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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_adm_router.py | python | DeploymentConfig.delete_volume_by_name | (self, volume) | return modified | delete a volume | delete a volume | [
"delete",
"a",
"volume"
] | def delete_volume_by_name(self, volume):
'''delete a volume '''
modified = False
exist_volume_mounts = self.get_volume_mounts()
exist_volumes = self.get_volumes()
del_idx = None
for idx, exist_volume in enumerate(exist_volumes):
if 'name' in exist_volume and exist_volume['name'] == volume['name']:
del_idx = idx
break
if del_idx != None:
del exist_volumes[del_idx]
modified = True
del_idx = None
for idx, exist_volume_mount in enumerate(exist_volume_mounts):
if 'name' in exist_volume_mount and exist_volume_mount['name'] == volume['name']:
del_idx = idx
break
if del_idx != None:
del exist_volume_mounts[idx]
modified = True
return modified | [
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | Lib/xml/sax/_exceptions.py | python | SAXException.__getitem__ | (self, ix) | Avoids weird error messages if someone does exception[ix] by
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fonttools/fonttools | 892322aaff6a89bea5927379ec06bc0da3dfb7df | Lib/fontTools/afmLib.py | python | AFM.write | (self, path, sep='\r') | Writes out an AFM font to the given path. | Writes out an AFM font to the given path. | [
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"""Writes out an AFM font to the given path."""
import time
lines = [ "StartFontMetrics 2.0",
"Comment Generated by afmLib; at %s" % (
time.strftime("%m/%d/%Y %H:%M:%S",
time.localtime(time.time())))]
# write comments, assuming (possibly wrongly!) they should
# all appear at the top
for comment in self._comments:
lines.append("Comment " + comment)
# write attributes, first the ones we know about, in
# a preferred order
attrs = self._attrs
for attr in preferredAttributeOrder:
if attr in attrs:
value = attrs[attr]
if attr == "FontBBox":
value = "%s %s %s %s" % value
lines.append(attr + " " + str(value))
# then write the attributes we don't know about,
# in alphabetical order
items = sorted(attrs.items())
for attr, value in items:
if attr in preferredAttributeOrder:
continue
lines.append(attr + " " + str(value))
# write char metrics
lines.append("StartCharMetrics " + repr(len(self._chars)))
items = [(charnum, (charname, width, box)) for charname, (charnum, width, box) in self._chars.items()]
def myKey(a):
"""Custom key function to make sure unencoded chars (-1)
end up at the end of the list after sorting."""
if a[0] == -1:
a = (0xffff,) + a[1:] # 0xffff is an arbitrary large number
return a
items.sort(key=myKey)
for charnum, (charname, width, (l, b, r, t)) in items:
lines.append("C %d ; WX %d ; N %s ; B %d %d %d %d ;" %
(charnum, width, charname, l, b, r, t))
lines.append("EndCharMetrics")
# write kerning info
lines.append("StartKernData")
lines.append("StartKernPairs " + repr(len(self._kerning)))
items = sorted(self._kerning.items())
for (leftchar, rightchar), value in items:
lines.append("KPX %s %s %d" % (leftchar, rightchar, value))
lines.append("EndKernPairs")
lines.append("EndKernData")
if self._composites:
composites = sorted(self._composites.items())
lines.append("StartComposites %s" % len(self._composites))
for charname, components in composites:
line = "CC %s %s ;" % (charname, len(components))
for basechar, xoffset, yoffset in components:
line = line + " PCC %s %s %s ;" % (basechar, xoffset, yoffset)
lines.append(line)
lines.append("EndComposites")
lines.append("EndFontMetrics")
writelines(path, lines, sep) | [
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Asana/python-asana | 9b54ab99423208bd6aa87dbfaa628c069430b127 | asana/resources/custom_fields.py | python | CustomFields.insert_enum_option | (self, custom_field, params={}, **options) | return self.client.post(path, params, **options) | Moves a particular enum option to be either before or after another specified enum option in the custom field.
Locked custom fields can only be reordered by the user who locked the field.
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----------
custom_field : {Gid} Globally unique identifier for the custom field.
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- enum_option : {Gid} The ID of the enum option to relocate.
- name : {String} The name of the enum option.
- [color] : {String} The color of the enum option. Defaults to 'none'.
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"""Moves a particular enum option to be either before or after another specified enum option in the custom field.
Locked custom fields can only be reordered by the user who locked the field.
Parameters
----------
custom_field : {Gid} Globally unique identifier for the custom field.
[data] : {Object} Data for the request
- enum_option : {Gid} The ID of the enum option to relocate.
- name : {String} The name of the enum option.
- [color] : {String} The color of the enum option. Defaults to 'none'.
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- [after_enum_option] : {Gid} An existing enum option within this custom field after which the new enum option should be inserted. Cannot be provided together with before_enum_option.
"""
path = "/custom_fields/%s/enum_options/insert" % (custom_field)
return self.client.post(path, params, **options) | [
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ricequant/rqalpha-mod-ctp | bfd40801f9a182226a911cac74660f62993eb6db | rqalpha_mod_ctp/ctp/pyctp/linux64_36/__init__.py | python | TraderApi.OnRtnRepealFromFutureToBankByBank | (self, pRspRepeal) | 银行发起冲正期货转银行通知 | 银行发起冲正期货转银行通知 | [
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] | def OnRtnRepealFromFutureToBankByBank(self, pRspRepeal):
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/gse/v20191112/gse_client.py | python | GseClient.DescribeFleetRelatedResources | (self, request) | 本接口（DescribeFleetRelatedResources）用于获取与游戏服务器舰队关联的资源信息，如别名、队列
:param request: Request instance for DescribeFleetRelatedResources.
:type request: :class:`tencentcloud.gse.v20191112.models.DescribeFleetRelatedResourcesRequest`
:rtype: :class:`tencentcloud.gse.v20191112.models.DescribeFleetRelatedResourcesResponse` | 本接口（DescribeFleetRelatedResources）用于获取与游戏服务器舰队关联的资源信息，如别名、队列 | [
"本接口（DescribeFleetRelatedResources）用于获取与游戏服务器舰队关联的资源信息，如别名、队列"
] | def DescribeFleetRelatedResources(self, request):
"""本接口（DescribeFleetRelatedResources）用于获取与游戏服务器舰队关联的资源信息，如别名、队列
:param request: Request instance for DescribeFleetRelatedResources.
:type request: :class:`tencentcloud.gse.v20191112.models.DescribeFleetRelatedResourcesRequest`
:rtype: :class:`tencentcloud.gse.v20191112.models.DescribeFleetRelatedResourcesResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeFleetRelatedResources", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeFleetRelatedResourcesResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/printing/precedence.py | python | precedence_FracElement | (item) | [] | def precedence_FracElement(item):
if item.denom == 1:
return precedence_PolyElement(item.numer)
else:
return PRECEDENCE["Mul"] | [
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pnprog/goreviewpartner | cbcc486cd4c51fb6fc3bc0a1eab61ff34298dadf | mss/linux.py | python | MSS.grab | (self, monitor) | return self.cls_image(data, monitor) | Retrieve all pixels from a monitor. Pixels have to be RGB. | Retrieve all pixels from a monitor. Pixels have to be RGB. | [
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# type: (Dict[str, int]) -> ScreenShot
""" Retrieve all pixels from a monitor. Pixels have to be RGB. """
# Convert PIL bbox style
if isinstance(monitor, tuple):
monitor = {
'left': monitor[0],
'top': monitor[1],
'width': monitor[2] - monitor[0],
'height': monitor[3] - monitor[1],
}
# Fix for XGetImage:
# expected LP_Display instance instead of LP_XWindowAttributes
root = ctypes.cast(self.root, ctypes.POINTER(Display))
ximage = self.xlib.XGetImage(self.display, root,
monitor['left'], monitor['top'],
monitor['width'], monitor['height'],
0x00ffffff, 2) # ZPIXMAP
if not ximage:
raise ScreenShotError('xlib.XGetImage() failed.', locals())
bits_per_pixel = ximage.contents.bits_per_pixel
if bits_per_pixel != 32:
raise ScreenShotError(('[XImage] bits per pixel value '
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data = ctypes.cast(ximage.contents.data, ctypes.POINTER(
ctypes.c_ubyte * monitor['height'] * monitor['width'] * 4))
data = bytearray(data.contents)
# Free
self.xlib.XDestroyImage(ximage)
ximage = None
return self.cls_image(data, monitor) | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | tokumx/datadog_checks/tokumx/vendor/pymongo/common.py | python | partition_node | (node) | return host, port | Split a host:port string into (host, int(port)) pair. | Split a host:port string into (host, int(port)) pair. | [
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host = node
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idx = node.rfind(':')
if idx != -1:
host, port = node[:idx], int(node[idx + 1:])
if host.startswith('['):
host = host[1:-1]
return host, port | [
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FSecureLABS/Jandroid | e31d0dab58a2bfd6ed8e0a387172b8bd7c893436 | libs/platform-tools/platform-tools_darwin/systrace/catapult/devil/devil/android/device_utils.py | python | DeviceUtils._GetDumpsysOutput | (self, extra_args, pattern=None) | Runs |dumpsys| command on the device and returns its output.
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"""
try:
cmd = ['dumpsys'] + extra_args
if pattern:
cmd = ' '.join(cmd_helper.SingleQuote(s) for s in cmd)
return self._RunPipedShellCommand(
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else:
cmd = ['dumpsys'] + extra_args
return self.RunShellCommand(cmd, check_return=True, large_output=True)
except device_errors.AdbShellCommandFailedError as e:
if e.status and isinstance(e.status, list) and not e.status[0]:
# If dumpsys succeeded but grep failed, there were no lines matching
# the given pattern.
return []
else:
raise | [
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anki/vector-python-sdk | d61fdb07c6278deba750f987b20441fff2df865f | examples/apps/remote_control/lib/flask_helpers.py | python | _delayed_open_web_browser | (url, delay, new=0, autoraise=True, specific_browser=None) | Spawn a thread and call sleep_and_open_web_browser from within it so that main thread can keep executing at the
same time. Insert a small sleep before opening a web-browser
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"""
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def _sleep_and_open_web_browser(url, delay, new, autoraise, specific_browser):
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# E.g. On OSX the following would use the Chrome browser app from that location
# specific_browser = 'open -a /Applications/Google\ Chrome.app %s'
if specific_browser:
browser = webbrowser.get(specific_browser)
browser.open(url, new=new, autoraise=autoraise)
thread = Thread(target=_sleep_and_open_web_browser,
kwargs=dict(url=url, new=new, autoraise=autoraise, delay=delay, specific_browser=specific_browser))
thread.daemon = True # Force to quit on main quitting
thread.start() | [
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yqyao/FCOS_PLUS | 0d20ba34ccc316650d8c30febb2eb40cb6eaae37 | maskrcnn_benchmark/data/build.py | python | build_dataset | (dataset_list, transforms, dataset_catalog, is_train=True) | return [dataset] | Arguments:
dataset_list (list[str]): Contains the names of the datasets, i.e.,
coco_2014_trian, coco_2014_val, etc
transforms (callable): transforms to apply to each (image, target) sample
dataset_catalog (DatasetCatalog): contains the information on how to
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is_train (bool): whether to setup the dataset for training or testing | Arguments:
dataset_list (list[str]): Contains the names of the datasets, i.e.,
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transforms (callable): transforms to apply to each (image, target) sample
dataset_catalog (DatasetCatalog): contains the information on how to
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"""
Arguments:
dataset_list (list[str]): Contains the names of the datasets, i.e.,
coco_2014_trian, coco_2014_val, etc
transforms (callable): transforms to apply to each (image, target) sample
dataset_catalog (DatasetCatalog): contains the information on how to
construct a dataset.
is_train (bool): whether to setup the dataset for training or testing
"""
if not isinstance(dataset_list, (list, tuple)):
raise RuntimeError(
"dataset_list should be a list of strings, got {}".format(dataset_list)
)
datasets = []
for dataset_name in dataset_list:
data = dataset_catalog.get(dataset_name)
factory = getattr(D, data["factory"])
args = data["args"]
# for COCODataset, we want to remove images without annotations
# during training
if data["factory"] == "COCODataset":
args["remove_images_without_annotations"] = is_train
if data["factory"] == "PascalVOCDataset":
args["use_difficult"] = not is_train
args["transforms"] = transforms
# make dataset from factory
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if not is_train:
return datasets
# for training, concatenate all datasets into a single one
dataset = datasets[0]
if len(datasets) > 1:
dataset = D.ConcatDataset(datasets)
return [dataset] | [
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decalage2/ViperMonkey | 631d242f43108226bb25ed91e773a274012dc8c2 | vipermonkey/api.py | python | Module.__init__ | (self, lines, deobfuscate=False) | Initializes a VBA module (or collection of loose lines)
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Initializes a VBA module (or collection of loose lines)
:param lines: String or list lines representing the code.
:param deobfuscate: Whether to deobfuscate the code first which may speed up processing.
"""
# TODO: pp spec for module?
# Instead of having a pyparsing spec, we are going to manually create the
# parsed object from code blocks.
super(Module, self).__init__(None, lines, deobfuscate=deobfuscate)
# We are also going to include a dummy first line so that _iter_code_blocks()
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self.lines = [''] + self.lines + [''] | [
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getdock/whitelist | 704bb41552f71d1a91ecf35e373d8f0b7434f144 | app/errors.py | python | AppError.__init__ | (self, key: str = None, details: Any = None, code: int = None) | [] | def __init__(self, key: str = None, details: Any = None, code: int = None):
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getsentry/sentry | 83b1f25aac3e08075e0e2495bc29efaf35aca18a | src/sentry/digests/backends/base.py | python | Backend.add | (
self,
key: str,
record: "Record",
increment_delay: Optional[int] = None,
maximum_delay: Optional[int] = None,
timestamp: Optional[float] = None,
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If another record exists in the timeline with the same record key, it
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The return value this function indicates whether or not the timeline is
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] | def add(
self,
key: str,
record: "Record",
increment_delay: Optional[int] = None,
maximum_delay: Optional[int] = None,
timestamp: Optional[float] = None,
) -> bool:
"""
Add a record to a timeline.
