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zatosource/zato | 2a9d273f06f9d776fbfeb53e73855af6e40fa208 | code/zato-server/src/zato/server/connection/cache.py | python | CacheAPI.sync_after_delete_by_suffix | (self, cache_type, data) | Synchronizes the state of this worker's cache after a .delete_by_suffix operation in another worker process. | Synchronizes the state of this worker's cache after a .delete_by_suffix operation in another worker process. | [
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self.caches[cache_type][data.cache_name].sync_after_delete_by_suffix(data) | [
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DamnWidget/anaconda | a9998fb362320f907d5ccbc6fcf5b62baca677c0 | anaconda_server/commands/complete_parameters.py | python | CompleteParameters.run | (self) | Run the command | Run the command | [
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name, value = p
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name = p[0]
value = None
name = name.replace('param ', '')
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completions.append('${%d:%s}' % (i + 1, name))
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completions.append('%s=${%d:%s}' % (name, i + 1, value))
self.callback({
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chipmuenk/pyfda | 665310b8548a940a575c0e5ff4bba94608d9ac26 | pyfda/input_widgets/freq_specs.py | python | FreqSpecs.sort_dict_freqs | (self) | - Sort visible filter dict frequency spec entries with ascending frequency if
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# Make sure normalized freqs are in the range ]0, 0.5[ and are different
# by at least MIN_FREQ_STEP
for i in range(self.n_cur_labels):
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logger.warning("Frequencies must be > 0, changed {0} from {1:.4g} to {2:.4g}."\
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f_specs[i] = MIN_FREQ + MIN_FREQ_STEP
if f_specs[i] >= MAX_FREQ:
logger.warning("Frequencies must be < f_S /2, changed {0} from {1:.4g} to {2:.4g}."\
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f_specs[i] = MAX_FREQ - MIN_FREQ_STEP
fb.fil[0][str(self.qlineedit[i].objectName())] = f_specs[i]
# check for (nearly) identical elements:
_, mult = unique_roots(f_specs, tol=MIN_FREQ_STEP)
ident = [x for x in mult if x > 1]
if ident:
logger.warning("Frequencies must differ by at least {0:.4g}"
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insarlab/MintPy | 4357b8c726dec8a3f936770e3f3dda92882685b7 | mintpy/simulation/simulation.py | python | sim_variable_timeseries_v1 | (tbase, scale=3., display=False) | return ts_sim | Simulate time variable displacement time-series
Parameters: tbase - 1D np.ndarray in size of (num_date,), temporal baseline in days
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Parameters: tbase - 1D np.ndarray in size of (num_date,), temporal baseline in days
Returns: ts_sim - 1D np.ndarray in size of (num_date,), displacement time-series in meters
"""
# Opt 2 - Time variable
num_date = len(tbase)
ts_sim = np.zeros(num_date, np.float32)
# comp 1 - exponential increase
idx1 = int(0.2*num_date)
ts_sim[idx1:] = 0.01 * np.log(tbase[idx1:] - tbase[idx1-1])
# comp 2 - step decrease + linear increase of 5 cm/yr
idx2 = int(0.5*num_date)
ts_sim[idx2:] = 0.03 + 0.05 * (tbase[idx2:] - tbase[idx2-1]) / 365.25
idx3 = int(0.8*num_date)
ts_sim[idx3:] = 0.
# comp 3 - overall linear descrease of 0.5 cm/yr
ts_sim += tbase * -0.005 / 365.25
# scale and reference to the 1st image
ts_sim *= 3.
ts_sim -= ts_sim[0]
if display:
fig, ax = plt.subplots(figsize=[6, 3])
ax.plot(tbase, ts_sim * 100., '--')
ax.set_xlabel('time [days]', fontsize=font_size)
ax.set_ylabel('displacement [cm]', fontsize=font_size)
ax.tick_params(direction='in', labelsize=font_size)
plt.show()
return ts_sim | [
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BigBrotherBot/big-brother-bot | 848823c71413c86e7f1ff9584f43e08d40a7f2c0 | b3/parsers/frontline/protocol.py | python | Client.login | (self, challenge) | Authenticate to the server
from Frontline documentation :
Open a TCP/IP streaming socket to the remote console port of the server
All commands sent and received are separated with new line characters '\n' or 0x04
The server will send back a string (without the quotes):
"WELCOME! Frontlines: Fuel of War (RCON) VER=2 CHALLENGE=38D384D07C"
Note: Challenge string length is not fixed and will vary
To generate a response use the MD5 algorithm to hash an ansi string:
ResponseString = MD5Hash( ChallengeStringFromServer + Password );
The client will send this string to the server to login:
"RESPONSE UserNameHere ResponseString"
If the login was successful the client will receive:
"Login Success!"
If the login failed, the client will be disconnected immediately
Once the client is logged in commands can be sent to be run and responses can come back | Authenticate to the server
from Frontline documentation :
Open a TCP/IP streaming socket to the remote console port of the server
All commands sent and received are separated with new line characters '\n' or 0x04
The server will send back a string (without the quotes):
"WELCOME! Frontlines: Fuel of War (RCON) VER=2 CHALLENGE=38D384D07C"
Note: Challenge string length is not fixed and will vary
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ResponseString = MD5Hash( ChallengeStringFromServer + Password );
The client will send this string to the server to login:
"RESPONSE UserNameHere ResponseString"
If the login was successful the client will receive:
"Login Success!"
If the login failed, the client will be disconnected immediately
Once the client is logged in commands can be sent to be run and responses can come back | [
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ResponseString = MD5Hash( ChallengeStringFromServer + Password );
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If the login was successful the client will receive:
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If the login failed, the client will be disconnected immediately
Once the client is logged in commands can be sent to be run and responses can come back
"""
self.console.info("logging to Frontline server with username %r" % self._username)
hashed_password = md5.new("%s%s" % (challenge, self._password)).hexdigest()
try:
self.send('RESPONSE %s %s' % (self._username, hashed_password))
except socket.error, e:
self.console.error(repr(e)) | [
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machawk1/wail | 199bd1bfbd5d1126f83b113ce5261c3312a7b9ef | bundledApps/WAIL.py | python | WAILStatusBar.__init__ | (self, parent) | [] | def __init__(self, parent):
wx.StatusBar.__init__(self, parent, -1)
self.parent = parent
self.SetFieldsCount(2)
self.SetStatusWidths([-2, 30])
self.sb_button = wx.Button(
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/twisted/python/util.py | python | untilConcludes | (f, *a, **kw) | Call C{f} with the given arguments, handling C{EINTR} by retrying.
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while True:
try:
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except (IOError, OSError) as e:
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/django/contrib/gis/gdal/field.py | python | OFTInteger.type | (self) | return 0 | GDAL uses OFTReals to represent OFTIntegers in created
shapefiles -- forcing the type here since the underlying field
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/musicbrainzngs/mbxml.py | python | parse_alias | (alias) | return result | [] | def parse_alias(alias):
result = {}
attribs = ["locale", "sort-name", "type", "primary",
"begin-date", "end-date"]
result.update(parse_attributes(attribs, alias))
result["alias"] = alias.text
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SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions | 014c4ca27a70b5907a183e942228004c989dcbe4 | selim_sef/zoo/senet.py | python | SEBottleneck.__init__ | (self, inplanes, planes, groups, reduction, stride=1,
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super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
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self.conv2 = nn.Conv2d(planes * 2, planes * 4, kernel_size=3,
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self.bn2 = BatchNorm2d(planes * 4)
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self.bn3 = BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
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hahwul/a2sv | cb24c4e8bd7c9f06eb74658039c20bbed4f8b2ef | module/M_heartbleed.py | python | parseresp | (s) | [] | def parseresp(s):
while True:
typ, ver, pay = recvmsg(s)
if typ == None:
zzzzz=1#showDisplay(displayMode,'Server closed connection without sending Server Hello.')
return 0
# Look for server hello done message.
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tweecode/twine | 45e6350a2b813ec4282440b78f901cf377ead1a9 | storyframe.py | python | StoryFrame.importFontDialog | (self, event=None) | Asks the user to choose a font file to import, then imports into the current story. | Asks the user to choose a font file to import, then imports into the current story. | [
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if dialog.ShowModal() == wx.ID_OK:
self.importFont(dialog.GetPath()) | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/ex-submodules/pillowtop/reindexer/reindexer.py | python | ElasticPillowReindexer.__init__ | (self, pillow_or_processor, change_provider, elasticsearch, index_info, in_place=False) | [] | def __init__(self, pillow_or_processor, change_provider, elasticsearch, index_info, in_place=False):
super(ElasticPillowReindexer, self).__init__(pillow_or_processor, change_provider)
self.es = elasticsearch
self.index_info = index_info
self.in_place = in_place | [
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tensorflow/benchmarks | 16af178ad312e8c1213efb27a5f227044228bfdf | scripts/tf_cnn_benchmarks/variable_mgr.py | python | VariableMgr.create_outer_variable_scope | (self, device_num) | Create the tf.variable_scope around all model graph operations. | Create the tf.variable_scope around all model graph operations. | [
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] | def create_outer_variable_scope(self, device_num):
"""Create the tf.variable_scope around all model graph operations."""
del device_num # unused by this implementation
assert False, 'Must be implemented in subclass' | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/sklearn/svm/classes.py | python | LinearSVC.fit | (self, X, y, sample_weight=None) | return self | Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
sample_weight : array-like, shape = [n_samples], optional
Array of weights that are assigned to individual
samples. If not provided,
then each sample is given unit weight.
Returns
-------
self : object
Returns self. | Fit the model according to the given training data. | [
"Fit",
"the",
"model",
"according",
"to",
"the",
"given",
"training",
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] | def fit(self, X, y, sample_weight=None):
"""Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
sample_weight : array-like, shape = [n_samples], optional
Array of weights that are assigned to individual
samples. If not provided,
then each sample is given unit weight.
Returns
-------
self : object
Returns self.
"""
# FIXME Remove l1/l2 support in 1.0 -----------------------------------
msg = ("loss='%s' has been deprecated in favor of "
"loss='%s' as of 0.16. Backward compatibility"
" for the loss='%s' will be removed in %s")
if self.loss in ('l1', 'l2'):
old_loss = self.loss
self.loss = {'l1': 'hinge', 'l2': 'squared_hinge'}.get(self.loss)
warnings.warn(msg % (old_loss, self.loss, old_loss, '1.0'),
DeprecationWarning)
# ---------------------------------------------------------------------
if self.C < 0:
raise ValueError("Penalty term must be positive; got (C=%r)"
% self.C)
X, y = check_X_y(X, y, accept_sparse='csr',
dtype=np.float64, order="C")
check_classification_targets(y)
self.classes_ = np.unique(y)
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
X, y, self.C, self.fit_intercept, self.intercept_scaling,
self.class_weight, self.penalty, self.dual, self.verbose,
self.max_iter, self.tol, self.random_state, self.multi_class,
self.loss, sample_weight=sample_weight)
if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
if self.fit_intercept:
intercept = self.intercept_[1] - self.intercept_[0]
self.intercept_ = np.array([intercept])
return self | [
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cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/meter/tools.py | python | slashMixedToFraction | (valueSrc: str) | return tuple(post), summedNumerator | Given a mixture if possible meter fraction representations, return a list
of pairs. If originally given as a summed numerator; break into separate
fractions and return True as the second element of the tuple
>>> meter.tools.slashMixedToFraction('4/4')
(((4, 4),), False)
>>> meter.tools.slashMixedToFraction('3/8+2/8')
(((3, 8), (2, 8)), False)
>>> meter.tools.slashMixedToFraction('3+2/8')
(((3, 8), (2, 8)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8')
(((3, 8), (2, 8), (5, 8)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8+3/4')
(((3, 8), (2, 8), (5, 8), (3, 4)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8+3/4+2+1+4/16')
(((3, 8), (2, 8), (5, 8), (3, 4), (2, 16), (1, 16), (4, 16)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8+3/4+2+1+4')
Traceback (most recent call last):
music21.exceptions21.MeterException: cannot match denominator to numerator in: 3+2+5/8+3/4+2+1+4
>>> meter.tools.slashMixedToFraction('3.0/4.0')
Traceback (most recent call last):
music21.exceptions21.TimeSignatureException: Cannot create time signature from "3.0/4.0"
Changed in v7 -- new location and returns a tuple as first value. | Given a mixture if possible meter fraction representations, return a list
of pairs. If originally given as a summed numerator; break into separate
fractions and return True as the second element of the tuple | [
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'''
Given a mixture if possible meter fraction representations, return a list
of pairs. If originally given as a summed numerator; break into separate
fractions and return True as the second element of the tuple
>>> meter.tools.slashMixedToFraction('4/4')
(((4, 4),), False)
>>> meter.tools.slashMixedToFraction('3/8+2/8')
(((3, 8), (2, 8)), False)
>>> meter.tools.slashMixedToFraction('3+2/8')
(((3, 8), (2, 8)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8')
(((3, 8), (2, 8), (5, 8)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8+3/4')
(((3, 8), (2, 8), (5, 8), (3, 4)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8+3/4+2+1+4/16')
(((3, 8), (2, 8), (5, 8), (3, 4), (2, 16), (1, 16), (4, 16)), True)
>>> meter.tools.slashMixedToFraction('3+2+5/8+3/4+2+1+4')
Traceback (most recent call last):
music21.exceptions21.MeterException: cannot match denominator to numerator in: 3+2+5/8+3/4+2+1+4
>>> meter.tools.slashMixedToFraction('3.0/4.0')
Traceback (most recent call last):
music21.exceptions21.TimeSignatureException: Cannot create time signature from "3.0/4.0"
Changed in v7 -- new location and returns a tuple as first value.
