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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/smtpd.py | python | PureProxy.__init__ | (self, *args, **kwargs) | [] | def __init__(self, *args, **kwargs):
if 'enable_SMTPUTF8' in kwargs and kwargs['enable_SMTPUTF8']:
raise ValueError("PureProxy does not support SMTPUTF8.")
super(PureProxy, self).__init__(*args, **kwargs) | [
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atlassian-api/atlassian-python-api | 6d8545a790c3aae10b75bdc225fb5c3a0aee44db | atlassian/confluence.py | python | Confluence.delete_page_property | (self, page_id, page_property) | return response | Delete the page (content) property e.g. delete key of hash
:param page_id: content_id format
:param page_property: key of property
:return: | Delete the page (content) property e.g. delete key of hash
:param page_id: content_id format
:param page_property: key of property
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"""
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:param page_property: key of property
:return:
"""
url = "rest/api/content/{page_id}/property/{page_property}".format(
page_id=page_id, page_property=str(page_property)
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try:
response = self.delete(path=url)
except HTTPError as e:
if e.response.status_code == 404:
# Raise ApiError as the documented reason is ambiguous
raise ApiError(
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reason=e,
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raise
return response | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/bdf/bdf_interface/get_card.py | python | GetCard.get_mklist | (self) | return mkarray | gets the MKLIST vector from MKAERO1/MKAERO2 | gets the MKLIST vector from MKAERO1/MKAERO2 | [
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"""gets the MKLIST vector from MKAERO1/MKAERO2"""
mklist = []
mkarray = np.array([])
for mkaero in self.mkaeros:
mklist += mkaero.mklist()
if mklist:
mkarray = np.hstack([mklist])
#new_array = [tuple(row) for row in mkarray]
#unique_pairs = np.lib.arraysetops.unique(new_array, axis=0).tolist()
return mkarray | [
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zetaops/ulakbus | bcc05abf17bbd6dbeec93809e4ad30885e94e83e | ulakbus/views/bap/bap_firma_basvuru_degerlendirme.py | python | BapFirmaBasvuruDegerlendirme.islem_mesaji_olustur | (self) | İşlem bitiminde, işlem mesajı oluşturulur ve gösterilir. | İşlem bitiminde, işlem mesajı oluşturulur ve gösterilir. | [
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] | def islem_mesaji_olustur(self):
"""
İşlem bitiminde, işlem mesajı oluşturulur ve gösterilir.
"""
self.ilgili_daveti_sil()
self.current.output['msgbox'] = {"type": "info",
"title": _(u"Firma Başvuru Kaydı Değerlendirme"),
"msg": _(u"""%s adlı firmanın kayıt başvurusu hakkındaki
kararınız firma yetkilisine başarıyla iletilmiştir.
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/plugins/attack/db/sqlmap/plugins/dbms/sybase/enumeration.py | python | Enumeration.getDbs | (self) | return kb.data.cachedDbs | [] | def getDbs(self):
if len(kb.data.cachedDbs) > 0:
return kb.data.cachedDbs
infoMsg = "fetching database names"
logger.info(infoMsg)
rootQuery = queries[DBMS.SYBASE].dbs
randStr = randomStr()
query = rootQuery.inband.query
if any(isTechniqueAvailable(_) for _ in (PAYLOAD.TECHNIQUE.UNION, PAYLOAD.TECHNIQUE.ERROR, PAYLOAD.TECHNIQUE.QUERY)) or conf.direct:
blinds = [False, True]
else:
blinds = [True]
for blind in blinds:
retVal = pivotDumpTable("(%s) AS %s" % (query, randStr), ['%s.name' % randStr], blind=blind)
if retVal:
kb.data.cachedDbs = retVal[0].values()[0]
break
if kb.data.cachedDbs:
kb.data.cachedDbs.sort()
return kb.data.cachedDbs | [
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facebookresearch/CrypTen | 90bf38b4f80726c808f322efb0ce430dcdf5e5ec | crypten/cuda/cuda_tensor.py | python | CUDALongTensor.clone | (self) | return result | Create a deep copy of the input tensor. | Create a deep copy of the input tensor. | [
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] | def clone(self):
"""Create a deep copy of the input tensor."""
# TODO: Rename this to __deepcopy__()?
result = CUDALongTensor()
result._tensor = self._tensor.clone()
return result | [
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phoenix2/phoenix | 2c83ee6e7c509d2a9378bcd15634a3740adf7666 | phoenix2/backend/ClientBase.py | python | ClientBase._deactivateCallbacks | (self) | Shut down the runCallback function. Typically used post-disconnect. | Shut down the runCallback function. Typically used post-disconnect. | [
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"""Shut down the runCallback function. Typically used post-disconnect.
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CloudBotIRC/CloudBot | 6c5a88f9961b6cd66a8f489a38feb2e8227ac9ea | cloudbot/clients/irc.py | python | IrcClient.__init__ | (self, bot, name, nick, *, channels=None, config=None,
server, port=6667, use_ssl=False, ignore_cert_errors=True, timeout=300, local_bind=False) | :type bot: cloudbot.bot.CloudBot
:type name: str
:type nick: str
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:type config: dict[str, unknown]
:type server: str
:type port: int
:type use_ssl: bool
:type ignore_cert_errors: bool
:type timeout: int | :type bot: cloudbot.bot.CloudBot
:type name: str
:type nick: str
:type channels: list[str]
:type config: dict[str, unknown]
:type server: str
:type port: int
:type use_ssl: bool
:type ignore_cert_errors: bool
:type timeout: int | [
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"""
:type bot: cloudbot.bot.CloudBot
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:type server: str
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"""
super().__init__(bot, name, nick, channels=channels, config=config)
self.use_ssl = use_ssl
self._ignore_cert_errors = ignore_cert_errors
self._timeout = timeout
self.server = server
self.port = port
self.local_bind = local_bind
# create SSL context
if self.use_ssl:
self.ssl_context = SSLContext(PROTOCOL_SSLv23)
if self._ignore_cert_errors:
self.ssl_context.verify_mode = ssl.CERT_NONE
else:
self.ssl_context.verify_mode = ssl.CERT_REQUIRED
else:
self.ssl_context = None
# if we're connected
self._connected = False
# if we've quit
self._quit = False
# transport and protocol
self._transport = None
self._protocol = None
self.capabilities = set(self.config.get('capabilities', [])) | [
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jazzband/django-hosts | b3fb2ab26adc725f09aceea042d3394ea47999c5 | django_hosts/managers.py | python | HostSiteManager.by_id | (self, site_id=None) | return self.get_queryset(site_id) | Returns a queryset matching the given site id. If not given
this falls back to the ``SITE_ID`` setting.
:param site_id: the ID of the site
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"""
Returns a queryset matching the given site id. If not given
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:param site_id: the ID of the site
:rtype: :class:`~django.db.models.query.QuerySet`
"""
return self.get_queryset(site_id) | [
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scikit-learn-contrib/DESlib | 64260ae7c6dd745ef0003cc6322c9f829c807708 | deslib/dcs/base.py | python | BaseDCS.estimate_competence | (self, competence_region, distances=None,
predictions=None) | Estimate the competence of each base classifier for the
classification of the query sample.
Parameters
----------
competence_region : array of shape (n_samples, n_neighbors)
Indices of the k nearest neighbors.
distances : array of shape (n_samples, n_neighbors)
Distances from the k nearest neighbors to the query.
predictions : array of shape (n_samples, n_classifiers)
Predictions of the base classifiers for the test examples.
Returns
-------
competences : array of shape (n_samples, n_classifiers)
Competence level estimated for each base classifier and test
example. | Estimate the competence of each base classifier for the
classification of the query sample. | [
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] | def estimate_competence(self, competence_region, distances=None,
predictions=None):
"""Estimate the competence of each base classifier for the
classification of the query sample.
Parameters
----------
competence_region : array of shape (n_samples, n_neighbors)
Indices of the k nearest neighbors.
distances : array of shape (n_samples, n_neighbors)
Distances from the k nearest neighbors to the query.
predictions : array of shape (n_samples, n_classifiers)
Predictions of the base classifiers for the test examples.
Returns
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competences : array of shape (n_samples, n_classifiers)
Competence level estimated for each base classifier and test
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/logging/handlers.py | python | TimedRotatingFileHandler.computeRollover | (self, currentTime) | return result | Work out the rollover time based on the specified time. | Work out the rollover time based on the specified time. | [
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"""
Work out the rollover time based on the specified time.
"""
result = currentTime + self.interval
# If we are rolling over at midnight or weekly, then the interval is already known.
# What we need to figure out is WHEN the next interval is. In other words,
# if you are rolling over at midnight, then your base interval is 1 day,
# but you want to start that one day clock at midnight, not now. So, we
# have to fudge the rolloverAt value in order to trigger the first rollover
# at the right time. After that, the regular interval will take care of
# the rest. Note that this code doesn't care about leap seconds. :)
if self.when == 'MIDNIGHT' or self.when.startswith('W'):
# This could be done with less code, but I wanted it to be clear
if self.utc:
t = time.gmtime(currentTime)
else:
t = time.localtime(currentTime)
currentHour = t[3]
currentMinute = t[4]
currentSecond = t[5]
# r is the number of seconds left between now and midnight
r = _MIDNIGHT - ((currentHour * 60 + currentMinute) * 60 +
currentSecond)
result = currentTime + r
# If we are rolling over on a certain day, add in the number of days until
# the next rollover, but offset by 1 since we just calculated the time
# until the next day starts. There are three cases:
# Case 1) The day to rollover is today; in this case, do nothing
# Case 2) The day to rollover is further in the interval (i.e., today is
# day 2 (Wednesday) and rollover is on day 6 (Sunday). Days to
# next rollover is simply 6 - 2 - 1, or 3.
# Case 3) The day to rollover is behind us in the interval (i.e., today
# is day 5 (Saturday) and rollover is on day 3 (Thursday).
# Days to rollover is 6 - 5 + 3, or 4. In this case, it's the
# number of days left in the current week (1) plus the number
# of days in the next week until the rollover day (3).
# The calculations described in 2) and 3) above need to have a day added.
# This is because the above time calculation takes us to midnight on this
# day, i.e. the start of the next day.
if self.when.startswith('W'):
day = t[6] # 0 is Monday
if day != self.dayOfWeek:
if day < self.dayOfWeek:
daysToWait = self.dayOfWeek - day
else:
daysToWait = 6 - day + self.dayOfWeek + 1
newRolloverAt = result + (daysToWait * (60 * 60 * 24))
if not self.utc:
dstNow = t[-1]
dstAtRollover = time.localtime(newRolloverAt)[-1]
if dstNow != dstAtRollover:
if not dstNow: # DST kicks in before next rollover, so we need to deduct an hour
addend = -3600
else: # DST bows out before next rollover, so we need to add an hour
addend = 3600
newRolloverAt += addend
result = newRolloverAt
return result | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/pymysql/connections.py | python | OKPacketWrapper.__init__ | (self, from_packet) | [] | def __init__(self, from_packet):
if not from_packet.is_ok_packet():
raise ValueError('Cannot create ' + str(self.__class__.__name__) +
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self.packet = from_packet
self.packet.advance(1)
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self.insert_id = self.packet.read_length_encoded_integer()
self.server_status, self.warning_count = self.read_struct('<HH')
self.message = self.packet.read_all()
self.has_next = self.server_status & SERVER_STATUS.SERVER_MORE_RESULTS_EXISTS | [
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ihabunek/toot | 1387d045eb10560444a4343e40ae9a16229ceed6 | toot/tui/app.py | python | TUI.async_load_timeline | (self, is_initial, timeline_name=None, local=None) | return self.run_in_thread(_load_statuses,
done_callback=_done_initial if is_initial else _done_next) | Asynchronously load a list of statuses. | Asynchronously load a list of statuses. | [
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"""Asynchronously load a list of statuses."""
def _load_statuses():
self.footer.set_message("Loading statuses...")
try:
data = next(self.timeline_generator)
except StopIteration:
return []
finally:
self.footer.clear_message()
return [self.make_status(s) for s in data]
def _done_initial(statuses):
"""Process initial batch of statuses, construct a Timeline."""
self.timeline = self.build_timeline(timeline_name, statuses, local)
self.timeline.refresh_status_details() # Draw first status
self.refresh_footer(self.timeline)
self.body = self.timeline
def _done_next(statuses):
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self.timeline.append_statuses(statuses)
return self.run_in_thread(_load_statuses,
done_callback=_done_initial if is_initial else _done_next) | [
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dsoprea/GDriveFS | d307b043ebc235d6444c8c1ae2e0bbb860e788a0 | gdrivefs/gdfuse.py | python | _GdfsMixin.release | (self, filepath, fh) | Close a file. | Close a file. | [
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"""Close a file."""
om = gdrivefs.opened_file.get_om()
try:
om.remove_by_fh(fh)
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/pycryptodomex-3.9.7/lib/Cryptodome/Protocol/KDF.py | python | bcrypt_check | (password, bcrypt_hash) | Verify if the provided password matches the given bcrypt hash.
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password (byte string or string):
The secret password or pass phrase to test.
It must be at most 72 bytes long.
It must not contain the zero byte.
Unicode strings will be encoded as UTF-8.
bcrypt_hash (byte string, bytearray):
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The secret password or pass phrase to test.
It must be at most 72 bytes long.
It must not contain the zero byte.
Unicode strings will be encoded as UTF-8.
bcrypt_hash (byte string, bytearray):
The reference bcrypt hash the password needs to be checked against.
