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Qihoo360/mongosync | 55b647e81c072ebe91daaa3b9dc1a953c3c22e19 | dep/mongo-cxx-driver/site_scons/buildscripts/cpplint.py | python | CheckBraces | (filename, clean_lines, linenum, error) | Looks for misplaced braces (e.g. at the end of line).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Looks for misplaced braces (e.g. at the end of line). | [
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] | def CheckBraces(filename, clean_lines, linenum, error):
"""Looks for misplaced braces (e.g. at the end of line).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
if Match(r'\s*{\s*$', line):
# We allow an open brace to start a line in the case where someone
# is using braces in a block to explicitly create a new scope,
# which is commonly used to control the lifetime of
# stack-allocated variables. We don't detect this perfectly: we
# just don't complain if the last non-whitespace character on the
# previous non-blank line is ';', ':', '{', or '}'.
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if not Search(r'[;:}{]\s*$', prevline):
error(filename, linenum, 'whitespace/braces', 4,
'{ should almost always be at the end of the previous line')
# An else clause should be on the same line as the preceding closing brace.
if Match(r'\s*else\s*', line):
prevline = GetPreviousNonBlankLine(clean_lines, linenum)[0]
if Match(r'\s*}\s*$', prevline):
error(filename, linenum, 'whitespace/newline', 4,
'An else should appear on the same line as the preceding }')
# If braces come on one side of an else, they should be on both.
# However, we have to worry about "else if" that spans multiple lines!
if Search(r'}\s*else[^{]*$', line) or Match(r'[^}]*else\s*{', line):
if Search(r'}\s*else if([^{]*)$', line): # could be multi-line if
# find the ( after the if
pos = line.find('else if')
pos = line.find('(', pos)
if pos > 0:
(endline, _, endpos) = CloseExpression(clean_lines, linenum, pos)
if endline[endpos:].find('{') == -1: # must be brace after if
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
else: # common case: else not followed by a multi-line if
error(filename, linenum, 'readability/braces', 5,
'If an else has a brace on one side, it should have it on both')
# Likewise, an else should never have the else clause on the same line
if Search(r'\belse [^\s{]', line) and not Search(r'\belse if\b', line):
error(filename, linenum, 'whitespace/newline', 4,
'Else clause should never be on same line as else (use 2 lines)')
# In the same way, a do/while should never be on one line
if Match(r'\s*do [^\s{]', line):
error(filename, linenum, 'whitespace/newline', 4,
'do/while clauses should not be on a single line')
# Braces shouldn't be followed by a ; unless they're defining a struct
# or initializing an array.
# We can't tell in general, but we can for some common cases.
prevlinenum = linenum
while True:
(prevline, prevlinenum) = GetPreviousNonBlankLine(clean_lines, prevlinenum)
if Match(r'\s+{.*}\s*;', line) and not prevline.count(';'):
line = prevline + line
else:
break
if (Search(r'{.*}\s*;', line) and
line.count('{') == line.count('}') and
not Search(r'struct|class|enum|\s*=\s*{', line)):
error(filename, linenum, 'readability/braces', 4,
"You don't need a ; after a }") | [
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] | https://github.com/Qihoo360/mongosync/blob/55b647e81c072ebe91daaa3b9dc1a953c3c22e19/dep/mongo-cxx-driver/site_scons/buildscripts/cpplint.py#L1993-L2064 | ||
sigmaai/self-driving-golf-cart | 8d891600af3d851add27a10ae45cf3c2108bb87c | ros/src/ros_carla_bridge/carla_ackermann_control/src/carla_ackermann_control/carla_ackermann_control_node.py | python | CarlaAckermannControl.reconfigure_pid_parameters | (self, ego_vehicle_control_parameter, _level) | return ego_vehicle_control_parameter | Callback for dynamic reconfigure call to set the PID parameters
:param ego_vehicle_control_parameter:
:type ego_vehicle_control_parameter: \
carla_ackermann_control.cfg.EgoVehicleControlParameterConfig | Callback for dynamic reconfigure call to set the PID parameters | [
"Callback",
"for",
"dynamic",
"reconfigure",
"call",
"to",
"set",
"the",
"PID",
"parameters"
] | def reconfigure_pid_parameters(self, ego_vehicle_control_parameter, _level):
"""
Callback for dynamic reconfigure call to set the PID parameters
:param ego_vehicle_control_parameter:
:type ego_vehicle_control_parameter: \
carla_ackermann_control.cfg.EgoVehicleControlParameterConfig
"""
rospy.loginfo("Reconfigure Request: "
"speed ({speed_Kp}, {speed_Ki}, {speed_Kd}),"
"accel ({accel_Kp}, {accel_Ki}, {accel_Kd}),"
"".format(**ego_vehicle_control_parameter))
self.speed_controller.tunings = (
ego_vehicle_control_parameter['speed_Kp'],
ego_vehicle_control_parameter['speed_Ki'],
ego_vehicle_control_parameter['speed_Kd']
)
self.accel_controller.tunings = (
ego_vehicle_control_parameter['accel_Kp'],
ego_vehicle_control_parameter['accel_Ki'],
ego_vehicle_control_parameter['accel_Kd']
)
return ego_vehicle_control_parameter | [
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | external_tools/ply_info/example/ansic/cparse.py | python | p_and_expression_2 | (t) | and_expression : and_expression AND equality_expression | and_expression : and_expression AND equality_expression | [
"and_expression",
":",
"and_expression",
"AND",
"equality_expression"
] | def p_and_expression_2(t):
'and_expression : and_expression AND equality_expression'
pass | [
"def",
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")",
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] | https://github.com/echronos/echronos/blob/c996f1d2c8af6c6536205eb319c1bf1d4d84569c/external_tools/ply_info/example/ansic/cparse.py#L661-L663 | ||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_root.py | python | _root_broyden1_doc | () | Options
-------
nit : int, optional
Number of iterations to make. If omitted (default), make as many
as required to meet tolerances.
disp : bool, optional
Print status to stdout on every iteration.
maxiter : int, optional
Maximum number of iterations to make. If more are needed to
meet convergence, `NoConvergence` is raised.
ftol : float, optional
Relative tolerance for the residual. If omitted, not used.
fatol : float, optional
Absolute tolerance (in max-norm) for the residual.
If omitted, default is 6e-6.
xtol : float, optional
Relative minimum step size. If omitted, not used.
xatol : float, optional
Absolute minimum step size, as determined from the Jacobian
approximation. If the step size is smaller than this, optimization
is terminated as successful. If omitted, not used.
tol_norm : function(vector) -> scalar, optional
Norm to use in convergence check. Default is the maximum norm.
line_search : {None, 'armijo' (default), 'wolfe'}, optional
Which type of a line search to use to determine the step size in
the direction given by the Jacobian approximation. Defaults to
'armijo'.
jac_options : dict, optional
Options for the respective Jacobian approximation.
alpha : float, optional
Initial guess for the Jacobian is (-1/alpha).
reduction_method : str or tuple, optional
Method used in ensuring that the rank of the Broyden
matrix stays low. Can either be a string giving the
name of the method, or a tuple of the form ``(method,
param1, param2, ...)`` that gives the name of the
method and values for additional parameters.
Methods available:
- ``restart``: drop all matrix columns. Has no
extra parameters.
- ``simple``: drop oldest matrix column. Has no
extra parameters.
- ``svd``: keep only the most significant SVD
components.
Extra parameters:
- ``to_retain``: number of SVD components to
retain when rank reduction is done.
Default is ``max_rank - 2``.
max_rank : int, optional
Maximum rank for the Broyden matrix.
Default is infinity (ie., no rank reduction). | Options
-------
nit : int, optional
Number of iterations to make. If omitted (default), make as many
as required to meet tolerances.
disp : bool, optional
Print status to stdout on every iteration.
maxiter : int, optional
Maximum number of iterations to make. If more are needed to
meet convergence, `NoConvergence` is raised.
ftol : float, optional
Relative tolerance for the residual. If omitted, not used.
fatol : float, optional
Absolute tolerance (in max-norm) for the residual.
If omitted, default is 6e-6.
xtol : float, optional
Relative minimum step size. If omitted, not used.
xatol : float, optional
Absolute minimum step size, as determined from the Jacobian
approximation. If the step size is smaller than this, optimization
is terminated as successful. If omitted, not used.
tol_norm : function(vector) -> scalar, optional
Norm to use in convergence check. Default is the maximum norm.
line_search : {None, 'armijo' (default), 'wolfe'}, optional
Which type of a line search to use to determine the step size in
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jac_options : dict, optional
Options for the respective Jacobian approximation.
alpha : float, optional
Initial guess for the Jacobian is (-1/alpha).
reduction_method : str or tuple, optional
Method used in ensuring that the rank of the Broyden
matrix stays low. Can either be a string giving the
name of the method, or a tuple of the form ``(method,
param1, param2, ...)`` that gives the name of the
method and values for additional parameters. | [
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"""
Options
-------
nit : int, optional
Number of iterations to make. If omitted (default), make as many
as required to meet tolerances.
disp : bool, optional
Print status to stdout on every iteration.
maxiter : int, optional
Maximum number of iterations to make. If more are needed to
meet convergence, `NoConvergence` is raised.
ftol : float, optional
Relative tolerance for the residual. If omitted, not used.
fatol : float, optional
Absolute tolerance (in max-norm) for the residual.
If omitted, default is 6e-6.
xtol : float, optional
Relative minimum step size. If omitted, not used.
xatol : float, optional
Absolute minimum step size, as determined from the Jacobian
approximation. If the step size is smaller than this, optimization
is terminated as successful. If omitted, not used.
tol_norm : function(vector) -> scalar, optional
Norm to use in convergence check. Default is the maximum norm.
line_search : {None, 'armijo' (default), 'wolfe'}, optional
Which type of a line search to use to determine the step size in
the direction given by the Jacobian approximation. Defaults to
'armijo'.
jac_options : dict, optional
Options for the respective Jacobian approximation.
alpha : float, optional
Initial guess for the Jacobian is (-1/alpha).
reduction_method : str or tuple, optional
Method used in ensuring that the rank of the Broyden
matrix stays low. Can either be a string giving the
name of the method, or a tuple of the form ``(method,
param1, param2, ...)`` that gives the name of the
method and values for additional parameters.
Methods available:
- ``restart``: drop all matrix columns. Has no
extra parameters.
- ``simple``: drop oldest matrix column. Has no
extra parameters.
- ``svd``: keep only the most significant SVD
components.
Extra parameters:
- ``to_retain``: number of SVD components to
retain when rank reduction is done.
Default is ``max_rank - 2``.
max_rank : int, optional
Maximum rank for the Broyden matrix.
Default is infinity (ie., no rank reduction).
"""
pass | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/display.py | python | DisplayObject.__init__ | (self, data=None, url=None, filename=None) | Create a display object given raw data.
When this object is returned by an expression or passed to the
display function, it will result in the data being displayed
in the frontend. The MIME type of the data should match the
subclasses used, so the Png subclass should be used for 'image/png'
data. If the data is a URL, the data will first be downloaded
and then displayed. If
Parameters
----------
data : unicode, str or bytes
The raw data or a URL or file to load the data from
url : unicode
A URL to download the data from.
filename : unicode
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"""Create a display object given raw data.
