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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/PartDesign/WizardShaft/SegmentFunction.py | python | SegmentFunctionSegment.hasStart | (self, xval) | return abs(self.start - xval) < 1E-9 | Return true if the start of this segment is xval | Return true if the start of this segment is xval | [
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"Return true if the start of this segment is xval"
#FIXME: 1E-9 is arbitrary here. But since units are in meters, 1E-9 is a nanometer...
return abs(self.start - xval) < 1E-9 | [
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/faster-rcnn/lib/rpn/generate.py | python | _get_image_blob | (im) | return blob, im_info | Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
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blob (ndarray): a data blob holding an image pyramid
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im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
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"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
assert len(cfg.TEST.SCALES) == 1
target_size = cfg.TEST.SCALES[0]
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_info = np.hstack((im.shape[:2], im_scale))[np.newaxis, :]
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_info | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/nn/init.py | python | sparse_ | (tensor, sparsity, std=0.01) | return tensor | r"""Fills the 2D input `Tensor` as a sparse matrix, where the
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenuBar.OnLeaveWindow | (self, event) | Handles the ``wx.EVT_LEAVE_WINDOW`` event for :class:`FlatMenuBar`.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/pytables.py | python | GenericTable.get_attrs | (self) | retrieve our attributes | retrieve our attributes | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py | python | StateSpaceModel._exogenous_noise_increasing | (self, current_times, exogenous_values, state) | return (start_mean + mean_addition,
start_covariance + covariance_addition,
previous_times) | Update state with exogenous regressors, increasing uncertainty.
Adds to the state mean a linear transformation of `exogenous_values`, and
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This update is useful for modeling changes relative to current state,
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Args:
current_times: A [batch size] Tensor of times for the exogenous values
being input.
exogenous_values: A [batch size x exogenous input dimension] Tensor of
exogenous values for each part of the batch.
state: A tuple of (mean, covariance, previous_times) having shapes
mean; [batch size x state dimension]
covariance; [batch size x state dimension x state dimension]
previous_times; [batch size]
Returns:
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"""Update state with exogenous regressors, increasing uncertainty.
Adds to the state mean a linear transformation of `exogenous_values`, and
increases uncertainty by constructing a covariance matrix based on
`exogenous_values` and adding it to the state covariance.
This update is useful for modeling changes relative to current state,
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Args:
current_times: A [batch size] Tensor of times for the exogenous values
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exogenous_values: A [batch size x exogenous input dimension] Tensor of
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state: A tuple of (mean, covariance, previous_times) having shapes
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"""
start_mean, start_covariance, previous_times = state
with variable_scope.variable_scope("exogenous_noise_increasing_mean"):
mean_addition = layers.fully_connected(
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tensor_shape.dimension_value(start_mean.shape[1]), activation_fn=None)
state_dimension = tensor_shape.dimension_value(start_covariance.shape[1])
with variable_scope.variable_scope("exogenous_noise_increasing_covariance"):
covariance_addition = (
math_utils.transform_to_covariance_matrices(
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return (start_mean + mean_addition,
start_covariance + covariance_addition,
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Cisco-Talos/moflow | ed71dfb0540d9e0d7a4c72f0881b58958d573728 | BAP-0.7-moflow/libtracewrap/libtrace/protobuf/python/google/protobuf/internal/wire_format.py | python | _VarUInt64ByteSizeNoTag | (uint64) | return 10 | Returns the number of bytes required to serialize a single varint
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if uint64 <= 0x7f: return 1
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/smtplib.py | python | SMTP.connect | (self, host='localhost', port=0) | return (code, msg) | Connect to a host on a given port.
If the hostname ends with a colon (`:') followed by a number, and
there is no port specified, that suffix will be stripped off and the
number interpreted as the port number to use.
Note: This method is automatically invoked by __init__, if a host is
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"""Connect to a host on a given port.
If the hostname ends with a colon (`:') followed by a number, and
there is no port specified, that suffix will be stripped off and the
number interpreted as the port number to use.
Note: This method is automatically invoked by __init__, if a host is
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"""
if not port and (host.find(':') == host.rfind(':')):
i = host.rfind(':')
if i >= 0:
host, port = host[:i], host[i + 1:]
try:
port = int(port)
except ValueError:
raise socket.error, "nonnumeric port"
if not port:
port = self.default_port
if self.debuglevel > 0:
print>>stderr, 'connect:', (host, port)
self.sock = self._get_socket(host, port, self.timeout)
(code, msg) = self.getreply()
if self.debuglevel > 0:
print>>stderr, "connect:", msg
return (code, msg) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | chrome/common/extensions/docs/server2/compiled_file_system.py | python | CompiledFileSystem._RecursiveList | (self, path) | return Future(delegate=Gettable(resolve)) | Returns a Future containing the recursive directory listing of |path| as
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def split_dirs_from_files(paths):
'''Returns a tuple (dirs, files) where |dirs| contains the directory
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'''
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if not first_layer_dirs:
return Future(value=first_layer_files)
second_layer_listing = self._file_system.Read(
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def resolve():
def get_from_future_listing(futures):
'''Recursively lists files from directory listing |futures|.
'''
dirs, files = [], []
for dir_name, listing in futures.Get().iteritems():
new_dirs, new_files = split_dirs_from_files(listing)
# |dirs| are paths for reading. Add the full prefix relative to
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dirs += add_prefix(dir_name, new_dirs)
# |files| are not for reading, they are for returning to the caller.
# This entire function set (i.e. GetFromFileListing) is defined to
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files += add_prefix(dir_name[len(path):], new_files)
if dirs:
files += get_from_future_listing(self._file_system.Read(dirs))
return files
return first_layer_files + get_from_future_listing(second_layer_listing)
return Future(delegate=Gettable(resolve)) | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/pyyaml/lib/yaml/__init__.py | python | YAMLObject.to_yaml | (cls, dumper, data) | return dumper.represent_yaml_object(cls.yaml_tag, data, cls,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/propgrid.py | python | PropertyGridManager.SetPageSplitterLeft | (*args, **kwargs) | return _propgrid.PropertyGridManager_SetPageSplitterLeft(*args, **kwargs) | SetPageSplitterLeft(self, int page, bool subProps=False) | SetPageSplitterLeft(self, int page, bool subProps=False) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/propgrid.py#L3586-L3588 | |
deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/algorithms/dqn.py | python | DQN.__init__ | (self,
session,
player_id,
state_representation_size,
num_actions,
hidden_layers_sizes=128,
replay_buffer_capacity=10000,
batch_size=128,
replay_buffer_class=ReplayBuffer,
learning_rate=0.01,
update_target_network_every=1000,
learn_every=10,
discount_factor=1.0,
min_buffer_size_to_learn=1000,
epsilon_start=1.0,
epsilon_end=0.1,
epsilon_decay_duration=int(1e6),
optimizer_str="sgd",
loss_str="mse") | Initialize the DQN agent. | Initialize the DQN agent. | [
"Initialize",
"the",
"DQN",
"agent",
"."
] | def __init__(self,
session,
player_id,
state_representation_size,
num_actions,
hidden_layers_sizes=128,
replay_buffer_capacity=10000,
batch_size=128,
replay_buffer_class=ReplayBuffer,
learning_rate=0.01,
update_target_network_every=1000,
learn_every=10,
discount_factor=1.0,
min_buffer_size_to_learn=1000,
epsilon_start=1.0,
epsilon_end=0.1,
epsilon_decay_duration=int(1e6),
optimizer_str="sgd",
loss_str="mse"):
"""Initialize the DQN agent."""
# This call to locals() is used to store every argument used to initialize
# the class instance, so it can be copied with no hyperparameter change.
self._kwargs = locals()
self.player_id = player_id
self._session = session
self._num_actions = num_actions
if isinstance(hidden_layers_sizes, int):
hidden_layers_sizes = [hidden_layers_sizes]
self._layer_sizes = hidden_layers_sizes
self._batch_size = batch_size
self._update_target_network_every = update_target_network_every
self._learn_every = learn_every
self._min_buffer_size_to_learn = min_buffer_size_to_learn
self._discount_factor = discount_factor
self._epsilon_start = epsilon_start
self._epsilon_end = epsilon_end
self._epsilon_decay_duration = epsilon_decay_duration
# TODO(author6) Allow for optional replay buffer config.
if not isinstance(replay_buffer_capacity, int):
raise ValueError("Replay buffer capacity not an integer.")
self._replay_buffer = replay_buffer_class(replay_buffer_capacity)
self._prev_timestep = None
self._prev_action = None
# Step counter to keep track of learning, eps decay and target network.
self._step_counter = 0
# Keep track of the last training loss achieved in an update step.
self._last_loss_value = None
# Create required TensorFlow placeholders to perform the Q-network updates.
self._info_state_ph = tf.placeholder(
shape=[None, state_representation_size],
dtype=tf.float32,
name="info_state_ph")
self._action_ph = tf.placeholder(
shape=[None], dtype=tf.int32, name="action_ph")
self._reward_ph = tf.placeholder(
shape=[None], dtype=tf.float32, name="reward_ph")
self._is_final_step_ph = tf.placeholder(
shape=[None], dtype=tf.float32, name="is_final_step_ph")
self._next_info_state_ph = tf.placeholder(
shape=[None, state_representation_size],
dtype=tf.float32,
name="next_info_state_ph")
self._legal_actions_mask_ph = tf.placeholder(
shape=[None, num_actions],
dtype=tf.float32,
name="legal_actions_mask_ph")
self._q_network = simple_nets.MLP(state_representation_size,
self._layer_sizes, num_actions)
self._q_values = self._q_network(self._info_state_ph)
self._target_q_network = simple_nets.MLP(state_representation_size,
self._layer_sizes, num_actions)
self._target_q_values = self._target_q_network(self._next_info_state_ph)
# Stop gradient to prevent updates to the target network while learning
self._target_q_values = tf.stop_gradient(self._target_q_values)
self._update_target_network = self._create_target_network_update_op(
self._q_network, self._target_q_network)
# Create the loss operations.
# Sum a large negative constant to illegal action logits before taking the
# max. This prevents illegal action values from being considered as target.
illegal_actions = 1 - self._legal_actions_mask_ph
illegal_logits = illegal_actions * ILLEGAL_ACTION_LOGITS_PENALTY
max_next_q = tf.reduce_max(
tf.math.add(tf.stop_gradient(self._target_q_values), illegal_logits),
axis=-1)
target = (
self._reward_ph +
(1 - self._is_final_step_ph) * self._discount_factor * max_next_q)
action_indices = tf.stack(
[tf.range(tf.shape(self._q_values)[0]), self._action_ph], axis=-1)
predictions = tf.gather_nd(self._q_values, action_indices)
self._savers = [("q_network", tf.train.Saver(self._q_network.variables)),
("target_q_network",
tf.train.Saver(self._target_q_network.variables))]
if loss_str == "mse":
loss_class = tf.losses.mean_squared_error
elif loss_str == "huber":
loss_class = tf.losses.huber_loss
else:
raise ValueError("Not implemented, choose from 'mse', 'huber'.")
