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google/skia | 82d65d0487bd72f5f7332d002429ec2dc61d2463 | infra/bots/recipe_modules/gold_upload/api.py | python | GoldUploadApi.upload | (self) | Attempt to upload files to Gold.
This module assumes setup has occurred for the vars and flavor modules. | Attempt to upload files to Gold.
This module assumes setup has occurred for the vars and flavor modules. | [
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"""Attempt to upload files to Gold.
This module assumes setup has occurred for the vars and flavor modules.
"""
revision = self.m.properties['revision']
results_dir = self.m.flavor.host_dirs.dm_dir
# Upload the images. It is preferred that the images are uploaded first
# so they exist whenever the json is processed.
image_dest_path = 'gs://%s/dm-images-v1' % self.m.properties['gs_bucket']
for ext in ['.png']:
files_to_upload = self.m.file.glob_paths(
'find %s images' % ext,
results_dir,
'*%s' % ext,
test_data=['someimage.png'])
# For some reason, glob returns results_dir when it should return nothing.
files_to_upload = [f for f in files_to_upload if str(f).endswith(ext)]
if len(files_to_upload) > 0:
self.m.gsutil.cp('%s images' % ext, results_dir.join('*%s' % ext),
image_dest_path, multithread=True)
summary_dest_path = 'gs://%s' % self.m.properties['gs_bucket']
ref = revision
# Trybot results are siloed by issue/patchset.
if self.m.vars.is_trybot:
summary_dest_path = '/'.join([summary_dest_path, 'trybot'])
ref = '%s_%s' % (str(self.m.vars.issue), str(self.m.vars.patchset))
# Compute the directory to upload results to
now = self.m.time.utcnow()
summary_dest_path = '/'.join([
summary_dest_path,
'dm-json-v1',
str(now.year ).zfill(4),
str(now.month).zfill(2),
str(now.day ).zfill(2),
str(now.hour ).zfill(2),
ref,
self.m.vars.builder_name,
str(int(calendar.timegm(now.utctimetuple())))])
# Directly upload dm.json if it exists.
json_file = results_dir.join(DM_JSON)
# -Z compresses the json file at rest with gzip.
self.m.gsutil.cp('dm.json', json_file,
summary_dest_path + '/' + DM_JSON, extra_args=['-Z']) | [
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||
microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | lldb/third_party/Python/module/pexpect-4.6/pexpect/popen_spawn.py | python | PopenSpawn.writelines | (self, sequence) | This calls write() for each element in the sequence.
The sequence can be any iterable object producing strings, typically a
list of strings. This does not add line separators. There is no return
value. | This calls write() for each element in the sequence. | [
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] | def writelines(self, sequence):
'''This calls write() for each element in the sequence.
The sequence can be any iterable object producing strings, typically a
list of strings. This does not add line separators. There is no return
value.
'''
for s in sequence:
self.send(s) | [
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||
Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBTypeSummary.SetFunctionCode | (self, *args) | return _lldb.SBTypeSummary_SetFunctionCode(self, *args) | SetFunctionCode(self, str data) | SetFunctionCode(self, str data) | [
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] | def SetFunctionCode(self, *args):
"""SetFunctionCode(self, str data)"""
return _lldb.SBTypeSummary_SetFunctionCode(self, *args) | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/sdb/db/manager/sdbmanager.py | python | SDBManager._build_filter_part | (self, cls, filters, order_by=None, select=None) | Build the filter part | Build the filter part | [
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] | def _build_filter_part(self, cls, filters, order_by=None, select=None):
"""
Build the filter part
"""
import types
query_parts = []
order_by_filtered = False
if order_by:
if order_by[0] == "-":
order_by_method = "DESC"
order_by = order_by[1:]
else:
order_by_method = "ASC"
if select:
if order_by and order_by in select:
order_by_filtered = True
query_parts.append("(%s)" % select)
if isinstance(filters, six.string_types):
query = "WHERE %s AND `__type__` = '%s'" % (filters, cls.__name__)
if order_by in ["__id__", "itemName()"]:
query += " ORDER BY itemName() %s" % order_by_method
elif order_by is not None:
query += " ORDER BY `%s` %s" % (order_by, order_by_method)
return query
for filter in filters:
filter_parts = []
filter_props = filter[0]
if not isinstance(filter_props, list):
filter_props = [filter_props]
for filter_prop in filter_props:
(name, op) = filter_prop.strip().split(" ", 1)
value = filter[1]
property = cls.find_property(name)
if name == order_by:
order_by_filtered = True
if types.TypeType(value) == list:
filter_parts_sub = []
for val in value:
val = self.encode_value(property, val)
if isinstance(val, list):
for v in val:
filter_parts_sub.append(self._build_filter(property, name, op, v))
else:
filter_parts_sub.append(self._build_filter(property, name, op, val))
filter_parts.append("(%s)" % (" OR ".join(filter_parts_sub)))
else:
val = self.encode_value(property, value)
if isinstance(val, list):
for v in val:
filter_parts.append(self._build_filter(property, name, op, v))
else:
filter_parts.append(self._build_filter(property, name, op, val))
query_parts.append("(%s)" % (" or ".join(filter_parts)))
type_query = "(`__type__` = '%s'" % cls.__name__
for subclass in self._get_all_decendents(cls).keys():
type_query += " or `__type__` = '%s'" % subclass
type_query += ")"
query_parts.append(type_query)
order_by_query = ""
if order_by:
if not order_by_filtered:
query_parts.append("`%s` LIKE '%%'" % order_by)
if order_by in ["__id__", "itemName()"]:
order_by_query = " ORDER BY itemName() %s" % order_by_method
else:
order_by_query = " ORDER BY `%s` %s" % (order_by, order_by_method)
if len(query_parts) > 0:
return "WHERE %s %s" % (" AND ".join(query_parts), order_by_query)
else:
return "" | [
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||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/layers/python/layers/layers.py | python | one_hot_encoding | (labels,
num_classes,
on_value=1.0,
off_value=0.0,
outputs_collections=None,
scope=None) | Transform numeric labels into onehot_labels using `tf.one_hot`.
Args:
labels: [batch_size] target labels.
num_classes: Total number of classes.
on_value: A scalar defining the on-value.
off_value: A scalar defining the off-value.
outputs_collections: Collection to add the outputs.
scope: Optional scope for name_scope.
Returns:
One-hot encoding of the labels. | Transform numeric labels into onehot_labels using `tf.one_hot`. | [
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] | def one_hot_encoding(labels,
num_classes,
on_value=1.0,
off_value=0.0,
outputs_collections=None,
scope=None):
"""Transform numeric labels into onehot_labels using `tf.one_hot`.
Args:
labels: [batch_size] target labels.
num_classes: Total number of classes.
on_value: A scalar defining the on-value.
off_value: A scalar defining the off-value.
outputs_collections: Collection to add the outputs.
scope: Optional scope for name_scope.
Returns:
One-hot encoding of the labels.
"""
with ops.name_scope(scope, 'OneHotEncoding', [labels, num_classes]) as sc:
labels = ops.convert_to_tensor(labels)
if labels.dtype == dtypes.int32:
labels = standard_ops.to_int64(labels)
outputs = standard_ops.one_hot(
labels, num_classes, on_value=on_value, off_value=off_value)
return utils.collect_named_outputs(outputs_collections, sc, outputs) | [
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||
sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/wsgiserver/ssl_pyopenssl.py | python | pyOpenSSLAdapter.get_context | (self) | return c | Return an SSL.Context from self attributes. | Return an SSL.Context from self attributes. | [
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] | def get_context(self):
"""Return an SSL.Context from self attributes."""
# See http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/442473
c = SSL.Context(SSL.SSLv23_METHOD)
c.use_privatekey_file(self.private_key)
if self.certificate_chain:
c.load_verify_locations(self.certificate_chain)
c.use_certificate_file(self.certificate)
return c | [
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|
KhronosGroup/SPIRV-Tools | 940127a77d3ad795a4a1422fbeaad50c9f19f2ea | utils/generate_grammar_tables.py | python | generate_capability_arrays | (caps) | return '\n'.join(arrays) | Returns the arrays of capabilities.
Arguments:
- caps: a sequence of sequence of capability names | Returns the arrays of capabilities. | [
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] | def generate_capability_arrays(caps):
"""Returns the arrays of capabilities.
Arguments:
- caps: a sequence of sequence of capability names
"""
caps = sorted(set([tuple(c) for c in caps if c]))
arrays = [
'static const SpvCapability {}[] = {};'.format(
get_capability_array_name(c), compose_capability_list(c))
for c in caps]
return '\n'.join(arrays) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | EvtHandler.ProcessEventLocally | (*args, **kwargs) | return _core_.EvtHandler_ProcessEventLocally(*args, **kwargs) | ProcessEventLocally(self, Event event) -> bool | ProcessEventLocally(self, Event event) -> bool | [
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"""ProcessEventLocally(self, Event event) -> bool"""
return _core_.EvtHandler_ProcessEventLocally(*args, **kwargs) | [
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|
rtbkit/rtbkit | 502d06acc3f8d90438946b6ae742190f2f4b4fbb | jml-build/jmlbuild.py | python | Parser.parse_func_default | (self, line) | return line | Function which don't have special handlers are parsed here. | Function which don't have special handlers are parsed here. | [
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"""
Function which don't have special handlers are parsed here.
"""
print_dbg("\tdefault_func: " + line)
params, line = self.parse_func_params(line)
return line | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | MenuBar.FindItemById | (*args, **kwargs) | return _core_.MenuBar_FindItemById(*args, **kwargs) | FindItemById(self, int id) -> MenuItem | FindItemById(self, int id) -> MenuItem | [
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"""FindItemById(self, int id) -> MenuItem"""
return _core_.MenuBar_FindItemById(*args, **kwargs) | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/model_pruning/python/layers/rnn_cells.py | python | MaskedLSTMCell.call | (self, inputs, state) | return m, new_state | Run one step of LSTM.
Args:
inputs: input Tensor, 2D, `[batch, num_units].
state: if `state_is_tuple` is False, this must be a state Tensor,
`2-D, [batch, state_size]`. If `state_is_tuple` is True, this must be a
tuple of state Tensors, both `2-D`, with column sizes `c_state` and
`m_state`.
Returns:
A tuple containing:
- A `2-D, [batch, output_dim]`, Tensor representing the output of the
LSTM after reading `inputs` when previous state was `state`.
Here output_dim is:
num_proj if num_proj was set,
num_units otherwise.
- Tensor(s) representing the new state of LSTM after reading `inputs` when
the previous state was `state`. Same type and shape(s) as `state`.
Raises:
ValueError: If input size cannot be inferred from inputs via
static shape inference. | Run one step of LSTM. | [
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"of",
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] | def call(self, inputs, state):
"""Run one step of LSTM.
Args:
inputs: input Tensor, 2D, `[batch, num_units].
state: if `state_is_tuple` is False, this must be a state Tensor,
`2-D, [batch, state_size]`. If `state_is_tuple` is True, this must be a
tuple of state Tensors, both `2-D`, with column sizes `c_state` and
`m_state`.
Returns:
A tuple containing:
- A `2-D, [batch, output_dim]`, Tensor representing the output of the
LSTM after reading `inputs` when previous state was `state`.
Here output_dim is:
num_proj if num_proj was set,
num_units otherwise.
- Tensor(s) representing the new state of LSTM after reading `inputs` when
the previous state was `state`. Same type and shape(s) as `state`.
Raises:
ValueError: If input size cannot be inferred from inputs via
static shape inference.
"""
num_proj = self._num_units if self._num_proj is None else self._num_proj
sigmoid = math_ops.sigmoid
if self._state_is_tuple:
(c_prev, m_prev) = state
else:
c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units])
m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj])
input_size = inputs.get_shape().with_rank(2).dims[1]
if input_size.value is None:
raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
lstm_matrix = math_ops.matmul(
array_ops.concat([inputs, m_prev], 1), self._masked_kernel)
lstm_matrix = nn_ops.bias_add(lstm_matrix, self._bias)
i, j, f, o = array_ops.split(
value=lstm_matrix, num_or_size_splits=4, axis=1)
# Diagonal connections
if self._use_peepholes:
c = (
sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev +
sigmoid(i + self._w_i_diag * c_prev) * self._activation(j))
else:
c = (
sigmoid(f + self._forget_bias) * c_prev +
sigmoid(i) * self._activation(j))
if self._cell_clip is not None:
# pylint: disable=invalid-unary-operand-type
c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
# pylint: enable=invalid-unary-operand-type
if self._use_peepholes:
m = sigmoid(o + self._w_o_diag * c) * self._activation(c)
else:
m = sigmoid(o) * self._activation(c)
if self._num_proj is not None:
m = math_ops.matmul(m, self._proj_kernel)
if self._proj_clip is not None:
# pylint: disable=invalid-unary-operand-type
m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
# pylint: enable=invalid-unary-operand-type
new_state = (
tf_rnn.LSTMStateTuple(c, m)
if self._state_is_tuple else array_ops.concat([c, m], 1))
return m, new_state | [
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|
SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pyedbglib/util/binary.py | python | pack_be16 | (value) | return bytearray([(value >> 8) & 0xFF, value & 0xFF]) | :param value: input value
:return: 16-bit big endian bytearray representation of the input value | :param value: input value
:return: 16-bit big endian bytearray representation of the input value | [
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] | def pack_be16(value):
"""
:param value: input value
:return: 16-bit big endian bytearray representation of the input value
"""
_check_input_value(value, 16)
return bytearray([(value >> 8) & 0xFF, value & 0xFF]) | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/backend.py | python | arange | (start, stop=None, step=1, dtype='int32') | return result | Creates a 1D tensor containing a sequence of integers.
