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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/tkinter/__init__.py | python | Text.tag_nextrange | (self, tagName, index1, index2=None) | return self.tk.splitlist(self.tk.call(
self._w, 'tag', 'nextrange', tagName, index1, index2)) | Return a list of start and end index for the first sequence of
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conan7882/GoogLeNet-Inception | 2a015d1e0f378fb3b5ba8509586b4e2cebb84156 | src/models/layers.py | python | batch_norm | (x, train=True, name='bn') | return tf.contrib.layers.batch_norm(
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proofit404/dependencies | 204e0cfadca801d64857f24aa4c74e7939ed9af0 | src/_dependencies/trace.py | python | _Trace.__str__ | (self) | return self.error + ":\n\n" + "\n".join(map(indentation, self.stack())) | [] | def __str__(self):
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pjlantz/Hale | 5c4c96f18f9a7ed0362e115007813c0b56dc3853 | src/modules/httpModule.py | python | HTTPClientFactory.clientConnectionLost | (self, connector, reason) | Called on lost connection to server | Called on lost connection to server | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/setuptools/pkg_resources/__init__.py | python | WorkingSet.run_script | (self, requires, script_name) | Locate distribution for `requires` and run `script_name` script | Locate distribution for `requires` and run `script_name` script | [
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name = ns['__name__']
ns.clear()
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francisck/DanderSpritz_docs | 86bb7caca5a957147f120b18bb5c31f299914904 | Python/Core/Lib/idlelib/ScriptBinding.py | python | ScriptBinding.getfilename | (self) | return filename | Get source filename. If not saved, offer to save (or create) file
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/process_group_dto.py | python | ProcessGroupDTO.__eq__ | (self, other) | return self.__dict__ == other.__dict__ | Returns true if both objects are equal | Returns true if both objects are equal | [
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Alexey-T/CudaText | 6a8b9a974c5d5029c6c273bde83198c83b3a5fb9 | app/py/sys/requests/utils.py | python | is_ipv4_address | (string_ip) | return True | :rtype: bool | :rtype: bool | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/zfs.py | python | filesystem_absent | (name, force=False, recursive=False) | return ret | ensure filesystem is absent on the system
name : string
name of filesystem
force : boolean
try harder to destroy the dataset (zfs destroy -f)
recursive : boolean
also destroy all the child datasets (zfs destroy -r)
.. warning::
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if not __utils__["zfs.is_dataset"](name):
ret = {
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dabeaz/ply | 559a7a1feabb0853be79d4f4dd9e9e9e5ea0d08c | example/ansic/cparse.py | python | p_abstract_declarator_opt_2 | (t) | abstract_declarator_opt : abstract_declarator | abstract_declarator_opt : abstract_declarator | [
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fabioz/PyDev.Debugger | 0f8c02a010fe5690405da1dd30ed72326191ce63 | pydevd.py | python | PyDB._exec | (self, is_module, entry_point_fn, module_name, file, globals, locals) | return globals | This function should have frames tracked by unhandled exceptions (the `_exec` name is important). | This function should have frames tracked by unhandled exceptions (the `_exec` name is important). | [
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Qirky/FoxDot | 76318f9630bede48ff3994146ed644affa27bfa4 | FoxDot/lib/Code/main_lib.py | python | FoxDotCode.__call__ | (self, code, verbose=True, verbose_error=None) | return response | Takes a string of FoxDot code and executes as Python | Takes a string of FoxDot code and executes as Python | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/calculus/util.py | python | AccumulationBounds.__ge__ | (self, other) | return super(AccumulationBounds, self).__ge__(other) | Returns True if range of values attained by `self` AccumulationBounds
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Examples
========
>>> from sympy import AccumBounds, oo
>>> AccumBounds(1, 3) >= AccumBounds(4, oo)
False
>>> AccumBounds(1, 4) >= AccumBounds(3, 4)
AccumBounds(1, 4) >= AccumBounds(3, 4)
>>> AccumBounds(1, oo) >= 1
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implus/PytorchInsight | 2864528f8b83f52c3df76f7c3804aa468b91e5cf | detection/mmdet/datasets/coco.py | python | CocoDataset._parse_ann_info | (self, ann_info, with_mask=True) | return ann | Parse bbox and mask annotation.
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ann_info (list[dict]): Annotation info of an image.
with_mask (bool): Whether to parse mask annotations.
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gt_bboxes_ignore = []
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# 1. mask: a binary map of the same size of the image.
# 2. polys: each mask consists of one or several polys, each poly is a
# list of float.
if with_mask:
gt_masks = []
gt_mask_polys = []
gt_poly_lens = []
for i, ann in enumerate(ann_info):
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w - 1, y1 + h - 1]
if ann['iscrowd']:
gt_bboxes_ignore.append(bbox)
else:
gt_bboxes.append(bbox)
gt_labels.append(self.cat2label[ann['category_id']])
if with_mask:
gt_masks.append(self.coco.annToMask(ann))
mask_polys = [
p for p in ann['segmentation'] if len(p) >= 6
] # valid polygons have >= 3 points (6 coordinates)
poly_lens = [len(p) for p in mask_polys]
gt_mask_polys.append(mask_polys)
gt_poly_lens.extend(poly_lens)
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
else:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
gt_labels = np.array([], dtype=np.int64)
if gt_bboxes_ignore:
gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
else:
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
ann = dict(
bboxes=gt_bboxes, labels=gt_labels, bboxes_ignore=gt_bboxes_ignore)
if with_mask:
ann['masks'] = gt_masks
# poly format is not used in the current implementation
ann['mask_polys'] = gt_mask_polys
ann['poly_lens'] = gt_poly_lens
return ann | [
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facebookresearch/hgnn | 2a22fdb479996c2318f85ad36d2750a383011773 | task/BaseTask.py | python | BaseTask.__init__ | (self, args, logger, criterion='max') | criterion: min/max | criterion: min/max | [
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criterion: min/max
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self.logger = logger
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CellProfiler/CellProfiler | a90e17e4d258c6f3900238be0f828e0b4bd1b293 | cellprofiler/gui/pipelinelistview.py | python | PipelineListCtrl.update_drop_insert_point | (self, index) | Change the visual drop indication
index - the first dropped module would end up here. | Change the visual drop indication | [
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] | def update_drop_insert_point(self, index):
"""Change the visual drop indication
index - the first dropped module would end up here.
"""
self.drop_insert_point = index
self.Refresh(eraseBackground=False) | [
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thangvubk/Cascade-RPN | c832973b8d849acbe4c0ebf75f353c7f48ec457b | mmdet/core/anchor/anchor_target.py | python | anchor_target | (anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
target_means,
target_stds,
cfg,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
sampling=True,
unmap_outputs=True) | return (labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, rois_list, num_total_pos, num_total_neg) | Compute regression and classification targets for anchors.
Args:
anchor_list (list[list]): Multi level anchors of each image.
valid_flag_list (list[list]): Multi level valid flags of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
target_means (Iterable): Mean value of regression targets.
target_stds (Iterable): Std value of regression targets.
cfg (dict): RPN train configs.
Returns:
tuple | Compute regression and classification targets for anchors. | [
"Compute",
"regression",
"and",
"classification",
"targets",
"for",
"anchors",
"."
] | def anchor_target(anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
target_means,
target_stds,
cfg,
gt_bboxes_ignore_list=None,
gt_labels_list=None,
label_channels=1,
sampling=True,
unmap_outputs=True):
"""Compute regression and classification targets for anchors.
Args:
anchor_list (list[list]): Multi level anchors of each image.
valid_flag_list (list[list]): Multi level valid flags of each image.
gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
img_metas (list[dict]): Meta info of each image.
target_means (Iterable): Mean value of regression targets.
target_stds (Iterable): Std value of regression targets.
cfg (dict): RPN train configs.
Returns:
tuple
"""
num_imgs = len(img_metas)
assert len(anchor_list) == len(valid_flag_list) == num_imgs
# anchor number of multi levels
num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
# concat all level anchors and flags to a single tensor
_anchor_list = []
_valid_flag_list = []
for i in range(num_imgs):
assert len(anchor_list[i]) == len(valid_flag_list[i])
_anchor_list.append(torch.cat(anchor_list[i]))
_valid_flag_list.append(torch.cat(valid_flag_list[i]))
# compute targets for each image
if gt_bboxes_ignore_list is None:
gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
if gt_labels_list is None:
gt_labels_list = [None for _ in range(num_imgs)]
(all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
pos_inds_list, neg_inds_list) = multi_apply(
anchor_target_single,
_anchor_list,
_valid_flag_list,
gt_bboxes_list,
gt_bboxes_ignore_list,
gt_labels_list,
img_metas,
target_means=target_means,
target_stds=target_stds,
cfg=cfg,
label_channels=label_channels,
sampling=sampling,
unmap_outputs=unmap_outputs)
# no valid anchors
if any([labels is None for labels in all_labels]):
return None
# sampled anchors of all images
num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
# split targets to a list w.r.t. multiple levels
labels_list = images_to_levels(all_labels, num_level_anchors)
label_weights_list = images_to_levels(all_label_weights, num_level_anchors)
bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors)
bbox_weights_list = images_to_levels(all_bbox_weights, num_level_anchors)
rois_list = images_to_levels(_anchor_list, num_level_anchors)
return (labels_list, label_weights_list, bbox_targets_list,
bbox_weights_list, rois_list, num_total_pos, num_total_neg) | [
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/object_detection/core/box_list_ops.py | python | to_normalized_coordinates | (boxlist, height, width,
check_range=True, scope=None) | Converts absolute box coordinates to normalized coordinates in [0, 1].
Usually one uses the dynamic shape of the image or conv-layer tensor:
boxlist = box_list_ops.to_normalized_coordinates(boxlist,
tf.shape(images)[1],
tf.shape(images)[2]),
This function raises an assertion failed error at graph execution time when
the maximum coordinate is smaller than 1.01 (which means that coordinates are
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Args:
boxlist: BoxList with coordinates in terms of pixel-locations.
height: Maximum value for height of absolute box coordinates.
width: Maximum value for width of absolute box coordinates.
check_range: If True, checks if the coordinates are normalized or not.
scope: name scope.
Returns:
boxlist with normalized coordinates in [0, 1]. | Converts absolute box coordinates to normalized coordinates in [0, 1]. | [
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check_range=True, scope=None):
"""Converts absolute box coordinates to normalized coordinates in [0, 1].
Usually one uses the dynamic shape of the image or conv-layer tensor:
boxlist = box_list_ops.to_normalized_coordinates(boxlist,
tf.shape(images)[1],
tf.shape(images)[2]),
This function raises an assertion failed error at graph execution time when
the maximum coordinate is smaller than 1.01 (which means that coordinates are
already normalized). The value 1.01 is to deal with small rounding errors.
Args:
boxlist: BoxList with coordinates in terms of pixel-locations.
height: Maximum value for height of absolute box coordinates.
width: Maximum value for width of absolute box coordinates.
check_range: If True, checks if the coordinates are normalized or not.
scope: name scope.
Returns:
boxlist with normalized coordinates in [0, 1].