self._loss = tf.reduce_mean(
loss_class(labels=target, predictions=predictions))
if optimizer_str == "adam":
self._optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
elif optimizer_str == "sgd":
self._optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate)
else:
raise ValueError("Not implemented, choose from 'adam' and 'sgd'.")
self._learn_step = self._optimizer.minimize(self._loss)
self._initialize() | [
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... | https://github.com/deepmind/open_spiel/blob/4ca53bea32bb2875c7385d215424048ae92f78c8/open_spiel/python/algorithms/dqn.py#L47-L174 | ||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/integrate/_ode.py | python | _transform_banded_jac | (bjac) | return newjac | Convert a real matrix of the form (for example)
[0 0 A B] [0 0 0 B]
[0 0 C D] [0 0 A D]
[E F G H] to [0 F C H]
[I J K L] [E J G L]
[I 0 K 0]
That is, every other column is shifted up one. | Convert a real matrix of the form (for example) | [
"Convert",
"a",
"real",
"matrix",
"of",
"the",
"form",
"(",
"for",
"example",
")"
] | def _transform_banded_jac(bjac):
"""
Convert a real matrix of the form (for example)
[0 0 A B] [0 0 0 B]
[0 0 C D] [0 0 A D]
[E F G H] to [0 F C H]
[I J K L] [E J G L]
[I 0 K 0]
That is, every other column is shifted up one.
"""
# Shift every other column.
newjac = zeros((bjac.shape[0] + 1, bjac.shape[1]))
newjac[1:, ::2] = bjac[:, ::2]
newjac[:-1, 1::2] = bjac[:, 1::2]
return newjac | [
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bristolcrypto/SPDZ-2 | 721abfae849625a02ea49aabc534f9cf41ca643f | Compiler/program.py | python | Program.curr_tape | (self) | return self._curr_tape | The tape that is currently running. | The tape that is currently running. | [
"The",
"tape",
"that",
"is",
"currently",
"running",
"."
] | def curr_tape(self):
""" The tape that is currently running."""
if self._curr_tape is None:
# Create a new main thread if necessary
self.restart_main_thread()
return self._curr_tape | [
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")",
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"_curr_tape"
] | https://github.com/bristolcrypto/SPDZ-2/blob/721abfae849625a02ea49aabc534f9cf41ca643f/Compiler/program.py#L315-L320 | |
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/protobuf/python/google/protobuf/descriptor_pool.py | python | DescriptorPool._ConvertEnumDescriptor | (self, enum_proto, package=None, file_desc=None,
containing_type=None, scope=None) | return desc | Make a protobuf EnumDescriptor given an EnumDescriptorProto protobuf.
Args:
enum_proto: The descriptor_pb2.EnumDescriptorProto protobuf message.
package: Optional package name for the new message EnumDescriptor.
file_desc: The file containing the enum descriptor.
containing_type: The type containing this enum.
scope: Scope containing available types.
Returns:
The added descriptor | Make a protobuf EnumDescriptor given an EnumDescriptorProto protobuf. | [
"Make",
"a",
"protobuf",
"EnumDescriptor",
"given",
"an",
"EnumDescriptorProto",
"protobuf",
"."
] | def _ConvertEnumDescriptor(self, enum_proto, package=None, file_desc=None,
containing_type=None, scope=None):
"""Make a protobuf EnumDescriptor given an EnumDescriptorProto protobuf.
Args:
enum_proto: The descriptor_pb2.EnumDescriptorProto protobuf message.
package: Optional package name for the new message EnumDescriptor.
file_desc: The file containing the enum descriptor.
containing_type: The type containing this enum.
scope: Scope containing available types.
Returns:
The added descriptor
"""
if package:
enum_name = '.'.join((package, enum_proto.name))
else:
enum_name = enum_proto.name
if file_desc is None:
file_name = None
else:
file_name = file_desc.name
values = [self._MakeEnumValueDescriptor(value, index)
for index, value in enumerate(enum_proto.value)]
desc = descriptor.EnumDescriptor(name=enum_proto.name,
full_name=enum_name,
filename=file_name,
file=file_desc,
values=values,
containing_type=containing_type,
options=enum_proto.options)
scope['.%s' % enum_name] = desc
self._enum_descriptors[enum_name] = desc
return desc | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/mox.py | python | MockMethod.GetPossibleGroup | (self) | return group | Returns a possible group from the end of the call queue or None if no
other methods are on the stack. | Returns a possible group from the end of the call queue or None if no
other methods are on the stack. | [
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"queue",
"or",
"None",
"if",
"no",
"other",
"methods",
"are",
"on",
"the",
"stack",
"."
] | def GetPossibleGroup(self):
"""Returns a possible group from the end of the call queue or None if no
other methods are on the stack.
"""
# Remove this method from the tail of the queue so we can add it to a group.
this_method = self._call_queue.pop()
assert this_method == self
# Determine if the tail of the queue is a group, or just a regular ordered
# mock method.
group = None
try:
group = self._call_queue[-1]
except IndexError:
pass
return group | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/ndarray/ndarray.py | python | maximum | (lhs, rhs) | return _ufunc_helper(
lhs,
rhs,
op.broadcast_maximum,
lambda x, y: x if x > y else y,
_internal._maximum_scalar,
None) | Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32) | Returns element-wise maximum of the input arrays with broadcasting. | [
"Returns",
"element",
"-",
"wise",
"maximum",
"of",
"the",
"input",
"arrays",
"with",
"broadcasting",
"."
] | def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_maximum,
lambda x, y: x if x > y else y,
_internal._maximum_scalar,
None) | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/dataframe.py | python | DataFrame._prepare_for_rowwise_op | (self, method, skipna) | return coerced, mask, common_dtype | Prepare a DataFrame for CuPy-based row-wise operations. | Prepare a DataFrame for CuPy-based row-wise operations. | [
"Prepare",
"a",
"DataFrame",
"for",
"CuPy",
"-",
"based",
"row",
"-",
"wise",
"operations",
"."
] | def _prepare_for_rowwise_op(self, method, skipna):
"""Prepare a DataFrame for CuPy-based row-wise operations."""
if method not in _cupy_nan_methods_map and any(
col.nullable for col in self._columns
):
msg = (
f"Row-wise operations to calculate '{method}' do not "
f"currently support columns with null values. "
f"Consider removing them with .dropna() "
f"or using .fillna()."
)
raise ValueError(msg)
is_pure_dt = all(is_datetime_dtype(dt) for dt in self.dtypes)
if not is_pure_dt:
filtered = self.select_dtypes(include=[np.number, np.bool_])
else:
filtered = self.copy(deep=False)
common_dtype = find_common_type(filtered.dtypes)
if filtered._num_columns < self._num_columns:
msg = (
"Row-wise operations currently only support int, float "
"and bool dtypes. Non numeric columns are ignored."
)
warnings.warn(msg)
if not skipna and any(col.nullable for col in filtered._columns):
mask = DataFrame(
{
name: filtered._data[name]._get_mask_as_column()
if filtered._data[name].nullable
else column.full(len(filtered._data[name]), True)
for name in filtered._data.names
}
)
mask = mask.all(axis=1)
else:
mask = None
coerced = filtered.astype(common_dtype, copy=False)
if is_pure_dt:
# Further convert into cupy friendly types
coerced = coerced.astype("int64", copy=False)
return coerced, mask, common_dtype | [
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0ad/0ad | f58db82e0e925016d83f4e3fa7ca599e3866e2af | source/tools/i18n/generateDebugTranslation.py | python | generate_long_strings | (root_path, input_file_name, output_file_name, languages=None) | Generate the 'long strings' debug catalog.
This catalog contains the longest singular and plural string,
found amongst all translated languages or a filtered subset.
It can be used to check if GUI elements are large enough.
The catalog is long.*.po | Generate the 'long strings' debug catalog.
This catalog contains the longest singular and plural string,
found amongst all translated languages or a filtered subset.
It can be used to check if GUI elements are large enough.
The catalog is long.*.po | [
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"amongst",
"all",
"translated",
"languages",
"or",
"a",
"filtered",
"subset",
".",
"It",
"can",
"be"... | def generate_long_strings(root_path, input_file_name, output_file_name, languages=None):
"""
Generate the 'long strings' debug catalog.
This catalog contains the longest singular and plural string,
found amongst all translated languages or a filtered subset.
It can be used to check if GUI elements are large enough.
The catalog is long.*.po
"""
print("Generating", output_file_name)
input_file_path = os.path.join(root_path, input_file_name)
output_file_path = os.path.join(root_path, output_file_name)
template_catalog = Catalog.readFrom(input_file_path)
# Pretend we write English to get plurals.
long_string_catalog = Catalog(locale="en")
# Fill catalog with English strings.
for message in template_catalog:
long_string_catalog.add(
id=message.id, string=message.id, context=message.context)
# Load existing translation catalogs.
existing_translation_catalogs = getCatalogs(input_file_path, languages)
# If any existing translation has more characters than the average expansion, use that instead.
for translation_catalog in existing_translation_catalogs:
for long_string_catalog_message in long_string_catalog:
translation_message = translation_catalog.get(
long_string_catalog_message.id, long_string_catalog_message.context)
if not translation_message or not translation_message.string:
continue
if not long_string_catalog_message.pluralizable or not translation_message.pluralizable:
if len(translation_message.string) > len(long_string_catalog_message.string):
long_string_catalog_message.string = translation_message.string
continue
longest_singular_string = translation_message.string[0]
longest_plural_string = translation_message.string[1 if len(
translation_message.string) > 1 else 0]
candidate_singular_string = long_string_catalog_message.string[0]
# There might be between 0 and infinite plural forms.
candidate_plural_string = ""
for candidate_string in long_string_catalog_message.string[1:]:
if len(candidate_string) > len(candidate_plural_string):
candidate_plural_string = candidate_string
changed = False
if len(candidate_singular_string) > len(longest_singular_string):
longest_singular_string = candidate_singular_string
changed = True
if len(candidate_plural_string) > len(longest_plural_string):
longest_plural_string = candidate_plural_string
changed = True
if changed:
long_string_catalog_message.string = [
longest_singular_string, longest_plural_string]
translation_message = long_string_catalog_message
long_string_catalog.writeTo(output_file_path) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py | python | LogisticRegressor.get_default_metrics | (cls, thresholds=None) | return metrics | Returns a dictionary of basic metrics for logistic regression.
