body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def get_lbs_for_center_crop(crop_size, data_shape):
'\n :param crop_size:\n :param data_shape: (b,c,x,y(,z)) must be the whole thing!\n :return:\n '
lbs = []
for i in range((len(data_shape) - 2)):
lbs.append(((data_shape[(i + 2)] - crop_size[i]) // 2))
return lbs | 5,384,268,792,373,000,000 | :param crop_size:
:param data_shape: (b,c,x,y(,z)) must be the whole thing!
:return: | data/crop_and_pad_augmentations.py | get_lbs_for_center_crop | bowang-lab/shape-attentive-unet | python | def get_lbs_for_center_crop(crop_size, data_shape):
'\n :param crop_size:\n :param data_shape: (b,c,x,y(,z)) must be the whole thing!\n :return:\n '
lbs = []
for i in range((len(data_shape) - 2)):
lbs.append(((data_shape[(i + 2)] - crop_size[i]) // 2))
return lbs |
def crop(data, seg=None, crop_size=128, margins=(0, 0, 0), crop_type='center', pad_mode='constant', pad_kwargs={'constant_values': 0}, pad_mode_seg='constant', pad_kwargs_seg={'constant_values': 0}):
'\n crops data and seg (seg may be None) to crop_size. Whether this will be achieved via center or random crop is... | -4,820,768,818,868,650,000 | crops data and seg (seg may be None) to crop_size. Whether this will be achieved via center or random crop is
determined by crop_type. Margin will be respected only for random_crop and will prevent the crops form being closer
than margin to the respective image border. crop_size can be larger than data_shape - margin -... | data/crop_and_pad_augmentations.py | crop | bowang-lab/shape-attentive-unet | python | def crop(data, seg=None, crop_size=128, margins=(0, 0, 0), crop_type='center', pad_mode='constant', pad_kwargs={'constant_values': 0}, pad_mode_seg='constant', pad_kwargs_seg={'constant_values': 0}):
'\n crops data and seg (seg may be None) to crop_size. Whether this will be achieved via center or random crop is... |
def pad_nd_image_and_seg(data, seg, new_shape=None, must_be_divisible_by=None, pad_mode_data='constant', np_pad_kwargs_data=None, pad_mode_seg='constant', np_pad_kwargs_seg=None):
'\n Pads data and seg to new_shape. new_shape is thereby understood as min_shape (if data/seg is already larger then\n new_shape t... | -694,869,453,499,894,400 | Pads data and seg to new_shape. new_shape is thereby understood as min_shape (if data/seg is already larger then
new_shape the shape stays the same for the dimensions this applies)
:param data:
:param seg:
:param new_shape: if none then only must_be_divisible_by is applied
:param must_be_divisible_by: UNet like archite... | data/crop_and_pad_augmentations.py | pad_nd_image_and_seg | bowang-lab/shape-attentive-unet | python | def pad_nd_image_and_seg(data, seg, new_shape=None, must_be_divisible_by=None, pad_mode_data='constant', np_pad_kwargs_data=None, pad_mode_seg='constant', np_pad_kwargs_seg=None):
'\n Pads data and seg to new_shape. new_shape is thereby understood as min_shape (if data/seg is already larger then\n new_shape t... |
def extract_leegstand(self):
'Create a column indicating leegstand (no inhabitants on the address).'
self.data['leegstand'] = (~ self.data.inwnrs.notnull())
self.version += '_leegstand'
self.save() | -4,992,713,222,237,245,000 | Create a column indicating leegstand (no inhabitants on the address). | codebase/datasets/adres_dataset.py | extract_leegstand | petercuret/woonfraude | python | def extract_leegstand(self):
self.data['leegstand'] = (~ self.data.inwnrs.notnull())
self.version += '_leegstand'
self.save() |
def enrich_with_woning_id(self):
'Add woning ids to the adres dataframe.'
adres_periodes = datasets.download_dataset('bwv_adres_periodes', 'bwv_adres_periodes')
self.data = self.data.merge(adres_periodes[['ads_id', 'wng_id']], how='left', left_on='adres_id', right_on='ads_id')
self.version += '_woningId... | 9,146,979,939,905,093,000 | Add woning ids to the adres dataframe. | codebase/datasets/adres_dataset.py | enrich_with_woning_id | petercuret/woonfraude | python | def enrich_with_woning_id(self):
adres_periodes = datasets.download_dataset('bwv_adres_periodes', 'bwv_adres_periodes')
self.data = self.data.merge(adres_periodes[['ads_id', 'wng_id']], how='left', left_on='adres_id', right_on='ads_id')
self.version += '_woningId'
self.save() |
def impute_values_for_bagless_addresses(self, adres):
'Impute values for adresses where no BAG-match could be found.'
clean.impute_missing_values(adres)
adres.fillna(value={'huisnummer_nummeraanduiding': 0, 'huisletter_nummeraanduiding': 'None', '_openbare_ruimte_naam_nummeraanduiding': 'None', 'huisnummer_... | -5,799,213,507,536,765,000 | Impute values for adresses where no BAG-match could be found. | codebase/datasets/adres_dataset.py | impute_values_for_bagless_addresses | petercuret/woonfraude | python | def impute_values_for_bagless_addresses(self, adres):
clean.impute_missing_values(adres)
adres.fillna(value={'huisnummer_nummeraanduiding': 0, 'huisletter_nummeraanduiding': 'None', '_openbare_ruimte_naam_nummeraanduiding': 'None', 'huisnummer_toevoeging_nummeraanduiding': 'None', 'type_woonobject_omschrij... |
def enrich_with_bag(self, bag):
'Enrich the adres data with information from the BAG data. Uses the bag dataframe as input.'
bag = self.prepare_bag(bag)
self.data = self.prepare_adres(self.data)
self.data = self.match_bwv_bag(self.data, bag)
self.data = self.replace_string_nan_adres(self.data)
s... | 2,526,807,197,943,869,400 | Enrich the adres data with information from the BAG data. Uses the bag dataframe as input. | codebase/datasets/adres_dataset.py | enrich_with_bag | petercuret/woonfraude | python | def enrich_with_bag(self, bag):
bag = self.prepare_bag(bag)
self.data = self.prepare_adres(self.data)
self.data = self.match_bwv_bag(self.data, bag)
self.data = self.replace_string_nan_adres(self.data)
self.data = self.impute_values_for_bagless_addresses(self.data)
self.version += '_bag'
... |
def enrich_with_personen_features(self, personen):
'Add aggregated features relating to persons to the address dataframe. Uses the personen dataframe as input.'
adres = self.data
today = pd.to_datetime('today')
personen['geboortedatum'] = pd.to_datetime(personen['geboortedatum'], errors='coerce')
ge... | -7,579,026,273,688,002,000 | Add aggregated features relating to persons to the address dataframe. Uses the personen dataframe as input. | codebase/datasets/adres_dataset.py | enrich_with_personen_features | petercuret/woonfraude | python | def enrich_with_personen_features(self, personen):
adres = self.data
today = pd.to_datetime('today')
personen['geboortedatum'] = pd.to_datetime(personen['geboortedatum'], errors='coerce')
geboortedatum_mode = personen['geboortedatum'].mode()[0]
personen['leeftijd'] = (today - personen['geboorte... |
def add_hotline_features(self, hotline):
'Add the hotline features to the adres dataframe.'
merge = self.data.merge(hotline, on='wng_id', how='left')
adres_groups = merge.groupby(by='adres_id')
hotline_counts = adres_groups['id'].agg(['count'])
hotline_counts.columns = ['aantal_hotline_meldingen']
... | 4,715,285,952,275,173,000 | Add the hotline features to the adres dataframe. | codebase/datasets/adres_dataset.py | add_hotline_features | petercuret/woonfraude | python | def add_hotline_features(self, hotline):
merge = self.data.merge(hotline, on='wng_id', how='left')
adres_groups = merge.groupby(by='adres_id')
hotline_counts = adres_groups['id'].agg(['count'])
hotline_counts.columns = ['aantal_hotline_meldingen']
self.data = self.data.merge(hotline_counts, on=... |
def SubPixel1D_v2(I, r):
'One-dimensional subpixel upsampling layer\n\n Based on https://github.com/Tetrachrome/subpixel/blob/master/subpixel.py\n '
with tf.compat.v1.name_scope('subpixel'):
(bsize, a, r) = I.get_shape().as_list()
bsize = tf.shape(input=I)[0]
X = tf.split(1, a, I)
... | 1,428,587,690,402,081,500 | One-dimensional subpixel upsampling layer
Based on https://github.com/Tetrachrome/subpixel/blob/master/subpixel.py | src/models/layers/subpixel.py | SubPixel1D_v2 | Lootwig/audio-super-res | python | def SubPixel1D_v2(I, r):
'One-dimensional subpixel upsampling layer\n\n Based on https://github.com/Tetrachrome/subpixel/blob/master/subpixel.py\n '
with tf.compat.v1.name_scope('subpixel'):
(bsize, a, r) = I.get_shape().as_list()
bsize = tf.shape(input=I)[0]
X = tf.split(1, a, I)
... |
def SubPixel1D(I, r):
'One-dimensional subpixel upsampling layer\n\n Calls a tensorflow function that directly implements this functionality.\n We assume input has dim (batch, width, r)\n '
with tf.compat.v1.name_scope('subpixel'):
X = tf.transpose(a=I, perm=[2, 1, 0])
X = tf.batch_to_space(X... | 6,580,163,009,517,961,000 | One-dimensional subpixel upsampling layer
Calls a tensorflow function that directly implements this functionality.