'''
pre = []
post = []
summedNumerator = False
value = valueSrc.strip() # rem whitespace
value = value.split('+')
for part in value:
if '/' in part:
tup = slashToTuple(part)
if tup is None:
raise TimeSignatureException(
f'Cannot create time signature from "{valueSrc}"')
pre.append([tup.numerator, tup.denominator])
else: # its just a numerator
try:
pre.append([int(part), None])
except ValueError:
raise Music21Exception(
'Cannot parse this file -- this error often comes '
+ 'up if the musicxml pickled file is out of date after a change '
+ 'in musicxml/__init__.py . '
+ 'Clear your temp directory of .p and .p.gz files and try again...; '
+ f'Time Signature: {valueSrc} ')
# when encountering a missing denominator, find the first defined
# and apply to all previous
for i in range(len(pre)):
if pre[i][1] is not None: # there is a denominator
post.append(tuple(pre[i]))
else: # search ahead for next defined denominator
summedNumerator = True
match = None
for j in range(i, len(pre)):
if pre[j][1] is not None:
match = pre[j][1]
break
if match is None:
raise MeterException(f'cannot match denominator to numerator in: {valueSrc}')
pre[i][1] = match
post.append(tuple(pre[i]))
return tuple(post), summedNumerator | [
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openstack/tempest | fe0ac89a5a1c43fa908a76759cd99eea3b1f9853 | tempest/lib/services/placement/resource_providers_client.py | python | ResourceProvidersClient.list_resource_providers | (self, **params) | return rest_client.ResponseBody(resp, body) | List resource providers.
For full list of available parameters, please refer to the official
API reference:
https://docs.openstack.org/api-ref/placement/#list-resource-providers | List resource providers. | [
"List",
"resource",
"providers",
"."
] | def list_resource_providers(self, **params):
"""List resource providers.
For full list of available parameters, please refer to the official
API reference:
https://docs.openstack.org/api-ref/placement/#list-resource-providers
"""
url = '/resource_providers'
if params:
url += '?%s' % urllib.urlencode(params)
resp, body = self.get(url)
self.expected_success(200, resp.status)
body = json.loads(body)
return rest_client.ResponseBody(resp, body) | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/attr/_make.py | python | _ClassBuilder._patch_original_class | (self) | return cls | Apply accumulated methods and return the class. | Apply accumulated methods and return the class. | [
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"the",
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] | def _patch_original_class(self):
"""
Apply accumulated methods and return the class.
"""
cls = self._cls
base_names = self._base_names
# Clean class of attribute definitions (`attr.ib()`s).
if self._delete_attribs:
for name in self._attr_names:
if (
name not in base_names
and getattr(cls, name, None) is not None
):
try:
delattr(cls, name)
except AttributeError:
# This can happen if a base class defines a class
# variable and we want to set an attribute with the
# same name by using only a type annotation.
pass
# Attach our dunder methods.
for name, value in self._cls_dict.items():
setattr(cls, name, value)
return cls | [
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marcusva/py-sdl2 | d549fc58de7aa204a119dc8dedef81b3cc888fb9 | sdl2/ext/window.py | python | Window.refresh | (self) | Updates the window to reflect any changes made to its surface.
.. note::
This only needs to be called if the window surface was acquired and
modified using :meth:`get_surface`. | Updates the window to reflect any changes made to its surface. | [
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"."
] | def refresh(self):
"""Updates the window to reflect any changes made to its surface.
.. note::
This only needs to be called if the window surface was acquired and
modified using :meth:`get_surface`.
"""
video.SDL_UpdateWindowSurface(self.window) | [
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TryCatchHCF/DumpsterFire | 43b46a274663ff694763db6e627975e160bc3597 | FireModules/OSCommand/os_win_powershell_script.py | python | os_win_powershell_script.__init__ | (self, moofStr) | return | [] | def __init__(self, moofStr):
self.moofStr = moofStr
self.commentsStr = "OSCommand/os_win_powershell_script"
self.psScriptFilepathStr = ""
return; | [
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/gdata/apps/emailsettings/client.py | python | EmailSettingsClient.update_imap | (self, username, enable, **kwargs) | return self.update(new_imap, **kwargs) | Update Google Mail IMAP settings.
Args:
username: string The name of the user.
enable: Boolean Whether to enable IMAP access.language
kwargs: The other parameters to pass to the update method.
Returns:
gdata.apps.emailsettings.data.EmailSettingsImap of the updated resource. | Update Google Mail IMAP settings.
Args:
username: string The name of the user.
enable: Boolean Whether to enable IMAP access.language
kwargs: The other parameters to pass to the update method. | [
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"""Update Google Mail IMAP settings.
Args:
username: string The name of the user.
enable: Boolean Whether to enable IMAP access.language
kwargs: The other parameters to pass to the update method.
Returns:
gdata.apps.emailsettings.data.EmailSettingsImap of the updated resource.
"""
uri = self.MakeEmailSettingsUri(username=username,
setting_id=SETTING_ID_IMAP)
new_imap = gdata.apps.emailsettings.data.EmailSettingsImap(
uri=uri, enable=enable)
return self.update(new_imap, **kwargs) | [
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/cinder/cinder/image/glance.py | python | GlanceClientWrapper.call | (self, context, method, *args, **kwargs) | Call a glance client method. If we get a connection error,
retry the request according to FLAGS.glance_num_retries. | Call a glance client method. If we get a connection error,
retry the request according to FLAGS.glance_num_retries. | [
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] | def call(self, context, method, *args, **kwargs):
"""
Call a glance client method. If we get a connection error,
retry the request according to FLAGS.glance_num_retries.
"""
version = self.version
if version in kwargs:
version = kwargs['version']
retry_excs = (glanceclient.exc.ServiceUnavailable,
glanceclient.exc.InvalidEndpoint,
glanceclient.exc.CommunicationError)
num_attempts = 1 + FLAGS.glance_num_retries
for attempt in xrange(1, num_attempts + 1):
client = self.client or self._create_onetime_client(context,
version)
try:
return getattr(client.images, method)(*args, **kwargs)
except retry_excs as e:
host = self.host
port = self.port
extra = "retrying"
error_msg = _("Error contacting glance server "
"'%(host)s:%(port)s' for '%(method)s', "
"%(extra)s.")
if attempt == num_attempts:
extra = 'done trying'
LOG.exception(error_msg, locals())
raise exception.GlanceConnectionFailed(host=host,
port=port,
reason=str(e))
LOG.exception(error_msg, locals())
time.sleep(1) | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/conpono/create_pretrain_data/books_preproc_pipeline.py | python | books_pipeline | () | return pipeline | Read Books Corpus filenames and create Beam pipeline. | Read Books Corpus filenames and create Beam pipeline. | [
"Read",
"Books",
"Corpus",
"filenames",
"and",
"create",
"Beam",
"pipeline",
"."
] | def books_pipeline():
"""Read Books Corpus filenames and create Beam pipeline."""
# set a random seed for reproducability
rng = random.Random(FLAGS.random_seed)
# BooksCorpus is organized into directories of genre and files of books
# adventure-all.txt seems to contain all the adventure books in 1 file
# romance-all.txt is the same. None of the other directories have this,
# so we will skip it to not double count those books
file_name_set = set()
input_files_by_genre = collections.defaultdict(list)
for path, _, fnames in tf.gfile.Walk(FLAGS.input_file):
genre = path.split("/")[-1]
for fname in fnames:
if fname == "adventure-all.txt" or fname == "romance-all.txt":
continue
if fname in file_name_set:
continue
file_name_set.add(fname)
input_files_by_genre[genre].append(path + "/" + fname)
# Sort genres and iterate in order for reproducability
train_files, dev_files, test_files = [], [], []
for genre, file_list in sorted(input_files_by_genre.items()):
rng.shuffle(file_list)
genre_size = len(file_list)
test_size = int(FLAGS.test_size * genre_size)
dev_size = int(FLAGS.dev_size * genre_size)
test_files.extend(file_list[:test_size])
dev_files.extend(file_list[test_size:test_size + dev_size])
train_files.extend(file_list[test_size + dev_size:])
assert len(file_list[:test_size]) + \
len(file_list[test_size:test_size+dev_size]) + \
len(file_list[test_size+dev_size:]) == len(file_list)
# make sure there is no test train overlap
for filename in train_files:
assert filename not in test_files
assert filename not in dev_files
for filename in dev_files:
assert filename not in test_files
rng.shuffle(train_files)
rng.shuffle(dev_files)
rng.shuffle(test_files)
def pipeline(root):
"""Beam pipeline for converting Books Corpus files to TF Examples."""
_ = (
root | "Create test files" >> beam.Create(test_files)
| "Read test files" >> beam.FlatMap(read_file)
| "test Shuffle" >> beam.Reshuffle()
| "Preproc test docs" >> beam.FlatMap(preproc_doc)
| "record test Shuffle" >> beam.Reshuffle()
| "Write to test tfrecord" >> beam.io.WriteToTFRecord(
FLAGS.output_file + "." + FLAGS.format + ".test.tfrecord",
num_shards=100))
_ = (
root | "Create dev files" >> beam.Create(dev_files)
| "Read dev files" >> beam.FlatMap(read_file)
| "dev Shuffle" >> beam.Reshuffle()
| "Preproc dev docs" >> beam.FlatMap(preproc_doc)
| "record dev Shuffle" >> beam.Reshuffle()
| "Write to dev tfrecord" >> beam.io.WriteToTFRecord(
FLAGS.output_file + "." + FLAGS.format + ".dev.tfrecord",
num_shards=100))
_ = (
root | "Create train files" >> beam.Create(train_files)
| "Read train files" >> beam.FlatMap(read_file)
| "train Shuffle" >> beam.Reshuffle()
| "Preproc train docs" >> beam.FlatMap(preproc_doc)
| "record train Shuffle" >> beam.Reshuffle()
| "Write to train tfrecord" >> beam.io.WriteToTFRecord(
FLAGS.output_file + "." + FLAGS.format + ".train.tfrecord",
num_shards=500))
return
return pipeline | [
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"# adventure-all.txt seems to contain all the ad... | https://github.com/google-research/language/blob/61fa7260ac7d690d11ef72ca863e45a37c0bdc80/language/conpono/create_pretrain_data/books_preproc_pipeline.py#L156-L234 | |
oracle/oci-python-sdk | 3c1604e4e212008fb6718e2f68cdb5ef71fd5793 | examples/load_balancer_path_route_sets_example.py | python | create_vcn_and_subnets | (virtual_network, compartment_id, first_ad, second_ad) | return {'vcn': vcn, 'subnets': [subnet_one, subnet_two]} | [] | def create_vcn_and_subnets(virtual_network, compartment_id, first_ad, second_ad):
# Create a VCN
vcn_name = 'python_sdk_test_lb_vcn'
cidr_block = "10.0.0.0/16"
result = virtual_network.create_vcn(
oci.core.models.CreateVcnDetails(
cidr_block=cidr_block,
display_name=vcn_name,
compartment_id=compartment_id
)
)
vcn = oci.wait_until(
virtual_network,
virtual_network.get_vcn(result.data.id),
'lifecycle_state',
'AVAILABLE',
max_wait_seconds=300
).data
print('Created VCN')
# Create a subnet
subnet_name = 'python_sdk_test_lb_subnet1'
subnet_cidr_block1 = "10.0.0.0/24"
result = virtual_network.create_subnet(
oci.core.models.CreateSubnetDetails(
compartment_id=compartment_id,
availability_domain=first_ad,
display_name=subnet_name,
vcn_id=vcn.id,
cidr_block=subnet_cidr_block1
)
)
subnet_one = oci.wait_until(
virtual_network,
virtual_network.get_subnet(result.data.id),
'lifecycle_state',
'AVAILABLE',
max_wait_seconds=300
).data
print('Created Subnet 1')
# Create another subnet
subnet_name = 'python_sdk_test_lb_subnet2'
subnet_cidr_block2 = "10.0.1.0/24"
result = virtual_network.create_subnet(
oci.core.models.CreateSubnetDetails(
compartment_id=compartment_id,
availability_domain=second_ad,
display_name=subnet_name,
vcn_id=vcn.id,
cidr_block=subnet_cidr_block2
)
)
subnet_two = oci.wait_until(
virtual_network,
virtual_network.get_subnet(result.data.id),
'lifecycle_state',
'AVAILABLE',
max_wait_seconds=300
).data
print('Created Subnet 2')
return {'vcn': vcn, 'subnets': [subnet_one, subnet_two]} | [
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plaid/plaid-python | 8c60fca608e426f3ff30da8857775946d29e122c | plaid/model/deductions.py | python | Deductions.__init__ | (self, breakdown, total, *args, **kwargs) | Deductions - a model defined in OpenAPI
Args:
breakdown ([DeductionsBreakdown]):
total (DeductionsTotal):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
subtotals ([Total]): [optional] # noqa: E501
totals ([Total]): [optional] # noqa: E501 | Deductions - a model defined in OpenAPI | [
"Deductions",
"-",
"a",
"model",
"defined",
"in",
"OpenAPI"
] | def __init__(self, breakdown, total, *args, **kwargs): # noqa: E501
"""Deductions - a model defined in OpenAPI
Args:
breakdown ([DeductionsBreakdown]):
total (DeductionsTotal):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
subtotals ([Total]): [optional] # noqa: E501
totals ([Total]): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.breakdown = breakdown
self.total = total
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value) | [
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... | https://github.com/plaid/plaid-python/blob/8c60fca608e426f3ff30da8857775946d29e122c/plaid/model/deductions.py#L119-L193 | ||
karanchahal/distiller | a17ec06cbeafcdd2aea19d7c7663033c951392f5 | models/vision/inception.py | python | Inception3.forward | (self, x) | [] | def forward(self, x):
x = self._transform_input(x)
x, aux = self._forward(x)
aux_defined = self.training and self.aux_logits
if torch.jit.is_scripting():
if not aux_defined:
warnings.warn("Scripted Inception3 always returns Inception3 Tuple")
return InceptionOutputs(x, aux)
else:
return self.eager_outputs(x, aux) | [
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alexmic/microtemplates | c45896b95bb577fdd831b54c7698e9856af7b170 | microtemplates/base.py | python | _Variable.render | (self, context) | return resolve(self.name, context) | [] | def render(self, context):
return resolve(self.name, context) | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/scattergl/_error_y.py | python | ErrorY.array | (self) | return self["array"] | Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray | Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series | [
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"""
Sets the data corresponding the length of each error bar.