Args:
thresholds: List of floating point thresholds to use for accuracy,
precision, and recall metrics. If None, defaults to [0.5].
Returns:
Dictionary mapping metrics string names to metrics functions. | Returns a dictionary of basic metrics for logistic regression. | [
"Returns",
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] | def get_default_metrics(cls, thresholds=None):
"""Returns a dictionary of basic metrics for logistic regression.
Args:
thresholds: List of floating point thresholds to use for accuracy,
precision, and recall metrics. If None, defaults to [0.5].
Returns:
Dictionary mapping metrics string names to metrics functions.
"""
if thresholds is None:
thresholds = [0.5]
metrics = {}
metrics[cls.PREDICTION_MEAN] = _predictions_streaming_mean
metrics[cls.TARGET_MEAN] = _targets_streaming_mean
# Also include the streaming mean of the label as an accuracy baseline, as
# a reminder to users.
metrics[cls.ACCURACY_BASELINE] = _targets_streaming_mean
metrics[cls.AUC] = metrics_lib.streaming_auc
for threshold in thresholds:
metrics[cls.ACCURACY_MEAN % threshold] = _make_streaming_with_threshold(
metrics_lib.streaming_accuracy, threshold)
# Precision for positive examples.
metrics[cls.PRECISION_MEAN % threshold] = _make_streaming_with_threshold(
metrics_lib.streaming_precision, threshold)
# Recall for positive examples.
metrics[cls.RECALL_MEAN % threshold] = _make_streaming_with_threshold(
metrics_lib.streaming_recall, threshold)
return metrics | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | buildscripts/cpplint.py | python | _BlockInfo.CheckEnd | (self, filename, clean_lines, linenum, error) | Run checks that applies to text after the closing brace.
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Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
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This is mostly used for checking end of namespace comments.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/nyan/nyan_structs.py | python | NyanObject._prepare_inheritance_content | (self, import_tree=None) | return output_str | Returns a string containing the nyan object's inheritance set
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output_str = "("
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sfqon = ".".join(import_tree.get_alias_fqon(
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else:
sfqon = ".".join(parent.get_fqon())
output_str += f"{sfqon}, "
output_str = output_str[:-2]
output_str += "):\n"
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amrayn/easyloggingpp | 8489989bb26c6371df103f6cbced3fbee1bc3c2f | tools/cpplint.py | python | _VerboseLevel | () | return _cpplint_state.verbose_level | Returns the module's verbosity setting. | Returns the module's verbosity setting. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/distutils/archive_util.py | python | make_zipfile | (base_name, base_dir, verbose=0, dry_run=0) | return zip_filename | Create a zip file from all the files under 'base_dir'.
The output zip file will be named 'base_name' + ".zip". Uses either the
"zipfile" Python module (if available) or the InfoZIP "zip" utility
(if installed and found on the default search path). If neither tool is
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] | def make_zipfile(base_name, base_dir, verbose=0, dry_run=0):
"""Create a zip file from all the files under 'base_dir'.
The output zip file will be named 'base_name' + ".zip". Uses either the
"zipfile" Python module (if available) or the InfoZIP "zip" utility
(if installed and found on the default search path). If neither tool is
available, raises DistutilsExecError. Returns the name of the output zip
file.
"""
try:
import zipfile
except ImportError:
zipfile = None
zip_filename = base_name + ".zip"
mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
# If zipfile module is not available, try spawning an external
# 'zip' command.
if zipfile is None:
if verbose:
zipoptions = "-r"
else:
zipoptions = "-rq"
try:
spawn(["zip", zipoptions, zip_filename, base_dir],
dry_run=dry_run)
except DistutilsExecError:
# XXX really should distinguish between "couldn't find
# external 'zip' command" and "zip failed".
raise DistutilsExecError, \
("unable to create zip file '%s': "
"could neither import the 'zipfile' module nor "
"find a standalone zip utility") % zip_filename
else:
log.info("creating '%s' and adding '%s' to it",
zip_filename, base_dir)
if not dry_run:
zip = zipfile.ZipFile(zip_filename, "w",
compression=zipfile.ZIP_DEFLATED)
if base_dir != os.curdir:
path = os.path.normpath(os.path.join(base_dir, ''))
zip.write(path, path)
log.info("adding '%s'", path)
for dirpath, dirnames, filenames in os.walk(base_dir):
for name in dirnames:
path = os.path.normpath(os.path.join(dirpath, name, ''))
zip.write(path, path)
log.info("adding '%s'", path)
for name in filenames:
path = os.path.normpath(os.path.join(dirpath, name))
if os.path.isfile(path):
zip.write(path, path)
log.info("adding '%s'" % path)
zip.close()
return zip_filename | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/storage_uri.py | python | BucketStorageUri.is_cloud_uri | (self) | return True | Returns True if this URI names a bucket or object. | Returns True if this URI names a bucket or object. | [
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"""Returns True if this URI names a bucket or object."""
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MegEngine/MegEngine | ce9ad07a27ec909fb8db4dd67943d24ba98fb93a | imperative/python/megengine/dtr/dtr.py | python | enable_sqrt_sampling | (mod) | return _enable_sqrt_sampling | r"""Get or set whether sqrt sampling is allowed. Sqrt sampling means that given
the size of the candidate set is N, only enumerate sqrt(N) tensors. When
the number of tensors is very high, enabling this optimization will speed
up the training.
Examples:
.. code-block::
import megengine as mge
mge.dtr.enable_sqrt_sampling = True | r"""Get or set whether sqrt sampling is allowed. Sqrt sampling means that given
the size of the candidate set is N, only enumerate sqrt(N) tensors. When
the number of tensors is very high, enabling this optimization will speed
up the training.
Examples:
.. code-block:: | [
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r"""Get or set whether sqrt sampling is allowed. Sqrt sampling means that given
the size of the candidate set is N, only enumerate sqrt(N) tensors. When
the number of tensors is very high, enabling this optimization will speed
up the training.
Examples:
.. code-block::
import megengine as mge
mge.dtr.enable_sqrt_sampling = True
"""
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/misc/doccer.py | python | inherit_docstring_from | (cls) | return _doc | This decorator modifies the decorated function's docstring by
replacing occurrences of '%(super)s' with the docstring of the
method of the same name from the class `cls`.
If the decorated method has no docstring, it is simply given the
docstring of `cls`s method.
Parameters
----------
cls : Python class or instance
A class with a method with the same name as the decorated method.
The docstring of the method in this class replaces '%(super)s' in the
docstring of the decorated method.
Returns
-------
f : function
The decorator function that modifies the __doc__ attribute
of its argument.
Examples
--------
In the following, the docstring for Bar.func created using the
docstring of `Foo.func`.
>>> class Foo(object):
... def func(self):
... '''Do something useful.'''
... return
...
>>> class Bar(Foo):
... @inherit_docstring_from(Foo)
... def func(self):
... '''%(super)s
... Do it fast.
... '''
... return
...
>>> b = Bar()
>>> b.func.__doc__
'Do something useful.\n Do it fast.\n ' | This decorator modifies the decorated function's docstring by
replacing occurrences of '%(super)s' with the docstring of the
method of the same name from the class `cls`. | [
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This decorator modifies the decorated function's docstring by
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If the decorated method has no docstring, it is simply given the
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In the following, the docstring for Bar.func created using the
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>>> class Foo(object):
... def func(self):
... '''Do something useful.'''
... return
...
>>> class Bar(Foo):
... @inherit_docstring_from(Foo)
... def func(self):
... '''%(super)s
... Do it fast.
... '''
... return
...
>>> b = Bar()
>>> b.func.__doc__
'Do something useful.\n Do it fast.\n '
"""
def _doc(func):
cls_docstring = getattr(cls, func.__name__).__doc__
func_docstring = func.__doc__
if func_docstring is None:
func.__doc__ = cls_docstring
else:
new_docstring = func_docstring % dict(super=cls_docstring)
func.__doc__ = new_docstring
return func
return _doc | [
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/ivy_parser.py | python | p_loc | (p) | loc : | loc : | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/ma/core.py | python | put | (a, indices, values, mode='raise') | Set storage-indexed locations to corresponding values.
This function is equivalent to `MaskedArray.put`, see that method
for details.
See Also
--------
MaskedArray.put | Set storage-indexed locations to corresponding values. | [
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"""
Set storage-indexed locations to corresponding values.
This function is equivalent to `MaskedArray.put`, see that method
for details.
See Also
--------
MaskedArray.put
"""
# We can't use 'frommethod', the order of arguments is different
try:
return a.put(indices, values, mode=mode)
except AttributeError:
return narray(a, copy=False).put(indices, values, mode=mode) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | RichTextBuffer_SetBulletRightMargin | (*args, **kwargs) | return _richtext.RichTextBuffer_SetBulletRightMargin(*args, **kwargs) | RichTextBuffer_SetBulletRightMargin(int margin) | RichTextBuffer_SetBulletRightMargin(int margin) | [
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"""RichTextBuffer_SetBulletRightMargin(int margin)"""
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ArduPilot/ardupilot | 6e684b3496122b8158ac412b609d00004b7ac306 | Tools/scripts/build_binaries.py | python | build_binaries.read_string_from_filepath | (self, filepath) | return content | returns content of filepath as a string | returns content of filepath as a string | [
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content = fh.read()
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Cantera/cantera | 0119484b261967ccb55a0066c020599cacc312e4 | interfaces/cython/cantera/ctml2yaml.py | python | main | () | Parse command line arguments and pass them to `convert`. | Parse command line arguments and pass them to `convert`. | [
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parser.add_argument("input", help="The input CTML filename. Must be specified.")
parser.add_argument("output", nargs="?", help="The output YAML filename. Optional.")
if len(sys.argv) not in [2, 3]:
if len(sys.argv) > 3:
print(
"ctml2yaml.py: error: unrecognized arguments:",
' '.join(sys.argv[3:]),
file=sys.stderr,
)
parser.print_help(sys.stderr)
sys.exit(1)
args = parser.parse_args()
input_file = Path(args.input)
if args.output is None:
output_file = input_file.with_suffix(".yaml")
else:
output_file = Path(args.output)
convert(input_file, output_file) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Wm.wm_overrideredirect | (self, boolean=None) | return self._getboolean(self.tk.call(
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/slim/python/slim/data/data_provider.py | python | DataProvider.__init__ | (self, items_to_tensors, num_samples) | Constructs the Data Provider.
