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NREL/EnergyPlus | fadc5973b85c70e8cc923efb69c144e808a26078 | src/EnergyPlus/api/datatransfer.py | python | DataExchange.warmup_flag | (self, state: c_void_p) | return self.api.warmupFlag(state) == 1 | Gets a flag for whether the warmup flag is currently on, signaling that EnergyPlus is still in the process of
converging on warmup days. The C API returns an integer where 1 is yes and 0 is no, this simply wraps that
with a bool conversion.
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:return: A boolean indicating whether the warmup flag is on. | Gets a flag for whether the warmup flag is currently on, signaling that EnergyPlus is still in the process of
converging on warmup days. The C API returns an integer where 1 is yes and 0 is no, this simply wraps that
with a bool conversion. | [
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converging on warmup days. The C API returns an integer where 1 is yes and 0 is no, this simply wraps that
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:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
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"""
return self.api.warmupFlag(state) == 1 | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Image.GetDataBuffer | (*args, **kwargs) | return _core_.Image_GetDataBuffer(*args, **kwargs) | GetDataBuffer(self) -> PyObject
Returns a writable Python buffer object that is pointing at the RGB
image data buffer inside the wx.Image. You need to ensure that you do
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"""
GetDataBuffer(self) -> PyObject
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not use this buffer object after the image has been destroyed.
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return _core_.Image_GetDataBuffer(*args, **kwargs) | [
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|
shizhuolin/PyCTP | 3b3e160ef7ace423a04a09cc034d939db2444ef1 | APIToPyCTP.py | python | generatestructcppheadercode | (codetree) | struct header | struct header | [
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] | def generatestructcppheadercode(codetree):
"""struct header"""
cppheadercodetemplate = "./src/UserApiStruct.h.template"
cppheadercodefile = "./src/UserApiStruct.h"
cppheadercode = ""
for (key, value) in codetree.items():
if(value['type'] == 'struct'):
cppheadercode += "\nPyObject *PyCTP_PyDict_FromStruct(" + key + " *p"+key.replace("CThostFtdc", "")+");"
cppheadercode += "\nint PyCTP_Struct_FromPyDict(" + key + " *p" + key.replace("CThostFtdc", "") + ", PyObject *dict);"
import re, chardet
with open(cppheadercodetemplate, 'rb') as f:
data = f.read()
encoding = chardet.detect(data)
sourcecode = data.decode(encoding['encoding'])
sourcecode = re.sub(r'\{\{\s*body\s*\}\}', cppheadercode, sourcecode)
with open(cppheadercodefile, 'wt') as f:
f.write(sourcecode) | [
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||
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/isolate/isolate.py | python | load_results | (resultfile) | return data | Loads the previous results as an optimization. | Loads the previous results as an optimization. | [
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"""Loads the previous results as an optimization."""
data = {}
if resultfile and os.path.isfile(resultfile):
resultfile = os.path.abspath(resultfile)
with open(resultfile, 'r') as f:
data = json.load(f)
logging.debug('Loaded %s' % resultfile)
else:
resultfile = os.path.abspath(resultfile)
logging.debug('%s was not found' % resultfile)
# Works with native os.path.sep but stores as '/'.
if 'files' in data and os.path.sep != '/':
data['files'] = dict(
(k.replace('/', os.path.sep), v)
for k, v in data['files'].iteritems())
return data | [
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|
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/quantize/quantize_transpiler.py | python | _quantized_scale_name | (var_name) | return "%s.scale" % (var_name) | Return quantized variable name for the input `var_name`. | Return quantized variable name for the input `var_name`. | [
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] | def _quantized_scale_name(var_name):
"""
Return quantized variable name for the input `var_name`.
"""
return "%s.scale" % (var_name) | [
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|
envoyproxy/envoy | 65541accdafe255e72310b4298d646e091da2d80 | tools/api_proto_plugin/type_context.py | python | TypeContext.extend_field | (self, index, name) | return self._extend([2, index], 'field', name) | Extend type context with a field.
Args:
index: field index in message.
name: field name. | Extend type context with a field. | [
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"""Extend type context with a field.
Args:
index: field index in message.
name: field name.
"""
return self._extend([2, index], 'field', name) | [
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|
openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/middle/passes/fusing/resnet_optimization.py | python | _simple_stride_prop | (graph: Graph, node: Node, spatial_dims, supported=True) | This function handles stride propagation for op nodes. If node is in supported ops dict so this is supported operation and we
can propagate stride directly via this op (stride_prop will be set by using bottom stride_prop), otherwise we can't and
stride_prop attr will be set as 1,1,1,1 | This function handles stride propagation for op nodes. If node is in supported ops dict so this is supported operation and we
can propagate stride directly via this op (stride_prop will be set by using bottom stride_prop), otherwise we can't and
stride_prop attr will be set as 1,1,1,1 | [
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can propagate stride directly via this op (stride_prop will be set by using bottom stride_prop), otherwise we can't and
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"""
next_ops = get_next_operation(node)
stride_props, all_ops_are_valid = _check_next_ops(next_ops)
if not supported or not all_ops_are_valid:
# We have to insert pooling layers
for op in next_ops:
if op.has_valid('stride_prop') and not np.array_equal(op.stride_prop[spatial_dims], mo_array([1, 1])) and \
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node['stride_prop'] = mo_array([1, 1, 1, 1])
return
for op in next_ops:
if op.soft_get('has_stride') == True:
op.stride = mo_array([1, 1, 1, 1])
log.debug("STRIDE PROP: {} {} strides was moved upper via {}".format(op.type, op.name, node.name))
node['stride_prop'] = mo_array(stride_props[0]) if len(stride_props) > 0 else mo_array([1, 1, 1, 1])
node['is_partial_inferred'] = False
_clean_fw_tensor_attrs(node.out_node()) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/py_vulcanize/third_party/rcssmin/_setup/py3/commands.py | python | Build.initialize_options | (self) | Prepare for new options | Prepare for new options | [
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""" Prepare for new options """
_build.build.initialize_options(self)
if 'build' in _option_defaults:
for opt_name, default in _option_defaults['build']:
setattr(self, opt_name, default) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/array_ops.py | python | Argmin.__init__ | (self, axis=-1, output_type=mstype.int32) | Initialize Argmin | Initialize Argmin | [
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] | def __init__(self, axis=-1, output_type=mstype.int32):
"""Initialize Argmin"""
self.init_prim_io_names(inputs=['x'], outputs=['output'])
validator.check_value_type("axis", axis, [int], self.name)
validator.check_type_name("output_type", output_type, [mstype.int32, mstype.int64], self.name)
self.axis = axis
self.add_prim_attr('output_type', output_type) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/summary/event_file_inspector.py | python | get_dict_to_print | (field_to_obs) | return output | Transform the field-to-obs mapping into a printable dictionary.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict with the keys and values to print to console. | Transform the field-to-obs mapping into a printable dictionary. | [
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"-",
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"-",
"obs",
"mapping",
"into",
"a",
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] | def get_dict_to_print(field_to_obs):
"""Transform the field-to-obs mapping into a printable dictionary.
Args:
field_to_obs: Dict that maps string field to `Observation` list.
Returns:
A dict with the keys and values to print to console.
"""
def compressed_steps(steps):
return {'num_steps': len(set(steps)),
'min_step': min(steps),
'max_step': max(steps),
'last_step': steps[-1],
'first_step': steps[0],
'outoforder_steps': get_out_of_order(steps)}
def full_steps(steps):
return {'steps': steps, 'outoforder_steps': get_out_of_order(steps)}
output = {}
for field, observations in field_to_obs.items():
if not observations:
output[field] = None
continue
steps = [x['step'] for x in observations]
if field in SHORT_FIELDS:
output[field] = compressed_steps(steps)
if field in LONG_FIELDS:
output[field] = full_steps(steps)
return output | [
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|
cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/affine/reshape.py | python | reshape.validate_arguments | (self) | Checks that the new shape has the same number of entries as the old. | Checks that the new shape has the same number of entries as the old. | [
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] | def validate_arguments(self) -> None:
"""Checks that the new shape has the same number of entries as the old.
"""
old_len = self.args[0].size
new_len = np.prod(self._shape, dtype=int)
if not old_len == new_len:
raise ValueError(
"Invalid reshape dimensions %s." % (self._shape,)
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||
luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0919-Complete-Binary-Tree-Inserter/0919.py | python | CBTInserter.__init__ | (self, root) | :type root: TreeNode | :type root: TreeNode | [
":",
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] | def __init__(self, root):
"""
:type root: TreeNode
"""
self.data = list()
self.data.append(root)
for node in self.data:
if node.left:
self.data.append(node.left)
if node.right:
self.data.append(node.right) | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_collections_abc.py | python | MutableMapping.clear | (self) | D.clear() -> None. Remove all items from D. | D.clear() -> None. Remove all items from D. | [
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] | def clear(self):
'D.clear() -> None. Remove all items from D.'
try:
while True:
self.popitem()
except KeyError:
pass | [
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||
intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | lldb/examples/python/bsd.py | python | Interactive.default | (self, line) | return True | Catch all for unknown command, which will exit the interpreter. | Catch all for unknown command, which will exit the interpreter. | [
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] | def default(self, line):
'''Catch all for unknown command, which will exit the interpreter.'''
print("unknown command: %s" % line)
return True | [
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/bayesflow/python/ops/monte_carlo_impl.py | python | expectation | (f, p, z=None, n=None, seed=None, name='expectation') | r"""Monte Carlo estimate of an expectation: `E_p[f(Z)]` with sample mean.
This `Op` returns
```
n^{-1} sum_{i=1}^n f(z_i), where z_i ~ p
\approx E_p[f(Z)]
```
User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
Args:
f: Callable mapping samples from `p` to `Tensors`.
p: `tf.contrib.distributions.Distribution`.
z: `Tensor` of samples from `p`, produced by `p.sample` for some `n`.
n: Integer `Tensor`. Number of samples to generate if `z` is not provided.
seed: Python integer to seed the random number generator.
name: A name to give this `Op`.
Returns:
A `Tensor` with the same `dtype` as `p`.
Example:
```python
N_samples = 10000
distributions = tf.contrib.distributions
dist = distributions.Uniform([0.0, 0.0], [1.0, 2.0])
elementwise_mean = lambda x: x
mean_sum = lambda x: tf.reduce_sum(x, 1)
estimate_elementwise_mean_tf = monte_carlo.expectation(elementwise_mean,
dist,
n=N_samples)
estimate_mean_sum_tf = monte_carlo.expectation(mean_sum,
dist,
n=N_samples)
with tf.Session() as sess:
estimate_elementwise_mean, estimate_mean_sum = (
sess.run([estimate_elementwise_mean_tf, estimate_mean_sum_tf]))
print estimate_elementwise_mean
>>> np.array([ 0.50018013 1.00097895], dtype=np.float32)
print estimate_mean_sum
>>> 1.49571
``` | r"""Monte Carlo estimate of an expectation: `E_p[f(Z)]` with sample mean. | [
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] | def expectation(f, p, z=None, n=None, seed=None, name='expectation'):
r"""Monte Carlo estimate of an expectation: `E_p[f(Z)]` with sample mean.
This `Op` returns
```
n^{-1} sum_{i=1}^n f(z_i), where z_i ~ p
\approx E_p[f(Z)]
```
User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
Args:
f: Callable mapping samples from `p` to `Tensors`.
p: `tf.contrib.distributions.Distribution`.
z: `Tensor` of samples from `p`, produced by `p.sample` for some `n`.
n: Integer `Tensor`. Number of samples to generate if `z` is not provided.
seed: Python integer to seed the random number generator.
name: A name to give this `Op`.
Returns:
A `Tensor` with the same `dtype` as `p`.
Example:
```python
N_samples = 10000
distributions = tf.contrib.distributions
dist = distributions.Uniform([0.0, 0.0], [1.0, 2.0])
elementwise_mean = lambda x: x
mean_sum = lambda x: tf.reduce_sum(x, 1)
estimate_elementwise_mean_tf = monte_carlo.expectation(elementwise_mean,
dist,
n=N_samples)
estimate_mean_sum_tf = monte_carlo.expectation(mean_sum,
dist,
n=N_samples)
with tf.Session() as sess:
estimate_elementwise_mean, estimate_mean_sum = (
sess.run([estimate_elementwise_mean_tf, estimate_mean_sum_tf]))
print estimate_elementwise_mean
>>> np.array([ 0.50018013 1.00097895], dtype=np.float32)
print estimate_mean_sum
>>> 1.49571
```
"""
with ops.name_scope(name, values=[n, z]):
z = _get_samples(p, z, n, seed)
return _sample_mean(f(z)) | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | PanedWindow.proxy_forget | (self) | return self.proxy("forget") | Remove the proxy from the display. | Remove the proxy from the display. | [
"Remove",
"the",
"proxy",
"from",
"the",
"display",
"."
] | def proxy_forget(self):
"""Remove the proxy from the display.
"""
return self.proxy("forget") | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/pdb.py | python | Pdb.do_display | (self, arg) | display [expression]
Display the value of the expression if it changed, each time execution
stops in the current frame.
Without expression, list all display expressions for the current frame. | display [expression] | [
"display",
"[",
"expression",
"]"
] | def do_display(self, arg):
"""display [expression]
Display the value of the expression if it changed, each time execution
stops in the current frame.
Without expression, list all display expressions for the current frame.
"""
if not arg:
self.message('Currently displaying:')
for item in self.displaying.get(self.curframe, {}).items():
self.message('%s: %r' % item)
else:
val = self._getval_except(arg)
self.displaying.setdefault(self.curframe, {})[arg] = val
self.message('display %s: %r' % (arg, val)) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/parso/py3/parso/cache.py | python | load_module | (hashed_grammar, file_io, cache_path=None) | Returns a module or None, if it fails. | Returns a module or None, if it fails. | [
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] | def load_module(hashed_grammar, file_io, cache_path=None):
"""
Returns a module or None, if it fails.
