code_tokens
stringlengths 60
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| doc
stringlengths 16
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def out ( self , output , newline = True ) : click . echo ( output , nl = newline )
| 1 |
making clickable text in python
|
Outputs a string to the console ( stdout ) .
|
cosqa-train-14300
|
def out(self, output, newline=True):
"""Outputs a string to the console (stdout)."""
click.echo(output, nl=newline)
|
def check_by_selector ( self , selector ) : elem = find_element_by_jquery ( world . browser , selector ) if not elem . is_selected ( ) : elem . click ( )
| 1 |
python webdriver checkbox checked
|
Check the checkbox matching the CSS selector .
|
cosqa-train-14301
|
def check_by_selector(self, selector):
"""Check the checkbox matching the CSS selector."""
elem = find_element_by_jquery(world.browser, selector)
if not elem.is_selected():
elem.click()
|
def timestamp_to_datetime ( timestamp ) : from datetime import datetime , timedelta obj = datetime . fromtimestamp ( timestamp [ 0 ] ) return obj + timedelta ( microseconds = int ( timestamp [ 1 ] ) )
| 0 |
manipulate timestamp datatype python
|
Convert an ARF timestamp to a datetime . datetime object ( naive local time )
|
cosqa-train-14302
|
def timestamp_to_datetime(timestamp):
"""Convert an ARF timestamp to a datetime.datetime object (naive local time)"""
from datetime import datetime, timedelta
obj = datetime.fromtimestamp(timestamp[0])
return obj + timedelta(microseconds=int(timestamp[1]))
|
def check_by_selector ( self , selector ) : elem = find_element_by_jquery ( world . browser , selector ) if not elem . is_selected ( ) : elem . click ( )
| 1 |
python webkit check element
|
Check the checkbox matching the CSS selector .
|
cosqa-train-14303
|
def check_by_selector(self, selector):
"""Check the checkbox matching the CSS selector."""
elem = find_element_by_jquery(world.browser, selector)
if not elem.is_selected():
elem.click()
|
def map ( cls , iterable , func , * a , * * kw ) : return cls ( func ( x , * a , * * kw ) for x in iterable )
| 1 |
map python 'function' object is not iterable
|
Iterable - first replacement of Python s built - in map () function .
|
cosqa-train-14304
|
def map(cls, iterable, func, *a, **kw):
"""
Iterable-first replacement of Python's built-in `map()` function.
"""
return cls(func(x, *a, **kw) for x in iterable)
|
def _close_websocket ( self ) : close_method = getattr ( self . _websocket , "close" , None ) if callable ( close_method ) : asyncio . ensure_future ( close_method ( ) , loop = self . _event_loop ) self . _websocket = None self . _dispatch_event ( event = "close" )
| 1 |
python websocket logout after send a message
|
Closes the websocket connection .
|
cosqa-train-14305
|
def _close_websocket(self):
"""Closes the websocket connection."""
close_method = getattr(self._websocket, "close", None)
if callable(close_method):
asyncio.ensure_future(close_method(), loop=self._event_loop)
self._websocket = None
self._dispatch_event(event="close")
|
def _group_dict_set ( iterator ) : d = defaultdict ( set ) for key , value in iterator : d [ key ] . add ( value ) return dict ( d )
| 1 |
mapping a set with a dictionary in python
|
Make a dict that accumulates the values for each key in an iterator of doubles .
|
cosqa-train-14306
|
def _group_dict_set(iterator):
"""Make a dict that accumulates the values for each key in an iterator of doubles.
:param iter[tuple[A,B]] iterator: An iterator
:rtype: dict[A,set[B]]
"""
d = defaultdict(set)
for key, value in iterator:
d[key].add(value)
return dict(d)
|
def _increase_file_handle_limit ( ) : logging . info ( 'Increasing file handle limit to {}' . format ( constants . FILE_HANDLE_LIMIT ) ) resource . setrlimit ( resource . RLIMIT_NOFILE , ( constants . FILE_HANDLE_LIMIT , resource . RLIM_INFINITY ) )
| 1 |
python windows increase file handle limit
|
Raise the open file handles permitted by the Dusty daemon process and its child processes . The number we choose here needs to be within the OS X default kernel hard limit which is 10240 .
|
cosqa-train-14307
|
def _increase_file_handle_limit():
"""Raise the open file handles permitted by the Dusty daemon process
and its child processes. The number we choose here needs to be within
the OS X default kernel hard limit, which is 10240."""
logging.info('Increasing file handle limit to {}'.format(constants.FILE_HANDLE_LIMIT))
resource.setrlimit(resource.RLIMIT_NOFILE,
(constants.FILE_HANDLE_LIMIT, resource.RLIM_INFINITY))
|
def asMaskedArray ( self ) : return ma . masked_array ( data = self . data , mask = self . mask , fill_value = self . fill_value )
| 1 |
masked array to numpy array python
|
Creates converts to a masked array
|
cosqa-train-14308
|
def asMaskedArray(self):
""" Creates converts to a masked array
"""
return ma.masked_array(data=self.data, mask=self.mask, fill_value=self.fill_value)
|
def acquire_nix ( lock_file ) : # pragma: no cover fd = os . open ( lock_file , OPEN_MODE ) try : fcntl . flock ( fd , fcntl . LOCK_EX | fcntl . LOCK_NB ) except ( IOError , OSError ) : os . close ( fd ) else : return fd
| 1 |
python windows lock file
|
Acquire a lock file on linux or osx .
|
cosqa-train-14309
|
def acquire_nix(lock_file): # pragma: no cover
"""Acquire a lock file on linux or osx."""
fd = os.open(lock_file, OPEN_MODE)
try:
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
except (IOError, OSError):
os.close(fd)
else:
return fd
|
def array ( self ) : return np . arange ( self . start , self . stop , self . step )
| 0 |
matlab equivalent of python arange
|
return the underlying numpy array
|
cosqa-train-14310
|
def array(self):
"""
return the underlying numpy array
"""
return np.arange(self.start, self.stop, self.step)
|
def __Logout ( si ) : try : if si : content = si . RetrieveContent ( ) content . sessionManager . Logout ( ) except Exception as e : pass
| 1 |
python windows session logout check
|
Disconnect ( logout ) service instance
|
cosqa-train-14311
|
def __Logout(si):
"""
Disconnect (logout) service instance
@param si: Service instance (returned from Connect)
"""
try:
if si:
content = si.RetrieveContent()
content.sessionManager.Logout()
except Exception as e:
pass
|
def clear_matplotlib_ticks ( self , axis = "both" ) : ax = self . get_axes ( ) plotting . clear_matplotlib_ticks ( ax = ax , axis = axis )
| 1 |
matplotlib python remove ticks
|
Clears the default matplotlib ticks .
