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mapsme/omim | 1892903b63f2c85b16ed4966d21fe76aba06b9ba | tools/python/maps_generator/generator/stages.py | python | country_stage_status | (stage: Type[Stage]) | return stage | It's helper decorator that works with status file. | It's helper decorator that works with status file. | [
"It",
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"decorator",
"that",
"works",
"with",
"status",
"file",
"."
] | def country_stage_status(stage: Type[Stage]) -> Type[Stage]:
"""It's helper decorator that works with status file."""
def new_apply(method):
def apply(obj: Stage, env: "Env", country: AnyStr, *args, **kwargs):
name = get_stage_name(obj)
_logger = DummyObject()
countries_meta = env.countries_meta
if "logger" in countries_meta[country]:
_logger, _ = countries_meta[country]["logger"]
if not env.is_accepted_stage(stage):
_logger.info(f"Stage {name} was not accepted.")
return
if "status" not in countries_meta[country]:
countries_meta[country]["status"] = status.Status()
country_status = countries_meta[country]["status"]
status_file = os.path.join(
env.paths.status_path, status.with_stat_ext(country)
)
country_status.init(status_file, name)
if country_status.need_skip():
_logger.warning(f"Stage {name} was skipped.")
return
country_status.update_status()
method(obj, env, country, *args, **kwargs)
return apply
stage.apply = new_apply(stage.apply)
return stage | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/archive_util.py | python | unpack_directory | (filename, extract_dir, progress_filter=default_filter) | Unpack" a directory, using the same interface as for archives
Raises ``UnrecognizedFormat`` if `filename` is not a directory | Unpack" a directory, using the same interface as for archives | [
"Unpack",
"a",
"directory",
"using",
"the",
"same",
"interface",
"as",
"for",
"archives"
] | def unpack_directory(filename, extract_dir, progress_filter=default_filter):
""""Unpack" a directory, using the same interface as for archives
Raises ``UnrecognizedFormat`` if `filename` is not a directory
"""
if not os.path.isdir(filename):
raise UnrecognizedFormat("%s is not a directory" % filename)
paths = {
filename: ('', extract_dir),
}
for base, dirs, files in os.walk(filename):
src, dst = paths[base]
for d in dirs:
paths[os.path.join(base, d)] = src + d + '/', os.path.join(dst, d)
for f in files:
target = os.path.join(dst, f)
target = progress_filter(src + f, target)
if not target:
# skip non-files
continue
ensure_directory(target)
f = os.path.join(base, f)
shutil.copyfile(f, target)
shutil.copystat(f, target) | [
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lilypond/lilypond | 2a14759372979f5b796ee802b0ee3bc15d28b06b | scripts/build/output-distance.py | python | FileLink.name | (self) | return os.path.join(self.prefix(), base) | Returns the \\sourcefilename for this test file | Returns the \\sourcefilename for this test file | [
"Returns",
"the",
"\\\\",
"sourcefilename",
"for",
"this",
"test",
"file"
] | def name(self) -> str:
"""Returns the \\sourcefilename for this test file"""
base = os.path.basename(self.file_names[1])
base = os.path.splitext(base)[0]
base = hash_to_original_name.get(base, base)
base = os.path.splitext(base)[0]
return os.path.join(self.prefix(), base) | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/graph_editor/edit.py | python | detach_outputs | (sgv, control_outputs=None) | return sgv_, output_placeholders | Detach the output of a subgraph view.
Args:
sgv: the subgraph view to be detached. This argument is converted to a
subgraph using the same rules as the function subgraph.make_view.
Note that sgv is modified in place.
control_outputs: a util.ControlOutputs instance or None. If not None the
control outputs are also detached.
Returns:
A tuple `(sgv, output_placeholders)` where
`sgv` is a new subgraph view of the detached subgraph;
`output_placeholders` is a list of the created output placeholders.
Raises:
StandardError: if sgv cannot be converted to a SubGraphView using
the same rules than the function subgraph.make_view. | Detach the output of a subgraph view. | [
"Detach",
"the",
"output",
"of",
"a",
"subgraph",
"view",
"."
] | def detach_outputs(sgv, control_outputs=None):
"""Detach the output of a subgraph view.
Args:
sgv: the subgraph view to be detached. This argument is converted to a
subgraph using the same rules as the function subgraph.make_view.
Note that sgv is modified in place.
control_outputs: a util.ControlOutputs instance or None. If not None the
control outputs are also detached.
Returns:
A tuple `(sgv, output_placeholders)` where
`sgv` is a new subgraph view of the detached subgraph;
`output_placeholders` is a list of the created output placeholders.
Raises:
StandardError: if sgv cannot be converted to a SubGraphView using
the same rules than the function subgraph.make_view.
"""
sgv = subgraph.make_view(sgv)
# only select outputs with consumers
sgv_ = sgv.remap_outputs([output_id
for output_id, output_t in enumerate(sgv.outputs)
if output_t.consumers()])
# create consumer subgraph and remap
consumers_sgv = subgraph.SubGraphView(sgv_.consumers())
consumers_sgv = consumers_sgv.remap_inputs(
[input_id for input_id, input_t in enumerate(consumers_sgv.inputs)
if input_t in sgv_.outputs])
with sgv_.graph.as_default():
output_placeholders = [
util.make_placeholder_from_tensor(input_t)
for input_t in consumers_sgv.inputs
]
reroute.swap_outputs(sgv_, output_placeholders)
if control_outputs is not None:
detach_control_outputs(sgv_, control_outputs)
return sgv_, output_placeholders | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/datasets/_twenty_newsgroups.py | python | strip_newsgroup_header | (text) | return after | Given text in "news" format, strip the headers, by removing everything
before the first blank line.
Parameters
----------
text : string
The text from which to remove the signature block. | Given text in "news" format, strip the headers, by removing everything
before the first blank line. | [
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"strip",
"the",
"headers",
"by",
"removing",
"everything",
"before",
"the",
"first",
"blank",
"line",
"."
] | def strip_newsgroup_header(text):
"""
Given text in "news" format, strip the headers, by removing everything
before the first blank line.
Parameters
----------
text : string
The text from which to remove the signature block.
"""
_before, _blankline, after = text.partition('\n\n')
return after | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/lookup_ops.py | python | InitializableLookupTableBase.__init__ | (self, default_value, initializer) | Construct a table object from a table reference.
If requires a table initializer object (subclass of `TableInitializerBase`).
It provides the table key and value types, as well as the op to initialize
the table. The caller is responsible to execute the initialization op.
Args:
default_value: The value to use if a key is missing in the table.
initializer: The table initializer to use. | Construct a table object from a table reference. | [
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"table",
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"."
] | def __init__(self, default_value, initializer):
"""Construct a table object from a table reference.
If requires a table initializer object (subclass of `TableInitializerBase`).
It provides the table key and value types, as well as the op to initialize
the table. The caller is responsible to execute the initialization op.
Args:
default_value: The value to use if a key is missing in the table.
initializer: The table initializer to use.
"""
super(InitializableLookupTableBase, self).__init__(initializer.key_dtype,
initializer.value_dtype)
self._default_value = ops.convert_to_tensor(
default_value, dtype=self._value_dtype)
self._default_value.get_shape().merge_with(tensor_shape.TensorShape([]))
if isinstance(initializer, trackable_base.Trackable):
self._initializer = self._track_trackable(initializer, "_initializer")
with ops.init_scope():
self._resource_handle = self._create_resource()
if (not context.executing_eagerly() and
ops.get_default_graph()._get_control_flow_context() is not None): # pylint: disable=protected-access
with ops.init_scope():
self._init_op = self._initialize()
else:
self._init_op = self._initialize() | [
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themighty1/lpfw | 7880c0a1aac26ecc33259633c0f866a157ab491e | gui/gui.py | python | queryDialog.closeEvent | (self, event) | in case when user closed the dialog without pressing allow or deny | in case when user closed the dialog without pressing allow or deny | [
"in",
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"when",
"user",
"closed",
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"or",
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] | def closeEvent(self, event):
"in case when user closed the dialog without pressing allow or deny"
print "in closeEvent"
msgQueue.put('ADD ' + b64encode(bytearray(self.path, encoding='utf-8')) + ' ' + self.pid + ' IGNORED') | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/more-itertools/py2/more_itertools/more.py | python | divide | (n, iterable) | return ret | Divide the elements from *iterable* into *n* parts, maintaining
order.
>>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6])
>>> list(group_1)
[1, 2, 3]
>>> list(group_2)
[4, 5, 6]
If the length of *iterable* is not evenly divisible by *n*, then the
length of the returned iterables will not be identical:
>>> children = divide(3, [1, 2, 3, 4, 5, 6, 7])
>>> [list(c) for c in children]
[[1, 2, 3], [4, 5], [6, 7]]
If the length of the iterable is smaller than n, then the last returned
iterables will be empty:
>>> children = divide(5, [1, 2, 3])
>>> [list(c) for c in children]
[[1], [2], [3], [], []]
This function will exhaust the iterable before returning and may require
significant storage. If order is not important, see :func:`distribute`,
which does not first pull the iterable into memory. | Divide the elements from *iterable* into *n* parts, maintaining
order. | [
"Divide",
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"from",
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"*",
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"."
] | def divide(n, iterable):
"""Divide the elements from *iterable* into *n* parts, maintaining
order.
>>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6])
>>> list(group_1)
[1, 2, 3]
>>> list(group_2)
[4, 5, 6]
If the length of *iterable* is not evenly divisible by *n*, then the
length of the returned iterables will not be identical:
>>> children = divide(3, [1, 2, 3, 4, 5, 6, 7])
>>> [list(c) for c in children]
[[1, 2, 3], [4, 5], [6, 7]]
If the length of the iterable is smaller than n, then the last returned
iterables will be empty:
>>> children = divide(5, [1, 2, 3])
>>> [list(c) for c in children]
[[1], [2], [3], [], []]
This function will exhaust the iterable before returning and may require
significant storage. If order is not important, see :func:`distribute`,
which does not first pull the iterable into memory.
"""
if n < 1:
raise ValueError('n must be at least 1')
seq = tuple(iterable)
q, r = divmod(len(seq), n)
ret = []
for i in range(n):
start = (i * q) + (i if i < r else r)
stop = ((i + 1) * q) + (i + 1 if i + 1 < r else r)
ret.append(iter(seq[start:stop]))
return ret | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/graph_editor/reroute.py | python | _RerouteMode.check | (cls, mode) | Check swap mode.
Args:
mode: an integer representing one of the modes.
Returns:
True if a is rerouted to b (mode is swap or a2b).
True if b is rerouted to a (mode is swap or b2a).
Raises:
ValueError: if mode is outside the enum range. | Check swap mode. | [
"Check",
"swap",
"mode",
"."
] | def check(cls, mode):
"""Check swap mode.
Args:
mode: an integer representing one of the modes.
Returns:
True if a is rerouted to b (mode is swap or a2b).
True if b is rerouted to a (mode is swap or b2a).
Raises:
ValueError: if mode is outside the enum range.
"""
if mode == cls.swap:
return True, True
elif mode == cls.b2a:
return False, True
elif mode == cls.a2b:
return True, False
else:
raise ValueError("Unknown _RerouteMode: {}".format(mode)) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/distribute/distributed_training_utils_v1.py | python | _build_network_on_replica | (model, mode, inputs=None, targets=None) | return updated_model | Build an updated model on replicas.
We create a new Keras model while sharing the variables from the old graph.
Building a new sub-graph is required since the original keras model creates
placeholders for the input and the output that are not accessible till we
call iterator.get_next() inside the step_fn for `fit`/`evaluate`/`predict`.
The sharing of weights and layers between the old and the new model guarantee
that we're using Strategy variables and any updates on either model are
reflected correctly in callbacks and loop iterations.
We need to make sure we share the optimizers between the old and the new model
as well so that optimizer state is not lost if the user is running fit
multiple times.