Values are plotted relative to the underlying data.
The 'array' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["array"] | [
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keiffster/program-y | 8c99b56f8c32f01a7b9887b5daae9465619d0385 | src/programy/parser/exceptions.py | python | MatcherException.__init__ | (self, message) | [] | def __init__(self, message):
Exception.__init__(self, message)
self.message = message | [
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carpedm20/LINE | 501d1515cb84dc0c353ef33e345901de5bf5427c | line/client.py | python | LineClient.longPoll | (self, count=50, debug=False) | Receive a list of operations that have to be processed by original
Line cleint.
:param count: number of operations to get from
:returns: a generator which returns operations
>>> for op in client.longPoll():
sender = op[0]
receiver = op[1]
message = op[2]
print "%s->%s : %s" % (sender, receiver, message) | Receive a list of operations that have to be processed by original
Line cleint. | [
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"that",
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"cleint",
"."
] | def longPoll(self, count=50, debug=False):
"""Receive a list of operations that have to be processed by original
Line cleint.
:param count: number of operations to get from
:returns: a generator which returns operations
>>> for op in client.longPoll():
sender = op[0]
receiver = op[1]
message = op[2]
print "%s->%s : %s" % (sender, receiver, message)
"""
"""Check is there any operations from LINE server"""
OT = OperationType
try:
operations = self._fetchOperations(self.revision, count)
except EOFError:
return
except TalkException as e:
if e.code == 9:
self.raise_error("user logged in to another machine")
else:
return
for operation in operations:
if debug:
print operation
if operation.type == OT.END_OF_OPERATION:
pass
elif operation.type == OT.SEND_MESSAGE:
pass
elif operation.type == OT.RECEIVE_MESSAGE:
message = LineMessage(self, operation.message)
raw_sender = operation.message._from
raw_receiver = operation.message.to
sender = self.getContactOrRoomOrGroupById(raw_sender)
receiver = self.getContactOrRoomOrGroupById(raw_receiver)
# If sender is not found, check member list of group chat sent to
if sender is None:
try:
for m in receiver.members:
if m.id == raw_sender:
sender = m
break
except (AttributeError, TypeError):
pass
# If sender is not found, check member list of room chat sent to
if sender is None:
try:
for m in receiver.contacts:
if m.id == raw_sender:
sender = m
break
except (AttributeError, TypeError):
pass
if sender is None or receiver is None:
self.refreshGroups()
self.refreshContacts()
self.refreshActiveRooms()
sender = self.getContactOrRoomOrGroupById(raw_sender)
receiver = self.getContactOrRoomOrGroupById(raw_receiver)
if sender is None or receiver is None:
contacts = self._getContacts([raw_sender, raw_receiver])
if contacts:
if len(contacts) == 2:
sender = LineContact(self, contacts[0])
receiver = LineContact(self, contacts[1])
yield (sender, receiver, message)
elif operation.type in [ 60, 61 ]:
pass
else:
print "[*] %s" % OT._VALUES_TO_NAMES[operation.type]
print operation
self.revision = max(operation.revision, self.revision) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/mistune.py | python | InlineGrammar.hard_wrap | (self) | Grammar for hard wrap linebreak. You don't need to add two
spaces at the end of a line. | Grammar for hard wrap linebreak. You don't need to add two
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] | def hard_wrap(self):
"""Grammar for hard wrap linebreak. You don't need to add two
spaces at the end of a line.
"""
self.linebreak = re.compile(r'^ *\n(?!\s*$)')
self.text = re.compile(
r'^[\s\S]+?(?=[\\<!\[_*`~]|https?://| *\n|$)'
) | [
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wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/venv/lib/python3.6/site-packages/pygame/midi.py | python | Input.read | (self, num_events) | return self._input.Read(num_events) | reads num_events midi events from the buffer.
Input.read(num_events): return midi_event_list
Reads from the Input buffer and gives back midi events.
[[[status,data1,data2,data3],timestamp],
[[status,data1,data2,data3],timestamp],...] | reads num_events midi events from the buffer.
Input.read(num_events): return midi_event_list | [
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] | def read(self, num_events):
"""reads num_events midi events from the buffer.
Input.read(num_events): return midi_event_list
Reads from the Input buffer and gives back midi events.
[[[status,data1,data2,data3],timestamp],
[[status,data1,data2,data3],timestamp],...]
"""
_check_init()
self._check_open()
return self._input.Read(num_events) | [
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vincentbernat/snimpy | edfb4978fe3d1cbca15e98ebc1e0f009c613c9de | snimpy/mib.py | python | path | (path=None) | Set or get a search path to libsmi.
When no path is provided, return the current path,
unmodified. Otherwise, set the path to the specified value.
:param path: The string to be used to change the search path or
`None` | Set or get a search path to libsmi. | [
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"""Set or get a search path to libsmi.
When no path is provided, return the current path,
unmodified. Otherwise, set the path to the specified value.
:param path: The string to be used to change the search path or
`None`
"""
if path is None:
# Get the path
path = _smi.smiGetPath()
if path == ffi.NULL:
raise SMIException("unable to get current libsmi path")
path = ffi.gc(path, _smi.free)
result = ffi.string(path)
return result.decode("utf8")
# Set the path
if not isinstance(path, bytes):
path = path.encode("utf8")
if _smi.smiSetPath(path) < 0:
raise SMIException("unable to set the path {}".format(path)) | [
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/liealgebras/type_e.py | python | TypeE.basic_root | (self, i, j) | return root | This is a method just to generate roots
with a -1 in the ith position and a 1
in the jth position. | This is a method just to generate roots
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"""
This is a method just to generate roots
with a -1 in the ith position and a 1
in the jth position.
"""
root = [0]*8
root[i] = -1
root[j] = 1
return root | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/layout/polar/_radialaxis.py | python | RadialAxis.ticksuffix | (self) | return self["ticksuffix"] | Sets a tick label suffix.
The 'ticksuffix' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str | Sets a tick label suffix.
The 'ticksuffix' property is a string and must be specified as:
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NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/asr/parts/submodules/conformer_modules.py | python | ConformerLayer.forward | (self, x, att_mask=None, pos_emb=None, pad_mask=None) | return x | Args:
x (torch.Tensor): input signals (B, T, d_model)
att_mask (torch.Tensor): attention masks(B, T, T)
pos_emb (torch.Tensor): (L, 1, d_model)
pad_mask (torch.tensor): padding mask
Returns:
x (torch.Tensor): (B, T, d_model) | Args:
x (torch.Tensor): input signals (B, T, d_model)
att_mask (torch.Tensor): attention masks(B, T, T)
pos_emb (torch.Tensor): (L, 1, d_model)
pad_mask (torch.tensor): padding mask
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att_mask (torch.Tensor): attention masks(B, T, T)
pos_emb (torch.Tensor): (L, 1, d_model)
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x = self.fc_factor * self.dropout(x) + residual
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x = self.norm_self_att(x)
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x = self.self_attn(query=x, key=x, value=x, mask=att_mask, pos_emb=pos_emb)
elif self.self_attention_model == 'abs_pos':
x = self.self_attn(query=x, key=x, value=x, mask=att_mask)
else:
x = None
x = self.dropout(x) + residual
residual = x
x = self.norm_conv(x)
x = self.conv(x, pad_mask)
x = self.dropout(x) + residual
residual = x
x = self.norm_feed_forward2(x)
x = self.feed_forward2(x)
x = self.fc_factor * self.dropout(x) + residual
x = self.norm_out(x)
return x | [
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deepmind/dm_control | 806a10e896e7c887635328bfa8352604ad0fedae | dm_control/suite/dog.py | python | Physics.joint_velocities | (self) | return self.data.qvel[qvel_index].copy() | Returns the velocity of all hinge joints (skipping free joints). | Returns the velocity of all hinge joints (skipping free joints). | [
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hinge_joints = self.model.jnt_type == _HINGE_TYPE
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qiucheng025/zao- | 3a5edf3607b3a523f95746bc69b688090c76d89a | tools/sort.py | python | Sort.splice_lists | (sorted_list, new_vals_list) | return new_list | This method replaces the value at index 1 in each sub-list in the
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new_list = []
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val_index_list = [i[0] for i in new_vals_list]
for i in tqdm(range(len(sorted_list)),
desc="Splicing",
file=sys.stdout):
current_img = sorted_list[i] if isinstance(sorted_list[i], str) else sorted_list[i][0]
new_val_index = val_index_list.index(current_img)
new_list.append([current_img, new_vals_list[new_val_index][1]])
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/roformer/modeling_flax_roformer.py | python | FlaxRoFormerOnlyMLMHead.__call__ | (self, hidden_states, shared_embedding=None) | return hidden_states | [] | def __call__(self, hidden_states, shared_embedding=None):
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rspivak/lsbasi | 07e1a14516156a21ebe2d82e0bae4bba5ad73dd6 | part4/calc4.py | python | Token.__str__ | (self) | return 'Token({type}, {value})'.format(
type=self.type,
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Token(INTEGER, 3)
Token(MUL, '*') | String representation of the class instance. | [
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"""String representation of the class instance.
Examples:
Token(INTEGER, 3)
Token(MUL, '*')
"""
return 'Token({type}, {value})'.format(
type=self.type,
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Lotayou/everybody_dance_now_pytorch | 41144e9b5a47d70d0fe9101ca6c748ecded27fd1 | util/util.py | python | uint82bin | (n, count=8) | return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) | returns the binary of integer n, count refers to amount of bits | returns the binary of integer n, count refers to amount of bits | [
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"""returns the binary of integer n, count refers to amount of bits"""
return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) | [
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RasaHQ/rasa | 54823b68c1297849ba7ae841a4246193cd1223a1 | rasa/core/processor.py | python | MessageProcessor.get_tracker | (self, conversation_id: Text) | return tracker | Get the tracker for a conversation.
In contrast to `fetch_tracker_and_update_session` this does not add any
`action_session_start` or `session_start` events at the beginning of a
conversation.
Args:
conversation_id: The ID of the conversation for which the history should be
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Returns:
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"""Get the tracker for a conversation.
In contrast to `fetch_tracker_and_update_session` this does not add any
`action_session_start` or `session_start` events at the beginning of a
conversation.
Args:
conversation_id: The ID of the conversation for which the history should be
retrieved.
Returns:
Tracker for the conversation. Creates an empty tracker in case it's a new
conversation.
"""
conversation_id = conversation_id or DEFAULT_SENDER_ID
tracker = self.tracker_store.get_or_create_tracker(
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tracker.model_id = self.model_metadata.model_id
return tracker | [
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Esri/ArcREST | ab240fde2b0200f61d4a5f6df033516e53f2f416 | src/arcrest/manageorg/_parameters.py | python | PublishCSVParameters.description | (self, value) | gets/sets the description | gets/sets the description | [
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openelections/openelections-core | 3c516d8c4cf1166b1868b738a248d48f3378c525 | openelex/us/vt/transform/transforms.py | python | CreateResultsTransform.get_candidate | (self, raw_resultDict, extra={}) | Get the Candidate model for a RawResult
Keyword arguments:
* extra - Dictionary of extra query parameters that will
be used to select the candidate. | Get the Candidate model for a RawResult | [
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Get the Candidate model for a RawResult
Keyword arguments:
* extra - Dictionary of extra query parameters that will
be used to select the candidate.