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display function, it will result in the data being displayed
in the frontend. The MIME type of the data should match the
subclasses used, so the Png subclass should be used for 'image/png'
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Parameters
----------
data : unicode, str or bytes
The raw data or a URL or file to load the data from
url : unicode
A URL to download the data from.
filename : unicode
Path to a local file to load the data from.
"""
if data is not None and isinstance(data, string_types):
if data.startswith('http') and url is None:
url = data
filename = None
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url = None
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self.data = data
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self.reload()
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | clang/bindings/python/clang/cindex.py | python | Cursor.is_abstract_record | (self) | return conf.lib.clang_CXXRecord_isAbstract(self) | Returns True if the cursor refers to a C++ record declaration
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facebook/openr | ed38bdfd6bf290084bfab4821b59f83e7b59315d | openr/py/openr/cli/commands/kvstore.py | python | KvNodesCmd.get_connected_nodes | (
self, adj_keys: kvstore_types.Publication, node_id: str
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"""
Build graph of adjacencies and return list of connected node from
current node-id
"""
import networkx as nx
edges = set()
graph = nx.Graph()
for adj_value in adj_keys.keyVals.values():
adj_db = serializer.deserialize_thrift_object(
adj_value.value, openr_types.AdjacencyDatabase
)
graph.add_node(adj_db.thisNodeName)
for adj in adj_db.adjacencies:
# Add edge only when we see the reverse side of it.
if (adj.otherNodeName, adj_db.thisNodeName, adj.otherIfName) in edges:
graph.add_edge(adj.otherNodeName, adj_db.thisNodeName)
continue
edges.add((adj_db.thisNodeName, adj.otherNodeName, adj.ifName))
# pyre-ignore[16]
return nx.node_connected_component(graph, node_id) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/telemetry/core/gpu_info.py | python | GPUInfo.feature_status | (self) | return self._feature_status | Returns an optional dictionary of graphics features and their status. | Returns an optional dictionary of graphics features and their status. | [
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jiaxiang-wu/quantized-cnn | 4d020e17026df90e40111d219e3eb74e0afb1588 | cpplint.py | python | CheckForNewlineAtEOF | (filename, lines, error) | Logs an error if there is no newline char at the end of the file.
Args:
filename: The name of the current file.
lines: An array of strings, each representing a line of the file.
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error(filename, len(lines) - 2, 'whitespace/ending_newline', 5,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/glcanvas.py | python | GLCanvas.SetCurrent | (*args) | return _glcanvas.GLCanvas_SetCurrent(*args) | SetCurrent(self, GLContext context) -> bool
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ApolloAuto/apollo | 463fb82f9e979d02dcb25044e60931293ab2dba0 | modules/tools/control_info/control_info.py | python | ControlInfo.longitudinal | (self) | Showing Longitudinal | Showing Longitudinal | [
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"""
Showing Longitudinal
"""
for loc, ax in numpy.ndenumerate(self.ax):
ax.clear()
self.ax[0, 0].plot(
self.canbustime, self.throttlefbk, label='Throttle Feedback')
self.ax[0, 0].plot(
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self.ax[0, 0].plot(
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self.ax[0, 0].grid(True)
self.ax[0, 0].set_title('Throttle Brake Info')
self.ax[0, 0].set_xlabel('Time')
self.ax[0, 1].plot(
self.speed_lookup, self.acceleration_lookup, label='Table Lookup')
self.ax[0, 1].plot(
self.target_speed, self.target_acceleration, label='Target')
self.ax[0, 1].legend(fontsize='medium')
self.ax[0, 1].grid(True)
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self.ax[0, 1].set_xlabel('Speed')
self.ax[0, 1].set_ylabel('Acceleration')
self.ax[1, 0].plot(self.canbustime, self.speed, label='Vehicle Speed')
self.ax[1, 0].plot(
self.target_time, self.target_speed, label='Target Speed')
self.ax[1, 0].plot(
self.target_time, self.target_acceleration, label='Target Acc')
self.ax[1, 0].plot(
self.localizationtime, self.imuforward, label='IMU Forward')
self.ax[1, 0].legend(fontsize='medium')
self.ax[1, 0].grid(True)
self.ax[1, 0].set_title('Speed Info')
self.ax[1, 0].set_xlabel('Time')
self.ax[1, 1].plot(
self.controltime, self.acceleration_lookup, label='Lookup Acc')
self.ax[1, 1].plot(self.controltime, self.acc_open, label='Acc Open')
self.ax[1, 1].plot(self.controltime, self.acc_close, label='Acc Close')
self.ax[1, 1].plot(
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self.ax[1, 1].plot(
self.controltime, self.speed_error, label='speed_error')
self.ax[1, 1].legend(fontsize='medium')
self.ax[1, 1].grid(True)
self.ax[1, 1].set_title('IMU Info')
self.ax[1, 1].set_xlabel('Time')
if len(self.mode_time) % 2 == 1:
self.mode_time.append(self.controltime[-1])
for i in range(0, len(self.mode_time), 2):
self.ax[0, 0].axvspan(
self.mode_time[i], self.mode_time[i + 1], fc='0.1', alpha=0.1)
self.ax[1, 0].axvspan(
self.mode_time[i], self.mode_time[i + 1], fc='0.1', alpha=0.1)
self.ax[1, 1].axvspan(
self.mode_time[i], self.mode_time[i + 1], fc='0.1', alpha=0.1)
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rsummers11/CADLab | 976ed959a0b5208bb4173127a7ef732ac73a9b6f | panreas_hnn/hed-globalweight/python/caffe/draw.py | python | choose_color_by_layertype | (layertype) | return color | Define colors for nodes based on the layer type. | Define colors for nodes based on the layer type. | [
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] | def choose_color_by_layertype(layertype):
"""Define colors for nodes based on the layer type.
"""
color = '#6495ED' # Default
if layertype == 'Convolution' or layertype == 'Deconvolution':
color = '#FF5050'
elif layertype == 'Pooling':
color = '#FF9900'
elif layertype == 'InnerProduct':
color = '#CC33FF'
return color | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py | python | _check_config_keys | (config, expected_keys) | Checks that a config has all expected_keys. | Checks that a config has all expected_keys. | [
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config, expected_keys)) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/function.py | python | _DefinedFunction._create_definition_if_needed | (self) | Creates the function definition if it's not created yet. | Creates the function definition if it's not created yet. | [
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"""Creates the function definition if it's not created yet."""
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/boost/boost_1_68_0/tools/build/src/build/type.py | python | all_bases | (type) | return result | Returns type and all of its bases, in the order of their distance from type. | Returns type and all of its bases, in the order of their distance from type. | [
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""" Returns type and all of its bases, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = []
while type:
result.append (type)
type = __types [type]['base']
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PrincetonUniversity/athena-public-version | 9c266692b9423743d8e23509b3ab266a232a92d2 | tst/style/cpplint.py | python | Error | (filename, linenum, category, confidence, message) | Logs the fact we've found a lint error.
We log where the error was found, and also our confidence in the error,
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filename: The name of the file containing the error.
linenum: The number of the line containing the error.
category: A string used to describe the "category" this bug
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confidence: A number from 1-5 representing a confidence score for
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and 1 meaning that it could be a legitimate construct.
message: The error message. | Logs the fact we've found a lint error. | [
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] | def Error(filename, linenum, category, confidence, message):
"""Logs the fact we've found a lint error.
We log where the error was found, and also our confidence in the error,
that is, how certain we are this is a legitimate style regression, and
not a misidentification or a use that's sometimes justified.
False positives can be suppressed by the use of
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Args:
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linenum: The number of the line containing the error.
category: A string used to describe the "category" this bug
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confidence: A number from 1-5 representing a confidence score for
the error, with 5 meaning that we are certain of the problem,
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message: The error message.
"""
if _ShouldPrintError(category, confidence, linenum):
_cpplint_state.IncrementErrorCount(category)
if _cpplint_state.output_format == 'vs7':
_cpplint_state.PrintError('%s(%s): error cpplint: [%s] %s [%d]\n' % (
filename, linenum, category, message, confidence))
elif _cpplint_state.output_format == 'eclipse':
sys.stderr.write('%s:%s: warning: %s [%s] [%d]\n' % (
filename, linenum, message, category, confidence))
elif _cpplint_state.output_format == 'junit':
_cpplint_state.AddJUnitFailure(filename, linenum, message, category,
confidence)
else:
final_message = '%s:%s: %s [%s] [%d]\n' % (
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/vision/models/resnet.py | python | resnet152 | (pretrained=False, **kwargs) | return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs) | ResNet 152-layer model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import resnet152
# build model
model = resnet152()
# build model and load imagenet pretrained weight
# model = resnet152(pretrained=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
print(out.shape) | ResNet 152-layer model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ | [
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"""ResNet 152-layer model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import resnet152
# build model
model = resnet152()
# build model and load imagenet pretrained weight
# model = resnet152(pretrained=True)
x = paddle.rand([1, 3, 224, 224])
out = model(x)
print(out.shape)
"""
return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/ConfigParser.py | python | ConfigParser.items | (self, section, raw=False, vars=None) | Return a list of tuples with (name, value) for each option
in the section.
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"""Return a list of tuples with (name, value) for each option
in the section.
All % interpolations are expanded in the return values, based on the
defaults passed into the constructor, unless the optional argument
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The section DEFAULT is special.
"""
d = self._defaults.copy()
try:
d.update(self._sections[section])
except KeyError:
if section != DEFAULTSECT:
raise NoSectionError(section)
# Update with the entry specific variables
if vars:
for key, value in vars.items():
d[self.optionxform(key)] = value
options = d.keys()
if "__name__" in options:
options.remove("__name__")
if raw:
return [(option, d[option])
for option in options]
else:
return [(option, self._interpolate(section, option, d[option], d))
for option in options] | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/framework/op_def_library.py | python | OpDefLibrary.add_op | (self, op_def) | Register an OpDef. May call apply_op with the name afterwards. | Register an OpDef. May call apply_op with the name afterwards. | [
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"""Register an OpDef. May call apply_op with the name afterwards."""
if not isinstance(op_def, op_def_pb2.OpDef):
raise TypeError("%s is %s, not an op_def_pb2.OpDef" %
(op_def, type(op_def)))
if not op_def.name:
raise ValueError("%s missing name." % op_def)
if op_def.name in self._ops:
raise RuntimeError("Op name %s registered twice." % op_def.name)
self._ops[op_def.name] = _OpInfo(op_def) | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/procrouting/proc.py | python | select_adc2 | (name, **kwargs) | Function selecting the algorithm for ADC(2) excited state energy
call and directing to specified or best-performance default modules. | Function selecting the algorithm for ADC(2) excited state energy
call and directing to specified or best-performance default modules. | [
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] | def select_adc2(name, **kwargs):
"""Function selecting the algorithm for ADC(2) excited state energy
call and directing to specified or best-performance default modules.