"""
with tf.name_scope(scope, 'ToNormalizedCoordinates'):
height = tf.cast(height, tf.float32)
width = tf.cast(width, tf.float32)
if check_range:
max_val = tf.reduce_max(boxlist.get())
max_assert = tf.Assert(tf.greater(max_val, 1.01),
['max value is lower than 1.01: ', max_val])
with tf.control_dependencies([max_assert]):
width = tf.identity(width)
return scale(boxlist, 1 / height, 1 / width) | [
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aws/aws-sam-cli | 2aa7bf01b2e0b0864ef63b1898a8b30577443acc | samcli/commands/local/cli_common/invoke_context.py | python | InvokeContext._get_debug_context | (
debug_ports: Optional[Tuple[int]],
debug_args: Optional[str],
debugger_path: Optional[str],
container_env_vars: Optional[Dict[str, str]],
debug_function: Optional[str] = None,
) | return DebugContext(
debug_ports=debug_ports,
debug_args=debug_args,
debugger_path=debugger_path,
debug_function=debug_function,
container_env_vars=container_env_vars,
) | Creates a DebugContext if the InvokeContext is in a debugging mode
Parameters
----------
debug_ports tuple(int)
Ports to bind the debugger to
debug_args str
Additional arguments passed to the debugger
debugger_path str
Path to the directory of the debugger to mount on Docker
container_env_vars dict
Dictionary containing debugging based environmental variables.
debug_function str
The Lambda function logicalId that will have the debugging options enabled in case of warm containers
option is enabled
Returns
-------
samcli.commands.local.lib.debug_context.DebugContext
Object representing the DebugContext
Raises
------
samcli.commands.local.cli_common.user_exceptions.DebugContext
When the debugger_path is not valid | Creates a DebugContext if the InvokeContext is in a debugging mode | [
"Creates",
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"DebugContext",
"if",
"the",
"InvokeContext",
"is",
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] | def _get_debug_context(
debug_ports: Optional[Tuple[int]],
debug_args: Optional[str],
debugger_path: Optional[str],
container_env_vars: Optional[Dict[str, str]],
debug_function: Optional[str] = None,
) -> DebugContext:
"""
Creates a DebugContext if the InvokeContext is in a debugging mode
Parameters
----------
debug_ports tuple(int)
Ports to bind the debugger to
debug_args str
Additional arguments passed to the debugger
debugger_path str
Path to the directory of the debugger to mount on Docker
container_env_vars dict
Dictionary containing debugging based environmental variables.
debug_function str
The Lambda function logicalId that will have the debugging options enabled in case of warm containers
option is enabled
Returns
-------
samcli.commands.local.lib.debug_context.DebugContext
Object representing the DebugContext
Raises
------
samcli.commands.local.cli_common.user_exceptions.DebugContext
When the debugger_path is not valid
"""
if debug_ports and debugger_path:
try:
debugger = Path(debugger_path).resolve(strict=True)
except OSError as error:
if error.errno == errno.ENOENT:
raise DebugContextException("'{}' could not be found.".format(debugger_path)) from error
raise error
if not debugger.is_dir():
raise DebugContextException("'{}' should be a directory with the debugger in it.".format(debugger_path))
debugger_path = str(debugger)
return DebugContext(
debug_ports=debug_ports,
debug_args=debug_args,
debugger_path=debugger_path,
debug_function=debug_function,
container_env_vars=container_env_vars,
) | [
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qutip/qutip | 52d01da181a21b810c3407812c670f35fdc647e8 | qutip/qobj.py | python | shape | (inpt) | Returns the shape attribute of a quantum object.
Parameters
----------
inpt : :class:`qutip.Qobj`
Input quantum object.
Returns
-------
shape : list
A ``list`` of the quantum objects shape.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.shape`
attribute is recommended. | Returns the shape attribute of a quantum object. | [
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] | def shape(inpt):
"""Returns the shape attribute of a quantum object.
Parameters
----------
inpt : :class:`qutip.Qobj`
Input quantum object.
Returns
-------
shape : list
A ``list`` of the quantum objects shape.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.shape`
attribute is recommended.
"""
if isinstance(inpt, Qobj):
return Qobj.shape
else:
return np.shape(inpt) | [
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Srinivas11789/PcapXray | dc2bd65e94b2031b9203ed68e549110f46f9cd9c | Source/Module/legacy/plotLanNetwork.py | python | plotLan.apply_custom_style | (self, graph, color) | return graph | [] | def apply_custom_style(self, graph, color):
style = {'edges': {
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'arrowhead': 'open',
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'fontsize': '12',
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graph.edge_attr.update(
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/pricing/v1/phone_number/country.py | python | CountryContext.__init__ | (self, version, iso_country) | Initialize the CountryContext
:param Version version: Version that contains the resource
:param iso_country: The ISO country code
:returns: twilio.rest.pricing.v1.phone_number.country.CountryContext
:rtype: twilio.rest.pricing.v1.phone_number.country.CountryContext | Initialize the CountryContext | [
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"""
Initialize the CountryContext
:param Version version: Version that contains the resource
:param iso_country: The ISO country code
:returns: twilio.rest.pricing.v1.phone_number.country.CountryContext
:rtype: twilio.rest.pricing.v1.phone_number.country.CountryContext
"""
super(CountryContext, self).__init__(version)
# Path Solution
self._solution = {'iso_country': iso_country, }
self._uri = '/PhoneNumbers/Countries/{iso_country}'.format(**self._solution) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/library/oc_label.py | python | Yedit.load | (self, content_type='yaml') | return self.yaml_dict | return yaml file | return yaml file | [
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''' return yaml file '''
contents = self.read()
if not contents and not self.content:
return None
if self.content:
if isinstance(self.content, dict):
self.yaml_dict = self.content
return self.yaml_dict
elif isinstance(self.content, str):
contents = self.content
# check if it is yaml
try:
if content_type == 'yaml' and contents:
# Try to set format attributes if supported
try:
self.yaml_dict.fa.set_block_style()
except AttributeError:
pass
# Try to use RoundTripLoader if supported.
try:
self.yaml_dict = yaml.load(contents, yaml.RoundTripLoader)
except AttributeError:
self.yaml_dict = yaml.safe_load(contents)
# Try to set format attributes if supported
try:
self.yaml_dict.fa.set_block_style()
except AttributeError:
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self.yaml_dict = json.loads(contents)
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raise YeditException('Problem with loading yaml file. {}'.format(err))
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/werkzeug/_internal.py | python | _get_environ | (obj) | return env | [] | def _get_environ(obj):
env = getattr(obj, 'environ', obj)
assert isinstance(env, dict), \
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return env | [
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conan-io/conan | 28ec09f6cbf1d7e27ec27393fd7bbc74891e74a8 | conans/client/build/compiler_flags.py | python | adjust_path | (path, settings, win_bash=False, subsystem=None) | return '"%s"' % path if ' ' in path else path | adjusts path to be safely passed to the compiler command line
for Windows bash, ensures path is in format according to the subsystem
for path with spaces, places double quotes around it
converts slashes to backslashes, or vice versa | adjusts path to be safely passed to the compiler command line
for Windows bash, ensures path is in format according to the subsystem
for path with spaces, places double quotes around it
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adjusts path to be safely passed to the compiler command line
for Windows bash, ensures path is in format according to the subsystem
for path with spaces, places double quotes around it
converts slashes to backslashes, or vice versa
"""
compiler = _base_compiler(settings)
if str(compiler) == 'Visual Studio':
path = path.replace('/', '\\')
else:
path = path.replace('\\', '/')
if win_bash:
path = unix_path(path, subsystem)
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heronsystems/adeptRL | d8554d134c1dfee6659baafd886684351c1dd982 | examples/custom_network_stub.py | python | MyCustomNetwork.new_internals | (self, device) | Define any initial hidden states here, move them to device if necessary.
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wuub/SublimeREPL | d17e8649c0d0008a364158d671ac0c7d33d0c896 | repls/sublimepython_repl.py | python | SublimePythonRepl.__init__ | (self, encoding) | [] | def __init__(self, encoding):
super(SublimePythonRepl, self).__init__(encoding, "python", "\n", False)
self._console = InterceptingConsole(encoding)
self._thread = threading.Thread(target=self._console.run)
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urwid/urwid | e2423b5069f51d318ea1ac0f355a0efe5448f7eb | urwid/decoration.py | python | BoxAdapter.mouse_event | (self, size, event, button, col, row, focus) | return self._original_widget.mouse_event((maxcol, self.height),
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benoitc/couchdbkit | 6be148640c00b54ee87a2f2d502e9d67fa5b45a8 | couchdbkit/consumer/ceventlet.py | python | ChangeConsumer._run | (self) | [] | def _run(self):
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | whois_lambda/requests/sessions.py | python | Session.put | (self, url, data=None, **kwargs) | return self.request('PUT', url, data=data, **kwargs) | Sends a PUT request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
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"""Sends a PUT request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
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return self.request('PUT', url, data=data, **kwargs) | [
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accel-brain/accel-brain-code | 86f489dc9be001a3bae6d053f48d6b57c0bedb95 | Accel-Brain-Base/accelbrainbase/controllablemodel/_mxnet/gan_controller.py | python | GANController.collect_params | (self, select=None) | return params_dict | Overrided `collect_params` in `mxnet.gluon.HybridBlok`. | Overrided `collect_params` in `mxnet.gluon.HybridBlok`. | [
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'''
Overrided `collect_params` in `mxnet.gluon.HybridBlok`.
'''
params_dict = self.__generative_model.collect_params(select)
params_dict.update(self.__discriminative_model.collect_params(select))
return params_dict | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/CPython/27/Lib/decimal.py | python | Decimal._round_down | (self, prec) | Also known as round-towards-0, truncate. | Also known as round-towards-0, truncate. | [
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"""Also known as round-towards-0, truncate."""
if _all_zeros(self._int, prec):
return 0
else:
return -1 | [
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wikimedia/pywikibot | 81a01ffaec7271bf5b4b170f85a80388420a4e78 | pywikibot/site/_basesite.py | python | BaseSite.category_on_one_line | (self) | return self.code in self.family.category_on_one_line | Return True if this site wants all category links on one line. | Return True if this site wants all category links on one line. | [
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] | def category_on_one_line(self):
# TODO: is this even needed? No family in the framework uses it.
"""Return True if this site wants all category links on one line."""
return self.code in self.family.category_on_one_line | [
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yt-project/yt | dc7b24f9b266703db4c843e329c6c8644d47b824 | yt/visualization/volume_rendering/image_handling.py | python | import_rgba | (name, h5=True) | return np.array([r, g, b, a]).swapaxes(0, 2).swapaxes(0, 1) | This function will read back in an HDF5 file, as saved by export_rgba, and
return the frames to the user. *name* is the name of the file to be read
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f.close()
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mylog.error("No support for fits import.")
return np.array([r, g, b, a]).swapaxes(0, 2).swapaxes(0, 1) | [
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fonttools/fonttools | 892322aaff6a89bea5927379ec06bc0da3dfb7df | Lib/fontTools/subset/__init__.py | python | neuter_lookups | (self, lookup_indices) | Sets lookups not in lookup_indices to None. | Sets lookups not in lookup_indices to None. | [
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"""Sets lookups not in lookup_indices to None."""
if self.table.LookupList:
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vibora-io/vibora | 4cda888f89aec6bfb2541ee53548ae1bf50fbf1b | vibora/client/session.py | python | HTTPEngine.get_pool | (self, protocol: str, host: str, port: int) | return self.pools[key] | :param protocol:
:param host:
:param port:
:return: | [] | def get_pool(self, protocol: str, host: str, port: int) -> ConnectionPool:
"""
:param protocol:
:param host:
:param port:
:return:
"""
key = (protocol, host, port)
if port in (0, None):
if protocol == 'https':
port = 443
else:
port = 80
try:
return self.pools[key]
except KeyError:
self.pools[key] = ConnectionPool(loop=self.loop, host=host, port=port, protocol=protocol,
keep_alive=self.session.keep_alive)
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joe42/CloudFusion | c4b94124e74a81e0634578c7754d62160081f7a1 | cloudfusion/store/chunk_caching_store.py | python | ChunkMultiprocessingCachingStore.__init__ | (self, store, cache_expiration_time=60, cache_size_in_mb=2000, cache_id=None, max_archive_size_in_mb = 4, cache_dir='/tmp/cloudfusion') | :param store: the store whose access should be cached
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:param cache_dir: Cache directory on local hard drive disk, default value is */tmp/cloudfusion*. | [
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"""
:param store: the store whose access should be cached
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:param cache_dir: Cache directory on local hard drive disk, default value is */tmp/cloudfusion*."""