Adding a record to a timeline also causes it to be added to the
schedule, if it is not already present.
If another record exists in the timeline with the same record key, it
will be overwritten.
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"""
raise NotImplementedError | [
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androguard/androguard | 8d091cbb309c0c50bf239f805cc1e0931b8dcddc | androguard/core/analysis/auto.py | python | DefaultAndroAnalysis.create_adex | (self, log, dexobj) | return vm_analysis | This method is called in order to create an Analysis object
:param log: an object which corresponds to a unique app
:param androguard.core.bytecodes.dvm.DalvikVMFormat dexobj: a :class:`DalvikVMFormat` object
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"""
This method is called in order to create an Analysis object
:param log: an object which corresponds to a unique app
:param androguard.core.bytecodes.dvm.DalvikVMFormat dexobj: a :class:`DalvikVMFormat` object
:rytpe: a :class:`~androguard.core.analysis.analysis.Analysis` object
"""
vm_analysis = analysis.Analysis(dexobj)
vm_analysis.create_xref()
return vm_analysis | [
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DamnWidget/anaconda | a9998fb362320f907d5ccbc6fcf5b62baca677c0 | anaconda_lib/autopep/autopep8_lib/autopep8.py | python | Reindenter.getline | (self) | return line | Line-getter for tokenize. | Line-getter for tokenize. | [
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"""Line-getter for tokenize."""
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line = ''
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line = self.lines[self.index]
self.index += 1
return line | [
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VirtueSecurity/aws-extender | d123b7e1a845847709ba3a481f11996bddc68a1c | BappModules/s3transfer/utils.py | python | CountCallbackInvoker.increment | (self) | Increment the count by one | Increment the count by one | [
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"""Increment the count by one"""
with self._lock:
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'incremented.'
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self._count += 1 | [
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ageitgey/face_recognition | 87a8449a359fbc0598e95b820e920ce285b8a9d9 | examples/facerec_ipcamera_knn.py | python | train | (train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False) | return knn_clf | Trains a k-nearest neighbors classifier for face recognition.
:param train_dir: directory that contains a sub-directory for each known person, with its name.
(View in source code to see train_dir example tree structure)
Structure:
<train_dir>/
├── <person1>/
│ ├── <somename1>.jpeg
│ ├── <somename2>.jpeg
│ ├── ...
├── <person2>/
│ ├── <somename1>.jpeg
│ └── <somename2>.jpeg
└── ...
:param model_save_path: (optional) path to save model on disk
:param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified
:param knn_algo: (optional) underlying data structure to support knn.default is ball_tree
:param verbose: verbosity of training
:return: returns knn classifier that was trained on the given data. | Trains a k-nearest neighbors classifier for face recognition. | [
"Trains",
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"-",
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"."
] | def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
"""
Trains a k-nearest neighbors classifier for face recognition.
:param train_dir: directory that contains a sub-directory for each known person, with its name.
(View in source code to see train_dir example tree structure)
Structure:
<train_dir>/
├── <person1>/
│ ├── <somename1>.jpeg
│ ├── <somename2>.jpeg
│ ├── ...
├── <person2>/
│ ├── <somename1>.jpeg
│ └── <somename2>.jpeg
└── ...
:param model_save_path: (optional) path to save model on disk
:param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified
:param knn_algo: (optional) underlying data structure to support knn.default is ball_tree
:param verbose: verbosity of training
:return: returns knn classifier that was trained on the given data.
"""
X = []
y = []
# Loop through each person in the training set
for class_dir in os.listdir(train_dir):
if not os.path.isdir(os.path.join(train_dir, class_dir)):
continue
# Loop through each training image for the current person
for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):
image = face_recognition.load_image_file(img_path)
face_bounding_boxes = face_recognition.face_locations(image)
if len(face_bounding_boxes) != 1:
# If there are no people (or too many people) in a training image, skip the image.
if verbose:
print("Image {} not suitable for training: {}".format(img_path, "Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face"))
else:
# Add face encoding for current image to the training set
X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])
y.append(class_dir)
# Determine how many neighbors to use for weighting in the KNN classifier
if n_neighbors is None:
n_neighbors = int(round(math.sqrt(len(X))))
if verbose:
print("Chose n_neighbors automatically:", n_neighbors)
# Create and train the KNN classifier
knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')
knn_clf.fit(X, y)
# Save the trained KNN classifier
if model_save_path is not None:
with open(model_save_path, 'wb') as f:
pickle.dump(knn_clf, f)
return knn_clf | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/redis/sentinel.py | python | SentinelManagedConnection.connect_to | (self, address) | [] | def connect_to(self, address):
self.host, self.port = address
super(SentinelManagedConnection, self).connect()
if self.connection_pool.check_connection:
self.send_command('PING')
if nativestr(self.read_response()) != 'PONG':
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NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/nlp/modules/common/bert_module.py | python | BertModule.input_example | (self) | return tuple([input_ids, token_type_ids, attention_mask]) | Generates input examples for tracing etc.
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A tuple of input examples. | Generates input examples for tracing etc.
Returns:
A tuple of input examples. | [
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"""
Generates input examples for tracing etc.
Returns:
A tuple of input examples.
"""
sample = next(self.parameters())
input_ids = torch.randint(low=0, high=2048, size=(2, 16), device=sample.device)
token_type_ids = torch.randint(low=0, high=1, size=(2, 16), device=sample.device)
attention_mask = torch.randint(low=0, high=1, size=(2, 16), device=sample.device)
return tuple([input_ids, token_type_ids, attention_mask]) | [
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pulp/pulp | a0a28d804f997b6f81c391378aff2e4c90183df9 | server/pulp/server/logs.py | python | _blacklist_loggers | () | Disable all the loggers in the LOG_BLACKLIST. | Disable all the loggers in the LOG_BLACKLIST. | [
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"""
Disable all the loggers in the LOG_BLACKLIST.
"""
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logger.propagate = False | [
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fedden/poker_ai | 73fb394b26623c897459ffa3e66d7a5cb47e9962 | poker_ai/games/short_deck/state.py | python | ShortDeckPokerState.is_terminal | (self) | return self._betting_stage in {"show_down", "terminal"} | Returns whether this state is terminal or not.
The state is terminal once all rounds of betting are complete and we
are at the show down stage of the game or if all players have folded. | Returns whether this state is terminal or not. | [
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"""Returns whether this state is terminal or not.
The state is terminal once all rounds of betting are complete and we
are at the show down stage of the game or if all players have folded.
"""
return self._betting_stage in {"show_down", "terminal"} | [
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Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/idlelib/CodeContext.py | python | CodeContext.update_code_context | (self) | Update context information and lines visible in the context pane. | Update context information and lines visible in the context pane. | [
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if self.topvisible == new_topvisible: # haven't scrolled
return
if self.topvisible < new_topvisible: # scroll down
lines, lastindent = self.get_context(new_topvisible, self.topvisible)
# retain only context info applicable to the region
# between topvisible and new_topvisible:
while self.info[-1][1] >= lastindent:
del self.info[-1]
elif self.topvisible > new_topvisible: # scroll up
stopindent = self.info[-1][1] + 1
# retain only context info associated
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self.label["text"] = '\n'.join(context_strings) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/numpy-1.16.0-py3.7-macosx-10.9-x86_64.egg/numpy/ma/mrecords.py | python | MaskedRecords.soften_mask | (self) | Forces the mask to soft | Forces the mask to soft | [
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"""
Forces the mask to soft
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tahoe-lafs/tahoe-lafs | 766a53b5208c03c45ca0a98e97eee76870276aa1 | src/allmydata/web/root.py | python | RootElement._describe_server | (server) | return {
"peerid": peerid,
"nickname": nickname,
"version": version,
"available_space": available_space,
} | Return a dict containing server stats. | Return a dict containing server stats. | [
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"."
] | def _describe_server(server):
"""Return a dict containing server stats."""
peerid = server.get_longname()
nickname = server.get_nickname()
version = server.get_announcement().get("my-version", "")
space = server.get_available_space()
if space is not None:
available_space = abbreviate_size(space)
else:
available_space = "N/A"
return {
"peerid": peerid,
"nickname": nickname,
"version": version,
"available_space": available_space,
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projecthamster/hamster-gtk | f51b45a77bcc343514ac1439d42ec024ef751a82 | hamster_gtk/overview/widgets/fact_grid.py | python | FactBox.__init__ | (self, fact) | Initialize widget. | Initialize widget. | [
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/pandas/core/internals.py | python | SingleBlockManager.fast_xs | (self, loc) | return self._block.values[loc] | fast path for getting a cross-section
return a view of the data | fast path for getting a cross-section
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"""
fast path for getting a cross-section
return a view of the data
"""
return self._block.values[loc] | [
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HewlettPackard/dlcookbook-dlbs | 863ac1d7e72ad2fcafc78d8a13f67d35bc00c235 | docker/build.py | python | DockerImage.__init__ | (self, prefix: t.Optional[t.Text], name: t.Text, tag: t.Text) | Args:
prefix: Image prefix (user name).
name: Image name (repository).
tag: Image tag.
Full name is PREFIX/NAME:VERSION | Args:
prefix: Image prefix (user name).
name: Image name (repository).
tag: Image tag.
Full name is PREFIX/NAME:VERSION | [
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prefix: Image prefix (user name).
name: Image name (repository).
tag: Image tag.
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self.prefix = prefix or DockerImage.Prefix.DLBS
self.name = name
self.tag = tag | [
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scikit-multiflow/scikit-multiflow | d073a706b5006cba2584761286b7fa17e74e87be | src/skmultiflow/lazy/knn_adwin.py | python | KNNADWINClassifier.reset | (self) | return super().reset() | Reset the estimator.
Resets the ADWIN Drift detector as well as the KNN model.
Returns
-------
KNNADWINClassifier
self | Reset the estimator. | [
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""" Reset the estimator.
Resets the ADWIN Drift detector as well as the KNN model.
Returns
-------
KNNADWINClassifier
self
"""
self.adwin = ADWIN()
return super().reset() | [
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openstack/oslo.config | 64c82a0829c6c5356ed2a44f14c1ea675aa493b7 | oslo_config/iniparser.py | python | BaseParser.new_section | (self, section) | Called when a new section is started. | Called when a new section is started. | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/tkinter/ttk.py | python | Style.theme_names | (self) | return self.tk.splitlist(self.tk.call(self._name, "theme", "names")) | Returns a list of all known themes. | Returns a list of all known themes. | [
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] | def theme_names(self):
"""Returns a list of all known themes."""
return self.tk.splitlist(self.tk.call(self._name, "theme", "names")) | [
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zllrunning/SiameseX.PyTorch | 36fec1e20ac04798d93d98b8b9618a5cf13e04ba | demo_rpn_utils/vot.py | python | VOT.__init__ | (self, region_format) | Constructor
Args:
region_format: Region format options | Constructor | [
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] | def __init__(self, region_format):
""" Constructor
Args:
region_format: Region format options
"""
assert(region_format in ['rectangle', 'polygon'])
if TRAX:
options = trax.server.ServerOptions(region_format, trax.image.PATH)
self._trax = trax.server.Server(options)
request = self._trax.wait()
assert(request.type == 'initialize')
if request.region.type == 'polygon':
self._region = Polygon([Point(x[0], x[1]) for x in request.region.points])
else:
self._region = Rectangle(request.region.x, request.region.y, request.region.width, request.region.height)
self._image = str(request.image)
self._trax.status(request.region)
else:
self._files = [x.strip('\n') for x in open('images.txt', 'r').readlines()]
self._frame = 0
self._region = convert_region(parse_region(open('region.txt', 'r').readline()), region_format)
self._result = [] | [
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wwqgtxx/wwqLyParse | 33136508e52821babd9294fdecffbdf02d73a6fc | wwqLyParse/lib/fallback_lib_py352/pycparser/c_lexer.py | python | CLexer.t_ppline_NEWLINE | (self, t) | r'\n | r'\n | [
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r'\n'
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self._error('line number missing in #line', t)
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self.lexer.lineno = int(self.pp_line)
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self.filename = self.pp_filename
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zenodo/zenodo | 3c45e52a742ad5a0a7788a67b02fbbc15ab4d8d5 | zenodo/modules/utils/grants.py | python | OpenAIREGrantsDump.__init__ | (self, path, rows_write_chunk_size=10000) | Initialize the OpenAIRE grants dump. | Initialize the OpenAIRE grants dump. | [
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] | def __init__(self, path, rows_write_chunk_size=10000):
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self.path = path
self.rows_write_chunk_size = rows_write_chunk_size | [
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mandiant/capa | c0851fc643793c012f5dd764482133c25c3216c8 | capa/features/extractors/viv/basicblock.py | python | extract_stackstring | (f, bb) | check basic block for stackstring indicators | check basic block for stackstring indicators | [
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] | def extract_stackstring(f, bb):
"""check basic block for stackstring indicators"""
if _bb_has_stackstring(f, bb):
yield Characteristic("stack string"), bb.va | [
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jparkhill/TensorMol | d52104dc7ee46eec8301d332a95d672270ac0bd1 | TensorMol/Containers/Mol.py | python | Mol.ForceFromXYZ | (self, path) | Reads the forces from the comment line in the md_dataset,
and if no forces exist sets them to zero. Switched on by
has_force=True in the ReadGDB9Unpacked routine | Reads the forces from the comment line in the md_dataset,
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"""
Reads the forces from the comment line in the md_dataset,
and if no forces exist sets them to zero. Switched on by
has_force=True in the ReadGDB9Unpacked routine
"""
try:
f = open(path, 'r')
lines = f.readlines()
natoms = int(lines[0])
forces=np.zeros((natoms,3))
read_forces = ((lines[1].strip().split(';'))[1]).replace("],[", ",").replace("[","").replace("]","").split(",")
for j in range(natoms):
for k in range(3):
forces[j,k] = float(read_forces[j*3+k])
self.properties['forces'] = forces
except Exception as Ex:
print("Reading Force Failed.", Ex) | [
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/cinder/cinder/utils.py | python | check_isinstance | (obj, cls) | return cls() | Checks that obj is of type cls, and lets PyLint infer types. | Checks that obj is of type cls, and lets PyLint infer types. | [
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"""Checks that obj is of type cls, and lets PyLint infer types."""
if isinstance(obj, cls):
return obj
raise Exception(_('Expected object of type: %s') % (str(cls)))
# TODO(justinsb): Can we make this better??
return cls() | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/prettytable/prettytable/v0_7_2/__init__.py | python | PrettyTable._get_sortby | (self) | return self._sortby | Name of field by which to sort rows
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openstack/oslo.config | 64c82a0829c6c5356ed2a44f14c1ea675aa493b7 | oslo_config/cfg.py | python | _Namespace._check_deprecated | (self, name, current, deprecated) | Check for usage of deprecated names.
