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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/traveltime/modelling.py | python | TravelTimeDijkstraModelling.createStartModel | (self, dataVals) | return sm | Create a starting model from data values (gradient or constant). | Create a starting model from data values (gradient or constant). | [
"Create",
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] | def createStartModel(self, dataVals):
"""Create a starting model from data values (gradient or constant)."""
sm = None
if self._useGradient is not None:
[vTop, vBot] = self._useGradient # something strange here!!!
pg.info('Create gradient starting model. {0}: {1}'.format(vTop,
vBot))
sm = createGradientModel2D(self.data,
self.paraDomain,
vTop, vBot)
else:
dists = shotReceiverDistances(self.data, full=True)
aSlow = 1. / (dists / dataVals)
# pg._r(self.regionManager().parameterCount())
sm = pg.Vector(self.regionManager().parameterCount(),
pg.math.median(aSlow))
pg.info('Create constant starting model:', sm[0])
return sm | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/vcs/__init__.py | python | VersionControl.check_destination | (self, dest, url, rev_options, rev_display) | return checkout | Prepare a location to receive a checkout/clone.
Return True if the location is ready for (and requires) a
checkout/clone, False otherwise. | Prepare a location to receive a checkout/clone. | [
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] | def check_destination(self, dest, url, rev_options, rev_display):
"""
Prepare a location to receive a checkout/clone.
Return True if the location is ready for (and requires) a
checkout/clone, False otherwise.
"""
checkout = True
prompt = False
if os.path.exists(dest):
checkout = False
if os.path.exists(os.path.join(dest, self.dirname)):
existing_url = self.get_url(dest)
if self.compare_urls(existing_url, url):
logger.info('%s in %s exists, and has correct URL (%s)' %
(self.repo_name.title(), display_path(dest),
url))
logger.notify('Updating %s %s%s' %
(display_path(dest), self.repo_name,
rev_display))
self.update(dest, rev_options)
else:
logger.warn('%s %s in %s exists with URL %s' %
(self.name, self.repo_name,
display_path(dest), existing_url))
prompt = ('(s)witch, (i)gnore, (w)ipe, (b)ackup ',
('s', 'i', 'w', 'b'))
else:
logger.warn('Directory %s already exists, '
'and is not a %s %s.' %
(dest, self.name, self.repo_name))
prompt = ('(i)gnore, (w)ipe, (b)ackup ', ('i', 'w', 'b'))
if prompt:
logger.warn('The plan is to install the %s repository %s' %
(self.name, url))
response = ask_path_exists('What to do? %s' % prompt[0],
prompt[1])
if response == 's':
logger.notify('Switching %s %s to %s%s' %
(self.repo_name, display_path(dest), url,
rev_display))
self.switch(dest, url, rev_options)
elif response == 'i':
# do nothing
pass
elif response == 'w':
logger.warn('Deleting %s' % display_path(dest))
rmtree(dest)
checkout = True
elif response == 'b':
dest_dir = backup_dir(dest)
logger.warn('Backing up %s to %s'
% (display_path(dest), dest_dir))
shutil.move(dest, dest_dir)
checkout = True
return checkout | [
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google/clif | cab24d6a105609a65c95a36a1712ae3c20c7b5df | clif/python/proto.py | python | GenerateFrom | (messages, proto_filename, clif_hdr, proto_hdr) | Traverse ast and generate output files. | Traverse ast and generate output files. | [
"Traverse",
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"files",
"."
] | def GenerateFrom(messages, proto_filename, clif_hdr, proto_hdr):
"""Traverse ast and generate output files."""
with open(FLAGS.header_out, 'w') as hout:
gen.WriteTo(hout, gen.Headlines(
proto_filename, [proto_hdr, 'clif/python/postconv.h']))
gen.WriteTo(hout, _GenHeader(messages))
with open(FLAGS.ccdeps_out, 'w') as cout:
gen.WriteTo(cout, gen.Headlines(
proto_filename, ['clif/python/runtime.h',
'clif/python/types.h',
clif_hdr]))
for ns, ts in itertools.groupby(messages, types.Namespace):
if ns == '::':
ns = 'clif'
gen.WriteTo(cout, gen.TypeConverters(ns, ts)) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/tensor_array_grad.py | python | _TensorArraySplitGrad | (op, flow) | return [None, grad, None, flow] | Gradient for TensorArraySplit.
Args:
op: Forward TensorArraySplit op.
flow: Gradient `Tensor` flow to TensorArraySplit.
Returns:
A grad `Tensor`, the gradient created in upstream ReadGrads or PackGrad. | Gradient for TensorArraySplit. | [
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] | def _TensorArraySplitGrad(op, flow):
"""Gradient for TensorArraySplit.
Args:
op: Forward TensorArraySplit op.
flow: Gradient `Tensor` flow to TensorArraySplit.
Returns:
A grad `Tensor`, the gradient created in upstream ReadGrads or PackGrad.
"""
handle = op.inputs[0]
dtype = op.get_attr("T")
grad_source = _GetGradSource(flow)
g = tensor_array_ops.TensorArray(dtype=dtype, handle=handle).grad(
source=grad_source, flow=flow)
grad = g.concat()
# handle, value, lengths, flow_in
return [None, grad, None, flow] | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBLaunchInfo.__init__ | (self, argv) | __init__(lldb::SBLaunchInfo self, char const ** argv) -> SBLaunchInfo | __init__(lldb::SBLaunchInfo self, char const ** argv) -> SBLaunchInfo | [
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"""__init__(lldb::SBLaunchInfo self, char const ** argv) -> SBLaunchInfo"""
this = _lldb.new_SBLaunchInfo(argv)
try:
self.this.append(this)
except __builtin__.Exception:
self.this = this | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/rbd_support/module.py | python | Module.perf_image_stats | (self,
pool_spec: Optional[str] = None,
sort_by: Optional[ImageSortBy] = None) | Retrieve current RBD IO performance stats | Retrieve current RBD IO performance stats | [
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] | def perf_image_stats(self,
pool_spec: Optional[str] = None,
sort_by: Optional[ImageSortBy] = None) -> Tuple[int, str, str]:
"""
Retrieve current RBD IO performance stats
"""
with self.perf.lock:
sort_by_name = sort_by.name if sort_by else OSD_PERF_QUERY_COUNTERS[0]
return self.perf.get_perf_stats(pool_spec, sort_by_name) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/ReflectometryILLAutoProcess.py | python | ReflectometryILLAutoProcess.compose_polarized_runs_list | (self, angle_index) | return run_inputs, run_names | Returns the lists of runs and names for different flipper configurations at the given angle_index | Returns the lists of runs and names for different flipper configurations at the given angle_index | [
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] | def compose_polarized_runs_list(self, angle_index):
"""Returns the lists of runs and names for different flipper configurations at the given angle_index"""
run_inputs = []
run_names = []
if self._rb00:
run00_name = self.make_name(self._rb00[angle_index]) + '_00'
run00_input = self.compose_run_string(self._rb00[angle_index])
run_names.append(run00_name)
run_inputs.append(run00_input)
if self._rb01:
run01_name = self.make_name(self._rb01[angle_index]) + '_01'
run01_input = self.compose_run_string(self._rb01[angle_index])
run_names.append(run01_name)
run_inputs.append(run01_input)
if self._rb10:
run10_name = self.make_name(self._rb10[angle_index]) + '_10'
run10_input = self.compose_run_string(self._rb10[angle_index])
run_names.append(run10_name)
run_inputs.append(run10_input)
if self._rb11:
run11_name = self.make_name(self._rb11[angle_index]) + '_11'
run11_input = self.compose_run_string(self._rb11[angle_index])
run_names.append(run11_name)
run_inputs.append(run11_input)
return run_inputs, run_names | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/llvmlite/binding/module.py | python | ModuleRef.name | (self) | return _decode_string(ffi.lib.LLVMPY_GetModuleName(self)) | The module's identifier. | The module's identifier. | [
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"""
The module's identifier.
"""
return _decode_string(ffi.lib.LLVMPY_GetModuleName(self)) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/layers/python/layers/layers.py | python | repeat | (inputs, repetitions, layer, *args, **kwargs) | Applies the same layer with the same arguments repeatedly.
```python
y = repeat(x, 3, conv2d, 64, [3, 3], scope='conv1')
# It is equivalent to:
x = conv2d(x, 64, [3, 3], scope='conv1/conv1_1')
x = conv2d(x, 64, [3, 3], scope='conv1/conv1_2')
y = conv2d(x, 64, [3, 3], scope='conv1/conv1_3')
```
If the `scope` argument is not given in `kwargs`, it is set to
`layer.__name__`, or `layer.func.__name__` (for `functools.partial`
objects). If neither `__name__` nor `func.__name__` is available, the
layers are called with `scope='stack'`.
Args:
inputs: A `Tensor` suitable for layer.
repetitions: Int, number of repetitions.
layer: A layer with arguments `(inputs, *args, **kwargs)`
*args: Extra args for the layer.
**kwargs: Extra kwargs for the layer.
Returns:
A tensor result of applying the layer, repetitions times.
Raises:
ValueError: If the op is unknown or wrong. | Applies the same layer with the same arguments repeatedly. | [
"Applies",
"the",
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"layer",
"with",
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"."
] | def repeat(inputs, repetitions, layer, *args, **kwargs):
"""Applies the same layer with the same arguments repeatedly.
```python
y = repeat(x, 3, conv2d, 64, [3, 3], scope='conv1')
# It is equivalent to:
x = conv2d(x, 64, [3, 3], scope='conv1/conv1_1')
x = conv2d(x, 64, [3, 3], scope='conv1/conv1_2')
y = conv2d(x, 64, [3, 3], scope='conv1/conv1_3')
```
If the `scope` argument is not given in `kwargs`, it is set to
`layer.__name__`, or `layer.func.__name__` (for `functools.partial`
objects). If neither `__name__` nor `func.__name__` is available, the
layers are called with `scope='stack'`.
Args:
inputs: A `Tensor` suitable for layer.
repetitions: Int, number of repetitions.
layer: A layer with arguments `(inputs, *args, **kwargs)`
*args: Extra args for the layer.
**kwargs: Extra kwargs for the layer.
Returns:
A tensor result of applying the layer, repetitions times.
Raises:
ValueError: If the op is unknown or wrong.
"""
scope = kwargs.pop('scope', None)
with variable_scope.variable_scope(scope, 'Repeat', [inputs]):
inputs = ops.convert_to_tensor(inputs)
if scope is None:
if hasattr(layer, '__name__'):
scope = layer.__name__
elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'):
scope = layer.func.__name__ # In case layer is a functools.partial.
else:
scope = 'repeat'
outputs = inputs
for i in range(repetitions):
kwargs['scope'] = scope + '_' + str(i+1)
outputs = layer(outputs, *args, **kwargs)
return outputs | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/osx_cocoa/gizmos.py | python | DynamicSashUnifyEvent.__init__ | (self, *args, **kwargs) | __init__(self, Object target) -> DynamicSashUnifyEvent | __init__(self, Object target) -> DynamicSashUnifyEvent | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/loading/cloud/frontend/memory_logs.py | python | MemoryLogs.Flush | (self) | return result | Stops collecting the logs and returns the logs collected since Start()
was called. | Stops collecting the logs and returns the logs collected since Start()
was called. | [
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"""Stops collecting the logs and returns the logs collected since Start()
was called.