The function arguments use the same convention as
Theano's arange: if only one argument is provided,
it is in fact the "stop" argument and "start" is 0.
The default type of the returned tensor is `'int32'` to
match TensorFlow's default.
Arguments:
start: Start value.
stop: Stop value.
step: Difference between two successive values.
dtype: Integer dtype to use.
Returns:
An integer tensor.
Example:
```python
>>> tf.keras.backend.arange(start=0, stop=10, step=1.5)
<tf.Tensor: id=96, shape=(7,), dtype=float32,
numpy=array([0. , 1.5, 3. , 4.5, 6. , 7.5, 9. ], dtype=float32)>
``` | Creates a 1D tensor containing a sequence of integers. | [
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"."
] | def arange(start, stop=None, step=1, dtype='int32'):
"""Creates a 1D tensor containing a sequence of integers.
The function arguments use the same convention as
Theano's arange: if only one argument is provided,
it is in fact the "stop" argument and "start" is 0.
The default type of the returned tensor is `'int32'` to
match TensorFlow's default.
Arguments:
start: Start value.
stop: Stop value.
step: Difference between two successive values.
dtype: Integer dtype to use.
Returns:
An integer tensor.
Example:
```python
>>> tf.keras.backend.arange(start=0, stop=10, step=1.5)
<tf.Tensor: id=96, shape=(7,), dtype=float32,
numpy=array([0. , 1.5, 3. , 4.5, 6. , 7.5, 9. ], dtype=float32)>
```
"""
# Match the behavior of numpy and Theano by returning an empty sequence.
if stop is None and start < 0:
start = 0
result = math_ops.range(start, limit=stop, delta=step, name='arange')
if dtype != 'int32':
result = cast(result, dtype)
return result | [
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|
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftfunctions/dxf.py | python | getDXF | (obj,
direction=None) | return get_dxf(obj,
direction=direction) | Return DXF string of the object. DEPRECATED. Use 'get_dxf'. | Return DXF string of the object. DEPRECATED. Use 'get_dxf'. | [
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] | def getDXF(obj,
direction=None):
"""Return DXF string of the object. DEPRECATED. Use 'get_dxf'."""
utils.use_instead("get_dxf")
return get_dxf(obj,
direction=direction) | [
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/closure_linter/closure_linter/common/tokenizer.py | python | Tokenizer.__CreateNormalToken | (self, mode, string, line, line_number) | return self._CreateToken(string, type, line, line_number) | Creates a normal token.
Args:
mode: The current mode.
string: The string to tokenize.
line: The line of text.
line_number: The line number within the file.
Returns:
A Token object, of the default type for the current mode. | Creates a normal token. | [
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"""Creates a normal token.
Args:
mode: The current mode.
string: The string to tokenize.
line: The line of text.
line_number: The line number within the file.
Returns:
A Token object, of the default type for the current mode.
"""
type = Type.NORMAL
if mode in self.default_types:
type = self.default_types[mode]
return self._CreateToken(string, type, line, line_number) | [
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|
hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py | python | train | () | Train CIFAR-10 for a number of steps. | Train CIFAR-10 for a number of steps. | [
"Train",
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"-",
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] | def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0), trainable=False)
# Calculate the learning rate schedule.
num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,
global_step,
decay_steps,
cifar10.LEARNING_RATE_DECAY_FACTOR,
staircase=True)
# Create an optimizer that performs gradient descent.
opt = tf.train.GradientDescentOptimizer(lr)
# Calculate the gradients for each model tower.
tower_grads = []
for i in xrange(FLAGS.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:
# Calculate the loss for one tower of the CIFAR model. This function
# constructs the entire CIFAR model but shares the variables across
# all towers.
loss = tower_loss(scope)
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
# Retain the summaries from the final tower.
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
# Calculate the gradients for the batch of data on this CIFAR tower.
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Add a summary to track the learning rate.
summaries.append(tf.scalar_summary('learning_rate', lr))
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
summaries.append(
tf.histogram_summary(var.op.name + '/gradients', grad))
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
summaries.append(tf.histogram_summary(var.op.name, var))
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
cifar10.MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op, variables_averages_op)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build the summary operation from the last tower summaries.
summary_op = tf.merge_summary(summaries)
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration / FLAGS.num_gpus
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step) | [
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||
ucb-bar/esp-llvm | 8aec2ae754fd66d4e73b9b777a9f20c4583a0f03 | bindings/python/llvm/object.py | python | Symbol.expire | (self) | Mark the object as expired to prevent future API accesses.
This is called internally by this module and it is unlikely that
external callers have a legitimate reason for using it. | Mark the object as expired to prevent future API accesses. | [
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"the",
"object",
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"expired",
"to",
"prevent",
"future",
"API",
"accesses",
"."
] | def expire(self):
"""Mark the object as expired to prevent future API accesses.
This is called internally by this module and it is unlikely that
external callers have a legitimate reason for using it.
"""
self.expired = True | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/stats/_multivariate.py | python | wishart_gen.rvs | (self, df, scale, size=1, random_state=None) | return _squeeze_output(out) | Draw random samples from a Wishart distribution.
Parameters
----------
%(_doc_default_callparams)s
size : integer or iterable of integers, optional
Number of samples to draw (default 1).
%(_doc_random_state)s
Returns
-------
rvs : ndarray
Random variates of shape (`size`) + (`dim`, `dim), where `dim` is
the dimension of the scale matrix.
Notes
-----
%(_doc_callparams_note)s | Draw random samples from a Wishart distribution. | [
"Draw",
"random",
"samples",
"from",
"a",
"Wishart",
"distribution",
"."
] | def rvs(self, df, scale, size=1, random_state=None):
"""
Draw random samples from a Wishart distribution.
Parameters
----------
%(_doc_default_callparams)s
size : integer or iterable of integers, optional
Number of samples to draw (default 1).
%(_doc_random_state)s
Returns
-------
rvs : ndarray
Random variates of shape (`size`) + (`dim`, `dim), where `dim` is
the dimension of the scale matrix.
Notes
-----
%(_doc_callparams_note)s
"""
n, shape = self._process_size(size)
dim, df, scale = self._process_parameters(df, scale)
# Cholesky decomposition of scale
C = scipy.linalg.cholesky(scale, lower=True)
out = self._rvs(n, shape, dim, df, C, random_state)
return _squeeze_output(out) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | uCSIsYiSyllables | (code) | return ret | Check whether the character is part of YiSyllables UCS Block | Check whether the character is part of YiSyllables UCS Block | [
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"whether",
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] | def uCSIsYiSyllables(code):
"""Check whether the character is part of YiSyllables UCS Block """
ret = libxml2mod.xmlUCSIsYiSyllables(code)
return ret | [
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|
may0324/DeepCompression-caffe | 0aff6c1287bda4cfc7f378ed8a16524e1afabd8c | scripts/cpp_lint.py | python | ParseNolintSuppressions | (filename, raw_line, linenum, error) | Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler. | Updates the global list of error-suppressions. | [
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"-",
"suppressions",
"."
] | def ParseNolintSuppressions(filename, raw_line, linenum, error):
"""Updates the global list of error-suppressions.
Parses any NOLINT comments on the current line, updating the global
error_suppressions store. Reports an error if the NOLINT comment
was malformed.
Args:
filename: str, the name of the input file.
raw_line: str, the line of input text, with comments.
linenum: int, the number of the current line.
error: function, an error handler.
"""
# FIXME(adonovan): "NOLINT(" is misparsed as NOLINT(*).
matched = _RE_SUPPRESSION.search(raw_line)
if matched:
if matched.group(1) == '_NEXT_LINE':
linenum += 1
category = matched.group(2)
if category in (None, '(*)'): # => "suppress all"
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else:
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category = category[1:-1]
if category in _ERROR_CATEGORIES:
_error_suppressions.setdefault(category, set()).add(linenum)
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papyrussolution/OpenPapyrus | bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91 | Src/OSF/protobuf-3.19.1/python/google/protobuf/internal/encoder.py | python | _StructPackEncoder | (wire_type, format) | return SpecificEncoder | Return a constructor for an encoder for a fixed-width field.
Args:
wire_type: The field's wire type, for encoding tags.
format: The format string to pass to struct.pack(). | Return a constructor for an encoder for a fixed-width field. | [
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"""
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|
mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Tool/gxx.py | python | generate | (env) | Add Builders and construction variables for g++ to an Environment. | Add Builders and construction variables for g++ to an Environment. | [
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"""Add Builders and construction variables for g++ to an Environment."""
static_obj, shared_obj = SCons.Tool.createObjBuilders(env)
if 'CXX' not in env:
env['CXX'] = env.Detect(compilers) or compilers[0]
cxx.generate(env)
# platform specific settings
if env['PLATFORM'] == 'aix':
env['SHCXXFLAGS'] = SCons.Util.CLVar('$CXXFLAGS -mminimal-toc')
env['STATIC_AND_SHARED_OBJECTS_ARE_THE_SAME'] = 1
env['SHOBJSUFFIX'] = '$OBJSUFFIX'
elif env['PLATFORM'] == 'hpux':
env['SHOBJSUFFIX'] = '.pic.o'
elif env['PLATFORM'] == 'sunos':
env['SHOBJSUFFIX'] = '.pic.o'
# determine compiler version
version = gcc.detect_version(env, env['CXX'])
if version:
env['CXXVERSION'] = version | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/datetime.py | python | timezone.__getinitargs__ | (self) | return (self._offset, self._name) | pickle support | pickle support | [
"pickle",
"support"
] | def __getinitargs__(self):
"""pickle support"""
if self._name is None:
return (self._offset,)
return (self._offset, self._name) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextBuffer_GetFloatingLayoutMode | (*args) | return _richtext.RichTextBuffer_GetFloatingLayoutMode(*args) | RichTextBuffer_GetFloatingLayoutMode() -> bool | RichTextBuffer_GetFloatingLayoutMode() -> bool | [
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"""RichTextBuffer_GetFloatingLayoutMode() -> bool"""
return _richtext.RichTextBuffer_GetFloatingLayoutMode(*args) | [
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|
eclipse/omr | 056e7c9ce9d503649190bc5bd9931fac30b4e4bc | jitbuilder/apigen/genutils.py | python | APIClass.as_type | (self) | return APIType(self.name(), self.api) | Returns an instance of APIType corresponding to the described class. | Returns an instance of APIType corresponding to the described class. | [
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|
OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/osr.py | python | SpatialReference.SetOS | (self, *args, **kwargs) | return _osr.SpatialReference_SetOS(self, *args, **kwargs) | r"""SetOS(SpatialReference self, double dfOriginLat, double dfCMeridian, double scale, double fe, double fn) -> OGRErr | r"""SetOS(SpatialReference self, double dfOriginLat, double dfCMeridian, double scale, double fe, double fn) -> OGRErr | [
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|
google/swiftshader | 8ccc63f045d5975fb67f9dfd3d2b8235b0526990 | third_party/SPIRV-Tools/utils/generate_grammar_tables.py | python | InstInitializer.__init__ | (self, opname, caps, exts, operands, version, lastVersion) | Initialization.