"""
p_time = file_io.get_last_modified()
if p_time is None:
return None
try:
module_cache_item = parser_cache[hashed_grammar][file_io.path]
if p_time <= module_cache_item.change_time:
module_cache_item.last_used = time.time()
return module_cache_item.node
except KeyError:
return _load_from_file_system(
hashed_grammar,
file_io.path,
p_time,
cache_path=cache_path
) | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Wm.wm_protocol | (self, name=None, func=None) | return self.tk.call(
'wm', 'protocol', self._w, name, command) | Bind function FUNC to command NAME for this widget.
Return the function bound to NAME if None is given. NAME could be
e.g. "WM_SAVE_YOURSELF" or "WM_DELETE_WINDOW". | Bind function FUNC to command NAME for this widget.
Return the function bound to NAME if None is given. NAME could be
e.g. "WM_SAVE_YOURSELF" or "WM_DELETE_WINDOW". | [
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Return the function bound to NAME if None is given. NAME could be
e.g. "WM_SAVE_YOURSELF" or "WM_DELETE_WINDOW"."""
if hasattr(func, '__call__'):
command = self._register(func)
else:
command = func
return self.tk.call(
'wm', 'protocol', self._w, name, command) | [
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|
turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/extensions.py | python | _build_native_function_call | (fn) | return _Closure(native_function_name, arglist) | If fn can be interpreted and handled as a native function: i.e.
fn is one of the extensions, or fn is a simple lambda closure using one of
the extensions.
fn = gl.extensions.add
fn = lambda x: gl.extensions.add(5)
Then, this returns a closure object, which describes the function call
which can then be passed to C++.
Returns a _Closure object on success, raises an exception on failure. | If fn can be interpreted and handled as a native function: i.e.
fn is one of the extensions, or fn is a simple lambda closure using one of
the extensions. | [
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] | def _build_native_function_call(fn):
"""
If fn can be interpreted and handled as a native function: i.e.
fn is one of the extensions, or fn is a simple lambda closure using one of
the extensions.
fn = gl.extensions.add
fn = lambda x: gl.extensions.add(5)
Then, this returns a closure object, which describes the function call
which can then be passed to C++.
Returns a _Closure object on success, raises an exception on failure.
"""
# See if fn is the native function itself
native_function_name = _get_toolkit_function_name_from_function(fn)
if native_function_name != "":
# yup!
# generate an "identity" argument list
argnames = _get_argument_list_from_toolkit_function_name(native_function_name)
arglist = [[0, i] for i in range(len(argnames))]
return _Closure(native_function_name, arglist)
# ok. its not a native function
from .util.lambda_closure_capture import translate
from .util.lambda_closure_capture import Parameter
# Lets see if it is a simple lambda
capture = translate(fn)
# ok. build up the closure arguments
# Try to pick up the lambda
function = _descend_namespace(capture.caller_globals, capture.closure_fn_name)
native_function_name = _get_toolkit_function_name_from_function(function)
if native_function_name == "":
raise RuntimeError("Lambda does not contain a native function")
argnames = _get_argument_list_from_toolkit_function_name(native_function_name)
# ok. build up the argument list. this is mildly annoying due to the mix of
# positional and named arguments
# make an argument list with a placeholder for everything first
arglist = [[-1, i] for i in argnames]
# loop through the positional arguments
for i in range(len(capture.positional_args)):
arg = capture.positional_args[i]
if type(arg) is Parameter:
# This is a lambda argument
# arg.name is the actual string of the argument
# here we need the index
arglist[i] = [0, capture.input_arg_names.index(arg.name)]
else:
# this is a captured value
arglist[i] = [1, arg]
# now. the named arguments are somewhat annoying
for i in capture.named_args:
arg = capture.named_args[i]
if type(arg) is Parameter:
# This is a lambda argument
# arg.name is the actual string of the argument
# here we need the index
arglist[argnames.index(i)] = [0, capture.input_arg_names.index(arg.name)]
else:
# this is a captured value
arglist[argnames.index(i)] = [1, arg]
# done. Make sure all arguments are filled
for i in arglist:
if i[0] == -1:
raise RuntimeError("Incomplete function specification")
# attempt to recursively break down any other functions
import inspect
for i in range(len(arglist)):
if arglist[i][0] == 1 and inspect.isfunction(arglist[i][1]):
try:
arglist[i][1] = _build_native_function_call(arglist[i][1])
except:
pass
return _Closure(native_function_name, arglist) | [
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|
google/gemmlowp | e844ffd17118c1e17d94e1ba4354c075a4577b88 | meta/generators/quantized_mul_kernels_common.py | python | _GenerateNxMLoadMultiplyAggregate | (emitter, registers, kernel_m, kernel_n,
aggregators, lhs, rhs, count) | Emit inner loop for N rows x M cols multiplication. | Emit inner loop for N rows x M cols multiplication. | [
"Emit",
"inner",
"loop",
"for",
"N",
"rows",
"x",
"M",
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"multiplication",
"."
] | def _GenerateNxMLoadMultiplyAggregate(emitter, registers, kernel_m, kernel_n,
aggregators, lhs, rhs, count):
"""Emit inner loop for N rows x M cols multiplication."""
emitter.EmitNewline()
emitter.EmitComment('General NxM lanes loop.')
emitter.EmitNumericalLabel(1)
emitter.EmitNewline()
emitter.EmitComment('Subtract counter.')
emitter.EmitSubs(count, count, emitter.ImmediateConstant(8))
emitter.EmitNewline()
lhs_load = [registers.DoubleRegister() for unused_i in range(kernel_m)]
rhs_load = [registers.DoubleRegister() for unused_i in range(kernel_n)]
emitter.EmitVLoadAE(8 * kernel_m, 8, lhs_load, lhs, 64)
emitter.EmitVLoadAE(8 * kernel_n, 8, rhs_load, rhs, 64)
emitter.EmitPldOffset(lhs, emitter.ImmediateConstant(64))
emitter.EmitPldOffset(rhs, emitter.ImmediateConstant(64))
results = [
registers.QuadRegister() for unused_i in range(kernel_m * kernel_n)
]
for row in range(kernel_m):
for col in range(kernel_n):
index = row * kernel_n + col
emitter.EmitVMull('u8', results[index], rhs_load[col], lhs_load[row])
for i in range(kernel_m * kernel_n):
emitter.EmitVPadal('u16', aggregators[i], results[i])
emitter.EmitNewline()
emitter.EmitComment('Loop break.')
emitter.EmitBgtBack(1)
registers.FreeRegisters(lhs_load + rhs_load + results) | [
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||
weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Source/bindings/scripts/idl_definitions.py | python | IdlDefinitions.__init__ | (self, idl_name, node) | Args: node: AST root node, class == 'File | Args: node: AST root node, class == 'File | [
"Args",
":",
"node",
":",
"AST",
"root",
"node",
"class",
"==",
"File"
] | def __init__(self, idl_name, node):
"""Args: node: AST root node, class == 'File'"""
self.callback_functions = {}
self.dictionaries = {}
self.enumerations = {}
self.implements = []
self.interfaces = {}
self.idl_name = idl_name
self.typedefs = {}
node_class = node.GetClass()
if node_class != 'File':
raise ValueError('Unrecognized node class: %s' % node_class)
children = node.GetChildren()
for child in children:
child_class = child.GetClass()
if child_class == 'Interface':
interface = IdlInterface(idl_name, child)
self.interfaces[interface.name] = interface
elif child_class == 'Exception':
exception = IdlException(idl_name, child)
# For simplicity, treat exceptions as interfaces
self.interfaces[exception.name] = exception
elif child_class == 'Typedef':
typedef = IdlTypedef(child)
self.typedefs[typedef.name] = typedef
elif child_class == 'Enum':
enumeration = IdlEnum(idl_name, child)
self.enumerations[enumeration.name] = enumeration
elif child_class == 'Callback':
callback_function = IdlCallbackFunction(idl_name, child)
self.callback_functions[callback_function.name] = callback_function
elif child_class == 'Implements':
self.implements.append(IdlImplement(child))
elif child_class == 'Dictionary':
dictionary = IdlDictionary(idl_name, child)
self.dictionaries[dictionary.name] = dictionary
else:
raise ValueError('Unrecognized node class: %s' % child_class) | [
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||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/peeking-iterator.py | python | PeekingIterator.next | (self) | return self.val_ | :rtype: int | :rtype: int | [
":",
"rtype",
":",
"int"
] | def next(self):
"""
:rtype: int
"""
self.val_ = self.peek()
self.has_peeked_ = False
self.has_next_ = self.iterator.hasNext()
return self.val_ | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiManager.AddPane | (*args, **kwargs) | return _aui.AuiManager_AddPane(*args, **kwargs) | AddPane(self, Window window, AuiPaneInfo paneInfo, Point dropPos) -> bool | AddPane(self, Window window, AuiPaneInfo paneInfo, Point dropPos) -> bool | [
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] | def AddPane(*args, **kwargs):
"""AddPane(self, Window window, AuiPaneInfo paneInfo, Point dropPos) -> bool"""
return _aui.AuiManager_AddPane(*args, **kwargs) | [
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|
thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | parseDTD | (ExternalID, SystemID) | return xmlDtd(_obj=ret) | Load and parse an external subset. | Load and parse an external subset. | [
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] | def parseDTD(ExternalID, SystemID):
"""Load and parse an external subset. """
ret = libxml2mod.xmlParseDTD(ExternalID, SystemID)
if ret is None:raise parserError('xmlParseDTD() failed')
return xmlDtd(_obj=ret) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/utils/encoding.py | python | auto_decode | (data) | return data.decode(
locale.getpreferredencoding(False) or sys.getdefaultencoding(),
) | Check a bytes string for a BOM to correctly detect the encoding
Fallback to locale.getpreferredencoding(False) like open() on Python3 | Check a bytes string for a BOM to correctly detect the encoding | [
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# type: (bytes) -> str
"""Check a bytes string for a BOM to correctly detect the encoding
Fallback to locale.getpreferredencoding(False) like open() on Python3"""
for bom, encoding in BOMS:
if data.startswith(bom):
return data[len(bom):].decode(encoding)
# Lets check the first two lines as in PEP263
for line in data.split(b'\n')[:2]:
if line[0:1] == b'#' and ENCODING_RE.search(line):
result = ENCODING_RE.search(line)
assert result is not None
encoding = result.groups()[0].decode('ascii')
return data.decode(encoding)
return data.decode(
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/urllib/request.py | python | URLopener.open_unknown | (self, fullurl, data=None) | Overridable interface to open unknown URL type. | Overridable interface to open unknown URL type. | [
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] | def open_unknown(self, fullurl, data=None):
"""Overridable interface to open unknown URL type."""
type, url = splittype(fullurl)
raise OSError('url error', 'unknown url type', type) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/docview.py | python | DocManager.FileHistoryUseMenu | (self, menu) | Use this menu for appending recently-visited document filenames, for
convenient access. Calling this function with a valid menu enables the
history list functionality.
Note that you can add multiple menus using this function, to be
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"""
Use this menu for appending recently-visited document filenames, for
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history list functionality.
Note that you can add multiple menus using this function, to be
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"""
if self._fileHistory:
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||
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/data_structures/sarray.py | python | SArray.all | (self) | Return True if every element of the SArray evaluates to True. For
numeric SArrays zeros and missing values (``None``) evaluate to False,
while all non-zero, non-missing values evaluate to True. For string,
list, and dictionary SArrays, empty values (zero length strings, lists
or dictionaries) or missing values (``None``) evaluate to False. All
other values evaluate to True.
Returns True on an empty SArray.
Returns
-------
out : bool
See Also
--------
any
Examples
--------
>>> turicreate.SArray([1, None]).all()
False
>>> turicreate.SArray([1, 0]).all()
False
>>> turicreate.SArray([1, 2]).all()
True
>>> turicreate.SArray(["hello", "world"]).all()
True
>>> turicreate.SArray(["hello", ""]).all()
False
>>> turicreate.SArray([]).all()
True | Return True if every element of the SArray evaluates to True. For
numeric SArrays zeros and missing values (``None``) evaluate to False,
while all non-zero, non-missing values evaluate to True. For string,
list, and dictionary SArrays, empty values (zero length strings, lists
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"""
Return True if every element of the SArray evaluates to True. For
numeric SArrays zeros and missing values (``None``) evaluate to False,
while all non-zero, non-missing values evaluate to True. For string,
list, and dictionary SArrays, empty values (zero length strings, lists
or dictionaries) or missing values (``None``) evaluate to False. All
other values evaluate to True.
Returns True on an empty SArray.
Returns
-------
out : bool
See Also
--------
any
Examples
--------
>>> turicreate.SArray([1, None]).all()
False
>>> turicreate.SArray([1, 0]).all()
False
>>> turicreate.SArray([1, 2]).all()
True
>>> turicreate.SArray(["hello", "world"]).all()
True
>>> turicreate.SArray(["hello", ""]).all()
False
>>> turicreate.SArray([]).all()
True
"""
with cython_context():
return self.__proxy__.all() | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/mrecords.py | python | _mrreconstruct | (subtype, baseclass, baseshape, basetype,) | return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,) | Build a new MaskedArray from the information stored in a pickle. | Build a new MaskedArray from the information stored in a pickle. | [
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] | def _mrreconstruct(subtype, baseclass, baseshape, basetype,):
"""
Build a new MaskedArray from the information stored in a pickle.