Args:
items_to_tensors: a dictionary of names to tensors.
num_samples: the number of samples in the dataset being provided. | Constructs the Data Provider. | [
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"""Constructs the Data Provider.
Args:
items_to_tensors: a dictionary of names to tensors.
num_samples: the number of samples in the dataset being provided.
"""
self._items_to_tensors = items_to_tensors
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | EventLoopBase.WakeUpIdle | (*args, **kwargs) | return _core_.EventLoopBase_WakeUpIdle(*args, **kwargs) | WakeUpIdle(self) | WakeUpIdle(self) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | _CppLintState.PrintErrorCounts | (self) | Print a summary of errors by category, and the total. | Print a summary of errors by category, and the total. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/imaplib.py | python | IMAP4.xatom | (self, name, *args) | return self._simple_command(name, *args) | Allow simple extension commands
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name = name.upper()
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tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/deephol/utilities/stats.py | python | merge_aggregate_stat | (target: deephol_stat_pb2.ProofAggregateStat,
source: deephol_stat_pb2.ProofAggregateStat) | Merge two aggregated statistics.
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target: Aggregeated statistics to be updated.
source: Statistics to be merged in. | Merge two aggregated statistics. | [
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source: deephol_stat_pb2.ProofAggregateStat):
"""Merge two aggregated statistics.
Args:
target: Aggregeated statistics to be updated.
source: Statistics to be merged in.
"""
target.num_theorems_attempted += source.num_theorems_attempted
target.num_theorems_proved += source.num_theorems_proved
target.num_theorems_with_bad_proof += source.num_theorems_with_bad_proof
target.num_nodes += source.num_nodes
target.num_reduced_nodes += source.num_reduced_nodes
target.num_closed_nodes += source.num_closed_nodes
target.time_spent_milliseconds += source.time_spent_milliseconds
merge_log_scale_histograms(target.proof_time_histogram,
source.proof_time_histogram)
merge_log_scale_histograms(target.proof_time_histogram_proved,
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merge_log_scale_histograms(target.proof_time_histogram_failed,
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merge_proof_tapp_stats(target.tapp_stat, source.tapp_stat)
merge_histograms(target.num_reduced_nodes_distribution,
source.num_reduced_nodes_distribution)
target.total_prediction_time += source.total_prediction_time
merge_log_scale_histograms(target.proof_prediction_time_histogram,
source.proof_prediction_time_histogram)
merge_log_scale_histograms(target.node_prediction_time_histogram,
source.node_prediction_time_histogram)
target.proof_closed_after_millis.extend(source.proof_closed_after_millis) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/D7AbsoluteCrossSections.py | python | D7AbsoluteCrossSections._read_experiment_properties | (self, ws) | Reads the user-provided dictionary that contains sample geometry (type, dimensions) and experimental conditions,
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self._sampleAndEnvironmentProperties = self.getProperty('SampleAndEnvironmentProperties').value
if 'InitialEnergy' not in self._sampleAndEnvironmentProperties:
h = physical_constants['Planck constant'][0] # in m^2 kg / s
neutron_mass = physical_constants['neutron mass'][0] # in0 kg
wavelength = mtd[ws][0].getRun().getLogData('monochromator.wavelength').value * 1e-10 # in m
joules_to_mev = 1e3 / physical_constants['electron volt'][0]
self._sampleAndEnvironmentProperties['InitialEnergy'] = \
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if self.getPropertyValue('NormalisationMethod') != 'None' and 'NMoles' not in self._sampleAndEnvironmentProperties:
sample_mass = self._sampleAndEnvironmentProperties['SampleMass'].value
formula_unit_mass = self._sampleAndEnvironmentProperties['FormulaUnitMass'].value
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Komnomnomnom/swigibpy | cfd307fdbfaffabc69a2dc037538d7e34a8b8daf | swigibpy.py | python | ComboLegList.size | (self) | return _swigibpy.ComboLegList_size(self) | size(ComboLegList self) -> std::vector< shared_ptr< ComboLeg > >::size_type | size(ComboLegList self) -> std::vector< shared_ptr< ComboLeg > >::size_type | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | cmake/developer_package/cpplint/cpplint.py | python | FileInfo.Split | (self) | return (project,) + os.path.splitext(rest) | Splits the file into the directory, basename, and extension.
For 'chrome/browser/browser.cc', Split() would
return ('chrome/browser', 'browser', '.cc')
Returns:
A tuple of (directory, basename, extension). | Splits the file into the directory, basename, and extension. | [
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A tuple of (directory, basename, extension).
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/urllib3/_collections.py | python | HTTPHeaderDict.itermerged | (self) | Iterate over all headers, merging duplicate ones together. | Iterate over all headers, merging duplicate ones together. | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBTypeNameSpecifier.__eq__ | (self, *args) | return _lldb.SBTypeNameSpecifier___eq__(self, *args) | __eq__(self, SBTypeNameSpecifier rhs) -> bool | __eq__(self, SBTypeNameSpecifier rhs) -> bool | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/otci/otci/otci.py | python | OTCI.srp_client_get_host_state | (self) | return self.__parse_str(self.execute_command('srp client host state')) | Get SRP client host state. | Get SRP client host state. | [
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google/clif | cab24d6a105609a65c95a36a1712ae3c20c7b5df | clif/pyclif.py | python | _GetHeaders | (ast) | return wrap_header | Scan AST for header files. | Scan AST for header files. | [
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# It's not moved to astutils yet because of asserts.
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if not included:
return None
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CaoWGG/TensorRT-CenterNet | f949252e37b51e60f873808f46d3683f15735e79 | onnx-tensorrt/third_party/onnx/third_party/pybind11/tools/clang/cindex.py | python | Type.get_size | (self) | return conf.lib.clang_Type_getSizeOf(self) | Retrieve the size of the record. | Retrieve the size of the record. | [
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Retrieve the size of the record.
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pybind/pybind11 | 6493f496e30c80f004772c906370c8f4db94b6ec | pybind11/setup_helpers.py | python | tmp_chdir | () | Prepare and enter a temporary directory, cleanup when done | Prepare and enter a temporary directory, cleanup when done | [
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try:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | StatusBar.SetFields | (self, items) | Set the values of the statusbar fields from a list of strings. | Set the values of the statusbar fields from a list of strings. | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | xmlDoc.htmlDocContentDumpOutput | (self, buf, encoding) | Dump an HTML document. Formating return/spaces are added. | Dump an HTML document. Formating return/spaces are added. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PropertyGridInterface.SetPropertyValidator | (*args, **kwargs) | return _propgrid.PropertyGridInterface_SetPropertyValidator(*args, **kwargs) | SetPropertyValidator(self, PGPropArg id, Validator validator) | SetPropertyValidator(self, PGPropArg id, Validator validator) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/core/numeric.py | python | normalize_axis_tuple | (axis, ndim, argname=None, allow_duplicate=False) | return axis | Normalizes an axis argument into a tuple of non-negative integer axes.
This handles shorthands such as ``1`` and converts them to ``(1,)``,
as well as performing the handling of negative indices covered by
`normalize_axis_index`.
By default, this forbids axes from being specified multiple times.
Used internally by multi-axis-checking logic.
.. versionadded:: 1.13.0
Parameters
----------
axis : int, iterable of int
The un-normalized index or indices of the axis.
ndim : int
The number of dimensions of the array that `axis` should be normalized
against.
argname : str, optional
A prefix to put before the error message, typically the name of the
argument.
allow_duplicate : bool, optional
If False, the default, disallow an axis from being specified twice.
Returns
-------
normalized_axes : tuple of int
The normalized axis index, such that `0 <= normalized_axis < ndim`
Raises
------
AxisError
If any axis provided is out of range
ValueError
If an axis is repeated
See also
--------
normalize_axis_index : normalizing a single scalar axis | Normalizes an axis argument into a tuple of non-negative integer axes. | [
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"""
Normalizes an axis argument into a tuple of non-negative integer axes.
This handles shorthands such as ``1`` and converts them to ``(1,)``,
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By default, this forbids axes from being specified multiple times.
Used internally by multi-axis-checking logic.
.. versionadded:: 1.13.0
Parameters
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axis : int, iterable of int
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normalize_axis_index : normalizing a single scalar axis
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# Optimization to speed-up the most common cases.
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if not allow_duplicate and len(set(axis)) != len(axis):
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/Input/BlockEditor.py | python | BlockEditor._applyAndClose | (self) | Apply any changes the user has made then close the window | Apply any changes the user has made then close the window | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | PageSetupDialogData.GetDefaultMinMargins | (*args, **kwargs) | return _windows_.PageSetupDialogData_GetDefaultMinMargins(*args, **kwargs) | GetDefaultMinMargins(self) -> bool | GetDefaultMinMargins(self) -> bool | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/distutils/misc_util.py | python | Configuration.set_options | (self, **options) | Configure Configuration instance.
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Configure Configuration instance.
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"""
for key, value in options.items():
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self.options[key] = value
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/html5lib/treebuilders/base.py | python | Node.insertBefore | (self, node, refNode) | Insert node as a child of the current node, before refNode in the
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/memory_stats/python/ops/memory_stats_ops.py | python | BytesLimit | () | return gen_memory_stats_ops.bytes_limit() | Generates an op that measures the total memory (in bytes) of a device. | Generates an op that measures the total memory (in bytes) of a device. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/aui/aui_utilities.py | python | IndentPressedBitmap | (rect, button_state) | return rect | Indents the input rectangle `rect` based on the value of `button_state`.