Arguments:
- opname: opcode name (with the 'Op' prefix)
- caps: a sequence of capability names required by this opcode
- exts: a sequence of names of extensions enabling this enumerant
- operands: a sequence of (operand-kind, operand-quantifier) tuples
- version: minimal SPIR-V version required for this opcode
- lastVersion: last version of SPIR-V that includes this opcode | Initialization. | [
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Arguments:
- opname: opcode name (with the 'Op' prefix)
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- exts: a sequence of names of extensions enabling this enumerant
- operands: a sequence of (operand-kind, operand-quantifier) tuples
- version: minimal SPIR-V version required for this opcode
- lastVersion: last version of SPIR-V that includes this opcode
"""
assert opname.startswith('Op')
self.opname = opname[2:] # Remove the "Op" prefix.
self.num_caps = len(caps)
self.caps_mask = get_capability_array_name(caps)
self.num_exts = len(exts)
self.exts = get_extension_array_name(exts)
self.operands = [convert_operand_kind(o) for o in operands]
self.fix_syntax()
operands = [o[0] for o in operands]
self.ref_type_id = 'IdResultType' in operands
self.def_result_id = 'IdResult' in operands
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self.lastVersion = convert_max_required_version(lastVersion) | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/turtle.py | python | RawTurtle.onrelease | (self, fun, btn=1, add=None) | Bind fun to mouse-button-release event on this turtle on canvas.
Arguments:
fun -- a function with two arguments, to which will be assigned
the coordinates of the clicked point on the canvas.
num -- number of the mouse-button defaults to 1 (left mouse button).
Example (for a MyTurtle instance named joe):
>>> class MyTurtle(Turtle):
... def glow(self,x,y):
... self.fillcolor("red")
... def unglow(self,x,y):
... self.fillcolor("")
...
>>> joe = MyTurtle()
>>> joe.onclick(joe.glow)
>>> joe.onrelease(joe.unglow)
Clicking on joe turns fillcolor red, unclicking turns it to
transparent. | Bind fun to mouse-button-release event on this turtle on canvas. | [
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"""Bind fun to mouse-button-release event on this turtle on canvas.
Arguments:
fun -- a function with two arguments, to which will be assigned
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SpaceNetChallenge/BuildingDetectors | 3def3c44b5847c744cd2f3356182892d92496579 | qinhaifang/src/caffe-mnc/scripts/cpp_lint.py | python | IsCppString | (line) | return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 | Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
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"""Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
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"""
line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
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] | https://github.com/SpaceNetChallenge/BuildingDetectors/blob/3def3c44b5847c744cd2f3356182892d92496579/qinhaifang/src/caffe-mnc/scripts/cpp_lint.py#L1045-L1059 |
|
tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/deephol/prover.py | python | Prover.prove_one | (self, search_tree: proof_search_tree.ProofSearchTree,
task: proof_assistant_pb2.ProverTask) | Prove a single-goal task.
This method can assume an already initialized search tree with node 0
being the sing goal specified in the task.
Args:
search_tree: The pre-initialized search tree.
task: Task to be performed.
Returns:
Error message on error, None otherwise. | Prove a single-goal task. | [
"Prove",
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"task",
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] | def prove_one(self, search_tree: proof_search_tree.ProofSearchTree,
task: proof_assistant_pb2.ProverTask) -> Optional[Text]:
"""Prove a single-goal task.
This method can assume an already initialized search tree with node 0
being the sing goal specified in the task.
Args:
search_tree: The pre-initialized search tree.
task: Task to be performed.
Returns:
Error message on error, None otherwise.
"""
raise NotImplementedError('Must define this.') | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/fractions.py | python | Fraction.__str__ | (self) | str(self) | str(self) | [
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] | def __str__(self):
"""str(self)"""
if self._denominator == 1:
return str(self._numerator)
else:
return '%s/%s' % (self._numerator, self._denominator) | [
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||
15172658790/Blog | 46e5036f5fbcad535af2255dc0e095cebcd8d710 | 计算机与信息类/数据结构/students/mbinary/allOone/allOone.py | python | AllOne.inc | (self, key,n=1) | Inserts a new key <Key> with value 1. Or increments an existing key by 1.
:type key: str
:rtype: void | Inserts a new key <Key> with value 1. Or increments an existing key by 1.
:type key: str
:rtype: void | [
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"""
Inserts a new key <Key> with value 1. Or increments an existing key by 1.
:type key: str
:rtype: void
"""
if key in self:
self[key]+=n
else:self[key]=n
for i in range(n): self.dll.incTo(key, self[key]) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/internal/extension_dict.py | python | _ExtensionDict.__init__ | (self, extended_message) | Args:
extended_message: Message instance for which we are the Extensions dict. | Args:
extended_message: Message instance for which we are the Extensions dict. | [
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"""
Args:
extended_message: Message instance for which we are the Extensions dict.
"""
self._extended_message = extended_message | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/types/npytypes.py | python | Record.typeof | (self, key) | return self.fields[key].type | Get the type of a field. | Get the type of a field. | [
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"""Get the type of a field.
"""
return self.fields[key].type | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/decimal.py | python | Decimal.is_subnormal | (self, context=None) | return self.adjusted() < context.Emin | Return True if self is subnormal; otherwise return False. | Return True if self is subnormal; otherwise return False. | [
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"""Return True if self is subnormal; otherwise return False."""
if self._is_special or not self:
return False
if context is None:
context = getcontext()
return self.adjusted() < context.Emin | [
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|
Yelp/MOE | 5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c | moe/optimal_learning/python/cpp_wrappers/optimization.py | python | GradientDescentOptimizer.optimize | (self, **kwargs) | C++ does not expose this endpoint. | C++ does not expose this endpoint. | [
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] | def optimize(self, **kwargs):
"""C++ does not expose this endpoint."""
raise NotImplementedError("C++ wrapper currently does not support optimization member functions.") | [
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tensorflow/ngraph-bridge | ea6422491ec75504e78a63db029e7f74ec3479a5 | examples/mnist/mnist_deep_simplified_distributed.py | python | weight_variable | (shape, name) | return weight_var | weight_variable generates a weight variable of a given shape. | weight_variable generates a weight variable of a given shape. | [
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] | def weight_variable(shape, name):
"""weight_variable generates a weight variable of a given shape."""
weight_var = tf.compat.v1.get_variable(name, shape)
return weight_var | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/decomposition/_dict_learning.py | python | dict_learning_online | (X, n_components=2, alpha=1, n_iter=100,
return_code=True, dict_init=None, callback=None,
batch_size=3, verbose=False, shuffle=True,
n_jobs=None, method='lars', iter_offset=0,
random_state=None, return_inner_stats=False,
inner_stats=None, return_n_iter=False,
positive_dict=False, positive_code=False,
method_max_iter=1000) | Solves a dictionary learning matrix factorization problem online.
Finds the best dictionary and the corresponding sparse code for
approximating the data matrix X by solving::
(U^*, V^*) = argmin 0.5 || X - U V ||_2^2 + alpha * || U ||_1
(U,V)
with || V_k ||_2 = 1 for all 0 <= k < n_components
where V is the dictionary and U is the sparse code. This is
accomplished by repeatedly iterating over mini-batches by slicing
the input data.
Read more in the :ref:`User Guide <DictionaryLearning>`.
Parameters
----------
X : array of shape (n_samples, n_features)
Data matrix.
n_components : int,
Number of dictionary atoms to extract.
alpha : float,
Sparsity controlling parameter.
n_iter : int,
Number of mini-batch iterations to perform.
return_code : boolean,
Whether to also return the code U or just the dictionary V.
dict_init : array of shape (n_components, n_features),
Initial value for the dictionary for warm restart scenarios.
callback : callable or None, optional (default: None)
callable that gets invoked every five iterations
batch_size : int,
The number of samples to take in each batch.
verbose : bool, optional (default: False)
To control the verbosity of the procedure.
shuffle : boolean,
Whether to shuffle the data before splitting it in batches.
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
method : {'lars', 'cd'}
lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
iter_offset : int, default 0
Number of previous iterations completed on the dictionary used for
initialization.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
return_inner_stats : boolean, optional
Return the inner statistics A (dictionary covariance) and B
(data approximation). Useful to restart the algorithm in an
online setting. If return_inner_stats is True, return_code is
ignored
inner_stats : tuple of (A, B) ndarrays
Inner sufficient statistics that are kept by the algorithm.
Passing them at initialization is useful in online settings, to
avoid losing the history of the evolution.
A (n_components, n_components) is the dictionary covariance matrix.
B (n_features, n_components) is the data approximation matrix
return_n_iter : bool
Whether or not to return the number of iterations.
positive_dict : bool
Whether to enforce positivity when finding the dictionary.
.. versionadded:: 0.20
positive_code : bool
Whether to enforce positivity when finding the code.
.. versionadded:: 0.20
method_max_iter : int, optional (default=1000)
Maximum number of iterations to perform when solving the lasso problem.
.. versionadded:: 0.22
Returns
-------
code : array of shape (n_samples, n_components),
the sparse code (only returned if `return_code=True`)
dictionary : array of shape (n_components, n_features),
the solutions to the dictionary learning problem
n_iter : int
Number of iterations run. Returned only if `return_n_iter` is
set to `True`.
See also
--------
dict_learning
DictionaryLearning
MiniBatchDictionaryLearning
SparsePCA
MiniBatchSparsePCA | Solves a dictionary learning matrix factorization problem online. | [
"Solves",
"a",
"dictionary",
"learning",
"matrix",
"factorization",
"problem",
"online",
"."
] | def dict_learning_online(X, n_components=2, alpha=1, n_iter=100,
return_code=True, dict_init=None, callback=None,
batch_size=3, verbose=False, shuffle=True,
n_jobs=None, method='lars', iter_offset=0,
random_state=None, return_inner_stats=False,
inner_stats=None, return_n_iter=False,
positive_dict=False, positive_code=False,
method_max_iter=1000):
"""Solves a dictionary learning matrix factorization problem online.
Finds the best dictionary and the corresponding sparse code for
approximating the data matrix X by solving::
(U^*, V^*) = argmin 0.5 || X - U V ||_2^2 + alpha * || U ||_1
(U,V)
with || V_k ||_2 = 1 for all 0 <= k < n_components
where V is the dictionary and U is the sparse code. This is
accomplished by repeatedly iterating over mini-batches by slicing
the input data.
Read more in the :ref:`User Guide <DictionaryLearning>`.
Parameters
----------
X : array of shape (n_samples, n_features)
Data matrix.
n_components : int,
Number of dictionary atoms to extract.
alpha : float,
Sparsity controlling parameter.
n_iter : int,
Number of mini-batch iterations to perform.
return_code : boolean,
Whether to also return the code U or just the dictionary V.
dict_init : array of shape (n_components, n_features),
Initial value for the dictionary for warm restart scenarios.
callback : callable or None, optional (default: None)
callable that gets invoked every five iterations
batch_size : int,
The number of samples to take in each batch.
verbose : bool, optional (default: False)
To control the verbosity of the procedure.
shuffle : boolean,
Whether to shuffle the data before splitting it in batches.
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
method : {'lars', 'cd'}
lars: uses the least angle regression method to solve the lasso problem
(linear_model.lars_path)
cd: uses the coordinate descent method to compute the
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.
iter_offset : int, default 0
Number of previous iterations completed on the dictionary used for
initialization.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
return_inner_stats : boolean, optional
Return the inner statistics A (dictionary covariance) and B
(data approximation). Useful to restart the algorithm in an
online setting. If return_inner_stats is True, return_code is
ignored
inner_stats : tuple of (A, B) ndarrays
Inner sufficient statistics that are kept by the algorithm.
Passing them at initialization is useful in online settings, to
avoid losing the history of the evolution.
A (n_components, n_components) is the dictionary covariance matrix.
B (n_features, n_components) is the data approximation matrix
return_n_iter : bool
Whether or not to return the number of iterations.
positive_dict : bool
Whether to enforce positivity when finding the dictionary.
.. versionadded:: 0.20
positive_code : bool
Whether to enforce positivity when finding the code.
.. versionadded:: 0.20
method_max_iter : int, optional (default=1000)
Maximum number of iterations to perform when solving the lasso problem.
.. versionadded:: 0.22
Returns
-------
code : array of shape (n_samples, n_components),
the sparse code (only returned if `return_code=True`)
dictionary : array of shape (n_components, n_features),
the solutions to the dictionary learning problem
n_iter : int
Number of iterations run. Returned only if `return_n_iter` is
set to `True`.
See also
--------
dict_learning
DictionaryLearning
MiniBatchDictionaryLearning
SparsePCA
MiniBatchSparsePCA
"""
if n_components is None:
n_components = X.shape[1]
if method not in ('lars', 'cd'):
raise ValueError('Coding method not supported as a fit algorithm.')
