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ucsb-seclab/difuze | bb59a12ff87ad5ae45d9c60e349891bf80d72877 | helper_scripts/components/bear_generate_preprocess.py | python | build_preprocessed | (compilation_commands, linker_commands, kernel_src_dir,
target_arch, clang_path, llvm_link_path, llvm_bit_code_out) | return True | The main method that performs the preprocessing.
:param compilation_commands: Parsed compilation commands from the json.
:param linker_commands: Parsed linker commands from the json.
:param kernel_src_dir: Path to the kernel source directory.
:param target_arch: Number representing target architecture.
:param clang_path: Path to clang.
:param llvm_link_path: Path to llvm-link
:param llvm_bit_code_out: Folder where all the linked bitcode files should be stored.
:return: True | The main method that performs the preprocessing.
:param compilation_commands: Parsed compilation commands from the json.
:param linker_commands: Parsed linker commands from the json.
:param kernel_src_dir: Path to the kernel source directory.
:param target_arch: Number representing target architecture.
:param clang_path: Path to clang.
:param llvm_link_path: Path to llvm-link
:param llvm_bit_code_out: Folder where all the linked bitcode files should be stored.
:return: True | [
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target_arch, clang_path, llvm_link_path, llvm_bit_code_out):
"""
The main method that performs the preprocessing.
:param compilation_commands: Parsed compilation commands from the json.
:param linker_commands: Parsed linker commands from the json.
:param kernel_src_dir: Path to the kernel source directory.
:param target_arch: Number representing target architecture.
:param clang_path: Path to clang.
:param llvm_link_path: Path to llvm-link
:param llvm_bit_code_out: Folder where all the linked bitcode files should be stored.
:return: True
"""
output_llvm_sh_file = os.path.join(llvm_bit_code_out, 'llvm_generate_preprocessed.sh')
fp_out = open(output_llvm_sh_file, 'w')
fp_out.write("#!/bin/bash\n")
log_info("Writing all preprocessing commands to", output_llvm_sh_file)
all_compilation_commands = []
for curr_compilation_command in compilation_commands:
wd, obj_file, bc_file, build_str = _get_llvm_preprocessing_str(clang_path, curr_compilation_command.curr_args,
kernel_src_dir, target_arch,
curr_compilation_command.work_dir,
curr_compilation_command.src_file,
curr_compilation_command.output_file,
llvm_bit_code_out)
all_compilation_commands.append((wd, build_str))
fp_out.write("cd " + wd + ";" + build_str + "\n")
fp_out.close()
log_info("Got", len(all_compilation_commands), "preprocessing commands.")
log_info("Running preprocessing commands in multiprocessing modea.")
p = Pool(cpu_count())
return_vals = p.map(run_program_with_wd, all_compilation_commands)
log_success("Finished running preprocessing commands.")
return True | [
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NVIDIA/MDL-SDK | aa9642b2546ad7b6236b5627385d882c2ed83c5d | src/mdl/jit/llvm/dist/bindings/python/llvm/object.py | python | Relocation.cache | (self) | Cache all cacheable properties on this instance. | Cache all cacheable properties on this instance. | [
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"""Cache all cacheable properties on this instance."""
getattr(self, 'address')
getattr(self, 'offset')
getattr(self, 'symbol')
getattr(self, 'type')
getattr(self, 'type_name')
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/osx_carbon/gizmos.py | python | TreeListCtrl.GetChildrenCount | (*args, **kwargs) | return _gizmos.TreeListCtrl_GetChildrenCount(*args, **kwargs) | GetChildrenCount(self, TreeItemId item, bool recursively=True) -> size_t | GetChildrenCount(self, TreeItemId item, bool recursively=True) -> size_t | [
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yan99033/CNN-SVO | d5591ea88103f8d1b26e5296129bf3b3196a14f1 | rpg_svo/svo_analysis/src/svo_analysis/tum_benchmark_tools/associate.py | python | read_file_list | (filename) | return dict(list) | Reads a trajectory from a text file.
File format:
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and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp.
Input:
filename -- File name
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dict -- dictionary of (stamp,data) tuples | Reads a trajectory from a text file.
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and "d1 d2 d3.." is arbitary data (e.g., a 3D position and 3D orientation) associated to this timestamp.
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filename -- File name
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openmm/openmm | cb293447c4fc8b03976dfe11399f107bab70f3d9 | wrappers/python/openmm/app/internal/pdbx/reader/PdbxParser.py | python | PdbxReader.__parser | (self, tokenizer, containerList) | Parser for PDBx data files and dictionaries.
Input - tokenizer() reentrant method recognizing data item names (_category.attribute)
quoted strings (single, double and multi-line semi-colon delimited), and unquoted
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containerList - list-type container for data and definition objects parsed from
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Input - tokenizer() reentrant method recognizing data item names (_category.attribute)
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curContainer = None
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curCategory = None
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | IconLocation.__init__ | (self, *args, **kwargs) | __init__(self, String filename=&wxPyEmptyString, int num=0) -> IconLocation | __init__(self, String filename=&wxPyEmptyString, int num=0) -> IconLocation | [
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"""__init__(self, String filename=&wxPyEmptyString, int num=0) -> IconLocation"""
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/utils/strdispatch.py | python | StrDispatch.dispatch | (self, key) | Get a seq of Commandchain objects that match key | Get a seq of Commandchain objects that match key | [
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if key in self.strs:
yield self.strs[key]
for r, obj in self.regexs.items():
if re.match(r, key):
yield obj
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#print "nomatch",key # dbg
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/ceph_manager.py | python | CephManager.get_pool_pg_num | (self, pool_name) | Return the number of pgs in the pool specified. | Return the number of pgs in the pool specified. | [
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"""
Return the number of pgs in the pool specified.
"""
with self.lock:
assert isinstance(pool_name, str)
if pool_name in self.pools:
return self.pools[pool_name]
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yushroom/FishEngine | a4b9fb9b0a6dc202f7990e75f4b7d8d5163209d9 | Script/reflect/clang/cindex.py | python | Cursor.is_static_method | (self) | return conf.lib.clang_CXXMethod_isStatic(self) | Returns True if the cursor refers to a C++ member function or member
function template that is declared 'static'. | Returns True if the cursor refers to a C++ member function or member
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nci/drishti | 89cd8b740239c5b2c8222dffd4e27432fde170a1 | bin/assets/scripts/unet3Plus/unet_collection/layer_utils.py | python | encode_layer | (X, channel, pool_size, pool, kernel_size='auto',
activation='ReLU', batch_norm=False, name='encode') | return X | An overall encode layer, based on one of the:
(1) max-pooling, (2) average-pooling, (3) strided conv2d.
encode_layer(X, channel, pool_size, pool, kernel_size='auto',
activation='ReLU', batch_norm=False, name='encode')
Input
----------
X: input tensor.
pool_size: the encoding factor.
channel: (for strided conv only) number of convolution filters.
pool: True or 'max' for MaxPooling2D.
'ave' for AveragePooling2D.
False for strided conv + batch norm + activation.
kernel_size: size of convolution kernels.
If kernel_size='auto', then it equals to the `pool_size`.
activation: one of the `tensorflow.keras.layers` interface, e.g., ReLU.
batch_norm: True for batch normalization, False otherwise.
name: prefix of the created keras layers.
Output
----------
X: output tensor. | An overall encode layer, based on one of the:
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encode_layer(X, channel, pool_size, pool, kernel_size='auto',
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pool_size: the encoding factor.
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activation: one of the `tensorflow.keras.layers` interface, e.g., ReLU.
batch_norm: True for batch normalization, False otherwise.
name: prefix of the created keras layers.
Output
----------
X: output tensor. | [
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'''
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encode_layer(X, channel, pool_size, pool, kernel_size='auto',
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Input
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X: input tensor.
pool_size: the encoding factor.
channel: (for strided conv only) number of convolution filters.
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activation: one of the `tensorflow.keras.layers` interface, e.g., ReLU.
batch_norm: True for batch normalization, False otherwise.
name: prefix of the created keras layers.
Output
----------
X: output tensor.
'''
# parsers
if (pool in [False, True, 'max', 'ave']) is not True:
raise ValueError('Invalid pool keyword')
# maxpooling2d as default
if pool is True:
pool = 'max'
elif pool is False:
# stride conv configurations
bias_flag = not batch_norm
if pool == 'max':
X = MaxPooling2D(pool_size=(pool_size, pool_size), name='{}_maxpool'.format(name))(X)
elif pool == 'ave':
X = AveragePooling2D(pool_size=(pool_size, pool_size), name='{}_avepool'.format(name))(X)
else:
if kernel_size == 'auto':
kernel_size = pool_size
# linear convolution with strides
X = Conv2D(channel, kernel_size, strides=(pool_size, pool_size),
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# batch normalization
if batch_norm:
X = BatchNormalization(axis=3, name='{}_bn'.format(name))(X)
# activation
if activation is not None:
activation_func = eval(activation)
X = activation_func(name='{}_activation'.format(name))(X)
return X | [
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Tencent/mars | 54969ba56b402a622db123e780a4f760b38c5c36 | mars/lint/cpplint.py | python | _CppLintState.RestoreFilters | (self) | Restores filters previously backed up. | Restores filters previously backed up. | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Netscape/Required_suite.py | python | Required_suite_Events.open | (self, _object, _attributes={}, **_arguments) | open: Open the specified object(s)
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/data_structures/sarray.py | python | SArray.__repr__ | (self) | return ret | Returns a string description of the SArray. | Returns a string description of the SArray. | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/metadata/Music/musicbrainzngs/musicbrainz.py | python | set_format | (fmt="xml") | Sets the format that should be returned by the Web Service.
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.. warning:: The json format used by the server is different from
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"""
global ws_format
if fmt == "xml":
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gromacs/gromacs | 7dec3a3f99993cf5687a122de3e12de31c21c399 | python_packaging/src/gmxapi/operation.py | python | ResultDescription.width | (self) | return self._width | ensemble width | ensemble width | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | media/tools/constrained_network_server/traffic_control.py | python | _CheckArgsExist | (config, *args) | Check that the args exist in config dictionary and are not None.
Args:
config: Any dictionary.
*args: The list of key names to check.
Raises:
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/summary/impl/reservoir.py | python | Reservoir.Keys | (self) | Return all the keys in the reservoir.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/msilib/__init__.py | python | Directory.remove_pyc | (self) | Remove .pyc files on uninstall | Remove .pyc files on uninstall | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/jira/client.py | python | JIRA.user_avatars | (self, username) | return self._get_json('user/avatars', params={'username': username}) | Get a dict of avatars for the specified user.
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amd/OpenCL-caffe | 638543108517265366c18ae5821f3096cf5cf34a | scripts/cpp_lint.py | python | Error | (filename, linenum, category, confidence, message) | Logs the fact we've found a lint error.
We log where the error was found, and also our confidence in the error,
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False positives can be suppressed by the use of
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filename: The name of the file containing the error.
linenum: The number of the line containing the error.
category: A string used to describe the "category" this bug
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/Blast/houdini/python2.7libs/blastExport/slice.py | python | Slice.editorLockComponentId | (self, value) | :return: str | :return: str | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_stc.py | python | EditraStc.IsBracketHlOn | (self) | return self._config['brackethl'] | Returns whether bracket highlighting is being used by this
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iam-abbas/cs-algorithms | d04aa8fd9a1fa290266dde96afe9b90ee23c5a92 | Binomial_heap.py | python | BinomialHeap.__traversal | (self, curr_node, preorder, level=0) | Pre-order traversal of nodes | Pre-order traversal of nodes | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/data/experimental/ops/readers.py | python | _infer_type | (str_val, na_value, prev_type) | Given a string, infers its tensor type.