"""
self._logger.removeHandler(self._log_handler)
self._log_handler.flush()
self._log_buffer.flush()
result = self._log_buffer.getvalue()
self._log_buffer.truncate(0)
return result | [
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apple/swift-clang | d7403439fc6641751840b723e7165fb02f52db95 | bindings/python/clang/cindex.py | python | TranslationUnit.cursor | (self) | return conf.lib.clang_getTranslationUnitCursor(self) | Retrieve the cursor that represents the given translation unit. | Retrieve the cursor that represents the given translation unit. | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | xpathParserContext.xpathNotFunction | (self, nargs) | Implement the not() XPath function boolean not(boolean) The
not function returns true if its argument is false, and
false otherwise. | Implement the not() XPath function boolean not(boolean) The
not function returns true if its argument is false, and
false otherwise. | [
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] | def xpathNotFunction(self, nargs):
"""Implement the not() XPath function boolean not(boolean) The
not function returns true if its argument is false, and
false otherwise. """
libxml2mod.xmlXPathNotFunction(self._o, nargs) | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/expressions/leaf.py | python | Leaf.is_pwl | (self) | return True | Leaf nodes are always piecewise linear. | Leaf nodes are always piecewise linear. | [
"Leaf",
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"always",
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] | def is_pwl(self) -> bool:
"""Leaf nodes are always piecewise linear.
"""
return True | [
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johmathe/shotdetect | 1ecf93a695c96fd7601a41ab5834f1117b9d7d50 | tools/cpplint.py | python | FileInfo.RepositoryName | (self) | return fullname | FullName after removing the local path to the repository.
If we have a real absolute path name here we can try to do something smart:
detecting the root of the checkout and truncating /path/to/checkout from
the name so that we get header guards that don't include things like
"C:\Documents and Settings\..." or "/home/username/..." in them and thus
people on different computers who have checked the source out to different
locations won't see bogus errors. | FullName after removing the local path to the repository. | [
"FullName",
"after",
"removing",
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"local",
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"to",
"the",
"repository",
"."
] | def RepositoryName(self):
"""FullName after removing the local path to the repository.
If we have a real absolute path name here we can try to do something smart:
detecting the root of the checkout and truncating /path/to/checkout from
the name so that we get header guards that don't include things like
"C:\Documents and Settings\..." or "/home/username/..." in them and thus
people on different computers who have checked the source out to different
locations won't see bogus errors.
"""
fullname = self.FullName()
if os.path.exists(fullname):
project_dir = os.path.dirname(fullname)
if os.path.exists(os.path.join(project_dir, ".svn")):
# If there's a .svn file in the current directory, we recursively look
# up the directory tree for the top of the SVN checkout
root_dir = project_dir
one_up_dir = os.path.dirname(root_dir)
while os.path.exists(os.path.join(one_up_dir, ".svn")):
root_dir = os.path.dirname(root_dir)
one_up_dir = os.path.dirname(one_up_dir)
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Not SVN <= 1.6? Try to find a git, hg, or svn top level directory by
# searching up from the current path.
root_dir = os.path.dirname(fullname)
while (root_dir != os.path.dirname(root_dir) and
not os.path.exists(os.path.join(root_dir, ".git")) and
not os.path.exists(os.path.join(root_dir, ".hg")) and
not os.path.exists(os.path.join(root_dir, ".svn"))):
root_dir = os.path.dirname(root_dir)
if (os.path.exists(os.path.join(root_dir, ".git")) or
os.path.exists(os.path.join(root_dir, ".hg")) or
os.path.exists(os.path.join(root_dir, ".svn"))):
prefix = os.path.commonprefix([root_dir, project_dir])
return fullname[len(prefix) + 1:]
# Don't know what to do; header guard warnings may be wrong...
return fullname | [
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Node/FS.py | python | File._morph | (self) | Turn a file system node into a File object. | Turn a file system node into a File object. | [
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] | def _morph(self):
"""Turn a file system node into a File object."""
self.scanner_paths = {}
if not hasattr(self, '_local'):
self._local = 0
if not hasattr(self, 'released_target_info'):
self.released_target_info = False
self.store_info = 1
self._func_exists = 4
self._func_get_contents = 3
# Initialize this Node's decider function to decide_source() because
# every file is a source file until it has a Builder attached...
self.changed_since_last_build = 4
# If there was already a Builder set on this entry, then
# we need to make sure we call the target-decider function,
# not the source-decider. Reaching in and doing this by hand
# is a little bogus. We'd prefer to handle this by adding
# an Entry.builder_set() method that disambiguates like the
# other methods, but that starts running into problems with the
# fragile way we initialize Dir Nodes with their Mkdir builders,
# yet still allow them to be overridden by the user. Since it's
# not clear right now how to fix that, stick with what works
# until it becomes clear...
if self.has_builder():
self.changed_since_last_build = 5 | [
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0ad/0ad | f58db82e0e925016d83f4e3fa7ca599e3866e2af | source/tools/fontbuilder2/Packer.py | python | CygonRectanglePacker.__init__ | (self, packingAreaWidth, packingAreaHeight) | Initializes a new rectangle packer
packingAreaWidth: Maximum width of the packing area
packingAreaHeight: Maximum height of the packing area | Initializes a new rectangle packer
packingAreaWidth: Maximum width of the packing area
packingAreaHeight: Maximum height of the packing area | [
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"""Initializes a new rectangle packer
packingAreaWidth: Maximum width of the packing area
packingAreaHeight: Maximum height of the packing area"""
RectanglePacker.__init__(self, packingAreaWidth, packingAreaHeight)
# Stores the height silhouette of the rectangles
self.heightSlices = []
# At the beginning, the packing area is a single slice of height 0
self.heightSlices.append(Point(0,0)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | MultiChoiceDialog.SetSelections | (*args, **kwargs) | return _windows_.MultiChoiceDialog_SetSelections(*args, **kwargs) | SetSelections(List selections)
Specify the items in the list that should be selected, using a list of
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"""
SetSelections(List selections)
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should be selected.
"""
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/framework/error_interpolation.py | python | create_graph_debug_info_def | (func_named_operations) | return graph_debug_info_def | Construct and returns a `GraphDebugInfo` protocol buffer.
Args:
func_named_operations: An iterable of (func_name, op.Operation) tuples
where the Operation instances have a _traceback members. The func_name
should be the empty string for operations in the top-level Graph.
Returns:
GraphDebugInfo protocol buffer.
Raises:
TypeError: If the arguments are not of the correct proto buffer type. | Construct and returns a `GraphDebugInfo` protocol buffer. | [
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] | def create_graph_debug_info_def(func_named_operations):
"""Construct and returns a `GraphDebugInfo` protocol buffer.
Args:
func_named_operations: An iterable of (func_name, op.Operation) tuples
where the Operation instances have a _traceback members. The func_name
should be the empty string for operations in the top-level Graph.
Returns:
GraphDebugInfo protocol buffer.
Raises:
TypeError: If the arguments are not of the correct proto buffer type.
"""
# Creates an empty GraphDebugInfoDef proto.
graph_debug_info_def = graph_debug_info_pb2.GraphDebugInfo()
# Gets the file names and line numbers for the exported node names. Also
# collects the unique file names.
all_file_names = set()
node_to_trace = {}
for func_name, op in func_named_operations:
try:
op_traceback = op.traceback
except AttributeError:
# Some ops synthesized on as part of function or control flow definition
# do not have tracebacks.
continue
# Gets the stack trace of the operation and then the file location.
node_name = op.name + "@" + func_name
node_to_trace[node_name] = _compute_useful_frames(op_traceback, 10)
for frame in node_to_trace[node_name]:
all_file_names.add(frame.filename)
# Sets the `files` field in the GraphDebugInfo proto
graph_debug_info_def.files.extend(all_file_names)
# Builds a mapping between file names and index of the `files` field, so we
# only store the indexes for the nodes in the GraphDebugInfo.
file_to_index = dict(
[(y, x) for x, y in enumerate(graph_debug_info_def.files)])
# Creates the FileLineCol proto for each node and sets the value in the
# GraphDebugInfo proto. We only store the file name index for each node to
# save the storage space.
for node_name, frames in node_to_trace.items():
trace_def = graph_debug_info_def.traces[node_name]
for frame in reversed(frames):
trace_def.file_line_cols.add(
file_index=file_to_index[frame.filename],
line=frame.lineno)
return graph_debug_info_def | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/make_archive.py | python | make_tar_archive | (opts) | Given the parsed options, generates the 'opt.output_filename'
tarball containing all the files in 'opt.input_filename' renamed
according to the mappings in 'opts.transformations'.
e.g. for an input file named "a/mongo/build/DISTSRC", and an
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purposes of compressing, are removed. | Given the parsed options, generates the 'opt.output_filename'
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'''Given the parsed options, generates the 'opt.output_filename'
tarball containing all the files in 'opt.input_filename' renamed
according to the mappings in 'opts.transformations'.
e.g. for an input file named "a/mongo/build/DISTSRC", and an
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All files to be compressed are copied into new directories as
required by 'opts.transformations'. Once the tarball has been
created, all temporary directory structures created for the
purposes of compressing, are removed.
'''
tar_options = "cvf"
if opts.archive_format is 'tgz':
tar_options += "z"
# clean and create a temp directory to copy files to
enclosing_archive_directory = tempfile.mkdtemp(
prefix='archive_',
dir=os.path.abspath('build')
)
output_tarfile = os.path.join(os.getcwd(), opts.output_filename)
tar_command = ["tar", tar_options, output_tarfile]
for input_filename in opts.input_filenames:
preferred_filename = get_preferred_filename(input_filename, opts.transformations)
temp_file_location = os.path.join(enclosing_archive_directory, preferred_filename)
enclosing_file_directory = os.path.dirname(temp_file_location)
if not os.path.exists(enclosing_file_directory):
os.makedirs(enclosing_file_directory)
print "copying %s => %s" % (input_filename, temp_file_location)
if os.path.isdir(input_filename):
shutil.copytree(input_filename, temp_file_location)
else:
shutil.copy2(input_filename, temp_file_location)
tar_command.append(preferred_filename)
print " ".join(tar_command)
# execute the full tar command
run_directory = os.path.join(os.getcwd(), enclosing_archive_directory)
proc = Popen(tar_command, stdout=PIPE, stderr=STDOUT, bufsize=0, cwd=run_directory)
proc.wait()
# delete temp directory
delete_directory(enclosing_archive_directory) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/frame.py | python | DataFrame.lookup | (self, row_labels, col_labels) | return result | Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
Parameters
----------
row_labels : sequence
The row labels to use for lookup.
col_labels : sequence
The column labels to use for lookup.
Returns
-------
numpy.ndarray
Examples
--------
values : ndarray
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] | def lookup(self, row_labels, col_labels) -> np.ndarray:
"""
Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
Parameters
----------
row_labels : sequence
The row labels to use for lookup.
col_labels : sequence
The column labels to use for lookup.
Returns
-------
numpy.ndarray
Examples
--------
values : ndarray
The found values
"""
n = len(row_labels)
if n != len(col_labels):
raise ValueError("Row labels must have same size as column labels")
thresh = 1000
if not self._is_mixed_type or n > thresh:
values = self.values
ridx = self.index.get_indexer(row_labels)
cidx = self.columns.get_indexer(col_labels)
if (ridx == -1).any():
raise KeyError("One or more row labels was not found")
if (cidx == -1).any():
raise KeyError("One or more column labels was not found")
flat_index = ridx * len(self.columns) + cidx
result = values.flat[flat_index]
else:
result = np.empty(n, dtype="O")
for i, (r, c) in enumerate(zip(row_labels, col_labels)):
result[i] = self._get_value(r, c)
if is_object_dtype(result):
result = lib.maybe_convert_objects(result)
return result | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/autopep8.py | python | _shorten_line_at_tokens_new | (tokens, source, indentation,
max_line_length) | Shorten the line taking its length into account.