_check_positive_coding(method, positive_code)
method = 'lasso_' + method
t0 = time.time()
n_samples, n_features = X.shape
# Avoid integer division problems
alpha = float(alpha)
random_state = check_random_state(random_state)
# Init V with SVD of X
if dict_init is not None:
dictionary = dict_init
else:
_, S, dictionary = randomized_svd(X, n_components,
random_state=random_state)
dictionary = S[:, np.newaxis] * dictionary
r = len(dictionary)
if n_components <= r:
dictionary = dictionary[:n_components, :]
else:
dictionary = np.r_[dictionary,
np.zeros((n_components - r, dictionary.shape[1]))]
if verbose == 1:
print('[dict_learning]', end=' ')
if shuffle:
X_train = X.copy()
random_state.shuffle(X_train)
else:
X_train = X
dictionary = check_array(dictionary.T, order='F', dtype=np.float64,
copy=False)
dictionary = np.require(dictionary, requirements='W')
X_train = check_array(X_train, order='C', dtype=np.float64, copy=False)
batches = gen_batches(n_samples, batch_size)
batches = itertools.cycle(batches)
# The covariance of the dictionary
if inner_stats is None:
A = np.zeros((n_components, n_components))
# The data approximation
B = np.zeros((n_features, n_components))
else:
A = inner_stats[0].copy()
B = inner_stats[1].copy()
# If n_iter is zero, we need to return zero.
ii = iter_offset - 1
for ii, batch in zip(range(iter_offset, iter_offset + n_iter), batches):
this_X = X_train[batch]
dt = (time.time() - t0)
if verbose == 1:
sys.stdout.write(".")
sys.stdout.flush()
elif verbose:
if verbose > 10 or ii % ceil(100. / verbose) == 0:
print("Iteration % 3i (elapsed time: % 3is, % 4.1fmn)"
% (ii, dt, dt / 60))
this_code = sparse_encode(this_X, dictionary.T, algorithm=method,
alpha=alpha, n_jobs=n_jobs,
check_input=False,
positive=positive_code,
max_iter=method_max_iter, verbose=verbose).T
# Update the auxiliary variables
if ii < batch_size - 1:
theta = float((ii + 1) * batch_size)
else:
theta = float(batch_size ** 2 + ii + 1 - batch_size)
beta = (theta + 1 - batch_size) / (theta + 1)
A *= beta
A += np.dot(this_code, this_code.T)
B *= beta
B += np.dot(this_X.T, this_code.T)
# Update dictionary
dictionary = _update_dict(dictionary, B, A, verbose=verbose,
random_state=random_state,
positive=positive_dict)
# XXX: Can the residuals be of any use?
# Maybe we need a stopping criteria based on the amount of
# modification in the dictionary
if callback is not None:
callback(locals())
if return_inner_stats:
if return_n_iter:
return dictionary.T, (A, B), ii - iter_offset + 1
else:
return dictionary.T, (A, B)
if return_code:
if verbose > 1:
print('Learning code...', end=' ')
elif verbose == 1:
print('|', end=' ')
code = sparse_encode(X, dictionary.T, algorithm=method, alpha=alpha,
n_jobs=n_jobs, check_input=False,
positive=positive_code, max_iter=method_max_iter,
verbose=verbose)
if verbose > 1:
dt = (time.time() - t0)
print('done (total time: % 3is, % 4.1fmn)' % (dt, dt / 60))
if return_n_iter:
return code, dictionary.T, ii - iter_offset + 1
else:
return code, dictionary.T
if return_n_iter:
return dictionary.T, ii - iter_offset + 1
else:
return dictionary.T | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/index.py | python | DatetimeIndex.year | (self) | return self._get_dt_field("year") | The year of the datetime.
Examples
--------
>>> import cudf
>>> import pandas as pd
>>> datetime_index = cudf.Index(pd.date_range("2000-01-01",
... periods=3, freq="Y"))
>>> datetime_index
DatetimeIndex(['2000-12-31', '2001-12-31', '2002-12-31'], dtype='datetime64[ns]')
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The year of the datetime.
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>>> datetime_index
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/linalg/blas.py | python | find_best_blas_type | (arrays=(), dtype=None) | return prefix, dtype, prefer_fortran | Find best-matching BLAS/LAPACK type.
Arrays are used to determine the optimal prefix of BLAS routines.
Parameters
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arrays : sequence of ndarrays, optional
Arrays can be given to determine optimal prefix of BLAS
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dtype : str or dtype, optional
Data-type specifier. Not used if `arrays` is non-empty.
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BLAS/LAPACK prefix character.
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>>> bla.find_best_blas_type((a,))
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>>> bla.find_best_blas_type((a*1j,))
('z', dtype('complex128'), False)
>>> bla.find_best_blas_type((b,))
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arrays : sequence of ndarrays, optional
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dtype = _np.dtype(dtype)
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dtypes = [ar.dtype for ar in arrays]
dtype = _np.find_common_type(dtypes, ())
try:
index = dtypes.index(dtype)
except ValueError:
index = 0
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prefer_fortran = True
prefix = _type_conv.get(dtype.char, 'd')
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mitsuba-renderer/mitsuba2 | 4e7628c6eed365904ca2ba536b795d1b03410344 | src/python/__init__.py | python | variant | () | return getattr(_tls, 'variant', None) | Returns the currently active variant | Returns the currently active variant | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | PyLocale.__init__ | (self, *args, **kwargs) | __init__(self, int language=-1, int flags=LOCALE_LOAD_DEFAULT) -> PyLocale | __init__(self, int language=-1, int flags=LOCALE_LOAD_DEFAULT) -> PyLocale | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | NotificationMessage.__init__ | (self, *args) | __init__(self) -> NotificationMessage
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/RNN/rnn_quantizer/nndct_shared/nndct_graph/base_graph.py | python | GraphBase.children | (self, node) | Get successors of a node in graph
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Executor.py | python | Executor.get_timestamp | (self) | return 0 | Fetch a time stamp for this Executor. We don't have one, of
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jeog/TDAmeritradeAPI | 91c738afd7d57b54f6231170bd64c2550fafd34d | python/tdma_api/get.py | python | TransactionHistoryGetter.get_symbol | (self) | return clib.get_str(self._abi('GetSymbol'), self._obj) | Returns search symbol being used. | Returns search symbol being used. | [
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"""Returns search symbol being used."""
return clib.get_str(self._abi('GetSymbol'), self._obj) | [
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/muelu/doc/Tutorial/tex/prepareTexTutorial.py | python | deleteDir | (path) | deletes the path entirely | deletes the path entirely | [
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"""deletes the path entirely"""
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros/roslib/src/roslib/names.py | python | resource_name_package | (name) | return name[:name.find(PRN_SEPARATOR)] | pkg/typeName -> pkg, typeName -> None
@param name: package resource name, e.g. 'std_msgs/String'
@type name: str
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@param name: package resource name, e.g. 'std_msgs/String'
@type name: str
@return: package name of resource
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/sparse_ops.py | python | sparse_maximum | (sp_a, sp_b, name=None) | return sparse_tensor.SparseTensor(out_indices, out_values, sp_a.dense_shape) | Returns the element-wise max of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Example:
```python
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[1]], [1], [7])
res = tf.sparse_maximum(sp_zero, sp_one).eval()
# "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]).
```
Args:
sp_a: a `SparseTensor` operand whose dtype is real, and indices
lexicographically ordered.
sp_b: the other `SparseTensor` operand with the same requirements (and the
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name: optional name of the operation.
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"""Returns the element-wise max of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Example:
```python
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[1]], [1], [7])
res = tf.sparse_maximum(sp_zero, sp_one).eval()
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```
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sp_b: the other `SparseTensor` operand with the same requirements (and the
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/_osx_support.py | python | _find_executable | (executable, path=None) | Tries to find 'executable' in the directories listed in 'path'.
A string listing directories separated by 'os.pathsep'; defaults to
os.environ['PATH']. Returns the complete filename or None if not found. | Tries to find 'executable' in the directories listed in 'path'. | [
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] | def _find_executable(executable, path=None):
"""Tries to find 'executable' in the directories listed in 'path'.
A string listing directories separated by 'os.pathsep'; defaults to
os.environ['PATH']. Returns the complete filename or None if not found.
"""
if path is None:
path = os.environ['PATH']
paths = path.split(os.pathsep)
base, ext = os.path.splitext(executable)
if (sys.platform == 'win32' or os.name == 'os2') and (ext != '.exe'):
executable = executable + '.exe'
if not os.path.isfile(executable):
for p in paths:
f = os.path.join(p, executable)
if os.path.isfile(f):
# the file exists, we have a shot at spawn working
return f
return None
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return executable | [
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tcpexmachina/remy | 687b5db29b81df7ae8737889c78b47e7f9788297 | scripts/plot.py | python | process_replot_argument | (replot_dir, results_dir) | return remyccs, link_ppt_range, console_dir | Reads the args.json file in a results directory, copies it to an
appropriate location in the current results directory and returns the link
speed range and a list of RemyCC files. | Reads the args.json file in a results directory, copies it to an
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speed range and a list of RemyCC files. | [
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"""Reads the args.json file in a results directory, copies it to an
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speed range and a list of RemyCC files."""
argsfilename = os.path.join(replot_dir, "args.json")
argsfile = open(argsfilename)
jsondict = json.load(argsfile)
argsfile.close()
args = jsondict["args"]
remyccs = args["remycc"]
link_ppt_range = np.logspace(np.log10(args["link_ppt"][0]), np.log10(args["link_ppt"][1]), args["num_points"])
console_dir = os.path.join(replot_dir, "outputs")
replots_dirname = os.path.join(results_dir, "replots", os.path.basename(replot_dir))
os.makedirs(replots_dirname, exist_ok=True)
target_filename = os.path.join(replots_dirname, "args.json")
shutil.copy(argsfilename, target_filename)
return remyccs, link_ppt_range, console_dir | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | FileTypeInfoSequence | (*args, **kwargs) | return val | FileTypeInfoSequence(wxArrayString sArray) -> FileTypeInfo | FileTypeInfoSequence(wxArrayString sArray) -> FileTypeInfo | [
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"""FileTypeInfoSequence(wxArrayString sArray) -> FileTypeInfo"""
val = _misc_.new_FileTypeInfoSequence(*args, **kwargs)
return val | [
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|
albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/base/service.py | python | GBaseService.__init__ | (self, email=None, password=None, source=None,
server='base.google.com', api_key=None, additional_headers=None,
handler=None, **kwargs) | Creates a client for the Google Base service.
Args:
email: string (optional) The user's email address, used for
authentication.
password: string (optional) The user's password.
source: string (optional) The name of the user's application.
server: string (optional) The name of the server to which a connection
will be opened. Default value: 'base.google.com'.
api_key: string (optional) The Google Base API key to use.
**kwargs: The other parameters to pass to gdata.service.GDataService
constructor. | Creates a client for the Google Base service. | [
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] | def __init__(self, email=None, password=None, source=None,
server='base.google.com', api_key=None, additional_headers=None,
handler=None, **kwargs):
"""Creates a client for the Google Base service.
Args:
email: string (optional) The user's email address, used for
authentication.
password: string (optional) The user's password.
source: string (optional) The name of the user's application.
server: string (optional) The name of the server to which a connection
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api_key: string (optional) The Google Base API key to use.
**kwargs: The other parameters to pass to gdata.service.GDataService
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"""
gdata.service.GDataService.__init__(
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||
danxuhk/ContinuousCRF-CNN | 2b6dcaf179620f118b225ed12c890414ca828e21 | scripts/cpp_lint.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:
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"""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).
"""
googlename = self.RepositoryName()
project, rest = os.path.split(googlename)
return (project,) + os.path.splitext(rest) | [
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|
windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Source/ThirdParty/FakeIt/2.0.2/build/coveralls.py | python | Repository.git | (self, *arguments) | return process.communicate()[0].decode('UTF-8') | Return output from git. | Return output from git. | [
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"""Return output from git."""
process = subprocess.Popen(['git'] + list(arguments),
stdout=subprocess.PIPE,
cwd=self.cwd)
return process.communicate()[0].decode('UTF-8') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | PrintPreview.GetFrame | (*args, **kwargs) | return _windows_.PrintPreview_GetFrame(*args, **kwargs) | GetFrame(self) -> Frame | GetFrame(self) -> Frame | [
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|
google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/text_format.py | python | _Parser._MergeMessageField | (self, tokenizer, message, field) | Merges a single scalar field into a message.
Args:
tokenizer: A tokenizer to parse the field value.
message: The message of which field is a member.
field: The descriptor of the field to be merged.
Raises:
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] | def _MergeMessageField(self, tokenizer, message, field):
"""Merges a single scalar field into a message.
Args:
tokenizer: A tokenizer to parse the field value.
message: The message of which field is a member.
field: The descriptor of the field to be merged.
Raises:
ParseError: In case of text parsing problems.