|
cosqa-train-14312
|
def clear_matplotlib_ticks(self, axis="both"):
"""Clears the default matplotlib ticks."""
ax = self.get_axes()
plotting.clear_matplotlib_ticks(ax=ax, axis=axis)
|
def Proxy ( f ) : def Wrapped ( self , * args ) : return getattr ( self , f ) ( * args ) return Wrapped
| 1 |
python wrapper function for a method
|
A helper to create a proxy method in a class .
|
cosqa-train-14313
|
def Proxy(f):
"""A helper to create a proxy method in a class."""
def Wrapped(self, *args):
return getattr(self, f)(*args)
return Wrapped
|
def is_square_matrix ( mat ) : mat = np . array ( mat ) if mat . ndim != 2 : return False shape = mat . shape return shape [ 0 ] == shape [ 1 ]
| 1 |
matrix in python to check accurecy
|
Test if an array is a square matrix .
|
cosqa-train-14314
|
def is_square_matrix(mat):
"""Test if an array is a square matrix."""
mat = np.array(mat)
if mat.ndim != 2:
return False
shape = mat.shape
return shape[0] == shape[1]
|
def save_dict_to_file ( filename , dictionary ) : with open ( filename , 'w' ) as f : writer = csv . writer ( f ) for k , v in iteritems ( dictionary ) : writer . writerow ( [ str ( k ) , str ( v ) ] )
| 1 |
python write a dictionary to file
|
Saves dictionary as CSV file .
|
cosqa-train-14315
|
def save_dict_to_file(filename, dictionary):
"""Saves dictionary as CSV file."""
with open(filename, 'w') as f:
writer = csv.writer(f)
for k, v in iteritems(dictionary):
writer.writerow([str(k), str(v)])
|
def argmax ( l , f = None ) : if f : l = [ f ( i ) for i in l ] return max ( enumerate ( l ) , key = lambda x : x [ 1 ] ) [ 0 ]
| 1 |
max function for a list of strings in python
|
http : // stackoverflow . com / questions / 5098580 / implementing - argmax - in - python
|
cosqa-train-14316
|
def argmax(l,f=None):
"""http://stackoverflow.com/questions/5098580/implementing-argmax-in-python"""
if f:
l = [f(i) for i in l]
return max(enumerate(l), key=lambda x:x[1])[0]
|
def _write_color_colorama ( fp , text , color ) : foreground , background , style = get_win_color ( color ) colorama . set_console ( foreground = foreground , background = background , style = style ) fp . write ( text ) colorama . reset_console ( )
| 0 |
python write colored text to file
|
Colorize text with given color .
|
cosqa-train-14317
|
def _write_color_colorama (fp, text, color):
"""Colorize text with given color."""
foreground, background, style = get_win_color(color)
colorama.set_console(foreground=foreground, background=background,
style=style)
fp.write(text)
colorama.reset_console()
|
def SegmentMax ( a , ids ) : func = lambda idxs : np . amax ( a [ idxs ] , axis = 0 ) return seg_map ( func , a , ids ) ,
| 1 |
maximum 2 dimentional array python
|
Segmented max op .
|
cosqa-train-14318
|
def SegmentMax(a, ids):
"""
Segmented max op.
"""
func = lambda idxs: np.amax(a[idxs], axis=0)
return seg_map(func, a, ids),
|
def write_fits ( data , header , file_name ) : hdu = fits . PrimaryHDU ( data ) hdu . header = header hdulist = fits . HDUList ( [ hdu ] ) hdulist . writeto ( file_name , overwrite = True ) logging . info ( "Wrote {0}" . format ( file_name ) ) return
| 1 |
python write fits header to another
|
Combine data and a fits header to write a fits file .
|
cosqa-train-14319
|
def write_fits(data, header, file_name):
"""
Combine data and a fits header to write a fits file.
Parameters
----------
data : numpy.ndarray
The data to be written.
header : astropy.io.fits.hduheader
The header for the fits file.
file_name : string
The file to write
Returns
-------
None
"""
hdu = fits.PrimaryHDU(data)
hdu.header = header
hdulist = fits.HDUList([hdu])
hdulist.writeto(file_name, overwrite=True)
logging.info("Wrote {0}".format(file_name))
return
|
def md5_string ( s ) : m = hashlib . md5 ( ) m . update ( s ) return str ( m . hexdigest ( ) )
| 0 |
md5 for python 3
|
Shortcut to create md5 hash : param s : : return :
|
cosqa-train-14320
|
def md5_string(s):
"""
Shortcut to create md5 hash
:param s:
:return:
"""
m = hashlib.md5()
m.update(s)
return str(m.hexdigest())
|
def write_string ( value , buff , byteorder = 'big' ) : data = value . encode ( 'utf-8' ) write_numeric ( USHORT , len ( data ) , buff , byteorder ) buff . write ( data )
| 1 |
python write or don't write bytecodes
|
Write a string to a file - like object .
|
cosqa-train-14321
|
def write_string(value, buff, byteorder='big'):
"""Write a string to a file-like object."""
data = value.encode('utf-8')
write_numeric(USHORT, len(data), buff, byteorder)
buff.write(data)
|
def get_file_md5sum ( path ) : with open ( path , 'rb' ) as fh : h = str ( hashlib . md5 ( fh . read ( ) ) . hexdigest ( ) ) return h
| 1 |
md5 of a file python
|
Calculate the MD5 hash for a file .
|
cosqa-train-14322
|
def get_file_md5sum(path):
"""Calculate the MD5 hash for a file."""
with open(path, 'rb') as fh:
h = str(hashlib.md5(fh.read()).hexdigest())
return h
|
def set_icon ( self , bmp ) : _icon = wx . EmptyIcon ( ) _icon . CopyFromBitmap ( bmp ) self . SetIcon ( _icon )
| 1 |
python wx set icon
|
Sets main window icon to given wx . Bitmap
|
cosqa-train-14323
|
def set_icon(self, bmp):
"""Sets main window icon to given wx.Bitmap"""
_icon = wx.EmptyIcon()
_icon.CopyFromBitmap(bmp)
self.SetIcon(_icon)
|
def start_task ( self , task ) : self . info ( "Calculating {}..." . format ( task ) ) self . tasks [ task ] = self . timer ( )
| 1 |
measure start of task in python
|
Begin logging of a task
|
cosqa-train-14324
|
def start_task(self, task):
"""Begin logging of a task
Stores the time this task was started in order to return
time lapsed when `complete_task` is called.