"""
reference = core.get_option('SCF', 'REFERENCE')
mtd_type = core.get_global_option('MP_TYPE')
module = core.get_global_option('QC_MODULE')
# Considering only adcc/adc
# TODO Actually one should do selection on a couple of other options here
# as well, e.g. adcc supports frozen-core and frozen-virtual,
# spin-specific states or spin-flip methods.
# But as far as I (mfherbst) know the BUILTIN ADC routine only supports
# singlet states and without freezing some core or some virtual orbitals.
func = None
if reference == 'RHF':
if mtd_type == 'CONV':
if module in {'ADCC', ''} and extras.addons("adcc"):
func = run_adcc
elif module in {'BUILTIN', ''}:
func = run_adc
if reference == 'UHF':
if mtd_type == 'CONV':
if module in ['ADCC', ''] and extras.addons("adcc"):
func = run_adcc
# Note: ROHF is theoretically available in adcc, but are not fully tested
# ... so will be added later.
if func is None:
raise ManagedMethodError(['select_adc2', name, 'MP_TYPE', mtd_type, reference, module])
if kwargs.pop('probe', False):
return
else:
return func(name, **kwargs) | [
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mavlink/mavros | a32232d57a5e91abf6737e454d4199cae29b369c | mavros/mavros/cmd/safety.py | python | safety | (client) | Tool to send safety commands to MAVLink device. | Tool to send safety commands to MAVLink device. | [
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] | def safety(client):
"""Tool to send safety commands to MAVLink device.""" | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | example/caffe/caffe_net.py | python | get_lenet | () | return lenet | LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
Haffner. "Gradient-based learning applied to document recognition."
Proceedings of the IEEE (1998) | LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
Haffner. "Gradient-based learning applied to document recognition."
Proceedings of the IEEE (1998) | [
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] | def get_lenet():
"""
LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick
Haffner. "Gradient-based learning applied to document recognition."
Proceedings of the IEEE (1998)
"""
data = mx.symbol.Variable('data')
# first conv
conv1 = mx.symbol.CaffeOp(data_0=data, num_weight=2, prototxt="layer{type:\"Convolution\" convolution_param { num_output: 20 kernel_size: 5 stride: 1} }")
act1 = mx.symbol.CaffeOp(data_0=conv1, prototxt="layer{type:\"TanH\"}")
pool1 = mx.symbol.CaffeOp(data_0=act1, prototxt="layer{type:\"Pooling\" pooling_param { pool: MAX kernel_size: 2 stride: 2}}")
# second conv
conv2 = mx.symbol.CaffeOp(data_0=pool1, num_weight=2, prototxt="layer{type:\"Convolution\" convolution_param { num_output: 50 kernel_size: 5 stride: 1} }")
act2 = mx.symbol.CaffeOp(data_0=conv2, prototxt="layer{type:\"TanH\"}")
pool2 = mx.symbol.CaffeOp(data_0=act2, prototxt="layer{type:\"Pooling\" pooling_param { pool: MAX kernel_size: 2 stride: 2}}")
fc1 = mx.symbol.CaffeOp(data_0=pool2, num_weight=2, prototxt="layer{type:\"InnerProduct\" inner_product_param{num_output: 500} }")
act3 = mx.symbol.CaffeOp(data_0=fc1, prototxt="layer{type:\"TanH\"}")
# second fullc
fc2 = mx.symbol.CaffeOp(data_0=act3, num_weight=2, prototxt="layer{type:\"InnerProduct\"inner_product_param{num_output: 10} }")
if use_caffe_loss:
label = mx.symbol.Variable('softmax_label')
lenet = mx.symbol.CaffeLoss(data=fc2, label=label, grad_scale=1, name='softmax', prototxt="layer{type:\"SoftmaxWithLoss\"}")
else:
lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax')
return lenet | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/backend.py | python | update_add | (x, increment) | return state_ops.assign_add(x, increment) | Update the value of `x` by adding `increment`.
Arguments:
x: A Variable.
increment: A tensor of same shape as `x`.
Returns:
The variable `x` updated. | Update the value of `x` by adding `increment`. | [
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] | def update_add(x, increment):
"""Update the value of `x` by adding `increment`.
Arguments:
x: A Variable.
increment: A tensor of same shape as `x`.
Returns:
The variable `x` updated.
"""
return state_ops.assign_add(x, increment) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/_extends/graph_kernel/model/model.py | python | Tensor.get_size | (self) | return size | Get size | Get size | [
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] | def get_size(self):
"""Get size"""
size = PrimLib.dtype_bytes(self.dtype)
for i in self.shape:
size *= i
return size | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/interactiveshell.py | python | InteractiveShell.getoutput | (self, cmd, split=True, depth=0) | return out | Get output (possibly including stderr) from a subprocess.
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported.
split : bool, optional
If True, split the output into an IPython SList. Otherwise, an
IPython LSString is returned. These are objects similar to normal
lists and strings, with a few convenience attributes for easier
manipulation of line-based output. You can use '?' on them for
details.
depth : int, optional
How many frames above the caller are the local variables which should
be expanded in the command string? The default (0) assumes that the
expansion variables are in the stack frame calling this function. | Get output (possibly including stderr) from a subprocess. | [
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"""Get output (possibly including stderr) from a subprocess.
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported.
split : bool, optional
If True, split the output into an IPython SList. Otherwise, an
IPython LSString is returned. These are objects similar to normal
lists and strings, with a few convenience attributes for easier
manipulation of line-based output. You can use '?' on them for
details.
depth : int, optional
How many frames above the caller are the local variables which should
be expanded in the command string? The default (0) assumes that the
expansion variables are in the stack frame calling this function.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
raise OSError("Background processes not supported.")
out = getoutput(self.var_expand(cmd, depth=depth+1))
if split:
out = SList(out.splitlines())
else:
out = LSString(out)
return out | [
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Kitware/VTK | 5b4df4d90a4f31194d97d3c639dd38ea8f81e8b8 | Wrapping/Python/vtkmodules/wx/wxVTKRenderWindow.py | python | wxVTKRenderWindow.OnButtonUp | (self, event) | Overridable event. | Overridable event. | [
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] | def OnButtonUp(self, event):
"""Overridable event.
"""
if event.LeftUp():
self.OnLeftUp(event)
elif event.RightUp():
self.OnRightUp(event)
elif event.MiddleUp():
self.OnMiddleUp(event)
# if not interacting, then do nothing more
if self._Mode:
if self._CurrentRenderer:
self.Render()
self._Mode = None | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Runtime/VART/vart/trace/vaitrace/tracer/function.py | python | functionTracer.process | (self, data, t_range=[]) | test_performanc-19925 [003] .... 110337.353720: _fun_9351538_entry: (0xffff8553b0d8) | test_performanc-19925 [003] .... 110337.353720: _fun_9351538_entry: (0xffff8553b0d8) | [
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""""test_performanc-19925 [003] .... 110337.353720: _fun_9351538_entry: (0xffff8553b0d8)"""
data = [l for l in data.get('ftrace', {}).get(
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data_out = []
for l in data:
ftraceSym = l.strip().split()[4].rsplit('_', 1)[0]
data_out.append(l.replace(ftraceSym, self.ftraceSymMap[ftraceSym]))
# for k in self.ftraceSymMap.keys():
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/_pyio.py | python | FileIO.closefd | (self) | return self._closefd | True if the file descriptor will be closed by close(). | True if the file descriptor will be closed by close(). | [
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"""True if the file descriptor will be closed by close()."""
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/tlslite/tlslite/utils/keyfactory.py | python | generateRSAKey | (bits, implementations=["openssl", "python"]) | Generate an RSA key with the specified bit length.
@type bits: int
@param bits: Desired bit length of the new key's modulus.
@rtype: L{tlslite.utils.RSAKey.RSAKey}
@return: A new RSA private key. | Generate an RSA key with the specified bit length. | [
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"""Generate an RSA key with the specified bit length.
@type bits: int
@param bits: Desired bit length of the new key's modulus.
@rtype: L{tlslite.utils.RSAKey.RSAKey}
@return: A new RSA private key.
"""
for implementation in implementations:
if implementation == "openssl" and cryptomath.m2cryptoLoaded:
return OpenSSL_RSAKey.generate(bits)
elif implementation == "python":
return Python_RSAKey.generate(bits)
raise ValueError("No acceptable implementations") | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/cloudsearch2/layer1.py | python | CloudSearchConnection.define_index_field | (self, domain_name, index_field) | return self._make_request(
action='DefineIndexField',
verb='POST',
path='/', params=params) | Configures an `IndexField` for the search domain. Used to
create new fields and modify existing ones. You must specify
the name of the domain you are configuring and an index field
configuration. The index field configuration specifies a
unique name, the index field type, and the options you want to
configure for the field. The options you can specify depend on
the `IndexFieldType`. If the field exists, the new
configuration replaces the old one. For more information, see
`Configuring Index Fields`_ in the Amazon CloudSearch
Developer Guide .
:type domain_name: string
:param domain_name: A string that represents the name of a domain.
Domain names are unique across the domains owned by an account
within an AWS region. Domain names start with a letter or number
and can contain the following characters: a-z (lowercase), 0-9, and
- (hyphen).
:type index_field: dict
:param index_field: The index field and field options you want to
configure. | Configures an `IndexField` for the search domain. Used to
create new fields and modify existing ones. You must specify
the name of the domain you are configuring and an index field
configuration. The index field configuration specifies a
unique name, the index field type, and the options you want to
configure for the field. The options you can specify depend on
the `IndexFieldType`. If the field exists, the new
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:type index_field: dict
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configure.
"""
params = {'DomainName': domain_name, }
self.build_complex_param(params, 'IndexField',
index_field)
return self._make_request(
action='DefineIndexField',
verb='POST',
path='/', params=params) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/s3/bucket.py | python | Bucket.validate_kwarg_names | (self, kwargs, names) | Checks that all named arguments are in the specified list of names.
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/crywaflib/compile_rules_linux_x64_linux_x64_clang.py | python | load_release_linux_x64_linux_x64_clang_settings | (conf) | Setup all compiler and linker settings shared over all linux_x64_linux_x64_clang configurations for
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Setup all compiler and linker settings shared over all linux_x64_linux_x64_clang configurations for
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"""
v = conf.env
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conf.load_release_cryengine_settings()
conf.load_release_clang_settings()
conf.load_release_linux_settings()
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/universe_generation/galaxy.py | python | DSet.bind_parent | (self, parent) | Bind to parent. | Bind to parent. | [
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apple/swift | 469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893 | utils/swift_build_support/swift_build_support/products/swiftinspect.py | python | SwiftInspect.test | (self, host_target) | Just run a single instance of the command for both .debug and
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/CGIHTTPServer.py | python | CGIHTTPRequestHandler.send_head | (self) | Version of send_head that support CGI scripts | Version of send_head that support CGI scripts | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | GridCellAttr.GetTextColour | (*args, **kwargs) | return _grid.GridCellAttr_GetTextColour(*args, **kwargs) | GetTextColour(self) -> Colour | GetTextColour(self) -> Colour | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | ThirdParty/cinemasci/paraview/tpl/cinemasci/cis/colormap.py | python | colormap.add_point | (self, point) | return | Add a point, a tuple of (x,o,r,g,b) to points. | Add a point, a tuple of (x,o,r,g,b) to points. | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distribution/dirichlet.py | python | Dirichlet.sample | (self, shape=()) | return _dirichlet(self.concentration.expand(self._extend_shape(shape))) | Sample from dirichlet distribution.