Raises:
ValueError: if the password does not match
"""
bcrypt_hash = tobytes(bcrypt_hash)
if len(bcrypt_hash) != 60:
raise ValueError("Incorrect length of the bcrypt hash: %d bytes instead of 60" % len(bcrypt_hash))
if bcrypt_hash[:4] != b'$2a$':
raise ValueError("Unsupported prefix")
p = re.compile(br'\$2a\$([0-9][0-9])\$([A-Za-z0-9./]{22,22})([A-Za-z0-9./]{31,31})')
r = p.match(bcrypt_hash)
if not r:
raise ValueError("Incorrect bcrypt hash format")
cost = int(r.group(1))
if not (4 <= cost <= 31):
raise ValueError("Incorrect cost")
salt = _bcrypt_decode(r.group(2))
bcrypt_hash2 = bcrypt(password, cost, salt)
secret = get_random_bytes(16)
mac1 = BLAKE2s.new(digest_bits=160, key=secret, data=bcrypt_hash).digest()
mac2 = BLAKE2s.new(digest_bits=160, key=secret, data=bcrypt_hash2).digest()
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cool-RR/python_toolbox | cb9ef64b48f1d03275484d707dc5079b6701ad0c | python_toolbox/combi/perming/perm_space.py | python | PermSpace.short_length_string | (self) | Short string describing size of space, e.g. "12!" | Short string describing size of space, e.g. "12!" | [
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tianzhi0549/FCOS | 0eb8ee0b7114a3ca42ad96cd89e0ac63a205461e | fcos_core/utils/env.py | python | setup_environment | () | Perform environment setup work. The default setup is a no-op, but this
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/helpers/device_registry.py | python | DeviceRegistry.async_schedule_save | (self) | Schedule saving the device registry. | Schedule saving the device registry. | [
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hvac/hvac | ec048ded30d21c13c21cfa950d148c8bfc1467b0 | hvac/api/system_backend/key.py | python | Key.cancel_rekey_verify | (self) | return self._adapter.delete(
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foxmask/django-th | 29aa84f8d4aa945dbef6cf580593b435cc708e31 | th_evernote/my_evernote.py | python | ServiceEvernote.auth | (self, request) | return client.get_authorize_url(request_token) | let's auth the user to the Service | let's auth the user to the Service | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/Django/django/contrib/gis/utils/ogrinspect.py | python | ogrinspect | (*args, **kwargs) | return '\n'.join(s for s in _ogrinspect(*args, **kwargs)) | Given a data source (either a string or a DataSource object) and a string
model name this function will generate a GeoDjango model.
Usage:
>>> from django.contrib.gis.utils import ogrinspect
>>> ogrinspect('/path/to/shapefile.shp','NewModel')
...will print model definition to stout
or put this in a python script and use to redirect the output to a new
model like:
$ python generate_model.py > myapp/models.py
# generate_model.py
from django.contrib.gis.utils import ogrinspect
shp_file = 'data/mapping_hacks/world_borders.shp'
model_name = 'WorldBorders'
print(ogrinspect(shp_file, model_name, multi_geom=True, srid=4326,
geom_name='shapes', blank=True))
Required Arguments
`datasource` => string or DataSource object to file pointer
`model name` => string of name of new model class to create
Optional Keyword Arguments
`geom_name` => For specifying the model name for the Geometry Field.
Otherwise will default to `geom`
`layer_key` => The key for specifying which layer in the DataSource to use;
defaults to 0 (the first layer). May be an integer index or a string
identifier for the layer.
`srid` => The SRID to use for the Geometry Field. If it can be determined,
the SRID of the datasource is used.
`multi_geom` => Boolean (default: False) - specify as multigeometry.
`name_field` => String - specifies a field name to return for the
`__unicode__` function (which will be generated if specified).
`imports` => Boolean (default: True) - set to False to omit the
`from django.contrib.gis.db import models` code from the
autogenerated models thus avoiding duplicated imports when building
more than one model by batching ogrinspect()
`decimal` => Boolean or sequence (default: False). When set to True
all generated model fields corresponding to the `OFTReal` type will
be `DecimalField` instead of `FloatField`. A sequence of specific
field names to generate as `DecimalField` may also be used.
`blank` => Boolean or sequence (default: False). When set to True all
generated model fields will have `blank=True`. If the user wants to
give specific fields to have blank, then a list/tuple of OGR field
names may be used.
`null` => Boolean (default: False) - When set to True all generated
model fields will have `null=True`. If the user wants to specify
give specific fields to have null, then a list/tuple of OGR field
names may be used.
Note: This routine calls the _ogrinspect() helper to do the heavy lifting. | Given a data source (either a string or a DataSource object) and a string
model name this function will generate a GeoDjango model. | [
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"""
Given a data source (either a string or a DataSource object) and a string
model name this function will generate a GeoDjango model.
Usage:
>>> from django.contrib.gis.utils import ogrinspect
>>> ogrinspect('/path/to/shapefile.shp','NewModel')
...will print model definition to stout
or put this in a python script and use to redirect the output to a new
model like:
$ python generate_model.py > myapp/models.py
# generate_model.py
from django.contrib.gis.utils import ogrinspect
shp_file = 'data/mapping_hacks/world_borders.shp'
model_name = 'WorldBorders'
print(ogrinspect(shp_file, model_name, multi_geom=True, srid=4326,
geom_name='shapes', blank=True))
Required Arguments
`datasource` => string or DataSource object to file pointer
`model name` => string of name of new model class to create
Optional Keyword Arguments
`geom_name` => For specifying the model name for the Geometry Field.
Otherwise will default to `geom`
`layer_key` => The key for specifying which layer in the DataSource to use;
defaults to 0 (the first layer). May be an integer index or a string
identifier for the layer.
`srid` => The SRID to use for the Geometry Field. If it can be determined,
the SRID of the datasource is used.
`multi_geom` => Boolean (default: False) - specify as multigeometry.
`name_field` => String - specifies a field name to return for the
`__unicode__` function (which will be generated if specified).
`imports` => Boolean (default: True) - set to False to omit the
`from django.contrib.gis.db import models` code from the
autogenerated models thus avoiding duplicated imports when building
more than one model by batching ogrinspect()
`decimal` => Boolean or sequence (default: False). When set to True
all generated model fields corresponding to the `OFTReal` type will
be `DecimalField` instead of `FloatField`. A sequence of specific
field names to generate as `DecimalField` may also be used.
`blank` => Boolean or sequence (default: False). When set to True all
generated model fields will have `blank=True`. If the user wants to
give specific fields to have blank, then a list/tuple of OGR field
names may be used.
`null` => Boolean (default: False) - When set to True all generated
model fields will have `null=True`. If the user wants to specify
give specific fields to have null, then a list/tuple of OGR field
names may be used.
Note: This routine calls the _ogrinspect() helper to do the heavy lifting.
"""
return '\n'.join(s for s in _ogrinspect(*args, **kwargs)) | [
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toastdriven/restless | ce18b02886cce6d182d60661cb5c2eeeac215cf6 | restless/resources.py | python | Resource.request_method | (self) | return self.request.method.upper() | Returns the HTTP method for the current request.
If you're integrating with a new web framework, you might need to
override this method within your subclass.
:returns: The HTTP method in uppercase
:rtype: string | Returns the HTTP method for the current request. | [
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"""
Returns the HTTP method for the current request.
If you're integrating with a new web framework, you might need to
override this method within your subclass.
:returns: The HTTP method in uppercase
:rtype: string
"""
# By default, Django-esque.
return self.request.method.upper() | [
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themix-project/oomox | 1bb0f3033736c56652e25c7d7b47c7fc499bfeb1 | plugins/icons_numix/oomox_plugin.py | python | NumixIconsExportDialog.do_export | (self) | [] | def do_export(self):
self.command = [
"bash",
os.path.join(PLUGIN_DIR, "change_color.sh"),
"-o", self.theme_name,
self.temp_theme_path,
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snapcore/snapcraft | b81550376df7f2d0dfe65f7bfb006a3107252450 | snapcraft/internal/cache/_file.py | python | FileCache.__init__ | (self, *, namespace: str = "files") | Create a FileCache under namespace.
:param str namespace: set the namespace for the cache
(default: "files"). | Create a FileCache under namespace. | [
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"""Create a FileCache under namespace.
:param str namespace: set the namespace for the cache
(default: "files").
"""
super().__init__()
self.file_cache = os.path.join(self.cache_root, namespace) | [
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spartan-array/spartan | fdcf059ce7e48688648d793d632dc5961f4e72b5 | spartan/expr/operator/checkpoint.py | python | checkpoint | (x, mode='disk') | return CheckpointExpr(src=lazify(x), path=FLAGS.checkpoint_path, mode=mode, ready=False) | Make a checkpoint for x
:param x: `numpy.ndarray` or `Expr`
:param mode: 'disk' or 'replica'
:rtype: `Expr` | Make a checkpoint for x | [
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'''
Make a checkpoint for x
:param x: `numpy.ndarray` or `Expr`
:param mode: 'disk' or 'replica'
:rtype: `Expr`
'''
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bit-team/backintime | e1ae23ddc0b4229053e3e9c6c61adcb7f3d8e9b3 | common/tools.py | python | pidsWithName | (name) | return [x for x in pids() if processName(x) == name[:15]] | Get all processes currently running with name ``name``.
Args:
name (str): name of a process like 'python3' or 'backintime'
Returns:
list: PIDs as int | Get all processes currently running with name ``name``. | [
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"""
Get all processes currently running with name ``name``.
Args:
name (str): name of a process like 'python3' or 'backintime'
Returns:
list: PIDs as int
"""
# /proc/###/stat stores just the first 16 chars of the process name
return [x for x in pids() if processName(x) == name[:15]] | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | pypy/module/cpyext/floatobject.py | python | PyFloat_AS_DOUBLE | (space, w_float) | return space.float_w(w_float) | Return a C double representation of the contents of w_float, but
without error checking. | Return a C double representation of the contents of w_float, but
without error checking. | [
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] | def PyFloat_AS_DOUBLE(space, w_float):
"""Return a C double representation of the contents of w_float, but
without error checking."""
return space.float_w(w_float) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/celery/backends/redis.py | python | RedisBackend.on_chord_part_return | (self, request, state, result,
propagate=None, **kwargs) | [] | def on_chord_part_return(self, request, state, result,
propagate=None, **kwargs):
app = self.app
tid, gid = request.id, request.group
if not gid or not tid:
return
client = self.client
jkey = self.get_key_for_group(gid, '.j')
tkey = self.get_key_for_group(gid, '.t')
result = self.encode_result(result, state)
with client.pipeline() as pipe:
_, readycount, totaldiff, _, _ = pipe \
.rpush(jkey, self.encode([1, tid, state, result])) \
.llen(jkey) \
.get(tkey) \
.expire(jkey, self.expires) \
.expire(tkey, self.expires) \
.execute()
totaldiff = int(totaldiff or 0)
try:
callback = maybe_signature(request.chord, app=app)
total = callback['chord_size'] + totaldiff
if readycount == total:
decode, unpack = self.decode, self._unpack_chord_result
with client.pipeline() as pipe:
resl, _, _ = pipe \
.lrange(jkey, 0, total) \
.delete(jkey) \
.delete(tkey) \
.execute()
try:
callback.delay([unpack(tup, decode) for tup in resl])
except Exception as exc: # pylint: disable=broad-except
logger.exception(
'Chord callback for %r raised: %r', request.group, exc)
return self.chord_error_from_stack(
callback,
ChordError('Callback error: {0!r}'.format(exc)),
)
except ChordError as exc:
logger.exception('Chord %r raised: %r', request.group, exc)
return self.chord_error_from_stack(callback, exc)
except Exception as exc: # pylint: disable=broad-except
logger.exception('Chord %r raised: %r', request.group, exc)
return self.chord_error_from_stack(
callback,
ChordError('Join error: {0!r}'.format(exc)),
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NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | TensorFlow2/LanguageModeling/ELECTRA/modeling.py | python | TFElectraMainLayer._prune_heads | (self, heads_to_prune) | Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel | Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel | [
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""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
raise NotImplementedError | [
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maas/maas | db2f89970c640758a51247c59bf1ec6f60cf4ab5 | src/provisioningserver/dns/commands/edit_named_options.py | python | parse_file | (config_path, options_file) | return config_dict | Read the named.conf.options file and parse it.
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"""Read the named.conf.options file and parse it.
Then insert the include statement that we need.