"""
_data = ndarray.__new__(baseclass, baseshape, basetype).view(subtype)
_mask = ndarray.__new__(ndarray, baseshape, 'b1')
return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,) | [
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|
SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pyedbglib/protocols/avr8protocol.py | python | Avr8Protocol.set_interface | (self, interface) | Sets the function field in the physical context
:param interface: physical interface setting | Sets the function field in the physical context | [
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"physical",
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] | def set_interface(self, interface):
"""
Sets the function field in the physical context
:param interface: physical interface setting
"""
self.set_byte(self.AVR8_CTXT_PHYSICAL, self.AVR8_PHY_INTERFACE, interface) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_caml.py | python | SyntaxData.GetKeywords | (self) | return [CAML_KW1, CAML_KW2, CAML_KW3] | Returns Specified Keywords List | Returns Specified Keywords List | [
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] | def GetKeywords(self):
"""Returns Specified Keywords List """
return [CAML_KW1, CAML_KW2, CAML_KW3] | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/cmd.py | python | Command.__init__ | (self, dist) | Create and initialize a new Command object. Most importantly,
invokes the 'initialize_options()' method, which is the real
initializer and depends on the actual command being
instantiated. | Create and initialize a new Command object. Most importantly,
invokes the 'initialize_options()' method, which is the real
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] | def __init__(self, dist):
"""Create and initialize a new Command object. Most importantly,
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initializer and depends on the actual command being
instantiated.
"""
# late import because of mutual dependence between these classes
from distutils.dist import Distribution
if not isinstance(dist, Distribution):
raise TypeError("dist must be a Distribution instance")
if self.__class__ is Command:
raise RuntimeError("Command is an abstract class")
self.distribution = dist
self.initialize_options()
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# "not defined, check self.distribution's copy", while 0 or 1 mean
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# XXX This needs to be fixed.
self._dry_run = None
# verbose is largely ignored, but needs to be set for
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self.verbose = dist.verbose
# Some commands define a 'self.force' option to ignore file
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# 'self.force' exists for all commands. So define it here
# just to be safe.
self.force = None
# The 'help' flag is just used for command-line parsing, so
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self.help = 0
# 'finalized' records whether or not 'finalize_options()' has been
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||
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/executor.py | python | Executor.close | (self) | Close the executor. This interface is used for distributed training (PServers mode).
This executor can not be used after calling the interface, because
this interface releases resources associated with the current Trainer.
Returns:
None
Examples:
.. code-block:: python
import paddle
cpu = paddle.CPUPlace()
exe = paddle.static.Executor(cpu)
# execute training or testing
exe.close() | Close the executor. This interface is used for distributed training (PServers mode).
This executor can not be used after calling the interface, because
this interface releases resources associated with the current Trainer. | [
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"""
Close the executor. This interface is used for distributed training (PServers mode).
This executor can not be used after calling the interface, because
this interface releases resources associated with the current Trainer.
Returns:
None
Examples:
.. code-block:: python
import paddle
cpu = paddle.CPUPlace()
exe = paddle.static.Executor(cpu)
# execute training or testing
exe.close()
"""
if not self._closed:
self._closed = True
for k, trainer_instance in self.trainer_caches.items():
self._default_executor.release_trainer(trainer_instance)
del trainer_instance
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | Region.IntersectRegion | (*args, **kwargs) | return _gdi_.Region_IntersectRegion(*args, **kwargs) | IntersectRegion(self, Region region) -> bool | IntersectRegion(self, Region region) -> bool | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cgutils.py | python | for_range_slice | (builder, start, stop, step, intp=None, inc=True) | Generate LLVM IR for a for-loop based on a slice. Yields a
(index, count) tuple where `index` is the slice index's value
inside the loop, and `count` the iteration count.
Parameters
-------------
builder : object
Builder object
start : int
The beginning value of the slice
stop : int
The end value of the slice
step : int
The step value of the slice
intp :
The data type
inc : boolean, optional
Signals whether the step is positive (True) or negative (False).
Returns
-----------
None | Generate LLVM IR for a for-loop based on a slice. Yields a
(index, count) tuple where `index` is the slice index's value
inside the loop, and `count` the iteration count. | [
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"""
Generate LLVM IR for a for-loop based on a slice. Yields a
(index, count) tuple where `index` is the slice index's value
inside the loop, and `count` the iteration count.
Parameters
-------------
builder : object
Builder object
start : int
The beginning value of the slice
stop : int
The end value of the slice
step : int
The step value of the slice
intp :
The data type
inc : boolean, optional
Signals whether the step is positive (True) or negative (False).
Returns
-----------
None
"""
if intp is None:
intp = start.type
bbcond = builder.append_basic_block("for.cond")
bbbody = builder.append_basic_block("for.body")
bbend = builder.append_basic_block("for.end")
bbstart = builder.basic_block
builder.branch(bbcond)
with builder.goto_block(bbcond):
index = builder.phi(intp, name="loop.index")
count = builder.phi(intp, name="loop.count")
if (inc):
pred = builder.icmp_signed('<', index, stop)
else:
pred = builder.icmp_signed('>', index, stop)
builder.cbranch(pred, bbbody, bbend)
with builder.goto_block(bbbody):
yield index, count
bbbody = builder.basic_block
incr = builder.add(index, step)
next_count = increment_index(builder, count)
terminate(builder, bbcond)
index.add_incoming(start, bbstart)
index.add_incoming(incr, bbbody)
count.add_incoming(ir.Constant(intp, 0), bbstart)
count.add_incoming(next_count, bbbody)
builder.position_at_end(bbend) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/pygments/lexers/__init__.py | python | get_lexer_for_filename | (_fn, code=None, **options) | Get a lexer for a filename. If multiple lexers match the filename
pattern, use ``analyze_text()`` to figure out which one is more
appropriate. | Get a lexer for a filename. If multiple lexers match the filename
pattern, use ``analyze_text()`` to figure out which one is more
appropriate. | [
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] | def get_lexer_for_filename(_fn, code=None, **options):
"""
Get a lexer for a filename. If multiple lexers match the filename
pattern, use ``analyze_text()`` to figure out which one is more
appropriate.
"""
matches = []
fn = basename(_fn)
for modname, name, _, filenames, _ in LEXERS.itervalues():
for filename in filenames:
if fnmatch.fnmatch(fn, filename):
if name not in _lexer_cache:
_load_lexers(modname)
matches.append(_lexer_cache[name])
for cls in find_plugin_lexers():
for filename in cls.filenames:
if fnmatch.fnmatch(fn, filename):
matches.append(cls)
if sys.version_info > (3,) and isinstance(code, bytes):
# decode it, since all analyse_text functions expect unicode
code = code.decode('latin1')
def get_rating(cls):
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# gets turned into 0.0. Run scripts/detect_missing_analyse_text.py
# to find lexers which need it overridden.
d = cls.analyse_text(code)
#print "Got %r from %r" % (d, cls)
return d
if code:
matches.sort(key=get_rating)
if matches:
#print "Possible lexers, after sort:", matches
return matches[-1](**options)
raise ClassNotFound('no lexer for filename %r found' % _fn) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.BackTab | (*args, **kwargs) | return _stc.StyledTextCtrl_BackTab(*args, **kwargs) | BackTab(self)
Dedent the selected lines. | BackTab(self) | [
"BackTab",
"(",
"self",
")"
] | def BackTab(*args, **kwargs):
"""
BackTab(self)
Dedent the selected lines.
"""
return _stc.StyledTextCtrl_BackTab(*args, **kwargs) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/multioutput.py | python | MultiOutputRegressor.score | (self, X, y, sample_weight=None) | return r2_score(y, self.predict(X), sample_weight=sample_weight,
multioutput='uniform_average') | Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression
sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
sum of squares ((y_true - y_true.mean()) ** 2).sum().
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.
Notes
-----
R^2 is calculated by weighting all the targets equally using
`multioutput='uniform_average'`.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Test samples.
y : array-like, shape (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : array-like, shape [n_samples], optional
Sample weights.
Returns
-------
score : float
R^2 of self.predict(X) wrt. y. | Returns the coefficient of determination R^2 of the prediction. | [
"Returns",
"the",
"coefficient",
"of",
"determination",
"R^2",
"of",
"the",
"prediction",
"."
] | def score(self, X, y, sample_weight=None):
"""Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression
sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
sum of squares ((y_true - y_true.mean()) ** 2).sum().
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.
Notes
-----
R^2 is calculated by weighting all the targets equally using
`multioutput='uniform_average'`.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Test samples.
y : array-like, shape (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : array-like, shape [n_samples], optional
Sample weights.
Returns
-------
score : float
R^2 of self.predict(X) wrt. y.
"""
# XXX remove in 0.19 when r2_score default for multioutput changes
from .metrics import r2_score
return r2_score(y, self.predict(X), sample_weight=sample_weight,
multioutput='uniform_average') | [
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|
BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/random.py | python | Random.betavariate | (self, alpha, beta) | Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1. | Beta distribution. | [
"Beta",
"distribution",
"."
] | def betavariate(self, alpha, beta):
"""Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
"""
# This version due to Janne Sinkkonen, and matches all the std
# texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
y = self.gammavariate(alpha, 1.)
if y == 0:
return 0.0
else:
return y / (y + self.gammavariate(beta, 1.)) | [
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||
msracver/Deep-Image-Analogy | 632b9287b42552e32dad64922967c8c9ec7fc4d3 | examples/pycaffe/tools.py | python | SimpleTransformer.deprocess | (self, im) | return np.uint8(im) | inverse of preprocess() | inverse of preprocess() | [
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"preprocess",
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] | def deprocess(self, im):
"""
inverse of preprocess()
"""
im = im.transpose(1, 2, 0)
im /= self.scale
im += self.mean
im = im[:, :, ::-1] # change to RGB
return np.uint8(im) | [
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/logging_ops.py | python | Print | (input_, data, message=None, first_n=None, summarize=None,
name=None) | return gen_logging_ops._print(input_, data, message, first_n, summarize, name) | Prints a list of tensors.
This is an identity op with the side effect of printing `data` when
evaluating.
Note: This op prints to the standard error. It is not currently compatible
with jupyter notebook (printing to the notebook *server's* output, not into
the notebook).
Args:
input_: A tensor passed through this op.
data: A list of tensors to print out when op is evaluated.
message: A string, prefix of the error message.
first_n: Only log `first_n` number of times. Negative numbers log always;
this is the default.
summarize: Only print this many entries of each tensor. If None, then a
maximum of 3 elements are printed per input tensor.
name: A name for the operation (optional).
Returns:
Same tensor as `input_`. | Prints a list of tensors. | [
"Prints",
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"."
] | def Print(input_, data, message=None, first_n=None, summarize=None,
name=None):
"""Prints a list of tensors.
This is an identity op with the side effect of printing `data` when
evaluating.
Note: This op prints to the standard error. It is not currently compatible
with jupyter notebook (printing to the notebook *server's* output, not into
the notebook).
Args:
input_: A tensor passed through this op.
data: A list of tensors to print out when op is evaluated.
message: A string, prefix of the error message.
first_n: Only log `first_n` number of times. Negative numbers log always;
this is the default.
summarize: Only print this many entries of each tensor. If None, then a
maximum of 3 elements are printed per input tensor.
name: A name for the operation (optional).
Returns:
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"""
return gen_logging_ops._print(input_, data, message, first_n, summarize, name) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | VersionInfo.__init__ | (self, *args, **kwargs) | __init__(self, String name, int major, int minor, int micro=0, String description=wxEmptyString,
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"""
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"""
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||
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | keepBlanksDefault | (val) | return ret | Set and return the previous value for default blanks text
nodes support. The 1.x version of the parser used an
heuristic to try to detect ignorable white spaces. As a
result the SAX callback was generating
xmlSAX2IgnorableWhitespace() callbacks instead of
characters() one, and when using the DOM output text nodes
containing those blanks were not generated. The 2.x and
later version will switch to the XML standard way and
ignorableWhitespace() are only generated when running the
parser in validating mode and when the current element
doesn't allow CDATA or mixed content. This function is
provided as a way to force the standard behavior on 1.X
libs and to switch back to the old mode for compatibility
when running 1.X client code on 2.X . Upgrade of 1.X code
should be done by using xmlIsBlankNode() commodity function
to detect the "empty" nodes generated. This value also
affect autogeneration of indentation when saving code if
blanks sections are kept, indentation is not generated. | Set and return the previous value for default blanks text
nodes support. The 1.x version of the parser used an
heuristic to try to detect ignorable white spaces. As a
result the SAX callback was generating
xmlSAX2IgnorableWhitespace() callbacks instead of
characters() one, and when using the DOM output text nodes
containing those blanks were not generated. The 2.x and
later version will switch to the XML standard way and
ignorableWhitespace() are only generated when running the
parser in validating mode and when the current element
doesn't allow CDATA or mixed content. This function is
provided as a way to force the standard behavior on 1.X
libs and to switch back to the old mode for compatibility
when running 1.X client code on 2.X . Upgrade of 1.X code
should be done by using xmlIsBlankNode() commodity function
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"""Set and return the previous value for default blanks text
nodes support. The 1.x version of the parser used an
heuristic to try to detect ignorable white spaces. As a
result the SAX callback was generating
xmlSAX2IgnorableWhitespace() callbacks instead of
characters() one, and when using the DOM output text nodes
containing those blanks were not generated. The 2.x and
later version will switch to the XML standard way and
ignorableWhitespace() are only generated when running the
parser in validating mode and when the current element
doesn't allow CDATA or mixed content. This function is
provided as a way to force the standard behavior on 1.X
libs and to switch back to the old mode for compatibility
when running 1.X client code on 2.X . Upgrade of 1.X code
should be done by using xmlIsBlankNode() commodity function
to detect the "empty" nodes generated. This value also
affect autogeneration of indentation when saving code if
blanks sections are kept, indentation is not generated. """
ret = libxml2mod.xmlKeepBlanksDefault(val)
return ret | [
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|
chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/yapf/yapf/yapflib/format_token.py | python | FormatToken.subtypes | (self) | return [Subtype.NONE] if value is None else value | Extra type information for directing formatting. | Extra type information for directing formatting. | [
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"""Extra type information for directing formatting."""
value = pytree_utils.GetNodeAnnotation(self.node,
pytree_utils.Annotation.SUBTYPE)
return [Subtype.NONE] if value is None else value | [
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/telemetry/page/page_set.py | python | PageSet.ReorderPageSet | (self, results_file) | return pages | Reorders this page set based on the results of a past run. | Reorders this page set based on the results of a past run. | [
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"""Reorders this page set based on the results of a past run."""
page_set_dict = {}
for page in self.pages:
page_set_dict[page.url] = page
pages = []
with open(results_file, 'rb') as csv_file:
csv_reader = csv.reader(csv_file)
csv_header = csv_reader.next()
if 'url' not in csv_header:
raise Exception('Unusable results_file.')
url_index = csv_header.index('url')
for csv_row in csv_reader:
if csv_row[url_index] in page_set_dict:
pages.append(page_set_dict[csv_row[url_index]])
else:
raise Exception('Unusable results_file.')
return pages | [
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|
BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/pdb.py | python | Pdb.displayhook | (self, obj) | Custom displayhook for the exec in default(), which prevents
assignment of the _ variable in the builtins. | Custom displayhook for the exec in default(), which prevents
assignment of the _ variable in the builtins. | [
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] | def displayhook(self, obj):
"""Custom displayhook for the exec in default(), which prevents
assignment of the _ variable in the builtins.
