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bbfamily/abu | 2de85ae57923a720dac99a545b4f856f6b87304b | abupy/UtilBu/ABuProgress.py | python | AbuProgress.show | (self, a_progress=None, ext='', p_format="{}:{}:{}%") | 进行进度控制显示主方法
:param ext: 可以添加额外的显示文字，str，默认空字符串
:param a_progress: 默认None, 即使用类内部计算的迭代次数进行进度显示
:param p_format: 进度显示格式，默认{}: {}%，即'self._label:round(self._progress / self._total * 100, 2))%' | 进行进度控制显示主方法
:param ext: 可以添加额外的显示文字，str，默认空字符串
:param a_progress: 默认None, 即使用类内部计算的迭代次数进行进度显示
:param p_format: 进度显示格式，默认{}: {}%，即'self._label:round(self._progress / self._total * 100, 2))%' | [
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"""
self.progress = a_progress if a_progress is not None else self.progress + 1
ps = round(self._progress / self._total * 100, 2)
if self._label is not None:
# 如果初始化label没有就只显示ui进度
self.f.write('\r')
self.f.write(p_format.format(self._label, ext, ps))
if ABuEnv.g_is_ipython:
if self.progress_widget is None:
self.progress_widget = FloatProgress(value=0, min=0, max=100)
display(self.progress_widget)
self.progress_widget.value = ps
# 这样会出现余数结束的情况，还是尽量使用上下文管理器控制结束
if self._progress == self._total:
self.f.write('\r')
if self.progress_widget is not None:
self.progress_widget.close() | [
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determined-ai/determined | f637264493acc14f12e66550cb51c520b5d27f6c | examples/features/data_layer_mnist_estimator/util.py | python | download_data | (download_directory) | return data_dir | Return the path of a directory with the MNIST dataset in TFRecord format.
The dataset will be downloaded into download_directory, if it is not already
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"""
Return the path of a directory with the MNIST dataset in TFRecord format.
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"""
if not tf.io.gfile.exists(download_directory):
tf.io.gfile.makedirs(download_directory)
filepath = os.path.join(download_directory, MNIST_TF_RECORDS_FILE)
if not tf.io.gfile.exists(filepath):
logging.info("Downloading {}".format(MNIST_TF_RECORDS_URL))
r = requests.get(MNIST_TF_RECORDS_URL)
with tf.io.gfile.GFile(filepath, "wb") as f:
f.write(r.content)
logging.info("Downloaded {} ({} bytes)".format(MNIST_TF_RECORDS_FILE, f.size()))
logging.info("Extracting {} to {}".format(MNIST_TF_RECORDS_FILE, download_directory))
with tarfile.open(filepath, mode="r:gz") as f:
f.extractall(path=download_directory)
data_dir = os.path.join(download_directory, "mnist-tfrecord")
assert tf.io.gfile.exists(data_dir)
return data_dir | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/hqwebapp/templatetags/hq_shared_tags.py | python | BOOL | (obj) | return 'true' if obj else 'false' | [] | def BOOL(obj):
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tribe29/checkmk | 6260f2512e159e311f426e16b84b19d0b8e9ad0c | cmk/gui/views/__init__.py | python | View.breadcrumb | (self) | return self._host_hierarchy_breadcrumb() | Render the breadcrumb for the current view
In case of views we not only have a hierarchy of
1. main menu
2. main menu topic
We also have a hierarchy between some of the views (see _host_hierarchy_breadcrumb). But
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We also have a hierarchy between some of the views (see _host_hierarchy_breadcrumb). But
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"""
# View without special hierarchy
if "host" not in self.spec["single_infos"] or "host" in self.missing_single_infos:
request_vars: HTTPVariables = [("view_name", self.name)]
request_vars += list(
visuals.get_singlecontext_vars(self.context, self.spec["single_infos"]).items()
)
breadcrumb = make_topic_breadcrumb(
mega_menu_registry.menu_monitoring(),
pagetypes.PagetypeTopics.get_topic(self.spec["topic"]),
)
breadcrumb.append(
BreadcrumbItem(
title=view_title(self.spec, self.context),
url=makeuri_contextless(request, request_vars),
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return breadcrumb
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return self._host_hierarchy_breadcrumb() | [
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GaoQ1/rasa_nlu_gq | aea2d037220f022b2773d0852cbcfbad582e8e25 | rasa_nlu_gao/models/bert/tokenization.py | python | _is_control | (char) | return False | Checks whether `chars` is a control character. | Checks whether `chars` is a control character. | [
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# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/research/power_flow/fubm/jacobian_based_acdc_power_flow.py | python | dSbus_dV_with_numba | (Ybus, V) | return sp.csc_matrix((dS_dVa, Ybus.indices, Ybus.indptr)), sp.csc_matrix((dS_dVm, Ybus.indices, Ybus.indptr)) | Call the numba sparse constructor of the derivatives
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:param V: Voltages vector
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:return: dS_dVm, dS_dVa in CSC format
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dS_dVm, dS_dVa = dSbus_dV_numba_sparse_csc(Ybus.data, Ybus.indptr, Ybus.indices, V)
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return sp.csc_matrix((dS_dVa, Ybus.indices, Ybus.indptr)), sp.csc_matrix((dS_dVm, Ybus.indices, Ybus.indptr)) | [
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mherrmann/selenium-python-helium | 02f9a5a872871999d683c84461ac0d0b3e9da192 | helium/__init__.py | python | doubleclick | (element) | :param element: The element or point to click.
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/distribute-0.6.34-py2.7.egg/setuptools/command/build_py.py | python | build_py._get_data_files | (self) | return data | Generate list of '(package,src_dir,build_dir,filenames)' tuples | Generate list of '(package,src_dir,build_dir,filenames)' tuples | [
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"""Generate list of '(package,src_dir,build_dir,filenames)' tuples"""
self.analyze_manifest()
data = []
for package in self.packages or ():
# Locate package source directory
src_dir = self.get_package_dir(package)
# Compute package build directory
build_dir = os.path.join(*([self.build_lib] + package.split('.')))
# Length of path to strip from found files
plen = len(src_dir)+1
# Strip directory from globbed filenames
filenames = [
file[plen:] for file in self.find_data_files(package, src_dir)
]
data.append( (package, src_dir, build_dir, filenames) )
return data | [
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CenterForOpenScience/osf.io | cc02691be017e61e2cd64f19b848b2f4c18dcc84 | osf/models/user.py | python | OSFUser.update_is_active | (self) | Update ``is_active`` to be consistent with the fields that
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grantjenks/free-python-games | 3f27c6555e2c72a9b1c668c4dd8506d7b8378d09 | freegames/cannon.py | python | inside | (xy) | return -200 < xy.x < 200 and -200 < xy.y < 200 | Return True if xy within screen. | Return True if xy within screen. | [
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YosaiProject/yosai | 7f96aa6b837ceae9bf3d7387cd7e35f5ab032575 | yosai/core/mgt/abcs.py | python | RememberMeManager.get_remembered_identifiers | (self, subject_context) | Based on the specified subject context map being used to build a
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openbmc/openbmc | 5f4109adae05f4d6925bfe960007d52f98c61086 | poky/bitbake/lib/bb/utils.py | python | environment | (**envvars) | Context manager to selectively update the environment with the specified mapping. | Context manager to selectively update the environment with the specified mapping. | [
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"""
Context manager to selectively update the environment with the specified mapping.
"""
backup = dict(os.environ)
try:
os.environ.update(envvars)
yield
finally:
for var in envvars:
if var in backup:
os.environ[var] = backup[var]
else:
del os.environ[var] | [
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pluralsight/guides-cms | 9e73a6bbc0f0747a66c08346d94b380892a49012 | pskb_website/models/file.py | python | _parse_stacks_line | (line) | return [_force_unicode(m.group()) for m in STACK_RE.finditer(line.lower())] | Parse list of stacks from line of text
:param line: Line of text to parse
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/sorting.py | python | OrderedList.__repr__ | (self) | return "<%s %r>" % (self.__class__.__name__, self.dict) | [] | def __repr__(self):
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splunk/splunk-sdk-python | ef88e9d3e90ab9d6cf48cf940c7376400ed759b8 | splunklib/client.py | python | Service.kvstore | (self) | return KVStoreCollections(self) | Returns the collection of KV Store collections.
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spectacles/CodeComplice | 8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62 | libs/codeintel2/tcl_parser.py | python | Parser._parse_name_list | (self) | return vars | [] | def _parse_name_list(self):
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/wsgiref/util.py | python | shift_path_info | (environ) | Shift a name from PATH_INFO to SCRIPT_NAME, returning it
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LGE-ARC-AdvancedAI/auptimizer | 50f6e3b4e0cb9146ca90fd74b9b24ca97ae22617 | src/aup/compression/torch/pruning/structured_pruning.py | python | StructuredWeightMasker.get_channel_sum | (self, wrapper, wrapper_idx) | Calculate the importance weight for each channel. If want to support the
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layer wrapper of this layer
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apache/bloodhound | c3e31294e68af99d4e040e64fbdf52394344df9e | bloodhound_search/bhsearch/api.py | python | IQueryParser.parse | (query_string, context) | Parse query from string | Parse query from string | [
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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/lib-tk/turtle.py | python | TurtleScreen.window_height | (self) | return self._window_size()[1] | Return the height of the turtle window.
Example (for a TurtleScreen instance named screen):
>>> screen.window_height()
480 | Return the height of the turtle window. | [
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480
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microsoft/debugpy | be8dd607f6837244e0b565345e497aff7a0c08bf | src/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/process.py | python | Process.get_mapped_filenames | (self, memoryMap = None) | return mappedFilenames | Retrieves the filenames for memory mapped files in the debugee.
@type memoryMap: list( L{win32.MemoryBasicInformation} )
@param memoryMap: (Optional) Memory map returned by L{get_memory_map}.
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@rtype: dict( int S{->} str )
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@param memoryMap: (Optional) Memory map returned by L{get_memory_map}.
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@rtype: dict( int S{->} str )
@return: Dictionary mapping memory addresses to file names.
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"""
hProcess = self.get_handle( win32.PROCESS_VM_READ |
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if not memoryMap:
memoryMap = self.get_memory_map()
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if mbi.Type not in (win32.MEM_IMAGE, win32.MEM_MAPPED):
continue
baseAddress = mbi.BaseAddress
fileName = ""
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fileName = win32.GetMappedFileName(hProcess, baseAddress)
fileName = PathOperations.native_to_win32_pathname(fileName)
except WindowsError:
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getavalon/core | 31e8cb4760e00e3db64443f6f932b7fd8e96d41d | avalon/vendor/requests/packages/urllib3/util/retry.py | python | Retry.is_retry | (self, method, status_code, has_retry_after=False) | return (self.total and self.respect_retry_after_header and
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NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/asr/modules/conv_asr.py | python | ConvASRDecoderClassification.input_example | (self) | return tuple([input_example]) | Generates input examples for tracing etc.
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xmindltd/xmind-sdk-python | 58b2c7f1971abd941cd0f28e88388ec93ed2c53d | xmind/core/__init__.py | python | Document.documentElement | (self) | return self._node.documentElement | Get root element of passed DOM implementation for manipulate | Get root element of passed DOM implementation for manipulate | [
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TensorSpeech/TensorflowTTS | 34358d82a4c91fd70344872f8ea8a405ea84aedb | tensorflow_tts/models/parallel_wavegan.py | python | TFResidualBlock.call | (self, x, c, training=False) | return x, s | Calculate forward propagation.
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x (Tensor): Input tensor (B, residual_channels, T).
c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T).