"""
try:
config_dict = parse_isc_string(options_file)
except ISCParseException as e:
raise ValueError(
"Failed to parse %s: %s" % (config_path, str(e))
) from e
options_block = config_dict.get("options", None)
if options_block is None:
# Something is horribly wrong with the file; bail out rather
# than doing anything drastic.
raise ValueError(
"Can't find options {} block in %s, bailing out without "
"doing anything." % config_path
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return config_dict | [
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bigaidream-projects/drmad | a4bb6010595d956f29c5a42a095bab76a60b29eb | gpu_ver/refcode/models/layers/conv_layers.py | python | pool_layer.__init__ | (self, rstream, index, x,
params, useRglrz, bnPhase,
poolShape, inFilters, outFilters, stride, ignore_border = False,
b=None, a=None, normParam=None, normParam2=None, rglrzParam=None) | Pooling layer + BN + noise | Pooling layer + BN + noise | [
"Pooling",
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"+",
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params, useRglrz, bnPhase,
poolShape, inFilters, outFilters, stride, ignore_border = False,
b=None, a=None, normParam=None, normParam2=None, rglrzParam=None):
'''
Pooling layer + BN + noise
'''
# batch normalization
if params.batchNorm and params.convLayers[index].bn:
_, b, a = t1_shared(params=params, rng=0, index=index, nIn=0, nOut=0,
outFilters=inFilters, filterShape=0, defineW=0)
self.b = b; self.a = a
if params.batchNorm and not params.aFix:
self.paramsT1 = [b, a]
else:
self.paramsT1 = [b]
if normParam is None:
normParam, paramsBN = bn_shared(params, inFilters, index)
self.normParam = normParam
self.paramsBN = paramsBN
x, updateBN = bn_layer(x, self.a, self.b, self.normParam, params, bnPhase)
self.updateBN = updateBN
# additive noise
self.paramsT2 = []
if 'addNoise' in params.rglrz and params.convLayers[index].noise:
if rglrzParam is None:
self.rglrzParam = {}
tempValue = params.rglrzInitial['addNoise'][index]
tempParam = np.asarray(tempValue, dtype=theano.config.floatX)
noizParam = theano.shared(value=tempParam, name='%s_%d' % ('addNoise', index), borrow=True)
self.rglrzParam['addNoise']=noizParam
if params.useT2 and 'addNoise' in params.rglrzTrain:
self.paramsT2 = [noizParam]
x = noiseup(x, useRglrz, noizParam, params.noiseT1, params, index, rstream)
# dropout
if dropout_conditions(params, index, 'type0') and params.convLayers[index].noise:
if ('dropOut' in params.rglrz):
drop = self.rglrzInitial['dropOut'][index]
elif 'dropOutB' in rglrzParam.keys():
drop = self.rglrzInitial['dropOutB'][index]
x = dropout(x, useRglrz, drop, params, inFilters, rstream)
# pooling
if cudasConv:
self.output = cudnn.dnn_pool(x, poolShape, stride = stride, mode = 'max')#, ignore_border = ignore_border)
else:
self.output = pool.pool_2d(x, ds = poolShape, st = stride, ignore_border = ignore_border, mode = 'max')
# batch normalization
if params.batchNorm and params.convLayers[index].bn and params.poolBNafter:
_, b, a = t1_shared(params=params, rng=0, index=index+20, nIn=0, nOut=0,
outFilters=outFilters, filterShape=0, defineW=0)
self.b = b; self.a = a
if params.batchNorm and not params.aFix:
self.paramsT1 = [b, a]
else:
self.paramsT1 = [b]
if normParam2 is None:
normParam2, paramsBN2 = bn_shared(params, outFilters, index+20)
self.normParam2 = normParam2
self.paramsBN += paramsBN2
self.output, updateBN2 = bn_layer(self.output, self.a, self.b, self.normParam2, params, bnPhase)
self.updateBN += updateBN2 | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/pillows/application.py | python | transform_app_for_es | (doc_dict) | return doc.to_json() | [] | def transform_app_for_es(doc_dict):
# perform any lazy migrations
doc = get_correct_app_class(doc_dict).wrap(doc_dict)
doc['@indexed_on'] = json_format_datetime(datetime.utcnow())
return doc.to_json() | [
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piergiaj/representation-flow-cvpr19 | b9dcb0a7750732210bccf84dbe6044cc33b48c17 | baseline_2d_resnets.py | python | resnet34 | (pretrained=False, mode='rgb', **kwargs) | return model | Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet | Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet | [
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"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if mode == 'flow':
model = ResNet(BasicBlock, [3, 4, 6, 3], inp=20, **kwargs)
else:
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3/s3navigation.py | python | S3NavigationItem.__setitem__ | (self, i, item) | Overwrite the component item at position i with item
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item: the item | Overwrite the component item at position i with item | [
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"""
Overwrite the component item at position i with item
Args:
i: the index within the component list
item: the item
"""
self.components.__setitem__(i, item) | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/pyparsing.py | python | ParseResults.__setitem__ | ( self, k, v, isinstance=isinstance ) | [] | def __setitem__( self, k, v, isinstance=isinstance ):
if isinstance(v,_ParseResultsWithOffset):
self.__tokdict[k] = self.__tokdict.get(k,list()) + [v]
sub = v[0]
elif isinstance(k,(int,slice)):
self.__toklist[k] = v
sub = v
else:
self.__tokdict[k] = self.__tokdict.get(k,list()) + [_ParseResultsWithOffset(v,0)]
sub = v
if isinstance(sub,ParseResults):
sub.__parent = wkref(self) | [
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web2py/web2py | 095905c4e010a1426c729483d912e270a51b7ba8 | gluon/contrib/login_methods/motp_auth.py | python | motp_auth | (db=DAL('sqlite://storage.sqlite'),
time_offset=60) | return motp_auth_aux | motp allows you to login with a one time password(OTP) generated on a motp client,
motp clients are available for practically all platforms.
to know more about OTP visit http://en.wikipedia.org/wiki/One-time_password
to know more visit http://motp.sourceforge.net
Written by Madhukar R Pai (madspai@gmail.com)
License : MIT or GPL v2
thanks and credits to the web2py community
to use motp_auth:
motp_auth.py has to be located in gluon/contrib/login_methods/ folder
first auth_user has to have 2 extra fields - motp_secret and motp_pin
for that define auth like shown below:
## after auth = Auth(db)
db.define_table(
auth.settings.table_user_name,
Field('first_name', length=128, default=''),
Field('last_name', length=128, default=''),
Field('email', length=128, default='', unique=True), # required
Field('password', 'password', length=512, # required
readable=False, label='Password'),
Field('motp_secret',length=512,default='',
label='MOTP Seceret'),
Field('motp_pin',length=128,default='',
label='MOTP PIN'),
Field('registration_key', length=512, # required
writable=False, readable=False, default=''),
Field('reset_password_key', length=512, # required
writable=False, readable=False, default=''),
Field('registration_id', length=512, # required
writable=False, readable=False, default=''))
##validators
custom_auth_table = db[auth.settings.table_user_name]
# get the custom_auth_table
custom_auth_table.first_name.requires = \
IS_NOT_EMPTY(error_message=auth.messages.is_empty)
custom_auth_table.last_name.requires = \
IS_NOT_EMPTY(error_message=auth.messages.is_empty)
custom_auth_table.password.requires = CRYPT()
custom_auth_table.email.requires = [
IS_EMAIL(error_message=auth.messages.invalid_email),
IS_NOT_IN_DB(db, custom_auth_table.email)]
auth.settings.table_user = custom_auth_table # tell auth to use custom_auth_table
## before auth.define_tables()
##after that:
from gluon.contrib.login_methods.motp_auth import motp_auth
auth.settings.login_methods.append(motp_auth(db=db))
##Instructions for using MOTP
- after configuring motp for web2py, Install a MOTP client on your phone (android,IOS, java, windows phone, etc)
- initialize the motp client (to reset a motp secret type in #**#),
During user creation enter the secret generated during initialization into the motp_secret field in auth_user and
similarly enter a pre-decided pin into the motp_pin
- done.. to login, just generate a fresh OTP by typing in the pin and use the OTP as password
###To Dos###
- both motp_secret and pin are stored in plain text! need to have some way of encrypting
- web2py stores the password in db on successful login (should not happen)
- maybe some utility or page to check the otp would be useful
- as of now user field is hardcoded to email. Some way of selecting user table and user field. | motp allows you to login with a one time password(OTP) generated on a motp client,
motp clients are available for practically all platforms.
to know more about OTP visit http://en.wikipedia.org/wiki/One-time_password
to know more visit http://motp.sourceforge.net | [
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time_offset=60):
"""
motp allows you to login with a one time password(OTP) generated on a motp client,
motp clients are available for practically all platforms.
to know more about OTP visit http://en.wikipedia.org/wiki/One-time_password
to know more visit http://motp.sourceforge.net
Written by Madhukar R Pai (madspai@gmail.com)
License : MIT or GPL v2
thanks and credits to the web2py community
to use motp_auth:
motp_auth.py has to be located in gluon/contrib/login_methods/ folder
first auth_user has to have 2 extra fields - motp_secret and motp_pin
for that define auth like shown below:
## after auth = Auth(db)
db.define_table(
auth.settings.table_user_name,
Field('first_name', length=128, default=''),
Field('last_name', length=128, default=''),
Field('email', length=128, default='', unique=True), # required
Field('password', 'password', length=512, # required
readable=False, label='Password'),
Field('motp_secret',length=512,default='',
label='MOTP Seceret'),
Field('motp_pin',length=128,default='',
label='MOTP PIN'),
Field('registration_key', length=512, # required
writable=False, readable=False, default=''),
Field('reset_password_key', length=512, # required
writable=False, readable=False, default=''),
Field('registration_id', length=512, # required
writable=False, readable=False, default=''))
##validators
custom_auth_table = db[auth.settings.table_user_name]
# get the custom_auth_table
custom_auth_table.first_name.requires = \
IS_NOT_EMPTY(error_message=auth.messages.is_empty)
custom_auth_table.last_name.requires = \
IS_NOT_EMPTY(error_message=auth.messages.is_empty)
custom_auth_table.password.requires = CRYPT()
custom_auth_table.email.requires = [
IS_EMAIL(error_message=auth.messages.invalid_email),
IS_NOT_IN_DB(db, custom_auth_table.email)]
auth.settings.table_user = custom_auth_table # tell auth to use custom_auth_table
## before auth.define_tables()
##after that:
from gluon.contrib.login_methods.motp_auth import motp_auth
auth.settings.login_methods.append(motp_auth(db=db))
##Instructions for using MOTP
- after configuring motp for web2py, Install a MOTP client on your phone (android,IOS, java, windows phone, etc)
- initialize the motp client (to reset a motp secret type in #**#),
During user creation enter the secret generated during initialization into the motp_secret field in auth_user and
similarly enter a pre-decided pin into the motp_pin
- done.. to login, just generate a fresh OTP by typing in the pin and use the OTP as password
###To Dos###
- both motp_secret and pin are stored in plain text! need to have some way of encrypting
- web2py stores the password in db on successful login (should not happen)
- maybe some utility or page to check the otp would be useful
- as of now user field is hardcoded to email. Some way of selecting user table and user field.
"""
def verify_otp(otp, pin, secret, offset=60):
epoch_time = int(time.time())
time_start = int(str(epoch_time - offset)[:-1])
time_end = int(str(epoch_time + offset)[:-1])
for t in range(time_start - 1, time_end + 1):
to_hash = str(t) + secret + pin
hash = md5(to_hash).hexdigest()[:6]
if otp == hash:
return True
return False
def motp_auth_aux(email,
password,
db=db,
offset=time_offset):
if db:
user_data = db(db.auth_user.email == email).select().first()
if user_data:
if user_data['motp_secret'] and user_data['motp_pin']:
motp_secret = user_data['motp_secret']
motp_pin = user_data['motp_pin']
otp_check = verify_otp(
password, motp_pin, motp_secret, offset=offset)
if otp_check:
return True
else:
return False
else:
return False
return False
return motp_auth_aux | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Doc/jinja2/environment.py | python | Environment.handle_exception | (self, exc_info=None, rendered=False, source_hint=None) | Exception handling helper. This is used internally to either raise
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"""Exception handling helper. This is used internally to either raise
rewritten exceptions or return a rendered traceback for the template.
"""
global _make_traceback
if exc_info is None:
exc_info = sys.exc_info()
# the debugging module is imported when it's used for the first time.
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if _make_traceback is None:
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traceback = _make_traceback(exc_info, source_hint)
if rendered and self.exception_formatter is not None:
return self.exception_formatter(traceback)
if self.exception_handler is not None:
self.exception_handler(traceback)
exc_type, exc_value, tb = traceback.standard_exc_info
raise exc_type, exc_value, tb | [
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ialbert/biostar-central | 2dc7bd30691a50b2da9c2833ba354056bc686afa | biostar/recipes/forms.py | python | clean_file | (fobj, user, project, check_name=True) | return fobj | [] | def clean_file(fobj, user, project, check_name=True):
if not fobj:
return fobj
check_size(fobj=fobj, maxsize=settings.MAX_FILE_SIZE_MB)
check_upload_limit(file=fobj, user=user)
# Check if this name already exists.
if check_name and Data.objects.filter(name=fobj.name, project=project).exists():
msg = "Name already exists. Upload another file or rename existing data."
raise forms.ValidationError(msg)
return fobj | [
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ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models/xlnet/run_classifier_thucnews.py | python | LCQMCProcessor.get_test_examples | (self, data_dir) | return self._create_examples(
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Qidian213/deep_sort_yolov3 | df913275777ecaee19b4a99ea1889ac6063c0a4b | convert.py | python | unique_config_sections | (config_file) | return output_stream | Convert all config sections to have unique names.
Adds unique suffixes to config sections for compability with configparser. | Convert all config sections to have unique names. | [
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] | def unique_config_sections(config_file):
"""Convert all config sections to have unique names.
Adds unique suffixes to config sections for compability with configparser.
"""
section_counters = defaultdict(int)
output_stream = io.StringIO()
with open(config_file) as fin:
for line in fin:
if line.startswith('['):
section = line.strip().strip('[]')
_section = section + '_' + str(section_counters[section])
section_counters[section] += 1
line = line.replace(section, _section)
output_stream.write(line)
output_stream.seek(0)
return output_stream | [
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Qiskit/qiskit-terra | b66030e3b9192efdd3eb95cf25c6545fe0a13da4 | qiskit/quantum_info/synthesis/clifford_decompose.py | python | _from_pair_paulis_to_type | (pauli_x, pauli_z, qubit) | return [type_x, type_z] | Converts a pair of Paulis pauli_x and pauli_z into a type | Converts a pair of Paulis pauli_x and pauli_z into a type | [
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"""Converts a pair of Paulis pauli_x and pauli_z into a type"""
type_x = [pauli_x.z[qubit], pauli_x.x[qubit]]
type_z = [pauli_z.z[qubit], pauli_z.x[qubit]]
return [type_x, type_z] | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/scatterpolar/marker/_line.py | python | Line.colorscale | (self) | return self["colorscale"] | Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array. The colorscale
must be an array containing arrays mapping a normalized value
to an rgb, rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and highest (1) values
are required. For example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the colorscale in
color space, use`marker.line.cmin` and `marker.line.cmax`.
