out=a)
>>> print a.asnumpy()
[[ 6. 6. 6.]
[ 6. 6. 6.]]
"""
_updater_proto = ctypes.CFUNCTYPE(
None, ctypes.c_int, NDArrayHandle, NDArrayHandle, ctypes.c_void_p)
self._updater_func = _updater_proto(_updater_wrapper(updater))
check_call(_LIB.MXKVStoreSetUpdater(self.handle, self._updater_func, None)) | [
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||
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/blackduck_hub.py | python | ReportManager.add_report_metric | (self, comp_name: str, metric: str) | Add a column to be included in the pretty table. | Add a column to be included in the pretty table. | [
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] | def add_report_metric(self, comp_name: str, metric: str):
"""Add a column to be included in the pretty table."""
comp_name = ReportManager._get_norm_comp_name(comp_name)
self._data.add_value(comp_name, metric) | [
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sailing-pmls/bosen | 06cb58902d011fbea5f9428f10ce30e621492204 | style_script/cpplint.py | python | Search | (pattern, s) | return _regexp_compile_cache[pattern].search(s) | Searches the string for the pattern, caching the compiled regexp. | Searches the string for the pattern, caching the compiled regexp. | [
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] | def Search(pattern, s):
"""Searches the string for the pattern, caching the compiled regexp."""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].search(s) | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/gslib/commands/perfdiag.py | python | PerfDiagCommand._RunOperation | (self, func) | return return_val | Runs an operation with retry logic.
Args:
func: The function to run.
Returns:
True if the operation succeeds, False if aborted. | Runs an operation with retry logic. | [
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] | def _RunOperation(self, func):
"""Runs an operation with retry logic.
Args:
func: The function to run.
Returns:
True if the operation succeeds, False if aborted.
"""
# We retry on httplib exceptions that can happen if the socket was closed
# by the remote party or the connection broke because of network issues.
# Only the BotoServerError is counted as a 5xx error towards the retry
# limit.
success = False
server_error_retried = 0
total_retried = 0
i = 0
return_val = None
while not success:
next_sleep = min(random.random() * (2 ** i) + 1, GetMaxRetryDelay())
try:
return_val = func()
self.total_requests += 1
success = True
except tuple(self.exceptions) as e:
total_retried += 1
if total_retried > self.MAX_TOTAL_RETRIES:
self.logger.info('Reached maximum total retries. Not retrying.')
break
if isinstance(e, ServiceException):
if e.status >= 500:
self.error_responses_by_code[e.status] += 1
self.total_requests += 1
self.request_errors += 1
server_error_retried += 1
time.sleep(next_sleep)
else:
raise
if server_error_retried > self.MAX_SERVER_ERROR_RETRIES:
self.logger.info(
'Reached maximum server error retries. Not retrying.')
break
else:
self.connection_breaks += 1
return return_val | [
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/check_ops.py | python | assert_integer_v2 | (x, message=None, name=None) | Assert that `x` is of integer dtype.
If `x` has a non-integer type, `message`, as well as the dtype of `x` are
printed, and `InvalidArgumentError` is raised.
This can always be checked statically, so this method returns nothing.
Args:
x: A `Tensor`.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_integer".
Raises:
TypeError: If `x.dtype` is not a non-quantized integer type. | Assert that `x` is of integer dtype. | [
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"""Assert that `x` is of integer dtype.
If `x` has a non-integer type, `message`, as well as the dtype of `x` are
printed, and `InvalidArgumentError` is raised.
This can always be checked statically, so this method returns nothing.
Args:
x: A `Tensor`.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_integer".
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||
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Node/Python.py | python | ValueNodeInfo.__getstate__ | (self) | return state | Return all fields that shall be pickled. Walk the slots in the class
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|
ideawu/ssdb-rocks | a3cbb322cafb2f493252829c608e2239df98c9ac | deps/cpy/antlr3/tree.py | python | TreeAdaptor.deleteChild | (self, t, i) | Remove ith child and shift children down from right. | Remove ith child and shift children down from right. | [
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"""Remove ith child and shift children down from right."""
raise NotImplementedError | [
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||
google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/constraint_solver/samples/cvrptw.py | python | create_distance_evaluator | (data) | return distance_evaluator | Creates callback to return distance between points. | Creates callback to return distance between points. | [
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] | def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in range(data['num_locations']):
_distances[from_node] = {}
for to_node in range(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data['locations'][from_node], data['locations'][to_node]))
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/nntplib.py | python | _NNTPBase.getwelcome | (self) | return self.welcome | Get the welcome message from the server
(this is read and squirreled away by __init__()).