"""
is_map_entry = _IsMapEntry(field)
if tokenizer.TryConsume('<'):
end_token = '>'
else:
tokenizer.Consume('{')
end_token = '}'
if (field.message_type.full_name == _ANY_FULL_TYPE_NAME and
tokenizer.TryConsume('[')):
packed_type_name = self._ConsumeAnyTypeUrl(tokenizer)
tokenizer.Consume(']')
tokenizer.TryConsume(':')
if tokenizer.TryConsume('<'):
expanded_any_end_token = '>'
else:
tokenizer.Consume('{')
expanded_any_end_token = '}'
if not self.descriptor_pool:
raise ParseError('Descriptor pool required to parse expanded Any field')
expanded_any_sub_message = _BuildMessageFromTypeName(packed_type_name,
self.descriptor_pool)
if not expanded_any_sub_message:
raise ParseError('Type %s not found in descriptor pool' %
packed_type_name)
while not tokenizer.TryConsume(expanded_any_end_token):
if tokenizer.AtEnd():
raise tokenizer.ParseErrorPreviousToken('Expected "%s".' %
(expanded_any_end_token,))
self._MergeField(tokenizer, expanded_any_sub_message)
if field.label == descriptor.FieldDescriptor.LABEL_REPEATED:
any_message = getattr(message, field.name).add()
else:
any_message = getattr(message, field.name)
any_message.Pack(expanded_any_sub_message)
elif field.label == descriptor.FieldDescriptor.LABEL_REPEATED:
if field.is_extension:
sub_message = message.Extensions[field].add()
elif is_map_entry:
sub_message = getattr(message, field.name).GetEntryClass()()
else:
sub_message = getattr(message, field.name).add()
else:
if field.is_extension:
sub_message = message.Extensions[field]
else:
sub_message = getattr(message, field.name)
sub_message.SetInParent()
while not tokenizer.TryConsume(end_token):
if tokenizer.AtEnd():
raise tokenizer.ParseErrorPreviousToken('Expected "%s".' % (end_token,))
self._MergeField(tokenizer, sub_message)
if is_map_entry:
value_cpptype = field.message_type.fields_by_name['value'].cpp_type
if value_cpptype == descriptor.FieldDescriptor.CPPTYPE_MESSAGE:
value = getattr(message, field.name)[sub_message.key]
value.MergeFrom(sub_message.value)
else:
getattr(message, field.name)[sub_message.key] = sub_message.value | [
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||
LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/jinja2/utils.py | python | LRUCache.__delitem__ | (self, key) | Remove an item from the cache dict.
Raise a `KeyError` if it does not exist. | Remove an item from the cache dict.
Raise a `KeyError` if it does not exist. | [
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"if",
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] | def __delitem__(self, key):
"""Remove an item from the cache dict.
Raise a `KeyError` if it does not exist.
"""
self._wlock.acquire()
try:
del self._mapping[key]
try:
self._remove(key)
except ValueError:
# __getitem__ is not locked, it might happen
pass
finally:
self._wlock.release() | [
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||
RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/Resources/ST1.14/Tools/sentrytribe.py | python | Sentrytribe.resend_message | (self, msg_id) | Resend fragments
requires a ping done first to find missing fragments | Resend fragments
requires a ping done first to find missing fragments | [
"Resend",
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"requires",
"a",
"ping",
"done",
"first",
"to",
"find",
"missing",
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] | def resend_message(self, msg_id):
"""
Resend fragments
requires a ping done first to find missing fragments
"""
if self.pending_msg_id == msg_id and msg_id in self.pending_messages.keys():
print "[+] Found saved message, only resending missing fragments"
for i in self.pending_messages[msg_id].keys():
if not self.pending_fragments[i-1]: # fragments starts at offset 0
self.send_data(CMD_EXECUTE, self.pending_messages[msg_id][i], msg_id, i, len(self.pending_messages[msg_id].keys()))
else:
print "Skipping", i
elif msg_id in self.pending_messages.keys():
print "[+] Found saved message, but couldn't find ping or missing fragments, resending everything"
frag_count = len(self.pending_messages[msg_id].keys())
for i in self.pending_messages[msg_id].keys():
self.send_data(CMD_EXECUTE, self.pending_messages[msg_id][i], msg_id, i, frag_count)
else:
raise Exception("Couldn't find pending message, did you ping? "+repr(msg_id)+" not in "+str(self.pending_messages.keys())) | [
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||
p4lang/p4c | 3272e79369f20813cc1a555a5eb26f44432f84a4 | tools/cpplint.py | python | NestingState.InNamespaceBody | (self) | return self.stack and isinstance(self.stack[-1], _NamespaceInfo) | Check if we are currently one level inside a namespace body.
Returns:
True if top of the stack is a namespace block, False otherwise. | Check if we are currently one level inside a namespace body. | [
"Check",
"if",
"we",
"are",
"currently",
"one",
"level",
"inside",
"a",
"namespace",
"body",
"."
] | def InNamespaceBody(self):
"""Check if we are currently one level inside a namespace body.
Returns:
True if top of the stack is a namespace block, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _NamespaceInfo) | [
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|
intel-iot-devkit/how-to-code-samples | b4ea616f36bbfa2e042beb1698f968cfd651d79f | access-control/python/iot_access_control/log.py | python | log | (event) | Publish message to MQTT server and data store. | Publish message to MQTT server and data store. | [
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] | def log(event):
"""
Publish message to MQTT server and data store.
"""
message = "{0} {1}".format(datetime.utcnow().isoformat(), event)
payload = {"value": message}
print(message)
send(payload) | [
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||
MVIG-SJTU/RMPE | 5188c230ec800c12be7369c3619615bc9b020aa4 | scripts/cpp_lint.py | python | ProcessFileData | (filename, file_extension, lines, error,
extra_check_functions=[]) | Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is terminated with a newline.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error | Performs lint checks and reports any errors to the given error function. | [
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"lint",
"checks",
"and",
"reports",
"any",
"errors",
"to",
"the",
"given",
"error",
"function",
"."
] | def ProcessFileData(filename, file_extension, lines, error,
extra_check_functions=[]):
"""Performs lint checks and reports any errors to the given error function.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
lines: An array of strings, each representing a line of the file, with the
last element being empty if the file is terminated with a newline.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
lines = (['// marker so line numbers and indices both start at 1'] + lines +
['// marker so line numbers end in a known way'])
include_state = _IncludeState()
function_state = _FunctionState()
nesting_state = _NestingState()
ResetNolintSuppressions()
CheckForCopyright(filename, lines, error)
if file_extension == 'h':
CheckForHeaderGuard(filename, lines, error)
RemoveMultiLineComments(filename, lines, error)
clean_lines = CleansedLines(lines)
for line in xrange(clean_lines.NumLines()):
ProcessLine(filename, file_extension, clean_lines, line,
include_state, function_state, nesting_state, error,
extra_check_functions)
nesting_state.CheckCompletedBlocks(filename, error)
CheckForIncludeWhatYouUse(filename, clean_lines, include_state, error)
# We check here rather than inside ProcessLine so that we see raw
# lines rather than "cleaned" lines.
CheckForBadCharacters(filename, lines, error)
CheckForNewlineAtEOF(filename, lines, error) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/dataview.py | python | DataViewRenderer.GetAlignment | (*args, **kwargs) | return _dataview.DataViewRenderer_GetAlignment(*args, **kwargs) | GetAlignment(self) -> int | GetAlignment(self) -> int | [
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"(",
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")",
"-",
">",
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] | def GetAlignment(*args, **kwargs):
"""GetAlignment(self) -> int"""
return _dataview.DataViewRenderer_GetAlignment(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/dataview.py#L1193-L1195 |
|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/collections.py | python | Counter.__init__ | (self, iterable=None, **kwds) | Create a new, empty Counter object. And if given, count elements
from an input iterable. Or, initialize the count from another mapping
of elements to their counts.
>>> c = Counter() # a new, empty counter
>>> c = Counter('gallahad') # a new counter from an iterable
>>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping
>>> c = Counter(a=4, b=2) # a new counter from keyword args | Create a new, empty Counter object. And if given, count elements
from an input iterable. Or, initialize the count from another mapping
of elements to their counts. | [
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'''Create a new, empty Counter object. And if given, count elements
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of elements to their counts.
>>> c = Counter() # a new, empty counter
>>> c = Counter('gallahad') # a new counter from an iterable
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>>> c = Counter(a=4, b=2) # a new counter from keyword args
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super(Counter, self).__init__()
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||
weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/wpt/wpt/tools/wptserve/wptserve/request.py | python | RequestHeaders.get_list | (self, key, default=missing) | Get all the header values for a particular field name as
a list | Get all the header values for a particular field name as
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"""Get all the header values for a particular field name as
a list"""
try:
return dict.__getitem__(self, key.lower())
except KeyError:
if default is not missing:
return default
else:
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||
okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_internal/pyproject.py | python | load_pyproject_toml | (
use_pep517, # type: Optional[bool]
pyproject_toml, # type: str
setup_py, # type: str
req_name # type: str
) | return (requires, backend, check) | Load the pyproject.toml file.
Parameters:
use_pep517 - Has the user requested PEP 517 processing? None
means the user hasn't explicitly specified.
pyproject_toml - Location of the project's pyproject.toml file
setup_py - Location of the project's setup.py file
req_name - The name of the requirement we're processing (for
error reporting)
Returns:
None if we should use the legacy code path, otherwise a tuple
(
requirements from pyproject.toml,
name of PEP 517 backend,
requirements we should check are installed after setting
up the build environment
) | Load the pyproject.toml file. | [
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] | def load_pyproject_toml(
use_pep517, # type: Optional[bool]
pyproject_toml, # type: str
setup_py, # type: str
req_name # type: str
):
# type: (...) -> Optional[Tuple[List[str], str, List[str]]]
"""Load the pyproject.toml file.
Parameters:
use_pep517 - Has the user requested PEP 517 processing? None
means the user hasn't explicitly specified.
pyproject_toml - Location of the project's pyproject.toml file
setup_py - Location of the project's setup.py file
req_name - The name of the requirement we're processing (for
error reporting)
Returns:
None if we should use the legacy code path, otherwise a tuple
(
requirements from pyproject.toml,
name of PEP 517 backend,
requirements we should check are installed after setting
up the build environment
)
"""
has_pyproject = os.path.isfile(pyproject_toml)
has_setup = os.path.isfile(setup_py)
if has_pyproject:
with io.open(pyproject_toml, encoding="utf-8") as f:
pp_toml = pytoml.load(f)
build_system = pp_toml.get("build-system")
else:
build_system = None
# The following cases must use PEP 517
# We check for use_pep517 being non-None and falsey because that means
# the user explicitly requested --no-use-pep517. The value 0 as
# opposed to False can occur when the value is provided via an
# environment variable or config file option (due to the quirk of
# strtobool() returning an integer in pip's configuration code).
if has_pyproject and not has_setup:
if use_pep517 is not None and not use_pep517:
raise InstallationError(
"Disabling PEP 517 processing is invalid: "
"project does not have a setup.py"
)
use_pep517 = True
elif build_system and "build-backend" in build_system:
if use_pep517 is not None and not use_pep517:
raise InstallationError(
"Disabling PEP 517 processing is invalid: "
"project specifies a build backend of {} "
"in pyproject.toml".format(
build_system["build-backend"]
)
)
use_pep517 = True
# If we haven't worked out whether to use PEP 517 yet,
# and the user hasn't explicitly stated a preference,
# we do so if the project has a pyproject.toml file.
elif use_pep517 is None:
use_pep517 = has_pyproject
# At this point, we know whether we're going to use PEP 517.
assert use_pep517 is not None
# If we're using the legacy code path, there is nothing further
# for us to do here.
if not use_pep517:
return None
if build_system is None:
# Either the user has a pyproject.toml with no build-system
# section, or the user has no pyproject.toml, but has opted in
# explicitly via --use-pep517.
# In the absence of any explicit backend specification, we
# assume the setuptools backend that most closely emulates the
# traditional direct setup.py execution, and require wheel and
# a version of setuptools that supports that backend.
build_system = {
"requires": ["setuptools>=40.8.0", "wheel"],
"build-backend": "setuptools.build_meta:__legacy__",
}
# If we're using PEP 517, we have build system information (either
# from pyproject.toml, or defaulted by the code above).
# Note that at this point, we do not know if the user has actually
# specified a backend, though.
assert build_system is not None
# Ensure that the build-system section in pyproject.toml conforms
# to PEP 518.
error_template = (
"{package} has a pyproject.toml file that does not comply "
"with PEP 518: {reason}"
)
# Specifying the build-system table but not the requires key is invalid
if "requires" not in build_system:
raise InstallationError(
error_template.format(package=req_name, reason=(
"it has a 'build-system' table but not "
"'build-system.requires' which is mandatory in the table"
))
)
# Error out if requires is not a list of strings
requires = build_system["requires"]
if not _is_list_of_str(requires):
raise InstallationError(error_template.format(
package=req_name,
reason="'build-system.requires' is not a list of strings.",
))
backend = build_system.get("build-backend")
check = [] # type: List[str]
if backend is None:
# If the user didn't specify a backend, we assume they want to use
# the setuptools backend. But we can't be sure they have included
# a version of setuptools which supplies the backend, or wheel
# (which is needed by the backend) in their requirements. So we
# make a note to check that those requirements are present once
# we have set up the environment.
# This is quite a lot of work to check for a very specific case. But
# the problem is, that case is potentially quite common - projects that
# adopted PEP 518 early for the ability to specify requirements to
# execute setup.py, but never considered needing to mention the build
# tools themselves. The original PEP 518 code had a similar check (but
# implemented in a different way).
backend = "setuptools.build_meta:__legacy__"
check = ["setuptools>=40.8.0", "wheel"]
return (requires, backend, check) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | StockGDI.GetCursor | (*args, **kwargs) | return _gdi_.StockGDI_GetCursor(*args, **kwargs) | GetCursor(int item) -> Cursor | GetCursor(int item) -> Cursor | [
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")",
"-",
">",
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] | def GetCursor(*args, **kwargs):
"""GetCursor(int item) -> Cursor"""
return _gdi_.StockGDI_GetCursor(*args, **kwargs) | [
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|
chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/exec_util.py | python | exec_cmd | (cmd, path, input_string=None) | return {'out': out.decode('utf-8'), 'err': err.decode('utf-8'), 'ret': ret} | Execute the specified command and return the result. | Execute the specified command and return the result. | [
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] | def exec_cmd(cmd, path, input_string=None):
""" Execute the specified command and return the result. """
out = ''
err = ''
ret = -1
parts = cmd.split()
try:
if input_string is None:
process = Popen(
parts,
cwd=path,
stdout=PIPE,
stderr=PIPE,
shell=(sys.platform == 'win32'))
out, err = process.communicate()
ret = process.returncode
else:
process = Popen(
parts,
cwd=path,
stdin=PIPE,
stdout=PIPE,
stderr=PIPE,
shell=(sys.platform == 'win32'))
out, err = process.communicate(input=input_string)
ret = process.returncode
except IOError as e:
(errno, strerror) = e.args
raise
except:
raise
return {'out': out.decode('utf-8'), 'err': err.decode('utf-8'), 'ret': ret} | [
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|
albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/photos/__init__.py | python | TagEntry.GetAlbumUri | (self) | return href[:pos] | Return the uri to the AlbumEntry containing this tag | Return the uri to the AlbumEntry containing this tag | [
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"the",
"uri",
"to",
"the",
"AlbumEntry",
"containing",
"this",
"tag"
] | def GetAlbumUri(self):
"""Return the uri to the AlbumEntry containing this tag"""
href = self.GetSelfLink().href
pos = href.find('/photoid')
if pos == -1:
return None
return href[:pos] | [
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|
weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/jinja2/utils.py | python | generate_lorem_ipsum | (n=5, html=True, min=20, max=100) | return Markup(u'\n'.join(u'<p>%s</p>' % escape(x) for x in result)) | Generate some lorem impsum for the template. | Generate some lorem impsum for the template. | [
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"for",
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"template",
"."
] | def generate_lorem_ipsum(n=5, html=True, min=20, max=100):
"""Generate some lorem impsum for the template."""
from jinja2.constants import LOREM_IPSUM_WORDS
from random import choice, randrange
words = LOREM_IPSUM_WORDS.split()
result = []
for _ in range(n):
next_capitalized = True
last_comma = last_fullstop = 0
word = None
last = None
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# each paragraph contains out of 20 to 100 words.
for idx, _ in enumerate(range(randrange(min, max))):
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word = choice(words)
if word != last:
last = word
break
if next_capitalized:
word = word.capitalize()
next_capitalized = False
# add commas
if idx - randrange(3, 8) > last_comma:
last_comma = idx
last_fullstop += 2
word += ','
# add end of sentences
if idx - randrange(10, 20) > last_fullstop:
last_comma = last_fullstop = idx
word += '.'
next_capitalized = True
p.append(word)
# ensure that the paragraph ends with a dot.
p = u' '.join(p)
if p.endswith(','):
p = p[:-1] + '.'
elif not p.endswith('.'):
p += '.'
result.append(p)
if not html:
return u'\n\n'.join(result)
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/parso/py3/parso/tree.py | python | search_ancestor | (node: 'NodeOrLeaf', *node_types: str) | return None | Recursively looks at the parents of a node and returns the first found node
that matches ``node_types``. Returns ``None`` if no matching node is found.
This function is deprecated, use :meth:`NodeOrLeaf.search_ancestor` instead.
:param node: The ancestors of this node will be checked.
:param node_types: type names that are searched for. | Recursively looks at the parents of a node and returns the first found node
that matches ``node_types``. Returns ``None`` if no matching node is found. | [
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"""
Recursively looks at the parents of a node and returns the first found node
that matches ``node_types``. Returns ``None`` if no matching node is found.
This function is deprecated, use :meth:`NodeOrLeaf.search_ancestor` instead.
:param node: The ancestors of this node will be checked.
:param node_types: type names that are searched for.
"""
n = node.parent
while n is not None:
if n.type in node_types:
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n = n.parent
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|
francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | cnx/tickds.py | python | TickDataSeries.getHighDataSeries | (self) | return self.__highDS | Returns a :class:`pyalgotrade.dataseries.DataSeries` with the high prices. | Returns a :class:`pyalgotrade.dataseries.DataSeries` with the high prices. | [
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"""Returns a :class:`pyalgotrade.dataseries.DataSeries` with the high prices."""
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|
oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/MSVSSettings.py | python | ConvertVCMacrosToMSBuild | (s) | return s | Convert the MSVS macros found in the string to the MSBuild equivalent.
This list is probably not exhaustive. Add as needed. | Convert the MSVS macros found in the string to the MSBuild equivalent. | [
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"""Convert the MSVS macros found in the string to the MSBuild equivalent.
This list is probably not exhaustive. Add as needed.
"""
if "$" in s:
replace_map = {
"$(ConfigurationName)": "$(Configuration)",
"$(InputDir)": "%(RelativeDir)",
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"$(InputName)": "%(Filename)",
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for old, new in replace_map.items():
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return s | [
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|
ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/ceph_manager.py | python | CephManager.expand_pool | (self, pool_name, by, max_pgs) | Increase the number of pgs in a pool | Increase the number of pgs in a pool | [
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"""
Increase the number of pgs in a pool
"""
with self.lock:
assert isinstance(pool_name, str)
assert isinstance(by, int)
assert pool_name in self.pools
if self.get_num_creating() > 0:
return False
if (self.pools[pool_name] + by) > max_pgs:
return False
self.log("increase pool size by %d" % (by,))
new_pg_num = self.pools[pool_name] + by
self.set_pool_property(pool_name, "pg_num", new_pg_num)
self.pools[pool_name] = new_pg_num
return True | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | BitmapFromBuffer | (width, height, dataBuffer, alphaBuffer=None) | Creates a `wx.Bitmap` from the data in dataBuffer. The dataBuffer
parameter must be a Python object that implements the buffer
interface, such as a string, array, etc. The dataBuffer object is
expected to contain a series of RGB bytes and be width*height*3
bytes long. A buffer object can optionally be supplied for the
image's alpha channel data, and it is expected to be width*height
bytes long. On Windows and Mac the RGB values are 'premultiplied'
by the alpha values. (The other platforms do the multiplication
themselves.)
Unlike `wx.ImageFromBuffer` the bitmap created with this function
does not share the memory buffer with the buffer object. This is
because the native pixel buffer format varies on different
platforms, and so instead an efficient as possible copy of the
data is made from the buffer objects to the bitmap's native pixel
buffer. For direct access to a bitmap's pixel buffer see
`wx.NativePixelData` and `wx.AlphaPixelData`.
:see: `wx.Bitmap`, `wx.BitmapFromBufferRGBA`, `wx.NativePixelData`,
`wx.AlphaPixelData`, `wx.ImageFromBuffer` | Creates a `wx.Bitmap` from the data in dataBuffer. The dataBuffer
parameter must be a Python object that implements the buffer
interface, such as a string, array, etc. The dataBuffer object is
expected to contain a series of RGB bytes and be width*height*3
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image's alpha channel data, and it is expected to be width*height
bytes long. On Windows and Mac the RGB values are 'premultiplied'
by the alpha values. (The other platforms do the multiplication
themselves.) | [
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"""
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interface, such as a string, array, etc. The dataBuffer object is
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platforms, and so instead an efficient as possible copy of the
data is made from the buffer objects to the bitmap's native pixel
buffer. For direct access to a bitmap's pixel buffer see
`wx.NativePixelData` and `wx.AlphaPixelData`.
:see: `wx.Bitmap`, `wx.BitmapFromBufferRGBA`, `wx.NativePixelData`,
`wx.AlphaPixelData`, `wx.ImageFromBuffer`
"""
if alphaBuffer is not None:
return _gdi_._BitmapFromBufferAlpha(width, height, dataBuffer, alphaBuffer)
else:
return _gdi_._BitmapFromBuffer(width, height, dataBuffer) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Pen.SetJoin | (*args, **kwargs) | return _gdi_.Pen_SetJoin(*args, **kwargs) | SetJoin(self, int join_style) | SetJoin(self, int join_style) | [
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|
NicknineTheEagle/TF2-Base | 20459c5a7fbc995b6bf54fa85c2f62a101e9fb64 | src/thirdparty/protobuf-2.3.0/python/google/protobuf/service.py | python | RpcController.Reset | (self) | Resets the RpcController to its initial state.
After the RpcController has been reset, it may be reused in
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"""Resets the RpcController to its initial state.
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"""
raise NotImplementedError | [
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||
carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/carla/agents/navigation/local_planner.py | python | LocalPlanner.__init__ | (self, vehicle, opt_dict={}) | :param vehicle: actor to apply to local planner logic onto
:param opt_dict: dictionary of arguments with different parameters:
dt: time between simulation steps
target_speed: desired cruise speed in Km/h
sampling_radius: distance between the waypoints part of the plan
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max_throttle: maximum throttle applied to the vehicle
max_brake: maximum brake applied to the vehicle
max_steering: maximum steering applied to the vehicle
offset: distance between the route waypoints and the center of the lane | :param vehicle: actor to apply to local planner logic onto
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target_speed: desired cruise speed in Km/h
sampling_radius: distance between the waypoints part of the plan
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longitudinal_control_dict: values of the longitudinal PID controller
max_throttle: maximum throttle applied to the vehicle
max_brake: maximum brake applied to the vehicle
max_steering: maximum steering applied to the vehicle
offset: distance between the route waypoints and the center of the lane
"""
self._vehicle = vehicle
self._world = self._vehicle.get_world()
self._map = self._world.get_map()
self._vehicle_controller = None
self.target_waypoint = None
self.target_road_option = None
self._waypoints_queue = deque(maxlen=10000)
self._min_waypoint_queue_length = 100
self._stop_waypoint_creation = False
# Base parameters
self._dt = 1.0 / 20.0
self._target_speed = 20.0 # Km/h
self._sampling_radius = 2.0
self._args_lateral_dict = {'K_P': 1.95, 'K_I': 0.05, 'K_D': 0.2, 'dt': self._dt}
self._args_longitudinal_dict = {'K_P': 1.0, 'K_I': 0.05, 'K_D': 0, 'dt': self._dt}
self._max_throt = 0.75
self._max_brake = 0.3
self._max_steer = 0.8
self._offset = 0
self._base_min_distance = 3.0
self._follow_speed_limits = False
# Overload parameters
if opt_dict:
if 'dt' in opt_dict:
self._dt = opt_dict['dt']
if 'target_speed' in opt_dict:
self._target_speed = opt_dict['target_speed']
if 'sampling_radius' in opt_dict:
self._sampling_radius = opt_dict['sampling_radius']
if 'lateral_control_dict' in opt_dict:
self._args_lateral_dict = opt_dict['lateral_control_dict']
if 'longitudinal_control_dict' in opt_dict:
self._args_longitudinal_dict = opt_dict['longitudinal_control_dict']
if 'max_throttle' in opt_dict:
self._max_throt = opt_dict['max_throttle']
if 'max_brake' in opt_dict:
self._max_brake = opt_dict['max_brake']
if 'max_steering' in opt_dict:
self._max_steer = opt_dict['max_steering']
if 'offset' in opt_dict:
self._offset = opt_dict['offset']
if 'base_min_distance' in opt_dict:
self._base_min_distance = opt_dict['base_min_distance']
if 'follow_speed_limits' in opt_dict:
self._follow_speed_limits = opt_dict['follow_speed_limits']
# initializing controller
self._init_controller() | [
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||
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/protobuf/python/google/protobuf/internal/containers.py | python | RepeatedScalarFieldContainer.extend | (self, elem_seq) | Extends by appending the given sequence. Similar to list.extend(). | Extends by appending the given sequence. Similar to list.extend(). | [
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] | def extend(self, elem_seq):
"""Extends by appending the given sequence. Similar to list.extend()."""