#prevent simultaneous access to store (synchronous use of __deepcopy__ by _store SyncThread and a different method):
self.store = SynchronizeProxy(store, private_methods_to_synchronize=['__deepcopy__'])
self.max_archive_size_in_mb = max_archive_size_in_mb
if cache_id == None:
cache_id = str(random.random())
self.logger = logging.getLogger(self.get_logging_handler())
self.logger.debug("creating ChunkTransparentMultiprocessingCachingStore object")
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self.logger.warning("Be aware of the synchronization issue https://github.com/joe42/CloudFusion/issues/16 \
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self.cache_expiration_time = cache_expiration_time
self.time_of_last_flush = time.time()
self.cache_dir = cache_dir[:-1] if cache_dir[-1:] == '/' else cache_dir # remove slash at the end
temp_dir = self.cache_dir+"/cachingstore_"+cache_id
cache = PersistentLRUCache(temp_dir, cache_expiration_time, cache_size_in_mb)
cache.set_resize_intervall(10)
self.entries = SynchronizeProxy( cache ) #[shares_resource: write self.entries]
self.sync_thread = ChunkStoreSyncThread(self.entries, self.store, temp_dir, self.logger)
self.sync_thread.start() | [
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elfi-dev/elfi | 07ac0ed5e81d5d5fb42de63db3cf9ccc9135b88c | elfi/methods/inference/parameter_inference.py | python | ParameterInference.prepare_new_batch | (self, batch_index) | Prepare values for a new batch.
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next batch_index to be submitted
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"""Prepare values for a new batch.
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do do preparations, e.g. in Bayesian optimization algorithm, the next acquisition
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kapicorp/kapitan | ad925478023f8b7a0ccc99762b77babf1064a8df | kapitan/resources.py | python | read_file | (search_paths, name) | return content of file in name | return content of file in name | [
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"""return content of file in name"""
for path in search_paths:
full_path = os.path.join(path, name)
logger.debug("read_file trying file %s", full_path)
if os.path.exists(full_path):
logger.debug("read_file found file at %s", full_path)
with io.open(full_path, newline="") as f:
return f.read()
raise IOError("Could not find file {}".format(name)) | [
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"... | https://github.com/kapicorp/kapitan/blob/ad925478023f8b7a0ccc99762b77babf1064a8df/kapitan/resources.py#L159-L169 | ||
theatlantic/django-nested-admin | 2554f51f6d05489dd2d81b24c60b59781fda4e8a | nested_admin/formsets.py | python | NestedInlineFormSetMixin.process_forms_pre_save | (self, forms) | return forms | Sort by the sortable_field_name of the formset, if it has been set,
and re-index the form positions (to account for gaps caused by blank
or deleted forms)
Allows customizable sorting and modification of self.forms before
they're iterated through in save().
Returns list of forms. | Sort by the sortable_field_name of the formset, if it has been set,
and re-index the form positions (to account for gaps caused by blank
or deleted forms) | [
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] | def process_forms_pre_save(self, forms):
"""
Sort by the sortable_field_name of the formset, if it has been set,
and re-index the form positions (to account for gaps caused by blank
or deleted forms)
Allows customizable sorting and modification of self.forms before
they're iterated through in save().
Returns list of forms.
"""
sort_field = getattr(self, 'sortable_field_name', None)
def get_position(form):
return getattr(form, 'cleaned_data', {sort_field: 0}).get(sort_field, 0)
if sort_field is not None:
forms.sort(key=get_position)
i = 0
for form in forms:
if self._should_delete_form(form):
# Skip deleted forms
continue
original_position = form.data.get(form.add_prefix(sort_field))
if ("%s" % i) != original_position:
# If this is an unchanged extra form, continue because
# this form will be skipped when saving
if not form.changed_data and not form._is_initial:
continue
# Set the sort field on the instance and in the form data
setattr(form.instance, sort_field, i)
with mutable_querydict(form.data):
form.data[form.add_prefix(sort_field)] = six.text_type(i)
# Force recalculation of changed_data
form.__dict__.pop('changed_data', None)
i += 1
return forms | [
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santhoshkolloju/Abstractive-Summarization-With-Transfer-Learning | 97ff2ae3ba9f2d478e174444c4e0f5349f28c319 | texar_repo/texar/modules/memory/memory_network.py | python | MemNetBase.get_default_embed_fn | (self, memory_size, embed_fn_hparams) | return _embed_fn, memory_dim | Creates a default embedding function. Can be used for A, C, or B
operation.
For B operation (i.e., query_embed_fn), :attr:`memory_size` must be 1.
The function is a combination of both memory embedding and temporal
embedding, with the combination method specified by "combine_mode" in
the `embed_fn_hparams`.
.. role:: python(code)
:language: python
Args:
embed_fn_hparams (dict or HParams): Hyperparameter of the
embedding function. See
:func:`~texar.modules.default_memnet_embed_fn` for details.
Returns:
A tuple `(embed_fn, memory_dim)`, where
- **`memory_dim`** is the dimension of memory entry embedding, \
inferred from :attr:`embed_fn_hparams`.
- If `combine_mode` == 'add', `memory_dim` is the \
embedder dimension.
- If `combine_mode` == 'concat', `memory_dim` is the sum \
of the memory embedder dimension and the temporal embedder \
dimension.
- **`embed_fn`** is an embedding function that takes in memory \
and returns memory embedding. \
Specifically, the function has signature \
:python:`memory_embedding= embed_fn(memory=None, soft_memory=None)`\
where one of `memory` and `soft_memory` is provided (but not both).
Args:
memory: An `int` Tensor of shape
`[batch_size, memory_size]`
containing memory indexes used for embedding lookup.
soft_memory: A Tensor of shape
`[batch_size, memory_size, raw_memory_dim]`
containing soft weights used to mix the embedding vectors.
Returns:
A Tensor of shape `[batch_size, memory_size, memory_dim]`
containing the memory entry embeddings. | Creates a default embedding function. Can be used for A, C, or B
operation. | [
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] | def get_default_embed_fn(self, memory_size, embed_fn_hparams):
"""Creates a default embedding function. Can be used for A, C, or B
operation.
For B operation (i.e., query_embed_fn), :attr:`memory_size` must be 1.
The function is a combination of both memory embedding and temporal
embedding, with the combination method specified by "combine_mode" in
the `embed_fn_hparams`.
.. role:: python(code)
:language: python
Args:
embed_fn_hparams (dict or HParams): Hyperparameter of the
embedding function. See
:func:`~texar.modules.default_memnet_embed_fn` for details.
Returns:
A tuple `(embed_fn, memory_dim)`, where
- **`memory_dim`** is the dimension of memory entry embedding, \
inferred from :attr:`embed_fn_hparams`.
- If `combine_mode` == 'add', `memory_dim` is the \
embedder dimension.
- If `combine_mode` == 'concat', `memory_dim` is the sum \
of the memory embedder dimension and the temporal embedder \
dimension.
- **`embed_fn`** is an embedding function that takes in memory \
and returns memory embedding. \
Specifically, the function has signature \
:python:`memory_embedding= embed_fn(memory=None, soft_memory=None)`\
where one of `memory` and `soft_memory` is provided (but not both).
Args:
memory: An `int` Tensor of shape
`[batch_size, memory_size]`
containing memory indexes used for embedding lookup.
soft_memory: A Tensor of shape
`[batch_size, memory_size, raw_memory_dim]`
containing soft weights used to mix the embedding vectors.
Returns:
A Tensor of shape `[batch_size, memory_size, memory_dim]`
containing the memory entry embeddings.
"""
# memory embedder
embedder = WordEmbedder(
vocab_size=self._raw_memory_dim,
hparams=embed_fn_hparams["embedding"]
)
# temporal embedder
temporal_embedder = PositionEmbedder(
position_size=memory_size,
hparams=embed_fn_hparams["temporal_embedding"]
)
combine = embed_fn_hparams['combine_mode']
if combine == 'add':
if embedder.dim != temporal_embedder.dim:
raise ValueError('`embedding` and `temporal_embedding` must '
'have the same dimension for "add" '
'combination.')
memory_dim = embedder.dim
elif combine == 'concat':
memory_dim = embedder.dim + temporal_embedder.dim
def _embed_fn(memory, soft_memory, mode=None):
if memory is None and soft_memory is None:
raise ValueError(
"Either `memory` or `soft_memory` is required.")
if memory is not None and soft_memory is not None:
raise ValueError(
"Must not specify `memory` and `soft_memory` at the "
"same time.")
embedded_memory = embedder(
ids=memory, soft_ids=soft_memory, mode=mode)
temporal_embedded = temporal_embedder(
sequence_length=tf.constant([memory_size]), mode=mode)
temporal_embedded = tf.tile(
temporal_embedded, [tf.shape(embedded_memory)[0], 1, 1])
if combine == 'add':
return tf.add(embedded_memory, temporal_embedded)
elif combine == 'concat':
return tf.concat([embedded_memory, temporal_embedded], axis=-1)
else:
raise ValueError('Unknown combine method: {}'.format(combine))
return _embed_fn, memory_dim | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/contrib/gis/gdal/raster/band.py | python | GDALBand.width | (self) | return capi.get_band_xsize(self._ptr) | Width (X axis) in pixels of the band. | Width (X axis) in pixels of the band. | [
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] | def width(self):
"""
Width (X axis) in pixels of the band.
"""
return capi.get_band_xsize(self._ptr) | [
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danielegrattarola/keras-gat | c870d198a6137201d354fddbe4f7250903ab483c | keras_gat/utils.py | python | sample_mask | (idx, l) | return np.array(mask, dtype=np.bool) | Create mask. | Create mask. | [
"Create",
"mask",
"."
] | def sample_mask(idx, l):
"""Create mask."""
mask = np.zeros(l)
mask[idx] = 1
return np.array(mask, dtype=np.bool) | [
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jtriley/StarCluster | bc7c950e73f193eac9aab986b6764939cfdad978 | starcluster/utils.py | python | is_valid_device | (dev) | Checks that dev matches the following regular expression:
/dev/sd[a-z]$ | Checks that dev matches the following regular expression:
/dev/sd[a-z]$ | [
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] | def is_valid_device(dev):
"""
Checks that dev matches the following regular expression:
/dev/sd[a-z]$
"""
regex = re.compile('/dev/sd[a-z]$')
try:
return regex.match(dev) is not None
except TypeError:
return False | [
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wishinlife/SyncY | 71597eb523cc411e5f41558570d3814d52cfe92e | syncy.py | python | SYCurl.__init__ | (self) | [] | def __init__(self):
self.__response = ''
self.__op = None
self.__fd = None
self.__startpos = 0
self.__endpos = None
self.__buffer = '' | [
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matplotlib/matplotlib | 8d7a2b9d2a38f01ee0d6802dd4f9e98aec812322 | lib/matplotlib/colorbar.py | python | Colorbar._long_axis | (self) | return self.ax.xaxis | Return the long axis | Return the long axis | [
"Return",
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"long",
"axis"
] | def _long_axis(self):
"""Return the long axis"""
if self.orientation == 'vertical':
return self.ax.yaxis
return self.ax.xaxis | [
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bmc/munkres | 23d11c83057c7d93fa7f4ebfa191f9b746374792 | munkres.py | python | Munkres.__step1 | (self) | return 2 | For each row of the matrix, find the smallest element and
subtract it from every element in its row. Go to Step 2. | For each row of the matrix, find the smallest element and
subtract it from every element in its row. Go to Step 2. | [
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] | def __step1(self) -> int:
"""
For each row of the matrix, find the smallest element and
subtract it from every element in its row. Go to Step 2.