Parameters
----------
task : str
Name of the task to be started
"""
self.info("Calculating {}...".format(task))
self.tasks[task] = self.timer()
|
def __call__ ( self , xy ) : x , y = xy return ( self . x ( x ) , self . y ( y ) )
| 1 |
python x and y coordiante
|
Project x and y
|
cosqa-train-14325
|
def __call__(self, xy):
"""Project x and y"""
x, y = xy
return (self.x(x), self.y(y))
|
def merge ( self , obj ) : for attribute in dir ( obj ) : if '__' in attribute : continue setattr ( self , attribute , getattr ( obj , attribute ) )
| 1 |
merge objects without overwrite python
|
This function merge another object s values with this instance
|
cosqa-train-14326
|
def merge(self, obj):
"""This function merge another object's values with this instance
:param obj: An object to be merged with into this layer
:type obj: object
"""
for attribute in dir(obj):
if '__' in attribute:
continue
setattr(self, attribute, getattr(obj, attribute))
|
def elXpath ( self , xpath , dom = None ) : if dom is None : dom = self . browser return expect ( dom . is_element_present_by_xpath , args = [ xpath ] )
| 1 |
python xpath elements exist
|
check if element is present by css
|
cosqa-train-14327
|
def elXpath(self, xpath, dom=None):
"""check if element is present by css"""
if dom is None:
dom = self.browser
return expect(dom.is_element_present_by_xpath, args=[xpath])
|
def dict_merge ( set1 , set2 ) : return dict ( list ( set1 . items ( ) ) + list ( set2 . items ( ) ) )
| 1 |
merging two similar dictionaries in python
|
Joins two dictionaries .
|
cosqa-train-14328
|
def dict_merge(set1, set2):
"""Joins two dictionaries."""
return dict(list(set1.items()) + list(set2.items()))
|
def serialize_yaml_tofile ( filename , resource ) : stream = file ( filename , "w" ) yaml . dump ( resource , stream , default_flow_style = False )
| 1 |
python yaml writ to json like file
|
Serializes a K8S resource to YAML - formatted file .
|
cosqa-train-14329
|
def serialize_yaml_tofile(filename, resource):
"""
Serializes a K8S resource to YAML-formatted file.
"""
stream = file(filename, "w")
yaml.dump(resource, stream, default_flow_style=False)
|
def fn_min ( self , a , axis = None ) : return numpy . nanmin ( self . _to_ndarray ( a ) , axis = axis )
| 1 |
minimum value of array numpy python
|
Return the minimum of an array ignoring any NaNs .
|
cosqa-train-14330
|
def fn_min(self, a, axis=None):
"""
Return the minimum of an array, ignoring any NaNs.
:param a: The array.
:return: The minimum value of the array.
"""
return numpy.nanmin(self._to_ndarray(a), axis=axis)
|
def guess_title ( basename ) : base , _ = os . path . splitext ( basename ) return re . sub ( r'[ _-]+' , r' ' , base ) . title ( )
| 1 |
python, detect file name
|
Attempt to guess the title from the filename
|
cosqa-train-14331
|
def guess_title(basename):
""" Attempt to guess the title from the filename """
base, _ = os.path.splitext(basename)
return re.sub(r'[ _-]+', r' ', base).title()
|
def _obj_cursor_to_dictionary ( self , cursor ) : if not cursor : return cursor cursor = json . loads ( json . dumps ( cursor , cls = BSONEncoder ) ) if cursor . get ( "_id" ) : cursor [ "id" ] = cursor . get ( "_id" ) del cursor [ "_id" ] return cursor
| 1 |
mongodb cursor to json object python3
|
Handle conversion of pymongo cursor into a JSON object formatted for UI consumption
|
cosqa-train-14332
|
def _obj_cursor_to_dictionary(self, cursor):
"""Handle conversion of pymongo cursor into a JSON object formatted for UI consumption
:param dict cursor: a mongo document that should be converted to primitive types for the client code
:returns: a primitive dictionary
:rtype: dict
"""
if not cursor:
return cursor
cursor = json.loads(json.dumps(cursor, cls=BSONEncoder))
if cursor.get("_id"):
cursor["id"] = cursor.get("_id")
del cursor["_id"]
return cursor
|
def dict_to_html_attrs ( dict_ ) : res = ' ' . join ( '%s="%s"' % ( k , v ) for k , v in dict_ . items ( ) ) return res
| 1 |
python, dict to html
|
Banana banana
|
cosqa-train-14333
|
def dict_to_html_attrs(dict_):
"""
Banana banana
"""
res = ' '.join('%s="%s"' % (k, v) for k, v in dict_.items())
return res
|
def mostCommonItem ( lst ) : # This elegant solution from: http://stackoverflow.com/a/1518632/1760218 lst = [ l for l in lst if l ] if lst : return max ( set ( lst ) , key = lst . count ) else : return None
| 1 |
most common item in list python
|
Choose the most common item from the list or the first item if all items are unique .
|
cosqa-train-14334
|
def mostCommonItem(lst):
"""Choose the most common item from the list, or the first item if all
items are unique."""
# This elegant solution from: http://stackoverflow.com/a/1518632/1760218
lst = [l for l in lst if l]
if lst:
return max(set(lst), key=lst.count)
else:
return None
|
def extract_module_locals ( depth = 0 ) : f = sys . _getframe ( depth + 1 ) global_ns = f . f_globals module = sys . modules [ global_ns [ '__name__' ] ] return ( module , f . f_locals )
| 1 |
python, get function stack
|
Returns ( module locals ) of the funciton depth frames away from the caller
|
cosqa-train-14335
|
def extract_module_locals(depth=0):
"""Returns (module, locals) of the funciton `depth` frames away from the caller"""
f = sys._getframe(depth + 1)
global_ns = f.f_globals
module = sys.modules[global_ns['__name__']]
return (module, f.f_locals)
|
def _most_common ( iterable ) : data = Counter ( iterable ) return max ( data , key = data . __getitem__ )
| 1 |
most common value in an array python
|
Returns the most common element in iterable .
|
cosqa-train-14336
|
def _most_common(iterable):
"""Returns the most common element in `iterable`."""
data = Counter(iterable)
return max(data, key=data.__getitem__)
|
def astype ( array , y ) : if isinstance ( y , autograd . core . Node ) : return array . astype ( numpy . array ( y . value ) . dtype ) return array . astype ( numpy . array ( y ) . dtype )
| 0 |
python, how to apply astype function
|
A functional form of the astype method .
|
cosqa-train-14337
|
def astype(array, y):
"""A functional form of the `astype` method.
