code_tokens
stringlengths 60
3.94k
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int64 0
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| doc
stringlengths 16
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stringlengths 0
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stringlengths 13
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|---|---|---|---|---|---|
async def _thread_coro ( self , * args ) : return await self . _loop . run_in_executor ( self . _executor , self . _function , * args )
| 0 |
corotine with thread asyncio python
|
Coroutine called by MapAsync . It s wrapping the call of run_in_executor to run the synchronous function as thread
|
cosqa-train-13000
|
async def _thread_coro(self, *args):
""" Coroutine called by MapAsync. It's wrapping the call of
run_in_executor to run the synchronous function as thread """
return await self._loop.run_in_executor(
self._executor, self._function, *args)
|
def update_screen ( self ) : self . clock . tick ( self . FPS ) pygame . display . update ( )
| 1 |
python full screen adjust to the screen
|
Refresh the screen . You don t need to override this except to update only small portins of the screen .
|
cosqa-train-13001
|
def update_screen(self):
"""Refresh the screen. You don't need to override this except to update only small portins of the screen."""
self.clock.tick(self.FPS)
pygame.display.update()
|
def sample_correlations ( self ) : C = np . corrcoef ( self . X . T ) corr_matrix = ExpMatrix ( genes = self . samples , samples = self . samples , X = C ) return corr_matrix
| 1 |
correlation matrix with respect to python
|
Returns an ExpMatrix containing all pairwise sample correlations .
|
cosqa-train-13002
|
def sample_correlations(self):
"""Returns an `ExpMatrix` containing all pairwise sample correlations.
Returns
-------
`ExpMatrix`
The sample correlation matrix.
"""
C = np.corrcoef(self.X.T)
corr_matrix = ExpMatrix(genes=self.samples, samples=self.samples, X=C)
return corr_matrix
|
def parameter_vector ( self ) : return np . array ( [ getattr ( self , k ) for k in self . parameter_names ] )
| 1 |
python function array type
|
An array of all parameters ( including frozen parameters )
|
cosqa-train-13003
|
def parameter_vector(self):
"""An array of all parameters (including frozen parameters)"""
return np.array([getattr(self, k) for k in self.parameter_names])
|
def count_rows_with_nans ( X ) : if X . ndim == 2 : return np . where ( np . isnan ( X ) . sum ( axis = 1 ) != 0 , 1 , 0 ) . sum ( )
| 1 |
count nans in matrix python
|
Count the number of rows in 2D arrays that contain any nan values .
|
cosqa-train-13004
|
def count_rows_with_nans(X):
"""Count the number of rows in 2D arrays that contain any nan values."""
if X.ndim == 2:
return np.where(np.isnan(X).sum(axis=1) != 0, 1, 0).sum()
|
def log_loss ( preds , labels ) : log_likelihood = np . sum ( labels * np . log ( preds ) ) / len ( preds ) return - log_likelihood
| 1 |
python function for logaritthms
|
Logarithmic loss with non - necessarily - binary labels .