"""
key = self._candidate_key(raw_resultDict)
try:
return self._candidate_cache[key]
except KeyError:
fields = self.get_candidate_fields(raw_resultDict)
fields.update(extra)
del fields['source']
try:
print(fields)
candidate = Candidate.objects.get(**fields)
except Candidate.DoesNotExist:
print(fields)
raise
self._candidate_cache[key] = candidate
return candidate | [
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pycollada/pycollada | 5b2d53333f03047b0fdfc25e394f8b77b57b62fc | collada/material.py | python | Material.__init__ | (self, id, name, effect, xmlnode=None) | Creates a material.
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A unique string identifier for the material
:param str name:
A name for the material
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The effect instantiated in this material
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A name for the material
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"""
self.id = id
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"""The name for the material"""
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self.xmlnode = xmlnode
"""ElementTree representation of the surface."""
else:
self.xmlnode = E.material(
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pydata/xarray | 9226c7ac87b3eb246f7a7e49f8f0f23d68951624 | xarray/core/dataarray.py | python | DataArray.swap_dims | (
self, dims_dict: Mapping[Any, Hashable] = None, **dims_kwargs
) | return self._from_temp_dataset(ds) | Returns a new DataArray with swapped dimensions.
Parameters
----------
dims_dict : dict-like
Dictionary whose keys are current dimension names and whose values
are new names.
**dims_kwargs : {existing_dim: new_dim, ...}, optional
The keyword arguments form of ``dims_dict``.
One of dims_dict or dims_kwargs must be provided.
Returns
-------
swapped : DataArray
DataArray with swapped dimensions.
Examples
--------
>>> arr = xr.DataArray(
... data=[0, 1],
... dims="x",
... coords={"x": ["a", "b"], "y": ("x", [0, 1])},
... )
>>> arr
<xarray.DataArray (x: 2)>
array([0, 1])
Coordinates:
* x (x) <U1 'a' 'b'
y (x) int64 0 1
>>> arr.swap_dims({"x": "y"})
<xarray.DataArray (y: 2)>
array([0, 1])
Coordinates:
x (y) <U1 'a' 'b'
* y (y) int64 0 1
>>> arr.swap_dims({"x": "z"})
<xarray.DataArray (z: 2)>
array([0, 1])
Coordinates:
x (z) <U1 'a' 'b'
y (z) int64 0 1
Dimensions without coordinates: z
See Also
--------
DataArray.rename
Dataset.swap_dims | Returns a new DataArray with swapped dimensions. | [
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self, dims_dict: Mapping[Any, Hashable] = None, **dims_kwargs
) -> "DataArray":
"""Returns a new DataArray with swapped dimensions.
Parameters
----------
dims_dict : dict-like
Dictionary whose keys are current dimension names and whose values
are new names.
**dims_kwargs : {existing_dim: new_dim, ...}, optional
The keyword arguments form of ``dims_dict``.
One of dims_dict or dims_kwargs must be provided.
Returns
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swapped : DataArray
DataArray with swapped dimensions.
Examples
--------
>>> arr = xr.DataArray(
... data=[0, 1],
... dims="x",
... coords={"x": ["a", "b"], "y": ("x", [0, 1])},
... )
>>> arr
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<xarray.DataArray (y: 2)>
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<xarray.DataArray (z: 2)>
array([0, 1])
Coordinates:
x (z) <U1 'a' 'b'
y (z) int64 0 1
Dimensions without coordinates: z
See Also
--------
DataArray.rename
Dataset.swap_dims
"""
dims_dict = either_dict_or_kwargs(dims_dict, dims_kwargs, "swap_dims")
ds = self._to_temp_dataset().swap_dims(dims_dict)
return self._from_temp_dataset(ds) | [
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scikit-learn-contrib/lightning | 3afcb4a9967a0d9e3961acd967705e42a593e448 | lightning/impl/datasets/samples_generator.py | python | make_classification | (n_samples=100, n_features=20, n_informative=2,
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n_clusters_per_class=2, weights=None, flip_y=0.01,
class_sep=1.0, hypercube=True, shift=0.0, scale=1.0,
shuffle=True, random_state=None) | return X, y | Generate a random n-class classification problem.
Parameters
----------
n_samples : int, optional (default=100)
The number of samples.
n_features : int, optional (default=20)
The total number of features. These comprise `n_informative`
informative features, `n_redundant` redundant features, `n_repeated`
duplicated features and `n_features-n_informative-n_redundant-
n_repeated` useless features drawn at random.
n_informative : int, optional (default=2)
The number of informative features. Each class is composed of a number
of gaussian clusters each located around the vertices of a hypercube
in a subspace of dimension `n_informative`. For each cluster,
informative features are drawn independently from N(0, 1) and then
randomly linearly combined in order to add covariance. The clusters
are then placed on the vertices of the hypercube.
n_redundant : int, optional (default=2)
The number of redundant features. These features are generated as
random linear combinations of the informative features.
n_repeated : int, optional (default=2)
The number of duplicated features, drawn randomly from the informative
and the redundant features.
n_classes : int, optional (default=2)
The number of classes (or labels) of the classification problem.
n_clusters_per_class : int, optional (default=2)
The number of clusters per class.
weights : list of floats or None (default=None)
The proportions of samples assigned to each class. If None, then
classes are balanced. Note that if `len(weights) == n_classes - 1`,
then the last class weight is automatically inferred.
flip_y : float, optional (default=0.01)
The fraction of samples whose class are randomly exchanged.
class_sep : float, optional (default=1.0)
The factor multiplying the hypercube dimension.
hypercube : boolean, optional (default=True)
If True, the clusters are put on the vertices of a hypercube. If
False, the clusters are put on the vertices of a random polytope.
shift : float or None, optional (default=0.0)
Shift all features by the specified value. If None, then features
are shifted by a random value drawn in [-class_sep, class_sep].
scale : float or None, optional (default=1.0)
Multiply all features by the specified value. If None, then features
are scaled by a random value drawn in [1, 100]. Note that scaling
happens after shifting.
shuffle : boolean, optional (default=True)
Shuffle the samples and the features.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
X : array of shape [n_samples, n_features]
The generated samples.
y : array of shape [n_samples]
The integer labels for class membership of each sample.
Notes
-----
The algorithm is adapted from Guyon [1] and was designed to generate
the "Madelon" dataset.
References
----------
.. [1] I. Guyon, "Design of experiments for the NIPS 2003 variable
selection benchmark", 2003. | Generate a random n-class classification problem. | [
"Generate",
"a",
"random",
"n",
"-",
"class",
"classification",
"problem",
"."
] | def make_classification(n_samples=100, n_features=20, n_informative=2,
n_redundant=2, n_repeated=0, n_classes=2,
n_clusters_per_class=2, weights=None, flip_y=0.01,
class_sep=1.0, hypercube=True, shift=0.0, scale=1.0,
shuffle=True, random_state=None):
"""Generate a random n-class classification problem.
Parameters
----------
n_samples : int, optional (default=100)
The number of samples.
n_features : int, optional (default=20)
The total number of features. These comprise `n_informative`
informative features, `n_redundant` redundant features, `n_repeated`
duplicated features and `n_features-n_informative-n_redundant-
n_repeated` useless features drawn at random.
n_informative : int, optional (default=2)
The number of informative features. Each class is composed of a number
of gaussian clusters each located around the vertices of a hypercube
in a subspace of dimension `n_informative`. For each cluster,
informative features are drawn independently from N(0, 1) and then
randomly linearly combined in order to add covariance. The clusters
are then placed on the vertices of the hypercube.
n_redundant : int, optional (default=2)
The number of redundant features. These features are generated as
random linear combinations of the informative features.
n_repeated : int, optional (default=2)
The number of duplicated features, drawn randomly from the informative
and the redundant features.
n_classes : int, optional (default=2)
The number of classes (or labels) of the classification problem.
n_clusters_per_class : int, optional (default=2)
The number of clusters per class.
weights : list of floats or None (default=None)
The proportions of samples assigned to each class. If None, then
classes are balanced. Note that if `len(weights) == n_classes - 1`,
then the last class weight is automatically inferred.
flip_y : float, optional (default=0.01)
The fraction of samples whose class are randomly exchanged.
class_sep : float, optional (default=1.0)
The factor multiplying the hypercube dimension.
hypercube : boolean, optional (default=True)
If True, the clusters are put on the vertices of a hypercube. If
False, the clusters are put on the vertices of a random polytope.
shift : float or None, optional (default=0.0)
Shift all features by the specified value. If None, then features
are shifted by a random value drawn in [-class_sep, class_sep].
scale : float or None, optional (default=1.0)
Multiply all features by the specified value. If None, then features
are scaled by a random value drawn in [1, 100]. Note that scaling
happens after shifting.
shuffle : boolean, optional (default=True)
Shuffle the samples and the features.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
X : array of shape [n_samples, n_features]
The generated samples.
y : array of shape [n_samples]
The integer labels for class membership of each sample.
Notes
-----
The algorithm is adapted from Guyon [1] and was designed to generate
the "Madelon" dataset.
References
----------
.. [1] I. Guyon, "Design of experiments for the NIPS 2003 variable
selection benchmark", 2003.
"""
generator = check_random_state(random_state)
# Count features, clusters and samples
if n_informative + n_redundant + n_repeated > n_features:
raise ValueError("Number of informative, redundant and repeated "
"features must sum to less than the number of total"
" features")
if 2 ** n_informative < n_classes * n_clusters_per_class:
raise ValueError("n_classes * n_clusters_per_class must"
" be smaller or equal 2 ** n_informative")
if weights and len(weights) not in [n_classes, n_classes - 1]:
raise ValueError("Weights specified but incompatible with number "
"of classes.")
n_useless = n_features - n_informative - n_redundant - n_repeated
n_clusters = n_classes * n_clusters_per_class
if weights and len(weights) == (n_classes - 1):
weights.append(1.0 - sum(weights))
if weights is None:
weights = [1.0 / n_classes] * n_classes
weights[-1] = 1.0 - sum(weights[:-1])
n_samples_per_cluster = []
for k in range(n_clusters):
n_samples_per_cluster.append(int(n_samples * weights[k % n_classes]
/ n_clusters_per_class))
for i in range(n_samples - sum(n_samples_per_cluster)):
n_samples_per_cluster[i % n_clusters] += 1
# Intialize X and y
X = np.zeros((n_samples, n_features))
y = np.zeros(n_samples, dtype=int)
# Build the polytope
C = np.array(list(product([-class_sep, class_sep], repeat=n_informative)))
generator.shuffle(C)
if not hypercube:
C[:n_clusters] *= generator.rand(n_clusters, 1)
C *= generator.rand(1, n_informative)
# Loop over all clusters
pos = 0
pos_end = 0
for k in range(n_clusters):
# Number of samples in cluster k
n_samples_k = n_samples_per_cluster[k]
# Define the range of samples
pos = pos_end
pos_end = pos + n_samples_k
# Assign labels
y[pos:pos_end] = k % n_classes
# Draw features at random
X[pos:pos_end, :n_informative] = generator.randn(n_samples_k,
n_informative)
# Multiply by a random matrix to create co-variance of the features
A = 2 * generator.rand(n_informative, n_informative) - 1
X[pos:pos_end, :n_informative] = np.dot(X[pos:pos_end, :n_informative],
A)
# Shift the cluster to a vertice
X[pos:pos_end, :n_informative] += np.tile(C[k, :], (n_samples_k, 1))
# Create redundant features
if n_redundant > 0:
B = 2 * generator.rand(n_informative, n_redundant) - 1
X[:, n_informative:n_informative + n_redundant] = \
np.dot(X[:, :n_informative], B)
# Repeat some features
if n_repeated > 0:
n = n_informative + n_redundant
indices = ((n - 1) * generator.rand(n_repeated) + 0.5).astype(np.intp)
X[:, n:n + n_repeated] = X[:, indices]
# Fill useless features
X[:, n_features - n_useless:] = generator.randn(n_samples, n_useless)
# Randomly flip labels
if flip_y >= 0.0:
for i in range(n_samples):
if generator.rand() < flip_y:
y[i] = generator.randint(n_classes)
# Randomly shift and scale
constant_shift = shift is not None
constant_scale = scale is not None
for f in range(n_features):
if not constant_shift:
shift = (2 * generator.rand() - 1) * class_sep
if not constant_scale:
scale = 1 + 100 * generator.rand()
X[:, f] += shift
X[:, f] *= scale
# Randomly permute samples and features
if shuffle:
X, y = shuffle_func(X, y, random_state=generator)
indices = np.arange(n_features)
generator.shuffle(indices)
X[:, :] = X[:, indices]
return X, y | [
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LabPy/lantz | 3e878e3f765a4295b0089d04e241d4beb7b8a65b | lantz/action.py | python | _dget | (adict, instance=MISSING) | Helper function to get an element by key
from a dictionary defaulting to key=MISSING | Helper function to get an element by key
from a dictionary defaulting to key=MISSING | [
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"""Helper function to get an element by key
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try:
return adict[instance]
except KeyError:
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linnanwang/AlphaX-NASBench101 | 87eb0d403def6dee43be7eb73520de074a2f9f03 | alphax-1-net/model.py | python | AuxiliaryHeadCIFAR.__init__ | (self, C, num_classes) | assuming input size 8x8 | assuming input size 8x8 | [
"assuming",
"input",
"size",
"8x8"
] | def __init__(self, C, num_classes):
"""assuming input size 8x8"""
super(AuxiliaryHeadCIFAR, self).__init__()
self.features = nn.Sequential(
nn.ReLU(inplace=True),
nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
nn.Conv2d(C, 128, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 768, 2, bias=False),
nn.BatchNorm2d(768),
nn.ReLU(inplace=True)
)
self.classifier = nn.Linear(768, num_classes) | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | medusa/providers/generic_provider.py | python | GenericProvider.create_magnet | (self, filename, result) | return False | Create a .magnet file containing the Magnet URI.