if not elem_seq:
return
new_values = []
for elem in elem_seq:
self._type_checker.CheckValue(elem)
new_values.append(elem)
self._values.extend(new_values)
self._message_listener.Modified() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/traitlets/py2/traitlets/traitlets.py | python | _validate_link | (*tuples) | Validate arguments for traitlet link functions | Validate arguments for traitlet link functions | [
"Validate",
"arguments",
"for",
"traitlet",
"link",
"functions"
] | def _validate_link(*tuples):
"""Validate arguments for traitlet link functions"""
for t in tuples:
if not len(t) == 2:
raise TypeError("Each linked traitlet must be specified as (HasTraits, 'trait_name'), not %r" % t)
obj, trait_name = t
if not isinstance(obj, HasTraits):
raise TypeError("Each object must be HasTraits, not %r" % type(obj))
if not trait_name in obj.traits():
raise TypeError("%r has no trait %r" % (obj, trait_name)) | [
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||
kiwix/kiwix-xulrunner | 38f4a10ae4b1585c16cb11730bb0dcc4924ae19f | android/gen-std-icon.py | python | copy_to | (src, dst) | copy source content (local or remote) to local file | copy source content (local or remote) to local file | [
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"content",
"(",
"local",
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] | def copy_to(src, dst):
''' copy source content (local or remote) to local file '''
local = None
if is_remote_path(src):
local = tempfile.NamedTemporaryFile(delete=False)
download_remote_file(src, local.name)
src = local.name
shutil.copy(src, dst)
if local is not None:
os.remove(local.name) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/xml/etree/ElementTree.py | python | Element.iterfind | (self, path, namespaces=None) | return ElementPath.iterfind(self, path, namespaces) | Find all matching subelements by tag name or path.
*path* is a string having either an element tag or an XPath,
*namespaces* is an optional mapping from namespace prefix to full name.
Return an iterable yielding all matching elements in document order. | Find all matching subelements by tag name or path. | [
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] | def iterfind(self, path, namespaces=None):
"""Find all matching subelements by tag name or path.
*path* is a string having either an element tag or an XPath,
*namespaces* is an optional mapping from namespace prefix to full name.
Return an iterable yielding all matching elements in document order.
"""
return ElementPath.iterfind(self, path, namespaces) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib2to3/fixes/fix_import.py | python | traverse_imports | (names) | Walks over all the names imported in a dotted_as_names node. | Walks over all the names imported in a dotted_as_names node. | [
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] | def traverse_imports(names):
"""
Walks over all the names imported in a dotted_as_names node.
"""
pending = [names]
while pending:
node = pending.pop()
if node.type == token.NAME:
yield node.value
elif node.type == syms.dotted_name:
yield "".join([ch.value for ch in node.children])
elif node.type == syms.dotted_as_name:
pending.append(node.children[0])
elif node.type == syms.dotted_as_names:
pending.extend(node.children[::-2])
else:
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gemrb/gemrb | 730206eed8d1dd358ca5e69a62f9e099aa22ffc6 | gemrb/GUIScripts/GUIOPT.py | python | OpenVideoOptionsWindow | () | return | Open video options window | Open video options window | [
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"video",
"options",
"window"
] | def OpenVideoOptionsWindow ():
"""Open video options window"""
global HelpTextArea
#GemRB.GetView("SUB_WIN", 0).Close()
Window = GemRB.LoadWindow (6, "GUIOPT")
Window.AddAlias("SUB_WIN", 0)
Window.SetFlags (WF_BORDERLESS, OP_OR)
HelpTextArea = GUIOPTControls.OptHelpText ('VideoOptions', Window, 33, 18038)
GUIOPTControls.OptDone (CloseVideoOptionsWindow, Window, 21)
GUIOPTControls.OptCancel (CloseVideoOptionsWindow, Window, 32)
GUIOPTControls.OptSlider (18038, 17203, HelpTextArea, Window, 3, 35, 17129, 'Brightness Correction', DisplayHelpBrightness, 4)
GUIOPTControls.OptSlider (18038, 17204, HelpTextArea, Window, 22, 36, 17128, 'Gamma Correction', DisplayHelpContrast)
GUIOPTControls.OptRadio (DisplayHelpBPP, Window, 5, 37, 'BitsPerPixel', 16)
GUIOPTControls.OptRadio (DisplayHelpBPP, Window, 6, 37, 'BitsPerPixel', 24)
GUIOPTControls.OptRadio (DisplayHelpBPP, Window, 7, 37, 'BitsPerPixel', 32)
GUIOPTControls.OptCheckbox (18038, 18000, HelpTextArea, Window, 9, 38, 17131, 'Full Screen', DisplayHelpFullScreen)
GUIOPTControls.OptCheckbox (18038, 20620, HelpTextArea, Window, 51, 50, 20617, 'Translucent Shadows')
GUIOPTControls.OptCheckbox (18038, 18004, HelpTextArea, Window, 40, 44, 17134, 'SoftMirrorBlt')
GUIOPTControls.OptCheckbox (18038, 18006, HelpTextArea, Window, 41, 46, 17136, 'SoftSrcKeyBlt') # software standard blit
GUIOPTControls.OptCheckbox (18038, 18007, HelpTextArea, Window, 42, 48, 17135, 'SoftBltFast') # software transparent blit
Window.ShowModal (MODAL_SHADOW_GRAY)
return | [
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] | https://github.com/gemrb/gemrb/blob/730206eed8d1dd358ca5e69a62f9e099aa22ffc6/gemrb/GUIScripts/GUIOPT.py#L124-L154 |
|
scanner-research/scanner | 04a0c4b4196341995985acd729c0788aab823e1c | python/scannerpy/client.py | python | Client.load_op | (self, so_path: str, proto_path: str = None) | r"""Loads a custom op into the Scanner runtime.
Parameters
----------
so_path
Path to the custom op's shared library (.so).
proto_path
Path to the custom op's arguments protobuf if one exists.
Raises
------
ScannerException
Raised when the master fails to load the op. | r"""Loads a custom op into the Scanner runtime. | [
"r",
"Loads",
"a",
"custom",
"op",
"into",
"the",
"Scanner",
"runtime",
"."
] | def load_op(self, so_path: str, proto_path: str = None):
r"""Loads a custom op into the Scanner runtime.
Parameters
----------
so_path
Path to the custom op's shared library (.so).
proto_path
Path to the custom op's arguments protobuf if one exists.
Raises
------
ScannerException
Raised when the master fails to load the op.
"""
if proto_path is not None:
protobufs.add_module(proto_path)
op_path = protobufs.OpPath()
op_path.path = so_path
self._try_rpc(
lambda: self._master.LoadOp(op_path, timeout=self._grpc_timeout))
self._modules.add((so_path, proto_path)) | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/asyncio/locks.py | python | Condition.wait | (self) | Wait until notified.
If the calling coroutine has not acquired the lock when this
method is called, a RuntimeError is raised.
This method releases the underlying lock, and then blocks
until it is awakened by a notify() or notify_all() call for
the same condition variable in another coroutine. Once
awakened, it re-acquires the lock and returns True. | Wait until notified. | [
"Wait",
"until",
"notified",
"."
] | async def wait(self):
"""Wait until notified.
If the calling coroutine has not acquired the lock when this
method is called, a RuntimeError is raised.
This method releases the underlying lock, and then blocks
until it is awakened by a notify() or notify_all() call for
the same condition variable in another coroutine. Once
awakened, it re-acquires the lock and returns True.
"""
if not self.locked():
raise RuntimeError('cannot wait on un-acquired lock')
self.release()
try:
fut = self._loop.create_future()
self._waiters.append(fut)
try:
await fut
return True
finally:
self._waiters.remove(fut)
finally:
# Must reacquire lock even if wait is cancelled
cancelled = False
while True:
try:
await self.acquire()
break
except futures.CancelledError:
cancelled = True
if cancelled:
raise futures.CancelledError | [
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||
ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/mozbuild/mozbuild/configure/libstdcxx.py | python | find_version | (e) | return encode_ver(last_version) | Given the value of environment variable CXX or HOST_CXX, find the
version of the libstdc++ it uses. | Given the value of environment variable CXX or HOST_CXX, find the
version of the libstdc++ it uses. | [
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"HOST_CXX",
"find",
"the",
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"the",
"libstdc",
"++",
"it",
"uses",
"."
] | def find_version(e):
"""Given the value of environment variable CXX or HOST_CXX, find the
version of the libstdc++ it uses.
"""
args = e.split()
args += ['-shared', '-Wl,-t']
p = subprocess.Popen(args, stderr=subprocess.STDOUT, stdout=subprocess.PIPE)
candidates = [x for x in p.stdout if 'libstdc++.so' in x]
if not candidates:
return ''
assert len(candidates) == 1
libstdcxx = parse_ld_line(candidates[-1])
p = subprocess.Popen(['readelf', '-V', libstdcxx], stdout=subprocess.PIPE)
versions = [parse_readelf_line(x)
for x in p.stdout.readlines() if 'Name: GLIBCXX' in x]
last_version = sorted(versions, cmp = cmp_ver)[-1]
return encode_ver(last_version) | [
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|
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/hang_analyzer/dumper.py | python | Dumper._find_debugger | (self, debugger) | Find the installed debugger.
:param debugger: debugger executable. | Find the installed debugger. | [
"Find",
"the",
"installed",
"debugger",
"."
] | def _find_debugger(self, debugger):
"""
Find the installed debugger.
:param debugger: debugger executable.
"""
raise NotImplementedError("_find_debugger must be implemented in OS-specific subclasses") | [
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||
turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/data_structures/sframe.py | python | SFrame.__getitem__ | (self, key) | This method does things based on the type of `key`.
If `key` is:
* str
selects column with name 'key'
* type
selects all columns with types matching the type
* list of str or type
selects all columns with names or type in the list
* SArray
Performs a logical filter. Expects given SArray to be the same
length as all columns in current SFrame. Every row
corresponding with an entry in the given SArray that is
equivalent to False is filtered from the result.
* int
Returns a single row of the SFrame (the `key`th one) as a dictionary.
* slice
Returns an SFrame including only the sliced rows. | This method does things based on the type of `key`. | [
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"method",
"does",
"things",
"based",
"on",
"the",
"type",
"of",
"key",
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] | def __getitem__(self, key):
"""
This method does things based on the type of `key`.
If `key` is:
* str
selects column with name 'key'
* type
selects all columns with types matching the type
* list of str or type
selects all columns with names or type in the list
* SArray
Performs a logical filter. Expects given SArray to be the same
length as all columns in current SFrame. Every row
corresponding with an entry in the given SArray that is
equivalent to False is filtered from the result.
* int
Returns a single row of the SFrame (the `key`th one) as a dictionary.
* slice
Returns an SFrame including only the sliced rows.
"""
if type(key) is SArray:
return self._row_selector(key)
elif type(key) is str:
return self.select_column(key)
elif type(key) is type:
return self.select_columns([key])
elif _is_non_string_iterable(key):
return self.select_columns(key)
elif isinstance(key, numbers.Integral):
sf_len = len(self)
if key < 0:
key = sf_len + key
if key >= sf_len:
raise IndexError("SFrame index out of range")
if not hasattr(self, '_cache') or self._cache is None:
self._cache = {}
try:
lb, ub, value_list = self._cache["getitem_cache"]
if lb <= key < ub:
return value_list[int(key - lb)]
except KeyError:
pass
# Not in cache, need to grab it. Smaller here than with sarray
# Do we have a good block size that won't cause memory to blow up?
if not "getitem_cache_blocksize" in self._cache:
block_size = \
(8*1024) // sum( (2 if dt in [int, long, float] else 8) for dt in self.column_types())
block_size = max(16, block_size)
self._cache["getitem_cache_blocksize"] = block_size
else:
block_size = self._cache["getitem_cache_blocksize"]
block_num = int(key // block_size)
lb = block_num * block_size
ub = min(sf_len, lb + block_size)
val_list = list(SFrame(_proxy = self.__proxy__.copy_range(lb, 1, ub)))
self._cache["getitem_cache"] = (lb, ub, val_list)
return val_list[int(key - lb)]
elif type(key) is slice:
start = key.start
stop = key.stop
step = key.step
if start is None:
start = 0
if stop is None:
stop = len(self)
if step is None:
step = 1
# handle negative indices
if start < 0:
start = len(self) + start
if stop < 0:
stop = len(self) + stop
return SFrame(_proxy = self.__proxy__.copy_range(start, step, stop))
else:
raise TypeError("Invalid index type: must be SArray, list, int, or str") | [
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||
hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/learn/python/learn/estimators/dnn_sampled_softmax_classifier.py | python | _DNNSampledSoftmaxClassifier.export | (self, export_dir, signature_fn=None,
input_fn=None, default_batch_size=1,
exports_to_keep=None) | return self._estimator.export(export_dir=export_dir,
signature_fn=signature_fn,
input_fn=input_fn or default_input_fn,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep) | Exports inference graph into given dir.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value. | Exports inference graph into given dir. | [
"Exports",
"inference",
"graph",
"into",
"given",
"dir",
"."
