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pyfa-org/Pyfa | feaa52c36beeda21ab380fc74d8f871b81d49729 | service/market.py | python | Market.getShipListDelayed | (self, id_, callback) | Background version of getShipList | Background version of getShipList | [
"Background",
"version",
"of",
"getShipList"
] | def getShipListDelayed(self, id_, callback):
"""Background version of getShipList"""
self.shipBrowserWorkerThread.queue.put((id_, callback)) | [
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joerick/pyinstrument | d3c45164a385021f366c1081baec18a1a226a573 | pyinstrument/util.py | python | file_is_a_tty | (file_obj: IO) | return hasattr(file_obj, "isatty") and file_obj.isatty() | [] | def file_is_a_tty(file_obj: IO) -> bool:
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Rapptz/discord.py | 45d498c1b76deaf3b394d17ccf56112fa691d160 | discord/ext/tasks/__init__.py | python | Loop.seconds | (self) | Optional[:class:`float`]: Read-only value for the number of seconds
between each iteration. ``None`` if an explicit ``time`` value was passed instead.
.. versionadded:: 2.0 | Optional[:class:`float`]: Read-only value for the number of seconds
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"""Optional[:class:`float`]: Read-only value for the number of seconds
between each iteration. ``None`` if an explicit ``time`` value was passed instead.
.. versionadded:: 2.0
"""
if self._seconds is not MISSING:
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issackelly/wemo | d6c1f502015d21b19fa7e518fe84b697cf58ec3d | wemo.py | python | off | () | return True if tagValue in ['0', 'Error'] else False | Turns off the first switch that it finds.
BinaryState is set to 'Error' in the case that it was already off. | Turns off the first switch that it finds. | [
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"""
Turns off the first switch that it finds.
BinaryState is set to 'Error' in the case that it was already off.
"""
resp = _send('SetBinaryState', {'BinaryState': (0, 'Boolean')})
tagValue = conn.extractSingleTag(resp, 'BinaryState')
return True if tagValue in ['0', 'Error'] else False | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/plat-freebsd4/IN.py | python | IN6_IS_ADDR_MC_ORGLOCAL | (a) | return | [] | def IN6_IS_ADDR_MC_ORGLOCAL(a): return | [
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weecology/retriever | e5ba505f7b9958c70e60155f3c5495899da27e7e | retriever/lib/scripts.py | python | get_script_citation | (dataset=None) | return citations | Get the citation list for a script | Get the citation list for a script | [
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] | def get_script_citation(dataset=None):
"""Get the citation list for a script"""
if dataset is not None:
dataset = dataset.strip()
if not dataset:
return [VERSION]
citations = []
scripts = name_matches(reload_scripts(), dataset)
if scripts:
citations = []
for script in scripts:
citations.append(script.citation)
return citations | [
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google/pytype | fa43edc95dd42ade6e3147d6580d63e778c9d506 | pytype/tools/xref/callgraph.py | python | FunctionMap.add_param_def | (self, ref, defn) | Add a function parameter definition. | Add a function parameter definition. | [
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"""Add a function parameter definition."""
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# Don't override a type inferred from call sites.
if param.name == defn.name and param.type in ('nothing', 'typing.Any'):
param.type = unwrap_type(self.index.get_pytd(ref.data[0]))
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wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/Snake/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/distlib/locators.py | python | SimpleScrapingLocator.get_page | (self, url) | return result | Get the HTML for an URL, possibly from an in-memory cache.
XXX TODO Note: this cache is never actually cleared. It's assumed that
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necessarily true for the default_locator). | Get the HTML for an URL, possibly from an in-memory cache. | [
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"""
Get the HTML for an URL, possibly from an in-memory cache.
XXX TODO Note: this cache is never actually cleared. It's assumed that
the data won't get stale over the lifetime of a locator instance (not
necessarily true for the default_locator).
"""
# http://peak.telecommunity.com/DevCenter/EasyInstall#package-index-api
scheme, netloc, path, _, _, _ = urlparse(url)
if scheme == 'file' and os.path.isdir(url2pathname(path)):
url = urljoin(ensure_slash(url), 'index.html')
if url in self._page_cache:
result = self._page_cache[url]
logger.debug('Returning %s from cache: %s', url, result)
else:
host = netloc.split(':', 1)[0]
result = None
if host in self._bad_hosts:
logger.debug('Skipping %s due to bad host %s', url, host)
else:
req = Request(url, headers={'Accept-encoding': 'identity'})
try:
logger.debug('Fetching %s', url)
resp = self.opener.open(req, timeout=self.timeout)
logger.debug('Fetched %s', url)
headers = resp.info()
content_type = headers.get('Content-Type', '')
if HTML_CONTENT_TYPE.match(content_type):
final_url = resp.geturl()
data = resp.read()
encoding = headers.get('Content-Encoding')
if encoding:
decoder = self.decoders[encoding] # fail if not found
data = decoder(data)
encoding = 'utf-8'
m = CHARSET.search(content_type)
if m:
encoding = m.group(1)
try:
data = data.decode(encoding)
except UnicodeError: # pragma: no cover
data = data.decode('latin-1') # fallback
result = Page(data, final_url)
self._page_cache[final_url] = result
except HTTPError as e:
if e.code != 404:
logger.exception('Fetch failed: %s: %s', url, e)
except URLError as e: # pragma: no cover
logger.exception('Fetch failed: %s: %s', url, e)
with self._lock:
self._bad_hosts.add(host)
except Exception as e: # pragma: no cover
logger.exception('Fetch failed: %s: %s', url, e)
finally:
self._page_cache[url] = result # even if None (failure)
return result | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/inject/plugins/dbms/postgresql/enumeration.py | python | Enumeration.getHostname | (self) | [] | def getHostname(self):
warnMsg = "on PostgreSQL it is not possible to enumerate the hostname"
logger.warn(warnMsg) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_vendored_deps/library/oc_clusterrole.py | python | Utils._write | (filename, contents) | Actually write the file contents to disk. This helps with mocking. | Actually write the file contents to disk. This helps with mocking. | [
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] | def _write(filename, contents):
''' Actually write the file contents to disk. This helps with mocking. '''
with open(filename, 'w') as sfd:
sfd.write(str(contents)) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.2/django/contrib/gis/db/backends/postgis/creation.py | python | PostGISCreation.sql_indexes_for_field | (self, model, f, style) | return output | Return any spatial index creation SQL for the field. | Return any spatial index creation SQL for the field. | [
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] | def sql_indexes_for_field(self, model, f, style):
"Return any spatial index creation SQL for the field."
from django.contrib.gis.db.models.fields import GeometryField
output = super(PostGISCreation, self).sql_indexes_for_field(model, f, style)
if isinstance(f, GeometryField):
gqn = self.connection.ops.geo_quote_name
qn = self.connection.ops.quote_name
db_table = model._meta.db_table
if f.geography:
# Geogrophy columns are created normally.
pass
else:
# Geometry columns are created by `AddGeometryColumn`
# stored procedure.
output.append(style.SQL_KEYWORD('SELECT ') +
style.SQL_TABLE('AddGeometryColumn') + '(' +
style.SQL_TABLE(gqn(db_table)) + ', ' +
style.SQL_FIELD(gqn(f.column)) + ', ' +
style.SQL_FIELD(str(f.srid)) + ', ' +
style.SQL_COLTYPE(gqn(f.geom_type)) + ', ' +
style.SQL_KEYWORD(str(f.dim)) + ');')
if not f.null:
# Add a NOT NULL constraint to the field
output.append(style.SQL_KEYWORD('ALTER TABLE ') +
style.SQL_TABLE(qn(db_table)) +
style.SQL_KEYWORD(' ALTER ') +
style.SQL_FIELD(qn(f.column)) +
style.SQL_KEYWORD(' SET NOT NULL') + ';')
if f.spatial_index:
# Spatial indexes created the same way for both Geometry and
# Geography columns
if f.geography:
index_opts = ''
else:
index_opts = ' ' + style.SQL_KEYWORD(self.geom_index_opts)
output.append(style.SQL_KEYWORD('CREATE INDEX ') +
style.SQL_TABLE(qn('%s_%s_id' % (db_table, f.column))) +
style.SQL_KEYWORD(' ON ') +
style.SQL_TABLE(qn(db_table)) +
style.SQL_KEYWORD(' USING ') +
style.SQL_COLTYPE(self.geom_index_type) + ' ( ' +
style.SQL_FIELD(qn(f.column)) + index_opts + ' );')
return output | [
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clips/clicr | 557f6d96a9afe245d9637e7e86315c2838878fc2 | dataset-code/json_to_plain.py | python | write_sareader | (i, fh_out) | :param i: {"id": "",
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fh_out.write(i["id"] + "\n\n") | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/ecoal_boiler/switch.py | python | EcoalSwitch.invalidate_ecoal_cache | (self) | Invalidate ecoal interface cache.
Forces that next read from ecaol interface to not use cache. | Invalidate ecoal interface cache. | [
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] | def invalidate_ecoal_cache(self):
"""Invalidate ecoal interface cache.
Forces that next read from ecaol interface to not use cache.
"""
self._ecoal_contr.status = None | [
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1040003585/WebScrapingWithPython | a770fa5b03894076c8c9539b1ffff34424ffc016 | portia_examle/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | _handle_ns | (packageName, path_item) | return subpath | Ensure that named package includes a subpath of path_item (if needed) | Ensure that named package includes a subpath of path_item (if needed) | [
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"""Ensure that named package includes a subpath of path_item (if needed)"""
importer = get_importer(path_item)
if importer is None:
return None
loader = importer.find_module(packageName)
if loader is None:
return None
module = sys.modules.get(packageName)
if module is None:
module = sys.modules[packageName] = types.ModuleType(packageName)
module.__path__ = []
_set_parent_ns(packageName)
elif not hasattr(module, '__path__'):
raise TypeError("Not a package:", packageName)
handler = _find_adapter(_namespace_handlers, importer)
subpath = handler(importer, path_item, packageName, module)
if subpath is not None:
path = module.__path__
path.append(subpath)
loader.load_module(packageName)
_rebuild_mod_path(path, packageName, module)
return subpath | [
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giswqs/geemap | ba8de85557a8a1bc9c8d19e75bac69416afe7fea | geemap/eefolium.py | python | Map.center_object | (self, ee_object, zoom=10) | Centers the map view on a given object.
Args:
ee_object (Element|Geometry): An Earth Engine object to center on - a geometry, image or feature.
zoom (int, optional): The zoom level, from 1 to 24. Defaults to 10. | Centers the map view on a given object. | [
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] | def center_object(self, ee_object, zoom=10):
"""Centers the map view on a given object.
Args:
ee_object (Element|Geometry): An Earth Engine object to center on - a geometry, image or feature.
zoom (int, optional): The zoom level, from 1 to 24. Defaults to 10.