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residual = x
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xa, xb = xa + ca, xb + cb
x = tf.nn.tanh(xa) * tf.nn.sigmoid(xb)
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trakt/Plex-Trakt-Scrobbler | aeb0bfbe62fad4b06c164f1b95581da7f35dce0b | Trakttv.bundle/Contents/Libraries/Shared/sortedcontainers/sortedset.py | python | SortedSet.discard | (self, value) | Remove the first occurrence of *value*. If *value* is not a member,
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NoGameNoLife00/mybolg | afe17ea5bfe405e33766e5682c43a4262232ee12 | libs/jinja2/filters.py | python | do_list | (value) | return list(value) | Convert the value into a list. If it was a string the returned list
will be a list of characters. | Convert the value into a list. If it was a string the returned list
will be a list of characters. | [
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] | def do_list(value):
"""Convert the value into a list. If it was a string the returned list
will be a list of characters.
"""
return list(value) | [
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spesmilo/electrum | bdbd59300fbd35b01605e66145458e5f396108e8 | electrum/util.py | python | random_shuffled_copy | (x: Iterable[T]) | return x_copy | Returns a shuffled copy of the input. | Returns a shuffled copy of the input. | [
"Returns",
"a",
"shuffled",
"copy",
"of",
"the",
"input",
"."
] | def random_shuffled_copy(x: Iterable[T]) -> List[T]:
"""Returns a shuffled copy of the input."""
x_copy = list(x) # copy
random.shuffle(x_copy) # shuffle in-place
return x_copy | [
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burness/tensorflow-101 | c775a54af86542940e6e69b7d90d8d7e8aa9aeb9 | finetuning/deployment/model_deploy.py | python | DeploymentConfig.clone_scope | (self, clone_index) | return scope | Name scope to create the clone.
Args:
clone_index: Int, representing the clone_index.
Returns:
A name_scope suitable for `tf.name_scope()`.
Raises:
ValueError: if `clone_index` is greater or equal to the number of clones". | Name scope to create the clone. | [
"Name",
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"clone",
"."
] | def clone_scope(self, clone_index):
"""Name scope to create the clone.
Args:
clone_index: Int, representing the clone_index.
Returns:
A name_scope suitable for `tf.name_scope()`.
Raises:
ValueError: if `clone_index` is greater or equal to the number of clones".
"""
if clone_index >= self._num_clones:
raise ValueError('clone_index must be less than num_clones')
scope = ''
if self._num_clones > 1:
scope = 'clone_%d' % clone_index
return scope | [
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bitcoin-core/HWI | 6871946c2176f2f9777b6ac8f0614d96d99bfa0e | hwilib/devices/ckcc/client.py | python | UnixSimulatorPipe.write | (self, buf) | return 65 if rv == 64 else rv | [] | def write(self, buf):
assert len(buf) == 65
self.pipe.settimeout(10)
rv = self.pipe.send(buf[1:])
return 65 if rv == 64 else rv | [
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futurecore/python-csp | 1f96b76de1531ecf6bf1759641eadb08266ff7e7 | csp/os_thread.py | python | _CSPOpMixin.join | (self) | Join only if self is running. | Join only if self is running. | [
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] | def join(self):
"""Join only if self is running."""
if self._Thread__started.is_set():
threading.Thread.join(self) | [
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XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py | python | Distribution.get_entry_map | (self, group=None) | return ep_map | Return the entry point map for `group`, or the full entry map | Return the entry point map for `group`, or the full entry map | [
"Return",
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"for",
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] | def get_entry_map(self, group=None):
"""Return the entry point map for `group`, or the full entry map"""
try:
ep_map = self._ep_map
except AttributeError:
ep_map = self._ep_map = EntryPoint.parse_map(
self._get_metadata('entry_points.txt'), self
)
if group is not None:
return ep_map.get(group, {})
return ep_map | [
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/object_detection/core/preprocessor.py | python | get_default_func_arg_map | (include_label_scores=False,
include_multiclass_scores=False,
include_instance_masks=False,
include_keypoints=False) | return prep_func_arg_map | Returns the default mapping from a preprocessor function to its args.
Args:
include_label_scores: If True, preprocessing functions will modify the
label scores, too.
include_multiclass_scores: If True, preprocessing functions will modify the
multiclass scores, too.
include_instance_masks: If True, preprocessing functions will modify the
instance masks, too.
include_keypoints: If True, preprocessing functions will modify the
keypoints, too.
Returns:
A map from preprocessing functions to the arguments they receive. | Returns the default mapping from a preprocessor function to its args. | [
"Returns",
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"default",
"mapping",
"from",
"a",
"preprocessor",
"function",
"to",
"its",
"args",
"."
] | def get_default_func_arg_map(include_label_scores=False,
include_multiclass_scores=False,
include_instance_masks=False,
include_keypoints=False):
"""Returns the default mapping from a preprocessor function to its args.
Args:
include_label_scores: If True, preprocessing functions will modify the
label scores, too.
include_multiclass_scores: If True, preprocessing functions will modify the
multiclass scores, too.
include_instance_masks: If True, preprocessing functions will modify the
instance masks, too.
include_keypoints: If True, preprocessing functions will modify the
keypoints, too.
Returns:
A map from preprocessing functions to the arguments they receive.
"""
groundtruth_label_scores = None
if include_label_scores:
groundtruth_label_scores = (fields.InputDataFields.groundtruth_label_scores)
multiclass_scores = None
if include_multiclass_scores:
multiclass_scores = (fields.InputDataFields.multiclass_scores)
groundtruth_instance_masks = None
if include_instance_masks:
groundtruth_instance_masks = (
fields.InputDataFields.groundtruth_instance_masks)
groundtruth_keypoints = None
if include_keypoints:
groundtruth_keypoints = fields.InputDataFields.groundtruth_keypoints
prep_func_arg_map = {
normalize_image: (fields.InputDataFields.image,),
random_horizontal_flip: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,
),
random_vertical_flip: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,
),
random_rotation90: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,
),
random_pixel_value_scale: (fields.InputDataFields.image,),
random_image_scale: (
fields.InputDataFields.image,
groundtruth_instance_masks,
),
random_rgb_to_gray: (fields.InputDataFields.image,),
random_adjust_brightness: (fields.InputDataFields.image,),
random_adjust_contrast: (fields.InputDataFields.image,),
random_adjust_hue: (fields.InputDataFields.image,),
random_adjust_saturation: (fields.InputDataFields.image,),
random_distort_color: (fields.InputDataFields.image,),
random_jitter_boxes: (fields.InputDataFields.groundtruth_boxes,),
random_crop_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores, multiclass_scores,
groundtruth_instance_masks, groundtruth_keypoints),
random_pad_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes),
random_crop_pad_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores, multiclass_scores),
random_crop_to_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
multiclass_scores,
groundtruth_instance_masks,
groundtruth_keypoints,
),
random_pad_to_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,
),
random_black_patches: (fields.InputDataFields.image,),
retain_boxes_above_threshold: (
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
multiclass_scores,
groundtruth_instance_masks,
groundtruth_keypoints,
),
image_to_float: (fields.InputDataFields.image,),
random_resize_method: (fields.InputDataFields.image,),
resize_to_range: (
fields.InputDataFields.image,
groundtruth_instance_masks,
),
resize_to_min_dimension: (
fields.InputDataFields.image,
groundtruth_instance_masks,
),
scale_boxes_to_pixel_coordinates: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_keypoints,
),
resize_image: (
fields.InputDataFields.image,
groundtruth_instance_masks,
),
subtract_channel_mean: (fields.InputDataFields.image,),
one_hot_encoding: (fields.InputDataFields.groundtruth_image_classes,),
rgb_to_gray: (fields.InputDataFields.image,),
ssd_random_crop: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores, multiclass_scores,
groundtruth_instance_masks, groundtruth_keypoints),
ssd_random_crop_pad: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores, multiclass_scores),
ssd_random_crop_fixed_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes, groundtruth_label_scores,
multiclass_scores, groundtruth_instance_masks, groundtruth_keypoints),
ssd_random_crop_pad_fixed_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
multiclass_scores,
groundtruth_instance_masks,
groundtruth_keypoints,
),
convert_class_logits_to_softmax: (multiclass_scores,),
}
return prep_func_arg_map | [
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spotify/gcp-audit | 2d17578e8845db670505291076cfb58ede031901 | gcp_audit/util/gcp.py | python | get_all_organizations | () | return [x['organizationId'] for x in resp['organizations']] | Get all organizations that the credentials have access to. | Get all organizations that the credentials have access to. | [
"Get",
"all",
"organizations",
"that",
"the",
"credentials",
"have",
"access",
"to",
"."
] | def get_all_organizations():
"""Get all organizations that the credentials have access to."""
resp = create_service('cloudresourcemanager', 'v1beta1') \
.organizations().list().execute()
return [x['organizationId'] for x in resp['organizations']] | [
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... | https://github.com/spotify/gcp-audit/blob/2d17578e8845db670505291076cfb58ede031901/gcp_audit/util/gcp.py#L128-L133 | |
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/olefile/olefile.py | python | OleFileIO._check_duplicate_stream | (self, first_sect, minifat=False) | Checks if a stream has not been already referenced elsewhere.
This method should only be called once for each known stream, and only
if stream size is not null.
:param first_sect: int, index of first sector of the stream in FAT
:param minifat: bool, if True, stream is located in the MiniFAT, else in the FAT | Checks if a stream has not been already referenced elsewhere.
This method should only be called once for each known stream, and only
if stream size is not null. | [
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] | def _check_duplicate_stream(self, first_sect, minifat=False):
"""
Checks if a stream has not been already referenced elsewhere.
This method should only be called once for each known stream, and only
if stream size is not null.
:param first_sect: int, index of first sector of the stream in FAT
:param minifat: bool, if True, stream is located in the MiniFAT, else in the FAT
"""
if minifat:
log.debug('_check_duplicate_stream: sect=%Xh in MiniFAT' % first_sect)
used_streams = self._used_streams_minifat
else:
log.debug('_check_duplicate_stream: sect=%Xh in FAT' % first_sect)
# some values can be safely ignored (not a real stream):
if first_sect in (DIFSECT,FATSECT,ENDOFCHAIN,FREESECT):
return
used_streams = self._used_streams_fat
#TODO: would it be more efficient using a dict or hash values, instead
# of a list of long ?
if first_sect in used_streams:
self._raise_defect(DEFECT_INCORRECT, 'Stream referenced twice')
else:
used_streams.append(first_sect) | [
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proycon/pynlpl | 7707f69a91caaa6cde037f0d0379f1d42500a68b | pynlpl/fsa.py | python | NFA._states | (self, state, processedstates=[]) | return processedstates | Iterate over all states in no particular order | Iterate over all states in no particular order | [
"Iterate",
"over",
"all",
"states",
"in",
"no",
"particular",
"order"
] | def _states(self, state, processedstates=[]): #pylint: disable=dangerous-default-value
"""Iterate over all states in no particular order"""
processedstates.append(state)
for nextstate in state.epsilon:
if not nextstate in processedstates:
self._states(nextstate, processedstates)
for _, nextstate in state.transitions:
if not nextstate in processedstates:
self._states(nextstate, processedstates)
return processedstates | [
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... | https://github.com/proycon/pynlpl/blob/7707f69a91caaa6cde037f0d0379f1d42500a68b/pynlpl/fsa.py#L97-L109 | |
scipy/scipy | e0a749f01e79046642ccfdc419edbf9e7ca141ad | scipy/sparse/linalg/_isolve/utils.py | python | make_system | (A, M, x0, b) | return A, M, x, b, postprocess | Make a linear system Ax=b
Parameters
----------
A : LinearOperator
sparse or dense matrix (or any valid input to aslinearoperator)
M : {LinearOperator, Nones}
preconditioner
sparse or dense matrix (or any valid input to aslinearoperator)
x0 : {array_like, str, None}
initial guess to iterative method.
``x0 = 'Mb'`` means using the nonzero initial guess ``M @ b``.