"""
C = self.C
n = self.n
for i in range(n):
vals = [x for x in self.C[i] if x is not DISALLOWED]
if len(vals) == 0:
# All values in this row are DISALLOWED. This matrix is
# unsolvable.
raise UnsolvableMatrix(
"Row {0} is entirely DISALLOWED.".format(i)
)
minval = min(vals)
# Find the minimum value for this row and subtract that minimum
# from every element in the row.
for j in range(n):
if self.C[i][j] is not DISALLOWED:
self.C[i][j] -= minval
return 2 | [
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... | https://github.com/bmc/munkres/blob/23d11c83057c7d93fa7f4ebfa191f9b746374792/munkres.py#L184-L205 | |
cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/alpha/analysis/aligner.py | python | StreamAligner.populateDistanceMatrix | (self) | Sets up the distance matrix for back-tracing
>>> note1 = note.Note('C#4')
>>> note2 = note.Note('C4')
Test 1: similar streams
>>> targetA = stream.Stream()
>>> sourceA = stream.Stream()
>>> targetA.append([note1, note2])
>>> sourceA.append([note1, note2])
>>> saA = alpha.analysis.aligner.StreamAligner(targetA, sourceA)
>>> saA.makeHashedStreams()
>>> saA.setupDistanceMatrix()
>>> saA.populateDistanceMatrix()
>>> saA.distanceMatrix
array([[0, 2, 4],
[2, 0, 2],
[4, 2, 0]])
Second Test
>>> targetB = stream.Stream()
>>> sourceB = stream.Stream()
>>> targetB.append([note1, note2])
>>> sourceB.append(note1)
>>> saB = alpha.analysis.aligner.StreamAligner(targetB, sourceB)
>>> saB.makeHashedStreams()
>>> saB.setupDistanceMatrix()
>>> saB.populateDistanceMatrix()
>>> saB.distanceMatrix
array([[0, 2],
[2, 0],
[4, 2]])
Third Test
>>> note3 = note.Note('D5')
>>> note3.quarterLength = 3
>>> note4 = note.Note('E3')
>>> targetC = stream.Stream()
>>> sourceC = stream.Stream()
>>> targetC.append([note1, note2, note4])
>>> sourceC.append([note3, note1, note4])
>>> saC = alpha.analysis.aligner.StreamAligner(targetC, sourceC)
>>> saC.makeHashedStreams()
>>> saC.setupDistanceMatrix()
>>> saC.populateDistanceMatrix()
>>> saC.distanceMatrix
array([[0, 2, 4, 6],
[2, 2, 2, 4],
[4, 4, 3, 3],
[6, 6, 5, 3]]) | Sets up the distance matrix for back-tracing | [
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"-",
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] | def populateDistanceMatrix(self):
'''
Sets up the distance matrix for back-tracing
>>> note1 = note.Note('C#4')
>>> note2 = note.Note('C4')
Test 1: similar streams
>>> targetA = stream.Stream()
>>> sourceA = stream.Stream()
>>> targetA.append([note1, note2])
>>> sourceA.append([note1, note2])
>>> saA = alpha.analysis.aligner.StreamAligner(targetA, sourceA)
>>> saA.makeHashedStreams()
>>> saA.setupDistanceMatrix()
>>> saA.populateDistanceMatrix()
>>> saA.distanceMatrix
array([[0, 2, 4],
[2, 0, 2],
[4, 2, 0]])
Second Test
>>> targetB = stream.Stream()
>>> sourceB = stream.Stream()
>>> targetB.append([note1, note2])
>>> sourceB.append(note1)
>>> saB = alpha.analysis.aligner.StreamAligner(targetB, sourceB)
>>> saB.makeHashedStreams()
>>> saB.setupDistanceMatrix()
>>> saB.populateDistanceMatrix()
>>> saB.distanceMatrix
array([[0, 2],
[2, 0],
[4, 2]])
Third Test
>>> note3 = note.Note('D5')
>>> note3.quarterLength = 3
>>> note4 = note.Note('E3')
>>> targetC = stream.Stream()
>>> sourceC = stream.Stream()
>>> targetC.append([note1, note2, note4])
>>> sourceC.append([note3, note1, note4])
>>> saC = alpha.analysis.aligner.StreamAligner(targetC, sourceC)
>>> saC.makeHashedStreams()
>>> saC.setupDistanceMatrix()
>>> saC.populateDistanceMatrix()
>>> saC.distanceMatrix
array([[0, 2, 4, 6],
[2, 2, 2, 4],
[4, 4, 3, 3],
[6, 6, 5, 3]])
'''
# calculate insert and delete costs based on the first tuple in the Source S
insertCost = self.insertCost(self.hashedSourceStream[0])
deleteCost = self.deleteCost(self.hashedSourceStream[0])
# setup all the entries in the first column, the target stream
for i in range(1, self.n + 1):
self.distanceMatrix[i][0] = self.distanceMatrix[i - 1][0] + insertCost
# setup all the entries in the first row, the source stream
for j in range(1, self.m + 1):
self.distanceMatrix[0][j] = self.distanceMatrix[0][j - 1] + deleteCost
# fill in rest of matrix
for i in range(1, self.n + 1):
for j in range(1, self.m + 1):
substCost = self.substitutionCost(self.hashedTargetStream[i - 1],
self.hashedSourceStream[j - 1])
previousValues = [self.distanceMatrix[i - 1][j] + insertCost,
self.distanceMatrix[i][j - 1] + deleteCost,
self.distanceMatrix[i - 1][j - 1] + substCost]
self.distanceMatrix[i][j] = min(previousValues) | [
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ACloudGuru/AdvancedCloudFormation | 6831cfbff1888951c9b2ede6cb87eeb71cfd8bf5 | 206-LambdaCustomEnhancements/autosubnet/netaddr/ip/__init__.py | python | IPNetwork.ipv4 | (self) | return ip | :return: A numerically equivalent version 4 `IPNetwork` object. \
Raises an `AddrConversionError` if IPv6 address cannot be \
converted to IPv4. | :return: A numerically equivalent version 4 `IPNetwork` object. \
Raises an `AddrConversionError` if IPv6 address cannot be \
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"""
:return: A numerically equivalent version 4 `IPNetwork` object. \
Raises an `AddrConversionError` if IPv6 address cannot be \
converted to IPv4.
"""
ip = None
klass = self.__class__
if self._module.version == 4:
ip = klass('%s/%d' % (self.ip, self.prefixlen))
elif self._module.version == 6:
if 0 <= self._value <= _ipv4.max_int:
addr = _ipv4.int_to_str(self._value)
ip = klass('%s/%d' % (addr, self.prefixlen - 96))
elif _ipv4.max_int <= self._value <= 0xffffffffffff:
addr = _ipv4.int_to_str(self._value - 0xffff00000000)
ip = klass('%s/%d' % (addr, self.prefixlen - 96))
else:
raise AddrConversionError('IPv6 address %s unsuitable for ' \
'conversion to IPv4!' % self)
return ip | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/vpc/v20170312/models.py | python | DirectConnectGatewayCcnRoute.__init__ | (self) | r"""
:param RouteId: 路由ID。
:type RouteId: str
:param DestinationCidrBlock: IDC网段。
:type DestinationCidrBlock: str
:param ASPath: `BGP`的`AS-Path`属性。
:type ASPath: list of str
:param Description: 备注
:type Description: str
:param UpdateTime: 最后更新时间
:type UpdateTime: str | r"""
:param RouteId: 路由ID。
:type RouteId: str
:param DestinationCidrBlock: IDC网段。
:type DestinationCidrBlock: str
:param ASPath: `BGP`的`AS-Path`属性。
:type ASPath: list of str
:param Description: 备注
:type Description: str
:param UpdateTime: 最后更新时间
:type UpdateTime: str | [
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:type RouteId: str
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:type DestinationCidrBlock: str
:param ASPath: `BGP`的`AS-Path`属性。
:type ASPath: list of str
:param Description: 备注
:type Description: str
:param UpdateTime: 最后更新时间
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self.RouteId = None
self.DestinationCidrBlock = None
self.ASPath = None
self.Description = None
self.UpdateTime = None | [
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deepinsight/insightface | c0b25f998a649f662c7136eb389abcacd7900e9d | detection/scrfd/mmdet/apis/inference.py | python | LoadImage.__call__ | (self, results) | return results | Call function to load images into results.
Args:
results (dict): A result dict contains the file name
of the image to be read.
Returns:
dict: ``results`` will be returned containing loaded image. | Call function to load images into results. | [
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"into",
"results",
"."
] | def __call__(self, results):
"""Call function to load images into results.
Args:
results (dict): A result dict contains the file name
of the image to be read.
Returns:
dict: ``results`` will be returned containing loaded image.
"""
if isinstance(results['img'], str):
results['filename'] = results['img']
results['ori_filename'] = results['img']
else:
results['filename'] = None
results['ori_filename'] = None
img = mmcv.imread(results['img'])
results['img'] = img
results['img_fields'] = ['img']
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
return results | [
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stopstalk/stopstalk-deployment | 10c3ab44c4ece33ae515f6888c15033db2004bb1 | aws_lambda/spoj_aws_lambda_function/lambda_code/pip/_vendor/requests/api.py | python | put | (url, data=None, **kwargs) | return request('put', url, data=data, **kwargs) | r"""Sends a PUT request.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary (will be form-encoded), bytes, or file-like object to send in the body of the :class:`Request`.
:param json: (optional) json data to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response | r"""Sends a PUT request. | [
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] | def put(url, data=None, **kwargs):
r"""Sends a PUT request.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary (will be form-encoded), bytes, or file-like object to send in the body of the :class:`Request`.
:param json: (optional) json data to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response
"""
return request('put', url, data=data, **kwargs) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/topology/simplicial_complex_morphism.py | python | SimplicialComplexMorphism.associated_chain_complex_morphism | (self,base_ring=ZZ,augmented=False,cochain=False) | return ChainComplexMorphism(matrices,
self.codomain().chain_complex(base_ring=base_ring,augmented=augmented,cochain=cochain),
self.domain().chain_complex(base_ring=base_ring,augmented=augmented,cochain=cochain)) | Return the associated chain complex morphism of ``self``.
EXAMPLES::
sage: S = simplicial_complexes.Sphere(1)
sage: T = simplicial_complexes.Sphere(2)
sage: H = Hom(S,T)
sage: f = {0:0,1:1,2:2}
sage: x = H(f)
sage: x
Simplicial complex morphism:
From: Minimal triangulation of the 1-sphere
To: Minimal triangulation of the 2-sphere
Defn: 0 |--> 0
1 |--> 1
2 |--> 2
sage: a = x.associated_chain_complex_morphism()
sage: a
Chain complex morphism:
From: Chain complex with at most 2 nonzero terms over Integer Ring
To: Chain complex with at most 3 nonzero terms over Integer Ring
sage: a._matrix_dictionary
{0: [1 0 0]
[0 1 0]
[0 0 1]
[0 0 0], 1: [1 0 0]
[0 1 0]
[0 0 0]
[0 0 1]
[0 0 0]
[0 0 0], 2: []}
sage: x.associated_chain_complex_morphism(augmented=True)
Chain complex morphism:
From: Chain complex with at most 3 nonzero terms over Integer Ring
To: Chain complex with at most 4 nonzero terms over Integer Ring
sage: x.associated_chain_complex_morphism(cochain=True)
Chain complex morphism:
From: Chain complex with at most 3 nonzero terms over Integer Ring
To: Chain complex with at most 2 nonzero terms over Integer Ring
sage: x.associated_chain_complex_morphism(augmented=True,cochain=True)
Chain complex morphism:
From: Chain complex with at most 4 nonzero terms over Integer Ring
To: Chain complex with at most 3 nonzero terms over Integer Ring
sage: x.associated_chain_complex_morphism(base_ring=GF(11))
Chain complex morphism:
From: Chain complex with at most 2 nonzero terms over Finite Field of size 11
To: Chain complex with at most 3 nonzero terms over Finite Field of size 11
Some simplicial maps which reverse the orientation of a few simplices::
sage: g = {0:1, 1:2, 2:0}
sage: H(g).associated_chain_complex_morphism()._matrix_dictionary
{0: [0 0 1]
[1 0 0]
[0 1 0]
[0 0 0], 1: [ 0 -1 0]
[ 0 0 -1]
[ 0 0 0]
[ 1 0 0]
[ 0 0 0]
[ 0 0 0], 2: []}
sage: X = SimplicialComplex([[0, 1]], is_mutable=False)
sage: Hom(X,X)({0:1, 1:0}).associated_chain_complex_morphism()._matrix_dictionary
{0: [0 1]
[1 0], 1: [-1]} | Return the associated chain complex morphism of ``self``. | [
"Return",
"the",
"associated",
"chain",
"complex",
"morphism",
"of",
"self",
"."