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"""Shorten the line taking its length into account.
The input is expected to be free of newlines except for inside
multiline strings and at the end.
"""
# Yield the original source so to see if it's a better choice than the
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yield indentation + source
parsed_tokens = _parse_tokens(tokens)
if parsed_tokens:
# Perform two reflows. The first one starts on the same line as the
# prefix. The second starts on the line after the prefix.
fixed = _reflow_lines(parsed_tokens, indentation, max_line_length,
start_on_prefix_line=True)
if fixed and check_syntax(normalize_multiline(fixed.lstrip())):
yield fixed
fixed = _reflow_lines(parsed_tokens, indentation, max_line_length,
start_on_prefix_line=False)
if fixed and check_syntax(normalize_multiline(fixed.lstrip())):
yield fixed | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/ops/array_ops.py | python | na_arithmetic_op | (left, right, op, str_rep: str) | return missing.dispatch_fill_zeros(op, left, right, result) | Return the result of evaluating op on the passed in values.
If native types are not compatible, try coersion to object dtype.
Parameters
----------
left : np.ndarray
right : np.ndarray or scalar
str_rep : str or None
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-------
array-like
Raises
------
TypeError : invalid operation | Return the result of evaluating op on the passed in values. | [
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"""
Return the result of evaluating op on the passed in values.
If native types are not compatible, try coersion to object dtype.
Parameters
----------
left : np.ndarray
right : np.ndarray or scalar
str_rep : str or None
Returns
-------
array-like
Raises
------
TypeError : invalid operation
"""
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, left, right)
except TypeError:
result = masked_arith_op(left, right, op)
return missing.dispatch_fill_zeros(op, left, right, result) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/ndarray/ndarray.py | python | NDArray.arccos | (self, *args, **kwargs) | return op.arccos(self, *args, **kwargs) | Convenience fluent method for :py:func:`arccos`.
The arguments are the same as for :py:func:`arccos`, with
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"""Convenience fluent method for :py:func:`arccos`.
The arguments are the same as for :py:func:`arccos`, with
this array as data.
"""
return op.arccos(self, *args, **kwargs) | [
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] | https://github.com/hpi-xnor/BMXNet/blob/ed0b201da6667887222b8e4b5f997c4f6b61943d/python/mxnet/ndarray/ndarray.py#L1340-L1346 | |
CanalTP/navitia | cb84ce9859070187e708818b058e6a7e0b7f891b | source/jormungandr/jormungandr/authentication.py | python | get_used_coverages | () | return the list of coverages used to generate the response | return the list of coverages used to generate the response | [
"return",
"the",
"list",
"of",
"coverages",
"used",
"to",
"generate",
"the",
"response"
] | def get_used_coverages():
"""
return the list of coverages used to generate the response
"""
if request.view_args and 'region' in request.view_args:
return [request.view_args['region']]
elif hasattr(g, 'used_coverages'):
return g.used_coverages
else:
return [] | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/eager/python/examples/revnet/blocks.py | python | FinalBlock.__init__ | (self, config) | Initialization.
Args:
config: tf.contrib.training.HParams object; specifies hyperparameters
Raises:
ValueError: Unsupported data format | Initialization. | [
"Initialization",
"."
] | def __init__(self, config):
"""Initialization.
Args:
config: tf.contrib.training.HParams object; specifies hyperparameters
Raises:
ValueError: Unsupported data format
"""
super(FinalBlock, self).__init__(dtype=config.dtype)
self.config = config
self.axis = 1 if self.config.data_format == "channels_first" else 3
f = self.config.filters[-1] # Number of filters
r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio
r *= self.config.init_stride
if self.config.init_max_pool:
r *= 2
if self.config.data_format == "channels_first":
w, h = self.config.input_shape[1], self.config.input_shape[2]
input_shape = (f, w // r, h // r)
elif self.config.data_format == "channels_last":
w, h = self.config.input_shape[0], self.config.input_shape[1]
input_shape = (w // r, h // r, f)
else:
raise ValueError("Data format should be either `channels_first`"
" or `channels_last`")
self.batch_norm = tf.keras.layers.BatchNormalization(
axis=self.axis,
input_shape=input_shape,
fused=self.config.fused,
dtype=self.config.dtype)
self.activation = tf.keras.layers.Activation("relu",
dtype=self.config.dtype)
self.global_avg_pool = tf.keras.layers.GlobalAveragePooling2D(
data_format=self.config.data_format, dtype=self.config.dtype)
self.dense = tf.keras.layers.Dense(
self.config.n_classes, dtype=self.config.dtype) | [
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cmu-db/noisepage | 79276e68fe83322f1249e8a8be96bd63c583ae56 | build-support/cpplint.py | python | _SetVerboseLevel | (level) | return _cpplint_state.SetVerboseLevel(level) | Sets the module's verbosity, and returns the previous setting. | Sets the module's verbosity, and returns the previous setting. | [
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] | def _SetVerboseLevel(level):
"""Sets the module's verbosity, and returns the previous setting."""
return _cpplint_state.SetVerboseLevel(level) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/multiprocessing/managers.py | python | Server.debug_info | (self, c) | Return some info --- useful to spot problems with refcounting | Return some info --- useful to spot problems with refcounting | [
"Return",
"some",
"info",
"---",
"useful",
"to",
"spot",
"problems",
"with",
"refcounting"
] | def debug_info(self, c):
'''
Return some info --- useful to spot problems with refcounting
'''
self.mutex.acquire()
try:
result = []
keys = self.id_to_obj.keys()
keys.sort()
for ident in keys:
if ident != '0':
result.append(' %s: refcount=%s\n %s' %
(ident, self.id_to_refcount[ident],
str(self.id_to_obj[ident][0])[:75]))
return '\n'.join(result)
finally:
self.mutex.release() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiToolBarItem.GetMinSize | (*args, **kwargs) | return _aui.AuiToolBarItem_GetMinSize(*args, **kwargs) | GetMinSize(self) -> Size | GetMinSize(self) -> Size | [
"GetMinSize",
"(",
"self",
")",
"-",
">",
"Size"
] | def GetMinSize(*args, **kwargs):
"""GetMinSize(self) -> Size"""
return _aui.AuiToolBarItem_GetMinSize(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/calendar.py | python | Calendar.monthdatescalendar | (self, year, month) | return [ dates[i:i+7] for i in range(0, len(dates), 7) ] | Return a matrix (list of lists) representing a month's calendar.
Each row represents a week; week entries are datetime.date values. | Return a matrix (list of lists) representing a month's calendar.
Each row represents a week; week entries are datetime.date values. | [
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] | def monthdatescalendar(self, year, month):
"""
Return a matrix (list of lists) representing a month's calendar.
Each row represents a week; week entries are datetime.date values.
"""
dates = list(self.itermonthdates(year, month))
return [ dates[i:i+7] for i in range(0, len(dates), 7) ] | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/deps/v8/third_party/jinja2/parser.py | python | Parser.parse | (self) | return result | Parse the whole template into a `Template` node. | Parse the whole template into a `Template` node. | [
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] | def parse(self):
"""Parse the whole template into a `Template` node."""
result = nodes.Template(self.subparse(), lineno=1)
result.set_environment(self.environment)
return result | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/wsgiref/simple_server.py | python | WSGIRequestHandler.handle | (self) | Handle a single HTTP request | Handle a single HTTP request | [
"Handle",
"a",
"single",
"HTTP",
"request"
] | def handle(self):
"""Handle a single HTTP request"""
self.raw_requestline = self.rfile.readline()
if not self.parse_request(): # An error code has been sent, just exit
return
handler = ServerHandler(
self.rfile, self.wfile, self.get_stderr(), self.get_environ()
)
handler.request_handler = self # backpointer for logging
handler.run(self.server.get_app()) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/importlib-metadata/py3/importlib_metadata/__init__.py | python | Distribution.requires | (self) | return reqs and list(reqs) | Generated requirements specified for this Distribution | Generated requirements specified for this Distribution | [
"Generated",
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"specified",
"for",
"this",
"Distribution"
] | def requires(self):
"""Generated requirements specified for this Distribution"""
reqs = self._read_dist_info_reqs() or self._read_egg_info_reqs()
return reqs and list(reqs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_flagship.py | python | SyntaxData.GetProperties | (self) | return [FOLD] | Returns a list of Extra Properties to set | Returns a list of Extra Properties to set | [
"Returns",
"a",
"list",
"of",
"Extra",
"Properties",
"to",
"set"
] | def GetProperties(self):
"""Returns a list of Extra Properties to set """
return [FOLD] | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/syntax/_flagship.py#L176-L178 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | FontPickerEvent.SetFont | (*args, **kwargs) | return _controls_.FontPickerEvent_SetFont(*args, **kwargs) | SetFont(self, Font c) | SetFont(self, Font c) | [
"SetFont",
"(",
"self",
"Font",
"c",
")"
] | def SetFont(*args, **kwargs):
"""SetFont(self, Font c)"""
return _controls_.FontPickerEvent_SetFont(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_controls.py#L7197-L7199 | |
bairdzhang/smallhardface | 76fa1d87a9602d9b13d7a7fe693fc7aec91cab80 | external/marcopede-face-eval-f2870fd85d48/loadData.py | python | loadDetections | (fn) | return dets | load detections from fn in the different formats | load detections from fn in the different formats | [
"load",
"detections",
"from",
"fn",
"in",
"the",
"different",
"formats"
] | def loadDetections(fn):
"""
load detections from fn in the different formats
"""
dets = []
print ("loading ", fn)
if os.path.splitext(fn)[1] == ".txt":
dets = loadDetectionsPascalFormat(fn)
elif os.path.splitext(fn)[1] == ".ramananmat":
dets = loadDetectionsRamanan(fn)
elif os.path.splitext(fn)[1] == ".shenmat":
dets = loadDetectionsShen(fn)
elif os.path.splitext(fn)[1] == ".mat":
dets = loadDetectionsYann(fn)
elif os.path.splitext(fn)[1] == ".csv":
dets = loadDetectionsCSV(fn)
else:
print (fn)
raise Exception("Detection file format not supported")
return dets | [
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apple/swift | 469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893 | utils/gyb.py | python | split_lines | (s) | return [line + '\n' for line in s.split('\n')] | Split s into a list of lines, each of which has a trailing newline
If the lines are later concatenated, the result is s, possibly
with a single appended newline. | Split s into a list of lines, each of which has a trailing newline | [
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] | def split_lines(s):
"""Split s into a list of lines, each of which has a trailing newline
If the lines are later concatenated, the result is s, possibly
with a single appended newline.
"""
return [line + '\n' for line in s.split('\n')] | [
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] | https://github.com/apple/swift/blob/469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893/utils/gyb.py#L49-L55 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextCtrl.WordPartRightExtend | (*args, **kwargs) | return _stc.StyledTextCtrl_WordPartRightExtend(*args, **kwargs) | WordPartRightExtend(self)
Move to the next change in capitalisation extending selection
to new caret position. | WordPartRightExtend(self) | [
"WordPartRightExtend",
"(",
"self",
")"
] | def WordPartRightExtend(*args, **kwargs):
"""
WordPartRightExtend(self)
Move to the next change in capitalisation extending selection
to new caret position.