"""
lat = 0
lon = 0
if isinstance(ee_object, ee.geometry.Geometry):
centroid = ee_object.centroid()
lon, lat = centroid.getInfo()["coordinates"]
bounds = [[lat, lon], [lat, lon]]
elif isinstance(ee_object, ee.featurecollection.FeatureCollection):
centroid = ee_object.geometry().centroid()
lon, lat = centroid.getInfo()["coordinates"]
bounds = [[lat, lon], [lat, lon]]
elif isinstance(ee_object, ee.image.Image):
geometry = ee_object.geometry()
coordinates = geometry.getInfo()["coordinates"][0]
bounds = [coordinates[0][::-1], coordinates[2][::-1]]
elif isinstance(ee_object, ee.imagecollection.ImageCollection):
geometry = ee_object.geometry()
coordinates = geometry.getInfo()["coordinates"][0]
bounds = [coordinates[0][::-1], coordinates[2][::-1]]
else:
bounds = [[0, 0], [0, 0]]
self.fit_bounds(bounds, max_zoom=zoom) | [
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npcole/npyscreen | 8ce31204e1de1fbd2939ffe2d8c3b3120e93a4d0 | npyscreen/wgmultiline.py | python | BufferPager.buffer | (self, lines, scroll_end=True, scroll_if_editing=False) | Add data to be displayed in the buffer. | Add data to be displayed in the buffer. | [
"Add",
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"to",
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] | def buffer(self, lines, scroll_end=True, scroll_if_editing=False):
"Add data to be displayed in the buffer."
self.values.extend(lines)
if scroll_end:
if not self.editing:
self.start_display_at = len(self.values) - len(self._my_widgets)
elif scroll_if_editing:
self.start_display_at = len(self.values) - len(self._my_widgets) | [
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CouchPotato/CouchPotatoV1 | 135b3331d1b88ef645e29b76f2d4cc4a732c9232 | app/controllers/config.py | python | ConfigController.index | (self) | return self.render({'trailerFormats':trailerFormats, 'foldernameResult':foldernameResult, 'filenameResult':filenameResult, 'config':config}) | Config form | Config form | [
"Config",
"form"
] | def index(self):
'''
Config form
'''
config = cherrypy.config.get('config')
renamer = self.cron.get('renamer')
replacements = {
'cd': ' cd1',
'cdNr': ' 1',
'ext': 'mkv',
'namethe': 'Big Lebowski, The',
'thename': 'The Big Lebowski',
'year': 1998,
'first': 'B',
'original': 'The.Big.Lebowski.1998.1080p.BluRay.x264.DTS-GROUP',
'group':'GROUP',
'audio':'DTS',
'video':'x264',
'quality': '1080p',
'sourcemedia': 'BluRay',
'resolution' : '1920x1080'
}
trailerFormats = self.cron.get('trailer').formats
foldernameResult = renamer.doReplace(config.get('Renamer', 'foldernaming'), replacements)
filenameResult = renamer.doReplace(config.get('Renamer', 'filenaming'), replacements)
return self.render({'trailerFormats':trailerFormats, 'foldernameResult':foldernameResult, 'filenameResult':filenameResult, 'config':config}) | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/encodings/cp862.py | python | Codec.encode | (self,input,errors='strict') | return codecs.charmap_encode(input,errors,encoding_map) | [] | def encode(self,input,errors='strict'):
return codecs.charmap_encode(input,errors,encoding_map) | [
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rasbt/mlxtend | 80964e439c1d150a00878f24b51bfeb2c8d2c3df | mlxtend/externals/pyprind/prog_class.py | python | Prog._check_stream | (self) | Determines which output stream (stdout, stderr, or custom) to use | Determines which output stream (stdout, stderr, or custom) to use | [
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] | def _check_stream(self):
"""Determines which output stream (stdout, stderr, or custom) to use"""
if self.stream:
try:
supported = ('PYCHARM_HOSTED' in os.environ or
os.isatty(sys.stdout.fileno()))
# a fix for IPython notebook "IOStream has no fileno."
except(UnsupportedOperation):
supported = True
else:
if self.stream is not None and hasattr(self.stream, 'write'):
self._stream_out = self.stream.write
self._stream_flush = self.stream.flush
if supported:
if self.stream == 1:
self._stream_out = sys.stdout.write
self._stream_flush = sys.stdout.flush
elif self.stream == 2:
self._stream_out = sys.stderr.write
self._stream_flush = sys.stderr.flush
else:
if self.stream is not None and hasattr(self.stream, 'write'):
self._stream_out = self.stream.write
self._stream_flush = self.stream.flush
else:
print('Warning: No valid output stream.') | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pip/_vendor/ipaddress.py | python | _BaseNetwork.num_addresses | (self) | return int(self.broadcast_address) - int(self.network_address) + 1 | Number of hosts in the current subnet. | Number of hosts in the current subnet. | [
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] | def num_addresses(self):
"""Number of hosts in the current subnet."""
return int(self.broadcast_address) - int(self.network_address) + 1 | [
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deepchem/deepchem | 054eb4b2b082e3df8e1a8e77f36a52137ae6e375 | contrib/DeepMHC/bd13_datasets.py | python | to_one_hot_array | (sequence) | return np.asarray(one_hot_array, dtype=np.int32) | Converts the sequence to one-hot-array. | Converts the sequence to one-hot-array. | [
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"-",
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"-",
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] | def to_one_hot_array(sequence):
"""Converts the sequence to one-hot-array."""
one_hot_array = list()
for letter in sequence:
one_hot_array.append([letter == i for i in aa_charset])
return np.asarray(one_hot_array, dtype=np.int32) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/google/appengine/_internal/django/utils/http.py | python | int_to_base36 | (i) | return ''.join(base36) | Converts an integer to a base36 string | Converts an integer to a base36 string | [
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"base36",
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] | def int_to_base36(i):
"""
Converts an integer to a base36 string
"""
digits = "0123456789abcdefghijklmnopqrstuvwxyz"
factor = 0
# Find starting factor
while True:
factor += 1
if i < 36 ** factor:
factor -= 1
break
base36 = []
# Construct base36 representation
while factor >= 0:
j = 36 ** factor
base36.append(digits[i / j])
i = i % j
factor -= 1
return ''.join(base36) | [
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KlugerLab/SpectralNet | e0993413ba4604258f979496e292fdcd26ea7a18 | src/core/util.py | python | LearningHandler.on_epoch_end | (self, epoch, logs=None) | return stop_training | Per epoch logic for managing learning rate and early stopping | Per epoch logic for managing learning rate and early stopping | [
"Per",
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"for",
"managing",
"learning",
"rate",
"and",
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] | def on_epoch_end(self, epoch, logs=None):
'''
Per epoch logic for managing learning rate and early stopping
'''
stop_training = False
# check if we need to stop or increase scheduler stage
if isinstance(logs, dict):
loss = logs['val_loss']
else:
loss = logs
if loss <= self.best_loss:
self.best_loss = loss
self.wait = 0
else:
self.wait += 1
if self.wait > self.patience:
self.scheduler_stage += 1
self.wait = 0
# calculate and set learning rate
lr = self.lr * np.power(self.drop, self.scheduler_stage)
K.set_value(self.lr_tensor, lr)
# built in stopping if lr is way too small
if lr <= 1e-7:
stop_training = True
# for keras
if hasattr(self, 'model') and self.model is not None:
self.model.stop_training = stop_training
return stop_training | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/utils/aiml_generator/generator.py | python | CollectionLoader.__init__ | (self, csv_file_name) | [] | def __init__(self, csv_file_name):
self.csv_file_name = csv_file_name | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/piston/oauth.py | python | OAuthSignatureMethod.build_signature | (self, oauth_request, oauth_consumer, oauth_token) | -> str. | -> str. | [
"-",
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] | def build_signature(self, oauth_request, oauth_consumer, oauth_token):
"""-> str."""
raise NotImplementedError | [
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ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models/albert/run_classifier.py | python | iFLYTEKDataProcessor._create_examples | (self, lines, set_type) | return examples | Creates examples for the training and dev sets. | Creates examples for the training and dev sets. | [
"Creates",
"examples",
"for",
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"training",
"and",
"dev",
"sets",
"."
] | def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = tokenization.convert_to_unicode(line[1])
text_b = None
label = tokenization.convert_to_unicode(line[0])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples | [
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JimmXinu/FanFicFare | bc149a2deb2636320fe50a3e374af6eef8f61889 | fanficfare/requestable.py | python | Requestable.do_decode | (self,data) | [] | def do_decode(self,data):
if not hasattr(data,'decode'):
## py3 str() from pickle doesn't have .decode and is
## already decoded. Should always be bytes now(Jan2021),
## but keeping this just in case.
return data
decode = self.getConfigList('website_encodings',
default=["utf8",
"Windows-1252",
"iso-8859-1"])
for code in decode:
try:
logger.debug("Encoding:%s"%code)
errors=None
if ':' in code:
(code,errors)=code.split(':')
if code == "auto":
if not chardet:
logger.info("chardet not available, skipping 'auto' encoding")
continue
detected = chardet.detect(data)
#print(detected)
if detected['confidence'] > float(self.getConfig("chardet_confidence_limit",0.9)):
logger.debug("using chardet detected encoding:%s(%s)"%(detected['encoding'],detected['confidence']))
code=detected['encoding']
else:
logger.debug("chardet confidence too low:%s(%s)"%(detected['encoding'],detected['confidence']))
continue
if errors == 'ignore': # only allow ignore.
return data.decode(code,errors='ignore')
else:
return data.decode(code)
except Exception as e:
logger.debug("code failed:"+code)
logger.debug(e)
logger.info("Could not decode story, tried:%s Stripping non-ASCII."%decode)
try:
# python2
return "".join([x for x in data if ord(x) < 128])
except TypeError:
# python3
return "".join([chr(x) for x in data if x < 128]) | [
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Borda/pyImSegm | 7584b40a8d5bba04d3bf46f540f22b5d923e4b03 | imsegm/utilities/drawing.py | python | draw_image_segm_points | (
ax,
img,
points,
labels=None,
slic=None,
color_slic='w',
lut_label_marker=DICT_LABEL_MARKER,
seg_contour=None,
) | on plane draw background image or segmentation, overlap with SLIC
contours, add contour of adative segmentation like annot. for centers
plot point with specific property (shape and colour) according label
:param ax: figure axis
:param ndarray img: image
:param list(tuple(int,int)) points: collection of points
:param list(int) labels: LUT labels for superpixels
:param ndarray slic: superpixel segmentation
:param str color_slic: color dor superpixels
:param dict lut_label_marker: dictionary {int: (str, str)} of label and markers
:param ndarray seg_contour: segmentation contour
>>> img = np.random.randint(0, 256, (100, 100))
>>> points = np.random.randint(0, 100, (25, 2))
>>> labels = np.random.randint(0, 5, len(points))
>>> slic = np.random.randint(0, 256, (100, 100))
>>> draw_image_segm_points(plt.Figure().gca(), img, points, labels, slic) | on plane draw background image or segmentation, overlap with SLIC
contours, add contour of adative segmentation like annot. for centers
plot point with specific property (shape and colour) according label | [
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"(",
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... | def draw_image_segm_points(
ax,
img,
points,
labels=None,
slic=None,
color_slic='w',
lut_label_marker=DICT_LABEL_MARKER,
seg_contour=None,
):
""" on plane draw background image or segmentation, overlap with SLIC
contours, add contour of adative segmentation like annot. for centers
plot point with specific property (shape and colour) according label
:param ax: figure axis
:param ndarray img: image
:param list(tuple(int,int)) points: collection of points
:param list(int) labels: LUT labels for superpixels
:param ndarray slic: superpixel segmentation
:param str color_slic: color dor superpixels
:param dict lut_label_marker: dictionary {int: (str, str)} of label and markers
:param ndarray seg_contour: segmentation contour
>>> img = np.random.randint(0, 256, (100, 100))
>>> points = np.random.randint(0, 100, (25, 2))
>>> labels = np.random.randint(0, 5, len(points))
>>> slic = np.random.randint(0, 256, (100, 100))
>>> draw_image_segm_points(plt.Figure().gca(), img, points, labels, slic)
"""
# background image or segmentation
if img.ndim == 2:
ax.imshow(img, alpha=0.3, cmap=plt.cm.gist_earth)
else:
ax.imshow(img)
if slic is not None:
ax.contour(slic, levels=np.unique(slic), alpha=0.5, colors=color_slic, linewidths=0.5)
# fig.gca().imshow(mark_boundaries(img, slic))
if seg_contour is not None and isinstance(seg_contour, np.ndarray):
if img.shape[:2] != seg_contour.shape[:2]:
raise ImageDimensionError('image size %r and segm. %r should match' % (img.shape, seg_contour.shape))
ax.contour(seg_contour, linewidths=3, levels=np.unique(seg_contour))
if labels is not None:
if len(points) != len(labels):
raise ValueError('number of points (%i) and labels (%i) should match' % (len(points), len(labels)))
for lb in lut_label_marker:
marker, clr = lut_label_marker[lb]
ax.plot(points[(labels == lb), 1], points[(labels == lb), 0], marker, color=clr)
else:
ax.plot(points[:, 1], points[:, 0], 'o', color=COLOR_ORANGE)
ax.set(xlim=[0, img.shape[1]], ylim=[img.shape[0], 0]) | [
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noamraph/dreampie | b09ee546ec099ee6549c649692ceb129e05fb229 | dreampielib/gui/odict.py | python | Items.__call__ | (self) | return self._main._items() | Pretend to be the items method. | Pretend to be the items method. | [
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] | def __call__(self):
"""Pretend to be the items method."""