Default is `None`, which means using the zero initial guess.
b : array_like
right hand side
Returns
-------
(A, M, x, b, postprocess)
A : LinearOperator
matrix of the linear system
M : LinearOperator
preconditioner
x : rank 1 ndarray
initial guess
b : rank 1 ndarray
right hand side
postprocess : function
converts the solution vector to the appropriate
type and dimensions (e.g. (N,1) matrix) | Make a linear system Ax=b | [
"Make",
"a",
"linear",
"system",
"Ax",
"=",
"b"
] | def make_system(A, M, x0, b):
"""Make a linear system Ax=b
Parameters
----------
A : LinearOperator
sparse or dense matrix (or any valid input to aslinearoperator)
M : {LinearOperator, Nones}
preconditioner
sparse or dense matrix (or any valid input to aslinearoperator)
x0 : {array_like, str, None}
initial guess to iterative method.
``x0 = 'Mb'`` means using the nonzero initial guess ``M @ b``.
Default is `None`, which means using the zero initial guess.
b : array_like
right hand side
Returns
-------
(A, M, x, b, postprocess)
A : LinearOperator
matrix of the linear system
M : LinearOperator
preconditioner
x : rank 1 ndarray
initial guess
b : rank 1 ndarray
right hand side
postprocess : function
converts the solution vector to the appropriate
type and dimensions (e.g. (N,1) matrix)
"""
A_ = A
A = aslinearoperator(A)
if A.shape[0] != A.shape[1]:
raise ValueError(f'expected square matrix, but got shape={(A.shape,)}')
N = A.shape[0]
b = asanyarray(b)
if not (b.shape == (N,1) or b.shape == (N,)):
raise ValueError(f'shapes of A {A.shape} and b {b.shape} are '
'incompatible')
if b.dtype.char not in 'fdFD':
b = b.astype('d') # upcast non-FP types to double
def postprocess(x):
return x
if hasattr(A,'dtype'):
xtype = A.dtype.char
else:
xtype = A.matvec(b).dtype.char
xtype = coerce(xtype, b.dtype.char)
b = asarray(b,dtype=xtype) # make b the same type as x
b = b.ravel()
# process preconditioner
if M is None:
if hasattr(A_,'psolve'):
psolve = A_.psolve
else:
psolve = id
if hasattr(A_,'rpsolve'):
rpsolve = A_.rpsolve
else:
rpsolve = id
if psolve is id and rpsolve is id:
M = IdentityOperator(shape=A.shape, dtype=A.dtype)
else:
M = LinearOperator(A.shape, matvec=psolve, rmatvec=rpsolve,
dtype=A.dtype)
else:
M = aslinearoperator(M)
if A.shape != M.shape:
raise ValueError('matrix and preconditioner have different shapes')
# set initial guess
if x0 is None:
x = zeros(N, dtype=xtype)
elif isinstance(x0, str):
if x0 == 'Mb': # use nonzero initial guess ``M @ b``
bCopy = b.copy()
x = M.matvec(bCopy)
else:
x = array(x0, dtype=xtype)
if not (x.shape == (N, 1) or x.shape == (N,)):
raise ValueError(f'shapes of A {A.shape} and '
f'x0 {x.shape} are incompatible')
x = x.ravel()
return A, M, x, b, postprocess | [
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0vercl0k/stuffz | 2ff82f4739d7e215c6140d4987efa8310db39d55 | ql-chall-python-2014/fuby_ql.py | python | PythonDecompiler.__reduce_conditional_blocks | (self, stmts) | return stmts | Reduce conditional blocks | Reduce conditional blocks | [
"Reduce",
"conditional",
"blocks"
] | def __reduce_conditional_blocks(self, stmts):
""" Reduce conditional blocks """
repass = True
while repass:
repass = False
i = 0
while i < len(stmts):
if isinstance(stmts[i], If) or isinstance(stmts[i], Elif):
j = i + 1
while j < len(stmts):
if isinstance(stmts[j], If) or isinstance(stmts[j], Elif):
# (el)if x: z elif y: z => (el)if x or y: z
if stmts[i].statements == stmts[j].statements:
stmts[i].expr = BinaryOp(stmts[i].expr, 'or', stmts[j].expr)
stmts.pop(j)
repass = True
continue
j = j + 1
i = i + 1
i = 0
while i < len(stmts):
# if x: if x and y: | if x: if not x or y:
# if y: => foo | if y: => z
# foo z | z
# [z] | else:
# z | z
if isinstance(stmts[i], If) or isinstance(stmts[i], Elif):
if len(stmts[i].statements) > 0 and isinstance(stmts[i].statements[0], If):
if i < len(stmts) - 1 and isinstance(stmts[i+1], Else):
if stmts[i].statements[0].statements == stmts[i+1].statements:
stmts[i].expr = BinaryOp(UnaryOp('not ', stmts[i].expr), 'or', stmts[i].statements[0].expr)
stmts[i].statements = stmts.pop(i+1).statements
repass = True
continue
if stmts[i].statements[1:] == stmts[1-len(stmts[i].statements):] or stmts[i].statements[1:] == stmts[i+1:] or len(stmts[i].statements) == 1:
stmts[i].expr = BinaryOp(stmts[i].expr, 'and', stmts[i].statements[0].expr)
stmts[i].statements = stmts[i].statements[0].statements
repass = True
continue
i = i + 1
return stmts | [
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scrapinghub/spidermon | f2b21e45e70796f583bbb97f39b823c31d242b17 | spidermon/results/monitor.py | python | MonitorResult._step_monitors_finished | (self) | return self._steps[settings.STEPS.MONITORS_FINISHED] | [] | def _step_monitors_finished(self):
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cbrgm/telegram-robot-rss | 58fe98de427121fdc152c8df0721f1891174e6c9 | venv/lib/python2.7/site-packages/future/backports/http/server.py | python | BaseHTTPRequestHandler.version_string | (self) | return self.server_version + ' ' + self.sys_version | Return the server software version string. | Return the server software version string. | [
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"""Return the server software version string."""
return self.server_version + ' ' + self.sys_version | [
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poppy-project/pypot | c5d384fe23eef9f6ec98467f6f76626cdf20afb9 | pypot/vrep/remoteApiBindings/vrep.py | python | simxStartSimulation | (clientID, operationMode) | return c_StartSimulation(clientID, operationMode) | Please have a look at the function description/documentation in the V-REP user manual | Please have a look at the function description/documentation in the V-REP user manual | [
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'''
Please have a look at the function description/documentation in the V-REP user manual
'''
return c_StartSimulation(clientID, operationMode) | [
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PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym | 14ab6fc82018e48de130e87671cca6c57456d1a5 | ch8/environment/carla_gym/envs/carla/driving_benchmark/experiment_suites/corl_2017.py | python | CoRL2017.build_experiments | (self) | return experiments_vector | Creates the whole set of experiment objects,
The experiments created depend on the selected Town. | Creates the whole set of experiment objects,
The experiments created depend on the selected Town. | [
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] | def build_experiments(self):
"""
Creates the whole set of experiment objects,
The experiments created depend on the selected Town.
"""
# We set the camera
# This single RGB camera is used on every experiment
camera = Camera('CameraRGB')
camera.set(FOV=100)
camera.set_image_size(800, 600)
camera.set_position(2.0, 0.0, 1.4)
camera.set_rotation(-15.0, 0, 0)
if self._city_name == 'Town01':
poses_tasks = self._poses_town01()
vehicles_tasks = [0, 0, 0, 20]
pedestrians_tasks = [0, 0, 0, 50]
else:
poses_tasks = self._poses_town02()
vehicles_tasks = [0, 0, 0, 15]
pedestrians_tasks = [0, 0, 0, 50]
experiments_vector = []
for weather in self.weathers:
for iteration in range(len(poses_tasks)):
poses = poses_tasks[iteration]
vehicles = vehicles_tasks[iteration]
pedestrians = pedestrians_tasks[iteration]
conditions = CarlaSettings()
conditions.set(
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=vehicles,
NumberOfPedestrians=pedestrians,
WeatherId=weather
)
# Add all the cameras that were set for this experiments
conditions.add_sensor(camera)
experiment = Experiment()
experiment.set(
Conditions=conditions,
Poses=poses,
Task=iteration,
Repetitions=1
)
experiments_vector.append(experiment)
return experiments_vector | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python-modules/twisted/twisted/lore/tree.py | python | fixLinks | (document, ext) | Rewrite links to XHTML lore input documents so they point to lore XHTML
output documents.
Any node with an C{href} attribute which does not contain a value starting
with C{http}, C{https}, C{ftp}, or C{mailto} and which does not have a
C{class} attribute of C{absolute} or which contains C{listing} and which
does point to an URL ending with C{html} will have that attribute value
rewritten so that the filename extension is C{ext} instead of C{html}.
@type document: A DOM Node or Document
@param document: The input document which contains all of the content to be
presented.
@type ext: C{str}
@param ext: The extension to use when selecting an output file name. This
replaces the extension of the input file name.
@return: C{None} | Rewrite links to XHTML lore input documents so they point to lore XHTML
output documents. | [
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"lore",
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"so",
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"output",
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"."
] | def fixLinks(document, ext):
"""
Rewrite links to XHTML lore input documents so they point to lore XHTML
output documents.
Any node with an C{href} attribute which does not contain a value starting
with C{http}, C{https}, C{ftp}, or C{mailto} and which does not have a
C{class} attribute of C{absolute} or which contains C{listing} and which
does point to an URL ending with C{html} will have that attribute value
rewritten so that the filename extension is C{ext} instead of C{html}.
@type document: A DOM Node or Document
@param document: The input document which contains all of the content to be
presented.
@type ext: C{str}
@param ext: The extension to use when selecting an output file name. This
replaces the extension of the input file name.
@return: C{None}
"""
supported_schemes=['http', 'https', 'ftp', 'mailto']
for node in domhelpers.findElementsWithAttribute(document, 'href'):
href = node.getAttribute("href")
if urlparse.urlparse(href)[0] in supported_schemes:
continue
if node.getAttribute("class") == "absolute":
continue
if node.getAttribute("class").find('listing') != -1:
continue
# This is a relative link, so it should be munged.
if href.endswith('html') or href[:href.rfind('#')].endswith('html'):
fname, fext = os.path.splitext(href)
if '#' in fext:
fext = ext+'#'+fext.split('#', 1)[1]
else:
fext = ext
node.setAttribute("href", fname + fext) | [
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biopython/biopython | 2dd97e71762af7b046d7f7f8a4f1e38db6b06c86 | Bio/PDB/QCPSuperimposer/__init__.py | python | QCPSuperimposer.get_rotran | (self) | return self.rot, self.tran | Right multiplying rotation matrix and translation. | Right multiplying rotation matrix and translation. | [
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"""Right multiplying rotation matrix and translation."""
if self.rot is None:
raise Exception("Nothing superimposed yet.")
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jwkvam/bowtie | 220cd41367a70f2e206db846278cb7b6fd3649eb | bowtie/_progress.py | python | Progress._instantiate | (self) | return ''.join(self._tags) | Instantiate a progress bar.
This is normally never used. | Instantiate a progress bar. | [
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] | def _instantiate(self) -> str:
"""Instantiate a progress bar.
This is normally never used.
"""
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emesene/emesene | 4548a4098310e21b16437bb36223a7f632a4f7bc | emesene/e3/xmpp/SleekXMPP/sleekxmpp/roster/multi.py | python | Roster.auto_subscribe | (self) | return self._auto_subscribe | Auto send requests for mutual subscriptions.
If True, auto send mutual subscription requests. | Auto send requests for mutual subscriptions. | [
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"""
Auto send requests for mutual subscriptions.
If True, auto send mutual subscription requests.