"""
# reproduce the behavior of the standard displayhook, not printing None
if obj is not None:
print repr(obj) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Sizer.PrependWindow | (self, *args, **kw) | return self.Prepend(*args, **kw) | Compatibility alias for `Prepend`. | Compatibility alias for `Prepend`. | [
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|
GoSSIP-SJTU/TripleDoggy | 03648d6b19c812504b14e8b98c8c7b3f443f4e54 | tools/clang/bindings/python/clang/cindex.py | python | TranslationUnit.reparse | (self, unsaved_files=None, options=0) | Reparse an already parsed translation unit.
In-memory contents for files can be provided by passing a list of pairs
as unsaved_files, the first items should be the filenames to be mapped
and the second should be the contents to be substituted for the
file. The contents may be passed as strings or file objects. | Reparse an already parsed translation unit. | [
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"""
Reparse an already parsed translation unit.
In-memory contents for files can be provided by passing a list of pairs
as unsaved_files, the first items should be the filenames to be mapped
and the second should be the contents to be substituted for the
file. The contents may be passed as strings or file objects.
"""
if unsaved_files is None:
unsaved_files = []
unsaved_files_array = 0
if len(unsaved_files):
unsaved_files_array = (_CXUnsavedFile * len(unsaved_files))()
for i,(name,value) in enumerate(unsaved_files):
if not isinstance(value, str):
# FIXME: It would be great to support an efficient version
# of this, one day.
value = value.read()
print(value)
if not isinstance(value, str):
raise TypeError('Unexpected unsaved file contents.')
unsaved_files_array[i].name = name
unsaved_files_array[i].contents = value
unsaved_files_array[i].length = len(value)
ptr = conf.lib.clang_reparseTranslationUnit(self, len(unsaved_files),
unsaved_files_array, options) | [
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||
SeisSol/SeisSol | 955fbeb8c5d40d3363a2da0edc611259aebe1653 | preprocessing/science/kinematic_models/FaultPlane.py | python | FaultPlane.upsample_fault | (self, spatial_order, spatial_zoom, temporal_zoom, proj, use_Yoffe=False, time_smoothing_kernel_as_dt_fraction=0.5) | return pf | increase spatial and temporal resolution of kinematic model by interpolation | increase spatial and temporal resolution of kinematic model by interpolation | [
"increase",
"spatial",
"and",
"temporal",
"resolution",
"of",
"kinematic",
"model",
"by",
"interpolation"
] | def upsample_fault(self, spatial_order, spatial_zoom, temporal_zoom, proj, use_Yoffe=False, time_smoothing_kernel_as_dt_fraction=0.5):
"increase spatial and temporal resolution of kinematic model by interpolation"
# time vector
ndt2 = (self.ndt - 1) * temporal_zoom + 1
ny2, nx2 = self.ny * spatial_zoom, self.nx * spatial_zoom
# resampled source
pf = FaultPlane()
pf.init_spatial_arrays(nx2, ny2)
pf.ndt = ndt2
pf.init_aSR()
pf.dt = self.dt / temporal_zoom
pf.compute_time_array()
# upsample spatially geometry (bilinear interpolation)
allarr = np.array([self.x, self.y, self.depth])
pf.x, pf.y, pf.depth = upsample_quantities(allarr, spatial_order=1, spatial_zoom=spatial_zoom, padding="extrapolate")
# upsample other quantities
allarr = np.array([self.t0, self.strike, self.dip, self.rake])
pf.t0, pf.strike, pf.dip, pf.rake = upsample_quantities(allarr, spatial_order, spatial_zoom, padding="edge")
# the interpolation may generate some acausality that we here prevent
pf.t0 = np.maximum(pf.t0, np.amin(self.t0))
allarr = np.array([self.slip1])
(pf.slip1,) = upsample_quantities(allarr, spatial_order, spatial_zoom, padding="constant", minimize_block_average_variations=True)
pf.compute_latlon_from_xy(proj)
pf.PSarea_cm2 = self.PSarea_cm2 / spatial_zoom ** 2
ratio_potency = np.sum(pf.slip1) * pf.PSarea_cm2 / (np.sum(self.slip1) * self.PSarea_cm2)
print(f"seismic potency ratio (upscaled over initial): {ratio_potency}")
if use_Yoffe:
allarr = np.array([self.rise_time, self.tacc])
pf.rise_time, pf.tacc = upsample_quantities(allarr, spatial_order, spatial_zoom, padding="edge")
pf.rise_time = np.maximum(pf.rise_time, np.amin(self.rise_time))
pf.tacc = np.maximum(pf.tacc, np.amin(self.tacc))
# see comment above explaining why the 1.27 factor
print("using ts = tacc / 1.27 to compute the regularized Yoffe")
ts = pf.tacc / 1.27
tr = pf.rise_time - 2.0 * ts
tr = np.maximum(tr, ts)
for j in range(pf.ny):
for i in range(pf.nx):
for k, tk in enumerate(pf.myt):
pf.aSR[j, i, k] = pf.slip1[j, i] * regularizedYoffe(tk, ts[j, i], tr[j, i])
else:
aSRa = np.zeros((pf.ny, pf.nx, self.ndt))
for k in range(self.ndt):
aSRa[:, :, k] = upsample_quantities(np.array([self.aSR[:, :, k]]), spatial_order, spatial_zoom, padding="constant")
# interpolate temporally the AST
for j in range(pf.ny):
for i in range(pf.nx):
# 1. upsample with linear interpolation
# 2. apply a gauss kernel to smooth out sharp edges
# 3. tapper the signal smoothly to 0 at both time ends
# 4. rescale SR to ensure integral (SR) = slip
f = interpolate.interp1d(self.myt, aSRa[j, i, :], kind="linear")
pf.aSR[j, i, :] = f(pf.myt)
tapper = cosine_taper(pf.ndt, self.dt / (pf.ndt * pf.dt))
pf.aSR[j, i, :] = tapper * ndimage.gaussian_filter1d(pf.aSR[j, i, :], time_smoothing_kernel_as_dt_fraction * self.dt / pf.dt, mode="constant")
# With a cubic interpolation, the interpolated slip1 may be negative which does not make sense.
if pf.slip1[j, i] < 0:
pf.aSR[j, i, :] = 0
continue
# should be the SR
integral_STF = np.trapz(np.abs(pf.aSR[j, i, :]), dx=pf.dt)
if abs(integral_STF) > 0:
pf.aSR[j, i, :] = pf.slip1[j, i] * pf.aSR[j, i, :] / integral_STF
return pf | [
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|
thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/VMM/VMMAll/IEMAllInstructionsPython.py | python | SimpleParser.parseTagOpDisEnum | (self, sTag, aasSections, iTagLine, iEndLine) | return True | Tag: \@opdisenum
Value: OP_XXXX
This is for select a specific (legacy) disassembler enum value for the
instruction. | Tag: \@opdisenum
Value: OP_XXXX | [
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] | def parseTagOpDisEnum(self, sTag, aasSections, iTagLine, iEndLine):
"""
Tag: \@opdisenum
Value: OP_XXXX
This is for select a specific (legacy) disassembler enum value for the
instruction.
"""
oInstr = self.ensureInstructionForOpTag(iTagLine);
# Flatten and split.
asWords = self.flattenAllSections(aasSections).split();
if len(asWords) != 1:
self.errorComment(iTagLine, '%s: expected exactly one value: %s' % (sTag, asWords,));
if not asWords:
return False;
sDisEnum = asWords[0];
if not self.oReDisEnum.match(sDisEnum):
return self.errorComment(iTagLine, '%s: invalid disassembler OP_XXXX enum: %s (pattern: %s)'
% (sTag, sDisEnum, self.oReDisEnum.pattern));
# Set it.
if oInstr.sDisEnum is not None:
return self.errorComment(iTagLine, '%s: attempting to overwrite "%s" with "%s"' % (sTag, oInstr.sDisEnum, sDisEnum,));
oInstr.sDisEnum = sDisEnum;
_ = iEndLine;
return True; | [
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|
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathOpTools.py | python | offsetWire | (wire, base, offset, forward, Side=None) | return orientWire(Part.Wire(edges), None) | offsetWire(wire, base, offset, forward) ... offsets the wire away from base and orients the wire accordingly.
The function tries to avoid most of the pitfalls of Part.makeOffset2D which is possible because all offsetting
happens in the XY plane. | offsetWire(wire, base, offset, forward) ... offsets the wire away from base and orients the wire accordingly.
The function tries to avoid most of the pitfalls of Part.makeOffset2D which is possible because all offsetting
happens in the XY plane. | [
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] | def offsetWire(wire, base, offset, forward, Side=None):
"""offsetWire(wire, base, offset, forward) ... offsets the wire away from base and orients the wire accordingly.
The function tries to avoid most of the pitfalls of Part.makeOffset2D which is possible because all offsetting
happens in the XY plane.
"""
PathLog.track("offsetWire")
if 1 == len(wire.Edges):
edge = wire.Edges[0]
curve = edge.Curve
if Part.Circle == type(curve) and wire.isClosed():
# it's a full circle and there are some problems with that, see
# https://www.freecadweb.org/wiki/Part%20Offset2D
# it's easy to construct them manually though
z = -1 if forward else 1
new_edge = Part.makeCircle(
curve.Radius + offset, curve.Center, FreeCAD.Vector(0, 0, z)
)
if base.isInside(new_edge.Vertexes[0].Point, offset / 2, True):
if offset > curve.Radius or PathGeom.isRoughly(offset, curve.Radius):
# offsetting a hole by its own radius (or more) makes the hole vanish
return None
if Side:
Side[0] = "Inside"
print("inside")
new_edge = Part.makeCircle(
curve.Radius - offset, curve.Center, FreeCAD.Vector(0, 0, -z)
)
return Part.Wire([new_edge])
if Part.Circle == type(curve) and not wire.isClosed():
# Process arc segment
z = -1 if forward else 1
l1 = math.sqrt(
(edge.Vertexes[0].Point.x - curve.Center.x) ** 2
+ (edge.Vertexes[0].Point.y - curve.Center.y) ** 2
)
l2 = math.sqrt(
(edge.Vertexes[1].Point.x - curve.Center.x) ** 2
+ (edge.Vertexes[1].Point.y - curve.Center.y) ** 2
)
# Calculate angles based on x-axis (0 - PI/2)
start_angle = math.acos((edge.Vertexes[0].Point.x - curve.Center.x) / l1)
end_angle = math.acos((edge.Vertexes[1].Point.x - curve.Center.x) / l2)
# Angles are based on x-axis (Mirrored on x-axis) -> negative y value means negative angle
if edge.Vertexes[0].Point.y < curve.Center.y:
start_angle *= -1
if edge.Vertexes[1].Point.y < curve.Center.y:
end_angle *= -1
if (
edge.Vertexes[0].Point.x > curve.Center.x
or edge.Vertexes[1].Point.x > curve.Center.x
) and curve.AngleXU < 0:
tmp = start_angle
start_angle = end_angle
end_angle = tmp
# Inside / Outside
if base.isInside(edge.Vertexes[0].Point, offset / 2, True):
offset *= -1
if Side:
Side[0] = "Inside"
# Create new arc
if curve.AngleXU > 0:
edge = Part.ArcOfCircle(
Part.Circle(
curve.Center, FreeCAD.Vector(0, 0, 1), curve.Radius + offset
),
start_angle,
end_angle,
).toShape()
else:
edge = Part.ArcOfCircle(
Part.Circle(
curve.Center, FreeCAD.Vector(0, 0, 1), curve.Radius - offset
),
start_angle,
end_angle,
).toShape()
return Part.Wire([edge])
if Part.Line == type(curve) or Part.LineSegment == type(curve):