Args:
shape (Sequence[int], optional): Sample shape. Defaults to empty tuple. | Sample from dirichlet distribution. | [
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Args:
shape (Sequence[int], optional): Sample shape. Defaults to empty tuple.
"""
shape = shape if isinstance(shape, tuple) else tuple(shape)
return _dirichlet(self.concentration.expand(self._extend_shape(shape))) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | PseudoDC.DrawRectanglePointSize | (*args, **kwargs) | return _gdi_.PseudoDC_DrawRectanglePointSize(*args, **kwargs) | DrawRectanglePointSize(self, Point pt, Size sz)
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/debug/debug_utils.py | python | add_debug_tensor_watch | (run_options,
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"""Add debug tensor watch option to RunOptions.
Args:
run_options: An instance of tensorflow.core.protobuf.config_pb2.RunOptions
node_name: Name of the node to watch.
output_slot: Output slot index of the tensor from the watched node.
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watch = watch_opts.add()
watch.node_name = node_name
watch.output_slot = output_slot
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watch.debug_ops.extend(debug_ops)
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/constraint_solver/samples/vrp_initial_routes.py | python | main | () | Solve the CVRP problem. | Solve the CVRP problem. | [
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"""Solve the CVRP problem."""
# Instantiate the data problem.
# [START data]
data = create_data_model()
# [END data]
# Create the routing index manager.
# [START index_manager]
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['depot'])
# [END index_manager]
# Create Routing Model.
# [START routing_model]
routing = pywrapcp.RoutingModel(manager)
# [END routing_model]
# Create and register a transit callback.
# [START transit_callback]
def distance_callback(from_index, to_index):
"""Returns the distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['distance_matrix'][from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# [END transit_callback]
# Define cost of each arc.
# [START arc_cost]
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# [END arc_cost]
# Add Distance constraint.
# [START distance_constraint]
dimension_name = 'Distance'
routing.AddDimension(
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True, # start cumul to zero
dimension_name)
distance_dimension = routing.GetDimensionOrDie(dimension_name)
distance_dimension.SetGlobalSpanCostCoefficient(100)
# [END distance_constraint]
# [START print_initial_solution]
initial_solution = routing.ReadAssignmentFromRoutes(data['initial_routes'],
True)
print('Initial solution:')
print_solution(data, manager, routing, initial_solution)
# [END print_initial_solution]
# Set default search parameters.
# [START parameters]
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
# [END parameters]
# Solve the problem.
# [START solve]
solution = routing.SolveFromAssignmentWithParameters(
initial_solution, search_parameters)
# [END solve]
# Print solution on console.
# [START print_solution]
if solution:
print('Solution after search:')
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/Blast/houdini/python2.7libs/blastExport/slice.py | python | Slice.translate | (self) | return self.__translate | :return: str | :return: str | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/py/editor.py | python | EditorFrame.bufferSaveAs | (self) | return cancel | Save buffer to a new filename. | Save buffer to a new filename. | [
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if self.buffer and self.buffer.doc.filedir:
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RobotLocomotion/drake | 0e18a34604c45ed65bc9018a54f7610f91cdad5b | bindings/pydrake/systems/planar_scenegraph_visualizer.py | python | PlanarSceneGraphVisualizer._build_body_patches | (self, use_random_colors,
substitute_collocated_mesh_files, inspector) | Generates body patches. self._patch_Blist stores a list of patches for
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vertices of a piece of visual geometry. | Generates body patches. self._patch_Blist stores a list of patches for
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"""
Generates body patches. self._patch_Blist stores a list of patches for
each body (starting at body id 1). A body patch is a list of all 3D
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"""
self._patch_Blist = {}
self._patch_Blist_colors = {}
for frame_id in inspector.GetAllFrameIds():
count = inspector.NumGeometriesForFrameWithRole(frame_id,
Role.kIllustration)
if count == 0:
continue
frame_name = self.frame_name(frame_id, inspector)
this_body_patches = []
this_body_colors = []
for g_id in inspector.GetGeometries(frame_id, Role.kIllustration):
X_BG = inspector.GetPoseInFrame(g_id)
shape = inspector.GetShape(g_id)
if isinstance(shape, Box):
# Draw a bounding box.
patch_G = np.vstack((
shape.width()/2.*np.array(
[-1, -1, 1, 1, -1, -1, 1, 1]),
shape.depth()/2.*np.array(
[-1, 1, -1, 1, -1, 1, -1, 1]),
shape.height()/2.*np.array(
[-1, -1, -1, -1, 1, 1, 1, 1])))
elif isinstance(shape, Sphere):
# Sphere is the only shape that allows a zero-measure. A
# zero-radius sphere is a point, and we skip it.
if shape.radius() == 0:
continue
lati, longi = np.meshgrid(np.arange(0., 2.*math.pi, 0.5),
np.arange(0., 2.*math.pi, 0.5))
lati = lati.ravel()
longi = longi.ravel()
patch_G = np.vstack([
np.sin(lati)*np.cos(longi),
np.sin(lati)*np.sin(longi),
np.cos(lati)])
patch_G *= shape.radius()
elif isinstance(shape, Cylinder):
radius = shape.radius()
length = shape.length()
# In the lcm geometry, cylinders are along +z
# https://github.com/RobotLocomotion/drake/blob/last_sha_with_original_matlab/drake/matlab/systems/plants/RigidBodyCylinder.m
# Two circles: one at bottom, one at top.
sample_pts = np.arange(0., 2.*math.pi, 0.25)
patch_G = np.hstack(
[np.array([
[radius*math.cos(pt),
radius*math.sin(pt),
-length/2.],
[radius*math.cos(pt),
radius*math.sin(pt),
length/2.]]).T
for pt in sample_pts])
elif isinstance(shape, (Mesh, Convex)):
filename = shape.filename()
base, ext = os.path.splitext(filename)
if (ext.lower() != ".obj"
and substitute_collocated_mesh_files):
# Check for a co-located .obj file (case insensitive).
for f in glob.glob(base + '.*'):
if f[-4:].lower() == '.obj':
filename = f
break
if filename[-4:].lower() != '.obj':
raise RuntimeError(
f"The given file {filename} is not "
f"supported and no alternate {base}"
".obj could be found.")
if not os.path.exists(filename):
raise FileNotFoundError(errno.ENOENT, os.strerror(
errno.ENOENT), filename)
# Get mesh scaling.
scale = shape.scale()
mesh = ReadObjToTriangleSurfaceMesh(filename, scale)
patch_G = np.vstack(mesh.vertices())
# Only store the vertices of the (3D) convex hull of the
# mesh, as any interior vertices will still be interior
# vertices after projection, and will therefore be removed
# in _update_body_fill_verts().
hull = spatial.ConvexHull(patch_G)
patch_G = np.vstack(
[patch_G[v, :] for v in hull.vertices]).T
elif isinstance(shape, HalfSpace):
# For a half space, we'll simply create a large box with
# the top face at z = 0, the bottom face at z = -1 and the
# far corners at +/- 50 in the x- and y- directions.
x = 50
y = 50
z = -1
patch_G = np.vstack((
x * np.array([-1, -1, 1, 1, -1, -1, 1, 1]),
y * np.array([-1, 1, -1, 1, -1, 1, -1, 1]),
z * np.array([-1, -1, -1, -1, 0, 0, 0, 0])))
# TODO(SeanCurtis-TRI): Provide support for capsule and
# ellipsoid.
else:
print("UNSUPPORTED GEOMETRY TYPE {} IGNORED".format(
type(shape)))
continue
# Compute pose in body.
patch_B = X_BG @ patch_G
# Close path if not closed.
if (patch_B[:, -1] != patch_B[:, 0]).any():
patch_B = np.hstack((patch_B, patch_B[:, 0][np.newaxis].T))
this_body_patches.append(patch_B)
if not use_random_colors:
# If we need to use random colors, we apply them after the
# fact. See below.
props = inspector.GetIllustrationProperties(g_id)
assert props is not None
rgba = props.GetPropertyOrDefault(
"phong", "diffuse", Rgba(0.9, 0.9, 0.9, 1.0))
color = np.array((rgba.r(), rgba.g(), rgba.b(), rgba.a()))
this_body_colors.append(color)
self._patch_Blist[frame_name] = this_body_patches
self._patch_Blist_colors[frame_name] = this_body_colors
# Spawn a random color generator. Each body will be given a unique
# color when using this random generator, with each visual element of
# the body colored the same.
if use_random_colors:
color = iter(plt.cm.rainbow(
np.linspace(0, 1, len(self._patch_Blist_colors))))
for name in self._patch_Blist_colors.keys():
this_color = next(color)
patch_count = len(self._patch_Blist[name])
self._patch_Blist_colors[name] = [this_color] * patch_count | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/signal/signaltools.py | python | resample_poly | (x, up, down, axis=0, window=('kaiser', 5.0)) | return y[tuple(keep)] | Resample `x` along the given axis using polyphase filtering.
The signal `x` is upsampled by the factor `up`, a zero-phase low-pass
FIR filter is applied, and then it is downsampled by the factor `down`.
The resulting sample rate is ``up / down`` times the original sample
rate. Values beyond the boundary of the signal are assumed to be zero
during the filtering step.
Parameters
----------
x : array_like
The data to be resampled.
up : int
The upsampling factor.
down : int
The downsampling factor.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : string, tuple, or array_like, optional
Desired window to use to design the low-pass filter, or the FIR filter
coefficients to employ. See below for details.
Returns
-------
resampled_x : array
The resampled array.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample : Resample up or down using the FFT method.
Notes
-----
This polyphase method will likely be faster than the Fourier method
in `scipy.signal.resample` when the number of samples is large and
prime, or when the number of samples is large and `up` and `down`
share a large greatest common denominator. The length of the FIR
filter used will depend on ``max(up, down) // gcd(up, down)``, and
the number of operations during polyphase filtering will depend on
the filter length and `down` (see `scipy.signal.upfirdn` for details).
The argument `window` specifies the FIR low-pass filter design.
If `window` is an array_like it is assumed to be the FIR filter
coefficients. Note that the FIR filter is applied after the upsampling
step, so it should be designed to operate on a signal at a sampling
frequency higher than the original by a factor of `up//gcd(up, down)`.
This function's output will be centered with respect to this array, so it
is best to pass a symmetric filter with an odd number of samples if, as
is usually the case, a zero-phase filter is desired.
For any other type of `window`, the functions `scipy.signal.get_window`
and `scipy.signal.firwin` are called to generate the appropriate filter
coefficients.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * down / float(up)``.
Examples
--------
Note that the end of the resampled data rises to meet the first
sample of the next cycle for the FFT method, and gets closer to zero
for the polyphase method:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f_fft = signal.resample(y, 100)
>>> f_poly = signal.resample_poly(y, 100, 20)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(xnew, f_fft, 'b.-', xnew, f_poly, 'r.-')
>>> plt.plot(x, y, 'ko-')
>>> plt.plot(10, y[0], 'bo', 10, 0., 'ro') # boundaries
>>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best')
>>> plt.show() | Resample `x` along the given axis using polyphase filtering. | [
"Resample",
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"filtering",
"."