"""
return self._auto_subscribe | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | futures/ctp/ApiStruct.py | python | SettlementRef.__init__ | (self, TradingDay='', SettlementID=0) | [] | def __init__(self, TradingDay='', SettlementID=0):
self.TradingDay = 'Date' #交易日, char[9]
self.SettlementID = '' | [
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translate/translate | 72816df696b5263abfe80ab59129b299b85ae749 | translate/storage/poheader.py | python | poheader.makeheaderdict | (
self,
charset="CHARSET",
encoding="ENCODING",
project_id_version=None,
pot_creation_date=None,
po_revision_date=None,
last_translator=None,
language_team=None,
mime_version=None,
plural_forms=None,
report_msgid_bugs_to=None,
**kwargs,
) | return update(defaultargs, add=True, **kwargs) | Create a header dictionary with useful defaults.
pot_creation_date can be None (current date) or a value (datetime or string)
po_revision_date can be None (form), False (=pot_creation_date), True (=now),
or a value (datetime or string)
:return: Dictionary with the header items
:rtype: dict of strings | Create a header dictionary with useful defaults. | [
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"defaults",
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] | def makeheaderdict(
self,
charset="CHARSET",
encoding="ENCODING",
project_id_version=None,
pot_creation_date=None,
po_revision_date=None,
last_translator=None,
language_team=None,
mime_version=None,
plural_forms=None,
report_msgid_bugs_to=None,
**kwargs,
):
"""Create a header dictionary with useful defaults.
pot_creation_date can be None (current date) or a value (datetime or string)
po_revision_date can be None (form), False (=pot_creation_date), True (=now),
or a value (datetime or string)
:return: Dictionary with the header items
:rtype: dict of strings
"""
if project_id_version is None:
project_id_version = "PACKAGE VERSION"
if pot_creation_date is None or pot_creation_date is True:
pot_creation_date = time.strftime("%Y-%m-%d %H:%M") + tzstring()
if isinstance(pot_creation_date, time.struct_time):
pot_creation_date = (
time.strftime("%Y-%m-%d %H:%M", pot_creation_date) + tzstring()
)
if po_revision_date is None:
po_revision_date = "YEAR-MO-DA HO:MI+ZONE"
elif po_revision_date is False:
po_revision_date = pot_creation_date
elif po_revision_date is True:
po_revision_date = time.strftime("%Y-%m-%d %H:%M") + tzstring()
if isinstance(po_revision_date, time.struct_time):
po_revision_date = (
time.strftime("%Y-%m-%d %H:%M", po_revision_date) + tzstring()
)
if last_translator is None:
last_translator = "FULL NAME <EMAIL@ADDRESS>"
if language_team is None:
language_team = "LANGUAGE <LL@li.org>"
if mime_version is None:
mime_version = "1.0"
if report_msgid_bugs_to is None:
report_msgid_bugs_to = ""
defaultargs = OrderedDict()
defaultargs["Project-Id-Version"] = project_id_version
defaultargs["Report-Msgid-Bugs-To"] = report_msgid_bugs_to
defaultargs["POT-Creation-Date"] = pot_creation_date
defaultargs["PO-Revision-Date"] = po_revision_date
defaultargs["Last-Translator"] = last_translator
defaultargs["Language-Team"] = language_team
defaultargs["MIME-Version"] = mime_version
defaultargs["Content-Type"] = "text/plain; charset=%s" % charset
defaultargs["Content-Transfer-Encoding"] = encoding
if plural_forms:
defaultargs["Plural-Forms"] = plural_forms
defaultargs["X-Generator"] = self.x_generator
return update(defaultargs, add=True, **kwargs) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/rings/number_field/number_field_ideal.py | python | NumberFieldFractionalIdeal.small_residue | (self, f) | return k(k.pari_nf().nfeltreduce(f, self.pari_hnf())) | r"""
Given an element `f` of the ambient number field, returns an
element `g` such that `f - g` belongs to the ideal self (which
must be integral), and `g` is small.
.. note::
The reduced representative returned is not uniquely determined.
ALGORITHM: Uses Pari function :pari:`nfeltreduce`.
EXAMPLES:
::
sage: k.<a> = NumberField(x^2 + 5)
sage: I = k.ideal(a)
sage: I.small_residue(14)
4
::
sage: K.<a> = NumberField(x^5 + 7*x^4 + 18*x^2 + x - 3)
sage: I = K.ideal(5)
sage: I.small_residue(a^2 -13)
a^2 + 5*a - 3 | r"""
Given an element `f` of the ambient number field, returns an
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Given an element `f` of the ambient number field, returns an
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must be integral), and `g` is small.
.. note::
The reduced representative returned is not uniquely determined.
ALGORITHM: Uses Pari function :pari:`nfeltreduce`.
EXAMPLES:
::
sage: k.<a> = NumberField(x^2 + 5)
sage: I = k.ideal(a)
sage: I.small_residue(14)
4
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sage: K.<a> = NumberField(x^5 + 7*x^4 + 18*x^2 + x - 3)
sage: I = K.ideal(5)
sage: I.small_residue(a^2 -13)
a^2 + 5*a - 3
"""
if not self.is_integral():
raise ValueError("The ideal must be integral")
k = self.number_field()
return k(k.pari_nf().nfeltreduce(f, self.pari_hnf())) | [
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neuropsychology/NeuroKit | d01111b9b82364d28da01c002e6cbfc45d9493d9 | neurokit2/eeg/mne_channel_add.py | python | mne_channel_add | (
raw, channel, channel_type=None, channel_name=None, sync_index_raw=0, sync_index_channel=0
) | return raw | Add channel as array to MNE.
Add a channel to a mne's Raw m/eeg file. It will basically synchronize the channel to the eeg data
following a particular index and add it.
Parameters
----------
raw : mne.io.Raw
Raw EEG data from MNE.
channel : list or array
The signal to be added.
channel_type : str
Channel type. Currently supported fields are 'ecg', 'bio', 'stim', 'eog', 'misc', 'seeg',
'ecog', 'mag', 'eeg', 'ref_meg', 'grad', 'emg', 'hbr' or 'hbo'.
channel_name : str
Desired channel name.
sync_index_raw : int or list
An index (e.g., the onset of the same event marked in the same signal), in the raw data, by
which to align the two inputs. This can be used in case the EEG data and the channel to add
do not have the same onsets and must be aligned through some common event.
sync_index_channel : int or list
An index (e.g., the onset of the same event marked in the same signal), in the channel to add,
by which to align the two inputs. This can be used in case the EEG data and the channel to add
do not have the same onsets and must be aligned through some common event.
Returns
----------
mne.io.Raw
Raw data in FIF format.
Example
----------
>>> import neurokit2 as nk
>>> import mne
>>>
>>> raw = nk.mne_data("filt-0-40_raw")
>>> ecg = nk.ecg_simulate(length=50000)
>>>
>>> # Let the 42nd sample point in the EEG signal correspond to the 333rd point in the ECG
>>> event_index_in_eeg = 42
>>> event_index_in_ecg = 333
>>>
>>> raw = nk.mne_channel_add(raw,
... ecg,
... sync_index_raw=event_index_in_eeg,
... sync_index_channel=event_index_in_ecg,
... channel_type="ecg") # doctest: +SKIP | Add channel as array to MNE. | [
"Add",
"channel",
"as",
"array",
"to",
"MNE",
"."
] | def mne_channel_add(
raw, channel, channel_type=None, channel_name=None, sync_index_raw=0, sync_index_channel=0
):
"""Add channel as array to MNE.
Add a channel to a mne's Raw m/eeg file. It will basically synchronize the channel to the eeg data
following a particular index and add it.
Parameters
----------
raw : mne.io.Raw
Raw EEG data from MNE.
channel : list or array
The signal to be added.
channel_type : str
Channel type. Currently supported fields are 'ecg', 'bio', 'stim', 'eog', 'misc', 'seeg',
'ecog', 'mag', 'eeg', 'ref_meg', 'grad', 'emg', 'hbr' or 'hbo'.
channel_name : str
Desired channel name.
sync_index_raw : int or list
An index (e.g., the onset of the same event marked in the same signal), in the raw data, by
which to align the two inputs. This can be used in case the EEG data and the channel to add
do not have the same onsets and must be aligned through some common event.
sync_index_channel : int or list
An index (e.g., the onset of the same event marked in the same signal), in the channel to add,
by which to align the two inputs. This can be used in case the EEG data and the channel to add
do not have the same onsets and must be aligned through some common event.
Returns
----------
mne.io.Raw
Raw data in FIF format.
Example
----------
>>> import neurokit2 as nk
>>> import mne
>>>
>>> raw = nk.mne_data("filt-0-40_raw")
>>> ecg = nk.ecg_simulate(length=50000)
>>>
>>> # Let the 42nd sample point in the EEG signal correspond to the 333rd point in the ECG
>>> event_index_in_eeg = 42
>>> event_index_in_ecg = 333
>>>
>>> raw = nk.mne_channel_add(raw,
... ecg,
... sync_index_raw=event_index_in_eeg,
... sync_index_channel=event_index_in_ecg,
... channel_type="ecg") # doctest: +SKIP
"""
# Try loading mne
try:
import mne
except ImportError:
raise ImportError(
"NeuroKit error: eeg_channel_add(): the 'mne' module is required for this function to run. ",
"Please install it first (`pip install mne`).",
)
if channel_name is None:
if isinstance(channel, pd.Series):
if channel.name is not None:
channel_name = channel.name
else:
channel_name = "Added_Channel"
else:
channel_name = "Added_Channel"
# Compute the distance between the two signals
diff = sync_index_channel - sync_index_raw
# Pre-empt the channel with nans if shorter or crop if longer
if diff > 0:
channel = list(channel)[diff : len(channel)]
channel = channel + [np.nan] * diff
if diff < 0:
channel = [np.nan] * abs(diff) + list(channel)
# Extend the channel with nans if shorter or crop if longer
if len(channel) < len(raw):
channel = list(channel) + [np.nan] * (len(raw) - len(channel))
else:
# Crop to fit the raw data
channel = list(channel)[0 : len(raw)]
old_verbosity_level = mne.set_log_level(verbose="WARNING", return_old_level=True)
# Create RawArray
info = mne.create_info([channel_name], raw.info["sfreq"], ch_types=channel_type)
channel = mne.io.RawArray([channel], info)
# Add channel
raw = raw.copy()
raw.add_channels([channel], force_update_info=True) # In-place
# Restore old verbosity level
mne.set_log_level(old_verbosity_level)
return raw | [
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svpcom/wifibroadcast | 51251b8c484b8c4f548aa3bbb1633e0edbb605dc | telemetry/mavlink.py | python | MAVLink.open_drone_id_authentication_send | (self, target_system, target_component, id_or_mac, authentication_type, data_page, page_count, length, timestamp, authentication_data, force_mavlink1=False) | return self.send(self.open_drone_id_authentication_encode(target_system, target_component, id_or_mac, authentication_type, data_page, page_count, length, timestamp, authentication_data), force_mavlink1=force_mavlink1) | Data for filling the OpenDroneID Authentication message. The
Authentication Message defines a field that can
provide a means of authenticity for the identity of
the UAS (Unmanned Aircraft System). The Authentication
message can have two different formats. Five data
pages are supported. For data page 0, the fields
PageCount, Length and TimeStamp are present and
AuthData is only 17 bytes. For data page 1 through 4,
PageCount, Length and TimeStamp are not present and
the size of AuthData is 23 bytes.
target_system : System ID (0 for broadcast). (type:uint8_t)
target_component : Component ID (0 for broadcast). (type:uint8_t)
id_or_mac : Only used for drone ID data received from other UAs. See detailed description at https://mavlink.io/en/services/opendroneid.html. (type:uint8_t)
authentication_type : Indicates the type of authentication. (type:uint8_t, values:MAV_ODID_AUTH_TYPE)
data_page : Allowed range is 0 - 4. (type:uint8_t)
page_count : This field is only present for page 0. Allowed range is 0 - 5. (type:uint8_t)
length : This field is only present for page 0. Total bytes of authentication_data from all data pages. Allowed range is 0 - 109 (17 + 23*4). [bytes] (type:uint8_t)
timestamp : This field is only present for page 0. 32 bit Unix Timestamp in seconds since 00:00:00 01/01/2019. [s] (type:uint32_t)
authentication_data : Opaque authentication data. For page 0, the size is only 17 bytes. For other pages, the size is 23 bytes. Shall be filled with nulls in the unused portion of the field. (type:uint8_t) | Data for filling the OpenDroneID Authentication message. The
Authentication Message defines a field that can
provide a means of authenticity for the identity of
the UAS (Unmanned Aircraft System). The Authentication
message can have two different formats. Five data
pages are supported. For data page 0, the fields
PageCount, Length and TimeStamp are present and
AuthData is only 17 bytes. For data page 1 through 4,
PageCount, Length and TimeStamp are not present and
the size of AuthData is 23 bytes. | [
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'''
Data for filling the OpenDroneID Authentication message. The
Authentication Message defines a field that can
provide a means of authenticity for the identity of
the UAS (Unmanned Aircraft System). The Authentication
message can have two different formats. Five data
pages are supported. For data page 0, the fields
PageCount, Length and TimeStamp are present and
AuthData is only 17 bytes. For data page 1 through 4,
PageCount, Length and TimeStamp are not present and
the size of AuthData is 23 bytes.