# offsetting a single edge doesn't work because there is an infinite
# possible planes into which the edge could be offset
# luckily, the plane here must be the XY-plane ...
p0 = edge.Vertexes[0].Point
v0 = edge.Vertexes[1].Point - p0
n = v0.cross(FreeCAD.Vector(0, 0, 1))
o = n.normalize() * offset
edge.translate(o)
# offset edde the other way if the result is inside
if base.isInside(
edge.valueAt((edge.FirstParameter + edge.LastParameter) / 2),
offset / 2,
True,
):
edge.translate(-2 * o)
# flip the edge if it's not on the right side of the original edge
if forward is not None:
v1 = edge.Vertexes[1].Point - p0
left = PathGeom.Side.Left == PathGeom.Side.of(v0, v1)
if left != forward:
edge = PathGeom.flipEdge(edge)
return Part.Wire([edge])
# if we get to this point the assumption is that makeOffset2D can deal with the edge
pass # pylint: disable=unnecessary-pass
owire = orientWire(wire.makeOffset2D(offset), True)
debugWire("makeOffset2D_%d" % len(wire.Edges), owire)
if wire.isClosed():
if not base.isInside(owire.Edges[0].Vertexes[0].Point, offset / 2, True):
PathLog.track("closed - outside")
if Side:
Side[0] = "Outside"
return orientWire(owire, forward)
PathLog.track("closed - inside")
if Side:
Side[0] = "Inside"
try:
owire = wire.makeOffset2D(-offset)
except Exception: # pylint: disable=broad-except
# most likely offsetting didn't work because the wire is a hole
# and the offset is too big - making the hole vanish
return None
# For negative offsets (holes) 'forward' is the other way
if forward is None:
return orientWire(owire, None)
return orientWire(owire, not forward)
# An edge is considered to be inside of shape if the mid point is inside
# Of the remaining edges we take the longest wire to be the engraving side
# Looking for a circle with the start vertex as center marks and end
# starting from there follow the edges until a circle with the end vertex as center is found
# if the traversed edges include any of the remaining from above, all those edges are remaining
# this is to also include edges which might partially be inside shape
# if they need to be discarded, split, that should happen in a post process
# Depending on the Axis of the circle, and which side remains we know if the wire needs to be flipped
# first, let's make sure all edges are oriented the proper way
edges = _orientEdges(wire.Edges)
# determine the start and end point
start = edges[0].firstVertex().Point
end = edges[-1].lastVertex().Point
debugWire("wire", wire)
debugWire("wedges", Part.Wire(edges))
# find edges that are not inside the shape
common = base.common(owire)
insideEndpoints = [e.lastVertex().Point for e in common.Edges]
insideEndpoints.append(common.Edges[0].firstVertex().Point)
def isInside(edge):
p0 = edge.firstVertex().Point
p1 = edge.lastVertex().Point
for p in insideEndpoints:
if PathGeom.pointsCoincide(p, p0, 0.01) or PathGeom.pointsCoincide(
p, p1, 0.01
):
return True
return False
outside = [e for e in owire.Edges if not isInside(e)]
# discard all edges that are not part of the longest wire
longestWire = None
for w in [Part.Wire(el) for el in Part.sortEdges(outside)]:
if not longestWire or longestWire.Length < w.Length:
longestWire = w
debugWire("outside", Part.Wire(outside))
debugWire("longest", longestWire)
def isCircleAt(edge, center):
"""isCircleAt(edge, center) ... helper function returns True if edge is a circle at the given center."""
if Part.Circle == type(edge.Curve) or Part.ArcOfCircle == type(edge.Curve):
return PathGeom.pointsCoincide(edge.Curve.Center, center)
return False
# split offset wire into edges to the left side and edges to the right side
collectLeft = False
collectRight = False
leftSideEdges = []
rightSideEdges = []
# traverse through all edges in order and start collecting them when we encounter
# an end point (circle centered at one of the end points of the original wire).
# should we come to an end point and determine that we've already collected the
# next side, we're done
for e in owire.Edges + owire.Edges:
if isCircleAt(e, start):
if PathGeom.pointsCoincide(e.Curve.Axis, FreeCAD.Vector(0, 0, 1)):
if not collectLeft and leftSideEdges:
break
collectLeft = True
collectRight = False
else:
if not collectRight and rightSideEdges:
break
collectLeft = False
collectRight = True
elif isCircleAt(e, end):
if PathGeom.pointsCoincide(e.Curve.Axis, FreeCAD.Vector(0, 0, 1)):
if not collectRight and rightSideEdges:
break
collectLeft = False
collectRight = True
else:
if not collectLeft and leftSideEdges:
break
collectLeft = True
collectRight = False
elif collectLeft:
leftSideEdges.append(e)
elif collectRight:
rightSideEdges.append(e)
debugWire("left", Part.Wire(leftSideEdges))
debugWire("right", Part.Wire(rightSideEdges))
# figure out if all the left sided edges or the right sided edges are the ones
# that are 'outside'. However, we return the full side.
edges = leftSideEdges
for e in longestWire.Edges:
for e0 in rightSideEdges:
if PathGeom.edgesMatch(e, e0):
edges = rightSideEdges
PathLog.debug("#use right side edges")
if not forward:
PathLog.debug("#reverse")
edges.reverse()
return orientWire(Part.Wire(edges), None)
# at this point we have the correct edges and they are in the order for forward
# traversal (climb milling). If that's not what we want just reverse the order,
# orientWire takes care of orienting the edges appropriately.
PathLog.debug("#use left side edges")
if not forward:
PathLog.debug("#reverse")
edges.reverse()
return orientWire(Part.Wire(edges), None) | [
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|
hunterlew/mstar_deeplearning_project | 3761624dcbd7d44af257200542d13d1444dc634a | classification/caffe/build/Release/pycaffe/caffe/coord_map.py | python | conv_params | (fn) | return (axis, np.array(params.get('stride', 1), ndmin=1),
(ks - 1) * dilation + 1,
np.array(params.get('pad', 0), ndmin=1)) | Extract the spatial parameters that determine the coordinate mapping:
kernel size, stride, padding, and dilation.
Implementation detail: Convolution, Deconvolution, and Im2col layers
define these in the convolution_param message, while Pooling has its
own fields in pooling_param. This method deals with these details to
extract canonical parameters. | Extract the spatial parameters that determine the coordinate mapping:
kernel size, stride, padding, and dilation. | [
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"""
Extract the spatial parameters that determine the coordinate mapping:
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Implementation detail: Convolution, Deconvolution, and Im2col layers
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"""
params = fn.params.get('convolution_param', fn.params)
axis = params.get('axis', 1)
ks = np.array(params['kernel_size'], ndmin=1)
dilation = np.array(params.get('dilation', 1), ndmin=1)
assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
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return (axis, np.array(params.get('stride', 1), ndmin=1),
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|
mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Tool/MSCommon/sdk.py | python | get_default_sdk | () | return InstalledSDKList[0] | Set up the default Platform/Windows SDK. | Set up the default Platform/Windows SDK. | [
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"/",
"Windows",
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"."
] | def get_default_sdk():
"""Set up the default Platform/Windows SDK."""
get_installed_sdks()
if not InstalledSDKList:
return None
return InstalledSDKList[0] | [
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|
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/rosdep2/model.py | python | RosdepDatabase.get_view_names | (self) | return self._rosdep_db.keys() | :returns: list of view names that are loaded into this database. | :returns: list of view names that are loaded into this database. | [
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] | def get_view_names(self):
"""
:returns: list of view names that are loaded into this database.
"""
return self._rosdep_db.keys() | [
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|
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/array_ops.py | python | _FillShape | (op) | return [tensor_util.constant_value_as_shape(op.inputs[0])] | Shape function for the Fill op.
This op takes a vector of dimensions and a scalar, and produces a
tensor with the given dimensions.
Args:
op: A Fill Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: If the shapes or arguments are known to be invalid. | Shape function for the Fill op. | [
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] | def _FillShape(op):
"""Shape function for the Fill op.
This op takes a vector of dimensions and a scalar, and produces a
tensor with the given dimensions.
Args:
op: A Fill Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: If the shapes or arguments are known to be invalid.
"""
op.inputs[0].get_shape().assert_has_rank(1)
op.inputs[1].get_shape().assert_has_rank(0)
fill_dims = tensor_util.constant_value(op.inputs[0])
if fill_dims is not None and any(d < 0 for d in fill_dims):
raise ValueError("Fill dimensions must be >= 0")
return [tensor_util.constant_value_as_shape(op.inputs[0])] | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_bookmark.py | python | BookmarkWindow.GotoBookmark | (self, mark) | Goto the bookmark in the editor
@param mark: BookMark | Goto the bookmark in the editor
@param mark: BookMark | [
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"""Goto the bookmark in the editor
@param mark: BookMark
"""
app = wx.GetApp()
mw = app.GetActiveWindow()
if mw:
nb = mw.GetNotebook()
buf = nb.FindBuffer(mark.Filename)
use_handle = True
if not buf:
nb.OpenPage(ebmlib.GetPathName(mark.Filename),
ebmlib.GetFileName(mark.Filename))
buf = nb.GetCurrentPage()
use_handle = False # Handle is invalid so use line number
if buf:
# Ensure the tab is the current one
nb.GotoPage(mark.Filename)
# Jump to the bookmark line
if use_handle:
lnum = buf.MarkerLineFromHandle(mark.Handle)
else:
lnum = mark.Line
buf.GotoLine(lnum)
else:
util.Log("[ed_bookmark][err] Failed to locate mainwindow") | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/symtable.py | python | Symbol.get_namespaces | (self) | return self.__namespaces | Return a list of namespaces bound to this name | Return a list of namespaces bound to this name | [
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"list",
"of",
"namespaces",
"bound",
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] | def get_namespaces(self):
"""Return a list of namespaces bound to this name"""
return self.__namespaces | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/_custom_op/_basic.py | python | _get_bias | (shape_bias) | return shb | _get_bias | _get_bias | [
"_get_bias"
] | def _get_bias(shape_bias):
"""_get_bias"""
bias_length = shape_bias[0]
if bias_length % 16 == 0:
shb = shape_bias
else:
bias_length = (bias_length // 16) * 16 + 16
shape_bias = []
shape_bias.append(bias_length)
shb = shape_bias
return shb | [
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|
snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TCh_GetUc | (*args) | return _snap.TCh_GetUc(*args) | TCh_GetUc(char const & Ch) -> char
Parameters:
Ch: char const & | TCh_GetUc(char const & Ch) -> char | [
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] | def TCh_GetUc(*args):
"""
TCh_GetUc(char const & Ch) -> char
Parameters:
Ch: char const &
"""
return _snap.TCh_GetUc(*args) | [
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|
ROCmSoftwarePlatform/hipCaffe | 4ec5d482515cce532348553b6db6d00d015675d5 | scripts/cpp_lint.py | python | ResetNolintSuppressions | () | Resets the set of NOLINT suppressions to empty. | Resets the set of NOLINT suppressions to empty. | [
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] | def ResetNolintSuppressions():
"Resets the set of NOLINT suppressions to empty."
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||
tkn-tub/ns3-gym | 19bfe0a583e641142609939a090a09dfc63a095f | src/visualizer/visualizer/core.py | python | Visualizer.__init__ | (self) | !
Initializer function.
@param self: class object.
@return none | !
Initializer function. | [
"!",
"Initializer",
"function",
"."
] | def __init__(self):
"""!
Initializer function.
@param self: class object.
@return none
"""
assert Visualizer.INSTANCE is None
Visualizer.INSTANCE = self
super(Visualizer, self).__init__()
self.nodes = {} # node index -> Node
self.channels = {} # id(ns3.Channel) -> Channel
self.window = None # toplevel window
self.canvas = None # GooCanvas.Canvas
self.time_label = None # Gtk.Label
self.play_button = None # Gtk.ToggleButton
self.zoom = None # Gtk.Adjustment
self._scrolled_window = None # Gtk.ScrolledWindow
self.links_group = GooCanvas.CanvasGroup()
self.channels_group = GooCanvas.CanvasGroup()
self.nodes_group = GooCanvas.CanvasGroup()
self._update_timeout_id = None
self.simulation = SimulationThread(self)
self.selected_node = None # node currently selected
self.speed = 1.0
self.information_windows = []
self._transmission_arrows = []
self._last_transmissions = []
self._drop_arrows = []
self._last_drops = []
self._show_transmissions_mode = None
self.set_show_transmissions_mode(ShowTransmissionsMode.ALL)
self._panning_state = None
self.node_size_adjustment = None
self.transmissions_smoothing_adjustment = None
self.sample_period = SAMPLE_PERIOD
self.node_drag_state = None
self.follow_node = None
self.shell_window = None
self.create_gui()
for plugin in plugins:
plugin(self) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/web-page-replay/platformsettings.py | python | _WindowsPlatformSettings.get_system_logging_handler | (self) | return DebugViewHandler() | Return a handler for the logging module (optional).
For Windows, output can be viewed with DebugView.
http://technet.microsoft.com/en-us/sysinternals/bb896647.aspx | Return a handler for the logging module (optional). | [
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"logging",
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"."
] | def get_system_logging_handler(self):
"""Return a handler for the logging module (optional).
For Windows, output can be viewed with DebugView.
http://technet.microsoft.com/en-us/sysinternals/bb896647.aspx
"""
import ctypes
output_debug_string = ctypes.windll.kernel32.OutputDebugStringA
output_debug_string.argtypes = [ctypes.c_char_p]
class DebugViewHandler(logging.Handler):
def emit(self, record):
output_debug_string('[wpr] ' + self.format(record))
return DebugViewHandler() | [
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|
BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/api/chat_api.py | python | ChatApi.chat_get | (self, **kwargs) | Get chat messages. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.chat_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:param float count: Number of results to fetch.
:param float start: Starting ID for results.
:param bool reverse: If true, will sort results newest first.