"""
return _stc.StyledTextCtrl_WordPartRightExtend(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/stc.py#L5128-L5135 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/roc/hsadrv/driver.py | python | Stream.auto_synchronize | (self) | A context manager that waits for all commands in this stream to execute
and commits any pending memory transfers upon exiting the context. | A context manager that waits for all commands in this stream to execute
and commits any pending memory transfers upon exiting the context. | [
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] | def auto_synchronize(self):
'''
A context manager that waits for all commands in this stream to execute
and commits any pending memory transfers upon exiting the context.
'''
yield self
self.synchronize() | [
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BSVino/DoubleAction | c550b168a3e919926c198c30240f506538b92e75 | mp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/reflection.py | python | _AddInitMethod | (message_descriptor, cls) | Adds an __init__ method to cls. | Adds an __init__ method to cls. | [
"Adds",
"an",
"__init__",
"method",
"to",
"cls",
"."
] | def _AddInitMethod(message_descriptor, cls):
"""Adds an __init__ method to cls."""
fields = message_descriptor.fields
def init(self, **kwargs):
self._cached_byte_size = 0
self._cached_byte_size_dirty = False
self._fields = {}
self._is_present_in_parent = False
self._listener = message_listener_mod.NullMessageListener()
self._listener_for_children = _Listener(self)
for field_name, field_value in kwargs.iteritems():
field = _GetFieldByName(message_descriptor, field_name)
if field is None:
raise TypeError("%s() got an unexpected keyword argument '%s'" %
(message_descriptor.name, field_name))
if field.label == _FieldDescriptor.LABEL_REPEATED:
copy = field._default_constructor(self)
if field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE: # Composite
for val in field_value:
copy.add().MergeFrom(val)
else: # Scalar
copy.extend(field_value)
self._fields[field] = copy
elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
copy = field._default_constructor(self)
copy.MergeFrom(field_value)
self._fields[field] = copy
else:
self._fields[field] = field_value
init.__module__ = None
init.__doc__ = None
cls.__init__ = init | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/misc/pilutil.py | python | imfilter | (arr, ftype) | return fromimage(im.filter(_tdict[ftype])) | Simple filtering of an image.
This function is only available if Python Imaging Library (PIL) is installed.
.. warning::
This function uses `bytescale` under the hood to rescale images to use
the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``.
It will also cast data for 2-D images to ``uint32`` for ``mode=None``
(which is the default).
Parameters
----------
arr : ndarray
The array of Image in which the filter is to be applied.
ftype : str
The filter that has to be applied. Legal values are:
'blur', 'contour', 'detail', 'edge_enhance', 'edge_enhance_more',
'emboss', 'find_edges', 'smooth', 'smooth_more', 'sharpen'.
Returns
-------
imfilter : ndarray
The array with filter applied.
Raises
------
ValueError
*Unknown filter type.* If the filter you are trying
to apply is unsupported. | Simple filtering of an image. | [
"Simple",
"filtering",
"of",
"an",
"image",
"."
] | def imfilter(arr, ftype):
"""
Simple filtering of an image.
This function is only available if Python Imaging Library (PIL) is installed.
.. warning::
This function uses `bytescale` under the hood to rescale images to use
the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``.
It will also cast data for 2-D images to ``uint32`` for ``mode=None``
(which is the default).
Parameters
----------
arr : ndarray
The array of Image in which the filter is to be applied.
ftype : str
The filter that has to be applied. Legal values are:
'blur', 'contour', 'detail', 'edge_enhance', 'edge_enhance_more',
'emboss', 'find_edges', 'smooth', 'smooth_more', 'sharpen'.
Returns
-------
imfilter : ndarray
The array with filter applied.
Raises
------
ValueError
*Unknown filter type.* If the filter you are trying
to apply is unsupported.
"""
_tdict = {'blur': ImageFilter.BLUR,
'contour': ImageFilter.CONTOUR,
'detail': ImageFilter.DETAIL,
'edge_enhance': ImageFilter.EDGE_ENHANCE,
'edge_enhance_more': ImageFilter.EDGE_ENHANCE_MORE,
'emboss': ImageFilter.EMBOSS,
'find_edges': ImageFilter.FIND_EDGES,
'smooth': ImageFilter.SMOOTH,
'smooth_more': ImageFilter.SMOOTH_MORE,
'sharpen': ImageFilter.SHARPEN
}
im = toimage(arr)
if ftype not in _tdict:
raise ValueError("Unknown filter type.")
return fromimage(im.filter(_tdict[ftype])) | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/tools/gyp/pylib/gyp/generator/msvs.py | python | _AddActionStep | (actions_dict, inputs, outputs, description, command) | Merge action into an existing list of actions.
Care must be taken so that actions which have overlapping inputs either don't
get assigned to the same input, or get collapsed into one.
Arguments:
actions_dict: dictionary keyed on input name, which maps to a list of
dicts describing the actions attached to that input file.
inputs: list of inputs
outputs: list of outputs
description: description of the action
command: command line to execute | Merge action into an existing list of actions. | [
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] | def _AddActionStep(actions_dict, inputs, outputs, description, command):
"""Merge action into an existing list of actions.
Care must be taken so that actions which have overlapping inputs either don't
get assigned to the same input, or get collapsed into one.
Arguments:
actions_dict: dictionary keyed on input name, which maps to a list of
dicts describing the actions attached to that input file.
inputs: list of inputs
outputs: list of outputs
description: description of the action
command: command line to execute
"""
# Require there to be at least one input (call sites will ensure this).
assert inputs
action = {
'inputs': inputs,
'outputs': outputs,
'description': description,
'command': command,
}
# Pick where to stick this action.
# While less than optimal in terms of build time, attach them to the first
# input for now.
chosen_input = inputs[0]
# Add it there.
if chosen_input not in actions_dict:
actions_dict[chosen_input] = []
actions_dict[chosen_input].append(action) | [
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alibaba/graph-learn | 54cafee9db3054dc310a28b856be7f97c7d5aee9 | examples/tf/seal/seal_link_predict.py | python | eval_hits | (y_pred_pos, y_pred_neg, k) | return {'hits@{}'.format(k): hitsK} | compute Hits@K
For each positive target node, the negative target nodes are the same.
y_pred_neg is an array.
rank y_pred_pos[i] against y_pred_neg for each i | compute Hits | [
"compute",
"Hits"
] | def eval_hits(y_pred_pos, y_pred_neg, k):
'''
compute Hits@K
For each positive target node, the negative target nodes are the same.
y_pred_neg is an array.
rank y_pred_pos[i] against y_pred_neg for each i
'''
if len(y_pred_neg) < k or len(y_pred_pos) == 0:
return {'hits@{}'.format(k): 1.}
kth_score_in_negative_edges = np.sort(y_pred_neg)[-k]
hitsK = float(np.sum(y_pred_pos > kth_score_in_negative_edges)) / len(y_pred_pos)
return {'hits@{}'.format(k): hitsK} | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/third_party/jinja2/environment.py | python | Template.get_corresponding_lineno | (self, lineno) | return 1 | Return the source line number of a line number in the
generated bytecode as they are not in sync. | Return the source line number of a line number in the
generated bytecode as they are not in sync. | [
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] | def get_corresponding_lineno(self, lineno):
"""Return the source line number of a line number in the
generated bytecode as they are not in sync.
"""
for template_line, code_line in reversed(self.debug_info):
if code_line <= lineno:
return template_line
return 1 | [
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/build/lib/google/protobuf/internal/decoder.py | python | BytesDecoder | (field_number, is_repeated, is_packed, key, new_default) | Returns a decoder for a bytes field. | Returns a decoder for a bytes field. | [
"Returns",
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"decoder",
"for",
"a",
"bytes",
"field",
"."
] | def BytesDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a bytes field."""
local_DecodeVarint = _DecodeVarint
assert not is_packed
if is_repeated:
tag_bytes = encoder.TagBytes(field_number,
wire_format.WIRETYPE_LENGTH_DELIMITED)
tag_len = len(tag_bytes)
def DecodeRepeatedField(buffer, pos, end, message, field_dict):
value = field_dict.get(key)
if value is None:
value = field_dict.setdefault(key, new_default(message))
while 1:
(size, pos) = local_DecodeVarint(buffer, pos)
new_pos = pos + size
if new_pos > end:
raise _DecodeError('Truncated string.')
value.append(buffer[pos:new_pos])
# Predict that the next tag is another copy of the same repeated field.
pos = new_pos + tag_len
if buffer[new_pos:pos] != tag_bytes or new_pos == end:
# Prediction failed. Return.
return new_pos
return DecodeRepeatedField
else:
def DecodeField(buffer, pos, end, message, field_dict):
(size, pos) = local_DecodeVarint(buffer, pos)
new_pos = pos + size
if new_pos > end:
raise _DecodeError('Truncated string.')
field_dict[key] = buffer[pos:new_pos]
return new_pos
return DecodeField | [
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kevinlin311tw/Caffe-DeepBinaryCode | 9eaa7662be47d49f475ecbeea2bd51be105270d2 | scripts/cpp_lint.py | python | ReverseCloseExpression | (clean_lines, linenum, pos) | return (line, 0, -1) | If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum. | If input points to ) or } or ] or >, finds the position that opens it. | [
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"it",
"."
] | def ReverseCloseExpression(clean_lines, linenum, pos):
"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
endchar = line[pos]
if endchar not in ')}]>':
return (line, 0, -1)
if endchar == ')': startchar = '('
if endchar == ']': startchar = '['
if endchar == '}': startchar = '{'
if endchar == '>': startchar = '<'
# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
line, pos, 0, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
line, len(line) - 1, num_open, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find startchar before beginning of file, give up
return (line, 0, -1) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.GetWordChars | (*args, **kwargs) | return _stc.StyledTextCtrl_GetWordChars(*args, **kwargs) | GetWordChars(self) -> String | GetWordChars(self) -> String | [
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"""GetWordChars(self) -> String"""
return _stc.StyledTextCtrl_GetWordChars(*args, **kwargs) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/decoder/beam_search_decoder.py | python | BeamSearchDecoder._parent_block | (self) | return parent_block | Getter of parent block.
Returns:
The parent block of decoder. | Getter of parent block. | [
"Getter",
"of",
"parent",
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"."
] | def _parent_block(self):
"""
Getter of parent block.
Returns:
The parent block of decoder.
"""
program = self._helper.main_program
parent_block_idx = program.current_block().parent_idx
if parent_block_idx < 0:
raise ValueError('Invalid block with index %d.' % parent_block_idx)
parent_block = program.block(parent_block_idx)
return parent_block | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/dtypes/common.py | python | needs_i8_conversion | (arr_or_dtype) | return (
is_datetime_or_timedelta_dtype(arr_or_dtype)
or is_datetime64tz_dtype(arr_or_dtype)
or is_period_dtype(arr_or_dtype)
) | Check whether the array or dtype should be converted to int64.
An array-like or dtype "needs" such a conversion if the array-like
or dtype is of a datetime-like dtype
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype should be converted to int64.
Examples
--------
>>> needs_i8_conversion(str)
False
>>> needs_i8_conversion(np.int64)
False
>>> needs_i8_conversion(np.datetime64)
True
>>> needs_i8_conversion(np.array(['a', 'b']))
False
>>> needs_i8_conversion(pd.Series([1, 2]))
False
>>> needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
True
>>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
True | Check whether the array or dtype should be converted to int64. | [
"Check",
"whether",
"the",
"array",
"or",
"dtype",
"should",
"be",
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"to",
"int64",
"."