Args:
array: The array or number to cast.
y: An array or number, as the input, whose type should be that of array.
Returns:
An array or number with the same dtype as `y`.
"""
if isinstance(y, autograd.core.Node):
return array.astype(numpy.array(y.value).dtype)
return array.astype(numpy.array(y).dtype)
|
def dict_merge ( set1 , set2 ) : return dict ( list ( set1 . items ( ) ) + list ( set2 . items ( ) ) )
| 0 |
most optimized way to merge 2 dictionaries in python
|
Joins two dictionaries .
|
cosqa-train-14338
|
def dict_merge(set1, set2):
"""Joins two dictionaries."""
return dict(list(set1.items()) + list(set2.items()))
|
def save ( self ) : if self . path : self . _saveState ( self . path ) else : self . saveAs ( )
| 1 |
python, model saved in session
|
save the current session override if session was saved earlier
|
cosqa-train-14339
|
def save(self):
"""save the current session
override, if session was saved earlier"""
if self.path:
self._saveState(self.path)
else:
self.saveAs()
|
def list_move_to_front ( l , value = 'other' ) : l = list ( l ) if value in l : l . remove ( value ) l . insert ( 0 , value ) return l
| 1 |
move an item in list to front python
|
if the value is in the list move it to the front and return it .
|
cosqa-train-14340
|
def list_move_to_front(l,value='other'):
"""if the value is in the list, move it to the front and return it."""
l=list(l)
if value in l:
l.remove(value)
l.insert(0,value)
return l
|
def user_return ( self , frame , return_value ) : pdb . Pdb . user_return ( self , frame , return_value )
| 1 |
python, pdb, step out of function, shortcut
|
This function is called when a return trap is set here .
|
cosqa-train-14341
|
def user_return(self, frame, return_value):
"""This function is called when a return trap is set here."""
pdb.Pdb.user_return(self, frame, return_value)
|
def move_up ( lines = 1 , file = sys . stdout ) : move . up ( lines ) . write ( file = file )
| 1 |
move cursor down line python
|
Move the cursor up a number of lines .
|
cosqa-train-14342
|
def move_up(lines=1, file=sys.stdout):
""" Move the cursor up a number of lines.
Esc[ValueA:
Moves the cursor up by the specified number of lines without changing
columns. If the cursor is already on the top line, ANSI.SYS ignores
this sequence.
"""
move.up(lines).write(file=file)
|
def string_to_identity ( identity_str ) : m = _identity_regexp . match ( identity_str ) result = m . groupdict ( ) log . debug ( 'parsed identity: %s' , result ) return { k : v for k , v in result . items ( ) if v }
| 1 |
python, turn a string into a dict
|
Parse string into Identity dictionary .
|
cosqa-train-14343
|
def string_to_identity(identity_str):
"""Parse string into Identity dictionary."""
m = _identity_regexp.match(identity_str)
result = m.groupdict()
log.debug('parsed identity: %s', result)
return {k: v for k, v in result.items() if v}
|
def move_up ( lines = 1 , file = sys . stdout ) : move . up ( lines ) . write ( file = file )
| 0 |
move cursor up and to beginning of line python
|
Move the cursor up a number of lines .
|
cosqa-train-14344
|
def move_up(lines=1, file=sys.stdout):
""" Move the cursor up a number of lines.
Esc[ValueA:
Moves the cursor up by the specified number of lines without changing
columns. If the cursor is already on the top line, ANSI.SYS ignores
this sequence.
"""
move.up(lines).write(file=file)
|
def get_url_args ( url ) : url_data = urllib . parse . urlparse ( url ) arg_dict = urllib . parse . parse_qs ( url_data . query ) return arg_dict
| 1 |
python2 url parse query to dict
|
Returns a dictionary from a URL params
|
cosqa-train-14345
|
def get_url_args(url):
""" Returns a dictionary from a URL params """
url_data = urllib.parse.urlparse(url)
arg_dict = urllib.parse.parse_qs(url_data.query)
return arg_dict
|
def _parse_array ( self , tensor_proto ) : try : from onnx . numpy_helper import to_array except ImportError as e : raise ImportError ( "Unable to import onnx which is required {}" . format ( e ) ) np_array = to_array ( tensor_proto ) . reshape ( tuple ( tensor_proto . dims ) ) return mx . nd . array ( np_array )
| 1 |
mxnet ndarray to python list
|
Grab data in TensorProto and convert to numpy array .
|
cosqa-train-14346
|
def _parse_array(self, tensor_proto):
"""Grab data in TensorProto and convert to numpy array."""
try:
from onnx.numpy_helper import to_array
except ImportError as e:
raise ImportError("Unable to import onnx which is required {}".format(e))
np_array = to_array(tensor_proto).reshape(tuple(tensor_proto.dims))
return mx.nd.array(np_array)
|
def list2dict ( list_of_options ) : d = { } for key , value in list_of_options : d [ key ] = value return d
| 1 |
python3 2 list to dictionary
|
Transforms a list of 2 element tuples to a dictionary
|
cosqa-train-14347
|
def list2dict(list_of_options):
"""Transforms a list of 2 element tuples to a dictionary"""
d = {}
for key, value in list_of_options:
d[key] = value
return d
|
def unit_ball_L2 ( shape ) : x = tf . Variable ( tf . zeros ( shape ) ) return constrain_L2 ( x )
| 1 |
name 'python' is not defined tensorflow
|
A tensorflow variable tranfomed to be constrained in a L2 unit ball .
|
cosqa-train-14348
|
def unit_ball_L2(shape):
"""A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
return constrain_L2(x)
|
def to_dicts ( recarray ) : for rec in recarray : yield dict ( zip ( recarray . dtype . names , rec . tolist ( ) ) )
| 0 |
python3 array to dict
|
convert record array to a dictionaries
|
cosqa-train-14349
|
def to_dicts(recarray):
"""convert record array to a dictionaries"""
for rec in recarray:
yield dict(zip(recarray.dtype.names, rec.tolist()))
|
def to_distribution_values ( self , values ) : with warnings . catch_warnings ( ) : warnings . simplefilter ( "ignore" ) # avoid RuntimeWarning: divide by zero encountered in log return numpy . log ( values )
| 1 |
natural log of array in python
|
Returns numpy array of natural logarithms of values .
|
cosqa-train-14350
|
def to_distribution_values(self, values):
"""
Returns numpy array of natural logarithms of ``values``.