Args:
model: Model to be replicated across Replicas
mode: Which of fit/eval/predict is building the distributed network
inputs: Input variables to be passed to the model
targets: Target tensor to be passed to model.compile
Returns:
A new model with shared layers with the old model. | Build an updated model on replicas. | [
"Build",
"an",
"updated",
"model",
"on",
"replicas",
"."
] | def _build_network_on_replica(model, mode, inputs=None, targets=None):
"""Build an updated model on replicas.
We create a new Keras model while sharing the variables from the old graph.
Building a new sub-graph is required since the original keras model creates
placeholders for the input and the output that are not accessible till we
call iterator.get_next() inside the step_fn for `fit`/`evaluate`/`predict`.
The sharing of weights and layers between the old and the new model guarantee
that we're using Strategy variables and any updates on either model are
reflected correctly in callbacks and loop iterations.
We need to make sure we share the optimizers between the old and the new model
as well so that optimizer state is not lost if the user is running fit
multiple times.
Args:
model: Model to be replicated across Replicas
mode: Which of fit/eval/predict is building the distributed network
inputs: Input variables to be passed to the model
targets: Target tensor to be passed to model.compile
Returns:
A new model with shared layers with the old model.
"""
# Need to do imports here since we run into a circular dependency error.
from tensorflow.python.keras import models # pylint: disable=g-import-not-at-top
from tensorflow.python.keras.engine import sequential # pylint: disable=g-import-not-at-top
# We rely on the internal methods to avoid having share_weights weights in the
# public API.
if isinstance(model, sequential.Sequential):
updated_model = models._clone_sequential_model(
model, input_tensors=inputs, layer_fn=models.share_weights)
else:
updated_model = models._clone_functional_model(
model, input_tensors=inputs, layer_fn=models.share_weights)
# Callable losses added directly to a functional Model need to be added
# here.
updated_model._callable_losses = model._callable_losses
# Recast all low precision outputs back to float32 since we only casted
# the inputs to bfloat16 and not targets. This is done so that we can preserve
# precision when calculating the loss value.
def _upcast_low_precision_outputs(output):
if output.dtype == dtypes.bfloat16:
return math_ops.cast(output, dtypes.float32)
else:
return output
updated_model.outputs = [_upcast_low_precision_outputs(o)
for o in updated_model.outputs]
if isinstance(targets, tuple):
targets = nest.flatten(targets)
if mode == ModeKeys.PREDICT and inputs is not None: # TPU predict case
_custom_compile_for_predict(updated_model)
else:
updated_model.compile(
model.optimizer,
model.loss,
metrics=metrics_module.clone_metrics(model._compile_metrics),
loss_weights=model.loss_weights,
sample_weight_mode=model.sample_weight_mode,
weighted_metrics=metrics_module.clone_metrics(
model._compile_weighted_metrics),
target_tensors=targets)
return updated_model | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | JoystickEvent.SetButtonState | (*args, **kwargs) | return _misc_.JoystickEvent_SetButtonState(*args, **kwargs) | SetButtonState(self, int state) | SetButtonState(self, int state) | [
"SetButtonState",
"(",
"self",
"int",
"state",
")"
] | def SetButtonState(*args, **kwargs):
"""SetButtonState(self, int state)"""
return _misc_.JoystickEvent_SetButtonState(*args, **kwargs) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/reduce4circleControl.py | python | CWSCDReductionControl.get_raw_data_workspace | (self, exp_no, scan_no, pt_no) | return ws | Get raw workspace | Get raw workspace | [
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"raw",
"workspace"
] | def get_raw_data_workspace(self, exp_no, scan_no, pt_no):
""" Get raw workspace
"""
try:
ws = self._myRawDataWSDict[(exp_no, scan_no, pt_no)]
assert isinstance(ws, mantid.dataobjects.Workspace2D)
except KeyError:
return None
return ws | [
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yuxng/DA-RNN | 77fbb50b4272514588a10a9f90b7d5f8d46974fb | lib/datasets/shapenet_single.py | python | shapenet_single.gt_roidb | (self) | return gt_roidb | Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls. | Return the database of ground-truth regions of interest. | [
"Return",
"the",
"database",
"of",
"ground",
"-",
"truth",
"regions",
"of",
"interest",
"."
] | def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = [self._load_shapenet_single_annotation(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/turtle.py | python | TNavigator.xcor | (self) | return self._position[0] | Return the turtle's x coordinate.
No arguments.
Example (for a Turtle instance named turtle):
>>> reset()
>>> turtle.left(60)
>>> turtle.forward(100)
>>> print turtle.xcor()
50.0 | Return the turtle's x coordinate. | [
"Return",
"the",
"turtle",
"s",
"x",
"coordinate",
"."
] | def xcor(self):
""" Return the turtle's x coordinate.
No arguments.
Example (for a Turtle instance named turtle):
>>> reset()
>>> turtle.left(60)
>>> turtle.forward(100)
>>> print turtle.xcor()
50.0
"""
return self._position[0] | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Node/FS.py | python | FS.Repository | (self, *dirs) | Specify Repository directories to search. | Specify Repository directories to search. | [
"Specify",
"Repository",
"directories",
"to",
"search",
"."
] | def Repository(self, *dirs):
"""Specify Repository directories to search."""
for d in dirs:
if not isinstance(d, SCons.Node.Node):
d = self.Dir(d)
self.Top.addRepository(d) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/metrics/cluster/bicluster.py | python | consensus_score | (a, b, similarity="jaccard") | return matrix[indices[:, 0], indices[:, 1]].sum() / max(n_a, n_b) | The similarity of two sets of biclusters.
Similarity between individual biclusters is computed. Then the
best matching between sets is found using the Hungarian algorithm.
The final score is the sum of similarities divided by the size of
the larger set.
Read more in the :ref:`User Guide <biclustering>`.
Parameters
----------
a : (rows, columns)
Tuple of row and column indicators for a set of biclusters.
b : (rows, columns)
Another set of biclusters like ``a``.
similarity : string or function, optional, default: "jaccard"
May be the string "jaccard" to use the Jaccard coefficient, or
any function that takes four arguments, each of which is a 1d
indicator vector: (a_rows, a_columns, b_rows, b_columns).
References
----------
* Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
for bicluster acquisition
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__. | The similarity of two sets of biclusters. | [
"The",
"similarity",
"of",
"two",
"sets",
"of",
"biclusters",
"."
] | def consensus_score(a, b, similarity="jaccard"):
"""The similarity of two sets of biclusters.
Similarity between individual biclusters is computed. Then the
best matching between sets is found using the Hungarian algorithm.
The final score is the sum of similarities divided by the size of
the larger set.
Read more in the :ref:`User Guide <biclustering>`.
Parameters
----------
a : (rows, columns)
Tuple of row and column indicators for a set of biclusters.
b : (rows, columns)
Another set of biclusters like ``a``.
similarity : string or function, optional, default: "jaccard"
May be the string "jaccard" to use the Jaccard coefficient, or
any function that takes four arguments, each of which is a 1d
indicator vector: (a_rows, a_columns, b_rows, b_columns).
References
----------
* Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
for bicluster acquisition
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__.
"""
if similarity == "jaccard":
similarity = _jaccard
matrix = _pairwise_similarity(a, b, similarity)
indices = linear_assignment(1. - matrix)
n_a = len(a[0])
n_b = len(b[0])
return matrix[indices[:, 0], indices[:, 1]].sum() / max(n_a, n_b) | [
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libfive/libfive | ab5e354cf6fd992f80aaa9432c52683219515c8a | libfive/bind/python/libfive/stdlib/transforms.py | python | rotate_y | (t, angle, center=(0, 0, 0)) | return Shape(stdlib.rotate_y(
args[0].ptr,
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tvec3(*[a.ptr for a in args[2]]))) | Rotate the given shape by an angle in radians
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | Float16 | (ctx=None) | return FPSortRef(Z3_mk_fpa_sort_16(ctx.ref()), ctx) | Floating-point 16-bit (half) sort. | Floating-point 16-bit (half) sort. | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TMemOut_New | (*args) | return _snap.TMemOut_New(*args) | TMemOut_New(PMem const & Mem) -> PSOut
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/zipfile.py | python | _EndRecData | (fpin) | return None | Return data from the "End of Central Directory" record, or None.
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endrec.append(comment)
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/third_party/jinja2/compiler.py | python | Frame.soft | (self) | return rv | Return a soft frame. A soft frame may not be modified as
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/pylinters.py | python | lint_all | (linters, config_dict, file_names) | Lint files command entry point based on working tree. | Lint files command entry point based on working tree. | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/gyp/pylib/gyp/xcodeproj_file.py | python | PBXProject.RootGroupsTakeOverOnlyChildren | (self, recurse=False) | Calls TakeOverOnlyChild for all groups in the main group. | Calls TakeOverOnlyChild for all groups in the main group. | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/ordered_dict.py | python | OrderedDict.__delitem__ | (self, key, dict_delitem=dict.__delitem__) | od.__delitem__(y) <==> del od[y] | od.__delitem__(y) <==> del od[y] | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/dumbdbm.py | python | open | (file, flag=None, mode=0666) | return _Database(file, mode) | Open the database file, filename, and return corresponding object.
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database is always opened for update, and will be created if it does
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database is always opened for update, and will be created if it does
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/optimize/lbfgsb.py | python | LbfgsInvHessProduct.__init__ | (self, sk, yk) | Construct the operator. | Construct the operator. | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_grad.py | python | _MeanGrad | (op, grad) | return math_ops.truediv(sum_grad, math_ops.cast(factor, sum_grad.dtype)), None | Gradient for Mean. | Gradient for Mean. | [
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] | def _MeanGrad(op, grad):
"""Gradient for Mean."""
sum_grad = _SumGrad(op, grad)[0]
input_shape = op.inputs[0]._shape_tuple() # pylint: disable=protected-access
output_shape = op.outputs[0]._shape_tuple() # pylint: disable=protected-access
if (input_shape is not None and output_shape is not None and
None not in input_shape and None not in output_shape):
input_size = np.prod(input_shape)
output_size = np.prod(output_shape)
factor = input_size // max(output_size, 1)
factor = constant_op.constant(factor, dtype=sum_grad.dtype)
else:
input_shape = array_ops.shape(op.inputs[0])
output_shape = array_ops.shape(op.outputs[0])
factor = _safe_shape_div(
math_ops.reduce_prod(input_shape), math_ops.reduce_prod(output_shape))
return math_ops.truediv(sum_grad, math_ops.cast(factor, sum_grad.dtype)), None | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/dataflow.py | python | DataFlowAnalysis.op_STORE_SLICE_1 | (self, info, inst) | TOS1[TOS:] = TOS2 | TOS1[TOS:] = TOS2 | [
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] | def op_STORE_SLICE_1(self, info, inst):
"""
TOS1[TOS:] = TOS2
"""
tos = info.pop()
tos1 = info.pop()
value = info.pop()
slicevar = info.make_temp()
indexvar = info.make_temp()
nonevar = info.make_temp()
info.append(inst, base=tos1, start=tos, slicevar=slicevar,
value=value, indexvar=indexvar, nonevar=nonevar) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/dnn.py | python | DNNClassifier.__init__ | (self,
hidden_units,
feature_columns=None,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=True,
config=None) | Initializes a DNNClassifier instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can also
be used to load checkpoints from the directory into a estimator to continue
training a previously saved model.
n_classes: number of target classes. Default is binary classification.
It must be greater than 1.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`.
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are
clipped to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
Returns:
A `DNNClassifier` estimator. | Initializes a DNNClassifier instance. | [
"Initializes",
"a",
"DNNClassifier",
"instance",
"."
] | def __init__(self,
hidden_units,
feature_columns=None,
model_dir=None,
n_classes=2,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=True,
config=None):
"""Initializes a DNNClassifier instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can also
be used to load checkpoints from the directory into a estimator to continue
training a previously saved model.
n_classes: number of target classes. Default is binary classification.