:param name: A tuple of the form (group, name) representing the group
and name where an opt value was found.
:param current: A tuple of the form (group, name) representing the
current name for an option.
:param deprecated: A list of tuples with the same format as the name
param which represent any deprecated names for an option.
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deprecation warning will be logged. | Check for usage of deprecated names. | [
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] | def _check_deprecated(self, name, current, deprecated):
"""Check for usage of deprecated names.
:param name: A tuple of the form (group, name) representing the group
and name where an opt value was found.
:param current: A tuple of the form (group, name) representing the
current name for an option.
:param deprecated: A list of tuples with the same format as the name
param which represent any deprecated names for an option.
If the name param matches any entries in this list a
deprecation warning will be logged.
"""
if name in deprecated and name not in self._emitted_deprecations:
self._emitted_deprecations.add(name)
current = (current[0] or 'DEFAULT', current[1])
format_dict = {'dep_option': name[1], 'dep_group': name[0],
'option': current[1], 'group': current[0]}
_report_deprecation(self._deprecated_opt_message, format_dict) | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/django/contrib/gis/gdal/geometries.py | python | OGRGeometry.__del__ | (self) | Deletes this Geometry. | Deletes this Geometry. | [
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"Deletes this Geometry."
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buildbot/buildbot | b9c558217c72e4c2463eedc7ec6d56736f7b01a8 | master/buildbot/www/change_hook.py | python | ChangeHookResource.makeHandler | (self, dialect) | return self._dialect_handlers[dialect] | create and cache the handler object for this dialect | create and cache the handler object for this dialect | [
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"""create and cache the handler object for this dialect"""
if dialect not in self.dialects:
m = "The dialect specified, '{}', wasn't whitelisted in change_hook".format(dialect)
log.msg(m)
log.msg("Note: if dialect is 'base' then it's possible your URL is "
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raise ValueError(m)
if dialect not in self._dialect_handlers:
if dialect not in self._plugins:
m = ("The dialect specified, '{}', is not registered as "
"a buildbot.webhook plugin").format(dialect)
log.msg(m)
raise ValueError(m)
options = self.dialects[dialect]
if isinstance(options, dict) and 'custom_class' in options:
klass = options['custom_class']
else:
klass = self._plugins.get(dialect)
self._dialect_handlers[dialect] = klass(self.master, self.dialects[dialect])
return self._dialect_handlers[dialect] | [
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GeekLiB/caffe-model | f21e52032c92f36eb6622846f6e6710bcd3f2054 | inception_v1.py | python | factorization_conv_bn_scale_relu | (bottom, num_output=64, kernel_size=3, stride=1, pad=0) | return conv, conv_bn, conv_scale, conv_relu | [] | def factorization_conv_bn_scale_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', std=1),
bias_filler=dict(type='constant', value=0.2))
conv_bn = L.BatchNorm(conv, use_global_stats=False, in_place=True)
conv_scale = L.Scale(conv, scale_param=dict(bias_term=True), in_place=True)
conv_relu = L.ReLU(conv, in_place=True)
return conv, conv_bn, conv_scale, conv_relu | [
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1012598167/flask_mongodb_game | 60c7e0351586656ec38f851592886338e50b4110 | python_flask/venv/Lib/site-packages/pip-19.0.3-py3.6.egg/pip/_internal/wheel.py | python | wheel_version | (source_dir) | Return the Wheel-Version of an extracted wheel, if possible.
Otherwise, return None if we couldn't parse / extract it. | Return the Wheel-Version of an extracted wheel, if possible. | [
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] | def wheel_version(source_dir):
# type: (Optional[str]) -> Optional[Tuple[int, ...]]
"""
Return the Wheel-Version of an extracted wheel, if possible.
Otherwise, return None if we couldn't parse / extract it.
"""
try:
dist = [d for d in pkg_resources.find_on_path(None, source_dir)][0]
wheel_data = dist.get_metadata('WHEEL')
wheel_data = Parser().parsestr(wheel_data)
version = wheel_data['Wheel-Version'].strip()
version = tuple(map(int, version.split('.')))
return version
except Exception:
return None | [
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/wifizoo/WifiZooEntities.py | python | AccessPoint.addClient | (self, aWifiClient) | return | [] | def addClient(self, aWifiClient):
self._ClientsList.append( aWifiClient )
return | [
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slackapi/python-slack-sdk | 2dee6656ffacb7de0c29bb2a6c2b51ec6b5dbce7 | slack_sdk/web/legacy_client.py | python | LegacyWebClient.chat_getPermalink | (
self,
*,
channel: str,
message_ts: str,
**kwargs,
) | return self.api_call("chat.getPermalink", http_verb="GET", params=kwargs) | Retrieve a permalink URL for a specific extant message
https://api.slack.com/methods/chat.getPermalink | Retrieve a permalink URL for a specific extant message
https://api.slack.com/methods/chat.getPermalink | [
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self,
*,
channel: str,
message_ts: str,
**kwargs,
) -> Union[Future, SlackResponse]:
"""Retrieve a permalink URL for a specific extant message
https://api.slack.com/methods/chat.getPermalink
"""
kwargs.update({"channel": channel, "message_ts": message_ts})
return self.api_call("chat.getPermalink", http_verb="GET", params=kwargs) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/_vendor/distlib/version.py | python | SemanticVersion.parse | (self, s) | return _semantic_key(s) | [] | def parse(self, s):
return _semantic_key(s) | [
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robhagemans/pcbasic | c3a043b46af66623a801e18a38175be077251ada | pcbasic/basic/memory/scalars.py | python | Scalars.__iter__ | (self) | return iterkeys(self._vars) | Return an iterable over all scalar names. | Return an iterable over all scalar names. | [
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] | def __iter__(self):
"""Return an iterable over all scalar names."""
return iterkeys(self._vars) | [
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/pprint.py | python | pformat | (object, indent=1, width=80, depth=None) | return PrettyPrinter(indent=indent, width=width, depth=depth).pformat(object) | Format a Python object into a pretty-printed representation. | Format a Python object into a pretty-printed representation. | [
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] | def pformat(object, indent=1, width=80, depth=None):
"""Format a Python object into a pretty-printed representation."""
return PrettyPrinter(indent=indent, width=width, depth=depth).pformat(object) | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/github/__init__.py | python | enable_console_debug_logging | () | This function sets up a very simple logging configuration (log everything on standard output) that is useful for troubleshooting. | This function sets up a very simple logging configuration (log everything on standard output) that is useful for troubleshooting. | [
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"""
This function sets up a very simple logging configuration (log everything on standard output) that is useful for troubleshooting.
"""
logger = logging.getLogger("github")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler()) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/celery/app/utils.py | python | Settings.humanize | (self, with_defaults=False, censored=True) | return '\n'.join(
'{0}: {1}'.format(key, pretty(value, width=50))
for key, value in items(self.table(with_defaults, censored))) | Return a human readable text showing configuration changes. | Return a human readable text showing configuration changes. | [
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] | def humanize(self, with_defaults=False, censored=True):
"""Return a human readable text showing configuration changes."""
return '\n'.join(
'{0}: {1}'.format(key, pretty(value, width=50))
for key, value in items(self.table(with_defaults, censored))) | [
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certbot/certbot | 30b066f08260b73fc26256b5484a180468b9d0a6 | certbot/certbot/interfaces.py | python | AccountStorage.load | (self, account_id: str) | Load an account by its id.
:raises .AccountNotFound: if account could not be found
:raises .AccountStorageError: if account could not be loaded
:returns: The account loaded
:rtype: .Account | Load an account by its id. | [
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] | def load(self, account_id: str) -> 'Account': # pragma: no cover
"""Load an account by its id.
:raises .AccountNotFound: if account could not be found
:raises .AccountStorageError: if account could not be loaded
:returns: The account loaded
:rtype: .Account
"""
raise NotImplementedError() | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | txweb2/stream.py | python | ProducerStream.finish | (self, failure=None) | Called by producer when it is done.
If the optional failure argument is passed a Failure instance,
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"""
self.closed = True
if not self.buffer:
self.length = 0
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deferred = self.deferred
self.deferred = None
if failure is not None:
self.failed = True
deferred.errback(failure)
else:
deferred.callback(None)
else:
if failure is not None:
self.failed = True
self.failure = failure | [
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oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | src/oci/waf/waf_client.py | python | WafClient.list_network_address_lists | (self, compartment_id, **kwargs) | Gets a list of all NetworkAddressLists in a compartment.
:param str compartment_id: (required)
The `OCID`__ of the compartment in which to list resources.
__ https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm
:param list[NetworkAddressListLifecycleState] lifecycle_state: (optional)
A filter to return only resources that match the given lifecycleState.
:param str display_name: (optional)
A filter to return only resources that match the entire display name given.
:param str id: (optional)
A filter to return only the NetworkAddressList with the given `OCID`__.
__ https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm
:param int limit: (optional)
The maximum number of items to return.
:param str page: (optional)
A token representing the position at which to start retrieving results.
This must come from the `opc-next-page` header field of a previous response.
:param str sort_order: (optional)
The sort order to use, either 'ASC' or 'DESC'.
Allowed values are: "ASC", "DESC"
:param str sort_by: (optional)
The field to sort by. Only one sort order may be provided.
Default order for timeCreated is descending.
Default order for displayName is ascending.
If no value is specified timeCreated is default.
Allowed values are: "timeCreated", "displayName"
:param str opc_request_id: (optional)
The client request ID for tracing.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.waf.models.NetworkAddressListCollection`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/waf/list_network_address_lists.py.html>`__ to see an example of how to use list_network_address_lists API. | Gets a list of all NetworkAddressLists in a compartment. | [
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"in",
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"."
] | def list_network_address_lists(self, compartment_id, **kwargs):
"""
Gets a list of all NetworkAddressLists in a compartment.
:param str compartment_id: (required)
The `OCID`__ of the compartment in which to list resources.
__ https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm
:param list[NetworkAddressListLifecycleState] lifecycle_state: (optional)
A filter to return only resources that match the given lifecycleState.
:param str display_name: (optional)
A filter to return only resources that match the entire display name given.
:param str id: (optional)
A filter to return only the NetworkAddressList with the given `OCID`__.
__ https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm
:param int limit: (optional)
The maximum number of items to return.
:param str page: (optional)
A token representing the position at which to start retrieving results.
This must come from the `opc-next-page` header field of a previous response.
:param str sort_order: (optional)
The sort order to use, either 'ASC' or 'DESC'.
Allowed values are: "ASC", "DESC"
:param str sort_by: (optional)
The field to sort by. Only one sort order may be provided.
Default order for timeCreated is descending.
Default order for displayName is ascending.
If no value is specified timeCreated is default.
Allowed values are: "timeCreated", "displayName"
:param str opc_request_id: (optional)
The client request ID for tracing.
:param obj retry_strategy: (optional)
A retry strategy to apply to this specific operation/call. This will override any retry strategy set at the client-level.
This should be one of the strategies available in the :py:mod:`~oci.retry` module. This operation will not retry by default, users can also use the convenient :py:data:`~oci.retry.DEFAULT_RETRY_STRATEGY` provided by the SDK to enable retries for it.
The specifics of the default retry strategy are described `here <https://docs.oracle.com/en-us/iaas/tools/python/latest/sdk_behaviors/retries.html>`__.
To have this operation explicitly not perform any retries, pass an instance of :py:class:`~oci.retry.NoneRetryStrategy`.
:return: A :class:`~oci.response.Response` object with data of type :class:`~oci.waf.models.NetworkAddressListCollection`
:rtype: :class:`~oci.response.Response`
:example:
Click `here <https://docs.cloud.oracle.com/en-us/iaas/tools/python-sdk-examples/latest/waf/list_network_address_lists.py.html>`__ to see an example of how to use list_network_address_lists API.