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# avoid RuntimeWarning: divide by zero encountered in log
return numpy.log(values)
|
def dt_to_ts ( value ) : if not isinstance ( value , datetime ) : return value return calendar . timegm ( value . utctimetuple ( ) ) + value . microsecond / 1000000.0
| 1 |
python3 datetime to integer timestamp
|
If value is a datetime convert to timestamp
|
cosqa-train-14351
|
def dt_to_ts(value):
""" If value is a datetime, convert to timestamp """
if not isinstance(value, datetime):
return value
return calendar.timegm(value.utctimetuple()) + value.microsecond / 1000000.0
|
def log_loss ( preds , labels ) : log_likelihood = np . sum ( labels * np . log ( preds ) ) / len ( preds ) return - log_likelihood
| 1 |
negative log likelihood python code tobit regression
|
Logarithmic loss with non - necessarily - binary labels .
|
cosqa-train-14352
|
def log_loss(preds, labels):
"""Logarithmic loss with non-necessarily-binary labels."""
log_likelihood = np.sum(labels * np.log(preds)) / len(preds)
return -log_likelihood
|
def filehash ( path ) : with open ( path , "rU" ) as f : return md5 ( py3compat . str_to_bytes ( f . read ( ) ) ) . hexdigest ( )
| 1 |
python3 file md5 hash
|
Make an MD5 hash of a file ignoring any differences in line ending characters .
|
cosqa-train-14353
|
def filehash(path):
"""Make an MD5 hash of a file, ignoring any differences in line
ending characters."""
with open(path, "rU") as f:
return md5(py3compat.str_to_bytes(f.read())).hexdigest()
|
def full_like ( array , value , dtype = None ) : shared = empty_like ( array , dtype ) shared [ : ] = value return shared
| 1 |
new array object that looks at the same data in python
|
Create a shared memory array with the same shape and type as a given array filled with value .
|
cosqa-train-14354
|
def full_like(array, value, dtype=None):
""" Create a shared memory array with the same shape and type as a given array, filled with `value`.
"""
shared = empty_like(array, dtype)
shared[:] = value
return shared
|
def caller_locals ( ) : import inspect frame = inspect . currentframe ( ) try : return frame . f_back . f_back . f_locals finally : del frame
| 1 |
python3 get function locals
|
Get the local variables in the caller s frame .
|
cosqa-train-14355
|
def caller_locals():
"""Get the local variables in the caller's frame."""
import inspect
frame = inspect.currentframe()
try:
return frame.f_back.f_back.f_locals
finally:
del frame
|
def __next__ ( self ) : # Retrieve the row, thereby incrementing the line number: row = super ( UnicodeReaderWithLineNumber , self ) . __next__ ( ) return self . lineno + 1 , row
| 1 |
next line to read in python
|
cosqa-train-14356
|
def __next__(self):
"""
:return: a pair (1-based line number in the input, row)
"""
# Retrieve the row, thereby incrementing the line number:
row = super(UnicodeReaderWithLineNumber, self).__next__()
return self.lineno + 1, row
|
|
def last_modified_date ( filename ) : mtime = os . path . getmtime ( filename ) dt = datetime . datetime . utcfromtimestamp ( mtime ) return dt . replace ( tzinfo = pytz . utc )
| 1 |
python3 get last modified time
|
Last modified timestamp as a UTC datetime
|
cosqa-train-14357
|
def last_modified_date(filename):
"""Last modified timestamp as a UTC datetime"""
mtime = os.path.getmtime(filename)
dt = datetime.datetime.utcfromtimestamp(mtime)
return dt.replace(tzinfo=pytz.utc)
|
def mag ( z ) : if isinstance ( z [ 0 ] , np . ndarray ) : return np . array ( list ( map ( np . linalg . norm , z ) ) ) else : return np . linalg . norm ( z )
| 1 |
norm of a numpy array python
|
Get the magnitude of a vector .
|
cosqa-train-14358
|
def mag(z):
"""Get the magnitude of a vector."""
if isinstance(z[0], np.ndarray):
return np.array(list(map(np.linalg.norm, z)))
else:
return np.linalg.norm(z)
|
def get_obj ( ref ) : oid = int ( ref ) return server . id2ref . get ( oid ) or server . id2obj [ oid ]
| 0 |
python3 get object id
|
Get object from string reference .
|
cosqa-train-14359
|
def get_obj(ref):
"""Get object from string reference."""
oid = int(ref)
return server.id2ref.get(oid) or server.id2obj[oid]
|
def normalize_path ( path ) : return os . path . normcase ( os . path . realpath ( os . path . expanduser ( path ) ) )
| 0 |
normalize path address python
|
Convert a path to its canonical case - normalized absolute version .
|
cosqa-train-14360
|
def normalize_path(path):
"""
Convert a path to its canonical, case-normalized, absolute version.
"""
return os.path.normcase(os.path.realpath(os.path.expanduser(path)))
|
def enable_gtk3 ( self , app = None ) : from pydev_ipython . inputhookgtk3 import create_inputhook_gtk3 self . set_inputhook ( create_inputhook_gtk3 ( self . _stdin_file ) ) self . _current_gui = GUI_GTK
| 0 |
python3 gtk how to detect gui does not response
|
Enable event loop integration with Gtk3 ( gir bindings ) .
|
cosqa-train-14361
|
def enable_gtk3(self, app=None):
"""Enable event loop integration with Gtk3 (gir bindings).
Parameters
----------
app : ignored
Ignored, it's only a placeholder to keep the call signature of all
gui activation methods consistent, which simplifies the logic of
supporting magics.
Notes
-----
This methods sets the PyOS_InputHook for Gtk3, which allows
the Gtk3 to integrate with terminal based applications like
IPython.
"""
from pydev_ipython.inputhookgtk3 import create_inputhook_gtk3
self.set_inputhook(create_inputhook_gtk3(self._stdin_file))
self._current_gui = GUI_GTK
|
def v_normalize ( v ) : vmag = v_magnitude ( v ) return [ v [ i ] / vmag for i in range ( len ( v ) ) ]
| 1 |
normalize vector python numpy
|
Normalizes the given vector . The vector given may have any number of dimensions .
|
cosqa-train-14362
|
def v_normalize(v):
"""
Normalizes the given vector.
The vector given may have any number of dimensions.
"""
vmag = v_magnitude(v)
return [ v[i]/vmag for i in range(len(v)) ]
|
def get_table_names ( connection ) : cursor = connection . cursor ( ) cursor . execute ( "SELECT name FROM sqlite_master WHERE type == 'table'" ) return [ name for ( name , ) in cursor ]
| 1 |
python3 how to print out sqlite table names
|
Return a list of the table names in the database .
|
cosqa-train-14363
|
def get_table_names(connection):
"""
Return a list of the table names in the database.