] | def export(self, export_dir, signature_fn=None,
input_fn=None, default_batch_size=1,
exports_to_keep=None):
"""Exports inference graph into given dir.
Args:
export_dir: A string containing a directory to write the exported graph
and checkpoints.
signature_fn: Function that returns a default signature and a named
signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
for features and `Tensor` or `dict` of `Tensor`s for predictions.
input_fn: If `use_deprecated_input_fn` is true, then a function that given
`Tensor` of `Example` strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, targets), where features is a dict of
string key to `Tensor` and targets is a `Tensor` that's currently not
used (and so can be `None`).
default_batch_size: Default batch size of the `Example` placeholder.
exports_to_keep: Number of exports to keep.
Returns:
The string path to the exported directory. NB: this functionality was
added ca. 2016/09/25; clients that depend on the return value may need
to handle the case where this function returns None because subclasses
are not returning a value.
"""
def default_input_fn(unused_estimator, examples):
return layers.parse_feature_columns_from_examples(
examples, self._feature_columns)
return self._estimator.export(export_dir=export_dir,
signature_fn=signature_fn,
input_fn=input_fn or default_input_fn,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | PixelDataBase.GetRowStride | (*args, **kwargs) | return _gdi_.PixelDataBase_GetRowStride(*args, **kwargs) | GetRowStride(self) -> int | GetRowStride(self) -> int | [
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"""GetRowStride(self) -> int"""
return _gdi_.PixelDataBase_GetRowStride(*args, **kwargs) | [
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|
BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/instrument.py | python | Instrument.high_price | (self, high_price) | Sets the high_price of this Instrument.
:param high_price: The high_price of this Instrument. # noqa: E501
:type: float | Sets the high_price of this Instrument. | [
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"""Sets the high_price of this Instrument.
:param high_price: The high_price of this Instrument. # noqa: E501
:type: float
"""
self._high_price = high_price | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aquabutton.py | python | __ToggleMixin.SetToggle | (self, flag) | Sets the button as toggled/not toggled.
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Sets the button as toggled/not toggled.
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"""
self.up = not flag
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||
ideawu/ssdb-rocks | a3cbb322cafb2f493252829c608e2239df98c9ac | deps/cpy/antlr3/treewizard.py | python | TreeWizard._visitPattern | (self, tree, pattern, visitor) | For all subtrees that match the pattern, execute the visit action. | For all subtrees that match the pattern, execute the visit action. | [
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"""
For all subtrees that match the pattern, execute the visit action.
"""
# Create a TreePattern from the pattern
tokenizer = TreePatternLexer(pattern)
parser = TreePatternParser(tokenizer, self, TreePatternTreeAdaptor())
tpattern = parser.pattern()
# don't allow invalid patterns
if (tpattern is None or tpattern.isNil()
or isinstance(tpattern, WildcardTreePattern)):
return
rootTokenType = tpattern.getType()
def rootvisitor(tree, parent, childIndex, labels):
labels = {}
if self._parse(tree, tpattern, labels):
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PaddlePaddle/PaddleOCR | b756bf5f8c90142e0d89d3db0163965c686b6ffe | ppocr/utils/e2e_utils/extract_textpoint_slow.py | python | add_id | (pos_list, image_id=0) | return new_list | Add id for gather feature, for inference. | Add id for gather feature, for inference. | [
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] | def add_id(pos_list, image_id=0):
"""
Add id for gather feature, for inference.
"""
new_list = []
for item in pos_list:
new_list.append((image_id, item[0], item[1]))
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/xcode_emulation.py | python | XcodeSettings.GetLdflags | (self, configname, product_dir, gyp_to_build_path, arch=None) | return ldflags | Returns flags that need to be passed to the linker.
Args:
configname: The name of the configuration to get ld flags for.
product_dir: The directory where products such static and dynamic
libraries are placed. This is added to the library search path.
gyp_to_build_path: A function that converts paths relative to the
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"""Returns flags that need to be passed to the linker.
Args:
configname: The name of the configuration to get ld flags for.
product_dir: The directory where products such static and dynamic
libraries are placed. This is added to the library search path.
gyp_to_build_path: A function that converts paths relative to the
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"""
self.configname = configname
ldflags = []
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self._Appendf(
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self._AppendPlatformVersionMinFlags(ldflags)
if 'SDKROOT' in self._Settings() and self._SdkPath():
ldflags.append('-isysroot ' + self._SdkPath())
for library_path in self._Settings().get('LIBRARY_SEARCH_PATHS', []):
ldflags.append('-L' + gyp_to_build_path(library_path))
if 'ORDER_FILE' in self._Settings():
ldflags.append('-Wl,-order_file ' +
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self._Settings()['ORDER_FILE']))
if arch is not None:
archs = [arch]
else:
assert self.configname
archs = self.GetActiveArchs(self.configname)
if len(archs) != 1:
# TODO: Supporting fat binaries will be annoying.
self._WarnUnimplemented('ARCHS')
archs = ['i386']
ldflags.append('-arch ' + archs[0])
# Xcode adds the product directory by default.
# Rewrite -L. to -L./ to work around http://www.openradar.me/25313838
ldflags.append('-L' + (product_dir if product_dir != '.' else './'))
install_name = self.GetInstallName()
if install_name and self.spec['type'] != 'loadable_module':
ldflags.append('-install_name ' + install_name.replace(' ', r'\ '))
for rpath in self._Settings().get('LD_RUNPATH_SEARCH_PATHS', []):
ldflags.append('-Wl,-rpath,' + rpath)
sdk_root = self._SdkPath()
if not sdk_root:
sdk_root = ''
config = self.spec['configurations'][self.configname]
framework_dirs = config.get('mac_framework_dirs', [])
for directory in framework_dirs:
ldflags.append('-F' + directory.replace('$(SDKROOT)', sdk_root))
if self._IsXCTest():
platform_root = self._XcodePlatformPath(configname)
if sdk_root and platform_root:
ldflags.append('-F' + platform_root + '/Developer/Library/Frameworks/')
ldflags.append('-framework XCTest')
is_extension = self._IsIosAppExtension() or self._IsIosWatchKitExtension()
if sdk_root and is_extension:
# Adds the link flags for extensions. These flags are common for all
# extensions and provide loader and main function.
# These flags reflect the compilation options used by xcode to compile
# extensions.
if XcodeVersion() < '0900':
ldflags.append('-lpkstart')
ldflags.append(sdk_root +
'/System/Library/PrivateFrameworks/PlugInKit.framework/PlugInKit')
else:
ldflags.append('-e _NSExtensionMain')
ldflags.append('-fapplication-extension')
self._Appendf(ldflags, 'CLANG_CXX_LIBRARY', '-stdlib=%s')
self.configname = None
return ldflags | [
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|
blocknetdx/blocknet | f85bdf3eeebb1ed8c2321ebd928232d4885b30b6 | contrib/devtools/security-check.py | python | check_ELF_Canary | (executable) | return ok | Check for use of stack canary | Check for use of stack canary | [
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"use",
"of",
"stack",
"canary"
] | def check_ELF_Canary(executable):
'''
Check for use of stack canary
'''
p = subprocess.Popen([READELF_CMD, '--dyn-syms', '-W', executable], stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE, universal_newlines=True)
(stdout, stderr) = p.communicate()
if p.returncode:
raise IOError('Error opening file')
ok = False
for line in stdout.splitlines():
if '__stack_chk_fail' in line:
ok = True
return ok | [
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|
miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/session_manager.py | python | SessionManager.recover_session | (self, master, saver=None, checkpoint_dir=None,
wait_for_checkpoint=False, max_wait_secs=7200,
config=None) | Creates a `Session`, recovering if possible.
Creates a new session on 'master'. If the session is not initialized
and can be recovered from a checkpoint, recover it.
Args:
master: `String` representation of the TensorFlow master to use.
saver: A `Saver` object used to restore a model.
checkpoint_dir: Path to the checkpoint files.
wait_for_checkpoint: Whether to wait for checkpoint to become available.
max_wait_secs: Maximum time to wait for checkpoints to become available.
config: Optional `ConfigProto` proto used to configure the session.
Returns:
A pair (sess, initialized) where 'initialized' is `True` if
the session could be recovered, `False` otherwise. | Creates a `Session`, recovering if possible. | [
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"a",
"Session",
"recovering",
"if",
"possible",
"."
] | def recover_session(self, master, saver=None, checkpoint_dir=None,
wait_for_checkpoint=False, max_wait_secs=7200,
config=None):
"""Creates a `Session`, recovering if possible.
Creates a new session on 'master'. If the session is not initialized
and can be recovered from a checkpoint, recover it.
Args:
master: `String` representation of the TensorFlow master to use.
saver: A `Saver` object used to restore a model.
checkpoint_dir: Path to the checkpoint files.
wait_for_checkpoint: Whether to wait for checkpoint to become available.
max_wait_secs: Maximum time to wait for checkpoints to become available.
config: Optional `ConfigProto` proto used to configure the session.
Returns:
A pair (sess, initialized) where 'initialized' is `True` if
the session could be recovered, `False` otherwise.
"""
self._target = master
sess = session.Session(self._target, graph=self._graph, config=config)
if self._local_init_op:
sess.run([self._local_init_op])
# If either saver or checkpoint_dir is not specified, cannot restore. Just
# return.
if not saver or not checkpoint_dir:
not_ready = self._model_not_ready(sess)
return sess, not_ready is None
# Waits up until max_wait_secs for checkpoint to become available.
wait_time = 0
ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
while not ckpt or not ckpt.model_checkpoint_path:
if wait_for_checkpoint and wait_time < max_wait_secs:
logging.info("Waiting for checkpoint to be available.")
time.sleep(self._recovery_wait_secs)
wait_time += self._recovery_wait_secs
ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
else:
return sess, False
# Loads the checkpoint and verifies that it makes the model ready.
saver.restore(sess, ckpt.model_checkpoint_path)
last_checkpoints = []
for fname in ckpt.all_model_checkpoint_paths:
fnames = gfile.Glob(fname)
if fnames:
mtime = gfile.Stat(fnames[0]).mtime
last_checkpoints.append((fname, mtime))
saver.set_last_checkpoints_with_time(last_checkpoints)
not_ready = self._model_not_ready(sess)
if not_ready:
logging.info("Restoring model from %s did not make model ready: %s",
ckpt.model_checkpoint_path, not_ready)
return sess, False
else:
logging.info("Restored model from %s", ckpt.model_checkpoint_path)
return sess, True | [
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] | https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/training/session_manager.py#L180-L239 |
||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/mixins/rubberband.py | python | normalizeBox | (box) | return (x, y, w, h) | Convert any negative measurements in the current
box to positive, and adjust the origin. | Convert any negative measurements in the current
box to positive, and adjust the origin. | [
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"measurements",
"in",
"the",
"current",
"box",
"to",
"positive",
"and",
"adjust",
"the",
"origin",
"."
] | def normalizeBox(box):
"""
Convert any negative measurements in the current
box to positive, and adjust the origin.
"""
x, y, w, h = box
if w < 0:
x += (w+1)
w *= -1
if h < 0:
y += (h+1)
h *= -1
return (x, y, w, h) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/html.py | python | HtmlCell.IsBefore | (*args, **kwargs) | return _html.HtmlCell_IsBefore(*args, **kwargs) | IsBefore(self, HtmlCell cell) -> bool | IsBefore(self, HtmlCell cell) -> bool | [
"IsBefore",
"(",
"self",
"HtmlCell",
"cell",
")",
"-",
">",
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] | def IsBefore(*args, **kwargs):
"""IsBefore(self, HtmlCell cell) -> bool"""
return _html.HtmlCell_IsBefore(*args, **kwargs) | [
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