"""
resource_path = "/networkAddressLists"
method = "GET"
# Don't accept unknown kwargs
expected_kwargs = [
"retry_strategy",
"lifecycle_state",
"display_name",
"id",
"limit",
"page",
"sort_order",
"sort_by",
"opc_request_id"
]
extra_kwargs = [_key for _key in six.iterkeys(kwargs) if _key not in expected_kwargs]
if extra_kwargs:
raise ValueError(
"list_network_address_lists got unknown kwargs: {!r}".format(extra_kwargs))
if 'sort_order' in kwargs:
sort_order_allowed_values = ["ASC", "DESC"]
if kwargs['sort_order'] not in sort_order_allowed_values:
raise ValueError(
"Invalid value for `sort_order`, must be one of {0}".format(sort_order_allowed_values)
)
if 'sort_by' in kwargs:
sort_by_allowed_values = ["timeCreated", "displayName"]
if kwargs['sort_by'] not in sort_by_allowed_values:
raise ValueError(
"Invalid value for `sort_by`, must be one of {0}".format(sort_by_allowed_values)
)
query_params = {
"compartmentId": compartment_id,
"lifecycleState": self.base_client.generate_collection_format_param(kwargs.get("lifecycle_state", missing), 'multi'),
"displayName": kwargs.get("display_name", missing),
"id": kwargs.get("id", missing),
"limit": kwargs.get("limit", missing),
"page": kwargs.get("page", missing),
"sortOrder": kwargs.get("sort_order", missing),
"sortBy": kwargs.get("sort_by", missing)
}
query_params = {k: v for (k, v) in six.iteritems(query_params) if v is not missing and v is not None}
header_params = {
"accept": "application/json",
"content-type": "application/json",
"opc-request-id": kwargs.get("opc_request_id", missing)
}
header_params = {k: v for (k, v) in six.iteritems(header_params) if v is not missing and v is not None}
retry_strategy = self.base_client.get_preferred_retry_strategy(
operation_retry_strategy=kwargs.get('retry_strategy'),
client_retry_strategy=self.retry_strategy
)
if retry_strategy:
if not isinstance(retry_strategy, retry.NoneRetryStrategy):
self.base_client.add_opc_client_retries_header(header_params)
retry_strategy.add_circuit_breaker_callback(self.circuit_breaker_callback)
return retry_strategy.make_retrying_call(
self.base_client.call_api,
resource_path=resource_path,
method=method,
query_params=query_params,
header_params=header_params,
response_type="NetworkAddressListCollection")
else:
return self.base_client.call_api(
resource_path=resource_path,
method=method,
query_params=query_params,
header_params=header_params,
response_type="NetworkAddressListCollection") | [
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gunthercox/ChatterBot | 4ff8af28567ed446ae796d37c246bb6a14032fe7 | chatterbot/comparisons.py | python | LevenshteinDistance.compare | (self, statement_a, statement_b) | return percent | Compare the two input statements.
:return: The percent of similarity between the text of the statements.
:rtype: float | Compare the two input statements. | [
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"""
Compare the two input statements.
:return: The percent of similarity between the text of the statements.
:rtype: float
"""
# Return 0 if either statement has a falsy text value
if not statement_a.text or not statement_b.text:
return 0
# Get the lowercase version of both strings
statement_a_text = str(statement_a.text.lower())
statement_b_text = str(statement_b.text.lower())
similarity = SequenceMatcher(
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# Calculate a decimal percent of the similarity
percent = round(similarity.ratio(), 2)
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biopython/biopython | 2dd97e71762af7b046d7f7f8a4f1e38db6b06c86 | Bio/Phylo/_utils.py | python | to_networkx | (tree) | return G | Convert a Tree object to a networkx graph.
The result is useful for graph-oriented analysis, and also interactive
plotting with pylab, matplotlib or pygraphviz, though the resulting diagram
is usually not ideal for displaying a phylogeny.
Requires NetworkX version 0.99 or later. | Convert a Tree object to a networkx graph. | [
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] | def to_networkx(tree):
"""Convert a Tree object to a networkx graph.
The result is useful for graph-oriented analysis, and also interactive
plotting with pylab, matplotlib or pygraphviz, though the resulting diagram
is usually not ideal for displaying a phylogeny.
Requires NetworkX version 0.99 or later.
"""
try:
import networkx
except ImportError:
raise MissingPythonDependencyError(
"Install NetworkX if you want to use to_networkx."
) from None
# NB (1/2010): the networkx API stabilized at v.1.0
# 1.0+: edges accept arbitrary data as kwargs, weights are floats
# 0.99: edges accept weight as a string, nothing else
# pre-0.99: edges accept no additional data
# Ubuntu Lucid LTS uses v0.99, let's support everything
if networkx.__version__ >= "1.0":
def add_edge(graph, n1, n2):
graph.add_edge(n1, n2, weight=n2.branch_length or 1.0)
# Copy branch color value as hex, if available
if hasattr(n2, "color") and n2.color is not None:
graph[n1][n2]["color"] = n2.color.to_hex()
elif hasattr(n1, "color") and n1.color is not None:
# Cascading color attributes
graph[n1][n2]["color"] = n1.color.to_hex()
n2.color = n1.color
# Copy branch weight value (float) if available
if hasattr(n2, "width") and n2.width is not None:
graph[n1][n2]["width"] = n2.width
elif hasattr(n1, "width") and n1.width is not None:
# Cascading width attributes
graph[n1][n2]["width"] = n1.width
n2.width = n1.width
elif networkx.__version__ >= "0.99":
def add_edge(graph, n1, n2):
graph.add_edge(n1, n2, (n2.branch_length or 1.0))
else:
def add_edge(graph, n1, n2):
graph.add_edge(n1, n2)
def build_subgraph(graph, top):
"""Walk down the Tree, building graphs, edges and nodes."""
for clade in top:
graph.add_node(clade.root)
add_edge(graph, top.root, clade.root)
build_subgraph(graph, clade)
if tree.rooted:
G = networkx.DiGraph()
else:
G = networkx.Graph()
G.add_node(tree.root)
build_subgraph(G, tree.root)
return G | [
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theotherp/nzbhydra | 4b03d7f769384b97dfc60dade4806c0fc987514e | libs/urllib.py | python | urlencode | (query, doseq=0) | return '&'.join(l) | Encode a sequence of two-element tuples or dictionary into a URL query string.
If any values in the query arg are sequences and doseq is true, each
sequence element is converted to a separate parameter.
If the query arg is a sequence of two-element tuples, the order of the
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"""Encode a sequence of two-element tuples or dictionary into a URL query string.
If any values in the query arg are sequences and doseq is true, each
sequence element is converted to a separate parameter.
If the query arg is a sequence of two-element tuples, the order of the
parameters in the output will match the order of parameters in the
input.
"""
if hasattr(query,"items"):
# mapping objects
query = query.items()
else:
# it's a bother at times that strings and string-like objects are
# sequences...
try:
# non-sequence items should not work with len()
# non-empty strings will fail this
if len(query) and not isinstance(query[0], tuple):
raise TypeError
# zero-length sequences of all types will get here and succeed,
# but that's a minor nit - since the original implementation
# allowed empty dicts that type of behavior probably should be
# preserved for consistency
except TypeError:
ty,va,tb = sys.exc_info()
raise TypeError, "not a valid non-string sequence or mapping object", tb
l = []
if not doseq:
# preserve old behavior
for k, v in query:
k = quote_plus(str(k))
v = quote_plus(str(v))
l.append(k + '=' + v)
else:
for k, v in query:
k = quote_plus(str(k))
if isinstance(v, str):
v = quote_plus(v)
l.append(k + '=' + v)
elif _is_unicode(v):
# is there a reasonable way to convert to ASCII?
# encode generates a string, but "replace" or "ignore"
# lose information and "strict" can raise UnicodeError
v = quote_plus(v.encode("ASCII","replace"))
l.append(k + '=' + v)
else:
try:
# is this a sufficient test for sequence-ness?
len(v)
except TypeError:
# not a sequence
v = quote_plus(str(v))
l.append(k + '=' + v)
else:
# loop over the sequence
for elt in v:
l.append(k + '=' + quote_plus(str(elt)))
return '&'.join(l) | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/multiprocessing/process.py | python | BaseProcess.terminate | (self) | Terminate process; sends SIGTERM signal or uses TerminateProcess() | Terminate process; sends SIGTERM signal or uses TerminateProcess() | [
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'''
Terminate process; sends SIGTERM signal or uses TerminateProcess()
'''
self._check_closed()
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CGATOxford/cgat | 326aad4694bdfae8ddc194171bb5d73911243947 | CGAT/IndexedGenome.py | python | Quicksect.get | (self, contig, start, end) | return [(x.start, x.end, x.value)
for x in self.mIndex[contig].find(start, end)] | return intervals overlapping with key. | return intervals overlapping with key. | [
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'''return intervals overlapping with key.'''
if contig not in self.mIndex:
raise KeyError("contig %s not in index" % contig)
return [(x.start, x.end, x.value)
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_internal/operations/prepare.py | python | RequirementPreparer.__init__ | (self, build_dir, download_dir, src_dir, wheel_download_dir,
progress_bar, build_isolation, req_tracker) | [] | def __init__(self, build_dir, download_dir, src_dir, wheel_download_dir,
progress_bar, build_isolation, req_tracker):
super(RequirementPreparer, self).__init__()
self.src_dir = src_dir
self.build_dir = build_dir
self.req_tracker = req_tracker
# Where still packed archives should be written to. If None, they are
# not saved, and are deleted immediately after unpacking.
self.download_dir = download_dir
# Where still-packed .whl files should be written to. If None, they are
# written to the download_dir parameter. Separate to download_dir to
# permit only keeping wheel archives for pip wheel.
if wheel_download_dir:
wheel_download_dir = normalize_path(wheel_download_dir)
self.wheel_download_dir = wheel_download_dir
# NOTE
# download_dir and wheel_download_dir overlap semantically and may
# be combined if we're willing to have non-wheel archives present in
# the wheelhouse output by 'pip wheel'.
self.progress_bar = progress_bar
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/docutils/parsers/rst/directives/__init__.py | python | choice | (argument, values) | Directive option utility function, supplied to enable options whose
argument must be a member of a finite set of possible values (must be
lower case). A custom conversion function must be written to use it. For
example::
from docutils.parsers.rst import directives
def yesno(argument):
return directives.choice(argument, ('yes', 'no'))
Raise ``ValueError`` if no argument is found or if the argument's value is
not valid (not an entry in the supplied list). | Directive option utility function, supplied to enable options whose
argument must be a member of a finite set of possible values (must be
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"""
Directive option utility function, supplied to enable options whose
argument must be a member of a finite set of possible values (must be
lower case). A custom conversion function must be written to use it. For
example::
from docutils.parsers.rst import directives
def yesno(argument):
return directives.choice(argument, ('yes', 'no'))
Raise ``ValueError`` if no argument is found or if the argument's value is
not valid (not an entry in the supplied list).
"""
try:
value = argument.lower().strip()
except AttributeError:
raise ValueError('must supply an argument; choose from %s'
% format_values(values))
if value in values:
return value
else:
raise ValueError('"%s" unknown; choose from %s'
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/compilers/c.py | python | Xc16CCompiler.get_include_args | (self, path: str, is_system: bool) | return ['-I' + path] | [] | def get_include_args(self, path: str, is_system: bool) -> T.List[str]:
if path == '':
path = '.'
return ['-I' + path] | [
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espnet/espnet | ea411f3f627b8f101c211e107d0ff7053344ac80 | espnet2/lm/seq_rnn_lm.py | python | SequentialRNNLM.score | (
self,
y: torch.Tensor,
state: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
x: torch.Tensor,
) | return logp, new_state | Score new token.
Args:
y: 1D torch.int64 prefix tokens.
state: Scorer state for prefix tokens
x: 2D encoder feature that generates ys.
Returns:
Tuple of
torch.float32 scores for next token (n_vocab)
and next state for ys | Score new token. | [
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"new",
"token",
"."
] | def score(
self,
y: torch.Tensor,
state: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
x: torch.Tensor,
) -> Tuple[torch.Tensor, Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]]:
"""Score new token.
Args:
y: 1D torch.int64 prefix tokens.
state: Scorer state for prefix tokens
x: 2D encoder feature that generates ys.
Returns:
Tuple of
torch.float32 scores for next token (n_vocab)
and next state for ys
"""
y, new_state = self(y[-1].view(1, 1), state)
logp = y.log_softmax(dim=-1).view(-1)
return logp, new_state | [
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k0retux/fuddly | fd2c4d0c9c7db49e4de654ce545f1661e6c4e8fd | framework/node_builder.py | python | State.init_specific | (self) | Can be overridden to express additional initializations | Can be overridden to express additional initializations | [
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Can be overridden to express additional initializations
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/arrow/locales.py | python | OdiaLocale._ordinal_number | (self, n: int) | return "" | [] | def _ordinal_number(self, n: int) -> str:
if n > 10 or n == 0:
return f"{n}ତମ"
if n in [1, 5, 7, 8, 9, 10]:
return f"{n}ମ"
if n in [2, 3]:
return f"{n}ୟ"
if n == 4:
return f"{n}ର୍ଥ"
if n == 6:
return f"{n}ଷ୍ଠ"
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zim-desktop-wiki/zim-desktop-wiki | fe717d7ee64e5c06d90df90eb87758e5e72d25c5 | zim/gui/applications.py | python | edit_config_file | (widget, configfile) | Edit a config file in an external editor.
See L{edit_file()} for details.
@param widget: a C{gtk} widget to use as parent for dialogs or C{None}
@param configfile: a L{ConfigFile} object | Edit a config file in an external editor.