target_system : System ID (0 for broadcast). (type:uint8_t)
target_component : Component ID (0 for broadcast). (type:uint8_t)
id_or_mac : Only used for drone ID data received from other UAs. See detailed description at https://mavlink.io/en/services/opendroneid.html. (type:uint8_t)
authentication_type : Indicates the type of authentication. (type:uint8_t, values:MAV_ODID_AUTH_TYPE)
data_page : Allowed range is 0 - 4. (type:uint8_t)
page_count : This field is only present for page 0. Allowed range is 0 - 5. (type:uint8_t)
length : This field is only present for page 0. Total bytes of authentication_data from all data pages. Allowed range is 0 - 109 (17 + 23*4). [bytes] (type:uint8_t)
timestamp : This field is only present for page 0. 32 bit Unix Timestamp in seconds since 00:00:00 01/01/2019. [s] (type:uint32_t)
authentication_data : Opaque authentication data. For page 0, the size is only 17 bytes. For other pages, the size is 23 bytes. Shall be filled with nulls in the unused portion of the field. (type:uint8_t)
'''
return self.send(self.open_drone_id_authentication_encode(target_system, target_component, id_or_mac, authentication_type, data_page, page_count, length, timestamp, authentication_data), force_mavlink1=force_mavlink1) | [
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ilastik/ilastik | 6acd2c554bc517e9c8ddad3623a7aaa2e6970c28 | ilastik/applets/counting/countingGuiBoxesInterface.py | python | BoxInterpreter.__init__ | (self, base: InterpreterABC, posModel: PositionModel, newBoxParent: QWidget) | Create new BoxInterpreter.
Args:
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Args:
newBoxParent: Parent for the widget that is temporarily shown when a user draws a new box.
"""
super().__init__()
self._base = base
self._posModel = posModel
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frappe/frappe | b64cab6867dfd860f10ccaf41a4ec04bc890b583 | frappe/desk/form/utils.py | python | add_comment | (reference_doctype, reference_name, content, comment_email, comment_by) | return doc.as_dict() | allow any logged user to post a comment | allow any logged user to post a comment | [
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] | def add_comment(reference_doctype, reference_name, content, comment_email, comment_by):
"""allow any logged user to post a comment"""
doc = frappe.get_doc(dict(
doctype='Comment',
reference_doctype=reference_doctype,
reference_name=reference_name,
comment_email=comment_email,
comment_type='Comment',
comment_by=comment_by
))
reference_doc = frappe.get_doc(reference_doctype, reference_name)
doc.content = extract_images_from_html(reference_doc, content, is_private=True)
doc.insert(ignore_permissions=True)
follow_document(doc.reference_doctype, doc.reference_name, frappe.session.user)
return doc.as_dict() | [
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Alexey-T/CudaText | 6a8b9a974c5d5029c6c273bde83198c83b3a5fb9 | app/cudatext.app/Contents/Resources/py/sys/requests/sessions.py | python | Session.mount | (self, prefix, adapter) | Registers a connection adapter to a prefix.
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] | def mount(self, prefix, adapter):
"""Registers a connection adapter to a prefix.
Adapters are sorted in descending order by prefix length.
"""
self.adapters[prefix] = adapter
keys_to_move = [k for k in self.adapters if len(k) < len(prefix)]
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self.adapters[key] = self.adapters.pop(key) | [
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micahhausler/container-transform | 68223fae98f30b8bb2ce0f02ba9e58afbc80f196 | container_transform/chronos.py | python | ChronosTransformer.emit_volumes | (self, volumes) | return [
self._build_volume(volume)
for volume
in volumes
] | [] | def emit_volumes(self, volumes):
return [
self._build_volume(volume)
for volume
in volumes
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/hqadmin/views/system.py | python | _get_submodules | () | return [
line.strip()[1:].split()[1]
for line in git.submodule()
] | returns something like
['corehq/apps/hqmedia/static/hqmedia/MediaUploader', ...] | returns something like
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] | def _get_submodules():
"""
returns something like
['corehq/apps/hqmedia/static/hqmedia/MediaUploader', ...]
"""
import sh
git = sh.git.bake(_tty_out=False)
return [
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for line in git.submodule()
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/categories/diagram_drawing.py | python | XypicDiagramDrawer._build_xypic_string | (diagram, grid, morphisms,
morphisms_str_info, diagram_format) | return result | Given a collection of :class:`ArrowStringDescription`
describing the morphisms of a diagram and the object layout
information of a diagram, produces the final Xy-pic picture. | Given a collection of :class:`ArrowStringDescription`
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"""
Given a collection of :class:`ArrowStringDescription`
describing the morphisms of a diagram and the object layout
information of a diagram, produces the final Xy-pic picture.
"""
# Build the mapping between objects and morphisms which have
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object_morphisms = {}
for obj in diagram.objects:
object_morphisms[obj] = []
for morphism in morphisms:
object_morphisms[morphism.domain].append(morphism)
result = "\\xymatrix%s{\n" % diagram_format
for i in xrange(grid.height):
for j in xrange(grid.width):
obj = grid[i, j]
if obj:
result += latex(obj) + " "
morphisms_to_draw = object_morphisms[obj]
for morphism in morphisms_to_draw:
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result += "& "
# Don't put the line break after the last row.
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salesforce/cloudsplaining | c932ea61de7b0f12d77bb4144fee343245d68d3f | cloudsplaining/command/scan_multi_account.py | python | scan_accounts | (
multi_account_config: MultiAccountConfig,
exclusions: Exclusions,
role_name: str,
write_data_file: bool,
profile: Optional[str] = None,
output_directory: Optional[str] = None,
output_bucket: Optional[str] = None,
) | Use this method as a library to scan multiple accounts | Use this method as a library to scan multiple accounts | [
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] | def scan_accounts(
multi_account_config: MultiAccountConfig,
exclusions: Exclusions,
role_name: str,
write_data_file: bool,
profile: Optional[str] = None,
output_directory: Optional[str] = None,
output_bucket: Optional[str] = None,
) -> None:
"""Use this method as a library to scan multiple accounts"""
# TODO: Speed improvements? Multithreading? This currently runs sequentially.
for target_account_name, target_account_id in multi_account_config.accounts.items():
print(
f"{OK_GREEN}Scanning account: {target_account_name} (ID: {target_account_id}){END}"
)
results = scan_account(
target_account_id=target_account_id,
target_role_name=role_name,
exclusions=exclusions,
profile=profile,
)
html_report = HTMLReport(
account_id=target_account_id,
account_name=target_account_name,
results=results,
minimize=True,
)
rendered_report = html_report.get_html_report()
if not output_directory and not output_bucket:
raise Exception(
"Please supply --output-bucket and/or --output-directory as arguments."
)
if output_bucket:
s3 = aws_login.get_boto3_resource(service="s3", profile=profile)
# Write the HTML file
output_file = f"{target_account_name}.html"
s3.Object(output_bucket, output_file).put(
ACL="bucket-owner-full-control", Body=rendered_report
)
utils.print_green(
f"Saved the HTML report to: s3://{output_bucket}/{output_file}"
)
# Write the JSON data file
if write_data_file:
output_file = f"{target_account_name}.json"
body = json.dumps(results, sort_keys=True, default=str, indent=4)
s3.Object(output_bucket, output_file).put(
ACL="bucket-owner-full-control", Body=body
)
utils.print_green(
f"Saved the JSON data to: s3://{output_bucket}/{output_file}"
)
if output_directory:
# Write the HTML file
html_output_file = os.path.join(output_directory, f"{target_account_name}.html")
utils.write_file(html_output_file, rendered_report)
utils.print_green(
f"Saved the HTML report to: {os.path.relpath(html_output_file)}"
)
# Write the JSON data file
if write_data_file:
results_data_file = os.path.join(
output_directory, f"{target_account_name}.json"
)
results_data_filepath = utils.write_results_data_file(
results, results_data_file
)
utils.print_green(
f"Saved the JSON data to: {os.path.relpath(results_data_filepath)}"
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hasgeek/hasjob | 38098e8034ee749704dea65394b366e8adc5c71f | hasjob/views/index.py | python | offline | () | return render_template('offline.html.jinja2') | [] | def offline():
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] | https://github.com/hasgeek/hasjob/blob/38098e8034ee749704dea65394b366e8adc5c71f/hasjob/views/index.py#L1234-L1235 | |||
mlflow/mlflow | 364aca7daf0fcee3ec407ae0b1b16d9cb3085081 | mlflow/utils/autologging_utils/client.py | python | MlflowAutologgingQueueingClient.create_run | (
self,
experiment_id: str,
start_time: Optional[int] = None,
tags: Optional[Dict[str, Any]] = None,
) | return run_id | Enqueues a CreateRun operation with the specified attributes, returning a `PendingRunId`
instance that can be used as input to other client logging APIs (e.g. `log_metrics`,
`log_params`, ...).
:return: A `PendingRunId` that can be passed as the `run_id` parameter to other client
logging APIs, such as `log_params` and `log_metrics`. | Enqueues a CreateRun operation with the specified attributes, returning a `PendingRunId`
instance that can be used as input to other client logging APIs (e.g. `log_metrics`,
`log_params`, ...). | [
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"(",
"e",
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"g",
".",
"log_metrics",
... | def create_run(
self,
experiment_id: str,
start_time: Optional[int] = None,
tags: Optional[Dict[str, Any]] = None,
) -> PendingRunId:
"""
Enqueues a CreateRun operation with the specified attributes, returning a `PendingRunId`
instance that can be used as input to other client logging APIs (e.g. `log_metrics`,
`log_params`, ...).
:return: A `PendingRunId` that can be passed as the `run_id` parameter to other client
logging APIs, such as `log_params` and `log_metrics`.
"""
tags = tags or {}
tags = _truncate_dict(
tags, max_key_length=MAX_ENTITY_KEY_LENGTH, max_value_length=MAX_TAG_VAL_LENGTH
)
run_id = PendingRunId()
self._get_pending_operations(run_id).enqueue(
create_run=_PendingCreateRun(
experiment_id=experiment_id,
start_time=start_time,
tags=[RunTag(key, str(value)) for key, value in tags.items()],
)
)
return run_id | [
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edgewall/trac | beb3e4eaf1e0a456d801a50a8614ecab06de29fc | trac/wiki/api.py | python | IWikiMacroProvider.get_macros | () | Return an iterable that provides the names of the provided macros. | Return an iterable that provides the names of the provided macros. | [
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"""Return an iterable that provides the names of the provided macros.
""" | [
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/euclidean.py | python | getJoinOfXIntersectionIndexes | ( xIntersectionIndexList ) | return xIntersections | Get joined x intersections from surrounding layers. | Get joined x intersections from surrounding layers. | [
"Get",
"joined",
"x",
"intersections",
"from",
"surrounding",
"layers",
"."