:param Rect `rect`: the button bitmap rectangle;
:param integer `button_state`: the button state. | Indents the input rectangle `rect` based on the value of `button_state`. | [
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Indents the input rectangle `rect` based on the value of `button_state`.
:param Rect `rect`: the button bitmap rectangle;
:param integer `button_state`: the button state.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/metrics/pairwise.py | python | pairwise_distances | (X, Y=None, metric="euclidean", n_jobs=None,
force_all_finite=True, **kwds) | return _parallel_pairwise(X, Y, func, n_jobs, **kwds) | Compute the distance matrix from a vector array X and optional Y.
This method takes either a vector array or a distance matrix, and returns
a distance matrix. If the input is a vector array, the distances are
computed. If the input is a distances matrix, it is returned instead.
This method provides a safe way to take a distance matrix as input, while
preserving compatibility with many other algorithms that take a vector
array.
If Y is given (default is None), then the returned matrix is the pairwise
distance between the arrays from both X and Y.
Valid values for metric are:
- From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
'manhattan']. These metrics support sparse matrix
inputs.
['nan_euclidean'] but it does not yet support sparse matrices.
- From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis',
'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']
See the documentation for scipy.spatial.distance for details on these
metrics. These metrics do not support sparse matrix inputs.
Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are
valid scipy.spatial.distance metrics), the scikit-learn implementation
will be used, which is faster and has support for sparse matrices (except
for 'cityblock'). For a verbose description of the metrics from
scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics
function.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
Y : array [n_samples_b, n_features], optional
An optional second feature array. Only allowed if
metric != "precomputed".
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by scipy.spatial.distance.pdist for its metric parameter, or
a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
If metric is "precomputed", X is assumed to be a distance matrix.
Alternatively, if metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays from X as input and return a value indicating
the distance between them.
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
force_all_finite : boolean or 'allow-nan', (default=True)
Whether to raise an error on np.inf and np.nan in array. The
possibilities are:
- True: Force all values of array to be finite.
- False: accept both np.inf and np.nan in array.
- 'allow-nan': accept only np.nan values in array. Values cannot
be infinite.
.. versionadded:: 0.22
**kwds : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a scipy.spatial.distance metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]
A distance matrix D such that D_{i, j} is the distance between the
ith and jth vectors of the given matrix X, if Y is None.
If Y is not None, then D_{i, j} is the distance between the ith array
from X and the jth array from Y.
See also
--------
pairwise_distances_chunked : performs the same calculation as this
function, but returns a generator of chunks of the distance matrix, in
order to limit memory usage.
paired_distances : Computes the distances between corresponding
elements of two arrays | Compute the distance matrix from a vector array X and optional Y. | [
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force_all_finite=True, **kwds):
""" Compute the distance matrix from a vector array X and optional Y.
This method takes either a vector array or a distance matrix, and returns
a distance matrix. If the input is a vector array, the distances are
computed. If the input is a distances matrix, it is returned instead.
This method provides a safe way to take a distance matrix as input, while
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If Y is given (default is None), then the returned matrix is the pairwise
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Valid values for metric are:
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'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
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See the documentation for scipy.spatial.distance for details on these
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Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are
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will be used, which is faster and has support for sparse matrices (except
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scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics
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Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \
[n_samples_a, n_features] otherwise
Array of pairwise distances between samples, or a feature array.
Y : array [n_samples_b, n_features], optional
An optional second feature array. Only allowed if
metric != "precomputed".
metric : string, or callable
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by scipy.spatial.distance.pdist for its metric parameter, or
a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
If metric is "precomputed", X is assumed to be a distance matrix.
Alternatively, if metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays from X as input and return a value indicating
the distance between them.
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
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Whether to raise an error on np.inf and np.nan in array. The
possibilities are:
- True: Force all values of array to be finite.
- False: accept both np.inf and np.nan in array.
- 'allow-nan': accept only np.nan values in array. Values cannot
be infinite.
.. versionadded:: 0.22
**kwds : optional keyword parameters
Any further parameters are passed directly to the distance function.
If using a scipy.spatial.distance metric, the parameters are still
metric dependent. See the scipy docs for usage examples.
Returns
-------
D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b]
A distance matrix D such that D_{i, j} is the distance between the
ith and jth vectors of the given matrix X, if Y is None.
If Y is not None, then D_{i, j} is the distance between the ith array
from X and the jth array from Y.
See also
--------
pairwise_distances_chunked : performs the same calculation as this
function, but returns a generator of chunks of the distance matrix, in
order to limit memory usage.
paired_distances : Computes the distances between corresponding
elements of two arrays
"""
if (metric not in _VALID_METRICS and
not callable(metric) and metric != "precomputed"):
raise ValueError("Unknown metric %s. "
"Valid metrics are %s, or 'precomputed', or a "
"callable" % (metric, _VALID_METRICS))
if metric == "precomputed":
X, _ = check_pairwise_arrays(X, Y, precomputed=True,
force_all_finite=force_all_finite)
whom = ("`pairwise_distances`. Precomputed distance "
" need to have non-negative values.")
check_non_negative(X, whom=whom)
return X
elif metric in PAIRWISE_DISTANCE_FUNCTIONS:
func = PAIRWISE_DISTANCE_FUNCTIONS[metric]
elif callable(metric):
func = partial(_pairwise_callable, metric=metric,
force_all_finite=force_all_finite, **kwds)
else:
if issparse(X) or issparse(Y):
raise TypeError("scipy distance metrics do not"
" support sparse matrices.")
dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None
if (dtype == bool and
(X.dtype != bool or (Y is not None and Y.dtype != bool))):
msg = "Data was converted to boolean for metric %s" % metric
warnings.warn(msg, DataConversionWarning)
X, Y = check_pairwise_arrays(X, Y, dtype=dtype,
force_all_finite=force_all_finite)
# precompute data-derived metric params
params = _precompute_metric_params(X, Y, metric=metric, **kwds)
kwds.update(**params)
if effective_n_jobs(n_jobs) == 1 and X is Y:
return distance.squareform(distance.pdist(X, metric=metric,
**kwds))
func = partial(distance.cdist, metric=metric, **kwds)
return _parallel_pairwise(X, Y, func, n_jobs, **kwds) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/estimators/head.py | python | loss_only_head | (loss_fn, head_name=None) | return _LossOnlyHead(loss_fn, head_name=head_name) | Creates a Head that contains only loss terms.
Loss only head holds additional loss terms to be added to other heads and
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Args:
loss_fn: a function that takes no argument and returns a list of
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head_name: a name for the head.
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loss_fn: a function that takes no argument and returns a list of
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/timeseries/python/timeseries/model.py | python | SequentialTimeSeriesModel._window_initializer | (self, times, state) | Prepare for training or prediction on a window of data.
Args:
times: A [batch size x window size] Tensor with times for each
observation.
state: Model-dependent state, each with size [batch size x ...]. The
number and type will typically be fixed by the model (for example a
mean and variance).
Returns:
Nothing | Prepare for training or prediction on a window of data. | [
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Args:
times: A [batch size x window size] Tensor with times for each
observation.
state: Model-dependent state, each with size [batch size x ...]. The
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/distributed/ClockDelta.py | python | ClockDelta.networkToLocalTime | (self, networkTime, now = None, bits = 16,
ticksPerSec=NetworkTimePrecision) | return now + float(diff) / ticksPerSec | networkToLocalTime(self, int networkTime)
Converts the indicated networkTime to the corresponding
localTime value. The time is assumed to be within +/- 5
minutes of the current local time given in now, or
getRealTime() if now is not specified. | networkToLocalTime(self, int networkTime) | [
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"""networkToLocalTime(self, int networkTime)
Converts the indicated networkTime to the corresponding
localTime value. The time is assumed to be within +/- 5
minutes of the current local time given in now, or
getRealTime() if now is not specified.
"""
if now is None:
now = self.globalClock.getRealTime()
# Are we in non-real-time mode (i.e. filming a movie)? If you
# set movie-network-time 1, then we'll circumvent this logic
# and always return now.
if self.globalClock.getMode() == ClockObject.MNonRealTime and \
ConfigVariableBool('movie-network-time', False):
return now
# First, determine what network time we have for 'now'.
ntime = int(math.floor(((now - self.delta) * ticksPerSec) + 0.5))
# The signed difference between these is the number of ticks
# by which the network time differs from 'now'.
if bits == 16:
diff = self.__signExtend(networkTime - ntime)
else:
# Assume the bits is either 16 or 32. If it's 32, no need
# to sign-extend. 32 bits gives us about 227 days of
# continuous timestamp.
diff = networkTime - ntime
return now + float(diff) / ticksPerSec | [
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | bindings/pyroot/pythonizations/python/ROOT/_pythonization/_roofit/_roojsonfactorywstool.py | python | RooJSONFactoryWSTool.gendoc | (cls) | return hs3 | Generate the importer and exporter documentation. | Generate the importer and exporter documentation. | [
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hs3[key] = {}
if not "import" in hs3[key]:
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hs3[key]["export"].append(
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"proxies": {str(a): str(b) for a, b in exporter.proxies},
}
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/slim/quantization/quantization_pass.py | python | _get_input_name_index | (op, input_var_name) | return res | Get the input name and index of the var_name in the op | Get the input name and index of the var_name in the op | [
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var_names = op.input(argname)
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/__init__.py | python | NameValueListToDict | (name_value_list) | return result | Takes an array of strings of the form 'NAME=VALUE' and creates a dictionary
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | lldb/examples/python/file_extract.py | python | FileExtract.get_n_sint32 | (self, n, fail_value=0) | Extract "n" int32_t integers from the binary file at the current file position, returns a list of integers | Extract "n" int32_t integers from the binary file at the current file position, returns a list of integers | [
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if s:
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | util/gerrit-bot/gerrit.py | python | GerritRestAPI.list_reviewers | (self, change_id) | return self._get(f"/changes/{change_id}/reviewers") | list reviewers of a change | list reviewers of a change | [
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0957-Prison-Cells-After-N-Days/0957.py | python | Solution.prisonAfterNDays | (self, cells, N) | return cells | :type cells: List[int]
:type N: int
:rtype: List[int] | :type cells: List[int]
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N = N % 14
if not N:
N = 14
for _ in range(N):
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cells[i] = 2 if not cells[i] & 1 else 3
for i in range(len(cells)):
cells[i] >>= 1
return cells | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Rect.Intersects | (*args, **kwargs) | return _core_.Rect_Intersects(*args, **kwargs) | Intersects(self, Rect rect) -> bool
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | prj/app/lib/util/crc16.py | python | Crc16Ccitt.add | (self, byte) | Add a new byte to the CRC engine. 'byte'
should be a python character. E.g: c.add('x') | Add a new byte to the CRC engine. 'byte'
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poly_s = byte ^ (self.state >> 8)
poly_t = poly_s ^ (poly_s >> 4)
result = s16l(self.state, 8) ^ poly_t ^ s16l(poly_t, 5) ^ s16l(poly_t, 12)
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/ptable.py | python | PTable.flatten | (self, **option) | return transforms.flatten(self, **option) | 对于每个Key和Value中的每个元素(value 1, value 2, ... value m)，构造(Key, value 1), (Key, value 2), ... (Key, value m)，结果使用PCollection表示
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google-ar/WebARonTango | e86965d2cbc652156b480e0fcf77c716745578cd | chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py | python | GETnHandler.WriteServiceImplementation | (self, func, f) | Overrriden from TypeHandler. | Overrriden from TypeHandler. | [
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# All except shm_id and shm_offset.