] | def needs_i8_conversion(arr_or_dtype) -> bool:
"""
Check whether the array or dtype should be converted to int64.
An array-like or dtype "needs" such a conversion if the array-like
or dtype is of a datetime-like dtype
Parameters
----------
arr_or_dtype : array-like
The array or dtype to check.
Returns
-------
boolean
Whether or not the array or dtype should be converted to int64.
Examples
--------
>>> needs_i8_conversion(str)
False
>>> needs_i8_conversion(np.int64)
False
>>> needs_i8_conversion(np.datetime64)
True
>>> needs_i8_conversion(np.array(['a', 'b']))
False
>>> needs_i8_conversion(pd.Series([1, 2]))
False
>>> needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
True
>>> needs_i8_conversion(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
True
"""
if arr_or_dtype is None:
return False
return (
is_datetime_or_timedelta_dtype(arr_or_dtype)
or is_datetime64tz_dtype(arr_or_dtype)
or is_period_dtype(arr_or_dtype)
) | [
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Manu343726/siplasplas | 9fae7559f87087cf8ef34f04bd1e774b84b2ea9c | reference/cindex.py | python | Cursor.objc_type_encoding | (self) | return self._objc_type_encoding | Return the Objective-C type encoding as a str. | Return the Objective-C type encoding as a str. | [
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] | def objc_type_encoding(self):
"""Return the Objective-C type encoding as a str."""
if not hasattr(self, '_objc_type_encoding'):
self._objc_type_encoding = \
conf.lib.clang_getDeclObjCTypeEncoding(self)
return self._objc_type_encoding | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | tools/coverage/pull_request.py | python | get_pull | (pull_id) | return pull | Args:
pull_id (int): Pull id.
Returns:
github.PullRequest.PullRequest | Args:
pull_id (int): Pull id. | [
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] | def get_pull(pull_id):
"""
Args:
pull_id (int): Pull id.
Returns:
github.PullRequest.PullRequest
"""
github = Github(token, timeout=60)
repo = github.get_repo('PaddlePaddle/Paddle')
pull = repo.get_pull(pull_id)
return pull | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | example/ssd/dataset/pycocotools/coco.py | python | COCO.loadNumpyAnnotations | (self, data) | return ann | Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list) | Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list) | [
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"""
Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
:param data (numpy.ndarray)
:return: annotations (python nested list)
"""
print('Converting ndarray to lists...')
assert(type(data) == np.ndarray)
print(data.shape)
assert(data.shape[1] == 7)
N = data.shape[0]
ann = []
for i in range(N):
if i % 1000000 == 0:
print('{}/{}'.format(i,N))
ann += [{
'image_id' : int(data[i, 0]),
'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
'score' : data[i, 5],
'category_id': int(data[i, 6]),
}]
return ann | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/help.py | python | HelpParser.handle_starttag | (self, tag, attrs) | Handle starttags in help.html. | Handle starttags in help.html. | [
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] | def handle_starttag(self, tag, attrs):
"Handle starttags in help.html."
class_ = ''
for a, v in attrs:
if a == 'class':
class_ = v
s = ''
if tag == 'div' and class_ == 'section':
self.show = True # Start main content.
elif tag == 'div' and class_ == 'sphinxsidebar':
self.show = False # End main content.
elif tag == 'p' and self.prevtag and not self.prevtag[0]:
# Begin a new block for <p> tags after a closed tag.
# Avoid extra lines, e.g. after <pre> tags.
lastline = self.text.get('end-1c linestart', 'end-1c')
s = '\n\n' if lastline and not lastline.isspace() else '\n'
elif tag == 'span' and class_ == 'pre':
self.chartags = 'pre'
elif tag == 'span' and class_ == 'versionmodified':
self.chartags = 'em'
elif tag == 'em':
self.chartags = 'em'
elif tag in ['ul', 'ol']:
if class_.find('simple') != -1:
s = '\n'
self.simplelist = True
else:
self.simplelist = False
self.indent()
elif tag == 'dl':
if self.level > 0:
self.nested_dl = True
elif tag == 'li':
s = '\n* ' if self.simplelist else '\n\n* '
elif tag == 'dt':
s = '\n\n' if not self.nested_dl else '\n' # Avoid extra line.
self.nested_dl = False
elif tag == 'dd':
self.indent()
s = '\n'
elif tag == 'pre':
self.pre = True
if self.show:
self.text.insert('end', '\n\n')
self.tags = 'preblock'
elif tag == 'a' and class_ == 'headerlink':
self.hdrlink = True
elif tag == 'h1':
self.tags = tag
elif tag in ['h2', 'h3']:
if self.show:
self.header = ''
self.text.insert('end', '\n\n')
self.tags = tag
if self.show:
self.text.insert('end', s, (self.tags, self.chartags))
self.prevtag = (True, tag) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextFileHandler.GetEncoding | (*args, **kwargs) | return _richtext.RichTextFileHandler_GetEncoding(*args, **kwargs) | GetEncoding(self) -> String | GetEncoding(self) -> String | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/config/win/get_msvc_config_real.py | python | VisualStudioVersion.ProjectVersion | (self) | return self.project_version | Get the version number of the vcproj or vcxproj files. | Get the version number of the vcproj or vcxproj files. | [
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stepcode/stepcode | 2a50010e6f6b8bd4843561e48fdb0fd4e8b87f39 | src/exp2python/python/SCL/Part21.py | python | Parser.p_exchange_file | (self, p) | exchange_file : check_p21_start_token header_section data_section_list check_p21_end_token | exchange_file : check_p21_start_token header_section data_section_list check_p21_end_token | [
"exchange_file",
":",
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"header_section",
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"check_p21_end_token"
] | def p_exchange_file(self, p):
"""exchange_file : check_p21_start_token header_section data_section_list check_p21_end_token"""
p[0] = P21File(p[2], p[3]) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/wheel_builder.py | python | _build_one | (
req, # type: InstallRequirement
output_dir, # type: str
verify, # type: bool
build_options, # type: List[str]
global_options, # type: List[str]
) | return wheel_path | Build one wheel.
:return: The filename of the built wheel, or None if the build failed. | Build one wheel. | [
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"wheel",
"."
] | def _build_one(
req, # type: InstallRequirement
output_dir, # type: str
verify, # type: bool
build_options, # type: List[str]
global_options, # type: List[str]
):
# type: (...) -> Optional[str]
"""Build one wheel.
:return: The filename of the built wheel, or None if the build failed.
"""
try:
ensure_dir(output_dir)
except OSError as e:
logger.warning(
"Building wheel for %s failed: %s",
req.name, e,
)
return None
# Install build deps into temporary directory (PEP 518)
with req.build_env:
wheel_path = _build_one_inside_env(
req, output_dir, build_options, global_options
)
if wheel_path and verify:
try:
_verify_one(req, wheel_path)
except (InvalidWheelFilename, UnsupportedWheel) as e:
logger.warning("Built wheel for %s is invalid: %s", req.name, e)
return None
return wheel_path | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/requests_aws4auth/aws4auth.py | python | AWS4Auth.get_canonical_headers | (cls, req, include=None) | return (cano_headers, signed_headers) | Generate the Canonical Headers section of the Canonical Request.
Return the Canonical Headers and the Signed Headers strs as a tuple
(canonical_headers, signed_headers).
req -- Requests PreparedRequest object
include -- List of headers to include in the canonical and signed
headers. It's primarily included to allow testing against
specific examples from Amazon. If omitted or None it
includes host, content-type and any header starting 'x-amz-'
except for x-amz-client context, which appears to break
mobile analytics auth if included. Except for the
x-amz-client-context exclusion these defaults are per the
AWS documentation. | Generate the Canonical Headers section of the Canonical Request. | [
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"Canonical",
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] | def get_canonical_headers(cls, req, include=None):
"""
Generate the Canonical Headers section of the Canonical Request.
Return the Canonical Headers and the Signed Headers strs as a tuple
(canonical_headers, signed_headers).
req -- Requests PreparedRequest object
include -- List of headers to include in the canonical and signed
headers. It's primarily included to allow testing against
specific examples from Amazon. If omitted or None it
includes host, content-type and any header starting 'x-amz-'
except for x-amz-client context, which appears to break
mobile analytics auth if included. Except for the
x-amz-client-context exclusion these defaults are per the
AWS documentation.
"""
if include is None:
include = cls.default_include_headers
include = [x.lower() for x in include]
headers = req.headers.copy()
# Temporarily include the host header - AWS requires it to be included
# in the signed headers, but Requests doesn't include it in a
# PreparedRequest
if 'host' not in headers:
headers['host'] = urlparse(req.url).netloc.split(':')[0]
# Aggregate for upper/lowercase header name collisions in header names,
# AMZ requires values of colliding headers be concatenated into a
# single header with lowercase name. Although this is not possible with
# Requests, since it uses a case-insensitive dict to hold headers, this
# is here just in case you duck type with a regular dict
cano_headers_dict = {}
for hdr, val in headers.items():
hdr = hdr.strip().lower()
val = cls.amz_norm_whitespace(val).strip()
if (hdr in include or '*' in include or
('x-amz-*' in include and hdr.startswith('x-amz-') and not
hdr == 'x-amz-client-context')):
vals = cano_headers_dict.setdefault(hdr, [])
vals.append(val)
# Flatten cano_headers dict to string and generate signed_headers
cano_headers = ''
signed_headers_list = []
for hdr in sorted(cano_headers_dict):
vals = cano_headers_dict[hdr]
val = ','.join(sorted(vals))
cano_headers += '{}:{}\n'.format(hdr, val)
signed_headers_list.append(hdr)
signed_headers = ';'.join(signed_headers_list)
return (cano_headers, signed_headers) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/mvn_linear_operator.py | python | _kl_brute_force | (a, b, name=None) | Batched KL divergence `KL(a || b)` for multivariate Normals.
With `X`, `Y` both multivariate Normals in `R^k` with means `mu_a`, `mu_b` and
covariance `C_a`, `C_b` respectively,
```
KL(a || b) = 0.5 * ( L - k + T + Q ),
L := Log[Det(C_b)] - Log[Det(C_a)]
T := trace(C_b^{-1} C_a),
Q := (mu_b - mu_a)^T C_b^{-1} (mu_b - mu_a),
```
This `Op` computes the trace by solving `C_b^{-1} C_a`. Although efficient
methods for solving systems with `C_b` may be available, a dense version of
(the square root of) `C_a` is used, so performance is `O(B s k**2)` where `B`
is the batch size, and `s` is the cost of solving `C_b x = y` for vectors `x`
and `y`.
Args:
a: Instance of `MultivariateNormalLinearOperator`.
b: Instance of `MultivariateNormalLinearOperator`.
name: (optional) name to use for created ops. Default "kl_mvn".
Returns:
Batchwise `KL(a || b)`. | Batched KL divergence `KL(a || b)` for multivariate Normals. | [
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"KL",
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"(",
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"||",
"b",
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"."
] | def _kl_brute_force(a, b, name=None):
"""Batched KL divergence `KL(a || b)` for multivariate Normals.
With `X`, `Y` both multivariate Normals in `R^k` with means `mu_a`, `mu_b` and
covariance `C_a`, `C_b` respectively,
```
KL(a || b) = 0.5 * ( L - k + T + Q ),
L := Log[Det(C_b)] - Log[Det(C_a)]
T := trace(C_b^{-1} C_a),
Q := (mu_b - mu_a)^T C_b^{-1} (mu_b - mu_a),
```
This `Op` computes the trace by solving `C_b^{-1} C_a`. Although efficient
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Args:
a: Instance of `MultivariateNormalLinearOperator`.
b: Instance of `MultivariateNormalLinearOperator`.
name: (optional) name to use for created ops. Default "kl_mvn".
