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value | identifier stringlengths 1 140 | parameters stringlengths 0 87.7k | argument_list stringclasses 1
value | return_statement stringlengths 0 426k | docstring stringlengths 0 64.3k | docstring_summary stringlengths 0 26.3k | docstring_tokens list | function stringlengths 18 4.83M | function_tokens list | url stringlengths 83 304 |
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autokey/autokey | 0c848b8b3e066f1261260ad580d5996408cf2b98 | lib/autokey/model.py | python | Phrase.should_prompt | (self, buffer) | return self.prompt | Get a value indicating whether the user should be prompted to select the phrase.
Always returns true if the phrase has been triggered using predictive mode. | Get a value indicating whether the user should be prompted to select the phrase.
Always returns true if the phrase has been triggered using predictive mode. | [
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] | def should_prompt(self, buffer):
"""
Get a value indicating whether the user should be prompted to select the phrase.
Always returns true if the phrase has been triggered using predictive mode.
"""
# TODO - re-enable me if restoring predictive functionality
#if TriggerMode.PREDICTIVE in self.modes:
# if self._should_trigger_predictive(buffer):
# return True
return self.prompt | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/gettext.py | python | NullTranslations.ngettext | (self, msgid1, msgid2, n) | [] | def ngettext(self, msgid1, msgid2, n):
if self._fallback:
return self._fallback.ngettext(msgid1, msgid2, n)
if n == 1:
return msgid1
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/wheel/signatures/__init__.py | python | get_ed25519ll | () | return ed25519ll | Lazy import-and-test of ed25519 module | Lazy import-and-test of ed25519 module | [
"Lazy",
"import",
"-",
"and",
"-",
"test",
"of",
"ed25519",
"module"
] | def get_ed25519ll():
"""Lazy import-and-test of ed25519 module"""
global ed25519ll
if not ed25519ll:
try:
import ed25519ll # fast (thousands / s)
except (ImportError, OSError): # pragma nocover
from . import ed25519py as ed25519ll # pure Python (hundreds / s)
test()
return ed25519ll | [
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grnet/synnefo | d06ec8c7871092131cdaabf6b03ed0b504c93e43 | snf-pithos-backend/pithos/backends/lib/sqlalchemy/groups.py | python | Groups.group_destroy | (self, owner) | Delete all groups belonging to owner. | Delete all groups belonging to owner. | [
"Delete",
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] | def group_destroy(self, owner):
"""Delete all groups belonging to owner."""
s = self.groups.delete().where(self.groups.c.owner == owner)
r = self.conn.execute(s)
r.close() | [
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tenable/pyTenable | 1ccab9fc6f6e4c9f1cfe5128f694388ea112719d | tenable/ot/graphql/assets.py | python | AssetSchema.to_object | (self, data, **kwargs) | return Asset(**data) | This method turns the schema into its corresponding object. | This method turns the schema into its corresponding object. | [
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] | def to_object(self, data, **kwargs):
'''This method turns the schema into its corresponding object.'''
return Asset(**data) | [
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gratipay/gratipay.com | dc4e953a8a5b96908e2f3ea7f8fef779217ba2b6 | gratipay/models/team/__init__.py | python | slugize | (name) | return slug | Create a slug from a team name. | Create a slug from a team name. | [
"Create",
"a",
"slug",
"from",
"a",
"team",
"name",
"."
] | def slugize(name):
""" Create a slug from a team name.
"""
if TEAM_NAME_PATTERN.match(name) is None:
raise InvalidTeamName
slug = name.strip()
for c in (',', ' '):
slug = slug.replace(c, '-') # Avoid % encoded characters in slug url.
while '--' in slug:
slug = slug.replace('--', '-')
slug = slug.strip('-')
return slug | [
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opensourcesec/CIRTKit | 58b8793ada69320ffdbdd4ecdc04a3bb2fa83c37 | modules/reversing/viper/peepdf/jsbeautifier/unpackers/packer.py | python | _replacestrings | (source) | return source | Strip string lookup table (list) and replace values in source. | Strip string lookup table (list) and replace values in source. | [
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] | def _replacestrings(source):
"""Strip string lookup table (list) and replace values in source."""
match = re.search(r'var *(_\w+)\=\["(.*?)"\];', source, re.DOTALL)
if match:
varname, strings = match.groups()
startpoint = len(match.group(0))
lookup = strings.split('","')
variable = '%s[%%d]' % varname
for index, value in enumerate(lookup):
source = source.replace(variable % index, '"%s"' % value)
return source[startpoint:]
return source | [
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scipy/scipy | e0a749f01e79046642ccfdc419edbf9e7ca141ad | scipy/sparse/linalg/_dsolve/linsolve.py | python | _get_umf_family | (A) | return family, A_new | Get umfpack family string given the sparse matrix dtype. | Get umfpack family string given the sparse matrix dtype. | [
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"string",
"given",
"the",
"sparse",
"matrix",
"dtype",
"."
] | def _get_umf_family(A):
"""Get umfpack family string given the sparse matrix dtype."""
_families = {
(np.float64, np.int32): 'di',
(np.complex128, np.int32): 'zi',
(np.float64, np.int64): 'dl',
(np.complex128, np.int64): 'zl'
}
f_type = np.sctypeDict[A.dtype.name]
i_type = np.sctypeDict[A.indices.dtype.name]
try:
family = _families[(f_type, i_type)]
except KeyError as e:
msg = 'only float64 or complex128 matrices with int32 or int64' \
' indices are supported! (got: matrix: %s, indices: %s)' \
% (f_type, i_type)
raise ValueError(msg) from e
# See gh-8278. Considered converting only if
# A.shape[0]*A.shape[1] > np.iinfo(np.int32).max,
# but that didn't always fix the issue.
family = family[0] + "l"
A_new = copy.copy(A)
A_new.indptr = np.array(A.indptr, copy=False, dtype=np.int64)
A_new.indices = np.array(A.indices, copy=False, dtype=np.int64)
return family, A_new | [
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dBeker/Faster-RCNN-TensorFlow-Python3 | 027e5603551b3b9053042a113b4c7be9579dbb4a | lib/datasets/voc_eval.py | python | parse_rec | (filename) | return objects | Parse a PASCAL VOC xml file | Parse a PASCAL VOC xml file | [
"Parse",
"a",
"PASCAL",
"VOC",
"xml",
"file"
] | def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects | [
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funnyzhou/FPN-Pytorch | 423a4499c4e826d17367762e821b51b9b1b0f2f3 | lib/datasets/json_dataset_evaluator.py | python | evaluate_box_proposals | (
json_dataset, roidb, thresholds=None, area='all', limit=None
) | return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
'gt_overlaps': gt_overlaps, 'num_pos': num_pos} | Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results. | Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results. | [
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json_dataset, roidb, thresholds=None, area='all', limit=None
):
"""Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results.
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = {
'all': 0,
'small': 1,
'medium': 2,
'large': 3,
'96-128': 4,
'128-256': 5,
'256-512': 6,
'512-inf': 7}
area_ranges = [
[0**2, 1e5**2], # all
[0**2, 32**2], # small
[32**2, 96**2], # medium
[96**2, 1e5**2], # large
[96**2, 128**2], # 96-128
[128**2, 256**2], # 128-256
[256**2, 512**2], # 256-512
[512**2, 1e5**2]] # 512-inf
assert area in areas, 'Unknown area range: {}'.format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = np.zeros(0)
num_pos = 0
for entry in roidb:
gt_inds = np.where(
(entry['gt_classes'] > 0) & (entry['is_crowd'] == 0))[0]
gt_boxes = entry['boxes'][gt_inds, :]
gt_areas = entry['seg_areas'][gt_inds]
valid_gt_inds = np.where(
(gt_areas >= area_range[0]) & (gt_areas <= area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
boxes = entry['boxes'][non_gt_inds, :]
if boxes.shape[0] == 0:
continue
if limit is not None and boxes.shape[0] > limit:
boxes = boxes[:limit, :]
overlaps = box_utils.bbox_overlaps(
boxes.astype(dtype=np.float32, copy=False),
gt_boxes.astype(dtype=np.float32, copy=False))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
for j in range(min(boxes.shape[0], gt_boxes.shape[0])):
# find which proposal box maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# and get the iou amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert gt_ovr >= 0
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert _gt_overlaps[j] == gt_ovr
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
'gt_overlaps': gt_overlaps, 'num_pos': num_pos} | [
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elbayadm/attn2d | 982653439dedc7306e484e00b3dfb90e2cd7c9e1 | examples/pervasive/modules/archive/pa_gatenet8.py | python | PAGateNet8.forward | (self, x,
encoder_mask=None,
decoder_mask=None,
incremental_state=None) | return features | Input : B, Tt, Ts, C
Output : B, Tt, Ts, C | Input : B, Tt, Ts, C
Output : B, Tt, Ts, C | [
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encoder_mask=None,
decoder_mask=None,
incremental_state=None):
"""
Input : B, Tt, Ts, C
Output : B, Tt, Ts, C
"""
if self.reduce_channels is not None:
x = self.reduce_channels(x)
features = self.gate_embeddings(x)
for layer, gate in zip(self.blocks, self.gates):
xlayer = layer(x,
encoder_mask=encoder_mask,
decoder_mask=decoder_mask,
incremental_state=incremental_state)
features += gate(xlayer)
x = self.depth_gate(x + xlayer)
return features | [
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angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/analyses/binary_optimizer.py | python | BinaryOptimizer._constant_propagation | (self, function, data_graph) | :param function:
:param networkx.MultiDiGraph data_graph:
:return: | [] | def _constant_propagation(self, function, data_graph): #pylint:disable=unused-argument
"""
:param function:
:param networkx.MultiDiGraph data_graph:
:return:
"""
# find all edge sequences that looks like const->reg->memory
for n0 in data_graph.nodes():
if not isinstance(n0.variable, SimConstantVariable):
continue
n1s = list(data_graph.successors(n0))
if len(n1s) != 1:
continue
n1 = n1s[0]
if not isinstance(n1.variable, SimRegisterVariable):
continue
if len(list(data_graph.predecessors(n1))) != 1:
continue
n2s = list(data_graph.successors(n1))
if len(n2s) != 1:
continue
n2 = n2s[0]
if not isinstance(n2.variable, SimMemoryVariable):
continue
n2_inedges = data_graph.in_edges(n2, data=True)
if len([ 0 for _, _, data in n2_inedges if 'type' in data and data['type'] == 'mem_data' ]) != 1:
continue
cp = ConstantPropagation(n0.variable.value, n0.location, n2.location)
self.constant_propagations.append(cp) | [
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BlackLight/platypush | a6b552504e2ac327c94f3a28b607061b6b60cf36 | platypush/plugins/mail/imap/__init__.py | python | MailImapPlugin.remove_flags | (self, messages: List[int], flags: Union[str, List[str]], folder: str = 'INBOX', **connect_args) | Remove a set of flags to the specified set of message IDs.
:param messages: List of message IDs.
:param flags: List of flags to be added. Examples:
.. code-block:: python
['Flagged']
['Seen', 'Deleted']
['Junk']
:param folder: IMAP folder (default: ``INBOX``).
:param connect_args: Arguments to pass to :meth:`._get_server_info` for server configuration override. | Remove a set of flags to the specified set of message IDs. | [
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] | def remove_flags(self, messages: List[int], flags: Union[str, List[str]], folder: str = 'INBOX', **connect_args):
"""
Remove a set of flags to the specified set of message IDs.
:param messages: List of message IDs.
:param flags: List of flags to be added. Examples:
.. code-block:: python
['Flagged']
['Seen', 'Deleted']
['Junk']
:param folder: IMAP folder (default: ``INBOX``).