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for arg in all_but_last_args:
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GLsizei num_values = 0;
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shadowed = func.GetInfo('shadowed')
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f.write(' LOCAL_COPY_REAL_GL_ERRORS_TO_WRAPPER("%s");\n' % func.name)
f.write(code)
func.WriteHandlerImplementation(f)
if shadowed:
code = """ result->SetNumResults(num_values);
return error::kNoError;
}
"""
else:
code = """ GLenum error = LOCAL_PEEK_GL_ERROR("%(func_name)s");
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}
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f.write(code % {'func_name': func.name}) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/api/map_job/abstract_datastore_input_reader.py | python | AbstractDatastoreInputReader._get_query_spec | (cls, params) | return model.QuerySpec(
entity_kind=cls._get_raw_entity_kind(entity_kind),
keys_only=bool(params.get(cls.KEYS_ONLY_PARAM, False)),
filters=filters,
batch_size=int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE)),
model_class_path=entity_kind,
app=app,
ns=ns) | Construct a model.QuerySpec from model.MapperSpec. | Construct a model.QuerySpec from model.MapperSpec. | [
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"""Construct a model.QuerySpec from model.MapperSpec."""
entity_kind = params[cls.ENTITY_KIND_PARAM]
filters = params.get(cls.FILTERS_PARAM)
app = params.get(cls._APP_PARAM)
ns = params.get(cls.NAMESPACE_PARAM)
return model.QuerySpec(
entity_kind=cls._get_raw_entity_kind(entity_kind),
keys_only=bool(params.get(cls.KEYS_ONLY_PARAM, False)),
filters=filters,
batch_size=int(params.get(cls.BATCH_SIZE_PARAM, cls._BATCH_SIZE)),
model_class_path=entity_kind,
app=app,
ns=ns) | [
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nasa/astrobee | 9241e67e6692810d6e275abb3165b6d02f4ca5ef | localization/sparse_mapping/tools/build_theia_map.py | python | which | (program) | return None | Find if a program is in the PATH | Find if a program is in the PATH | [
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"""Find if a program is in the PATH"""
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fpath, fname = os.path.split(program)
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return program
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/cpplint.py | python | ReverseCloseExpression | (clean_lines, linenum, pos) | return (line, 0, -1) | If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
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linenum: The number of the line to check.
pos: A position on the line.
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(line, 0, -1) if we never find the matching opening brace. Note
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return is the 'cleansed' line at linenum. | If input points to ) or } or ] or >, finds the position that opens it. | [
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"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
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(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
if line[pos] not in ')}]>':
return (line, 0, -1)
# Check last line
(start_pos, stack) = FindStartOfExpressionInLine(line, pos, [])
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while stack and linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, stack) = FindStartOfExpressionInLine(line, len(line) - 1, stack)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find start of expression before beginning of file, give up
return (line, 0, -1) | [
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tangzhenyu/Scene-Text-Understanding | 0f7ffc7aea5971a50cdc03d33d0a41075285948b | ctpn_crnn_ocr/CTPN/caffe/scripts/cpp_lint.py | python | CheckForHeaderGuard | (filename, lines, error) | Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found. | Checks that the file contains a header guard. | [
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] | def CheckForHeaderGuard(filename, lines, error):
"""Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
cppvar = GetHeaderGuardCPPVariable(filename)
ifndef = None
ifndef_linenum = 0
define = None
endif = None
endif_linenum = 0
for linenum, line in enumerate(lines):
linesplit = line.split()
if len(linesplit) >= 2:
# find the first occurrence of #ifndef and #define, save arg
if not ifndef and linesplit[0] == '#ifndef':
# set ifndef to the header guard presented on the #ifndef line.
ifndef = linesplit[1]
ifndef_linenum = linenum
if not define and linesplit[0] == '#define':
define = linesplit[1]
# find the last occurrence of #endif, save entire line
if line.startswith('#endif'):
endif = line
endif_linenum = linenum
if not ifndef:
error(filename, 0, 'build/header_guard', 5,
'No #ifndef header guard found, suggested CPP variable is: %s' %
cppvar)
return
if not define:
error(filename, 0, 'build/header_guard', 5,
'No #define header guard found, suggested CPP variable is: %s' %
cppvar)
return
# The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
# for backward compatibility.
if ifndef != cppvar:
error_level = 0
if ifndef != cppvar + '_':
error_level = 5
ParseNolintSuppressions(filename, lines[ifndef_linenum], ifndef_linenum,
error)
error(filename, ifndef_linenum, 'build/header_guard', error_level,
'#ifndef header guard has wrong style, please use: %s' % cppvar)
if define != ifndef:
error(filename, 0, 'build/header_guard', 5,
'#ifndef and #define don\'t match, suggested CPP variable is: %s' %
cppvar)
return
if endif != ('#endif // %s' % cppvar):
error_level = 0
if endif != ('#endif // %s' % (cppvar + '_')):
error_level = 5
ParseNolintSuppressions(filename, lines[endif_linenum], endif_linenum,
error)
error(filename, endif_linenum, 'build/header_guard', error_level,
'#endif line should be "#endif // %s"' % cppvar) | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | lldb/third_party/Python/module/ptyprocess-0.6.0/ptyprocess/ptyprocess.py | python | PtyProcess.readline | (self) | return s | Read one line from the pseudoterminal, and return it as unicode.
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"""Read one line from the pseudoterminal, and return it as unicode.
Can block if there is nothing to read. Raises :exc:`EOFError` if the
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"""
try:
s = self.fileobj.readline()
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# Linux-style EOF
self.flag_eof = True
raise EOFError('End Of File (EOF). Exception style platform.')
raise
if s == b'':
# BSD-style EOF (also appears to work on recent Solaris (OpenIndiana))
self.flag_eof = True
raise EOFError('End Of File (EOF). Empty string style platform.')
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/eager/tape.py | python | record_operation | (op_type, output_tensors, input_tensors, backward_function) | Records the operation on all tapes in the stack. | Records the operation on all tapes in the stack. | [
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"""Records the operation on all tapes in the stack."""
for t in _tape_stack.stack:
t.record_operation(op_type, output_tensors,
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/ValidationKit/common/utils.py | python | formatIntervalHours | (cHours) | return sRet[:-1] | Format a hours interval into a nice 1w 2d 1h string. | Format a hours interval into a nice 1w 2d 1h string. | [
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""" Format a hours interval into a nice 1w 2d 1h string. """
# Simple special cases.
if cHours < 24:
return '%sh' % (cHours,);
# Generic and a bit slower.
cWeeks = cHours / (7 * 24);
cHours %= 7 * 24;
cDays = cHours / 24;
cHours %= 24;
sRet = '';
if cWeeks > 0:
sRet = '%sw ' % (cWeeks,);
if cDays > 0:
sRet = '%sd ' % (cDays,);
if cHours > 0:
sRet += '%sh ' % (cHours,);
assert sRet; assert sRet[-1] == ' ';
return sRet[:-1]; | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/minimum-increment-to-make-array-unique.py | python | Solution.minIncrementForUnique | (self, A) | return result | :type A: List[int]
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"""
:type A: List[int]
:rtype: int
"""
A.sort()
A.append(float("inf"))
result, duplicate = 0, 0
for i in xrange(1, len(A)):
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duplicate += 1
result -= A[i]
else:
move = min(duplicate, A[i]-A[i-1]-1)
duplicate -= move
result += move*A[i-1] + move*(move+1)//2
return result | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | syzygy/scripts/benchmark/chrome_control.py | python | GetPreload | () | Reads Chrome.dll preload settings from the registry.
Returns:
The percentage of chrome.dll that will be preloaded. | Reads Chrome.dll preload settings from the registry. | [
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"""Reads Chrome.dll preload settings from the registry.
Returns:
The percentage of chrome.dll that will be preloaded.
"""
try:
with _winreg.OpenKey(_winreg.HKEY_CURRENT_USER, _CHROME_FRAME_KEY) as key:
percentage = _GetDWORDValue(key, _PREREAD_PERCENTAGE_VALUE)
if percentage is None:
percentage = 0 if _GetDWORDValue(key, _PREREAD_VALUE) == 0 else 100
return percentage
except exceptions.WindowsError, ex:
# We expect specific errors on non-present key or values.
if ex.errno is not winerror.ERROR_FILE_NOT_FOUND:
raise
else:
return 100 | [
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forkineye/ESPixelStick | 22926f1c0d1131f1369fc7cad405689a095ae3cb | dist/bin/pyserial/examples/port_publisher.py | python | Forwarder.close | (self) | Close all resources and unpublish service | Close all resources and unpublish service | [
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"""Close all resources and unpublish service"""
if self.log is not None:
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self.alive = False
self.unpublish()
if self.server_socket:
self.server_socket.close()
if self.socket:
self.handle_disconnect()
self.serial.close()
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callback = self.on_close
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callback(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | ConfigBase_Create | (*args) | return _misc_.ConfigBase_Create(*args) | ConfigBase_Create() -> ConfigBase
Create and return a new global config object. This function will
create the "best" implementation of wx.Config available for the
current platform. | ConfigBase_Create() -> ConfigBase | [
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"""
ConfigBase_Create() -> ConfigBase
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current platform.