:param filename: base filename (without extension).
:param result: SearchResult object.
:returns: True if the magnet file is created and verified. | Create a .magnet file containing the Magnet URI. | [
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"."
] | def create_magnet(self, filename, result):
"""
Create a .magnet file containing the Magnet URI.
:param filename: base filename (without extension).
:param result: SearchResult object.
:returns: True if the magnet file is created and verified.
"""
filename_ext = '{filename}.magnet'.format(filename=filename)
log.info('Saving magnet file {result} to {location}',
{'result': result.name, 'location': filename_ext})
with open(filename_ext, 'w', encoding='utf-8') as fp:
fp.write(result.url)
if self._verify_magnet(filename_ext):
log.info('Saved .magnet file {result} to {location}',
{'result': result.name, 'location': filename_ext})
chmod_as_parent(filename)
return True
return False | [
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PyHDI/veriloggen | 2382d200deabf59cfcfd741f5eba371010aaf2bb | veriloggen/thread/fifo.py | python | FIFO.empty | (self) | return self.rif.empty | [] | def empty(self):
return self.rif.empty | [
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MaybeShewill-CV/lanenet-lane-detection | 94be8d9a66c7bbda5ad99b10492bc406c8aee45f | semantic_segmentation_zoo/vgg16_based_fcn.py | python | VGG16FCN._vgg16_fcn_encode | (self, input_tensor, name) | return | :param input_tensor:
:param name:
:return: | [] | def _vgg16_fcn_encode(self, input_tensor, name):
"""
:param input_tensor:
:param name:
:return:
"""
with tf.variable_scope(name_or_scope=name):
# encode stage 1
conv_1_1 = self._vgg16_conv_stage(
input_tensor=input_tensor, k_size=3,
out_dims=64, name='conv1_1',
need_layer_norm=True
)
conv_1_2 = self._vgg16_conv_stage(
input_tensor=conv_1_1, k_size=3,
out_dims=64, name='conv1_2',
need_layer_norm=True
)
self._net_intermediate_results['encode_stage_1_share'] = {
'data': conv_1_2,
'shape': conv_1_2.get_shape().as_list()
}
# encode stage 2
pool1 = self.maxpooling(
inputdata=conv_1_2, kernel_size=2,
stride=2, name='pool1'
)
conv_2_1 = self._vgg16_conv_stage(
input_tensor=pool1, k_size=3,
out_dims=128, name='conv2_1',
need_layer_norm=True
)
conv_2_2 = self._vgg16_conv_stage(
input_tensor=conv_2_1, k_size=3,
out_dims=128, name='conv2_2',
need_layer_norm=True
)
self._net_intermediate_results['encode_stage_2_share'] = {
'data': conv_2_2,
'shape': conv_2_2.get_shape().as_list()
}
# encode stage 3
pool2 = self.maxpooling(
inputdata=conv_2_2, kernel_size=2,
stride=2, name='pool2'
)
conv_3_1 = self._vgg16_conv_stage(
input_tensor=pool2, k_size=3,
out_dims=256, name='conv3_1',
need_layer_norm=True
)
conv_3_2 = self._vgg16_conv_stage(
input_tensor=conv_3_1, k_size=3,
out_dims=256, name='conv3_2',
need_layer_norm=True
)
conv_3_3 = self._vgg16_conv_stage(
input_tensor=conv_3_2, k_size=3,
out_dims=256, name='conv3_3',
need_layer_norm=True
)
self._net_intermediate_results['encode_stage_3_share'] = {
'data': conv_3_3,
'shape': conv_3_3.get_shape().as_list()
}
# encode stage 4
pool3 = self.maxpooling(
inputdata=conv_3_3, kernel_size=2,
stride=2, name='pool3'
)
conv_4_1 = self._vgg16_conv_stage(
input_tensor=pool3, k_size=3,
out_dims=512, name='conv4_1',
need_layer_norm=True
)
conv_4_2 = self._vgg16_conv_stage(
input_tensor=conv_4_1, k_size=3,
out_dims=512, name='conv4_2',
need_layer_norm=True
)
conv_4_3 = self._vgg16_conv_stage(
input_tensor=conv_4_2, k_size=3,
out_dims=512, name='conv4_3',
need_layer_norm=True
)
self._net_intermediate_results['encode_stage_4_share'] = {
'data': conv_4_3,
'shape': conv_4_3.get_shape().as_list()
}
# encode stage 5 for binary segmentation
pool4 = self.maxpooling(
inputdata=conv_4_3, kernel_size=2,
stride=2, name='pool4'
)
conv_5_1_binary = self._vgg16_conv_stage(
input_tensor=pool4, k_size=3,
out_dims=512, name='conv5_1_binary',
need_layer_norm=True
)
conv_5_2_binary = self._vgg16_conv_stage(
input_tensor=conv_5_1_binary, k_size=3,
out_dims=512, name='conv5_2_binary',
need_layer_norm=True
)
conv_5_3_binary = self._vgg16_conv_stage(
input_tensor=conv_5_2_binary, k_size=3,
out_dims=512, name='conv5_3_binary',
need_layer_norm=True
)
self._net_intermediate_results['encode_stage_5_binary'] = {
'data': conv_5_3_binary,
'shape': conv_5_3_binary.get_shape().as_list()
}
# encode stage 5 for instance segmentation
conv_5_1_instance = self._vgg16_conv_stage(
input_tensor=pool4, k_size=3,
out_dims=512, name='conv5_1_instance',
need_layer_norm=True
)
conv_5_2_instance = self._vgg16_conv_stage(
input_tensor=conv_5_1_instance, k_size=3,
out_dims=512, name='conv5_2_instance',
need_layer_norm=True
)
conv_5_3_instance = self._vgg16_conv_stage(
input_tensor=conv_5_2_instance, k_size=3,
out_dims=512, name='conv5_3_instance',
need_layer_norm=True
)
self._net_intermediate_results['encode_stage_5_instance'] = {
'data': conv_5_3_instance,
'shape': conv_5_3_instance.get_shape().as_list()
}
return | [
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inspurer/WorkAttendanceSystem | 1221e2d67bdf5bb15fe99517cc3ded58ccb066df | V2.0/venv/Lib/site-packages/pip-9.0.1-py3.5.egg/pip/_vendor/distro.py | python | version | (pretty=False, best=False) | return _distro.version(pretty, best) | Return the version of the current Linux distribution, as a human-readable
string.
If *pretty* is false, the version is returned without codename (e.g.
"7.0").
If *pretty* is true, the codename in parenthesis is appended, if the
codename is non-empty (e.g. "7.0 (Maipo)").
Some distributions provide version numbers with different precisions in
the different sources of distribution information. Examining the different
sources in a fixed priority order does not always yield the most precise
version (e.g. for Debian 8.2, or CentOS 7.1).
The *best* parameter can be used to control the approach for the returned
version:
If *best* is false, the first non-empty version number in priority order of
the examined sources is returned.
If *best* is true, the most precise version number out of all examined
sources is returned.
**Lookup hierarchy:**
In all cases, the version number is obtained from the following sources.
If *best* is false, this order represents the priority order:
* the value of the "VERSION_ID" attribute of the os-release file,
* the value of the "Release" attribute returned by the lsb_release
command,
* the version number parsed from the "<version_id>" field of the first line
of the distro release file,
* the version number parsed from the "PRETTY_NAME" attribute of the
os-release file, if it follows the format of the distro release files.
* the version number parsed from the "Description" attribute returned by
the lsb_release command, if it follows the format of the distro release
files. | Return the version of the current Linux distribution, as a human-readable
string. | [
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"""
Return the version of the current Linux distribution, as a human-readable
string.
If *pretty* is false, the version is returned without codename (e.g.
"7.0").
If *pretty* is true, the codename in parenthesis is appended, if the
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Some distributions provide version numbers with different precisions in
the different sources of distribution information. Examining the different
sources in a fixed priority order does not always yield the most precise
version (e.g. for Debian 8.2, or CentOS 7.1).
The *best* parameter can be used to control the approach for the returned
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If *best* is false, the first non-empty version number in priority order of
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If *best* is true, the most precise version number out of all examined
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**Lookup hierarchy:**
In all cases, the version number is obtained from the following sources.
If *best* is false, this order represents the priority order:
* the value of the "VERSION_ID" attribute of the os-release file,
* the value of the "Release" attribute returned by the lsb_release
command,
* the version number parsed from the "<version_id>" field of the first line
of the distro release file,
* the version number parsed from the "PRETTY_NAME" attribute of the
os-release file, if it follows the format of the distro release files.
* the version number parsed from the "Description" attribute returned by
the lsb_release command, if it follows the format of the distro release
files.
"""
return _distro.version(pretty, best) | [
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/access_policy_summary_dto.py | python | AccessPolicySummaryDTO.versioned_component_id | (self, versioned_component_id) | Sets the versioned_component_id of this AccessPolicySummaryDTO.
The ID of the corresponding component that is under version control
:param versioned_component_id: The versioned_component_id of this AccessPolicySummaryDTO.
:type: str | Sets the versioned_component_id of this AccessPolicySummaryDTO.
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:param versioned_component_id: The versioned_component_id of this AccessPolicySummaryDTO.
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reddit-archive/reddit | 753b17407e9a9dca09558526805922de24133d53 | r2/r2/models/query_cache.py | python | CachedQueryMutator.send | (self) | Commit the mutations batched up so far and potentially do pruning.
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This is automatically called by __exit__ when used as a context
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"""
self.mutator.send()
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lightkurve/lightkurve | 70d1c4cd1ab30f24c83e54bdcea4dd16624bfd9c | src/lightkurve/targetpixelfile.py | python | TargetPixelFileFactory._make_aperture_extension | (self) | return hdu | Create the aperture mask extension (i.e. extension #2). | Create the aperture mask extension (i.e. extension #2). | [
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"""Create the aperture mask extension (i.e. extension #2)."""