If the response code is 200, posting is allowed;
if it 201, posting is not allowed. | Get the welcome message from the server
(this is read and squirreled away by __init__()).
If the response code is 200, posting is allowed;
if it 201, posting is not allowed. | [
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"""Get the welcome message from the server
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If the response code is 200, posting is allowed;
if it 201, posting is not allowed."""
if self.debugging: print('*welcome*', repr(self.welcome))
return self.welcome | [
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|
apache/mesos | 97d9a4063332aae3825d78de71611657e05cf5e2 | support/verify-reviews.py | python | main | () | Main function to verify the submitted reviews. | Main function to verify the submitted reviews. | [
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"""Main function to verify the submitted reviews."""
review_requests_url = \
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review_requests = api(review_requests_url)
review_ids = []
for review_request in reversed(review_requests["review_requests"]):
if (NUM_REVIEWS == -1 or len(review_ids) < NUM_REVIEWS) and \
needs_verification(review_request):
if not SKIP_VERIFY:
verify_review(review_request)
review_ids.append(str(review_request["id"]))
write_review_ids(review_ids) | [
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||
hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/ops/nn_ops.py | python | _calc_bias_add_flops | (graph, node) | return ops.OpStats("flops", input_count) | Calculates the computing needed for BiasAdd. | Calculates the computing needed for BiasAdd. | [
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] | def _calc_bias_add_flops(graph, node):
"""Calculates the computing needed for BiasAdd."""
input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
input_shape.assert_is_fully_defined()
input_count = np.prod(input_shape.as_list())
return ops.OpStats("flops", input_count) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/mongosymb.py | python | PathDbgFileResolver.__init__ | (self, bin_path_guess) | Initialize PathDbgFileResolver. | Initialize PathDbgFileResolver. | [
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] | def __init__(self, bin_path_guess):
"""Initialize PathDbgFileResolver."""
self._bin_path_guess = os.path.realpath(bin_path_guess)
self.mci_build_dir = None | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/framework/tensor_shape.py | python | Dimension.__gt__ | (self, other) | Returns True if `self` is known to be greater than `other`.
Dimensions are compared as follows:
Dimension(m) > Dimension(n) == m > n
Dimension(m) > Dimension(None) == None
Dimension(None) > Dimension(n) == None
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"""Returns True if `self` is known to be greater than `other`.
Dimensions are compared as follows:
Dimension(m) > Dimension(n) == m > n
Dimension(m) > Dimension(None) == None
Dimension(None) > Dimension(n) == None
Dimension(None) > Dimension(None) == None
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other: Another Dimension.
Returns:
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"""
other = as_dimension(other)
if self._value is None or other.value is None:
return None
else:
return self._value > other.value | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/download.py | python | unpack_file_url | (link, location, download_dir=None) | Unpack link into location.
If download_dir is provided and link points to a file, make a copy
of the link file inside download_dir. | Unpack link into location.
If download_dir is provided and link points to a file, make a copy
of the link file inside download_dir. | [
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] | def unpack_file_url(link, location, download_dir=None):
"""Unpack link into location.
If download_dir is provided and link points to a file, make a copy
of the link file inside download_dir."""
link_path = url_to_path(link.url_without_fragment)
# If it's a url to a local directory
if os.path.isdir(link_path):
if os.path.isdir(location):
rmtree(location)
shutil.copytree(link_path, location, symlinks=True)
if download_dir:
logger.info('Link is a directory, ignoring download_dir')
return
# if link has a hash, let's confirm it matches
if link.hash:
link_path_hash = _get_hash_from_file(link_path, link)
_check_hash(link_path_hash, link)
# If a download dir is specified, is the file already there and valid?
already_downloaded_path = None
if download_dir:
already_downloaded_path = _check_download_dir(link, download_dir)
if already_downloaded_path:
from_path = already_downloaded_path
else:
from_path = link_path
content_type = mimetypes.guess_type(from_path)[0]
# unpack the archive to the build dir location. even when only downloading
# archives, they have to be unpacked to parse dependencies
unpack_file(from_path, location, content_type, link)
# a download dir is specified and not already downloaded
if download_dir and not already_downloaded_path:
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||
TimoSaemann/caffe-segnet-cudnn5 | abcf30dca449245e101bf4ced519f716177f0885 | scripts/cpp_lint.py | python | _ShouldPrintError | (category, confidence, linenum) | return True | If confidence >= verbose, category passes filter and is not suppressed. | If confidence >= verbose, category passes filter and is not suppressed. | [
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"""If confidence >= verbose, category passes filter and is not suppressed."""