return self._main._items() | [
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mdiazcl/fuzzbunch-debian | 2b76c2249ade83a389ae3badb12a1bd09901fd2c | windows/Resources/Python/Core/Lib/mailbox.py | python | _mboxMMDFMessage.set_from | (self, from_, time_=None) | return | Set "From " line, formatting and appending time_ if specified. | Set "From " line, formatting and appending time_ if specified. | [
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] | def set_from(self, from_, time_=None):
"""Set "From " line, formatting and appending time_ if specified."""
if time_ is not None:
if time_ is True:
time_ = time.gmtime()
from_ += ' ' + time.asctime(time_)
self._from = from_
return | [
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tracim/tracim | a0e9746fde5a4c45b4e0f0bfa2caf9522b8c4e21 | backend/tracim_backend/lib/core/storage.py | python | StorageLib.get_one_page_pdf_preview | (
self,
depot_file: UploadedFile,
page_number: int,
filename: str,
default_filename: str,
original_file_extension: str = "",
force_download: bool = None,
last_modified: datetime = None,
) | return HapicFile(
file_path=pdf_preview_path,
filename=filename,
as_attachment=force_download,
last_modified=last_modified,
) | Helper to get one page pdf preview for controller | Helper to get one page pdf preview for controller | [
"Helper",
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"pdf",
"preview",
"for",
"controller"
] | def get_one_page_pdf_preview(
self,
depot_file: UploadedFile,
page_number: int,
filename: str,
default_filename: str,
original_file_extension: str = "",
force_download: bool = None,
last_modified: datetime = None,
) -> HapicFile:
"""
Helper to get one page pdf preview for controller
"""
with self.preview_generator_filepath_context(
depot_file=depot_file, original_file_extension=original_file_extension
) as file_path:
preview_page_number = self.page_number_validator(
preview_generator_page_number=page_number,
depot_file=depot_file,
file_path=file_path,
original_file_extension=original_file_extension,
)
pdf_preview_path = self.preview_manager.get_pdf_preview(
file_path, page=preview_page_number, file_ext=original_file_extension
)
# INFO - G.M - 2019-08-08 - use given filename in all case but none or
# "raw", where filename returned will a custom one.
if not filename or filename == "raw":
filename = default_filename
return HapicFile(
file_path=pdf_preview_path,
filename=filename,
as_attachment=force_download,
last_modified=last_modified,
) | [
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taoshen58/BiBloSA | ec67cbdc411278dd29e8888e9fd6451695efc26c | exp_SC/src/utils/tree/shift_reduce.py | python | shift_reduce_constitucy | (parent_idx_seq) | return op_stack | This file is implemented to solve: tree sequence to constituency transition sequence:
input is a list of father node idx of constituency tree (1 based)
output is a list of element which is a one of
[
1. 1 for reduce and 2 for shift
2. 1 based node index
2. a list of 0-based index to reduce
]
@author: Anonymity | This file is implemented to solve: tree sequence to constituency transition sequence: | [
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":",
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] | def shift_reduce_constitucy(parent_idx_seq):
'''
This file is implemented to solve: tree sequence to constituency transition sequence:
input is a list of father node idx of constituency tree (1 based)
output is a list of element which is a one of
[
1. 1 for reduce and 2 for shift
2. 1 based node index
2. a list of 0-based index to reduce
]
@author: Anonymity
'''
node_num = len(parent_idx_seq) # all node number in the parsing tree
child_and_parent_idx = [(child_idx+1, parent_idx) # list of (child_idx, parent_idx) pair
for child_idx,parent_idx in enumerate(parent_idx_seq)]
# runtime variable:
shifted = [0] * node_num # 0 for shifted and 0 for un-shifted
children = []
parents = []
used = [] # 0 for un-used, 1 for used
op_stack = [] # to restore the operation as the output
while True:
# check whether reduce
do_reduce = False
try:
last_idx = parents[-1]
# count
count=sum([1 for u,p_idx in zip(used,parents) if u==0 and p_idx==last_idx ])
# check if count satisfy the number of 'last_idx''s children num
children_num = sum([1 for _,p_idx in child_and_parent_idx if p_idx==last_idx])
if count == children_num:
do_reduce = True
except IndexError:
pass # len(parents) == 0
# reduce or shift
if do_reduce:
reduce_idx = parents[-1]
reduce_parent_idx = child_and_parent_idx[reduce_idx-1][1]
# mark used
reduce_idxs = []
for idx_n,p in enumerate(parents):
if used[idx_n] == 0 and p == reduce_idx:
used[idx_n] = 1
reduce_idxs.append(idx_n)
shifted[reduce_idx-1] = 1
children.append(reduce_idx)
parents.append(reduce_parent_idx)
used.append(0)
op_stack.append((2,reduce_idx,reduce_idxs))
if reduce_parent_idx == 0:
break
else: # do shift
# get pointer
pointer = 0
for idx_s in range(node_num):
if shifted[idx_s] == 0:
pointer = idx_s
shifted[idx_s] = 1
break
children.append(child_and_parent_idx[pointer][0])
parents.append(child_and_parent_idx[pointer][1])
used.append(0)
#op_stack.append(-child_and_parent_idx[pointer][0])
op_stack.append((1,child_and_parent_idx[pointer][0],[] ))
return op_stack | [
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sixty-north/cosmic-ray | cf7fb7c3cc564db1e8d53b8e8848c9f46e61a879 | src/cosmic_ray/tools/survival_rate.py | python | format_survival_rate | (estimate, confidence, fail_over, session_file) | Calculate the survival rate of a session. | Calculate the survival rate of a session. | [
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] | def format_survival_rate(estimate, confidence, fail_over, session_file):
"""Calculate the survival rate of a session."""
confidence = float(confidence)
try:
z_score = SUPPORTED_Z_SCORES[int(float(confidence) * 10)]
except KeyError:
raise ValueError("Unsupported confidence interval: {0}".format(confidence))
with use_db(session_file, WorkDB.Mode.open) as db:
rate = survival_rate(db)
num_items = db.num_work_items
num_complete = db.num_results
if estimate:
conf_int = math.sqrt(rate * (100 - rate) / num_complete) * z_score * (1 - math.sqrt(num_complete / num_items))
min_rate = rate - conf_int
print("{:.2f} {:.2f} {:.2f}".format(min_rate, rate, rate + conf_int))
else:
print("{:.2f}".format(rate))
min_rate = rate
if fail_over and min_rate > float(fail_over):
sys.exit(1) | [
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number5/cloud-init | 19948dbaf40309355e1a2dbef116efb0ce66245c | cloudinit/net/activators.py | python | NetworkManagerActivator.bring_up_interface | (device_name: str) | return _alter_interface(cmd, device_name) | Bring up interface using nmcli.
Return True is successful, otherwise return False | Bring up interface using nmcli. | [
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] | def bring_up_interface(device_name: str) -> bool:
"""Bring up interface using nmcli.
Return True is successful, otherwise return False
"""
cmd = ["nmcli", "connection", "up", "ifname", device_name]
return _alter_interface(cmd, device_name) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/requests/utils.py | python | from_key_val_list | (value) | return OrderedDict(value) | Take an object and test to see if it can be represented as a
dictionary. Unless it can not be represented as such, return an
OrderedDict, e.g.,
::
>>> from_key_val_list([('key', 'val')])
OrderedDict([('key', 'val')])
>>> from_key_val_list('string')
ValueError: need more than 1 value to unpack
>>> from_key_val_list({'key': 'val'})
OrderedDict([('key', 'val')])
:rtype: OrderedDict | Take an object and test to see if it can be represented as a
dictionary. Unless it can not be represented as such, return an
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"""Take an object and test to see if it can be represented as a
dictionary. Unless it can not be represented as such, return an
OrderedDict, e.g.,
::
>>> from_key_val_list([('key', 'val')])
OrderedDict([('key', 'val')])
>>> from_key_val_list('string')
ValueError: need more than 1 value to unpack
>>> from_key_val_list({'key': 'val'})
OrderedDict([('key', 'val')])
:rtype: OrderedDict
"""
if value is None:
return None
if isinstance(value, (str, bytes, bool, int)):
raise ValueError('cannot encode objects that are not 2-tuples')
return OrderedDict(value) | [
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | src/codeintel/play/core.py | python | UpdateUIEvent_ResetUpdateTime | (*args, **kwargs) | return _core.UpdateUIEvent_ResetUpdateTime(*args, **kwargs) | UpdateUIEvent_ResetUpdateTime() | UpdateUIEvent_ResetUpdateTime() | [
"UpdateUIEvent_ResetUpdateTime",
"()"
] | def UpdateUIEvent_ResetUpdateTime(*args, **kwargs):
"""UpdateUIEvent_ResetUpdateTime()"""
return _core.UpdateUIEvent_ResetUpdateTime(*args, **kwargs) | [
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gabrieleangeletti/Deep-Learning-TensorFlow | ddeb1f2848da7b7bee166ad2152b4afc46bb2086 | yadlt/utils/utilities.py | python | expand_args | (**args_to_expand) | return args_to_expand | Expand the given lists into the length of the layers.
This is used as a convenience so that the user does not need to specify the
complete list of parameters for model initialization.
IE the user can just specify one parameter and this function will expand it | Expand the given lists into the length of the layers. | [
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"length",
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] | def expand_args(**args_to_expand):
"""Expand the given lists into the length of the layers.
This is used as a convenience so that the user does not need to specify the
complete list of parameters for model initialization.
IE the user can just specify one parameter and this function will expand it
"""
layers = args_to_expand['layers']
try:
items = args_to_expand.iteritems()
except AttributeError:
items = args_to_expand.items()
for key, val in items:
if isinstance(val, list) and len(val) != len(layers):
args_to_expand[key] = [val[0] for _ in layers]
return args_to_expand | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/docutils/utils/math/math2html.py | python | NumberGenerator.create | (self, type) | return counter | Create a counter for the given type. | Create a counter for the given type. | [
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] | def create(self, type):
"Create a counter for the given type."
if self.isnumbered(type) and self.getlevel(type) > 1:
index = self.orderedlayouts.index(type)
above = self.orderedlayouts[index - 1]
master = self.getcounter(above)
return self.createdependent(type, master)
counter = NumberCounter(type)
if self.isroman(type):
counter.setmode('I')
return counter | [
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theotherp/nzbhydra | 4b03d7f769384b97dfc60dade4806c0fc987514e | libs/passlib/apache.py | python | _CommonFile._encode_user | (self, user) | return self._encode_field(user, "user") | user-specific wrapper for _encode_field() | user-specific wrapper for _encode_field() | [
"user",
"-",
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] | def _encode_user(self, user):
"""user-specific wrapper for _encode_field()"""
return self._encode_field(user, "user") | [
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FrancoisSchnell/GPicSync | 07d7c4b7da44e4e6665abb94bbb9ef6da0e779d1 | src/gpicsync-GUI.py | python | GUI.selectTZ | (self, evt) | Choose a selected timezone | Choose a selected timezone | [
"Choose",
"a",
"selected",
"timezone"
] | def selectTZ(self, evt):
"""Choose a selected timezone"""
self.timezone = timezones[evt.GetId()-3000]
self.tzButton.SetLabel(self.timezone)
self.utcLabel.Disable()
self.utcEntry.Disable() | [
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tensorflow/lattice | 784eca50cbdfedf39f183cc7d298c9fe376b69c0 | tensorflow_lattice/python/premade_lib.py | python | build_linear_combination_layer | (ensemble_outputs, model_config, dtype) | return linear_layer.Linear(
num_input_dims=num_input_dims,
monotonicities=['increasing'] * num_input_dims,
normalization_order=normalization_order,
use_bias=model_config.use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
dtype=dtype,
name=OUTPUT_LINEAR_COMBINATION_LAYER_NAME)(
linear_input) | Creates a `tfl.layers.Linear` layer initialized to be an average.
Args:
ensemble_outputs: Ensemble outputs to be linearly combined.
model_config: Model configuration object describing model architecture.