mask = 3 * np.ones((self.n_rows, self.n_cols), dtype="int32")
hdu = fits.ImageHDU(mask)
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template = self._header_template(2)
for kw in template:
if kw not in ["XTENSION", "NAXIS1", "NAXIS2", "CHECKSUM", "BITPIX"]:
try:
hdu.header[kw] = (self.keywords[kw], self.keywords.comments[kw])
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hdu.header[kw] = (template[kw], template.comments[kw])
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hdu.header[keyword] = "" # override wcs keywords
hdu.header["EXTNAME"] = "APERTURE"
return hdu | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/writing.py | python | SegmentWriter._merge_segments | (self, mergetype, optimize, merge) | return mergetype(self, self.segments) | [] | def _merge_segments(self, mergetype, optimize, merge):
# The writer supports two ways of setting mergetype/optimize/merge:
# as attributes or as keyword arguments to commit(). Originally there
# were just the keyword arguments, but then I added the ability to use
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# explicitly called commit(), hence the attributes
mergetype = mergetype if mergetype is not None else self.mergetype
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mergetype = OPTIMIZE
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mergetype = NO_MERGE
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mergetype = MERGE_SMALL
# Call the merge policy function. The policy may choose to merge
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vertexproject/synapse | 8173f43cb5fba5ca2648d12a659afb432139b0a7 | synapse/lib/agenda.py | python | Agenda._addappt | (self, iden, appt) | Updates the data structures to add an appointment | Updates the data structures to add an appointment | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/ctp/futures/ApiStruct.py | python | ExchangeExecOrder.__init__ | (self, Volume=0, RequestID=0, BusinessUnit='', OffsetFlag=OF_Open, HedgeFlag=HF_Speculation, ActionType=ACTP_Exec, PosiDirection=PD_Net, ReservePositionFlag=EOPF_Reserve, CloseFlag=EOCF_AutoClose, ExecOrderLocalID='', ExchangeID='', ParticipantID='', ClientID='', ExchangeInstID='', TraderID='', InstallID=0, OrderSubmitStatus=OSS_InsertSubmitted, NotifySequence=0, TradingDay='', SettlementID=0, ExecOrderSysID='', InsertDate='', InsertTime='', CancelTime='', ExecResult=OER_NoExec, ClearingPartID='', SequenceNo=0) | [] | def __init__(self, Volume=0, RequestID=0, BusinessUnit='', OffsetFlag=OF_Open, HedgeFlag=HF_Speculation, ActionType=ACTP_Exec, PosiDirection=PD_Net, ReservePositionFlag=EOPF_Reserve, CloseFlag=EOCF_AutoClose, ExecOrderLocalID='', ExchangeID='', ParticipantID='', ClientID='', ExchangeInstID='', TraderID='', InstallID=0, OrderSubmitStatus=OSS_InsertSubmitted, NotifySequence=0, TradingDay='', SettlementID=0, ExecOrderSysID='', InsertDate='', InsertTime='', CancelTime='', ExecResult=OER_NoExec, ClearingPartID='', SequenceNo=0):
self.Volume = '' #数量, int
self.RequestID = '' #请求编号, int
self.BusinessUnit = '' #业务单元, char[21]
self.OffsetFlag = '' #开平标志, char
self.HedgeFlag = '' #投机套保标志, char
self.ActionType = '' #执行类型, char
self.PosiDirection = '' #保留头寸申请的持仓方向, char
self.ReservePositionFlag = 'ExecOrderPositionFlag' #期权行权后是否保留期货头寸的标记, char
self.CloseFlag = 'ExecOrderCloseFlag' #期权行权后生成的头寸是否自动平仓, char
self.ExecOrderLocalID = 'OrderLocalID' #本地执行宣告编号, char[13]
self.ExchangeID = '' #交易所代码, char[9]
self.ParticipantID = '' #会员代码, char[11]
self.ClientID = '' #客户代码, char[11]
self.ExchangeInstID = '' #合约在交易所的代码, char[31]
self.TraderID = '' #交易所交易员代码, char[21]
self.InstallID = '' #安装编号, int
self.OrderSubmitStatus = '' #执行宣告提交状态, char
self.NotifySequence = 'SequenceNo' #报单提示序号, int
self.TradingDay = 'Date' #交易日, char[9]
self.SettlementID = '' #结算编号, int
self.ExecOrderSysID = '' #执行宣告编号, char[21]
self.InsertDate = 'Date' #报单日期, char[9]
self.InsertTime = 'Time' #插入时间, char[9]
self.CancelTime = 'Time' #撤销时间, char[9]
self.ExecResult = '' #执行结果, char
self.ClearingPartID = 'ParticipantID' #结算会员编号, char[11]
self.SequenceNo = '' | [
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bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Lib/http/cookies.py | python | BaseCookie.value_decode | (self, val) | return val, val | real_value, coded_value = value_decode(STRING)
Called prior to setting a cookie's value from the network
representation. The VALUE is the value read from HTTP
header.
Override this function to modify the behavior of cookies. | real_value, coded_value = value_decode(STRING)
Called prior to setting a cookie's value from the network
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AmesCornish/buttersink | 5cc37e30d9f8071fcf3497dca8b8a91b910321ea | buttersink/send.py | python | TLV_PUT_BYTES | (attrs, attrNum, value) | Put a tag-length-value encoded attribute as bytes. | Put a tag-length-value encoded attribute as bytes. | [
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Anaconda-Platform/anaconda-project | df5ec33c12591e6512436d38d36c6132fa2e9618 | anaconda_project/requirements_registry/registry.py | python | RequirementsRegistry.find_requirement_by_env_var | (self, env_var, options) | return EnvVarRequirement(registry=self, env_var=env_var, options=options) | Create a requirement instance given an environment variable name.
Args:
env_var (str): environment variable name
options (dict): options from the project file for this requirement
Returns:
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"""Create a requirement instance given an environment variable name.
Args:
env_var (str): environment variable name
options (dict): options from the project file for this requirement
Returns:
instance of Requirement
"""
from .requirement import EnvVarRequirement
return EnvVarRequirement(registry=self, env_var=env_var, options=options) | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/django/forms/fields.py | python | DecimalField.validate | (self, value) | [] | def validate(self, value):
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/lib-tk/ttk.py | python | Treeview.delete | (self, *items) | Delete all specified items and all their descendants. The root
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mitre-attack/attack-scripts | b94e05c0a29a6fdfc9701c721fcf03cdf9f7945b | scripts/techniques_data_sources_vis.py | python | write_DPT | (output_directory) | writes a CSV that links techniques, defenses, permissions, and tactics.
specifically, the output will link:
a specific technique, to
a defense the technique bypassess, to
the permissions needed to run the technique,
repeated for each defense and tactic that is linked to the technique
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/limits/storage.py | python | RedisSentinelStorage.get | (self, key) | return super(RedisStorage, self).get(key, self.storage_slave) | :param str key: the key to get the counter value for | :param str key: the key to get the counter value for | [
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jodal/pyspotify | 770aee08de274951b63e60bdd8835188b58e7862 | spotify/track.py | python | Track.album | (self) | return spotify.Album(self._session, sp_album=sp_album, add_ref=True) | The album of the track.
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"""
spotify.Error.maybe_raise(self.error, ignores=[spotify.ErrorType.IS_LOADING])
if not self.is_loaded:
return None
sp_album = lib.sp_track_album(self._sp_track)
return spotify.Album(self._session, sp_album=sp_album, add_ref=True) | [
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jerryli27/TwinGAN | 4e5593445778dfb77af9f815b3f4fcafc35758dc | nets/nasnet/nasnet.py | python | nasnet_cifar_arg_scope | (weight_decay=5e-4,
batch_norm_decay=0.9,
batch_norm_epsilon=1e-5) | Defines the default arg scope for the NASNet-A Cifar model.
Args:
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: Decay for batch norm moving average.
batch_norm_epsilon: Small float added to variance to avoid dividing by zero
in batch norm.
Returns:
An `arg_scope` to use for the NASNet Cifar Model. | Defines the default arg scope for the NASNet-A Cifar model. | [
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batch_norm_decay=0.9,
batch_norm_epsilon=1e-5):
"""Defines the default arg scope for the NASNet-A Cifar model.
Args:
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: Decay for batch norm moving average.
batch_norm_epsilon: Small float added to variance to avoid dividing by zero
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Returns:
An `arg_scope` to use for the NASNet Cifar Model.
"""
batch_norm_params = {
# Decay for the moving averages.
'decay': batch_norm_decay,
# epsilon to prevent 0s in variance.
'epsilon': batch_norm_epsilon,
'scale': True,
'fused': True,
}
weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
weights_initializer = tf.contrib.layers.variance_scaling_initializer(
mode='FAN_OUT')
with arg_scope([slim.fully_connected, slim.conv2d, slim.separable_conv2d],
weights_regularizer=weights_regularizer,
weights_initializer=weights_initializer):
with arg_scope([slim.fully_connected],
activation_fn=None, scope='FC'):
with arg_scope([slim.conv2d, slim.separable_conv2d],
activation_fn=None, biases_initializer=None):
with arg_scope([slim.batch_norm], **batch_norm_params) as sc:
return sc | [
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thatmattlove/hyperglass | c52a6f609843177671d38bcad59b8bd658f46b64 | hyperglass/models/config/devices.py | python | Device.__hash__ | (self) | return hash((self.name,)) | Make device object hashable so the object can be deduplicated with set(). | Make device object hashable so the object can be deduplicated with set(). | [
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HymanLiuTS/flaskTs | 286648286976e85d9b9a5873632331efcafe0b21 | flasky/lib/python2.7/site-packages/werkzeug/datastructures.py | python | ETags.to_header | (self) | return ', '.join(
['"%s"' % x for x in self._strong] +
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"""Convert the etags set into a HTTP header string."""
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BMW-InnovationLab/BMW-TensorFlow-Training-GUI | 4f10d1f00f9ac312ca833e5b28fd0f8952cfee17 | training_api/research/object_detection/inputs.py | python | create_eval_input_fn | (eval_config, eval_input_config, model_config) | return _eval_input_fn | Creates an eval `input` function for `Estimator`.
# TODO(ronnyvotel,rathodv): Allow batch sizes of more than 1 for eval.
Args:
eval_config: An eval_pb2.EvalConfig.
eval_input_config: An input_reader_pb2.InputReader.
model_config: A model_pb2.DetectionModel.
Returns:
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] | def create_eval_input_fn(eval_config, eval_input_config, model_config):
"""Creates an eval `input` function for `Estimator`.
# TODO(ronnyvotel,rathodv): Allow batch sizes of more than 1 for eval.
Args:
eval_config: An eval_pb2.EvalConfig.
eval_input_config: An input_reader_pb2.InputReader.
model_config: A model_pb2.DetectionModel.
Returns:
`input_fn` for `Estimator` in EVAL mode.
"""
def _eval_input_fn(params=None):
"""Returns `features` and `labels` tensor dictionaries for evaluation.
Args:
params: Parameter dictionary passed from the estimator.
Returns:
A tf.data.Dataset that holds (features, labels) tuple.
features: Dictionary of feature tensors.
features[fields.InputDataFields.image] is a [1, H, W, C] float32 tensor
with preprocessed images.
features[HASH_KEY] is a [1] int32 tensor representing unique
identifiers for the images.
features[fields.InputDataFields.true_image_shape] is a [1, 3]
int32 tensor representing the true image shapes, as preprocessed
images could be padded.
features[fields.InputDataFields.original_image] is a [1, H', W', C]
float32 tensor with the original image.
labels: Dictionary of groundtruth tensors.
labels[fields.InputDataFields.groundtruth_boxes] is a [1, num_boxes, 4]
float32 tensor containing the corners of the groundtruth boxes.
labels[fields.InputDataFields.groundtruth_classes] is a
[num_boxes, num_classes] float32 one-hot tensor of classes.
labels[fields.InputDataFields.groundtruth_area] is a [1, num_boxes]
float32 tensor containing object areas.
labels[fields.InputDataFields.groundtruth_is_crowd] is a [1, num_boxes]
bool tensor indicating if the boxes enclose a crowd.
labels[fields.InputDataFields.groundtruth_difficult] is a [1, num_boxes]
int32 tensor indicating if the boxes represent difficult instances.
-- Optional --
labels[fields.InputDataFields.groundtruth_instance_masks] is a
[1, num_boxes, H, W] float32 tensor containing only binary values,
which represent instance masks for objects.
Raises:
TypeError: if the `eval_config`, `eval_input_config` or `model_config`
are not of the correct type.
"""
params = params or {}
if not isinstance(eval_config, eval_pb2.EvalConfig):
raise TypeError('For eval mode, the `eval_config` must be a '
'train_pb2.EvalConfig.')
if not isinstance(eval_input_config, input_reader_pb2.InputReader):
raise TypeError('The `eval_input_config` must be a '
'input_reader_pb2.InputReader.')
if not isinstance(model_config, model_pb2.DetectionModel):
raise TypeError('The `model_config` must be a '
'model_pb2.DetectionModel.')
def transform_and_pad_input_data_fn(tensor_dict):
"""Combines transform and pad operation."""
num_classes = config_util.get_number_of_classes(model_config)
model = model_builder.build(model_config, is_training=False)
image_resizer_config = config_util.get_image_resizer_config(model_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
transform_data_fn = functools.partial(
transform_input_data, model_preprocess_fn=model.preprocess,
image_resizer_fn=image_resizer_fn,
num_classes=num_classes,
data_augmentation_fn=None,
retain_original_image=eval_config.retain_original_images)
tensor_dict = pad_input_data_to_static_shapes(
tensor_dict=transform_data_fn(tensor_dict),
max_num_boxes=eval_input_config.max_number_of_boxes,
num_classes=config_util.get_number_of_classes(model_config),
spatial_image_shape=config_util.get_spatial_image_size(
image_resizer_config))
return (_get_features_dict(tensor_dict), _get_labels_dict(tensor_dict))
dataset = INPUT_BUILDER_UTIL_MAP['dataset_build'](
eval_input_config,
batch_size=1, # Currently only support batch size of 1 for eval.
transform_input_data_fn=transform_and_pad_input_data_fn)
return dataset
return _eval_input_fn | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/stringprep.py | python | in_table_c21_c22 | (code) | return unicodedata.category(code) == "Cc" or \
ord(code) in c22_specials | [] | def in_table_c21_c22(code):
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fablabnbg/inkscape-silhouette | e0ad65115b4b2b0a5b60dfecd9a3ca03ec1d6bec | silhouette/pyusb-1.0.2/usb/_objfinalizer.py | python | _AutoFinalizedObjectBase.finalize | (self) | Finalizes the object if not already done.
Returns: None | Finalizes the object if not already done. | [
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"""Finalizes the object if not already done.
Returns: None
"""
# this is the "public" finalize method
raise NotImplementedError(
"finalize() must be implemented by AutoFinalizedObject."
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/logging/__init__.py | python | BufferingFormatter.__init__ | (self, linefmt=None) | Optionally specify a formatter which will be used to format each
individual record. | Optionally specify a formatter which will be used to format each
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"""
Optionally specify a formatter which will be used to format each
individual record.