] | def associated_chain_complex_morphism(self,base_ring=ZZ,augmented=False,cochain=False):
"""
Return the associated chain complex morphism of ``self``.
EXAMPLES::
sage: S = simplicial_complexes.Sphere(1)
sage: T = simplicial_complexes.Sphere(2)
sage: H = Hom(S,T)
sage: f = {0:0,1:1,2:2}
sage: x = H(f)
sage: x
Simplicial complex morphism:
From: Minimal triangulation of the 1-sphere
To: Minimal triangulation of the 2-sphere
Defn: 0 |--> 0
1 |--> 1
2 |--> 2
sage: a = x.associated_chain_complex_morphism()
sage: a
Chain complex morphism:
From: Chain complex with at most 2 nonzero terms over Integer Ring
To: Chain complex with at most 3 nonzero terms over Integer Ring
sage: a._matrix_dictionary
{0: [1 0 0]
[0 1 0]
[0 0 1]
[0 0 0], 1: [1 0 0]
[0 1 0]
[0 0 0]
[0 0 1]
[0 0 0]
[0 0 0], 2: []}
sage: x.associated_chain_complex_morphism(augmented=True)
Chain complex morphism:
From: Chain complex with at most 3 nonzero terms over Integer Ring
To: Chain complex with at most 4 nonzero terms over Integer Ring
sage: x.associated_chain_complex_morphism(cochain=True)
Chain complex morphism:
From: Chain complex with at most 3 nonzero terms over Integer Ring
To: Chain complex with at most 2 nonzero terms over Integer Ring
sage: x.associated_chain_complex_morphism(augmented=True,cochain=True)
Chain complex morphism:
From: Chain complex with at most 4 nonzero terms over Integer Ring
To: Chain complex with at most 3 nonzero terms over Integer Ring
sage: x.associated_chain_complex_morphism(base_ring=GF(11))
Chain complex morphism:
From: Chain complex with at most 2 nonzero terms over Finite Field of size 11
To: Chain complex with at most 3 nonzero terms over Finite Field of size 11
Some simplicial maps which reverse the orientation of a few simplices::
sage: g = {0:1, 1:2, 2:0}
sage: H(g).associated_chain_complex_morphism()._matrix_dictionary
{0: [0 0 1]
[1 0 0]
[0 1 0]
[0 0 0], 1: [ 0 -1 0]
[ 0 0 -1]
[ 0 0 0]
[ 1 0 0]
[ 0 0 0]
[ 0 0 0], 2: []}
sage: X = SimplicialComplex([[0, 1]], is_mutable=False)
sage: Hom(X,X)({0:1, 1:0}).associated_chain_complex_morphism()._matrix_dictionary
{0: [0 1]
[1 0], 1: [-1]}
"""
max_dim = max(self.domain().dimension(),self.codomain().dimension())
min_dim = min(self.domain().dimension(),self.codomain().dimension())
matrices = {}
if augmented is True:
m = matrix(base_ring,1,1,1)
if not cochain:
matrices[-1] = m
else:
matrices[-1] = m.transpose()
for dim in range(min_dim+1):
X_faces = self.domain()._n_cells_sorted(dim)
Y_faces = self.codomain()._n_cells_sorted(dim)
num_faces_X = len(X_faces)
num_faces_Y = len(Y_faces)
mval = [0 for i in range(num_faces_X*num_faces_Y)]
for i in X_faces:
y, oriented = self(i, orientation=True)
if y.dimension() < dim:
pass
else:
mval[X_faces.index(i)+(Y_faces.index(y)*num_faces_X)] = oriented
m = matrix(base_ring,num_faces_Y,num_faces_X,mval,sparse=True)
if not cochain:
matrices[dim] = m
else:
matrices[dim] = m.transpose()
for dim in range(min_dim+1,max_dim+1):
try:
l1 = len(self.codomain().n_cells(dim))
except KeyError:
l1 = 0
try:
l2 = len(self.domain().n_cells(dim))
except KeyError:
l2 = 0
m = zero_matrix(base_ring,l1,l2,sparse=True)
if not cochain:
matrices[dim] = m
else:
matrices[dim] = m.transpose()
if not cochain:
return ChainComplexMorphism(matrices,
self.domain().chain_complex(base_ring=base_ring,augmented=augmented,cochain=cochain),
self.codomain().chain_complex(base_ring=base_ring,augmented=augmented,cochain=cochain))
return ChainComplexMorphism(matrices,
self.codomain().chain_complex(base_ring=base_ring,augmented=augmented,cochain=cochain),
self.domain().chain_complex(base_ring=base_ring,augmented=augmented,cochain=cochain)) | [
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/sets.py | python | BaseSet.__xor__ | (self, other) | return self.symmetric_difference(other) | Return the symmetric difference of two sets as a new set.
(I.e. all elements that are in exactly one of the sets.) | Return the symmetric difference of two sets as a new set. | [
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"""Return the symmetric difference of two sets as a new set.
(I.e. all elements that are in exactly one of the sets.)
"""
if not isinstance(other, BaseSet):
return NotImplemented
return self.symmetric_difference(other) | [
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ucsb-seclab/karonte | 427ac313e596f723e40768b95d13bd7a9fc92fd8 | eval/multi_bin/all_bins/run_utils.py | python | get_memcp_like | (p) | return summarized_f | [] | def get_memcp_like(p):
addrs = get_dyn_sym_addrs(p, ['strcmp', 'strcasecmp'])
summarized_f = {}
for f in addrs:
summarized_f[f] = summary_functions.memcmp_unsized
return summarized_f | [
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airbnb/streamalert | 26cf1d08432ca285fd4f7410511a6198ca104bbb | streamalert/rules_engine/rules_engine.py | python | RulesEngine._configure_dynamic_outputs | (cls, record, rule) | return [
output
for dynamic_output_function in rule.dynamic_outputs_set
for output in cls._call_dynamic_output_function(
dynamic_output_function, rule.name, args_list
)
] | Generate list of outputs from dynamic_outputs
Args:
record (dict): Record to pass through to the dynamic_output function
rule (rule.Rule): Attributes for the rule which triggered the alert
Returns:
list: list of additional outputs to append to the current set | Generate list of outputs from dynamic_outputs | [
"Generate",
"list",
"of",
"outputs",
"from",
"dynamic_outputs"
] | def _configure_dynamic_outputs(cls, record, rule):
"""Generate list of outputs from dynamic_outputs
Args:
record (dict): Record to pass through to the dynamic_output function
rule (rule.Rule): Attributes for the rule which triggered the alert
Returns:
list: list of additional outputs to append to the current set
"""
args_list = [record]
if rule.context:
# Pass context to dynamic_output function if context exists
args_list.append(rule.context)
return [
output
for dynamic_output_function in rule.dynamic_outputs_set
for output in cls._call_dynamic_output_function(
dynamic_output_function, rule.name, args_list
)
] | [
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tensorflow/model-analysis | e38c23ce76eff039548ce69e3160ed4d7984f2fc | tensorflow_model_analysis/eval_saved_model/graph_ref.py | python | load_legacy_features_and_labels | (
meta_graph_def: tf.compat.v1.MetaGraphDef, graph: tf.Graph
) | return (features_map, labels_map) | Loads legacy features and labels nodes.
Args:
meta_graph_def: MetaGraphDef to lookup nodes in.
graph: TensorFlow graph to lookup the nodes in.
Returns:
Tuple of (features_map, labels_map) | Loads legacy features and labels nodes. | [
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"nodes",
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] | def load_legacy_features_and_labels(
meta_graph_def: tf.compat.v1.MetaGraphDef, graph: tf.Graph
) -> Tuple[Dict[str, types.TensorType], Dict[str, types.TensorType]]:
"""Loads legacy features and labels nodes.
Args:
meta_graph_def: MetaGraphDef to lookup nodes in.
graph: TensorFlow graph to lookup the nodes in.
Returns:
Tuple of (features_map, labels_map)
"""
encoded_features_map = collections.OrderedDict(
get_node_map_in_graph(meta_graph_def, encoding.FEATURES_COLLECTION,
[encoding.NODE_SUFFIX], graph))
features_map = collections.OrderedDict()
for key in encoded_features_map:
features_map[key] = encoded_features_map[key][encoding.NODE_SUFFIX]
encoded_labels_map = collections.OrderedDict(
get_node_map_in_graph(meta_graph_def, encoding.LABELS_COLLECTION,
[encoding.NODE_SUFFIX], graph))
labels_map = collections.OrderedDict()
for key in encoded_labels_map:
labels_map[key] = encoded_labels_map[key][encoding.NODE_SUFFIX]
# Assume that KeyType is only Text
# pytype: disable=bad-return-type
return (features_map, labels_map) | [
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sqlalchemy/alembic | 85152025ddba1dbeb51b467f40eb36b795d2ca37 | alembic/autogenerate/api.py | python | compare_metadata | (context: "MigrationContext", metadata: "MetaData") | return migration_script.upgrade_ops.as_diffs() | Compare a database schema to that given in a
:class:`~sqlalchemy.schema.MetaData` instance.
The database connection is presented in the context
of a :class:`.MigrationContext` object, which
provides database connectivity as well as optional
comparison functions to use for datatypes and
server defaults - see the "autogenerate" arguments
at :meth:`.EnvironmentContext.configure`
for details on these.
The return format is a list of "diff" directives,
each representing individual differences::
from alembic.migration import MigrationContext
from alembic.autogenerate import compare_metadata
from sqlalchemy.schema import SchemaItem
from sqlalchemy.types import TypeEngine
from sqlalchemy import (create_engine, MetaData, Column,
Integer, String, Table, text)
import pprint
engine = create_engine("sqlite://")
with engine.begin() as conn:
conn.execute(text('''
create table foo (
id integer not null primary key,
old_data varchar,
x integer
)'''))
conn.execute(text('''
create table bar (
data varchar
)'''))
metadata = MetaData()
Table('foo', metadata,
Column('id', Integer, primary_key=True),
Column('data', Integer),
Column('x', Integer, nullable=False)
)
Table('bat', metadata,
Column('info', String)
)
mc = MigrationContext.configure(engine.connect())
diff = compare_metadata(mc, metadata)
pprint.pprint(diff, indent=2, width=20)
Output::
[ ( 'add_table',
Table('bat', MetaData(bind=None),
Column('info', String(), table=<bat>), schema=None)),
( 'remove_table',
Table(u'bar', MetaData(bind=None),
Column(u'data', VARCHAR(), table=<bar>), schema=None)),
( 'add_column',
None,
'foo',
Column('data', Integer(), table=<foo>)),
( 'remove_column',
None,
'foo',
Column(u'old_data', VARCHAR(), table=None)),
[ ( 'modify_nullable',
None,
'foo',
u'x',
{ 'existing_server_default': None,
'existing_type': INTEGER()},
True,
False)]]
:param context: a :class:`.MigrationContext`
instance.
:param metadata: a :class:`~sqlalchemy.schema.MetaData`
instance.
.. seealso::
:func:`.produce_migrations` - produces a :class:`.MigrationScript`
structure based on metadata comparison. | Compare a database schema to that given in a
:class:`~sqlalchemy.schema.MetaData` instance. | [
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"""Compare a database schema to that given in a
:class:`~sqlalchemy.schema.MetaData` instance.
The database connection is presented in the context
of a :class:`.MigrationContext` object, which
provides database connectivity as well as optional
comparison functions to use for datatypes and
server defaults - see the "autogenerate" arguments
at :meth:`.EnvironmentContext.configure`
for details on these.