:param float channel_id: Channel id. GET /chat/channels for ids. Leave blank for all.
:return: list[Chat]
If the method is called asynchronously,
returns the request thread. | Get chat messages. # noqa: E501 | [
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] | def chat_get(self, **kwargs): # noqa: E501
"""Get chat messages. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.chat_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:param float count: Number of results to fetch.
:param float start: Starting ID for results.
:param bool reverse: If true, will sort results newest first.
:param float channel_id: Channel id. GET /chat/channels for ids. Leave blank for all.
:return: list[Chat]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.chat_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.chat_get_with_http_info(**kwargs) # noqa: E501
return data | [
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||
RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/fuzzbunch/redirection.py | python | RedirectionManager.config_redirect | (self, plugin, do_redir) | return id | Configure whether the plug-in should perform redirection
plugin - An instance of a plugin
do_redir - Should we do redirection? (True or False) | Configure whether the plug-in should perform redirection
plugin - An instance of a plugin
do_redir - Should we do redirection? (True or False) | [
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"""Configure whether the plug-in should perform redirection
plugin - An instance of a plugin
do_redir - Should we do redirection? (True or False)"""
redir = plugin.getRedirection()
# Make a new session dictionary here
session_data = {
'params' : {}, #
'remote' : [], #
'local' : [] #
}
if do_redir:
id = uuid.uuid4()
else:
id = 0
try:
self.io.newline()
self.io.print_success("Configure Plugin Local Tunnels")
for l in redir['local']:
if do_redir:
self.redirect_local(l, plugin, session_data)
else:
self.straight_local(l, plugin)
self.io.newline()
self.io.print_success("Configure Plugin Remote Tunnels")
for r in redir['remote']:
if do_redir:
self.redirect_remote(r, plugin, session_data)
else:
self.straight_remote(r, plugin)
except exception.PromptErr:
for key,val in session_data['params'].items():
plugin.set(key, val)
raise
self.io.newline()
# Store info into the cache so that we can restore it in post_exec
if id:
self.session_cache[id] = session_data
return id | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/tf_asymmetry_fitting/tf_asymmetry_fitting_model.py | python | TFAsymmetryFittingModel._get_all_fit_function_parameter_values_for_tf_single_function | (self, tf_single_function: IFunction) | return [self._get_normalisation_from_tf_fit_function(tf_single_function)] + parameter_values | Returns the required parameters values including normalisation from a TF asymmetry single function. | Returns the required parameters values including normalisation from a TF asymmetry single function. | [
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] | def _get_all_fit_function_parameter_values_for_tf_single_function(self, tf_single_function: IFunction) -> list:
"""Returns the required parameters values including normalisation from a TF asymmetry single function."""
normal_single_function = self._get_normal_fit_function_from(tf_single_function)
parameter_values, _ = self.get_fit_function_parameter_values(normal_single_function)
return [self._get_normalisation_from_tf_fit_function(tf_single_function)] + parameter_values | [
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|
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/distutils/fcompiler/__init__.py | python | load_all_fcompiler_classes | () | Cache all the FCompiler classes found in modules in the
numpy.distutils.fcompiler package. | Cache all the FCompiler classes found in modules in the
numpy.distutils.fcompiler package. | [
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] | def load_all_fcompiler_classes():
"""Cache all the FCompiler classes found in modules in the
numpy.distutils.fcompiler package.
"""
from glob import glob
global fcompiler_class, fcompiler_aliases
if fcompiler_class is not None:
return
pys = os.path.join(os.path.dirname(__file__), '*.py')
fcompiler_class = {}
fcompiler_aliases = {}
for fname in glob(pys):
module_name, ext = os.path.splitext(os.path.basename(fname))
module_name = 'numpy.distutils.fcompiler.' + module_name
__import__ (module_name)
module = sys.modules[module_name]
if hasattr(module, 'compilers'):
for cname in module.compilers:
klass = getattr(module, cname)
desc = (klass.compiler_type, klass, klass.description)
fcompiler_class[klass.compiler_type] = desc
for alias in klass.compiler_aliases:
if alias in fcompiler_aliases:
raise ValueError("alias %r defined for both %s and %s"
% (alias, klass.__name__,
fcompiler_aliases[alias][1].__name__))
fcompiler_aliases[alias] = desc | [
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||
BVLC/caffe | 9b891540183ddc834a02b2bd81b31afae71b2153 | scripts/cpp_lint.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.
error: The function to call with any errors found. | Logs an error if there is no newline char at the end of the file. | [
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] | def 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.
error: The function to call with any errors found.
"""
# The array lines() was created by adding two newlines to the
# original file (go figure), then splitting on \n.
# To verify that the file ends in \n, we just have to make sure the
# last-but-two element of lines() exists and is empty.
if len(lines) < 3 or lines[-2]:
error(filename, len(lines) - 2, 'whitespace/ending_newline', 5,
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||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/debug/cli/curses_ui.py | python | CursesUI._scroll_output | (self, direction, line_index=None) | Scroll the output pad.
Args:
direction: _SCROLL_REFRESH, _SCROLL_UP, _SCROLL_DOWN, _SCROLL_UP_A_LINE,
_SCROLL_DOWN_A_LINE, _SCROLL_HOME, _SCROLL_END, _SCROLL_TO_LINE_INDEX
line_index: (int) Specifies the zero-based line index to scroll to.
Applicable only if direction is _SCROLL_TO_LINE_INDEX.
Raises:
ValueError: On invalid scroll direction.
TypeError: If line_index is not int and direction is
_SCROLL_TO_LINE_INDEX. | Scroll the output pad. | [
"Scroll",
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"."
] | def _scroll_output(self, direction, line_index=None):
"""Scroll the output pad.
Args:
direction: _SCROLL_REFRESH, _SCROLL_UP, _SCROLL_DOWN, _SCROLL_UP_A_LINE,
_SCROLL_DOWN_A_LINE, _SCROLL_HOME, _SCROLL_END, _SCROLL_TO_LINE_INDEX
line_index: (int) Specifies the zero-based line index to scroll to.
Applicable only if direction is _SCROLL_TO_LINE_INDEX.
Raises:
ValueError: On invalid scroll direction.
TypeError: If line_index is not int and direction is
_SCROLL_TO_LINE_INDEX.
"""
if not self._output_pad:
# No output pad is present. Do nothing.
return
if direction == _SCROLL_REFRESH:
pass
elif direction == _SCROLL_UP:
# Scroll up.
self._output_pad_row -= int(self._output_num_rows / 3)
if self._output_pad_row < 0:
self._output_pad_row = 0
elif direction == _SCROLL_DOWN:
# Scroll down.
self._output_pad_row += int(self._output_num_rows / 3)
if (self._output_pad_row >
self._output_pad_height - self._output_pad_screen_height - 1):
self._output_pad_row = (
self._output_pad_height - self._output_pad_screen_height - 1)
elif direction == _SCROLL_UP_A_LINE:
# Scroll up a line
if self._output_pad_row - 1 >= 0:
self._output_pad_row -= 1
elif direction == _SCROLL_DOWN_A_LINE:
# Scroll down a line
if self._output_pad_row + 1 < (
self._output_pad_height - self._output_pad_screen_height):
self._output_pad_row += 1
elif direction == _SCROLL_HOME:
# Scroll to top
self._output_pad_row = 0
elif direction == _SCROLL_END:
# Scroll to bottom
self._output_pad_row = (
self._output_pad_height - self._output_pad_screen_height - 1)
elif direction == _SCROLL_TO_LINE_INDEX:
if not isinstance(line_index, int):
raise TypeError("Invalid line_index type (%s) under mode %s" %
(type(line_index), _SCROLL_TO_LINE_INDEX))
self._output_pad_row = line_index
else:
raise ValueError("Unsupported scroll mode: %s" % direction)
self._nav_history.update_scroll_position(self._output_pad_row)
# Actually scroll the output pad: refresh with new location.
output_pad_top = self._output_pad_screen_location.top
if self._main_menu_pad:
output_pad_top += 1
self._screen_scroll_output_pad(self._output_pad, self._output_pad_row, 0,
output_pad_top,
self._output_pad_screen_location.left,
self._output_pad_screen_location.bottom,
self._output_pad_screen_location.right)
self._screen_render_nav_bar()
self._screen_render_menu_pad()
self._scroll_info = self._compile_ui_status_summary()
self._screen_draw_text_line(
self._output_scroll_row,
self._scroll_info,
color=self._STATUS_BAR_COLOR_PAIR) | [
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||
ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/webrtc/tools/compare_videos.py | python | _ParseArgs | () | return options | Registers the command-line options. | Registers the command-line options. | [
"Registers",
"the",
"command",
"-",
"line",
"options",
"."
] | def _ParseArgs():
"""Registers the command-line options."""
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage=usage)
parser.add_option('--label', type='string', default='MY_TEST',
help=('Label of the test, used to identify different '
'tests. Default: %default'))
parser.add_option('--ref_video', type='string',
help='Reference video to compare with (YUV).')
parser.add_option('--test_video', type='string',
help=('Test video to be compared with the reference '
'video (YUV).'))
parser.add_option('--frame_analyzer', type='string',
help='Path to the frame analyzer executable.')
parser.add_option('--barcode_decoder', type='string',
help=('Path to the barcode decoder script. By default, we '
'will assume we can find it in barcode_tools/'
'relative to this directory.'))
parser.add_option('--ffmpeg_path', type='string',
help=('The path to where the ffmpeg executable is located. '
'If omitted, it will be assumed to be present in the '
'PATH with the name ffmpeg[.exe].'))
parser.add_option('--zxing_path', type='string',
help=('The path to where the zxing executable is located. '
'If omitted, it will be assumed to be present in the '
'PATH with the name zxing[.exe].'))
parser.add_option('--stats_file', type='string', default='stats.txt',
help=('Path to the temporary stats file to be created and '
'used. Default: %default'))
parser.add_option('--yuv_frame_width', type='int', default=640,
help='Width of the YUV file\'s frames. Default: %default')
parser.add_option('--yuv_frame_height', type='int', default=480,
help='Height of the YUV file\'s frames. Default: %default')
options, _args = parser.parse_args()
if not options.ref_video:
parser.error('You must provide a path to the reference video!')
if not os.path.exists(options.ref_video):
parser.error('Cannot find the reference video at %s' % options.ref_video)
if not options.test_video:
parser.error('You must provide a path to the test video!')
if not os.path.exists(options.test_video):
parser.error('Cannot find the test video at %s' % options.test_video)
if not options.frame_analyzer:
parser.error('You must provide the path to the frame analyzer executable!')
if not os.path.exists(options.frame_analyzer):
parser.error('Cannot find frame analyzer executable at %s!' %
options.frame_analyzer)
return options | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/results_tab_widget/results_tab_presenter.py | python | ResultsTabPresenter.on_new_fit_performed | (self, fit_info=None) | React to a new fit created in the fitting tab | React to a new fit created in the fitting tab | [
"React",
"to",
"a",
"new",
"fit",
"created",
"in",
"the",
"fitting",
"tab"
] | def on_new_fit_performed(self, fit_info=None):
"""React to a new fit created in the fitting tab"""
# It's possible that this call can come in on a thread that
# is different to the one that the view lives on.
# In order to update the GUI we use invokeMethod with the assumption
# that 'self' lives on the same thread as the view and Qt forces
# the call to the chose method to be done on the thread the
# view lives on. This avoids errors from painting on non-gui threads.
new_fit_name = ";"
if fit_info:
new_fit_list = fit_info.output_workspace_names()
if new_fit_list and len(new_fit_list)>0:
new_fit_name = new_fit_list[0]
QMetaObject.invokeMethod(self, "_on_new_fit_performed_impl", Q_ARG(str, new_fit_name)) | [
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||
SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pyedbglib/protocols/avr8protocol.py | python | Avr8Protocol.run | (self) | Resumes core execution | Resumes core execution | [
"Resumes",
"core",
"execution"
] | def run(self):
"""Resumes core execution"""
self.logger.debug("AVR core resume")
self.check_response(self.jtagice3_command_response(bytearray([self.CMD_AVR8_RUN, self.CMD_VERSION0]))) | [
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||
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/legacy_tf_layers/core.py | python | dense | (
inputs, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None) | return layer.apply(inputs) | Functional interface for the densely-connected layer.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Args:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor the same shape as `inputs` except the last dimension is of
size `units`.
Raises:
ValueError: if eager execution is enabled. | Functional interface for the densely-connected layer. | [
"Functional",
"interface",
"for",
"the",
"densely",
"-",
"connected",
"layer",
"."
] | def dense(
inputs, units,
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
reuse=None):
"""Functional interface for the densely-connected layer.
This layer implements the operation:
`outputs = activation(inputs * kernel + bias)`
where `activation` is the activation function passed as the `activation`
argument (if not `None`), `kernel` is a weights matrix created by the layer,
and `bias` is a bias vector created by the layer
(only if `use_bias` is `True`).
Args:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix.
If `None` (default), weights are initialized using the default
initializer used by `tf.compat.v1.get_variable`.
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the
kernel after being updated by an `Optimizer` (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the
bias after being updated by an `Optimizer`.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor the same shape as `inputs` except the last dimension is of
size `units`.
Raises:
ValueError: if eager execution is enabled.