:param connect_args: Arguments to pass to :meth:`._get_server_info` for server configuration override.
"""
with self.connect(**connect_args) as client:
client.select_folder(folder)
client.remove_flags(messages, self._convert_flags(flags)) | [
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lmb-freiburg/mv3d | 7118c2fe37071ed236e6457b95f6efb361b746ff | utils/renderer.py | python | Renderer.deactivateLightSources | (self) | [] | def deactivateLightSources(self):
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weinbe58/QuSpin | 5bbc3204dbf5c227a87a44f0dacf39509cba580c | docs/downloads/9fcc9e398d1dd9d5f23ac37f6401eb0b/example16.py | python | make_basis | (N_half) | return (states_b+shift_states_b.T).ravel() | Generates a list of integers to represent external, user-imported basis | Generates a list of integers to represent external, user-imported basis | [
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""" Generates a list of integers to represent external, user-imported basis """
old_basis = spin_basis_general(N_half,m=0)
#
states = old_basis.states
shift_states = np.left_shift(states,N_half)
#
shape=states.shape+states.shape
#
states_b = np.broadcast_to(states,shape)
shift_states_b = np.broadcast_to(shift_states,shape)
# this does the kronecker sum in a more memory efficient way.
return (states_b+shift_states_b.T).ravel() | [
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/rich/pretty.py | python | _get_attr_fields | (obj: Any) | return _attr_module.fields(type(obj)) if _attr_module is not None else [] | Get fields for an attrs object. | Get fields for an attrs object. | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_git/build/lib/base.py | python | GitCLI._push | (self, remote, src_branch, dest_branch) | return results | Do a git checkout to <branch> | Do a git checkout to <branch> | [
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] | def _push(self, remote, src_branch, dest_branch):
''' Do a git checkout to <branch> '''
push_branches = src_branch + ":" + dest_branch
cmd = ["push", remote, push_branches]
results = self.git_cmd(cmd, output=True, output_type='raw')
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wummel/linkchecker | c2ce810c3fb00b895a841a7be6b2e78c64e7b042 | linkcheck/i18n.py | python | get_encoded_writer | (out=sys.stdout, encoding=None, errors='replace') | return Writer(out, errors) | Get wrapped output writer with given encoding and error handling. | Get wrapped output writer with given encoding and error handling. | [
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"""Get wrapped output writer with given encoding and error handling."""
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encoding = default_encoding
Writer = codecs.getwriter(encoding)
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PaddlePaddle/Research | 2da0bd6c72d60e9df403aff23a7802779561c4a1 | KG/CoKE/bin/pathquery_data_preprocess.py | python | Graph.walk_all | (self, start, path) | return set_s | walk from start and get all the paths
:param start: start entity
:param path: (r1, r2, ...,rk)
:return: entities set for candidates path | walk from start and get all the paths
:param start: start entity
:param path: (r1, r2, ...,rk)
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"""
walk from start and get all the paths
:param start: start entity
:param path: (r1, r2, ...,rk)
:return: entities set for candidates path
"""
set_s = set()
set_t = set()
set_s.add(start)
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_tset = set(self.neighbors[_s][r]) #tupe to set
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set_t.clear()
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dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/team_log.py | python | EventDetails.team_profile_remove_logo_details | (cls, val) | return cls('team_profile_remove_logo_details', val) | Create an instance of this class set to the
``team_profile_remove_logo_details`` tag with value ``val``.
:param TeamProfileRemoveLogoDetails val:
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"""
Create an instance of this class set to the
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:param TeamProfileRemoveLogoDetails val:
:rtype: EventDetails
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nitishsrivastava/deepnet | f4e4ff207923e01552c96038a1e2c29eb5d16160 | cudamat/cudamat.py | python | CUDAMatrix.set_selected_columns | (self, indices, source) | return self | copies all columns of source into some columns of self.
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err_code = _cudamat.setSelectedRows(self.p_mat, source.p_mat, indices.p_mat)
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raise generate_exception(err_code)
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dmlc/gluon-cv | 709bc139919c02f7454cb411311048be188cde64 | gluoncv/model_zoo/icnet.py | python | get_icnet_resnet50_citys | (**kwargs) | return get_icnet(dataset='citys', backbone='resnet50', **kwargs) | r"""Image Cascade Network
Parameters
----------
dataset : str, default citys
The dataset that model pretrained on. (default: cityscapes)
backbone : string
Pre-trained dilated backbone network type (default:'resnet50'). | r"""Image Cascade Network | [
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] | def get_icnet_resnet50_citys(**kwargs):
r"""Image Cascade Network
Parameters
----------
dataset : str, default citys
The dataset that model pretrained on. (default: cityscapes)
backbone : string
Pre-trained dilated backbone network type (default:'resnet50').
"""
return get_icnet(dataset='citys', backbone='resnet50', **kwargs) | [
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flosell/trailscraper | 2509b8da81b49edf375a44fbc22a58fd9e2ea928 | trailscraper/cli.py | python | select | (log_dir, filter_assumed_role_arn, use_cloudtrail_api, from_s, to_s) | Finds all CloudTrail records matching the given filters and prints them. | Finds all CloudTrail records matching the given filters and prints them. | [
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"""Finds all CloudTrail records matching the given filters and prints them."""
log_dir = os.path.expanduser(log_dir)
from_date = time_utils.parse_human_readable_time(from_s)
to_date = time_utils.parse_human_readable_time(to_s)
if use_cloudtrail_api:
records = CloudTrailAPIRecordSource().load_from_api(from_date, to_date)
else:
records = LocalDirectoryRecordSource(log_dir).load_from_dir(from_date, to_date)
filtered_records = filter_records(records, filter_assumed_role_arn, from_date, to_date)
filtered_records_as_json = [record.raw_source for record in filtered_records]
click.echo(json.dumps({"Records": filtered_records_as_json})) | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/sqlalchemy/engine/base.py | python | Connection.closed | (self) | return '_Connection__connection' not in self.__dict__ \
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"""Return True if this connection is closed."""
return '_Connection__connection' not in self.__dict__ \
and not self.__can_reconnect | [
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anyoptimization/pymoo | c6426a721d95c932ae6dbb610e09b6c1b0e13594 | pymoo/experimental/algorithms/moadawa.py | python | MOEADAWA.__init__ | (self,
ref_dirs,
n_neighbors=20,
decomposition=Tchebicheff(),
prob_neighbor_mating=0.9,
rate_update_weight=0.05,
rate_evol=0.8,
wag=20,
archive_size_multiplier=1.5,
use_new_ref_dirs_initialization=True,
display=MultiObjectiveDisplay(),
**kwargs) | MOEAD-AWA Algorithm.
Parameters
----------
ref_dirs
n_neighbors
decomposition
prob_neighbor_mating
rate_update_weight
rate_evol
wag
archive_size_multiplier
use_new_ref_dirs_initialization
display
kwargs | [] | def __init__(self,
ref_dirs,
n_neighbors=20,
decomposition=Tchebicheff(),
prob_neighbor_mating=0.9,
rate_update_weight=0.05,
rate_evol=0.8,
wag=20,
archive_size_multiplier=1.5,
use_new_ref_dirs_initialization=True,
display=MultiObjectiveDisplay(),
**kwargs):
"""
MOEAD-AWA Algorithm.
Parameters
----------
ref_dirs
n_neighbors
decomposition
prob_neighbor_mating
rate_update_weight
rate_evol
wag
archive_size_multiplier
use_new_ref_dirs_initialization
display
kwargs
"""
self.n_neighbors = n_neighbors
self.prob_neighbor_mating = prob_neighbor_mating
self.decomp = decomposition
self.rate_update_weight = rate_update_weight
self.rate_evol = rate_evol
self.wag = wag
self.EP = None
self.nEP = np.ceil(len(ref_dirs) * archive_size_multiplier)
set_if_none(kwargs, 'pop_size', len(ref_dirs))
set_if_none(kwargs, 'sampling', FloatRandomSampling())
set_if_none(kwargs, 'crossover', SBX(prob=1.0, eta=20))
set_if_none(kwargs, 'mutation', PM(prob=None, eta=20))
set_if_none(kwargs, 'survival', None)
set_if_none(kwargs, 'selection', None)
super().__init__(display=display, **kwargs)
# initialized when problem is known
self.ref_dirs = ref_dirs
if use_new_ref_dirs_initialization:
self.ref_dirs = 1.0 / (self.ref_dirs + 1e-6)
self.ref_dirs = self.ref_dirs / np.sum(self.ref_dirs, axis=1)[:, None]
if self.ref_dirs.shape[0] < self.n_neighbors:
print("Setting number of neighbours to population size: %s" % self.ref_dirs.shape[0])
self.n_neighbors = self.ref_dirs.shape[0]
self.nds = NonDominatedSorting()
# compute neighbors of reference directions using euclidean distance
self._update_neighbors() | [
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kivy/kivy | fbf561f73ddba9941b1b7e771f86264c6e6eef36 | kivy/uix/modalview.py | python | ModalView.dismiss | (self, *_args, **kwargs) | Close the view if it is open.
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view = ModalView()
view.dismiss(force=True)
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""" Close the view if it is open.
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view = ModalView()
view.dismiss(force=True)
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"""
if not self._is_open:
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self.dispatch('on_pre_dismiss')
if self.dispatch('on_dismiss') is True:
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if kwargs.get('animation', True):
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self._anim_alpha = 0
self._real_remove_widget() | [
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cjdrake/pyeda | 554ee53aa678f4b61bcd7e07ba2c74ddc749d665 | pyeda/boolalg/bfarray.py | python | farray.uand | (self) | return reduce(operator.and_, self._items, self.ftype.box(1)) | Unary AND reduction operator | Unary AND reduction operator | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_vendored_deps/library/oo_iam_kms.py | python | AwsIamKms.__init__ | (self) | constructor | constructor | [
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facebookresearch/demucs | 7317db81a34349e028ec943b199c9b9cdda47a12 | demucs/demucs.py | python | DConv.__init__ | (self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4,
norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True,
kernel=3, dilate=True) | Args:
channels: input/output channels for residual branch.
compress: amount of channel compression inside the branch.
depth: number of layers in the residual branch. Each layer has its own
projection, and potentially LSTM and attention.
init: initial scale for LayerNorm.
norm: use GroupNorm.
attn: use LocalAttention.
heads: number of heads for the LocalAttention.
ndecay: number of decay controls in the LocalAttention.
lstm: use LSTM.
gelu: Use GELU activation.
kernel: kernel size for the (dilated) convolutions.
dilate: if true, use dilation, increasing with the depth. | Args:
channels: input/output channels for residual branch.
compress: amount of channel compression inside the branch.
depth: number of layers in the residual branch. Each layer has its own
projection, and potentially LSTM and attention.
init: initial scale for LayerNorm.
norm: use GroupNorm.
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heads: number of heads for the LocalAttention.
ndecay: number of decay controls in the LocalAttention.
lstm: use LSTM.
gelu: Use GELU activation.
kernel: kernel size for the (dilated) convolutions.