Returns:
Batchwise `KL(a || b)`.
"""
def squared_frobenius_norm(x):
"""Helper to make KL calculation slightly more readable."""
# http://mathworld.wolfram.com/FrobeniusNorm.html
return math_ops.square(linalg_ops.norm(x, ord="fro", axis=[-2, -1]))
# TODO(b/35041439): See also b/35040945. Remove this function once LinOp
# supports something like:
# A.inverse().solve(B).norm(order='fro', axis=[-1, -2])
def is_diagonal(x):
"""Helper to identify if `LinearOperator` has only a diagonal component."""
return (isinstance(x, linalg.LinearOperatorIdentity) or
isinstance(x, linalg.LinearOperatorScaledIdentity) or
isinstance(x, linalg.LinearOperatorDiag))
with ops.name_scope(name, "kl_mvn", values=[a.loc, b.loc] +
a.scale.graph_parents + b.scale.graph_parents):
# Calculation is based on:
# http://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians
# and,
# https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
# i.e.,
# If Ca = AA', Cb = BB', then
# tr[inv(Cb) Ca] = tr[inv(B)' inv(B) A A']
# = tr[inv(B) A A' inv(B)']
# = tr[(inv(B) A) (inv(B) A)']
# = sum_{ij} (inv(B) A)_{ij}**2
# = ||inv(B) A||_F**2
# where ||.||_F is the Frobenius norm and the second equality follows from
# the cyclic permutation property.
if is_diagonal(a.scale) and is_diagonal(b.scale):
# Using `stddev` because it handles expansion of Identity cases.
b_inv_a = (a.stddev() / b.stddev())[..., array_ops.newaxis]
else:
b_inv_a = b.scale.solve(a.scale.to_dense())
kl_div = (b.scale.log_abs_determinant()
- a.scale.log_abs_determinant()
+ 0.5 * (
- math_ops.cast(a.scale.domain_dimension_tensor(), a.dtype)
+ squared_frobenius_norm(b_inv_a)
+ squared_frobenius_norm(b.scale.solve(
(b.mean() - a.mean())[..., array_ops.newaxis]))))
kl_div.set_shape(array_ops.broadcast_static_shape(
a.batch_shape, b.batch_shape))
return kl_div | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TBool.GetPrimHashCd | (self) | return _snap.TBool_GetPrimHashCd(self) | GetPrimHashCd(TBool self) -> int
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"""
GetPrimHashCd(TBool self) -> int
Parameters:
self: TBool const *
"""
return _snap.TBool_GetPrimHashCd(self) | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/common.py | python | EnsureDirExists | (path) | Make sure the directory for |path| exists. | Make sure the directory for |path| exists. | [
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] | def EnsureDirExists(path):
"""Make sure the directory for |path| exists."""
try:
os.makedirs(os.path.dirname(path))
except OSError:
pass | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/ndarray.py | python | NDArray.ndim | (self) | return len(self.shape) | Returns the number of dimensions of this array
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.ndim
1
>>> x = mx.nd.array([[1, 2], [3, 4]])
>>> x.ndim
2 | Returns the number of dimensions of this array | [
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"""Returns the number of dimensions of this array
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.ndim
1
>>> x = mx.nd.array([[1, 2], [3, 4]])
>>> x.ndim
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"""
return len(self.shape) | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/showbase/Messenger.py | python | Messenger.__methodRepr | (self, method) | return functionName | return string version of class.method or method. | return string version of class.method or method. | [
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] | def __methodRepr(self, method):
"""
return string version of class.method or method.
"""
if isinstance(method, types.MethodType):
functionName = method.__self__.__class__.__name__ + '.' + \
method.__func__.__name__
else:
if hasattr(method, "__name__"):
functionName = method.__name__
else:
return ""
return functionName | [
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sfzhang15/RefineDet | 52b6fe23dc1a160fe710b7734576dca509bf4fae | python/caffe/pycaffe.py | python | _Net_forward_all | (self, blobs=None, **kwargs) | return all_outs | Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict. | Run net forward in batches. | [
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"."
] | def _Net_forward_all(self, blobs=None, **kwargs):
"""
Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict.
"""
# Collect outputs from batches
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
for batch in self._batch(kwargs):
outs = self.forward(blobs=blobs, **batch)
for out, out_blob in six.iteritems(outs):
all_outs[out].extend(out_blob.copy())
# Package in ndarray.
for out in all_outs:
all_outs[out] = np.asarray(all_outs[out])
# Discard padding.
pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out in all_outs:
all_outs[out] = all_outs[out][:-pad]
return all_outs | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pyrsistent/_field_common.py | python | _restore_seq_field_pickle | (checked_class, item_type, data) | return _restore_pickle(type_, data) | Unpickling function for auto-generated PVec/PSet field types. | Unpickling function for auto-generated PVec/PSet field types. | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/ndarray.py | python | NDArray.log | (self, *args, **kwargs) | return op.log(self, *args, **kwargs) | Convenience fluent method for :py:func:`log`.
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The arguments are the same as for :py:func:`log`, with
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"""
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/third_party/bottle/bottle.py | python | BaseResponse.delete_cookie | (self, key, **kwargs) | Delete a cookie. Be sure to use the same `domain` and `path`
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kwargs['max_age'] = -1
kwargs['expires'] = 0
self.set_cookie(key, '', **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_basestc.py | python | EditraBaseStc.GetFileName | (self) | return self.file.GetPath() | Returns the full path name of the current file
@return: full path name of document | Returns the full path name of the current file
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return self.file.GetPath() | [
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/fusion/portableglobe/cutter/cgi-bin/common/form_wrap.py | python | FormWrap.getvalue_url | (self, key, default_val=None) | return val | Gets an URL value (absolute or relative).
Args:
key: requested argument.
default_val: a default value to return if requested argument is not
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"""Gets an URL value (absolute or relative).
Args:
key: requested argument.
default_val: a default value to return if requested argument is not
present.
Returns:
value for requested argument.
"""
logging.debug("getvalue_url() - key: %s", key)
val = self._getvalue_raw(key, default_val)
if not val:
return val
if self._do_sanitize:
val = self._sanitize_url(val)
logging.debug("getvalue_url() - key: %s, val: %s", key, val)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/encodings/punycode.py | python | selective_find | (str, char, index, pos) | Return a pair (index, pos), indicating the next occurrence of
char in str. index is the position of the character considering
only ordinals up to and including char, and pos is the position in
the full string. index/pos is the starting position in the full
string. | Return a pair (index, pos), indicating the next occurrence of
char in str. index is the position of the character considering
only ordinals up to and including char, and pos is the position in
the full string. index/pos is the starting position in the full
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/distutils/command/sdist.py | python | sdist.make_release_tree | (self, base_dir, files) | Create the directory tree that will become the source
distribution archive. All directories implied by the filenames in
'files' are created under 'base_dir', and then we hard link or copy
(if hard linking is unavailable) those files into place.
Essentially, this duplicates the developer's source tree, but in a
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'files' are created under 'base_dir', and then we hard link or copy
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Essentially, this duplicates the developer's source tree, but in a
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"""Create the directory tree that will become the source
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(if hard linking is unavailable) those files into place.
Essentially, this duplicates the developer's source tree, but in a
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"""
# Create all the directories under 'base_dir' necessary to
# put 'files' there; the 'mkpath()' is just so we don't die
# if the manifest happens to be empty.
self.mkpath(base_dir)
dir_util.create_tree(base_dir, files, dry_run=self.dry_run)
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if hasattr(os, 'link'): # can make hard links on this system
link = 'hard'
msg = "making hard links in %s..." % base_dir
else: # nope, have to copy
link = None
msg = "copying files to %s..." % base_dir
if not files:
log.warn("no files to distribute -- empty manifest?")
else:
log.info(msg)
for file in files:
if not os.path.isfile(file):
log.warn("'%s' not a regular file -- skipping" % file)
else:
dest = os.path.join(base_dir, file)
self.copy_file(file, dest, link=link)
self.distribution.metadata.write_pkg_info(base_dir) | [
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bh107/bohrium | 5b83e7117285fefc7779ed0e9acb0f8e74c7e068 | bridge/npbackend/bohrium/masking.py | python | masked_get | (ary, bool_mask) | return ary[reorganization.nonzero(bool_mask)] | Get the elements of 'ary' specified by 'bool_mask'. | Get the elements of 'ary' specified by 'bool_mask'. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mhlib.py | python | MH.__init__ | (self, path = None, profile = None) | Constructor. | Constructor. | [
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path = os.path.expanduser(path)
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self.path = path | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/datasets/species_distributions.py | python | fetch_species_distributions | (data_home=None,
download_if_missing=True) | return bunch | Loader for species distribution dataset from Phillips et. al. (2006)
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
data_home : optional, default: None
Specify another download and cache folder for the datasets. By default
all scikit learn data is stored in '~/scikit_learn_data' subfolders.
download_if_missing : optional, True by default
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
--------
The data is returned as a Bunch object with the following attributes:
coverages : array, shape = [14, 1592, 1212]
These represent the 14 features measured at each point of the map grid.
The latitude/longitude values for the grid are discussed below.
Missing data is represented by the value -9999.
train : record array, shape = (1623,)
The training points for the data. Each point has three fields:
- train['species'] is the species name
- train['dd long'] is the longitude, in degrees
- train['dd lat'] is the latitude, in degrees
test : record array, shape = (619,)
The test points for the data. Same format as the training data.
Nx, Ny : integers
The number of longitudes (x) and latitudes (y) in the grid
x_left_lower_corner, y_left_lower_corner : floats
The (x,y) position of the lower-left corner, in degrees
grid_size : float
The spacing between points of the grid, in degrees
Notes
------
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
<http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
References
----------
* `"Maximum entropy modeling of species geographic distributions"
<http://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf>`_
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.
Notes
-----
* See examples/applications/plot_species_distribution_modeling.py
for an example of using this dataset with scikit-learn | Loader for species distribution dataset from Phillips et. al. (2006) | [
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download_if_missing=True):
"""Loader for species distribution dataset from Phillips et. al. (2006)
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
data_home : optional, default: None
Specify another download and cache folder for the datasets. By default
all scikit learn data is stored in '~/scikit_learn_data' subfolders.
download_if_missing : optional, True by default
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
--------
The data is returned as a Bunch object with the following attributes:
coverages : array, shape = [14, 1592, 1212]
These represent the 14 features measured at each point of the map grid.
The latitude/longitude values for the grid are discussed below.
Missing data is represented by the value -9999.
train : record array, shape = (1623,)
The training points for the data. Each point has three fields:
- train['species'] is the species name
- train['dd long'] is the longitude, in degrees
- train['dd lat'] is the latitude, in degrees
test : record array, shape = (619,)
The test points for the data. Same format as the training data.
Nx, Ny : integers
The number of longitudes (x) and latitudes (y) in the grid
x_left_lower_corner, y_left_lower_corner : floats
The (x,y) position of the lower-left corner, in degrees
grid_size : float
The spacing between points of the grid, in degrees
Notes
------
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
<http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
References
----------
* `"Maximum entropy modeling of species geographic distributions"
<http://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf>`_
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.