The return format is a list of "diff" directives,
each representing individual differences::
from alembic.migration import MigrationContext
from alembic.autogenerate import compare_metadata
from sqlalchemy.schema import SchemaItem
from sqlalchemy.types import TypeEngine
from sqlalchemy import (create_engine, MetaData, Column,
Integer, String, Table, text)
import pprint
engine = create_engine("sqlite://")
with engine.begin() as conn:
conn.execute(text('''
create table foo (
id integer not null primary key,
old_data varchar,
x integer
)'''))
conn.execute(text('''
create table bar (
data varchar
)'''))
metadata = MetaData()
Table('foo', metadata,
Column('id', Integer, primary_key=True),
Column('data', Integer),
Column('x', Integer, nullable=False)
)
Table('bat', metadata,
Column('info', String)
)
mc = MigrationContext.configure(engine.connect())
diff = compare_metadata(mc, metadata)
pprint.pprint(diff, indent=2, width=20)
Output::
[ ( 'add_table',
Table('bat', MetaData(bind=None),
Column('info', String(), table=<bat>), schema=None)),
( 'remove_table',
Table(u'bar', MetaData(bind=None),
Column(u'data', VARCHAR(), table=<bar>), schema=None)),
( 'add_column',
None,
'foo',
Column('data', Integer(), table=<foo>)),
( 'remove_column',
None,
'foo',
Column(u'old_data', VARCHAR(), table=None)),
[ ( 'modify_nullable',
None,
'foo',
u'x',
{ 'existing_server_default': None,
'existing_type': INTEGER()},
True,
False)]]
:param context: a :class:`.MigrationContext`
instance.
:param metadata: a :class:`~sqlalchemy.schema.MetaData`
instance.
.. seealso::
:func:`.produce_migrations` - produces a :class:`.MigrationScript`
structure based on metadata comparison.
"""
migration_script = produce_migrations(context, metadata)
return migration_script.upgrade_ops.as_diffs() | [
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nansencenter/nansat | 5700ec673fbf522c19b8dedcb01cc15f7cd29a6a | nansat/vrt.py | python | VRT.create_bands | (self, metadata_dict) | Generic function called from the mappers to create bands
in the VRT dataset from an input dictionary of metadata
Parameters
----------
metadata_dict : list of dict with params of input bands and generated bands.
Each dict has:
'src' : dictionary with parameters of the sources:
'dst' : dictionary with parameters of the generated bands
Notes
---------
Adds bands to the self.dataset based on info in metaDict
See Also
---------
VRT.create_band() | Generic function called from the mappers to create bands
in the VRT dataset from an input dictionary of metadata | [
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""" Generic function called from the mappers to create bands
in the VRT dataset from an input dictionary of metadata
Parameters
----------
metadata_dict : list of dict with params of input bands and generated bands.
Each dict has:
'src' : dictionary with parameters of the sources:
'dst' : dictionary with parameters of the generated bands
Notes
---------
Adds bands to the self.dataset based on info in metaDict
See Also
---------
VRT.create_band()
"""
for band_dict in metadata_dict:
src = band_dict['src']
dst = band_dict.get('dst', None)
self.create_band(src, dst)
self.logger.debug('Creating band - OK!')
self.dataset.FlushCache() | [
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/hachoir_core/tools.py | python | makePrintable | (data, charset, quote=None, to_unicode=False, smart=True) | return data | r"""
Prepare a string to make it printable in the specified charset.
It escapes control characters. Characters with code bigger than 127
are escaped if data type is 'str' or if charset is "ASCII".
Examples with Unicode:
>>> aged = unicode("âgé", "UTF-8")
>>> repr(aged) # text type is 'unicode'
"u'\\xe2g\\xe9'"
>>> makePrintable("abc\0", "UTF-8")
'abc\\0'
>>> makePrintable(aged, "latin1")
'\xe2g\xe9'
>>> makePrintable(aged, "latin1", quote='"')
'"\xe2g\xe9"'
Examples with string encoded in latin1:
>>> aged_latin = unicode("âgé", "UTF-8").encode("latin1")
>>> repr(aged_latin) # text type is 'str'
"'\\xe2g\\xe9'"
>>> makePrintable(aged_latin, "latin1")
'\\xe2g\\xe9'
>>> makePrintable("", "latin1")
''
>>> makePrintable("a", "latin1", quote='"')
'"a"'
>>> makePrintable("", "latin1", quote='"')
'(empty)'
>>> makePrintable("abc", "latin1", quote="'")
"'abc'"
Control codes:
>>> makePrintable("\0\x03\x0a\x10 \x7f", "latin1")
'\\0\\3\\n\\x10 \\x7f'
Quote character may also be escaped (only ' and "):
>>> print makePrintable("a\"b", "latin-1", quote='"')
"a\"b"
>>> print makePrintable("a\"b", "latin-1", quote="'")
'a"b'
>>> print makePrintable("a'b", "latin-1", quote="'")
'a\'b' | r"""
Prepare a string to make it printable in the specified charset.
It escapes control characters. Characters with code bigger than 127
are escaped if data type is 'str' or if charset is "ASCII". | [
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Prepare a string to make it printable in the specified charset.
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are escaped if data type is 'str' or if charset is "ASCII".
Examples with Unicode:
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'abc\\0'
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>>> makePrintable(aged, "latin1", quote='"')
'"\xe2g\xe9"'
Examples with string encoded in latin1:
>>> aged_latin = unicode("âgé", "UTF-8").encode("latin1")
>>> repr(aged_latin) # text type is 'str'
"'\\xe2g\\xe9'"
>>> makePrintable(aged_latin, "latin1")
'\\xe2g\\xe9'
>>> makePrintable("", "latin1")
''
>>> makePrintable("a", "latin1", quote='"')
'"a"'
>>> makePrintable("", "latin1", quote='"')
'(empty)'
>>> makePrintable("abc", "latin1", quote="'")
"'abc'"
Control codes:
>>> makePrintable("\0\x03\x0a\x10 \x7f", "latin1")
'\\0\\3\\n\\x10 \\x7f'
Quote character may also be escaped (only ' and "):
>>> print makePrintable("a\"b", "latin-1", quote='"')
"a\"b"
>>> print makePrintable("a\"b", "latin-1", quote="'")
'a"b'
>>> print makePrintable("a'b", "latin-1", quote="'")
'a\'b'
"""
if data:
if not isinstance(data, unicode):
data = unicode(data, "ISO-8859-1")
charset = "ASCII"
data = regex_control_code.sub(
lambda regs: controlchars[ord(regs.group(1))], data)
if quote:
if quote in "\"'":
data = data.replace(quote, '\\' + quote)
data = ''.join((quote, data, quote))
elif quote:
data = "(empty)"
data = data.encode(charset, "backslashreplace")
if smart:
# Replace \x00\x01 by \0\1
data = re.sub(r"\\x0([0-7])(?=[^0-7]|$)", r"\\\1", data)
if to_unicode:
data = unicode(data, charset)
return data | [
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reiinakano/xcessiv | a48dff7d370c84eb5c243bde87164c1f5fd096d5 | xcessiv/views.py | python | create_base_learner | (id) | This creates a single base learner from a base learner origin and queues it up | This creates a single base learner from a base learner origin and queues it up | [
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] | def create_base_learner(id):
"""This creates a single base learner from a base learner origin and queues it up"""
path = functions.get_path_from_query_string(request)
with functions.DBContextManager(path) as session:
base_learner_origin = session.query(models.BaseLearnerOrigin).filter_by(id=id).first()
if base_learner_origin is None:
raise exceptions.UserError('Base learner origin {} not found'.format(id), 404)
if not base_learner_origin.final:
raise exceptions.UserError('Base learner origin {} is not final'.format(id))
req_body = request.get_json()
# Retrieve full hyperparameters
est = base_learner_origin.return_estimator()
hyperparameters = functions.import_object_from_string_code(req_body['source'],
'params')
est.set_params(**hyperparameters)
hyperparameters = functions.make_serializable(est.get_params())
base_learners = session.query(models.BaseLearner).\
filter_by(base_learner_origin_id=id,
hyperparameters=hyperparameters).all()
if base_learners:
raise exceptions.UserError('Base learner exists with given hyperparameters')
base_learner = models.BaseLearner(hyperparameters,
'queued',
base_learner_origin)
if 'single_searches' not in base_learner_origin.description:
base_learner_origin.description['single_searches'] = []
base_learner_origin.description['single_searches'] += ([req_body['source']])
session.add(base_learner)
session.add(base_learner_origin)
session.commit()
with Connection(get_redis_connection()):
rqtasks.generate_meta_features.delay(path, base_learner.id)
return jsonify(base_learner.serialize) | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/storage/utils/processors.py | python | TextFile.close | (self) | [] | def close(self):
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quic/aimet | dae9bae9a77ca719aa7553fefde4768270fc3518 | TrainingExtensions/torch/src/python/aimet_torch/cross_layer_equalization.py | python | HighBiasFold.bias_fold | (cls_set_info_list: List[ClsSetInfo], bn_layers: Dict[Union[torch.nn.Conv2d, torch.nn.ConvTranspose2d],
torch.nn.BatchNorm2d]) | Folds bias values greater than 3 * sigma to next layer's bias
:param cls_set_info_list: List of info elements for each cls set
:param bn_layers: Key: Conv/Linear layer Value: Corresponding folded BN layer
:return: None | Folds bias values greater than 3 * sigma to next layer's bias | [
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] | def bias_fold(cls_set_info_list: List[ClsSetInfo], bn_layers: Dict[Union[torch.nn.Conv2d, torch.nn.ConvTranspose2d],
torch.nn.BatchNorm2d]):
"""
Folds bias values greater than 3 * sigma to next layer's bias
:param cls_set_info_list: List of info elements for each cls set
:param bn_layers: Key: Conv/Linear layer Value: Corresponding folded BN layer
:return: None
"""
if not bn_layers:
logger.info('High Bias folding is not supported for models without BatchNorm Layers')
return
for cls_set_info in cls_set_info_list:
for cls_pair_info in cls_set_info.cls_pair_info_list:
if (cls_pair_info.layer1.bias is None) or (cls_pair_info.layer2.bias is None) or \
(cls_pair_info.layer1 not in bn_layers):
continue
prev_layer_params, curr_layer_params = HighBiasFold.call_mo_high_bias_fold(cls_pair_info, bn_layers)
prev_layer_bias_shape = cls_pair_info.layer1.weight.shape[0]
# Transpose weight back to N, C, H, W for transposed Conv2D
if isinstance(cls_pair_info.layer1, torch.nn.ConvTranspose2d) and cls_pair_info.layer1.groups == 1:
prev_layer_bias_shape = cls_pair_info.layer1.weight.shape[1]
cls_pair_info.layer1.bias.data = torch.from_numpy(np.reshape(prev_layer_params.bias,
prev_layer_bias_shape))
cls_pair_info.layer1.bias.data = cls_pair_info.layer1.bias.data.type(torch.FloatTensor)
cls_pair_info.layer2.bias.data = torch.from_numpy(np.reshape(curr_layer_params.bias,
curr_layer_params.weightShape[0]))
cls_pair_info.layer2.bias.data = cls_pair_info.layer2.bias.data.type(torch.FloatTensor) | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/decimal.py | python | Decimal.log10 | (self, context=None) | return ans | Returns the base 10 logarithm of self. | Returns the base 10 logarithm of self. | [
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] | def log10(self, context=None):
"""Returns the base 10 logarithm of self."""
if context is None:
context = getcontext()
# log10(NaN) = NaN
ans = self._check_nans(context=context)
if ans:
return ans
# log10(0.0) == -Infinity
if not self:
return _NegativeInfinity
# log10(Infinity) = Infinity
if self._isinfinity() == 1:
return _Infinity
# log10(negative or -Infinity) raises InvalidOperation
if self._sign == 1:
return context._raise_error(InvalidOperation,
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# log10(10**n) = n
if self._int[0] == '1' and self._int[1:] == '0'*(len(self._int) - 1):
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c, e = op.int, op.exp
p = context.prec
# correctly rounded result: repeatedly increase precision
# until result is unambiguously roundable
places = p-self._log10_exp_bound()+2
while True:
coeff = _dlog10(c, e, places)
# assert len(str(abs(coeff)))-p >= 1
if coeff % (5*10**(len(str(abs(coeff)))-p-1)):
break
places += 3
ans = _dec_from_triple(int(coeff<0), str(abs(coeff)), -places)
context = context._shallow_copy()
rounding = context._set_rounding(ROUND_HALF_EVEN)
ans = ans._fix(context)
context.rounding = rounding
return ans | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/gevent/pool.py | python | GroupMappingMixin.map_async | (self, func, iterable, callback=None) | return Greenlet.spawn(self.map_cb, func, iterable, callback) | A variant of the map() method which returns a Greenlet object that is executing
the map function.