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super().__init__()
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self.channels = channels
self.compress = compress
self.depth = abs(depth)
dilate = depth > 0
norm_fn: tp.Callable[[int], nn.Module]
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padding = dilation * (kernel // 2)
mods = [
nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),
norm_fn(hidden), act(),
nn.Conv1d(hidden, 2 * channels, 1),
norm_fn(2 * channels), nn.GLU(1),
LayerScale(channels, init),
]
if attn:
mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))
if lstm:
mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))
layer = nn.Sequential(*mods)
self.layers.append(layer) | [
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skylander86/lambda-text-extractor | 6da52d077a2fc571e38bfe29c33ae68f6443cd5a | lib-linux_x64/pptx/slide.py | python | NotesSlide.notes_text_frame | (self) | return notes_placeholder.text_frame | Return the text frame of the notes placeholder on this notes slide,
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/urllib/parse.py | python | splittag | (url) | return url, None | splittag('/path#tag') --> '/path', 'tag'. | splittag('/path#tag') --> '/path', 'tag'. | [
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"""splittag('/path#tag') --> '/path', 'tag'."""
path, delim, tag = url.rpartition('#')
if delim:
return path, tag
return url, None | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/decimal.py | python | Decimal.__ne__ | (self, other, context=None) | return self._cmp(other) != 0 | [] | def __ne__(self, other, context=None):
self, other = _convert_for_comparison(self, other, equality_op=True)
if other is NotImplemented:
return other
if self._check_nans(other, context):
return True
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pytroll/satpy | 09e51f932048f98cce7919a4ff8bd2ec01e1ae98 | satpy/readers/yaml_reader.py | python | _get_FCI_L1c_FDHSI_chunk_height | (chunk_width, chunk_n) | return chunk_height | Get the height in pixels of a FCI L1c FDHSI chunk given the chunk width and number (starting from 1). | Get the height in pixels of a FCI L1c FDHSI chunk given the chunk width and number (starting from 1). | [
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if chunk_width == 11136:
# 1km resolution case
if chunk_n in [3, 5, 8, 10, 13, 15, 18, 20, 23, 25, 28, 30, 33, 35, 38, 40]:
chunk_height = 279
else:
chunk_height = 278
elif chunk_width == 5568:
# 2km resolution case
if chunk_n in [5, 10, 15, 20, 25, 30, 35, 40]:
chunk_height = 140
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chunk_height = 139
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/idlelib/macosxSupport.py | python | tkVersionWarning | (root) | Returns a string warning message if the Tk version in use appears to
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/quantum/quantum/agent/linux/interface.py | python | LinuxInterfaceDriver.init_l3 | (self, device_name, ip_cidrs, namespace=None) | Set the L3 settings for the interface using data from the port.
ip_cidrs: list of 'X.X.X.X/YY' strings | Set the L3 settings for the interface using data from the port.
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ip_cidrs: list of 'X.X.X.X/YY' strings
"""
device = ip_lib.IPDevice(device_name,
self.root_helper,
namespace=namespace)
previous = {}
for address in device.addr.list(scope='global', filters=['permanent']):
previous[address['cidr']] = address['ip_version']
# add new addresses
for ip_cidr in ip_cidrs:
net = netaddr.IPNetwork(ip_cidr)
if ip_cidr in previous:
del previous[ip_cidr]
continue
device.addr.add(net.version, ip_cidr, str(net.broadcast))
# clean up any old addresses
for ip_cidr, ip_version in previous.items():
device.addr.delete(ip_version, ip_cidr) | [
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cbrgm/telegram-robot-rss | 58fe98de427121fdc152c8df0721f1891174e6c9 | venv/lib/python2.7/site-packages/setuptools/command/bdist_egg.py | python | make_zipfile | (zip_filename, base_dir, verbose=0, dry_run=0, compress=True,
mode='w') | return zip_filename | Create a zip file from all the files under 'base_dir'. The output
zip file will be named 'base_dir' + ".zip". Uses either the "zipfile"
Python module (if available) or the InfoZIP "zip" utility (if installed
and found on the default search path). If neither tool is available,
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"""Create a zip file from all the files under 'base_dir'. The output
zip file will be named 'base_dir' + ".zip". Uses either the "zipfile"
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"""
import zipfile
mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
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for name in names:
path = os.path.normpath(os.path.join(dirname, name))
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openstack/horizon | 12bb9fe5184c9dd3329ba17b3d03c90887dbcc3d | openstack_dashboard/usage/quotas.py | python | QuotaUsage.tally | (self, name, value) | Adds to the "used" metric for the given quota. | Adds to the "used" metric for the given quota. | [
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value = value or 0 # Protection against None.
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if 'used' not in self.usages[name]:
self.usages[name]['used'] = 0
# Increment our usage and update the "available" metric.
self.usages[name]['used'] += int(value) # Fail if can't coerce to int.
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | apps/impala/gen-py/hive_metastore/ThriftHiveMetastore.py | python | Iface.getMetaConf | (self, key) | Parameters:
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | library/hachoir_core/tools.py | python | timestampUUID60 | (value) | Convert UUID 60-bit timestamp to string. The timestamp format is
a 60-bit number which represents number of 100ns since the
the 15 October 1582 at 00:00. Result is an unicode string.
>>> timestampUUID60(0)
datetime.datetime(1582, 10, 15, 0, 0)
>>> timestampUUID60(130435676263032368)
datetime.datetime(1996, 2, 14, 5, 13, 46, 303236) | Convert UUID 60-bit timestamp to string. The timestamp format is
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":",
... | def timestampUUID60(value):
"""
Convert UUID 60-bit timestamp to string. The timestamp format is
a 60-bit number which represents number of 100ns since the
the 15 October 1582 at 00:00. Result is an unicode string.
>>> timestampUUID60(0)
datetime.datetime(1582, 10, 15, 0, 0)
>>> timestampUUID60(130435676263032368)
datetime.datetime(1996, 2, 14, 5, 13, 46, 303236)
"""
if not isinstance(value, (float, int, long)):
raise TypeError("an integer or float is required")
if value < 0:
raise ValueError("value have to be a positive or nul integer")
try:
return UUID60_TIMESTAMP_T0 + timedelta(microseconds=value/10)
except OverflowError:
raise ValueError(_("timestampUUID60() overflow (value=%s)") % value) | [
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IronLanguages/ironpython2 | 51fdedeeda15727717fb8268a805f71b06c0b9f1 | Src/StdLib/Lib/ConfigParser.py | python | RawConfigParser.options | (self, section) | return opts.keys() | Return a list of option names for the given section name. | Return a list of option names for the given section name. | [
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"""Return a list of option names for the given section name."""
try:
opts = self._sections[section].copy()
except KeyError:
raise NoSectionError(section)
opts.update(self._defaults)
if '__name__' in opts:
del opts['__name__']
return opts.keys() | [
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mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pywin/Lib/SocketServer.py | python | TCPServer.server_close | (self) | Called to clean-up the server.
May be overridden. | Called to clean-up the server. | [
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] | def server_close(self):
"""Called to clean-up the server.
May be overridden.
"""
self.socket.close() | [
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Franck-Dernoncourt/NeuroNER | 3817feaf290c1f6e03ae23ea964e68c88d0e7a88 | neuroner/train.py | python | prediction_step | (sess, dataset, dataset_type, model, transition_params_trained,
stats_graph_folder, epoch_number, parameters, dataset_filepaths) | return all_predictions, all_y_true, output_filepath | Predict. | Predict. | [
"Predict",
"."
] | def prediction_step(sess, dataset, dataset_type, model, transition_params_trained,
stats_graph_folder, epoch_number, parameters, dataset_filepaths):
"""
Predict.
"""
if dataset_type == 'deploy':
print('Predict labels for the {0} set'.format(dataset_type))
else:
print('Evaluate model on the {0} set'.format(dataset_type))
all_predictions = []
all_y_true = []
output_filepath = os.path.join(stats_graph_folder, '{1:03d}_{0}.txt'.format(dataset_type,
epoch_number))
output_file = codecs.open(output_filepath, 'w', 'UTF-8')
original_conll_file = codecs.open(dataset_filepaths[dataset_type], 'r', 'UTF-8')
for i in range(len(dataset.token_indices[dataset_type])):
feed_dict = {
model.input_token_indices: dataset.token_indices[dataset_type][i],
model.input_token_character_indices: dataset.character_indices_padded[dataset_type][i],
model.input_token_lengths: dataset.token_lengths[dataset_type][i],
model.input_label_indices_vector: dataset.label_vector_indices[dataset_type][i],
model.dropout_keep_prob: 1.
}
unary_scores, predictions = sess.run([model.unary_scores,
model.predictions], feed_dict)
if parameters['use_crf']:
predictions, _ = tf.contrib.crf.viterbi_decode(unary_scores,
transition_params_trained)
predictions = predictions[1:-1]
else:
predictions = predictions.tolist()
assert(len(predictions) == len(dataset.tokens[dataset_type][i]))
output_string = ''
prediction_labels = [dataset.index_to_label[prediction] for prediction in predictions]
unary_score_list = unary_scores.tolist()[1:-1]
gold_labels = dataset.labels[dataset_type][i]
if parameters['tagging_format'] == 'bioes':
prediction_labels = utils_nlp.bioes_to_bio(prediction_labels)
gold_labels = utils_nlp.bioes_to_bio(gold_labels)
for prediction, token, gold_label, scores in zip(prediction_labels,
dataset.tokens[dataset_type][i], gold_labels, unary_score_list):
while True:
line = original_conll_file.readline()
split_line = line.strip().split(' ')
if '-DOCSTART-' in split_line[0] or len(split_line) == 0 \
or len(split_line[0]) == 0:
continue
else:
token_original = split_line[0]
if parameters['tagging_format'] == 'bioes':
split_line.pop()
gold_label_original = split_line[-1]
assert(token == token_original and gold_label == gold_label_original)
break
split_line.append(prediction)
try:
if parameters['output_scores']:
# space separated scores
scores = ' '.join([str(i) for i in scores])
split_line.append('{}'.format(scores))
except KeyError:
pass
output_string += ' '.join(split_line) + '\n'
output_file.write(output_string+'\n')
all_predictions.extend(predictions)
all_y_true.extend(dataset.label_indices[dataset_type][i])
output_file.close()
original_conll_file.close()
if dataset_type != 'deploy':
if parameters['main_evaluation_mode'] == 'conll':
# run perl evaluation script in python package
# conll_evaluation_script = os.path.join('.', 'conlleval')
package_name = 'neuroner'
root_dir = os.path.dirname(pkg_resources.resource_filename(package_name,
'__init__.py'))
conll_evaluation_script = os.path.join(root_dir, 'conlleval')
conll_output_filepath = '{0}_conll_evaluation.txt'.format(output_filepath)
shell_command = 'perl {0} < {1} > {2}'.format(conll_evaluation_script,
output_filepath, conll_output_filepath)
os.system(shell_command)
with open(conll_output_filepath, 'r') as f:
classification_report = f.read()
print(classification_report)
else:
new_y_pred, new_y_true, new_label_indices, new_label_names, _, _ = remap_labels(all_predictions,
all_y_true, dataset, parameters['main_evaluation_mode'])
print(sklearn.metrics.classification_report(new_y_true, new_y_pred,
digits=4, labels=new_label_indices, target_names=new_label_names))
return all_predictions, all_y_true, output_filepath | [
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RasaHQ/rasa | 54823b68c1297849ba7ae841a4246193cd1223a1 | rasa/engine/training/hooks.py | python | TrainingHook.on_before_node | (
self,
node_name: Text,
execution_context: ExecutionContext,
config: Dict[Text, Any],
received_inputs: Dict[Text, Any],
) | return {"fingerprint_key": fingerprint_key} | Calculates the run fingerprint for use in `on_after_node`. | Calculates the run fingerprint for use in `on_after_node`. | [
"Calculates",
"the",
"run",
"fingerprint",
"for",
"use",
"in",
"on_after_node",
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] | def on_before_node(
self,
node_name: Text,
execution_context: ExecutionContext,
config: Dict[Text, Any],
received_inputs: Dict[Text, Any],
) -> Dict:
"""Calculates the run fingerprint for use in `on_after_node`."""