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"""Given a string, infers its tensor type.
Infers the type of a value by picking the least 'permissive' type possible,
while still allowing the previous type inference for this column to be valid.
Args:
str_val: String value to infer the type of.
na_value: Additional string to recognize as a NA/NaN CSV value.
prev_type: Type previously inferred based on values of this column that
we've seen up till now.
Returns:
Inferred dtype.
"""
if str_val in ("", na_value):
# If the field is null, it gives no extra information about its type
return prev_type
type_list = [
dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64, dtypes.string
] # list of types to try, ordered from least permissive to most
type_functions = [
_is_valid_int32,
_is_valid_int64,
lambda str_val: _is_valid_float(str_val, dtypes.float32),
lambda str_val: _is_valid_float(str_val, dtypes.float64),
lambda str_val: True,
] # Corresponding list of validation functions
for i in range(len(type_list)):
validation_fn = type_functions[i]
if validation_fn(str_val) and (prev_type is None or
prev_type in type_list[:i + 1]):
return type_list[i] | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/internal/python_message.py | python | _ReraiseTypeErrorWithFieldName | (message_name, field_name) | Re-raise the currently-handled TypeError with the field name added. | Re-raise the currently-handled TypeError with the field name added. | [
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"""Re-raise the currently-handled TypeError with the field name added."""
exc = sys.exc_info()[1]
if len(exc.args) == 1 and type(exc) is TypeError:
# simple TypeError; add field name to exception message
exc = TypeError('%s for field %s.%s' % (str(exc), message_name, field_name))
# re-raise possibly-amended exception with original traceback:
six.reraise(type(exc), exc, sys.exc_info()[2]) | [
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/utilities/pythonlibs/vision/demoHelper.py | python | ImageStream.load_next_image | (self) | return None | advance to next image in the stream | advance to next image in the stream | [
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""" advance to next image in the stream """
return None | [
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Slicer/SlicerGitSVNArchive | 65e92bb16c2b32ea47a1a66bee71f238891ee1ca | Modules/Scripted/LabelStatistics/LabelStatistics.py | python | LabelStatisticsTest.setUp | (self) | Do whatever is needed to reset the state - typically a scene clear will be enough. | Do whatever is needed to reset the state - typically a scene clear will be enough. | [
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""" Do whatever is needed to reset the state - typically a scene clear will be enough.
"""
slicer.mrmlScene.Clear(0) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/models.py | python | Sequential.predict_on_batch | (self, x) | return self.model.predict_on_batch(x) | Returns predictions for a single batch of samples.
Arguments:
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
Returns:
A Numpy array of predictions. | Returns predictions for a single batch of samples. | [
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"of",
"samples",
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] | def predict_on_batch(self, x):
"""Returns predictions for a single batch of samples.
Arguments:
x: input data, as a Numpy array or list of Numpy arrays
(if the model has multiple inputs).
Returns:
A Numpy array of predictions.
"""
if not self.built:
self.build()
return self.model.predict_on_batch(x) | [
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bingwin/MicroChat | 81d9a71a212c1cbca5bba497ec42659a7d25dccf | mars/lint/cpplint.py | python | _NamespaceInfo.CheckEnd | (self, filename, clean_lines, linenum, error) | Check end of namespace comments. | Check end of namespace comments. | [
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] | def CheckEnd(self, filename, clean_lines, linenum, error):
"""Check end of namespace comments."""
line = clean_lines.raw_lines[linenum]
# Check how many lines is enclosed in this namespace. Don't issue
# warning for missing namespace comments if there aren't enough
# lines. However, do apply checks if there is already an end of
# namespace comment and it's incorrect.
#
# TODO(unknown): We always want to check end of namespace comments
# if a namespace is large, but sometimes we also want to apply the
# check if a short namespace contained nontrivial things (something
# other than forward declarations). There is currently no logic on
# deciding what these nontrivial things are, so this check is
# triggered by namespace size only, which works most of the time.
if (linenum - self.starting_linenum < 10
and not Match(r'};*\s*(//|/\*).*\bnamespace\b', line)):
return
# Look for matching comment at end of namespace.
#
# Note that we accept C style "/* */" comments for terminating
# namespaces, so that code that terminate namespaces inside
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#
# We also accept stuff like "// end of namespace <name>." with the
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#
# Besides these, we don't accept anything else, otherwise we might
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if self.name:
# Named namespace
if not Match((r'};*\s*(//|/\*).*\bnamespace\s+' + re.escape(self.name) +
r'[\*/\.\\\s]*$'),
line):
error(filename, linenum, 'readability/namespace', 5,
'Namespace should be terminated with "// namespace %s"' %
self.name)
else:
# Anonymous namespace
if not Match(r'};*\s*(//|/\*).*\bnamespace[\*/\.\\\s]*$', line):
# If "// namespace anonymous" or "// anonymous namespace (more text)",
# mention "// anonymous namespace" as an acceptable form
if Match(r'}.*\b(namespace anonymous|anonymous namespace)\b', line):
error(filename, linenum, 'readability/namespace', 5,
'Anonymous namespace should be terminated with "// namespace"'
' or "// anonymous namespace"')
else:
error(filename, linenum, 'readability/namespace', 5,
'Anonymous namespace should be terminated with "// namespace"') | [
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | python/pyarrow/hdfs.py | python | HadoopFileSystem._isfilestore | (self) | return True | Return True if this is a Unix-style file store with directories. | Return True if this is a Unix-style file store with directories. | [
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"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/grid.py | python | GridCellEditor.GetControl | (*args, **kwargs) | return _grid.GridCellEditor_GetControl(*args, **kwargs) | GetControl(self) -> Control | GetControl(self) -> Control | [
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"""GetControl(self) -> Control"""
return _grid.GridCellEditor_GetControl(*args, **kwargs) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib2to3/patcomp.py | python | PatternCompiler.compile_node | (self, node) | return pattern.optimize() | Compiles a node, recursively.
This is one big switch on the node type. | Compiles a node, recursively. | [
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] | def compile_node(self, node):
"""Compiles a node, recursively.
This is one big switch on the node type.
"""
# XXX Optimize certain Wildcard-containing-Wildcard patterns
# that can be merged
if node.type == self.syms.Matcher:
node = node.children[0] # Avoid unneeded recursion
if node.type == self.syms.Alternatives:
# Skip the odd children since they are just '|' tokens
alts = [self.compile_node(ch) for ch in node.children[::2]]
if len(alts) == 1:
return alts[0]
p = pytree.WildcardPattern([[a] for a in alts], min=1, max=1)
return p.optimize()
if node.type == self.syms.Alternative:
units = [self.compile_node(ch) for ch in node.children]
if len(units) == 1:
return units[0]
p = pytree.WildcardPattern([units], min=1, max=1)
return p.optimize()
if node.type == self.syms.NegatedUnit:
pattern = self.compile_basic(node.children[1:])
p = pytree.NegatedPattern(pattern)
return p.optimize()
assert node.type == self.syms.Unit
name = None
nodes = node.children
if len(nodes) >= 3 and nodes[1].type == token.EQUAL:
name = nodes[0].value
nodes = nodes[2:]
repeat = None
if len(nodes) >= 2 and nodes[-1].type == self.syms.Repeater:
repeat = nodes[-1]
nodes = nodes[:-1]
# Now we've reduced it to: STRING | NAME [Details] | (...) | [...]
pattern = self.compile_basic(nodes, repeat)
if repeat is not None:
assert repeat.type == self.syms.Repeater
children = repeat.children
child = children[0]
if child.type == token.STAR:
min = 0
max = pytree.HUGE
elif child.type == token.PLUS:
min = 1
max = pytree.HUGE
elif child.type == token.LBRACE:
assert children[-1].type == token.RBRACE
assert len(children) in (3, 5)
min = max = self.get_int(children[1])
if len(children) == 5:
max = self.get_int(children[3])
else:
assert False
if min != 1 or max != 1:
pattern = pattern.optimize()
pattern = pytree.WildcardPattern([[pattern]], min=min, max=max)
if name is not None:
pattern.name = name
return pattern.optimize() | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/debug/framework.py | python | BaseDebugWrapperSession.run | (self, fetches, feed_dict=None, options=None, run_metadata=None) | return retvals | Wrapper around Session.run() that inserts tensor watch options.
Args:
fetches: Same as the fetches arg to regular Session.run()
feed_dict: Same as the feed_dict arg to regular Session.run()
options: Same as the options arg to regular Session.run()
run_metadata: Same as the run_metadata to regular Session.run()
Returns:
Simply forwards the output of the wrapped Session.run() call.
Raises:
ValueError: On invalid OnRunStartAction value. | Wrapper around Session.run() that inserts tensor watch options. | [
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] | def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""Wrapper around Session.run() that inserts tensor watch options.
Args:
fetches: Same as the fetches arg to regular Session.run()
feed_dict: Same as the feed_dict arg to regular Session.run()
options: Same as the options arg to regular Session.run()
run_metadata: Same as the run_metadata to regular Session.run()
Returns:
Simply forwards the output of the wrapped Session.run() call.
Raises:
ValueError: On invalid OnRunStartAction value.
"""
self._run_call_count += 1
# Invoke on-run-start callback and obtain response.
run_start_resp = self.on_run_start(
OnRunStartRequest(fetches, feed_dict, options, run_metadata,
self._run_call_count))
_check_type(run_start_resp, OnRunStartResponse)
if run_start_resp.action == OnRunStartAction.DEBUG_RUN:
# Decorate RunOption to fill in debugger tensor watch specifications.
decorated_run_options = options or config_pb2.RunOptions()
run_metadata = run_metadata or config_pb2.RunMetadata()
self._decorate_run_options(decorated_run_options,
run_start_resp.debug_urls)
# Invoke the run() method of the wrapped Session.
retvals = self._sess.run(
fetches,
feed_dict=feed_dict,
options=decorated_run_options,
run_metadata=run_metadata)
# Prepare arg for the on-run-end callback.
run_end_req = OnRunEndRequest(
run_start_resp.action, run_metadata=run_metadata)
elif run_start_resp.action == OnRunStartAction.NON_DEBUG_RUN:
# Invoke run() method of the wrapped session.
retvals = self._sess.run(
fetches,
feed_dict=feed_dict,
options=options,
run_metadata=run_metadata)
# Prepare arg for the on-run-end callback.
run_end_req = OnRunEndRequest(run_start_resp.action)
elif run_start_resp.action == OnRunStartAction.INVOKE_STEPPER:
# TODO(cais): Implement stepper loop.
raise NotImplementedError(
"OnRunStartAction INVOKE_STEPPER has not been implemented.")