We assume input has dim (batch, width, r) | src/models/layers/subpixel.py | SubPixel1D | Lootwig/audio-super-res | python | def SubPixel1D(I, r):
'One-dimensional subpixel upsampling layer\n\n Calls a tensorflow function that directly implements this functionality.\n We assume input has dim (batch, width, r)\n '
with tf.compat.v1.name_scope('subpixel'):
X = tf.transpose(a=I, perm=[2, 1, 0])
X = tf.batch_to_space(X... |
def SubPixel1D_multichan(I, r):
'One-dimensional subpixel upsampling layer\n\n Calls a tensorflow function that directly implements this functionality.\n We assume input has dim (batch, width, r).\n\n Works with multiple channels: (B,L,rC) -> (B,rL,C)\n '
with tf.compat.v1.name_scope('subpixel'):
(_... | -7,981,073,372,711,496,000 | One-dimensional subpixel upsampling layer
Calls a tensorflow function that directly implements this functionality.
We assume input has dim (batch, width, r).
Works with multiple channels: (B,L,rC) -> (B,rL,C) | src/models/layers/subpixel.py | SubPixel1D_multichan | Lootwig/audio-super-res | python | def SubPixel1D_multichan(I, r):
'One-dimensional subpixel upsampling layer\n\n Calls a tensorflow function that directly implements this functionality.\n We assume input has dim (batch, width, r).\n\n Works with multiple channels: (B,L,rC) -> (B,rL,C)\n '
with tf.compat.v1.name_scope('subpixel'):
(_... |
def dkim_sign(message, dkim_domain=None, dkim_key=None, dkim_selector=None, dkim_headers=None):
'Return signed email message if dkim package and settings are available.'
try:
import dkim
except ImportError:
pass
else:
if (dkim_domain and dkim_key):
sig = dkim.sign(mes... | -6,159,254,177,365,536,000 | Return signed email message if dkim package and settings are available. | django_ses/__init__.py | dkim_sign | mlissner/django-ses | python | def dkim_sign(message, dkim_domain=None, dkim_key=None, dkim_selector=None, dkim_headers=None):
try:
import dkim
except ImportError:
pass
else:
if (dkim_domain and dkim_key):
sig = dkim.sign(message, dkim_selector, dkim_domain, dkim_key, include_headers=dkim_headers)... |
def cast_nonzero_to_float(val):
'Cast nonzero number to float; on zero or None, return None'
if (not val):
return None
return float(val) | 6,612,048,108,139,969,000 | Cast nonzero number to float; on zero or None, return None | django_ses/__init__.py | cast_nonzero_to_float | mlissner/django-ses | python | def cast_nonzero_to_float(val):
if (not val):
return None
return float(val) |
def open(self):
'Create a connection to the AWS API server. This can be reused for\n sending multiple emails.\n '
if self.connection:
return False
try:
self.connection = boto3.client('ses', aws_access_key_id=self._access_key_id, aws_secret_access_key=self._access_key, region_na... | -3,722,438,059,502,486,000 | Create a connection to the AWS API server. This can be reused for
sending multiple emails. | django_ses/__init__.py | open | mlissner/django-ses | python | def open(self):
'Create a connection to the AWS API server. This can be reused for\n sending multiple emails.\n '
if self.connection:
return False
try:
self.connection = boto3.client('ses', aws_access_key_id=self._access_key_id, aws_secret_access_key=self._access_key, region_na... |
def close(self):
'Close any open HTTP connections to the API server.\n '
self.connection = None | 3,509,590,564,129,190,400 | Close any open HTTP connections to the API server. | django_ses/__init__.py | close | mlissner/django-ses | python | def close(self):
'\n '
self.connection = None |
def send_messages(self, email_messages):
'Sends one or more EmailMessage objects and returns the number of\n email messages sent.\n '
if (not email_messages):
return
new_conn_created = self.open()
if (not self.connection):
return
num_sent = 0
source = settings.AWS_S... | -3,148,640,440,429,157,000 | Sends one or more EmailMessage objects and returns the number of
email messages sent. | django_ses/__init__.py | send_messages | mlissner/django-ses | python | def send_messages(self, email_messages):
'Sends one or more EmailMessage objects and returns the number of\n email messages sent.\n '
if (not email_messages):
return
new_conn_created = self.open()
if (not self.connection):
return
num_sent = 0
source = settings.AWS_S... |
def run_python_tests():
' Runs the Python tests.\n Returns:\n True if the tests all succeed, False if there are failures. '
print('Starting tests...')
loader = unittest.TestLoader()
dir_path = os.path.dirname(os.path.realpath(__file__))
suite = loader.discover('rhodopsin/tests', top_level_dir=di... | 6,912,438,203,725,193,000 | Runs the Python tests.
Returns:
True if the tests all succeed, False if there are failures. | run_tests.py | run_python_tests | djpetti/rhodopsin | python | def run_python_tests():
' Runs the Python tests.\n Returns:\n True if the tests all succeed, False if there are failures. '
print('Starting tests...')
loader = unittest.TestLoader()
dir_path = os.path.dirname(os.path.realpath(__file__))
suite = loader.discover('rhodopsin/tests', top_level_dir=di... |
def upgrade():
'Migrations for the upgrade.'
op.execute("\n UPDATE db_dbnode SET type = 'data.bool.Bool.' WHERE type = 'data.base.Bool.';\n UPDATE db_dbnode SET type = 'data.float.Float.' WHERE type = 'data.base.Float.';\n UPDATE db_dbnode SET type = 'data.int.Int.' WHERE type =... | -5,629,107,005,712,645,000 | Migrations for the upgrade. | aiida/storage/psql_dos/migrations/versions/django_0009_base_data_plugin_type_string.py | upgrade | mkrack/aiida-core | python | def upgrade():
op.execute("\n UPDATE db_dbnode SET type = 'data.bool.Bool.' WHERE type = 'data.base.Bool.';\n UPDATE db_dbnode SET type = 'data.float.Float.' WHERE type = 'data.base.Float.';\n UPDATE db_dbnode SET type = 'data.int.Int.' WHERE type = 'data.base.Int.';\n ... |
def downgrade():
'Migrations for the downgrade.'
op.execute("\n UPDATE db_dbnode SET type = 'data.base.Bool.' WHERE type = 'data.bool.Bool.';\n UPDATE db_dbnode SET type = 'data.base.Float.' WHERE type = 'data.float.Float.';\n UPDATE db_dbnode SET type = 'data.base.Int.' WHERE t... | 3,713,483,839,730,805,000 | Migrations for the downgrade. | aiida/storage/psql_dos/migrations/versions/django_0009_base_data_plugin_type_string.py | downgrade | mkrack/aiida-core | python | def downgrade():
op.execute("\n UPDATE db_dbnode SET type = 'data.base.Bool.' WHERE type = 'data.bool.Bool.';\n UPDATE db_dbnode SET type = 'data.base.Float.' WHERE type = 'data.float.Float.';\n UPDATE db_dbnode SET type = 'data.base.Int.' WHERE type = 'data.int.Int.';\n ... |
def VirtualMachineRuntimeInfo(vim, *args, **kwargs):
'The RuntimeInfo data object type provides information about the execution state\n and history of a virtual machine.'
obj = vim.client.factory.create('{urn:vim25}VirtualMachineRuntimeInfo')
if ((len(args) + len(kwargs)) < 7):
raise IndexError((... | -7,396,303,371,140,011,000 | The RuntimeInfo data object type provides information about the execution state
and history of a virtual machine. | pyvisdk/do/virtual_machine_runtime_info.py | VirtualMachineRuntimeInfo | Infinidat/pyvisdk | python | def VirtualMachineRuntimeInfo(vim, *args, **kwargs):
'The RuntimeInfo data object type provides information about the execution state\n and history of a virtual machine.'