"""
warnings.warn('`tf.layers.dense` is deprecated and '
'will be removed in a future version. '
'Please use `tf.keras.layers.Dense` instead.')
layer = Dense(units,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
trainable=trainable,
name=name,
_scope=name,
_reuse=reuse)
return layer.apply(inputs) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/distutils/ccompiler.py | python | CCompiler.add_include_dir | (self, dir) | Add 'dir' to the list of directories that will be searched for
header files. The compiler is instructed to search directories in
the order in which they are supplied by successive calls to
'add_include_dir()'. | Add 'dir' to the list of directories that will be searched for
header files. The compiler is instructed to search directories in
the order in which they are supplied by successive calls to
'add_include_dir()'. | [
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] | def add_include_dir(self, dir):
"""Add 'dir' to the list of directories that will be searched for
header files. The compiler is instructed to search directories in
the order in which they are supplied by successive calls to
'add_include_dir()'.
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self.include_dirs.append(dir) | [
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||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/client/session.py | python | BaseSession.close | (self) | Closes this session.
Calling this method frees all resources associated with the session.
Raises:
tf.errors.OpError: Or one of its subclasses if an error occurs while
closing the TensorFlow session. | Closes this session. | [
"Closes",
"this",
"session",
"."
] | def close(self):
"""Closes this session.
Calling this method frees all resources associated with the session.
Raises:
tf.errors.OpError: Or one of its subclasses if an error occurs while
closing the TensorFlow session.
"""
if self._session and not self._closed:
self._closed = True
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/SocketServer.py | python | ThreadingMixIn.process_request | (self, request, client_address) | Start a new thread to process the request. | Start a new thread to process the request. | [
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] | def process_request(self, request, client_address):
"""Start a new thread to process the request."""
t = threading.Thread(target = self.process_request_thread,
args = (request, client_address))
t.daemon = self.daemon_threads
t.start() | [
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||
quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/internal/cpp_message.py | python | _AddMessageMethods | (message_descriptor, cls) | Adds the methods to a protocol message class. | Adds the methods to a protocol message class. | [
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"a",
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"class",
"."
] | def _AddMessageMethods(message_descriptor, cls):
"""Adds the methods to a protocol message class."""
if message_descriptor.is_extendable:
def ClearExtension(self, extension):
self.Extensions.ClearExtension(extension)
def HasExtension(self, extension):
return self.Extensions.HasExtension(extension)
def HasField(self, field_name):
return self._cmsg.HasField(field_name)
def ClearField(self, field_name):
child_cmessage = None
if field_name in self._composite_fields:
child_field = self._composite_fields[field_name]
del self._composite_fields[field_name]
child_cdescriptor = self.__descriptors[field_name]
# TODO(anuraag): Support clearing repeated message fields as well.
if (child_cdescriptor.label != _LABEL_REPEATED and
child_cdescriptor.cpp_type == _CPPTYPE_MESSAGE):
child_field._owner = None
child_cmessage = child_field._cmsg
if child_cmessage is not None:
self._cmsg.ClearField(field_name, child_cmessage)
else:
self._cmsg.ClearField(field_name)
def Clear(self):
cmessages_to_release = []
for field_name, child_field in self._composite_fields.iteritems():
child_cdescriptor = self.__descriptors[field_name]
# TODO(anuraag): Support clearing repeated message fields as well.
if (child_cdescriptor.label != _LABEL_REPEATED and
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self.MergeFromString(serialized)
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self._cmsg.MergeFrom(msg._cmsg)
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extension = cls._extensions_by_name[field_name]
all_fields.append((extension, self.Extensions[extension]))
else:
field_descriptor = fields_by_name[field_name]
all_fields.append(
(field_descriptor, getattr(self, field_name)))
all_fields.sort(key=lambda item: item[0].number)
return all_fields
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def __str__(self):
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/ma/core.py | python | _MaskedBinaryOperation.__init__ | (self, mbfunc, fillx=0, filly=0) | abfunc(fillx, filly) must be defined.
abfunc(x, filly) = x for all x to enable reduce. | abfunc(fillx, filly) must be defined. | [
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"""
abfunc(fillx, filly) must be defined.
abfunc(x, filly) = x for all x to enable reduce.
"""
super().__init__(mbfunc)
self.fillx = fillx
self.filly = filly
ufunc_domain[mbfunc] = None
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TStr.GetPrimHashCd | (self) | return _snap.TStr_GetPrimHashCd(self) | GetPrimHashCd(TStr self) -> int
Parameters:
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GetPrimHashCd(TStr self) -> int
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/train/callback/_landscape.py | python | SummaryLandscape._check_device_ids | (device_ids) | Check device_ids type and value. | Check device_ids type and value. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/pytables.py | python | Fixed.validate_version | (self, where=None) | return True | are we trying to operate on an old version? | are we trying to operate on an old version? | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/package_index.py | python | interpret_distro_name | (
location, basename, metadata, py_version=None, precedence=SOURCE_DIST,
platform=None
) | Generate alternative interpretations of a source distro name
Note: if `location` is a filesystem filename, you should call
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platform=None
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"""Generate alternative interpretations of a source distro name
Note: if `location` is a filesystem filename, you should call
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parts = basename.split('-')
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return
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"# to match a request for it. It's still a potential problem, though, and",
"# in the long run PyPI and the distutils should go for \"safe\" names and",
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] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/package_index.py#L151-L183 |
||
apache/impala | 8ddac48f3428c86f2cbd037ced89cfb903298b12 | shell/ext-py/six-1.14.0/six.py | python | add_metaclass | (metaclass) | return wrapper | Class decorator for creating a class with a metaclass. | Class decorator for creating a class with a metaclass. | [
"Class",
"decorator",
"for",
"creating",
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"class",
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] | def add_metaclass(metaclass):
"""Class decorator for creating a class with a metaclass."""
def wrapper(cls):
orig_vars = cls.__dict__.copy()
slots = orig_vars.get('__slots__')
if slots is not None:
if isinstance(slots, str):
slots = [slots]
for slots_var in slots:
orig_vars.pop(slots_var)
orig_vars.pop('__dict__', None)
orig_vars.pop('__weakref__', None)
if hasattr(cls, '__qualname__'):
orig_vars['__qualname__'] = cls.__qualname__
return metaclass(cls.__name__, cls.__bases__, orig_vars)
return wrapper | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/xml/sax/saxutils.py | python | prepare_input_source | (source, base = "") | return source | This function takes an InputSource and an optional base URL and
returns a fully resolved InputSource object ready for reading. | This function takes an InputSource and an optional base URL and
returns a fully resolved InputSource object ready for reading. | [
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] | def prepare_input_source(source, base = ""):
"""This function takes an InputSource and an optional base URL and
returns a fully resolved InputSource object ready for reading."""
if type(source) in _StringTypes:
source = xmlreader.InputSource(source)
elif hasattr(source, "read"):
f = source
source = xmlreader.InputSource()
source.setByteStream(f)
if hasattr(f, "name"):
source.setSystemId(f.name)
if source.getByteStream() is None:
try:
sysid = source.getSystemId()
basehead = os.path.dirname(os.path.normpath(base))
encoding = sys.getfilesystemencoding()
if isinstance(sysid, unicode):
if not isinstance(basehead, unicode):
try:
basehead = basehead.decode(encoding)
except UnicodeDecodeError:
sysid = sysid.encode(encoding)
else:
if isinstance(basehead, unicode):
try:
sysid = sysid.decode(encoding)
except UnicodeDecodeError:
basehead = basehead.encode(encoding)
sysidfilename = os.path.join(basehead, sysid)
isfile = os.path.isfile(sysidfilename)
except UnicodeError:
isfile = False
if isfile:
source.setSystemId(sysidfilename)
f = open(sysidfilename, "rb")
else:
source.setSystemId(urlparse.urljoin(base, source.getSystemId()))
f = urllib.urlopen(source.getSystemId())
source.setByteStream(f)
return source | [
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] | https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/xml/sax/saxutils.py#L303-L346 |
|
google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/lib/recfunctions.py | python | append_fields | (base, names, data, dtypes=None,
fill_value= -1, usemask=True, asrecarray=False) | return _fix_output(output, usemask=usemask, asrecarray=asrecarray) | Add new fields to an existing array.
The names of the fields are given with the `names` arguments,
the corresponding values with the `data` arguments.
If a single field is appended, `names`, `data` and `dtypes` do not have
to be lists but just values.
Parameters
----------
base : array
Input array to extend.
names : string, sequence
String or sequence of strings corresponding to the names
of the new fields.
data : array or sequence of arrays
Array or sequence of arrays storing the fields to add to the base.
dtypes : sequence of datatypes, optional
Datatype or sequence of datatypes.
If None, the datatypes are estimated from the `data`.
fill_value : {float}, optional
Filling value used to pad missing data on the shorter arrays.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : {False, True}, optional
Whether to return a recarray (MaskedRecords) or not. | Add new fields to an existing array. | [
"Add",
"new",
"fields",
"to",
"an",
"existing",
"array",
"."
] | def append_fields(base, names, data, dtypes=None,
fill_value= -1, usemask=True, asrecarray=False):
"""
Add new fields to an existing array.
The names of the fields are given with the `names` arguments,
the corresponding values with the `data` arguments.
If a single field is appended, `names`, `data` and `dtypes` do not have
to be lists but just values.
Parameters
----------
base : array
Input array to extend.
names : string, sequence
String or sequence of strings corresponding to the names
of the new fields.
data : array or sequence of arrays
Array or sequence of arrays storing the fields to add to the base.
dtypes : sequence of datatypes, optional
Datatype or sequence of datatypes.
If None, the datatypes are estimated from the `data`.
fill_value : {float}, optional
Filling value used to pad missing data on the shorter arrays.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : {False, True}, optional
Whether to return a recarray (MaskedRecords) or not.
"""
# Check the names
if isinstance(names, (tuple, list)):
if len(names) != len(data):
msg = "The number of arrays does not match the number of names"
raise ValueError(msg)
elif isinstance(names, basestring):
names = [names, ]
data = [data, ]
#
if dtypes is None:
data = [np.array(a, copy=False, subok=True) for a in data]
data = [a.view([(name, a.dtype)]) for (name, a) in zip(names, data)]
else :
if not isinstance(dtypes, (tuple, list)):
dtypes = [dtypes, ]
if len(data) != len(dtypes):
if len(dtypes) == 1:
dtypes = dtypes * len(data)
else:
msg = "The dtypes argument must be None, a dtype, or a list."
raise ValueError(msg)
data = [np.array(a, copy=False, subok=True, dtype=d).view([(n, d)])
for (a, n, d) in zip(data, names, dtypes)]
#
base = merge_arrays(base, usemask=usemask, fill_value=fill_value)
if len(data) > 1:
data = merge_arrays(data, flatten=True, usemask=usemask,
fill_value=fill_value)
else:
data = data.pop()
#
output = ma.masked_all(max(len(base), len(data)),
dtype=base.dtype.descr + data.dtype.descr)
output = recursive_fill_fields(base, output)
output = recursive_fill_fields(data, output)
#
return _fix_output(output, usemask=usemask, asrecarray=asrecarray) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/interactiveshell.py | python | InteractiveShell.reset | (self, new_session=True, aggressive=False) | Clear all internal namespaces, and attempt to release references to
user objects.
If new_session is True, a new history session will be opened. | Clear all internal namespaces, and attempt to release references to
user objects. | [
"Clear",
"all",
"internal",
"namespaces",
"and",
"attempt",
"to",
"release",
"references",
"to",
"user",
"objects",
"."
] | def reset(self, new_session=True, aggressive=False):
"""Clear all internal namespaces, and attempt to release references to
user objects.
If new_session is True, a new history session will be opened.
"""
# Clear histories
self.history_manager.reset(new_session)
# Reset counter used to index all histories
if new_session:
self.execution_count = 1
# Reset last execution result
self.last_execution_succeeded = True
self.last_execution_result = None
# Flush cached output items
if self.displayhook.do_full_cache:
self.displayhook.flush()
# The main execution namespaces must be cleared very carefully,
# skipping the deletion of the builtin-related keys, because doing so
# would cause errors in many object's __del__ methods.
if self.user_ns is not self.user_global_ns:
self.user_ns.clear()
ns = self.user_global_ns
drop_keys = set(ns.keys())
drop_keys.discard('__builtin__')
drop_keys.discard('__builtins__')
drop_keys.discard('__name__')
for k in drop_keys:
del ns[k]
self.user_ns_hidden.clear()
# Restore the user namespaces to minimal usability
self.init_user_ns()
if aggressive and not hasattr(self, "_sys_modules_keys"):
print("Cannot restore sys.module, no snapshot")
elif aggressive:
print("culling sys module...")