It must be greater than 1.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`.
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are
clipped to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
Returns:
A `DNNClassifier` estimator.
"""
_changing(feature_columns)
super(DNNClassifier, self).__init__(
model_dir=model_dir,
n_classes=n_classes,
weight_column_name=weight_column_name,
dnn_feature_columns=feature_columns,
dnn_optimizer=optimizer,
dnn_hidden_units=hidden_units,
dnn_activation_fn=activation_fn,
dnn_dropout=dropout,
gradient_clip_norm=gradient_clip_norm,
enable_centered_bias=enable_centered_bias,
config=config)
self.feature_columns = feature_columns
self.optimizer = optimizer
self.activation_fn = activation_fn
self.dropout = dropout
self.hidden_units = hidden_units
self._feature_columns_inferred = False | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TIntHI.IsEnd | (self) | return _snap.TIntHI_IsEnd(self) | IsEnd(TIntHI self) -> bool
Parameters:
self: THashKeyDatI< TInt,TInt > const * | IsEnd(TIntHI self) -> bool | [
"IsEnd",
"(",
"TIntHI",
"self",
")",
"-",
">",
"bool"
] | def IsEnd(self):
"""
IsEnd(TIntHI self) -> bool
Parameters:
self: THashKeyDatI< TInt,TInt > const *
"""
return _snap.TIntHI_IsEnd(self) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBAddress.GetOffset | (self) | return _lldb.SBAddress_GetOffset(self) | GetOffset(self) -> addr_t | GetOffset(self) -> addr_t | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | uCSIsOsmanya | (code) | return ret | Check whether the character is part of Osmanya UCS Block | Check whether the character is part of Osmanya UCS Block | [
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"""Check whether the character is part of Osmanya UCS Block """
ret = libxml2mod.xmlUCSIsOsmanya(code)
return ret | [
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infinit/elle | a8154593c42743f45b9df09daf62b44630c24a02 | drake/src/drake/__init__.py | python | BaseNode.name_relative | (self) | return self.__name.name_relative | Node name, relative to the current drakefile. | Node name, relative to the current drakefile. | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/training/saver.py | python | saver_from_object_based_checkpoint | (checkpoint_path,
var_list=None,
builder=None,
names_to_keys=None,
cached_saver=None) | return cached_saver | Return a `Saver` which reads from an object-based checkpoint.
This function validates that all variables in the variables list are remapped
in the object-based checkpoint (or `names_to_keys` dict if provided). A
saver will be created with the list of remapped variables.
The `cached_saver` argument allows the user to pass in a previously created
saver, so multiple `saver.restore()` calls don't pollute the graph when graph
building. This assumes that keys are consistent, meaning that the
1) `checkpoint_path` checkpoint, and
2) checkpoint used to create the `cached_saver`
are the same type of object-based checkpoint. If this argument is set, this
function will simply validate that all variables have been remapped by the
checkpoint at `checkpoint_path`.
Note that in general, `tf.train.Checkpoint` should be used to restore/save an
object-based checkpoint.
Args:
checkpoint_path: string, path to object-based checkpoint
var_list: list of `Variables` that appear in the checkpoint. If `None`,
`var_list` will be set to all saveable objects.
builder: a `BaseSaverBuilder` instance. If `None`, a new `BulkSaverBuilder`
will be created.
names_to_keys: dict mapping string tensor names to checkpooint keys. If
`None`, this dict will be generated from the checkpoint file.
cached_saver: Cached `Saver` object with remapped variables.
Returns:
`Saver` with remapped variables for reading from an object-based checkpoint.
Raises:
ValueError if the checkpoint provided is not an object-based checkpoint.
NotFoundError: If one of the variables in `var_list` can not be found in the
checkpoint. This could mean the checkpoint or `names_to_keys` mapping is
missing the variable. | Return a `Saver` which reads from an object-based checkpoint. | [
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var_list=None,
builder=None,
names_to_keys=None,
cached_saver=None):
"""Return a `Saver` which reads from an object-based checkpoint.
This function validates that all variables in the variables list are remapped
in the object-based checkpoint (or `names_to_keys` dict if provided). A
saver will be created with the list of remapped variables.
The `cached_saver` argument allows the user to pass in a previously created
saver, so multiple `saver.restore()` calls don't pollute the graph when graph
building. This assumes that keys are consistent, meaning that the
1) `checkpoint_path` checkpoint, and
2) checkpoint used to create the `cached_saver`
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Note that in general, `tf.train.Checkpoint` should be used to restore/save an
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Args:
checkpoint_path: string, path to object-based checkpoint
var_list: list of `Variables` that appear in the checkpoint. If `None`,
`var_list` will be set to all saveable objects.
builder: a `BaseSaverBuilder` instance. If `None`, a new `BulkSaverBuilder`
will be created.
names_to_keys: dict mapping string tensor names to checkpooint keys. If
`None`, this dict will be generated from the checkpoint file.
cached_saver: Cached `Saver` object with remapped variables.
Returns:
`Saver` with remapped variables for reading from an object-based checkpoint.
Raises:
ValueError if the checkpoint provided is not an object-based checkpoint.
NotFoundError: If one of the variables in `var_list` can not be found in the
checkpoint. This could mean the checkpoint or `names_to_keys` mapping is
missing the variable.
"""
if names_to_keys is None:
try:
names_to_keys = object_graph_key_mapping(checkpoint_path)
except errors.NotFoundError:
raise ValueError("Checkpoint in %s not an object-based checkpoint." %
checkpoint_path)
if var_list is None:
var_list = variables._all_saveable_objects() # pylint: disable=protected-access
if builder is None:
builder = BulkSaverBuilder()
saveables = saveable_object_util.validate_and_slice_inputs(var_list)
current_names = set()
for saveable in saveables:
for spec in saveable.specs:
current_names.add(spec.name)
previous_names = set(names_to_keys.keys())
missing_names = current_names - previous_names
if missing_names:
extra_names = previous_names - current_names
intersecting_names = previous_names.intersection(current_names)
raise errors.NotFoundError(
None,
None,
message=(
"\n\nExisting variables not in the checkpoint: %s\n\n"
"Variables names when this checkpoint was written which don't "
"exist now: %s\n\n"
"(%d variable name(s) did match)\n\n"
"Could not find some variables in the checkpoint (see names "
"above). Saver was attempting to load an object-based checkpoint "
"(saved using tf.train.Checkpoint or tf.keras.Model.save_weights) "
"using variable names. If the checkpoint was written with eager "
"execution enabled, it's possible that variable names have "
"changed (for example missing a '_1' suffix). It's also "
"possible that there are new variables which did not exist "
"when the checkpoint was written. You can construct a "
"Saver(var_list=...) with only the variables which previously "
"existed, and if variable names have changed you may need to "
"make this a dictionary with the old names as keys. If you're "
"using an Estimator, you'll need to return a tf.train.Saver "
"inside a tf.train.Scaffold from your model_fn.") %
(", ".join(sorted(missing_names)), ", ".join(
sorted(extra_names)), len(intersecting_names)))
for saveable in saveables:
for spec in saveable.specs:
spec.name = names_to_keys[spec.name]
if cached_saver is None:
return Saver(saveables)
return cached_saver | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/_strptime.py | python | TimeRE.__init__ | (self, locale_time=None) | Create keys/values.
Order of execution is important for dependency reasons. | Create keys/values. | [
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"""Create keys/values.
Order of execution is important for dependency reasons.
"""
if locale_time:
self.locale_time = locale_time
else:
self.locale_time = LocaleTime()
base = super(TimeRE, self)
base.__init__({
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'd': r"(?P<d>3[0-1]|[1-2]\d|0[1-9]|[1-9]| [1-9])",
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'H': r"(?P<H>2[0-3]|[0-1]\d|\d)",
'I': r"(?P<I>1[0-2]|0[1-9]|[1-9])",
'j': r"(?P<j>36[0-6]|3[0-5]\d|[1-2]\d\d|0[1-9]\d|00[1-9]|[1-9]\d|0[1-9]|[1-9])",
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# W is set below by using 'U'
'y': r"(?P<y>\d\d)",
#XXX: Does 'Y' need to worry about having less or more than
# 4 digits?
'Y': r"(?P<Y>\d\d\d\d)",
'A': self.__seqToRE(self.locale_time.f_weekday, 'A'),
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'B': self.__seqToRE(self.locale_time.f_month[1:], 'B'),
'b': self.__seqToRE(self.locale_time.a_month[1:], 'b'),
'p': self.__seqToRE(self.locale_time.am_pm, 'p'),
'Z': self.__seqToRE((tz for tz_names in self.locale_time.timezone
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'Z'),
'%': '%'})
base.__setitem__('W', base.__getitem__('U').replace('U', 'W'))
base.__setitem__('c', self.pattern(self.locale_time.LC_date_time))
base.__setitem__('x', self.pattern(self.locale_time.LC_date))
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | util/gem5art/artifact/gem5art/artifact/_artifactdb.py | python | ArtifactMongoDB.__init__ | (self, uri: str) | Initialize the mongodb connection and grab pointers to the databases
uri is the location of the database in a mongodb compatible form.
http://dochub.mongodb.org/core/connections. | Initialize the mongodb connection and grab pointers to the databases
uri is the location of the database in a mongodb compatible form.
http://dochub.mongodb.org/core/connections. | [
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"""
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aimerykong/Low-Rank-Bilinear-Pooling | 487eb2c857fd9c95357a5166b0c15ad0fe135b28 | caffe-20160312/scripts/cpp_lint.py | python | _IsTestFilename | (filename) | Determines if the given filename has a suffix that identifies it as a test.
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filename: The input filename.
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if (filename.endswith('_test.cc') or
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/stats/_multivariate.py | python | ortho_group_gen._process_parameters | (self, dim) | return dim | Dimension N must be specified; it cannot be inferred. | Dimension N must be specified; it cannot be inferred. | [
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Dimension N must be specified; it cannot be inferred.
"""
if dim is None or not np.isscalar(dim) or dim <= 1 or dim != int(dim):
raise ValueError("Dimension of rotation must be specified,"
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/pkg_resources/__init__.py | python | get_supported_platform | () | return plat | Return this platform's maximum compatible version.
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"""Return this platform's maximum compatible version.
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distutils. But what we want when checking compatibility is to know the
version of Mac OS X that we are *running*. To allow usage of packages that
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If this condition occurs for any other platform with a version in its
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"""
plat = get_build_platform()
m = macosVersionString.match(plat)
if m is not None and sys.platform == "darwin":
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return plat | [
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greenheartgames/greenworks | 3ea4ab490b56676de3f0a237c74bcfdb17323e60 | deps/cpplint/cpplint.py | python | CleanseComments | (line) | return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line) | Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/ndarray/ndarray.py | python | NDArray.square | (self, *args, **kwargs) | return op.square(self, *args, **kwargs) | Convenience fluent method for :py:func:`square`.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/tornado/tornado-6/tornado/web.py | python | Application.add_handlers | (self, host_pattern: str, host_handlers: _RuleList) | Appends the given handlers to our handler list.
Host patterns are processed sequentially in the order they were
added. All matching patterns will be considered. | Appends the given handlers to our handler list. | [
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] | def add_handlers(self, host_pattern: str, host_handlers: _RuleList) -> None:
"""Appends the given handlers to our handler list.
Host patterns are processed sequentially in the order they were
added. All matching patterns will be considered.
"""
host_matcher = HostMatches(host_pattern)
rule = Rule(host_matcher, _ApplicationRouter(self, host_handlers))
self.default_router.rules.insert(-1, rule)
if self.default_host is not None:
self.wildcard_router.add_rules(
[(DefaultHostMatches(self, host_matcher.host_pattern), host_handlers)]
) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/s3transfer/manager.py | python | TransferManager.download | (self, bucket, key, fileobj, extra_args=None,
subscribers=None) | return self._submit_transfer(
call_args, DownloadSubmissionTask, extra_main_kwargs) | Downloads a file from S3
:type bucket: str
:param bucket: The name of the bucket to download from
:type key: str
:param key: The name of the key to download from
:type fileobj: str or seekable file-like object
:param fileobj: The name of a file to download or a seekable file-like
object to download. It is recommended to use a filename because
file-like objects may result in higher memory usage.