"""
if linefmt:
self.linefmt = linefmt
else:
self.linefmt = _defaultFormatter | [
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ProjectQ-Framework/ProjectQ | 0d32c1610ba4e9aefd7f19eb52dadb4fbe5f9005 | projectq/ops/_gates.py | python | BarrierGate.__str__ | (self) | return "Barrier" | Return a string representation of the object. | Return a string representation of the object. | [
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bashtage/linearmodels | 9256269f01ff8c5f85e65342d66149a5636661b6 | linearmodels/panel/results.py | python | PanelResults._out_of_sample | (
self, exog: OptionalArrayLike, data: Optional[DataFrame], missing: bool
) | return pred | Interface between model predict and predict for OOS fits | Interface between model predict and predict for OOS fits | [
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self, exog: OptionalArrayLike, data: Optional[DataFrame], missing: bool
) -> DataFrame:
"""Interface between model predict and predict for OOS fits"""
if exog is not None and data is not None:
raise ValueError(
"Predictions can only be constructed using one "
"of exog or data, but not both."
)
pred = self.model.predict(self.params, exog=exog, data=data)
if not missing:
pred = pred.loc[pred.notnull().all(1)]
return pred | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | dist/lib/python2.7/fractions.py | python | Fraction.__le__ | (a, b) | return a._richcmp(b, operator.le) | a <= b | a <= b | [
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aliyun/aliyun-oss-python-sdk | 5f2afa0928a58c7c1cc6317ac147f3637481f6fd | oss2/models.py | python | TaggingRule.len | (self) | return len(self.tagging_rule) | [] | def len(self):
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cal-pratt/SheetVision | f1ce1c9c97d5c922aa95b9120152f9c62fab829d | MIDIUtil-0.89/MIDIUtil-0.89/src/midiutil/MidiFile.py | python | MIDIFile.shiftTracks | (self, offset=0) | Shift tracks to be zero-origined, or origined at offset.
Note that the shifting of the time in the tracks uses the MIDIEventList -- in other
words it is assumed to be called in the stage where the MIDIEventList has been
created. This function, however, it meant to operate on the eventList itself. | Shift tracks to be zero-origined, or origined at offset.
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origin = 1000000 # A little silly, but we'll assume big enough
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event.time = adjustedTime + offset
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/SailPointIdentityNow/Integrations/SailPointIdentityNow/SailPointIdentityNow.py | python | Client.send_request | (self, url_suffix: str, method: str, params=None, json_data=None) | return self._http_request(url_suffix=url_suffix, method=method, json_data=json_data, params=params,
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:type url_suffix: ``str``
:param url_suffix: IdentityNow API endpoint ext suffix.
:type method: ``str``
:param method: HTTP method, e.g. 'GET', 'POST', 'PUT', 'DELETE'.
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"""
Perform a HTTP request to IdentityNow API.
:type url_suffix: ``str``
:param url_suffix: IdentityNow API endpoint ext suffix.
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"""
if url_suffix is None or method is None:
return None
return self._http_request(url_suffix=url_suffix, method=method, json_data=json_data, params=params,
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/compiler/ast.py | python | Break.getChildren | (self) | return () | [] | def getChildren(self):
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] | https://github.com/oilshell/oil/blob/94388e7d44a9ad879b12615f6203b38596b5a2d3/Python-2.7.13/Lib/compiler/ast.py#L272-L273 | |||
intuition-io/intuition | cd517e6b3b315a743eb4d0d0dc294e264ab913ce | intuition/data/remote.py | python | lookup_symbol | (company) | return json.loads(request.text[39:-1])["ResultSet"]["Result"] \
if request.ok else {'error': request.reason} | Fetch company info
>>> lookup_symbol("Apple Inc.")
{u'exch': u'NMS',
u'market': u'NASDAQ',
u'name': u'Apple Inc.',
u'symbol': u'AAPL',
u'type': u'Equity'} | Fetch company info
>>> lookup_symbol("Apple Inc.")
{u'exch': u'NMS',
u'market': u'NASDAQ',
u'name': u'Apple Inc.',
u'symbol': u'AAPL',
u'type': u'Equity'} | [
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''' Fetch company info
>>> lookup_symbol("Apple Inc.")
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u'symbol': u'AAPL',
u'type': u'Equity'}
'''
request = requests.get(FINANCE_URLS['info_lookup'].format(company))
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karlch/vimiv | acbb83e003805e5304131be1f73d7f66528606d6 | vimiv/transform.py | python | Transform.rotate_auto | (self) | Autorotate all pictures in the current pathlist. | Autorotate all pictures in the current pathlist. | [
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] | def rotate_auto(self):
"""Autorotate all pictures in the current pathlist."""
autorotate = imageactions.Autorotate(self._app.get_paths())
self.threads_running = True
autorotate.connect("completed", self._on_autorotate_completed)
autorotate.run() | [
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yuantailing/ctw-baseline | 081c8361b0ed0675bf619ab6316ebf8415e93f46 | classification/slim/nets/inception_resnet_v2.py | python | inception_resnet_v2_base | (inputs,
final_endpoint='Conv2d_7b_1x1',
output_stride=16,
align_feature_maps=False,
scope=None) | Inception model from http://arxiv.org/abs/1602.07261.
Constructs an Inception Resnet v2 network from inputs to the given final
endpoint. This method can construct the network up to the final inception
block Conv2d_7b_1x1.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
output_stride: A scalar that specifies the requested ratio of input to
output spatial resolution. Only supports 8 and 16.
align_feature_maps: When true, changes all the VALID paddings in the network
to SAME padding so that the feature maps are aligned.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or if the output_stride is not 8 or 16, or if the output_stride is 8 and
we request an end point after 'PreAuxLogits'. | Inception model from http://arxiv.org/abs/1602.07261. | [
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final_endpoint='Conv2d_7b_1x1',
output_stride=16,
align_feature_maps=False,
scope=None):
"""Inception model from http://arxiv.org/abs/1602.07261.
Constructs an Inception Resnet v2 network from inputs to the given final
endpoint. This method can construct the network up to the final inception
block Conv2d_7b_1x1.
Args:
inputs: a tensor of size [batch_size, height, width, channels].
final_endpoint: specifies the endpoint to construct the network up to. It
can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
output_stride: A scalar that specifies the requested ratio of input to
output spatial resolution. Only supports 8 and 16.
align_feature_maps: When true, changes all the VALID paddings in the network
to SAME padding so that the feature maps are aligned.
scope: Optional variable_scope.
Returns:
tensor_out: output tensor corresponding to the final_endpoint.
end_points: a set of activations for external use, for example summaries or
losses.
Raises:
ValueError: if final_endpoint is not set to one of the predefined values,
or if the output_stride is not 8 or 16, or if the output_stride is 8 and
we request an end point after 'PreAuxLogits'.
"""
if output_stride != 8 and output_stride != 16:
raise ValueError('output_stride must be 8 or 16.')
padding = 'SAME' if align_feature_maps else 'VALID'
end_points = {}
def add_and_check_final(name, net):
end_points[name] = net
return name == final_endpoint
with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# 149 x 149 x 32
net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding,
scope='Conv2d_1a_3x3')
if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points
# 147 x 147 x 32
net = slim.conv2d(net, 32, 3, padding=padding,
scope='Conv2d_2a_3x3')
if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points
# 147 x 147 x 64
net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3')
if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points
# 73 x 73 x 64
net = slim.max_pool2d(net, 3, stride=2, padding=padding,
scope='MaxPool_3a_3x3')
if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points
# 73 x 73 x 80
net = slim.conv2d(net, 80, 1, padding=padding,
scope='Conv2d_3b_1x1')
if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points
# 71 x 71 x 192
net = slim.conv2d(net, 192, 3, padding=padding,
scope='Conv2d_4a_3x3')
if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points
# 35 x 35 x 192
net = slim.max_pool2d(net, 3, stride=2, padding=padding,
scope='MaxPool_5a_3x3')
if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points
# 35 x 35 x 320
with tf.variable_scope('Mixed_5b'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5,
scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3,
scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3,
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME',
scope='AvgPool_0a_3x3')
tower_pool_1 = slim.conv2d(tower_pool, 64, 1,
scope='Conv2d_0b_1x1')
net = tf.concat(
[tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3)
if add_and_check_final('Mixed_5b', net): return net, end_points
# TODO(alemi): Register intermediate endpoints
net = slim.repeat(net, 10, block35, scale=0.17)
# 17 x 17 x 1088 if output_stride == 8,
# 33 x 33 x 1088 if output_stride == 16
use_atrous = output_stride == 8
with tf.variable_scope('Mixed_6a'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2,
padding=padding,
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3,
scope='Conv2d_0b_3x3')
tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3,
stride=1 if use_atrous else 2,
padding=padding,
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2,
padding=padding,
scope='MaxPool_1a_3x3')
net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3)
if add_and_check_final('Mixed_6a', net): return net, end_points
# TODO(alemi): register intermediate endpoints
with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1):
net = slim.repeat(net, 20, block17, scale=0.10)
if add_and_check_final('PreAuxLogits', net): return net, end_points
if output_stride == 8:
# TODO(gpapan): Properly support output_stride for the rest of the net.
raise ValueError('output_stride==8 is only supported up to the '
'PreAuxlogits end_point for now.')
# 8 x 8 x 2080
with tf.variable_scope('Mixed_7a'):
with tf.variable_scope('Branch_0'):
tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2,
padding=padding,
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2,
padding=padding,
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1')
tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3,
scope='Conv2d_0b_3x3')
tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2,
padding=padding,
scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_3'):
tower_pool = slim.max_pool2d(net, 3, stride=2,
padding=padding,
scope='MaxPool_1a_3x3')
net = tf.concat(
[tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3)
if add_and_check_final('Mixed_7a', net): return net, end_points
# TODO(alemi): register intermediate endpoints
net = slim.repeat(net, 9, block8, scale=0.20)
net = block8(net, activation_fn=None)
# 8 x 8 x 1536
net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1')
if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points
raise ValueError('final_endpoint (%s) not recognized', final_endpoint) | [
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OpenCobolIDE/OpenCobolIDE | c78d0d335378e5fe0a5e74f53c19b68b55e85388 | open_cobol_ide/extlibs/pygments/lexers/templates.py | python | CssDjangoLexer.__init__ | (self, **options) | [] | def __init__(self, **options):
super(CssDjangoLexer, self).__init__(CssLexer, DjangoLexer, **options) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | DistInfoDistribution._parsed_pkg_info | (self) | Parse and cache metadata | Parse and cache metadata | [
"Parse",
"and",
"cache",
"metadata"
] | def _parsed_pkg_info(self):
"""Parse and cache metadata"""
try:
return self._pkg_info
except AttributeError:
metadata = self.get_metadata(self.PKG_INFO)
self._pkg_info = email.parser.Parser().parsestr(metadata)
return self._pkg_info | [
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Tencent/bk-bcs-saas | 2b437bf2f5fd5ce2078f7787c3a12df609f7679d | bcs-app/backend/container_service/clusters/flow_views/node.py | python | CreateNode.create | (self) | return Response({}) | 添加节点
1. 检查节点是否可用
2. 触发OPS api | 添加节点
1. 检查节点是否可用
2. 触发OPS api | [
"添加节点",
"1",
".",
"检查节点是否可用",
"2",
".",
"触发OPS",
"api"
] | def create(self):
"""添加节点
1. 检查节点是否可用
2. 触发OPS api
"""
# 校验集群edit权限
self.check_perm()
# 校验数据
self.check_data()
self.ip_list = [ip.split(',')[0] for ip in self.data['ip']]
# 检测IP是否被占用
can_use_hosts(self.project_info["cc_app_id"], self.username, self.ip_list)
self.project_nodes = paas_cc.get_all_cluster_hosts(self.access_token)
self.check_node_ip()
# 获取已经存在的IP，直接更新使用
removed_ips, remained_ips = self.get_removed_remained_ips()
# 更新IP
self.update_nodes(removed_ips)
# 添加IP
self.add_nodes(remained_ips)
# 获取节点是否需要NAT
cluster_info = self.get_cluster_info()
self.need_nat = cluster_info.get('need_nat', True)
# 现阶段平台侧不主动创建CMDB set&module，赋值为空列表
self.module_id_list = []
# 请求ops api
with client.ContextActivityLogClient(
project_id=self.project_id,
user=self.username,
resource_type=ACTIVITY_RESOURCE_TYPE,
resource=','.join(self.ip_list)[:32],
).log_add():
# 更新所有节点为初始化中
node_info_list = self.update_cluster_nodes(self.ip_list)
log = self.create_node_by_bcs(node_info_list)
if not log.is_finished and log.is_polling:
log.polling_task()
return Response({}) | [
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Scalsol/mega.pytorch | a6aa6e0537b82d70da94228100a51e6a53d98f82 | mega_core/modeling/balanced_positive_negative_sampler.py | python | BalancedPositiveNegativeSampler.__init__ | (self, batch_size_per_image, positive_fraction) | Arguments:
batch_size_per_image (int): number of elements to be selected per image
positive_fraction (float): percentage of positive elements per batch | Arguments:
batch_size_per_image (int): number of elements to be selected per image
positive_fraction (float): percentage of positive elements per batch | [
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"""
Arguments:
batch_size_per_image (int): number of elements to be selected per image
positive_fraction (float): percentage of positive elements per batch
"""
self.batch_size_per_image = batch_size_per_image
self.positive_fraction = positive_fraction | [
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facebookresearch/hydra | 9b2f4d54b328d1551aa70a241a1d638cbe046367 | build_helpers/build_helpers.py | python | ANTLRCommand.run | (self) | Run command. | Run command. | [
"Run",
"command",
"."