Should be one of the model configs in `tfl.configs`.
dtype: dtype
Returns:
A `tfl.layers.Linear` instance. | Creates a `tfl.layers.Linear` layer initialized to be an average. | [
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] | def build_linear_combination_layer(ensemble_outputs, model_config, dtype):
"""Creates a `tfl.layers.Linear` layer initialized to be an average.
Args:
ensemble_outputs: Ensemble outputs to be linearly combined.
model_config: Model configuration object describing model architecture.
Should be one of the model configs in `tfl.configs`.
dtype: dtype
Returns:
A `tfl.layers.Linear` instance.
"""
if isinstance(ensemble_outputs, list):
num_input_dims = len(ensemble_outputs)
linear_input = tf.keras.layers.Concatenate(axis=1)(ensemble_outputs)
else:
num_input_dims = int(ensemble_outputs.shape[1])
linear_input = ensemble_outputs
kernel_initializer = tf.keras.initializers.Constant(1.0 / num_input_dims)
bias_initializer = tf.keras.initializers.Constant(0)
if (not model_config.output_calibration and
model_config.output_min is None and model_config.output_max is None):
normalization_order = None
else:
# We need to use weighted average to keep the output range.
normalization_order = 1
# Bias term cannot be used when this layer should have bounded output.
if model_config.use_bias:
raise ValueError('Cannot use a bias term in linear combination with '
'output bounds or output calibration')
return linear_layer.Linear(
num_input_dims=num_input_dims,
monotonicities=['increasing'] * num_input_dims,
normalization_order=normalization_order,
use_bias=model_config.use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
dtype=dtype,
name=OUTPUT_LINEAR_COMBINATION_LAYER_NAME)(
linear_input) | [
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MaurizioFD/RecSys2019_DeepLearning_Evaluation | 0fb6b7f5c396f8525316ed66cf9c9fdb03a5fa9b | Base/Incremental_Training_Early_Stopping.py | python | Incremental_Training_Early_Stopping._prepare_model_for_validation | (self) | This function is executed before the evaluation of the current model
It should ensure the current object "self" can be passed to the evaluator object
E.G. if the epoch is done via Cython or PyTorch, this function should get the new parameter values from
the cython or pytorch objects into the self. pyhon object
:return: | This function is executed before the evaluation of the current model
It should ensure the current object "self" can be passed to the evaluator object | [
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"""
This function is executed before the evaluation of the current model
It should ensure the current object "self" can be passed to the evaluator object
E.G. if the epoch is done via Cython or PyTorch, this function should get the new parameter values from
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:return:
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raise NotImplementedError() | [
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] | https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation/blob/0fb6b7f5c396f8525316ed66cf9c9fdb03a5fa9b/Base/Incremental_Training_Early_Stopping.py#L66-L75 | ||
wizyoung/googletranslate.popclipext | a3c465685a5a75213e2ec8517eb98d336984bc50 | src/urllib3/contrib/_securetransport/low_level.py | python | _load_items_from_file | (keychain, path) | return (identities, certificates) | Given a single file, loads all the trust objects from it into arrays and
the keychain.
Returns a tuple of lists: the first list is a list of identities, the
second a list of certs. | Given a single file, loads all the trust objects from it into arrays and
the keychain.
Returns a tuple of lists: the first list is a list of identities, the
second a list of certs. | [
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"""
Given a single file, loads all the trust objects from it into arrays and
the keychain.
Returns a tuple of lists: the first list is a list of identities, the
second a list of certs.
"""
certificates = []
identities = []
result_array = None
with open(path, "rb") as f:
raw_filedata = f.read()
try:
filedata = CoreFoundation.CFDataCreate(
CoreFoundation.kCFAllocatorDefault, raw_filedata, len(raw_filedata)
)
result_array = CoreFoundation.CFArrayRef()
result = Security.SecItemImport(
filedata, # cert data
None, # Filename, leaving it out for now
None, # What the type of the file is, we don't care
None, # what's in the file, we don't care
0, # import flags
None, # key params, can include passphrase in the future
keychain, # The keychain to insert into
ctypes.byref(result_array), # Results
)
_assert_no_error(result)
# A CFArray is not very useful to us as an intermediary
# representation, so we are going to extract the objects we want
# and then free the array. We don't need to keep hold of keys: the
# keychain already has them!
result_count = CoreFoundation.CFArrayGetCount(result_array)
for index in range(result_count):
item = CoreFoundation.CFArrayGetValueAtIndex(result_array, index)
item = ctypes.cast(item, CoreFoundation.CFTypeRef)
if _is_cert(item):
CoreFoundation.CFRetain(item)
certificates.append(item)
elif _is_identity(item):
CoreFoundation.CFRetain(item)
identities.append(item)
finally:
if result_array:
CoreFoundation.CFRelease(result_array)
CoreFoundation.CFRelease(filedata)
return (identities, certificates) | [
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FitMachineLearning/FitML | a60f49fce1799ca4b11b48307441325b6272719a | PolicyGradient/BipedalWalker_v1.0.py | python | actor_experience_replay | () | train_B = train_B[tW.flatten()>0]
print("%8d were better results than pr"%np.alen(tX_train))
tX = tX[train_B,:]
tY = tY[train_B,:]
tW = tW[train_B,:]
tR = tR[train_B,:]
#print("tW",tW) | train_B = train_B[tW.flatten()>0] | [
"train_B",
"=",
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"[",
"tW",
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] | def actor_experience_replay():
tSA = (memorySA)
tR = (memoryR)
tX = (memoryS)
tY = (memoryA)
tW = (memoryW)
target = tR.mean() #+ math.fabs( tR.mean() - tR.max() )/2 #+ math.fabs( tR.mean() - tR.max() )/4
train_C = np.arange(np.alen(tR))
train_C = train_C[tR.flatten()>target]
tX = tX[train_C,:]
tY = tY[train_C,:]
tW = tW[train_C,:]
tR = tR[train_C,:]
train_A = np.random.randint(tY.shape[0],size=int(min(experience_replay_size,np.alen(tR) )))
tX = tX[train_A,:]
tY = tY[train_A,:]
tW = tW[train_A,:]
tR = tR[train_A,:]
train_B = np.arange(np.alen(tR))
tX_train = np.zeros(shape=(1,num_env_variables))
tY_train = np.zeros(shape=(1,num_env_actions))
for i in range(np.alen(train_B)):
#pr = predictTotalRewards(tX[i],tY[i])
''' YOU CAN"T USE predictTotalRewards
IF YOU DON"T TRAIN THE QMODEL
if tR[i][0] < pr:
tW[i][0] = -1
else:
'''
d = math.fabs( memoryR.max() - target)
tW[i] = math.fabs(tR[i]-(target+0.000000000005)) / d
tW[i] = math.exp(1-(1/tW[i]**2))
if tW[i]> np.random.rand(1):
tX_train = np.vstack((tX_train,tX[i]))
tY_train = np.vstack((tY_train,tY[i]))
#print ("tW",tW[i],"exp", math.exp(1-(1/tW[i]**2)))
#tW[i] = math.exp(1-(1/tW[i]**2))
#tW[i] = 1
#print("tW[i] %3.1f tR %3.2f pr %3.2f "%(tW[i],tR[i],pr))
'''
train_B = train_B[tW.flatten()>0]
print("%8d were better results than pr"%np.alen(tX_train))
tX = tX[train_B,:]
tY = tY[train_B,:]
tW = tW[train_B,:]
tR = tR[train_B,:]
#print("tW",tW)
'''
print("%8d were better results than pr"%np.alen(tX_train))
''' REMOVE FIRST ELEMENT BEFORE TRAINING '''
tX_train = tX_train[1:]
tY_train = tY_train[1:]
print("%8d were better After removing first element"%np.alen(tX_train))
if np.alen(tX_train)>0:
#tW = scale_weights(tR,tW)
#print("# setps short listed ", np.alen(tR))
action_predictor_model.fit(tX_train,tY_train, batch_size=mini_batch, nb_epoch=training_epochs,verbose=0) | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/merchant_center_link_service/client.py | python | MerchantCenterLinkServiceClientMeta.get_transport_class | (
cls, label: str = None,
) | return next(iter(cls._transport_registry.values())) | Return an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use. | Return an appropriate transport class. | [
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] | def get_transport_class(
cls, label: str = None,
) -> Type[MerchantCenterLinkServiceTransport]:
"""Return an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use.
"""
# If a specific transport is requested, return that one.
if label:
return cls._transport_registry[label]
# No transport is requested; return the default (that is, the first one
# in the dictionary).
return next(iter(cls._transport_registry.values())) | [
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cloudera/impyla | 0c736af4cad2bade9b8e313badc08ec50e81c948 | impala/_thrift_gen/hive_metastore/ThriftHiveMetastore.py | python | exchange_partition_args.__eq__ | (self, other) | return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ | [] | def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/inject/thirdparty/xdot/xdot.py | python | ZoomAreaAction.abort | (self) | [] | def abort(self):
self.dot_widget.queue_draw() | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/Jinja2-2.6-py2.7.egg/jinja2/lexer.py | python | TokenStream.skip | (self, n=1) | Got n tokens ahead. | Got n tokens ahead. | [
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/gen/plug/docgen/stylesheet.py | python | StyleSheet.add_paragraph_style | (self, name, style) | Add a paragraph style to the style sheet.
:param name: The name of the :class:`.ParagraphStyle`
:param style: :class:`.ParagraphStyle` instance to be added. | Add a paragraph style to the style sheet. | [
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"""
Add a paragraph style to the style sheet.
:param name: The name of the :class:`.ParagraphStyle`
:param style: :class:`.ParagraphStyle` instance to be added.
"""
self.para_styles[name] = ParagraphStyle(style) | [
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | src/codeintel/play/core.py | python | Rect.Set | (*args, **kwargs) | return _core.Rect_Set(*args, **kwargs) | Set(int x=0, int y=0, int width=0, int height=0)
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] | def Set(*args, **kwargs):
"""
Set(int x=0, int y=0, int width=0, int height=0)
Set all rectangle properties.
"""
return _core.Rect_Set(*args, **kwargs) | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v9/services/services/user_list_service/transports/base.py | python | UserListServiceTransport.__init__ | (
self,
*,
host: str = "googleads.googleapis.com",
credentials: ga_credentials.Credentials = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) | Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library. | Instantiate the transport. | [
"Instantiate",
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"transport",
"."
] | def __init__(
self,
*,
host: str = "googleads.googleapis.com",
credentials: ga_credentials.Credentials = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
"""
# Save the hostname. Default to port 443 (HTTPS) if none is specified.
if ":" not in host:
host += ":443"
self._host = host
# If no credentials are provided, then determine the appropriate
# defaults.
if credentials is None:
credentials, _ = google.auth.default(scopes=self.AUTH_SCOPES)
# Save the credentials.
self._credentials = credentials
# Lifted into its own function so it can be stubbed out during tests.
self._prep_wrapped_messages(client_info) | [
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/functions/combinatorial/numbers.py | python | nP | (n, k=None, replacement=False) | return Integer(_nP(n, k, replacement)) | Return the number of permutations of ``n`` items taken ``k`` at a time.
Possible values for ``n``:
integer - set of length ``n``
sequence - converted to a multiset internally
multiset - {element: multiplicity}
If ``k`` is None then the total of all permutations of length 0
through the number of items represented by ``n`` will be returned.
If ``replacement`` is True then a given item can appear more than once
in the ``k`` items. (For example, for 'ab' permutations of 2 would
include 'aa', 'ab', 'ba' and 'bb'.) The multiplicity of elements in
``n`` is ignored when ``replacement`` is True but the total number
of elements is considered since no element can appear more times than
the number of elements in ``n``.