"""
return _misc_.ConfigBase_Create(*args) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibar.py | python | AuiToolBarItem.SetSizerItem | (self, s) | Associates a sizer item to this toolbar item.
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Associates a sizer item to this toolbar item.
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"""
self.sizer_item = s | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListCtrl.GetClassDefaultAttributes | (self, variant) | return attr | Returns the default font and colours which are used by the control. This is
useful if you want to use the same font or colour in your own control as in
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for example, :meth:`Button.GetClassDefaultAttributes` () will typically return the
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returned by, say, :meth:`ListCtrl.GetClassDefaultAttributes` ().
:note: The :class:`VisualAttributes` structure has at least the fields `font`,
`colFg` and `colBg`. All of them may be invalid if it was not possible to
determine the default control appearance or, especially for the background
colour, if the field doesn't make sense as is the case for `colBg` for the
controls with themed background.
:note: Overridden from :class:`PyControl`.
"""
attr = wx.VisualAttributes()
attr.colFg = wx.SystemSettings.GetColour(wx.SYS_COLOUR_LISTBOXTEXT)
attr.colBg = wx.SystemSettings.GetColour(wx.SYS_COLOUR_LISTBOX)
attr.font = wx.SystemSettings.GetFont(wx.SYS_DEFAULT_GUI_FONT)
return attr | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/protobuf/python/stubout.py | python | StubOutForTesting.Set | (self, parent, child_name, new_child) | Replace child_name's old definition with new_child, in the context
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method is being replaced. The named child is set to new_child, while
the prior definition is saved away for later, when UnsetAll() is called.
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"""
old_child = getattr(parent, child_name)
old_attribute = parent.__dict__.get(child_name)
if old_attribute is not None and isinstance(old_attribute, staticmethod):
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self.cache.append((parent, old_child, child_name))
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eldar/deepcut-cnn | 928bf2f224fce132f6e4404b4c95fb017297a5e0 | python/caffe/pycaffe.py | python | _Net_forward_all | (self, blobs=None, **kwargs) | return all_outs | Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict. | Run net forward in batches. | [
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"""
Run net forward in batches.
Parameters
----------
blobs : list of blobs to extract as in forward()
kwargs : Keys are input blob names and values are blob ndarrays.
Refer to forward().
Returns
-------
all_outs : {blob name: list of blobs} dict.
"""
# Collect outputs from batches
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
for batch in self._batch(kwargs):
outs = self.forward(blobs=blobs, **batch)
for out, out_blob in outs.iteritems():
all_outs[out].extend(out_blob.copy())
# Package in ndarray.
for out in all_outs:
all_outs[out] = np.asarray(all_outs[out])
# Discard padding.
pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
if pad:
for out in all_outs:
all_outs[out] = all_outs[out][:-pad]
return all_outs | [
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wy1iu/LargeMargin_Softmax_Loss | c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec | python/caffe/pycaffe.py | python | _Net_backward | (self, diffs=None, start=None, end=None, **kwargs) | return {out: self.blobs[out].diff for out in outputs} | Backward pass: prepare diffs and run the net backward.
Parameters
----------
diffs : list of diffs to return in addition to bottom diffs.
kwargs : Keys are output blob names and values are diff ndarrays.
If None, top diffs are taken from forward loss.
start : optional name of layer at which to begin the backward pass
end : optional name of layer at which to finish the backward pass
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outs: {blob name: diff ndarray} dict. | Backward pass: prepare diffs and run the net backward. | [
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"""
Backward pass: prepare diffs and run the net backward.
Parameters
----------
diffs : list of diffs to return in addition to bottom diffs.
kwargs : Keys are output blob names and values are diff ndarrays.
If None, top diffs are taken from forward loss.
start : optional name of layer at which to begin the backward pass
end : optional name of layer at which to finish the backward pass
(inclusive)
Returns
-------
outs: {blob name: diff ndarray} dict.
"""
if diffs is None:
diffs = []
if start is not None:
start_ind = list(self._layer_names).index(start)
else:
start_ind = len(self.layers) - 1
if end is not None:
end_ind = list(self._layer_names).index(end)
outputs = set([end] + diffs)
else:
end_ind = 0
outputs = set(self.inputs + diffs)
if kwargs:
if set(kwargs.keys()) != set(self.outputs):
raise Exception('Top diff arguments do not match net outputs.')
# Set top diffs according to defined shapes and make arrays single and
# C-contiguous as Caffe expects.
for top, diff in six.iteritems(kwargs):
if diff.shape[0] != self.blobs[top].shape[0]:
raise Exception('Diff is not batch sized')
self.blobs[top].diff[...] = diff
self._backward(start_ind, end_ind)
# Unpack diffs to extract
return {out: self.blobs[out].diff for out in outputs} | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/boto3/resources/collection.py | python | ResourceCollection.all | (self) | return self._clone() | Get all items from the collection, optionally with a custom
page size and item count limit.
This method returns an iterable generator which yields
individual resource instances. Example use::
# Iterate through items
>>> for queue in sqs.queues.all():
... print(queue.url)
'https://url1'
'https://url2'
# Convert to list
>>> queues = list(sqs.queues.all())
>>> len(queues)
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"""
Get all items from the collection, optionally with a custom
page size and item count limit.
This method returns an iterable generator which yields
individual resource instances. Example use::
# Iterate through items
>>> for queue in sqs.queues.all():
... print(queue.url)
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# Convert to list
>>> queues = list(sqs.queues.all())
>>> len(queues)
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"""
return self._clone() | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/logging/__init__.py | python | Handler.flush | (self) | Ensure all logging output has been flushed.
This version does nothing and is intended to be implemented by
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"""
Ensure all logging output has been flushed.
This version does nothing and is intended to be implemented by
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"""
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shogun-toolbox/shogun | 9b8d856971af5a295dd6ad70623ae45647a6334c | examples/meta/generator/parse.py | python | FastParser.p_elementAccess | (self, p) | elementAccess : identifier LSQUARE indexList RSQUARE | elementAccess : identifier LSQUARE indexList RSQUARE | [
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":",
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p[0] = {"ElementAccess": [p[1],
{"IndexList": p[3]}]} | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/pexpect/pexpect.py | python | spawn.isatty | (self) | return os.isatty(self.child_fd) | This returns True if the file descriptor is open and connected to a
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"""This returns True if the file descriptor is open and connected to a
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return os.isatty(self.child_fd) | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/molecule.py | python | Molecule.__init__ | (self,
molinit=None,
dtype=None,
geom=None,
elea=None,
elez=None,
elem=None,
mass=None,
real=None,
elbl=None,
name=None,
units='Angstrom',
input_units_to_au=None,
fix_com=None,
fix_orientation=None,
fix_symmetry=None,
fragment_separators=None,
fragment_charges=None,
fragment_multiplicities=None,
molecular_charge=None,
molecular_multiplicity=None,
comment=None,
provenance=None,
connectivity=None,
enable_qm=True,
enable_efp=True,
missing_enabled_return_qm='none',
missing_enabled_return_efp='none',
missing_enabled_return='error',
tooclose=0.1,
zero_ghost_fragments=False,
nonphysical=False,
mtol=1.e-3,
verbose=1) | Initialize Molecule object from LibmintsMolecule | Initialize Molecule object from LibmintsMolecule | [
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] | def __init__(self,
molinit=None,
dtype=None,
geom=None,
elea=None,
elez=None,
elem=None,
mass=None,
real=None,
elbl=None,
name=None,
units='Angstrom',
input_units_to_au=None,
fix_com=None,
fix_orientation=None,
fix_symmetry=None,
fragment_separators=None,
fragment_charges=None,
fragment_multiplicities=None,
molecular_charge=None,
molecular_multiplicity=None,
comment=None,
provenance=None,
connectivity=None,
enable_qm=True,
enable_efp=True,
missing_enabled_return_qm='none',
missing_enabled_return_efp='none',
missing_enabled_return='error',
tooclose=0.1,
zero_ghost_fragments=False,
nonphysical=False,
mtol=1.e-3,
verbose=1):
"""Initialize Molecule object from LibmintsMolecule"""
super(Molecule, self).__init__()
if molinit is not None or geom is not None:
if isinstance(molinit, dict):
molrec = molinit
elif isinstance(molinit, str):
compound_molrec = qcel.molparse.from_string(
molstr=molinit,
dtype=dtype,
name=name,
fix_com=fix_com,
fix_orientation=fix_orientation,
fix_symmetry=fix_symmetry,
return_processed=False,
enable_qm=enable_qm,
enable_efp=enable_efp,
missing_enabled_return_qm=missing_enabled_return_qm,
missing_enabled_return_efp=missing_enabled_return_efp,
verbose=verbose)
molrec = compound_molrec['qm']
elif molinit is None and geom is not None:
molrec = qcel.molparse.from_arrays(
geom=geom,
elea=elea,
elez=elez,
elem=elem,
mass=mass,
real=real,
elbl=elbl,
name=name,
units=units,
input_units_to_au=input_units_to_au,
fix_com=fix_com,
fix_orientation=fix_orientation,
fix_symmetry=fix_symmetry,
fragment_separators=fragment_separators,
fragment_charges=fragment_charges,
fragment_multiplicities=fragment_multiplicities,
molecular_charge=molecular_charge,
molecular_multiplicity=molecular_multiplicity,
comment=comment,
provenance=provenance,
connectivity=connectivity,
domain='qm',
missing_enabled_return=missing_enabled_return,
tooclose=tooclose,
zero_ghost_fragments=zero_ghost_fragments,
nonphysical=nonphysical,
mtol=mtol,
verbose=verbose)
# ok, got the molrec dictionary; now build the thing
self._internal_from_dict(molrec, verbose=verbose)
# The comment line
self.tagline = "" | [
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | python/pyarrow/filesystem.py | python | FileSystem.read_parquet | (self, path, columns=None, metadata=None, schema=None,
use_threads=True, use_pandas_metadata=False) | return dataset.read(columns=columns, use_threads=use_threads,
use_pandas_metadata=use_pandas_metadata) | Read Parquet data from path in file system. Can read from a single file
or a directory of files.
Parameters
----------
path : str
Single file path or directory
columns : List[str], optional
Subset of columns to read.
metadata : pyarrow.parquet.FileMetaData
Known metadata to validate files against.
schema : pyarrow.parquet.Schema
Known schema to validate files against. Alternative to metadata
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use_threads : bool, default True
Perform multi-threaded column reads.
use_pandas_metadata : bool, default False
If True and file has custom pandas schema metadata, ensure that
index columns are also loaded.