"""
cursor = connection.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type == 'table'")
return [name for (name,) in cursor]
|
def _to_array ( value ) : if isinstance ( value , ( tuple , list ) ) : return array ( value ) elif isinstance ( value , ( float , int ) ) : return np . float64 ( value ) else : return value
| 1 |
np array from list python
|
As a convenience turn Python lists and tuples into NumPy arrays .
|
cosqa-train-14364
|
def _to_array(value):
"""As a convenience, turn Python lists and tuples into NumPy arrays."""
if isinstance(value, (tuple, list)):
return array(value)
elif isinstance(value, (float, int)):
return np.float64(value)
else:
return value
|
def getvariable ( name ) : import inspect fr = inspect . currentframe ( ) try : while fr : fr = fr . f_back vars = fr . f_locals if name in vars : return vars [ name ] except : pass return None
| 1 |
python3 inspect get local variable
|
Get the value of a local variable somewhere in the call stack .
|
cosqa-train-14365
|
def getvariable(name):
"""Get the value of a local variable somewhere in the call stack."""
import inspect
fr = inspect.currentframe()
try:
while fr:
fr = fr.f_back
vars = fr.f_locals
if name in vars:
return vars[name]
except:
pass
return None
|
def _histplot_bins ( column , bins = 100 ) : col_min = np . min ( column ) col_max = np . max ( column ) return range ( col_min , col_max + 2 , max ( ( col_max - col_min ) // bins , 1 ) )
| 1 |
number of bins in histogram python
|
Helper to get bins for histplot .
|
cosqa-train-14366
|
def _histplot_bins(column, bins=100):
"""Helper to get bins for histplot."""
col_min = np.min(column)
col_max = np.max(column)
return range(col_min, col_max + 2, max((col_max - col_min) // bins, 1))
|
def _is_target_a_directory ( link , rel_target ) : target = os . path . join ( os . path . dirname ( link ) , rel_target ) return os . path . isdir ( target )
| 0 |
python3 isdir check directory or symbol link
|
If creating a symlink from link to a target determine if target is a directory ( relative to dirname ( link )) .
|
cosqa-train-14367
|
def _is_target_a_directory(link, rel_target):
"""
If creating a symlink from link to a target, determine if target
is a directory (relative to dirname(link)).
"""
target = os.path.join(os.path.dirname(link), rel_target)
return os.path.isdir(target)
|
def sem ( inlist ) : sd = stdev ( inlist ) n = len ( inlist ) return sd / math . sqrt ( n )
| 1 |
number of standard deviations python from a fit
|
Returns the estimated standard error of the mean ( sx - bar ) of the values in the passed list . sem = stdev / sqrt ( n )
|
cosqa-train-14368
|
def sem(inlist):
"""
Returns the estimated standard error of the mean (sx-bar) of the
values in the passed list. sem = stdev / sqrt(n)
Usage: lsem(inlist)
"""
sd = stdev(inlist)
n = len(inlist)
return sd / math.sqrt(n)
|
def merge ( self , other ) : newstart = min ( self . _start , other . start ) newend = max ( self . _end , other . end ) return Range ( newstart , newend )
| 1 |
python3 merge two ranges
|
Merge this range object with another ( ranges need not overlap or abut ) .
|
cosqa-train-14369
|
def merge(self, other):
"""
Merge this range object with another (ranges need not overlap or abut).
:returns: a new Range object representing the interval containing both
ranges.
"""
newstart = min(self._start, other.start)
newend = max(self._end, other.end)
return Range(newstart, newend)
|
def algo_exp ( x , m , t , b ) : return m * np . exp ( - t * x ) + b
| 1 |
numerically solve exponential equations in python
|
mono - exponential curve .
|
cosqa-train-14370
|
def algo_exp(x, m, t, b):
"""mono-exponential curve."""
return m*np.exp(-t*x)+b
|
def to_str ( obj ) : if not isinstance ( obj , str ) and PY3 and isinstance ( obj , bytes ) : obj = obj . decode ( 'utf-8' ) return obj if isinstance ( obj , string_types ) else str ( obj )
| 1 |
python3 move a byte object to string
|
Attempts to convert given object to a string object
|
cosqa-train-14371
|
def to_str(obj):
"""Attempts to convert given object to a string object
"""
if not isinstance(obj, str) and PY3 and isinstance(obj, bytes):
obj = obj.decode('utf-8')
return obj if isinstance(obj, string_types) else str(obj)
|
def _array2cstr ( arr ) : out = StringIO ( ) np . save ( out , arr ) return b64encode ( out . getvalue ( ) )
| 1 |
numpy array to string python
|
Serializes a numpy array to a compressed base64 string
|
cosqa-train-14372
|
def _array2cstr(arr):
""" Serializes a numpy array to a compressed base64 string """
out = StringIO()
np.save(out, arr)
return b64encode(out.getvalue())
|
def one_hot ( x , size , dtype = np . float32 ) : return np . array ( x [ ... , np . newaxis ] == np . arange ( size ) , dtype )
| 1 |
python3 numpy generate onehot vector
|
Make a n + 1 dim one - hot array from n dim int - categorical array .
|
cosqa-train-14373
|
def one_hot(x, size, dtype=np.float32):
"""Make a n+1 dim one-hot array from n dim int-categorical array."""
return np.array(x[..., np.newaxis] == np.arange(size), dtype)
|
def read_numpy ( fh , byteorder , dtype , count , offsetsize ) : dtype = 'b' if dtype [ - 1 ] == 's' else byteorder + dtype [ - 1 ] return fh . read_array ( dtype , count )
| 1 |
python3 numpy load bytes object has no attribute read
|
Read tag data from file and return as numpy array .
|
cosqa-train-14374
|
def read_numpy(fh, byteorder, dtype, count, offsetsize):
"""Read tag data from file and return as numpy array."""
dtype = 'b' if dtype[-1] == 's' else byteorder+dtype[-1]
return fh.read_array(dtype, count)
|
def fetch ( self ) : api = self . doapi_manager return api . _domain ( api . request ( self . url ) [ "domain" ] )
| 1 |
odoo python return domain
|
Fetch & return a new Domain object representing the domain s current state
|
cosqa-train-14375
|
def fetch(self):
"""
Fetch & return a new `Domain` object representing the domain's current
state
:rtype: Domain
:raises DOAPIError: if the API endpoint replies with an error (e.g., if
the domain no longer exists)
"""
api = self.doapi_manager
return api._domain(api.request(self.url)["domain"])
|
def run ( self ) : try : self . run_checked ( ) except KeyboardInterrupt : thread . interrupt_main ( ) except Exception : self . internal_error ( )
| 1 |
python3 raise keyboard interrupt programatically
|
Handle keyboard interrupt and other errors .
