function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def url_unescape(value, encoding='utf-8', plus=True):
"""Decodes the given value from a URL.
The argument may be either a byte or unicode string.
If encoding is None, the result will be a byte string. Otherwise,
the result is a unicode string in the specified encoding.
If ``p... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def utf8(value):
# type: (typing.Union[bytes,unicode_type,None])->typing.Union[bytes,None]
"""Converts a string argument to a byte string.
If the argument is already a byte string or None, it is returned unchanged.
Otherwise it must be a unicode string and is encoded as utf8.
"""
if isinstance(... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def to_unicode(value):
"""Converts a string argument to a unicode string.
If the argument is already a unicode string or None, it is returned
unchanged. Otherwise it must be a byte string and is decoded as utf8.
"""
if isinstance(value, _TO_UNICODE_TYPES):
return value
if not isinstanc... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def to_basestring(value):
"""Converts a string argument to a subclass of basestring.
In python2, byte and unicode strings are mostly interchangeable,
so functions that deal with a user-supplied argument in combination
with ascii string constants can use either and should return the type
the user su... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def linkify(text, shorten=False, extra_params="",
require_protocol=False, permitted_protocols=["http", "https"]):
"""Converts plain text into HTML with links.
For example: ``linkify("Hello http://tornadoweb.org!")`` would return
``Hello <a href="http://tornadoweb.org">http://tornadoweb.org</a>!... | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _build_unicode_map():
unicode_map = {}
for name, value in htmlentitydefs.name2codepoint.items():
unicode_map[name] = unichr(value)
return unicode_map | unnikrishnankgs/va | [
1,
5,
1,
10,
1496432585
] |
def _cls(self):
return ds.TransformedDistribution | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def testCachedSamplesWithoutInverse(self):
with self.test_session() as sess:
mu = 3.0
sigma = 0.02
log_normal = self._cls()(
distribution=ds.Normal(loc=mu, scale=sigma),
bijector=bs.Exp(event_ndims=0))
sample = log_normal.sample(1)
sample_val, log_pdf_val = sess.ru... | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def testEntropy(self):
with self.test_session():
shift = np.array([[-1, 0, 1], [-1, -2, -3]], dtype=np.float32)
diag = np.array([[1, 2, 3], [2, 3, 2]], dtype=np.float32)
actual_mvn_entropy = np.concatenate([
[stats.multivariate_normal(shift[i], np.diag(diag[i]**2)).entropy()]
f... | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def _cls(self):
return ds.TransformedDistribution | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def _testMVN(self,
base_distribution_class,
base_distribution_kwargs,
batch_shape=(),
event_shape=(),
not_implemented_message=None):
with self.test_session() as sess:
# Overriding shapes must be compatible w/bijector; most bijectors ar... | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def testScalarBatchNonScalarEvent(self):
self._testMVN(
base_distribution_class=ds.MultivariateNormalDiag,
base_distribution_kwargs={"loc": [0., 0., 0.],
"scale_diag": [1., 1, 1]},
batch_shape=[2],
not_implemented_message="not implemented")
with... | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def testNonScalarBatchNonScalarEvent(self):
with self.test_session():
# Can't override event_shape and/or batch_shape for non_scalar batch,
# non-scalar event.
with self.assertRaisesRegexp(ValueError, "base distribution not scalar"):
self._cls()(
distribution=ds.MultivariateNor... | npuichigo/ttsflow | [
16,
6,
16,
1,
1500635633
] |
def __init__(self,params,parent):
self.params=params
self.parent=parent | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def run(self): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def subplot(*args):
import pylab | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def __init__(self,parent=None,title='',direction='H',
size=(750,750),lfname=None,params=None):
self.fig=None | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Body(self): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Stopping(self):
return self.stopping | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Yield(self):
wx.Yield() | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def ResetTitle(self): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Plot(self,sim): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Run_Pause(self,event):
if not self.running: | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def __load_sim__(self,lfname): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Reset_Simulation(self,event=None):
if not os.path.exists(self.tmpfile):
return | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Restart(self,event=None): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Load_Simulation(self,event=None):
self.canvas.Show(False)
if self.modified:
(root,sfname)=os.path.split(self.params['save_sim_file'])
dlg=MessageDialog(self,
text="Do you want to save the changes you made to %s?" % sfname,
... | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Save_Simulation(self,event=None): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Save_Simulation_As(self,event=None): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Set_Simulation_Parameters(self,event):
self.canvas.Show(False)
set_simulation_parameters(self.params,self)
self.canvas.Show(True) | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Set_Input_Parameters(self,event):
self.canvas.Show(False)
set_input_parameters(self.params,self)
self.canvas.Show(True) | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Set_Output_Parameters(self,event):
self.canvas.Show(False)
set_output_parameters(self.params,self)
self.canvas.Show(True) | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Set_Weight_Parameters(self,event):
self.canvas.Show(False)
set_weight_parameters(self.params,self)
self.canvas.Show(True) | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Set_Parameter_Structure(self,event):
set_parameter_structure(self.params,self) | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def CreateMenu(self): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Display(self,event=None):
self.canvas.Show(False)
dlg = FileDialog(self, "Choose Display Module",default_dir=os.getcwd()+"/",
wildcard='Python Plot Files|plot*.py|All Files|*.*')
result = dlg.ShowModal()
dlg.Destroy()
if result == 'ok':
l... | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def About(self,event):
win=AboutWindow()
win.Show() | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Nop(self,event):
self.canvas.Show(False)
dlg = MessageDialog(self, "Error","Function Not Implemented",icon='error')
dlg.ShowModal()
dlg.Destroy()
self.canvas.Show(True) | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def Quit(self,event=None): | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def run(lfname=None,params=None,use_splash=True):
if use_splash:
app1=Application(splash.SplashFrame)
app1.Run()
app = Application(MainFrame, title="Plasticity",lfname=lfname,
params=params)
app.Run() | bblais/plasticity | [
5,
3,
5,
1,
1430486979
] |
def test_api_endpoint_existence(todolist_app):
with todolist_app.test_client() as client:
resp = client.get('/tasks')
assert resp.status_code == 200 | inkmonk/flask-sqlalchemy-booster | [
8,
3,
8,
8,
1430659799
] |
def __init__(self, upload):
self.upload = upload | dirkmoors/drf-tus | [
21,
20,
21,
2,
1488992116
] |
def handle_save(self):
pass | dirkmoors/drf-tus | [
21,
20,
21,
2,
1488992116
] |
def finish(self):
# Trigger signal
signals.saved.send(sender=self.__class__, instance=self)
# Finish
self.upload.finish()
self.upload.save() | dirkmoors/drf-tus | [
21,
20,
21,
2,
1488992116
] |
def handle_save(self):
# Save temporary field to file field
file_field = getattr(self.upload, self.destination_file_field)
file_field.save(self.upload.filename, File(open(self.upload.temporary_file_path)))
# Finish upload
self.finish() | dirkmoors/drf-tus | [
21,
20,
21,
2,
1488992116
] |
def __getattr__(cls, name):
return MagicMock() | ageitgey/face_recognition | [
47526,
12782,
47526,
704,
1488577959
] |
def combinationSum(self, candidates, target):
candidates.sort()
self.result = []
self.dfs(candidates,target,0,[])
return self.result | UmassJin/Leetcode | [
85,
40,
85,
57,
1426803902
] |
def dfs(self,candidates,target,start,reslist):
length = len(candidates)
if target == 0:
return self.result.append(reslist) | UmassJin/Leetcode | [
85,
40,
85,
57,
1426803902
] |
def combinationSum(self, candidates, target):
self.result = []
self.dfs(candidates,0,target,[])
return self.result | UmassJin/Leetcode | [
85,
40,
85,
57,
1426803902
] |
def dfs(self,can,cursum,target,res):
if cursum > target: return
if cursum == target:
self.result.append(res)
return
for i in xrange(len(can)):
if not res or res[len(res)-1] <= can[i]:
self.dfs(can,cursum+can[i],target,res+[can[i]]) | UmassJin/Leetcode | [
85,
40,
85,
57,
1426803902
] |
def __init__(self, **kwargs):
"""Initialization method.
Args:
**kwargs: Keyword arguments.
Kwargs:
hash_table (str): The hash table package id.
remote (str): The remote ckan url.
api_key (str): The ckan api key.
ua (str): The user age... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def delete_table(self, resource_id, **kwargs):
"""Deletes a datastore table.
Args:
resource_id (str): The datastore resource id.
**kwargs: Keyword arguments that are passed to datastore_create.
Kwargs:
force (bool): Delete resource even if read-only.
... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def get_hash(self, resource_id):
"""Gets the hash of a datastore table.
Args:
resource_id (str): The datastore resource id.
Returns:
str: The datastore resource hash.
Raises:
NotFound: If `hash_table_id` isn't set or not in datastore.
No... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def fetch_resource(self, resource_id, user_agent=None, stream=True):
"""Fetches a single resource from filestore.
Args:
resource_id (str): The filestore resource id.
Kwargs:
user_agent (str): The user agent.
stream (bool): Stream content (default: True).
... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def _update_filestore(self, func, *args, **kwargs):
"""Helps create or update a single resource on filestore.
To create a resource, you must supply either `url`, `filepath`, or
`fileobj`.
Args:
func (func): The resource passed to resource_create.