:type extra_args: dict
:param extra_args: Extra arguments that may be passed to the
client operation
:type subscribers: list(s3transfer.subscribers.BaseSubscriber)
:param subscribers: The list of subscribers to be invoked in the
order provided based on the event emit during the process of
the transfer request.
:rtype: s3transfer.futures.TransferFuture
:returns: Transfer future representing the download | Downloads a file from S3 | [
"Downloads",
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subscribers=None):
"""Downloads a file from S3
:type bucket: str
:param bucket: The name of the bucket to download from
:type key: str
:param key: The name of the key to download from
:type fileobj: str or seekable file-like object
:param fileobj: The name of a file to download or a seekable file-like
object to download. It is recommended to use a filename because
file-like objects may result in higher memory usage.
:type extra_args: dict
:param extra_args: Extra arguments that may be passed to the
client operation
:type subscribers: list(s3transfer.subscribers.BaseSubscriber)
:param subscribers: The list of subscribers to be invoked in the
order provided based on the event emit during the process of
the transfer request.
:rtype: s3transfer.futures.TransferFuture
:returns: Transfer future representing the download
"""
if extra_args is None:
extra_args = {}
if subscribers is None:
subscribers = []
self._validate_all_known_args(extra_args, self.ALLOWED_DOWNLOAD_ARGS)
call_args = CallArgs(
bucket=bucket, key=key, fileobj=fileobj, extra_args=extra_args,
subscribers=subscribers
)
extra_main_kwargs = {'io_executor': self._io_executor}
if self._bandwidth_limiter:
extra_main_kwargs['bandwidth_limiter'] = self._bandwidth_limiter
return self._submit_transfer(
call_args, DownloadSubmissionTask, extra_main_kwargs) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/fx/experimental/unification/multipledispatch/dispatcher.py | python | Dispatcher.help | (self, *args, **kwargs) | Print docstring for the function corresponding to inputs | Print docstring for the function corresponding to inputs | [
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""" Print docstring for the function corresponding to inputs """
print(self._help(*args)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/lambda-code/SanitizationLambda/sanitization_lambda.py | python | __validate_event | (event) | Validate content of event received. | Validate content of event received. | [
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''' Validate content of event received. '''
if 'Records' not in event:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/quopri.py | python | decode | (input, output, header=False) | Read 'input', apply quoted-printable decoding, and write to 'output'.
'input' and 'output' are binary file objects.
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If 'header' is true, decode underscore as space (per RFC 1522)."""
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return
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# Strip trailing whitespace
while n > 0 and line[n-1:n] in b" \t\r":
n = n-1
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partial = 1
while i < n:
c = line[i:i+1]
if c == b'_' and header:
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elif i+1 == n and not partial:
partial = 1; break
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new = new + ESCAPE; i = i+2
elif i+2 < n and ishex(line[i+1:i+2]) and ishex(line[i+2:i+3]):
new = new + bytes((unhex(line[i+1:i+3]),)); i = i+3
else: # Bad escape sequence -- leave it in
new = new + c; i = i+1
if not partial:
output.write(new + b'\n')
new = b''
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/math/symbolic_io.py | python | exprFromStr | (context,string,fmt=None,add=False) | Returns an Expression from a string. In auto mode, this reads in constants in klampt.loader JSON-
compatible format, standard variables in the form "x", user data in the form of strings prepended with $
(e.g., "$x"), and named expression references in the form of strings prepended with @.
Args:
context (Context): the context containing possible functions in string
string (str): the string to parse.
fmt (str, optional): specifies a format for the string. Can be None (auto), 'auto', or 'json'
add (bool, optional): if true, adds all variables referenced in the string to the context.
Otherwise, undefined variables are referred to as user data.
An exception is raised on parsing failure.
(Parsing is a little slow, so try not to use it in tight inner loops)
Returns:
(Expression): the expression represented by str. | Returns an Expression from a string. In auto mode, this reads in constants in klampt.loader JSON-
compatible format, standard variables in the form "x", user data in the form of strings prepended with $
(e.g., "$x"), and named expression references in the form of strings prepended with @. | [
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"""Returns an Expression from a string. In auto mode, this reads in constants in klampt.loader JSON-
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Returns:
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"""
if len(string) == 0:
raise ValueError("Empty string provided")
if fmt == None:
if string[0] == '{':
fmt = 'json'
else:
fmt = 'auto'
if fmt == 'auto':
import re,ast
USERDATA_MARKER = '___'
EXPR_MARKER = '____'
TAGLIST_NAME = '__tagexprlist__'
taglist = context.expressions.copy()
def __settag__(self,tagname,taglist):
assert isinstance(tagname,ConstantExpression) and isinstance(tagname.value,str)
taglist[tagname.value] = self
return self
def __gettag__(tagname,taglist):
assert isinstance(tagname,ConstantExpression) and isinstance(tagname.value,str)
return taglist[tagname.value]
Expression.__settag__ = __settag__
x = re.sub(r"\$(\w+)", r"___\1",string)
x = re.sub(r"\#(\w+)", r'.__settag__("\1",__tagexprlist__)',x)
x = re.sub(r"\@(\w+)", r'__gettag__("\1",__tagexprlist__)',x)
#print "Substituted string",x
tree = ast.parse(x,mode='eval')
missing_functions = []
missing_names = []
userdata = {}
#hack to easily access functions with the class.attribute syntax
allFunctions = _builtin_functions.copy()
for name,func in context.customFunctions.items():
path = name.split('.')
if len(path) == 1:
allFunctions[name] = func
else:
if path[0] not in allFunctions:
allFunctions[path[0]] = _Object()
root = allFunctions[path[0]]
for n in path[1:-1]:
if not hasattr(root,n):
setattr(root,n,_Object())
root = getattr(root,n)
setattr(root,path[-1],func)
allFunctions[TAGLIST_NAME] = taglist
allFunctions['__gettag__'] = __gettag__
class RewriteVarNames(ast.NodeTransformer):
def __init__(self):
self.infunc = False
def visit_Call(self,node):
self.infunc = True
self.generic_visit(node)
return node
def visit_Name(self, node):
if self.infunc:
self.infunc = False
if node.id not in allFunctions:
missing_functions.append(node.id)
return node
if node.id.startswith(USERDATA_MARKER):
basename = node.id[len(USERDATA_MARKER):]
userdata[node.id] = expr(basename)
else:
if node.id in context.variableDict:
userdata[node.id] = expr(context.variableDict[node.id])
elif add:
userdata[node.id] = expr(context.addVar(node.id,'N'))
elif node.id == TAGLIST_NAME:
pass
else:
missing_names.append(node.id)
userdata[node.id] = expr(node.id)
return node
def visit_Num(self, node):
return ast.copy_location(ast.Call(func=ast.copy_location(ast.Name(id="_const",ctx=ast.Load()),node),args=[node],keywords=[]),node)
def visit_Str(self, node):
return ast.copy_location(ast.Call(func=ast.copy_location(ast.Name(id="_const",ctx=ast.Load()),node),args=[node],keywords=[]),node)
def visit_List(self, node):
args = []
for idx, item in enumerate(node.elts):
args.append(self.visit(item))
return ast.copy_location(ast.Call(func=ast.copy_location(ast.Name(id="_convert_list",ctx=ast.Load()),node),args=args,keywords=[]),node)
def visit_Tuple(self, node):
args = []
for idx, item in enumerate(node.elts):
args.append(self.visit(item))
return ast.copy_location(ast.Call(func=ast.copy_location(ast.Name(id="_convert_list",ctx=ast.Load()),node),args=args,keywords=[]),node)
#print "old tree:",ast.dump(tree)
newtree = RewriteVarNames().visit(tree)
#print "new tree:",ast.dump(newtree)
if len(missing_functions) > 0:
raise ValueError("Undefined functions "+','.join(missing_functions))
if len(missing_names) > 0:
raise ValueError("Undefined variable "+','.join(missing_names))
allFunctions['_const'] = const
allFunctions['_convert_list'] = lambda *args:array(*args)
ctree = compile(newtree, filename="<ast>", mode="eval")
res = eval(ctree,allFunctions,userdata)
delattr(Expression,'__settag__')
return res
elif fmt == 'json':
import json
obj = json.parse(str)
return exprFromJson(context,obj)
else:
raise ValueError("Invalid format "+fmt) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/cryengine_modules.py | python | GetPlatformSpecificSettings | (ctx, dict, entry, _platform, configuration) | return returnValue | Util function to apply flags based on current platform | Util function to apply flags based on current platform | [
"Util",
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"""
Util function to apply flags based on current platform
"""
def _to_list( value ):
if isinstance(value,list):
return value
return [ value ]
returnValue = []
platforms = ctx.get_current_platform_list(_platform)
# Check for entry in <platform>_<entry> style
for platform in platforms:
platform_entry = platform + '_' + entry
if not platform_entry in dict:
continue # No platfrom specific entry found
returnValue += _to_list( dict[platform_entry] )
if configuration == []:
return [] # Dont try to check for configurations if we dont have any
# Check for 'test' or 'dedicated' tagged entries
if _platform and _platform != 'project_generator':
platform_details = ctx.get_target_platform_detail(_platform)
configuration_details = platform_details.get_configuration(configuration)
if configuration_details.is_test:
test_entry = 'test_{}'.format(entry)
if test_entry in dict:
returnValue += _to_list(dict[test_entry])
for platform in platforms:
platform_test_entry = '{}_test_{}'.format(platform, entry)
if platform_test_entry in dict:
returnValue += _to_list(dict[platform_test_entry])
if configuration_details.is_server:
test_entry = 'dedicated_{}'.format(entry)
if test_entry in dict:
returnValue += _to_list(dict[test_entry])
for platform in platforms:
platform_test_entry = '{}_dedicated_{}'.format(platform, entry)
if platform_test_entry in dict:
returnValue += _to_list(dict[platform_test_entry])
# Check for entry in <configuration>_<entry> style
configuration_entry = configuration + '_' + entry
if configuration_entry in dict:
returnValue += _to_list( dict[configuration_entry] )
# Check for entry in <platform>_<configuration>_<entry> style
for platform in platforms:
platform_configuration_entry = platform + '_' + configuration + '_' + entry
if not platform_configuration_entry in dict:
continue # No platfrom /configuration specific entry found
returnValue += _to_list( dict[platform_configuration_entry] )
return returnValue | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/turtle.py | python | TNavigator.pos | (self) | return self._position | Return the turtle's current location (x,y), as a Vec2D-vector.
Aliases: pos | position
No arguments.
Example (for a Turtle instance named turtle):
>>> turtle.pos()
(0.00, 240.00) | Return the turtle's current location (x,y), as a Vec2D-vector. | [
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"""Return the turtle's current location (x,y), as a Vec2D-vector.
Aliases: pos | position
No arguments.
Example (for a Turtle instance named turtle):
>>> turtle.pos()
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"""
return self._position | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/fusion/portableglobe/servers/search_services/search_service_geplaces.py | python | RegisterSearchService | (search_services) | return search_services[SEARCH_SERVICE_NAME] | Creates a new search service object and adds it by name to the dict.
Args:
search_services: dict to which the new search service should be added
using its name as the key.
Returns:
the new search service object. | Creates a new search service object and adds it by name to the dict. | [
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] | def RegisterSearchService(search_services):
"""Creates a new search service object and adds it by name to the dict.
Args:
search_services: dict to which the new search service should be added
using its name as the key.
Returns:
the new search service object.
