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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | PyApp.ResumeProcessingOfPendingEvents | (*args, **kwargs) | return _core_.PyApp_ResumeProcessingOfPendingEvents(*args, **kwargs) | ResumeProcessingOfPendingEvents(self)
Resume (after having been suspended) the processing of pending events. | ResumeProcessingOfPendingEvents(self) | [
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"""
ResumeProcessingOfPendingEvents(self)
Resume (after having been suspended) the processing of pending events.
"""
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tzlaine/text | b576f19c2643ac1b08080844fa6044df6939c5c7 | benchmark-v1.2.0/tools/gbench/util.py | python | remove_benchmark_flags | (prefix, benchmark_flags) | return [f for f in benchmark_flags if not f.startswith(prefix)] | Return a new list containing the specified benchmark_flags except those
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/sponge_ops.py | python | DihedralForce.__init__ | (self, dihedral_numbers) | Initialize DihedralForce. | Initialize DihedralForce. | [
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"""Initialize DihedralForce."""
validator.check_value_type('dihedral_numbers', dihedral_numbers, int, self.name)
self.dihedral_numbers = dihedral_numbers
self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'atom_d', 'ipn', 'pk',
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | Clipboard.SetData | (*args, **kwargs) | return _misc_.Clipboard_SetData(*args, **kwargs) | SetData(self, DataObject data) -> bool
Set the clipboard data, this is the same as `Clear` followed by
`AddData`.
:see: `wx.DataObject` | SetData(self, DataObject data) -> bool | [
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"""
SetData(self, DataObject data) -> bool
Set the clipboard data, this is the same as `Clear` followed by
`AddData`.
:see: `wx.DataObject`
"""
return _misc_.Clipboard_SetData(*args, **kwargs) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/lookup/lookup_ops.py | python | MutableHashTable.lookup | (self, keys, name=None) | return values | Looks up `keys` in a table, outputs the corresponding values.
The `default_value` is used for keys not present in the table.
Args:
keys: Keys to look up. Can be a tensor of any shape. Must match the
table's key_dtype.
name: A name for the operation (optional).
Returns:
A tensor containing the values in the same shape as `keys` using the
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"""Looks up `keys` in a table, outputs the corresponding values.
The `default_value` is used for keys not present in the table.
Args:
keys: Keys to look up. Can be a tensor of any shape. Must match the
table's key_dtype.
name: A name for the operation (optional).
Returns:
A tensor containing the values in the same shape as `keys` using the
table's value type.
Raises:
TypeError: when `keys` do not match the table data types.
"""
if keys.dtype != self._key_dtype:
raise TypeError("Signature mismatch. Keys must be dtype %s, got %s." %
(self._key_dtype, keys.dtype))
with ops.op_scope([self._table_ref, keys], name,
"%s_lookup_table_find" % self._name) as name:
# pylint: disable=protected-access
values = gen_data_flow_ops._lookup_table_find(self._table_ref,
keys,
self._default_value,
name=name)
# pylint: enable=protected-access
values.set_shape(keys.get_shape().concatenate(self._value_shape))
return values | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/pyserial/serial/rfc2217.py | python | TelnetSubnegotiation.isReady | (self) | return self.state == ACTIVE | \
check if answer from server has been received. when server rejects
the change, raise a ValueError. | \
check if answer from server has been received. when server rejects
the change, raise a ValueError. | [
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"""\
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if self.state == REALLY_INACTIVE:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | EraseEvent.GetDC | (*args, **kwargs) | return _core_.EraseEvent_GetDC(*args, **kwargs) | GetDC(self) -> DC
Returns the device context the event handler should draw upon. If
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"""
GetDC(self) -> DC
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that instead.
"""
return _core_.EraseEvent_GetDC(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextParagraphLayoutBox.HasCharacterAttributes | (*args, **kwargs) | return _richtext.RichTextParagraphLayoutBox_HasCharacterAttributes(*args, **kwargs) | HasCharacterAttributes(self, RichTextRange range, RichTextAttr style) -> bool | HasCharacterAttributes(self, RichTextRange range, RichTextAttr style) -> bool | [
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return _richtext.RichTextParagraphLayoutBox_HasCharacterAttributes(*args, **kwargs) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBMemoryRegionInfo.__ne__ | (self, *args) | return _lldb.SBMemoryRegionInfo___ne__(self, *args) | __ne__(self, SBMemoryRegionInfo rhs) -> bool | __ne__(self, SBMemoryRegionInfo rhs) -> bool | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/descriptor.py | python | MakeDescriptor | (desc_proto, package='', build_file_if_cpp=True,
syntax=None) | return Descriptor(desc_proto.name, desc_name, None, None, fields,
list(nested_types.values()), list(enum_types.values()), [],
options=desc_proto.options) | Make a protobuf Descriptor given a DescriptorProto protobuf.
Handles nested descriptors. Note that this is limited to the scope of defining
a message inside of another message. Composite fields can currently only be
resolved if the message is defined in the same scope as the field.
Args:
desc_proto: The descriptor_pb2.DescriptorProto protobuf message.
package: Optional package name for the new message Descriptor (string).
build_file_if_cpp: Update the C++ descriptor pool if api matches.
Set to False on recursion, so no duplicates are created.
syntax: The syntax/semantics that should be used. Set to "proto3" to get
proto3 field presence semantics.
Returns:
A Descriptor for protobuf messages. | Make a protobuf Descriptor given a DescriptorProto protobuf. | [
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syntax=None):
"""Make a protobuf Descriptor given a DescriptorProto protobuf.
Handles nested descriptors. Note that this is limited to the scope of defining
a message inside of another message. Composite fields can currently only be
resolved if the message is defined in the same scope as the field.
Args:
desc_proto: The descriptor_pb2.DescriptorProto protobuf message.
package: Optional package name for the new message Descriptor (string).
build_file_if_cpp: Update the C++ descriptor pool if api matches.
Set to False on recursion, so no duplicates are created.
syntax: The syntax/semantics that should be used. Set to "proto3" to get
proto3 field presence semantics.
Returns:
A Descriptor for protobuf messages.
"""
if api_implementation.Type() == 'cpp' and build_file_if_cpp:
# The C++ implementation requires all descriptors to be backed by the same
# definition in the C++ descriptor pool. To do this, we build a
# FileDescriptorProto with the same definition as this descriptor and build
# it into the pool.
from google.protobuf import descriptor_pb2
file_descriptor_proto = descriptor_pb2.FileDescriptorProto()
file_descriptor_proto.message_type.add().MergeFrom(desc_proto)
# Generate a random name for this proto file to prevent conflicts with any
# imported ones. We need to specify a file name so the descriptor pool
# accepts our FileDescriptorProto, but it is not important what that file
# name is actually set to.
proto_name = str(uuid.uuid4())
if package:
file_descriptor_proto.name = os.path.join(package.replace('.', '/'),
proto_name + '.proto')
file_descriptor_proto.package = package
else:
file_descriptor_proto.name = proto_name + '.proto'
_message.default_pool.Add(file_descriptor_proto)
result = _message.default_pool.FindFileByName(file_descriptor_proto.name)
if _USE_C_DESCRIPTORS:
return result.message_types_by_name[desc_proto.name]
full_message_name = [desc_proto.name]
if package: full_message_name.insert(0, package)
# Create Descriptors for enum types
enum_types = {}
for enum_proto in desc_proto.enum_type:
full_name = '.'.join(full_message_name + [enum_proto.name])
enum_desc = EnumDescriptor(
enum_proto.name, full_name, None, [
EnumValueDescriptor(enum_val.name, ii, enum_val.number)
for ii, enum_val in enumerate(enum_proto.value)])
enum_types[full_name] = enum_desc
# Create Descriptors for nested types
nested_types = {}
for nested_proto in desc_proto.nested_type:
full_name = '.'.join(full_message_name + [nested_proto.name])
# Nested types are just those defined inside of the message, not all types
# used by fields in the message, so no loops are possible here.
nested_desc = MakeDescriptor(nested_proto,
package='.'.join(full_message_name),
build_file_if_cpp=False,
syntax=syntax)
nested_types[full_name] = nested_desc
fields = []
for field_proto in desc_proto.field:
full_name = '.'.join(full_message_name + [field_proto.name])
enum_desc = None
nested_desc = None
if field_proto.HasField('type_name'):
type_name = field_proto.type_name
full_type_name = '.'.join(full_message_name +
[type_name[type_name.rfind('.')+1:]])
if full_type_name in nested_types:
nested_desc = nested_types[full_type_name]
elif full_type_name in enum_types:
enum_desc = enum_types[full_type_name]
# Else type_name references a non-local type, which isn't implemented
field = FieldDescriptor(
field_proto.name, full_name, field_proto.number - 1,
field_proto.number, field_proto.type,
FieldDescriptor.ProtoTypeToCppProtoType(field_proto.type),
field_proto.label, None, nested_desc, enum_desc, None, False, None,
options=field_proto.options, has_default_value=False)
fields.append(field)
desc_name = '.'.join(full_message_name)
return Descriptor(desc_proto.name, desc_name, None, None, fields,
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/internal/_parameterized.py | python | named_parameters | (*testcases) | return _ParameterDecorator(_FIRST_ARG, testcases) | A decorator for creating parameterized tests.
See the module docstring for a usage example. The first element of
each parameter tuple should be a string and will be appended to the
name of the test method.
Args:
*testcases: Parameters for the decorated method, either a single
iterable, or a list of tuples.
Returns:
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"""A decorator for creating parameterized tests.
See the module docstring for a usage example. The first element of
each parameter tuple should be a string and will be appended to the
name of the test method.
Args:
*testcases: Parameters for the decorated method, either a single
iterable, or a list of tuples.
Returns:
A test generator to be handled by TestGeneratorMetaclass.
"""
return _ParameterDecorator(_FIRST_ARG, testcases) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/Tix.py | python | CheckList.open | (self, entrypath) | Open the entry given by entryPath if its mode is open. | Open the entry given by entryPath if its mode is open. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/prefilter.py | python | PrefilterManager.init_transformers | (self) | Create the default transformers. | Create the default transformers. | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBSection.GetFileAddress | (self) | return _lldb.SBSection_GetFileAddress(self) | GetFileAddress(self) -> addr_t | GetFileAddress(self) -> addr_t | [
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jsupancic/deep_hand_pose | 22cbeae1a8410ff5d37c060c7315719d0a5d608f | scripts/cpp_lint.py | python | FindPreviousMatchingAngleBracket | (clean_lines, linenum, init_prefix) | return False | Find the corresponding < that started a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_prefix: Part of the current line before the initial >.
Returns:
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Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_prefix: Part of the current line before the initial >.
Returns:
True if a matching bracket exists.
"""
line = init_prefix
nesting_stack = ['>']
while True:
# Find the previous operator
match = Search(r'^(.*)([<>(),;\[\]])[^<>(),;\[\]]*$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(2)
line = match.group(1)
if nesting_stack[-1] == '>':
# Expecting opening angle bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator == '<':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma before a bracket, this is most likely a
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return True
else:
# Got some other operator.
return False
else:
# Expecting opening parenthesis or opening bracket
if operator in ('>', ')', ']'):
nesting_stack.append(operator)
elif operator in ('(', '['):
nesting_stack.pop()
else:
# Scan the previous line
linenum -= 1
if linenum < 0:
break
line = clean_lines.elided[linenum]
# Exhausted all earlier lines and still no matching angle bracket.
return False | [
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | scripts/cpp_lint.py | python | CloseExpression | (clean_lines, linenum, pos) | return (line, clean_lines.NumLines(), -1) | If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
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"""If input points to ( or { or [ or <, finds the position that closes it.
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Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
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startchar = line[pos]
if startchar not in '({[<':
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if startchar == '[': endchar = ']'
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(end_pos, num_open) = FindEndOfExpressionInLine(
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/pythonapi.py | python | PythonAPI.long_from_unsigned_int | (self, ival) | Same as long_from_signed_int, but for unsigned values. | Same as long_from_signed_int, but for unsigned values. | [
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"""
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"""
bits = ival.type.width
if bits <= self.ulong.width:
return self.long_from_ulong(self.builder.zext(ival, self.ulong))
elif bits <= self.ulonglong.width:
return self.long_from_ulonglong(self.builder.zext(ival, self.ulonglong))
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | KeyEvent.GetRawKeyCode | (*args, **kwargs) | return _core_.KeyEvent_GetRawKeyCode(*args, **kwargs) | GetRawKeyCode(self) -> unsigned int
Returns the raw key code for this event. This is a platform-dependent
scan code which should only be used in advanced
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"""
GetRawKeyCode(self) -> unsigned int
Returns the raw key code for this event. This is a platform-dependent
scan code which should only be used in advanced
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netease-youdao/hex | d7b8773dae8dde63f3807cef1d48c017077db727 | tools/make_hex_module.py | python | transfer_files | (cef_dir, script_dir, transfer_cfg, output_dir, quiet) | Transfer files based on the specified configuration. | Transfer files based on the specified configuration. | [
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""" Transfer files based on the specified configuration. """
if not path_exists(transfer_cfg):
return
configs = eval_file(transfer_cfg)
for cfg in configs:
dst = os.path.join(output_dir, cfg['target'])
# perform a copy if source is specified
if not cfg['source'] is None:
src = os.path.join(cef_dir, cfg['source'])
dst_path = os.path.dirname(dst)
make_dir(dst_path, quiet)
copy_file(src, dst, quiet)
# place a readme file in the destination directory
readme = os.path.join(dst_path, 'README-TRANSFER.txt')
if not path_exists(readme):
copy_file(os.path.join(script_dir, 'distrib/README-TRANSFER.txt'), readme)
open(readme, 'ab').write(cfg['source']+"\n")
# perform any required post-processing
if 'post-process' in cfg:
post = cfg['post-process']
if post == 'normalize_headers':
new_path = ''
if cfg.has_key('new_header_path'):
new_path = cfg['new_header_path']
normalize_headers(dst, new_path) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/multi_process_runner.py | python | get_barrier | () | return _barrier | Returns a `multiprocessing.Barrier` for `multi_process_runner.run`.