Notes
-----
* See examples/applications/plot_species_distribution_modeling.py
for an example of using this dataset with scikit-learn
"""
data_home = get_data_home(data_home)
if not exists(data_home):
makedirs(data_home)
# Define parameters for the data files. These should not be changed
# unless the data model changes. They will be saved in the npz file
# with the downloaded data.
extra_params = dict(x_left_lower_corner=-94.8,
Nx=1212,
y_left_lower_corner=-56.05,
Ny=1592,
grid_size=0.05)
dtype = np.int16
archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)
if not exists(archive_path):
print('Downloading species data from %s to %s' % (SAMPLES_URL,
data_home))
X = np.load(BytesIO(urlopen(SAMPLES_URL).read()))
for f in X.files:
fhandle = BytesIO(X[f])
if 'train' in f:
train = _load_csv(fhandle)
if 'test' in f:
test = _load_csv(fhandle)
print('Downloading coverage data from %s to %s' % (COVERAGES_URL,
data_home))
X = np.load(BytesIO(urlopen(COVERAGES_URL).read()))
coverages = []
for f in X.files:
fhandle = BytesIO(X[f])
print(' - converting', f)
coverages.append(_load_coverage(fhandle))
coverages = np.asarray(coverages, dtype=dtype)
bunch = Bunch(coverages=coverages,
test=test,
train=train,
**extra_params)
joblib.dump(bunch, archive_path, compress=9)
else:
bunch = joblib.load(archive_path)
return bunch | [
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isc-projects/kea | c5836c791b63f42173bb604dd5f05d7110f3e716 | hammer.py | python | _configure_pgsql | (system, features) | Configure PostgreSQL DB | Configure PostgreSQL DB | [
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""" Configure PostgreSQL DB """
# execute() calls will set cwd='/tmp' when switching user to postgres to
# avoid the error:
# could not change as postgres user directory to "/home/jenkins": Permission denied
if system in ['fedora', 'centos']:
# https://fedoraproject.org/wiki/PostgreSQL
exitcode = execute('sudo ls /var/lib/pgsql/data/postgresql.conf', raise_error=False)
if exitcode != 0:
if system == 'centos':
execute('sudo postgresql-setup initdb')
else:
execute('sudo postgresql-setup --initdb --unit postgresql')
elif system == 'freebsd':
# Stop any hypothetical existing postgres service.
execute('sudo service postgresql stop || true')
# Get the path to the data directory e.g. /var/db/postgres/data11 for
# FreeBSD 12 and /var/db/postgres/data13 for FreeBSD 13.
_, output = execute('ls -1d /var/db/postgres/data*', capture=True)
var_db_postgres_data = output.rstrip()
# Create postgres internals.
execute('sudo test ! -d {} && sudo /usr/local/etc/rc.d/postgresql oneinitdb || true'.format(var_db_postgres_data))
# if the file '/var/db/postgres/data*/postmaster.opts' does not exist the 'restart' of postgresql will fail with error:
# pg_ctl: could not read file "/var/db/postgres/data*/postmaster.opts"
# the initial start of the postgresql will create the 'postmaster.opts' file
execute('sudo test ! -f {}/postmaster.opts && sudo service postgresql onestart || true'.format(var_db_postgres_data))
_enable_postgresql(system)
_restart_postgresql(system)
# Change auth-method to 'md5' on all connections.
cmd = "sudo -u postgres psql -t -c 'SHOW hba_file' | xargs"
_, output = execute(cmd, capture=True, cwd='/tmp')
hba_file = output.rstrip()
_change_postgresql_auth_method('host', 'md5', hba_file)
_change_postgresql_auth_method('local', 'md5', hba_file)
# Make sure hba file has a postgres superuser entry. It needs to be placed
# before any other local auth method for higher priority. Let's simulate
# that by putting it just after the auth header.
if 0 != execute("sudo cat {} | grep -E '^local.*all.*postgres'".format(hba_file), raise_error=False):
auth_header='# TYPE DATABASE USER ADDRESS METHOD'
postgres_auth_line='local all postgres ident'
# The "\\" followed by newline is for BSD support.
execute("""sudo sed -i.bak '/{}/a\\
{}
' '{}'""".format(auth_header, postgres_auth_line, hba_file))
_restart_postgresql(system)
cmd = """bash -c \"cat <<EOF | sudo -u postgres psql postgres
DROP DATABASE IF EXISTS keatest;
DROP USER IF EXISTS keatest;
DROP USER IF EXISTS keatest_readonly;
CREATE USER keatest WITH PASSWORD 'keatest';
CREATE USER keatest_readonly WITH PASSWORD 'keatest';
CREATE DATABASE keatest;
GRANT ALL PRIVILEGES ON DATABASE keatest TO keatest;
ALTER DATABASE keatest SET TIMEZONE='{}';\n""".format(_get_local_timezone())
cmd += 'EOF\n"'
execute(cmd, cwd='/tmp')
cmd = """bash -c \"cat <<EOF | sudo -u postgres psql -U keatest keatest
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO keatest_readonly;\n"""
cmd += 'EOF\n"'
env = os.environ.copy()
env['PGPASSWORD'] = 'keatest'
execute(cmd, cwd='/tmp', env=env)
if 'forge' in features:
cmd = "bash -c \"cat <<EOF | sudo -u postgres psql postgres\n"
cmd += "DROP DATABASE IF EXISTS keadb;\n"
cmd += "DROP USER IF EXISTS keauser;\n"
cmd += "CREATE USER keauser WITH PASSWORD 'keapass';\n"
cmd += "CREATE DATABASE keadb;\n"
cmd += "GRANT ALL PRIVILEGES ON DATABASE keauser TO keadb;\n"
cmd += "EOF\n\""
execute(cmd, cwd='/tmp')
log.info('postgresql just configured') | [
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"... | https://github.com/isc-projects/kea/blob/c5836c791b63f42173bb604dd5f05d7110f3e716/hammer.py#L1213-L1297 | ||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/plot.py | python | PlotGraphics.getYLabel | (self) | return self.yLabel | Get y axis label string | Get y axis label string | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBBreakpointLocation.GetQueueName | (self) | return _lldb.SBBreakpointLocation_GetQueueName(self) | GetQueueName(SBBreakpointLocation self) -> char const * | GetQueueName(SBBreakpointLocation self) -> char const * | [
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"""GetQueueName(SBBreakpointLocation self) -> char const *"""
return _lldb.SBBreakpointLocation_GetQueueName(self) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Context.py | python | Context.end_msg | (self, result, color=None) | Print the end of a 'Checking for' message. See :py:meth:`waflib.Context.Context.msg` | Print the end of a 'Checking for' message. See :py:meth:`waflib.Context.Context.msg` | [
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"""Print the end of a 'Checking for' message. See :py:meth:`waflib.Context.Context.msg`"""
self.in_msg -= 1
if self.in_msg:
return
defcolor = 'GREEN'
if result == True:
msg = 'ok'
elif result == False:
msg = 'not found'
defcolor = 'YELLOW'
else:
msg = str(result)
#self.to_log(msg)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | StaticLine.GetClassDefaultAttributes | (*args, **kwargs) | return _controls_.StaticLine_GetClassDefaultAttributes(*args, **kwargs) | GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
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user's system, especially if it uses themes.
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"""
GetClassDefaultAttributes(int variant=WINDOW_VARIANT_NORMAL) -> VisualAttributes
Get the default attributes for this class. This is useful if you want
to use the same font or colour in your own control as in a standard
control -- which is a much better idea than hard coding specific
colours or fonts which might look completely out of place on the
user's system, especially if it uses themes.
The variant parameter is only relevant under Mac currently and is
ignore under other platforms. Under Mac, it will change the size of
the returned font. See `wx.Window.SetWindowVariant` for more about
this.
"""
return _controls_.StaticLine_GetClassDefaultAttributes(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/logging/config.py | python | _install_loggers | (cp, handlers, disable_existing) | Create and install loggers | Create and install loggers | [
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"install",
"loggers"
] | def _install_loggers(cp, handlers, disable_existing):
"""Create and install loggers"""
# configure the root first
llist = cp["loggers"]["keys"]
llist = llist.split(",")
llist = list(_strip_spaces(llist))
llist.remove("root")
section = cp["logger_root"]
root = logging.root
log = root
if "level" in section:
level = section["level"]
log.setLevel(level)
for h in root.handlers[:]:
root.removeHandler(h)
hlist = section["handlers"]
if len(hlist):
hlist = hlist.split(",")
hlist = _strip_spaces(hlist)
for hand in hlist:
log.addHandler(handlers[hand])
#and now the others...
#we don't want to lose the existing loggers,
#since other threads may have pointers to them.
#existing is set to contain all existing loggers,
#and as we go through the new configuration we
#remove any which are configured. At the end,
#what's left in existing is the set of loggers
#which were in the previous configuration but
#which are not in the new configuration.
existing = list(root.manager.loggerDict.keys())
#The list needs to be sorted so that we can
#avoid disabling child loggers of explicitly
#named loggers. With a sorted list it is easier
#to find the child loggers.
existing.sort()
#We'll keep the list of existing loggers
#which are children of named loggers here...
child_loggers = []
#now set up the new ones...
for log in llist:
section = cp["logger_%s" % log]
qn = section["qualname"]
propagate = section.getint("propagate", fallback=1)
logger = logging.getLogger(qn)
if qn in existing:
i = existing.index(qn) + 1 # start with the entry after qn
prefixed = qn + "."
pflen = len(prefixed)
num_existing = len(existing)
while i < num_existing:
if existing[i][:pflen] == prefixed:
child_loggers.append(existing[i])
i += 1
existing.remove(qn)
if "level" in section:
level = section["level"]
logger.setLevel(level)
for h in logger.handlers[:]:
logger.removeHandler(h)
logger.propagate = propagate
logger.disabled = 0
hlist = section["handlers"]
if len(hlist):
hlist = hlist.split(",")
hlist = _strip_spaces(hlist)
for hand in hlist:
logger.addHandler(handlers[hand])
#Disable any old loggers. There's no point deleting
#them as other threads may continue to hold references
#and by disabling them, you stop them doing any logging.
#However, don't disable children of named loggers, as that's
#probably not what was intended by the user.
#for log in existing:
# logger = root.manager.loggerDict[log]
# if log in child_loggers:
# logger.level = logging.NOTSET
# logger.handlers = []
# logger.propagate = 1
# elif disable_existing_loggers:
# logger.disabled = 1
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apiaryio/drafter | 4634ebd07f6c6f257cc656598ccd535492fdfb55 | tools/gyp/pylib/gyp/MSVSVersion.py | python | VisualStudioVersion.DefaultToolset | (self) | return self.default_toolset | Returns the msbuild toolset version that will be used in the absence
of a user override. | Returns the msbuild toolset version that will be used in the absence
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/socksipy-branch/socks.py | python | socksocket.connect | (self, destpair) | connect(self, despair)
Connects to the specified destination through a proxy.
destpar - A tuple of the IP/DNS address and the port number.
(identical to socket's connect).
To select the proxy server use setproxy(). | connect(self, despair)
Connects to the specified destination through a proxy.
destpar - A tuple of the IP/DNS address and the port number.
(identical to socket's connect).