graph_component_class = self._get_graph_component_class(
execution_context, node_name
)
fingerprint_key = fingerprinting.calculate_fingerprint_key(
graph_component_class=graph_component_class,
config=config,
inputs=received_inputs,
)
return {"fingerprint_key": fingerprint_key} | [
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ianmiell/shutit | ef724e1ed4dcc544e594200e0b6cdfa53d04a95f | shutit.py | python | create_session | (docker_image=None,
docker_rm=None,
echo=False,
loglevel='',
nocolor=False,
session_type='bash',
vagrant_session_name=None,
vagrant_image='ubuntu/xenial64',
vagrant_gui=False,
vagrant_memory='1024',
vagrant_num_machines='1',
vagrant_provider='virtualbox',
vagrant_root_folder=None,
vagrant_swapsize='2G',
vagrant_version='1.8.6',
vagrant_virt_method='virtualbox',
vagrant_cpu='1',
video=-1,
walkthrough=False) | Creates a distinct ShutIt session. Sessions can be of type:
bash - a bash shell is spawned and
vagrant - a Vagrantfile is created and 'vagrant up'ped | Creates a distinct ShutIt session. Sessions can be of type: | [
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"ShutIt",
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".",
"Sessions",
"can",
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"type",
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] | def create_session(docker_image=None,
docker_rm=None,
echo=False,
loglevel='',
nocolor=False,
session_type='bash',
vagrant_session_name=None,
vagrant_image='ubuntu/xenial64',
vagrant_gui=False,
vagrant_memory='1024',
vagrant_num_machines='1',
vagrant_provider='virtualbox',
vagrant_root_folder=None,
vagrant_swapsize='2G',
vagrant_version='1.8.6',
vagrant_virt_method='virtualbox',
vagrant_cpu='1',
video=-1,
walkthrough=False):
"""Creates a distinct ShutIt session. Sessions can be of type:
bash - a bash shell is spawned and
vagrant - a Vagrantfile is created and 'vagrant up'ped
"""
assert session_type in ('bash','docker','vagrant'), shutit_util.print_debug()
shutit_global_object = shutit_global.shutit_global_object
if video != -1 and video > 0:
walkthrough = True
if session_type in ('bash','docker'):
return shutit_global_object.create_session(session_type,
docker_image=docker_image,
rm=docker_rm,
echo=echo,
walkthrough=walkthrough,
walkthrough_wait=video,
nocolor=nocolor,
loglevel=loglevel)
elif session_type == 'vagrant':
if vagrant_session_name is None:
vagrant_session_name = 'shutit' + shutit_util.random_id()
if isinstance(vagrant_num_machines, int):
vagrant_num_machines = str(vagrant_num_machines)
assert isinstance(vagrant_num_machines, str)
assert isinstance(int(vagrant_num_machines), int)
if vagrant_root_folder is None:
vagrant_root_folder = shutit_global.shutit_global_object.owd
return create_session_vagrant(vagrant_session_name,
vagrant_num_machines,
vagrant_image,
vagrant_provider,
vagrant_gui,
vagrant_memory,
vagrant_swapsize,
echo,
walkthrough,
nocolor,
video,
vagrant_version,
vagrant_virt_method,
vagrant_root_folder,
vagrant_cpu,
loglevel) | [
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ncoudray/DeepPATH | 62bf7e7f74a80889f1e07890b8fe814f076f780d | DeepPATH_code/01_training/xClasses/inception/data/nc_build_imagenet_data.py | python | _process_image | (filename, coder) | return image_data, height, width | Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels. | Process a single image file. | [
"Process",
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"image",
"file",
"."
] | def _process_image(filename, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with tf.gfile.FastGFile(filename, 'r') as f:
image_data = f.read()
# Clean the dirty data.
if _is_png(filename):
# 1 image is a PNG.
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
elif _is_cmyk(filename):
# 22 JPEG images are in CMYK colorspace.
print('Converting CMYK to RGB for %s' % filename)
image_data = coder.cmyk_to_rgb(image_data)
# Decode the RGB JPEG.
image = coder.decode_jpeg(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/paramiko/client.py | python | SSHClient.open_sftp | (self) | return self._transport.open_sftp_client() | Open an SFTP session on the SSH server.
:return: a new `.SFTPClient` session object | Open an SFTP session on the SSH server. | [
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"""
Open an SFTP session on the SSH server.
:return: a new `.SFTPClient` session object
"""
return self._transport.open_sftp_client() | [
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guillermooo/Vintageous | f958207009902052aed5fcac09745f1742648604 | vi/mappings.py | python | Mappings.resolve | (self, sequence=None, mode=None, check_user_mappings=True) | Looks at the current global state and returns the command mapped to
the available sequence. It may be a 'missing' command.
@sequence
If a @sequence is passed, it is used instead of the global state's.
This is necessary for some commands that aren't name spaces but act
as them (for example, ys from the surround plugin).
@mode
If different than `None`, it will be used instead of the global
state's. This is necessary when we are in operator pending mode
and we receive a new action. By combining the existing action's
name with name of the action just received we could find a new
action.
For example, this is the case of g~~. | Looks at the current global state and returns the command mapped to
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] | def resolve(self, sequence=None, mode=None, check_user_mappings=True):
"""
Looks at the current global state and returns the command mapped to
the available sequence. It may be a 'missing' command.
@sequence
If a @sequence is passed, it is used instead of the global state's.
This is necessary for some commands that aren't name spaces but act
as them (for example, ys from the surround plugin).
@mode
If different than `None`, it will be used instead of the global
state's. This is necessary when we are in operator pending mode
and we receive a new action. By combining the existing action's
name with name of the action just received we could find a new
action.
For example, this is the case of g~~.
"""
# we usually need to look at the partial sequence, but some commands do weird things,
# like ys, which isn't a namespace but behaves as such sometimes.
seq = sequence or self.state.partial_sequence
seq = to_bare_command_name(seq)
# TODO: Use same structure as in mappings (nested dicst).
command = None
if check_user_mappings:
self.state.logger.info('[Mappings] checking user mappings')
# TODO: We should be able to force a mode here too as, below.
command = self.expand_first(seq)
if command:
self.state.logger.info('[Mappings] {0} equals command: {1}'.format(seq, command))
return command
# return {'name': command.mapping, 'type': cmd_types.USER}
else:
self.state.logger.info('[Mappings] looking up >{0}<'.format(seq))
command = seq_to_command(self.state, seq, mode=mode)
self.state.logger.info('[Mappings] got {0}'.format(command))
return command | [
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yandex/yandex-tank | b41bcc04396c4ed46fc8b28a261197320854fd33 | yandextank/plugins/Autostop/cumulative_criterions.py | python | TotalNetCodesCriterion.__init__ | (self, autostop, param_str) | [] | def __init__(self, autostop, param_str):
AbstractCriterion.__init__(self)
self.seconds_count = 0
params = param_str.split(',')
self.codes_mask = params[0].lower()
self.codes_regex = re.compile(self.codes_mask.replace("x", '.'))
self.autostop = autostop
self.data = deque()
self.second_window = deque()
level_str = params[1].strip()
if level_str[-1:] == '%':
self.level = float(level_str[:-1])
self.is_relative = True
else:
self.level = int(level_str)
self.is_relative = False
self.seconds_limit = expand_to_seconds(params[2])
self.tag = params[3].strip() if len(params) == 4 else None | [
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google-research/motion_imitation | d0e7b963c5a301984352d25a3ee0820266fa4218 | mpc_controller/static_gait_controller.py | python | StaticGaitController.__init__ | (self, robot) | [] | def __init__(self, robot):
self._robot = robot
self._toe_ids = tuple(robot.urdf_loader.get_end_effector_id_dict().values())
self._wait_count = 0
self._stepper = foot_stepper.FootStepper(self._robot.pybullet_client,
self._toe_ids, toe_pos_local_ref) | [
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aws-samples/aws-cdk-examples | 4ac65cc171044d1f6dbb8b131c77abb44014d6c6 | csharp/elasticbeanstalk/elasticbeanstalk-bg-pipeline/resources/blue_green.py | python | put_job_failure | (job, message) | Notify CodePipeline of a failed job
Args:
job: The CodePipeline job ID
message: A message to be logged relating to the job status
Raises:
Exception: Any exception thrown by .put_job_failure_result() | Notify CodePipeline of a failed job
Args:
job: The CodePipeline job ID
message: A message to be logged relating to the job status
Raises:
Exception: Any exception thrown by .put_job_failure_result() | [
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message: A message to be logged relating to the job status
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print('Putting job failure')
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/gdata/docs/data.py | python | Resource.get_resumable_edit_media_link | (self) | return self.get_link(RESUMABLE_EDIT_MEDIA_LINK_REL) | Extracts the Resource's resumable update link.
Returns:
A gdata.data.FeedLink object. | Extracts the Resource's resumable update link. | [
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"""Extracts the Resource's resumable update link.
Returns:
A gdata.data.FeedLink object.
"""
return self.get_link(RESUMABLE_EDIT_MEDIA_LINK_REL) | [
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miyosuda/unreal | 31d4886149412fa248f6efa490ab65bd2f425cde | model/model.py | python | conv_initializer | (kernel_width, kernel_height, input_channels, dtype=tf.float32) | return _initializer | [] | def conv_initializer(kernel_width, kernel_height, input_channels, dtype=tf.float32):
def _initializer(shape, dtype=dtype, partition_info=None):
d = 1.0 / np.sqrt(input_channels * kernel_width * kernel_height)
return tf.random_uniform(shape, minval=-d, maxval=d)
return _initializer | [
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"... | https://github.com/miyosuda/unreal/blob/31d4886149412fa248f6efa490ab65bd2f425cde/model/model.py#L19-L23 | |||
radlab/sparrow | afb8efadeb88524f1394d1abe4ea66c6fd2ac744 | deploy/third_party/boto-2.1.1/boto/gs/resumable_upload_handler.py | python | ResumableUploadHandler._check_final_md5 | (self, key, etag) | Checks that etag from server agrees with md5 computed before upload.
This is important, since the upload could have spanned a number of
hours and multiple processes (e.g., gsutil runs), and the user could
change some of the file and not realize they have inconsistent data. | Checks that etag from server agrees with md5 computed before upload.
This is important, since the upload could have spanned a number of
hours and multiple processes (e.g., gsutil runs), and the user could
change some of the file and not realize they have inconsistent data. | [
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This is important, since the upload could have spanned a number of
hours and multiple processes (e.g., gsutil runs), and the user could
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"""
if key.bucket.connection.debug >= 1:
print 'Checking md5 against etag.'
if key.md5 != etag.strip('"\''):
# Call key.open_read() before attempting to delete the
# (incorrect-content) key, so we perform that request on a
# different HTTP connection. This is neededb because httplib
# will return a "Response not ready" error if you try to perform
# a second transaction on the connection.
key.open_read()
key.close()
key.delete()
raise ResumableUploadException(
'File changed during upload: md5 signature doesn\'t match etag '
'(incorrect uploaded object deleted)',
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/setuptools/config.py | python | ConfigMetadataHandler._parse_version | (self, value) | return version | Parses `version` option value.
:param value:
:rtype: str | Parses `version` option value. | [
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"""Parses `version` option value.
:param value:
:rtype: str
"""
version = self._parse_attr(value)
if callable(version):
version = version()
if not isinstance(version, string_types):
if hasattr(version, '__iter__'):
version = '.'.join(map(str, version))
else:
version = '%s' % version
return version | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/contour/_colorbar.py | python | ColorBar.tickprefix | (self) | return self["tickprefix"] | Sets a tick label prefix.