`tf.__internal__.distribute.multi_process_runner.get_barrier()` returns
a `multiprocessing.Barrier` object which can be used within `fn` of
`tf.__internal__.distribute.multi_process_runner` to wait with
`barrier.wait()` call until all other tasks have also reached the
`barrier.wait()` call, before they can proceed individually.
Note that all tasks (subprocesses) have to reach `barrier.wait()` call to
proceed. Currently it is not supported to block on only a subset of tasks
in the cluster.
Example:
```python
def fn():
some_work_to_be_done_by_all_tasks()
tf.__internal__.distribute.multi_process_runner.get_barrier().wait()
# The barrier guarantees that at this point, all tasks have finished
# `some_work_to_be_done_by_all_tasks()`
some_other_work_to_be_done_by_all_tasks()
result = tf.__internal__.distribute.multi_process_runner.run(
fn=fn,
cluster_spec=(
tf.__internal__
.distribute.multi_process_runner.create_cluster_spec(
num_workers=2)))
```
Returns:
A `multiprocessing.Barrier` for `multi_process_runner.run`. | Returns a `multiprocessing.Barrier` for `multi_process_runner.run`. | [
"Returns",
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"""Returns a `multiprocessing.Barrier` for `multi_process_runner.run`.
`tf.__internal__.distribute.multi_process_runner.get_barrier()` returns
a `multiprocessing.Barrier` object which can be used within `fn` of
`tf.__internal__.distribute.multi_process_runner` to wait with
`barrier.wait()` call until all other tasks have also reached the
`barrier.wait()` call, before they can proceed individually.
Note that all tasks (subprocesses) have to reach `barrier.wait()` call to
proceed. Currently it is not supported to block on only a subset of tasks
in the cluster.
Example:
```python
def fn():
some_work_to_be_done_by_all_tasks()
tf.__internal__.distribute.multi_process_runner.get_barrier().wait()
# The barrier guarantees that at this point, all tasks have finished
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result = tf.__internal__.distribute.multi_process_runner.run(
fn=fn,
cluster_spec=(
tf.__internal__
.distribute.multi_process_runner.create_cluster_spec(
num_workers=2)))
```
Returns:
A `multiprocessing.Barrier` for `multi_process_runner.run`.
"""
if _barrier is None:
raise ValueError(
'barrier is not defined. It is likely because you are calling '
'get_barrier() in the main process. get_barrier() can only be called '
'in the subprocesses.'
)
return _barrier | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathEngraveBase.py | python | ObjectOp.opSetDefaultValues | (self, obj, job) | opSetDefaultValues(obj) ... set depths for engraving | opSetDefaultValues(obj) ... set depths for engraving | [
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"""opSetDefaultValues(obj) ... set depths for engraving"""
if PathOp.FeatureDepths & self.opFeatures(obj):
if job and len(job.Model.Group) > 0:
bb = job.Proxy.modelBoundBox(job)
obj.OpStartDepth = bb.ZMax
obj.OpFinalDepth = bb.ZMax - max(obj.StepDown.Value, 0.1)
else:
obj.OpFinalDepth = -0.1 | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/collections/__init__.py | python | ChainMap.popitem | (self) | Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty. | Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty. | [
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'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'
try:
return self.maps[0].popitem()
except KeyError:
raise KeyError('No keys found in the first mapping.') | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/javascripttokenizer.py | python | JavaScriptTokenizer.BuildMatchers | (cls) | return {
# Matchers for basic text mode.
JavaScriptModes.TEXT_MODE: [
# Check a big group - strings, starting comments, and regexes - all
# of which could be intertwined. 'string with /regex/',
# /regex with 'string'/, /* comment with /regex/ and string */ (and
# so on)
Matcher(cls.START_DOC_COMMENT, Type.START_DOC_COMMENT,
JavaScriptModes.DOC_COMMENT_MODE),
Matcher(cls.START_BLOCK_COMMENT, Type.START_BLOCK_COMMENT,
JavaScriptModes.BLOCK_COMMENT_MODE),
Matcher(cls.END_OF_LINE_SINGLE_LINE_COMMENT,
Type.START_SINGLE_LINE_COMMENT),
Matcher(cls.START_SINGLE_LINE_COMMENT,
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JavaScriptModes.LINE_COMMENT_MODE),
Matcher(cls.SINGLE_QUOTE, Type.SINGLE_QUOTE_STRING_START,
JavaScriptModes.SINGLE_QUOTE_STRING_MODE),
Matcher(cls.DOUBLE_QUOTE, Type.DOUBLE_QUOTE_STRING_START,
JavaScriptModes.DOUBLE_QUOTE_STRING_MODE),
Matcher(cls.REGEX, Type.REGEX),
# Next we check for start blocks appearing outside any of the items
# above.
Matcher(cls.START_BLOCK, Type.START_BLOCK),
Matcher(cls.END_BLOCK, Type.END_BLOCK),
# Then we search for function declarations.
Matcher(cls.FUNCTION_DECLARATION, Type.FUNCTION_DECLARATION,
JavaScriptModes.FUNCTION_MODE),
# Next, we convert non-function related parens to tokens.
Matcher(cls.OPENING_PAREN, Type.START_PAREN),
Matcher(cls.CLOSING_PAREN, Type.END_PAREN),
# Next, we convert brackets to tokens.
Matcher(cls.OPENING_BRACKET, Type.START_BRACKET),
Matcher(cls.CLOSING_BRACKET, Type.END_BRACKET),
# Find numbers. This has to happen before operators because
# scientific notation numbers can have + and - in them.
Matcher(cls.NUMBER, Type.NUMBER),
# Find operators and simple assignments
Matcher(cls.SIMPLE_LVALUE, Type.SIMPLE_LVALUE),
Matcher(cls.OPERATOR, Type.OPERATOR),
# Find key words and whitespace.
Matcher(keyword, Type.KEYWORD),
Matcher(cls.WHITESPACE, Type.WHITESPACE),
# Find identifiers.
Matcher(cls.IDENTIFIER, Type.IDENTIFIER),
# Finally, we convert semicolons to tokens.
Matcher(cls.SEMICOLON, Type.SEMICOLON)],
# Matchers for single quote strings.
JavaScriptModes.SINGLE_QUOTE_STRING_MODE: [
Matcher(cls.SINGLE_QUOTE_TEXT, Type.STRING_TEXT),
Matcher(cls.SINGLE_QUOTE, Type.SINGLE_QUOTE_STRING_END,
JavaScriptModes.TEXT_MODE)],
# Matchers for double quote strings.
JavaScriptModes.DOUBLE_QUOTE_STRING_MODE: [
Matcher(cls.DOUBLE_QUOTE_TEXT, Type.STRING_TEXT),
Matcher(cls.DOUBLE_QUOTE, Type.DOUBLE_QUOTE_STRING_END,
JavaScriptModes.TEXT_MODE)],
# Matchers for block comments.
JavaScriptModes.BLOCK_COMMENT_MODE: [
# First we check for exiting a block comment.
Matcher(cls.END_BLOCK_COMMENT, Type.END_BLOCK_COMMENT,
JavaScriptModes.TEXT_MODE),
# Match non-comment-ending text..
Matcher(cls.BLOCK_COMMENT_TEXT, Type.COMMENT)],
# Matchers for doc comments.
JavaScriptModes.DOC_COMMENT_MODE: cls.COMMON_DOC_MATCHERS + [
Matcher(cls.DOC_COMMENT_TEXT, Type.COMMENT)],
JavaScriptModes.DOC_COMMENT_LEX_SPACES_MODE: cls.COMMON_DOC_MATCHERS + [
Matcher(cls.WHITESPACE, Type.COMMENT),
Matcher(cls.DOC_COMMENT_NO_SPACES_TEXT, Type.COMMENT)],
# Matchers for single line comments.
JavaScriptModes.LINE_COMMENT_MODE: [
# We greedy match until the end of the line in line comment mode.
Matcher(cls.ANYTHING, Type.COMMENT, JavaScriptModes.TEXT_MODE)],
# Matchers for code after the function keyword.
JavaScriptModes.FUNCTION_MODE: [
# Must match open paren before anything else and move into parameter
# mode, otherwise everything inside the parameter list is parsed
# incorrectly.
Matcher(cls.OPENING_PAREN, Type.START_PARAMETERS,
JavaScriptModes.PARAMETER_MODE),
Matcher(cls.WHITESPACE, Type.WHITESPACE),
Matcher(cls.IDENTIFIER, Type.FUNCTION_NAME)],
# Matchers for function parameters
JavaScriptModes.PARAMETER_MODE: [
# When in function parameter mode, a closing paren is treated
# specially. Everything else is treated as lines of parameters.
Matcher(cls.CLOSING_PAREN_WITH_SPACE, Type.END_PARAMETERS,
JavaScriptModes.TEXT_MODE),
Matcher(cls.PARAMETERS, Type.PARAMETERS,
JavaScriptModes.PARAMETER_MODE)]} | Builds the token matcher group.
The token matcher groups work as follows: it is a list of Matcher objects.
The matchers will be tried in this order, and the first to match will be
returned. Hence the order is important because the matchers that come first
overrule the matchers that come later.
Returns:
The completed token matcher group. | Builds the token matcher group. | [
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] | def BuildMatchers(cls):
"""Builds the token matcher group.
The token matcher groups work as follows: it is a list of Matcher objects.
The matchers will be tried in this order, and the first to match will be
returned. Hence the order is important because the matchers that come first
overrule the matchers that come later.
Returns:
The completed token matcher group.
"""
# Match a keyword string followed by a non-identifier character in order to
# not match something like doSomething as do + Something.
keyword = re.compile('(%s)((?=[^%s])|$)' % (
'|'.join(cls.KEYWORD_LIST), cls.IDENTIFIER_CHAR))
return {
# Matchers for basic text mode.
JavaScriptModes.TEXT_MODE: [
# Check a big group - strings, starting comments, and regexes - all
# of which could be intertwined. 'string with /regex/',
# /regex with 'string'/, /* comment with /regex/ and string */ (and
# so on)
Matcher(cls.START_DOC_COMMENT, Type.START_DOC_COMMENT,
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Matcher(cls.START_BLOCK_COMMENT, Type.START_BLOCK_COMMENT,
JavaScriptModes.BLOCK_COMMENT_MODE),
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Type.START_SINGLE_LINE_COMMENT),
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Type.START_SINGLE_LINE_COMMENT,