If callback is specified then it should be a callable which accepts a
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"""
A variant of the map() method which returns a Greenlet object that is executing
the map function.
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single argument.
"""
return Greenlet.spawn(self.map_cb, func, iterable, callback) | [
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nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | lib/sqlalchemy/orm/relationships.py | python | JoinCondition._annotate_local | (self) | Annotate the primaryjoin and secondaryjoin
structures with 'local' annotations.
This annotates all column elements found
simultaneously in the parent table
and the join condition that don't have a
'remote' annotation set up from
_annotate_remote() or user-defined. | Annotate the primaryjoin and secondaryjoin
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] | def _annotate_local(self):
"""Annotate the primaryjoin and secondaryjoin
structures with 'local' annotations.
This annotates all column elements found
simultaneously in the parent table
and the join condition that don't have a
'remote' annotation set up from
_annotate_remote() or user-defined.
"""
if self._has_annotation(self.primaryjoin, "local"):
return
if self._local_remote_pairs:
local_side = util.column_set([l for (l, r)
in self._local_remote_pairs])
else:
local_side = util.column_set(self.parent_selectable.c)
def locals_(elem):
if "remote" not in elem._annotations and \
elem in local_side:
return elem._annotate({"local": True})
self.primaryjoin = visitors.replacement_traverse(
self.primaryjoin, {}, locals_
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automl/RoBO | 91366b12a1a3deb8e80dd08599e0eaf4df28adc1 | robo/models/base_model.py | python | BaseModel.train | (self, X, y) | Trains the model on the provided data.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of input dimensions.
y: np.ndarray (N,)
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"""
Trains the model on the provided data.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of input dimensions.
y: np.ndarray (N,)
The corresponding target values of the input data points.
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pass | [
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ahmetcemturan/SFACT | 7576e29ba72b33e5058049b77b7b558875542747 | fabmetheus_utilities/svg_reader.py | python | PathReader.processPathWordA | (self) | Process path word A. | Process path word A. | [
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] | def processPathWordA(self):
'Process path word A.'
self.addPathArc( self.getComplexByExtraIndex( 6 ) ) | [
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jendrikseipp/rednotebook | 3ec73124af8679371b4fdb379824149f7407006d | rednotebook/journal.py | python | Journal.get_journal_path | (self) | Retrieve the path from optional args or return standard value if args
not present | Retrieve the path from optional args or return standard value if args
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"""
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"""
if not args.journal:
data_dir = self.config.read("dataDir", self.dirs.data_dir)
if not os.path.isabs(data_dir):
data_dir = os.path.join(self.dirs.app_dir, data_dir)
data_dir = os.path.normpath(data_dir)
return data_dir
# path_arg can be e.g. data (under .rednotebook), data (elsewhere),
# or an absolute path /home/username/myjournal
# Try to find the journal under the standard location or at the given
# absolute or relative location
path_arg = args.journal
logging.debug('Trying to find journal "%s"' % path_arg)
paths_to_check = [path_arg, os.path.join(self.dirs.journal_user_dir, path_arg)]
for path in paths_to_check:
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logging.error(
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caronc/apprise | e5945e0be1b7051ab4890bf78949058304f2d641 | apprise/plugins/NotifyLametric.py | python | NotifyLametric.parse_url | (url) | return results | Parses the URL and returns enough arguments that can allow
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"""
Parses the URL and returns enough arguments that can allow
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"""
results = NotifyBase.parse_url(url, verify_host=False)
if not results:
# We're done early as we couldn't load the results
return results
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# schema://user@host
# This becomes the password
results['password'] = results['user']
results['user'] = None
# Priority Handling
if 'priority' in results['qsd'] and results['qsd']['priority']:
results['priority'] = NotifyLametric.unquote(
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# Icon Type
if 'icon' in results['qsd'] and results['qsd']['icon']:
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# Icon Type
if 'icon_type' in results['qsd'] and results['qsd']['icon_type']:
results['icon_type'] = NotifyLametric.unquote(
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# Sound
if 'sound' in results['qsd'] and results['qsd']['sound']:
results['sound'] = NotifyLametric.unquote(
results['qsd']['sound'].strip().lower())
# API Key (Device Mode)
if 'apikey' in results['qsd'] and results['qsd']['apikey']:
# Extract API Key from an argument
results['apikey'] = \
NotifyLametric.unquote(results['qsd']['apikey'])
# App ID
if 'app' in results['qsd'] \
and results['qsd']['app']:
# Extract the App ID from an argument
results['app_id'] = \
NotifyLametric.unquote(results['qsd']['app'])
# App Version
if 'app_ver' in results['qsd'] \
and results['qsd']['app_ver']:
# Extract the App ID from an argument
results['app_ver'] = \
NotifyLametric.unquote(results['qsd']['app_ver'])
if 'token' in results['qsd'] and results['qsd']['token']:
# Extract Application Access Token from an argument
results['app_token'] = \
NotifyLametric.unquote(results['qsd']['token'])
# Mode override
if 'mode' in results['qsd'] and results['qsd']['mode']:
results['mode'] = NotifyLametric.unquote(
results['qsd']['mode'].strip().lower())
else:
# We can try to detect the mode based on the validity of the
# hostname. We can also scan the validity of the Application
# Access token
#
# This isn't a surfire way to do things though; it's best to
# specify the mode= flag
results['mode'] = LametricMode.DEVICE \
if ((is_hostname(results['host']) or
is_ipaddr(results['host'])) and
# make sure password is not an Access Token
(results['password'] and not
LAMETRIC_IS_APP_TOKEN.match(results['password'])) and
# Scan for app_ flags
next((f for f in results.keys() \
if f.startswith('app_')), None) is None) \
else LametricMode.CLOUD
# Handle defaults if not set
if results['mode'] == LametricMode.DEVICE:
# Device Mode Defaults
if 'apikey' not in results:
results['apikey'] = \
NotifyLametric.unquote(results['password'])
else:
# CLOUD Mode Defaults
if 'app_id' not in results:
results['app_id'] = \
NotifyLametric.unquote(results['host'])
if 'app_token' not in results:
results['app_token'] = \
NotifyLametric.unquote(results['password'])
# Set cycles
try:
results['cycles'] = abs(int(results['qsd'].get('cycles')))
except (TypeError, ValueError):
# Not a valid integer; ignore entry
pass
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Tencent/QT4i | 75f8705c194505b483c6b7464da8522cd53ba679 | qt4i/driver/host.py | python | RPCServerHost.push_file_data | (self, data, file_path, override=True) | return os.path.isfile(file_path) | push file to rpc server
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'''
if os.path.exists(file_path) and override:
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keras-team/keras | 5caa668b6a415675064a730f5eb46ecc08e40f65 | keras/applications/efficientnet_v2.py | python | round_filters | (filters, width_coefficient, min_depth, depth_divisor) | return int(new_filters) | Round number of filters based on depth multiplier. | Round number of filters based on depth multiplier. | [
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filters *= width_coefficient
minimum_depth = min_depth or depth_divisor
new_filters = max(
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Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/build/inline_copy/lib/scons-3.1.2/SCons/Tool/mssdk.py | python | generate | (env) | Add construction variables for an MS SDK to an Environment. | Add construction variables for an MS SDK to an Environment. | [
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freyta/7Eleven-Python | e3702fec61743950370e1f35121b2a313e080210 | API_REQUESTS/api/account.py | python | logout | (deviceSecret, accessToken) | return(response.content) | [] | def logout(deviceSecret, accessToken):
payload = '""'
tssa = generateTssa(BASE_URL + "account/logout", "POST", payload, accessToken)
headers = {'User-Agent':'Apache-HttpClient/UNAVAILABLE (java 1.4)',
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/calendar.py | python | TextCalendar.formatweekday | (self, day, width) | return names[day][:width].center(width) | Returns a formatted week day name. | Returns a formatted week day name. | [
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names = day_abbr
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BigBrotherBot/big-brother-bot | 848823c71413c86e7f1ff9584f43e08d40a7f2c0 | b3/plugins/callvote/__init__.py | python | CallvotePlugin.getLevel | (self, level) | return mingroup.name | Return the group name associated to the given group level | Return the group name associated to the given group level | [
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henkelis/sonospy | 841f52010fd6e1e932d8f1a8896ad4e5a0667b8a | sonospy/gui/extractTab.py | python | ExtractPanel.setButtons | (self, state) | Toggle for the button states. | Toggle for the button states. | [
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Toggle for the button states.
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/mongodb/v20190725/models.py | python | DescribeCurrentOpResponse.__init__ | (self) | r"""
:param TotalCount: 符合查询条件的操作总数
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:type CurrentOps: list of CurrentOp
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
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:type TotalCount: int
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:type CurrentOps: list of CurrentOp
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:param CurrentOps: 当前操作列表
:type CurrentOps: list of CurrentOp
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/SQLAlchemy-1.3.17/lib/sqlalchemy/sql/dml.py | python | ValuesBase.return_defaults | (self, *cols) | Make use of a :term:`RETURNING` clause for the purpose
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E.g.::
stmt = table.insert().values(data='newdata').return_defaults()
result = connection.execute(stmt)
server_created_at = result.returned_defaults['created_at']
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its ``.key``.
This method differs from :meth:`.UpdateBase.returning` in these ways:
1. :meth:`.ValuesBase.return_defaults` is only intended for use with
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While the RETURNING construct in the general sense supports
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:meth:`.ValuesBase.return_defaults` is intended only for an
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:meth:`.ValuesBase.return_defaults` is used. By contrast,
:meth:`.UpdateBase.returning` leaves the RETURNING result-set
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2. It is compatible with the existing logic to fetch auto-generated
primary key values, also known as "implicit returning". Backends
that support RETURNING will automatically make use of RETURNING in
order to fetch the value of newly generated primary keys; while the
:meth:`.UpdateBase.returning` method circumvents this behavior,
:meth:`.ValuesBase.return_defaults` leaves it intact.
3. It can be called against any backend. Backends that don't support
RETURNING will skip the usage of the feature, rather than raising
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:meth:`.ValuesBase.return_defaults` is used by the ORM to provide
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:func:`.mapper`.
:param cols: optional list of column key names or
:class:`_schema.Column`
objects. If omitted, all column expressions evaluated on the server
are added to the returning list.
.. versionadded:: 0.9.0
.. seealso::
:meth:`.UpdateBase.returning`
:attr:`_engine.ResultProxy.returned_defaults` | Make use of a :term:`RETURNING` clause for the purpose
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stmt = table.insert().values(data='newdata').return_defaults()
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order to fetch the value of newly generated primary keys; while the
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3. It can be called against any backend. Backends that don't support
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:meth:`.ValuesBase.return_defaults` is used by the ORM to provide
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:param cols: optional list of column key names or
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objects. If omitted, all column expressions evaluated on the server
are added to the returning list.
.. versionadded:: 0.9.0
.. seealso::
:meth:`.UpdateBase.returning`
:attr:`_engine.ResultProxy.returned_defaults`
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jilljenn/tryalgo | 46da31aea9a0ff58819e360c987f9506b21f6e9d | tryalgo/closest_points.py | python | cell | (point, size) | return (int(x // size), int(y // size)) | returns the grid cell coordinates containing the given point.