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See L{edit_file()} for details.
@param widget: a C{gtk} widget to use as parent for dialogs or C{None}
@param configfile: a L{ConfigFile} object
'''
configfile.touch()
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SCSSoftware/BlenderTools | 96f323d3bdf2d8cb8ed7f882dcdf036277a802dd | addon/io_scs_tools/exp/pip/sign.py | python | Sign.set_position | (self, position) | Sets position of the sign.
:param position: position of the sign
:type position: tuple | mathutils.Vector | Sets position of the sign. | [
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"""Sets position of the sign.
:param position: position of the sign
:type position: tuple | mathutils.Vector
"""
self.__position = position | [
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cagbal/ros_people_object_detection_tensorflow | 982ffd4a54b8059638f5cd4aa167299c7fc9e61f | src/object_detection/eval_util.py | python | visualize_detection_results | (result_dict,
tag,
global_step,
categories,
summary_dir='',
export_dir='',
agnostic_mode=False,
show_groundtruth=False,
groundtruth_box_visualization_color='black',
min_score_thresh=.5,
max_num_predictions=20,
skip_scores=False,
skip_labels=False,
keep_image_id_for_visualization_export=False) | Visualizes detection results and writes visualizations to image summaries.
This function visualizes an image with its detected bounding boxes and writes
to image summaries which can be viewed on tensorboard. It optionally also
writes images to a directory. In the case of missing entry in the label map,
unknown class name in the visualization is shown as "N/A".
Args:
result_dict: a dictionary holding groundtruth and detection
data corresponding to each image being evaluated. The following keys
are required:
'original_image': a numpy array representing the image with shape
[1, height, width, 3]
'detection_boxes': a numpy array of shape [N, 4]
'detection_scores': a numpy array of shape [N]
'detection_classes': a numpy array of shape [N]
The following keys are optional:
'groundtruth_boxes': a numpy array of shape [N, 4]
'groundtruth_keypoints': a numpy array of shape [N, num_keypoints, 2]
Detections are assumed to be provided in decreasing order of score and for
display, and we assume that scores are probabilities between 0 and 1.
tag: tensorboard tag (string) to associate with image.
global_step: global step at which the visualization are generated.
categories: a list of dictionaries representing all possible categories.
Each dict in this list has the following keys:
'id': (required) an integer id uniquely identifying this category
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'
'supercategory': (optional) string representing the supercategory
e.g., 'animal', 'vehicle', 'food', etc
summary_dir: the output directory to which the image summaries are written.
export_dir: the output directory to which images are written. If this is
empty (default), then images are not exported.
agnostic_mode: boolean (default: False) controlling whether to evaluate in
class-agnostic mode or not.
show_groundtruth: boolean (default: False) controlling whether to show
groundtruth boxes in addition to detected boxes
groundtruth_box_visualization_color: box color for visualizing groundtruth
boxes
min_score_thresh: minimum score threshold for a box to be visualized
max_num_predictions: maximum number of detections to visualize
skip_scores: whether to skip score when drawing a single detection
skip_labels: whether to skip label when drawing a single detection
keep_image_id_for_visualization_export: whether to keep image identifier in
filename when exported to export_dir
Raises:
ValueError: if result_dict does not contain the expected keys (i.e.,
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'detection_classes') | Visualizes detection results and writes visualizations to image summaries. | [
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"and",
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"visualizations",
"to",
"image",
"summaries",
"."
] | def visualize_detection_results(result_dict,
tag,
global_step,
categories,
summary_dir='',
export_dir='',
agnostic_mode=False,
show_groundtruth=False,
groundtruth_box_visualization_color='black',
min_score_thresh=.5,
max_num_predictions=20,
skip_scores=False,
skip_labels=False,
keep_image_id_for_visualization_export=False):
"""Visualizes detection results and writes visualizations to image summaries.
This function visualizes an image with its detected bounding boxes and writes
to image summaries which can be viewed on tensorboard. It optionally also
writes images to a directory. In the case of missing entry in the label map,
unknown class name in the visualization is shown as "N/A".
Args:
result_dict: a dictionary holding groundtruth and detection
data corresponding to each image being evaluated. The following keys
are required:
'original_image': a numpy array representing the image with shape
[1, height, width, 3]
'detection_boxes': a numpy array of shape [N, 4]
'detection_scores': a numpy array of shape [N]
'detection_classes': a numpy array of shape [N]
The following keys are optional:
'groundtruth_boxes': a numpy array of shape [N, 4]
'groundtruth_keypoints': a numpy array of shape [N, num_keypoints, 2]
Detections are assumed to be provided in decreasing order of score and for
display, and we assume that scores are probabilities between 0 and 1.
tag: tensorboard tag (string) to associate with image.
global_step: global step at which the visualization are generated.
categories: a list of dictionaries representing all possible categories.
Each dict in this list has the following keys:
'id': (required) an integer id uniquely identifying this category
'name': (required) string representing category name
e.g., 'cat', 'dog', 'pizza'
'supercategory': (optional) string representing the supercategory
e.g., 'animal', 'vehicle', 'food', etc
summary_dir: the output directory to which the image summaries are written.
export_dir: the output directory to which images are written. If this is
empty (default), then images are not exported.
agnostic_mode: boolean (default: False) controlling whether to evaluate in
class-agnostic mode or not.
show_groundtruth: boolean (default: False) controlling whether to show
groundtruth boxes in addition to detected boxes
groundtruth_box_visualization_color: box color for visualizing groundtruth
boxes
min_score_thresh: minimum score threshold for a box to be visualized
max_num_predictions: maximum number of detections to visualize
skip_scores: whether to skip score when drawing a single detection
skip_labels: whether to skip label when drawing a single detection
keep_image_id_for_visualization_export: whether to keep image identifier in
filename when exported to export_dir
Raises:
ValueError: if result_dict does not contain the expected keys (i.e.,
'original_image', 'detection_boxes', 'detection_scores',
'detection_classes')
"""
detection_fields = fields.DetectionResultFields
input_fields = fields.InputDataFields
if not set([
input_fields.original_image,
detection_fields.detection_boxes,
detection_fields.detection_scores,
detection_fields.detection_classes,
]).issubset(set(result_dict.keys())):
raise ValueError('result_dict does not contain all expected keys.')
if show_groundtruth and input_fields.groundtruth_boxes not in result_dict:
raise ValueError('If show_groundtruth is enabled, result_dict must contain '
'groundtruth_boxes.')
logging.info('Creating detection visualizations.')
category_index = label_map_util.create_category_index(categories)
image = np.squeeze(result_dict[input_fields.original_image], axis=0)
detection_boxes = result_dict[detection_fields.detection_boxes]
detection_scores = result_dict[detection_fields.detection_scores]
detection_classes = np.int32((result_dict[
detection_fields.detection_classes]))
detection_keypoints = result_dict.get(detection_fields.detection_keypoints)
detection_masks = result_dict.get(detection_fields.detection_masks)
detection_boundaries = result_dict.get(detection_fields.detection_boundaries)
# Plot groundtruth underneath detections
if show_groundtruth:
groundtruth_boxes = result_dict[input_fields.groundtruth_boxes]
groundtruth_keypoints = result_dict.get(input_fields.groundtruth_keypoints)
vis_utils.visualize_boxes_and_labels_on_image_array(
image=image,
boxes=groundtruth_boxes,
classes=None,
scores=None,
category_index=category_index,
keypoints=groundtruth_keypoints,
use_normalized_coordinates=False,
max_boxes_to_draw=None,
groundtruth_box_visualization_color=groundtruth_box_visualization_color)
vis_utils.visualize_boxes_and_labels_on_image_array(
image,
detection_boxes,
detection_classes,
detection_scores,
category_index,
instance_masks=detection_masks,
instance_boundaries=detection_boundaries,
keypoints=detection_keypoints,
use_normalized_coordinates=False,
max_boxes_to_draw=max_num_predictions,
min_score_thresh=min_score_thresh,
agnostic_mode=agnostic_mode,
skip_scores=skip_scores,
skip_labels=skip_labels)
if export_dir:
if keep_image_id_for_visualization_export and result_dict[fields.
InputDataFields()
.key]:
export_path = os.path.join(export_dir, 'export-{}-{}.png'.format(
tag, result_dict[fields.InputDataFields().key]))
else:
export_path = os.path.join(export_dir, 'export-{}.png'.format(tag))
vis_utils.save_image_array_as_png(image, export_path)
summary = tf.Summary(value=[
tf.Summary.Value(
tag=tag,
image=tf.Summary.Image(
encoded_image_string=vis_utils.encode_image_array_as_png_str(
image)))
])
summary_writer = tf.summary.FileWriterCache.get(summary_dir)
summary_writer.add_summary(summary, global_step)
logging.info('Detection visualizations written to summary with tag %s.', tag) | [
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modflowpy/flopy | eecd1ad193c5972093c9712e5c4b7a83284f0688 | flopy/utils/postprocessing.py | python | get_specific_discharge | (
vectors,
model,
head=None,
position="centers",
) | return qx, qy, qz | Get the discharge vector at cell centers at a given time. For "classical"
MODFLOW versions, we calculate it from the flow rate across cell faces.
For MODFLOW 6, we directly take it from MODFLOW output (this requires
setting the option "save_specific_discharge" in the NPF package).
Parameters
----------
vectors : tuple, np.recarray
either a tuple of (flow right face, flow front face, flow lower face)
numpy arrays from a MODFLOW-2005 compatible Cell Budget File
or
a specific discharge recarray from a MODFLOW 6 Cell Budget File
model : object
flopy model object
head : np.ndarray
numpy array of head values for a specific model
position : str
Position at which the specific discharge will be calculated. Possible
values are "centers" (default), "faces" and "vertices".
Returns
-------
(qx, qy, qz) : tuple
Discharge vector.
qx, qy, qz are ndarrays of size (nlay, nrow, ncol) for a structured
grid or size (nlay, ncpl) for an unstructured grid.
The sign of qy is such that the y axis is considered to increase
in the north direction.
The sign of qz is such that the z axis is considered to increase
in the upward direction.
Note: if a head array is provided, inactive and dry cells are
set to NaN. | Get the discharge vector at cell centers at a given time. For "classical"
MODFLOW versions, we calculate it from the flow rate across cell faces.
For MODFLOW 6, we directly take it from MODFLOW output (this requires
setting the option "save_specific_discharge" in the NPF package). | [
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"6",
"we",
... | def get_specific_discharge(
vectors,
model,
head=None,
position="centers",
):
"""
Get the discharge vector at cell centers at a given time. For "classical"
MODFLOW versions, we calculate it from the flow rate across cell faces.
For MODFLOW 6, we directly take it from MODFLOW output (this requires
setting the option "save_specific_discharge" in the NPF package).
Parameters
----------
vectors : tuple, np.recarray
either a tuple of (flow right face, flow front face, flow lower face)
numpy arrays from a MODFLOW-2005 compatible Cell Budget File
or
a specific discharge recarray from a MODFLOW 6 Cell Budget File
model : object
flopy model object
head : np.ndarray
numpy array of head values for a specific model
position : str
Position at which the specific discharge will be calculated. Possible
values are "centers" (default), "faces" and "vertices".
Returns
-------
(qx, qy, qz) : tuple
Discharge vector.
qx, qy, qz are ndarrays of size (nlay, nrow, ncol) for a structured
grid or size (nlay, ncpl) for an unstructured grid.
The sign of qy is such that the y axis is considered to increase
in the north direction.
The sign of qz is such that the z axis is considered to increase
in the upward direction.
Note: if a head array is provided, inactive and dry cells are
set to NaN.