The 'tickprefix' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str | Sets a tick label prefix.
The 'tickprefix' property is a string and must be specified as:
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"""
Sets a tick label prefix.
The 'tickprefix' property is a string and must be specified as:
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Returns
-------
str
"""
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idiap/importance-sampling | 9c9cab2ac91081ae2b64f99891504155057c09e3 | scripts/variance_reduction.py | python | build_grad_batched | (network, batch_size) | return inner | Compute the average gradient by splitting the inputs in batches of size
'batch_size' and averaging. | Compute the average gradient by splitting the inputs in batches of size
'batch_size' and averaging. | [
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] | def build_grad_batched(network, batch_size):
"""Compute the average gradient by splitting the inputs in batches of size
'batch_size' and averaging."""
grad = build_grad(network)
def inner(inputs):
X, y, w = inputs
N = len(X)
g = 0
for i in range(0, N, batch_size):
g = g + w[i:i+batch_size].sum() * grad([
X[i:i+batch_size],
y[i:i+batch_size],
w[i:i+batch_size]
])[0]
return [g / w.sum()]
return inner | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/elasticsearch.py | python | pipeline_get | (id, hosts=None, profile=None) | .. versionadded:: 2017.7.0
Retrieve Ingest pipeline definition. Available since Elasticsearch 5.0.
id
Pipeline id
CLI Example:
.. code-block:: bash
salt myminion elasticsearch.pipeline_get mypipeline | .. versionadded:: 2017.7.0 | [
"..",
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] | def pipeline_get(id, hosts=None, profile=None):
"""
.. versionadded:: 2017.7.0
Retrieve Ingest pipeline definition. Available since Elasticsearch 5.0.
id
Pipeline id
CLI Example:
.. code-block:: bash
salt myminion elasticsearch.pipeline_get mypipeline
"""
es = _get_instance(hosts, profile)
try:
return es.ingest.get_pipeline(id=id)
except elasticsearch.NotFoundError:
return None
except elasticsearch.TransportError as e:
raise CommandExecutionError(
"Cannot create pipeline {}, server returned code {} with message {}".format(
id, e.status_code, e.error
)
)
except AttributeError:
raise CommandExecutionError("Method is applicable only for Elasticsearch 5.0+") | [
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iyah4888/SIGGRAPH18SSS | 8bb634316a1234f639cf4e6d26c671cc43491d48 | kaffe/graph.py | python | GraphBuilder.load | (self) | Load the layer definitions from the prototxt. | Load the layer definitions from the prototxt. | [
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'''Load the layer definitions from the prototxt.'''
self.params = get_caffe_resolver().NetParameter()
with open(self.def_path, 'rb') as def_file:
text_format.Merge(def_file.read(), self.params) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/_vendor/ipaddress.py | python | IPv6Address.teredo | (self) | return (IPv4Address((self._ip >> 64) & 0xFFFFFFFF),
IPv4Address(~self._ip & 0xFFFFFFFF)) | Tuple of embedded teredo IPs.
Returns:
Tuple of the (server, client) IPs or None if the address
doesn't appear to be a teredo address (doesn't start with
2001::/32) | Tuple of embedded teredo IPs. | [
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] | def teredo(self):
"""Tuple of embedded teredo IPs.
Returns:
Tuple of the (server, client) IPs or None if the address
doesn't appear to be a teredo address (doesn't start with
2001::/32)
"""
if (self._ip >> 96) != 0x20010000:
return None
return (IPv4Address((self._ip >> 64) & 0xFFFFFFFF),
IPv4Address(~self._ip & 0xFFFFFFFF)) | [
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quip/quip-api | 19f3b32a05ed092a70dc2c616e214aaff8a06de2 | samples/twitterbot/quip.py | python | QuipClient.get_users | (self, ids) | return self._fetch_json("users/", post_data={"ids": ",".join(ids)}) | Returns a dictionary of users for the given IDs. | Returns a dictionary of users for the given IDs. | [
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return self._fetch_json("users/", post_data={"ids": ",".join(ids)}) | [
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/voiper/sulley/impacket/dcerpc/dcerpc.py | python | MSRPCBindAck.get_header_size | (self) | return self._SIZE + var_size | [] | def get_header_size(self):
var_size = len(self.get_bytes()) - self._SIZE
# assert var_size > 0
return self._SIZE + var_size | [
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kerlomz/captcha_platform | f7d719bd1239a987996e266bd7fe35c96003b378 | config.py | python | Model.model_conf | (self) | [] | def model_conf(self) -> dict:
with open(self.model_conf_path, 'r', encoding="utf-8") as sys_fp:
sys_stream = sys_fp.read()
return yaml.load(sys_stream, Loader=yaml.SafeLoader) | [
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renemarc/home-assistant-config | 775d60ad436cd0f432d2260e503b530920041165 | custom_components/hacs/hacsbase/__init__.py | python | Hacs.prosess_queue | (self, notarealarg=None) | Recuring tasks for installed repositories. | Recuring tasks for installed repositories. | [
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"""Recuring tasks for installed repositories."""
if not self.queue.has_pending_tasks:
self.logger.debug("Nothing in the queue")
return
if self.queue.running:
self.logger.debug("Queue is already running")
return
can_update = await get_fetch_updates_for(self.github)
if can_update == 0:
self.logger.info(
"HACS is ratelimited, repository updates will resume later."
)
else:
self.system.status.background_task = True
self.hass.bus.async_fire("hacs/status", {})
await self.queue.execute(can_update)
self.system.status.background_task = False
self.hass.bus.async_fire("hacs/status", {}) | [
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | lms/djangoapps/grades/course_grade.py | python | CourseGrade._compute_percent | (grader_result) | return round_away_from_zero(grader_result['percent'] * 100 + 0.05) / 100 | Computes and returns the grade percentage from the given
result from the grader. | Computes and returns the grade percentage from the given
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"""
Computes and returns the grade percentage from the given
result from the grader.
"""
# Confused about the addition of .05 here? See https://openedx.atlassian.net/browse/TNL-6972
return round_away_from_zero(grader_result['percent'] * 100 + 0.05) / 100 | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib/xml/sax/xmlreader.py | python | InputSource.setByteStream | (self, bytefile) | Set the byte stream (a Python file-like object which does
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The SAX parser will ignore this if there is also a character
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"""Set the byte stream (a Python file-like object which does
not perform byte-to-character conversion) for this input
source.
The SAX parser will ignore this if there is also a character
stream specified, but it will use a byte stream in preference
to opening a URI connection itself.
If the application knows the character encoding of the byte
stream, it should set it with the setEncoding method."""
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hyde/hyde | 7f415402cc3e007a746eb2b5bc102281fdb415bd | hyde/ext/plugins/css.py | python | LessCSSPlugin.begin_text_resource | (self, resource, text) | return text | Replace @import statements with {% include %} statements. | Replace | [
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Replace @import statements with {% include %} statements.
"""
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return text
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path = match.groups(1)[0]
afile = File(resource.source_file.parent.child(path))
if len(afile.kind.strip()) == 0:
afile = File(afile.path + '.less')
ref = self.site.content.resource_from_path(afile.path)
if not ref:
raise HydeException(
"Cannot import from path [%s]" % afile.path)
ref.is_processable = False
return self.template.get_include_statement(ref.relative_path)
text = self.import_finder.sub(import_to_include, text)
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PowerScript/KatanaFramework | 0f6ad90a88de865d58ec26941cb4460501e75496 | lib/future/src/future/backports/http/cookiejar.py | python | CookieJar.clear_expired_cookies | (self) | Discard all expired cookies.
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mongodb/mongo-python-driver | c760f900f2e4109a247c2ffc8ad3549362007772 | pymongo/pool.py | python | Pool.connect | (self) | return sock_info | Connect to Mongo and return a new SocketInfo.
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Can raise ConnectionFailure.
Note that the pool does not keep a reference to the socket -- you
must call return_socket() when you're done with it.
"""
with self.lock:
conn_id = self.next_connection_id
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listeners = self.opts._event_listeners
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listeners.publish_connection_created(self.address, conn_id)
try:
sock = _configured_socket(self.address, self.opts)
except BaseException as error:
if self.enabled_for_cmap:
listeners.publish_connection_closed(
self.address, conn_id, ConnectionClosedReason.ERROR)
if isinstance(error, (IOError, OSError, _SSLError)):
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try:
if self.handshake:
sock_info.hello()
self.is_writable = sock_info.is_writable
sock_info.authenticate()
except BaseException:
sock_info.close_socket(ConnectionClosedReason.ERROR)
raise
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my8100/scrapydweb | 7a3b81dba2cba4279c9465064a693bb277ac20e9 | scrapydweb/views/operations/deploy.py | python | DeployUploadView.search_scrapy_cfg_path | (self, search_path, func_walk=os.walk, retry=True) | [] | def search_scrapy_cfg_path(self, search_path, func_walk=os.walk, retry=True):
try:
for dirpath, dirnames, filenames in func_walk(search_path):
self.scrapy_cfg_searched_paths.append(os.path.abspath(dirpath))
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except UnicodeDecodeError:
msg = "Found illegal filenames in %s" % search_path
self.logger.error(msg)
flash(msg, self.WARN)
if PY2 and retry:
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else:
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self.logger.error("scrapy.cfg not found in: %s", search_path)
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urwid/urwid | e2423b5069f51d318ea1ac0f355a0efe5448f7eb | urwid/widget.py | python | Edit.position_coords | (self,maxcol,pos) | return x,y | Return (*x*, *y*) coordinates for an offset into self.edit_text. | Return (*x*, *y*) coordinates for an offset into self.edit_text. | [
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] | def position_coords(self,maxcol,pos):
"""
Return (*x*, *y*) coordinates for an offset into self.edit_text.
"""
p = pos + len(self.caption)
trans = self.get_line_translation(maxcol)
x,y = calc_coords(self.get_text()[0], trans,p)
return x,y | [
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arsenetar/dupeguru | eb57d269fcc1392fac9d49eb10d597a9c66fcc82 | core/exclude.py | python | ExcludeDict.error | (self, regex) | return self._excluded.get(regex).get("error") | Return the compilation error message for regex string | Return the compilation error message for regex string | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/markdown/markdown/blockprocessors.py | python | ListIndentProcessor.create_item | (self, parent, block) | Create a new li and parse the block with it as the parent. | Create a new li and parse the block with it as the parent. | [
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""" Create a new li and parse the block with it as the parent. """
li = markdown.etree.SubElement(parent, 'li')
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websauna/websauna | a57de54fb8a3fae859f24f373f0292e1e4b3c344 | websauna/system/model/retry.py | python | retryable | (tm: t.Optional[TransactionManager] = None, get_tm: t.Optional[t.Callable] = None) | return _transaction_retry_wrapper | Function decorator for§ SQL Serialized transaction conflict resolution through retries.
You need to give either ``tm`` or ``get_tm`` argument.
* New transaction is started when entering the decorated function
* If there is already a transaction in progress when entering the decorated function raise an error
* Commit when existing the decorated function
* If the commit fails due to a SQL serialization conflict then try to rerun the decorated function max ``tm.retry_attempt_count`` times. Usually this is configured in TODO.