*args: Postional... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def update_filestore(self, resource_id, **kwargs):
"""Updates a single resource on filestore.
Args:
resource_id (str): The filestore resource id.
**kwargs: Keyword arguments that are passed to resource_create.
Kwargs:
url (str): New file url (for file link).... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def find_ids(self, packages, **kwargs):
default = {'rid': '', 'pname': ''}
kwargs.update({'method': self.query, 'default': default})
return pr.find(packages, **kwargs) | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def create_hash_table(self, verbose=False):
kwargs = {
'resource_id': self.hash_table_id,
'fields': [
{'id': 'datastore_id', 'type': 'text'},
{'id': 'hash', 'type': 'text'}],
'primary_key': 'datastore_id'
}
if verbose:
... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def get_update_date(self, item):
timestamps = {
'revision_timestamp': 'revision',
'last_modified': 'resource',
'metadata_modified': 'package'
}
for key, value in timestamps.items():
if key in item:
timestamp = item[key]
... | reubano/ckanutils | [
3,
3,
3,
3,
1434441316
] |
def register(self, tag):
return functools.partial(self._register, tag) | afg984/pyardrone | [
27,
4,
27,
4,
1439552654
] |
def build_dataset(reader, phi_list, class_func, vectorizer=None, verbose=False):
"""Core general function for building experimental
hand-generated feature datasets. | ptoman/icgauge | [
9,
2,
9,
1,
1460743801
] |
def experiment_features(
train_reader=data_readers.toy,
assess_reader=None,
train_size=0.7,
phi_list=[fe.manual_content_flags],
class_func=lt.identity_class_func,
train_func=training.fit_logistic_at_with_crossvalidation,
score_func=scipy.stats.stats.pearsonr,
... | ptoman/icgauge | [
9,
2,
9,
1,
1460743801
] |
def get_score_example_pairs(y, y_hat, examples):
""" Return a list of dicts: {truth score, predicted score, example} """
paired_results = sorted(zip(y, y_hat), key=lambda x: x[0]-x[1])
performance = []
for i, (truth, prediction) in enumerate(paired_results):
performance.append({"truth": truth, ... | ptoman/icgauge | [
9,
2,
9,
1,
1460743801
] |
def experiment_features_iterated(
train_reader=data_readers.toy,
assess_reader=None,
train_size=0.7,
phi_list=[fe.manual_content_flags],
class_func=lt.identity_class_func,
train_func=training.fit_logistic_at_with_crossvalidation,
score_func=utils.safe_weighted_... | ptoman/icgauge | [
9,
2,
9,
1,
1460743801
] |
def try_pull_image(self, name, tag="latest"):
'''
Pull an image
'''
self.log("(Try) Pulling image %s:%s" % (name, tag)) | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def log(self, msg, pretty_print=False):
qb_log(msg) | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def __init__(self, client, results):
super(ImageManager, self).__init__()
self.client = client
self.results = results
parameters = self.client.module.params
self.check_mode = self.client.check_mode
self.archive_path = parameters.get('archive_path')
self.contain... | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def fail(self, msg):
self.client.fail(msg) | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def absent(self):
'''
Handles state = 'absent', which removes an image.
:return None
'''
image = self.client.find_image(self.name, self.tag)
if image:
name = self.name
if self.tag:
name = "%s:%s" % (self.name, self.tag)
... | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def push_image(self, name, tag=None):
'''
If the name of the image contains a repository path, then push the image.
:param name Name of the image to push.
:param tag Use a specific tag.