obj = vim.client.factory.create('{urn:vim25}VirtualMachineRuntimeInfo')
if ((len(args) + len(kwargs)) < 7):
raise IndexError((... |
def __init__(self, list=None):
' A list of particle ids and names can be given to the constructor.\n '
self._list = []
if (list != None):
self._list = list | -4,374,892,717,127,874,000 | A list of particle ids and names can be given to the constructor. | FWCore/GuiBrowsers/python/Vispa/Plugins/EdmBrowser/ParticleDataList.py | __init__ | 7quantumphysics/cmssw | python | def __init__(self, list=None):
' \n '
self._list = []
if (list != None):
self._list = list |
def addParticle(self, ids, names, particleData):
' Add a paricle with (multiple) ids and names to the list.\n '
if (not (isinstance(ids, list) and isinstance(names, list))):
raise TypeError("addParticle needs to lists as input: e.g. [1,-1],['d','dbar']")
self._list += [(ids, names, particleDa... | -4,326,403,141,763,996,700 | Add a paricle with (multiple) ids and names to the list. | FWCore/GuiBrowsers/python/Vispa/Plugins/EdmBrowser/ParticleDataList.py | addParticle | 7quantumphysics/cmssw | python | def addParticle(self, ids, names, particleData):
' \n '
if (not (isinstance(ids, list) and isinstance(names, list))):
raise TypeError("addParticle needs to lists as input: e.g. [1,-1],['d','dbar']")
self._list += [(ids, names, particleData)] |
def getDefaultName(self, name):
" Return the default (first in list) name given any of the particle's names.\n "
for items in self._list:
if (name in items[1]):
return items[1][0]
return name | -5,270,448,408,394,749,000 | Return the default (first in list) name given any of the particle's names. | FWCore/GuiBrowsers/python/Vispa/Plugins/EdmBrowser/ParticleDataList.py | getDefaultName | 7quantumphysics/cmssw | python | def getDefaultName(self, name):
" \n "
for items in self._list:
if (name in items[1]):
return items[1][0]
return name |
def getDefaultId(self, id):
" Return the default (first in list) id given any of the particle's ids.\n "
for items in self._list:
if (id in items[0]):
return items[0][0]
return id | -8,276,303,927,237,939,000 | Return the default (first in list) id given any of the particle's ids. | FWCore/GuiBrowsers/python/Vispa/Plugins/EdmBrowser/ParticleDataList.py | getDefaultId | 7quantumphysics/cmssw | python | def getDefaultId(self, id):
" \n "
for items in self._list:
if (id in items[0]):
return items[0][0]
return id |
def getIdFromName(self, name):
" Return the default (first in list) id given any of the particle's names.\n "
for items in self._list:
if (name in items[1]):
return items[0][0]
return 0 | 344,722,637,239,240,640 | Return the default (first in list) id given any of the particle's names. | FWCore/GuiBrowsers/python/Vispa/Plugins/EdmBrowser/ParticleDataList.py | getIdFromName | 7quantumphysics/cmssw | python | def getIdFromName(self, name):
" \n "
for items in self._list:
if (name in items[1]):
return items[0][0]
return 0 |
def getNameFromId(self, id):
" Return the default (first in list) name given any of the particle's ids.\n "
for items in self._list:
if (id in items[0]):
return items[1][0]
return 'unknown' | 7,222,615,436,292,470,000 | Return the default (first in list) name given any of the particle's ids. | FWCore/GuiBrowsers/python/Vispa/Plugins/EdmBrowser/ParticleDataList.py | getNameFromId | 7quantumphysics/cmssw | python | def getNameFromId(self, id):
" \n "
for items in self._list:
if (id in items[0]):
return items[1][0]
return 'unknown' |
def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.Predict = channel.unary_unary('/onnxruntime.server.PredictionService/Predict', request_serializer=predict__pb2.PredictRequest.SerializeToString, response_deserializer=predict__pb2.PredictResponse.Fr... | -8,563,973,921,117,573,000 | Constructor.
Args:
channel: A grpc.Channel. | chapter2_training/cifar10/evaluate/src/proto/prediction_service_pb2_grpc.py | __init__ | akiueno/ml-system-in-actions | python | def __init__(self, channel):
'Constructor.\n\n Args:\n channel: A grpc.Channel.\n '
self.Predict = channel.unary_unary('/onnxruntime.server.PredictionService/Predict', request_serializer=predict__pb2.PredictRequest.SerializeToString, response_deserializer=predict__pb2.PredictResponse.Fr... |
def Predict(self, request, context):
'Missing associated documentation comment in .proto file.'
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | 3,231,770,545,470,701,600 | Missing associated documentation comment in .proto file. | chapter2_training/cifar10/evaluate/src/proto/prediction_service_pb2_grpc.py | Predict | akiueno/ml-system-in-actions | python | def Predict(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') |
def test_valid_distribution(self):
'Test for a valid distribution.'
plugin = Plugin(distribution='norm')
self.assertEqual(plugin.distribution, stats.norm)
self.assertEqual(plugin.shape_parameters, []) | 6,153,720,595,872,060,000 | Test for a valid distribution. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_valid_distribution | LaurenceBeard/improver | python | def test_valid_distribution(self):
plugin = Plugin(distribution='norm')
self.assertEqual(plugin.distribution, stats.norm)
self.assertEqual(plugin.shape_parameters, []) |
def test_valid_distribution_with_shape_parameters(self):
'Test for a valid distribution with shape parameters.'
plugin = Plugin(distribution='truncnorm', shape_parameters=[0, np.inf])
self.assertEqual(plugin.distribution, stats.truncnorm)
self.assertEqual(plugin.shape_parameters, [0, np.inf]) | 8,800,657,711,953,296,000 | Test for a valid distribution with shape parameters. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_valid_distribution_with_shape_parameters | LaurenceBeard/improver | python | def test_valid_distribution_with_shape_parameters(self):
plugin = Plugin(distribution='truncnorm', shape_parameters=[0, np.inf])
self.assertEqual(plugin.distribution, stats.truncnorm)
self.assertEqual(plugin.shape_parameters, [0, np.inf]) |
def test_invalid_distribution(self):
'Test for an invalid distribution.'
msg = 'The distribution requested'
with self.assertRaisesRegex(AttributeError, msg):
Plugin(distribution='elephant') | -2,428,895,901,677,872,600 | Test for an invalid distribution. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_invalid_distribution | LaurenceBeard/improver | python | def test_invalid_distribution(self):
msg = 'The distribution requested'
with self.assertRaisesRegex(AttributeError, msg):
Plugin(distribution='elephant') |
def test_basic(self):
'Test string representation'
expected_string = '<ConvertLocationAndScaleParameters: distribution: norm; shape_parameters: []>'
result = str(Plugin())
self.assertEqual(result, expected_string) | -7,172,860,046,943,809,000 | Test string representation | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_basic | LaurenceBeard/improver | python | def test_basic(self):
expected_string = '<ConvertLocationAndScaleParameters: distribution: norm; shape_parameters: []>'
result = str(Plugin())
self.assertEqual(result, expected_string) |
def setUp(self):
'Set up values for testing.'
self.location_parameter = np.array([(- 1), 0, 1])
self.scale_parameter = np.array([1, 1.5, 2]) | -2,617,334,872,451,485,000 | Set up values for testing. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | setUp | LaurenceBeard/improver | python | def setUp(self):
self.location_parameter = np.array([(- 1), 0, 1])
self.scale_parameter = np.array([1, 1.5, 2]) |
def test_truncated_at_zero(self):
'Test scaling shape parameters implying a truncation at zero.'
expected = [np.array([1.0, 0, (- 0.5)]), np.array([np.inf, np.inf, np.inf])]
shape_parameters = [0, np.inf]
plugin = Plugin(distribution='truncnorm', shape_parameters=shape_parameters)
plugin._rescale_sh... | 5,019,037,578,939,496,000 | Test scaling shape parameters implying a truncation at zero. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_truncated_at_zero | LaurenceBeard/improver | python | def test_truncated_at_zero(self):
expected = [np.array([1.0, 0, (- 0.5)]), np.array([np.inf, np.inf, np.inf])]
shape_parameters = [0, np.inf]
plugin = Plugin(distribution='truncnorm', shape_parameters=shape_parameters)
plugin._rescale_shape_parameters(self.location_parameter, self.scale_parameter)
... |
def test_discrete_shape_parameters(self):
'Test scaling discrete shape parameters.'
expected = [np.array([(- 3), (- 2.666667), (- 2.5)]), np.array([7, 4, 2.5])]
shape_parameters = [(- 4), 6]
plugin = Plugin(distribution='truncnorm', shape_parameters=shape_parameters)
plugin._rescale_shape_parameters... | -8,064,486,279,954,355,000 | Test scaling discrete shape parameters. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_discrete_shape_parameters | LaurenceBeard/improver | python | def test_discrete_shape_parameters(self):
expected = [np.array([(- 3), (- 2.666667), (- 2.5)]), np.array([7, 4, 2.5])]
shape_parameters = [(- 4), 6]
plugin = Plugin(distribution='truncnorm', shape_parameters=shape_parameters)
plugin._rescale_shape_parameters(self.location_parameter, self.scale_para... |
def test_alternative_distribution(self):
'Test specifying a distribution other than truncated normal. In\n this instance, no rescaling is applied.'
shape_parameters = [0, np.inf]
plugin = Plugin(distribution='norm', shape_parameters=shape_parameters)
plugin._rescale_shape_parameters(self.location... | -6,880,325,163,840,395,000 | Test specifying a distribution other than truncated normal. In
this instance, no rescaling is applied. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_alternative_distribution | LaurenceBeard/improver | python | def test_alternative_distribution(self):
'Test specifying a distribution other than truncated normal. In\n this instance, no rescaling is applied.'
shape_parameters = [0, np.inf]
plugin = Plugin(distribution='norm', shape_parameters=shape_parameters)
plugin._rescale_shape_parameters(self.location... |
def test_no_shape_parameters_exception(self):
'Test raising an exception when shape parameters are not specified\n for the truncated normal distribution.'
plugin = Plugin(distribution='truncnorm')
msg = 'For the truncated normal distribution'
with self.assertRaisesRegex(ValueError, msg):
... | -1,700,685,026,259,694,600 | Test raising an exception when shape parameters are not specified
for the truncated normal distribution. | improver_tests/ensemble_copula_coupling/ensemble_copula_coupling/test_ConvertLocationAndScaleParameters.py | test_no_shape_parameters_exception | LaurenceBeard/improver | python | def test_no_shape_parameters_exception(self):
'Test raising an exception when shape parameters are not specified\n for the truncated normal distribution.'
plugin = Plugin(distribution='truncnorm')
msg = 'For the truncated normal distribution'
with self.assertRaisesRegex(ValueError, msg):
... |
def harmonic_mean(x):
'\n The `harmonic mean`_ is a kind of average that is calculated as\n the reciprocal_ of the arithmetic mean of the reciprocals.\n It is appropriate when calculating averages of rates_.\n\n .. _`harmonic mean`: https://en.wikipedia.org/wiki/Harmonic_mean\n .. _reciprocal: https:... | 591,122,178,774,666,200 | The `harmonic mean`_ is a kind of average that is calculated as
the reciprocal_ of the arithmetic mean of the reciprocals.