# There are three ways we might decide not to print an error message:
# a "NOLINT(category)" comment appears in the source,
# the verbosity level isn't high enough, or the filters filter it out.
if IsErrorSuppressedByNolint(category, linenum):
return False
if confidence < _cpplint_state.verbose_level:
return False
is_filtered = False
for one_filter in _Filters():
if one_filter.startswith('-'):
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elif one_filter.startswith('+'):
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/idl_parser/idl_parser.py | python | IDLParser.p_TypeSuffix | (self, p) | TypeSuffix : '[' integer ']' TypeSuffix
| '[' ']' TypeSuffix
| '?' TypeSuffixStartingWithArray
| | TypeSuffix : '[' integer ']' TypeSuffix
| '[' ']' TypeSuffix
| '?' TypeSuffixStartingWithArray
| | [
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] | def p_TypeSuffix(self, p):
"""TypeSuffix : '[' integer ']' TypeSuffix
| '[' ']' TypeSuffix
| '?' TypeSuffixStartingWithArray
| """
if len(p) == 5:
p[0] = self.BuildNamed('Array', p, 2, p[4])
if len(p) == 4:
p[0] = self.BuildProduction('Array', p, 1, p[3])
if len(p) == 3:
p[0] = ListFromConcat(self.BuildTrue('NULLABLE'), p[2]) | [
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||
omnisci/omniscidb | b9c95f1bd602b4ffc8b0edf18bfad61031e08d86 | python/omnisci/thrift/OmniSci.py | python | Client.get_license_claims | (self, session, nonce) | return self.recv_get_license_claims() | Parameters:
- session
- nonce | Parameters:
- session
- nonce | [
"Parameters",
":",
"-",
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"""
Parameters:
- session
- nonce
"""
self.send_get_license_claims(session, nonce)
return self.recv_get_license_claims() | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/contexts/fitting_contexts/basic_fitting_context.py | python | BasicFittingContext.chi_squared | (self) | return self._chi_squared | Returns all of the chi squared values. | Returns all of the chi squared values. | [
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"""Returns all of the chi squared values."""
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|
oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/MSVSUserFile.py | python | Writer.AddConfig | (self, name) | Adds a configuration to the project.
Args:
name: Configuration name. | Adds a configuration to the project. | [
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"""Adds a configuration to the project.
Args:
name: Configuration name.
"""
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Image.InsertHandler | (*args, **kwargs) | return _core_.Image_InsertHandler(*args, **kwargs) | InsertHandler(ImageHandler handler) | InsertHandler(ImageHandler handler) | [
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|
hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/ndarray/ndarray.py | python | NDArray.__gt__ | (self, other) | return greater(self, other) | x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) | x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) | [
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|
stitchEm/stitchEm | 0f399501d41ab77933677f2907f41f80ceb704d7 | lib/bindings/samples/server/glfw.py | python | _GLFWimage.unwrap | (self) | return self.width, self.height, pixels | Returns a nested python sequence. | Returns a nested python sequence. | [
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] | def unwrap(self):
"""
Returns a nested python sequence.
"""
pixels = [[[int(c) for c in p] for p in l] for l in self.pixels_array]
return self.width, self.height, pixels | [
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/linalg/python/ops/linear_operator_util.py | python | matrix_adjoint | (a, name="matrix_adjoint") | Transposes last two dimensions of tensor `a`, and takes complex conjugate.
If `a` is real valued, the result is equivalent to `matrix_transpose`.
For example:
```python
# Matrix with no batch dimension.
# 'x' is [[1 2 3j]
# [4 5 -6j]]
tf.matrix_adjoint(x) ==> [[1 4]
[2 5]
[-3j 6j]]
# Matrix with two batch dimensions.
# x.shape is [1, 2, 3, 4]
# tf.matrix_adjoint(x) is shape [1, 2, 4, 3]
```
Note that `tf.matmul` provides kwargs allowing for adjoint of arguments. This
is done with minimal cost, and is preferable to using this function. E.g.