Alternatively, `colorscale` may be a palette name string of the
following list: Blackbody,Bluered,Blues,Cividis,Earth,Electric,
Greens,Greys,Hot,Jet,Picnic,Portland,Rainbow,RdBu,Reds,Viridis,
YlGnBu,YlOrRd.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of colors that will be spaced evenly to create the colorscale.
Many predefined colorscale lists are included in the sequential, diverging,
and cyclical modules in the plotly.colors package.
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
'ylorrd'].
Appending '_r' to a named colorscale reverses it.
Returns
-------
str | Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array. The colorscale
must be an array containing arrays mapping a normalized value
to an rgb, rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and highest (1) values
are required. For example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the colorscale in
color space, use`marker.line.cmin` and `marker.line.cmax`.
Alternatively, `colorscale` may be a palette name string of the
following list: Blackbody,Bluered,Blues,Cividis,Earth,Electric,
Greens,Greys,Hot,Jet,Picnic,Portland,Rainbow,RdBu,Reds,Viridis,
YlGnBu,YlOrRd.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of colors that will be spaced evenly to create the colorscale.
Many predefined colorscale lists are included in the sequential, diverging,
and cyclical modules in the plotly.colors package.
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
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must be an array containing arrays mapping a normalized value
to an rgb, rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and highest (1) values
are required. For example, `[[0, 'rgb(0,0,255)'], [1,
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The 'colorscale' property is a colorscale and may be
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and cyclical modules in the plotly.colors package.
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
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'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
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Appending '_r' to a named colorscale reverses it.
Returns
-------
str
"""
return self["colorscale"] | [
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django/channels | 6af1bc3ab45f55e3f47d0d1d059d5db0a18a9581 | channels/generic/websocket.py | python | WebsocketConsumer.receive | (self, text_data=None, bytes_data=None) | Called with a decoded WebSocket frame. | Called with a decoded WebSocket frame. | [
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Called with a decoded WebSocket frame.
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SublimeText-Markdown/MarkdownEditing | ec66bbee7396a3fec10a29cfc0fa30ec073a4e91 | plugins/folding.py | python | urls_to_fold | (view) | return [url for url in url_regions(view) if not any(url.contains(sel) for sel in view.sel())] | Returns a list of url regions to fold.
Returns all url regions but those a caret is placed within or which are partly selected.
:param view: The view
:param region: The region urls to return for.
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"""
Returns a list of url regions to fold.
Returns all url regions but those a caret is placed within or which are partly selected.
:param view: The view
:param region: The region urls to return for.
:returns: A list of regions
"""
if not view.settings().get("mde.auto_fold_link.enabled", True):
return []
return [url for url in url_regions(view) if not any(url.contains(sel) for sel in view.sel())] | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/tools/sqli/thirdparty/chardet/mbcharsetprober.py | python | MultiByteCharSetProber.feed | (self, aBuf) | return self.get_state() | [] | def feed(self, aBuf):
aLen = len(aBuf)
for i in xrange(0, aLen):
codingState = self._mCodingSM.next_state(aBuf[i])
if codingState == eError:
if constants._debug:
sys.stderr.write(self.get_charset_name() + ' prober hit error at byte ' + str(i) + '\n')
self._mState = constants.eNotMe
break
elif codingState == eItsMe:
self._mState = constants.eFoundIt
break
elif codingState == eStart:
charLen = self._mCodingSM.get_current_charlen()
if i == 0:
self._mLastChar[1] = aBuf[0]
self._mDistributionAnalyzer.feed(self._mLastChar, charLen)
else:
self._mDistributionAnalyzer.feed(aBuf[i-1:i+1], charLen)
self._mLastChar[0] = aBuf[aLen - 1]
if self.get_state() == constants.eDetecting:
if self._mDistributionAnalyzer.got_enough_data() and \
(self.get_confidence() > constants.SHORTCUT_THRESHOLD):
self._mState = constants.eFoundIt
return self.get_state() | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/io/vasp/sets.py | python | MPNonSCFSet.override_from_prev_calc | (self, prev_calc_dir=".") | return self | Update the input set to include settings from a previous calculation.
Args:
prev_calc_dir (str): The path to the previous calculation directory.
Returns:
The input set with the settings (structure, k-points, incar, etc)
updated using the previous VASP run. | Update the input set to include settings from a previous calculation. | [
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] | def override_from_prev_calc(self, prev_calc_dir="."):
"""
Update the input set to include settings from a previous calculation.
Args:
prev_calc_dir (str): The path to the previous calculation directory.
Returns:
The input set with the settings (structure, k-points, incar, etc)
updated using the previous VASP run.
"""
vasprun, outcar = get_vasprun_outcar(prev_calc_dir)
self.prev_incar = vasprun.incar
# Get a Magmom-decorated structure
self._structure = get_structure_from_prev_run(vasprun, outcar)
if self.standardize:
warnings.warn(
"Use of standardize=True with from_prev_run is not "
"recommended as there is no guarantee the copied "
"files will be appropriate for the standardized"
" structure. copy_chgcar is enforced to be false."
)
self.copy_chgcar = False
# Turn off spin when magmom for every site is smaller than 0.02.
if outcar and outcar.magnetization:
site_magmom = np.array([i["tot"] for i in outcar.magnetization])
ispin = 2 if np.any(site_magmom[np.abs(site_magmom) > 0.02]) else 1
elif vasprun.is_spin:
ispin = 2
else:
ispin = 1
nbands = int(np.ceil(vasprun.parameters["NBANDS"] * self.nbands_factor))
self.prev_incar.update({"ISPIN": ispin, "NBANDS": nbands})
files_to_transfer = {}
if self.copy_chgcar:
chgcars = sorted(glob.glob(str(Path(prev_calc_dir) / "CHGCAR*")))
if chgcars:
files_to_transfer["CHGCAR"] = str(chgcars[-1])
self.files_to_transfer.update(files_to_transfer)
# multiply the reciprocal density if needed:
if self.small_gap_multiply:
gap = vasprun.eigenvalue_band_properties[0]
if gap <= self.small_gap_multiply[0]:
self.reciprocal_density = self.reciprocal_density * self.small_gap_multiply[1]
self.kpoints_line_density = self.kpoints_line_density * self.small_gap_multiply[1]
# automatic setting of nedos using the energy range and the energy step dedos
if self.nedos == 0:
emax = max(eigs.max() for eigs in vasprun.eigenvalues.values())
emin = min(eigs.min() for eigs in vasprun.eigenvalues.values())
self.nedos = int((emax - emin) / self.dedos)
return self | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/_pyio.py | python | BytesIO.getvalue | (self) | return bytes(self._buffer) | Return the bytes value (contents) of the buffer | Return the bytes value (contents) of the buffer | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/numpy/lib/nanfunctions.py | python | nansum | (a, axis=None, dtype=None, out=None, keepdims=np._NoValue) | return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) | Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.
In NumPy versions <= 1.8.0 Nan is returned for slices that are all-NaN or
empty. In later versions zero is returned.
Parameters
----------
a : array_like
Array containing numbers whose sum is desired. If `a` is not an
array, a conversion is attempted.
axis : int, optional
Axis along which the sum is computed. The default is to compute the
sum of the flattened array.
dtype : data-type, optional
The type of the returned array and of the accumulator in which the
elements are summed. By default, the dtype of `a` is used. An
exception is when `a` has an integer type with less precision than
the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.
.. versionadded:: 1.8.0
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
`doc.ufuncs` for details. The casting of NaN to integer can yield
unexpected results.
.. versionadded:: 1.8.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `mean` or `sum` methods
of sub-classes of `ndarray`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
.. versionadded:: 1.8.0
Returns
-------
nansum : ndarray.
A new array holding the result is returned unless `out` is
specified, in which it is returned. The result has the same
size as `a`, and the same shape as `a` if `axis` is not None
or `a` is a 1-d array.
See Also
--------
numpy.sum : Sum across array propagating NaNs.
isnan : Show which elements are NaN.
isfinite: Show which elements are not NaN or +/-inf.
Notes
-----
If both positive and negative infinity are present, the sum will be Not
A Number (NaN).
Examples
--------
>>> np.nansum(1)
1
>>> np.nansum([1])
1
>>> np.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
array([ 2., 1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
>>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan | Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero. | [
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"""
Return the sum of array elements over a given axis treating Not a
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In NumPy versions <= 1.8.0 Nan is returned for slices that are all-NaN or
empty. In later versions zero is returned.
Parameters
----------
a : array_like
Array containing numbers whose sum is desired. If `a` is not an
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axis : int, optional
Axis along which the sum is computed. The default is to compute the
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dtype : data-type, optional
The type of the returned array and of the accumulator in which the
elements are summed. By default, the dtype of `a` is used. An
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the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.
.. versionadded:: 1.8.0
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
`doc.ufuncs` for details. The casting of NaN to integer can yield
unexpected results.
.. versionadded:: 1.8.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `mean` or `sum` methods
of sub-classes of `ndarray`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
.. versionadded:: 1.8.0
Returns
-------
nansum : ndarray.
A new array holding the result is returned unless `out` is
specified, in which it is returned. The result has the same
size as `a`, and the same shape as `a` if `axis` is not None
or `a` is a 1-d array.
See Also
--------
numpy.sum : Sum across array propagating NaNs.
isnan : Show which elements are NaN.
isfinite: Show which elements are not NaN or +/-inf.
Notes
-----
If both positive and negative infinity are present, the sum will be Not
A Number (NaN).
Examples
--------
>>> np.nansum(1)
1
>>> np.nansum([1])
1
>>> np.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
array([ 2., 1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
>>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan
"""
a, mask = _replace_nan(a, 0)
return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) | [
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aws/aws-sam-cli | 2aa7bf01b2e0b0864ef63b1898a8b30577443acc | samcli/lib/utils/sam_logging.py | python | SamCliLogger.configure_logger | (logger, formatter, level) | Configure a Logger with the level provided and also the first handler's formatter.
If there is no handler in the logger, a new StreamHandler will be added.
Parameters
----------
logger logging.getLogger
Logger to configure
formatter logging.formatter
Formatter for the logger | Configure a Logger with the level provided and also the first handler's formatter.
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"""
Configure a Logger with the level provided and also the first handler's formatter.
If there is no handler in the logger, a new StreamHandler will be added.
Parameters
----------
logger logging.getLogger
Logger to configure
formatter logging.formatter
Formatter for the logger
"""
handlers = logger.handlers
if handlers:
log_stream_handler = handlers[0]
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log_stream_handler = logging.StreamHandler()
logger.addHandler(log_stream_handler)
log_stream_handler.setLevel(logging.DEBUG)
log_stream_handler.setFormatter(formatter)
logger.setLevel(level)
logger.propagate = False | [
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cylc/cylc-flow | 5ec221143476c7c616c156b74158edfbcd83794a | cylc/flow/terminal.py | python | get_width | (default=80) | Return the terminal width or `default` if it is not determinable. | Return the terminal width or `default` if it is not determinable. | [
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] | def get_width(default=80):
"""Return the terminal width or `default` if it is not determinable."""
# stty can have different install locs so don't use absolute path
proc = Popen(['stty', 'size'], stdout=PIPE, stderr=PIPE) # nosec
if proc.wait():
return default
try:
return int(proc.communicate()[0].split()[1]) or default
except (IndexError, ValueError):
return default | [
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frerich/clcache | cae73d8255d78db8ba11e23c51fd2c9a89e7475b | clcache/__main__.py | python | CacheFileStrategy.__init__ | (self, cacheDirectory=None) | [] | def __init__(self, cacheDirectory=None):
self.dir = cacheDirectory
if not self.dir:
try:
self.dir = os.environ["CLCACHE_DIR"]
except KeyError:
self.dir = os.path.join(os.path.expanduser("~"), "clcache")
manifestsRootDir = os.path.join(self.dir, "manifests")
ensureDirectoryExists(manifestsRootDir)
self.manifestRepository = ManifestRepository(manifestsRootDir)
compilerArtifactsRootDir = os.path.join(self.dir, "objects")
ensureDirectoryExists(compilerArtifactsRootDir)
self.compilerArtifactsRepository = CompilerArtifactsRepository(compilerArtifactsRootDir)
self.configuration = Configuration(os.path.join(self.dir, "config.txt"))
self.statistics = Statistics(os.path.join(self.dir, "stats.txt")) | [
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sdaps/sdaps | 51d1072185223f5e48512661e2c1e8399d63e876 | sdaps/script.py | python | add_project_argument | (parser) | [] | def add_project_argument(parser):
parser.add_argument('project', type=str, help=_("project directory|The SDAPS project.")) | [
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lutris/lutris | 66675a4d5537f6b2a2ba2b6df0b3cdf8924c823a | lutris/util/wine/wine.py | python | is_gstreamer_build | (wine_path) | return system.path_exists(os.path.join(base_path, "lib64/gstreamer-1.0")) | Returns whether a wine build ships with gstreamer libraries.