It is appropriate when calculating averages of rates_.
.. _`harmonic mean`: https://en.wikipedia.org/wiki/Harmonic_mean
.. _reciprocal: https://en.wikipedia.org/wiki/Multiplicative_inverse
.. _rat... | simplestatistics/statistics/harmonic_mean.py | harmonic_mean | sheriferson/simple-statistics-py | python | def harmonic_mean(x):
'\n The `harmonic mean`_ is a kind of average that is calculated as\n the reciprocal_ of the arithmetic mean of the reciprocals.\n It is appropriate when calculating averages of rates_.\n\n .. _`harmonic mean`: https://en.wikipedia.org/wiki/Harmonic_mean\n .. _reciprocal: https:... |
@pytest.mark.regions(['ap-southeast-1'])
@pytest.mark.instances(['c5.xlarge'])
@pytest.mark.oss(['alinux2'])
@pytest.mark.schedulers(['slurm', 'awsbatch'])
@pytest.mark.usefixtures('region', 'instance')
def test_tag_propagation(pcluster_config_reader, clusters_factory, scheduler, os):
"\n Verify tags from variou... | -7,428,828,917,190,505,000 | Verify tags from various sources are propagated to the expected resources.
The following resources are checked for tags:
- main CFN stack
- head node
- head node's root EBS volume
- compute node (traditional schedulers)
- compute node's root EBS volume (traditional schedulers)
- shared EBS volume | tests/integration-tests/tests/tags/test_tag_propagation.py | test_tag_propagation | eshpc/aws-parallelcluster | python | @pytest.mark.regions(['ap-southeast-1'])
@pytest.mark.instances(['c5.xlarge'])
@pytest.mark.oss(['alinux2'])
@pytest.mark.schedulers(['slurm', 'awsbatch'])
@pytest.mark.usefixtures('region', 'instance')
def test_tag_propagation(pcluster_config_reader, clusters_factory, scheduler, os):
"\n Verify tags from variou... |
def convert_tags_dicts_to_tags_list(tags_dicts):
'Convert dicts of the form {key: value} to a list like [{"Key": key, "Value": value}].'
tags_list = []
for tags_dict in tags_dicts:
tags_list.extend([{'Key': key, 'Value': value} for (key, value) in tags_dict.items()])
return tags_list | -4,554,017,946,200,980,000 | Convert dicts of the form {key: value} to a list like [{"Key": key, "Value": value}]. | tests/integration-tests/tests/tags/test_tag_propagation.py | convert_tags_dicts_to_tags_list | eshpc/aws-parallelcluster | python | def convert_tags_dicts_to_tags_list(tags_dicts):
tags_list = []
for tags_dict in tags_dicts:
tags_list.extend([{'Key': key, 'Value': value} for (key, value) in tags_dict.items()])
return tags_list |
def get_cloudformation_tags(region, stack_name):
"\n Return the tags for the CFN stack with the given name\n\n The returned values is a list like the following:\n [\n {'Key': 'Key2', 'Value': 'Value2'},\n {'Key': 'Key1', 'Value': 'Value1'},\n ]\n "
cfn_client = boto3.client('cloudfo... | -6,683,868,679,622,842,000 | Return the tags for the CFN stack with the given name
The returned values is a list like the following:
[
{'Key': 'Key2', 'Value': 'Value2'},
{'Key': 'Key1', 'Value': 'Value1'},
] | tests/integration-tests/tests/tags/test_tag_propagation.py | get_cloudformation_tags | eshpc/aws-parallelcluster | python | def get_cloudformation_tags(region, stack_name):
"\n Return the tags for the CFN stack with the given name\n\n The returned values is a list like the following:\n [\n {'Key': 'Key2', 'Value': 'Value2'},\n {'Key': 'Key1', 'Value': 'Value1'},\n ]\n "
cfn_client = boto3.client('cloudfo... |
def get_main_stack_tags(cluster):
"Return the tags for the cluster's main CFN stack."
return get_cloudformation_tags(cluster.region, cluster.cfn_name) | -7,796,513,982,243,440,000 | Return the tags for the cluster's main CFN stack. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_main_stack_tags | eshpc/aws-parallelcluster | python | def get_main_stack_tags(cluster):
return get_cloudformation_tags(cluster.region, cluster.cfn_name) |
def get_head_node_instance_id(cluster):
"Return the given cluster's head node's instance ID."
return cluster.cfn_resources.get('HeadNode') | -6,527,855,288,032,906,000 | Return the given cluster's head node's instance ID. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_head_node_instance_id | eshpc/aws-parallelcluster | python | def get_head_node_instance_id(cluster):
return cluster.cfn_resources.get('HeadNode') |
def get_ec2_instance_tags(instance_id, region):
'Return a list of tags associated with the given EC2 instance.'
logging.info('Getting tags for instance %s', instance_id)
return boto3.client('ec2', region_name=region).describe_instances(InstanceIds=[instance_id]).get('Reservations')[0].get('Instances')[0].ge... | 9,049,807,806,296,432,000 | Return a list of tags associated with the given EC2 instance. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_ec2_instance_tags | eshpc/aws-parallelcluster | python | def get_ec2_instance_tags(instance_id, region):
logging.info('Getting tags for instance %s', instance_id)
return boto3.client('ec2', region_name=region).describe_instances(InstanceIds=[instance_id]).get('Reservations')[0].get('Instances')[0].get('Tags') |
def get_tags_for_volume(volume_id, region):
'Return the tags attached to the given EBS volume.'
logging.info('Getting tags for volume %s', volume_id)
return boto3.client('ec2', region_name=region).describe_volumes(VolumeIds=[volume_id]).get('Volumes')[0].get('Tags') | -1,241,565,648,266,099,700 | Return the tags attached to the given EBS volume. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_tags_for_volume | eshpc/aws-parallelcluster | python | def get_tags_for_volume(volume_id, region):
logging.info('Getting tags for volume %s', volume_id)
return boto3.client('ec2', region_name=region).describe_volumes(VolumeIds=[volume_id]).get('Volumes')[0].get('Tags') |
def get_head_node_root_volume_tags(cluster, os):
"Return the given cluster's head node's root volume's tags."
head_node_instance_id = get_head_node_instance_id(cluster)
root_volume_id = get_root_volume_id(head_node_instance_id, cluster.region, os)
return get_tags_for_volume(root_volume_id, cluster.regio... | 1,240,287,457,644,547,000 | Return the given cluster's head node's root volume's tags. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_head_node_root_volume_tags | eshpc/aws-parallelcluster | python | def get_head_node_root_volume_tags(cluster, os):
head_node_instance_id = get_head_node_instance_id(cluster)
root_volume_id = get_root_volume_id(head_node_instance_id, cluster.region, os)
return get_tags_for_volume(root_volume_id, cluster.region) |
def get_head_node_tags(cluster):
"Return the given cluster's head node's tags."
head_node_instance_id = get_head_node_instance_id(cluster)
return get_ec2_instance_tags(head_node_instance_id, cluster.region) | -2,295,178,007,714,998,800 | Return the given cluster's head node's tags. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_head_node_tags | eshpc/aws-parallelcluster | python | def get_head_node_tags(cluster):
head_node_instance_id = get_head_node_instance_id(cluster)
return get_ec2_instance_tags(head_node_instance_id, cluster.region) |
def get_compute_node_root_volume_tags(cluster, os):
"Return the given cluster's compute node's root volume's tags."
compute_nodes = cluster.get_cluster_instance_ids(node_type='Compute')
assert_that(compute_nodes).is_length(1)
root_volume_id = get_root_volume_id(compute_nodes[0], cluster.region, os)
... | -3,110,508,624,131,773,400 | Return the given cluster's compute node's root volume's tags. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_compute_node_root_volume_tags | eshpc/aws-parallelcluster | python | def get_compute_node_root_volume_tags(cluster, os):
compute_nodes = cluster.get_cluster_instance_ids(node_type='Compute')
assert_that(compute_nodes).is_length(1)
root_volume_id = get_root_volume_id(compute_nodes[0], cluster.region, os)
return get_tags_for_volume(root_volume_id, cluster.region) |
def get_compute_node_tags(cluster):
"Return the given cluster's compute node's tags."