"""
classical_budget = False
spdis, tqx, tqy, tqz = None, None, None, None
modelgrid = model.modelgrid
if head is not None:
head.shape = modelgrid.shape
if isinstance(vectors, (list, tuple)):
classical_budget = True
for ix, vector in enumerate(vectors):
if vector is None:
continue
else:
tshp = list(modelgrid.shape)[::-1]
tshp[ix] += 1
ext_shape = tuple(tshp[::-1])
break
if vectors[ix].shape == modelgrid.shape:
tqx = np.zeros(
(modelgrid.nlay, modelgrid.nrow, modelgrid.ncol + 1),
dtype=np.float32,
)
tqy = np.zeros(
(modelgrid.nlay, modelgrid.nrow + 1, modelgrid.ncol),
dtype=np.float32,
)
tqz = np.zeros(
(modelgrid.nlay + 1, modelgrid.nrow, modelgrid.ncol),
dtype=np.float32,
)
if vectors[0] is not None:
tqx[:, :, 1:] = vectors[0]
if vectors[1] is not None:
tqy[:, 1:, :] = -vectors[1]
if len(vectors) > 2 and vectors[2] is not None:
tqz[1:, :, :] = -vectors[2]
elif vectors[ix].shape == ext_shape:
if vectors[0] is not None:
tqx = vectors[0]
if vectors[1] is not None:
tqy = vectors[1]
if len(vectors) > 2 and vectors[2] is not None:
tqz = vectors[2]
else:
raise IndexError(
"Classical budget components must have "
"the same shape as the modelgrid"
)
else:
spdis = vectors
if classical_budget:
# get saturated thickness (head - bottom elev for unconfined layer)
if head is None:
sat_thk = modelgrid.remove_confining_beds(modelgrid.thick)
else:
sat_thk = modelgrid.saturated_thick(
head, mask=[model.hdry, model.hnoflo]
)
sat_thk.shape = modelgrid.shape
# inform modelgrid of no-flow and dry cells
modelgrid = model.modelgrid
if modelgrid._idomain is None:
modelgrid._idomain = model.dis.ibound
if head is not None:
noflo_or_dry = np.logical_or(
head == model.hnoflo, head == model.hdry
)
modelgrid._idomain[noflo_or_dry] = 0
# get cross section areas along x
delc = np.reshape(modelgrid.delc, (1, modelgrid.nrow, 1))
cross_area_x = delc * sat_thk
# get cross section areas along y
delr = np.reshape(modelgrid.delr, (1, 1, modelgrid.ncol))
cross_area_y = delr * sat_thk
# get cross section areas along z
cross_area_z = np.ones(modelgrid.shape) * delc * delr
# calculate qx, qy, qz
if position == "centers":
qx = np.zeros(modelgrid.shape, dtype=np.float32)
qy = np.zeros(modelgrid.shape, dtype=np.float32)
cross_area_x = (
delc[:] * 0.5 * (sat_thk[:, :, :-1] + sat_thk[:, :, 1:])
)
cross_area_y = (
delr * 0.5 * (sat_thk[:, 1:, :] + sat_thk[:, :-1, :])
)
qx[:, :, 1:] = (
0.5 * (tqx[:, :, 2:] + tqx[:, :, 1:-1]) / cross_area_x
)
qx[:, :, 0] = 0.5 * tqx[:, :, 1] / cross_area_x[:, :, 0]
qy[:, 1:, :] = (
0.5 * (tqy[:, 2:, :] + tqy[:, 1:-1, :]) / cross_area_y
)
qy[:, 0, :] = 0.5 * tqy[:, 1, :] / cross_area_y[:, 0, :]
qz = 0.5 * (tqz[1:, :, :] + tqz[:-1, :, :]) / cross_area_z
elif position == "faces" or position == "vertices":
cross_area_x = modelgrid.array_at_faces(cross_area_x, "x")
cross_area_y = modelgrid.array_at_faces(cross_area_y, "y")
cross_area_z = modelgrid.array_at_faces(cross_area_z, "z")
qx = tqx / cross_area_x
qy = tqy / cross_area_y
qz = tqz / cross_area_z
else:
raise ValueError(f'"{position}" is not a valid value for position')
if position == "vertices":
qx = modelgrid.array_at_verts(qx)
qy = modelgrid.array_at_verts(qy)
qz = modelgrid.array_at_verts(qz)
else:
nnodes = model.modelgrid.nnodes
qx = np.full((nnodes), np.nan, dtype=np.float64)
qy = np.full((nnodes), np.nan, dtype=np.float64)
qz = np.full((nnodes), np.nan, dtype=np.float64)
idx = np.array(spdis["node"]) - 1
qx[idx] = spdis["qx"]
qy[idx] = spdis["qy"]
qz[idx] = spdis["qz"]
qx.shape = modelgrid.shape
qy.shape = modelgrid.shape
qz.shape = modelgrid.shape
# set no-flow and dry cells to NaN
if head is not None and position == "centers":
noflo_or_dry = np.logical_or(head == model.hnoflo, head == model.hdry)
qx[noflo_or_dry] = np.nan
qy[noflo_or_dry] = np.nan
qz[noflo_or_dry] = np.nan
return qx, qy, qz | [
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"... | https://github.com/modflowpy/flopy/blob/eecd1ad193c5972093c9712e5c4b7a83284f0688/flopy/utils/postprocessing.py#L543-L724 | |
sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/modform/cuspidal_submodule.py | python | CuspidalSubmodule_wt1_gH._transformation_matrix | (self) | return self.__transformation_matrix | r"""
Return the transformation matrix between a basis of Galois orbits of
forms with character, and the echelon-form basis.
EXAMPLES::
sage: CuspForms(GammaH(31, [7]), 1)._transformation_matrix()
[1]
sage: CuspForms(GammaH(124, [33]), 1)._transformation_matrix() # long time
[ 1 1 0 0 0 0 1]
[ 0 0 0 0 0 1 0]
[ 1 0 1 1 -1 -1 1]
[ 0 0 0 0 0 0 1]
[ 0 1 0 0 0 0 0]
[ 1 0 0 0 -1 0 1]
[ 0 0 1 0 0 -1 0] | r"""
Return the transformation matrix between a basis of Galois orbits of
forms with character, and the echelon-form basis. | [
"r",
"Return",
"the",
"transformation",
"matrix",
"between",
"a",
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"orbits",
"of",
"forms",
"with",
"character",
"and",
"the",
"echelon",
"-",
"form",
"basis",
"."
] | def _transformation_matrix(self):
r"""
Return the transformation matrix between a basis of Galois orbits of
forms with character, and the echelon-form basis.
EXAMPLES::
sage: CuspForms(GammaH(31, [7]), 1)._transformation_matrix()
[1]
sage: CuspForms(GammaH(124, [33]), 1)._transformation_matrix() # long time
[ 1 1 0 0 0 0 1]
[ 0 0 0 0 0 1 0]
[ 1 0 1 1 -1 -1 1]
[ 0 0 0 0 0 0 1]
[ 0 1 0 0 0 0 0]
[ 1 0 0 0 -1 0 1]
[ 0 0 1 0 0 -1 0]
"""
self.q_expansion_basis() # triggers iterative computation
return self.__transformation_matrix | [
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] | https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/modular/modform/cuspidal_submodule.py#L436-L455 | |
gem/oq-engine | 1bdb88f3914e390abcbd285600bfd39477aae47c | openquake/hazardlib/mfd/truncated_gr.py | python | TruncatedGRMFD._set_a | (self, tmr) | Recalculate an ``a`` value preserving a total moment rate ``tmr`` ::
a = (log10((tmr * bi) / (10 ** (bi*max_mag) - 10 ** (bi*min_mag)))
- 9.05 - log10(b))
where ``bi = 1.5 - b``. If ``bi == 0`` the following formula is used:
a = log10(tmr / (max_mag - min_mag)) - 9.05 - log10(b) | Recalculate an ``a`` value preserving a total moment rate ``tmr`` :: | [
"Recalculate",
"an",
"a",
"value",
"preserving",
"a",
"total",
"moment",
"rate",
"tmr",
"::"
] | def _set_a(self, tmr):
"""
Recalculate an ``a`` value preserving a total moment rate ``tmr`` ::
a = (log10((tmr * bi) / (10 ** (bi*max_mag) - 10 ** (bi*min_mag)))
- 9.05 - log10(b))
where ``bi = 1.5 - b``. If ``bi == 0`` the following formula is used:
a = log10(tmr / (max_mag - min_mag)) - 9.05 - log10(b)
"""
bi = 1.5 - self.b_val
if bi == 0.0:
self.a_val = (math.log10(tmr / (self.max_mag - self.min_mag))
- 9.05
- math.log10(self.b_val))
else:
self.a_val = (math.log10(tmr * bi / (10 ** (bi * self.max_mag)
- 10 ** (bi * self.min_mag)))
- 9.05
- math.log10(self.b_val)) | [
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/models/Ubuntu_12/paramiko/channel.py | python | Channel.resize_pty | (self, width=80, height=24, width_pixels=0, height_pixels=0) | Resize the pseudo-terminal. This can be used to change the width and
height of the terminal emulation created in a previous L{get_pty} call.
@param width: new width (in characters) of the terminal screen
@type width: int
@param height: new height (in characters) of the terminal screen
@type height: int
@param width_pixels: new width (in pixels) of the terminal screen
@type width_pixels: int
@param height_pixels: new height (in pixels) of the terminal screen
@type height_pixels: int
@raise SSHException: if the request was rejected or the channel was
closed | Resize the pseudo-terminal. This can be used to change the width and
height of the terminal emulation created in a previous L{get_pty} call. | [
"Resize",
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"pseudo",
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"previous",
"L",
"{",
"get_pty",
"}",
"call",
"."
] | def resize_pty(self, width=80, height=24, width_pixels=0, height_pixels=0):
"""
Resize the pseudo-terminal. This can be used to change the width and
height of the terminal emulation created in a previous L{get_pty} call.
@param width: new width (in characters) of the terminal screen
@type width: int
@param height: new height (in characters) of the terminal screen
@type height: int
@param width_pixels: new width (in pixels) of the terminal screen
@type width_pixels: int
@param height_pixels: new height (in pixels) of the terminal screen
@type height_pixels: int
@raise SSHException: if the request was rejected or the channel was
closed
"""
if self.closed or self.eof_received or self.eof_sent or not self.active:
raise SSHException('Channel is not open')
m = Message()
m.add_byte(chr(MSG_CHANNEL_REQUEST))
m.add_int(self.remote_chanid)
m.add_string('window-change')
m.add_boolean(False)
m.add_int(width)
m.add_int(height)
m.add_int(width_pixels)
m.add_int(height_pixels)
self.transport._send_user_message(m) | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pymongo/collection.py | python | Collection.delete_many | (self, filter, collation=None, session=None) | return DeleteResult(
self._delete_retryable(
filter, True,
write_concern=write_concern,
collation=collation, session=session),
write_concern.acknowledged) | Delete one or more documents matching the filter.
>>> db.test.count_documents({'x': 1})
3
>>> result = db.test.delete_many({'x': 1})
>>> result.deleted_count
3
>>> db.test.count_documents({'x': 1})
0
:Parameters:
- `filter`: A query that matches the documents to delete.
- `collation` (optional): An instance of
:class:`~pymongo.collation.Collation`. This option is only supported
on MongoDB 3.4 and above.
- `session` (optional): a
:class:`~pymongo.client_session.ClientSession`.
:Returns:
- An instance of :class:`~pymongo.results.DeleteResult`.
.. versionchanged:: 3.6
Added ``session`` parameter.
.. versionchanged:: 3.4
Added the `collation` option.
.. versionadded:: 3.0 | Delete one or more documents matching the filter. | [
"Delete",
"one",
"or",
"more",
"documents",
"matching",
"the",
"filter",
"."
] | def delete_many(self, filter, collation=None, session=None):
"""Delete one or more documents matching the filter.
>>> db.test.count_documents({'x': 1})
3
>>> result = db.test.delete_many({'x': 1})
>>> result.deleted_count
3
>>> db.test.count_documents({'x': 1})
0
:Parameters:
- `filter`: A query that matches the documents to delete.
- `collation` (optional): An instance of
:class:`~pymongo.collation.Collation`. This option is only supported
on MongoDB 3.4 and above.
- `session` (optional): a
:class:`~pymongo.client_session.ClientSession`.
:Returns:
- An instance of :class:`~pymongo.results.DeleteResult`.
.. versionchanged:: 3.6
Added ``session`` parameter.
.. versionchanged:: 3.4
Added the `collation` option.
.. versionadded:: 3.0
"""
write_concern = self._write_concern_for(session)
return DeleteResult(
self._delete_retryable(
filter, True,
write_concern=write_concern,
collation=collation, session=session),
write_concern.acknowledged) | [
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fossasia/x-mario-center | fe67afe28d995dcf4e2498e305825a4859566172 | softwarecenter/ui/gtk3/dialogs/dependency_dialogs.py | python | confirm_install | (parent, datadir, app, db, icons) | return _confirm_internal(parent, datadir, app, db, icons, primary,
button_text, depends, cache) | Confirm install of the given app
(currently only shows a dialog if a installed app needs to be removed
in order to install the application) | Confirm install of the given app | [
"Confirm",
"install",
"of",
"the",
"given",
"app"
] | def confirm_install(parent, datadir, app, db, icons):
"""Confirm install of the given app
(currently only shows a dialog if a installed app needs to be removed
in order to install the application)
"""
cache = db._aptcache
distro = get_distro()
appdetails = app.get_details(db)
if not appdetails.pkg:
return True
depends = cache.get_packages_removed_on_install(appdetails.pkg)
if not depends:
return True
(primary, button_text) = distro.get_install_warning_text(cache,
appdetails.pkg, app.name, depends)
return _confirm_internal(parent, datadir, app, db, icons, primary,
button_text, depends, cache) | [
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ConnorJL/GPT2 | 936fe2a21fad221cb07d0157c00fbb0780c7d114 | experimental/tpu_survival.py | python | TPUSurvival.tpu_name | (self) | return '{}'.format(self.prefix) | Format tpu_name to be used in creation and deletion calls. | Format tpu_name to be used in creation and deletion calls. | [
"Format",
"tpu_name",
"to",
"be",
"used",
"in",
"creation",
"and",
"deletion",
"calls",
"."
] | def tpu_name(self):
"""Format tpu_name to be used in creation and deletion calls."""
return '{}'.format(self.prefix) | [
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")",
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")"
] | https://github.com/ConnorJL/GPT2/blob/936fe2a21fad221cb07d0157c00fbb0780c7d114/experimental/tpu_survival.py#L53-L55 | |
naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/lib-tk/Tkinter.py | python | Canvas.addtag_above | (self, newtag, tagOrId) | Add tag NEWTAG to all items above TAGORID. | Add tag NEWTAG to all items above TAGORID. | [
"Add",
"tag",
"NEWTAG",
"to",
"all",
"items",
"above",
"TAGORID",
"."