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timonwong/OmniMarkupPreviewer | 21921ac7a99d2b5924a2219b33679a5b53621392 | OmniMarkupLib/Renderers/libs/python3/genshi/path.py | python | Axis.forname | (cls, name) | return getattr(cls, name.upper().replace('-', '_'), None) | Return the axis constant for the given name, or `None` if no such
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/corporate_memberships/models.py | python | CorpMembership.is_join_pending | (self) | return not self.renewal and self.status_detail in [
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alexgolec/tda-api | 3a5232178ecaad423bd530764e547f968a822ec1 | tda/client/base.py | python | BaseClient._datetime_as_millis | (self, var_name, dt) | return int(dt.timestamp() * 1000) | Converts datetime objects to compatible millisecond values | Converts datetime objects to compatible millisecond values | [
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Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/qobj/converters/pulse_instruction.py | python | QobjToInstructionConverter.bind_pulse | (self, pulse) | Bind the supplied pulse to a converter method by pulse name.
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/pyparsing.py | python | MatchFirst.__init__ | ( self, exprs, savelist = False ) | [] | def __init__( self, exprs, savelist = False ):
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mayank93/Twitter-Sentiment-Analysis | f095c6ca6bf69787582b5dabb140fefaf278eb37 | front-end/web2py/gluon/dal.py | python | BaseAdapter.file_close | (self, fileobj) | to be used ONLY for files that on GAE may not be on filesystem | to be used ONLY for files that on GAE may not be on filesystem | [
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ClusterHQ/powerstrip | b18dc7261b001720149849b8ed8f2a31880d76c1 | powerstrip/powerstrip.py | python | DockerProxyClient.handleHeader | (self, key, value) | return proxy.ProxyClient.handleHeader(self, key, value) | [] | def handleHeader(self, key, value):
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angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/analyses/variable_recovery/engine_vex.py | python | SimEngineVRVEX._handle_Load | (self, expr: pyvex.IRExpr.Load) | return self._load(addr, size) | [] | def _handle_Load(self, expr: pyvex.IRExpr.Load) -> RichR:
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davidbau/ganseeing | 93cea2c8f391aef001ddf9dcb35c43990681a47c | seeing/upsegmodel/resnet.py | python | conv3x3 | (in_planes, out_planes, stride=1) | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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igraph/python-igraph | e9f83e8af08f24ea025596e745917197d8b44d94 | src/igraph/cut.py | python | Flow.flow | (self) | return self._flow | Returns the flow values for each edge.
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deepfakes/faceswap | 09c7d8aca3c608d1afad941ea78e9fd9b64d9219 | plugins/convert/color/_base.py | python | Adjustment.set_config | (self, configfile, config) | return retval | Set the config to either global config or passed in config | Set the config to either global config or passed in config | [
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achael/eht-imaging | bbd3aeb06bef52bf89fa1c06de71e5509a5b0015 | ehtim/caltable.py | python | Caltable.copy | (self) | return new_caltable | Copy the observation object.
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/fileinput.py | python | hook_compressed | (filename, mode) | [] | def hook_compressed(filename, mode):
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JetBrains/colorSchemeTool | 5536207cf92f66509318e8fc51dacc2b5a80cac7 | colorSchemeTool.py | python | capitalize_colors | (elem) | [] | def capitalize_colors(elem):
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/repackage/pip/pip/_vendor/requests/adapters.py | python | HTTPAdapter.request_url | (self, request, proxies) | return url | Obtain the url to use when making the final request.
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] | def request_url(self, request, proxies):
"""Obtain the url to use when making the final request.
If the message is being sent through a HTTP proxy, the full URL has to
be used. Otherwise, we should only use the path portion of the URL.
This should not be called from user code, and is only exposed for use
when subclassing the
:class:`HTTPAdapter <requests.adapters.HTTPAdapter>`.
:param request: The :class:`PreparedRequest <PreparedRequest>` being sent.
:param proxies: A dictionary of schemes or schemes and hosts to proxy URLs.
"""
proxy = select_proxy(request.url, proxies)
scheme = urlparse(request.url).scheme
if proxy and scheme != 'https':
url = urldefragauth(request.url)
else:
url = request.path_url
return url | [
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/pillow/build/lib.linux-i686-2.7/PIL/IcoImagePlugin.py | python | IcoFile.frame | (self, idx) | return im | Get an image from frame idx | Get an image from frame idx | [
"Get",
"an",
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"from",
"frame",
"idx"
] | def frame(self, idx):
"""
Get an image from frame idx
"""
header = self.entry[idx]
self.buf.seek(header['offset'])
data = self.buf.read(8)
self.buf.seek(header['offset'])
if data[:8] == PngImagePlugin._MAGIC:
# png frame
im = PngImagePlugin.PngImageFile(self.buf)
else:
# XOR + AND mask bmp frame
im = BmpImagePlugin.DibImageFile(self.buf)
# change tile dimension to only encompass XOR image
im.size = (im.size[0], int(im.size[1] / 2))
d, e, o, a = im.tile[0]
im.tile[0] = d, (0, 0) + im.size, o, a
# figure out where AND mask image starts
mode = a[0]
bpp = 8
for k in BmpImagePlugin.BIT2MODE.keys():
if mode == BmpImagePlugin.BIT2MODE[k][1]:
bpp = k
break
if 32 == bpp:
# 32-bit color depth icon image allows semitransparent areas
# PIL's DIB format ignores transparency bits, recover them.
# The DIB is packed in BGRX byte order where X is the alpha
# channel.
# Back up to start of bmp data
self.buf.seek(o)
# extract every 4th byte (eg. 3,7,11,15,...)
alpha_bytes = self.buf.read(im.size[0] * im.size[1] * 4)[3::4]
# convert to an 8bpp grayscale image
mask = Image.frombuffer(
'L', # 8bpp
im.size, # (w, h)
alpha_bytes, # source chars
'raw', # raw decoder
('L', 0, -1) # 8bpp inverted, unpadded, reversed
)
else:
# get AND image from end of bitmap
w = im.size[0]
if (w % 32) > 0:
# bitmap row data is aligned to word boundaries
w += 32 - (im.size[0] % 32)
# the total mask data is
# padded row size * height / bits per char
and_mask_offset = o + int(im.size[0] * im.size[1] *
(bpp / 8.0))
total_bytes = int((w * im.size[1]) / 8)
self.buf.seek(and_mask_offset)
maskData = self.buf.read(total_bytes)
# convert raw data to image
mask = Image.frombuffer(
'1', # 1 bpp
im.size, # (w, h)
maskData, # source chars
'raw', # raw decoder
('1;I', int(w/8), -1) # 1bpp inverted, padded, reversed
)
# now we have two images, im is XOR image and mask is AND image
# apply mask image as alpha channel
im = im.convert('RGBA')
im.putalpha(mask)
return im | [
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"... | https://github.com/entropy1337/infernal-twin/blob/10995cd03312e39a48ade0f114ebb0ae3a711bb8/Modules/build/pillow/build/lib.linux-i686-2.7/PIL/IcoImagePlugin.py#L153-L235 | |
edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/esp8266_clients/venv/lib/python2.7/site-packages/paho/mqtt/client.py | python | Client.loop_forever | (self, timeout=1.0, max_packets=1, retry_first_connection=False) | return rc | This function call loop() for you in an infinite blocking loop. It
is useful for the case where you only want to run the MQTT client loop
in your program.
loop_forever() will handle reconnecting for you. If you call
disconnect() in a callback it will return.
timeout: The time in seconds to wait for incoming/outgoing network
traffic before timing out and returning.
max_packets: Not currently used.
retry_first_connection: Should the first connection attempt be retried on failure.
Raises socket.error on first connection failures unless retry_first_connection=True | This function call loop() for you in an infinite blocking loop. It
is useful for the case where you only want to run the MQTT client loop
in your program. | [
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"client",
"loop",
"in",
"your",
"program",
... | def loop_forever(self, timeout=1.0, max_packets=1, retry_first_connection=False):
"""This function call loop() for you in an infinite blocking loop. It
is useful for the case where you only want to run the MQTT client loop
in your program.
loop_forever() will handle reconnecting for you. If you call
disconnect() in a callback it will return.
timeout: The time in seconds to wait for incoming/outgoing network
traffic before timing out and returning.
max_packets: Not currently used.
retry_first_connection: Should the first connection attempt be retried on failure.
Raises socket.error on first connection failures unless retry_first_connection=True
"""
run = True
while run:
if self._state == mqtt_cs_connect_async:
try:
self.reconnect()
except socket.error:
if not retry_first_connection:
raise
self._easy_log(MQTT_LOG_DEBUG, "Connection failed, retrying")
time.sleep(1)
else:
break
while run:
rc = MQTT_ERR_SUCCESS
while rc == MQTT_ERR_SUCCESS:
rc = self.loop(timeout, max_packets)
# We don't need to worry about locking here, because we've
# either called loop_forever() when in single threaded mode, or
# in multi threaded mode when loop_stop() has been called and
# so no other threads can access _current_out_packet,
# _out_packet or _messages.
if (self._thread_terminate is True
and self._current_out_packet is None
and len(self._out_packet) == 0
and len(self._out_messages) == 0):
rc = 1
run = False
self._state_mutex.acquire()
if self._state == mqtt_cs_disconnecting or run is False or self._thread_terminate is True:
run = False
self._state_mutex.release()
else:
self._state_mutex.release()
time.sleep(1)
self._state_mutex.acquire()
if self._state == mqtt_cs_disconnecting or run is False or self._thread_terminate is True:
run = False
self._state_mutex.release()
else:
self._state_mutex.release()
try:
self.reconnect()
except socket.error as err:
pass
return rc | [
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django-oscar/django-oscar | ffcc530844d40283b6b1552778a140536b904f5f | src/oscar/apps/shipping/repository.py | python | Repository.get_available_shipping_methods | (
self, basket, shipping_addr=None, **kwargs) | return self.methods | Return a list of all applicable shipping method instances for a given
basket, address etc. This method is intended to be overridden. | Return a list of all applicable shipping method instances for a given
basket, address etc. This method is intended to be overridden. | [
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] | def get_available_shipping_methods(
self, basket, shipping_addr=None, **kwargs):
"""
Return a list of all applicable shipping method instances for a given
basket, address etc. This method is intended to be overridden.
"""
return self.methods | [
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pfnet-research/xfeat | b199c3cdacef408b3d2b1d920b22c243cfe0667c | xfeat/cat_encoder/_basic_encoder.py | python | LabelEncoder.transform | (self, input_df: XDataFrame) | return new_df | Transform data frame.
Args:
input_df (XDataFrame): Input data frame.
Returns:
XDataFrame : Output data frame. | Transform data frame. | [
"Transform",
"data",
"frame",
"."
] | def transform(self, input_df: XDataFrame) -> XDataFrame:
"""Transform data frame.
Args:
input_df (XDataFrame): Input data frame.
Returns:
XDataFrame : Output data frame.
"""
new_df = input_df.copy()
input_cols = self._input_cols
if not input_cols:
input_cols = new_df.columns.tolist()
if self._exclude_cols:
for col in self._exclude_cols:
input_cols.remove(col)
for col in input_cols:
out_col = self._output_prefix + col + self._output_suffix
if cudf_is_available() and isinstance(new_df, cudf.DataFrame):
X = self._uniques[col].get_indexer(new_df[col].to_array())
else:
X = self._uniques[col].get_indexer(new_df[col])
if self._unseen == "n_unique":
missing_values = new_df[col].isna()
unseen_values = np.invert(new_df[col].isin(self._uniques[col]))
unseen_mask = np.bitwise_xor(missing_values, unseen_values)
X[unseen_mask] = len(self._uniques[col])
new_df[out_col] = X
return new_df | [
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