compute_nodes = cluster.get_cluster_instance_ids(node_type='Compute')
assert_that(compute_nodes).is_length(1)
return get_ec2_instance_tags(compute_nodes[0], cluster.region) | -1,093,552,564,996,228,600 | Return the given cluster's compute node's tags. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_compute_node_tags | eshpc/aws-parallelcluster | python | def get_compute_node_tags(cluster):
compute_nodes = cluster.get_cluster_instance_ids(node_type='Compute')
assert_that(compute_nodes).is_length(1)
return get_ec2_instance_tags(compute_nodes[0], cluster.region) |
def get_ebs_volume_tags(volume_id, region):
'Return the tags associated with the given EBS volume.'
return boto3.client('ec2', region_name=region).describe_volumes(VolumeIds=[volume_id]).get('Volumes')[0].get('Tags') | -2,903,476,295,029,446,000 | Return the tags associated with the given EBS volume. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_ebs_volume_tags | eshpc/aws-parallelcluster | python | def get_ebs_volume_tags(volume_id, region):
return boto3.client('ec2', region_name=region).describe_volumes(VolumeIds=[volume_id]).get('Volumes')[0].get('Tags') |
def get_shared_volume_tags(cluster):
"Return the given cluster's EBS volume's tags."
shared_volume = cluster.cfn_resources.get('EBS0')
return get_ebs_volume_tags(shared_volume, cluster.region) | -29,601,883,307,549,850 | Return the given cluster's EBS volume's tags. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_shared_volume_tags | eshpc/aws-parallelcluster | python | def get_shared_volume_tags(cluster):
shared_volume = cluster.cfn_resources.get('EBS0')
return get_ebs_volume_tags(shared_volume, cluster.region) |
def get_pcluster_version():
'Return the installed version of the pclsuter CLI.'
return json.loads(sp.check_output('pcluster version'.split()).decode().strip()).get('version') | -6,709,317,349,835,332,000 | Return the installed version of the pclsuter CLI. | tests/integration-tests/tests/tags/test_tag_propagation.py | get_pcluster_version | eshpc/aws-parallelcluster | python | def get_pcluster_version():
return json.loads(sp.check_output('pcluster version'.split()).decode().strip()).get('version') |
def make_deterministic(seed=0):
"Make results deterministic. If seed == -1, do not make deterministic.\n Running your script in a deterministic way might slow it down.\n Note that for some packages (eg: sklearn's PCA) this function is not enough.\n "
seed = int(seed)
if (seed == (- 1)):
... | 2,571,610,496,660,509,700 | Make results deterministic. If seed == -1, do not make deterministic.
Running your script in a deterministic way might slow it down.
Note that for some packages (eg: sklearn's PCA) this function is not enough. | commons.py | make_deterministic | gmberton/CosPlace | python | def make_deterministic(seed=0):
"Make results deterministic. If seed == -1, do not make deterministic.\n Running your script in a deterministic way might slow it down.\n Note that for some packages (eg: sklearn's PCA) this function is not enough.\n "
seed = int(seed)
if (seed == (- 1)):
... |
def setup_logging(output_folder, exist_ok=False, console='debug', info_filename='info.log', debug_filename='debug.log'):
'Set up logging files and console output.\n Creates one file for INFO logs and one for DEBUG logs.\n Args:\n output_folder (str): creates the folder where to save the files.\n ... | 3,354,185,008,153,865,000 | Set up logging files and console output.
Creates one file for INFO logs and one for DEBUG logs.
Args:
output_folder (str): creates the folder where to save the files.
exist_ok (boolean): if False throw a FileExistsError if output_folder already exists
debug (str):
if == "debug" prints on console deb... | commons.py | setup_logging | gmberton/CosPlace | python | def setup_logging(output_folder, exist_ok=False, console='debug', info_filename='info.log', debug_filename='debug.log'):
'Set up logging files and console output.\n Creates one file for INFO logs and one for DEBUG logs.\n Args:\n output_folder (str): creates the folder where to save the files.\n ... |
def donchian(high, low, lower_length=None, upper_length=None, offset=None, **kwargs):
'Indicator: Donchian Channels (DC)'
high = verify_series(high)
low = verify_series(low)
lower_length = (int(lower_length) if (lower_length and (lower_length > 0)) else 20)
upper_length = (int(upper_length) if (uppe... | -6,520,702,824,064,578,000 | Indicator: Donchian Channels (DC) | pandas_ta/volatility/donchian.py | donchian | MyBourse/pandas-ta | python | def donchian(high, low, lower_length=None, upper_length=None, offset=None, **kwargs):
high = verify_series(high)
low = verify_series(low)
lower_length = (int(lower_length) if (lower_length and (lower_length > 0)) else 20)
upper_length = (int(upper_length) if (upper_length and (upper_length > 0)) el... |
def as_create_table(self, table_name, overwrite=False):
'Reformats the query into the create table as query.\n\n Works only for the single select SQL statements, in all other cases\n the sql query is not modified.\n :param superset_query: string, sql query that will be executed\n :param ... | 5,869,634,862,788,180,000 | Reformats the query into the create table as query.
Works only for the single select SQL statements, in all other cases
the sql query is not modified.
:param superset_query: string, sql query that will be executed
:param table_name: string, will contain the results of the
query execution
:param overwrite, boolean,... | superset/sql_parse.py | as_create_table | AmberCa/incubator-superset | python | def as_create_table(self, table_name, overwrite=False):
'Reformats the query into the create table as query.\n\n Works only for the single select SQL statements, in all other cases\n the sql query is not modified.\n :param superset_query: string, sql query that will be executed\n :param ... |
def display(request):
'Function view to display form in the standard manner.'
if (request.method == 'POST'):
form = FiboForm(request.POST)
if form.is_valid():
fibo = form.save(commit=False)
evensum = fibo.evenFiboSum()
fibo.save()
return render(req... | 4,477,814,417,209,123,300 | Function view to display form in the standard manner. | problem2/views.py | display | byteknacker/eulerapps | python | def display(request):
if (request.method == 'POST'):
form = FiboForm(request.POST)
if form.is_valid():
fibo = form.save(commit=False)
evensum = fibo.evenFiboSum()
fibo.save()
return render(request, 'problem2/solution2.html', {'evensum': evensum, '... |
@staticmethod
def _get_series(i=0):
'\n\n :return:\n '
config = configparser.ConfigParser()
config.read('config.ini')
fourier_folder = config['Folder']['Output']
first_file = os.path.join(fourier_folder, os.listdir(fourier_folder)[i])
with open(first_file, 'r') as b:
j = js... | 8,986,104,419,332,724,000 | :return: | test/test_b_plot.py | _get_series | cperales/Fourier-Clustering-song | python | @staticmethod
def _get_series(i=0):
'\n\n \n '
config = configparser.ConfigParser()
config.read('config.ini')
fourier_folder = config['Folder']['Output']
first_file = os.path.join(fourier_folder, os.listdir(fourier_folder)[i])
with open(first_file, 'r') as b:
j = json.load(... |
@staticmethod
def _get_song(i=0):
'\n\n :return:\n '
config = configparser.ConfigParser()
config.read('config.ini')
song_folder = config['Folder']['Temp']
first_song = os.listdir(song_folder)[i]
(rate, aud_data) = read(os.path.join(song_folder, first_song))
if (len(aud_data) !=... | -8,418,300,708,451,570,000 | :return: | test/test_b_plot.py | _get_song | cperales/Fourier-Clustering-song | python | @staticmethod
def _get_song(i=0):
'\n\n \n '
config = configparser.ConfigParser()
config.read('config.ini')
song_folder = config['Folder']['Temp']
first_song = os.listdir(song_folder)[i]
(rate, aud_data) = read(os.path.join(song_folder, first_song))
if (len(aud_data) != len(aud... |
def test_diff(self):
'\n\n :return:\n '
config = configparser.ConfigParser()
config.read('config.ini')
image_folder = config['Folder']['Image']
(song_1, name_1) = self._get_series(i=0)
(song_2, name_2) = self._get_series(i=1)
diff_plot(song_1, song_2, filename=(name_1.split()[2... | 1,652,916,689,913,852,700 | :return: | test/test_b_plot.py | test_diff | cperales/Fourier-Clustering-song | python | def test_diff(self):
'\n\n \n '
config = configparser.ConfigParser()
config.read('config.ini')
image_folder = config['Folder']['Image']
(song_1, name_1) = self._get_series(i=0)
(song_2, name_2) = self._get_series(i=1)
diff_plot(song_1, song_2, filename=(name_1.split()[2].split(... |
def test_song(self):
'\n\n :return:\n '
config = configparser.ConfigParser()
config.read('config.ini')
image_folder = config['Folder']['Image']
(aud_data, name) = self._get_song()
song_plot(aud_data, filename=name.split('.')[0], folder=image_folder) | -8,779,366,337,030,944,000 | :return: | test/test_b_plot.py | test_song | cperales/Fourier-Clustering-song | python | def test_song(self):
'\n\n \n '
config = configparser.ConfigParser()
config.read('config.ini')
image_folder = config['Folder']['Image']
(aud_data, name) = self._get_song()
song_plot(aud_data, filename=name.split('.')[0], folder=image_folder) |
@staticmethod
def get_supported_channels() -> list:
'List of supported channels.'