Example:
.. code-block:: python
from websauna.system.model.retry import retryable
def deposit_eth(web3: Web3, dbsession: Session, opid: UUID):
@retryable(tm=dbsession.transaction_manager)
def perform_tx():
op = dbsession.query(CryptoOperation).get(opid)
op.mark_performed()
op.mark_broadcasted()
# Transaction confirmation count updater will make sure we have enough blocks,
# and then will call mark_completed()
perform_tx()
Example using class based transaction manager resolver:
.. code-block:: python
from websauna.system.model.retry import retryable
class OperationQueueManager:
def __init__(self, web3: Web3, dbsession: Session, asset_network_id, registry: Registry):
assert isinstance(registry, Registry)
assert isinstance(asset_network_id, UUID)
self.web3 = web3
self.dbsession = dbsession
self.asset_network_id = asset_network_id
self.registry = registry
self.tm = self.dbsession.transaction_manager
def _get_tm(*args, **kargs):
self = args[0]
return self.tm
@retryable(get_tm=_get_tm)
def get_waiting_operation_ids(self) -> List[Tuple[UUID, CryptoOperationType]]:
wait_list = self.dbsession.query(CryptoOperation, CryptoOperation.id, CryptoOperation.state, CryptoOperation.operation_type).filter_by(network_id=self.asset_network_id, state=CryptoOperationState.waiting)
# Flatten
wait_list = [(o.id, o.operation_type) for o in wait_list]
return wait_list
def run_waiting_operations(self):
# Performed inside TX retry boundary
ops = self.get_waiting_operation_ids()
Transaction manager needs ``retry_attempt_count`` attribute set by Websauna framework.
:param tm: Transaction manager used to control the TX execution
:param get_tm: Factory function that is called with ``args`` and ``kwargs`` to get the transaction manager | Function decorator for§ SQL Serialized transaction conflict resolution through retries. | [
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"decorator",
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] | def retryable(tm: t.Optional[TransactionManager] = None, get_tm: t.Optional[t.Callable] = None):
"""Function decorator for§ SQL Serialized transaction conflict resolution through retries.
You need to give either ``tm`` or ``get_tm`` argument.
* New transaction is started when entering the decorated function
* If there is already a transaction in progress when entering the decorated function raise an error
* Commit when existing the decorated function
* If the commit fails due to a SQL serialization conflict then try to rerun the decorated function max ``tm.retry_attempt_count`` times. Usually this is configured in TODO.
Example:
.. code-block:: python
from websauna.system.model.retry import retryable
def deposit_eth(web3: Web3, dbsession: Session, opid: UUID):
@retryable(tm=dbsession.transaction_manager)
def perform_tx():
op = dbsession.query(CryptoOperation).get(opid)
op.mark_performed()
op.mark_broadcasted()
# Transaction confirmation count updater will make sure we have enough blocks,
# and then will call mark_completed()
perform_tx()
Example using class based transaction manager resolver:
.. code-block:: python
from websauna.system.model.retry import retryable
class OperationQueueManager:
def __init__(self, web3: Web3, dbsession: Session, asset_network_id, registry: Registry):
assert isinstance(registry, Registry)
assert isinstance(asset_network_id, UUID)
self.web3 = web3
self.dbsession = dbsession
self.asset_network_id = asset_network_id
self.registry = registry
self.tm = self.dbsession.transaction_manager
def _get_tm(*args, **kargs):
self = args[0]
return self.tm
@retryable(get_tm=_get_tm)
def get_waiting_operation_ids(self) -> List[Tuple[UUID, CryptoOperationType]]:
wait_list = self.dbsession.query(CryptoOperation, CryptoOperation.id, CryptoOperation.state, CryptoOperation.operation_type).filter_by(network_id=self.asset_network_id, state=CryptoOperationState.waiting)
# Flatten
wait_list = [(o.id, o.operation_type) for o in wait_list]
return wait_list
def run_waiting_operations(self):
# Performed inside TX retry boundary
ops = self.get_waiting_operation_ids()
Transaction manager needs ``retry_attempt_count`` attribute set by Websauna framework.
:param tm: Transaction manager used to control the TX execution
:param get_tm: Factory function that is called with ``args`` and ``kwargs`` to get the transaction manager
"""
def _transaction_retry_wrapper(func):
@wraps(func)
def decorated_func(*args, **kwargs):
global _retry_count
if get_tm:
manager = get_tm(*args, **kwargs)
else:
# Get how many attempts we want to do
manager = tm
assert manager, "No transaction manager available for retry"
# Make sure we don't re-enter to transaction
ensure_transactionless(transaction_manager=manager)
retry_attempt_count = getattr(manager, "retry_attempt_count", None)
if retry_attempt_count is None:
raise NotRetryable("TransactionManager is not configured with default retry attempt count")
# Run attempt loop
latest_exc = None
for num in range(retry_attempt_count):
if num >= 1:
logger.info("Transaction attempt #%d for function %s", num + 1, func)
txn = manager.begin()
# Expose retry count for testing
manager.latest_retry_count = num
try:
val = func(*args, **kwargs)
try:
txn.commit()
except ValueError as ve:
# Means there was a nested transaction begin
raise TooDeepInTransactions("Looks like transaction.commit() failed - usually this means that the wrapped function {} begun its own transaction and ruined transaction state management".format(func)) from ve
return val
except Exception as e:
if is_retryable(txn, e):
latest_exc = e
continue
else:
txn.abort() # We could not commit
raise e
raise CannotRetryAnymore("Out of transaction retry attempts, tried {} times".format(num + 1)) from latest_exc
return decorated_func
return _transaction_retry_wrapper | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/setuptools/command/egg_info.py | python | egg_info.delete_file | (self, filename) | Delete `filename` (if not a dry run) after announcing it | Delete `filename` (if not a dry run) after announcing it | [
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"""Delete `filename` (if not a dry run) after announcing it"""
log.info("deleting %s", filename)
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whoosh-community/whoosh | 5421f1ab3bb802114105b3181b7ce4f44ad7d0bb | src/whoosh/searching.py | python | Results.key_terms | (self, fieldname, docs=10, numterms=5,
model=classify.Bo1Model, normalize=True) | return expander.expanded_terms(numterms, normalize=normalize) | Returns the 'numterms' most important terms from the top 'docs'
documents in these results. "Most important" is generally defined as
terms that occur frequently in the top hits but relatively infrequently
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:param docs: Look at this many of the top documents of the results.
:param numterms: Return this number of important terms.
:param model: The classify.ExpansionModel to use. See the classify
module.
:returns: list of unicode strings. | Returns the 'numterms' most important terms from the top 'docs'
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model=classify.Bo1Model, normalize=True):
"""Returns the 'numterms' most important terms from the top 'docs'
documents in these results. "Most important" is generally defined as
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expander = classify.Expander(reader, fieldname, model=model)
for _, docnum in self.top_n[:docs]:
expander.add_document(docnum)
return expander.expanded_terms(numterms, normalize=normalize) | [
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deepchem/deepchem | 054eb4b2b082e3df8e1a8e77f36a52137ae6e375 | deepchem/models/layers.py | python | WeaveLayer.__init__ | (self,
n_atom_input_feat: int = 75,
n_pair_input_feat: int = 14,
n_atom_output_feat: int = 50,
n_pair_output_feat: int = 50,
n_hidden_AA: int = 50,
n_hidden_PA: int = 50,
n_hidden_AP: int = 50,
n_hidden_PP: int = 50,
update_pair: bool = True,
init: str = 'glorot_uniform',
activation: str = 'relu',
batch_normalize: bool = True,
batch_normalize_kwargs: Dict = {"renorm": True},
**kwargs) | Parameters
----------
n_atom_input_feat: int, optional (default 75)
Number of features for each atom in input.
n_pair_input_feat: int, optional (default 14)
Number of features for each pair of atoms in input.
n_atom_output_feat: int, optional (default 50)
Number of features for each atom in output.
n_pair_output_feat: int, optional (default 50)
Number of features for each pair of atoms in output.
n_hidden_AA: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_PA: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_AP: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_PP: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
update_pair: bool, optional (default True)
Whether to calculate for pair features,
could be turned off for last layer
init: str, optional (default 'glorot_uniform')
Weight initialization for filters.
activation: str, optional (default 'relu')
Activation function applied
batch_normalize: bool, optional (default True)
If this is turned on, apply batch normalization before applying
activation functions on convolutional layers.
batch_normalize_kwargs: Dict, optional (default `{renorm=True}`)
Batch normalization is a complex layer which has many potential
argumentswhich change behavior. This layer accepts user-defined
parameters which are passed to all `BatchNormalization` layers in
`WeaveModel`, `WeaveLayer`, and `WeaveGather`. | Parameters
----------
n_atom_input_feat: int, optional (default 75)
Number of features for each atom in input.
n_pair_input_feat: int, optional (default 14)
Number of features for each pair of atoms in input.
n_atom_output_feat: int, optional (default 50)
Number of features for each atom in output.
n_pair_output_feat: int, optional (default 50)
Number of features for each pair of atoms in output.
n_hidden_AA: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_PA: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_AP: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_PP: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
update_pair: bool, optional (default True)
Whether to calculate for pair features,
could be turned off for last layer
init: str, optional (default 'glorot_uniform')
Weight initialization for filters.
activation: str, optional (default 'relu')
Activation function applied
batch_normalize: bool, optional (default True)
If this is turned on, apply batch normalization before applying
activation functions on convolutional layers.
batch_normalize_kwargs: Dict, optional (default `{renorm=True}`)
Batch normalization is a complex layer which has many potential
argumentswhich change behavior. This layer accepts user-defined
parameters which are passed to all `BatchNormalization` layers in
`WeaveModel`, `WeaveLayer`, and `WeaveGather`. | [
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n_atom_input_feat: int = 75,
n_pair_input_feat: int = 14,
n_atom_output_feat: int = 50,
n_pair_output_feat: int = 50,
n_hidden_AA: int = 50,
n_hidden_PA: int = 50,
n_hidden_AP: int = 50,
n_hidden_PP: int = 50,
update_pair: bool = True,
init: str = 'glorot_uniform',
activation: str = 'relu',
batch_normalize: bool = True,
batch_normalize_kwargs: Dict = {"renorm": True},
**kwargs):
"""
Parameters
----------
n_atom_input_feat: int, optional (default 75)
Number of features for each atom in input.
n_pair_input_feat: int, optional (default 14)
Number of features for each pair of atoms in input.
n_atom_output_feat: int, optional (default 50)
Number of features for each atom in output.
n_pair_output_feat: int, optional (default 50)
Number of features for each pair of atoms in output.
n_hidden_AA: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_PA: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_AP: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
n_hidden_PP: int, optional (default 50)
Number of units(convolution depths) in corresponding hidden layer
update_pair: bool, optional (default True)
Whether to calculate for pair features,
could be turned off for last layer
init: str, optional (default 'glorot_uniform')
Weight initialization for filters.
activation: str, optional (default 'relu')
Activation function applied
batch_normalize: bool, optional (default True)
If this is turned on, apply batch normalization before applying
activation functions on convolutional layers.
batch_normalize_kwargs: Dict, optional (default `{renorm=True}`)
Batch normalization is a complex layer which has many potential
argumentswhich change behavior. This layer accepts user-defined
parameters which are passed to all `BatchNormalization` layers in
`WeaveModel`, `WeaveLayer`, and `WeaveGather`.