] | def resample_poly(x, up, down, axis=0, window=('kaiser', 5.0)):
"""
Resample `x` along the given axis using polyphase filtering.
The signal `x` is upsampled by the factor `up`, a zero-phase low-pass
FIR filter is applied, and then it is downsampled by the factor `down`.
The resulting sample rate is ``up / down`` times the original sample
rate. Values beyond the boundary of the signal are assumed to be zero
during the filtering step.
Parameters
----------
x : array_like
The data to be resampled.
up : int
The upsampling factor.
down : int
The downsampling factor.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : string, tuple, or array_like, optional
Desired window to use to design the low-pass filter, or the FIR filter
coefficients to employ. See below for details.
Returns
-------
resampled_x : array
The resampled array.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample : Resample up or down using the FFT method.
Notes
-----
This polyphase method will likely be faster than the Fourier method
in `scipy.signal.resample` when the number of samples is large and
prime, or when the number of samples is large and `up` and `down`
share a large greatest common denominator. The length of the FIR
filter used will depend on ``max(up, down) // gcd(up, down)``, and
the number of operations during polyphase filtering will depend on
the filter length and `down` (see `scipy.signal.upfirdn` for details).
The argument `window` specifies the FIR low-pass filter design.
If `window` is an array_like it is assumed to be the FIR filter
coefficients. Note that the FIR filter is applied after the upsampling
step, so it should be designed to operate on a signal at a sampling
frequency higher than the original by a factor of `up//gcd(up, down)`.
This function's output will be centered with respect to this array, so it
is best to pass a symmetric filter with an odd number of samples if, as
is usually the case, a zero-phase filter is desired.
For any other type of `window`, the functions `scipy.signal.get_window`
and `scipy.signal.firwin` are called to generate the appropriate filter
coefficients.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * down / float(up)``.
Examples
--------
Note that the end of the resampled data rises to meet the first
sample of the next cycle for the FFT method, and gets closer to zero
for the polyphase method:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f_fft = signal.resample(y, 100)
>>> f_poly = signal.resample_poly(y, 100, 20)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(xnew, f_fft, 'b.-', xnew, f_poly, 'r.-')
>>> plt.plot(x, y, 'ko-')
>>> plt.plot(10, y[0], 'bo', 10, 0., 'ro') # boundaries
>>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best')
>>> plt.show()
"""
x = asarray(x)
if up != int(up):
raise ValueError("up must be an integer")
if down != int(down):
raise ValueError("down must be an integer")
up = int(up)
down = int(down)
if up < 1 or down < 1:
raise ValueError('up and down must be >= 1')
# Determine our up and down factors
# Use a rational approximation to save computation time on really long
# signals
g_ = gcd(up, down)
up //= g_
down //= g_
if up == down == 1:
return x.copy()
n_out = x.shape[axis] * up
n_out = n_out // down + bool(n_out % down)
if isinstance(window, (list, np.ndarray)):
window = array(window) # use array to force a copy (we modify it)
if window.ndim > 1:
raise ValueError('window must be 1-D')
half_len = (window.size - 1) // 2
h = window
else:
# Design a linear-phase low-pass FIR filter
max_rate = max(up, down)
f_c = 1. / max_rate # cutoff of FIR filter (rel. to Nyquist)
half_len = 10 * max_rate # reasonable cutoff for our sinc-like function
h = firwin(2 * half_len + 1, f_c, window=window)
h *= up
# Zero-pad our filter to put the output samples at the center
n_pre_pad = (down - half_len % down)
n_post_pad = 0
n_pre_remove = (half_len + n_pre_pad) // down
# We should rarely need to do this given our filter lengths...
while _output_len(len(h) + n_pre_pad + n_post_pad, x.shape[axis],
up, down) < n_out + n_pre_remove:
n_post_pad += 1
h = np.concatenate((np.zeros(n_pre_pad, dtype=h.dtype), h,
np.zeros(n_post_pad, dtype=h.dtype)))
n_pre_remove_end = n_pre_remove + n_out
# filter then remove excess
y = upfirdn(h, x, up, down, axis=axis)
keep = [slice(None), ]*x.ndim
keep[axis] = slice(n_pre_remove, n_pre_remove_end)
return y[tuple(keep)] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/mailbox.py | python | Maildir.remove | (self, key) | Remove the keyed message; raise KeyError if it doesn't exist. | Remove the keyed message; raise KeyError if it doesn't exist. | [
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"""Remove the keyed message; raise KeyError if it doesn't exist."""
os.remove(os.path.join(self._path, self._lookup(key))) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/requests/models.py | python | RequestHooksMixin.deregister_hook | (self, event, hook) | Deregister a previously registered hook.
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neopenx/Dragon | 0e639a7319035ddc81918bd3df059230436ee0a1 | Dragon/python/dragon/core/tensor.py | python | Tensor.__rdiv__ | (self, other) | return output | Calculate y / x.
Parameters
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other : Tensor
The y.
Returns
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Tensor
The output tensor. | Calculate y / x. | [
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"""Calculate y / x.
Parameters
----------
other : Tensor
The y.
Returns
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Tensor
The output tensor.
"""
if not isinstance(other, Tensor):
if not isinstance(other, np.ndarray):
if not isinstance(other, list): other = [other]
other = np.array(other, dtype=np.float32)
tensor = Tensor(GetTensorName())
ws.FeedTensor(tensor, other)
other = tensor
output = self.CreateOperator(inputs=[other, self], nout=1, op_type='RDiv')
if self.shape is not None:
output.shape = self.shape[:]
return output | [
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RoboJackets/robocup-software | bce13ce53ddb2ecb9696266d980722c34617dc15 | rj_gameplay/stp/skill/action_behavior.py | python | ActionBehavior.tick_once | (
self, robot: rc.Robot, world_state: rc.WorldState, ctx=None
) | return {self.robot.id: [self.action]} | Ticks its action using the robot given (if root) or the robot from its parent.
This will probably become tick() or spin() once action server is implemented
TODO: Should return a list of robot intents | Ticks its action using the robot given (if root) or the robot from its parent.
This will probably become tick() or spin() once action server is implemented
TODO: Should return a list of robot intents | [
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self, robot: rc.Robot, world_state: rc.WorldState, ctx=None
) -> RobotActions:
"""
Ticks its action using the robot given (if root) or the robot from its parent.
This will probably become tick() or spin() once action server is implemented
TODO: Should return a list of robot intents
"""
if robot is not None:
self.robot = robot
else:
self.robot = self.parent.robot
self.action.robot_id = robot.id
if world_state is not None:
self.world_state = world_state
else:
self.world_state = self.parent.world_state
# if robot is None:
# self.robot = self.parent.robot
self.ctx = ctx
super().tick_once()
return {self.robot.id: [self.action]} | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/ctc_ops.py | python | ctc_loss_v2 | (labels,
logits,
label_length,
logit_length,
logits_time_major=True,
unique=None,
blank_index=None,
name=None) | return ctc_loss_dense(
labels=labels,
logits=logits,
label_length=label_length,
logit_length=logit_length,
logits_time_major=logits_time_major,
unique=unique,
blank_index=blank_index,
name=name) | Computes CTC (Connectionist Temporal Classification) loss.
This op implements the CTC loss as presented in (Graves et al., 2006).
Notes:
- Same as the "Classic CTC" in TensorFlow 1.x's tf.compat.v1.nn.ctc_loss
setting of preprocess_collapse_repeated=False, ctc_merge_repeated=True
- Labels may be supplied as either a dense, zero-padded tensor with a
vector of label sequence lengths OR as a SparseTensor.
- On TPU and GPU: Only dense padded labels are supported.
- On CPU: Caller may use SparseTensor or dense padded labels but calling with
a SparseTensor will be significantly faster.
- Default blank label is 0 rather num_classes - 1, unless overridden by
blank_index.
Args:
labels: tensor of shape [batch_size, max_label_seq_length] or SparseTensor
logits: tensor of shape [frames, batch_size, num_labels], if
logits_time_major == False, shape is [batch_size, frames, num_labels].
label_length: tensor of shape [batch_size], None if labels is SparseTensor
Length of reference label sequence in labels.
logit_length: tensor of shape [batch_size] Length of input sequence in
logits.
logits_time_major: (optional) If True (default), logits is shaped [time,
batch, logits]. If False, shape is [batch, time, logits]
unique: (optional) Unique label indices as computed by
ctc_unique_labels(labels). If supplied, enable a faster, memory efficient
implementation on TPU.
blank_index: (optional) Set the class index to use for the blank label.
Negative values will start from num_classes, ie, -1 will reproduce the
ctc_loss behavior of using num_classes - 1 for the blank symbol. There is
some memory/performance overhead to switching from the default of 0 as an
additional shifted copy of the logits may be created.
name: A name for this `Op`. Defaults to "ctc_loss_dense".
Returns:
loss: tensor of shape [batch_size], negative log probabilities.
References:
Connectionist Temporal Classification - Labeling Unsegmented Sequence Data
with Recurrent Neural Networks:
[Graves et al., 2006](https://dl.acm.org/citation.cfm?id=1143891)
([pdf](http://www.cs.toronto.edu/~graves/icml_2006.pdf)) | Computes CTC (Connectionist Temporal Classification) loss. | [
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] | def ctc_loss_v2(labels,
logits,
label_length,
logit_length,
logits_time_major=True,
unique=None,
blank_index=None,
name=None):
"""Computes CTC (Connectionist Temporal Classification) loss.
This op implements the CTC loss as presented in (Graves et al., 2006).
Notes:
- Same as the "Classic CTC" in TensorFlow 1.x's tf.compat.v1.nn.ctc_loss
setting of preprocess_collapse_repeated=False, ctc_merge_repeated=True
- Labels may be supplied as either a dense, zero-padded tensor with a
vector of label sequence lengths OR as a SparseTensor.
- On TPU and GPU: Only dense padded labels are supported.
- On CPU: Caller may use SparseTensor or dense padded labels but calling with
a SparseTensor will be significantly faster.
- Default blank label is 0 rather num_classes - 1, unless overridden by
blank_index.
Args:
labels: tensor of shape [batch_size, max_label_seq_length] or SparseTensor
logits: tensor of shape [frames, batch_size, num_labels], if
logits_time_major == False, shape is [batch_size, frames, num_labels].
label_length: tensor of shape [batch_size], None if labels is SparseTensor
Length of reference label sequence in labels.
logit_length: tensor of shape [batch_size] Length of input sequence in
logits.
logits_time_major: (optional) If True (default), logits is shaped [time,
batch, logits]. If False, shape is [batch, time, logits]
unique: (optional) Unique label indices as computed by
ctc_unique_labels(labels). If supplied, enable a faster, memory efficient
implementation on TPU.
blank_index: (optional) Set the class index to use for the blank label.
Negative values will start from num_classes, ie, -1 will reproduce the
ctc_loss behavior of using num_classes - 1 for the blank symbol. There is
some memory/performance overhead to switching from the default of 0 as an
additional shifted copy of the logits may be created.
name: A name for this `Op`. Defaults to "ctc_loss_dense".