return list(ChannelMap.channel_map.keys()) | 313,114,182,041,640,400 | List of supported channels. | scripts/channel_map.py | get_supported_channels | artelk/performance | python | @staticmethod
def get_supported_channels() -> list:
return list(ChannelMap.channel_map.keys()) |
@staticmethod
def get_supported_frameworks() -> list:
'List of supported frameworks'
frameworks = [ChannelMap.channel_map[channel]['tfm'] for channel in ChannelMap.channel_map]
return set(frameworks) | 4,910,586,788,561,729,000 | List of supported frameworks | scripts/channel_map.py | get_supported_frameworks | artelk/performance | python | @staticmethod
def get_supported_frameworks() -> list:
frameworks = [ChannelMap.channel_map[channel]['tfm'] for channel in ChannelMap.channel_map]
return set(frameworks) |
@staticmethod
def get_target_framework_monikers(channels: list) -> list:
'\n Translates channel names to Target Framework Monikers (TFMs).\n '
monikers = [ChannelMap.get_target_framework_moniker(channel) for channel in channels]
return list(set(monikers)) | -8,264,586,632,849,845,000 | Translates channel names to Target Framework Monikers (TFMs). | scripts/channel_map.py | get_target_framework_monikers | artelk/performance | python | @staticmethod
def get_target_framework_monikers(channels: list) -> list:
'\n \n '
monikers = [ChannelMap.get_target_framework_moniker(channel) for channel in channels]
return list(set(monikers)) |
@staticmethod
def get_target_framework_moniker(channel: str) -> str:
'\n Translate channel name to Target Framework Moniker (TFM)\n '
if (channel in ChannelMap.channel_map):
return ChannelMap.channel_map[channel]['tfm']
else:
raise Exception(('Channel %s is not supported. Suppo... | 9,109,701,814,379,510,000 | Translate channel name to Target Framework Moniker (TFM) | scripts/channel_map.py | get_target_framework_moniker | artelk/performance | python | @staticmethod
def get_target_framework_moniker(channel: str) -> str:
'\n \n '
if (channel in ChannelMap.channel_map):
return ChannelMap.channel_map[channel]['tfm']
else:
raise Exception(('Channel %s is not supported. Supported channels %s' % (channel, ChannelMap.get_supported_c... |
@staticmethod
def get_channel_from_target_framework_moniker(target_framework_moniker: str) -> str:
'Translate Target Framework Moniker (TFM) to channel name'
for channel in ChannelMap.channel_map:
if (ChannelMap.channel_map[channel]['tfm'] == target_framework_moniker):
return channel
rai... | 6,853,412,562,388,000,000 | Translate Target Framework Moniker (TFM) to channel name | scripts/channel_map.py | get_channel_from_target_framework_moniker | artelk/performance | python | @staticmethod
def get_channel_from_target_framework_moniker(target_framework_moniker: str) -> str:
for channel in ChannelMap.channel_map:
if (ChannelMap.channel_map[channel]['tfm'] == target_framework_moniker):
return channel
raise Exception(('Framework %s is not supported. Supported fr... |
def normalize_imagenet(x):
' Normalize input images according to ImageNet standards.\n Args:\n x (tensor): input images\n '
x = x.clone()
x[:, 0] = ((x[:, 0] - 0.485) / 0.229)
x[:, 1] = ((x[:, 1] - 0.456) / 0.224)
x[:, 2] = ((x[:, 2] - 0.406) / 0.225)
return x | -5,227,346,449,647,160,000 | Normalize input images according to ImageNet standards.
Args:
x (tensor): input images | examples/ImageRecon/OccNet/architectures.py | normalize_imagenet | AOE-khkhan/kaolin | python | def normalize_imagenet(x):
' Normalize input images according to ImageNet standards.\n Args:\n x (tensor): input images\n '
x = x.clone()
x[:, 0] = ((x[:, 0] - 0.485) / 0.229)
x[:, 1] = ((x[:, 1] - 0.456) / 0.224)
x[:, 2] = ((x[:, 2] - 0.406) / 0.225)
return x |
def get_prior_z(device):
' Returns prior distribution for latent code z.\n Args:\n cfg (dict): imported yaml config\n device (device): pytorch device\n '
z_dim = 0
p0_z = dist.Normal(torch.zeros(z_dim, device=device), torch.ones(z_dim, device=device))
return p0_z | 8,228,995,010,554,023,000 | Returns prior distribution for latent code z.
Args:
cfg (dict): imported yaml config
device (device): pytorch device | examples/ImageRecon/OccNet/architectures.py | get_prior_z | AOE-khkhan/kaolin | python | def get_prior_z(device):
' Returns prior distribution for latent code z.\n Args:\n cfg (dict): imported yaml config\n device (device): pytorch device\n '
z_dim = 0
p0_z = dist.Normal(torch.zeros(z_dim, device=device), torch.ones(z_dim, device=device))
return p0_z |
def forward(self, p, inputs, sample=True, **kwargs):
' Performs a forward pass through the network.\n Args:\n p (tensor): sampled points\n inputs (tensor): conditioning input\n sample (bool): whether to sample for z\n '
batch_size = p.size(0)
c = self.encode_in... | -8,092,593,553,562,814,000 | Performs a forward pass through the network.
Args:
p (tensor): sampled points
inputs (tensor): conditioning input
sample (bool): whether to sample for z | examples/ImageRecon/OccNet/architectures.py | forward | AOE-khkhan/kaolin | python | def forward(self, p, inputs, sample=True, **kwargs):
' Performs a forward pass through the network.\n Args:\n p (tensor): sampled points\n inputs (tensor): conditioning input\n sample (bool): whether to sample for z\n '
batch_size = p.size(0)
c = self.encode_in... |
def compute_elbo(self, p, occ, inputs, **kwargs):
' Computes the expectation lower bound.\n Args:\n p (tensor): sampled points\n occ (tensor): occupancy values for p\n inputs (tensor): conditioning input\n '
c = self.encode_inputs(inputs)
q_z = self.infer_z(p, ... | -2,864,902,931,423,070,000 | Computes the expectation lower bound.
Args:
p (tensor): sampled points
occ (tensor): occupancy values for p
inputs (tensor): conditioning input | examples/ImageRecon/OccNet/architectures.py | compute_elbo | AOE-khkhan/kaolin | python | def compute_elbo(self, p, occ, inputs, **kwargs):
' Computes the expectation lower bound.\n Args:\n p (tensor): sampled points\n occ (tensor): occupancy values for p\n inputs (tensor): conditioning input\n '
c = self.encode_inputs(inputs)
q_z = self.infer_z(p, ... |
def encode_inputs(self, inputs):
' Encodes the input.\n Args:\n input (tensor): the input\n '
c = self.encoder(inputs)
return c | 5,463,329,561,843,520,000 | Encodes the input.
Args:
input (tensor): the input | examples/ImageRecon/OccNet/architectures.py | encode_inputs | AOE-khkhan/kaolin | python | def encode_inputs(self, inputs):
' Encodes the input.\n Args:\n input (tensor): the input\n '
c = self.encoder(inputs)
return c |
def decode(self, p, z, c, **kwargs):
' Returns occupancy probabilities for the sampled points.\n Args:\n p (tensor): points\n z (tensor): latent code z\n c (tensor): latent conditioned code c\n '
logits = self.decoder(p, z, c, **kwargs)
p_r = dist.Bernoulli(log... | -400,121,044,428,680,000 | Returns occupancy probabilities for the sampled points.
Args:
p (tensor): points
z (tensor): latent code z
c (tensor): latent conditioned code c | examples/ImageRecon/OccNet/architectures.py | decode | AOE-khkhan/kaolin | python | def decode(self, p, z, c, **kwargs):
' Returns occupancy probabilities for the sampled points.\n Args:\n p (tensor): points\n z (tensor): latent code z\n c (tensor): latent conditioned code c\n '
logits = self.decoder(p, z, c, **kwargs)
p_r = dist.Bernoulli(log... |
def infer_z(self, p, occ, c, **kwargs):
' Infers z.\n Args:\n p (tensor): points tensor\n occ (tensor): occupancy values for occ\n c (tensor): latent conditioned code c\n '
batch_size = p.size(0)
mean_z = torch.empty(batch_size, 0).to(self.device)
logstd_z ... | 6,820,978,492,670,022,000 | Infers z.
Args:
p (tensor): points tensor
occ (tensor): occupancy values for occ
c (tensor): latent conditioned code c | examples/ImageRecon/OccNet/architectures.py | infer_z | AOE-khkhan/kaolin | python | def infer_z(self, p, occ, c, **kwargs):
' Infers z.\n Args:\n p (tensor): points tensor\n occ (tensor): occupancy values for occ\n c (tensor): latent conditioned code c\n '
batch_size = p.size(0)
mean_z = torch.empty(batch_size, 0).to(self.device)
logstd_z ... |
def get_z_from_prior(self, size=torch.Size([]), sample=True):
' Returns z from prior distribution.\n Args:\n size (Size): size of z\n sample (bool): whether to sample\n '
if sample:
z = self.p0_z.sample(size).to(self.device)
else:
z = self.p0_z.mean.to(sel... | -7,939,061,773,836,317,000 | Returns z from prior distribution.