Examples
========
>>> from sympy.functions.combinatorial.numbers import nP
>>> from sympy.utilities.iterables import multiset_permutations, multiset
>>> nP(3, 2)
6
>>> nP('abc', 2) == nP(multiset('abc'), 2) == 6
True
>>> nP('aab', 2)
3
>>> nP([1, 2, 2], 2)
3
>>> [nP(3, i) for i in range(4)]
[1, 3, 6, 6]
>>> nP(3) == sum(_)
True
When ``replacement`` is True, each item can have multiplicity
equal to the length represented by ``n``:
>>> nP('aabc', replacement=True)
121
>>> [len(list(multiset_permutations('aaaabbbbcccc', i))) for i in range(5)]
[1, 3, 9, 27, 81]
>>> sum(_)
121
See Also
========
sympy.utilities.iterables.multiset_permutations
References
==========
.. [1] https://en.wikipedia.org/wiki/Permutation | Return the number of permutations of ``n`` items taken ``k`` at a time. | [
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] | def nP(n, k=None, replacement=False):
"""Return the number of permutations of ``n`` items taken ``k`` at a time.
Possible values for ``n``:
integer - set of length ``n``
sequence - converted to a multiset internally
multiset - {element: multiplicity}
If ``k`` is None then the total of all permutations of length 0
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If ``replacement`` is True then a given item can appear more than once
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include 'aa', 'ab', 'ba' and 'bb'.) The multiplicity of elements in
``n`` is ignored when ``replacement`` is True but the total number
of elements is considered since no element can appear more times than
the number of elements in ``n``.
Examples
========
>>> from sympy.functions.combinatorial.numbers import nP
>>> from sympy.utilities.iterables import multiset_permutations, multiset
>>> nP(3, 2)
6
>>> nP('abc', 2) == nP(multiset('abc'), 2) == 6
True
>>> nP('aab', 2)
3
>>> nP([1, 2, 2], 2)
3
>>> [nP(3, i) for i in range(4)]
[1, 3, 6, 6]
>>> nP(3) == sum(_)
True
When ``replacement`` is True, each item can have multiplicity
equal to the length represented by ``n``:
>>> nP('aabc', replacement=True)
121
>>> [len(list(multiset_permutations('aaaabbbbcccc', i))) for i in range(5)]
[1, 3, 9, 27, 81]
>>> sum(_)
121
See Also
========
sympy.utilities.iterables.multiset_permutations
References
==========
.. [1] https://en.wikipedia.org/wiki/Permutation
"""
try:
n = as_int(n)
except ValueError:
return Integer(_nP(_multiset_histogram(n), k, replacement))
return Integer(_nP(n, k, replacement)) | [
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quodlibet/mutagen | 399513b167ed00c4b7a9ef98dfe591a276efb701 | mutagen/_util.py | python | fileobj_name | (fileobj) | return value | Returns:
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value = getattr(fileobj, "name", u"")
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/matplotlib/category.py | python | StrCategoryFormatter._text | (value) | return value | Converts text values into utf-8 or ascii strings. | Converts text values into utf-8 or ascii strings. | [
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"""
if isinstance(value, bytes):
value = value.decode(encoding='utf-8')
elif not isinstance(value, str):
value = str(value)
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Podshot/MCEdit-Unified | 90abfb170c65b877ac67193e717fa3a3ded635dd | frustum.py | python | viewingMatrix | (projection=None, model=None) | return numpy.dot(model, projection) | Calculate the total viewing matrix from given data
projection -- the projection matrix, if not provided
than the result of glGetDoublev( GL_PROJECTION_MATRIX)
will be used.
model -- the model-view matrix, if not provided
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projection -- the projection matrix, if not provided
than the result of glGetDoublev( GL_PROJECTION_MATRIX)
will be used.
model -- the model-view matrix, if not provided
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"""
if projection is None:
projection = GL.glGetDoublev(GL.GL_PROJECTION_MATRIX)
if model is None:
model = GL.glGetDoublev(GL.GL_MODELVIEW_MATRIX)
# hmm, this will likely fail on 64-bit platforms :(
if projection is None or model is None:
context_log.warn(
"""A NULL matrix was returned from glGetDoublev: proj=%s modelView=%s""",
projection, model,
)
if projection:
return projection
if model:
return model
else:
return numpy.identity(4, 'd')
if numpy.allclose(projection, -1.79769313e+308):
context_log.warn(
"""Attempt to retrieve projection matrix when uninitialised %s, model=%s""",
projection, model,
)
return model
if numpy.allclose(model, -1.79769313e+308):
context_log.warn(
"""Attempt to retrieve model-view matrix when uninitialised %s, projection=%s""",
model, projection,
)
return projection
return numpy.dot(model, projection) | [
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AutoViML/Auto_ViML | 3f24c349a1b62612db7835065e25201497408f30 | autoviml/Auto_ViML.py | python | return_dictionary_list | (lst_of_tuples) | return orDict | Returns a dictionary of lists if you send in a list of Tuples | Returns a dictionary of lists if you send in a list of Tuples | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/distutils/versionpredicate.py | python | VersionPredicate.__str__ | (self) | [] | def __str__(self):
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LouYu2015/analysis_on_the_story_of_a_stone | 5acd070d026221bdca26694524e7281d79591c68 | dict_creat.py | python | entropy_of_list | (nodes) | return entropy | Calculate entropy from a list of nodes.
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count_sum = sum(node_counts)
probs = [count / count_sum for count in node_counts]
entropy = -sum([prob * math.log(prob, 2) for prob in probs])
for node in nodes:
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jsvine/pdfplumber | b904dd6a7c5164c5e051fa9b4121f8af6a105ae9 | pdfplumber/utils.py | python | merge_bboxes | (bboxes) | return (
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/multiprocessing/managers.py | python | BaseManager.connect | (self) | Connect manager object to the server process | Connect manager object to the server process | [
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Connect manager object to the server process
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dispatch(conn, None, 'dummy')
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wenxinxu/resnet-in-tensorflow | 8ba8d8905e099cd7e1b1cf1f84b89f603f7613a0 | cifar10_input.py | python | _read_one_batch | (path, is_random_label) | return data, label | The training data contains five data batches in total. The validation data has only one
batch. This function takes the directory of one batch of data and returns the images and
corresponding labels as numpy arrays
:param path: the directory of one batch of data
:param is_random_label: do you want to use random labels?
:return: image numpy arrays and label numpy arrays | The training data contains five data batches in total. The validation data has only one
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:param path: the directory of one batch of data
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fo.close()
data = dicts['data']
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Dvlv/Tkinter-By-Example | 30721f15f7bea41489a7c46819beb5282781e563 | Code/Chapter6-3.py | python | Editor.select_all | (self, event=None) | return "break" | [] | def select_all(self, event=None):
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/btoe/v20210303/btoe_client.py | python | BtoeClient.CreateVideoDeposit | (self, request) | 功能迭代，已上线更高版本的接口2021-05-14
用户通过本接口向BTOE写入待存证的视频的原文件或下载URL，BTOE对视频原文件存储后，将其Hash值存证上链，并生成含有电子签章的区块链存证电子凭证。视频文件支持格式：mp4、avi、mkv、mov、flv,wmv,rmvb,3gp；文件大小限制：直接上传原文件不大于5MB，下载URL文件大小不大于200 MB。
:param request: Request instance for CreateVideoDeposit.
:type request: :class:`tencentcloud.btoe.v20210303.models.CreateVideoDepositRequest`
:rtype: :class:`tencentcloud.btoe.v20210303.models.CreateVideoDepositResponse` | 功能迭代，已上线更高版本的接口2021-05-14 | [
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"""功能迭代，已上线更高版本的接口2021-05-14
用户通过本接口向BTOE写入待存证的视频的原文件或下载URL，BTOE对视频原文件存储后，将其Hash值存证上链，并生成含有电子签章的区块链存证电子凭证。视频文件支持格式：mp4、avi、mkv、mov、flv,wmv,rmvb,3gp；文件大小限制：直接上传原文件不大于5MB，下载URL文件大小不大于200 MB。
:param request: Request instance for CreateVideoDeposit.
:type request: :class:`tencentcloud.btoe.v20210303.models.CreateVideoDepositRequest`
:rtype: :class:`tencentcloud.btoe.v20210303.models.CreateVideoDepositResponse`
"""
try:
params = request._serialize()
body = self.call("CreateVideoDeposit", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.CreateVideoDepositResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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illidanlab/Simulator | d99e2b1352a1697e5930f9b45c4704db97e33f8f | simulator/envs.py | python | CityReal.construct_map_simulation | (self, M, N, n) | Connect node to its neighbors based on a simulated M by N map
:param M: M row index matrix
:param N: N column index matrix
:param n: n - sided polygon | Connect node to its neighbors based on a simulated M by N map
:param M: M row index matrix
:param N: N column index matrix
:param n: n - sided polygon | [
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:param n: n - sided polygon
"""
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HymanLiuTS/flaskTs | 286648286976e85d9b9a5873632331efcafe0b21 | flasky/local/lib/python2.7/warnings.py | python | catch_warnings.__init__ | (self, record=False, module=None) | Specify whether to record warnings and if an alternative module
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tensorflow/tensorboard | 61d11d99ef034c30ba20b6a7840c8eededb9031c | tensorboard/plugins/mesh/summary.py | python | _get_tensor_summary | (
name,
display_name,
description,
tensor,
content_type,
components,
json_config,
collections,
) | return tensor_summary | Creates a tensor summary with summary metadata.
Args:
name: Uniquely identifiable name of the summary op. Could be replaced by
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summary.
display_name: Will be used as the display name in TensorBoard.
Defaults to `tag`.
description: A longform readable description of the summary data. Markdown
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tensor: Tensor to display in summary.
content_type: Type of content inside the Tensor.
components: Bitmask representing present parts (vertices, colors, etc.) that
belong to the summary.
json_config: A string, JSON-serialized dictionary of ThreeJS classes
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collections: List of collections to add this summary to.
Returns:
Tensor summary with metadata. | Creates a tensor summary with summary metadata. | [
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name,
display_name,
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content_type,
components,
json_config,
collections,
):
"""Creates a tensor summary with summary metadata.
Args:
name: Uniquely identifiable name of the summary op. Could be replaced by
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summary.
display_name: Will be used as the display name in TensorBoard.
Defaults to `tag`.
description: A longform readable description of the summary data. Markdown
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tensor: Tensor to display in summary.
content_type: Type of content inside the Tensor.
components: Bitmask representing present parts (vertices, colors, etc.) that
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json_config: A string, JSON-serialized dictionary of ThreeJS classes
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collections: List of collections to add this summary to.
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shape = tensor.shape.as_list()
shape = [dim if dim is not None else -1 for dim in shape]
tensor_metadata = metadata.create_summary_metadata(
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shape,
description,
json_config=json_config,
)
tensor_summary = tf.compat.v1.summary.tensor_summary(
metadata.get_instance_name(name, content_type),
tensor,
summary_metadata=tensor_metadata,
collections=collections,
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return tensor_summary | [
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subbarayudu-j/TheAlgorithms-Python | bb29dc55faf5b98b1e9f0b1f11a9dd15525386a3 | other/primelib.py | python | getPrimeNumbers | (N) | return ans | input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)' | input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)' | [
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"""
input: positive integer 'N' > 2
returns a list of prime numbers from 2 up to N (inclusive)
This function is more efficient as function 'sieveEr(...)'
"""
# precondition
assert isinstance(N,int) and (N > 2), "'N' must been an int and > 2"
ans = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2,N+1):
if isPrime(number):
ans.append(number)
# precondition
assert isinstance(ans,list), "'ans' must been from type list"
return ans | [
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debian-calibre/calibre | 020fc81d3936a64b2ac51459ecb796666ab6a051 | src/calibre/ebooks/rtf2xml/border_parse.py | python | BorderParse.parse_border | (self, line) | return border_dict | Requires:
line -- line with border definition in it
Returns:
?