"""
if SEARCH_SERVICE_NAME in search_services.keys():
print "Warning: replacing existing %s service." % SEARCH_SERVICE_NAME
search_services[SEARCH_SERVICE_NAME] = GEPlacesSearch()
return search_services[SEARCH_SERVICE_NAME] | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/normal_conjugate_posteriors.py | python | normal_congugates_known_sigma_predictive | (prior, sigma, s, n) | return Normal(
mu=(prior.mu/sigma0_2 + s/sigma_2) * sigmap_2,
sigma=math_ops.sqrt(sigmap_2 + sigma_2)) | Posterior predictive Normal distribution w. conjugate prior on the mean.
This model assumes that `n` observations (with sum `s`) come from a
Normal with unknown mean `mu` (described by the Normal `prior`)
and known variance `sigma^2`. The "known sigma predictive"
is the distribution of new observations, conditioned on the existing
observations and our prior.
Accepts a prior Normal distribution object, having parameters
`mu0` and `sigma0`, as well as known `sigma` values of the predictive
distribution(s) (also assumed Normal),
and statistical estimates `s` (the sum(s) of the observations) and
`n` (the number(s) of observations).
Calculates the Normal distribution(s) `p(x | sigma^2)`:
```
p(x | sigma^2) = int N(x | mu, sigma^2) N(mu | prior.mu, prior.sigma^2) dmu
= N(x | prior.mu, 1/(sigma^2 + prior.sigma^2))
```
Returns the predictive posterior distribution object, with parameters
`(mu', sigma'^2)`, where:
```
sigma_n^2 = 1/(1/sigma0^2 + n/sigma^2),
mu' = (mu0/sigma0^2 + s/sigma^2) * sigma_n^2.
sigma'^2 = sigma_n^2 + sigma^2,
```
Distribution parameters from `prior`, as well as `sigma`, `s`, and `n`.
will broadcast in the case of multidimensional sets of parameters.
Args:
prior: `Normal` object of type `dtype`:
the prior distribution having parameters `(mu0, sigma0)`.
sigma: tensor of type `dtype`, taking values `sigma > 0`.
The known stddev parameter(s).
s: Tensor of type `dtype`. The sum(s) of observations.
n: Tensor of type `int`. The number(s) of observations.
Returns:
A new Normal predictive distribution object.
Raises:
TypeError: if dtype of `s` does not match `dtype`, or `prior` is not a
Normal object. | Posterior predictive Normal distribution w. conjugate prior on the mean. | [
"Posterior",
"predictive",
"Normal",
"distribution",
"w",
".",
"conjugate",
"prior",
"on",
"the",
"mean",
"."
] | def normal_congugates_known_sigma_predictive(prior, sigma, s, n):
"""Posterior predictive Normal distribution w. conjugate prior on the mean.
This model assumes that `n` observations (with sum `s`) come from a
Normal with unknown mean `mu` (described by the Normal `prior`)
and known variance `sigma^2`. The "known sigma predictive"
is the distribution of new observations, conditioned on the existing
observations and our prior.
Accepts a prior Normal distribution object, having parameters
`mu0` and `sigma0`, as well as known `sigma` values of the predictive
distribution(s) (also assumed Normal),
and statistical estimates `s` (the sum(s) of the observations) and
`n` (the number(s) of observations).
Calculates the Normal distribution(s) `p(x | sigma^2)`:
```
p(x | sigma^2) = int N(x | mu, sigma^2) N(mu | prior.mu, prior.sigma^2) dmu
= N(x | prior.mu, 1/(sigma^2 + prior.sigma^2))
```
Returns the predictive posterior distribution object, with parameters
`(mu', sigma'^2)`, where:
```
sigma_n^2 = 1/(1/sigma0^2 + n/sigma^2),
mu' = (mu0/sigma0^2 + s/sigma^2) * sigma_n^2.
sigma'^2 = sigma_n^2 + sigma^2,
```
Distribution parameters from `prior`, as well as `sigma`, `s`, and `n`.
will broadcast in the case of multidimensional sets of parameters.
Args:
prior: `Normal` object of type `dtype`:
the prior distribution having parameters `(mu0, sigma0)`.
sigma: tensor of type `dtype`, taking values `sigma > 0`.
The known stddev parameter(s).
s: Tensor of type `dtype`. The sum(s) of observations.
n: Tensor of type `int`. The number(s) of observations.
Returns:
A new Normal predictive distribution object.
Raises:
TypeError: if dtype of `s` does not match `dtype`, or `prior` is not a
Normal object.
"""
if not isinstance(prior, Normal):
raise TypeError("Expected prior to be an instance of type Normal")
if s.dtype != prior.dtype:
raise TypeError(
"Observation sum s.dtype does not match prior dtype: %s vs. %s"
% (s.dtype, prior.dtype))
n = math_ops.cast(n, prior.dtype)
sigma0_2 = math_ops.square(prior.sigma)
sigma_2 = math_ops.square(sigma)
sigmap_2 = 1.0/(1/sigma0_2 + n/sigma_2)
return Normal(
mu=(prior.mu/sigma0_2 + s/sigma_2) * sigmap_2,
sigma=math_ops.sqrt(sigmap_2 + sigma_2)) | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/training/saver.py | python | BaseSaverBuilder._AddShardedSaveOpsForV2 | (self, checkpoint_prefix, per_device) | Add ops to save the params per shard, for the V2 format.
Note that the sharded save procedure for the V2 format is different from
V1: there is a special "merge" step that merges the small metadata produced
from each device.
Args:
checkpoint_prefix: scalar String Tensor. Interpreted *NOT AS A
FILENAME*, but as a prefix of a V2 checkpoint;
per_device: A list of (device, BaseSaverBuilder.VarToSave) pairs, as
returned by _GroupByDevices().
Returns:
An op to save the variables, which, when evaluated, returns the prefix
"<user-fed prefix>" only and does not include the sharded spec suffix. | Add ops to save the params per shard, for the V2 format. | [
"Add",
"ops",
"to",
"save",
"the",
"params",
"per",
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"for",
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] | def _AddShardedSaveOpsForV2(self, checkpoint_prefix, per_device):
"""Add ops to save the params per shard, for the V2 format.
Note that the sharded save procedure for the V2 format is different from
V1: there is a special "merge" step that merges the small metadata produced
from each device.
Args:
checkpoint_prefix: scalar String Tensor. Interpreted *NOT AS A
FILENAME*, but as a prefix of a V2 checkpoint;
per_device: A list of (device, BaseSaverBuilder.VarToSave) pairs, as
returned by _GroupByDevices().
Returns:
An op to save the variables, which, when evaluated, returns the prefix
"<user-fed prefix>" only and does not include the sharded spec suffix.
"""
# IMPLEMENTATION DETAILS: most clients should skip.
#
# Suffix for any well-formed "checkpoint_prefix", when sharded.
# Transformations:
# * Users pass in "save_path" in save() and restore(). Say "myckpt".
# * checkpoint_prefix gets fed <save_path><_SHARDED_SUFFIX>.
#
# Example:
# During runtime, a temporary directory is first created, which contains
# files
#
# <train dir>/myckpt_temp/
# part-?????-of-?????{.index, .data-00000-of-00001}
#
# Before .save() finishes, they will be (hopefully, atomically) renamed to
#
# <train dir>/
# myckpt{.index, .data-?????-of-?????}
#
# Users only need to interact with the user-specified prefix, which is
# "<train dir>/myckpt" in this case. Save() and Restore() work with the
# prefix directly, instead of any physical pathname. (On failure and
# subsequent restore, an outdated and orphaned temporary directory can be
# safely removed.)
_SHARDED_SUFFIX = "_temp_%s/part" % uuid.uuid4().hex
tmp_checkpoint_prefix = string_ops.string_join(
[checkpoint_prefix, _SHARDED_SUFFIX])
num_shards = len(per_device)
sharded_saves = []
sharded_prefixes = []
num_shards_tensor = constant_op.constant(num_shards, name="num_shards")
last_device = None
for shard, (device, saveables) in enumerate(per_device):
last_device = device
with ops.device(device):
sharded_filename = self.sharded_filename(tmp_checkpoint_prefix, shard,
num_shards_tensor)
sharded_prefixes.append(sharded_filename)
sharded_saves.append(self._AddSaveOps(sharded_filename, saveables))
with ops.control_dependencies([x.op for x in sharded_saves]):
# Co-locates the merge step with the last device.
with ops.device(last_device):
# V2 format write path consists of a metadata merge step. Once merged,
# attempts to delete the temporary directory, "<user-fed prefix>_temp".
merge_step = gen_io_ops.merge_v2_checkpoints(
sharded_prefixes, checkpoint_prefix, delete_old_dirs=True)
with ops.control_dependencies([merge_step]):
# Returns the prefix "<user-fed prefix>" only. DOES NOT include the
# sharded spec suffix.
return array_ops.identity(checkpoint_prefix) | [
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] | https://github.com/hughperkins/tf-coriander/blob/970d3df6c11400ad68405f22b0c42a52374e94ca/tensorflow/python/training/saver.py#L233-L301 | ||
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/utils/asynchronous.py | python | BlockingAsyncTaskWithCallback.abort | (self) | Cancel an asynchronous execution | Cancel an asynchronous execution | [
"Cancel",
"an",
"asynchronous",
"execution"
] | def abort(self):
"""Cancel an asynchronous execution"""
# Implementation is based on
# https://stackoverflow.com/questions/5019436/python-how-to-terminate-a-blocking-thread
ctypes.pythonapi.PyThreadState_SetAsyncExc(ctypes.c_long(self.task.ident),
ctypes.py_object(KeyboardInterrupt))
#now try and cancel the running algorithm
alg = IAlgorithm._algorithmInThread(self.task.ident)
if alg is not None:
alg.cancel()
time.sleep(0.1) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/basic_fitting/basic_fitting_view.py | python | BasicFittingView.current_dataset_name | (self) | return self.workspace_selector.current_dataset_name | Returns the selected dataset name. | Returns the selected dataset name. | [
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"dataset",
"name",
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] | def current_dataset_name(self) -> str:
"""Returns the selected dataset name."""
return self.workspace_selector.current_dataset_name | [
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keyboardio/Kaleidoscope | d59604e98b2439d108647f15be52984a6837d360 | bin/cpplint.py | python | _CppLintState.ResetErrorCounts | (self) | Sets the module's error statistic back to zero. | Sets the module's error statistic back to zero. | [
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] | def ResetErrorCounts(self):
"""Sets the module's error statistic back to zero."""
self.error_count = 0
self.errors_by_category = {} | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/PRESUBMIT.py | python | _CheckWatchlist | (input_api, output_api) | return errors | Check that the WATCHLIST file parses correctly. | Check that the WATCHLIST file parses correctly. | [
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] | def _CheckWatchlist(input_api, output_api):
"""Check that the WATCHLIST file parses correctly."""
errors = []
for f in input_api.AffectedFiles():
if f.LocalPath() != 'WATCHLISTS':
continue
import StringIO
import logging
import watchlists
log_buffer = StringIO.StringIO()
log_handler = logging.StreamHandler(log_buffer)
log_handler.setFormatter(
logging.Formatter('%(levelname)s: %(message)s'))
logger = logging.getLogger()
logger.addHandler(log_handler)
wl = watchlists.Watchlists(input_api.change.RepositoryRoot())
logger.removeHandler(log_handler)
log_handler.flush()
log_buffer.flush()
if log_buffer.getvalue():
errors.append(output_api.PresubmitError(
'Cannot parse WATCHLISTS file, please resolve.',
log_buffer.getvalue().splitlines()))
return errors | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/fx/experimental/schema_type_annotation.py | python | AnnotateTypesWithSchema._extract_python_return_type | (self, target : Target) | return sig.return_annotation if sig.return_annotation is not inspect.Signature.empty else None | Given a Python call target, try to extract the Python return annotation
if it is available, otherwise return None
Args:
target (Callable): Python callable to get return annotation for
Returns:
Optional[Any]: Return annotation from the `target`, or None if it was
not available. | Given a Python call target, try to extract the Python return annotation
if it is available, otherwise return None | [
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] | def _extract_python_return_type(self, target : Target) -> Optional[Any]:
"""
Given a Python call target, try to extract the Python return annotation
if it is available, otherwise return None
Args:
target (Callable): Python callable to get return annotation for
Returns:
Optional[Any]: Return annotation from the `target`, or None if it was
not available.