current_keys = set(sys.modules.keys())
for k in current_keys - self._sys_modules_keys:
if k.startswith("multiprocessing"):
continue
del sys.modules[k]
# Restore the default and user aliases
self.alias_manager.clear_aliases()
self.alias_manager.init_aliases()
# Now define aliases that only make sense on the terminal, because they
# need direct access to the console in a way that we can't emulate in
# GUI or web frontend
if os.name == 'posix':
for cmd in ('clear', 'more', 'less', 'man'):
if cmd not in self.magics_manager.magics['line']:
self.alias_manager.soft_define_alias(cmd, cmd)
# Flush the private list of module references kept for script
# execution protection
self.clear_main_mod_cache() | [
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||
KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/ContactStructuralMechanicsApplication/python_scripts/adaptative_remeshing_contact_structural_mechanics_analysis.py | python | AdaptativeRemeshingContactStructuralMechanicsAnalysis.Initialize | (self) | Initializing the Analysis | Initializing the Analysis | [
"Initializing",
"the",
"Analysis"
] | def Initialize(self):
""" Initializing the Analysis """
super(AdaptativeRemeshingContactStructuralMechanicsAnalysis, self).Initialize()
computing_model_part = self._GetSolver().GetComputingModelPart()
if not self.process_remesh:
convergence_criteria = self._GetSolver()._GetConvergenceCriterion()
convergence_criteria.Initialize(computing_model_part)
# Ensuring to have conditions on the BC before remesh
is_surface = False
for elem in computing_model_part.Elements:
geom = elem.GetGeometry()
if geom.WorkingSpaceDimension() != geom.LocalSpaceDimension():
is_surface = True
break
if not is_surface:
list_model_parts = []
# We need to detect the conditions in the boundary conditions
if self.project_parameters.Has("constraints_process_list"):
constraints_process_list = self.project_parameters["constraints_process_list"]
for i in range(0,constraints_process_list.size()):
item = constraints_process_list[i]
list_model_parts.append(item["Parameters"]["model_part_name"].GetString())
skin_detection_parameters = KM.Parameters("""
{
"list_model_parts_to_assign_conditions" : []
}
""")
for name_mp in list_model_parts:
skin_detection_parameters["list_model_parts_to_assign_conditions"].Append(name_mp)
if computing_model_part.ProcessInfo[KM.DOMAIN_SIZE] == 2:
detect_skin = KM.SkinDetectionProcess2D(computing_model_part, skin_detection_parameters)
else:
detect_skin = KM.SkinDetectionProcess3D(computing_model_part, skin_detection_parameters)
detect_skin.Execute()
self._GetSolver().SetEchoLevel(self.echo_level) | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py | python | Text.dlineinfo | (self, index) | return self._getints(self.tk.call(self._w, 'dlineinfo', index)) | Return tuple (x,y,width,height,baseline) giving the bounding box
and baseline position of the visible part of the line containing
the character at INDEX. | Return tuple (x,y,width,height,baseline) giving the bounding box
and baseline position of the visible part of the line containing
the character at INDEX. | [
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] | def dlineinfo(self, index):
"""Return tuple (x,y,width,height,baseline) giving the bounding box
and baseline position of the visible part of the line containing
the character at INDEX."""
return self._getints(self.tk.call(self._w, 'dlineinfo', index)) | [
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|
hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | clang/bindings/python/clang/cindex.py | python | Cursor.is_static_method | (self) | return conf.lib.clang_CXXMethod_isStatic(self) | Returns True if the cursor refers to a C++ member function or member
function template that is declared 'static'. | Returns True if the cursor refers to a C++ member function or member
function template that is declared 'static'. | [
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] | def is_static_method(self):
"""Returns True if the cursor refers to a C++ member function or member
function template that is declared 'static'.
"""
return conf.lib.clang_CXXMethod_isStatic(self) | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/nn/probability/distribution/distribution.py | python | Distribution._set_sd | (self) | Set standard deviation based on the availability of `_sd` and `_var`. | Set standard deviation based on the availability of `_sd` and `_var`. | [
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] | def _set_sd(self):
"""
Set standard deviation based on the availability of `_sd` and `_var`.
"""
if hasattr(self, '_sd'):
self._call_sd = self._sd
elif hasattr(self, '_var'):
self._call_sd = self._calc_sd_from_var
else:
self._call_sd = self._raise_not_implemented_error('sd') | [
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||
google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | protobuf/python/google/protobuf/internal/python_message.py | python | _AddPropertiesForField | (field, cls) | Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field.
Args:
field: A FieldDescriptor for this field.
cls: The class we're constructing. | Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
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] | def _AddPropertiesForField(field, cls):
"""Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field.
Args:
field: A FieldDescriptor for this field.
cls: The class we're constructing.
"""
# Catch it if we add other types that we should
# handle specially here.
assert _FieldDescriptor.MAX_CPPTYPE == 10
constant_name = field.name.upper() + "_FIELD_NUMBER"
setattr(cls, constant_name, field.number)
if field.label == _FieldDescriptor.LABEL_REPEATED:
_AddPropertiesForRepeatedField(field, cls)
elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
_AddPropertiesForNonRepeatedCompositeField(field, cls)
else:
_AddPropertiesForNonRepeatedScalarField(field, cls) | [
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||
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/code_coverage/croc_html.py | python | CrocHtml.AddSectionHeader | (self, table, caption, itemtype, is_file=False) | Adds a section header to the coverage table.
Args:
table: Table to add rows to.
caption: Caption for section, if not None.
itemtype: Type of items in this section, if not None.
is_file: Are items in this section files? | Adds a section header to the coverage table. | [
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] | def AddSectionHeader(self, table, caption, itemtype, is_file=False):
"""Adds a section header to the coverage table.
Args:
table: Table to add rows to.
caption: Caption for section, if not None.
itemtype: Type of items in this section, if not None.
is_file: Are items in this section files?
"""
if caption is not None:
table.E('tr').E('td', e_class='secdesc', colspan=8).Text(caption)
sec_hdr = table.E('tr')
if itemtype is not None:
sec_hdr.E('td', e_class='section').Text(itemtype)
sec_hdr.E('td', e_class='section').Text('Coverage')
sec_hdr.E('td', e_class='section', colspan=3).Text(
'Lines executed / instrumented / missing')
graph = sec_hdr.E('td', e_class='section')
graph.E('span', style='color:#00FF00').Text('exe')
graph.Text(' / ')
graph.E('span', style='color:#FFFF00').Text('inst')
graph.Text(' / ')
graph.E('span', style='color:#FF0000').Text('miss')
if is_file:
sec_hdr.E('td', e_class='section').Text('Language')
sec_hdr.E('td', e_class='section').Text('Group')
else:
sec_hdr.E('td', e_class='section', colspan=2) | [
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||
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py | python | DirichletMultinomial.variance | (self, name="mean") | Class variances for every batch member.
The variance for each batch member is defined as the following:
```
Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
(n + alpha_0) / (1 + alpha_0)
```
where `alpha_0 = sum_j alpha_j`.
The covariance between elements in a batch is defined as:
```
Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
(n + alpha_0) / (1 + alpha_0)
```
Args:
name: The name for this op.
Returns:
A `Tensor` representing the variances for each batch member. | Class variances for every batch member. | [
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] | def variance(self, name="mean"):
"""Class variances for every batch member.
The variance for each batch member is defined as the following:
```
Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
(n + alpha_0) / (1 + alpha_0)
```
where `alpha_0 = sum_j alpha_j`.
The covariance between elements in a batch is defined as:
```
Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
(n + alpha_0) / (1 + alpha_0)
```
Args:
name: The name for this op.
Returns:
A `Tensor` representing the variances for each batch member.
"""
alpha = self._alpha
alpha_sum = self._alpha_sum
n = self._n
with ops.name_scope(self.name):
with ops.op_scope([alpha, alpha_sum, n], name):
expanded_alpha_sum = array_ops.expand_dims(alpha_sum, -1)
shared_factor = n * (expanded_alpha_sum + n) / (
expanded_alpha_sum + 1) * array_ops.ones_like(alpha)
mean_no_n = alpha / expanded_alpha_sum
expanded_mean_no_n = array_ops.expand_dims(mean_no_n, -1)
variance = -math_ops.batch_matmul(
expanded_mean_no_n, expanded_mean_no_n, adj_y=True)
variance += array_ops.batch_matrix_diag(mean_no_n)
variance *= array_ops.expand_dims(shared_factor, -1)
return variance | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/generic.py | python | NDFrame._construct_axes_dict_from | (self, axes, **kwargs) | return d | Return an axes dictionary for the passed axes. | Return an axes dictionary for the passed axes. | [
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] | def _construct_axes_dict_from(self, axes, **kwargs):
"""Return an axes dictionary for the passed axes."""
d = {a: ax for a, ax in zip(self._AXIS_ORDERS, axes)}
d.update(kwargs)
return d | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Point2D.GetFloor | (*args, **kwargs) | return _core_.Point2D_GetFloor(*args, **kwargs) | GetFloor() -> (x,y)
Convert to integer | GetFloor() -> (x,y) | [
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"""
GetFloor() -> (x,y)
Convert to integer
"""
return _core_.Point2D_GetFloor(*args, **kwargs) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/terminal/pt_inputhooks/wx.py | python | inputhook_wx2 | (context) | return 0 | Run the wx event loop, polling for stdin.
This version runs the wx eventloop for an undetermined amount of time,
during which it periodically checks to see if anything is ready on
stdin. If anything is ready on stdin, the event loop exits.
The argument to elr.Run controls how often the event loop looks at stdin.
This determines the responsiveness at the keyboard. A setting of 1000
enables a user to type at most 1 char per second. I have found that a
setting of 10 gives good keyboard response. We can shorten it further,
but eventually performance would suffer from calling select/kbhit too
often. | Run the wx event loop, polling for stdin. | [
"Run",
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"wx",
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"for",
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] | def inputhook_wx2(context):
"""Run the wx event loop, polling for stdin.
This version runs the wx eventloop for an undetermined amount of time,
during which it periodically checks to see if anything is ready on
stdin. If anything is ready on stdin, the event loop exits.
The argument to elr.Run controls how often the event loop looks at stdin.
This determines the responsiveness at the keyboard. A setting of 1000
enables a user to type at most 1 char per second. I have found that a
setting of 10 gives good keyboard response. We can shorten it further,
but eventually performance would suffer from calling select/kbhit too
often.
"""
app = wx.GetApp()
if app is not None:
assert wx.Thread_IsMain()
elr = EventLoopRunner()
# As this time is made shorter, keyboard response improves, but idle
# CPU load goes up. 10 ms seems like a good compromise.
elr.Run(time=10, # CHANGE time here to control polling interval
input_is_ready=context.input_is_ready)
return 0 | [
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|
weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | v8_7_5/tools/grokdump.py | python | InspectionShell.do_known_oldspace | (self, address) | Teach V8 heap layout information to the inspector.
Set the first old space page by passing any pointer into that page. | Teach V8 heap layout information to the inspector. | [
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] | def do_known_oldspace(self, address):
"""
Teach V8 heap layout information to the inspector.
Set the first old space page by passing any pointer into that page.
"""
address = self.ParseAddressExpr(address)
page_address = address & ~self.heap.PageAlignmentMask()
self.padawan.known_first_old_page = page_address | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/plotting/_matplotlib/style.py | python | _derive_colors | (
*,
color: Color | Collection[Color] | None,
colormap: str | Colormap | None,
color_type: str,
num_colors: int,
) | Derive colors from either `colormap`, `color_type` or `color` inputs.
Get a list of colors either from `colormap`, or from `color`,
or from `color_type` (if both `colormap` and `color` are None).
Parameters
----------
color : str or sequence, optional
Color(s) to be used for deriving sequence of colors.
Can be either be a single color (single color string, or sequence of floats
representing a single color), or a sequence of colors.
colormap : :py:class:`matplotlib.colors.Colormap`, optional
Matplotlib colormap.
When provided, the resulting colors will be derived from the colormap.
color_type : {"default", "random"}, optional
Type of colors to derive. Used if provided `color` and `colormap` are None.
Ignored if either `color` or `colormap`` are not None.
num_colors : int
Number of colors to be extracted.
Returns
-------
list
List of colors extracted.
Warns
-----
UserWarning
If both `colormap` and `color` are provided.
Parameter `color` will override. | Derive colors from either `colormap`, `color_type` or `color` inputs. | [
"Derive",
"colors",
"from",
"either",
"colormap",
"color_type",
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"inputs",
"."
] | def _derive_colors(
*,
color: Color | Collection[Color] | None,
colormap: str | Colormap | None,
color_type: str,
num_colors: int,
) -> list[Color]:
"""
Derive colors from either `colormap`, `color_type` or `color` inputs.
Get a list of colors either from `colormap`, or from `color`,
or from `color_type` (if both `colormap` and `color` are None).
Parameters
----------
color : str or sequence, optional
Color(s) to be used for deriving sequence of colors.
Can be either be a single color (single color string, or sequence of floats
representing a single color), or a sequence of colors.
colormap : :py:class:`matplotlib.colors.Colormap`, optional
Matplotlib colormap.
When provided, the resulting colors will be derived from the colormap.
color_type : {"default", "random"}, optional
Type of colors to derive. Used if provided `color` and `colormap` are None.
Ignored if either `color` or `colormap`` are not None.
num_colors : int
Number of colors to be extracted.
Returns
-------
list
List of colors extracted.
Warns
-----
UserWarning
If both `colormap` and `color` are provided.
Parameter `color` will override.
"""
if color is None and colormap is not None:
return _get_colors_from_colormap(colormap, num_colors=num_colors)
elif color is not None:
if colormap is not None:
warnings.warn(
"'color' and 'colormap' cannot be used simultaneously. Using 'color'"
)
return _get_colors_from_color(color)
else:
return _get_colors_from_color_type(color_type, num_colors=num_colors) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | ListItem.GetBackgroundColour | (*args, **kwargs) | return _controls_.ListItem_GetBackgroundColour(*args, **kwargs) | GetBackgroundColour(self) -> Colour | GetBackgroundColour(self) -> Colour | [
"GetBackgroundColour",
"(",
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] | def GetBackgroundColour(*args, **kwargs):
"""GetBackgroundColour(self) -> Colour"""
return _controls_.ListItem_GetBackgroundColour(*args, **kwargs) | [
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|
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/geographic_msgs/msg/_RoutePath.py | python | RoutePath.__init__ | (self, *args, **kwds) | Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
header,network,segments,props
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields. | Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
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Constructor. Any message fields that are implicitly/explicitly
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changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
header,network,segments,props
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
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"""
if args or kwds:
super(RoutePath, self).__init__(*args, **kwds)
#message fields cannot be None, assign default values for those that are
if self.header is None:
self.header = std_msgs.msg.Header()
if self.network is None:
self.network = uuid_msgs.msg.UniqueID()
if self.segments is None:
self.segments = []
if self.props is None:
self.props = []
else:
self.header = std_msgs.msg.Header()
self.network = uuid_msgs.msg.UniqueID()
self.segments = []
self.props = [] | [
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