```
# Good! Adjoint is taken at minimal additional cost.
tf.matmul(matrix, b, adjoint_b=True)
# Inefficient!
tf.matmul(matrix, tf.matrix_adjoint(b))
```
Args:
a: A `Tensor` with `rank >= 2`.
name: A name for the operation (optional).
Returns:
A batch matrix `Tensor` with same `dtype` as `a`.
Raises:
ValueError: If `a` is determined statically to have `rank < 2`. | Transposes last two dimensions of tensor `a`, and takes complex conjugate. | [
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"""Transposes last two dimensions of tensor `a`, and takes complex conjugate.
If `a` is real valued, the result is equivalent to `matrix_transpose`.
For example:
```python
# Matrix with no batch dimension.
# 'x' is [[1 2 3j]
# [4 5 -6j]]
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name: A name for the operation (optional).
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A batch matrix `Tensor` with same `dtype` as `a`.
Raises:
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"""
with ops.name_scope(name, values=[a]):
a = ops.convert_to_tensor(a, name="a")
a_transpose = array_ops.matrix_transpose(a)
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||
grpc/grpc | 27bc6fe7797e43298dc931b96dc57322d0852a9f | examples/python/cancellation/search.py | python | _bytestrings_of_length | (length) | Generates a stream containing all bytestrings of a given length.
Args:
length: A positive integer length.
Yields:
All bytestrings of length `length`. | Generates a stream containing all bytestrings of a given length. | [
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"""Generates a stream containing all bytestrings of a given length.
Args:
length: A positive integer length.
Yields:
All bytestrings of length `length`.
"""
for digits in itertools.product(range(_BYTE_MAX), repeat=length):
yield b''.join(struct.pack('B', i) for i in digits) | [
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||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/learn/python/learn/estimators/run_config.py | python | ClusterConfig.get_task_id | () | return int(task_index) if task_index else 0 | Returns task index from `TF_CONFIG` environmental variable.
If you have a ClusterConfig instance, you can just access its task_id
property instead of calling this function and re-parsing the environmental
variable.
Returns:
`TF_CONFIG['task']['index']`. Defaults to 0. | Returns task index from `TF_CONFIG` environmental variable. | [
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"""Returns task index from `TF_CONFIG` environmental variable.
If you have a ClusterConfig instance, you can just access its task_id
property instead of calling this function and re-parsing the environmental
variable.
Returns:
`TF_CONFIG['task']['index']`. Defaults to 0.
"""
config = json.loads(os.environ.get('TF_CONFIG') or '{}')
task_env = config.get('task', {})
task_index = task_env.get('index')
return int(task_index) if task_index else 0 | [
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|
libornovax/master_thesis_code | 6eca474ed3cae673afde010caef338cf7349f839 | scripts/nets/macc_net_generator.py | python | MACCNetGenerator._add_layer | (self, line, outfile, deploy) | Adds one layer to the PROTOTXT file specified by the line.
Input:
line: string with layer description (one line from the config file)
outfile: File handle into which we will write the layer
deploy: True/False | Adds one layer to the PROTOTXT file specified by the line. | [
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] | def _add_layer(self, line, outfile, deploy):
"""
Adds one layer to the PROTOTXT file specified by the line.
Input:
line: string with layer description (one line from the config file)
outfile: File handle into which we will write the layer
deploy: True/False
"""
layer_type = line[:4]
if layer_type == 'conv':
# Convolutional layer
outfile.write(self._layer_conv(line, deploy))
outfile.write(self._layer_relu())
elif layer_type == 'pool':
# Pooling layer
outfile.write(self._layer_pool())
elif layer_type == 'macc':
# Multiscale accumulator - this is also a convolutional layer, but with
# 1 output channel
outfile.write(self._layer_macc(line, deploy)) | [
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||
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/drill/view/DrillTableWidget.py | python | DrillTableWidget.eraseRow | (self, position) | Erase the contents of a whole row (if it exists).
Args:
position (int): row index | Erase the contents of a whole row (if it exists). | [
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] | def eraseRow(self, position):
"""
Erase the contents of a whole row (if it exists).
Args:
position (int): row index
"""
if self._disabled:
return
n_rows = self.rowCount()
if ((position < 0) or (position >= n_rows)):
return
for column in range(self.columnCount()):
item = self.item(position, column)
if item:
item.setData(Qt.EditRole, None) | [
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