JavaScriptModes.LINE_COMMENT_MODE),
Matcher(cls.SINGLE_QUOTE, Type.SINGLE_QUOTE_STRING_START,
JavaScriptModes.SINGLE_QUOTE_STRING_MODE),
Matcher(cls.DOUBLE_QUOTE, Type.DOUBLE_QUOTE_STRING_START,
JavaScriptModes.DOUBLE_QUOTE_STRING_MODE),
Matcher(cls.REGEX, Type.REGEX),
# Next we check for start blocks appearing outside any of the items
# above.
Matcher(cls.START_BLOCK, Type.START_BLOCK),
Matcher(cls.END_BLOCK, Type.END_BLOCK),
# Then we search for function declarations.
Matcher(cls.FUNCTION_DECLARATION, Type.FUNCTION_DECLARATION,
JavaScriptModes.FUNCTION_MODE),
# Next, we convert non-function related parens to tokens.
Matcher(cls.OPENING_PAREN, Type.START_PAREN),
Matcher(cls.CLOSING_PAREN, Type.END_PAREN),
# Next, we convert brackets to tokens.
Matcher(cls.OPENING_BRACKET, Type.START_BRACKET),
Matcher(cls.CLOSING_BRACKET, Type.END_BRACKET),
# Find numbers. This has to happen before operators because
# scientific notation numbers can have + and - in them.
Matcher(cls.NUMBER, Type.NUMBER),
# Find operators and simple assignments
Matcher(cls.SIMPLE_LVALUE, Type.SIMPLE_LVALUE),
Matcher(cls.OPERATOR, Type.OPERATOR),
# Find key words and whitespace.
Matcher(keyword, Type.KEYWORD),
Matcher(cls.WHITESPACE, Type.WHITESPACE),
# Find identifiers.
Matcher(cls.IDENTIFIER, Type.IDENTIFIER),
# Finally, we convert semicolons to tokens.
Matcher(cls.SEMICOLON, Type.SEMICOLON)],
# Matchers for single quote strings.
JavaScriptModes.SINGLE_QUOTE_STRING_MODE: [
Matcher(cls.SINGLE_QUOTE_TEXT, Type.STRING_TEXT),
Matcher(cls.SINGLE_QUOTE, Type.SINGLE_QUOTE_STRING_END,
JavaScriptModes.TEXT_MODE)],
# Matchers for double quote strings.
JavaScriptModes.DOUBLE_QUOTE_STRING_MODE: [
Matcher(cls.DOUBLE_QUOTE_TEXT, Type.STRING_TEXT),
Matcher(cls.DOUBLE_QUOTE, Type.DOUBLE_QUOTE_STRING_END,
JavaScriptModes.TEXT_MODE)],
# Matchers for block comments.
JavaScriptModes.BLOCK_COMMENT_MODE: [
# First we check for exiting a block comment.
Matcher(cls.END_BLOCK_COMMENT, Type.END_BLOCK_COMMENT,
JavaScriptModes.TEXT_MODE),
# Match non-comment-ending text..
Matcher(cls.BLOCK_COMMENT_TEXT, Type.COMMENT)],
# Matchers for doc comments.
JavaScriptModes.DOC_COMMENT_MODE: cls.COMMON_DOC_MATCHERS + [
Matcher(cls.DOC_COMMENT_TEXT, Type.COMMENT)],
JavaScriptModes.DOC_COMMENT_LEX_SPACES_MODE: cls.COMMON_DOC_MATCHERS + [
Matcher(cls.WHITESPACE, Type.COMMENT),
Matcher(cls.DOC_COMMENT_NO_SPACES_TEXT, Type.COMMENT)],
# Matchers for single line comments.
JavaScriptModes.LINE_COMMENT_MODE: [
# We greedy match until the end of the line in line comment mode.
Matcher(cls.ANYTHING, Type.COMMENT, JavaScriptModes.TEXT_MODE)],
# Matchers for code after the function keyword.
JavaScriptModes.FUNCTION_MODE: [
# Must match open paren before anything else and move into parameter
# mode, otherwise everything inside the parameter list is parsed
# incorrectly.
Matcher(cls.OPENING_PAREN, Type.START_PARAMETERS,
JavaScriptModes.PARAMETER_MODE),
Matcher(cls.WHITESPACE, Type.WHITESPACE),
Matcher(cls.IDENTIFIER, Type.FUNCTION_NAME)],
# Matchers for function parameters
JavaScriptModes.PARAMETER_MODE: [
# When in function parameter mode, a closing paren is treated
# specially. Everything else is treated as lines of parameters.
Matcher(cls.CLOSING_PAREN_WITH_SPACE, Type.END_PARAMETERS,
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Matcher(cls.PARAMETERS, Type.PARAMETERS,
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wy1iu/LargeMargin_Softmax_Loss | c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec | python/caffe/coord_map.py | python | compose | (base_map, next_map) | return ax, a1 * a2, a1 * b2 + b1 | Compose a base coord map with scale a1, shift b1 with a further coord map
with scale a2, shift b2. The scales multiply and the further shift, b2,
is scaled by base coord scale a1. | Compose a base coord map with scale a1, shift b1 with a further coord map
with scale a2, shift b2. The scales multiply and the further shift, b2,
is scaled by base coord scale a1. | [
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Compose a base coord map with scale a1, shift b1 with a further coord map
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is scaled by base coord scale a1.
"""
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/graph_analytics/_model_base.py | python | GraphAnalyticsModel._result_fields | (self) | return {"graph": "SGraph. See m['graph']"} | Return results information
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Return results information
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | RichTextObject.Dump | (*args, **kwargs) | return _richtext.RichTextObject_Dump(*args, **kwargs) | Dump(self) -> String | Dump(self) -> String | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/osr.py | python | SpatialReference.GetAuthorityCode | (self, *args) | return _osr.SpatialReference_GetAuthorityCode(self, *args) | r"""GetAuthorityCode(SpatialReference self, char const * target_key) -> char const * | r"""GetAuthorityCode(SpatialReference self, char const * target_key) -> char const * | [
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widelands/widelands | e9f047d46a23d81312237d52eabf7d74e8de52d6 | utils/find_unused_includes.py | python | find_includes | (file_to_check) | return files | Returns a set of includes. | Returns a set of includes. | [
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line = line.strip()
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continue
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if match and len(match.groups()) == 1:
include_file = match.groups()[0]
if os.path.isfile(include_file):
files.add(include_file)
return files | [
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albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/contacts/client.py | python | ContactsClient.get_feed_uri | (self, kind='contacts', contact_list=None, projection='full',
scheme="http") | return '%s/m8/feeds/%s/%s/%s' % (prefix, kind, contact_list, projection) | Builds a feed URI.
Args:
kind: The type of feed to return, typically 'groups' or 'contacts'.
Default value: 'contacts'.
contact_list: The contact list to return a feed for.
Default value: self.contact_list.
projection: The projection to apply to the feed contents, for example
'full', 'base', 'base/12345', 'full/batch'. Default value: 'full'.
scheme: The URL scheme such as 'http' or 'https', None to return a
relative URI without hostname.
Returns:
A feed URI using the given kind, contact list, and projection.
Example: '/m8/feeds/contacts/default/full'. | Builds a feed URI. | [
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"""Builds a feed URI.
Args:
kind: The type of feed to return, typically 'groups' or 'contacts'.
Default value: 'contacts'.
contact_list: The contact list to return a feed for.
Default value: self.contact_list.
projection: The projection to apply to the feed contents, for example
'full', 'base', 'base/12345', 'full/batch'. Default value: 'full'.
scheme: The URL scheme such as 'http' or 'https', None to return a
relative URI without hostname.
Returns:
A feed URI using the given kind, contact list, and projection.
Example: '/m8/feeds/contacts/default/full'.
"""
contact_list = contact_list or self.contact_list
if kind == 'profiles':
contact_list = 'domain/%s' % contact_list
prefix = scheme and '%s://%s' % (scheme, self.server) or ''
return '%s/m8/feeds/%s/%s/%s' % (prefix, kind, contact_list, projection) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/data/experimental/ops/io.py | python | save | (dataset,
path,
compression=None,
shard_func=None,
checkpoint_args=None) | Saves the content of the given dataset.
Example usage:
>>> import tempfile
>>> path = os.path.join(tempfile.gettempdir(), "saved_data")
>>> # Save a dataset
>>> dataset = tf.data.Dataset.range(2)
>>> tf.data.experimental.save(dataset, path)
>>> new_dataset = tf.data.experimental.load(path)
>>> for elem in new_dataset:
... print(elem)
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)
The saved dataset is saved in multiple file "shards". By default, the dataset
output is divided to shards in a round-robin fashion but custom sharding can
be specified via the `shard_func` function. For example, you can save the
dataset to using a single shard as follows:
```python
dataset = make_dataset()
def custom_shard_func(element):
return 0
dataset = tf.data.experimental.save(
path="/path/to/data", ..., shard_func=custom_shard_func)
```
To enable checkpointing, pass in `checkpoint_args` to the `save` method
as follows:
```python
dataset = tf.data.Dataset.range(100)
save_dir = "..."
checkpoint_prefix = "..."
step_counter = tf.Variable(0, trainable=False)
checkpoint_args = {
"checkpoint_interval": 50,
"step_counter": step_counter,
"directory": checkpoint_prefix,
"max_to_keep": 20,
}
dataset.save(dataset, save_dir, checkpoint_args=checkpoint_args)
```
NOTE: The directory layout and file format used for saving the dataset is
considered an implementation detail and may change. For this reason, datasets
saved through `tf.data.experimental.save` should only be consumed through
`tf.data.experimental.load`, which is guaranteed to be backwards compatible.
Args:
dataset: The dataset to save.
path: Required. A directory to use for saving the dataset.
compression: Optional. The algorithm to use to compress data when writing
it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`.
shard_func: Optional. A function to control the mapping of dataset elements
to file shards. The function is expected to map elements of the input
dataset to int64 shard IDs. If present, the function will be traced and
executed as graph computation.
checkpoint_args: Optional args for checkpointing which will be passed into
the `tf.train.CheckpointManager`. If `checkpoint_args` are not specified,
then checkpointing will not be performed. The `save()` implementation
creates a `tf.train.Checkpoint` object internally, so users should not
set the `checkpoint` argument in `checkpoint_args`.
Raises:
ValueError if `checkpoint` is passed into `checkpoint_args`. | Saves the content of the given dataset. | [
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path,
compression=None,
shard_func=None,
checkpoint_args=None):
"""Saves the content of the given dataset.
Example usage:
>>> import tempfile
>>> path = os.path.join(tempfile.gettempdir(), "saved_data")
>>> # Save a dataset
>>> dataset = tf.data.Dataset.range(2)
>>> tf.data.experimental.save(dataset, path)
>>> new_dataset = tf.data.experimental.load(path)
>>> for elem in new_dataset:
... print(elem)
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)
The saved dataset is saved in multiple file "shards". By default, the dataset
output is divided to shards in a round-robin fashion but custom sharding can
be specified via the `shard_func` function. For example, you can save the
dataset to using a single shard as follows:
```python
dataset = make_dataset()
def custom_shard_func(element):
return 0
dataset = tf.data.experimental.save(
path="/path/to/data", ..., shard_func=custom_shard_func)
```
To enable checkpointing, pass in `checkpoint_args` to the `save` method
as follows:
```python
dataset = tf.data.Dataset.range(100)
save_dir = "..."
checkpoint_prefix = "..."
step_counter = tf.Variable(0, trainable=False)
checkpoint_args = {
"checkpoint_interval": 50,
"step_counter": step_counter,
"directory": checkpoint_prefix,
"max_to_keep": 20,
}
dataset.save(dataset, save_dir, checkpoint_args=checkpoint_args)
```
NOTE: The directory layout and file format used for saving the dataset is
considered an implementation detail and may change. For this reason, datasets
saved through `tf.data.experimental.save` should only be consumed through
`tf.data.experimental.load`, which is guaranteed to be backwards compatible.
Args:
dataset: The dataset to save.
path: Required. A directory to use for saving the dataset.
compression: Optional. The algorithm to use to compress data when writing
it. Supported options are `GZIP` and `NONE`. Defaults to `NONE`.
shard_func: Optional. A function to control the mapping of dataset elements
to file shards. The function is expected to map elements of the input
dataset to int64 shard IDs. If present, the function will be traced and
executed as graph computation.
checkpoint_args: Optional args for checkpointing which will be passed into
the `tf.train.CheckpointManager`. If `checkpoint_args` are not specified,
then checkpointing will not be performed. The `save()` implementation
creates a `tf.train.Checkpoint` object internally, so users should not
set the `checkpoint` argument in `checkpoint_args`.
Raises:
ValueError if `checkpoint` is passed into `checkpoint_args`.
"""
if (context.executing_eagerly() and checkpoint_args
and compat.forward_compatible(2021, 6, 29)):
save_dataset = _SaveDataset(dataset, path, shard_func, compression)
save_iterator = iter(save_dataset)
if "checkpoint" in checkpoint_args:
raise ValueError(
"'Invalid `checkpoint_args`. `checkpoint_args` are not allowed "
"to include 'checkpoint'."
)
checkpoint = tracking.util.Checkpoint(iterator=save_iterator)
checkpoint_args["checkpoint"] = checkpoint
manager = checkpoint_management.CheckpointManager(**checkpoint_args)
checkpoint.restore(manager.latest_checkpoint)
for _ in enumerate(save_iterator):
if "step_counter" in checkpoint_args:
checkpoint_args["step_counter"].assign_add(delta=1)
manager.save(check_interval=True)
else:
dataset, shard_func, use_shard_func, path = _set_save_dataset_attributes(
dataset, shard_func, path)
gen_experimental_dataset_ops.save_dataset(
dataset._variant_tensor, # pylint: disable=protected-access
path=path,
shard_func_other_args=shard_func.captured_inputs,
compression=compression,
shard_func=shard_func,
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_trackers.py | python | ghostTracker.remove | (self) | Remove the ghost when switching to and from subelement mode. | Remove the ghost when switching to and from subelement mode. | [
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] | def remove(self):
"""Remove the ghost when switching to and from subelement mode."""
if self.switch:
self.finalize() | [
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strukturag/libheif | 0082fea96ee70a20c8906a0373bedec0c01777bc | scripts/cpplint.py | python | _ShouldPrintError | (category, confidence, linenum) | return True | If confidence >= verbose, category passes filter and is not suppressed. | If confidence >= verbose, category passes filter and is not suppressed. | [
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"""If confidence >= verbose, category passes filter and is not suppressed."""
# There are three ways we might decide not to print an error message:
# a "NOLINT(category)" comment appears in the source,
# the verbosity level isn't high enough, or the filters filter it out.
if IsErrorSuppressedByNolint(category, linenum):
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is_filtered = False
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elif one_filter.startswith('+'):
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if is_filtered:
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/compat/__init__.py | python | add_metaclass | (metaclass) | return wrapper | Class decorator for creating a class with a metaclass. | Class decorator for creating a class with a metaclass. | [
"Class",
"decorator",
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"creating",
"a",
"class",
"with",
"a",
"metaclass",
"."
] | def add_metaclass(metaclass):
"""Class decorator for creating a class with a metaclass."""
def wrapper(cls):
orig_vars = cls.__dict__.copy()
orig_vars.pop('__dict__', None)
orig_vars.pop('__weakref__', None)
for slots_var in orig_vars.get('__slots__', ()):
orig_vars.pop(slots_var)
return metaclass(cls.__name__, cls.__bases__, orig_vars)
return wrapper | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/mpcd/update.py | python | sort.set_period | (self, period) | Change the sorting period.
Args:
period (int): New period to set.
Examples::
sorter.set_period(100)
sorter.set_period(1)
While the simulation is running, the action of each updater
is executed every *period* time steps. Changing the period does
not change the phase set when the analyzer was first created. | Change the sorting period. | [
"Change",
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"sorting",
"period",
"."
] | def set_period(self, period):
""" Change the sorting period.
Args:
period (int): New period to set.
Examples::
sorter.set_period(100)
sorter.set_period(1)
While the simulation is running, the action of each updater
is executed every *period* time steps. Changing the period does
not change the phase set when the analyzer was first created.