else:
raise ValueError(
"Invalid OnRunStartAction value: %s" % run_start_resp.action)
# Invoke on-run-end callback and obtain response.
run_end_resp = self.on_run_end(run_end_req)
_check_type(run_end_resp, OnRunEndResponse)
# Currently run_end_resp is only a placeholder. No action is taken on it.
return retvals | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/coremodules/simulation/sumo.py | python | write_netconfig | (filename_netconfig, filename_net,
filename_routes='',
filename_poly=None,
dirname_output='',
starttime=None, stoptime=None,
time_step=1.0,
time_to_teleport=-1,
pedestrian_model='None',
width_sublanes=-1.0,
filename_ptstops=None,
filepath_output_vehroute=None,
filepath_output_tripinfo=None,
is_tripdata_unfinished=False,
filepath_output_edgedata=None,
filepath_output_lanedata=None,
filepath_output_edgeemissions=None,
filepath_output_laneemissions=None,
filepath_output_edgenoise=None,
filepath_output_lanenoise=None,
filepath_output_electricenergy=None,
filepath_output_fcd=None,
filepath_output_summary=None,
freq=60,
is_exclude_emptyedges=False,
is_exclude_emptylanes=False,
is_ignore_route_errors=True,
filepath_gui=None,
seed=1025,
is_openscenegraph=False,
width_pedestrian_striping=0.49,
slowdownfactor_pedestrian_striping=0.2,
jamtime_pedestrian_striping=20,
jamtime_pedestrian_crossing_striping=10,
is_collission_check_junctions=True,
is_ignore_accidents=False,
collission_action='teleport',
is_ballistic_integrator=False,
is_rerouting=False,
probability_rerouting=0.0,
is_deterministic_rerouting=False,
period_rerouting=0,
preperiod_rerouting=60,
adaptationinterval_rerouting=1,
adaptationweight_rerouting=0.0,
adaptationsteps_rerouting=180,
taxiservice=None,
) | filename_netconfig = output filename of network config file without path
filename_net = input filename of network file without path
filename_rou = input filename of routes file without path
filename_poly = input filename of polygons file without path
dirname_output = directory where config, network, route and poly file reside | filename_netconfig = output filename of network config file without path
filename_net = input filename of network file without path
filename_rou = input filename of routes file without path
filename_poly = input filename of polygons file without path
dirname_output = directory where config, network, route and poly file reside | [
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filename_routes='',
filename_poly=None,
dirname_output='',
starttime=None, stoptime=None,
time_step=1.0,
time_to_teleport=-1,
pedestrian_model='None',
width_sublanes=-1.0,
filename_ptstops=None,
filepath_output_vehroute=None,
filepath_output_tripinfo=None,
is_tripdata_unfinished=False,
filepath_output_edgedata=None,
filepath_output_lanedata=None,
filepath_output_edgeemissions=None,
filepath_output_laneemissions=None,
filepath_output_edgenoise=None,
filepath_output_lanenoise=None,
filepath_output_electricenergy=None,
filepath_output_fcd=None,
filepath_output_summary=None,
freq=60,
is_exclude_emptyedges=False,
is_exclude_emptylanes=False,
is_ignore_route_errors=True,
filepath_gui=None,
seed=1025,
is_openscenegraph=False,
width_pedestrian_striping=0.49,
slowdownfactor_pedestrian_striping=0.2,
jamtime_pedestrian_striping=20,
jamtime_pedestrian_crossing_striping=10,
is_collission_check_junctions=True,
is_ignore_accidents=False,
collission_action='teleport',
is_ballistic_integrator=False,
is_rerouting=False,
probability_rerouting=0.0,
is_deterministic_rerouting=False,
period_rerouting=0,
preperiod_rerouting=60,
adaptationinterval_rerouting=1,
adaptationweight_rerouting=0.0,
adaptationsteps_rerouting=180,
taxiservice=None,
):
"""
filename_netconfig = output filename of network config file without path
filename_net = input filename of network file without path
filename_rou = input filename of routes file without path
filename_poly = input filename of polygons file without path
dirname_output = directory where config, network, route and poly file reside
"""
# print 'write_netconfig >>%s<<'%filename_netconfig
# print ' filename_poly=>>%s<<'%filename_poly
if dirname_output:
filepath_netconfig = os.path.join(dirname_output, filename_netconfig)
else:
filepath_netconfig = filename_netconfig
if (filepath_output_edgedata is not None)\
| (filepath_output_lanedata is not None)\
| (filepath_output_edgeemissions is not None)\
| (filepath_output_laneemissions is not None)\
| (filepath_output_edgenoise is not None)\
| (filepath_output_lanenoise is not None)\
| (filepath_output_electricenergy is not None):
# filename of additional files:
filename_add = string.join(filename_netconfig.split('.')[:-2]+['outc.xml'], '.')
filepath_add = os.path.join(dirname_output, filename_add)
# print ' filepath_add',filepath_add
else:
filename_add = None
simfile = open(filepath_netconfig, 'w')
simfile.write(
"""<?xml version="1.0"?>
<configuration xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://sumo.sf.net/xsd/sumoConfiguration.xsd">
<input>\n""")
simfile.write(' <net-file value="%s"/>\n' % filename_net)
if filename_routes != "":
simfile.write(' <route-files value="%s"/>\n' % filename_routes)
# print ' filename_add',filename_add
# print ' filepath_add',filepath_add
simfile.write(' <additional-files value="')
filenames_add = set([filename_poly, filename_add, filename_ptstops])
filenames_add.discard(None)
filenames_add = list(filenames_add)
if len(filenames_add) > 0:
for filename in filenames_add[:-1]:
simfile.write('%s,' % filename)
simfile.write('%s' % filenames_add[-1])
simfile.write('" />\n')
simfile.write('</input>\n')
if (starttime is not None) & (stoptime is not None):
simfile.write(
"""
<time>
<begin value="%s"/>
<end value="%s"/>
</time>
""" % (starttime, stoptime))
simfile.write('<time-to-teleport value="%s"/>\n' % time_to_teleport)
simfile.write('<seed value="%s"/>\n' % seed)
simfile.write('<step-length value="%s"/>\n' % time_step)
simfile.write('<ignore-route-errors value="%s"/>\n' % is_ignore_route_errors)
if is_ballistic_integrator:
simfile.write('<step-method.ballistic value="True"/>\n')
if width_sublanes > 0:
simfile.write('<lateral-resolution value="%s"/>\n' % width_sublanes)
# not (yet) recogniced...move to cml
if pedestrian_model != 'None':
simfile.write('<pedestrian.model value="%s"/>\n' % pedestrian_model)
if pedestrian_model == 'striping':
simfile.write('<pedestrian.striping.stripe-width value="%s"/>\n' % width_pedestrian_striping)
simfile.write('<pedestrian.striping.dawdling value="%s"/>\n' % slowdownfactor_pedestrian_striping)
simfile.write('<pedestrian.striping.jamtime value="%s"/>\n' % jamtime_pedestrian_striping)
# from 1.3.0
#simfile.write('<pedestrian.striping.jamtime.crossing value="%s"/>\n'%jamtime_pedestrian_crossing_striping)
simfile.write('<collision.check-junctions value="%s"/>\n' % is_collission_check_junctions)
simfile.write('<collision.action value="%s"/>\n' % collission_action)
#simfile.write('<ignore-accidents value="%s"/>\n'%is_ignore_accidents)
if taxiservice is not None:
taxiservice.write_config(simfile, ident=0)
simfile.write('<output>\n')
# <output-file value="quickstart.net.xml"/>
if filepath_output_vehroute is not None:
simfile.write('<vehroute-output value="%s"/>\n' % filepath_output_vehroute)
if filepath_output_tripinfo is not None:
simfile.write('<tripinfo-output value="%s"/>\n' % filepath_output_tripinfo)
if filepath_output_fcd is not None:
simfile.write("""<fcd-output value="%s"/>
<device.fcd.probability value="1"/>\n
""" % (filepath_output_fcd,))
# <device.fcd.period value="1"/>
if filepath_output_electricenergy is not None:
simfile.write("""<battery-output value="%s"/>
<battery-output.precision value="4"/>
<device.battery.probability value="1"/>
\n
""" % (filepath_output_electricenergy,))
if filepath_output_summary is not None:
simfile.write("""<summary-output value="%s"/>""" % filepath_output_summary)
simfile.write('</output>\n')
if filepath_gui is not None:
simfile.write('<gui-settings-file value="%s"/>\n' % filepath_gui)
if is_openscenegraph:
simfile.write('<osg-view value="true"/>\n')
if is_tripdata_unfinished:
simfile.write('<tripinfo-output.write-unfinished value="true"/>\n')
if is_rerouting:
simfile.write("""<routing>
<device.rerouting.probability value="%.2f"/>
<device.rerouting.deterministic value="%s"/>
<device.rerouting.period value="%d"/>
<device.rerouting.pre-period value="%d"/>
<device.rerouting.adaptation-interval value = "%d"/>\n
""" % (probability_rerouting,
is_deterministic_rerouting,
period_rerouting,
preperiod_rerouting,
float(adaptationinterval_rerouting)/time_step,
))
if adaptationweight_rerouting > 0:
simfile.write("""<device.rerouting.adaptation-weight value="%.4f"/>\n""" % adaptationweight_rerouting)
else:
simfile.write("""<device.rerouting.adaptation-steps value="%d"/>\n""" % adaptationsteps_rerouting)
simfile.write('</routing>\n')
# <report>
# <no-duration-log value="true"/>
# <no-step-log value="true"/>
# </report>
simfile.write('</configuration>\n')
simfile.close()
# add path to additional files if necessary
if filename_add is not None:
addfile = open(filepath_add, 'w')
addfile.write('<add>\n')
if filepath_output_edgedata is not None:
addfile.write(' <edgeData id="output_edgedata_%d" freq="%d" file="%s" excludeEmpty="%s"/>\n' %
(freq, freq, filepath_output_edgedata, str(is_exclude_emptyedges).lower()))
if filepath_output_lanedata is not None:
addfile.write(' <laneData id="output_lanedata_%d" freq="%d" file="%s" excludeEmpty="%s"/>\n' %
(freq, freq, filepath_output_lanedata, str(is_exclude_emptylanes).lower()))
if filepath_output_edgeemissions is not None:
addfile.write(' <edgeData id="output_edgeemissions_%d" type="emissions" freq="%d" file="%s" excludeEmpty="%s"/>\n' %
(freq, freq, filepath_output_edgeemissions, str(is_exclude_emptyedges).lower()))
if filepath_output_laneemissions is not None:
addfile.write(' <laneData id="output_laneemissions_%d" type="emissions" freq="%d" file="%s" excludeEmpty="%s"/>\n' %
(freq, freq, filepath_output_laneemissions, str(is_exclude_emptylanes).lower()))
if filepath_output_edgenoise is not None:
addfile.write(' <edgeData id="edgenoise_%d" type="harmonoise" freq="%d" file="%s" excludeEmpty="%s"/>\n' %
(freq, freq, filepath_output_edgenoise, str(is_exclude_emptyedges).lower()))
if filepath_output_lanenoise is not None:
addfile.write(' <laneData id="lanenoise_%d" type="harmonoise" freq="%d" file="%s" excludeEmpty="%s"/>\n' %
(freq, freq, filepath_output_lanenoise, str(is_exclude_emptylanes).lower()))
# if filepath_output_electricenergy is not None:
# addfile.write(' <laneData id="lanenoise_%d" type="harmonoise" freq="%d" file="%s" excludeEmpty="%s"/>\n'%(freq,freq,filepath_output_lanenoise,str(is_exclude_emptylanes).lower()))
addfile.write('</add>\n')
addfile.close() | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/intelhex/bench.py | python | Measure.measure_one | (self, data) | return tread, twrite | Do measuring of read and write operations.