This allows to set GST_PLUGIN_SYSTEM_PATH_1_0 for the builds that support it. | Returns whether a wine build ships with gstreamer libraries.
This allows to set GST_PLUGIN_SYSTEM_PATH_1_0 for the builds that support it. | [
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] | def is_gstreamer_build(wine_path):
"""Returns whether a wine build ships with gstreamer libraries.
This allows to set GST_PLUGIN_SYSTEM_PATH_1_0 for the builds that support it.
"""
base_path = os.path.dirname(os.path.dirname(wine_path))
return system.path_exists(os.path.join(base_path, "lib64/gstreamer-1.0")) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/sql/elements.py | python | _only_column_elements_or_none | (element, name) | [] | def _only_column_elements_or_none(element, name):
if element is None:
return None
else:
return _only_column_elements(element, name) | [
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menpo/menpofit | 5f2f45bab26df206d43292fd32d19cd8f62f0443 | menpofit/lk/residual.py | python | Residual.steepest_descent_update | (self, sdi, image, template) | r"""
Calculates the steepest descent parameter updates.
These are defined, for the forward additive algorithm, as:
.. math::
\sum_x [ \nabla I \frac{\partial W}{\partial p} ]^T [ T(x) - I(W(x;p)) ]
Parameters
----------
J : ``(N, n_params)`` `ndarray`
The steepest descent images.
image : `menpo.image.Image`
Either the warped image or the template (depending on the framework)
template : `menpo.image.Image`
Either the warped image or the template (depending on the framework)
Returns
-------
sd_delta_p : ``(n_params,)`` `ndarray`
The steepest descent parameter updates. | r"""
Calculates the steepest descent parameter updates. | [
"r",
"Calculates",
"the",
"steepest",
"descent",
"parameter",
"updates",
"."
] | def steepest_descent_update(self, sdi, image, template):
r"""
Calculates the steepest descent parameter updates.
These are defined, for the forward additive algorithm, as:
.. math::
\sum_x [ \nabla I \frac{\partial W}{\partial p} ]^T [ T(x) - I(W(x;p)) ]
Parameters
----------
J : ``(N, n_params)`` `ndarray`
The steepest descent images.
image : `menpo.image.Image`
Either the warped image or the template (depending on the framework)
template : `menpo.image.Image`
Either the warped image or the template (depending on the framework)
Returns
-------
sd_delta_p : ``(n_params,)`` `ndarray`
The steepest descent parameter updates.
"""
pass | [
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user-cont/conu | 0d8962560f6f7f17fe1be0d434a4809e2a0ea51d | conu/backend/docker/image.py | python | DockerImage.save_to | (self, image) | Save this image to another DockerImage
:param image: DockerImage
:return: | Save this image to another DockerImage | [
"Save",
"this",
"image",
"to",
"another",
"DockerImage"
] | def save_to(self, image):
""" Save this image to another DockerImage
:param image: DockerImage
:return:
"""
if not isinstance(image, self.__class__):
raise ConuException("Invalid target image type", type(image))
self.copy(image.name, image.tag,
target_transport=image.transport, target_path=image.path,
logs=False) | [
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nipreps/mriqc | ad8813035136acc1a80c96ac1fbbdde1d82b14ee | mriqc/workflows/utils.py | python | fwhm_dict | (fwhm) | return {
"fwhm_x": fwhm[0],
"fwhm_y": fwhm[1],
"fwhm_z": fwhm[2],
"fwhm_avg": fwhm[3],
} | Convert a list of FWHM into a dictionary | Convert a list of FWHM into a dictionary | [
"Convert",
"a",
"list",
"of",
"FWHM",
"into",
"a",
"dictionary"
] | def fwhm_dict(fwhm):
"""Convert a list of FWHM into a dictionary"""
fwhm = [float(f) for f in fwhm]
return {
"fwhm_x": fwhm[0],
"fwhm_y": fwhm[1],
"fwhm_z": fwhm[2],
"fwhm_avg": fwhm[3],
} | [
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sabnzbd/sabnzbd | 52d21e94d3cc6e30764a833fe2a256783d1a8931 | sabnzbd/filesystem.py | python | sanitize_filename | (name: str) | return name + ext | Return filename with illegal chars converted to legal ones
and with the par2 extension always in lowercase | Return filename with illegal chars converted to legal ones
and with the par2 extension always in lowercase | [
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"always",
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] | def sanitize_filename(name: str) -> str:
"""Return filename with illegal chars converted to legal ones
and with the par2 extension always in lowercase
"""
if not name:
return name
illegal = CH_ILLEGAL
legal = CH_LEGAL
if sabnzbd.WIN32 or sabnzbd.cfg.sanitize_safe():
# Remove all bad Windows chars too
illegal += CH_ILLEGAL_WIN
legal += CH_LEGAL_WIN
if ":" in name and sabnzbd.DARWIN:
# Compensate for the foolish way par2 on macOS handles a colon character
name = name[name.rfind(":") + 1 :]
lst = []
for ch in name.strip():
if ch in illegal:
ch = legal[illegal.find(ch)]
lst.append(ch)
name = "".join(lst)
if sabnzbd.WIN32 or sabnzbd.cfg.sanitize_safe():
name = replace_win_devices(name)
if not name:
name = "unknown"
# now split name into name, ext
name, ext = os.path.splitext(name)
# If filename is too long (more than DEF_FILE_MAX bytes), brute-force truncate it,
# preserving the extension (max ext length 20)
# Note: some filesystem can handle up to 255 UTF chars (which is more than 255 bytes) in the filename,
# but we stay on the safe side: max DEF_FILE_MAX bytes
if len(utob(name)) + len(utob(ext)) > DEF_FILE_MAX:
logging.debug("Filename %s is too long, so truncating", name + ext)
# Too long filenames are often caused by incorrect non-ascii chars,
# so brute-force remove those non-ascii chars
name = ubtou(name.encode("ascii", "ignore"))
# Now it's plain ASCII, so no need for len(str.encode()) anymore; plain len() is enough
if len(name) + len(ext) > DEF_FILE_MAX:
# still too long, limit the extension
maxextlength = 20 # max length of an extension
if len(ext) > maxextlength:
# allow first <maxextlength> chars, including the starting dot
ext = ext[:maxextlength]
if len(name) + len(ext) > DEF_FILE_MAX:
# Still too long, limit the basename
name = name[: DEF_FILE_MAX - len(ext)]
lowext = ext.lower()
if lowext == ".par2" and lowext != ext:
ext = lowext
return name + ext | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/random.py | python | Random.gauss | (self, mu, sigma) | return mu + z*sigma | Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls. | Gaussian distribution. | [
"Gaussian",
"distribution",
"."
] | def gauss(self, mu, sigma):
"""Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
"""
# When x and y are two variables from [0, 1), uniformly
# distributed, then
#
# cos(2*pi*x)*sqrt(-2*log(1-y))
# sin(2*pi*x)*sqrt(-2*log(1-y))
#
# are two *independent* variables with normal distribution
# (mu = 0, sigma = 1).
# (Lambert Meertens)
# (corrected version; bug discovered by Mike Miller, fixed by LM)
# Multithreading note: When two threads call this function
# simultaneously, it is possible that they will receive the
# same return value. The window is very small though. To
# avoid this, you have to use a lock around all calls. (I
# didn't want to slow this down in the serial case by using a
# lock here.)
random = self.random
z = self.gauss_next
self.gauss_next = None
if z is None:
x2pi = random() * TWOPI
g2rad = _sqrt(-2.0 * _log(1.0 - random()))
z = _cos(x2pi) * g2rad
self.gauss_next = _sin(x2pi) * g2rad
return mu + z*sigma | [
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uqfoundation/mystic | 154e6302d1f2f94e8f13e88ecc5f24241cc28ac7 | models/storn.py | python | Corana.function | (self,coeffs) | return r | r"""evaluates a 4-D Corana's parabola function for a list of coeffs
f(x) = \sum_(i=0)^(3) f_0(x)
Where for \abs(x_i - z_i) < 0.05:
f_0(x) = 0.15*(z_i - 0.05*\sign(z_i))^(2) * d_i
and otherwise:
f_0(x) = d_i * x_(i)^(2),
with z_i = \floor(\abs(x_i/0.2)+0.49999)*\sign(x_i)*0.2
and d_i = 1,1000,10,100.
For len(x) == 1, x = x_0,0,0,0;
for len(x) == 2, x = x_0,0,x_1,0;
for len(x) == 3, x = x_0,0,x_1,x_2;
for len(x) >= 4, x = x_0,x_1,x_2,x_3.
Inspect with mystic_model_plotter using::
mystic.models.corana -b "-1:1:.01, -1:1:.01" -d -x 1
The minimum is f(x)=0 for \abs(x_i) < 0.05 for all i. | r"""evaluates a 4-D Corana's parabola function for a list of coeffs | [
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r"""evaluates a 4-D Corana's parabola function for a list of coeffs
f(x) = \sum_(i=0)^(3) f_0(x)
Where for \abs(x_i - z_i) < 0.05:
f_0(x) = 0.15*(z_i - 0.05*\sign(z_i))^(2) * d_i
and otherwise:
f_0(x) = d_i * x_(i)^(2),
with z_i = \floor(\abs(x_i/0.2)+0.49999)*\sign(x_i)*0.2
and d_i = 1,1000,10,100.
For len(x) == 1, x = x_0,0,0,0;
for len(x) == 2, x = x_0,0,x_1,0;
for len(x) == 3, x = x_0,0,x_1,x_2;
for len(x) >= 4, x = x_0,x_1,x_2,x_3.
Inspect with mystic_model_plotter using::
mystic.models.corana -b "-1:1:.01, -1:1:.01" -d -x 1
The minimum is f(x)=0 for \abs(x_i) < 0.05 for all i."""
d = [1., 1000., 10., 100.]
_d = [0, 3, 1, 2] # ordering for lower dimensions
#x = asarray(coeffs) #XXX: converting to numpy.array slows by 10x
x = [0.]*4 # ensure that there are 4 coefficients
if len(coeffs) < 4:
_x = x[:]
_x[:len(coeffs)]=coeffs
for i in range(4):
x[_d.index(i)] = _x[i]
else:
x = coeffs
r = 0
for j in range(4):
zj = floor( abs(x[j]/0.2) + 0.49999 ) * sign(x[j]) * 0.2
if abs(x[j]-zj) < 0.05:
r += 0.15 * pow(zj - 0.05*sign(zj), 2) * d[j]
else:
r += d[j] * x[j] * x[j]
return r | [
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pypa/setuptools | 9f37366aab9cd8f6baa23e6a77cfdb8daf97757e | setuptools/_vendor/packaging/version.py | python | LegacyVersion.epoch | (self) | return -1 | [] | def epoch(self) -> int:
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lfz/Guided-Denoise | 8881ab768d16eaf87342da4ff7dc8271e183e205 | Attackset/fgsm_ensv3_resv2_inresv2_random/nets/mobilenet_v1.py | python | mobilenet_v1 | (inputs,
num_classes=1000,
dropout_keep_prob=0.999,
is_training=True,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1') | return logits, end_points | Mobilenet v1 model for classification.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes.
dropout_keep_prob: the percentage of activation values that are retained.
is_training: whether is training or not.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: Input rank is invalid. | Mobilenet v1 model for classification. | [
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"classification",
"."
] | def mobilenet_v1(inputs,
num_classes=1000,
dropout_keep_prob=0.999,
is_training=True,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=tf.contrib.layers.softmax,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1'):
"""Mobilenet v1 model for classification.
Args:
inputs: a tensor of shape [batch_size, height, width, channels].
num_classes: number of predicted classes.
dropout_keep_prob: the percentage of activation values that are retained.
is_training: whether is training or not.
min_depth: Minimum depth value (number of channels) for all convolution ops.
Enforced when depth_multiplier < 1, and not an active constraint when
depth_multiplier >= 1.
depth_multiplier: Float multiplier for the depth (number of channels)
for all convolution ops. The value must be greater than zero. Typical
usage will be to set this value in (0, 1) to reduce the number of
parameters or computation cost of the model.
conv_defs: A list of ConvDef namedtuples specifying the net architecture.
prediction_fn: a function to get predictions out of logits.
spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the pre-softmax activations, a tensor of size
[batch_size, num_classes]
end_points: a dictionary from components of the network to the corresponding
activation.
Raises:
ValueError: Input rank is invalid.
"""
input_shape = inputs.get_shape().as_list()
if len(input_shape) != 4:
raise ValueError('Invalid input tensor rank, expected 4, was: %d' %
len(input_shape))
with tf.variable_scope(scope, 'MobilenetV1', [inputs, num_classes],
reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net, end_points = mobilenet_v1_base(inputs, scope=scope,
min_depth=min_depth,
depth_multiplier=depth_multiplier,
conv_defs=conv_defs)
with tf.variable_scope('Logits'):
kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7])
net = slim.avg_pool2d(net, kernel_size, padding='VALID',
scope='AvgPool_1a')
end_points['AvgPool_1a'] = net
# 1 x 1 x 1024
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
normalizer_fn=None, scope='Conv2d_1c_1x1')
if spatial_squeeze:
logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
end_points['Logits'] = logits
if prediction_fn:
end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
return logits, end_points | [
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MartinThoma/algorithms | 6199cfa3446e1056c7b4d75ca6e306e9e56fd95b | finite-groups/finiteGroupChecks.py | python | generate_empty_conjunction | (nr_of_elements) | return conjunction | Generate a conjunction which maps everything to 0.