"""
self.period = period
self._cpp.setPeriod(hoomd.context.current.system.getCurrentTimeStep(),
self.period) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | StandardDialogLayoutAdapter_DoReparentControls | (*args, **kwargs) | return _windows_.StandardDialogLayoutAdapter_DoReparentControls(*args, **kwargs) | StandardDialogLayoutAdapter_DoReparentControls(Window parent, Window reparentTo, Sizer buttonSizer=None) | StandardDialogLayoutAdapter_DoReparentControls(Window parent, Window reparentTo, Sizer buttonSizer=None) | [
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] | def StandardDialogLayoutAdapter_DoReparentControls(*args, **kwargs):
"""StandardDialogLayoutAdapter_DoReparentControls(Window parent, Window reparentTo, Sizer buttonSizer=None)"""
return _windows_.StandardDialogLayoutAdapter_DoReparentControls(*args, **kwargs) | [
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yuxng/DA-RNN | 77fbb50b4272514588a10a9f90b7d5f8d46974fb | lib/datasets/shapenet_single.py | python | shapenet_single.image_path_from_index | (self, index) | return image_path | Construct an image path from the image's "index" identifier. | Construct an image path from the image's "index" identifier. | [
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"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._data_path, index + '_rgba' + self._image_ext)
assert os.path.exists(image_path), \
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return image_path | [
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alibaba/AliSQLBackup | 26573de135d115b100dbbfb9698274463ade5c8d | libevent/event_rpcgen.py | python | Struct.PrintTags | (self, file) | Prints the tag definitions for a structure. | Prints the tag definitions for a structure. | [
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] | def PrintTags(self, file):
"""Prints the tag definitions for a structure."""
print >>file, '/* Tag definition for %s */' % self._name
print >>file, 'enum %s_ {' % self._name.lower()
for entry in self._entries:
print >>file, ' %s=%d,' % (self.EntryTagName(entry),
entry.Tag())
print >>file, ' %s_MAX_TAGS' % (self._name.upper())
print >>file, '};\n' | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/gan/python/train.py | python | gan_train_ops | (
model,
loss,
generator_optimizer,
discriminator_optimizer,
check_for_unused_update_ops=True,
# Optional args to pass directly to the `create_train_op`.
**kwargs) | return namedtuples.GANTrainOps(gen_train_op, disc_train_op, global_step_inc) | Returns GAN train ops.
The highest-level call in TFGAN. It is composed of functions that can also
be called, should a user require more control over some part of the GAN
training process.
Args:
model: A GANModel.
loss: A GANLoss.
generator_optimizer: The optimizer for generator updates.
discriminator_optimizer: The optimizer for the discriminator updates.
check_for_unused_update_ops: If `True`, throws an exception if there are
update ops outside of the generator or discriminator scopes.
**kwargs: Keyword args to pass directly to
`training.create_train_op` for both the generator and
discriminator train op.
Returns:
A GANTrainOps tuple of (generator_train_op, discriminator_train_op) that can
be used to train a generator/discriminator pair. | Returns GAN train ops. | [
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"."
] | def gan_train_ops(
model,
loss,
generator_optimizer,
discriminator_optimizer,
check_for_unused_update_ops=True,
# Optional args to pass directly to the `create_train_op`.
**kwargs):
"""Returns GAN train ops.
The highest-level call in TFGAN. It is composed of functions that can also
be called, should a user require more control over some part of the GAN
training process.
Args:
model: A GANModel.
loss: A GANLoss.
generator_optimizer: The optimizer for generator updates.
discriminator_optimizer: The optimizer for the discriminator updates.
check_for_unused_update_ops: If `True`, throws an exception if there are
update ops outside of the generator or discriminator scopes.
**kwargs: Keyword args to pass directly to
`training.create_train_op` for both the generator and
discriminator train op.
Returns:
A GANTrainOps tuple of (generator_train_op, discriminator_train_op) that can
be used to train a generator/discriminator pair.
"""
# Create global step increment op.
global_step = training_util.get_or_create_global_step()
global_step_inc = global_step.assign_add(1)
# Get generator and discriminator update ops. We split them so that update
# ops aren't accidentally run multiple times. For now, throw an error if
# there are update ops that aren't associated with either the generator or
# the discriminator. Might modify the `kwargs` dictionary.
gen_update_ops, dis_update_ops = _get_update_ops(
kwargs, model.generator_scope.name, model.discriminator_scope.name,
check_for_unused_update_ops)
generator_global_step = None
if isinstance(generator_optimizer,
sync_replicas_optimizer.SyncReplicasOptimizer):
# TODO(joelshor): Figure out a way to get this work without including the
# dummy global step in the checkpoint.
# WARNING: Making this variable a local variable causes sync replicas to
# hang forever.
generator_global_step = variable_scope.get_variable(
'dummy_global_step_generator',
shape=[],
dtype=global_step.dtype.base_dtype,
initializer=init_ops.zeros_initializer(),
trainable=False,
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
gen_update_ops += [generator_global_step.assign(global_step)]
with ops.name_scope('generator_train'):
gen_train_op = training.create_train_op(
total_loss=loss.generator_loss,
optimizer=generator_optimizer,
variables_to_train=model.generator_variables,
global_step=generator_global_step,
update_ops=gen_update_ops,
**kwargs)
discriminator_global_step = None
if isinstance(discriminator_optimizer,
sync_replicas_optimizer.SyncReplicasOptimizer):
# See comment above `generator_global_step`.
discriminator_global_step = variable_scope.get_variable(
'dummy_global_step_discriminator',
shape=[],
dtype=global_step.dtype.base_dtype,
initializer=init_ops.zeros_initializer(),
trainable=False,
collections=[ops.GraphKeys.GLOBAL_VARIABLES])
dis_update_ops += [discriminator_global_step.assign(global_step)]
with ops.name_scope('discriminator_train'):
disc_train_op = training.create_train_op(
total_loss=loss.discriminator_loss,
optimizer=discriminator_optimizer,
variables_to_train=model.discriminator_variables,
global_step=discriminator_global_step,
update_ops=dis_update_ops,
**kwargs)
return namedtuples.GANTrainOps(gen_train_op, disc_train_op, global_step_inc) | [
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sigmaai/self-driving-golf-cart | 8d891600af3d851add27a10ae45cf3c2108bb87c | ros/src/ros_carla_bridge/carla_ego_vehicle/src/carla_ego_vehicle/carla_ego_vehicle.py | python | CarlaEgoVehicle.setup_sensors | (self, sensors) | return actors | Create the sensors defined by the user and attach them to the ego-vehicle
:param sensors: list of sensors
:return: | Create the sensors defined by the user and attach them to the ego-vehicle
:param sensors: list of sensors
:return: | [
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"""
Create the sensors defined by the user and attach them to the ego-vehicle
:param sensors: list of sensors
:return:
"""
actors = []
bp_library = self.world.get_blueprint_library()
for sensor_spec in sensors:
try:
bp = bp_library.find(str(sensor_spec['type']))
bp.set_attribute('role_name', str(sensor_spec['id']))
if sensor_spec['type'].startswith('sensor.camera'):
bp.set_attribute('image_size_x', str(sensor_spec['width']))
bp.set_attribute('image_size_y', str(sensor_spec['height']))
bp.set_attribute('fov', str(sensor_spec['fov']))
try:
bp.set_attribute('sensor_tick', str(sensor_spec['sensor_tick']))
except KeyError:
pass
sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],
z=sensor_spec['z'])
sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],
roll=sensor_spec['roll'],
yaw=sensor_spec['yaw'])
elif sensor_spec['type'].startswith('sensor.lidar'):
bp.set_attribute('range', str(sensor_spec['range']))
bp.set_attribute('rotation_frequency', str(sensor_spec['rotation_frequency']))
bp.set_attribute('channels', str(sensor_spec['channels']))
bp.set_attribute('upper_fov', str(sensor_spec['upper_fov']))
bp.set_attribute('lower_fov', str(sensor_spec['lower_fov']))
bp.set_attribute('points_per_second', str(sensor_spec['points_per_second']))
try:
bp.set_attribute('sensor_tick', str(sensor_spec['sensor_tick']))
except KeyError:
pass
sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],
z=sensor_spec['z'])
sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],
roll=sensor_spec['roll'],
yaw=sensor_spec['yaw'])
elif sensor_spec['type'].startswith('sensor.other.gnss'):
sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],
z=sensor_spec['z'])
sensor_rotation = carla.Rotation()
except KeyError as e:
rospy.logfatal(
"Sensor will not be spawned, because sensor spec is invalid: '{}'".format(e))
continue
# create sensor
sensor_transform = carla.Transform(sensor_location, sensor_rotation)
sensor = self.world.spawn_actor(bp, sensor_transform,
attach_to=self.player)
actors.append(sensor)
return actors | [
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] | https://github.com/sigmaai/self-driving-golf-cart/blob/8d891600af3d851add27a10ae45cf3c2108bb87c/ros/src/ros_carla_bridge/carla_ego_vehicle/src/carla_ego_vehicle/carla_ego_vehicle.py#L165-L220 | |
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftgeoutils/faces.py | python | removeSplitter | (shape) | return None | Return a face from removing the splitter in a list of faces.
This is an alternative, shared edge-based version of Part.removeSplitter.
Returns a face, or `None` if the operation failed. | Return a face from removing the splitter in a list of faces. | [
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"removing",
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"splitter",
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] | def removeSplitter(shape):
"""Return a face from removing the splitter in a list of faces.
This is an alternative, shared edge-based version of Part.removeSplitter.
Returns a face, or `None` if the operation failed.
"""
lookup = dict()
for f in shape.Faces:
for e in f.Edges:
h = e.hashCode()
if h in lookup:
lookup[h].append(e)
else:
lookup[h] = [e]
edges = [e[0] for e in lookup.values() if len(e) == 1]
try:
face = Part.Face(Part.Wire(edges))
except Part.OCCError:
# operation failed
return None
else:
if face.isValid():
return face
return None | [
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tkn-tub/ns3-gym | 19bfe0a583e641142609939a090a09dfc63a095f | src/visualizer/visualizer/ipython_view.py | python | IterableIPShell.__init__ | (self,argv=None,user_ns=None,user_global_ns=None,
cin=None, cout=None,cerr=None, input_func=None) | ! Initializer
@param self: this object
@param argv: Command line options for IPython
@param user_ns: User namespace.
@param user_global_ns: User global namespace.
@param cin: Console standard input.
@param cout: Console standard output.
@param cerr: Console standard error.
@param input_func: Replacement for builtin raw_input()
@return none | ! Initializer | [
"!",
"Initializer"
] | def __init__(self,argv=None,user_ns=None,user_global_ns=None,
cin=None, cout=None,cerr=None, input_func=None):
"""! Initializer
@param self: this object
@param argv: Command line options for IPython
@param user_ns: User namespace.
@param user_global_ns: User global namespace.
@param cin: Console standard input.
@param cout: Console standard output.
@param cerr: Console standard error.
@param input_func: Replacement for builtin raw_input()
@return none
"""
io = IPython.utils.io
if input_func:
if parse_version(IPython.release.version) >= parse_version("1.2.1"):
IPython.terminal.interactiveshell.raw_input_original = input_func
else:
IPython.frontend.terminal.interactiveshell.raw_input_original = input_func
if cin:
io.stdin = io.IOStream(cin)
if cout:
io.stdout = io.IOStream(cout)
if cerr:
io.stderr = io.IOStream(cerr)
# This is to get rid of the blockage that occurs during
# IPython.Shell.InteractiveShell.user_setup()
io.raw_input = lambda x: None
os.environ['TERM'] = 'dumb'
excepthook = sys.excepthook
from IPython.config.loader import Config
cfg = Config()
cfg.InteractiveShell.colors = "Linux"
# InteractiveShell's __init__ overwrites io.stdout,io.stderr with
# sys.stdout, sys.stderr, this makes sure they are right
#
old_stdout, old_stderr = sys.stdout, sys.stderr
sys.stdout, sys.stderr = io.stdout.stream, io.stderr.stream
# InteractiveShell inherits from SingletonConfigurable, so use instance()
#
if parse_version(IPython.release.version) >= parse_version("1.2.1"):
self.IP = IPython.terminal.embed.InteractiveShellEmbed.instance(\
config=cfg, user_ns=user_ns)
else:
self.IP = IPython.frontend.terminal.embed.InteractiveShellEmbed.instance(\
config=cfg, user_ns=user_ns)
sys.stdout, sys.stderr = old_stdout, old_stderr
self.IP.system = lambda cmd: self.shell(self.IP.var_expand(cmd),
header='IPython system call: ')
# local_ns=user_ns)
#global_ns=user_global_ns)
#verbose=self.IP.rc.system_verbose)
self.IP.raw_input = input_func
sys.excepthook = excepthook
self.iter_more = 0
self.history_level = 0
self.complete_sep = re.compile('[\s\{\}\[\]\(\)]')
self.updateNamespace({'exit':lambda:None})
self.updateNamespace({'quit':lambda:None})
self.IP.readline_startup_hook(self.IP.pre_readline)
# Workaround for updating namespace with sys.modules
#
self.__update_namespace() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/framemanager.py | python | AuiManager.DrawHintRect | (self, pane_window, pt, offset) | Calculates the hint rectangle by calling :meth:`CalculateHintRect`. If there is a
rectangle, it shows it by calling :meth:`ShowHint`, otherwise it hides any hint
rectangle currently shown.
:param Window `pane_window`: it is the window pointer of the pane being dragged;
:param Point `pt`: is the mouse position, in client coordinates;
:param Point `offset`: describes the offset that the mouse is from the upper-left
corner of the item being dragged. | Calculates the hint rectangle by calling :meth:`CalculateHintRect`. If there is a
rectangle, it shows it by calling :meth:`ShowHint`, otherwise it hides any hint
rectangle currently shown. | [
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Calculates the hint rectangle by calling :meth:`CalculateHintRect`. If there is a
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rectangle currently shown.