] | def getJoinOfXIntersectionIndexes( xIntersectionIndexList ):
'Get joined x intersections from surrounding layers.'
xIntersections = []
solidTable = {}
solid = False
xIntersectionIndexList.sort()
for xIntersectionIndex in xIntersectionIndexList:
toggleHashtable(solidTable, xIntersectionIndex.index, '')
oldSolid = solid
solid = len(solidTable) > 0
if oldSolid != solid:
xIntersections.append(xIntersectionIndex.x)
return xIntersections | [
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deep-spin/entmax | f18bab9318f9d2471a36545ee0b4c97be6d48a87 | entmax/root_finding.py | python | sparsemax_bisect | (X, dim=-1, n_iter=50, ensure_sum_one=True) | return SparsemaxBisectFunction.apply(X, dim, n_iter, ensure_sum_one) | sparsemax: normalizing sparse transform (a la softmax), via bisection.
Solves the projection:
min_p ||x - p||_2 s.t. p >= 0, sum(p) == 1.
Parameters
----------
X : torch.Tensor
The input tensor.
dim : int
The dimension along which to apply sparsemax.
n_iter : int
Number of bisection iterations. For float32, 24 iterations should
suffice for machine precision.
ensure_sum_one : bool,
Whether to divide the result by its sum. If false, the result might
sum to close but not exactly 1, which might cause downstream problems.
Note: This function does not yet support normalizing along anything except
the last dimension. Please use transposing and views to achieve more
general behavior.
Returns
-------
P : torch tensor, same shape as X
The projection result, such that P.sum(dim=dim) == 1 elementwise. | sparsemax: normalizing sparse transform (a la softmax), via bisection. | [
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":",
"normalizing",
"sparse",
"transform",
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"a",
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"softmax",
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] | def sparsemax_bisect(X, dim=-1, n_iter=50, ensure_sum_one=True):
"""sparsemax: normalizing sparse transform (a la softmax), via bisection.
Solves the projection:
min_p ||x - p||_2 s.t. p >= 0, sum(p) == 1.
Parameters
----------
X : torch.Tensor
The input tensor.
dim : int
The dimension along which to apply sparsemax.
n_iter : int
Number of bisection iterations. For float32, 24 iterations should
suffice for machine precision.
ensure_sum_one : bool,
Whether to divide the result by its sum. If false, the result might
sum to close but not exactly 1, which might cause downstream problems.
Note: This function does not yet support normalizing along anything except
the last dimension. Please use transposing and views to achieve more
general behavior.
Returns
-------
P : torch tensor, same shape as X
The projection result, such that P.sum(dim=dim) == 1 elementwise.
"""
return SparsemaxBisectFunction.apply(X, dim, n_iter, ensure_sum_one) | [
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gammapy/gammapy | 735b25cd5bbed35e2004d633621896dcd5295e8b | gammapy/datasets/map.py | python | MapDataset.to_region_map_dataset | (self, region, name=None) | return self.__class__(**kwargs) | Integrate the map dataset in a given region.
Counts and background of the dataset are integrated in the given region,
taking the safe mask into accounts. The exposure is averaged in the
region again taking the safe mask into account. The PSF and energy
dispersion kernel are taken at the center of the region.
Parameters
----------
region : `~regions.SkyRegion`
Region from which to extract the spectrum
name : str
Name of the new dataset.
Returns
-------
dataset : `~gammapy.datasets.MapDataset`
the resulting reduced dataset | Integrate the map dataset in a given region. | [
"Integrate",
"the",
"map",
"dataset",
"in",
"a",
"given",
"region",
"."
] | def to_region_map_dataset(self, region, name=None):
"""Integrate the map dataset in a given region.
Counts and background of the dataset are integrated in the given region,
taking the safe mask into accounts. The exposure is averaged in the
region again taking the safe mask into account. The PSF and energy
dispersion kernel are taken at the center of the region.
Parameters
----------
region : `~regions.SkyRegion`
Region from which to extract the spectrum
name : str
Name of the new dataset.
Returns
-------
dataset : `~gammapy.datasets.MapDataset`
the resulting reduced dataset
"""
name = make_name(name)
kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
if self.mask_safe:
kwargs["mask_safe"] = self.mask_safe.to_region_nd_map(region, func=np.any)
if self.mask_fit:
kwargs["mask_fit"] = self.mask_fit.to_region_nd_map(region, func=np.any)
if self.counts:
kwargs["counts"] = self.counts.to_region_nd_map(
region, np.sum, weights=self.mask_safe
)
if self.stat_type == "cash" and self.background:
kwargs["background"] = self.background.to_region_nd_map(
region, func=np.sum, weights=self.mask_safe
)
if self.exposure:
kwargs["exposure"] = self.exposure.to_region_nd_map(region, func=np.mean)
region = region.center if region else None
# TODO: Compute average psf in region
if self.psf:
kwargs["psf"] = self.psf.to_region_nd_map(region)
# TODO: Compute average edisp in region
if self.edisp is not None:
kwargs["edisp"] = self.edisp.to_region_nd_map(region)
return self.__class__(**kwargs) | [
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aravindsrinivas/flowpp | 737fadb2218c1e2810a91b523498f97def2c30de | flows_imagenet/logistic.py | python | mixlogistic_logcdf | (*, x, prior_logits, means, logscales) | return tf.reduce_logsumexp(
tf.nn.log_softmax(prior_logits, axis=-1) + logistic_logcdf(
x=tf.expand_dims(x, -1), mean=means, logscale=logscales),
axis=-1
) | log cumulative distribution function of a mixture of logistics | log cumulative distribution function of a mixture of logistics | [
"log",
"cumulative",
"distribution",
"function",
"of",
"a",
"mixture",
"of",
"logistics"
] | def mixlogistic_logcdf(*, x, prior_logits, means, logscales):
"""log cumulative distribution function of a mixture of logistics"""
assert (len(x.get_shape()) + 1 == len(prior_logits.get_shape()) ==
len(means.get_shape()) == len(logscales.get_shape()))
return tf.reduce_logsumexp(
tf.nn.log_softmax(prior_logits, axis=-1) + logistic_logcdf(
x=tf.expand_dims(x, -1), mean=means, logscale=logscales),
axis=-1
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Antergos/Cnchi | 13ac2209da9432d453e0097cf48a107640b563a9 | src/pages/advanced.py | python | InstallationAdvanced.ssd_cell_toggled | (self, _widget, path) | User confirms selected disk is a ssd disk (or not) | User confirms selected disk is a ssd disk (or not) | [
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] | def ssd_cell_toggled(self, _widget, path):
""" User confirms selected disk is a ssd disk (or not) """
disk_path = self.partition_treeview.store[path][PartitionTreeview.COL_PATH]
self.ssd[disk_path] = self.partition_treeview.store[path][PartitionTreeview.COL_SSD_ACTIVE] | [
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Podshot/MCEdit-Unified | 90abfb170c65b877ac67193e717fa3a3ded635dd | pymclevel/cachefunc.py | python | lru_cache | (maxsize=100) | return decorating_function | Least-recently-used cache decorator.
Arguments to the cached function must be hashable.
Cache performance statistics stored in f.hits and f.misses.
Clear the cache with f.clear().
http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used | Least-recently-used cache decorator. | [
"Least",
"-",
"recently",
"-",
"used",
"cache",
"decorator",
"."
] | def lru_cache(maxsize=100):
'''Least-recently-used cache decorator.
Arguments to the cached function must be hashable.
Cache performance statistics stored in f.hits and f.misses.
Clear the cache with f.clear().
http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
'''
maxqueue = maxsize * 10
def decorating_function(user_function,
len=len, iter=iter, tuple=tuple, sorted=sorted, KeyError=KeyError):
cache = {} # mapping of args to results
queue = collections.deque() # order that keys have been used
refcount = Counter() # times each key is in the queue
sentinel = object() # marker for looping around the queue
kwd_mark = object() # separate positional and keyword args
# lookup optimizations (ugly but fast)
queue_append, queue_popleft = queue.append, queue.popleft
queue_appendleft, queue_pop = queue.appendleft, queue.pop
@functools.wraps(user_function)
def wrapper(*args, **kwds):
# cache key records both positional and keyword args
key = args
if kwds:
key += (kwd_mark,) + tuple(sorted(kwds.items()))
# record recent use of this key
queue_append(key)
refcount[key] += 1
# get cache entry or compute if not found
try:
result = cache[key]
wrapper.hits += 1
except KeyError:
result = user_function(*args, **kwds)
cache[key] = result
wrapper.misses += 1
# purge least recently used cache entry
if len(cache) > maxsize:
key = queue_popleft()
refcount[key] -= 1
while refcount[key]:
key = queue_popleft()
refcount[key] -= 1
del cache[key], refcount[key]
# periodically compact the queue by eliminating duplicate keys
# while preserving order of most recent access
if len(queue) > maxqueue:
refcount.clear()
queue_appendleft(sentinel)
for key in ifilterfalse(refcount.__contains__,
iter(queue_pop, sentinel)):
queue_appendleft(key)
refcount[key] = 1
return result
def clear():
cache.clear()
queue.clear()
refcount.clear()
wrapper.hits = wrapper.misses = 0
wrapper.hits = wrapper.misses = 0
wrapper.clear = clear
return wrapper
return decorating_function | [
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pvlib/pvlib-python | 1ab0eb20f9cd9fb9f7a0ddf35f81283f2648e34a | pvlib/pvsystem.py | python | PVSystem.get_cell_temperature | (self, poa_global, temp_air, wind_speed, model,
effective_irradiance=None) | return tuple(
array.get_cell_temperature(poa_global, temp_air, wind_speed,
model, effective_irradiance)
for array, poa_global, temp_air, wind_speed, effective_irradiance
in zip(
self.arrays, poa_global, temp_air, wind_speed,
effective_irradiance
)
) | Determine cell temperature using the method specified by ``model``.
Parameters
----------
poa_global : numeric or tuple of numeric
Total incident irradiance in W/m^2.
temp_air : numeric or tuple of numeric
Ambient dry bulb temperature in degrees C.
wind_speed : numeric or tuple of numeric
Wind speed in m/s.
model : str
Supported models include ``'sapm'``, ``'pvsyst'``,
``'faiman'``, ``'fuentes'``, and ``'noct_sam'``
effective_irradiance : numeric or tuple of numeric, optional
The irradiance that is converted to photocurrent in W/m^2.
Only used for some models.
Returns
-------
numeric or tuple of numeric
Values in degrees C.
See Also
--------
Array.get_cell_temperature
Notes
-----
The `temp_air` and `wind_speed` parameters may be passed as tuples
to provide different values for each Array in the system. If passed as
a tuple the length must be the same as the number of Arrays. If not
passed as a tuple then the same value is used for each Array. | Determine cell temperature using the method specified by ``model``. | [
"Determine",
"cell",
"temperature",
"using",
"the",
"method",
"specified",
"by",
"model",
"."
] | def get_cell_temperature(self, poa_global, temp_air, wind_speed, model,
effective_irradiance=None):
"""
Determine cell temperature using the method specified by ``model``.
Parameters
----------
poa_global : numeric or tuple of numeric
Total incident irradiance in W/m^2.
temp_air : numeric or tuple of numeric
Ambient dry bulb temperature in degrees C.
wind_speed : numeric or tuple of numeric
Wind speed in m/s.
model : str
Supported models include ``'sapm'``, ``'pvsyst'``,
``'faiman'``, ``'fuentes'``, and ``'noct_sam'``
effective_irradiance : numeric or tuple of numeric, optional
The irradiance that is converted to photocurrent in W/m^2.
Only used for some models.
Returns
-------
numeric or tuple of numeric
Values in degrees C.
See Also
--------
Array.get_cell_temperature
Notes
-----
The `temp_air` and `wind_speed` parameters may be passed as tuples
to provide different values for each Array in the system. If passed as
a tuple the length must be the same as the number of Arrays. If not
passed as a tuple then the same value is used for each Array.