] | def addtag_above(self, newtag, tagOrId):
"""Add tag NEWTAG to all items above TAGORID."""
self.addtag(newtag, 'above', tagOrId) | [
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] | https://github.com/naftaliharris/tauthon/blob/5587ceec329b75f7caf6d65a036db61ac1bae214/Lib/lib-tk/Tkinter.py#L2245-L2247 | ||
Bitmessage/PyBitmessage | 97612b049e0453867d6d90aa628f8e7b007b4d85 | src/bitmessageqt/settings.py | python | SettingsDialog.adjust_from_config | (self, config) | Adjust all widgets state according to config settings | Adjust all widgets state according to config settings | [
"Adjust",
"all",
"widgets",
"state",
"according",
"to",
"config",
"settings"
] | def adjust_from_config(self, config):
"""Adjust all widgets state according to config settings"""
# pylint: disable=too-many-branches,too-many-statements
if not self.parent.tray.isSystemTrayAvailable():
self.groupBoxTray.setEnabled(False)
self.groupBoxTray.setTitle(_translate(
"MainWindow", "Tray (not available in your system)"))
for setting in (
'minimizetotray', 'trayonclose', 'startintray'):
config.set('bitmessagesettings', setting, 'false')
else:
self.checkBoxMinimizeToTray.setChecked(
config.getboolean('bitmessagesettings', 'minimizetotray'))
self.checkBoxTrayOnClose.setChecked(
config.safeGetBoolean('bitmessagesettings', 'trayonclose'))
self.checkBoxStartInTray.setChecked(
config.getboolean('bitmessagesettings', 'startintray'))
self.checkBoxHideTrayConnectionNotifications.setChecked(
config.getboolean(
'bitmessagesettings', 'hidetrayconnectionnotifications'))
self.checkBoxShowTrayNotifications.setChecked(
config.getboolean('bitmessagesettings', 'showtraynotifications'))
self.checkBoxStartOnLogon.setChecked(
config.getboolean('bitmessagesettings', 'startonlogon'))
self.checkBoxWillinglySendToMobile.setChecked(
config.safeGetBoolean(
'bitmessagesettings', 'willinglysendtomobile'))
self.checkBoxUseIdenticons.setChecked(
config.safeGetBoolean('bitmessagesettings', 'useidenticons'))
self.checkBoxReplyBelow.setChecked(
config.safeGetBoolean('bitmessagesettings', 'replybelow'))
if state.appdata == paths.lookupExeFolder():
self.checkBoxPortableMode.setChecked(True)
else:
try:
tempfile.NamedTemporaryFile(
dir=paths.lookupExeFolder(), delete=True
).close() # should autodelete
except Exception:
self.checkBoxPortableMode.setDisabled(True)
if 'darwin' in sys.platform:
self.checkBoxMinimizeToTray.setDisabled(True)
self.checkBoxMinimizeToTray.setText(_translate(
"MainWindow",
"Minimize-to-tray not yet supported on your OS."))
self.checkBoxShowTrayNotifications.setDisabled(True)
self.checkBoxShowTrayNotifications.setText(_translate(
"MainWindow",
"Tray notifications not yet supported on your OS."))
if 'win' not in sys.platform and not self.parent.desktop:
self.checkBoxStartOnLogon.setDisabled(True)
self.checkBoxStartOnLogon.setText(_translate(
"MainWindow", "Start-on-login not yet supported on your OS."))
# On the Network settings tab:
self.lineEditTCPPort.setText(str(
config.get('bitmessagesettings', 'port')))
self.checkBoxUPnP.setChecked(
config.safeGetBoolean('bitmessagesettings', 'upnp'))
self.checkBoxUDP.setChecked(
config.safeGetBoolean('bitmessagesettings', 'udp'))
self.checkBoxAuthentication.setChecked(
config.getboolean('bitmessagesettings', 'socksauthentication'))
self.checkBoxSocksListen.setChecked(
config.getboolean('bitmessagesettings', 'sockslisten'))
self.checkBoxOnionOnly.setChecked(
config.safeGetBoolean('bitmessagesettings', 'onionservicesonly'))
self._proxy_type = getSOCKSProxyType(config)
self.comboBoxProxyType.setCurrentIndex(
0 if not self._proxy_type
else self.comboBoxProxyType.findText(self._proxy_type))
self.comboBoxProxyTypeChanged(self.comboBoxProxyType.currentIndex())
self.lineEditSocksHostname.setText(
config.get('bitmessagesettings', 'sockshostname'))
self.lineEditSocksPort.setText(str(
config.get('bitmessagesettings', 'socksport')))
self.lineEditSocksUsername.setText(
config.get('bitmessagesettings', 'socksusername'))
self.lineEditSocksPassword.setText(
config.get('bitmessagesettings', 'sockspassword'))
self.lineEditMaxDownloadRate.setText(str(
config.get('bitmessagesettings', 'maxdownloadrate')))
self.lineEditMaxUploadRate.setText(str(
config.get('bitmessagesettings', 'maxuploadrate')))
self.lineEditMaxOutboundConnections.setText(str(
config.get('bitmessagesettings', 'maxoutboundconnections')))
# Demanded difficulty tab
self.lineEditTotalDifficulty.setText(str((float(
config.getint(
'bitmessagesettings', 'defaultnoncetrialsperbyte')
) / defaults.networkDefaultProofOfWorkNonceTrialsPerByte)))
self.lineEditSmallMessageDifficulty.setText(str((float(
config.getint(
'bitmessagesettings', 'defaultpayloadlengthextrabytes')
) / defaults.networkDefaultPayloadLengthExtraBytes)))
# Max acceptable difficulty tab
self.lineEditMaxAcceptableTotalDifficulty.setText(str((float(
config.getint(
'bitmessagesettings', 'maxacceptablenoncetrialsperbyte')
) / defaults.networkDefaultProofOfWorkNonceTrialsPerByte)))
self.lineEditMaxAcceptableSmallMessageDifficulty.setText(str((float(
config.getint(
'bitmessagesettings', 'maxacceptablepayloadlengthextrabytes')
) / defaults.networkDefaultPayloadLengthExtraBytes)))
# OpenCL
self.comboBoxOpenCL.setEnabled(openclpow.openclAvailable())
self.comboBoxOpenCL.clear()
self.comboBoxOpenCL.addItem("None")
self.comboBoxOpenCL.addItems(openclpow.vendors)
self.comboBoxOpenCL.setCurrentIndex(0)
for i in range(self.comboBoxOpenCL.count()):
if self.comboBoxOpenCL.itemText(i) == config.safeGet(
'bitmessagesettings', 'opencl'):
self.comboBoxOpenCL.setCurrentIndex(i)
break
# Namecoin integration tab
nmctype = config.get('bitmessagesettings', 'namecoinrpctype')
self.lineEditNamecoinHost.setText(
config.get('bitmessagesettings', 'namecoinrpchost'))
self.lineEditNamecoinPort.setText(str(
config.get('bitmessagesettings', 'namecoinrpcport')))
self.lineEditNamecoinUser.setText(
config.get('bitmessagesettings', 'namecoinrpcuser'))
self.lineEditNamecoinPassword.setText(
config.get('bitmessagesettings', 'namecoinrpcpassword'))
if nmctype == "namecoind":
self.radioButtonNamecoinNamecoind.setChecked(True)
elif nmctype == "nmcontrol":
self.radioButtonNamecoinNmcontrol.setChecked(True)
self.lineEditNamecoinUser.setEnabled(False)
self.labelNamecoinUser.setEnabled(False)
self.lineEditNamecoinPassword.setEnabled(False)
self.labelNamecoinPassword.setEnabled(False)
else:
assert False
# Message Resend tab
self.lineEditDays.setText(str(
config.get('bitmessagesettings', 'stopresendingafterxdays')))
self.lineEditMonths.setText(str(
config.get('bitmessagesettings', 'stopresendingafterxmonths'))) | [
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"setEnabled"... | https://github.com/Bitmessage/PyBitmessage/blob/97612b049e0453867d6d90aa628f8e7b007b4d85/src/bitmessageqt/settings.py#L83-L237 | ||
llSourcell/Neural_Network_Voices | 6ab8ccf92138c2b2a91afee4fd406cbb5d70d1a6 | data_load.py | python | get_mfccs_and_spectrogram | (wav_file, trim=True, duration=None, random_crop=False) | return _get_mfcc_log_spec_and_log_mel_spec(wav, hp_default.preemphasis, hp_default.n_fft,
hp_default.win_length, hp_default.hop_length) | This is applied in `train2`, `test2` or `convert` phase. | This is applied in `train2`, `test2` or `convert` phase. | [
"This",
"is",
"applied",
"in",
"train2",
"test2",
"or",
"convert",
"phase",
"."
] | def get_mfccs_and_spectrogram(wav_file, trim=True, duration=None, random_crop=False):
'''This is applied in `train2`, `test2` or `convert` phase.
'''
# Load
wav, _ = librosa.load(wav_file, sr=hp_default.sr)
# Trim
if trim:
wav, _ = librosa.effects.trim(wav)
if random_crop:
wav = wav_random_crop(wav, hp_default.sr, duration)
# Padding or crop
if duration:
length = hp_default.sr * duration
wav = librosa.util.fix_length(wav, length)
return _get_mfcc_log_spec_and_log_mel_spec(wav, hp_default.preemphasis, hp_default.n_fft,
hp_default.win_length, hp_default.hop_length) | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/irc/irc/server.py | python | IRCClient.handle_join | (self, params) | Handle the JOINing of a user to a channel. Valid channel names start
with a # and consist of a-z, A-Z, 0-9 and/or '_'. | Handle the JOINing of a user to a channel. Valid channel names start
with a # and consist of a-z, A-Z, 0-9 and/or '_'. | [
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"-",
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"A",
"-",
"Z",
"0",
"-",
"9",
"and",
"/",
"or",
"_",
"."
] | def handle_join(self, params):
"""
Handle the JOINing of a user to a channel. Valid channel names start
with a # and consist of a-z, A-Z, 0-9 and/or '_'.
"""
channel_names = params.split(' ', 1)[0] # Ignore keys
for channel_name in channel_names.split(','):
r_channel_name = channel_name.strip()
# Valid channel name?
if not re.match('^#([a-zA-Z0-9_])+$', r_channel_name):
raise IRCError.from_name('nosuchchannel',
'%s :No such channel' % r_channel_name)
# Add user to the channel (create new channel if not exists)
channel = self.server.channels.setdefault(r_channel_name, IRCChannel(r_channel_name))
channel.clients.add(self)
# Add channel to user's channel list
self.channels[channel.name] = channel
# Send the topic
response_join = ':%s TOPIC %s :%s' % (channel.topic_by, channel.name, channel.topic)
self.send_queue.append(response_join)
# Send join message to everybody in the channel, including yourself and
# send user list of the channel back to the user.
response_join = ':%s JOIN :%s' % (self.client_ident(), r_channel_name)
for client in channel.clients:
client.send_queue.append(response_join)
nicks = [client.nick for client in channel.clients]
response_userlist = ':%s 353 %s = %s :%s' % (self.server.servername, self.nick, channel.name, ' '.join(nicks))
self.send_queue.append(response_userlist)
response = ':%s 366 %s %s :End of /NAMES list' % (self.server.servername, self.nick, channel.name)
self.send_queue.append(response) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/ply-3.11/example/ansic/cparse.py | python | p_enum_specifier_2 | (t) | enum_specifier : ENUM LBRACE enumerator_list RBRACE | enum_specifier : ENUM LBRACE enumerator_list RBRACE | [
"enum_specifier",
":",
"ENUM",
"LBRACE",
"enumerator_list",
"RBRACE"
] | def p_enum_specifier_2(t):
'enum_specifier : ENUM LBRACE enumerator_list RBRACE'
pass | [
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YvanYin/VNL_Monocular_Depth_Prediction | a11b1efd4b91714e27fd2799d2c01fced4dc2f9e | tools/train_nyu_metric.py | python | train | (train_dataloader, model, epoch, loss_func,
optimizer, scheduler, training_stats, val_dataloader=None, val_err=[], ignore_step=-1) | Train the model in steps | Train the model in steps | [
"Train",
"the",
"model",
"in",
"steps"
] | def train(train_dataloader, model, epoch, loss_func,
optimizer, scheduler, training_stats, val_dataloader=None, val_err=[], ignore_step=-1):
"""
Train the model in steps
"""
model.train()
epoch_steps = math.ceil(len(train_dataloader) / cfg.TRAIN.BATCHSIZE)
base_steps = epoch_steps * epoch + ignore_step if ignore_step != -1 else epoch_steps * epoch
for i, data in enumerate(train_dataloader):
if ignore_step != -1 and i > epoch_steps - ignore_step:
return
scheduler.step() # decay lr every iteration
training_stats.IterTic()
out = model(data)
losses = loss_func.criterion(out['b_fake_softmax'], out['b_fake_logit'], data, epoch)
optimizer.optim(losses)
step = base_steps + i + 1
training_stats.UpdateIterStats(losses)
training_stats.IterToc()
training_stats.LogIterStats(step, epoch, optimizer.optimizer, val_err[0])
# validate the model
if step % cfg.TRAIN.VAL_STEP == 0 and step != 0 and val_dataloader is not None:
model.eval()
val_err[0] = val(val_dataloader, model)
# training mode
model.train()
# save checkpoint
if step % cfg.TRAIN.SNAPSHOT_ITERS == 0 and step != 0:
save_ckpt(train_args, step, epoch, model, optimizer.optimizer, scheduler, val_err[0]) | [
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wikimedia/pywikibot | 81a01ffaec7271bf5b4b170f85a80388420a4e78 | scripts/coordinate_import.py | python | CoordImportRobot.try_import_coordinates_from_page | (self, page, item) | Try import coordinate from the given page to the given item.
:return: whether any coordinates were found and the import
was successful | Try import coordinate from the given page to the given item. | [
"Try",
"import",
"coordinate",
"from",
"the",
"given",
"page",
"to",
"the",
"given",
"item",
"."
] | def try_import_coordinates_from_page(self, page, item) -> bool:
"""
Try import coordinate from the given page to the given item.