:param Window `pane_window`: it is the window pointer of the pane being dragged;
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/ert/ves.py | python | VESManager.invert | (self, data=None, err=None, ab2=None, mn2=None, **kwargs) | return super(VESManager, self).invert(data=data, err=err, **kwargs) | Invert measured data.
Parameters
----------
Keyword Arguments
----------------
**kwargs
Additional kwargs inherited from %(MethodManager1d.invert) and
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Returns
-------
model : pg.Vector
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"""Invert measured data.
Parameters
----------
Keyword Arguments
----------------
**kwargs
Additional kwargs inherited from %(MethodManager1d.invert) and
%(Inversion.run)
Returns
-------
model : pg.Vector
inversion result
"""
if ab2 is not None and mn2 is not None:
self.fop.setDataSpace(ab2=ab2, mn2=mn2)
if data is not None:
if self.complex:
nData = len(data)//2
self.dataTrans = pg.trans.TransCumulative()
self.dataTrans.add(self.rhoaTrans, nData)
self.dataTrans.add(self.phiaTrans, nData)
else:
self.dataTrans = pg.trans.TransLog()
self.inv.dataTrans = self.dataTrans
if 'layerLimits' not in kwargs:
kwargs['layerLimits'] = [min(self.fop.mn2)/5,
max(self.fop.ab2)/2]
if 'paraLimits' in kwargs and self.complex:
pL = kwargs['paraLimits'][1]
kwargs['paraLimits'][1] = [pL[0]/1000, pL[1]/1000]
return super(VESManager, self).invert(data=data, err=err, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/stub.py | python | Stubber.activate | (self) | Activates the stubber on the client | Activates the stubber on the client | [
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"""
Activates the stubber on the client
"""
self.client.meta.events.register_first(
'before-parameter-build.*.*',
self._assert_expected_params,
unique_id=self._expected_params_event_id)
self.client.meta.events.register(
'before-call.*.*',
self._get_response_handler,
unique_id=self._event_id) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | Palette.GetRGB | (*args, **kwargs) | return _gdi_.Palette_GetRGB(*args, **kwargs) | GetRGB(self, int pixel) -> (success, R,G,B) | GetRGB(self, int pixel) -> (success, R,G,B) | [
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"""GetRGB(self, int pixel) -> (success, R,G,B)"""
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/google_appengine_cloudstorage/cloudstorage/errors.py | python | check_status | (status, expected, path, headers=None,
resp_headers=None, extras=None) | Check HTTP response status is expected.
Args:
status: HTTP response status. int.
expected: a list of expected statuses. A list of ints.
path: filename or a path prefix.
headers: HTTP request headers.
resp_headers: HTTP response headers.
extras: extra info to be logged verbatim if error occurs.
Raises:
AuthorizationError: if authorization failed.
NotFoundError: if an object that's expected to exist doesn't.
TimeoutError: if HTTP request timed out.
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FatalError: if any other unexpected errors occurred. | Check HTTP response status is expected. | [
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] | def check_status(status, expected, path, headers=None,
resp_headers=None, extras=None):
"""Check HTTP response status is expected.
Args:
status: HTTP response status. int.
expected: a list of expected statuses. A list of ints.
path: filename or a path prefix.
headers: HTTP request headers.
resp_headers: HTTP response headers.
extras: extra info to be logged verbatim if error occurs.
Raises:
AuthorizationError: if authorization failed.
NotFoundError: if an object that's expected to exist doesn't.
TimeoutError: if HTTP request timed out.
ServerError: if server experienced some errors.
FatalError: if any other unexpected errors occurred.
"""
if status in expected:
return
msg = ('Expect status %r from Google Storage. But got status %d.\n'
'Path: %r.\n'
'Request headers: %r.\n'
'Response headers: %r.\n'
'Extra info: %r.\n' %
(expected, status, path, headers, resp_headers, extras))
if status == httplib.UNAUTHORIZED:
raise AuthorizationError(msg)
elif status == httplib.FORBIDDEN:
raise ForbiddenError(msg)
elif status == httplib.NOT_FOUND:
raise NotFoundError(msg)
elif status == httplib.REQUEST_TIMEOUT:
raise TimeoutError(msg)
elif status == httplib.REQUESTED_RANGE_NOT_SATISFIABLE:
raise InvalidRange(msg)
elif (status == httplib.OK and 308 in expected and
httplib.OK not in expected):
raise FileClosedError(msg)
elif status >= 500:
raise ServerError(msg)
else:
raise FatalError(msg) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/setuptools/command/install_lib.py | python | install_lib._gen_exclusion_paths | () | Generate file paths to be excluded for namespace packages (bytecode
cache files). | Generate file paths to be excluded for namespace packages (bytecode
cache files). | [
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"""
Generate file paths to be excluded for namespace packages (bytecode
cache files).
"""
# always exclude the package module itself
yield '__init__.py'
yield '__init__.pyc'
yield '__init__.pyo'
if not hasattr(sys, 'implementation'):
return
base = os.path.join('__pycache__', '__init__.' + sys.implementation.cache_tag)
yield base + '.pyc'
yield base + '.pyo'
yield base + '.opt-1.pyc'
yield base + '.opt-2.pyc' | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | GraphicsPath.AddArc | (*args) | return _gdi_.GraphicsPath_AddArc(*args) | AddArc(self, Double x, Double y, Double r, Double startAngle, Double endAngle,
bool clockwise=True)
AddArc(self, Point2D c, Double r, Double startAngle, Double endAngle,
bool clockwise=True)
Adds an arc of a circle centering at (x,y) with radius (r) from
startAngle to endAngle | AddArc(self, Double x, Double y, Double r, Double startAngle, Double endAngle,
bool clockwise=True)
AddArc(self, Point2D c, Double r, Double startAngle, Double endAngle,
bool clockwise=True) | [
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"""
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zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | scripts/cpp_lint.py | python | ReverseCloseExpression | (clean_lines, linenum, pos) | return (line, 0, -1) | If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
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If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
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Args:
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line = clean_lines.elided[linenum]
endchar = line[pos]
if endchar not in ')}]>':
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if endchar == '}': startchar = '{'
if endchar == '>': startchar = '<'
# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
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if start_pos > -1:
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while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
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if start_pos > -1:
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neopenx/Dragon | 0e639a7319035ddc81918bd3df059230436ee0a1 | Dragon/python/dragon/vm/tensorflow/ops/nn_ops.py | python | conv2d_transpose | (value, filter, output_shape, strides, padding='SAME',
data_format='NHWC', name=None) | Compute 2D deconvolution according to the given 4D ``input`` and ``filter``.
For **NHWC** format, filter should be as ``[filter_height, filter_width, out_channels, in_channels]``.
For **NCHW** format, filter should be as ``[in_channels, out_channels, filter_height, filter_width]``.
``output_shape`` will be ignored if padding algorithm is **VALID**.
Parameters
----------
input : Tensor
The input tensor.
filter : Tensor
The filter tensor.
output_shape : list of int
The deterministic output shape for **SAME** padding.
strides : list of int
The strides with length 4.
padding : str
The padding algorithm. ``VALID`` or ``SAME``.
data_format : str
The data format. ``NHWC`` or ``NCHW``.
name : str
The optional name for this operator.
Returns
-------
Tensor
The output tensor. | Compute 2D deconvolution according to the given 4D ``input`` and ``filter``. | [
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"deconvolution",
"according",
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] | def conv2d_transpose(value, filter, output_shape, strides, padding='SAME',
data_format='NHWC', name=None):
"""Compute 2D deconvolution according to the given 4D ``input`` and ``filter``.
For **NHWC** format, filter should be as ``[filter_height, filter_width, out_channels, in_channels]``.
For **NCHW** format, filter should be as ``[in_channels, out_channels, filter_height, filter_width]``.
``output_shape`` will be ignored if padding algorithm is **VALID**.
Parameters
----------
input : Tensor
The input tensor.
filter : Tensor
The filter tensor.
output_shape : list of int
The deterministic output shape for **SAME** padding.
strides : list of int
The strides with length 4.
padding : str
The padding algorithm. ``VALID`` or ``SAME``.
data_format : str
The data format. ``NHWC`` or ``NCHW``.
name : str
The optional name for this operator.
Returns
-------
Tensor
The output tensor.
"""
if filter.shape is None:
raise ValueError('filter must have a valid shape.')
else:
if len(filter.shape) != 4:
raise ValueError('filter must be a 4D Tensor.')
if len(strides) != 4:
raise ValueError('strides must be a list with length 4.')
if not isinstance(output_shape, list):
raise TypeError('output_shape should be a list.')
if len(output_shape) != 4:
raise ValueError('output_shape should be a list with length 4.')
if data_format == 'NHWC':
output = ops.Conv2dTranspose([value, filter],
num_output=filter.shape[2],
kernel_size=filter.shape[0:2],
stride=strides[1:3],
padding=padding,
data_format=data_format,
output_shape=output_shape)
return output
elif data_format == 'NCHW':
output = ops.Conv2dTranspose([value, filter],
num_output=filter.shape[1],
kernel_size=filter.shape[2:4],
stride=strides[2:4],
padding=padding,
data_format=data_format,
output_shape=output_shape)
return output
else:
raise ValueError('Unknown data format: {}'.format(data_format)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py | python | SymbolFunction.detect | (self, pos) | return pos.current() in SymbolFunction.commandmap | Find the symbol | Find the symbol | [
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] | def detect(self, pos):
"Find the symbol"
return pos.current() in SymbolFunction.commandmap | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/aui.py | python | PyAuiTabArt.GetFlags | (*args, **kwargs) | return _aui.PyAuiTabArt_GetFlags(*args, **kwargs) | GetFlags(self) -> int | GetFlags(self) -> int | [
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return _aui.PyAuiTabArt_GetFlags(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/dataview.py | python | DataViewTreeStoreNode.GetIcon | (*args, **kwargs) | return _dataview.DataViewTreeStoreNode_GetIcon(*args, **kwargs) | GetIcon(self) -> Icon | GetIcon(self) -> Icon | [
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"""GetIcon(self) -> Icon"""
return _dataview.DataViewTreeStoreNode_GetIcon(*args, **kwargs) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Tux/PersistentToolbars.py | python | addBottom | (name, toolbars) | addBottom("name", ["toolbars"])
Description:
Look at addTop for more information. | addBottom("name", ["toolbars"]) | [
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] | def addBottom(name, toolbars):
"""addBottom("name", ["toolbars"])
Description:
Look at addTop for more information."""
p = App.ParamGet("User parameter:Tux/PersistentToolbars/System")
p.GetGroup(name).SetBool("Saved", 1)
p.GetGroup(name).SetString("Bottom", ",".join(toolbars)) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/offline_debug/dbg_services.py | python | DbgServices.add_watchpoint | (self, watchpoint_id, watch_condition, check_node_list, parameter_list) | return self.dbg_instance.AddWatchpoint(watchpoint_id, watch_condition, check_node_list, parameter_list_inst) | Adding watchpoint to Debug Service instance.
Args:
watchpoint_id (int): Watchpoint id
watch_condition (int): A representation of the condition to be checked.
check_node_list (dict): Dictionary of node names (str or '*' to check all nodes) as key,
mapping to rank_id (list of ints or '*' to check all devices),
root_graph_id (list of ints or '*' to check all graphs) and is_output (bool).
parameter_list (list): List of parameters in watchpoint. Parameters should be instances of Parameter class.
Each parameter describes the value to be checked in watchpoint.
Returns:
Debug Service instance with added watchpoint.
Examples:
>>> from mindspore.ccsrc.debug.debugger.offline_debug import dbg_services
>>> d = dbg_services.DbgServices(dump_file_path="dump_file_path")
>>> d_init = d.initialize(is_sync_mode=True)
>>> d_wp = d_init.add_watchpoint(watchpoint_id=1,
... watch_condition=6,
... check_node_list={"conv2.bias" : {"rank_id": [0],
... root_graph_id: [0], "is_output": True}},
... parameter_list=[dbg_services.Parameter(name="param",
... disabled=False,
... value=0.0,
... hit=False,
... actual_value=0.0)]) | Adding watchpoint to Debug Service instance. | [
"Adding",
"watchpoint",
"to",
"Debug",
"Service",
"instance",
"."
] | def add_watchpoint(self, watchpoint_id, watch_condition, check_node_list, parameter_list):
"""
Adding watchpoint to Debug Service instance.
Args:
watchpoint_id (int): Watchpoint id
watch_condition (int): A representation of the condition to be checked.
check_node_list (dict): Dictionary of node names (str or '*' to check all nodes) as key,
mapping to rank_id (list of ints or '*' to check all devices),
root_graph_id (list of ints or '*' to check all graphs) and is_output (bool).
parameter_list (list): List of parameters in watchpoint. Parameters should be instances of Parameter class.
Each parameter describes the value to be checked in watchpoint.
Returns:
Debug Service instance with added watchpoint.