] | def run(self) -> None:
"""Run command."""
root_dir = abspath(dirname(__file__))
project_root = abspath(dirname(basename(__file__)))
for grammar in [
"hydra/grammar/OverrideLexer.g4",
"hydra/grammar/OverrideParser.g4",
]:
command = [
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"-jar",
join(root_dir, "bin/antlr-4.8-complete.jar"),
"-Dlanguage=Python3",
"-o",
join(project_root, "hydra/grammar/gen/"),
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join(project_root, grammar),
]
self.announce(
f"Generating parser for Python3: {command}", level=distutils.log.INFO
)
subprocess.check_call(command) | [
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mahmoud/boltons | 270e974975984f662f998c8f6eb0ebebd964de82 | boltons/ioutils.py | python | SpooledBytesIO.seek | (self, pos, mode=0) | return self.buffer.seek(pos, mode) | [] | def seek(self, pos, mode=0):
return self.buffer.seek(pos, mode) | [
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")"
] | https://github.com/mahmoud/boltons/blob/270e974975984f662f998c8f6eb0ebebd964de82/boltons/ioutils.py#L269-L270 | |||
BillBillBillBill/Tickeys-linux | 2df31b8665004c58a5d4ab05277f245267d96364 | tickeys/kivy_32/kivy/core/window/__init__.py | python | WindowBase.toggle_fullscreen | (self) | Toggle between fullscreen and windowed mode.
.. deprecated:: 1.9.0
Use :attr:`fullscreen` instead. | Toggle between fullscreen and windowed mode. | [
"Toggle",
"between",
"fullscreen",
"and",
"windowed",
"mode",
"."
] | def toggle_fullscreen(self):
'''Toggle between fullscreen and windowed mode.
.. deprecated:: 1.9.0
Use :attr:`fullscreen` instead.
'''
pass | [
"def",
"toggle_fullscreen",
"(",
"self",
")",
":",
"pass"
] | https://github.com/BillBillBillBill/Tickeys-linux/blob/2df31b8665004c58a5d4ab05277f245267d96364/tickeys/kivy_32/kivy/core/window/__init__.py#L645-L651 | ||
softlayer/softlayer-python | cdef7d63c66413197a9a97b0414de9f95887a82a | SoftLayer/CLI/block/set_note.py | python | cli | (env, volume_id, note) | Set note for an existing block storage volume. | Set note for an existing block storage volume. | [
"Set",
"note",
"for",
"an",
"existing",
"block",
"storage",
"volume",
"."
] | def cli(env, volume_id, note):
"""Set note for an existing block storage volume."""
block_manager = SoftLayer.BlockStorageManager(env.client)
block_volume_id = helpers.resolve_id(block_manager.resolve_ids, volume_id, 'Block Volume')
result = block_manager.volume_set_note(block_volume_id, note)
if result:
click.echo("Set note successfully!")
else:
click.echo("Note could not be set! Please verify your options and try again.") | [
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... | https://github.com/softlayer/softlayer-python/blob/cdef7d63c66413197a9a97b0414de9f95887a82a/SoftLayer/CLI/block/set_note.py#L17-L28 | ||
akkana/scripts | bb59210acd3cef9a7eaccf5e082feff9d2b1b97d | gitbranchsync.py | python | check_push_status | (repo, silent=False) | return modfiles, alllocaldiffs, allremotediffs | Does this repo have changes that haven't been pushed upstream?
Print any info, but also return the number of changes that differ.
If silent is set, don't print anything, just calculate and return. | Does this repo have changes that haven't been pushed upstream?
Print any info, but also return the number of changes that differ.
If silent is set, don't print anything, just calculate and return. | [
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"a... | def check_push_status(repo, silent=False):
"""Does this repo have changes that haven't been pushed upstream?
Print any info, but also return the number of changes that differ.
If silent is set, don't print anything, just calculate and return.
"""
modfiles = 0
# git status --porcelain -uno
porcelain = repo.git.status(porcelain=True).splitlines()
for l in porcelain:
if not l.startswith("?? "):
if not silent:
if not modfiles:
print("Need to commit locally modified files:")
print(l)
modfiles += 1
if not silent:
print("")
differences = 0
alllocaldiffs = 0
allremotediffs = 0
# Another workaround for the git 2.11.0 bug:
try:
heads = repo.heads
except TypeError as e:
print("Skipping repo %s due to git bug:\n %s" % (repo.git_dir, str(e)))
return None, None, None
for ref in heads:
localdiffs, remotediffs = comprefs(ref)
alllocaldiffs += localdiffs
allremotediffs += remotediffs
upstream = ref.tracking_branch()
if not upstream:
# Can't push if there's no upstream!
continue
if localdiffs or remotediffs:
if not differences:
if not silent:
print("Need to push:")
needspush = True
elif not silent:
print("Up to date with %s" % upstream.name)
if localdiffs > 0 and not silent:
print(" %s is ahead of %s by %d commits" % (ref.name,
upstream.name,
localdiffs))
if remotediffs > 0 and not silent:
print(" %s is ahead of %s by %d commits" % (upstream.name,
ref.name, remotediffs))
# Perhaps temporarily, compare with the output of what I used before,
# git for-each-ref --format="%(refname:short) %(push:track)" refs/heads | fgrep '[ahead'
foreachref = repo.git.execute(['git', 'for-each-ref',
'--format="%(refname:short) %(push:track)"',
'refs/heads']).splitlines()
# git.execute weirdly adds " at the beginning and end of each line.
foundref = False
for line in foreachref:
if '[ahead' in line:
if line.startswith('"') and line.endswith('"'):
line = line[1:-1]
if not foundref:
if not silent:
print("for-each-ref says:")
foundref = True
if not silent:
print(" " + line)
return modfiles, alllocaldiffs, allremotediffs | [
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"fo... | https://github.com/akkana/scripts/blob/bb59210acd3cef9a7eaccf5e082feff9d2b1b97d/gitbranchsync.py#L62-L131 | |
bookwyrm-social/bookwyrm | 0c2537e27a2cdbc0136880dfbbf170d5fec72986 | bookwyrm/models/import_job.py | python | ImportItem.rating | (self) | return None | x/5 star rating for a book | x/5 star rating for a book | [
"x",
"/",
"5",
"star",
"rating",
"for",
"a",
"book"
] | def rating(self):
"""x/5 star rating for a book"""
if self.normalized_data.get("rating"):
return float(self.normalized_data.get("rating"))
return None | [
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] | https://github.com/bookwyrm-social/bookwyrm/blob/0c2537e27a2cdbc0136880dfbbf170d5fec72986/bookwyrm/models/import_job.py#L168-L172 | |
vslavik/bakefile | 0757295c3e4ac23cd1e0767c77c14c2256ed16e1 | 3rdparty/antlr3/python-runtime/antlr3/recognizers.py | python | BaseRecognizer.endResync | (self) | A hook to listen in on the token consumption during error recovery.
The DebugParser subclasses this to fire events to the listenter. | A hook to listen in on the token consumption during error recovery.
The DebugParser subclasses this to fire events to the listenter. | [
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"to",
"the",
"listenter",
"."
] | def endResync(self):
"""
A hook to listen in on the token consumption during error recovery.
The DebugParser subclasses this to fire events to the listenter.
"""
pass | [
"def",
"endResync",
"(",
"self",
")",
":",
"pass"
] | https://github.com/vslavik/bakefile/blob/0757295c3e4ac23cd1e0767c77c14c2256ed16e1/3rdparty/antlr3/python-runtime/antlr3/recognizers.py#L485-L491 | ||
dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/groups/models.py | python | Group.user_in_group | (cls, user_id, group_id) | [] | def user_in_group(cls, user_id, group_id):
if not user_id or not group_id:
return False
c = cls.get_db().view(
'groups/by_user',
key=user_id,
startkey_docid=group_id,
endkey_docid=group_id
).count()
if c == 0:
return False
elif c == 1:
return True
else:
raise Exception(
"This should just logically not be possible unless the group "
"has the user in there twice"
) | [
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"... | https://github.com/dimagi/commcare-hq/blob/d67ff1d3b4c51fa050c19e60c3253a79d3452a39/corehq/apps/groups/models.py#L255-L272 | ||||
ztosec/hunter | 4ee5cca8dc5fc5d7e631e935517bd0f493c30a37 | HunterCelery/common/http_util.py | python | get_parent_route | (url) | return result | 获得父级路由
:param url:
:return: | 获得父级路由
:param url:
:return: | [
"获得父级路由",
":",
"param",
"url",
":",
":",
"return",
":"
] | def get_parent_route(url):
"""
获得父级路由
:param url:
:return:
"""
parameter = re.split(r'/[^/]*$', url, re.S)
try:
result = parameter[0] + "/"
except IndexError:
result = url + "/"
if result == "https://" or result == "http://":
result = url + "/"
return result | [
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zzzeek/sqlalchemy | fc5c54fcd4d868c2a4c7ac19668d72f506fe821e | lib/sqlalchemy/sql/compiler.py | python | IdentifierPreparer._requires_quotes_illegal_chars | (self, value) | return not self.legal_characters.match(util.text_type(value)) | Return True if the given identifier requires quoting, but
not taking case convention into account. | Return True if the given identifier requires quoting, but
not taking case convention into account. | [
"Return",
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"given",
"identifier",
"requires",
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"but",
"not",
"taking",
"case",
"convention",
"into",
"account",
"."
] | def _requires_quotes_illegal_chars(self, value):
"""Return True if the given identifier requires quoting, but
not taking case convention into account."""
return not self.legal_characters.match(util.text_type(value)) | [
"def",
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"(",
"util",
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"text_type",
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")"
] | https://github.com/zzzeek/sqlalchemy/blob/fc5c54fcd4d868c2a4c7ac19668d72f506fe821e/lib/sqlalchemy/sql/compiler.py#L5124-L5127 | |
veusz/veusz | 5a1e2af5f24df0eb2a2842be51f2997c4999c7fb | veusz/windows/mainwindow.py | python | MainWindow.dropEvent | (self, event) | Respond to a drop event on the current window | Respond to a drop event on the current window | [
"Respond",
"to",
"a",
"drop",
"event",
"on",
"the",
"current",
"window"
] | def dropEvent(self, event):
"""Respond to a drop event on the current window"""
if event.mimeData().hasUrls():
files = self._getVeuszDropFiles(event)
if files:
if self.document.isBlank():
self.openFileInWindow(files[0])
else:
self.CreateWindow(files[0])
for filename in files[1:]:
self.CreateWindow(filename) | [
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... | https://github.com/veusz/veusz/blob/5a1e2af5f24df0eb2a2842be51f2997c4999c7fb/veusz/windows/mainwindow.py#L254-L264 | ||
googlearchive/appengine-flask-skeleton | 8c25461d003a0bd99a9ff3b339c2791ee6919242 | lib/jinja2/parser.py | python | Parser.parse_assign_target | (self, with_tuple=True, name_only=False,
extra_end_rules=None) | return target | Parse an assignment target. As Jinja2 allows assignments to
tuples, this function can parse all allowed assignment targets. Per
default assignments to tuples are parsed, that can be disable however
by setting `with_tuple` to `False`. If only assignments to names are
wanted `name_only` can be set to `True`. The `extra_end_rules`
parameter is forwarded to the tuple parsing function. | Parse an assignment target. As Jinja2 allows assignments to
tuples, this function can parse all allowed assignment targets. Per
default assignments to tuples are parsed, that can be disable however
by setting `with_tuple` to `False`. If only assignments to names are
wanted `name_only` can be set to `True`. The `extra_end_rules`
parameter is forwarded to the tuple parsing function. | [
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"assignment",
"targets",
".",
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"to",
"tuples",
"are",
"parsed",
"that",
"... | def parse_assign_target(self, with_tuple=True, name_only=False,
extra_end_rules=None):
"""Parse an assignment target. As Jinja2 allows assignments to
tuples, this function can parse all allowed assignment targets. Per
default assignments to tuples are parsed, that can be disable however
by setting `with_tuple` to `False`. If only assignments to names are
wanted `name_only` can be set to `True`. The `extra_end_rules`
parameter is forwarded to the tuple parsing function.
"""
if name_only:
token = self.stream.expect('name')
target = nodes.Name(token.value, 'store', lineno=token.lineno)
else:
if with_tuple:
target = self.parse_tuple(simplified=True,
extra_end_rules=extra_end_rules)
else:
target = self.parse_primary()
target.set_ctx('store')
if not target.can_assign():
self.fail('can\'t assign to %r' % target.__class__.
__name__.lower(), target.lineno)
return target | [
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