"""
assert callable(target)
try:
sig = inspect.signature(target)
except (ValueError, TypeError):
return None
return sig.return_annotation if sig.return_annotation is not inspect.Signature.empty else None | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/p4util/fcidump.py | python | write_eigenvalues | (eigs, mo_idx) | return eigs_dump | Prepare multi-line string with one-particle eigenvalues to be written to the FCIDUMP file. | Prepare multi-line string with one-particle eigenvalues to be written to the FCIDUMP file. | [
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] | def write_eigenvalues(eigs, mo_idx):
"""Prepare multi-line string with one-particle eigenvalues to be written to the FCIDUMP file.
"""
eigs_dump = ''
iorb = 0
for h, block in enumerate(eigs):
for idx, x in np.ndenumerate(block):
eigs_dump += '{:29.20E}{:4d}{:4d}{:4d}{:4d}\n'.format(x, mo_idx(iorb), 0, 0, 0)
iorb += 1
return eigs_dump | [
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ros-planning/moveit | ee48dc5cedc981d0869352aa3db0b41469c2735c | moveit_commander/src/moveit_commander/robot.py | python | RobotCommander.get_robot_markers | (self, *args) | return mrkr | Get a MarkerArray of the markers that make up this robot
Usage:
(): get's all markers for current state
state (RobotState): gets markers for a particular state
values (dict): get markers with given values
values, links (dict, list): get markers with given values and these links
group (string): get all markers for a group
group, values (string, dict): get all markers for a group with desired values | Get a MarkerArray of the markers that make up this robot | [
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"""Get a MarkerArray of the markers that make up this robot
Usage:
(): get's all markers for current state
state (RobotState): gets markers for a particular state
values (dict): get markers with given values
values, links (dict, list): get markers with given values and these links
group (string): get all markers for a group
group, values (string, dict): get all markers for a group with desired values
"""
mrkr = MarkerArray()
if not args:
conversions.msg_from_string(mrkr, self._r.get_robot_markers())
else:
if isinstance(args[0], RobotState):
msg_str = conversions.msg_to_string(args[0])
conversions.msg_from_string(mrkr, self._r.get_robot_markers(msg_str))
elif isinstance(args[0], dict):
conversions.msg_from_string(mrkr, self._r.get_robot_markers(*args))
elif isinstance(args[0], str):
conversions.msg_from_string(mrkr, self._r.get_group_markers(*args))
else:
raise MoveItCommanderException("Unexpected type")
return mrkr | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/android.py | python | adb_copy_output.set_device | (self, device) | Sets the android device (serial number from adb devices command) to copy the file to | Sets the android device (serial number from adb devices command) to copy the file to | [
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'''Sets the android device (serial number from adb devices command) to copy the file to'''
self.device = device | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/pkg_resources/__init__.py | python | find_eggs_in_zip | (importer, path_item, only=False) | Find eggs in zip files; possibly multiple nested eggs. | Find eggs in zip files; possibly multiple nested eggs. | [
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] | def find_eggs_in_zip(importer, path_item, only=False):
"""
Find eggs in zip files; possibly multiple nested eggs.
"""
if importer.archive.endswith('.whl'):
# wheels are not supported with this finder
# they don't have PKG-INFO metadata, and won't ever contain eggs
return
metadata = EggMetadata(importer)
if metadata.has_metadata('PKG-INFO'):
yield Distribution.from_filename(path_item, metadata=metadata)
if only:
# don't yield nested distros
return
for subitem in metadata.resource_listdir(''):
if _is_egg_path(subitem):
subpath = os.path.join(path_item, subitem)
dists = find_eggs_in_zip(zipimport.zipimporter(subpath), subpath)
for dist in dists:
yield dist
elif subitem.lower().endswith('.dist-info'):
subpath = os.path.join(path_item, subitem)
submeta = EggMetadata(zipimport.zipimporter(subpath))
submeta.egg_info = subpath
yield Distribution.from_location(path_item, subitem, submeta) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/arrays/datetimes.py | python | DatetimeArray._local_timestamps | (self) | return tzconversion.tz_convert_from_utc(self.asi8, self.tz) | Convert to an i8 (unix-like nanosecond timestamp) representation
while keeping the local timezone and not using UTC.
This is used to calculate time-of-day information as if the timestamps
were timezone-naive. | Convert to an i8 (unix-like nanosecond timestamp) representation
while keeping the local timezone and not using UTC.
This is used to calculate time-of-day information as if the timestamps
were timezone-naive. | [
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] | def _local_timestamps(self) -> np.ndarray:
"""
Convert to an i8 (unix-like nanosecond timestamp) representation
while keeping the local timezone and not using UTC.
This is used to calculate time-of-day information as if the timestamps
were timezone-naive.
"""
if self.tz is None or timezones.is_utc(self.tz):
return self.asi8
return tzconversion.tz_convert_from_utc(self.asi8, self.tz) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/lib/utils.py | python | get_include | () | return d | Return the directory that contains the NumPy \\*.h header files.
Extension modules that need to compile against NumPy should use this
function to locate the appropriate include directory.
Notes
-----
When using ``distutils``, for example in ``setup.py``.
::
import numpy as np
...
Extension('extension_name', ...
include_dirs=[np.get_include()])
... | Return the directory that contains the NumPy \\*.h header files. | [
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"""
Return the directory that contains the NumPy \\*.h header files.
Extension modules that need to compile against NumPy should use this
function to locate the appropriate include directory.
Notes
-----
When using ``distutils``, for example in ``setup.py``.
::
import numpy as np
...
Extension('extension_name', ...
include_dirs=[np.get_include()])
...
"""
import numpy
if numpy.show_config is None:
# running from numpy source directory
d = os.path.join(os.path.dirname(numpy.__file__), 'core', 'include')
else:
# using installed numpy core headers
import numpy.core as core
d = os.path.join(os.path.dirname(core.__file__), 'include')
return d | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/requests/requests/api.py | python | get | (url, **kwargs) | return request('get', url, **kwargs) | Sends a GET request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes. | Sends a GET request. Returns :class:`Response` object. | [
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] | def get(url, **kwargs):
"""Sends a GET request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes.
"""
kwargs.setdefault('allow_redirects', True)
return request('get', url, **kwargs) | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/Tools/javaw.py | python | javac.post_run | (self) | List class files created | List class files created | [
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"""
List class files created
"""
for node in self.generator.outdir.ant_glob('**/*.class', quiet=True):
self.generator.bld.node_sigs[node] = self.uid()
self.generator.bld.task_sigs[self.uid()] = self.cache_sig | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | clang/bindings/python/clang/cindex.py | python | Cursor.get_num_template_arguments | (self) | return conf.lib.clang_Cursor_getNumTemplateArguments(self) | Returns the number of template args associated with this cursor. | Returns the number of template args associated with this cursor. | [
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"""Returns the number of template args associated with this cursor."""
return conf.lib.clang_Cursor_getNumTemplateArguments(self) | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/Devices/EFI/Firmware/BaseTools/Scripts/UpdateBuildVersions.py | python | ParseOptions | () | return(parser.parse_args()) | Parse the command-line options.
The options for this tool will be passed along to the MkBinPkg tool. | Parse the command-line options.
The options for this tool will be passed along to the MkBinPkg tool. | [
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"""
Parse the command-line options.
The options for this tool will be passed along to the MkBinPkg tool.
"""
parser = ArgumentParser(
usage=("%s [options]" % __execname__),
description=__copyright__,
conflict_handler='resolve')
# Standard Tool Options
parser.add_argument("--version", action="version",
version=__execname__ + " " + __version__)
parser.add_argument("-s", "--silent", action="store_true",
dest="silent",
help="All output will be disabled, pass/fail determined by the exit code")
parser.add_argument("-v", "--verbose", action="store_true",
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help="Enable verbose output")
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parser.add_argument("--revert", action="store_true",
dest="REVERT", default=False,
help="Revert the BuildVersion files only")
parser.add_argument("--svn-test", action="store_true",
dest="TEST_SVN", default=False,
help="Test if the svn command is available")
parser.add_argument("--svnFlag", action="store_true",
dest="HAVE_SVN", default=False,
help=SUPPRESS)
return(parser.parse_args()) | [
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rdiankov/openrave | d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7 | python/interfaces/TaskManipulation.py | python | TaskManipulation.ReleaseActive | (self,movingdir=None,execute=None,outputtraj=None,outputfinal=None,translationstepmult=None,finestep=None,outputtrajobj=None) | return final,traj | See :ref:`module-taskmanipulation-releaseactive` | See :ref:`module-taskmanipulation-releaseactive` | [
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"""See :ref:`module-taskmanipulation-releaseactive`
"""
cmd = 'ReleaseActive '
if movingdir is not None:
assert(len(movingdir) == self.robot.GetActiveDOF())
cmd += 'movingdir %s '%(' '.join(str(f) for f in movingdir))
if execute is not None:
cmd += 'execute %d '%execute
if (outputtraj is not None and outputtraj) or (outputtrajobj is not None and outputtrajobj):
cmd += 'outputtraj '
if outputfinal:
cmd += 'outputfinal'
if translationstepmult is not None:
cmd += 'translationstepmult %.15e '%translationstepmult
if finestep is not None:
cmd += 'finestep %.15e '%finestep
res = self.prob.SendCommand(cmd)
if res is None:
raise PlanningError('ReleaseActive')
resvalues = res
if outputfinal:
resvaluesSplit = resvalues.split()
final = array([float64(resvaluesSplit[i]) for i in range(self.robot.GetActiveDOF())])
resvalues = resvalues[sum(len(resvaluesSplit[i])+1 for i in range(self.robot.GetActiveDOF())):]
else:
final=None
if (outputtraj is not None and outputtraj) or (outputtrajobj is not None and outputtrajobj):
traj = resvalues
if outputtrajobj is not None and outputtrajobj:
newtraj = RaveCreateTrajectory(self.prob.GetEnv(),'')
newtraj.deserialize(traj)
traj = newtraj
else:
traj = None
return final,traj | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/graph_editor/transform.py | python | _TmpInfo.new_name | (self, name) | return name_ | Compute a destination name from a source name.
Args:
name: the name to be "transformed".
Returns:
The transformed name.
Raises:
ValueError: if the source scope is used (that is, not an empty string)
and the source name does not belong to the source scope. | Compute a destination name from a source name. | [
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] | def new_name(self, name):
"""Compute a destination name from a source name.
Args:
name: the name to be "transformed".
Returns:
The transformed name.
Raises:
ValueError: if the source scope is used (that is, not an empty string)
and the source name does not belong to the source scope.
"""
scope = self.scope
if not name.startswith(scope):
raise ValueError("{} does not belong to source scope: {}.".format(
name, scope))
rel_name = name[len(scope):]
name_ = self.scope_ + rel_name
return name_ | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/generator/eclipse.py | python | GetJavaSourceDirs | (target_list, target_dicts, toplevel_dir) | Generates a sequence of all likely java package root directories. | Generates a sequence of all likely java package root directories. | [
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] | def GetJavaSourceDirs(target_list, target_dicts, toplevel_dir):
'''Generates a sequence of all likely java package root directories.'''
for target_name in target_list:
target = target_dicts[target_name]
for action in target.get('actions', []):
for input_ in action['inputs']:
if (os.path.splitext(input_)[1] == '.java' and
not input_.startswith('$')):
dir_ = os.path.dirname(os.path.join(os.path.dirname(target_name),
input_))
# If there is a parent 'src' or 'java' folder, navigate up to it -
# these are canonical package root names in Chromium. This will
# break if 'src' or 'java' exists in the package structure. This
# could be further improved by inspecting the java file for the
# package name if this proves to be too fragile in practice.
parent_search = dir_
while os.path.basename(parent_search) not in ['src', 'java']:
parent_search, _ = os.path.split(parent_search)
if not parent_search or parent_search == toplevel_dir:
# Didn't find a known root, just return the original path
yield dir_
break
else:
yield parent_search | [
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apache/qpid-proton | 6bcdfebb55ea3554bc29b1901422532db331a591 | python/proton/_endpoints.py | python | Connection.remote_desired_capabilities | (self) | return dat2obj(pn_connection_remote_desired_capabilities(self._impl)) | The capabilities desired by the remote peer for this connection.