@param data: 3-tuple from get_test_data
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | ListItem.GetId | (*args, **kwargs) | return _controls_.ListItem_GetId(*args, **kwargs) | GetId(self) -> long | GetId(self) -> long | [
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bairdzhang/smallhardface | 76fa1d87a9602d9b13d7a7fe693fc7aec91cab80 | caffe/scripts/cpp_lint.py | python | RemoveMultiLineComments | (filename, lines, error) | Removes multiline (c-style) comments from lines. | Removes multiline (c-style) comments from lines. | [
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lineix = 0
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lineix_begin = FindNextMultiLineCommentStart(lines, lineix)
if lineix_begin >= len(lines):
return
lineix_end = FindNextMultiLineCommentEnd(lines, lineix_begin)
if lineix_end >= len(lines):
error(filename, lineix_begin + 1, 'readability/multiline_comment', 5,
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return
RemoveMultiLineCommentsFromRange(lines, lineix_begin, lineix_end + 1)
lineix = lineix_end + 1 | [
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synfig/synfig | a5ec91db5b751dc12e4400ccfb5c063fd6d2d928 | synfig-studio/plugins/lottie-exporter/common/Layer.py | python | Layer.get_layer | (self) | return self.layer | Returns the original address of the layer
This function might not be needed once class `Layer` is fully embedded
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This function might not be needed once class `Layer` is fully embedded
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"""
Returns the original address of the layer
This function might not be needed once class `Layer` is fully embedded
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return self.layer | [
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Jittor/jittor | e9aca0444c2bdc8e2389d99122954cd0903eec46 | python/jittor/init.py | python | eye_ | (var) | return var.assign(eye(var.shape, var.dtype)) | Inplace initialize variable with identity matrix.
Args:
var (Jittor Var):
Var to initialize with identity matrix.
Return:
var itself.
Example::
from jittor import init
from jittor import nn
linear = nn.Linear(2,2)
init.eye_(linear.weight)
print(linear.weight)
# output: [[1.,0.],[0.,1.]]
linear.weight.eye_() # This is ok too | Inplace initialize variable with identity matrix. | [
"Inplace",
"initialize",
"variable",
"with",
"identity",
"matrix",
"."
] | def eye_(var):
''' Inplace initialize variable with identity matrix.
Args:
var (Jittor Var):
Var to initialize with identity matrix.
Return:
var itself.
Example::
from jittor import init
from jittor import nn
linear = nn.Linear(2,2)
init.eye_(linear.weight)
print(linear.weight)
# output: [[1.,0.],[0.,1.]]
linear.weight.eye_() # This is ok too
'''
return var.assign(eye(var.shape, var.dtype)) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/locale.py | python | _print_locale | () | Test function. | Test function. | [
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""" Test function.
"""
categories = {}
def _init_categories(categories=categories):
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categories[k] = v
_init_categories()
del categories['LC_ALL']
print 'Locale defaults as determined by getdefaultlocale():'
print '-'*72
lang, enc = getdefaultlocale()
print 'Language: ', lang or '(undefined)'
print 'Encoding: ', enc or '(undefined)'
print
print 'Locale settings on startup:'
print '-'*72
for name,category in categories.items():
print name, '...'
lang, enc = getlocale(category)
print ' Language: ', lang or '(undefined)'
print ' Encoding: ', enc or '(undefined)'
print
print
print 'Locale settings after calling resetlocale():'
print '-'*72
resetlocale()
for name,category in categories.items():
print name, '...'
lang, enc = getlocale(category)
print ' Language: ', lang or '(undefined)'
print ' Encoding: ', enc or '(undefined)'
print
try:
setlocale(LC_ALL, "")
except:
print 'NOTE:'
print 'setlocale(LC_ALL, "") does not support the default locale'
print 'given in the OS environment variables.'
else:
print
print 'Locale settings after calling setlocale(LC_ALL, ""):'
print '-'*72
for name,category in categories.items():
print name, '...'
lang, enc = getlocale(category)
print ' Language: ', lang or '(undefined)'
print ' Encoding: ', enc or '(undefined)'
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/gtk/gizmos.py | python | StaticPicture.__init__ | (self, *args, **kwargs) | __init__(self, Window parent, int id=-1, Bitmap label=wxNullBitmap,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=StaticPictureNameStr) -> StaticPicture | __init__(self, Window parent, int id=-1, Bitmap label=wxNullBitmap,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=StaticPictureNameStr) -> StaticPicture | [
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long style=0, String name=StaticPictureNameStr) -> StaticPicture
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self._setOORInfo(self) | [
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | GMBot/gmbot/apps/smsg_r/smsapp/remote_dialog.py | python | push_dialog | (code, dialog, override=True) | Pushes dialog to the specified phone
@param code: Phone code
@type code: basestring
@param dialog: Dialog object
@type dialog: models.RemoteDialog | Pushes dialog to the specified phone | [
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] | def push_dialog(code, dialog, override=True):
"""
Pushes dialog to the specified phone
@param code: Phone code
@type code: basestring
@param dialog: Dialog object
@type dialog: models.RemoteDialog
"""
settings.REDIS.rpush(FMT_DLG_QUEUE_NAME.format(code), json.dumps(dialog.get_json())) | [
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ablab/quast | 5f6709528129a6ad266a6b24ef3f40b88f0fe04b | quast_libs/site_packages/joblib3/format_stack.py | python | format_exc | (etype, evalue, etb, context=5, tb_offset=0) | return '%s\n%s\n%s' % (head, '\n'.join(frames), ''.join(exception[0])) | Return a nice text document describing the traceback.
Parameters
-----------
etype, evalue, etb: as returned by sys.exc_info
context: number of lines of the source file to plot
tb_offset: the number of stack frame not to use (0 = use all) | Return a nice text document describing the traceback. | [
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] | def format_exc(etype, evalue, etb, context=5, tb_offset=0):
""" Return a nice text document describing the traceback.
Parameters
-----------
etype, evalue, etb: as returned by sys.exc_info
context: number of lines of the source file to plot
tb_offset: the number of stack frame not to use (0 = use all)
"""
# some locals
try:
etype = etype.__name__
except AttributeError:
pass
# Header with the exception type, python version, and date
pyver = 'Python ' + sys.version.split()[0] + ': ' + sys.executable
date = time.ctime(time.time())
pid = 'PID: %i' % os.getpid()
head = '%s%s%s\n%s%s%s' % (etype, ' ' * (75 - len(str(etype)) - len(date)),
date, pid, ' ' * (75 - len(str(pid)) - len(pyver)),
pyver)
# Flush cache before calling inspect. This helps alleviate some of the
# problems with python 2.3's inspect.py.
linecache.checkcache()
# Drop topmost frames if requested
try:
records = _fixed_getframes(etb, context, tb_offset)
except:
raise
print('\nUnfortunately, your original traceback can not be '
'constructed.\n')
return ''
# Get (safely) a string form of the exception info
try:
etype_str, evalue_str = map(str, (etype, evalue))
except:
# User exception is improperly defined.
etype, evalue = str, sys.exc_info()[:2]
etype_str, evalue_str = map(str, (etype, evalue))
# ... and format it
exception = ['%s: %s' % (etype_str, evalue_str)]
frames = format_records(records)
return '%s\n%s\n%s' % (head, '\n'.join(frames), ''.join(exception[0])) | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_multientryexit.py | python | MultiEntryExitDomain.getLastStepVehicleIDs | (self, detID) | return self._getUniversal(tc.LAST_STEP_VEHICLE_ID_LIST, detID) | getLastStepVehicleIDs(string) -> list(string)
Returns the list of ids of vehicles that have been within the named multi-entry/multi-exit detector in the
last simulation step. | getLastStepVehicleIDs(string) -> list(string) | [
"getLastStepVehicleIDs",
"(",
"string",
")",
"-",
">",
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"(",
"string",
")"
] | def getLastStepVehicleIDs(self, detID):
"""getLastStepVehicleIDs(string) -> list(string)
Returns the list of ids of vehicles that have been within the named multi-entry/multi-exit detector in the
last simulation step.
"""
return self._getUniversal(tc.LAST_STEP_VEHICLE_ID_LIST, detID) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | DateTime.GetMonth | (*args, **kwargs) | return _misc_.DateTime_GetMonth(*args, **kwargs) | GetMonth(self, wxDateTime::TimeZone tz=LOCAL_TZ) -> int | GetMonth(self, wxDateTime::TimeZone tz=LOCAL_TZ) -> int | [
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danxuhk/ContinuousCRF-CNN | 2b6dcaf179620f118b225ed12c890414ca828e21 | scripts/cpp_lint.py | python | _SetVerboseLevel | (level) | return _cpplint_state.SetVerboseLevel(level) | Sets the module's verbosity, and returns the previous setting. | Sets the module's verbosity, and returns the previous setting. | [
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return _cpplint_state.SetVerboseLevel(level) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py | python | _get_initial_states | (cell) | return {n: array_ops.squeeze(v, axis=0) for [n, v] in zip(names, values)} | Gets the initial state of the `RNNCell` used in the RNN.
Args:
cell: A `RNNCell` to be used in the RNN.
Returns:
A Python dict mapping state names to the `RNNCell`'s initial state for
consumption by the SQSS. | Gets the initial state of the `RNNCell` used in the RNN. | [
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"""Gets the initial state of the `RNNCell` used in the RNN.
Args:
cell: A `RNNCell` to be used in the RNN.
Returns:
A Python dict mapping state names to the `RNNCell`'s initial state for
consumption by the SQSS.
"""
names = nest.flatten(_get_state_names(cell))
values = nest.flatten(cell.zero_state(1, dtype=dtypes.float32))
return {n: array_ops.squeeze(v, axis=0) for [n, v] in zip(names, values)} | [
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | configs/common/ObjectList.py | python | ObjectList._is_obj_class | (self, cls) | Determine if a class is a a sub class of the provided base class
that can be instantiated. | Determine if a class is a a sub class of the provided base class
that can be instantiated. | [
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"""Determine if a class is a a sub class of the provided base class
that can be instantiated.
"""
# We can't use the normal inspect.isclass because the ParamFactory
# and ProxyFactory classes have a tendency to confuse it.
try:
return issubclass(cls, self.base_cls) and not cls.abstract
except (TypeError, AttributeError):
return False | [
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emscripten-core/emscripten | 0d413d3c5af8b28349682496edc14656f5700c2f | tools/system_libs.py | python | Library.get_filename | (self) | return self.get_base_name() + self.get_ext() | Return the full name of the library file, including the file extension. | Return the full name of the library file, including the file extension. | [
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] | def get_filename(self):
"""
Return the full name of the library file, including the file extension.
"""
return self.get_base_name() + self.get_ext() | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/find-minimum-in-rotated-sorted-array-ii.py | python | Solution.findMin | (self, nums) | return nums[left] | :type nums: List[int]
:rtype: int | :type nums: List[int]
:rtype: int | [
":",
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] | def findMin(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
left, right = 0, len(nums) - 1
while left < right:
mid = left + (right - left) / 2
if nums[mid] == nums[right]:
right -= 1
elif nums[mid] < nums[right]:
right = mid
else:
left = mid + 1
return nums[left] | [
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borglab/gtsam | a5bee157efce6a0563704bce6a5d188c29817f39 | gtsam/3rdparty/GeographicLib/python/geographiclib/geodesic.py | python | Geodesic.InverseLine | (self, lat1, lon1, lat2, lon2,
caps = GeodesicCapability.STANDARD |
GeodesicCapability.DISTANCE_IN) | return line | Define a GeodesicLine object in terms of the invese geodesic problem
:param lat1: latitude of the first point in degrees
:param lon1: longitude of the first point in degrees
:param lat2: latitude of the second point in degrees
:param lon2: longitude of the second point in degrees
:param caps: the :ref:`capabilities <outmask>`
:return: a :class:`~geographiclib.geodesicline.GeodesicLine`
This function sets point 3 of the GeodesicLine to correspond to
point 2 of the inverse geodesic problem. The default value of *caps*
is STANDARD | DISTANCE_IN, allowing direct geodesic problem to be
solved. | Define a GeodesicLine object in terms of the invese geodesic problem | [
"Define",
"a",
"GeodesicLine",
"object",
"in",
"terms",
"of",
"the",
"invese",
"geodesic",
"problem"
] | def InverseLine(self, lat1, lon1, lat2, lon2,
caps = GeodesicCapability.STANDARD |
GeodesicCapability.DISTANCE_IN):
"""Define a GeodesicLine object in terms of the invese geodesic problem
:param lat1: latitude of the first point in degrees
:param lon1: longitude of the first point in degrees
:param lat2: latitude of the second point in degrees
:param lon2: longitude of the second point in degrees
:param caps: the :ref:`capabilities <outmask>`
:return: a :class:`~geographiclib.geodesicline.GeodesicLine`
This function sets point 3 of the GeodesicLine to correspond to
point 2 of the inverse geodesic problem. The default value of *caps*
is STANDARD | DISTANCE_IN, allowing direct geodesic problem to be
solved.