Logic: | Requires:
line -- line with border definition in it
Returns:
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Logic: | [
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"""
Requires:
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Returns:
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Logic:
"""
border_dict = {}
border_style_dict = {}
border_style_list = []
border_type = self.__border_dict.get(line[6:16])
if not border_type:
sys.stderr.write(
'module is border_parse.py\n'
'function is parse_border\n'
'token does not have a dictionary value\n'
'token is "%s"' % line
)
return border_dict
att_line = line[20:-1]
atts = att_line.split('|')
# cw<bd<bor-cel-ri<nu<
# border has no value--should be no lines
if len(atts) == 1 and atts[0] == '':
border_dict[border_type] = 'none'
return border_dict
# border-paragraph-right
for att in atts:
values = att.split(':')
if len(values) ==2:
att = values[0]
value = values[1]
else:
value = 'true'
style_att = self.__border_style_dict.get(att)
if style_att:
att = '%s-%s' % (border_type, att)
border_style_dict[att] = value
border_style_list.append(style_att)
else:
att = self.__border_dict.get(att)
if not att:
sys.stderr.write(
'module is border_parse_def.py\n'
'function is parse_border\n'
'token does not have an att value\n'
'line is "%s"' % line
)
att = '%s-%s' % (border_type, att)
border_dict[att] = value
new_border_dict = self.__determine_styles(border_type, border_style_list)
border_dict.update(new_border_dict)
return border_dict | [
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DonnchaC/shadowbrokers-exploits | 42d8265db860b634717da4faa668b2670457cf7e | windows/fuzzbunch/pluginfinder.py | python | addplugins | (fb, type, location, constructor, manager=pluginmanager.PluginManager, bin=True) | @brief Enumerate available plugins and add them to the fuzzbunch pluginmanager
@param fb Fuzzbunch object
@param type String with the type of plugin to add (Exploit, Payload, Touch, etc...)
@param location The disk location to look for that type of plugin
@param constructor Constructor to use to instantiate the plugin | @brief Enumerate available plugins and add them to the fuzzbunch pluginmanager | [
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"""
@brief Enumerate available plugins and add them to the fuzzbunch pluginmanager
@param fb Fuzzbunch object
@param type String with the type of plugin to add (Exploit, Payload, Touch, etc...)
@param location The disk location to look for that type of plugin
@param constructor Constructor to use to instantiate the plugin
"""
# Get a list of tuples for the available plugins in 'location'
# Each entry will be (config, executable, fbfile)
#
# XXX Should we ensure the directories exist and load nothing, or
# fail, in the event the directory doesn't exist already.
#
plugins = getpluginlist(location, bin)
manager = fb.register_manager(type, manager)
for plugin in plugins:
try:
manager.add_plugin(plugin, constructor)
except exception.PluginXmlErr:
# We encountered an error in the plugin's XML file. We don't want
# this to kill execution of Fuzzbunch
import os.path
(d,f) = os.path.split(plugin[0])
n = f.split('-')[0]
fb.io.pre_input(None)
fb.io.print_warning("Failed to load %s - XML Error" % (str(n)))
fb.io.post_input() | [
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CPqD/RouteFlow | 3f406b9c1a0796f40a86eb1194990cdd2c955f4d | pox/pox/lib/graph/graph.py | python | Graph.disconnect_node | (self, node1) | Disconnecte node from all neighbours | Disconnecte node from all neighbours | [
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""" Disconnecte node from all neighbours """
for neighbor in self.neighbors(node1):
self.disconnect_nodes(node1, neighbor) | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/logging/__init__.py | python | Filterer.addFilter | (self, filter) | Add the specified filter to this handler. | Add the specified filter to this handler. | [
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"""
Add the specified filter to this handler.
"""
if not (filter in self.filters):
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intrig-unicamp/mininet-wifi | 3c8a8f63bd4aa043aa9c1ad16f304dec2916f5ba | mn_wifi/sixLoWPAN/net.py | python | Mininet_IoT.pingAll | (self, timeout=None) | return self.ping6(timeout=timeout) | Ping between all hosts.
returns: ploss packet loss percentage | Ping between all hosts.
returns: ploss packet loss percentage | [
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"""Ping between all hosts.
returns: ploss packet loss percentage"""
return self.ping6(timeout=timeout) | [
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/_vendor/distro.py | python | LinuxDistribution.__repr__ | (self) | return \
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self.os_release_file,
self.distro_release_file,
self._os_release_info,
self._lsb_release_info,
self._distro_release_info) | Return repr of all info | Return repr of all info | [
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] | def __repr__(self):
"""Return repr of all info
"""
return \
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"distro_release_file={1!r}, " \
"_os_release_info={2!r}, " \
"_lsb_release_info={3!r}, " \
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/asyncio/proactor_events.py | python | _ProactorDatagramTransport.sendto | (self, data, addr=None) | [] | def sendto(self, data, addr=None):
if not isinstance(data, (bytes, bytearray, memoryview)):
raise TypeError('data argument must be bytes-like object (%r)',
type(data))
if not data:
return
if self._address is not None and addr not in (None, self._address):
raise ValueError(
f'Invalid address: must be None or {self._address}')
if self._conn_lost and self._address:
if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:
logger.warning('socket.sendto() raised exception.')
self._conn_lost += 1
return
# Ensure that what we buffer is immutable.
self._buffer.append((bytes(data), addr))
if self._write_fut is None:
# No current write operations are active, kick one off
self._loop_writing()
# else: A write operation is already kicked off
self._maybe_pause_protocol() | [
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sametmax/Django--an-app-at-a-time | 99eddf12ead76e6dfbeb09ce0bae61e282e22f8a | ignore_this_directory/django/db/migrations/autodetector.py | python | MigrationAutodetector.suggest_name | (cls, ops) | return "auto_%s" % get_migration_name_timestamp() | Given a set of operations, suggest a name for the migration they might
represent. Names are not guaranteed to be unique, but put some effort
into the fallback name to avoid VCS conflicts if possible. | Given a set of operations, suggest a name for the migration they might
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"""
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into the fallback name to avoid VCS conflicts if possible.
"""
if len(ops) == 1:
if isinstance(ops[0], operations.CreateModel):
return ops[0].name_lower
elif isinstance(ops[0], operations.DeleteModel):
return "delete_%s" % ops[0].name_lower
elif isinstance(ops[0], operations.AddField):
return "%s_%s" % (ops[0].model_name_lower, ops[0].name_lower)
elif isinstance(ops[0], operations.RemoveField):
return "remove_%s_%s" % (ops[0].model_name_lower, ops[0].name_lower)
elif ops:
if all(isinstance(o, operations.CreateModel) for o in ops):
return "_".join(sorted(o.name_lower for o in ops))
return "auto_%s" % get_migration_name_timestamp() | [
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kevingo/system-design-primer-zh-tw | d664bbc3dbcde7e8d399a4a579f28ba7822209c4 | solutions/system_design/query_cache/query_cache_snippets.py | python | QueryApi.parse_query | (self, query) | Remove markup, break text into terms, deal with typos,
normalize capitalization, convert to use boolean operations. | Remove markup, break text into terms, deal with typos,
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PyMVPA/PyMVPA | 76c476b3de8264b0bb849bf226da5674d659564e | doc/sphinxext/autosummary/__init__.py | python | autosummary_toc_visit_html | (self, node) | Hide autosummary toctree list in HTML output. | Hide autosummary toctree list in HTML output. | [
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] | def autosummary_toc_visit_html(self, node):
"""Hide autosummary toctree list in HTML output."""
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/models/Ubuntu_12/pyasn1/type/univ.py | python | OctetString.__rmul__ | (self, value) | return self * value | [] | def __rmul__(self, value): return self * value | [
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brainiak/brainiak | ee093597c6c11597b0a59e95b48d2118e40394a5 | brainiak/funcalign/sssrm.py | python | SSSRM._loss_lr_subject | (self, data, labels, w, theta, bias) | return self.alpha / samples / self.gamma * (sum_exp_values.sum() - aux) | Compute the Loss MLR for a single subject (without regularization)
Parameters
----------
data : array, shape=[voxels, samples]
The fMRI data of subject i for the classification task.
labels : array of int, shape=[samples]
The labels for the data samples in data.
w : array, shape=[voxels, features]
The orthogonal transform (mapping) :math:`W_i` for subject i.
theta : array, shape=[classes, features]
The MLR class plane parameters.
bias : array, shape=[classes]
The MLR class biases.
Returns
-------
loss : float
The loss MLR for the subject | Compute the Loss MLR for a single subject (without regularization) | [
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"""Compute the Loss MLR for a single subject (without regularization)
Parameters
----------
data : array, shape=[voxels, samples]
The fMRI data of subject i for the classification task.
labels : array of int, shape=[samples]
The labels for the data samples in data.
w : array, shape=[voxels, features]
The orthogonal transform (mapping) :math:`W_i` for subject i.
theta : array, shape=[classes, features]
The MLR class plane parameters.
bias : array, shape=[classes]
The MLR class biases.
Returns
-------
loss : float
The loss MLR for the subject
"""
if data is None:
return 0.0
samples = data.shape[1]
thetaT_wi_zi_plus_bias = theta.T.dot(w.T.dot(data)) + bias
sum_exp, max_value, _ = utils.sumexp_stable(thetaT_wi_zi_plus_bias)
sum_exp_values = np.log(sum_exp) + max_value
aux = 0.0
for sample in range(samples):
label = labels[sample]
aux += thetaT_wi_zi_plus_bias[label, sample]
return self.alpha / samples / self.gamma * (sum_exp_values.sum() - aux) | [
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pyvista/pyvista | 012dbb95a9aae406c3cd4cd94fc8c477f871e426 | pyvista/core/pointset.py | python | StructuredGrid.dimensions | (self, dims) | Set the dataset dimensions. Pass a length three tuple of integers. | Set the dataset dimensions. Pass a length three tuple of integers. | [
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nx, ny, nz = dims[0], dims[1], dims[2]
self.SetDimensions(nx, ny, nz)
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nadineproject/nadine | c41c8ef7ffe18f1853029c97eecc329039b4af6c | nadine/models/membership.py | python | MemberGroups.get_member_groups | () | return group_list | [] | def get_member_groups():
group_list = []
for package in MembershipPackage.objects.filter(enabled=True).order_by('name'):
if User.helper.members_by_package(package).count() > 0:
package_name = package.name
group_list.append((package_name, "%s Members" % package_name))
for g, d in sorted(list(MemberGroups.GROUP_DICT.items()), key=operator.itemgetter(0)):
group_list.append((g, d))
return group_list | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/com.oracle.graal.python.benchmarks/python/meso/chaos-sized2.py | python | GVector.__str__ | (self) | return "<%f, %f, %f>" % (self.x, self.y, self.z) | [] | def __str__(self):
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inkandswitch/livebook | 93c8d467734787366ad084fc3566bf5cbe249c51 | public/pypyjs/modules/numpy/distutils/misc_util.py | python | get_npy_pkg_dir | () | return d | Return the path where to find the npy-pkg-config directory. | Return the path where to find the npy-pkg-config directory. | [
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"""Return the path where to find the npy-pkg-config directory."""