Args:
size (Size): size of z
sample (bool): whether to sample | examples/ImageRecon/OccNet/architectures.py | get_z_from_prior | AOE-khkhan/kaolin | python | def get_z_from_prior(self, size=torch.Size([]), sample=True):
' Returns z from prior distribution.\n Args:\n size (Size): size of z\n sample (bool): whether to sample\n '
if sample:
z = self.p0_z.sample(size).to(self.device)
else:
z = self.p0_z.mean.to(sel... |
def register(self, model, model_admin=None, **kwargs):
'\n Registers the given model with the given admin class. Once a model is\n registered in self.registry, we also add it to app registries in\n self.apps.\n\n If no model_admin is passed, it will use ModelAdmin2. If keyword\n a... | 1,695,026,397,503,695,600 | Registers the given model with the given admin class. Once a model is
registered in self.registry, we also add it to app registries in
self.apps.
If no model_admin is passed, it will use ModelAdmin2. If keyword
arguments are given they will be passed to the admin class on
instantiation.
If a model is already register... | djadmin2/core.py | register | PowerOlive/django-admin2 | python | def register(self, model, model_admin=None, **kwargs):
'\n Registers the given model with the given admin class. Once a model is\n registered in self.registry, we also add it to app registries in\n self.apps.\n\n If no model_admin is passed, it will use ModelAdmin2. If keyword\n a... |
def deregister(self, model):
'\n Deregisters the given model. Remove the model from the self.app as well\n\n If the model is not already registered, this will raise\n ImproperlyConfigured.\n '
try:
del self.registry[model]
except KeyError:
raise ImproperlyConfigur... | -226,734,680,756,163,400 | Deregisters the given model. Remove the model from the self.app as well
If the model is not already registered, this will raise
ImproperlyConfigured. | djadmin2/core.py | deregister | PowerOlive/django-admin2 | python | def deregister(self, model):
'\n Deregisters the given model. Remove the model from the self.app as well\n\n If the model is not already registered, this will raise\n ImproperlyConfigured.\n '
try:
del self.registry[model]
except KeyError:
raise ImproperlyConfigur... |
def register_app_verbose_name(self, app_label, app_verbose_name):
'\n Registers the given app label with the given app verbose name.\n\n If a app_label is already registered, this will raise\n ImproperlyConfigured.\n '
if (app_label in self.app_verbose_names):
raise Improperl... | 8,412,480,849,148,175,000 | Registers the given app label with the given app verbose name.
If a app_label is already registered, this will raise
ImproperlyConfigured. | djadmin2/core.py | register_app_verbose_name | PowerOlive/django-admin2 | python | def register_app_verbose_name(self, app_label, app_verbose_name):
'\n Registers the given app label with the given app verbose name.\n\n If a app_label is already registered, this will raise\n ImproperlyConfigured.\n '
if (app_label in self.app_verbose_names):
raise Improperl... |
def deregister_app_verbose_name(self, app_label):
'\n Deregisters the given app label. Remove the app label from the\n self.app_verbose_names as well.\n\n If the app label is not already registered, this will raise\n ImproperlyConfigured.\n '
try:
del self.app_verbose_... | -2,633,586,113,666,253,300 | Deregisters the given app label. Remove the app label from the
self.app_verbose_names as well.
If the app label is not already registered, this will raise
ImproperlyConfigured. | djadmin2/core.py | deregister_app_verbose_name | PowerOlive/django-admin2 | python | def deregister_app_verbose_name(self, app_label):
'\n Deregisters the given app label. Remove the app label from the\n self.app_verbose_names as well.\n\n If the app label is not already registered, this will raise\n ImproperlyConfigured.\n '
try:
del self.app_verbose_... |
def autodiscover(self):
'\n Autodiscovers all admin2.py modules for apps in INSTALLED_APPS by\n trying to import them.\n '
for app_name in [x for x in settings.INSTALLED_APPS]:
try:
import_module(('%s.admin2' % app_name))
except ImportError as e:
if (... | 4,519,707,043,250,492,400 | Autodiscovers all admin2.py modules for apps in INSTALLED_APPS by
trying to import them. | djadmin2/core.py | autodiscover | PowerOlive/django-admin2 | python | def autodiscover(self):
'\n Autodiscovers all admin2.py modules for apps in INSTALLED_APPS by\n trying to import them.\n '
for app_name in [x for x in settings.INSTALLED_APPS]:
try:
import_module(('%s.admin2' % app_name))
except ImportError as e:
if (... |
def get_admin_by_name(self, name):
'\n Returns the admin instance that was registered with the passed in\n name.\n '
for object_admin in self.registry.values():
if (object_admin.name == name):
return object_admin
raise ValueError(u'No object admin found with name {}'... | 1,111,493,410,733,876,500 | Returns the admin instance that was registered with the passed in
name. | djadmin2/core.py | get_admin_by_name | PowerOlive/django-admin2 | python | def get_admin_by_name(self, name):
'\n Returns the admin instance that was registered with the passed in\n name.\n '
for object_admin in self.registry.values():
if (object_admin.name == name):
return object_admin
raise ValueError(u'No object admin found with name {}'... |
def save(query: List[str], save_path: str, downloader, m3u_file: Optional[str]=None) -> None:
'\n Save metadata from spotify to the disk.\n\n ### Arguments\n - query: list of strings to search for.\n - save_path: Path to the file to save the metadata to.\n - threads: Number of threads to use.\n\n ... | 1,037,826,605,912,516,600 | Save metadata from spotify to the disk.
### Arguments
- query: list of strings to search for.
- save_path: Path to the file to save the metadata to.
- threads: Number of threads to use.
### Notes
- This function is multi-threaded. | spotdl/console/save.py | save | phcreery/spotdl-v4 | python | def save(query: List[str], save_path: str, downloader, m3u_file: Optional[str]=None) -> None:
'\n Save metadata from spotify to the disk.\n\n ### Arguments\n - query: list of strings to search for.\n - save_path: Path to the file to save the metadata to.\n - threads: Number of threads to use.\n\n ... |
def get_arguments():
'Parse all the arguments provided from the CLI.\n\n Returns:\n A list of parsed arguments.\n '
parser = argparse.ArgumentParser(description='DeepLab-ResNet Network')
parser.add_argument('--model', type=str, default=MODEL, help='Model Choice (DeeplabMulti/DeeplabVGG/Oracle).')... | -3,601,046,404,071,038,000 | Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments. | generate_plabel_dark_zurich.py | get_arguments | qimw/UACDA | python | def get_arguments():
'Parse all the arguments provided from the CLI.\n\n Returns:\n A list of parsed arguments.\n '
parser = argparse.ArgumentParser(description='DeepLab-ResNet Network')
parser.add_argument('--model', type=str, default=MODEL, help='Model Choice (DeeplabMulti/DeeplabVGG/Oracle).')... |
def main():
'Create the model and start the evaluation process.'
args = get_arguments()
(w, h) = map(int, args.input_size.split(','))
config_path = os.path.join(os.path.dirname(args.restore_from), 'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
args.model =... | -2,165,387,849,207,418,400 | Create the model and start the evaluation process. | generate_plabel_dark_zurich.py | main | qimw/UACDA | python | def main():
args = get_arguments()
(w, h) = map(int, args.input_size.split(','))
config_path = os.path.join(os.path.dirname(args.restore_from), 'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
args.model = config['model']
print(('ModelType:%s' % args.mo... |
def __init__(self, runscontainer, marginal_threshold=0.05):
'Wrapper for parameter_importance to save the importance-object/ extract the results. We want to show the\n top X most important parameter-fanova-plots.\n\n Parameters\n ----------\n runscontainer: RunsContainer\n con... | -2,845,748,282,511,785,500 | Wrapper for parameter_importance to save the importance-object/ extract the results. We want to show the
top X most important parameter-fanova-plots.
Parameters
----------
runscontainer: RunsContainer
contains all important information about the configurator runs
marginal_threshold: float
parameter/s must be a... | cave/analyzer/parameter_importance/fanova.py | __init__ | automl/CAVE | python | def __init__(self, runscontainer, marginal_threshold=0.05):
'Wrapper for parameter_importance to save the importance-object/ extract the results. We want to show the\n top X most important parameter-fanova-plots.\n\n Parameters\n ----------\n runscontainer: RunsContainer\n con... |
def parse_pairwise(p):
"parse pimp's way of having pairwise parameters as key as str and return list of individuals"
res = [tmp.strip("' ") for tmp in p.strip('[]').split(',')]
return res | 8,489,956,221,889,464,000 | parse pimp's way of having pairwise parameters as key as str and return list of individuals | cave/analyzer/parameter_importance/fanova.py | parse_pairwise | automl/CAVE | python | def parse_pairwise(p):
res = [tmp.strip("' ") for tmp in p.strip('[]').split(',')]
return res |
def test_create_failure_recovery(self):
'Check that rollback still works with dynamic metadata.\n\n This test fails the second instance.\n '
tmpl = {'HeatTemplateFormatVersion': '2012-12-12', 'Resources': {'AResource': {'Type': 'OverwrittenFnGetRefIdType', 'Properties': {'Foo': 'abc'}}, 'BResource... | 8,971,451,799,159,772,000 | Check that rollback still works with dynamic metadata.