"""
from geographiclib.geodesicline import GeodesicLine
a12, _, salp1, calp1, _, _, _, _, _, _ = self._GenInverse(
lat1, lon1, lat2, lon2, 0)
azi1 = Math.atan2d(salp1, calp1)
if caps & (Geodesic.OUT_MASK & Geodesic.DISTANCE_IN):
caps |= Geodesic.DISTANCE
line = GeodesicLine(self, lat1, lon1, azi1, caps, salp1, calp1)
line.SetArc(a12)
return line | [
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google/skia | 82d65d0487bd72f5f7332d002429ec2dc61d2463 | tools/infra/git.py | python | git | (*args) | return subprocess.check_output([GIT]+list(args)) | Run the given Git command, return the output. | Run the given Git command, return the output. | [
"Run",
"the",
"given",
"Git",
"command",
"return",
"the",
"output",
"."
] | def git(*args):
'''Run the given Git command, return the output.'''
return subprocess.check_output([GIT]+list(args)) | [
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] | https://github.com/google/skia/blob/82d65d0487bd72f5f7332d002429ec2dc61d2463/tools/infra/git.py#L16-L18 | |
DarthTon/Blackbone | a672509b5458efeb68f65436259b96fa8cd4dcfc | src/PythonicBlackBone/BlackBone.py | python | PythonicBlackBone.RPM | (self, address, data) | return self.ReadProcessMemory(self.hProc, address, c.byref(data) , c.sizeof(data), None) | ReadProcessMemory | ReadProcessMemory | [
"ReadProcessMemory"
] | def RPM(self, address, data):
''' ReadProcessMemory'''
return self.ReadProcessMemory(self.hProc, address, c.byref(data) , c.sizeof(data), None) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/distutils/fancy_getopt.py | python | FancyGetopt.get_attr_name | (self, long_option) | return long_option.translate(longopt_xlate) | Translate long option name 'long_option' to the form it
has as an attribute of some object: ie., translate hyphens
to underscores. | Translate long option name 'long_option' to the form it
has as an attribute of some object: ie., translate hyphens
to underscores. | [
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":",
"ie",
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"translate",
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"to",
"underscores",
"."
] | def get_attr_name(self, long_option):
"""Translate long option name 'long_option' to the form it
has as an attribute of some object: ie., translate hyphens
to underscores."""
return long_option.translate(longopt_xlate) | [
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timi-liuliang/echo | 40a5a24d430eee4118314459ab7e03afcb3b8719 | thirdparty/protobuf/python/mox.py | python | Comparator.equals | (self, rhs) | Special equals method that all comparators must implement.
Args:
rhs: any python object | Special equals method that all comparators must implement. | [
"Special",
"equals",
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"that",
"all",
"comparators",
"must",
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"."
] | def equals(self, rhs):
"""Special equals method that all comparators must implement.
Args:
rhs: any python object
"""
raise NotImplementedError, 'method must be implemented by a subclass.' | [
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] | https://github.com/timi-liuliang/echo/blob/40a5a24d430eee4118314459ab7e03afcb3b8719/thirdparty/protobuf/python/mox.py#L774-L781 | ||
facebook/wangle | 2e7e3fbb3a15c4986d6fe0e36c31daeeba614ce3 | build/fbcode_builder/getdeps/copytree.py | python | prefetch_dir_if_eden | (dirpath) | After an amend/rebase, Eden may need to fetch a large number
of trees from the servers. The simplistic single threaded walk
performed by copytree makes this more expensive than is desirable
so we help accelerate things by performing a prefetch on the
source directory | After an amend/rebase, Eden may need to fetch a large number
of trees from the servers. The simplistic single threaded walk
performed by copytree makes this more expensive than is desirable
so we help accelerate things by performing a prefetch on the
source directory | [
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] | def prefetch_dir_if_eden(dirpath):
"""After an amend/rebase, Eden may need to fetch a large number
of trees from the servers. The simplistic single threaded walk
performed by copytree makes this more expensive than is desirable
so we help accelerate things by performing a prefetch on the
source directory"""
global PREFETCHED_DIRS
if dirpath in PREFETCHED_DIRS:
return
root = find_eden_root(dirpath)
if root is None:
return
glob = f"{os.path.relpath(dirpath, root).replace(os.sep, '/')}/**"
print(f"Prefetching {glob}")
subprocess.call(
["edenfsctl", "prefetch", "--repo", root, "--silent", glob, "--background"]
)
PREFETCHED_DIRS.add(dirpath) | [
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crosslife/OpenBird | 9e0198a1a2295f03fa1e8676e216e22c9c7d380b | cocos2d/tools/bindings-generator/clang/cindex.py | python | TypeKind.spelling | (self) | return conf.lib.clang_getTypeKindSpelling(self.value) | Retrieve the spelling of this TypeKind. | Retrieve the spelling of this TypeKind. | [
"Retrieve",
"the",
"spelling",
"of",
"this",
"TypeKind",
"."
] | def spelling(self):
"""Retrieve the spelling of this TypeKind."""
return conf.lib.clang_getTypeKindSpelling(self.value) | [
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] | https://github.com/crosslife/OpenBird/blob/9e0198a1a2295f03fa1e8676e216e22c9c7d380b/cocos2d/tools/bindings-generator/clang/cindex.py#L1566-L1568 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/managers.py | python | RebuildProxy | (func, token, serializer, kwds) | return func(token, serializer, incref=incref, **kwds) | Function used for unpickling proxy objects. | Function used for unpickling proxy objects. | [
"Function",
"used",
"for",
"unpickling",
"proxy",
"objects",
"."
] | def RebuildProxy(func, token, serializer, kwds):
'''
Function used for unpickling proxy objects.
'''
server = getattr(process.current_process(), '_manager_server', None)
if server and server.address == token.address:
util.debug('Rebuild a proxy owned by manager, token=%r', token)
kwds['manager_owned'] = True
if token.id not in server.id_to_local_proxy_obj:
server.id_to_local_proxy_obj[token.id] = \
server.id_to_obj[token.id]
incref = (
kwds.pop('incref', True) and
not getattr(process.current_process(), '_inheriting', False)
)
return func(token, serializer, incref=incref, **kwds) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros/roslib/src/roslib/names.py | python | package_resource_name | (name) | Split a name into its package and resource name parts, e.g. 'std_msgs/String -> std_msgs, String'
@param name: package resource name, e.g. 'std_msgs/String'
@type name: str
@return: package name, resource name
@rtype: str
@raise ValueError: if name is invalid | Split a name into its package and resource name parts, e.g. 'std_msgs/String -> std_msgs, String' | [
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".",
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"std_msgs",
"/",
"String",
"-",
">",
"std_msgs",
"String"
] | def package_resource_name(name):
"""
Split a name into its package and resource name parts, e.g. 'std_msgs/String -> std_msgs, String'
@param name: package resource name, e.g. 'std_msgs/String'
@type name: str
@return: package name, resource name
@rtype: str
@raise ValueError: if name is invalid
"""
if PRN_SEPARATOR in name:
val = tuple(name.split(PRN_SEPARATOR))
if len(val) != 2:
raise ValueError("invalid name [%s]"%name)
else:
return val
else:
return '', name | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Spreadsheet/App/Spreadsheet_legacy.py | python | export | (exportList,filename) | called when freecad exports a csv file | called when freecad exports a csv file | [
"called",
"when",
"freecad",
"exports",
"a",
"csv",
"file"
] | def export(exportList,filename):
"called when freecad exports a csv file"
import csv, Draft
if not exportList:
print("Spreadsheet: Nothing to export")
return
obj = exportList[0]
if Draft.getType(obj) != "Spreadsheet":
print("Spreadsheet: The selected object is not a spreadsheet")
return
if not obj.Proxy._cells:
print("Spreadsheet: The selected spreadsheet contains no cell")
return
numcols = ("abcdefghijklmnopqrstuvwxyz".index(str(obj.Proxy.cols[-1])))+1
numrows = int(obj.Proxy.rows[-1])
with pyopen(filename, 'wb') as csvfile:
csvfile = csv.writer(csvfile)
for i in range(numrows):
r = []
for j in range(numcols):
key = "abcdefghijklmnopqrstuvwxyz"[j]+str(i+1)
if key in obj.Proxy._cells.keys():
r.append(str(obj.Proxy.getFunction(key)))
else:
r.append("")
csvfile.writerow(r)
print("successfully exported ",filename) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/ArchWall.py | python | _CommandWall.setAlign | (self,i) | Simple callback for the interactive mode gui widget to set alignment. | Simple callback for the interactive mode gui widget to set alignment. | [
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self.Align = ["Center","Left","Right"][i]
FreeCAD.ParamGet("User parameter:BaseApp/Preferences/Mod/Arch").SetInt("WallAlignment",i) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/xml/sax/_exceptions.py | python | SAXParseException.__init__ | (self, msg, exception, locator) | Creates the exception. The exception parameter is allowed to be None. | Creates the exception. The exception parameter is allowed to be None. | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/mooseutils/gitutils.py | python | git_version | () | return (int(match.group('major')), int(match.group('minor')), int(match.group('patch'))) | Return the version number as a tuple (major, minor, patch) | Return the version number as a tuple (major, minor, patch) | [
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"""
Return the version number as a tuple (major, minor, patch)
"""
out = mooseutils.check_output(['git', '--version'], encoding='utf-8')
match = re.search(r'(?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)', out)
if match is None:
raise SystemError("git --version failed to return correctly formatted version number")
return (int(match.group('major')), int(match.group('minor')), int(match.group('patch'))) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/arrays/period.py | python | PeriodArray.to_timestamp | (self, freq=None, how="start") | return DatetimeArray._from_sequence(new_data, freq="infer") | Cast to DatetimeArray/Index.
Parameters
----------
freq : str or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise.
how : {'s', 'e', 'start', 'end'}
Whether to use the start or end of the time period being converted.
Returns
-------
DatetimeArray/Index | Cast to DatetimeArray/Index. | [
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"""
Cast to DatetimeArray/Index.
Parameters
----------
freq : str or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise.
how : {'s', 'e', 'start', 'end'}
Whether to use the start or end of the time period being converted.