Examples:
>>> from mindspore.ccsrc.debug.debugger.offline_debug import dbg_services
>>> d = dbg_services.DbgServices(dump_file_path="dump_file_path")
>>> d_init = d.initialize(is_sync_mode=True)
>>> d_wp = d_init.add_watchpoint(watchpoint_id=1,
... watch_condition=6,
... check_node_list={"conv2.bias" : {"rank_id": [0],
... root_graph_id: [0], "is_output": True}},
... parameter_list=[dbg_services.Parameter(name="param",
... disabled=False,
... value=0.0,
... hit=False,
... actual_value=0.0)])
"""
logger.info("in Python AddWatchpoint")
for node_name, node_info in check_node_list.items():
for info_name, info_param in node_info.items():
check_node_list = self.transform_check_node_list(info_name, info_param, node_name, check_node_list)
parameter_list_inst = []
for elem in parameter_list:
parameter_list_inst.append(elem.instance)
return self.dbg_instance.AddWatchpoint(watchpoint_id, watch_condition, check_node_list, parameter_list_inst) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/wheel.py | python | check_compatibility | (version, name) | Raises errors or warns if called with an incompatible Wheel-Version.
Pip should refuse to install a Wheel-Version that's a major series
ahead of what it's compatible with (e.g 2.0 > 1.1); and warn when
installing a version only minor version ahead (e.g 1.2 > 1.1).
version: a 2-tuple representing a Wheel-Version (Major, Minor)
name: name of wheel or package to raise exception about
:raises UnsupportedWheel: when an incompatible Wheel-Version is given | Raises errors or warns if called with an incompatible Wheel-Version. | [
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"errors",
"or",
"warns",
"if",
"called",
"with",
"an",
"incompatible",
"Wheel",
"-",
"Version",
"."
] | def check_compatibility(version, name):
"""
Raises errors or warns if called with an incompatible Wheel-Version.
Pip should refuse to install a Wheel-Version that's a major series
ahead of what it's compatible with (e.g 2.0 > 1.1); and warn when
installing a version only minor version ahead (e.g 1.2 > 1.1).
version: a 2-tuple representing a Wheel-Version (Major, Minor)
name: name of wheel or package to raise exception about
:raises UnsupportedWheel: when an incompatible Wheel-Version is given
"""
if not version:
raise UnsupportedWheel(
"%s is in an unsupported or invalid wheel" % name
)
if version[0] > VERSION_COMPATIBLE[0]:
raise UnsupportedWheel(
"%s's Wheel-Version (%s) is not compatible with this version "
"of pip" % (name, '.'.join(map(str, version)))
)
elif version > VERSION_COMPATIBLE:
logger.warning(
'Installing from a newer Wheel-Version (%s)',
'.'.join(map(str, version)),
) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_vim.py | python | EditraCommander.IsAtLineStart | (self) | return self._GetCol() == 0 | Is the cursor currently at the start of a line
@return: bool | Is the cursor currently at the start of a line
@return: bool | [
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"currently",
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] | def IsAtLineStart(self):
"""Is the cursor currently at the start of a line
@return: bool
"""
return self._GetCol() == 0 | [
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rbgirshick/caffe-fast-rcnn | 28a579eaf0668850705598b3075b8969f22226d9 | 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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"-",
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] | 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"
_error_suppressions.setdefault(None, set()).add(linenum)
else:
if category.startswith('(') and category.endswith(')'):
category = category[1:-1]
if category in _ERROR_CATEGORIES:
_error_suppressions.setdefault(category, set()).add(linenum)
else:
error(filename, linenum, 'readability/nolint', 5,
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/rnn_grad.py | python | _block_lstm_grad | (op, *grads) | return (None, x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad,
wco_grad, b_grad) | Gradient for the BlockLSTM op. | Gradient for the BlockLSTM op. | [
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"""Gradient for the BlockLSTM op."""
seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b = op.inputs
i, cs, f, o, ci, co, h = op.outputs
_, cs_grad, _, _, _, _, h_grad = grads
(x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad,
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return (None, x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | DCFontChanger.__init__ | (self, *args, **kwargs) | __init__(self, DC dc, Font font) -> DCFontChanger
wx.wxDCFontChanger sets the DC's font when it is constructed,
and then restores the old font whrn it goes out of scope. | __init__(self, DC dc, Font font) -> DCFontChanger | [
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"""
__init__(self, DC dc, Font font) -> DCFontChanger
wx.wxDCFontChanger sets the DC's font when it is constructed,
and then restores the old font whrn it goes out of scope.
"""
_gdi_.DCFontChanger_swiginit(self,_gdi_.new_DCFontChanger(*args, **kwargs)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/cli/req_command.py | python | SessionCommandMixin._get_index_urls | (cls, options) | return index_urls or None | Return a list of index urls from user-provided options. | Return a list of index urls from user-provided options. | [
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# type: (Values) -> Optional[List[str]]
"""Return a list of index urls from user-provided options."""
index_urls = []
if not getattr(options, "no_index", False):
url = getattr(options, "index_url", None)
if url:
index_urls.append(url)
urls = getattr(options, "extra_index_urls", None)
if urls:
index_urls.extend(urls)
# Return None rather than an empty list
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/numbers.py | python | Complex.__pow__ | (self, exponent) | self**exponent; should promote to float or complex when necessary. | self**exponent; should promote to float or complex when necessary. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/webapp2/webapp2.py | python | RequestHandler.error | (self, code) | Clears the response and sets the given HTTP status code.
This doesn't stop code execution; for this, use :meth:`abort`.
:param code:
HTTP status error code (e.g., 501). | Clears the response and sets the given HTTP status code. | [
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"""Clears the response and sets the given HTTP status code.
This doesn't stop code execution; for this, use :meth:`abort`.
:param code:
HTTP status error code (e.g., 501).
"""
self.response.status = code
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPMT_SIGNATURE.sigAlg | (self) | return signature.GetUnionSelector() if signature else TPM_ALG_ID.NULL | Selector of the algorithm used to construct the signature | Selector of the algorithm used to construct the signature | [
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""" Selector of the algorithm used to construct the signature """
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/configdialog.py | python | HighPage.save_as_new_theme | (self) | Prompt for new theme name and create the theme.
Methods:
get_new_theme_name
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Methods:
get_new_theme_name
create_new
"""
new_theme_name = self.get_new_theme_name('New Theme Name:')
if new_theme_name:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/setuptools/package_index.py | python | unique_everseen | (iterable, key=None) | List unique elements, preserving order. Remember all elements ever seen. | List unique elements, preserving order. Remember all elements ever seen. | [
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"List unique elements, preserving order. Remember all elements ever seen."
# unique_everseen('AAAABBBCCDAABBB') --> A B C D
# unique_everseen('ABBCcAD', str.lower) --> A B C D
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seen_add = seen.add
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/external/bazel_tools/tools/android/build_incremental_dexmanifest.py | python | DexmanifestBuilder.Checksum | (self, filename) | return h.hexdigest() | Compute the SHA-256 checksum of a file. | Compute the SHA-256 checksum of a file. | [
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"""Compute the SHA-256 checksum of a file."""
h = hashlib.sha256()
with file(filename, "r") as f:
while True:
data = f.read(65536)
if not data:
break
h.update(data)
return h.hexdigest() | [
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telefonicaid/fiware-orion | 27c3202b9ddcfb9e3635a0af8d373f76e89b1d24 | scripts/cpplint.py | python | _GetTextInside | (text, start_pattern) | return text[start_position:position - 1] | Retrieves all the text between matching open and close parentheses.
Given a string of lines and a regular expression string, retrieve all the text
following the expression and between opening punctuation symbols like
(, [, or {, and the matching close-punctuation symbol. This properly nested
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printf(a(), b(c()));
a call to _GetTextInside(text, r'printf\(') will return 'a(), b(c())'.
start_pattern must match string having an open punctuation symbol at the end.
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text: The lines to extract text. Its comments and strings must be elided.
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start_pattern: The regexp string indicating where to start extracting
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Returns:
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None if either the opening string or ending punctuation could not be found. | Retrieves all the text between matching open and close parentheses. | [
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"""Retrieves all the text between matching open and close parentheses.
Given a string of lines and a regular expression string, retrieve all the text
following the expression and between opening punctuation symbols like
(, [, or {, and the matching close-punctuation symbol. This properly nested
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start_pattern: The regexp string indicating where to start extracting
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Returns:
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None if either the opening string or ending punctuation could not be found.
"""
# TODO(sugawarayu): Audit cpplint.py to see what places could be profitably
# rewritten to use _GetTextInside (and use inferior regexp matching today).
# Give opening punctuations to get the matching close-punctuations.
matching_punctuation = {'(': ')', '{': '}', '[': ']'}
closing_punctuation = set(matching_punctuation.itervalues())
# Find the position to start extracting text.
match = re.search(start_pattern, text, re.M)
if not match: # start_pattern not found in text.
return None
start_position = match.end(0)
assert start_position > 0, (
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assert text[start_position - 1] in matching_punctuation, (
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# Stack of closing punctuations we expect to have in text after position.
punctuation_stack = [matching_punctuation[text[start_position - 1]]]
position = start_position
while punctuation_stack and position < len(text):
if text[position] == punctuation_stack[-1]:
punctuation_stack.pop()
elif text[position] in closing_punctuation:
# A closing punctuation without matching opening punctuations.
return None
elif text[position] in matching_punctuation:
punctuation_stack.append(matching_punctuation[text[position]])
position += 1
if punctuation_stack:
# Opening punctuations left without matching close-punctuations.
return None
# punctuations match.
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s9xie/DSN | 065e49898d239f5c96be558616b2556eabc50351 | python/caffe/drawnet.py | python | draw_net_to_file | (caffe_net, filename) | Draws a caffe net, and saves it to file using the format given as the
file extension. Use '.raw' to output raw text that you can manually feed
to graphviz to draw graphs. | Draws a caffe net, and saves it to file using the format given as the
file extension. Use '.raw' to output raw text that you can manually feed
to graphviz to draw graphs. | [
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ext = filename[filename.rfind('.')+1:]
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Window.Show | (*args, **kwargs) | return _core_.Window_Show(*args, **kwargs) | Show(self, bool show=True) -> bool
Shows or hides the window. You may need to call Raise for a top level
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/lib2to3/pgen2/parse.py | python | Parser.shift | (self, type, value, newstate, context) | Shift a token. (Internal) | Shift a token. (Internal) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/cython/Cython/Compiler/ExprNodes.py | python | make_dedup_key | (outer_type, item_nodes) | return outer_type, tuple(item_keys) | Recursively generate a deduplication key from a sequence of values.
Includes Cython node types to work around the fact that (1, 2.0) == (1.0, 2), for example.
@param outer_type: The type of the outer container.
@param item_nodes: A sequence of constant nodes that will be traversed recursively.
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else (node.type, node.constant_result,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/utils.py | python | copy_and_modify | (src_file, dst_file, pattern_and_replace_tuples) | Read a source file, apply replacements, and save to a target file
:param src_file: The source file to read and do the replacements
:param dst_file: The target file
:param pattern_and_replace_tuples: Pattern+Replacement tuples | Read a source file, apply replacements, and save to a target file | [
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Read a source file, apply replacements, and save to a target file
:param src_file: The source file to read and do the replacements
:param dst_file: The target file
:param pattern_and_replace_tuples: Pattern+Replacement tuples
"""
if not os.path.exists(src_file):
raise ValueError('Source file ({}) does not exist.'.format(src_file))
with open(src_file,'r') as src_file_file:
src_file_content = src_file_file.read()
for pattern_str, replacement_str in pattern_and_replace_tuples:
src_file_content = re.sub(pattern_str, replacement_str, src_file_content)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/cubecolourdialog.py | python | ColourPanel.OnPaint | (self, event) | Handles the ``wx.EVT_PAINT`` for :class:`ColourPanel`.
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"""
Handles the ``wx.EVT_PAINT`` for :class:`ColourPanel`.
:param `event`: a :class:`PaintEvent` event to be processed.
"""
pdc = wx.PaintDC(self)
dc = wx.GCDC(pdc)
mem_dc = wx.MemoryDC()
rect = self.GetClientRect()
bmp = wx.EmptyBitmap(rect.width, rect.height)
mem_dc.SelectObject(bmp)
backBrush = wx.Brush(self.GetParent().GetBackgroundColour())
mem_dc.SetBackground(backBrush)
mem_dc.Clear()
mem_dc.SetBrush(wx.WHITE_BRUSH)
mem_dc.DrawRectangleRect(rect)
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gcdc = wx.GCDC(mem_dc)
colour_gcdc = wx.Colour(self._colour.r, self._colour.g, self._colour.b, self._colour._alpha)
gcdc.SetBrush(wx.Brush(colour_gcdc))
gcdc.SetPen(wx.Pen(colour_gcdc))
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Tk.__init__ | (self, screenName=None, baseName=None, className='Tk',
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readprofile).
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self.children = {}
self._tkloaded = 0
# to avoid recursions in the getattr code in case of failure, we
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self.tk = None
if baseName is None:
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baseName = os.path.basename(sys.argv[0])
baseName, ext = os.path.splitext(baseName)
if ext not in ('.py', '.pyc', '.pyo'):
baseName = baseName + ext
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/generators.py | python | PyGeneratorLower.init_generator_state | (self, lower) | NULL-initialize all generator state variables, to avoid spurious
decref's on cleanup. | NULL-initialize all generator state variables, to avoid spurious
decref's on cleanup. | [
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"."
] | def init_generator_state(self, lower):
"""
NULL-initialize all generator state variables, to avoid spurious
decref's on cleanup.