Returns:
loss: tensor of shape [batch_size], negative log probabilities.
References:
Connectionist Temporal Classification - Labeling Unsegmented Sequence Data
with Recurrent Neural Networks:
[Graves et al., 2006](https://dl.acm.org/citation.cfm?id=1143891)
([pdf](http://www.cs.toronto.edu/~graves/icml_2006.pdf))
"""
if isinstance(labels, sparse_tensor.SparseTensor):
if blank_index is None:
raise ValueError(
"Argument `blank_index` must be provided when labels is a "
"SparseTensor.")
if blank_index < 0:
blank_index += _get_dim(logits, 2)
if blank_index != _get_dim(logits, 2) - 1:
logits = array_ops.concat([
logits[:, :, :blank_index],
logits[:, :, blank_index + 1:],
logits[:, :, blank_index:blank_index + 1],
],
axis=2)
labels = sparse_tensor.SparseTensor(
labels.indices,
array_ops.where(labels.values < blank_index, labels.values,
labels.values - 1), labels.dense_shape)
return ctc_loss(
labels=labels,
inputs=logits,
sequence_length=logit_length,
time_major=logits_time_major)
if blank_index is None:
blank_index = 0
return ctc_loss_dense(
labels=labels,
logits=logits,
label_length=label_length,
logit_length=logit_length,
logits_time_major=logits_time_major,
unique=unique,
blank_index=blank_index,
name=name) | [
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | utils/cpplint.py | python | _CppLintState.ResetErrorCounts | (self) | Sets the module's error statistic back to zero. | Sets the module's error statistic back to zero. | [
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"""Sets the module's error statistic back to zero."""
self.error_count = 0
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | build/android/pylib/results/report_results.py | python | LogFull | (results, test_type, test_package, annotation=None,
flakiness_server=None) | Log the tests results for the test suite.
The results will be logged three different ways:
1. Log to stdout.
2. Log to local files for aggregating multiple test steps
(on buildbots only).
3. Log to flakiness dashboard (on buildbots only).
Args:
results: An instance of TestRunResults object.
test_type: Type of the test (e.g. 'Instrumentation', 'Unit test', etc.).
test_package: Test package name (e.g. 'ipc_tests' for gtests,
'ContentShellTest' for instrumentation tests)
annotation: If instrumenation test type, this is a list of annotations
(e.g. ['Smoke', 'SmallTest']).
flakiness_server: If provider, upload the results to flakiness dashboard
with this URL. | Log the tests results for the test suite. | [
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"suite",
"."
] | def LogFull(results, test_type, test_package, annotation=None,
flakiness_server=None):
"""Log the tests results for the test suite.
The results will be logged three different ways:
1. Log to stdout.
2. Log to local files for aggregating multiple test steps
(on buildbots only).
3. Log to flakiness dashboard (on buildbots only).
Args:
results: An instance of TestRunResults object.
test_type: Type of the test (e.g. 'Instrumentation', 'Unit test', etc.).
test_package: Test package name (e.g. 'ipc_tests' for gtests,
'ContentShellTest' for instrumentation tests)
annotation: If instrumenation test type, this is a list of annotations
(e.g. ['Smoke', 'SmallTest']).
flakiness_server: If provider, upload the results to flakiness dashboard
with this URL.
"""
if not results.DidRunPass():
logging.critical('*' * 80)
logging.critical('Detailed Logs')
logging.critical('*' * 80)
for line in results.GetLogs().splitlines():
logging.critical(line)
logging.critical('*' * 80)
logging.critical('Summary')
logging.critical('*' * 80)
for line in results.GetGtestForm().splitlines():
logging.critical(line)
logging.critical('*' * 80)
if os.environ.get('BUILDBOT_BUILDERNAME'):
# It is possible to have multiple buildbot steps for the same
# instrumenation test package using different annotations.
if annotation and len(annotation) == 1:
suite_name = annotation[0]
else:
suite_name = test_package
_LogToFile(results, test_type, suite_name)
if flakiness_server:
_LogToFlakinessDashboard(results, test_type, test_package,
flakiness_server) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_txt.py | python | FileReadJob.Cancel | (self) | Cancel the running task | Cancel the running task | [
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"""Cancel the running task"""
self.cancel = True | [
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | lldb/third_party/Python/module/pexpect-2.4/pexpect.py | python | spawn.waitnoecho | (self, timeout=-1) | This waits until the terminal ECHO flag is set False. This returns
True if the echo mode is off. This returns False if the ECHO flag was
not set False before the timeout. This can be used to detect when the
child is waiting for a password. Usually a child application will turn
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p = pexpect.spawn ('ssh user@example.com')
p.waitnoecho()
p.sendline(mypassword)
If timeout is None then this method to block forever until ECHO flag is
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child is waiting for a password. Usually a child application will turn
off echo mode when it is waiting for the user to enter a password. For
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not set False before the timeout. This can be used to detect when the
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | MenuBar.Attach | (*args, **kwargs) | return _core_.MenuBar_Attach(*args, **kwargs) | Attach(self, wxFrame frame) | Attach(self, wxFrame frame) | [
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"""Attach(self, wxFrame frame)"""
return _core_.MenuBar_Attach(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/imaplib.py | python | IMAP4.store | (self, message_set, command, flags) | return self._untagged_response(typ, dat, 'FETCH') | Alters flag dispositions for messages in mailbox.
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"""Alters flag dispositions for messages in mailbox.
(typ, [data]) = <instance>.store(message_set, command, flags)
"""
if (flags[0],flags[-1]) != ('(',')'):
flags = '(%s)' % flags # Avoid quoting the flags
typ, dat = self._simple_command('STORE', message_set, command, flags)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | DataViewListCtrl.AppendItem | (*args, **kwargs) | return _dataview.DataViewListCtrl_AppendItem(*args, **kwargs) | AppendItem(self, wxVariantVector values, UIntPtr data=None) | AppendItem(self, wxVariantVector values, UIntPtr data=None) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/systrace/systrace/decorators.py | python | ShouldSkip | (test, device) | return False | Returns whether the test should be skipped and the reason for it. | Returns whether the test should be skipped and the reason for it. | [
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"""Returns whether the test should be skipped and the reason for it."""
if hasattr(test, '_disabled_strings'):
disabled_devices = getattr(test, '_disabled_strings')
return device in disabled_devices
return False | [
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CaoWGG/TensorRT-CenterNet | f949252e37b51e60f873808f46d3683f15735e79 | onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang/cindex.py | python | Cursor.is_move_constructor | (self) | return conf.lib.clang_CXXConstructor_isMoveConstructor(self) | Returns True if the cursor refers to a C++ move constructor. | Returns True if the cursor refers to a C++ move constructor. | [
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"""
return conf.lib.clang_CXXConstructor_isMoveConstructor(self) | [
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gnina/gnina | b9ae032f52fc7a8153987bde09c0efa3620d8bb6 | caffe/scripts/cpp_lint.py | python | IsErrorSuppressedByNolint | (category, linenum) | return (linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set())) | Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
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"""Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
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return (linenum in _error_suppressions.get(category, set()) or
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | bindings/pyroot/cppyy/cppyy-backend/cling/setup.py | python | get_prefix | () | return prefix | cppyy-cling installation. | cppyy-cling installation. | [
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] | def get_prefix():
"""cppyy-cling installation."""
global prefix
if prefix is None:
prefix = os.path.join(get_builddir(), 'install', 'cppyy_backend')
return prefix | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiToolBarItem.SetActive | (*args, **kwargs) | return _aui.AuiToolBarItem_SetActive(*args, **kwargs) | SetActive(self, bool b) | SetActive(self, bool b) | [
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"""SetActive(self, bool b)"""
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TFIn.__init__ | (self, *args) | __init__(TFIn self, TStr FNm) -> TFIn
Parameters:
FNm: TStr const &
__init__(TFIn self, TStr FNm, bool & OpenedP) -> TFIn
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OpenedP: bool & | __init__(TFIn self, TStr FNm) -> TFIn | [
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Parameters:
FNm: TStr const &
__init__(TFIn self, TStr FNm, bool & OpenedP) -> TFIn
Parameters:
FNm: TStr const &
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_snap.TFIn_swiginit(self,_snap.new_TFIn(*args)) | [
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neopenx/Dragon | 0e639a7319035ddc81918bd3df059230436ee0a1 | Dragon/python/dragon/vm/theano/compile/function.py | python | GraphDef_Device | (meta_graph) | Inject the device option into GraphDef.
Parameters
----------
meta_graph : dragon_pb2.GraphDef
The definition of meta graph.
Returns
-------
None
References
----------
`config.EnableCPU()`_ - How to use CPU device.
`config.EnableCUDA(*args, **kwargs)`_ - How to use CUDA device.
`config.SetRandomSeed(*args, **kwargs)`_ - How to set random seed. | Inject the device option into GraphDef. | [
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] | def GraphDef_Device(meta_graph):
"""Inject the device option into GraphDef.
Parameters
----------
meta_graph : dragon_pb2.GraphDef
The definition of meta graph.
Returns
-------
None
References
----------
`config.EnableCPU()`_ - How to use CPU device.
`config.EnableCUDA(*args, **kwargs)`_ - How to use CUDA device.
`config.SetRandomSeed(*args, **kwargs)`_ - How to set random seed.
"""
from dragon.config import option
if option['device'] is not 'None':
supports = {'CPU': 0, 'CUDA': 1}
device_option = pb.DeviceOption()
device_option.device_type = supports[option['device']]
device_option.gpu_id = option['gpu_id']
device_option.random_seed = option['random_seed']
if option['use_cudnn']: device_option.engine = 'CUDNN'
meta_graph.device_option.CopyFrom(device_option) | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/egt/alpharank_visualizer.py | python | NetworkPlot._draw_network | (self) | Draws the NetworkX object representing the underlying graph. | Draws the NetworkX object representing the underlying graph. | [
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] | def _draw_network(self):
"""Draws the NetworkX object representing the underlying graph."""
plt.clf()
if self.num_populations == 1:
node_sizes = 5000
node_border_width = 1.
else:
node_sizes = 15000
node_border_width = 3.