This operation will return a :class:`Data` object that
is valid until the connection object is freed. This :class:`Data`
object will be empty until the remote connection is opened as
indicated by the :const:`REMOTE_ACTIVE` flag.
:type: :class:`Data` | The capabilities desired by the remote peer for this connection. | [
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] | def remote_desired_capabilities(self):
"""
The capabilities desired by the remote peer for this connection.
This operation will return a :class:`Data` object that
is valid until the connection object is freed. This :class:`Data`
object will be empty until the remote connection is opened as
indicated by the :const:`REMOTE_ACTIVE` flag.
:type: :class:`Data`
"""
return dat2obj(pn_connection_remote_desired_capabilities(self._impl)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/_vendor/packaging/specifiers.py | python | BaseSpecifier.__hash__ | (self) | Returns a hash value for this Specifier like object. | Returns a hash value for this Specifier like object. | [
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] | def __hash__(self) -> int:
"""
Returns a hash value for this Specifier like object.
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_snapper.py | python | Snapper.getApparentPoint | (self, x, y) | return pt | Return a 3D point, projected on the current working plane. | Return a 3D point, projected on the current working plane. | [
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"current",
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] | def getApparentPoint(self, x, y):
"""Return a 3D point, projected on the current working plane."""
view = Draft.get3DView()
pt = view.getPoint(x, y)
if self.mask != "z":
if hasattr(App,"DraftWorkingPlane"):
if view.getCameraType() == "Perspective":
camera = view.getCameraNode()
p = camera.getField("position").getValue()
dv = pt.sub(App.Vector(p[0], p[1], p[2]))
else:
dv = view.getViewDirection()
return App.DraftWorkingPlane.projectPoint(pt, dv)
return pt | [
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openmm/openmm | cb293447c4fc8b03976dfe11399f107bab70f3d9 | wrappers/python/openmm/app/statedatareporter.py | python | StateDataReporter.report | (self, simulation, state) | Generate a report.
Parameters
----------
simulation : Simulation
The Simulation to generate a report for
state : State
The current state of the simulation | Generate a report. | [
"Generate",
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"report",
"."
] | def report(self, simulation, state):
"""Generate a report.
Parameters
----------
simulation : Simulation
The Simulation to generate a report for
state : State
The current state of the simulation
"""
if not self._hasInitialized:
self._initializeConstants(simulation)
headers = self._constructHeaders()
if not self._append:
print('#"%s"' % ('"'+self._separator+'"').join(headers), file=self._out)
try:
self._out.flush()
except AttributeError:
pass
self._initialClockTime = time.time()
self._initialSimulationTime = state.getTime()
self._initialSteps = simulation.currentStep
self._hasInitialized = True
# Check for errors.
self._checkForErrors(simulation, state)
# Query for the values
values = self._constructReportValues(simulation, state)
# Write the values.
print(self._separator.join(str(v) for v in values), file=self._out)
try:
self._out.flush()
except AttributeError:
pass | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | ContextLoadResponse.fromBytes | (buffer) | return TpmBuffer(buffer).createObj(ContextLoadResponse) | Returns new ContextLoadResponse object constructed from its
marshaled representation in the given byte buffer | Returns new ContextLoadResponse object constructed from its
marshaled representation in the given byte buffer | [
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""" Returns new ContextLoadResponse object constructed from its
marshaled representation in the given byte buffer
"""
return TpmBuffer(buffer).createObj(ContextLoadResponse) | [
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trailofbits/llvm-sanitizer-tutorial | d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99 | llvm/tools/clang/tools/scan-build-py/libscanbuild/arguments.py | python | parse_args_for_scan_build | () | return args | Parse and validate command-line arguments for scan-build. | Parse and validate command-line arguments for scan-build. | [
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] | def parse_args_for_scan_build():
""" Parse and validate command-line arguments for scan-build. """
from_build_command = True
parser = create_analyze_parser(from_build_command)
args = parser.parse_args()
reconfigure_logging(args.verbose)
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JavierIH/zowi | 830c1284154b8167c9131deb9c45189fd9e67b54 | code/python-client/Oscillator.py | python | Oscillator.O | (self, value) | Attribute: Set the offset | Attribute: Set the offset | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/auto_bisect/query_crbug.py | python | QuerySingleIssue | (issue_id, url_template=SINGLE_ISSUE_URL) | return json.loads(response) | Queries the tracker for a specific issue. Returns a dict.
This uses the deprecated Issue Tracker API to fetch a JSON representation of
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issue_id: An int or string representing the issue id.
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the issue details.
Args:
issue_id: An int or string representing the issue id.
url_template: URL to query the tracker with '%s' instead of the bug id.
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Raises:
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assert str(issue_id).isdigit()
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return json.loads(response) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/ec2/volume.py | python | Volume.create_snapshot | (self, description=None, dry_run=False) | return self.connection.create_snapshot(
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:param description: A description of the snapshot.
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fabianschenk/REVO | eb949c0fdbcdf0be09a38464eb0592a90803947f | thirdparty/Sophus/py/sophus/se2.py | python | Se2.__init__ | (self, so2, t) | internally represented by a unit complex number z and a translation
2-vector | internally represented by a unit complex number z and a translation
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/extern/__init__.py | python | VendorImporter.find_module | (self, fullname, path=None) | return self | Return self when fullname starts with root_name and the
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_grad/grad_math_ops.py | python | get_bprop_truncate_div | (self) | return bprop | Grad definition for `TruncateDiv` operation. | Grad definition for `TruncateDiv` operation. | [
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"""Grad definition for `TruncateDiv` operation."""
def bprop(x, y, out, dout):
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/nn/probability/distribution/exponential.py | python | Exponential._mode | (self, rate=None) | return self.fill(self.dtype, self.shape(rate), 0.) | r"""
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rate = self._check_param_type(rate)
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | xmlDoc.addDocEntity | (self, name, type, ExternalID, SystemID, content) | return __tmp | Register a new entity for this document. | Register a new entity for this document. | [
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ret = libxml2mod.xmlAddDocEntity(self._o, name, type, ExternalID, SystemID, content)
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Misc.selection_own | (self, **kw) | Become owner of X selection.
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | models/AI-Model-Zoo/caffe-xilinx/tools/extra/parse_log.py | python | write_csv | (output_filename, dict_list, delimiter, verbose=False) | Write a CSV file | Write a CSV file | [
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return
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dict_writer.writeheader()
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Tencent/Pebble | 68315f176d9e328a233ace29b7579a829f89879f | tools/blade/src/blade/proto_library_target.py | python | ProtoLibrary._check_proto_srcs_name | (self, srcs_list) | _check_proto_srcs_name.
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"""_check_proto_srcs_name.
Checks whether the proto file's name ends with 'proto'.
"""
err = 0
for src in srcs_list:
base_name = os.path.basename(src)
pos = base_name.rfind('.')
if pos == -1:
err = 1
file_suffix = base_name[pos + 1:]
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/tools/docs/py_guide_parser.py | python | md_files_in_dir | (py_guide_src_dir) | return [(full, f) for full, f in all_in_dir
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PropertyGrid.SelectProperty | (*args, **kwargs) | return _propgrid.PropertyGrid_SelectProperty(*args, **kwargs) | SelectProperty(self, PGPropArg id, bool focus=False) -> bool | SelectProperty(self, PGPropArg id, bool focus=False) -> bool | [
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RegrowthStudios/SoACode-Public | c3ddd69355b534d5e70e2e6d0c489b4e93ab1ffe | utils/git-hooks/cpplint/cpplint.py | python | ResetNolintSuppressions | () | Resets the set of NOLINT suppressions to empty. | Resets the set of NOLINT suppressions to empty. | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/vision/ops.py | python | roi_align | (x,
boxes,
boxes_num,
output_size,
spatial_scale=1.0,
sampling_ratio=-1,
aligned=True,
name=None) | This operator implements the roi_align layer.
Region of Interest (RoI) Align operator (also known as RoI Align) is to
perform bilinear interpolation on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7), as described in Mask R-CNN.
Dividing each region proposal into equal-sized sections with the pooled_width
and pooled_height. Location remains the origin result.
In each ROI bin, the value of the four regularly sampled locations are
computed directly through bilinear interpolation. The output is the mean of
four locations. Thus avoid the misaligned problem.
Args:
x (Tensor): Input feature, 4D-Tensor with the shape of [N,C,H,W],
where N is the batch size, C is the input channel, H is Height,
W is weight. The data type is float32 or float64.
boxes (Tensor): Boxes (RoIs, Regions of Interest) to pool over. It
should be a 2-D Tensor of shape (num_boxes, 4). The data type is
float32 or float64. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
the top left coordinates, and (x2, y2) is the bottom right coordinates.
boxes_num (Tensor): The number of boxes contained in each picture in
the batch, the data type is int32.
output_size (int or Tuple[int, int]): The pooled output size(h, w), data
type is int32. If int, h and w are both equal to output_size.
spatial_scale (float32): Multiplicative spatial scale factor to translate
ROI coords from their input scale to the scale used when pooling.
Default: 1.0
sampling_ratio (int32): number of sampling points in the interpolation
grid used to compute the output value of each pooled output bin.
If > 0, then exactly ``sampling_ratio x sampling_ratio`` sampling
points per bin are used.
If <= 0, then an adaptive number of grid points are used (computed
as ``ceil(roi_width / output_width)``, and likewise for height).
Default: -1
aligned (bool): If False, use the legacy implementation. If True, pixel
shift the box coordinates it by -0.5 for a better alignment with the
two neighboring pixel indices. This version is used in Detectron2.
Default: True
name(str, optional): For detailed information, please refer to :
ref:`api_guide_Name`. Usually name is no need to set and None by
default.
Returns:
Tensor: The output of ROIAlignOp is a 4-D tensor with shape (num_boxes,
channels, pooled_h, pooled_w). The data type is float32 or float64.
Examples:
.. code-block:: python
import paddle
from paddle.vision.ops import roi_align
data = paddle.rand([1, 256, 32, 32])
boxes = paddle.rand([3, 4])
boxes[:, 2] += boxes[:, 0] + 3
boxes[:, 3] += boxes[:, 1] + 4
boxes_num = paddle.to_tensor([3]).astype('int32')
align_out = roi_align(data, boxes, boxes_num, output_size=3)
assert align_out.shape == [3, 256, 3, 3] | This operator implements the roi_align layer.
Region of Interest (RoI) Align operator (also known as RoI Align) is to
perform bilinear interpolation on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7), as described in Mask R-CNN. | [
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boxes,
boxes_num,
output_size,
spatial_scale=1.0,
sampling_ratio=-1,
aligned=True,
name=None):
"""
This operator implements the roi_align layer.
Region of Interest (RoI) Align operator (also known as RoI Align) is to
perform bilinear interpolation on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7), as described in Mask R-CNN.
Dividing each region proposal into equal-sized sections with the pooled_width
and pooled_height. Location remains the origin result.
In each ROI bin, the value of the four regularly sampled locations are
computed directly through bilinear interpolation. The output is the mean of
four locations. Thus avoid the misaligned problem.