Parameters
----------
nr_of_elements : int
Returns
-------
list | Generate a conjunction which maps everything to 0. | [
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] | def generate_empty_conjunction(nr_of_elements):
"""
Generate a conjunction which maps everything to 0.
Parameters
----------
nr_of_elements : int
Returns
-------
list
"""
conjunction = []
for i in range(nr_of_elements):
line = []
for j in range(nr_of_elements):
line.append(0)
conjunction.append(line)
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pyansys/pymapdl | c07291fc062b359abf0e92b95a92d753a95ef3d7 | ansys/mapdl/core/_commands/preproc/booleans.py | python | Booleans.lsba | (self, nl="", na="", sepo="", keepl="", keepa="", **kwargs) | return self.run(command, **kwargs) | Subtracts areas from lines.
APDL Command: LSBA
Parameters
----------
nl
Line (or lines, if picking is used) to be subtracted from. If ALL,
use all selected lines. If NL = P, graphical picking is enabled
and all remaining command fields are ignored (valid only in the
GUI). A component name may also be substituted for NL.
na
Area (or areas, if picking is used) to be subtracted. If ALL, use
all selected areas. A component name may also be substituted for
NA.
sepo
Behavior if the intersection of the lines and the areas is a
keypoint or keypoints:
(blank) - The resulting lines will share keypoint(s) where they touch.
SEPO - The resulting lines will have separate, but coincident keypoint(s) where they
touch.
keepl
Specifies whether NL lines are to be deleted:
(blank) - Use the setting of KEEP on the BOPTN command.
DELETE - Delete NL lines after LSBA operation (override BOPTN command settings).
KEEP - Keep NL lines after LSBA operation (override BOPTN command settings).
keepa
Specifies whether NA areas are to be deleted:
(blank) - Use the setting of KEEP on the BOPTN command.
DELETE - Delete areas after LSBA operation (override BOPTN command settings).
KEEP - Keep areas after LSBA operation (override BOPTN command settings).
Notes
-----
Generates new lines by subtracting the regions common to both NL lines
and NA areas (the intersection) from the NL lines. The intersection
can be a line(s) or keypoint(s). If the intersection is a keypoint and
SEPO is blank, the NL line is divided at the keypoint and the resulting
lines will be connected, sharing a common keypoint where they touch.
If SEPO is set to SEPO, NL is divided into two unconnected lines with
separate keypoints where they touch. See the Modeling and Meshing
Guide for an illustration. See the BOPTN command for an explanation of
the options available to Boolean operations. Element attributes and
solid model boundary conditions assigned to the original entities will
not be transferred to the new entities generated. | Subtracts areas from lines. | [
"Subtracts",
"areas",
"from",
"lines",
"."
] | def lsba(self, nl="", na="", sepo="", keepl="", keepa="", **kwargs):
"""Subtracts areas from lines.
APDL Command: LSBA
Parameters
----------
nl
Line (or lines, if picking is used) to be subtracted from. If ALL,
use all selected lines. If NL = P, graphical picking is enabled
and all remaining command fields are ignored (valid only in the
GUI). A component name may also be substituted for NL.
na
Area (or areas, if picking is used) to be subtracted. If ALL, use
all selected areas. A component name may also be substituted for
NA.
sepo
Behavior if the intersection of the lines and the areas is a
keypoint or keypoints:
(blank) - The resulting lines will share keypoint(s) where they touch.
SEPO - The resulting lines will have separate, but coincident keypoint(s) where they
touch.
keepl
Specifies whether NL lines are to be deleted:
(blank) - Use the setting of KEEP on the BOPTN command.
DELETE - Delete NL lines after LSBA operation (override BOPTN command settings).
KEEP - Keep NL lines after LSBA operation (override BOPTN command settings).
keepa
Specifies whether NA areas are to be deleted:
(blank) - Use the setting of KEEP on the BOPTN command.
DELETE - Delete areas after LSBA operation (override BOPTN command settings).
KEEP - Keep areas after LSBA operation (override BOPTN command settings).
Notes
-----
Generates new lines by subtracting the regions common to both NL lines
and NA areas (the intersection) from the NL lines. The intersection
can be a line(s) or keypoint(s). If the intersection is a keypoint and
SEPO is blank, the NL line is divided at the keypoint and the resulting
lines will be connected, sharing a common keypoint where they touch.
If SEPO is set to SEPO, NL is divided into two unconnected lines with
separate keypoints where they touch. See the Modeling and Meshing
Guide for an illustration. See the BOPTN command for an explanation of
the options available to Boolean operations. Element attributes and
solid model boundary conditions assigned to the original entities will
not be transferred to the new entities generated.
"""
command = f"LSBA,{nl},{na},{sepo},{keepl},{keepa}"
return self.run(command, **kwargs) | [
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stefankoegl/kdtree | 587edc7056d7735177ad56a84ad5abccdea91693 | kdtree.py | python | KDNode.is_valid | (self) | return all(c.is_valid() for c, _ in self.children) or self.is_leaf | Checks recursively if the tree is valid
It is valid if each node splits correctly | Checks recursively if the tree is valid | [
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""" Checks recursively if the tree is valid
It is valid if each node splits correctly """
if not self:
return True
if self.left and self.data[self.axis] < self.left.data[self.axis]:
return False
if self.right and self.data[self.axis] > self.right.data[self.axis]:
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return all(c.is_valid() for c, _ in self.children) or self.is_leaf | [
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paulwinex/pw_MultiScriptEditor | e447e99f87cb07e238baf693b7e124e50efdbc51 | multi_script_editor/managers/nuke/main.py | python | NodeConstructor.__new__ | (self,S, ) | T.__new__(S, ...) -> a new object with type S, a subtype of T | T.__new__(S, ...) -> a new object with type S, a subtype of T | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/rings/padics/lattice_precision.py | python | pRational.unit_part | (self) | return self.__class__(p, x, 0, valuation=0) | r"""
Return the unit part of this element, that is the part ``u``
in the writing ``u * p^v`` with ``u`` coprime to `p`.
TESTS::
sage: from sage.rings.padics.lattice_precision import pRational
sage: x = pRational(2, 123456, 7); x
2^7 * 123456
sage: x.unit_part()
1929 | r"""
Return the unit part of this element, that is the part ``u``
in the writing ``u * p^v`` with ``u`` coprime to `p`. | [
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] | def unit_part(self):
r"""
Return the unit part of this element, that is the part ``u``
in the writing ``u * p^v`` with ``u`` coprime to `p`.
TESTS::
sage: from sage.rings.padics.lattice_precision import pRational
sage: x = pRational(2, 123456, 7); x
2^7 * 123456
sage: x.unit_part()
1929
"""
if self.is_zero():
raise ValueError("the unit part of zero is not defined")
p = self.p
val = self.valuation()
x = self.x / (p ** (val-self.exponent))
return self.__class__(p, x, 0, valuation=0) | [
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Miserlou/Zappa | 5a11c17f5ecf0568bdb73b4baf6fb08ff0184f39 | zappa/cli.py | python | ZappaCLI.check_for_update | (self) | Print a warning if there's a new Zappa version available. | Print a warning if there's a new Zappa version available. | [
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] | def check_for_update(self):
"""
Print a warning if there's a new Zappa version available.
"""
try:
version = pkg_resources.require("zappa")[0].version
updateable = check_new_version_available(version)
if updateable:
click.echo(click.style("Important!", fg="yellow", bold=True) +
" A new version of " + click.style("Zappa", bold=True) + " is available!")
click.echo("Upgrade with: " + click.style("pip install zappa --upgrade", bold=True))
click.echo("Visit the project page on GitHub to see the latest changes: " +
click.style("https://github.com/Miserlou/Zappa", bold=True))
except Exception as e: # pragma: no cover
print(e)
return | [
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golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | thirdparty_libs/django/core/mail/backends/locmem.py | python | EmailBackend.send_messages | (self, messages) | return len(messages) | Redirect messages to the dummy outbox | Redirect messages to the dummy outbox | [
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] | def send_messages(self, messages):
"""Redirect messages to the dummy outbox"""
for message in messages: # .message() triggers header validation
message.message()
mail.outbox.extend(messages)
return len(messages) | [
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stellargraph/stellargraph | 3c2c8c18ab4c5c16660f350d8e23d7dc39e738de | scripts/notebook_text_checker.py | python | no_leading_block_quotes | (cells) | A block quote at the start of a cell doesn't recieve the necessary separating comment in rST
A rST quote is just indented text, which can merge with earlier directives unless there's a
separating comment. Within a single cell, nbsphinx/nbconvert handles this correctly, but it
doesn't when the quote is at the start of the cell. See:
- https://github.com/spatialaudio/nbsphinx/issues/450
- https://github.com/stellargraph/stellargraph/pull/1398 | A block quote at the start of a cell doesn't recieve the necessary separating comment in rST | [
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] | def no_leading_block_quotes(cells):
"""
A block quote at the start of a cell doesn't recieve the necessary separating comment in rST
A rST quote is just indented text, which can merge with earlier directives unless there's a
separating comment. Within a single cell, nbsphinx/nbconvert handles this correctly, but it
doesn't when the quote is at the start of the cell. See:
- https://github.com/spatialaudio/nbsphinx/issues/450
- https://github.com/stellargraph/stellargraph/pull/1398
"""
errors = []
for cell in cells:
if not isinstance(cell, MarkdownCell):
continue
# unfortunately, the cloud runner cells break this rule (and there doesn't seem to be a good
# way to avoid it), so skip them, and we just have to be careful that they get formatted
# correctly.
if "CloudRunner" in cell.metadata.get("tags", []):
continue
first = cell.ast.first_child
if is_block_quote(first):
errors.append(
message_with_line(
cell,
f"Found a block quote (like `> ...`) as the first element of a cell; this must be avoided because it may cause problems during the reStructuredText conversion (https://github.com/spatialaudio/nbsphinx/issues/450). Consider: moving the block quote",
sourcepos=first.sourcepos,
)
)
if errors:
raise FormattingError(errors) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/xdrlib.py | python | Packer.reset | (self) | [] | def reset(self):
self.__buf = _StringIO() | [
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DataDog/dd-trace-py | 13f9c6c1a8b4820365b299ab204f2bb5189d2a49 | ddtrace/vendor/psutil/_pslinux.py | python | virtual_memory | () | return svmem(total, avail, percent, used, free,
active, inactive, buffers, cached, shared, slab) | Report virtual memory stats.
This implementation matches "free" and "vmstat -s" cmdline
utility values and procps-ng-3.3.12 source was used as a reference
(2016-09-18):
https://gitlab.com/procps-ng/procps/blob/
24fd2605c51fccc375ab0287cec33aa767f06718/proc/sysinfo.c
For reference, procps-ng-3.3.10 is the version available on Ubuntu
16.04.
Note about "available" memory: up until psutil 4.3 it was
calculated as "avail = (free + buffers + cached)". Now
"MemAvailable:" column (kernel 3.14) from /proc/meminfo is used as
it's more accurate.
That matches "available" column in newer versions of "free". | Report virtual memory stats.
This implementation matches "free" and "vmstat -s" cmdline
utility values and procps-ng-3.3.12 source was used as a reference
(2016-09-18):
https://gitlab.com/procps-ng/procps/blob/
24fd2605c51fccc375ab0287cec33aa767f06718/proc/sysinfo.c
For reference, procps-ng-3.3.10 is the version available on Ubuntu
16.04. | [
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"""Report virtual memory stats.
This implementation matches "free" and "vmstat -s" cmdline
utility values and procps-ng-3.3.12 source was used as a reference
(2016-09-18):
https://gitlab.com/procps-ng/procps/blob/
24fd2605c51fccc375ab0287cec33aa767f06718/proc/sysinfo.c
For reference, procps-ng-3.3.10 is the version available on Ubuntu
16.04.
Note about "available" memory: up until psutil 4.3 it was
calculated as "avail = (free + buffers + cached)". Now
"MemAvailable:" column (kernel 3.14) from /proc/meminfo is used as
it's more accurate.
That matches "available" column in newer versions of "free".