vmin, vmax = 0, np.max(self.pi) + 0.1
nx.draw_networkx_nodes(
self.g,
self.pos,
node_size=node_sizes,
node_color=self.node_colors,
edgecolors="k",
cmap=plt.cm.Blues,
vmin=vmin,
vmax=vmax,
linewidths=node_border_width)
nx.draw_networkx_edges(
self.g,
self.pos,
node_size=node_sizes,
arrowstyle="->",
arrowsize=10,
edge_color=self.edge_colors,
edge_cmap=plt.cm.Blues,
width=5)
nx.draw_networkx_edge_labels(self.g, self.pos, edge_labels=self.edge_labels)
if self.num_populations > 1:
subnode_separation = 0.1
subgraph = nx.Graph()
for i_population in range(self.num_populations):
subgraph.add_node(i_population)
for i_strat_profile in self.g:
x, y = self.pos[i_strat_profile]
if self.num_populations == 1:
node_text = "$\\pi_{" + self.state_labels[i_strat_profile] + "}=$"
node_text += str(np.round(self.pi[i_strat_profile], decimals=2))
else:
node_text = "" # No text for multi-population case as plot gets messy
txt = plt.text(
x,
y,
node_text,
horizontalalignment="center",
verticalalignment="center",
fontsize=12)
txt.set_path_effects(
[PathEffects.withStroke(linewidth=3, foreground="w")])
if self.num_populations > 1:
sub_pos = nx.circular_layout(subgraph)
subnode_labels = dict()
strat_profile = utils.get_strat_profile_from_id(
self.num_strats_per_population, i_strat_profile)
for i_population in subgraph.nodes():
i_strat = strat_profile[i_population]
subnode_labels[i_population] = "$s^{" + str(i_population + 1) + "}="
subnode_labels[i_population] += (
self.state_labels[i_population][i_strat] + "$")
# Adjust the node positions generated by NetworkX's circular_layout(),
# such that the node for the 1st strategy starts on the left.
sub_pos[i_population] = (-sub_pos[i_population] * subnode_separation +
self.pos[i_strat_profile])
nx.draw(
subgraph,
pos=sub_pos,
with_labels=True,
width=0.,
node_color="w",
labels=subnode_labels,
node_size=2500) | [
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Netflix/NfWebCrypto | 499faf4eb9f9ccf0b21dc728e974970f54bd6c52 | plugin/ppapi/ppapi/generators/idl_parser.py | python | IDLParser.p_expression_binop | (self, p) | expression : expression LSHIFT expression
| expression RSHIFT expression
| expression '|' expression
| expression '&' expression
| expression '^' expression
| expression '+' expression
| expression '-' expression
| expression '*' expression
| expression '/' expression | expression : expression LSHIFT expression
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| expression '*' expression
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p[0] = "%s %s %s" % (str(p[1]), str(p[2]), str(p[3]))
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | PrintPreview.SetCurrentPage | (*args, **kwargs) | return _windows_.PrintPreview_SetCurrentPage(*args, **kwargs) | SetCurrentPage(self, int pageNum) -> bool | SetCurrentPage(self, int pageNum) -> bool | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/functools.py | python | _c3_mro | (cls, abcs=None) | return _c3_merge(
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If no *abcs* are given, the algorithm works exactly like the built-in C3
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If given, *abcs* is a list of abstract base classes that should be inserted
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__len__) are inserted directly after the last ABC explicitly listed in the
MRO of said class. If two implicit ABCs end up next to each other in the
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"""
for i, base in enumerate(reversed(cls.__bases__)):
if hasattr(base, '__abstractmethods__'):
boundary = len(cls.__bases__) - i
break # Bases up to the last explicit ABC are considered first.
else:
boundary = 0
abcs = list(abcs) if abcs else []
explicit_bases = list(cls.__bases__[:boundary])
abstract_bases = []
other_bases = list(cls.__bases__[boundary:])
for base in abcs:
if issubclass(cls, base) and not any(
issubclass(b, base) for b in cls.__bases__
):
# If *cls* is the class that introduces behaviour described by
# an ABC *base*, insert said ABC to its MRO.
abstract_bases.append(base)
for base in abstract_bases:
abcs.remove(base)
explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases]
other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
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verilog-to-routing/vtr-verilog-to-routing | d9719cf7374821156c3cee31d66991cb85578562 | vtr_flow/scripts/python_libs/vtr/log_parse.py | python | RangeAbsPassRequirement.min_value | (self) | return self._min_value | Return min value of ratio range | Return min value of ratio range | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/dataview.py | python | DataViewTreeStoreContainerNode.IsExpanded | (*args, **kwargs) | return _dataview.DataViewTreeStoreContainerNode_IsExpanded(*args, **kwargs) | IsExpanded(self) -> bool | IsExpanded(self) -> bool | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/RNN/rnn_quantizer/nndct_shared/utils/registry.py | python | Registry.lookup | (self, name) | Looks up "name".
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Args:
name: a string specifying the registry key for the obj.
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Raises:
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/command/sdist.py | python | sdist.add_defaults | (self) | Add all the default files to self.filelist:
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- all files defined in data_files.
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got_it = 0
for fn in alts:
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else:
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else:
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adventuregamestudio/ags | efa89736d868e9dfda4200149d33ba8637746399 | Common/libsrc/freetype-2.1.3/src/tools/docmaker/content.py | python | ContentProcessor.add_markup | ( self ) | add a new markup section | add a new markup section | [
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marks = self.markup_lines
if len(marks) > 0 and not string.strip(marks[-1]):
self.markup_lines = marks[:-1]
m = DocMarkup( self.markup, self.markup_lines )
self.markups.append( m )
self.markup = None
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emscripten-core/emscripten | 0d413d3c5af8b28349682496edc14656f5700c2f | third_party/ply/example/ansic/cparse.py | python | p_iteration_statement_2 | (t) | iteration_statement : FOR LPAREN expression_opt SEMI expression_opt SEMI expression_opt RPAREN statement | iteration_statement : FOR LPAREN expression_opt SEMI expression_opt SEMI expression_opt RPAREN statement | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py | python | BaseWidget.__init__ | (self, master, widgetName, cnf={}, kw={}, extra=()) | Construct a widget with the parent widget MASTER, a name WIDGETNAME
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cnf = _cnfmerge((cnf, kw))
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/tools/grit/grit/tool/build.py | python | RcBuilder.GenerateDepfile | (self, depfile, depdir, first_ids_file, depend_on_stamp) | Generate a depfile that contains the imlicit dependencies of the input
grd. The depfile will be in the same format as a makefile, and will contain
references to files relative to |depdir|. It will be put in |depfile|.
For example, supposing we have three files in a directory src/
src/
blah.grd <- depends on input{1,2}.xtb
input1.xtb
input2.xtb
and we run
grit -i blah.grd -o ../out/gen \
--depdir ../out \
--depfile ../out/gen/blah.rd.d
from the directory src/ we will generate a depfile ../out/gen/blah.grd.d
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gen/blah.h: ../src/input1.xtb ../src/input2.xtb
Where "gen/blah.h" is the first output (Ninja expects the .d file to list
the first output in cases where there is more than one). If the flag
--depend-on-stamp is specified, "gen/blah.rd.d.stamp" will be used that is
'touched' whenever a new depfile is generated.
Note that all paths in the depfile are relative to ../out, the depdir. | Generate a depfile that contains the imlicit dependencies of the input
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'''Generate a depfile that contains the imlicit dependencies of the input
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For example, supposing we have three files in a directory src/
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blah.grd <- depends on input{1,2}.xtb
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and we run
grit -i blah.grd -o ../out/gen \
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Note that all paths in the depfile are relative to ../out, the depdir.
'''
depfile = os.path.abspath(depfile)
depdir = os.path.abspath(depdir)
infiles = self.res.GetInputFiles()
# We want to trigger a rebuild if the first ids change.
if first_ids_file is not None:
infiles.append(first_ids_file)
if (depend_on_stamp):
output_file = depfile + ".stamp"
# Touch the stamp file before generating the depfile.
with open(output_file, 'a'):
os.utime(output_file, None)
else:
# Get the first output file relative to the depdir.
outputs = self.res.GetOutputFiles()
output_file = os.path.join(self.output_directory,
outputs[0].GetOutputFilename())
output_file = os.path.relpath(output_file, depdir)
# The path prefix to prepend to dependencies in the depfile.
prefix = os.path.relpath(os.getcwd(), depdir)
deps_text = ' '.join([os.path.join(prefix, i) for i in infiles])
depfile_contents = output_file + ': ' + deps_text
self.MakeDirectoriesTo(depfile)
outfile = self.fo_create(depfile, 'w', encoding='utf-8')
outfile.write(depfile_contents) | [
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sfzhang15/FaceBoxes | b52cc92f9362d3adc08d54666aeb9ebb62fdb7da | python/caffe/coord_map.py | python | coord_map | (fn) | Define the coordinate mapping by its
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- scale: output coord[i * scale] <- input_coord[i]
- shift: output coord[i] <- output_coord[i + shift]
s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
(None, 1, 0) since it is independent of axis and does not transform coords. | Define the coordinate mapping by its
- axis
- scale: output coord[i * scale] <- input_coord[i]
- shift: output coord[i] <- output_coord[i + shift]
s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
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s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
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"""
if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
axis, stride, ks, pad = conv_params(fn)
return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
elif fn.type_name == 'Deconvolution':
axis, stride, ks, pad = conv_params(fn)
return axis, stride, (ks - 1) / 2 - pad
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return None, 1, 0
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/_pydecimal.py | python | Decimal.log10 | (self, context=None) | return ans | Returns the base 10 logarithm of self. | Returns the base 10 logarithm of self. | [
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"""Returns the base 10 logarithm of self."""
if context is None:
context = getcontext()
# log10(NaN) = NaN
ans = self._check_nans(context=context)
if ans:
return ans
# log10(0.0) == -Infinity
if not self:
return _NegativeInfinity
# log10(Infinity) = Infinity
if self._isinfinity() == 1:
return _Infinity
# log10(negative or -Infinity) raises InvalidOperation
if self._sign == 1:
return context._raise_error(InvalidOperation,
'log10 of a negative value')
# log10(10**n) = n
if self._int[0] == '1' and self._int[1:] == '0'*(len(self._int) - 1):
# answer may need rounding
ans = Decimal(self._exp + len(self._int) - 1)
else:
# result is irrational, so necessarily inexact
op = _WorkRep(self)
c, e = op.int, op.exp
p = context.prec
# correctly rounded result: repeatedly increase precision
# until result is unambiguously roundable
places = p-self._log10_exp_bound()+2
while True:
coeff = _dlog10(c, e, places)
# assert len(str(abs(coeff)))-p >= 1
if coeff % (5*10**(len(str(abs(coeff)))-p-1)):
break
places += 3
ans = _dec_from_triple(int(coeff<0), str(abs(coeff)), -places)
context = context._shallow_copy()
rounding = context._set_rounding(ROUND_HALF_EVEN)
ans = ans._fix(context)
context.rounding = rounding
return ans | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/pyparsing.py | python | ParserElement.__rand__ | (self, other ) | return other & self | Implementation of & operator when left operand is not a :class:`ParserElement` | Implementation of & operator when left operand is not a :class:`ParserElement` | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextFileHandler.SetVisible | (*args, **kwargs) | return _richtext.RichTextFileHandler_SetVisible(*args, **kwargs) | SetVisible(self, bool visible) | SetVisible(self, bool visible) | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf_kafka/versioneer.py | python | render_pep440_pre | (pieces) | return rendered | TAG[.post.devDISTANCE] -- No -dirty.