Args:
x (Tensor): Input feature, 4D-Tensor with the shape of [N,C,H,W],
where N is the batch size, C is the input channel, H is Height,
W is weight. The data type is float32 or float64.
boxes (Tensor): Boxes (RoIs, Regions of Interest) to pool over. It
should be a 2-D Tensor of shape (num_boxes, 4). The data type is
float32 or float64. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
the top left coordinates, and (x2, y2) is the bottom right coordinates.
boxes_num (Tensor): The number of boxes contained in each picture in
the batch, the data type is int32.
output_size (int or Tuple[int, int]): The pooled output size(h, w), data
type is int32. If int, h and w are both equal to output_size.
spatial_scale (float32): Multiplicative spatial scale factor to translate
ROI coords from their input scale to the scale used when pooling.
Default: 1.0
sampling_ratio (int32): number of sampling points in the interpolation
grid used to compute the output value of each pooled output bin.
If > 0, then exactly ``sampling_ratio x sampling_ratio`` sampling
points per bin are used.
If <= 0, then an adaptive number of grid points are used (computed
as ``ceil(roi_width / output_width)``, and likewise for height).
Default: -1
aligned (bool): If False, use the legacy implementation. If True, pixel
shift the box coordinates it by -0.5 for a better alignment with the
two neighboring pixel indices. This version is used in Detectron2.
Default: True
name(str, optional): For detailed information, please refer to :
ref:`api_guide_Name`. Usually name is no need to set and None by
default.
Returns:
Tensor: The output of ROIAlignOp is a 4-D tensor with shape (num_boxes,
channels, pooled_h, pooled_w). The data type is float32 or float64.
Examples:
.. code-block:: python
import paddle
from paddle.vision.ops import roi_align
data = paddle.rand([1, 256, 32, 32])
boxes = paddle.rand([3, 4])
boxes[:, 2] += boxes[:, 0] + 3
boxes[:, 3] += boxes[:, 1] + 4
boxes_num = paddle.to_tensor([3]).astype('int32')
align_out = roi_align(data, boxes, boxes_num, output_size=3)
assert align_out.shape == [3, 256, 3, 3]
"""
check_type(output_size, 'output_size', (int, tuple), 'roi_align')
if isinstance(output_size, int):
output_size = (output_size, output_size)
pooled_height, pooled_width = output_size
if in_dygraph_mode():
assert boxes_num is not None, "boxes_num should not be None in dygraph mode."
align_out = _C_ops.roi_align(
x, boxes, boxes_num, "pooled_height", pooled_height, "pooled_width",
pooled_width, "spatial_scale", spatial_scale, "sampling_ratio",
sampling_ratio, "aligned", aligned)
return align_out
else:
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'roi_align')
check_variable_and_dtype(boxes, 'boxes', ['float32', 'float64'],
'roi_align')
helper = LayerHelper('roi_align', **locals())
dtype = helper.input_dtype()
align_out = helper.create_variable_for_type_inference(dtype)
inputs = {
"X": x,
"ROIs": boxes,
}
if boxes_num is not None:
inputs['RoisNum'] = boxes_num
helper.append_op(
type="roi_align",
inputs=inputs,
outputs={"Out": align_out},
attrs={
"pooled_height": pooled_height,
"pooled_width": pooled_width,
"spatial_scale": spatial_scale,
"sampling_ratio": sampling_ratio,
"aligned": aligned,
})
return align_out | [
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google/orbit | 7c0a530f402f0c3753d0bc52f8e3eb620f65d017 | third_party/include-what-you-use/fix_includes.py | python | ParseOneFile | (f, iwyu_record) | return file_lines | Given a file object, read and classify the lines of the file.
For each file that iwyu_output mentions, we return a list of LineInfo
objects, which is a parsed version of each line, including not only
its content but its 'type', its 'key', etc.
Arguments:
f: an iterable object returning lines from a file.
iwyu_record: the IWYUOutputRecord struct for this source file.
Returns:
An array of LineInfo objects. The first element is always a dummy
element, so the first line of the file is at retval[1], matching
the way iwyu counts line numbers. | Given a file object, read and classify the lines of the file. | [
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"""Given a file object, read and classify the lines of the file.
For each file that iwyu_output mentions, we return a list of LineInfo
objects, which is a parsed version of each line, including not only
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Arguments:
f: an iterable object returning lines from a file.
iwyu_record: the IWYUOutputRecord struct for this source file.
Returns:
An array of LineInfo objects. The first element is always a dummy
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"""
file_lines = [LineInfo(None)]
for line in f:
file_lines.append(LineInfo(line))
_CalculateLineTypesAndKeys(file_lines, iwyu_record)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | ConfigBase.Exists | (*args, **kwargs) | return _misc_.ConfigBase_Exists(*args, **kwargs) | Exists(self, String name) -> bool
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Exists(self, String name) -> bool
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citizenfx/fivem | 88276d40cc7baf8285d02754cc5ae42ec7a8563f | vendor/chromium/base/win/embedded_i18n/create_string_rc.py | python | GrdHandler.__init__ | (self, string_id_set) | Constructs a handler that reads selected strings from a .grd file.
The dict attribute |messages| is populated with the strings that are read.
Args:
string_id_set: An optional set of message identifiers to extract; all
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"""Constructs a handler that reads selected strings from a .grd file.
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Args:
string_id_set: An optional set of message identifiers to extract; all
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"""
sax.handler.ContentHandler.__init__(self)
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self.referenced_xtb_files = []
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0142-Linked-List-Cycle-II/0142.py | python | Solution.findDuplicate | (self, nums) | return -1 | :type nums: List[int]
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"""
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"""
if len(nums) > 1:
slow = nums[0]
fast = nums[nums[0]]
while slow != fast:
slow = nums[slow]
fast = nums[nums[fast]]
entry = 0
while entry != slow:
entry = nums[entry]
slow = nums[slow]
return entry
return -1 | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/condor_scripts/adstash/utils.py | python | time_remaining | (starttime, timeout, positive=True) | return timeout - elapsed | Return the remaining time (in seconds) until starttime + timeout
Returns 0 if there is no time remaining | Return the remaining time (in seconds) until starttime + timeout
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/strings.py | python | cat_core | (list_of_columns: List, sep: str) | return np.sum(arr_with_sep, axis=0) | Auxiliary function for :meth:`str.cat`
Parameters
----------
list_of_columns : list of numpy arrays
List of arrays to be concatenated with sep;
these arrays may not contain NaNs!
sep : string
The separator string for concatenating the columns.
Returns
-------
nd.array
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"""
Auxiliary function for :meth:`str.cat`
Parameters
----------
list_of_columns : list of numpy arrays
List of arrays to be concatenated with sep;
these arrays may not contain NaNs!
sep : string
The separator string for concatenating the columns.
Returns
-------
nd.array
The concatenation of list_of_columns with sep.
"""
if sep == "":
# no need to interleave sep if it is empty
arr_of_cols = np.asarray(list_of_columns, dtype=object)
return np.sum(arr_of_cols, axis=0)
list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
list_with_sep[::2] = list_of_columns
arr_with_sep = np.asarray(list_with_sep, dtype=object)
return np.sum(arr_with_sep, axis=0) | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Alignment/MuonAlignment/python/svgfig.py | python | Ticks.interpret | (self) | Evaluate and return optimal ticks and miniticks according to
the standard minitick specification.
Normally only used internally. | Evaluate and return optimal ticks and miniticks according to
the standard minitick specification. | [
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] | def interpret(self):
"""Evaluate and return optimal ticks and miniticks according to
the standard minitick specification.
Normally only used internally.
"""
if self.labels == None or self.labels == False:
format = lambda x: ""
elif self.labels == True:
format = unumber
elif isinstance(self.labels, str):
format = lambda x: (self.labels % x)
elif callable(self.labels):
format = self.labels
else: raise TypeError("labels must be None/False, True, a format string, or a number->string function")
# Now for the ticks
ticks = self.ticks
# Case 1: ticks is None/False
if ticks == None or ticks == False: return {}, []
# Case 2: ticks is the number of desired ticks
elif isinstance(ticks, (int, long)):
if ticks == True: ticks = -10
if self.logbase == None:
ticks = self.compute_ticks(ticks, format)
else:
ticks = self.compute_logticks(self.logbase, ticks, format)
# Now for the miniticks
if self.miniticks == True:
if self.logbase == None:
return ticks, self.compute_miniticks(ticks)
else:
return ticks, self.compute_logminiticks(self.logbase)
elif isinstance(self.miniticks, (int, long)):
return ticks, self.regular_miniticks(self.miniticks)
elif getattr(self.miniticks, "__iter__", False):
return ticks, self.miniticks
elif self.miniticks == False or self.miniticks == None:
return ticks, []
else:
raise TypeError("miniticks must be None/False, True, a number of desired miniticks, or a list of numbers")
# Cases 3 & 4: ticks is iterable
elif getattr(ticks, "__iter__", False):
# Case 3: ticks is some kind of list
if not isinstance(ticks, dict):
output = {}
eps = _epsilon * (self.high - self.low)
for x in ticks:
if format == unumber and abs(x) < eps:
output[x] = u"0"
else:
output[x] = format(x)
ticks = output
# Case 4: ticks is a dict
else: pass
# Now for the miniticks
if self.miniticks == True:
if self.logbase == None:
return ticks, self.compute_miniticks(ticks)
else:
return ticks, self.compute_logminiticks(self.logbase)
elif isinstance(self.miniticks, (int, long)):
return ticks, self.regular_miniticks(self.miniticks)
elif getattr(self.miniticks, "__iter__", False):
return ticks, self.miniticks
elif self.miniticks == False or self.miniticks == None:
return ticks, []
else:
raise TypeError("miniticks must be None/False, True, a number of desired miniticks, or a list of numbers")
else:
raise TypeError("ticks must be None/False, a number of desired ticks, a list of numbers, or a dictionary of explicit markers") | [
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] | https://github.com/cms-sw/cmssw/blob/fd9de012d503d3405420bcbeec0ec879baa57cf2/Alignment/MuonAlignment/python/svgfig.py#L2508-L2600 | ||
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/robotsim.py | python | RobotModel.interpolate | (self, a, b, u) | return _robotsim.RobotModel_interpolate(self, a, b, u) | interpolate(RobotModel self, doubleVector a, doubleVector b, double u)
Interpolates smoothly between two configurations, properly taking into account
nonstandard joints.
Returns:
(list of n floats): The configuration that is u fraction of the way
from a to b | interpolate(RobotModel self, doubleVector a, doubleVector b, double u) | [
"interpolate",
"(",
"RobotModel",
"self",
"doubleVector",
"a",
"doubleVector",
"b",
"double",
"u",
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] | def interpolate(self, a, b, u):
"""
interpolate(RobotModel self, doubleVector a, doubleVector b, double u)
Interpolates smoothly between two configurations, properly taking into account
nonstandard joints.
Returns:
(list of n floats): The configuration that is u fraction of the way
from a to b
"""
return _robotsim.RobotModel_interpolate(self, a, b, u) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/linear_model/base.py | python | LinearClassifierMixin._predict_proba_lr | (self, X) | Probability estimation for OvR logistic regression.
Positive class probabilities are computed as
1. / (1. + np.exp(-self.decision_function(X)));
multiclass is handled by normalizing that over all classes. | Probability estimation for OvR logistic regression. | [
"Probability",
"estimation",
"for",
"OvR",
"logistic",
"regression",
"."
] | def _predict_proba_lr(self, X):
"""Probability estimation for OvR logistic regression.
Positive class probabilities are computed as
1. / (1. + np.exp(-self.decision_function(X)));
multiclass is handled by normalizing that over all classes.
"""
prob = self.decision_function(X)
prob *= -1
np.exp(prob, prob)
prob += 1
np.reciprocal(prob, prob)
if prob.ndim == 1:
return np.vstack([1 - prob, prob]).T
else:
# OvR normalization, like LibLinear's predict_probability
prob /= prob.sum(axis=1).reshape((prob.shape[0], -1))
return prob | [
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