"""
super(WeaveLayer, self).__init__(**kwargs)
self.init = init # Set weight initialization
self.activation = activation # Get activations
self.activation_fn = activations.get(activation)
self.update_pair = update_pair # last weave layer does not need to update
self.n_hidden_AA = n_hidden_AA
self.n_hidden_PA = n_hidden_PA
self.n_hidden_AP = n_hidden_AP
self.n_hidden_PP = n_hidden_PP
self.n_hidden_A = n_hidden_AA + n_hidden_PA
self.n_hidden_P = n_hidden_AP + n_hidden_PP
self.batch_normalize = batch_normalize
self.batch_normalize_kwargs = batch_normalize_kwargs
self.n_atom_input_feat = n_atom_input_feat
self.n_pair_input_feat = n_pair_input_feat
self.n_atom_output_feat = n_atom_output_feat
self.n_pair_output_feat = n_pair_output_feat
self.W_AP, self.b_AP, self.W_PP, self.b_PP, self.W_P, self.b_P = None, None, None, None, None, None | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/http/server.py | python | SimpleHTTPRequestHandler.copyfile | (self, source, outputfile) | Copy all data between two file objects.
The SOURCE argument is a file object open for reading
(or anything with a read() method) and the DESTINATION
argument is a file object open for writing (or
anything with a write() method).
The only reason for overriding this would be to change
the block size or perhaps to replace newlines by CRLF
-- note however that this the default server uses this
to copy binary data as well. | Copy all data between two file objects. | [
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] | def copyfile(self, source, outputfile):
"""Copy all data between two file objects.
The SOURCE argument is a file object open for reading
(or anything with a read() method) and the DESTINATION
argument is a file object open for writing (or
anything with a write() method).
The only reason for overriding this would be to change
the block size or perhaps to replace newlines by CRLF
-- note however that this the default server uses this
to copy binary data as well.
"""
shutil.copyfileobj(source, outputfile) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/inject/thirdparty/gprof2dot/gprof2dot.py | python | SleepyParser.parse_symbols | (self) | [] | def parse_symbols(self):
lines = self.database.read('symbols.txt').splitlines()
for line in lines:
mo = self._symbol_re.match(line)
if mo:
symbol_id, module, procname, sourcefile, sourceline = mo.groups()
function_id = ':'.join([module, procname])
try:
function = self.profile.functions[function_id]
except KeyError:
function = Function(function_id, procname)
function.module = module
function[SAMPLES] = 0
self.profile.add_function(function)
self.symbols[symbol_id] = function | [
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nadineproject/nadine | c41c8ef7ffe18f1853029c97eecc329039b4af6c | nadine/models/profile.py | python | UserQueryHelper.payers | (self, target_date=None) | return User.objects.filter(id__in=combined_set).distinct() | Return a set of Users that are paying for the active memberships | Return a set of Users that are paying for the active memberships | [
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] | def payers(self, target_date=None):
''' Return a set of Users that are paying for the active memberships '''
# I tried to make this method as easy to read as possible. -- JLS
# This joins the following sets:
# Individuals paying for their own membership,
# Organization leads of active organizations,
# Users paying for other's memberships
active_paid_subscriptions = ResourceSubscription.objects.active_subscriptions(target_date).filter(monthly_rate__gt=0)
paid_by_self = active_paid_subscriptions.filter(paid_by__isnull=True)
paid_by_other = active_paid_subscriptions.filter(paid_by__isnull=False)
other_payers = paid_by_other.annotate(payer=F('paid_by')).values('payer')
is_individual_membership = Q(membership__individualmembership__isnull=False)
individual_payers = paid_by_self.filter(is_individual_membership).annotate(payer=F('membership__individualmembership__user')).values('payer')
is_organizaion_membership = Q(membership__organizationmembership__isnull=False)
organization_leads = paid_by_self.filter(is_organizaion_membership).annotate(payer=F('membership__organizationmembership__organization__lead')).values('payer')
combined_set = (individual_payers | organization_leads | other_payers)
return User.objects.filter(id__in=combined_set).distinct() | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/ctp/futures/ApiStruct.py | python | BrokerUserPassword.__init__ | (self, BrokerID='', UserID='', Password='') | [] | def __init__(self, BrokerID='', UserID='', Password=''):
self.BrokerID = '' #经纪公司代码, char[11]
self.UserID = '' #用户代码, char[16]
self.Password = '' | [
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alanhamlett/pip-update-requirements | ce875601ef278c8ce00ad586434a978731525561 | pur/packages/pip/_vendor/ipaddress.py | python | _IPAddressBase._prefix_from_ip_int | (cls, ip_int) | return prefixlen | Return prefix length from the bitwise netmask.
Args:
ip_int: An integer, the netmask in expanded bitwise format
Returns:
An integer, the prefix length.
Raises:
ValueError: If the input intermingles zeroes & ones | Return prefix length from the bitwise netmask. | [
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] | def _prefix_from_ip_int(cls, ip_int):
"""Return prefix length from the bitwise netmask.
Args:
ip_int: An integer, the netmask in expanded bitwise format
Returns:
An integer, the prefix length.
Raises:
ValueError: If the input intermingles zeroes & ones
"""
trailing_zeroes = _count_righthand_zero_bits(ip_int,
cls._max_prefixlen)
prefixlen = cls._max_prefixlen - trailing_zeroes
leading_ones = ip_int >> trailing_zeroes
all_ones = (1 << prefixlen) - 1
if leading_ones != all_ones:
byteslen = cls._max_prefixlen // 8
details = _compat_to_bytes(ip_int, byteslen, 'big')
msg = 'Netmask pattern %r mixes zeroes & ones'
raise ValueError(msg % details)
return prefixlen | [
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respeaker/get_started_with_respeaker | ec859759fcec7e683a5e09328a8ea307046f353d | files/usr/lib/python2.7/site-packages/serial/sermsdos.py | python | Serial.getCD | (self) | Eead terminal status line | Eead terminal status line | [
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"""Eead terminal status line"""
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/codec/whoosh2.py | python | OLD_NUMERIC.parse_query | (self, fieldname, qstring, boost=1.0) | return query.Term(fieldname, text, boost=boost) | [] | def parse_query(self, fieldname, qstring, boost=1.0):
from whoosh import query
if qstring == "*":
return query.Every(fieldname, boost=boost)
try:
text = self.to_text(qstring)
except Exception:
e = sys.exc_info()[1]
return query.error_query(e)
return query.Term(fieldname, text, boost=boost) | [
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zedshaw/learn-more-python-the-hard-way-solutions | 7886c860f69d69739a41d6490b8dc3fa777f227b | ex33_parsers/ex33.py | python | plus | (tokens, left) | return {'type': 'PLUS', 'left': left, 'right': right} | plus = expression PLUS expression | plus = expression PLUS expression | [
"plus",
"=",
"expression",
"PLUS",
"expression"
] | def plus(tokens, left):
"""plus = expression PLUS expression"""
match(tokens, 'PLUS')
right = expression(tokens)
return {'type': 'PLUS', 'left': left, 'right': right} | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | check_server/check_scripts/8.py | python | check.index_check | (self) | return False | [] | def index_check(self):
res = http('get',host,port,'/index.php?file=news&cid=1&page=1&test=eval&time=%s'%str(my_time),'',headers)
if 'lalalala' in res:
return True
if debug:
print "[fail!] index_fail"
return False | [
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ustayready/CredKing | 68b612e4cdf01d2b65b14ab2869bb8a5531056ee | plugins/gmail/bs4/element.py | python | Tag.index | (self, element) | Find the index of a child by identity, not value. Avoids issues with
tag.contents.index(element) getting the index of equal elements. | Find the index of a child by identity, not value. Avoids issues with
tag.contents.index(element) getting the index of equal elements. | [
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"""
Find the index of a child by identity, not value. Avoids issues with
tag.contents.index(element) getting the index of equal elements.
"""
for i, child in enumerate(self.contents):
if child is element:
return i
raise ValueError("Tag.index: element not in tag") | [
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amansrivastava17/embedding-as-service | 8cd4087cbd39ecb57ee732c029dea465ad364a5e | server/embedding_as_service/text/xlnet/models/tpu_estimator.py | python | _clone_export_output_with_tensors | (export_output, tensors) | Clones `export_output` but with new `tensors`.
Args:
export_output: an `ExportOutput` object such as `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
tensors: a list of `Tensors` used to construct a new `export_output`.
Returns:
A dict similar to `export_output` but with `tensors`.
Raises:
ValueError: if `export_output` is not one of `ClassificationOutput`,
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] | def _clone_export_output_with_tensors(export_output, tensors):
"""Clones `export_output` but with new `tensors`.
Args:
export_output: an `ExportOutput` object such as `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
tensors: a list of `Tensors` used to construct a new `export_output`.
Returns:
A dict similar to `export_output` but with `tensors`.
Raises:
ValueError: if `export_output` is not one of `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
"""
if isinstance(export_output, export_output_lib.ClassificationOutput):
if len(tensors) != 2:
raise ValueError('tensors must be of length 2; '
'got {}.'.format(len(tensors)))
return export_output_lib.ClassificationOutput(*tensors)
elif isinstance(export_output, export_output_lib.RegressionOutput):
if len(tensors) != 1:
raise ValueError('tensors must be of length 1; '
'got {}'.format(len(tensors)))
return export_output_lib.RegressionOutput(*tensors)
elif isinstance(export_output, export_output_lib.PredictOutput):
return export_output_lib.PredictOutput(
dict(zip(export_output.outputs.keys(), tensors)))
else:
raise ValueError(
'`export_output` must be have type `ClassificationOutput`, '
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xiaobai1217/MBMD | 246f3434bccb9c8357e0f698995b659578bf1afb | core/man_meta_arch.py | python | MANMetaArch._add_box_predictions_to_feature_maps | (self, feature_maps,reuse=None) | return box_encodings, class_predictions_with_background | Adds box predictors to each feature map and returns concatenated results.
Args:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
Returns:
box_encodings: 4-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
class_predictions_with_background: 2-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions (at class index 0).
Raises:
RuntimeError: if the number of feature maps extracted via the
extract_features method does not match the length of the
num_anchors_per_locations list that was passed to the constructor.
RuntimeError: if box_encodings from the box_predictor does not have
shape of the form [batch_size, num_anchors, 1, code_size]. | Adds box predictors to each feature map and returns concatenated results. | [
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"""Adds box predictors to each feature map and returns concatenated results.
Args:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
Returns:
box_encodings: 4-D float tensor of shape [batch_size, num_anchors,
box_code_dimension] containing predicted boxes.
class_predictions_with_background: 2-D float tensor of shape
[batch_size, num_anchors, num_classes+1] containing class predictions
(logits) for each of the anchors. Note that this tensor *includes*
background class predictions (at class index 0).
Raises:
RuntimeError: if the number of feature maps extracted via the
extract_features method does not match the length of the
num_anchors_per_locations list that was passed to the constructor.
RuntimeError: if box_encodings from the box_predictor does not have
shape of the form [batch_size, num_anchors, 1, code_size].