:return: whether any coordinates were found and the import
was successful
"""
coordinate = page.coordinates(primary_only=True)
if not coordinate:
return False
newclaim = pywikibot.Claim(self.repo, self.prop)
newclaim.setTarget(coordinate)
source = self.getSource(page.site)
if source:
newclaim.addSource(source)
pywikibot.output('Adding {}, {} to {}'.format(
coordinate.lat, coordinate.lon, item.title()))
# todo: handle exceptions using self.user_add_claim
try:
item.addClaim(newclaim)
except CoordinateGlobeUnknownError as e:
pywikibot.output('Skipping unsupported globe: {}'.format(e.args))
return False
else:
return True | [
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geofront-auth/geofront | 1db8d3c1a7ae1d59caa3a5bb2a36a6e466b226f6 | geofront/backends/dbapi.py | python | DatabaseKeyStore._execute | (self, cursor, sql: str, params: Tuple[str, ...]) | To support various paramstyles. See the following specification:
http://legacy.python.org/dev/peps/pep-0249/#paramstyle | To support various paramstyles. See the following specification: | [
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":"
] | def _execute(self, cursor, sql: str, params: Tuple[str, ...]) -> None:
"""To support various paramstyles. See the following specification:
http://legacy.python.org/dev/peps/pep-0249/#paramstyle
"""
final_params = cast(Union[Tuple[str, ...], Mapping[str, str]], params)
paramstyle = self.db_module.paramstyle # type: ignore
if paramstyle == 'format':
sql = sql.replace('?', '%s')
elif paramstyle != 'qmark':
if paramstyle == 'numeric':
fmt = ':{}'
i = 1
else:
if paramstyle == 'named':
fmt = ':p{}'
else: # pyformat
fmt = '%(p{})s'
final_params = \
{'p' + str(i): val for i, val in enumerate(params)}
i = 0
while '?' in sql:
sql = sql.replace('?', fmt.format(i), 1)
i += 1
cursor.execute(sql, final_params) | [
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secynic/ipwhois | a5d5b65ce3b1d4b2c20bba2e981968f54e1b5e9e | ipwhois/scripts/ipwhois_cli.py | python | IPWhoisCLI.generate_output_network | (self, json_data=None, hr=True, show_name=False,
colorize=True) | return output | The function for generating CLI output RDAP network results.
Args:
json_data (:obj:`dict`): The data to process. Defaults to None.
hr (:obj:`bool`): Enable human readable key translations. Defaults
to True.
show_name (:obj:`bool`): Show human readable name (default is to
only show short). Defaults to False.
colorize (:obj:`bool`): Colorize the console output with ANSI
colors. Defaults to True.
Returns:
str: The generated output. | The function for generating CLI output RDAP network results. | [
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] | def generate_output_network(self, json_data=None, hr=True, show_name=False,
colorize=True):
"""
The function for generating CLI output RDAP network results.
Args:
json_data (:obj:`dict`): The data to process. Defaults to None.
hr (:obj:`bool`): Enable human readable key translations. Defaults
to True.
show_name (:obj:`bool`): Show human readable name (default is to
only show short). Defaults to False.
colorize (:obj:`bool`): Colorize the console output with ANSI
colors. Defaults to True.
Returns:
str: The generated output.
"""
if json_data is None:
json_data = {}
output = generate_output(
line='0',
short=HR_RDAP['network']['_short'] if hr else 'network',
name=HR_RDAP['network']['_name'] if (hr and show_name) else None,
is_parent=True,
colorize=colorize
)
for key, val in json_data['network'].items():
if key in ['links', 'status']:
output += self.generate_output_list(
source='network',
key=key,
val=val,
line='1',
hr=hr,
show_name=show_name,
colorize=colorize
)
elif key in ['notices', 'remarks']:
output += self.generate_output_notices(
source='network',
key=key,
val=val,
line='1',
hr=hr,
show_name=show_name,
colorize=colorize
)
elif key == 'events':
output += self.generate_output_events(
source='network',
key=key,
val=val,
line='1',
hr=hr,
show_name=show_name,
colorize=colorize
)
elif key not in ['raw']:
output += generate_output(
line='1',
short=HR_RDAP['network'][key]['_short'] if hr else key,
name=HR_RDAP['network'][key]['_name'] if (
hr and show_name) else None,
value=val,
colorize=colorize
)
return output | [
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hypothesis/h | 25ef1b8d94889df86ace5a084f1aa0effd9f4e25 | h/search/config.py | python | delete_index | (client, index_name) | Delete an unused index.
This must be an actual index and not an alias. | Delete an unused index. | [
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"""
Delete an unused index.
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pygments/lexers/graphics.py | python | _shortened | (word) | return '|'.join(word[:dpos] + word[dpos+1:i] + r'\b'
for i in range(len(word), dpos, -1)) | [] | def _shortened(word):
dpos = word.find('$')
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seppius-xbmc-repo/ru | d0879d56ec8243b2c7af44fda5cf3d1ff77fd2e2 | plugin.video.online.anidub.com/resources/lib/BeautifulSoup.py | python | PageElement.findAllPrevious | (self, name=None, attrs={}, text=None, limit=None,
**kwargs) | return self._findAll(name, attrs, text, limit, self.previousGenerator,
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eerimoq/asn1tools | 30eb88e287cc1616903858aa96ee8791a4d7bf1c | asn1tools/codecs/compiler.py | python | Compiler.pre_process_components_of | (self, type_descriptors, module_name) | COMPONENTS OF expansion. | COMPONENTS OF expansion. | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/com.oracle.graal.python.benchmarks/python/meso/srad_rodinia-sized2.py | python | srad | (nIter, size_R, cols, rows, J, dN, dS, dE, dW, c, r1, r2, c1, c2, Lambda) | [] | def srad(nIter, size_R, cols, rows, J, dN, dS, dE, dW, c, r1, r2, c1, c2, Lambda):
for ii in range(nIter):
Sum = 0.
Sum2 = 0.
for i in range(r1, r2+1):
for j in range(c1, c2+1):
tmp = J[i * cols + j]
Sum += tmp
Sum2 += tmp*tmp
meanROI = Sum / size_R
varROI = (Sum2 / size_R) - meanROI*meanROI
q0sqr = varROI / (meanROI*meanROI)
srad_1(q0sqr, cols, rows, J, dN, dS, dE, dW, c)
srad_2(cols, rows, J, dN, dS, dE, dW, c, Lambda) | [
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pip/_vendor/distlib/locators.py | python | Locator.prefer_url | (self, url1, url2) | return result | Choose one of two URLs where both are candidates for distribution
archives for the same version of a distribution (for example,
.tar.gz vs. zip).
The current implementation favours https:// URLs over http://, archives
from PyPI over those from other locations, wheel compatibility (if a
wheel) and then the archive name. | Choose one of two URLs where both are candidates for distribution
archives for the same version of a distribution (for example,
.tar.gz vs. zip). | [
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"""
Choose one of two URLs where both are candidates for distribution
archives for the same version of a distribution (for example,
.tar.gz vs. zip).
The current implementation favours https:// URLs over http://, archives
from PyPI over those from other locations, wheel compatibility (if a
wheel) and then the archive name.
"""
result = url2
if url1:
s1 = self.score_url(url1)
s2 = self.score_url(url2)
if s1 > s2:
result = url1
if result != url2:
logger.debug('Not replacing %r with %r', url1, url2)
else:
logger.debug('Replacing %r with %r', url1, url2)
return result | [
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markj3d/Red9_StudioPack | 1d40a8bf84c45ce7eaefdd9ccfa3cdbeb1471919 | core/Red9_Meta.py | python | MetaHUDNode.getHudDisplays | (self) | return ['MetaHUDConnector%s' % attr for attr in self.monitorAttrs] | each line in the HUD is actually a separate HUD in itself so we need
to carefully manage this list | each line in the HUD is actually a separate HUD in itself so we need
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'''
each line in the HUD is actually a separate HUD in itself so we need
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'''
return ['MetaHUDConnector%s' % attr for attr in self.monitorAttrs] | [
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quic/aimet | dae9bae9a77ca719aa7553fefde4768270fc3518 | TrainingExtensions/torch/src/python/aimet_torch/adaround/adaround_optimizer.py | python | AdaroundOptimizer.adaround_module | (cls, module: torch.nn.Module, quant_module: StaticGridQuantWrapper,
orig_model: torch.nn.Module, quant_model: torch.nn.Module,
act_func: Union[torch.nn.Module, None], cached_dataset: Dataset,
opt_params: AdaroundHyperParameters) | Adaround module
:param module: Original module
:param quant_module: Quantized wrapper module
:param orig_model: The original, un quantized, model
:param quant_model: QuantSim model
:param act_func: Activation function
:param cached_dataset: Cached dataset
:param opt_params: Optimization parameters | Adaround module
:param module: Original module
:param quant_module: Quantized wrapper module
:param orig_model: The original, un quantized, model
:param quant_model: QuantSim model
:param act_func: Activation function
:param cached_dataset: Cached dataset
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act_func: Union[torch.nn.Module, None], cached_dataset: Dataset,
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assert isinstance(quant_module, StaticGridQuantWrapper), '%s is not wrapper module.' % quant_module
assert quant_module.param_quantizers['weight'], '%s does not have weight quantizer.' % quant_module
# Get input and output data of batch size to compute reconstruction error of output activations
# before and after optimization
iterator = iter(cached_dataset)
inp_data, out_data = ActivationSampler.sample_activation(module, quant_module, orig_model,
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recons_err_hard, recons_err_soft = cls._compute_recons_metrics(quant_module, act_func, inp_data, out_data)
logger.debug("Before opt, Recons. error metrics using soft rounding=%f and hard rounding=%f", recons_err_soft,
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# Optimize weight rounding
cls._optimize_rounding(module, quant_module, orig_model, quant_model, act_func, cached_dataset, opt_params)
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quant_module.param_quantizers['weight'].use_soft_rounding = False | [
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Ultimaker/Uranium | 66da853cd9a04edd3a8a03526fac81e83c03f5aa | UM/SortedList.py | python | SortedList.bisect_right | (self, value) | return self._loc(pos, idx) | Return an index to insert `value` in the sorted list.
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Similar to the `bisect` module in the standard library.
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>>> sl.bisect_right(12)
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Similar to `bisect_left`, but if `value` is already present, the
insertion point with be after (to the right of) any existing values.
Similar to the `bisect` module in the standard library.
Runtime complexity: `O(log(n))` -- approximate.
>>> sl = SortedList([10, 11, 12, 13, 14])
>>> sl.bisect_right(12)
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:param value: insertion index of value in sorted list
:return: index
"""
_maxes = self._maxes
if not _maxes:
return 0
pos = bisect_right(_maxes, value)
if pos == len(_maxes):
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idx = bisect_right(self._lists[pos], value)
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ukBaz/python-bluezero | e6b4e96342de6c66571a6660d711c018f8c6b470 | bluezero/central.py | python | Central.services_available | (self) | return self.rmt_device.services_available | Get a list of Service UUIDs available on this device | Get a list of Service UUIDs available on this device | [
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SebKuzminsky/pycam | 55e3129f518e470040e79bb00515b4bfcf36c172 | pycam/Utils/threading.py | python | ProcessStatistics.__str__ | (self) | return os.linesep.join([str(item)
for item in self.processes.values() + self.queues.values()]) | [] | def __str__(self):
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/lib-tk/turtle.py | python | _Root.set_geometry | (self, width, height, startx, starty) | [] | def set_geometry(self, width, height, startx, starty):
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/ex-submodules/pillowtop/management/commands/ptop_run_docs.py | python | Command.log | (self, string) | [] | def log(self, string):
timestamp = datetime.datetime.utcnow().replace(microsecond=0)
print("[{}] {}".format(timestamp, string)) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/categories/baseclasses.py | python | Morphism.__mul__ | (self, other) | return self.compose(other) | r"""
Composes self with the supplied morphism.
The semantics of this operation is given by the following
equation: ``g * f == g.compose(f)`` for composable morphisms
``g`` and ``f``.
See Also
========
compose | r"""
Composes self with the supplied morphism. | [
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r"""
Composes self with the supplied morphism.
The semantics of this operation is given by the following
equation: ``g * f == g.compose(f)`` for composable morphisms
``g`` and ``f``.
See Also
========
compose
"""
return self.compose(other) | [
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NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | TensorFlow/LanguageModeling/BERT/tokenization.py | python | _is_control | (char) | return False | Checks whether `chars` is a control character. | Checks whether `chars` is a control character. | [
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] | def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False | [
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learningequality/ka-lite | 571918ea668013dcf022286ea85eff1c5333fb8b | kalite/packages/bundled/django/core/serializers/__init__.py | python | _load_serializers | () | Register built-in and settings-defined serializers. This is done lazily so
that user code has a chance to (e.g.) set up custom settings without
needing to be careful of import order. | Register built-in and settings-defined serializers. This is done lazily so
that user code has a chance to (e.g.) set up custom settings without
needing to be careful of import order. | [
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"""
Register built-in and settings-defined serializers. This is done lazily so
that user code has a chance to (e.g.) set up custom settings without
needing to be careful of import order.
"""
global _serializers
serializers = {}
for format in BUILTIN_SERIALIZERS:
register_serializer(format, BUILTIN_SERIALIZERS[format], serializers)
if hasattr(settings, "SERIALIZATION_MODULES"):
for format in settings.SERIALIZATION_MODULES:
register_serializer(format, settings.SERIALIZATION_MODULES[format], serializers)
_serializers = serializers | [
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"... | https://github.com/learningequality/ka-lite/blob/571918ea668013dcf022286ea85eff1c5333fb8b/kalite/packages/bundled/django/core/serializers/__init__.py#L112-L125 |
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