This test fails the second instance. | heat/tests/test_stack.py | test_create_failure_recovery | openstack/heat | python | def test_create_failure_recovery(self):
'Check that rollback still works with dynamic metadata.\n\n This test fails the second instance.\n '
tmpl = {'HeatTemplateFormatVersion': '2012-12-12', 'Resources': {'AResource': {'Type': 'OverwrittenFnGetRefIdType', 'Properties': {'Foo': 'abc'}}, 'BResource... |
def test_store_saves_owner(self):
'owner_id attribute of Store is saved to the database when stored.'
self.stack = stack.Stack(self.ctx, 'owner_stack', self.tmpl)
stack_ownee = stack.Stack(self.ctx, 'ownee_stack', self.tmpl, owner_id=self.stack.id)
stack_ownee.store()
db_stack = stack_object.Stack.g... | -2,445,248,347,015,333,400 | owner_id attribute of Store is saved to the database when stored. | heat/tests/test_stack.py | test_store_saves_owner | openstack/heat | python | def test_store_saves_owner(self):
self.stack = stack.Stack(self.ctx, 'owner_stack', self.tmpl)
stack_ownee = stack.Stack(self.ctx, 'ownee_stack', self.tmpl, owner_id=self.stack.id)
stack_ownee.store()
db_stack = stack_object.Stack.get_by_id(self.ctx, stack_ownee.id)
self.assertEqual(self.stack.... |
def test_store_saves_creds(self):
'A user_creds entry is created on first stack store.'
cfg.CONF.set_default('deferred_auth_method', 'password')
self.stack = stack.Stack(self.ctx, 'creds_stack', self.tmpl)
self.stack.store()
db_stack = stack_object.Stack.get_by_id(self.ctx, self.stack.id)
user_c... | -9,213,545,049,745,668,000 | A user_creds entry is created on first stack store. | heat/tests/test_stack.py | test_store_saves_creds | openstack/heat | python | def test_store_saves_creds(self):
cfg.CONF.set_default('deferred_auth_method', 'password')
self.stack = stack.Stack(self.ctx, 'creds_stack', self.tmpl)
self.stack.store()
db_stack = stack_object.Stack.get_by_id(self.ctx, self.stack.id)
user_creds_id = db_stack.user_creds_id
self.assertIsNot... |
def test_store_saves_creds_trust(self):
'A user_creds entry is created on first stack store.'
cfg.CONF.set_override('deferred_auth_method', 'trusts')
self.patchobject(keystone.KeystoneClientPlugin, '_create', return_value=fake_ks.FakeKeystoneClient(user_id='auser123'))
self.stack = stack.Stack(self.ctx,... | -2,844,117,463,037,988,400 | A user_creds entry is created on first stack store. | heat/tests/test_stack.py | test_store_saves_creds_trust | openstack/heat | python | def test_store_saves_creds_trust(self):
cfg.CONF.set_override('deferred_auth_method', 'trusts')
self.patchobject(keystone.KeystoneClientPlugin, '_create', return_value=fake_ks.FakeKeystoneClient(user_id='auser123'))
self.stack = stack.Stack(self.ctx, 'creds_stack', self.tmpl)
self.stack.store()
... |
def test_stored_context_err(self):
'Test stored_context error path.'
self.stack = stack.Stack(self.ctx, 'creds_stack', self.tmpl)
ex = self.assertRaises(exception.Error, self.stack.stored_context)
expected_err = 'Attempt to use stored_context with no user_creds'
self.assertEqual(expected_err, str(ex... | 4,702,206,411,824,754,000 | Test stored_context error path. | heat/tests/test_stack.py | test_stored_context_err | openstack/heat | python | def test_stored_context_err(self):
self.stack = stack.Stack(self.ctx, 'creds_stack', self.tmpl)
ex = self.assertRaises(exception.Error, self.stack.stored_context)
expected_err = 'Attempt to use stored_context with no user_creds'
self.assertEqual(expected_err, str(ex)) |
def test_load_honors_owner(self):
'Loading a stack from the database will set the owner_id.\n\n Loading a stack from the database will set the owner_id of the\n resultant stack appropriately.\n '
self.stack = stack.Stack(self.ctx, 'owner_stack', self.tmpl)
stack_ownee = stack.Stack(self... | 7,915,637,699,835,126,000 | Loading a stack from the database will set the owner_id.
Loading a stack from the database will set the owner_id of the
resultant stack appropriately. | heat/tests/test_stack.py | test_load_honors_owner | openstack/heat | python | def test_load_honors_owner(self):
'Loading a stack from the database will set the owner_id.\n\n Loading a stack from the database will set the owner_id of the\n resultant stack appropriately.\n '
self.stack = stack.Stack(self.ctx, 'owner_stack', self.tmpl)
stack_ownee = stack.Stack(self... |
def test_stack_load_no_param_value_validation(self):
'Test stack loading with disabled parameter value validation.'
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n flavor:\n type: string\n description: A flavor.\n ... | 3,320,778,543,385,687,000 | Test stack loading with disabled parameter value validation. | heat/tests/test_stack.py | test_stack_load_no_param_value_validation | openstack/heat | python | def test_stack_load_no_param_value_validation(self):
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n flavor:\n type: string\n description: A flavor.\n constraints:\n - custom_constraint: ... |
def test_encrypt_parameters_false_parameters_stored_plaintext(self):
'Test stack loading with disabled parameter value validation.'
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: valu... | 1,876,205,515,915,799,000 | Test stack loading with disabled parameter value validation. | heat/tests/test_stack.py | test_encrypt_parameters_false_parameters_stored_plaintext | openstack/heat | python | def test_encrypt_parameters_false_parameters_stored_plaintext(self):
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n param2:\n type: string\n ... |
def test_parameters_stored_encrypted_decrypted_on_load(self):
'Test stack loading with disabled parameter value validation.'
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n ... | -1,018,863,454,073,504,000 | Test stack loading with disabled parameter value validation. | heat/tests/test_stack.py | test_parameters_stored_encrypted_decrypted_on_load | openstack/heat | python | def test_parameters_stored_encrypted_decrypted_on_load(self):
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n param2:\n type: string\n ... |
def test_parameters_created_encrypted_updated_decrypted(self):
'Test stack loading with disabled parameter value validation.'
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n ... | -1,445,745,818,561,343,700 | Test stack loading with disabled parameter value validation. | heat/tests/test_stack.py | test_parameters_created_encrypted_updated_decrypted | openstack/heat | python | def test_parameters_created_encrypted_updated_decrypted(self):
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n param2:\n type: string\n ... |
def test_parameters_stored_decrypted_successful_load(self):
'Test stack loading with disabled parameter value validation.'
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n ... | 8,764,875,422,488,754,000 | Test stack loading with disabled parameter value validation. | heat/tests/test_stack.py | test_parameters_stored_decrypted_successful_load | openstack/heat | python | def test_parameters_stored_decrypted_successful_load(self):
tmpl = template_format.parse('\n heat_template_version: 2013-05-23\n parameters:\n param1:\n type: string\n description: value1.\n param2:\n type: string\n ... |
def serve_paste(app, global_conf, **kw):
'pserve / paster serve / waitress replacement / integration\n\n You can pass as parameters:\n\n transports = websockets, xhr-multipart, xhr-longpolling, etc...\n policy_server = True\n '
serve(app, **kw)
return 0 | -5,353,821,925,766,461,000 | pserve / paster serve / waitress replacement / integration
You can pass as parameters:
transports = websockets, xhr-multipart, xhr-longpolling, etc...
policy_server = True | socketio/server.py | serve_paste | jykim16/gevent-socketio | python | def serve_paste(app, global_conf, **kw):
'pserve / paster serve / waitress replacement / integration\n\n You can pass as parameters:\n\n transports = websockets, xhr-multipart, xhr-longpolling, etc...\n policy_server = True\n '
serve(app, **kw)
return 0 |
def __init__(self, *args, **kwargs):
'This is just like the standard WSGIServer __init__, except with a\n few additional ``kwargs``:\n\n :param resource: The URL which has to be identified as a\n socket.io request. Defaults to the /socket.io/ URL.\n\n :param transports: Optional lis... | 2,082,469,375,877,520,000 | This is just like the standard WSGIServer __init__, except with a
few additional ``kwargs``:
:param resource: The URL which has to be identified as a
socket.io request. Defaults to the /socket.io/ URL.
:param transports: Optional list of transports to allow. List of
strings, each string should be one of
... | socketio/server.py | __init__ | jykim16/gevent-socketio | python | def __init__(self, *args, **kwargs):
'This is just like the standard WSGIServer __init__, except with a\n few additional ``kwargs``:\n\n :param resource: The URL which has to be identified as a\n socket.io request. Defaults to the /socket.io/ URL.\n\n :param transports: Optional lis... |
def get_socket(self, sessid=''):
'Return an existing or new client Socket.'
socket = self.sockets.get(sessid)
if (sessid and (not socket)):
return None
if (socket is None):
socket = Socket(self, self.config)
self.sockets[socket.sessid] = socket
else:
socket.incr_hits(... | 537,170,999,850,106,900 | Return an existing or new client Socket. | socketio/server.py | get_socket | jykim16/gevent-socketio | python | def get_socket(self, sessid=):
socket = self.sockets.get(sessid)
if (sessid and (not socket)):
return None
if (socket is None):
socket = Socket(self, self.config)
self.sockets[socket.sessid] = socket
else:
socket.incr_hits()
return socket |
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