"""
poa_global = self._validate_per_array(poa_global)
temp_air = self._validate_per_array(temp_air, system_wide=True)
wind_speed = self._validate_per_array(wind_speed, system_wide=True)
# Not used for all models, but Array.get_cell_temperature handles it
effective_irradiance = self._validate_per_array(effective_irradiance,
system_wide=True)
return tuple(
array.get_cell_temperature(poa_global, temp_air, wind_speed,
model, effective_irradiance)
for array, poa_global, temp_air, wind_speed, effective_irradiance
in zip(
self.arrays, poa_global, temp_air, wind_speed,
effective_irradiance
)
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PaddlePaddle/PaddleX | 2bab73f81ab54e328204e7871e6ae4a82e719f5d | paddlex/ppdet/engine/callbacks.py | python | LogPrinter.on_epoch_end | (self, status) | [] | def on_epoch_end(self, status):
if dist.get_world_size() < 2 or dist.get_rank() == 0:
mode = status['mode']
if mode == 'eval':
sample_num = status['sample_num']
cost_time = status['cost_time']
logger.info('Total sample number: {}, averge FPS: {}'.format(
sample_num, sample_num / cost_time)) | [
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WikiTeam/wikiteam | 7f1f9985f667c417501f0dc872ab4fe416b6acda | wikiteam/mediawiki.py | python | mwSaveSiteInfo | (config={}) | Save a file with site info | Save a file with site info | [
"Save",
"a",
"file",
"with",
"site",
"info"
] | def mwSaveSiteInfo(config={}):
""" Save a file with site info """
if config['api']:
if os.path.exists('%s/siteinfo.json' % (config['path'])):
sys.stderr.write('siteinfo.json exists, do not overwrite')
else:
sys.stderr.write('Downloading site info as siteinfo.json')
# MediaWiki 1.13+
raw = wikiteam.getURL(url=config['api'], data={
'action': 'query',
'meta': 'siteinfo',
'siprop': 'general|namespaces|statistics|dbrepllag|interwikimap|namespacealiases|specialpagealiases|usergroups|extensions|skins|magicwords|fileextensions|rightsinfo',
'sinumberingroup': 1,
'format': 'json'})
wikiteam.delay(config=config)
# MediaWiki 1.11-1.12
if not 'query' in wikiteam.getJSON(raw):
raw = wikiteam.getURL(url=config['api'], data={
'action': 'query',
'meta': 'siteinfo',
'siprop': 'general|namespaces|statistics|dbrepllag|interwikimap',
'format': 'json'})
# MediaWiki 1.8-1.10
if not 'query' in wikiteam.getJSON(raw):
raw = wikiteam.getURL(url=config['api'], data={
'action': 'query',
'meta': 'siteinfo',
'siprop': 'general|namespaces',
'format': 'json'})
result = wikiteam.getJSON(raw)
wikiteam.delay(config=config)
with open('%s/siteinfo.json' % (config['path']), 'w') as outfile:
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testerSunshine/12306 | a495af88346a0d794493c6030f6a6207debb5824 | inter/GetSuccessRate.py | python | getSuccessRate.data_apr | (self) | return data | secretList 9vqa9%2B%2F%2Fsdozmm22hpSeDTGqRUwSuA2D0r%2BmU%2BLZj7MK7CDuf5Ep1xpxl4Dyxfmoah%2BaB9TZSesU%0AkxBbo5oNgR1vqMfvq66VP0T7tpQtH%2BbVGBz1FolZG8jDD%2FHqnz%2FnvdBP416Og6WGS14O%2F3iBSwT8%0AkRPsNF0Vq0U082g0tlJtP%2BPn7TzW3z7TDCceMJIjFcfEOA%2BW%2BuK%2Bpy6jCQMv0TmlkXf5aKcGnE02%0APuv4I8nF%2BOWjWzv9CrJyiCZiWaXd%2Bi7p69V3a9dhF787UgS660%2BqKRFB4RLwAfic3MkAlfpGWhMY%0ACfARVQ%3D%3D#O
_json_att
候补一次只能补一个座位，默认取TICKET_TYPE第一个
:return: | secretList 9vqa9%2B%2F%2Fsdozmm22hpSeDTGqRUwSuA2D0r%2BmU%2BLZj7MK7CDuf5Ep1xpxl4Dyxfmoah%2BaB9TZSesU%0AkxBbo5oNgR1vqMfvq66VP0T7tpQtH%2BbVGBz1FolZG8jDD%2FHqnz%2FnvdBP416Og6WGS14O%2F3iBSwT8%0AkRPsNF0Vq0U082g0tlJtP%2BPn7TzW3z7TDCceMJIjFcfEOA%2BW%2BuK%2Bpy6jCQMv0TmlkXf5aKcGnE02%0APuv4I8nF%2BOWjWzv9CrJyiCZiWaXd%2Bi7p69V3a9dhF787UgS660%2BqKRFB4RLwAfic3MkAlfpGWhMY%0ACfARVQ%3D%3D#O
_json_att
候补一次只能补一个座位，默认取TICKET_TYPE第一个
:return: | [
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"""
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"""
ticker = TickerConfig.PASSENGER_TICKER_STR.get(TickerConfig.SET_TYPE[0])
data = OrderedDict()
data["successSecret"] = f"{self.secretList}#{ticker}"
data["_json_att"] = ""
return data | [
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angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/misc/bug_report.py | python | print_native_info | () | [] | def print_native_info():
print("######### Native Module Info ##########")
for module, path in native_modules.items():
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print("%s: %s" % (module, str(eval(path))))
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/ctypes/macholib/framework.py | python | framework_info | (filename) | return is_framework.groupdict() | A framework name can take one of the following four forms:
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)
Note that SomeVersion and Suffix are optional and may be None
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is_framework = STRICT_FRAMEWORK_RE.match(filename)
if not is_framework:
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/clients/events/tcpsocket/client.py | python | ClientConnection.receive_data | (self) | return json.loads(json_data, encoding="utf-8") | [] | def receive_data(self):
json_data = self._clientsocket.recv(self._max_buffer).decode()
YLogger.debug(self, "Received: %s", json_data)
return json.loads(json_data, encoding="utf-8") | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/urllib/request.py | python | Request.get_host | (self) | return self.host | [] | def get_host(self):
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git-cola/git-cola | b48b8028e0c3baf47faf7b074b9773737358163d | cola/cmds.py | python | PrepareCommitMessageHook.__init__ | (self, context) | [] | def __init__(self, context):
super(PrepareCommitMessageHook, self).__init__(context)
self.old_commitmsg = self.model.commitmsg | [
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mbj4668/pyang | 97523476e7ada8609d27fd47880e1b5061073dc3 | pyang/statements.py | python | has_type | (typestmt, names) | return None | Return type with name if `type` has name as one of its base types,
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if typestmt.arg in names:
return typestmt
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r = has_type(t, names)
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typedef = getattr(typestmt, 'i_typedef', None)
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lvpengyuan/masktextspotter.caffe2 | da99ef31f5ccb4de5248bb881d5b4a291910c8ae | lib/modeling/detector.py | python | DetectionModelHelper.UpdateWorkspaceLr | (self, cur_iter) | return new_lr | Updates the model's current learning rate and the workspace (learning
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# The workspace is the one source of truth for the lr
# The lr is always the same on all GPUs
cur_lr = workspace.FetchBlob('gpu_0/lr')[0]
new_lr = lr_policy.get_lr_at_iter(cur_iter)
# There are no type conversions between the lr in Python and the lr in
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if cur_lr != new_lr:
ratio = _get_lr_change_ratio(cur_lr, new_lr)
if ratio > cfg.SOLVER.LOG_LR_CHANGE_THRESHOLD:
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format(cur_lr, new_lr, cur_iter))
self._SetNewLr(cur_lr, new_lr)
return new_lr | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/rumps/rumps.py | python | SliderMenuItem.__repr__ | (self) | return '<{0}: [value: {1}; callback: {2}]>'.format(
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annoviko/pyclustering | bf4f51a472622292627ec8c294eb205585e50f52 | pyclustering/utils/__init__.py | python | draw_dynamics_set | (dynamics, xtitle=None, ytitle=None, xlim=None, ylim=None, xlabels=False, ylabels=False) | !
@brief Draws dynamic of each neuron (oscillator) in an oscillatory network.
@param[in] dynamics (list): List of network outputs that are represented by values of output of oscillators (used by y axis).
@param[in] xtitle (string): Title for Y.
@param[in] ytitle (string): Title for X.
@param[in] xlim (double): X limit.
@param[in] ylim (double): Y limit.
@param[in] xlabels (bool): If True - shows X labels.
@param[in] ylabels (bool): If True - shows Y labels. | ! | [
"!"
] | def draw_dynamics_set(dynamics, xtitle=None, ytitle=None, xlim=None, ylim=None, xlabels=False, ylabels=False):
"""!
@brief Draws dynamic of each neuron (oscillator) in an oscillatory network.
@param[in] dynamics (list): List of network outputs that are represented by values of output of oscillators (used by y axis).
@param[in] xtitle (string): Title for Y.
@param[in] ytitle (string): Title for X.
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@param[in] ylim (double): Y limit.
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@param[in] ylabels (bool): If True - shows Y labels.
"""
# Calculate edge for comfortable representation.
number_dynamics = len(dynamics)
if number_dynamics == 1:
figure, _ = draw_dynamics(dynamics[0][0], dynamics[0][1], xtitle, ytitle, xlim, ylim, xlabels, ylabels)
plt.close(figure)
return
number_cols = int(numpy.ceil(number_dynamics ** 0.5))
number_rows = int(numpy.ceil(number_dynamics / number_cols))
real_index = 0, 0
double_indexer = True
if (number_cols == 1) or (number_rows == 1):
real_index = 0
double_indexer = False
figure, axarr = plt.subplots(number_rows, number_cols)
#plt.setp([ax for ax in axarr], visible = False);
for dynamic in dynamics:
axarr[real_index] = draw_dynamics(dynamic[0], dynamic[1], xtitle, ytitle, xlim, ylim, xlabels, ylabels, axis=axarr[real_index])
#plt.setp(axarr[real_index], visible = True);
if double_indexer is True:
real_index = real_index[0], real_index[1] + 1
if real_index[1] >= number_cols:
real_index = real_index[0] + 1, 0
else:
real_index += 1
plt.show()
plt.close(figure) | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/contourcarpet/_colorbar.py | python | ColorBar.lenmode | (self) | return self["lenmode"] | Determines whether this color bar's length (i.e. the measure in
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SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions | 014c4ca27a70b5907a183e942228004c989dcbe4 | number13/number13/src/Mask_RCNN/mrcnn/model.py | python | log2_graph | (x) | return tf.log(x) / tf.log(2.0) | Implementatin of Log2. TF doesn't have a native implemenation. | Implementatin of Log2. TF doesn't have a native implemenation. | [
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"""Implementatin of Log2. TF doesn't have a native implemenation."""