Returns
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DatetimeArray/Index
"""
from pandas.core.arrays import DatetimeArray
how = libperiod._validate_end_alias(how)
end = how == "E"
if end:
if freq == "B":
# roll forward to ensure we land on B date
adjust = Timedelta(1, "D") - Timedelta(1, "ns")
return self.to_timestamp(how="start") + adjust
else:
adjust = Timedelta(1, "ns")
return (self + self.freq).to_timestamp(how="start") - adjust
if freq is None:
base, mult = libfrequencies.get_freq_code(self.freq)
freq = libfrequencies.get_to_timestamp_base(base)
else:
freq = Period._maybe_convert_freq(freq)
base, mult = libfrequencies.get_freq_code(freq)
new_data = self.asfreq(freq, how=how)
new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)
return DatetimeArray._from_sequence(new_data, freq="infer") | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/tarfile.py | python | TarInfo._proc_builtin | (self, tarfile) | return self | Process a builtin type or an unknown type which
will be treated as a regular file. | Process a builtin type or an unknown type which
will be treated as a regular file. | [
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self.offset_data = tarfile.fileobj.tell()
offset = self.offset_data
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tarfile.offset = offset
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self._apply_pax_info(tarfile.pax_headers, tarfile.encoding, tarfile.errors)
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HyeonwooNoh/caffe | d9e8494a2832d67b25dee37194c7bcb9d52d0e42 | scripts/cpp_lint.py | python | CheckEmptyBlockBody | (filename, clean_lines, linenum, error) | Look for empty loop/conditional body with only a single semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Look for empty loop/conditional body with only a single semicolon. | [
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"""Look for empty loop/conditional body with only a single semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
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#
# We also check "if" blocks here, since an empty conditional block
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line = clean_lines.elided[linenum]
matched = Match(r'\s*(for|while|if)\s*\(', line)
if matched:
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if end_pos >= 0 and Match(r';', end_line[end_pos:]):
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error(filename, end_linenum, 'whitespace/empty_conditional_body', 5,
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else:
error(filename, end_linenum, 'whitespace/empty_loop_body', 5,
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/framework/tensor_shape.py | python | TensorShape.merge_with | (self, other) | Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` containing the combined information of `self` and
`other`.
Raises:
ValueError: If `self` and `other` are not compatible. | Returns a `TensorShape` combining the information in `self` and `other`. | [
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"""Returns a `TensorShape` combining the information in `self` and `other`.
The dimensions in `self` and `other` are merged elementwise,
according to the rules defined for `Dimension.merge_with()`.
Args:
other: Another `TensorShape`.
Returns:
A `TensorShape` containing the combined information of `self` and
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Raises:
ValueError: If `self` and `other` are not compatible.
"""
other = as_shape(other)
if self._dims is None:
return other
else:
try:
self.assert_same_rank(other)
new_dims = []
for i, dim in enumerate(self._dims):
new_dims.append(dim.merge_with(other[i]))
return TensorShape(new_dims)
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raise ValueError("Shapes %s and %s are not compatible" %
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/src/robotsim.py | python | RobotModel.randomizeConfig | (self, unboundedScale: "double"=1.0) | return _robotsim.RobotModel_randomizeConfig(self, unboundedScale) | r"""
randomizeConfig(RobotModel self, double unboundedScale=1.0)
Samples a random configuration and updates the robot's pose. Properly handles
non-normal joints and handles DOFs with infinite bounds using a centered
Laplacian distribution with the given scaling term.
.. note::
Python random module seeding does not affect the result. | r"""
randomizeConfig(RobotModel self, double unboundedScale=1.0) | [
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randomizeConfig(RobotModel self, double unboundedScale=1.0)
Samples a random configuration and updates the robot's pose. Properly handles
non-normal joints and handles DOFs with infinite bounds using a centered
Laplacian distribution with the given scaling term.
.. note::
Python random module seeding does not affect the result.
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rootm0s/Protectors | 5b3f4d11687a5955caf9c3af30666c4bfc2c19ab | OWASP-ZSC/module/readline_windows/pyreadline/console/consolebase.py | python | baseconsole.write_scrolling | (self, text, attr=None) | write text at current cursor position while watching for scrolling.
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/mox.py | python | And.equals | (self, rhs) | return True | Checks whether all Comparators are equal to rhs.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/groupby/groupby.py | python | GroupBy._agg_py_fallback | (
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# If we are DataFrameGroupBy and went through a SeriesGroupByPath
# then we need to reshape
# GH#32223 includes case with IntegerArray values, ndarray res_values
# test_groupby_duplicate_columns with object dtype values
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | python/artm/regularizers.py | python | LabelRegularizationPhiRegularizer.__init__ | (self, name=None, tau=1.0, gamma=None, class_ids=None,
topic_names=None, dictionary=None, config=None) | :param str name: the identifier of regularizer, will be auto-generated if not specified
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See SmoothSparsePhiRegularizer documentation for further details.
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See SmoothSparsePhiRegularizer documentation for further details.
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facebookresearch/ELF | 1f790173095cd910976d9f651b80beb872ec5d12 | vendor/pybind11/tools/clang/cindex.py | python | SourceLocation.file | (self) | return self._get_instantiation()[0] | Get the file represented by this source location. | Get the file represented by this source location. | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/requests/requests/packages/urllib3/poolmanager.py | python | ProxyManager._set_proxy_headers | (self, url, headers=None) | return headers_ | Sets headers needed by proxies: specifically, the Accept and Host
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/string_ops.py | python | _reduce_join_reduction_dims | (x, axis, reduction_indices) | Returns range(rank(x) - 1, 0, -1) if reduction_indices is None. | Returns range(rank(x) - 1, 0, -1) if reduction_indices is None. | [
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ApolloAuto/apollo | 463fb82f9e979d02dcb25044e60931293ab2dba0 | modules/tools/sensor_calibration/extract_data.py | python | Extractor.sanity_check_path | (self, path) | Sanity check wrapper | Sanity check wrapper | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/types/abstract.py | python | ArrayCompatible.as_array | (self) | The equivalent array type, for operations supporting array-compatible
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"""
The equivalent array type, for operations supporting array-compatible
objects (such as ufuncs).
""" | [
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libtcod/libtcod | 613b5c20dc3d222600c202bee39707500aee2a25 | scripts/update_copyrights.py | python | update_file | (path: Path, copyright: str, args: argparse.Namespace) | Adds or replaces the copyright banner for a source file.
`path` is the file path.
`copyright` is the up-to-date copyright banner. | Adds or replaces the copyright banner for a source file. | [
"Adds",
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"."
] | def update_file(path: Path, copyright: str, args: argparse.Namespace):
"""Adds or replaces the copyright banner for a source file.
`path` is the file path.
`copyright` is the up-to-date copyright banner.
"""
path = path.resolve()
source = path.read_text(encoding="utf-8")
match = re.match(
pattern=r"(/\*.*?Copyright ©.*?\*/\n?)?\n*(.*)",
string=source,
flags=re.DOTALL | re.MULTILINE,
)
assert match
old_copyright, source = match.groups()
if old_copyright == copyright:
if args.verbose:
print(f"Banner is up-to-date {path}")
return
elif old_copyright is None:
print(f"Adding missing banner to {path}")
else:
print(f"Updating banner for {path}")
if not args.dry_run:
path.write_text(copyright + source, encoding="utf-8") | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/jinja2/filters.py | python | do_round | (value, precision=0, method='common') | return func(value * (10 ** precision)) / (10 ** precision) | Round the number to a given precision. The first
parameter specifies the precision (default is ``0``), the
second the rounding method:
- ``'common'`` rounds either up or down
- ``'ceil'`` always rounds up
- ``'floor'`` always rounds down
If you don't specify a method ``'common'`` is used.
.. sourcecode:: jinja
{{ 42.55|round }}
-> 43.0
{{ 42.55|round(1, 'floor') }}
-> 42.5
Note that even if rounded to 0 precision, a float is returned. If
you need a real integer, pipe it through `int`:
.. sourcecode:: jinja
{{ 42.55|round|int }}
-> 43 | Round the number to a given precision. The first
parameter specifies the precision (default is ``0``), the
second the rounding method: | [
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"""Round the number to a given precision. The first
parameter specifies the precision (default is ``0``), the
second the rounding method:
- ``'common'`` rounds either up or down
- ``'ceil'`` always rounds up
- ``'floor'`` always rounds down
If you don't specify a method ``'common'`` is used.
.. sourcecode:: jinja
{{ 42.55|round }}
-> 43.0
{{ 42.55|round(1, 'floor') }}
-> 42.5
Note that even if rounded to 0 precision, a float is returned. If
you need a real integer, pipe it through `int`:
.. sourcecode:: jinja
{{ 42.55|round|int }}
-> 43
"""
if not method in ('common', 'ceil', 'floor'):
raise FilterArgumentError('method must be common, ceil or floor')
if method == 'common':
return round(value, precision)
func = getattr(math, method)
return func(value * (10 ** precision)) / (10 ** precision) | [
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falkTX/Carla | 74a1ae82c90db85f20550ddcdc8a927b8fb7e414 | source/modules/lilv/lilv-0.24.0/bindings/python/lilv.py | python | Plugin.get_optional_features | (self) | return Nodes(plugin_get_optional_features(self.plugin)) | Get the LV2 Features optionally supported by a plugin.
Hosts MAY ignore optional plugin features for whatever reasons. Plugins
MUST operate (at least somewhat) if they are instantiated without being
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"""Get the LV2 Features optionally supported by a plugin.
Hosts MAY ignore optional plugin features for whatever reasons. Plugins
MUST operate (at least somewhat) if they are instantiated without being
passed optional features.
"""
return Nodes(plugin_get_optional_features(self.plugin)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/configdialog.py | python | HighPage.create_new | (self, new_theme_name) | Create a new custom theme with the given name.
Create the new theme based on the previously active theme
with the current changes applied. Once it is saved, then
activate the new theme.
Attributes accessed:
builtin_name
custom_name
Attributes updated:
customlist
theme_source
Method:
save_new
set_theme_type | Create a new custom theme with the given name. | [
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"given",
"name",
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] | def create_new(self, new_theme_name):
"""Create a new custom theme with the given name.
Create the new theme based on the previously active theme
with the current changes applied. Once it is saved, then
activate the new theme.
Attributes accessed:
builtin_name
custom_name
Attributes updated:
customlist
theme_source
Method:
save_new
set_theme_type
"""
if self.theme_source.get():
theme_type = 'default'
theme_name = self.builtin_name.get()
else:
theme_type = 'user'
theme_name = self.custom_name.get()
new_theme = idleConf.GetThemeDict(theme_type, theme_name)
# Apply any of the old theme's unsaved changes to the new theme.
if theme_name in changes['highlight']:
theme_changes = changes['highlight'][theme_name]
for element in theme_changes:
new_theme[element] = theme_changes[element]
# Save the new theme.
self.save_new(new_theme_name, new_theme)
# Change GUI over to the new theme.
custom_theme_list = idleConf.GetSectionList('user', 'highlight')
custom_theme_list.sort()
self.customlist.SetMenu(custom_theme_list, new_theme_name)
self.theme_source.set(0)
self.set_theme_type() | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | lldb/examples/python/bsd.py | python | Archive.get_object_dicts | (self) | return object_dicts | Returns an array of object dictionaries that contain they following
keys:
'object': the actual bsd.Object instance
'symdefs': an array of symbol names that the object contains
as found in the "__.SYMDEF" item in the archive | Returns an array of object dictionaries that contain they following
keys:
'object': the actual bsd.Object instance
'symdefs': an array of symbol names that the object contains
as found in the "__.SYMDEF" item in the archive | [
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'''
Returns an array of object dictionaries that contain they following
keys:
'object': the actual bsd.Object instance
'symdefs': an array of symbol names that the object contains
as found in the "__.SYMDEF" item in the archive
'''
symdefs = self.get_symdef()
symdef_dict = {}
if symdefs:
for (name, offset) in symdefs:
if offset in symdef_dict:
object_dict = symdef_dict[offset]
else:
object_dict = {
'object': self.get_object_at_offset(offset),
'symdefs': []
}
symdef_dict[offset] = object_dict
object_dict['symdefs'].append(name)
object_dicts = []
for offset in sorted(symdef_dict):
object_dicts.append(symdef_dict[offset])
return object_dicts | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cuda/api.py | python | is_cuda_array | (obj) | return hasattr(obj, '__cuda_array_interface__') | Test if the object has defined the `__cuda_array_interface__` attribute.