"""
num_anchors_per_location_list = (
self._anchor_generator.num_anchors_per_location())
if len(feature_maps) != len(num_anchors_per_location_list):
raise RuntimeError('the number of feature maps must match the '
'length of self.anchors.NumAnchorsPerLocation().')
box_encodings_list = []
cls_predictions_with_background_list = []
for idx, (feature_map, num_anchors_per_location
) in enumerate(zip(feature_maps, num_anchors_per_location_list)):
box_predictor_scope = 'BoxPredictor'
box_predictions = self._box_predictor.predict(feature_map,
num_anchors_per_location,
box_predictor_scope,reuse=reuse)
box_encodings = box_predictions[bpredictor.BOX_ENCODINGS]
cls_predictions_with_background = box_predictions[
bpredictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
box_encodings_shape = box_encodings.get_shape().as_list()
if len(box_encodings_shape) != 4 or box_encodings_shape[2] != 1:
raise RuntimeError('box_encodings from the box_predictor must be of '
'shape `[batch_size, num_anchors, 1, code_size]`; '
'actual shape', box_encodings_shape)
box_encodings = tf.squeeze(box_encodings, axis=2)
box_encodings_list.append(box_encodings)
cls_predictions_with_background_list.append(
cls_predictions_with_background)
num_predictions = sum(
[tf.shape(box_encodings)[1] for box_encodings in box_encodings_list])
num_anchors = self.anchors.num_boxes()
anchors_assert = tf.assert_equal(num_anchors, num_predictions, [
'Mismatch: number of anchors vs number of predictions', num_anchors,
num_predictions
])
with tf.control_dependencies([anchors_assert]):
box_encodings = tf.concat(box_encodings_list, 1)
class_predictions_with_background = tf.concat(
cls_predictions_with_background_list, 1)
return box_encodings, class_predictions_with_background | [
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tlsfuzzer/tlslite-ng | 8720db53067ba4f7bb7b5a32d682033d8b5446f9 | tlslite/bufferedsocket.py | python | BufferedSocket.__init__ | (self, socket) | Associate socket with the object | Associate socket with the object | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/contourcarpet/_contours.py | python | Contours.showlines | (self) | return self["showlines"] | Determines whether or not the contour lines are drawn. Has an
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The 'showlines' property must be specified as a bool
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Returns
-------
bool | Determines whether or not the contour lines are drawn. Has an
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The 'showlines' property must be specified as a bool
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Determines whether or not the contour lines are drawn. Has an
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The 'showlines' property must be specified as a bool
(either True, or False)
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649453932/Bert-Chinese-Text-Classification-Pytorch | 050a7b0dc75d8a2d7fd526002c4642d5329a0c27 | pytorch_pretrained/modeling_gpt2.py | python | GPT2Config.to_json_string | (self) | return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" | Serializes this instance to a JSON string. | Serializes this instance to a JSON string. | [
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alexsax/pytorch-visdom | 6e46601d71ea417ec2a5b39316d2a0ea8ac921cf | trainer/plugins/visdom_logger.py | python | VisdomTextLogger.__init__ | (self, fields, interval=None, win=None, env=None, opts={}, update_type=valid_update_types[0]) | Args:
fields: The fields to log. May either be the name of some stat (e.g. ProgressMonitor)
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interval: A List of 2-tuples where each tuple contains (k, HOOK_TIME).
k (int): The logger will be called every 'k' HOOK_TIMES
HOOK_TIME (string): The logger will be called at the given hook
update_type: One of {'REPLACE', 'APPEND'}. Default 'REPLACE'.
Examples:
>>> progress_m = ProgressMonitor()
>>> logger = VisdomTextLogger(["progress"], [(2, 'iteration')])
>>> train.register_plugin(progress_m)
>>> train.register_plugin(logger) | Args:
fields: The fields to log. May either be the name of some stat (e.g. ProgressMonitor)
will have `stat_name='progress'`, in which case all of the fields under
`log_HOOK_fields` will be logged. Finer-grained control can be specified
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k (int): The logger will be called every 'k' HOOK_TIMES
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k (int): The logger will be called every 'k' HOOK_TIMES
HOOK_TIME (string): The logger will be called at the given hook
update_type: One of {'REPLACE', 'APPEND'}. Default 'REPLACE'.
Examples:
>>> progress_m = ProgressMonitor()
>>> logger = VisdomTextLogger(["progress"], [(2, 'iteration')])
>>> train.register_plugin(progress_m)
>>> train.register_plugin(logger)
'''
super(VisdomTextLogger, self).__init__(fields, interval, win, env, opts)
self.text = ''
if update_type not in self.valid_update_types:
raise ValueError("update type '{}' not found. Must be one of {}".format(update_type, self.valid_update_types))
self.update_type = update_type
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Nuitka/Nuitka | 39262276993757fa4e299f497654065600453fc9 | nuitka/build/inline_copy/lib/scons-2.3.2/SCons/Tool/docbook/__init__.py | python | __extend_targets_sources | (target, source) | return target, source | Prepare the lists of target and source files. | Prepare the lists of target and source files. | [
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target = [target]
if not source:
source = target[:]
elif not SCons.Util.is_List(source):
source = [source]
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target.extend(source[len(target):])
return target, source | [
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Esri/ArcREST | ab240fde2b0200f61d4a5f6df033516e53f2f416 | src/arcrest/packages/ntlm3/ntlm.py | python | create_NT_hashed_password_v1 | (passwd, user=None, domain=None) | return digest | create NT hashed password | create NT hashed password | [
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"hashed",
"password"
] | def create_NT_hashed_password_v1(passwd, user=None, domain=None):
"create NT hashed password"
# if the passwd provided is already a hash, we just return the second half
if re.match(r'^[\w]{32}:[\w]{32}$', passwd):
return binascii.unhexlify(passwd.split(':')[1])
digest = hashlib.new('md4', passwd.encode('utf-16le')).digest()
return digest | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/tarfile.py | python | TarInfo._apply_pax_info | (self, pax_headers, encoding, errors) | Replace fields with supplemental information from a previous
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for keyword, value in pax_headers.items():
if keyword == "GNU.sparse.name":
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elif keyword == "GNU.sparse.size":
setattr(self, "size", int(value))
elif keyword == "GNU.sparse.realsize":
setattr(self, "size", int(value))
elif keyword in PAX_FIELDS:
if keyword in PAX_NUMBER_FIELDS:
try:
value = PAX_NUMBER_FIELDS[keyword](value)
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value = 0
if keyword == "path":
value = value.rstrip("/")
setattr(self, keyword, value)
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Tools/pybench/CommandLine.py | python | Application.handle__examples | (self,arg) | return 0 | [] | def handle__examples(self,arg):
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/interfaces/kenzo.py | python | KenzoChainComplex.tensor_product | (self, other) | return KenzoChainComplex(__tnsr_prdc__(self._kenzo, other._kenzo)) | r"""
Return the tensor product of ``self`` and ``other``.
INPUT:
- ``other`` -- The Kenzo object with which to compute the tensor product
OUTPUT:
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EXAMPLES::
sage: from sage.interfaces.kenzo import Sphere # optional - kenzo
sage: s2 = Sphere(2) # optional - kenzo
sage: s3 = Sphere(3) # optional - kenzo
sage: p = s2.tensor_product(s3) # optional - kenzo
sage: type(p) # optional - kenzo
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r"""
Return the tensor product of ``self`` and ``other``.
INPUT:
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OUTPUT:
- A :class:`KenzoChainComplex`
EXAMPLES::
sage: from sage.interfaces.kenzo import Sphere # optional - kenzo
sage: s2 = Sphere(2) # optional - kenzo
sage: s3 = Sphere(3) # optional - kenzo
sage: p = s2.tensor_product(s3) # optional - kenzo
sage: type(p) # optional - kenzo
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/mpmath/functions/zetazeros.py | python | separate_zeros_in_block | (ctx, zero_number_block, T, V, limitloop=None,
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limitloop = ctx.inf
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newT = [a]
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u = V[n]
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w = ctx.siegelz(b)
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u = V[n]
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a = b2
v = u
T = newT
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kMax = 0
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kMax=k1
dtSec = dtMax
dtMax = dt
elif (dt<dtMax) and(dt >dtSec):
dtSec = dt
if dtMax>3*dtSec:
f = lambda x: ctx.rs_z(x,derivative=1)
t0=T[kMax-1]
t1 = T[kMax]
t=ctx.findroot(f, (t0,t1), solver ='illinois',verify=False, verbose=False)
v = ctx.siegelz(t)
if (t0<t) and (t<t1) and (v*V[kMax]<0):
T.insert(kMax,t)
V.insert(kMax,v)
variations = count_variations(V)
if variations == zero_number_block:
separated = True
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separated = False
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"loopnumber",
... | https://github.com/omz/PythonistaAppTemplate/blob/f560f93f8876d82a21d108977f90583df08d55af/PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/mpmath/functions/zetazeros.py#L67-L135 | |
mks0601/A-Convolutional-Neural-Network-Cascade-for-Face-Detection | 2a7e8464fff041d128a8dc536ae200ca51784045 | data.py | python | load_db_calib_train | (dim) | return x_db | [] | def load_db_calib_train(dim):
print "Loading calibration training db..."
annot_dir = param.db_dir + "AFLW/aflw/data/"
annot_fp = open(annot_dir + "annot", "r")
raw_data = annot_fp.readlines()
#pos image cropping
x_db = [0 for _ in xrange(len(raw_data))]
for i,line in enumerate(raw_data):
parsed_line = line.split(',')
filename = parsed_line[0][3:-1]
xmin = int(parsed_line[1])
ymin = int(parsed_line[2])
xmax = xmin + int(parsed_line[3])
ymax = ymin + int(parsed_line[4][:-2])
img = Image.open(param.pos_dir+filename)
#truncated image(error)
if i == 8160 or i == 14884 or i == 14886:
continue
#check if gray
if len(np.shape(np.asarray(img))) != param.input_channel:
img = np.asarray(img)
img = np.reshape(img,(np.shape(img)[0],np.shape(img)[1],1))
img = np.concatenate((img,img,img),axis=2)
img = Image.fromarray(img)
if xmax >= img.size[0]:
xmax = img.size[0]-1
if ymax >= img.size[1]:
ymax = img.size[1]-1
x_db_list = [0 for _ in xrange(param.cali_patt_num)]
for si,s in enumerate(param.cali_scale):
for xi,x in enumerate(param.cali_off_x):
for yi,y in enumerate(param.cali_off_y):
new_xmin = xmin - x*float(xmax-xmin)/s
new_ymin = ymin - y*float(ymax-ymin)/s
new_xmax = new_xmin+float(xmax-xmin)/s
new_ymax = new_ymin+float(ymax-ymin)/s
new_xmin = int(new_xmin)
new_ymin = int(new_ymin)
new_xmax = int(new_xmax)
new_ymax = int(new_ymax)
if new_xmin < 0 or new_ymin < 0 or new_xmax >= img.size[0] or new_ymax >= img.size[1]:
continue
cropped_img = util.img2array(img.crop((new_xmin, new_ymin, new_xmax, new_ymax)),dim)
calib_idx = si*len(param.cali_off_x)*len(param.cali_off_y)+xi*len(param.cali_off_y)+yi
#for debugging
#cropped_img.save(param.pos_dir + str(i) + ".jpg")
x_db_list[calib_idx] = [cropped_img,calib_idx]
x_db_list = [elem for elem in x_db_list if type(elem) != int]
if len(x_db_list) > 0:
x_db[i] = x_db_list
x_db = [elem for elem in x_db if type(elem) != int]
x_db = [x_db[i][j] for i in xrange(len(x_db)) for j in xrange(len(x_db[i]))]
return x_db | [
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... | https://github.com/mks0601/A-Convolutional-Neural-Network-Cascade-for-Face-Detection/blob/2a7e8464fff041d128a8dc536ae200ca51784045/data.py#L185-L259 | |||
haiwen/seahub | e92fcd44e3e46260597d8faa9347cb8222b8b10d | scripts/setup-seafile-mysql.py | python | Utils.must_copy | (src, dst) | Copy src to dst, exit on failure | Copy src to dst, exit on failure | [
"Copy",
"src",
"to",
"dst",
"exit",
"on",
"failure"
] | def must_copy(src, dst):
'''Copy src to dst, exit on failure'''
try:
shutil.copy(src, dst)
except Exception as e:
Utils.error('failed to copy %s to %s: %s' % (src, dst, e)) | [
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","... | https://github.com/haiwen/seahub/blob/e92fcd44e3e46260597d8faa9347cb8222b8b10d/scripts/setup-seafile-mysql.py#L140-L145 |
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