:return: None
'''
repository = name
if not tag:
reposito... | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def build_image(self):
'''
Build an image
:return: image dict
'''
params = dict(
path=self.path,
tag=self.name,
rm=self.rm,
nocache=self.nocache,
stream=True,
timeout=self.http_timeout,
pull=self... | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def log(self, msg, pretty_print=False):
return qb_log(msg) | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def warn( self, warning ):
self.results['warnings'].append( str(warning) ) | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def qb_debug(name, message, **payload):
if not qb.ipc.stdio.client.log.connected:
return False | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def main():
argument_spec = dict(
archive_path=dict(type='path'),
container_limits=dict(type='dict'),
dockerfile=dict(type='str'),
force=dict(type='bool', default=False),
http_timeout=dict(type='int'),
load_path=dict(type='path'),
name=dict(type='str', require... | nrser/qb | [
1,
1,
1,
8,
1448301308
] |
def setUp(self):
entry_Li = ComputedEntry("Li", -1.90753119)
with open(os.path.join(PymatgenTest.TEST_FILES_DIR, "LiTiO2_batt.json")) as f:
entries_LTO = json.load(f, cls=MontyDecoder)
self.ie_LTO = InsertionElectrode.from_entries(entries_LTO, entry_Li)
with open(os.pat... | materialsproject/pymatgen | [
1063,
732,
1063,
235,
1319343039
] |
def testPlotly(self):
plotter = VoltageProfilePlotter(xaxis="frac_x")
plotter.add_electrode(self.ie_LTO, "LTO insertion")
plotter.add_electrode(self.ce_FF, "FeF3 conversion")
fig = plotter.get_plotly_figure()
self.assertEqual(fig.layout.xaxis.title.text, "Atomic Fraction of Li")
... | materialsproject/pymatgen | [
1063,
732,
1063,
235,
1319343039
] |
def __init__(self, songs_data=None):
if songs_data is None:
self.songs_data = []
else:
self.songs_data = songs_data | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def songs(self):
for s in self.songs_data:
yield s | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def num_features(self):
if len(self.songs_data):
return self.songs_data[0].X.shape[1] | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def size(self):
return len(self.songs_data) | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def __init__(self, audio_path, label_path):
if not os.path.isfile(audio_path):
raise IOError("Audio file at %s does not exist" % audio_path)
if label_path and not os.path.isfile(label_path):
raise IOError("MIDI file at %s does not exist" % label_path)
self.audio_path = a... | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def x(self):
return self.__x | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def x(self, x):
self.__x = x | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def X(self):
return self.__X | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def X(self, X):
if hasattr(self, 'Y') and self.Y.shape[0] != X.shape[0]:
raise ValueError("Number of feature frames must equal number of label frames")
self.__X = X | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def Y(self):
return self.__Y | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def Y(self, Y):
if hasattr(self, 'X') and self.X.shape[0] != Y.shape[0]:
raise ValueError("Number of label frames must equal number of feature frames")
self.__Y = Y | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def num_pitches(self):
if hasattr(self, 'Y'):
return np.shape(self.Y)[1]
return 0 | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def num_features(self):
if hasattr(self, 'X'):
return self.X.shape[1] | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def num_frames(self):
if hasattr(self, 'X'):
return self.X.shape[0] | Guitar-Machine-Learning-Group/guitar-transcriber | [
1,
2,
1,
1,
1476390274
] |
def setHome(home):
global __home
__home = home | ManiacalLabs/PixelWeb | [
18,
6,
18,
10,
1445800144
] |
def initConfig():
try:
if not os.path.exists(__home):
print "Creating {}".format(__home)
os.makedirs(__home)
except:
print "Failed to initialize PixelWeb config!" | ManiacalLabs/PixelWeb | [
18,
6,
18,
10,
1445800144
] |
def writeConfig(file, data, key = None, path=None):
if not path:
path = __home
base = data
if key:
base = readConfig(file, path=path)
base[key] = data
with open(path + "/" + file + ".json", "w") as fp:
json.dump(base, fp, indent=4, sort_keys=True) | ManiacalLabs/PixelWeb | [
18,
6,
18,
10,
1445800144
] |
def readServerConfig():
data = readConfig("config", path=__home)
base = paramsToDict(BASE_SERVER_CONFIG.params)
if len(data.keys()) == 0:
data = paramsToDict(BASE_SERVER_CONFIG.params)
elif len(data.keys()) != len(base.keys()):
data.upgrade(base)
return d(data) | ManiacalLabs/PixelWeb | [
18,
6,
18,
10,
1445800144
] |
def test_segment_pools():
### Test Segment ID Pool Operations
# Get all configured Segment Pools
get_segment_resp = client_session.read('vdnSegmentPools')
client_session.view_response(get_segment_resp)
# Add a Segment Pool
segments_create_body = client_session.extract_resource_body_example('vd... | vmware/nsxramlclient | [
41,
33,
41,
9,
1440806810
] |
def has_ext_modules(self):
return True | ryfeus/lambda-packs | [
1086,
234,
1086,
13,
1476901359
] |
def finalize_options(self):
ret = InstallCommandBase.finalize_options(self)
self.install_headers = os.path.join(self.install_purelib,
'tensorflow', 'include')
return ret | ryfeus/lambda-packs | [
1086,
234,
1086,
13,
1476901359
] |
def initialize_options(self):
self.install_dir = None
self.force = 0
self.outfiles = [] | ryfeus/lambda-packs | [
1086,
234,
1086,
13,
1476901359
] |
def mkdir_and_copy_file(self, header):
install_dir = os.path.join(self.install_dir, os.path.dirname(header))
# Get rid of some extra intervening directories so we can have fewer
# directories for -I
install_dir = re.sub('/google/protobuf_archive/src', '', install_dir)
# Copy eigen code into tensorf... | ryfeus/lambda-packs | [
1086,
234,
1086,
13,
1476901359
] |
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