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rendered = pieces["closest-tag"]
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/joblib/joblib/externals/loky/backend/popen_loky_win32.py | python | main | () | Run code specified by data received over pipe | Run code specified by data received over pipe | [
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Run code specified by data received over pipe
'''
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from_parent = os.fdopen(fd, 'rb')
process.current_process()._inheriting = True
preparation_data = load(from_parent)
spawn.prepare(preparation_data)
self = load(from_parent)
process.current_process()._inheriting = False
from_parent.close()
exitcode = self._bootstrap()
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apiaryio/drafter | 4634ebd07f6c6f257cc656598ccd535492fdfb55 | tools/gyp/pylib/gyp/ordered_dict.py | python | OrderedDict.__repr__ | (self, _repr_running={}) | od.__repr__() <==> repr(od) | od.__repr__() <==> repr(od) | [
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kismetwireless/kismet | a7c0dc270c960fb1f58bd9cec4601c201885fd4e | capture_bt_geiger/KismetCaptureBtGeiger/kismetexternal/__init__.py | python | Datasource.set_probesource_cb | (self, cb) | Set callback for datasource probing
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pathlib2/pathlib2/__init__.py | python | _Flavour.join_parsed_parts | (self, drv, root, parts, drv2, root2, parts2) | return drv2, root2, parts2 | Join the two paths represented by the respective
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/cookielib.py | python | CookiePolicy.set_ok | (self, cookie, request) | Return true if (and only if) cookie should be accepted from server.
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apple/foundationdb | f7118ad406f44ab7a33970fc8370647ed0085e18 | bindings/python/fdb/directory_impl.py | python | DirectoryLayer.exists | (self, tr, path=()) | return True | Returns whether or not the specified directory exists. | Returns whether or not the specified directory exists. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | Misc.bell | (self, displayof=0) | Ring a display's bell. | Ring a display's bell. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/threading.py | python | Thread._set_tstate_lock | (self) | Set a lock object which will be released by the interpreter when
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | grc/core/ports/_virtual_connections.py | python | _sources_from_virtual_sink_port | (sink_port, _traversed=None) | return list(chain(*source_ports_per_virtual_connection)) | Resolve the source port that is connected to the given virtual sink port.
Use the get source from virtual source to recursively resolve subsequent ports. | Resolve the source port that is connected to the given virtual sink port.
Use the get source from virtual source to recursively resolve subsequent ports. | [
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] | def _sources_from_virtual_sink_port(sink_port, _traversed=None):
"""
Resolve the source port that is connected to the given virtual sink port.
Use the get source from virtual source to recursively resolve subsequent ports.
"""
source_ports_per_virtual_connection = (
# there can be multiple ports per virtual connection
_sources_from_virtual_source_port(
c.source_port, _traversed) # type: list
for c in sink_port.connections(enabled=True)
)
# concatenate generated lists of ports
return list(chain(*source_ports_per_virtual_connection)) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/clang/bindings/python/clang/cindex.py | python | Cursor.is_converting_constructor | (self) | return conf.lib.clang_CXXConstructor_isConvertingConstructor(self) | Returns True if the cursor refers to a C++ converting constructor. | Returns True if the cursor refers to a C++ converting constructor. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibook.py | python | AuiNotebook.GetAGWWindowStyleFlag | (self) | return self._agwFlags | Returns the AGW-specific style of the window.
:see: :meth:`SetAGWWindowStyleFlag` for a list of possible AGW-specific window styles. | Returns the AGW-specific style of the window. | [
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"""
Returns the AGW-specific style of the window.
:see: :meth:`SetAGWWindowStyleFlag` for a list of possible AGW-specific window styles.
"""
return self._agwFlags | [
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tuttleofx/TuttleOFX | 36fc4cae15092a84ea8c29b9c6658c7cabfadb6e | applications/sam/common/samUtils.py | python | Sam._displayCommandLineHelp | (self, parser) | Display sam command line help. | Display sam command line help. | [
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] | def _displayCommandLineHelp(self, parser):
"""
Display sam command line help.
"""
if not self.command:
raise NotImplementedError
subparser = getSubParser(parser, self.command)
# if sam command is called from sam main command line
if subparser is not None:
puts(subparser.format_help())
else:
parser.print_help() | [
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ideawu/ssdb-rocks | a3cbb322cafb2f493252829c608e2239df98c9ac | deps/cpy/antlr3/streams.py | python | CommonTokenStream.setTokenSource | (self, tokenSource) | Reset this token stream by setting its token source. | Reset this token stream by setting its token source. | [
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self.p = -1
self.channel = DEFAULT_CHANNEL | [
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isl-org/Open3D | 79aec3ddde6a571ce2f28e4096477e52ec465244 | python/open3d/visualization/tensorboard_plugin/colormap.py | python | Colormap.calc_color_array | (self, values, range_min, range_max) | return [tex[int(u * n)] for u in u_array] | Generate the color array based on the minimum and maximum range passed.
Args:
values: The index of values.
range_min: The minimum value in the range.
range_max: The maximum value in the range.
Returns:
An array of color index based on the range passed. | Generate the color array based on the minimum and maximum range passed. | [
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] | def calc_color_array(self, values, range_min, range_max):
"""Generate the color array based on the minimum and maximum range passed.
Args:
values: The index of values.
range_min: The minimum value in the range.
range_max: The maximum value in the range.
Returns:
An array of color index based on the range passed.
"""
u_array = self.calc_u_array(values, range_min, range_max)
tex = [[1.0, 0.0, 1.0]] * 128
n = float(len(tex) - 1)
idx = 0
for tex_idx in range(0, len(tex)):
x = float(tex_idx) / n
while idx < len(self.points) and x > self.points[idx].value:
idx += 1
if idx == 0:
tex[tex_idx] = self.points[0].color
elif idx == len(self.points):
tex[tex_idx] = self.points[-1].color
else:
p0 = self.points[idx - 1]
p1 = self.points[idx]
dist = p1.value - p0.value
# Calc weights between 0 and 1
w0 = 1.0 - (x - p0.value) / dist
w1 = (x - p0.value) / dist
c = [
w0 * p0.color[0] + w1 * p1.color[0],
w0 * p0.color[1] + w1 * p1.color[1],
w0 * p0.color[2] + w1 * p1.color[2]
]
tex[tex_idx] = c
return [tex[int(u * n)] for u in u_array] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/webapp2/webapp2.py | python | SimpleRoute.regex | (self) | return re.compile(self.template) | Lazy regex compiler. | Lazy regex compiler. | [
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"regex",
"compiler",
"."
] | def regex(self):
"""Lazy regex compiler."""
if not self.template.startswith('^'):
self.template = '^' + self.template
if not self.template.endswith('$'):
self.template += '$'
return re.compile(self.template) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/numpy/math_ops.py | python | copysign | (x1, x2, dtype=None) | return res | Changes the sign of `x1` to that of `x2`, element-wise.
If `x2` is a scalar, its sign will be copied to all elements of `x1`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Complex inputs are not supported now.
Args:
x1 (Union[int, float, list, tuple, Tensor]): Values to change the sign of.
x2 (Union[int, float, list, tuple, Tensor]): The sign of x2 is copied to x1. If `x1.shape != x2.shape`,
they must be broadcastable to a common shape (which becomes the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. The values of `x1` with the sign of `x2`. This is a scalar if both `x1` and `x2` are scalars.
Raises:
TypeError: If dtype of the input is not in the given types or
the input can not be converted to tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.copysign(np.array([1, -1, -1]), np.array([-1, 1, -1]))
>>> print(output)
[-1 1 -1] | Changes the sign of `x1` to that of `x2`, element-wise. | [
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] | def copysign(x1, x2, dtype=None):
"""
Changes the sign of `x1` to that of `x2`, element-wise.
If `x2` is a scalar, its sign will be copied to all elements of `x1`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Complex inputs are not supported now.
Args:
x1 (Union[int, float, list, tuple, Tensor]): Values to change the sign of.
x2 (Union[int, float, list, tuple, Tensor]): The sign of x2 is copied to x1. If `x1.shape != x2.shape`,
they must be broadcastable to a common shape (which becomes the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Defaults to None. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. The values of `x1` with the sign of `x2`. This is a scalar if both `x1` and `x2` are scalars.
Raises:
TypeError: If dtype of the input is not in the given types or
the input can not be converted to tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.copysign(np.array([1, -1, -1]), np.array([-1, 1, -1]))
>>> print(output)
[-1 1 -1]
"""
if not isinstance(x1, (int, float, list, tuple, Tensor)):
_raise_type_error('integer, float, list, tuple or Tensor are expected, but got', x1)
if not isinstance(x2, (int, float, list, tuple, Tensor)):
_raise_type_error('integer, float, list, tuple or Tensor are expected, but got', x2)
x1, x2 = _to_tensor(x1, x2)
shape_out = _infer_out_shape(F.shape(x1), F.shape(x2))
x1 = _broadcast_to_shape(x1, shape_out)
x2 = _broadcast_to_shape(x2, shape_out)
if _check_same_type(F.dtype(x1), mstype.bool_) or _check_same_type(F.dtype(x2), mstype.bool_):
_raise_type_error("sign does not accept dtype bool.")
original_dtype = x1.dtype
if not _check_is_float(original_dtype):
pos_tensor = F.absolute(x1.astype('float32')).astype(original_dtype)
else:
pos_tensor = F.absolute(x1)
neg_tensor = F.neg_tensor(pos_tensor)
less_zero = F.less(x2, 0)
res = F.select(less_zero, neg_tensor, pos_tensor)
if dtype is not None and not _check_same_type(F.dtype(res), dtype):
res = F.cast(res, dtype)
return res | [
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google-ar/WebARonTango | e86965d2cbc652156b480e0fcf77c716745578cd | chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py | python | CustomHandler.WriteImmediateCmdSet | (self, func, f) | Overrriden from TypeHandler. | Overrriden from TypeHandler. | [
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"TypeHandler",
"."
] | def WriteImmediateCmdSet(self, func, f):
"""Overrriden from TypeHandler."""
copy_args = func.MakeCmdArgString("_", False)
f.write(" void* Set(void* cmd%s) {\n" %
func.MakeTypedCmdArgString("_", True))
self.WriteImmediateCmdGetTotalSize(func, f)
f.write(" static_cast<ValueType*>(cmd)->Init(%s);\n" % copy_args)
f.write(" return NextImmediateCmdAddressTotalSize<ValueType>("
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f.write(" }\n")
f.write("\n") | [
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] | https://github.com/google-ar/WebARonTango/blob/e86965d2cbc652156b480e0fcf77c716745578cd/chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py#L5722-L5732 | ||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/binary-search-tree-iterator-ii.py | python | BSTIterator.next | (self) | return self.__vals[self.__pos] | :rtype: int | :rtype: int | [
":",
"rtype",
":",
"int"
] | def next(self):
"""
:rtype: int
"""
self.__pos += 1
if self.__pos == len(self.__vals):
node = self.__stk.pop()
self.__traversalLeft(node.right)
self.__vals.append(node.val)
return self.__vals[self.__pos] | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/core/shape_base.py | python | hstack | (tup) | Stack arrays in sequence horizontally (column wise).
Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by `hsplit`.
Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays together.
hsplit : Split array along second axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)``
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]]) | Stack arrays in sequence horizontally (column wise). | [
"Stack",
"arrays",
"in",
"sequence",
"horizontally",
"(",
"column",
"wise",
")",
"."
] | def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
Take a sequence of arrays and stack them horizontally to make
a single array. Rebuild arrays divided by `hsplit`.
Parameters
----------
tup : sequence of ndarrays
All arrays must have the same shape along all but the second axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays together.
hsplit : Split array along second axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=1)``
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrs = [atleast_1d(_m) for _m in tup]
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
else:
return _nx.concatenate(arrs, 1) | [
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