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/idlelib/config_key.py | python | GetKeysDialog.bind_ok | (self, keys) | Return True if Tcl accepts the new keys else show message. | Return True if Tcl accepts the new keys else show message. | [
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except TclError as err:
self.showerror(
title=self.keyerror_title, parent=self,
message=(f'The entered key sequence is not accepted.\n\n'
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return False
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self.unbind(keys, binding)
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getsentry/sentry | 83b1f25aac3e08075e0e2495bc29efaf35aca18a | src/sentry/stacktraces/processing.py | python | _has_system_frames | (frames) | return bool(system_frames) and len(frames) != system_frames | Determines whether there are any frames in the stacktrace with in_app=false. | Determines whether there are any frames in the stacktrace with in_app=false. | [
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conan7882/adversarial-autoencoders | 4960f252784a7dd2fbe203d7dad65938b57ee9c2 | src/models/layers.py | python | drop_out | (layer_dict, is_training, inputs=None, keep_prob=0.5) | return layer_dict['cur_input'] | [] | def drop_out(layer_dict, is_training, inputs=None, keep_prob=0.5):
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Jenyay/outwiker | 50530cf7b3f71480bb075b2829bc0669773b835b | plugins/webpage/webpage/libs/bs4/element.py | python | Tag.__init__ | (self, parser=None, builder=None, name=None, namespace=None,
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self.cdata_list_attributes = cdata_list_attributes
self.preserve_whitespace_tags = preserve_whitespace_tags
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/verify/v2/service/entity/new_factor.py | python | NewFactorInstance.binding | (self) | return self._properties['binding'] | :returns: Unique external identifier of the Entity
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techbliss/Python_editor | 4c2b67bd0b1a2b9b9403f5ae31cf8f13c247c981 | 7.2/Add to ida python folder/sipconfig.py | python | ProgramMakefile.__init__ | (self, configuration, build_file=None, install_dir=None,
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"""
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self._install_dir = install_dir
self._manifest = ("embed_manifest_exe" in self.optional_list("CONFIG"))
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malllabiisc/WordGCN | 8a9418a5326a4abb2b1ae4910a51fc7dd9f1a23d | syngcn.py | python | SynGCN.load_data | (self) | Loads the text corpus and C++ batch creation script
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voc2id: Mapping of word to its unique identifier
id2voc: Inverse of voc2id
id2freq: Mapping of word id to its frequency in the corpus
wrd_list: List of words for which embedding is required
embed_dims: Dimension of the embedding
voc_size: Total number of words in vocabulary
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Loads the text corpus and C++ batch creation script
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voc2id: Mapping of word to its unique identifier
id2voc: Inverse of voc2id
id2freq: Mapping of word id to its frequency in the corpus
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self.logger.info("Loading data")
self.voc2id = read_mappings('./data/voc2id.txt'); self.voc2id = {k: int(v) for k, v in self.voc2id.items()}
self.id2freq = read_mappings('./data/id2freq.txt'); self.id2freq = {int(k): int(v) for k, v in self.id2freq.items()}
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self.lib.init()
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self.edges = np.zeros(self.p.max_dep_len * self.p.batch_size*3, dtype=np.int32)
self.wrds = np.zeros(self.p.max_sent_len * self.p.batch_size, dtype=np.int32)
self.samp = np.zeros(self.p.max_sent_len * self.p.batch_size, dtype=np.int32)
self.negs = np.zeros(self.p.max_sent_len * self.p.num_neg * self.p.batch_size, dtype=np.int32)
self.wlen = np.zeros(self.p.batch_size, dtype=np.int32)
self.elen = np.zeros(self.p.batch_size, dtype=np.int32)
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Symbo1/wsltools | 0b6e536fc85c707a1c81f0296c4e91ca835396a1 | wsltools/utils/faker/providers/address/ko_KR/__init__.py | python | Provider.land_address | (self) | return self.generator.parse(pattern) | :example 세종특별자치시 어진동 507 | :example 세종특별자치시 어진동 507 | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/twisted/spread/pb.py | python | Broker.sendDecRef | (self, objectID) | (internal) Send a DECREF directive.
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plaid/plaid-python | 8c60fca608e426f3ff30da8857775946d29e122c | plaid/model/link_token_create_response.py | python | LinkTokenCreateResponse.openapi_types | () | return {
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'expiration': (datetime,), # noqa: E501
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ring04h/wyportmap | c4201e2313504e780a7f25238eba2a2d3223e739 | sqlalchemy/engine/base.py | python | Transaction.rollback | (self) | Roll back this :class:`.Transaction`. | Roll back this :class:`.Transaction`. | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/apscheduler/schedulers/asyncio.py | python | AsyncIOScheduler.wakeup | (self) | [] | def wakeup(self):
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"_start_timer",
"(",
"wait_seconds",
")"
] | https://github.com/rembo10/headphones/blob/b3199605be1ebc83a7a8feab6b1e99b64014187c/lib/apscheduler/schedulers/asyncio.py#L61-L64 | ||||
larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/_pydecimal.py | python | Decimal.logb | (self, context=None) | return ans._fix(context) | Returns the exponent of the magnitude of self's MSD.
The result is the integer which is the exponent of the magnitude
of the most significant digit of self (as though it were truncated
to a single digit while maintaining the value of that digit and
without limiting the resulting exponent). | Returns the exponent of the magnitude of self's MSD. | [
"Returns",
"the",
"exponent",
"of",
"the",
"magnitude",
"of",
"self",
"s",
"MSD",
"."
] | def logb(self, context=None):
""" Returns the exponent of the magnitude of self's MSD.
The result is the integer which is the exponent of the magnitude
of the most significant digit of self (as though it were truncated
to a single digit while maintaining the value of that digit and
without limiting the resulting exponent).
"""
# logb(NaN) = NaN
ans = self._check_nans(context=context)
if ans:
return ans
if context is None:
context = getcontext()
# logb(+/-Inf) = +Inf
if self._isinfinity():
return _Infinity
# logb(0) = -Inf, DivisionByZero
if not self:
return context._raise_error(DivisionByZero, 'logb(0)', 1)
# otherwise, simply return the adjusted exponent of self, as a
# Decimal. Note that no attempt is made to fit the result
# into the current context.
ans = Decimal(self.adjusted())
return ans._fix(context) | [
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... | https://github.com/larryhastings/gilectomy/blob/4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a/Lib/_pydecimal.py#L3361-L3389 | |
tlsnotary/tlsnotary | be3346ca8e754c7f1d027c33b183f7e799d2b0b0 | src/auditee/tlsnotary-auditee.py | python | commit_session | (tlsn_session,response,sf) | return reply[1][len('sha1hmac_for_MS:'):] | Commit the encrypted server response and other data to auditor | Commit the encrypted server response and other data to auditor | [
"Commit",
"the",
"encrypted",
"server",
"response",
"and",
"other",
"data",
"to",
"auditor"
] | def commit_session(tlsn_session,response,sf):
'''Commit the encrypted server response and other data to auditor'''
commit_dir = join(current_session_dir, 'commit')
if not os.path.exists(commit_dir): os.makedirs(commit_dir)
#Serialization of RC4 'IV' requires concatenating the box,x,y elements of the RC4 state tuple
IV = shared.rc4_state_to_bytearray(tlsn_session.IV_after_finished) \
if tlsn_session.chosen_cipher_suite in [4,5] else tlsn_session.IV_after_finished
stuff_to_be_committed = {'response':response,'IV':IV,
'cs':str(tlsn_session.chosen_cipher_suite),
'md5hmac':tlsn_session.p_auditee,'domain':tlsn_session.server_name}
for k,v in stuff_to_be_committed.iteritems():
with open(join(commit_dir,k+sf),'wb') as f: f.write(v)
commit_hash = sha256(response).digest()
md5hmac_hash = sha256(tlsn_session.p_auditee).digest()
reply = send_and_recv('commit_hash:'+commit_hash+md5hmac_hash)
if reply[0] != 'success':
raise Exception ('Failed to receive a reply')
if not reply[1].startswith('sha1hmac_for_MS:'):
raise Exception ('bad reply. Expected sha1hmac_for_MS')
return reply[1][len('sha1hmac_for_MS:'):] | [
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inasafe/inasafe | 355eb2ce63f516b9c26af0c86a24f99e53f63f87 | safe/gui/tools/extent_selector_dialog.py | python | ExtentSelectorDialog._populate_bookmarks_list | (self) | Read the sqlite database and populate the bookmarks list.
If no bookmarks are found, the bookmarks radio button will be disabled
and the label will be shown indicating that the user should add
bookmarks in QGIS first.
Every bookmark are reprojected to mapcanvas crs. | Read the sqlite database and populate the bookmarks list. | [
"Read",
"the",
"sqlite",
"database",
"and",
"populate",
"the",
"bookmarks",
"list",
"."
] | def _populate_bookmarks_list(self):
"""Read the sqlite database and populate the bookmarks list.
If no bookmarks are found, the bookmarks radio button will be disabled
and the label will be shown indicating that the user should add
bookmarks in QGIS first.
Every bookmark are reprojected to mapcanvas crs.
"""
# Connect to the QGIS sqlite database and check if the table exists.
# noinspection PyArgumentList
db_file_path = QgsApplication.qgisUserDatabaseFilePath()
db = sqlite3.connect(db_file_path)
cursor = db.cursor()
cursor.execute(
'SELECT COUNT(*) '
'FROM sqlite_master '
'WHERE type=\'table\' '
'AND name=\'tbl_bookmarks\';')
number_of_rows = cursor.fetchone()[0]
if number_of_rows > 0:
cursor.execute(
'SELECT * '
'FROM tbl_bookmarks;')
bookmarks = cursor.fetchall()
canvas_crs = self.canvas.mapSettings().destinationCrs()
for bookmark in bookmarks:
name = bookmark[1]
srid = bookmark[7]
rectangle = QgsRectangle(
bookmark[3], bookmark[4], bookmark[5], bookmark[6])
if srid != canvas_crs.srsid():
transform = QgsCoordinateTransform(
QgsCoordinateReferenceSystem(srid),
canvas_crs,
QgsProject.instance()
)
try:
rectangle = transform.transform(rectangle)
except QgsCsException:
rectangle = QgsRectangle()
if rectangle.isEmpty():
pass
self.bookmarks_list.addItem(name, rectangle)
if self.bookmarks_list.currentIndex() >= 0:
self.create_bookmarks_label.hide()
else:
self.create_bookmarks_label.show()
self.hazard_exposure_bookmark.setDisabled(True)
self.bookmarks_list.hide() | [
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"conne... | https://github.com/inasafe/inasafe/blob/355eb2ce63f516b9c26af0c86a24f99e53f63f87/safe/gui/tools/extent_selector_dialog.py#L365-L420 | ||
maas/maas | db2f89970c640758a51247c59bf1ec6f60cf4ab5 | src/maasserver/models/interface.py | python | Interface.update_neighbour | (self, ip, mac, time, vid=None) | return neighbour | Updates the neighbour table for this interface. | Updates the neighbour table for this interface. | [
"Updates",
"the",
"neighbour",
"table",
"for",
"this",
"interface",
"."
] | def update_neighbour(self, ip, mac, time, vid=None):
"""Updates the neighbour table for this interface."""
from maasserver.models.neighbour import Neighbour
deleted = Neighbour.objects.delete_and_log_obsolete_neighbours(
ip, mac, interface=self, vid=vid
)
neighbour, created = Neighbour.objects.get_or_create(
defaults={"time": time},
ip=ip,
mac_address=mac,
vid=vid,
interface=self,
)
if created:
# If we deleted a previous neighbour, then we have already
# generated a log statement about this neighbour.
if not deleted:
maaslog.info(
f"{self.get_log_string()}: "
"New MAC, IP binding "
f"observed{Neighbour.objects.get_vid_log_snippet(vid)}: "
f"{mac}, {ip}"
)
else:
neighbour.time = time
neighbour.count += 1
neighbour.save(update_fields=["time", "count", "updated"])
return neighbour | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/tione/v20191022/models.py | python | DescribeTrainingJobRequest.__init__ | (self) | r"""
:param TrainingJobName: 训练任务名称
:type TrainingJobName: str | r"""
:param TrainingJobName: 训练任务名称
:type TrainingJobName: str | [
"r",
":",
"param",
"TrainingJobName",
":",
"训练任务名称",
":",
"type",
"TrainingJobName",
":",
"str"
] | def __init__(self):
r"""
:param TrainingJobName: 训练任务名称
:type TrainingJobName: str
"""
self.TrainingJobName = None | [
"def",
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] | https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/tione/v20191022/models.py#L1316-L1321 | ||
wbond/package_control | cfaaeb57612023e3679ecb7f8cd7ceac9f57990d | package_control/providers/gitlab_repository_provider.py | python | GitLabRepositoryProvider.get_broken_dependencies | (self) | return {}.items() | For API-compatibility with RepositoryProvider | For API-compatibility with RepositoryProvider | [
"For",
"API",
"-",
"compatibility",
"with",
"RepositoryProvider"
] | def get_broken_dependencies(self):
"""
For API-compatibility with RepositoryProvider
"""
return {}.items() | [
"def",
"get_broken_dependencies",
"(",
"self",
")",
":",
"return",
"{",
"}",
".",
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] | https://github.com/wbond/package_control/blob/cfaaeb57612023e3679ecb7f8cd7ceac9f57990d/package_control/providers/gitlab_repository_provider.py#L79-L84 |
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