To select the proxy server use setproxy(). | [
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# Do a minimal input check first
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portnum = self.__proxy[2]
else:
portnum = 1080
_orgsocket.connect(self, (self.__proxy[1], portnum))
self.__negotiatesocks5(destpair[0], destpair[1])
elif self.__proxy[0] == PROXY_TYPE_SOCKS4:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/s3transfer/futures.py | python | TransferFuture.__init__ | (self, meta=None, coordinator=None) | The future associated to a submitted transfer request
:type meta: TransferMeta
:param meta: The metadata associated to the request. This object
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:type coordinator: TransferCoordinator
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/calendar.py | python | CalendarCtrlBase.Mark | (*args, **kwargs) | return _calendar.CalendarCtrlBase_Mark(*args, **kwargs) | Mark(self, size_t day, bool mark) | Mark(self, size_t day, bool mark) | [
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wesnoth/wesnoth | 6ccac5a5e8ff75303c9190c0da60580925cb32c0 | data/tools/wesnoth/wmliterator3.py | python | isMacroCloser | (elem) | return type(elem) == type("") and elem == closeMacroType | Are we looking at a macro closer? | Are we looking at a macro closer? | [
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"Are we looking at a macro closer?"
if isinstance(elem, WmlIterator):
elem = elem.element
return type(elem) == type("") and elem == closeMacroType | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Window.SetInitialSize | (*args, **kwargs) | return _core_.Window_SetInitialSize(*args, **kwargs) | SetInitialSize(self, Size size=DefaultSize)
A 'Smart' SetSize that will fill in default size components with the
window's *best size* values. Also set's the minsize for use with sizers. | SetInitialSize(self, Size size=DefaultSize) | [
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"""
SetInitialSize(self, Size size=DefaultSize)
A 'Smart' SetSize that will fill in default size components with the
window's *best size* values. Also set's the minsize for use with sizers.
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return _core_.Window_SetInitialSize(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/frame.py | python | DataFrame.from_records | (
cls,
data,
index=None,
exclude=None,
columns=None,
coerce_float: bool = False,
nrows: int | None = None,
) | return cls(mgr) | Convert structured or record ndarray to DataFrame.
Creates a DataFrame object from a structured ndarray, sequence of
tuples or dicts, or DataFrame.
Parameters
----------
data : structured ndarray, sequence of tuples or dicts, or DataFrame
Structured input data.
index : str, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use.
exclude : sequence, default None
Columns or fields to exclude.
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns).
coerce_float : bool, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets.
nrows : int, default None
Number of rows to read if data is an iterator.
Returns
-------
DataFrame
See Also
--------
DataFrame.from_dict : DataFrame from dict of array-like or dicts.
DataFrame : DataFrame object creation using constructor.
Examples
--------
Data can be provided as a structured ndarray:
>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
... dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of dicts:
>>> data = [{'col_1': 3, 'col_2': 'a'},
... {'col_1': 2, 'col_2': 'b'},
... {'col_1': 1, 'col_2': 'c'},
... {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of tuples with corresponding columns:
>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d | Convert structured or record ndarray to DataFrame. | [
"Convert",
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] | def from_records(
cls,
data,
index=None,
exclude=None,
columns=None,
coerce_float: bool = False,
nrows: int | None = None,
) -> DataFrame:
"""
Convert structured or record ndarray to DataFrame.
Creates a DataFrame object from a structured ndarray, sequence of
tuples or dicts, or DataFrame.
Parameters
----------
data : structured ndarray, sequence of tuples or dicts, or DataFrame
Structured input data.
index : str, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use.
exclude : sequence, default None
Columns or fields to exclude.
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns).
coerce_float : bool, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets.
nrows : int, default None
Number of rows to read if data is an iterator.
Returns
-------
DataFrame
See Also
--------
DataFrame.from_dict : DataFrame from dict of array-like or dicts.
DataFrame : DataFrame object creation using constructor.
Examples
--------
Data can be provided as a structured ndarray:
>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
... dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of dicts:
>>> data = [{'col_1': 3, 'col_2': 'a'},
... {'col_1': 2, 'col_2': 'b'},
... {'col_1': 1, 'col_2': 'c'},
... {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of tuples with corresponding columns:
>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
"""
# Make a copy of the input columns so we can modify it
if columns is not None:
columns = ensure_index(columns)
if is_iterator(data):
if nrows == 0:
return cls()
try:
first_row = next(data)
except StopIteration:
return cls(index=index, columns=columns)
dtype = None
if hasattr(first_row, "dtype") and first_row.dtype.names:
dtype = first_row.dtype
values = [first_row]
if nrows is None:
values += data
else:
values.extend(itertools.islice(data, nrows - 1))
if dtype is not None:
data = np.array(values, dtype=dtype)
else:
data = values
if isinstance(data, dict):
if columns is None:
columns = arr_columns = ensure_index(sorted(data))
arrays = [data[k] for k in columns]
else:
arrays = []
arr_columns_list = []
for k, v in data.items():
if k in columns:
arr_columns_list.append(k)
arrays.append(v)
arr_columns = Index(arr_columns_list)
arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns)
elif isinstance(data, (np.ndarray, DataFrame)):
arrays, columns = to_arrays(data, columns)
arr_columns = columns
else:
arrays, arr_columns = to_arrays(data, columns)
if coerce_float:
for i, arr in enumerate(arrays):
if arr.dtype == object:
# error: Argument 1 to "maybe_convert_objects" has
# incompatible type "Union[ExtensionArray, ndarray]";
# expected "ndarray"
arrays[i] = lib.maybe_convert_objects(
arr, # type: ignore[arg-type]
try_float=True,
)
arr_columns = ensure_index(arr_columns)
if columns is None:
columns = arr_columns
if exclude is None:
exclude = set()
else:
exclude = set(exclude)
result_index = None
if index is not None:
if isinstance(index, str) or not hasattr(index, "__iter__"):
i = columns.get_loc(index)
exclude.add(index)
if len(arrays) > 0:
result_index = Index(arrays[i], name=index)
else:
result_index = Index([], name=index)
else:
try:
index_data = [arrays[arr_columns.get_loc(field)] for field in index]
except (KeyError, TypeError):
# raised by get_loc, see GH#29258
result_index = index
else:
result_index = ensure_index_from_sequences(index_data, names=index)
exclude.update(index)
if any(exclude):
arr_exclude = [x for x in exclude if x in arr_columns]
to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
arr_columns = arr_columns.drop(arr_exclude)
columns = columns.drop(exclude)
manager = get_option("mode.data_manager")
mgr = arrays_to_mgr(arrays, arr_columns, result_index, columns, typ=manager)
return cls(mgr) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/pbr/packaging.py | python | override_get_script_args | (
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/numbers.py | python | Complex.imag | (self) | Retrieve the imaginary component of this number.
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HyeonwooNoh/caffe | d9e8494a2832d67b25dee37194c7bcb9d52d0e42 | scripts/cpp_lint.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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ablab/spades | 3a754192b88540524ce6fb69eef5ea9273a38465 | webvis/pydot.py | python | Dot.write | (self, path, prog=None, format='raw') | return True | Writes a graph to a file.
Given a filename 'path' it will open/create and truncate
such file and write on it a representation of the graph
defined by the dot object and in the format specified by
'format'.
The format 'raw' is used to dump the string representation
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Returns True or False according to the success of the write
operation.
There's also the preferred possibility of using:
write_'format'(path, prog='program')
which are automatically defined for all the supported formats.
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Given a filename 'path' it will open/create and truncate
such file and write on it a representation of the graph
defined by the dot object and in the format specified by
'format'.
The format 'raw' is used to dump the string representation
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The output can be processed by any of graphviz tools, defined
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Returns True or False according to the success of the write
operation.
There's also the preferred possibility of using:
write_'format'(path, prog='program')
which are automatically defined for all the supported formats.
[write_ps(), write_gif(), write_dia(), ...]
"""
if prog is None:
prog = self.prog
dot_fd = file(path, "w+b")
if format == 'raw':
data = self.to_string()
if isinstance(data, basestring):
if not isinstance(data, unicode):
try:
data = unicode(data, 'utf-8')
except:
pass
try:
data = data.encode('utf-8')
except:
pass
dot_fd.write(data)
else:
dot_fd.write(self.create(prog, format))
dot_fd.close()
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/Chem/ChemUtils/SDFToCSV.py | python | initParser | () | return parser | Initialize the parser for the CLI | Initialize the parser for the CLI | [
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""" Initialize the parser for the CLI """
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parser.add_argument('--key', '-k', metavar='keyCol', default=None, dest='keyCol')
parser.add_argument('--chiral', default=False, action='store_true', dest='useChirality')
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/asyncio/events.py | python | set_event_loop_policy | (policy) | Set the current event loop policy.
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If policy is None, the default policy is restored."""
global _event_loop_policy
assert policy is None or isinstance(policy, AbstractEventLoopPolicy)
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danxuhk/ContinuousCRF-CNN | 2b6dcaf179620f118b225ed12c890414ca828e21 | scripts/cpp_lint.py | python | Match | (pattern, s) | return _regexp_compile_cache[pattern].match(s) | Matches the string with the pattern, caching the compiled regexp. | Matches the string with the pattern, caching the compiled regexp. | [
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/carla/agents/navigation/behavior_agent.py | python | BehaviorAgent.collision_and_car_avoid_manager | (self, waypoint) | return vehicle_state, vehicle, distance | This module is in charge of warning in case of a collision
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:param location: current location of the agent
:param waypoint: current waypoint of the agent
:return vehicle_state: True if there is a vehicle nearby, False if not
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:param waypoint: current waypoint of the agent
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vehicle_list = self._world.get_actors().filter("*vehicle*")
def dist(v): return v.get_location().distance(waypoint.transform.location)
vehicle_list = [v for v in vehicle_list if dist(v) < 45 and v.id != self._vehicle.id]
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/hypertreelist.py | python | TreeListColumnInfo.SetEditable | (self, edit) | return self | Sets the column as editable or non-editable.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/turtle.py | python | RawTurtle._update | (self) | Perform a Turtle-data update. | Perform a Turtle-data update. | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Script/Main.py | python | _scons_syntax_error | (e) | Handle syntax errors. Print out a message and show where the error
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"sys... | https://github.com/SoarGroup/Soar/blob/a1c5e249499137a27da60533c72969eef3b8ab6b/scons/scons-local-4.1.0/SCons/Script/Main.py#L542-L550 | ||
google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/service_reflection.py | python | GeneratedServiceType.__init__ | (cls, name, bases, dictionary) | Creates a message service class.
Args:
name: Name of the class (ignored, but required by the metaclass
protocol).
bases: Base classes of the class being constructed.
dictionary: The class dictionary of the class being constructed.
dictionary[_DESCRIPTOR_KEY] must contain a ServiceDescriptor object
describing this protocol service type. | Creates a message service class. | [
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] | def __init__(cls, name, bases, dictionary):
"""Creates a message service class.
Args:
name: Name of the class (ignored, but required by the metaclass
protocol).
bases: Base classes of the class being constructed.
dictionary: The class dictionary of the class being constructed.
dictionary[_DESCRIPTOR_KEY] must contain a ServiceDescriptor object
describing this protocol service type.
"""
# Don't do anything if this class doesn't have a descriptor. This happens
# when a service class is subclassed.
if GeneratedServiceType._DESCRIPTOR_KEY not in dictionary:
return
descriptor = dictionary[GeneratedServiceType._DESCRIPTOR_KEY]
service_builder = _ServiceBuilder(descriptor)
service_builder.BuildService(cls) | [
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freesurfer/freesurfer | 6dbe527d43ffa611acb2cd112e9469f9bfec8e36 | python/freesurfer/surface.py | python | Surface.compute_tangents | (self) | Compute and cache vertex tangents along primary curvature direction. | Compute and cache vertex tangents along primary curvature direction. | [
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] | def compute_tangents(self):
'''Compute and cache vertex tangents along primary curvature direction.'''
bindings.surf.compute_tangents(self) | [
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