"""
lower.builder.store(Constant.null(self.gen_state_ptr.type.pointee),
self.gen_state_ptr) | [
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rdiankov/openrave | d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7 | sandbox/mintime/RRT_Smooth.py | python | RRT | (robot,goal_config) | return rave_traj | Find a RRT trajectory using OpenRAVE's bi-rrt
Linear smoothing by dichotomy to reduce the number of via-points
Return a rave trajectory
robot -- the robot
goal_config -- goal configuration | Find a RRT trajectory using OpenRAVE's bi-rrt
Linear smoothing by dichotomy to reduce the number of via-points
Return a rave trajectory
robot -- the robot
goal_config -- goal configuration | [
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] | def RRT(robot,goal_config):
"""Find a RRT trajectory using OpenRAVE's bi-rrt
Linear smoothing by dichotomy to reduce the number of via-points
Return a rave trajectory
robot -- the robot
goal_config -- goal configuration
"""
params = Planner.PlannerParameters()
params.SetRobotActiveJoints(robot)
params.SetGoalConfig(goal_config)
params.SetExtraParameters('<_postprocessing planner="shortcut_linear"><_nmaxiterations>40</_nmaxiterations></_postprocessing>')
env=robot.GetEnv()
planner=RaveCreatePlanner(env,'birrt')
planner.InitPlan(robot, params)
traj = RaveCreateTrajectory(env,'')
planner.PlanPath(traj)
traj_rrt=[]
for i in range(traj.GetNumWaypoints()):
traj_rrt.append(traj.GetWaypoint(i))
# Linear smoothing by dichotomy
via_points=linear_smooth_dichotomy(robot,traj_rrt,0.001)
dt_vect=array([1]*len(via_points))
dt_vect[0]=0
rave_traj=RaveCreateTrajectory(env,'')
spec = robot.GetActiveConfigurationSpecification('linear')
spec.AddDeltaTimeGroup()
rave_traj.Init(spec)
rave_traj.Insert(0,c_[via_points,dt_vect].flatten())
return rave_traj | [
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forkineye/ESPixelStick | 22926f1c0d1131f1369fc7cad405689a095ae3cb | dist/bin/pyserial/examples/wxTerminal.py | python | TerminalFrame.StartThread | (self) | Start the receiver thread | Start the receiver thread | [
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"""Start the receiver thread"""
self.thread = threading.Thread(target=self.ComPortThread)
self.thread.setDaemon(1)
self.alive.set()
self.thread.start()
self.serial.rts = True
self.serial.dtr = True
self.frame_terminal_menubar.Check(ID_RTS, self.serial.rts)
self.frame_terminal_menubar.Check(ID_DTR, self.serial.dtr) | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/learn/python/learn/dataframe/estimator_utils.py | python | to_feature_columns_and_input_fn | (dataframe,
base_input_keys_with_defaults,
feature_keys,
target_keys=None,
**kwargs) | return feature_columns, input_fn | Build a list of FeatureColumns and an input_fn for use with Estimator.
Args:
dataframe: the underlying dataframe
base_input_keys_with_defaults: a dict from the names of columns to be
considered base features to their default values. These columns will be
fed via input_fn.
feature_keys: the names of columns from which to generate FeatureColumns.
These may include base features and/or derived features.
target_keys: the names of columns to be used as targets. None is
acceptable for unsupervised learning.
**kwargs: Additional keyword arguments, unused here.
Returns:
A tuple of two elements:
* A list of `FeatureColumn`s to be used when constructing an Estimator
* An input_fn, i.e. a function that returns a pair of dicts
(features, targets), each mapping string names to Tensors.
the feature dict provides mappings for all the base columns required
by the FeatureColumns.
Raises:
ValueError: when the feature and target key sets are non-disjoint, or the
base_input and target sets are non-disjoint. | Build a list of FeatureColumns and an input_fn for use with Estimator. | [
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] | def to_feature_columns_and_input_fn(dataframe,
base_input_keys_with_defaults,
feature_keys,
target_keys=None,
**kwargs):
"""Build a list of FeatureColumns and an input_fn for use with Estimator.
Args:
dataframe: the underlying dataframe
base_input_keys_with_defaults: a dict from the names of columns to be
considered base features to their default values. These columns will be
fed via input_fn.
feature_keys: the names of columns from which to generate FeatureColumns.
These may include base features and/or derived features.
target_keys: the names of columns to be used as targets. None is
acceptable for unsupervised learning.
**kwargs: Additional keyword arguments, unused here.
Returns:
A tuple of two elements:
* A list of `FeatureColumn`s to be used when constructing an Estimator
* An input_fn, i.e. a function that returns a pair of dicts
(features, targets), each mapping string names to Tensors.
the feature dict provides mappings for all the base columns required
by the FeatureColumns.
Raises:
ValueError: when the feature and target key sets are non-disjoint, or the
base_input and target sets are non-disjoint.
"""
if feature_keys is None or not feature_keys:
raise ValueError("feature_keys must be specified.")
if target_keys is None:
target_keys = []
base_input_keys = base_input_keys_with_defaults.keys()
in_two = (set(feature_keys) & set(target_keys)) or (set(base_input_keys) &
set(target_keys))
if in_two:
raise ValueError("Columns cannot be used for both features and targets: %s"
% ", ".join(in_two))
# Obtain the feature series in the alternate universe
new_feature_series_dict, feature_specs = _build_alternate_universe(
dataframe, base_input_keys_with_defaults, feature_keys)
# TODO(soergel): Allow non-real, non-dense DataFrameColumns
for key in new_feature_series_dict.keys():
spec = feature_specs[key]
if not (
isinstance(spec, parsing_ops.FixedLenFeature)
and (spec.dtype.is_integer or spec.dtype.is_floating)):
raise ValueError("For now, only real dense columns can be passed from "
"DataFrame to Estimator. %s is %s of %s" % (
(key, type(spec).__name__, spec.dtype)))
# Make FeatureColumns from these
feature_columns = [feature_column.DataFrameColumn(name, s)
for name, s in new_feature_series_dict.items()]
# Make a new DataFrame with only the Series needed for input_fn.
# This is important to avoid starting queue feeders that won't be used.
limited_dataframe = dataframe.select_columns(
list(base_input_keys) + list(target_keys))
# Build an input_fn suitable for use with Estimator.
def input_fn():
"""An input_fn() for feeding the given set of DataFrameColumns."""
# It's important to build all the tensors together in one DataFrame.
# If we did df.select() for both key sets and then build those, the two
# resulting DataFrames would be shuffled independently.
tensors = limited_dataframe.build(**kwargs)
base_input_features = {key: tensors[key] for key in base_input_keys}
targets = {key: tensors[key] for key in target_keys}
# TODO(soergel): Remove this special case when b/30367437 is fixed.
if len(targets) == 1:
targets = list(targets.values())[0]
return base_input_features, targets
return feature_columns, input_fn | [
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priyankchheda/algorithms | c361aa9071573fa9966d5b02d05e524815abcf2b | binary_search_tree/compare_two_bst.py | python | main | () | operational function | operational function | [
"operational",
"function"
] | def main():
""" operational function """
root1 = Node(5)
root1.left = Node(3)
root1.right = Node(8)
root1.left.left = Node(2)
root1.left.right = Node(4)
root2 = Node(5)
root2.left = Node(3)
root2.right = Node(8)
root2.left.left = Node(2)
print(is_identical(root1, root2))
root2.left.right = Node(4)
print(is_identical(root1, root2)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py | python | CommandBit.parseliteral | (self, pos) | return bracket.literal | Parse a literal bracket. | Parse a literal bracket. | [
"Parse",
"a",
"literal",
"bracket",
"."
] | def parseliteral(self, pos):
"Parse a literal bracket."
self.factory.clearskipped(pos)
if not self.factory.detecttype(Bracket, pos):
if not pos.isvalue():
Trace.error('No literal parameter found at: ' + pos.identifier())
return None
return pos.globvalue()
bracket = Bracket().setfactory(self.factory)
self.add(bracket.parseliteral(pos))
return bracket.literal | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/IntegrateSingePtSubWindow.py | python | IntegrateSinglePtIntensityWindow.change_scan_number | (self, increment) | return | change the scan number in the
:param increment:
:return: | change the scan number in the
:param increment:
:return: | [
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] | def change_scan_number(self, increment):
"""
change the scan number in the
:param increment:
:return:
"""
# FIXME - 20180809 - This behaviors weird... Need debugging output - TODO
# get the list of scan number from the table, in future, a real-time updated list shall be used.
run_number_list = list()
for irow in range(self.ui.tableView_summary.rowCount()):
run_number_list.append(self.ui.tableView_summary.get_scan_number(irow))
curr_scan = int(self.ui.lineEdit_Scan.text())
try:
curr_scan_index = run_number_list.index(curr_scan)
except IndexError:
curr_scan_index = 0
next_scan_index = curr_scan_index + increment
next_scan_index = (next_scan_index + len(run_number_list)) % len(run_number_list)
# set
self.ui.lineEdit_Scan.setText('{}'.format(run_number_list[next_scan_index]))
return | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | StatusBar.GetFieldsCount | (*args, **kwargs) | return _windows_.StatusBar_GetFieldsCount(*args, **kwargs) | GetFieldsCount(self) -> int | GetFieldsCount(self) -> int | [
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"""GetFieldsCount(self) -> int"""
return _windows_.StatusBar_GetFieldsCount(*args, **kwargs) | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/ops/einsum.py | python | Einsum.adjust_equation_with_NCHW_layout | (node_name: str, equation: str, input_ranks: list, output_rank: int,
input_correct_layout_mask: list, output_correct_layout_mask: bool) | return permuted_equation, is_inputs_adjusted, is_output_adjusted | In order to satisfy NCHW layout, subscripts for tensors with rank greater than three must be adjusted by moving labels
of the last dimension to the second position in the subscript. There is an exception for such tensors when
the label is ellipsis and it covers multiple tail dimensions. The method returns equation with adjusted subscripts
to NCHW layout along with a boolean mask to indicate which subscripts are adjusted.
:param node_name: Einsum node name for which equation is adjusted
:param equation: Equation to be adjusted
:param input_ranks: a list of input ranks
:param output_rank: output rank
:return: adjusted equation, boolean mask for inputs, and boolean flag if output subscript is adjusted | In order to satisfy NCHW layout, subscripts for tensors with rank greater than three must be adjusted by moving labels
of the last dimension to the second position in the subscript. There is an exception for such tensors when
the label is ellipsis and it covers multiple tail dimensions. The method returns equation with adjusted subscripts
to NCHW layout along with a boolean mask to indicate which subscripts are adjusted. | [
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input_correct_layout_mask: list, output_correct_layout_mask: bool) -> (
str, list, bool):
"""
In order to satisfy NCHW layout, subscripts for tensors with rank greater than three must be adjusted by moving labels
of the last dimension to the second position in the subscript. There is an exception for such tensors when
the label is ellipsis and it covers multiple tail dimensions. The method returns equation with adjusted subscripts
to NCHW layout along with a boolean mask to indicate which subscripts are adjusted.
:param node_name: Einsum node name for which equation is adjusted
:param equation: Equation to be adjusted
:param input_ranks: a list of input ranks
:param output_rank: output rank
:return: adjusted equation, boolean mask for inputs, and boolean flag if output subscript is adjusted
"""
is_inputs_adjusted = []
input_subscripts, output_subscript = Einsum.parse_equation(node_name, equation)
num_inputs = len(input_ranks)
assert len(input_subscripts) == num_inputs, "The number of inputs must match a number " \
"of input subscripts"
assert len(input_correct_layout_mask) == num_inputs, "The number of inputs must match a number " \
"elements in input_correct_layout_mask list"
# permute labels in input subscripts and mark inputs for which inference in NCHW layout is acceptable
# in case ellipsis covering multiple dimensions in the end, the permutation is impossible
# so the corresponding input must be in the original format (NHWC)
permuted_input_subscripts = []
for input_ind in range(num_inputs):
input_subscript = input_subscripts[input_ind]
input_rank = input_ranks[input_ind]
labels = Einsum.extract_subscript_labels(node_name, input_subscript)
num_broadcasted_dims = input_rank - len(labels) + 1
if input_correct_layout_mask[input_ind]:
is_inputs_adjusted.append(True)
elif input_rank > 3 and (labels[-1] != "..." or labels[-1] == "..." and num_broadcasted_dims == 1):
is_inputs_adjusted.append(True)
labels.insert(1, labels[-1])
del labels[-1]
else:
is_inputs_adjusted.append(False)
permuted_input_subscript = ''.join(labels)
permuted_input_subscripts.append(permuted_input_subscript)
# perform the same procedure for the output subscript as for the inputs subscripts
labels = Einsum.extract_subscript_labels(node_name, output_subscript)
num_broadcasted_dims = output_rank - len(labels) + 1
if output_correct_layout_mask:
is_output_adjusted = True
elif output_rank > 3 and (labels[-1] != "..." or labels[-1] == "..." and num_broadcasted_dims == 1):
is_output_adjusted = True
labels.insert(1, labels[-1])
del labels[-1]
else:
is_output_adjusted = False
permuted_output_subscript = ''.join(labels)
# concatenate the left and right hands of the resulted equation
left_hand = ','.join(permuted_input_subscripts)
right_hand = permuted_output_subscript
permuted_equation = left_hand + "->" + right_hand
return permuted_equation, is_inputs_adjusted, is_output_adjusted | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/robotsim.py | python | PointCloud.setProperty | (self, *args) | return _robotsim.PointCloud_setProperty(self, *args) | setProperty(PointCloud self, int index, int pindex, double value)
setProperty(PointCloud self, int index, std::string const & pname, double value)
Sets the property named pname of point index to the given value. | setProperty(PointCloud self, int index, int pindex, double value)
setProperty(PointCloud self, int index, std::string const & pname, double value) | [
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setProperty(PointCloud self, int index, std::string const & pname, double value)
Sets the property named pname of point index to the given value.