Does not verify the validity of the interface. | Test if the object has defined the `__cuda_array_interface__` attribute. | [
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] | def is_cuda_array(obj):
"""Test if the object has defined the `__cuda_array_interface__` attribute.
Does not verify the validity of the interface.
"""
return hasattr(obj, '__cuda_array_interface__') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | MenuItem.SetId | (*args, **kwargs) | return _core_.MenuItem_SetId(*args, **kwargs) | SetId(self, int id) | SetId(self, int id) | [
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"""SetId(self, int id)"""
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francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | cnx/tickds.py | python | TickDataSeries.getVolumeDataSeries | (self) | return self.__volumeDS | Returns a :class:`pyalgotrade.dataseries.DataSeries` with the volume. | Returns a :class:`pyalgotrade.dataseries.DataSeries` with the volume. | [
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"""Returns a :class:`pyalgotrade.dataseries.DataSeries` with the volume."""
return self.__volumeDS | [
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/fit/polar.py | python | PolarFittingSeA.__init__ | (self,
descrpt : tf.Tensor,
neuron : List[int] = [120,120,120],
resnet_dt : bool = True,
sel_type : List[int] = None,
fit_diag : bool = True,
scale : List[float] = None,
shift_diag : bool = True, # YWolfeee: will support the user to decide whether to use this function
#diag_shift : List[float] = None, YWolfeee: will not support the user to assign a shift
seed : int = None,
activation_function : str = 'tanh',
precision : str = 'default',
uniform_seed: bool = False
) | Constructor
Parameters
----------
descrpt : tf.Tensor
The descrptor
neuron : List[int]
Number of neurons in each hidden layer of the fitting net
resnet_dt : bool
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
sel_type : List[int]
The atom types selected to have an atomic polarizability prediction. If is None, all atoms are selected.
fit_diag : bool
Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
scale : List[float]
The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
diag_shift : List[float]
The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.
seed : int
Random seed for initializing the network parameters.
activation_function : str
The activation function in the embedding net. Supported options are {0}
precision : str
The precision of the embedding net parameters. Supported options are {1}
uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed | Constructor | [
"Constructor"
] | def __init__ (self,
descrpt : tf.Tensor,
neuron : List[int] = [120,120,120],
resnet_dt : bool = True,
sel_type : List[int] = None,
fit_diag : bool = True,
scale : List[float] = None,
shift_diag : bool = True, # YWolfeee: will support the user to decide whether to use this function
#diag_shift : List[float] = None, YWolfeee: will not support the user to assign a shift
seed : int = None,
activation_function : str = 'tanh',
precision : str = 'default',
uniform_seed: bool = False
) -> None:
"""
Constructor
Parameters
----------
descrpt : tf.Tensor
The descrptor
neuron : List[int]
Number of neurons in each hidden layer of the fitting net
resnet_dt : bool
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
sel_type : List[int]
The atom types selected to have an atomic polarizability prediction. If is None, all atoms are selected.
fit_diag : bool
Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
scale : List[float]
The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
diag_shift : List[float]
The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.
seed : int
Random seed for initializing the network parameters.
activation_function : str
The activation function in the embedding net. Supported options are {0}
precision : str
The precision of the embedding net parameters. Supported options are {1}
uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
"""
if not isinstance(descrpt, DescrptSeA) :
raise RuntimeError('PolarFittingSeA only supports DescrptSeA')
self.ntypes = descrpt.get_ntypes()
self.dim_descrpt = descrpt.get_dim_out()
# args = ClassArg()\
# .add('neuron', list, default = [120,120,120], alias = 'n_neuron')\
# .add('resnet_dt', bool, default = True)\
# .add('fit_diag', bool, default = True)\
# .add('diag_shift', [list,float], default = [0.0 for ii in range(self.ntypes)])\
# .add('scale', [list,float], default = [1.0 for ii in range(self.ntypes)])\
# .add('sel_type', [list,int], default = [ii for ii in range(self.ntypes)], alias = 'pol_type')\
# .add('seed', int)\
# .add("activation_function", str , default = "tanh")\
# .add('precision', str, default = "default")
# class_data = args.parse(jdata)
self.n_neuron = neuron
self.resnet_dt = resnet_dt
self.sel_type = sel_type
self.fit_diag = fit_diag
self.seed = seed
self.uniform_seed = uniform_seed
self.seed_shift = one_layer_rand_seed_shift()
#self.diag_shift = diag_shift
self.shift_diag = shift_diag
self.scale = scale
self.fitting_activation_fn = get_activation_func(activation_function)
self.fitting_precision = get_precision(precision)
if self.sel_type is None:
self.sel_type = [ii for ii in range(self.ntypes)]
if self.scale is None:
self.scale = [1.0 for ii in range(self.ntypes)]
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if type(self.sel_type) is not list:
self.sel_type = [self.sel_type]
self.constant_matrix = np.zeros(len(self.sel_type)) # len(sel_type) x 1, store the average diagonal value
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dscharrer/innoextract | 5519d364cc8898f906f6285d81a87ab8c5469cde | cmake/cpplint.py | python | _CppLintState.SetVerboseLevel | (self, level) | return last_verbose_level | Sets the module's verbosity, and returns the previous setting. | Sets the module's verbosity, and returns the previous setting. | [
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last_verbose_level = self.verbose_level
self.verbose_level = level
return last_verbose_level | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/linalg/linalg_impl.py | python | logdet | (matrix, name=None) | Computes log of the determinant of a hermitian positive definite matrix.
```python
# Compute the determinant of a matrix while reducing the chance of over- or
underflow:
A = ... # shape 10 x 10
det = tf.exp(tf.logdet(A)) # scalar
```
Args:
matrix: A `Tensor`. Must be `float32`, `float64`, `complex64`, or
`complex128` with shape `[..., M, M]`.
name: A name to give this `Op`. Defaults to `logdet`.
Returns:
The natural log of the determinant of `matrix`.
@compatibility(numpy)
Equivalent to numpy.linalg.slogdet, although no sign is returned since only
hermitian positive definite matrices are supported.
@end_compatibility | Computes log of the determinant of a hermitian positive definite matrix. | [
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"""Computes log of the determinant of a hermitian positive definite matrix.
```python
# Compute the determinant of a matrix while reducing the chance of over- or
underflow:
A = ... # shape 10 x 10
det = tf.exp(tf.logdet(A)) # scalar
```
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matrix: A `Tensor`. Must be `float32`, `float64`, `complex64`, or
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name: A name to give this `Op`. Defaults to `logdet`.
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"""
# This uses the property that the log det(A) = 2*sum(log(real(diag(C))))
# where C is the cholesky decomposition of A.
with ops.name_scope(name, 'logdet', [matrix]):
chol = gen_linalg_ops.cholesky(matrix)
return 2.0 * math_ops.reduce_sum(
math_ops.log(math_ops.real(array_ops.matrix_diag_part(chol))),
reduction_indices=[-1]) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/protobuf/python/mox.py | python | ExpectedMethodCallsError.__init__ | (self, expected_methods) | Init exception.
Args:
# expected_methods: A sequence of MockMethod objects that should have been
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expected_methods: [MockMethod]
Raises:
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"""Init exception.
Args:
# expected_methods: A sequence of MockMethod objects that should have been
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expected_methods: [MockMethod]
Raises:
ValueError: if expected_methods contains no methods.
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if not expected_methods:
raise ValueError("There must be at least one expected method")
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/run/__init__.py | python | to_local_args | (input_args=None) | return ["run"] + [arg for arg in (suites_arg, storage_engine_arg) if arg is not None
] + other_local_args + positional_args | Return a command line invocation for resmoke.py suitable for being run outside of Evergreen.
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"""
Return a command line invocation for resmoke.py suitable for being run outside of Evergreen.
This function parses the 'args' list of command line arguments, removes any Evergreen-centric
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if input_args is None:
input_args = sys.argv[1:]
if input_args[0] != 'run':
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(parser, parsed_args) = main_parser.parse(input_args)
# If --originSuite was specified, then we replace the value of --suites with it. This is done to
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origin_suite = getattr(parsed_args, "origin_suite", None)
if origin_suite is not None:
setattr(parsed_args, "suite_files", origin_suite)
# Replace --runAllFeatureFlagTests with an explicit list of feature flags. The former relies on
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run_all_feature_flag_tests = getattr(parsed_args, "run_all_feature_flag_tests", None)
if run_all_feature_flag_tests is not None:
setattr(parsed_args, "additional_feature_flags", config.ENABLED_FEATURE_FLAGS)
del parsed_args.run_all_feature_flag_tests
del parsed_args.run_all_feature_flags_no_tests
# The top-level parser has one subparser that contains all subcommand parsers.
command_subparser = [
action for action in parser._actions # pylint: disable=protected-access
if action.dest == "command"
][0]
run_parser = command_subparser.choices.get("run")
suites_arg = None
storage_engine_arg = None
other_local_args = []
positional_args = []
def format_option(option_name, option_value):
"""
Return <option_name>=<option_value>.
This function assumes that 'option_name' is always "--" prefix and isn't "-" prefixed.
"""
return f"{option_name}={option_value}"
# Trim the argument namespace of any args we don't want to return.
for group in run_parser._action_groups: # pylint: disable=protected-access
arg_dests_visited = set()
for action in group._group_actions: # pylint: disable=protected-access
arg_dest = action.dest
arg_value = getattr(parsed_args, arg_dest, None)
# Some arguments, such as --shuffle and --shuffleMode, update the same dest variable.
# To not print out multiple arguments that will update the same dest, we will skip once
# one such argument has been visited.
if arg_dest in arg_dests_visited:
continue
else:
arg_dests_visited.add(arg_dest)
# If the arg doesn't exist in the parsed namespace, skip.
# This is mainly for "--help".
if not hasattr(parsed_args, arg_dest):
continue
# Skip any evergreen centric args.
elif group.title in [
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]:
continue
elif group.title == 'positional arguments':
positional_args.extend(arg_value)
# Keep all remaining args.
else:
arg_name = action.option_strings[-1]
# If an option has the same value as the default, we don't need to specify it.
if getattr(parsed_args, arg_dest, None) == action.default:
continue
# These are arguments that take no value.
elif action.nargs == 0:
other_local_args.append(arg_name)
elif isinstance(action, argparse._AppendAction): # pylint: disable=protected-access
args = [format_option(arg_name, elem) for elem in arg_value]
other_local_args.extend(args)
else:
arg = format_option(arg_name, arg_value)
# We track the value for the --suites and --storageEngine command line options
# separately in order to more easily sort them to the front.
if arg_dest == "suite_files":
suites_arg = arg
elif arg_dest == "storage_engine":
storage_engine_arg = arg
else:
other_local_args.append(arg)
return ["run"] + [arg for arg in (suites_arg, storage_engine_arg) if arg is not None
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] | https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/resmokelib/run/__init__.py#L1188-L1295 | |
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/wiredtiger/dist/stat.py | python | print_defines | () | Print the #defines for the wiredtiger.in file. | Print the #defines for the wiredtiger.in file. | [
"Print",
"the",
"#defines",
"for",
"the",
"wiredtiger",
".",
"in",
"file",
"."