|
cosqa-train-14376
|
def run (self):
"""Handle keyboard interrupt and other errors."""
try:
self.run_checked()
except KeyboardInterrupt:
thread.interrupt_main()
except Exception:
self.internal_error()
|
def autoconvert ( string ) : for fn in ( boolify , int , float ) : try : return fn ( string ) except ValueError : pass return string
| 1 |
only take certain type into def python
|
Try to convert variables into datatypes .
|
cosqa-train-14377
|
def autoconvert(string):
"""Try to convert variables into datatypes."""
for fn in (boolify, int, float):
try:
return fn(string)
except ValueError:
pass
return string
|
def _stdin_ready_posix ( ) : infds , outfds , erfds = select . select ( [ sys . stdin ] , [ ] , [ ] , 0 ) return bool ( infds )
| 1 |
python3 stdin check if pending char
|
Return True if there s something to read on stdin ( posix version ) .
|
cosqa-train-14378
|
def _stdin_ready_posix():
"""Return True if there's something to read on stdin (posix version)."""
infds, outfds, erfds = select.select([sys.stdin],[],[],0)
return bool(infds)
|
def list_i2str ( ilist ) : slist = [ ] for el in ilist : slist . append ( str ( el ) ) return slist
| 1 |
onvert list into array in python
|
Convert an integer list into a string list .
|
cosqa-train-14379
|
def list_i2str(ilist):
"""
Convert an integer list into a string list.
"""
slist = []
for el in ilist:
slist.append(str(el))
return slist
|
def askopenfilename ( * * kwargs ) : try : from Tkinter import Tk import tkFileDialog as filedialog except ImportError : from tkinter import Tk , filedialog root = Tk ( ) root . withdraw ( ) root . update ( ) filenames = filedialog . askopenfilename ( * * kwargs ) root . destroy ( ) return filenames
| 1 |
python3 tkinter filedialog askopenfilename
|
Return file name ( s ) from Tkinter s file open dialog .
|
cosqa-train-14380
|
def askopenfilename(**kwargs):
"""Return file name(s) from Tkinter's file open dialog."""
try:
from Tkinter import Tk
import tkFileDialog as filedialog
except ImportError:
from tkinter import Tk, filedialog
root = Tk()
root.withdraw()
root.update()
filenames = filedialog.askopenfilename(**kwargs)
root.destroy()
return filenames
|
def open_file ( file , mode ) : if hasattr ( file , "read" ) : return file if hasattr ( file , "open" ) : return file . open ( mode ) return open ( file , mode )
| 1 |
open a file in r and w mode in python
|
Open a file .
|
cosqa-train-14381
|
def open_file(file, mode):
"""Open a file.
:arg file: file-like or path-like object.
:arg str mode: ``mode`` argument for :func:`open`.
"""
if hasattr(file, "read"):
return file
if hasattr(file, "open"):
return file.open(mode)
return open(file, mode)
|
def timedelta_seconds ( timedelta ) : return ( timedelta . total_seconds ( ) if hasattr ( timedelta , "total_seconds" ) else timedelta . days * 24 * 3600 + timedelta . seconds + timedelta . microseconds / 1000000. )
| 1 |
python3 total number of seconds in timedelta
|
Returns the total timedelta duration in seconds .
|
cosqa-train-14382
|
def timedelta_seconds(timedelta):
"""Returns the total timedelta duration in seconds."""
return (timedelta.total_seconds() if hasattr(timedelta, "total_seconds")
else timedelta.days * 24 * 3600 + timedelta.seconds +
timedelta.microseconds / 1000000.)
|
def load_image ( fname ) : with open ( fname , "rb" ) as f : i = Image . open ( fname ) #i.load() return i
| 1 |
opening an image in python
|
read an image from file - PIL doesnt close nicely
|
cosqa-train-14383
|
def load_image(fname):
""" read an image from file - PIL doesnt close nicely """
with open(fname, "rb") as f:
i = Image.open(fname)
#i.load()
return i
|
def re_raise ( self ) : if self . exc_info is not None : six . reraise ( type ( self ) , self , self . exc_info [ 2 ] ) else : raise self
| 1 |
python3 traceback remove raise code
|
Raise this exception with the original traceback
|
cosqa-train-14384
|
def re_raise(self):
""" Raise this exception with the original traceback """
if self.exc_info is not None:
six.reraise(type(self), self, self.exc_info[2])
else:
raise self
|
def load ( filename ) : path , name = os . path . split ( filename ) path = path or '.' with util . indir ( path ) : return pickle . load ( open ( name , 'rb' ) )
| 1 |
opening an pickle file python
|
Load the state from the given file moving to the file s directory during load ( temporarily moving back after loaded )
|
cosqa-train-14385
|
def load(filename):
"""
Load the state from the given file, moving to the file's directory during
load (temporarily, moving back after loaded)
Parameters
----------
filename : string
name of the file to open, should be a .pkl file
"""
path, name = os.path.split(filename)
path = path or '.'
with util.indir(path):
return pickle.load(open(name, 'rb'))
|
def remote_file_exists ( self , url ) : status = requests . head ( url ) . status_code if status != 200 : raise RemoteFileDoesntExist
| 1 |
pythonrequests check if file exists
|
Checks whether the remote file exists .
|
cosqa-train-14386
|
def remote_file_exists(self, url):
""" Checks whether the remote file exists.
:param url:
The url that has to be checked.
:type url:
String
:returns:
**True** if remote file exists and **False** if it doesn't exist.
"""
status = requests.head(url).status_code
if status != 200:
raise RemoteFileDoesntExist
|
def get_order ( self , codes ) : return sorted ( codes , key = lambda e : [ self . ev2idx . get ( e ) ] )
| 1 |
ordering names in lexiographical order python
|
Return evidence codes in order shown in code2name .
|
cosqa-train-14387
|
def get_order(self, codes):
"""Return evidence codes in order shown in code2name."""
return sorted(codes, key=lambda e: [self.ev2idx.get(e)])
|
def insort_no_dup ( lst , item ) : import bisect ix = bisect . bisect_left ( lst , item ) if lst [ ix ] != item : lst [ ix : ix ] = [ item ]
| 1 |
quckiest way to insert something into a sorted list python
|
If item is not in lst add item to list at its sorted position
|
cosqa-train-14388
|
def insort_no_dup(lst, item):
"""
If item is not in lst, add item to list at its sorted position
"""
import bisect
ix = bisect.bisect_left(lst, item)
if lst[ix] != item:
lst[ix:ix] = [item]
|
def merge ( left , right , how = 'inner' , key = None , left_key = None , right_key = None , left_as = 'left' , right_as = 'right' ) : return join ( left , right , how , key , left_key , right_key , join_fn = make_union_join ( left_as , right_as ) )
| 0 |
outer join without the intersection python
|
Performs a join using the union join function .