"""
missing_fields = []
mems = {}
with open_binary('%s/meminfo' % get_procfs_path()) as f:
for line in f:
fields = line.split()
mems[fields[0]] = int(fields[1]) * 1024
# /proc doc states that the available fields in /proc/meminfo vary
# by architecture and compile options, but these 3 values are also
# returned by sysinfo(2); as such we assume they are always there.
total = mems[b'MemTotal:']
free = mems[b'MemFree:']
try:
buffers = mems[b'Buffers:']
except KeyError:
# https://github.com/giampaolo/psutil/issues/1010
buffers = 0
missing_fields.append('buffers')
try:
cached = mems[b"Cached:"]
except KeyError:
cached = 0
missing_fields.append('cached')
else:
# "free" cmdline utility sums reclaimable to cached.
# Older versions of procps used to add slab memory instead.
# This got changed in:
# https://gitlab.com/procps-ng/procps/commit/
# 05d751c4f076a2f0118b914c5e51cfbb4762ad8e
cached += mems.get(b"SReclaimable:", 0) # since kernel 2.6.19
try:
shared = mems[b'Shmem:'] # since kernel 2.6.32
except KeyError:
try:
shared = mems[b'MemShared:'] # kernels 2.4
except KeyError:
shared = 0
missing_fields.append('shared')
try:
active = mems[b"Active:"]
except KeyError:
active = 0
missing_fields.append('active')
try:
inactive = mems[b"Inactive:"]
except KeyError:
try:
inactive = \
mems[b"Inact_dirty:"] + \
mems[b"Inact_clean:"] + \
mems[b"Inact_laundry:"]
except KeyError:
inactive = 0
missing_fields.append('inactive')
try:
slab = mems[b"Slab:"]
except KeyError:
slab = 0
used = total - free - cached - buffers
if used < 0:
# May be symptomatic of running within a LCX container where such
# values will be dramatically distorted over those of the host.
used = total - free
# - starting from 4.4.0 we match free's "available" column.
# Before 4.4.0 we calculated it as (free + buffers + cached)
# which matched htop.
# - free and htop available memory differs as per:
# http://askubuntu.com/a/369589
# http://unix.stackexchange.com/a/65852/168884
# - MemAvailable has been introduced in kernel 3.14
try:
avail = mems[b'MemAvailable:']
except KeyError:
avail = calculate_avail_vmem(mems)
if avail < 0:
avail = 0
missing_fields.append('available')
# If avail is greater than total or our calculation overflows,
# that's symptomatic of running within a LCX container where such
# values will be dramatically distorted over those of the host.
# https://gitlab.com/procps-ng/procps/blob/
# 24fd2605c51fccc375ab0287cec33aa767f06718/proc/sysinfo.c#L764
if avail > total:
avail = free
percent = usage_percent((total - avail), total, round_=1)
# Warn about missing metrics which are set to 0.
if missing_fields:
msg = "%s memory stats couldn't be determined and %s set to 0" % (
", ".join(missing_fields),
"was" if len(missing_fields) == 1 else "were")
warnings.warn(msg, RuntimeWarning)
return svmem(total, avail, percent, used, free,
active, inactive, buffers, cached, shared, slab) | [
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pydoit/doit | cf7edfbe73fafebd1b2a6f1d3be8b69fde41383d | doit/tools.py | python | set_trace | () | start debugger, make sure stdout shows pdb output.
output is not restored. | start debugger, make sure stdout shows pdb output.
output is not restored. | [
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"""start debugger, make sure stdout shows pdb output.
output is not restored.
"""
import pdb
import sys
debugger = pdb.Pdb(stdin=sys.__stdin__, stdout=sys.__stdout__)
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NervanaSystems/neon | 8c3fb8a93b4a89303467b25817c60536542d08bd | examples/faster-rcnn/proposal_layer.py | python | ProposalLayer._sample_fg_bg | (self, max_overlaps) | return keep_inds, int(fg_rois_per_this_image) | Return sample of at most fg_fraction * num_rois foreground indicies, padding
the remaining num_rois with background indicies. Foreground and background labels
are determined based on max_overlaps and the thresholds fg_thresh, bg_thresh_hi,
bg_thresh_lo.
Returns:
keep_inds (array): (num_rois,) sampled indicies of bboxes.
fg_rois_per_this_image (int): number of fg rois sampled from the image. | Return sample of at most fg_fraction * num_rois foreground indicies, padding
the remaining num_rois with background indicies. Foreground and background labels
are determined based on max_overlaps and the thresholds fg_thresh, bg_thresh_hi,
bg_thresh_lo.
Returns:
keep_inds (array): (num_rois,) sampled indicies of bboxes.
fg_rois_per_this_image (int): number of fg rois sampled from the image. | [
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"""Return sample of at most fg_fraction * num_rois foreground indicies, padding
the remaining num_rois with background indicies. Foreground and background labels
are determined based on max_overlaps and the thresholds fg_thresh, bg_thresh_hi,
bg_thresh_lo.
Returns:
keep_inds (array): (num_rois,) sampled indicies of bboxes.
fg_rois_per_this_image (int): number of fg rois sampled from the image.
"""
# Split proposals into foreground and background based on overlap
fg_inds = np.where(max_overlaps >= self.fg_thresh)[0]
fg_rois_per_image = np.round(self.fg_fraction * self.num_rois)
# Guard against the case when an image has fewer than fg_rois_per_image foreground RoIs
fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size)
# Sample foreground regions without replacement
if fg_inds.size > 0 and not self.deterministic:
fg_inds = self.be.rng.choice(fg_inds, size=int(fg_rois_per_this_image), replace=False)
elif fg_inds.size > 0:
fg_inds = fg_inds[:fg_rois_per_this_image]
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((max_overlaps < self.bg_thresh_hi) &
(max_overlaps >= self.bg_thresh_lo))[0]
# Compute number of background RoIs to take from this image (guarding
# against there being fewer than desired)
bg_rois_per_this_image = self.num_rois - fg_rois_per_this_image
bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size)
# Sample background regions without replacement
if bg_inds.size > 0 and not self.deterministic:
bg_inds = self.be.rng.choice(bg_inds, size=int(bg_rois_per_this_image), replace=False)
elif bg_inds.size > 0:
bg_inds = bg_inds[:bg_rois_per_this_image]
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds)
return keep_inds, int(fg_rois_per_this_image) | [
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/venv/lib/python2.7/site-packages/pip/_vendor/requests/utils.py | python | get_netrc_auth | (url) | Returns the Requests tuple auth for a given url from netrc. | Returns the Requests tuple auth for a given url from netrc. | [
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"."
] | def get_netrc_auth(url):
"""Returns the Requests tuple auth for a given url from netrc."""
try:
from netrc import netrc, NetrcParseError
netrc_path = None
for f in NETRC_FILES:
try:
loc = os.path.expanduser('~/{0}'.format(f))
except KeyError:
# os.path.expanduser can fail when $HOME is undefined and
# getpwuid fails. See http://bugs.python.org/issue20164 &
# https://github.com/kennethreitz/requests/issues/1846
return
if os.path.exists(loc):
netrc_path = loc
break
# Abort early if there isn't one.
if netrc_path is None:
return
ri = urlparse(url)
# Strip port numbers from netloc
host = ri.netloc.split(':')[0]
try:
_netrc = netrc(netrc_path).authenticators(host)
if _netrc:
# Return with login / password
login_i = (0 if _netrc[0] else 1)
return (_netrc[login_i], _netrc[2])
except (NetrcParseError, IOError):
# If there was a parsing error or a permissions issue reading the file,
# we'll just skip netrc auth
pass
# AppEngine hackiness.
except (ImportError, AttributeError):
pass | [
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atomistic-machine-learning/schnetpack | dacf6076d43509dfd8b6694a846ac8453ae39b5e | src/schnetpack/md/parsers/md_setup.py | python | SetupCalculator._setup | (self, md_initializer) | Main routine for loading the model, preparing it for the calculator and setting
up the main :obj:`schnetpack.md.calculator`.
Args:
md_initializer (schnetpack.md.parser.MDSimulation): Parent MDSimulation class. | Main routine for loading the model, preparing it for the calculator and setting
up the main :obj:`schnetpack.md.calculator`. | [
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] | def _setup(self, md_initializer):
"""
Main routine for loading the model, preparing it for the calculator and setting
up the main :obj:`schnetpack.md.calculator`.
Args:
md_initializer (schnetpack.md.parser.MDSimulation): Parent MDSimulation class.
"""
calculator = self.target_config_block
calculator_dict = {}
# Load model, else get options
for key in calculator:
# TODO Figure out conventions for ensemble
if key == "model_file" or key == "model_files":
if calculator[CalculatorInit.kind] in self.basic_models:
model = self._load_model_schnetpack(
calculator["model_file"], md_initializer.device
).to(md_initializer.device)
calculator_dict["model"] = model
elif calculator[CalculatorInit.kind] in self.ensemble_models:
models = [
self._load_model_schnetpack(model, md_initializer.device).to(
md_initializer.device
)
for model in calculator["model_files"]
]
calculator_dict["models"] = models
elif calculator[CalculatorInit.kind] == "sgdml":
model = self._load_model_sgdml(calculator["model_file"]).to(
md_initializer.device
)
calculator_dict["model"] = model
else:
raise ValueError(
f"Unrecognized ML calculator {calculator[CalculatorInit.kind]}"
)
elif key == "neighbor_list":
# Check for neighbor list
calculator_dict[key] = self.neighbor_list[calculator[key]]
else:
calculator_dict[key] = calculator[key]
calculator = CalculatorInit(calculator_dict).initialized
md_initializer.calculator = calculator | [
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kylebebak/Requester | 4a9f9f051fa5fc951a8f7ad098a328261ca2db97 | deps/urllib3/packages/ordered_dict.py | python | OrderedDict.__reversed__ | (self) | od.__reversed__() <==> reversed(od) | od.__reversed__() <==> reversed(od) | [
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'od.__reversed__() <==> reversed(od)'
root = self.__root
curr = root[0]
while curr is not root:
yield curr[2]
curr = curr[0] | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/min/pydoc.py | python | Helper.listkeywords | (self) | [] | def listkeywords(self):
self.output.write('''
Here is a list of the Python keywords. Enter any keyword to get more help.
''')
self.list(self.keywords.keys()) | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/numpy-1.10.0.dev0_046311a-py3.3-win-amd64.egg/numpy/lib/type_check.py | python | mintypecode | (typechars,typeset='GDFgdf',default='d') | return l[0][1] | Return the character for the minimum-size type to which given types can
be safely cast.
The returned type character must represent the smallest size dtype such
that an array of the returned type can handle the data from an array of
all types in `typechars` (or if `typechars` is an array, then its
dtype.char).
Parameters
----------
typechars : list of str or array_like
If a list of strings, each string should represent a dtype.
If array_like, the character representation of the array dtype is used.
typeset : str or list of str, optional
The set of characters that the returned character is chosen from.
The default set is 'GDFgdf'.
default : str, optional
The default character, this is returned if none of the characters in
`typechars` matches a character in `typeset`.
Returns
-------
typechar : str
The character representing the minimum-size type that was found.
See Also
--------
dtype, sctype2char, maximum_sctype
Examples
--------
>>> np.mintypecode(['d', 'f', 'S'])
'd'
>>> x = np.array([1.1, 2-3.j])
>>> np.mintypecode(x)
'D'
>>> np.mintypecode('abceh', default='G')
'G' | Return the character for the minimum-size type to which given types can
be safely cast. | [
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] | def mintypecode(typechars,typeset='GDFgdf',default='d'):
"""
Return the character for the minimum-size type to which given types can
be safely cast.
The returned type character must represent the smallest size dtype such
that an array of the returned type can handle the data from an array of
all types in `typechars` (or if `typechars` is an array, then its
dtype.char).
Parameters
----------
typechars : list of str or array_like
If a list of strings, each string should represent a dtype.
If array_like, the character representation of the array dtype is used.
typeset : str or list of str, optional
The set of characters that the returned character is chosen from.
The default set is 'GDFgdf'.
default : str, optional
The default character, this is returned if none of the characters in
`typechars` matches a character in `typeset`.
Returns
-------
typechar : str
The character representing the minimum-size type that was found.
See Also
--------
dtype, sctype2char, maximum_sctype
Examples
--------
>>> np.mintypecode(['d', 'f', 'S'])
'd'
>>> x = np.array([1.1, 2-3.j])
>>> np.mintypecode(x)
'D'
>>> np.mintypecode('abceh', default='G')
'G'
"""
typecodes = [(isinstance(t, str) and t) or asarray(t).dtype.char
for t in typechars]
intersection = [t for t in typecodes if t in typeset]
if not intersection:
return default
if 'F' in intersection and 'd' in intersection:
return 'D'
l = []
for t in intersection:
i = _typecodes_by_elsize.index(t)
l.append((i, t))
l.sort()
return l[0][1] | [
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donnemartin/data-science-ipython-notebooks | 5b3c00d462c6e9200315afe46d0093948621eb95 | scikit-learn/fig_code/svm_gui.py | python | View.plot_support_vectors | (self, support_vectors) | Plot the support vectors by placing circles over the
corresponding data points and adds the circle collection
to the contours list. | Plot the support vectors by placing circles over the
corresponding data points and adds the circle collection
to the contours list. | [
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] | def plot_support_vectors(self, support_vectors):
"""Plot the support vectors by placing circles over the
corresponding data points and adds the circle collection
to the contours list."""
cs = self.ax.scatter(support_vectors[:, 0], support_vectors[:, 1],
s=80, edgecolors="k", facecolors="none")
self.contours.append(cs) | [
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fsspec/filesystem_spec | 76da18cf5a9697f480e5a0f6d1013d71676af131 | fsspec/utils.py | python | stringify_path | (filepath) | Attempt to convert a path-like object to a string.
Parameters
----------
filepath: object to be converted
Returns
-------
filepath_str: maybe a string version of the object
Notes
-----
Objects supporting the fspath protocol (Python 3.6+) are coerced
according to its __fspath__ method.
For backwards compatibility with older Python version, pathlib.Path
objects are specially coerced.
Any other object is passed through unchanged, which includes bytes,
strings, buffers, or anything else that's not even path-like. | Attempt to convert a path-like object to a string. | [
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"path",
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"."
] | def stringify_path(filepath):
"""Attempt to convert a path-like object to a string.
Parameters
----------
filepath: object to be converted
Returns
-------
filepath_str: maybe a string version of the object
Notes
-----
Objects supporting the fspath protocol (Python 3.6+) are coerced
according to its __fspath__ method.
For backwards compatibility with older Python version, pathlib.Path
objects are specially coerced.
Any other object is passed through unchanged, which includes bytes,
strings, buffers, or anything else that's not even path-like.
"""
if isinstance(filepath, str):
return filepath
elif hasattr(filepath, "__fspath__"):
return filepath.__fspath__()
elif isinstance(filepath, pathlib.Path):
return str(filepath)
elif hasattr(filepath, "path"):
return filepath.path
else:
return filepath | [
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rwth-i6/returnn | f2d718a197a280b0d5f0fd91a7fcb8658560dddb | tools/bliss-collect-seq-lens.py | python | main | () | Main entry. | Main entry. | [
"Main",
"entry",
"."