# XXX: import here for bootstrapping reasons
import numpy
d = os.path.join(os.path.dirname(numpy.__file__),
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return d | [
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EdinburghNLP/XSum | 7b95485c3e861d7caff76a8b4c6b3fd3bf7ee9b1 | XSum-Topic-ConvS2S/fairseq/trainer.py | python | Trainer.valid_step | (self, sample) | return agg_logging_output | Do forward pass in evaluation mode. | Do forward pass in evaluation mode. | [
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] | def valid_step(self, sample):
"""Do forward pass in evaluation mode."""
sample = self._prepare_sample(sample, volatile=True)
# forward pass
loss, sample_sizes, logging_outputs, ooms_fwd = self._forward(sample, eval=True)
assert not ooms_fwd, 'Ran out of memory during validation'
# aggregate stats and logging outputs
ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
grad_denom = self.criterion.__class__.grad_denom(sample_sizes)
agg_logging_output = self.criterion.__class__.aggregate_logging_outputs(logging_outputs)
# update loss meters for validation
if 'loss' in agg_logging_output:
self.meters['valid_loss'].update(agg_logging_output['loss'], grad_denom)
# criterions can optionally log the NLL loss too
if 'nll_loss' in agg_logging_output:
self.meters['valid_nll_loss'].update(agg_logging_output['nll_loss'], ntokens)
return agg_logging_output | [
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SFDO-Tooling/CumulusCI | 825ae1f122b25dc41761c52a4ddfa1938d2a4b6e | cumulusci/cli/ui.py | python | CliTable.echo | (self, plain=False, box_style: box.Box = None) | Print this table to the global Console using console.print(). | Print this table to the global Console using console.print(). | [
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"""
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"""
orig_box = self._table.box
if plain:
self._table.box = box.ASCII2
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self._table.box = box_style or orig_box
console = rich.get_console()
console.print(self._table)
self._table.box = orig_box | [
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nicolargo/ubuntupostinstall | a1713c47122ae01e8de7ab1e346815241b520126 | ubuntu-14.04-postinstall.py | python | getpassword | (description = "") | Read password (with confirmation) | Read password (with confirmation) | [
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"""
Read password (with confirmation)
"""
if (description != ""):
sys.stdout.write ("%s\n" % description)
password1 = getpass.getpass(_("Password: "));
password2 = getpass.getpass(_("Password (confirm): "));
if (password1 == password2):
return password1
else:
sys.stdout.write (colors.ORANGE + _("[Warning] Password did not match, please try again") + colors.NO + "\n")
return getpassword() | [
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dropbox/stone | b7b64320631b3a4d2f10681dca64e0718ebe68ee | stone/frontend/parser.py | python | ParserFactory.p_kw_arg | (self, p) | kw_arg : ID EQ primitive
| ID EQ type_ref | kw_arg : ID EQ primitive
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"""kw_arg : ID EQ primitive
| ID EQ type_ref"""
p[0] = {p[1]: p[3]} | [
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dronekit/dronekit-python | 1d89e82807ce543bae5e36426641b717e187c589 | dronekit/__init__.py | python | Vehicle.play_tune | (self, tune) | Play a tune on the vehicle | Play a tune on the vehicle | [
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'''Play a tune on the vehicle'''
msg = self.message_factory.play_tune_encode(0, 0, tune)
self.send_mavlink(msg) | [
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DataDog/dd-agent | 526559be731b6e47b12d7aa8b6d45cb8d9ac4d68 | checks/__init__.py | python | AgentCheck._roll_up_instance_metadata | (self) | Concatenate and flush instance metadata. | Concatenate and flush instance metadata. | [
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] | def _roll_up_instance_metadata(self):
"""
Concatenate and flush instance metadata.
"""
self.svc_metadata.append(dict((k, v) for (k, v) in self._instance_metadata))
self._instance_metadata = [] | [
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beeware/ouroboros | a29123c6fab6a807caffbb7587cf548e0c370296 | ouroboros/lib2to3/refactor.py | python | RefactoringTool.traverse_by | (self, fixers, traversal) | Traverse an AST, applying a set of fixers to each node.
This is a helper method for refactor_tree().
Args:
fixers: a list of fixer instances.
traversal: a generator that yields AST nodes.
Returns:
None | Traverse an AST, applying a set of fixers to each node. | [
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] | def traverse_by(self, fixers, traversal):
"""Traverse an AST, applying a set of fixers to each node.
This is a helper method for refactor_tree().
Args:
fixers: a list of fixer instances.
traversal: a generator that yields AST nodes.
Returns:
None
"""
if not fixers:
return
for node in traversal:
for fixer in fixers[node.type]:
results = fixer.match(node)
if results:
new = fixer.transform(node, results)
if new is not None:
node.replace(new)
node = new | [
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google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/server/grr_response_server/gui/api_call_router_with_approval_checks.py | python | ApiCallRouterWithApprovalChecks.CreateClientApproval | (self, args, context=None) | return self.delegate.CreateClientApproval(args, context=context) | [] | def CreateClientApproval(self, args, context=None):
# Everybody can create a user client approval.
return self.delegate.CreateClientApproval(args, context=context) | [
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/mimify.py | python | mimify_part | (ifile, ofile, is_mime) | return | Convert an 8bit part of a MIME mail message to quoted-printable. | Convert an 8bit part of a MIME mail message to quoted-printable. | [
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] | def mimify_part(ifile, ofile, is_mime):
"""Convert an 8bit part of a MIME mail message to quoted-printable."""
has_cte = is_qp = is_base64 = 0
multipart = None
must_quote_body = must_quote_header = has_iso_chars = 0
header = []
header_end = ''
message = []
message_end = ''
hfile = HeaderFile(ifile)
while 1:
line = hfile.readline()
if not line:
break
if not must_quote_header and iso_char.search(line):
must_quote_header = 1
if mv.match(line):
is_mime = 1
if cte.match(line):
has_cte = 1
if qp.match(line):
is_qp = 1
elif base64_re.match(line):
is_base64 = 1
mp_res = mp.match(line)
if mp_res:
multipart = '--' + mp_res.group(1)
if he.match(line):
header_end = line
break
header.append(line)
while 1:
line = ifile.readline()
if not line:
break
if multipart:
if line == multipart + '--\n':
message_end = line
break
if line == multipart + '\n':
message_end = line
break
if is_base64:
message.append(line)
continue
if is_qp:
while line[-2:] == '=\n':
line = line[:-2]
newline = ifile.readline()
if newline[:len(QUOTE)] == QUOTE:
newline = newline[len(QUOTE):]
line = line + newline
line = mime_decode(line)
message.append(line)
if not has_iso_chars:
if iso_char.search(line):
has_iso_chars = must_quote_body = 1
if not must_quote_body:
if len(line) > MAXLEN:
must_quote_body = 1
for line in header:
if must_quote_header:
line = mime_encode_header(line)
chrset_res = chrset.match(line)
if chrset_res:
if has_iso_chars:
if chrset_res.group(2).lower() == 'us-ascii':
line = '%s%s%s' % (chrset_res.group(1),
CHARSET,
chrset_res.group(3))
else:
line = '%sus-ascii%s' % chrset_res.group(1, 3)
if has_cte and cte.match(line):
line = 'Content-Transfer-Encoding: '
if is_base64:
line = line + 'base64\n'
elif must_quote_body:
line = line + 'quoted-printable\n'
else:
line = line + '7bit\n'
ofile.write(line)
if (must_quote_header or must_quote_body) and not is_mime:
ofile.write('Mime-Version: 1.0\n')
ofile.write('Content-Type: text/plain; ')
if has_iso_chars:
ofile.write('charset="%s"\n' % CHARSET)
else:
ofile.write('charset="us-ascii"\n')
if must_quote_body and not has_cte:
ofile.write('Content-Transfer-Encoding: quoted-printable\n')
ofile.write(header_end)
for line in message:
if must_quote_body:
line = mime_encode(line, 0)
ofile.write(line)
ofile.write(message_end)
line = message_end
while multipart:
if line == multipart + '--\n':
while 1:
line = ifile.readline()
if not line:
return
if must_quote_body:
line = mime_encode(line, 0)
ofile.write(line)
if line == multipart + '\n':
nifile = File(ifile, multipart)
mimify_part(nifile, ofile, 1)
line = nifile.peek
if not line:
break
ofile.write(line)
continue
while 1:
line = ifile.readline()
if not line:
return
if must_quote_body:
line = mime_encode(line, 0)
ofile.write(line)
return | [
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mimic/scripts/extern/pyqtgraph_0_11_0/pyqtgraph/graphicsItems/AxisItem.py | python | AxisItem.generateDrawSpecs | (self, p) | return (axisSpec, tickSpecs, textSpecs) | Calls tickValues() and tickStrings() to determine where and how ticks should
be drawn, then generates from this a set of drawing commands to be
interpreted by drawPicture(). | Calls tickValues() and tickStrings() to determine where and how ticks should
be drawn, then generates from this a set of drawing commands to be
interpreted by drawPicture(). | [
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"""
Calls tickValues() and tickStrings() to determine where and how ticks should
be drawn, then generates from this a set of drawing commands to be
interpreted by drawPicture().
"""
profiler = debug.Profiler()
#bounds = self.boundingRect()
bounds = self.mapRectFromParent(self.geometry())
linkedView = self.linkedView()
if linkedView is None or self.grid is False:
tickBounds = bounds
else:
tickBounds = linkedView.mapRectToItem(self, linkedView.boundingRect())
if self.orientation == 'left':
span = (bounds.topRight(), bounds.bottomRight())
tickStart = tickBounds.right()
tickStop = bounds.right()
tickDir = -1
axis = 0
elif self.orientation == 'right':
span = (bounds.topLeft(), bounds.bottomLeft())
tickStart = tickBounds.left()
tickStop = bounds.left()
tickDir = 1
axis = 0
elif self.orientation == 'top':
span = (bounds.bottomLeft(), bounds.bottomRight())
tickStart = tickBounds.bottom()
tickStop = bounds.bottom()
tickDir = -1
axis = 1
elif self.orientation == 'bottom':
span = (bounds.topLeft(), bounds.topRight())
tickStart = tickBounds.top()
tickStop = bounds.top()
tickDir = 1
axis = 1
#print tickStart, tickStop, span
## determine size of this item in pixels
points = list(map(self.mapToDevice, span))
if None in points:
return
lengthInPixels = Point(points[1] - points[0]).length()
if lengthInPixels == 0:
return
# Determine major / minor / subminor axis ticks
if self._tickLevels is None:
tickLevels = self.tickValues(self.range[0], self.range[1], lengthInPixels)
tickStrings = None
else:
## parse self.tickLevels into the formats returned by tickLevels() and tickStrings()
tickLevels = []
tickStrings = []
for level in self._tickLevels:
values = []
strings = []
tickLevels.append((None, values))
tickStrings.append(strings)
for val, strn in level:
values.append(val)
strings.append(strn)
## determine mapping between tick values and local coordinates
dif = self.range[1] - self.range[0]
if dif == 0:
xScale = 1
offset = 0
else:
if axis == 0:
xScale = -bounds.height() / dif
offset = self.range[0] * xScale - bounds.height()
else:
xScale = bounds.width() / dif
offset = self.range[0] * xScale
xRange = [x * xScale - offset for x in self.range]
xMin = min(xRange)
xMax = max(xRange)
profiler('init')
tickPositions = [] # remembers positions of previously drawn ticks
## compute coordinates to draw ticks
## draw three different intervals, long ticks first
tickSpecs = []
for i in range(len(tickLevels)):
tickPositions.append([])
ticks = tickLevels[i][1]
## length of tick
tickLength = self.style['tickLength'] / ((i*0.5)+1.0)
lineAlpha = 255 / (i+1)
if self.grid is not False:
lineAlpha *= self.grid/255. * np.clip((0.05 * lengthInPixels / (len(ticks)+1)), 0., 1.)
for v in ticks:
## determine actual position to draw this tick
x = (v * xScale) - offset
if x < xMin or x > xMax: ## last check to make sure no out-of-bounds ticks are drawn
tickPositions[i].append(None)
continue
tickPositions[i].append(x)
p1 = [x, x]
p2 = [x, x]
p1[axis] = tickStart
p2[axis] = tickStop
if self.grid is False:
p2[axis] += tickLength*tickDir
tickPen = self.pen()
color = tickPen.color()
color.setAlpha(int(lineAlpha))
tickPen.setColor(color)
tickSpecs.append((tickPen, Point(p1), Point(p2)))
profiler('compute ticks')
if self.style['stopAxisAtTick'][0] is True:
minTickPosition = min(map(min, tickPositions))
if axis == 0:
stop = max(span[0].y(), minTickPosition)
span[0].setY(stop)
else:
stop = max(span[0].x(), minTickPosition)
span[0].setX(stop)
if self.style['stopAxisAtTick'][1] is True:
maxTickPosition = max(map(max, tickPositions))
if axis == 0:
stop = min(span[1].y(), maxTickPosition)
span[1].setY(stop)
else:
stop = min(span[1].x(), maxTickPosition)
span[1].setX(stop)
axisSpec = (self.pen(), span[0], span[1])
textOffset = self.style['tickTextOffset'][axis] ## spacing between axis and text
#if self.style['autoExpandTextSpace'] is True:
#textWidth = self.textWidth
#textHeight = self.textHeight
#else:
#textWidth = self.style['tickTextWidth'] ## space allocated for horizontal text
#textHeight = self.style['tickTextHeight'] ## space allocated for horizontal text
textSize2 = 0
textRects = []
textSpecs = [] ## list of draw
# If values are hidden, return early
if not self.style['showValues']:
return (axisSpec, tickSpecs, textSpecs)
for i in range(min(len(tickLevels), self.style['maxTextLevel']+1)):
## Get the list of strings to display for this level
if tickStrings is None:
spacing, values = tickLevels[i]
strings = self.tickStrings(values, self.autoSIPrefixScale * self.scale, spacing)
else:
strings = tickStrings[i]
if len(strings) == 0:
continue
## ignore strings belonging to ticks that were previously ignored
for j in range(len(strings)):
if tickPositions[i][j] is None:
strings[j] = None
## Measure density of text; decide whether to draw this level
rects = []
for s in strings:
if s is None:
rects.append(None)
else:
br = p.boundingRect(QtCore.QRectF(0, 0, 100, 100), QtCore.Qt.AlignCenter, asUnicode(s))
## boundingRect is usually just a bit too large
## (but this probably depends on per-font metrics?)
br.setHeight(br.height() * 0.8)
rects.append(br)
textRects.append(rects[-1])
if len(textRects) > 0:
## measure all text, make sure there's enough room
if axis == 0:
textSize = np.sum([r.height() for r in textRects])
textSize2 = np.max([r.width() for r in textRects])
else:
textSize = np.sum([r.width() for r in textRects])
textSize2 = np.max([r.height() for r in textRects])
else:
textSize = 0
textSize2 = 0
if i > 0: ## always draw top level
## If the strings are too crowded, stop drawing text now.
## We use three different crowding limits based on the number
## of texts drawn so far.
textFillRatio = float(textSize) / lengthInPixels
finished = False
for nTexts, limit in self.style['textFillLimits']:
if len(textSpecs) >= nTexts and textFillRatio >= limit:
finished = True
break
if finished:
break
#spacing, values = tickLevels[best]
#strings = self.tickStrings(values, self.scale, spacing)
# Determine exactly where tick text should be drawn
for j in range(len(strings)):
vstr = strings[j]
if vstr is None: ## this tick was ignored because it is out of bounds
continue
vstr = asUnicode(vstr)
x = tickPositions[i][j]
#textRect = p.boundingRect(QtCore.QRectF(0, 0, 100, 100), QtCore.Qt.AlignCenter, vstr)
textRect = rects[j]
height = textRect.height()
width = textRect.width()
#self.textHeight = height
offset = max(0,self.style['tickLength']) + textOffset
if self.orientation == 'left':
textFlags = QtCore.Qt.TextDontClip|QtCore.Qt.AlignRight|QtCore.Qt.AlignVCenter
rect = QtCore.QRectF(tickStop-offset-width, x-(height/2), width, height)
elif self.orientation == 'right':
textFlags = QtCore.Qt.TextDontClip|QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter
rect = QtCore.QRectF(tickStop+offset, x-(height/2), width, height)
elif self.orientation == 'top':
textFlags = QtCore.Qt.TextDontClip|QtCore.Qt.AlignCenter|QtCore.Qt.AlignBottom
rect = QtCore.QRectF(x-width/2., tickStop-offset-height, width, height)
elif self.orientation == 'bottom':
textFlags = QtCore.Qt.TextDontClip|QtCore.Qt.AlignCenter|QtCore.Qt.AlignTop
rect = QtCore.QRectF(x-width/2., tickStop+offset, width, height)
#p.setPen(self.pen())
#p.drawText(rect, textFlags, vstr)
textSpecs.append((rect, textFlags, vstr))
profiler('compute text')
## update max text size if needed.
self._updateMaxTextSize(textSize2)
return (axisSpec, tickSpecs, textSpecs) | [
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magenta/magenta | be6558f1a06984faff6d6949234f5fe9ad0ffdb5 | magenta/models/gansynth/lib/networks.py | python | ResolutionSchedule.scale_factor | (self, block_id) | return self._scale_base**(self._num_resolutions - block_id) | Returns the scale factor for network block `block_id`. | Returns the scale factor for network block `block_id`. | [
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] | def scale_factor(self, block_id):
"""Returns the scale factor for network block `block_id`."""
if block_id < 1 or block_id > self._num_resolutions:
raise ValueError('`block_id` must be in [1, {}]'.format(
self._num_resolutions))
return self._scale_base**(self._num_resolutions - block_id) | [
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google/flax | 89e1126cc43588c6946bc85506987dc812909575 | flax/core/scope.py | python | Scope.make_rng | (self, name: str) | return random.fold_in(self.rngs[name], self.rng_counters[name]) | Generates A PRNGKey from a PRNGSequence with name `name`. | Generates A PRNGKey from a PRNGSequence with name `name`. | [
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] | def make_rng(self, name: str) -> PRNGKey:
"""Generates A PRNGKey from a PRNGSequence with name `name`."""
if not self.has_rng(name):
raise errors.InvalidRngError(f'{self.name} needs PRNG for "{name}"')
self._check_valid()
self._validate_trace_level()
self.rng_counters[name] += 1
return random.fold_in(self.rngs[name], self.rng_counters[name]) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/sre_compile.py | python | _compile | (code, pattern, flags) | [] | def _compile(code, pattern, flags):
# internal: compile a (sub)pattern
emit = code.append
_len = len
LITERAL_CODES = _LITERAL_CODES
REPEATING_CODES = _REPEATING_CODES
SUCCESS_CODES = _SUCCESS_CODES
ASSERT_CODES = _ASSERT_CODES
if (flags & SRE_FLAG_IGNORECASE and
not (flags & SRE_FLAG_LOCALE) and
flags & SRE_FLAG_UNICODE):
fixes = _ignorecase_fixes
else:
fixes = None
for op, av in pattern:
if op in LITERAL_CODES:
if flags & SRE_FLAG_IGNORECASE:
lo = _sre.getlower(av, flags)
if fixes and lo in fixes:
emit(OPCODES[IN_IGNORE])
skip = _len(code); emit(0)
if op is NOT_LITERAL:
emit(OPCODES[NEGATE])
for k in (lo,) + fixes[lo]:
emit(OPCODES[LITERAL])
emit(k)
emit(OPCODES[FAILURE])
code[skip] = _len(code) - skip
else:
emit(OPCODES[OP_IGNORE[op]])
emit(lo)
else:
emit(OPCODES[op])
emit(av)
elif op is IN:
if flags & SRE_FLAG_IGNORECASE:
emit(OPCODES[OP_IGNORE[op]])
def fixup(literal, flags=flags):
return _sre.getlower(literal, flags)
else:
emit(OPCODES[op])
fixup = None
skip = _len(code); emit(0)
_compile_charset(av, flags, code, fixup, fixes)
code[skip] = _len(code) - skip
elif op is ANY:
if flags & SRE_FLAG_DOTALL:
emit(OPCODES[ANY_ALL])
else:
emit(OPCODES[ANY])
elif op in REPEATING_CODES:
if flags & SRE_FLAG_TEMPLATE:
raise error, "internal: unsupported template operator"
emit(OPCODES[REPEAT])
skip = _len(code); emit(0)
emit(av[0])
emit(av[1])
_compile(code, av[2], flags)
emit(OPCODES[SUCCESS])
code[skip] = _len(code) - skip
elif _simple(av) and op is not REPEAT:
if op is MAX_REPEAT:
emit(OPCODES[REPEAT_ONE])
else:
emit(OPCODES[MIN_REPEAT_ONE])
skip = _len(code); emit(0)
emit(av[0])
emit(av[1])
_compile(code, av[2], flags)
emit(OPCODES[SUCCESS])
code[skip] = _len(code) - skip
else:
emit(OPCODES[REPEAT])
skip = _len(code); emit(0)
emit(av[0])
emit(av[1])
_compile(code, av[2], flags)
code[skip] = _len(code) - skip
if op is MAX_REPEAT:
emit(OPCODES[MAX_UNTIL])
else:
emit(OPCODES[MIN_UNTIL])
elif op is SUBPATTERN:
if av[0]:
emit(OPCODES[MARK])
emit((av[0]-1)*2)
# _compile_info(code, av[1], flags)
_compile(code, av[1], flags)
if av[0]:
emit(OPCODES[MARK])
emit((av[0]-1)*2+1)
elif op in SUCCESS_CODES:
emit(OPCODES[op])
elif op in ASSERT_CODES:
emit(OPCODES[op])
skip = _len(code); emit(0)
if av[0] >= 0:
emit(0) # look ahead
else:
lo, hi = av[1].getwidth()
if lo != hi:
raise error, "look-behind requires fixed-width pattern"
emit(lo) # look behind
_compile(code, av[1], flags)
emit(OPCODES[SUCCESS])
code[skip] = _len(code) - skip
elif op is CALL:
emit(OPCODES[op])
skip = _len(code); emit(0)
_compile(code, av, flags)
emit(OPCODES[SUCCESS])
code[skip] = _len(code) - skip
elif op is AT:
emit(OPCODES[op])
if flags & SRE_FLAG_MULTILINE:
av = AT_MULTILINE.get(av, av)
if flags & SRE_FLAG_LOCALE:
av = AT_LOCALE.get(av, av)
elif flags & SRE_FLAG_UNICODE:
av = AT_UNICODE.get(av, av)
emit(ATCODES[av])
elif op is BRANCH:
emit(OPCODES[op])
tail = []
tailappend = tail.append
for av in av[1]:
skip = _len(code); emit(0)
# _compile_info(code, av, flags)
_compile(code, av, flags)
emit(OPCODES[JUMP])
tailappend(_len(code)); emit(0)
code[skip] = _len(code) - skip
emit(0) # end of branch
for tail in tail:
code[tail] = _len(code) - tail
elif op is CATEGORY:
emit(OPCODES[op])
if flags & SRE_FLAG_LOCALE:
av = CH_LOCALE[av]
elif flags & SRE_FLAG_UNICODE:
av = CH_UNICODE[av]
emit(CHCODES[av])
elif op is GROUPREF:
if flags & SRE_FLAG_IGNORECASE:
emit(OPCODES[OP_IGNORE[op]])
else:
emit(OPCODES[op])
emit(av-1)
elif op is GROUPREF_EXISTS:
emit(OPCODES[op])
emit(av[0]-1)
skipyes = _len(code); emit(0)
_compile(code, av[1], flags)
if av[2]:
emit(OPCODES[JUMP])
skipno = _len(code); emit(0)
code[skipyes] = _len(code) - skipyes + 1
_compile(code, av[2], flags)
code[skipno] = _len(code) - skipno
else:
code[skipyes] = _len(code) - skipyes + 1
else:
raise ValueError, ("unsupported operand type", op) | [
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eliben/pyelftools | 8f7a0becaface09435c4374947548b7851e3d1a2 | elftools/construct/adapters.py | python | NoneOf._validate | (self, obj, context) | return obj not in self.invalids | [] | def _validate(self, obj, context):
return obj not in self.invalids | [
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fortharris/Pcode | 147962d160a834c219e12cb456abc130826468e4 | rope/refactor/sourceutils.py | python | find_minimum_indents | (source_code) | return result | [] | def find_minimum_indents(source_code):
result = 80
lines = source_code.split('\n')
for line in lines:
if line.strip() == '':
continue
result = min(result, codeanalyze.count_line_indents(line))
return result | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | examples/research_projects/bertabs/modeling_bertabs.py | python | TransformerDecoderState.map_batch_fn | (self, fn) | [] | def map_batch_fn(self, fn):
def _recursive_map(struct, batch_dim=0):
for k, v in struct.items():
if v is not None:
if isinstance(v, dict):
_recursive_map(v)
else:
struct[k] = fn(v, batch_dim)
self.src = fn(self.src, 0)
if self.cache is not None:
_recursive_map(self.cache) | [
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WolframResearch/WolframClientForPython | 27cffef560eea8d16c02fe4086f42363604284b6 | wolframclient/serializers/encoders/pandas.py | python | normalized_prop_pandas_series_head | (serializer) | return prop | Check property `pandas_series_head` only if specified (not None). | Check property `pandas_series_head` only if specified (not None). | [
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] | def normalized_prop_pandas_series_head(serializer):
""" Check property `pandas_series_head` only if specified (not None). """
prop = serializer.get_property("pandas_series_head", d=None)
if prop and prop not in PANDAS_PROPERTIES["pandas_series_head"]:
raise ValueError(
"Invalid value for property 'pandas_series_head'. Expecting one of (%s), got %s."
% (", ".join(PANDAS_PROPERTIES["pandas_series_head"]), prop)
)
return prop | [
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