Returns
-------
table : pyarrow.Table | Read Parquet data from path in file system. Can read from a single file
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] | def read_parquet(self, path, columns=None, metadata=None, schema=None,
use_threads=True, use_pandas_metadata=False):
"""
Read Parquet data from path in file system. Can read from a single file
or a directory of files.
Parameters
----------
path : str
Single file path or directory
columns : List[str], optional
Subset of columns to read.
metadata : pyarrow.parquet.FileMetaData
Known metadata to validate files against.
schema : pyarrow.parquet.Schema
Known schema to validate files against. Alternative to metadata
argument.
use_threads : bool, default True
Perform multi-threaded column reads.
use_pandas_metadata : bool, default False
If True and file has custom pandas schema metadata, ensure that
index columns are also loaded.
Returns
-------
table : pyarrow.Table
"""
from pyarrow.parquet import ParquetDataset
dataset = ParquetDataset(path, schema=schema, metadata=metadata,
filesystem=self)
return dataset.read(columns=columns, use_threads=use_threads,
use_pandas_metadata=use_pandas_metadata) | [
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/ivy_parser.py | python | p_action_call_callatom | (p) | action : CALL callatom | action : CALL callatom | [
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p[0] = CallAction(p[2])
p[0].lineno = get_lineno(p,1) | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/third_party/jinja2/lexer.py | python | get_lexer | (environment) | return lexer | Return a lexer which is probably cached. | Return a lexer which is probably cached. | [
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"""Return a lexer which is probably cached."""
key = (environment.block_start_string,
environment.block_end_string,
environment.variable_start_string,
environment.variable_end_string,
environment.comment_start_string,
environment.comment_end_string,
environment.line_statement_prefix,
environment.line_comment_prefix,
environment.trim_blocks,
environment.lstrip_blocks,
environment.newline_sequence,
environment.keep_trailing_newline)
lexer = _lexer_cache.get(key)
if lexer is None:
lexer = Lexer(environment)
_lexer_cache[key] = lexer
return lexer | [
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"environm... | https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/deps/v8/third_party/jinja2/lexer.py#L391-L409 | |
y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/FortranCommon.py | python | DialectAddToEnv | (env, dialect, suffixes, ppsuffixes, support_module = 0) | Add dialect specific construction variables. | Add dialect specific construction variables. | [
"Add",
"dialect",
"specific",
"construction",
"variables",
"."
] | def DialectAddToEnv(env, dialect, suffixes, ppsuffixes, support_module = 0):
"""Add dialect specific construction variables."""
ComputeFortranSuffixes(suffixes, ppsuffixes)
fscan = SCons.Scanner.Fortran.FortranScan("%sPATH" % dialect)
for suffix in suffixes + ppsuffixes:
SCons.Tool.SourceFileScanner.add_scanner(suffix, fscan)
env.AppendUnique(FORTRANSUFFIXES = suffixes + ppsuffixes)
compaction, compppaction, shcompaction, shcompppaction = \
CreateDialectActions(dialect)
static_obj, shared_obj = SCons.Tool.createObjBuilders(env)
for suffix in suffixes:
static_obj.add_action(suffix, compaction)
shared_obj.add_action(suffix, shcompaction)
static_obj.add_emitter(suffix, FortranEmitter)
shared_obj.add_emitter(suffix, ShFortranEmitter)
for suffix in ppsuffixes:
static_obj.add_action(suffix, compppaction)
shared_obj.add_action(suffix, shcompppaction)
static_obj.add_emitter(suffix, FortranEmitter)
shared_obj.add_emitter(suffix, ShFortranEmitter)
if '%sFLAGS' % dialect not in env:
env['%sFLAGS' % dialect] = SCons.Util.CLVar('')
if 'SH%sFLAGS' % dialect not in env:
env['SH%sFLAGS' % dialect] = SCons.Util.CLVar('$%sFLAGS' % dialect)
# If a tool does not define fortran prefix/suffix for include path, use C ones
if 'INC%sPREFIX' % dialect not in env:
env['INC%sPREFIX' % dialect] = '$INCPREFIX'
if 'INC%sSUFFIX' % dialect not in env:
env['INC%sSUFFIX' % dialect] = '$INCSUFFIX'
env['_%sINCFLAGS' % dialect] = '$( ${_concat(INC%sPREFIX, %sPATH, INC%sSUFFIX, __env__, RDirs, TARGET, SOURCE)} $)' % (dialect, dialect, dialect)
if support_module == 1:
env['%sCOM' % dialect] = '$%s -o $TARGET -c $%sFLAGS $_%sINCFLAGS $_FORTRANMODFLAG $SOURCES' % (dialect, dialect, dialect)
env['%sPPCOM' % dialect] = '$%s -o $TARGET -c $%sFLAGS $CPPFLAGS $_CPPDEFFLAGS $_%sINCFLAGS $_FORTRANMODFLAG $SOURCES' % (dialect, dialect, dialect)
env['SH%sCOM' % dialect] = '$SH%s -o $TARGET -c $SH%sFLAGS $_%sINCFLAGS $_FORTRANMODFLAG $SOURCES' % (dialect, dialect, dialect)
env['SH%sPPCOM' % dialect] = '$SH%s -o $TARGET -c $SH%sFLAGS $CPPFLAGS $_CPPDEFFLAGS $_%sINCFLAGS $_FORTRANMODFLAG $SOURCES' % (dialect, dialect, dialect)
else:
env['%sCOM' % dialect] = '$%s -o $TARGET -c $%sFLAGS $_%sINCFLAGS $SOURCES' % (dialect, dialect, dialect)
env['%sPPCOM' % dialect] = '$%s -o $TARGET -c $%sFLAGS $CPPFLAGS $_CPPDEFFLAGS $_%sINCFLAGS $SOURCES' % (dialect, dialect, dialect)
env['SH%sCOM' % dialect] = '$SH%s -o $TARGET -c $SH%sFLAGS $_%sINCFLAGS $SOURCES' % (dialect, dialect, dialect)
env['SH%sPPCOM' % dialect] = '$SH%s -o $TARGET -c $SH%sFLAGS $CPPFLAGS $_CPPDEFFLAGS $_%sINCFLAGS $SOURCES' % (dialect, dialect, dialect) | [
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svn2github/webrtc | 0e4615a75ed555ec866cd5543bfea586f3385ceb | webrtc/tools/barcode_tools/barcode_decoder.py | python | _read_barcode_from_text_file | (barcode_file_name) | return barcode | Reads the decoded barcode for a .txt file.
Args:
barcode_file_name(string): The name of the .txt file.
Return:
(string): The decoded barcode. | Reads the decoded barcode for a .txt file. | [
"Reads",
"the",
"decoded",
"barcode",
"for",
"a",
".",
"txt",
"file",
"."
] | def _read_barcode_from_text_file(barcode_file_name):
"""Reads the decoded barcode for a .txt file.
Args:
barcode_file_name(string): The name of the .txt file.
Return:
(string): The decoded barcode.
"""
barcode_file = open(barcode_file_name, 'r')
barcode = barcode_file.read()
barcode_file.close()
return barcode | [
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... | https://github.com/svn2github/webrtc/blob/0e4615a75ed555ec866cd5543bfea586f3385ceb/webrtc/tools/barcode_tools/barcode_decoder.py#L155-L166 | |
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/print-immutable-linked-list-in-reverse.py | python | Solution3.printLinkedListInReverse | (self, head) | :type head: ImmutableListNode
:rtype: None | :type head: ImmutableListNode
:rtype: None | [
":",
"type",
"head",
":",
"ImmutableListNode",
":",
"rtype",
":",
"None"
] | def printLinkedListInReverse(self, head):
"""
:type head: ImmutableListNode
:rtype: None
"""
tail = None
while head != tail:
curr = head
while curr.getNext() != tail:
curr = curr.getNext()
curr.printValue()
tail = curr | [
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"(... | https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/print-immutable-linked-list-in-reverse.py#L59-L70 | ||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/pkg_resources/__init__.py | python | _initialize | (g=globals()) | Set up global resource manager (deliberately not state-saved) | Set up global resource manager (deliberately not state-saved) | [
"Set",
"up",
"global",
"resource",
"manager",
"(",
"deliberately",
"not",
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"-",
"saved",
")"
] | def _initialize(g=globals()):
"Set up global resource manager (deliberately not state-saved)"
manager = ResourceManager()
g['_manager'] = manager
g.update(
(name, getattr(manager, name))
for name in dir(manager)
if not name.startswith('_')
) | [
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chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/cef_parser.py | python | obj_function.get_capi_parts | (self, defined_structs=[], prefix=None) | return {'retval': retval, 'name': name, 'args': args} | Return the parts of the C API function definition. | Return the parts of the C API function definition. | [
"Return",
"the",
"parts",
"of",
"the",
"C",
"API",
"function",
"definition",
"."
] | def get_capi_parts(self, defined_structs=[], prefix=None):
""" Return the parts of the C API function definition. """
retval = ''
dict = self.retval.get_type().get_capi(defined_structs)
if dict['format'] == 'single':
retval = dict['value']
name = self.get_capi_name(prefix)
args = []
if isinstance(self, obj_function_virtual):
# virtual functions get themselves as the first argument
str = 'struct _' + self.parent.get_capi_name() + '* self'
if isinstance(self, obj_function_virtual) and self.is_const():
# const virtual functions get const self pointers
str = 'const ' + str
args.append(str)
if len(self.arguments) > 0:
for cls in self.arguments:
type = cls.get_type()
dict = type.get_capi(defined_structs)
if dict['format'] == 'single':
args.append(dict['value'])
elif dict['format'] == 'multi-arg':
# add an additional argument for the size of the array
type_name = type.get_name()
if type.is_const():
# for const arrays pass the size argument by value
args.append('size_t ' + type_name + 'Count')
else:
# for non-const arrays pass the size argument by address
args.append('size_t* ' + type_name + 'Count')
args.append(dict['value'])
return {'retval': retval, 'name': name, 'args': args} | [
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... | https://github.com/chromiumembedded/cef/blob/80caf947f3fe2210e5344713c5281d8af9bdc295/tools/cef_parser.py#L1208-L1243 |
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