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return _robotsim.PointCloud_setProperty(self, *args) | [
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NVIDIA/MDL-SDK | aa9642b2546ad7b6236b5627385d882c2ed83c5d | src/mdl/jit/llvm/dist/utils/lit/lit/ProgressBar.py | python | TerminalController.__init__ | (self, term_stream=sys.stdout) | Create a `TerminalController` and initialize its attributes
with appropriate values for the current terminal.
`term_stream` is the stream that will be used for terminal
output; if this stream is not a tty, then the terminal is
assumed to be a dumb terminal (i.e., have no capabilities). | Create a `TerminalController` and initialize its attributes
with appropriate values for the current terminal.
`term_stream` is the stream that will be used for terminal
output; if this stream is not a tty, then the terminal is
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"""
Create a `TerminalController` and initialize its attributes
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`term_stream` is the stream that will be used for terminal
output; if this stream is not a tty, then the terminal is
assumed to be a dumb terminal (i.e., have no capabilities).
"""
# Curses isn't available on all platforms
try: import curses
except: return
# If the stream isn't a tty, then assume it has no capabilities.
if not term_stream.isatty(): return
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self.LINES = curses.tigetnum('lines')
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if set_fg_ansi:
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set_bg = self._tigetstr('setb')
if set_bg:
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if set_bg_ansi:
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | lldb/third_party/Python/module/pexpect-4.6/pexpect/spawnbase.py | python | SpawnBase.compile_pattern_list | (self, patterns) | return compiled_pattern_list | This compiles a pattern-string or a list of pattern-strings.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/stats/morestats.py | python | ansari | (x, y) | return AnsariResult(AB, pval) | Perform the Ansari-Bradley test for equal scale parameters
The Ansari-Bradley test is a non-parametric test for the equality
of the scale parameter of the distributions from which two
samples were drawn.
Parameters
----------
x, y : array_like
arrays of sample data
Returns
-------
statistic : float
The Ansari-Bradley test statistic
pvalue : float
The p-value of the hypothesis test
See Also
--------
fligner : A non-parametric test for the equality of k variances
mood : A non-parametric test for the equality of two scale parameters
Notes
-----
The p-value given is exact when the sample sizes are both less than
55 and there are no ties, otherwise a normal approximation for the
p-value is used.
References
----------
.. [1] Sprent, Peter and N.C. Smeeton. Applied nonparametric statistical
methods. 3rd ed. Chapman and Hall/CRC. 2001. Section 5.8.2. | Perform the Ansari-Bradley test for equal scale parameters | [
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"""
Perform the Ansari-Bradley test for equal scale parameters
The Ansari-Bradley test is a non-parametric test for the equality
of the scale parameter of the distributions from which two
samples were drawn.
Parameters
----------
x, y : array_like
arrays of sample data
Returns
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statistic : float
The Ansari-Bradley test statistic
pvalue : float
The p-value of the hypothesis test
See Also
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fligner : A non-parametric test for the equality of k variances
mood : A non-parametric test for the equality of two scale parameters
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-----
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55 and there are no ties, otherwise a normal approximation for the
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.. [1] Sprent, Peter and N.C. Smeeton. Applied nonparametric statistical
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if m < 1:
raise ValueError("Not enough other observations.")
if n < 1:
raise ValueError("Not enough test observations.")
N = m + n
xy = r_[x, y] # combine
rank = stats.rankdata(xy)
symrank = amin(array((rank, N - rank + 1)), 0)
AB = np.sum(symrank[:n], axis=0)
uxy = unique(xy)
repeats = (len(uxy) != len(xy))
exact = ((m < 55) and (n < 55) and not repeats)
if repeats and (m < 55 or n < 55):
warnings.warn("Ties preclude use of exact statistic.")
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total = np.sum(a1, axis=0)
if ind < len(a1)/2.0:
cind = int(ceil(ind))
if ind == cind:
pval = 2.0 * np.sum(a1[:cind+1], axis=0) / total
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pval = 2.0 * np.sum(a1[:cind], axis=0) / total
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find = int(floor(ind))
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else:
pval = 2.0 * np.sum(a1[find+1:], axis=0) / total
return AnsariResult(AB, min(1.0, pval))
# otherwise compute normal approximation
if N % 2: # N odd
mnAB = n * (N+1.0)**2 / 4.0 / N
varAB = n * m * (N+1.0) * (3+N**2) / (48.0 * N**2)
else:
mnAB = n * (N+2.0) / 4.0
varAB = m * n * (N+2) * (N-2.0) / 48 / (N-1.0)
if repeats: # adjust variance estimates
# compute np.sum(tj * rj**2,axis=0)
fac = np.sum(symrank**2, axis=0)
if N % 2: # N odd
varAB = m * n * (16*N*fac - (N+1)**4) / (16.0 * N**2 * (N-1))
else: # N even
varAB = m * n * (16*fac - N*(N+2)**2) / (16.0 * N * (N-1))
z = (AB - mnAB) / sqrt(varAB)
pval = distributions.norm.sf(abs(z)) * 2.0
return AnsariResult(AB, pval) | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/MSVSProject.py | python | Writer._GetSpecForConfiguration | (self, config_type, config_name, attrs, tools) | return specification | Returns the specification for a configuration.
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config_type: Type of configuration node.
config_name: Configuration name.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
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config_type: Type of configuration node.
config_name: Configuration name.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
Returns:
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# Handle defaults
if not attrs:
attrs = {}
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tools = []
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node_attrs = attrs.copy()
node_attrs['Name'] = config_name
specification = [config_type, node_attrs]
# Add tool nodes and their attributes
if tools:
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/pylib/utils/logdog_helper.py | python | get_viewer_url | (name) | return get_logdog_client().get_viewer_url(name) | Get Logdog viewer URL.
Args:
name: Name of the logdog stream.
Returns:
Link to view uploaded binary in logdog viewer. | Get Logdog viewer URL. | [
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"""Get Logdog viewer URL.
Args:
name: Name of the logdog stream.
Returns:
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"""
return get_logdog_client().get_viewer_url(name) | [
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herbstluftwm/herbstluftwm | 23ef0274bd4d317208eae5fea72b21478a71431b | doc/gendoc.py | python | TokenStream.try_match | (self, *args) | return True | if the next tokens match the list in the *args
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/html.py | python | HtmlContainerCell.GetFirstChild | (*args, **kwargs) | return _html.HtmlContainerCell_GetFirstChild(*args, **kwargs) | GetFirstChild(self) -> HtmlCell | GetFirstChild(self) -> HtmlCell | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | benchmarks/sparse/dlmc/utils.py | python | load_dlmc_dataset | (dataset_path, operation, hidden_size, sparsity, device, requires_grad, n_limit=math.inf) | load_dlmc_dataset loads a DLMC dataset for a matmul performance test.
Args:
dataset_path:
path of the dataset from DLMC collection.
operation:
This value allows tensors for `sparse@sparse`|`sparse@dense`|`sparse@vector` operations.
hidden_size
This value allows tensors of varying sizes.
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This value allows tensors of varying sparsities.
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Whether to place the Tensor on a GPU or CPU.
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Loads the dataset for backward test.
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dataset_path:
path of the dataset from DLMC collection.
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Args:
dataset_path:
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if operation == 'sparse@sparse' or operation == "sparse@dense":
collection = load_spmm_dataset(dataset_path, hidden_size, sparsity, operation, device, n_limit)
elif operation == 'sparse@vector':
collection = load_spmv_dataset(dataset_path, hidden_size, sparsity, device, n_limit)
scipy_vars = {}
backward_vars = {}
for x, y in collection:
if device == 'cpu':
scipy_vars = {
"sx": to_coo_scipy(x) if x.is_sparse else x.numpy(),
"sy": to_coo_scipy(y) if y.is_sparse else y.numpy(),
}
if not requires_grad:
dx = x.to_dense() if x.is_sparse else x
dy = y.to_dense() if y.is_sparse else y
else:
c = sparse_grad_output(x, y)
backward_vars = {
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}
x.requires_grad_(True)
y.requires_grad_(True)
dx = x.to_dense().detach() if x.is_sparse else x.clone().detach()
dy = y.to_dense().detach() if y.is_sparse else y.clone().detach()
dx.requires_grad_(True)
dy.requires_grad_(True)
yield {
"x": x,
"y": y,
"dx": dx,
"dy": dy,
**scipy_vars,
**backward_vars
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Node/__init__.py | python | Node.get_build_scanner_path | (self, scanner) | return self.get_executor().get_build_scanner_path(scanner) | Fetch the appropriate scanner path for this node. | Fetch the appropriate scanner path for this node. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/protorpc/protorpc/protobuf.py | python | decode_message | (message_type, encoded_message) | return message | Decode protocol buffer to Message instance.
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message_type: Message type to decode data to.
encoded_message: Encoded version of message as string.
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Decoded instance of message_type.
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DecodeError if an error occurs during decoding, such as incompatible
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"""Decode protocol buffer to Message instance.
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message_type: Message type to decode data to.
encoded_message: Encoded version of message as string.
Returns:
Decoded instance of message_type.
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message = message_type()
message_array = array.array('B')
message_array.fromstring(encoded_message)
try:
decoder = _Decoder(message_array, 0, len(message_array))
while decoder.avail() > 0:
# Decode tag and variant information.
encoded_tag = decoder.getVarInt32()
tag = encoded_tag >> _WIRE_TYPE_BITS
wire_type = encoded_tag & _WIRE_TYPE_MASK
try:
found_wire_type_decoder = _WIRE_TYPE_TO_DECODER_MAP[wire_type]
except:
raise messages.DecodeError('No such wire type %d' % wire_type)
if tag < 1:
raise messages.DecodeError('Invalid tag value %d' % tag)
try:
field = message.field_by_number(tag)
except KeyError:
# Unexpected tags are ok.
field = None
wire_type_decoder = found_wire_type_decoder
else:
expected_wire_type = _VARIANT_TO_WIRE_TYPE[field.variant]
if expected_wire_type != wire_type:
raise messages.DecodeError('Expected wire type %s but found %s' % (
_WIRE_TYPE_NAME[expected_wire_type],
_WIRE_TYPE_NAME[wire_type]))
wire_type_decoder = _VARIANT_TO_DECODER_MAP[field.variant]
value = wire_type_decoder(decoder)
# Save unknown fields and skip additional processing.
if not field:
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# interpret the value later.
variant = _WIRE_TYPE_TO_VARIANT_MAP.get(wire_type)
if variant:
message.set_unrecognized_field(tag, value, variant)
continue
# Special case Enum and Message types.
if isinstance(field, messages.EnumField):
try:
value = field.type(value)
except TypeError:
raise messages.DecodeError('Invalid enum value %s' % value)
elif isinstance(field, messages.MessageField):
value = decode_message(field.message_type, value)
value = field.value_from_message(value)
# Merge value in to message.
if field.repeated:
values = getattr(message, field.name)
if values is None:
setattr(message, field.name, [value])
else:
values.append(value)
else:
setattr(message, field.name, value)
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message.check_initialized()
return message | [
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takemaru/graphillion | 51879f92bb96b53ef8f914ef37a05252ce383617 | graphillion/graphset.py | python | GraphSet.update | (self, *others) | return self | Updates `self`, adding graphs from all others.
Examples:
>>> graph1 = []
>>> graph2 = [(1, 2)]
>>> graph3 = [(1, 2), (1, 4)]
>>> gs1 = GraphSet([graph1, graph2])
>>> gs2 = GraphSet([graph2, graph3])
>>> gs1 |= gs2
>>> gs1
GraphSet([[], [(1, 2)], [(1, 2), (1, 4)]])
Returns:
A new GraphSet object.
See Also:
union() | Updates `self`, adding graphs from all others. | [
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] | https://github.com/takemaru/graphillion/blob/51879f92bb96b53ef8f914ef37a05252ce383617/graphillion/graphset.py#L311-L331 | |
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/eager/execution_callbacks.py | python | InfOrNanError._get_error_message | (self) | return msg | Get the error message describing this InfOrNanError object. | Get the error message describing this InfOrNanError object. | [
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"""Get the error message describing this InfOrNanError object."""
name_str = (("'%s'" % self._op_name) if self._op_name is not None
else str(self._op_name))
msg = "Output %d of %d of TFE operation %s (name: %s) contains " % (
self._output_index + 1, self._num_outputs, self._op_type, name_str)
if self._inf_count and self._nan_count:
msg += "%d inf(s) and %d nan(s) " % (self._inf_count, self._nan_count)
elif self._inf_count:
msg += "%d inf(s) " % self._inf_count
else:
msg += "%d nan(s) " % self._nan_count
msg += "out of a total of %d element(s). Tensor value: %s" % (
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/datetime.py | python | date.replace | (self, year=None, month=None, day=None) | return type(self)(year, month, day) | Return a new date with new values for the specified fields. | Return a new date with new values for the specified fields. | [
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"""Return a new date with new values for the specified fields."""
if year is None:
year = self._year
if month is None:
month = self._month
if day is None:
day = self._day
return type(self)(year, month, day) | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBFrame.GetDescription | (self, *args) | return _lldb.SBFrame_GetDescription(self, *args) | GetDescription(self, SBStream description) -> bool | GetDescription(self, SBStream description) -> bool | [
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return _lldb.SBFrame_GetDescription(self, *args) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | PropertyGridInterface.GetFirstChild | (*args, **kwargs) | return _propgrid.PropertyGridInterface_GetFirstChild(*args, **kwargs) | GetFirstChild(self, PGPropArg id) -> PGProperty | GetFirstChild(self, PGPropArg id) -> PGProperty | [
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"""GetFirstChild(self, PGPropArg id) -> PGProperty"""
return _propgrid.PropertyGridInterface_GetFirstChild(*args, **kwargs) | [
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