|
cosqa-train-14389
|
def merge(left, right, how='inner', key=None, left_key=None, right_key=None,
left_as='left', right_as='right'):
""" Performs a join using the union join function. """
return join(left, right, how, key, left_key, right_key,
join_fn=make_union_join(left_as, right_as))
|
def _fetch_all_as_dict ( self , cursor ) : desc = cursor . description return [ dict ( zip ( [ col [ 0 ] for col in desc ] , row ) ) for row in cursor . fetchall ( ) ]
| 0 |
query result to a list mysql python
|
Iterates over the result set and converts each row to a dictionary
|
cosqa-train-14390
|
def _fetch_all_as_dict(self, cursor):
"""
Iterates over the result set and converts each row to a dictionary
:return: A list of dictionaries where each row is a dictionary
:rtype: list of dict
"""
desc = cursor.description
return [
dict(zip([col[0] for col in desc], row))
for row in cursor.fetchall()
]
|
def tab ( self , output ) : import csv csvwriter = csv . writer ( self . outfile , dialect = csv . excel_tab ) csvwriter . writerows ( output )
| 1 |
output the query to a excel file python
|
Output data in excel - compatible tab - delimited format
|
cosqa-train-14391
|
def tab(self, output):
"""Output data in excel-compatible tab-delimited format"""
import csv
csvwriter = csv.writer(self.outfile, dialect=csv.excel_tab)
csvwriter.writerows(output)
|
def test ( ) : dns = ReverseDNS ( ) print ( dns . lookup ( '192.168.0.1' ) ) print ( dns . lookup ( '8.8.8.8' ) ) # Test cache print ( dns . lookup ( '8.8.8.8' ) )
| 1 |
question 2what python function is used to perform a dns lookup
|
Test for ReverseDNS class
|
cosqa-train-14392
|
def test():
"""Test for ReverseDNS class"""
dns = ReverseDNS()
print(dns.lookup('192.168.0.1'))
print(dns.lookup('8.8.8.8'))
# Test cache
print(dns.lookup('8.8.8.8'))
|
def add_widgets ( self , * widgets_or_spacings ) : layout = self . layout ( ) for widget_or_spacing in widgets_or_spacings : if isinstance ( widget_or_spacing , int ) : layout . addSpacing ( widget_or_spacing ) else : layout . addWidget ( widget_or_spacing )
| 0 |
padding or spacing kivy python
|
Add widgets / spacing to dialog vertical layout
|
cosqa-train-14393
|
def add_widgets(self, *widgets_or_spacings):
"""Add widgets/spacing to dialog vertical layout"""
layout = self.layout()
for widget_or_spacing in widgets_or_spacings:
if isinstance(widget_or_spacing, int):
layout.addSpacing(widget_or_spacing)
else:
layout.addWidget(widget_or_spacing)
|
def _sort_r ( sorted , processed , key , deps , dependency_tree ) : if key in processed : return processed . add ( key ) for dep_key in deps : dep_deps = dependency_tree . get ( dep_key ) if dep_deps is None : log . debug ( '"%s" not found, skipped' , Repr ( dep_key ) ) continue _sort_r ( sorted , processed , dep_key , dep_deps , dependency_tree ) sorted . append ( ( key , deps ) )
| 1 |
quick sort recursion python
|
Recursive topological sort implementation .
|
cosqa-train-14394
|
def _sort_r(sorted, processed, key, deps, dependency_tree):
"""Recursive topological sort implementation."""
if key in processed:
return
processed.add(key)
for dep_key in deps:
dep_deps = dependency_tree.get(dep_key)
if dep_deps is None:
log.debug('"%s" not found, skipped', Repr(dep_key))
continue
_sort_r(sorted, processed, dep_key, dep_deps, dependency_tree)
sorted.append((key, deps))
|
def save_list ( key , * values ) : return json . dumps ( { key : [ _get_json ( value ) for value in values ] } )
| 1 |
pass a list to json function python
|
Convert the given list of parameters to a JSON object .
|
cosqa-train-14395
|
def save_list(key, *values):
"""Convert the given list of parameters to a JSON object.
JSON object is of the form:
{ key: [values[0], values[1], ... ] },
where values represent the given list of parameters.
"""
return json.dumps({key: [_get_json(value) for value in values]})
|
def readwav ( filename ) : from scipy . io . wavfile import read as readwav samplerate , signal = readwav ( filename ) return signal , samplerate
| 1 |
quickly read wave files python
|
Read a WAV file and returns the data and sample rate
|
cosqa-train-14396
|
def readwav(filename):
"""Read a WAV file and returns the data and sample rate
::
from spectrum.io import readwav
readwav()
"""
from scipy.io.wavfile import read as readwav
samplerate, signal = readwav(filename)
return signal, samplerate
|
def iget_list_column_slice ( list_ , start = None , stop = None , stride = None ) : if isinstance ( start , slice ) : slice_ = start else : slice_ = slice ( start , stop , stride ) return ( row [ slice_ ] for row in list_ )
| 1 |
pass a slice of list in python as variable
|
iterator version of get_list_column
|
cosqa-train-14397
|
def iget_list_column_slice(list_, start=None, stop=None, stride=None):
""" iterator version of get_list_column """
if isinstance(start, slice):
slice_ = start
else:
slice_ = slice(start, stop, stride)
return (row[slice_] for row in list_)
|
def rnormal ( mu , tau , size = None ) : return np . random . normal ( mu , 1. / np . sqrt ( tau ) , size )
| 1 |
random normal distribution in python
|
Random normal variates .
|
cosqa-train-14398
|
def rnormal(mu, tau, size=None):
"""
Random normal variates.
"""
return np.random.normal(mu, 1. / np.sqrt(tau), size)
|
def dimensions ( self ) : size = self . pdf . getPage ( 0 ) . mediaBox return { 'w' : float ( size [ 2 ] ) , 'h' : float ( size [ 3 ] ) }
| 1 |
pdfpages python size page
|
Get width and height of a PDF
|
cosqa-train-14399
|
def dimensions(self):
"""Get width and height of a PDF"""
size = self.pdf.getPage(0).mediaBox
return {'w': float(size[2]), 'h': float(size[3])}
|
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