] | def main():
"""
Main entry.
"""
arg_parser = ArgumentParser()
arg_parser.add_argument("bliss_filename", nargs="+")
arg_parser.add_argument("--output", default="/dev/stdout")
args = arg_parser.parse_args()
if args.output.endswith(".gz"):
out = gzip.GzipFile(args.output, mode="wb")
else:
out = open(args.output, "wb")
out.write(b"{\n")
for bliss_item in itertools.chain(*[iter_bliss(fn) for fn in args.bliss_filename]):
assert isinstance(bliss_item, BlissItem)
seq_len = round(bliss_item.delta_time * 100.) # assume 10ms frames, round
out.write(b"%r: %i,\n" % (bliss_item.segment_name, seq_len))
out.write(b"}\n")
out.close() | [
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johnroper100/CrowdMaster | dfd72c424c8cc8d1877830ff9dd2e00226f518c3 | cm_channels/cm_formationChannels.py | python | Formation.registerOld | (self, agent, formID, val) | Adds an object that is a formation target | Adds an object that is a formation target | [
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else:
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/managed_placement_view_service/client.py | python | ManagedPlacementViewServiceClient.common_location_path | (project: str, location: str,) | return "projects/{project}/locations/{location}".format(
project=project, location=location,
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] | def common_location_path(project: str, location: str,) -> str:
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return "projects/{project}/locations/{location}".format(
project=project, location=location,
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Patrowl/PatrowlEngines | 61dce987b662177396fa2f914ae07fd651179daf | engines/shhgit/engine-shhgit.py | python | clean_scan | (scan_id) | return engine.clean_scan(scan_id) | Clean scan identified by id. | Clean scan identified by id. | [
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"""Clean scan identified by id."""
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/core/leoNodes.py | python | Position.is_at_ignore | (self) | return g.match_word(p.h, 0, '@ignore') | Return True if p is an @ignore node. | Return True if p is an | [
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"""Return True if p is an @ignore node."""
p = self
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frol/flask-restplus-server-example | fa5a2cdbc1d78ac60fa822208bbf656b71e38c58 | tasks/app/env.py | python | enter | (context, install_dependencies=True, upgrade_db=True) | Enter into IPython notebook shell with an initialized app. | Enter into IPython notebook shell with an initialized app. | [
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] | def enter(context, install_dependencies=True, upgrade_db=True):
"""
Enter into IPython notebook shell with an initialized app.
"""
if install_dependencies:
context.invoke_execute(context, 'app.dependencies.install')
if upgrade_db:
context.invoke_execute(context, 'app.db.upgrade')
context.invoke_execute(
context,
'app.db.init_development_data',
upgrade_db=False,
skip_on_failure=True
)
import pprint
from werkzeug import script
import flask
import app
flask_app = app.create_app()
def shell_context():
context = dict(pprint=pprint.pprint)
context.update(vars(flask))
context.update(vars(app))
return context
with flask_app.app_context():
script.make_shell(shell_context, use_ipython=True)() | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/wheel/cli/pack.py | python | pack | (directory, dest_dir, build_number) | Repack a previously unpacked wheel directory into a new wheel file.
The .dist-info/WHEEL file must contain one or more tags so that the target
wheel file name can be determined.
:param directory: The unpacked wheel directory
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] | def pack(directory, dest_dir, build_number):
"""Repack a previously unpacked wheel directory into a new wheel file.
The .dist-info/WHEEL file must contain one or more tags so that the target
wheel file name can be determined.
:param directory: The unpacked wheel directory
:param dest_dir: Destination directory (defaults to the current directory)
"""
# Find the .dist-info directory
dist_info_dirs = [fn for fn in os.listdir(directory)
if os.path.isdir(os.path.join(directory, fn)) and DIST_INFO_RE.match(fn)]
if len(dist_info_dirs) > 1:
raise WheelError('Multiple .dist-info directories found in {}'.format(directory))
elif not dist_info_dirs:
raise WheelError('No .dist-info directories found in {}'.format(directory))
# Determine the target wheel filename
dist_info_dir = dist_info_dirs[0]
name_version = DIST_INFO_RE.match(dist_info_dir).group('namever')
# Read the tags and the existing build number from .dist-info/WHEEL
existing_build_number = None
wheel_file_path = os.path.join(directory, dist_info_dir, 'WHEEL')
with open(wheel_file_path) as f:
tags = []
for line in f:
if line.startswith('Tag: '):
tags.append(line.split(' ')[1].rstrip())
elif line.startswith('Build: '):
existing_build_number = line.split(' ')[1].rstrip()
if not tags:
raise WheelError('No tags present in {}/WHEEL; cannot determine target wheel filename'
.format(dist_info_dir))
# Set the wheel file name and add/replace/remove the Build tag in .dist-info/WHEEL
build_number = build_number if build_number is not None else existing_build_number
if build_number is not None:
if build_number:
name_version += '-' + build_number
if build_number != existing_build_number:
replacement = ('Build: %s\r\n' % build_number).encode('ascii') if build_number else b''
with open(wheel_file_path, 'rb+') as f:
wheel_file_content = f.read()
if not BUILD_NUM_RE.subn(replacement, wheel_file_content)[1]:
wheel_file_content += replacement
f.truncate()
f.write(wheel_file_content)
# Reassemble the tags for the wheel file
impls = sorted({tag.split('-')[0] for tag in tags})
abivers = sorted({tag.split('-')[1] for tag in tags})
platforms = sorted({tag.split('-')[2] for tag in tags})
tagline = '-'.join(['.'.join(impls), '.'.join(abivers), '.'.join(platforms)])
# Repack the wheel
wheel_path = os.path.join(dest_dir, '{}-{}.whl'.format(name_version, tagline))
with WheelFile(wheel_path, 'w') as wf:
print("Repacking wheel as {}...".format(wheel_path), end='')
sys.stdout.flush()
wf.write_files(directory)
print('OK') | [
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microsoft/azure-devops-python-api | 451cade4c475482792cbe9e522c1fee32393139e | azure-devops/azure/devops/v6_0/notification/notification_client.py | python | NotificationClient.update_subscriber | (self, update_parameters, subscriber_id) | return self._deserialize('NotificationSubscriber', response) | UpdateSubscriber.
[Preview API] Update delivery preferences of a notifications subscriber.
:param :class:`<NotificationSubscriberUpdateParameters> <azure.devops.v6_0.notification.models.NotificationSubscriberUpdateParameters>` update_parameters:
:param str subscriber_id: ID of the user or group.
:rtype: :class:`<NotificationSubscriber> <azure.devops.v6_0.notification.models.NotificationSubscriber>` | UpdateSubscriber.
[Preview API] Update delivery preferences of a notifications subscriber.
:param :class:`<NotificationSubscriberUpdateParameters> <azure.devops.v6_0.notification.models.NotificationSubscriberUpdateParameters>` update_parameters:
:param str subscriber_id: ID of the user or group.
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"""UpdateSubscriber.
[Preview API] Update delivery preferences of a notifications subscriber.
:param :class:`<NotificationSubscriberUpdateParameters> <azure.devops.v6_0.notification.models.NotificationSubscriberUpdateParameters>` update_parameters:
:param str subscriber_id: ID of the user or group.
:rtype: :class:`<NotificationSubscriber> <azure.devops.v6_0.notification.models.NotificationSubscriber>`
"""
route_values = {}
if subscriber_id is not None:
route_values['subscriberId'] = self._serialize.url('subscriber_id', subscriber_id, 'str')
content = self._serialize.body(update_parameters, 'NotificationSubscriberUpdateParameters')
response = self._send(http_method='PATCH',
location_id='4d5caff1-25ba-430b-b808-7a1f352cc197',
version='6.0-preview.1',
route_values=route_values,
content=content)
return self._deserialize('NotificationSubscriber', response) | [
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aleju/imgaug | 0101108d4fed06bc5056c4a03e2bcb0216dac326 | imgaug/augmenters/blend.py | python | BlendAlphaElementwise.factor | (self) | return self.mask_generator.parameter | [] | def factor(self):
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and3rson/clay | c271cecf6b6ea6465abcdd2444171b1a565a60a3 | clay/vlc.py | python | libvlc_audio_equalizer_get_preset_name | (u_index) | return f(u_index) | Get the name of a particular equalizer preset.
This name can be used, for example, to prepare a preset label or menu in a user
interface.
@param u_index: index of the preset, counting from zero.
@return: preset name, or None if there is no such preset.
@version: LibVLC 2.2.0 or later. | Get the name of a particular equalizer preset.
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'''Get the name of a particular equalizer preset.
This name can be used, for example, to prepare a preset label or menu in a user
interface.
@param u_index: index of the preset, counting from zero.
@return: preset name, or None if there is no such preset.
@version: LibVLC 2.2.0 or later.
'''
f = _Cfunctions.get('libvlc_audio_equalizer_get_preset_name', None) or \
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return f(u_index) | [
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yihui-he/KL-Loss | 66c0ed9e886a2218f4cf88c0efd4f40199bff54a | detectron/utils/keypoints.py | python | get_keypoints | () | return keypoints, keypoint_flip_map | Get the COCO keypoints and their left/right flip coorespondence map. | Get the COCO keypoints and their left/right flip coorespondence map. | [
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] | def get_keypoints():
"""Get the COCO keypoints and their left/right flip coorespondence map."""
# Keypoints are not available in the COCO json for the test split, so we
# provide them here.
keypoints = [
'nose',
'left_eye',
'right_eye',
'left_ear',
'right_ear',
'left_shoulder',
'right_shoulder',
'left_elbow',
'right_elbow',
'left_wrist',
'right_wrist',
'left_hip',
'right_hip',
'left_knee',
'right_knee',
'left_ankle',
'right_ankle'
]
keypoint_flip_map = {
'left_eye': 'right_eye',
'left_ear': 'right_ear',
'left_shoulder': 'right_shoulder',
'left_elbow': 'right_elbow',
'left_wrist': 'right_wrist',
'left_hip': 'right_hip',
'left_knee': 'right_knee',
'left_ankle': 'right_ankle'
}
return keypoints, keypoint_flip_map | [
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blackfeather-wang/ISDA-for-Deep-Networks | b66a594482557dada126211d65a4e9b6f4328423 | Image classification on CIFAR/train.py | python | AverageMeter.update | (self, val, n=1) | [] | def update(self, val, n=1):
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pillone/usntssearch | 24b5e5bc4b6af2589d95121c4d523dc58cb34273 | NZBmegasearch/werkzeug/routing.py | python | Map.iter_rules | (self, endpoint=None) | return iter(self._rules) | Iterate over all rules or the rules of an endpoint.
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"""Iterate over all rules or the rules of an endpoint.
:param endpoint: if provided only the rules for that endpoint
are returned.
:return: an iterator
"""
self.update()
if endpoint is not None:
return iter(self._rules_by_endpoint[endpoint])
return iter(self._rules) | [
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/plugins/view/noteview.py | python | NoteView.remove_tag | (self, transaction, note_handle, tag_handle) | Remove the given tag from the given note. | Remove the given tag from the given note. | [
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"""
Remove the given tag from the given note.
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note = self.dbstate.db.get_note_from_handle(note_handle)
note.remove_tag(tag_handle)
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maas/maas | db2f89970c640758a51247c59bf1ec6f60cf4ab5 | src/maasserver/models/domain.py | python | validate_domain_name | (value) | Django validator: `value` must be a valid DNS Zone name. | Django validator: `value` must be a valid DNS Zone name. | [
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":",
"value",
"must",
"be",
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"DNS",
"Zone",
"name",
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] | def validate_domain_name(value):
"""Django validator: `value` must be a valid DNS Zone name."""
namespec = re.compile("^%s$" % NAMESPEC)
if not namespec.search(value) or len(value) > 255:
raise ValidationError("Invalid domain name: %s." % value) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/combinat.py | python | CombinatorialClass.__next_from_iterator | (self, obj) | return None | Default implementation for next which uses iterator.
EXAMPLES::
sage: C = CombinatorialClass()
sage: C.list = lambda: [1,2,3]
sage: C.next(2) # indirect doctest
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] | def __next_from_iterator(self, obj):
"""
Default implementation for next which uses iterator.
EXAMPLES::
sage: C = CombinatorialClass()
sage: C.list = lambda: [1,2,3]
sage: C.next(2) # indirect doctest
3
"""
found = False
for i in self:
if found:
return i
if i == obj:
found = True
return None | [
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conjure-up/conjure-up | d2bf8ab8e71ff01321d0e691a8d3e3833a047678 | conjureup/hooklib/writer.py | python | success | (msg) | Returns a successful step | Returns a successful step | [
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] | def success(msg):
""" Returns a successful step
"""
print(msg)
sys.exit(0) | [
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learningequality/ka-lite | 571918ea668013dcf022286ea85eff1c5333fb8b | kalite/packages/bundled/django/contrib/localflavor/pl/forms.py | python | PLPESELField.has_valid_checksum | (self, number) | return result % 10 == 0 | Calculates a checksum with the provided algorithm. | Calculates a checksum with the provided algorithm. | [
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] | def has_valid_checksum(self, number):
"""
Calculates a checksum with the provided algorithm.
"""
multiple_table = (1, 3, 7, 9, 1, 3, 7, 9, 1, 3, 1)
result = 0
for i in range(len(number)):
result += int(number[i]) * multiple_table[i]
return result % 10 == 0 | [
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