] | def print_defines():
'''Print the #defines for the wiredtiger.in file.'''
f.write('''
/*!
* @name Connection statistics
* @anchor statistics_keys
* @anchor statistics_conn
* Statistics are accessed through cursors with \c "statistics:" URIs.
* Individual statistics can be queried through the cursor using the following
* keys. See @ref data_statistics for more information.
* @{
*/
''')
print_defines_one('CONN', 1000, sorted_conn_stats)
f.write('''
/*!
* @}
* @name Statistics for data sources
* @anchor statistics_dsrc
* @{
*/
''')
print_defines_one('DSRC', 2000, sorted_dsrc_statistics)
f.write('''
/*!
* @}
* @name Statistics for join cursors
* @anchor statistics_join
* @{
*/
''')
print_defines_one('JOIN', 3000, join_stats)
f.write('''
/*!
* @}
* @name Statistics for session
* @anchor statistics_session
* @{
*/
''')
print_defines_one('SESSION', 4000, session_stats)
f.write('/*! @} */\n') | [
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giuspen/cherrytree | 84712f206478fcf9acf30174009ad28c648c6344 | pygtk2/modules/exports.py | python | Export2Txt.get_single_txt_filepath | (self, proposed_name) | return ret_filepath | Prepare for the txt file save | Prepare for the txt file save | [
"Prepare",
"for",
"the",
"txt",
"file",
"save"
] | def get_single_txt_filepath(self, proposed_name):
"""Prepare for the txt file save"""
ret_filepath = support.dialog_file_save_as(proposed_name + ".txt",
filter_pattern="*.txt",
filter_name=_("Plain Text Document"),
curr_folder=self.dad.pick_dir_export,
parent=self.dad.window)
if ret_filepath:
if not ret_filepath.endswith(".txt"): ret_filepath += ".txt"
self.dad.pick_dir_export = os.path.dirname(ret_filepath)
return ret_filepath | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/examples/image_retraining/retrain.py | python | get_or_create_bottleneck | (sess, image_lists, label_name, index, image_dir,
category, bottleneck_dir, jpeg_data_tensor,
bottleneck_tensor) | return bottleneck_values | Retrieves or calculates bottleneck values for an image.
If a cached version of the bottleneck data exists on-disk, return that,
otherwise calculate the data and save it to disk for future use.
Args:
sess: The current active TensorFlow Session.
image_lists: Dictionary of training images for each label.
label_name: Label string we want to get an image for.
index: Integer offset of the image we want. This will be modulo-ed by the
available number of images for the label, so it can be arbitrarily large.
image_dir: Root folder string of the subfolders containing the training
images.
category: Name string of which set to pull images from - training, testing,
or validation.
bottleneck_dir: Folder string holding cached files of bottleneck values.
jpeg_data_tensor: The tensor to feed loaded jpeg data into.
bottleneck_tensor: The output tensor for the bottleneck values.
Returns:
Numpy array of values produced by the bottleneck layer for the image. | Retrieves or calculates bottleneck values for an image. | [
"Retrieves",
"or",
"calculates",
"bottleneck",
"values",
"for",
"an",
"image",
"."
] | def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
category, bottleneck_dir, jpeg_data_tensor,
bottleneck_tensor):
"""Retrieves or calculates bottleneck values for an image.
If a cached version of the bottleneck data exists on-disk, return that,
otherwise calculate the data and save it to disk for future use.
Args:
sess: The current active TensorFlow Session.
image_lists: Dictionary of training images for each label.
label_name: Label string we want to get an image for.
index: Integer offset of the image we want. This will be modulo-ed by the
available number of images for the label, so it can be arbitrarily large.
image_dir: Root folder string of the subfolders containing the training
images.
category: Name string of which set to pull images from - training, testing,
or validation.
bottleneck_dir: Folder string holding cached files of bottleneck values.
jpeg_data_tensor: The tensor to feed loaded jpeg data into.
bottleneck_tensor: The output tensor for the bottleneck values.
Returns:
Numpy array of values produced by the bottleneck layer for the image.
"""
label_lists = image_lists[label_name]
sub_dir = label_lists['dir']
sub_dir_path = os.path.join(bottleneck_dir, sub_dir)
ensure_dir_exists(sub_dir_path)
bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
bottleneck_dir, category)
if not os.path.exists(bottleneck_path):
print('Creating bottleneck at ' + bottleneck_path)
image_path = get_image_path(image_lists, label_name, index, image_dir,
category)
if not gfile.Exists(image_path):
tf.logging.fatal('File does not exist %s', image_path)
image_data = gfile.FastGFile(image_path, 'rb').read()
bottleneck_values = run_bottleneck_on_image(sess, image_data,
jpeg_data_tensor,
bottleneck_tensor)
bottleneck_string = ','.join(str(x) for x in bottleneck_values)
with open(bottleneck_path, 'w') as bottleneck_file:
bottleneck_file.write(bottleneck_string)
with open(bottleneck_path, 'r') as bottleneck_file:
bottleneck_string = bottleneck_file.read()
bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
return bottleneck_values | [
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] | https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/examples/image_retraining/retrain.py#L374-L422 | |
google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/core/numerictypes.py | python | obj2sctype | (rep, default=None) | return res.type | Return the scalar dtype or NumPy equivalent of Python type of an object.
Parameters
----------
rep : any
The object of which the type is returned.
default : any, optional
If given, this is returned for objects whose types can not be
determined. If not given, None is returned for those objects.
Returns
-------
dtype : dtype or Python type
The data type of `rep`.
See Also
--------
sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
Examples
--------
>>> np.obj2sctype(np.int32)
<type 'numpy.int32'>
>>> np.obj2sctype(np.array([1., 2.]))
<type 'numpy.float64'>
>>> np.obj2sctype(np.array([1.j]))
<type 'numpy.complex128'>
>>> np.obj2sctype(dict)
<type 'numpy.object_'>
>>> np.obj2sctype('string')
<type 'numpy.string_'>
>>> np.obj2sctype(1, default=list)
<type 'list'> | Return the scalar dtype or NumPy equivalent of Python type of an object. | [
"Return",
"the",
"scalar",
"dtype",
"or",
"NumPy",
"equivalent",
"of",
"Python",
"type",
"of",
"an",
"object",
"."
] | def obj2sctype(rep, default=None):
"""
Return the scalar dtype or NumPy equivalent of Python type of an object.
Parameters
----------
rep : any
The object of which the type is returned.
default : any, optional
If given, this is returned for objects whose types can not be
determined. If not given, None is returned for those objects.
Returns
-------
dtype : dtype or Python type
The data type of `rep`.
See Also
--------
sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
Examples
--------
>>> np.obj2sctype(np.int32)
<type 'numpy.int32'>
>>> np.obj2sctype(np.array([1., 2.]))
<type 'numpy.float64'>
>>> np.obj2sctype(np.array([1.j]))
<type 'numpy.complex128'>
>>> np.obj2sctype(dict)
<type 'numpy.object_'>
>>> np.obj2sctype('string')
<type 'numpy.string_'>
>>> np.obj2sctype(1, default=list)
<type 'list'>
"""
try:
if issubclass(rep, generic):
return rep
except TypeError:
pass
if isinstance(rep, dtype):
return rep.type
if isinstance(rep, type):
return _python_type(rep)
if isinstance(rep, ndarray):
return rep.dtype.type
try:
res = dtype(rep)
except:
return default
return res.type | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/email/_header_value_parser.py | python | get_obs_local_part | (value) | return obs_local_part, value | obs-local-part = word *("." word) | obs-local-part = word *("." word) | [
"obs",
"-",
"local",
"-",
"part",
"=",
"word",
"*",
"(",
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")"
] | def get_obs_local_part(value):
""" obs-local-part = word *("." word)
"""
obs_local_part = ObsLocalPart()
last_non_ws_was_dot = False
while value and (value[0]=='\\' or value[0] not in PHRASE_ENDS):
if value[0] == '.':
if last_non_ws_was_dot:
obs_local_part.defects.append(errors.InvalidHeaderDefect(
"invalid repeated '.'"))
obs_local_part.append(DOT)
last_non_ws_was_dot = True
value = value[1:]
continue
elif value[0]=='\\':
obs_local_part.append(ValueTerminal(value[0],
'misplaced-special'))
value = value[1:]
obs_local_part.defects.append(errors.InvalidHeaderDefect(
"'\\' character outside of quoted-string/ccontent"))
last_non_ws_was_dot = False
continue
if obs_local_part and obs_local_part[-1].token_type != 'dot':
obs_local_part.defects.append(errors.InvalidHeaderDefect(
"missing '.' between words"))
try:
token, value = get_word(value)
last_non_ws_was_dot = False
except errors.HeaderParseError:
if value[0] not in CFWS_LEADER:
raise
token, value = get_cfws(value)
obs_local_part.append(token)
if (obs_local_part[0].token_type == 'dot' or
obs_local_part[0].token_type=='cfws' and
obs_local_part[1].token_type=='dot'):
obs_local_part.defects.append(errors.InvalidHeaderDefect(
"Invalid leading '.' in local part"))
if (obs_local_part[-1].token_type == 'dot' or
obs_local_part[-1].token_type=='cfws' and
obs_local_part[-2].token_type=='dot'):
obs_local_part.defects.append(errors.InvalidHeaderDefect(
"Invalid trailing '.' in local part"))
if obs_local_part.defects:
obs_local_part.token_type = 'invalid-obs-local-part'
return obs_local_part, value | [
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apple/swift-clang | d7403439fc6641751840b723e7165fb02f52db95 | bindings/python/clang/cindex.py | python | Cursor.kind | (self) | return CursorKind.from_id(self._kind_id) | Return the kind of this cursor. | Return the kind of this cursor. | [
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return CursorKind.from_id(self._kind_id) | [
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aimerykong/Low-Rank-Bilinear-Pooling | 487eb2c857fd9c95357a5166b0c15ad0fe135b28 | caffe-20160312/python/caffe/pycaffe.py | python | _Net_params | (self) | return OrderedDict([(name, lr.blobs)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_basinhopping.py | python | Metropolis.__call__ | (self, **kwargs) | return bool(self.accept_reject(kwargs["f_new"],
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"""
f_new and f_old are mandatory in kwargs
"""
return bool(self.accept_reject(kwargs["f_new"],
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/network/download.py | python | parse_content_disposition | (content_disposition, default_filename) | return filename or default_filename | Parse the "filename" value from a Content-Disposition header, and
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# type: (str, str) -> str
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/profiler/parser/hccl_parser.py | python | HcclParser._parse_link_cost | (self, result_dict, key, link_type_dict) | Parse link cost. | Parse link cost. | [
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] | def _parse_link_cost(self, result_dict, key, link_type_dict):
"""Parse link cost."""
for link_type_key, link_type_value in link_type_dict.items():
if link_type_key == CommunicationInfo.RDMA.value:
# Divide information by thread.
rdma_infos = []
threads_dict = self._divide_communication_info_by_thread(link_type_value)
for thread_value in threads_dict.values():
rdma_info = self._calculate_adma_link_info(thread_value)
rdma_infos.append(rdma_info)
rdma_total_cost = np.sum(rdma_infos, axis=0).tolist()
result_dict[key][link_type_key] = rdma_total_cost
if link_type_key == CommunicationInfo.SDMA.value:
sdma_total_cost = self._calculate_sdma_link_info(link_type_value)
result_dict[key][link_type_key] = sdma_total_cost | [
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