|
cosqa-train-13005
|
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 _calc_overlap_count ( markers1 : dict , markers2 : dict , ) : overlaps = np . zeros ( ( len ( markers1 ) , len ( markers2 ) ) ) j = 0 for marker_group in markers1 : tmp = [ len ( markers2 [ i ] . intersection ( markers1 [ marker_group ] ) ) for i in markers2 . keys ( ) ] overlaps [ j , : ] = tmp j += 1 return overlaps
| 1 |
count number of overlaps in two python nested lists
|
Calculate overlap count between the values of two dictionaries
|
cosqa-train-13006
|
def _calc_overlap_count(
markers1: dict,
markers2: dict,
):
"""Calculate overlap count between the values of two dictionaries
Note: dict values must be sets
"""
overlaps=np.zeros((len(markers1), len(markers2)))
j=0
for marker_group in markers1:
tmp = [len(markers2[i].intersection(markers1[marker_group])) for i in markers2.keys()]
overlaps[j,:] = tmp
j += 1
return overlaps
|
def hex_escape ( bin_str ) : printable = string . ascii_letters + string . digits + string . punctuation + ' ' return '' . join ( ch if ch in printable else r'0x{0:02x}' . format ( ord ( ch ) ) for ch in bin_str )
| 1 |
python function for printing all characters in binary
|
Hex encode a binary string
|
cosqa-train-13007
|
def hex_escape(bin_str):
"""
Hex encode a binary string
"""
printable = string.ascii_letters + string.digits + string.punctuation + ' '
return ''.join(ch if ch in printable else r'0x{0:02x}'.format(ord(ch)) for ch in bin_str)
|
def count_list ( the_list ) : count = the_list . count result = [ ( item , count ( item ) ) for item in set ( the_list ) ] result . sort ( ) return result
| 1 |
count unique values in a list in python
|
Generates a count of the number of times each unique item appears in a list
|
cosqa-train-13008
|
def count_list(the_list):
"""
Generates a count of the number of times each unique item appears in a list
"""
count = the_list.count
result = [(item, count(item)) for item in set(the_list)]
result.sort()
return result
|
def to_snake_case ( s ) : return re . sub ( '([^_A-Z])([A-Z])' , lambda m : m . group ( 1 ) + '_' + m . group ( 2 ) . lower ( ) , s )
| 1 |
python function lowercase variable name
|
Converts camel - case identifiers to snake - case .
|
cosqa-train-13009
|
def to_snake_case(s):
"""Converts camel-case identifiers to snake-case."""
return re.sub('([^_A-Z])([A-Z])', lambda m: m.group(1) + '_' + m.group(2).lower(), s)
|
def coverage ( ctx , opts = "" ) : return test ( ctx , coverage = True , include_slow = True , opts = opts )
| 1 |
coverage python and unit tests
|
Execute all tests ( normal and slow ) with coverage enabled .
|
cosqa-train-13010
|
def coverage(ctx, opts=""):
"""
Execute all tests (normal and slow) with coverage enabled.
"""
return test(ctx, coverage=True, include_slow=True, opts=opts)
|
def _to_numeric ( val ) : if isinstance ( val , ( int , float , datetime . datetime , datetime . timedelta ) ) : return val return float ( val )
| 1 |
python function return datatype
|
Helper function for conversion of various data types into numeric representation .
|
cosqa-train-13011
|
def _to_numeric(val):
"""
Helper function for conversion of various data types into numeric representation.
"""
if isinstance(val, (int, float, datetime.datetime, datetime.timedelta)):
return val
return float(val)
|
def _array2cstr ( arr ) : out = StringIO ( ) np . save ( out , arr ) return b64encode ( out . getvalue ( ) )
| 1 |
cpmvertarray to string python 3
|
Serializes a numpy array to a compressed base64 string
|
cosqa-train-13012
|
def _array2cstr(arr):
""" Serializes a numpy array to a compressed base64 string """
out = StringIO()
np.save(out, arr)
return b64encode(out.getvalue())
|
def unique ( input_list ) : output = [ ] for item in input_list : if item not in output : output . append ( item ) return output
| 1 |
python function that check for elements appearing twice in a list
|
Return a list of unique items ( similar to set functionality ) .
|
cosqa-train-13013
|
def unique(input_list):
"""
Return a list of unique items (similar to set functionality).
Parameters
----------
input_list : list
A list containg some items that can occur more than once.
Returns
-------
list
A list with only unique occurances of an item.
"""
output = []
for item in input_list:
if item not in output:
output.append(item)
return output
|
def main ( ctx , connection ) : ctx . obj = Manager ( connection = connection ) ctx . obj . bind ( )
| 1 |
create a bound python
|
Command line interface for PyBEL .
|
cosqa-train-13014
|
def main(ctx, connection):
"""Command line interface for PyBEL."""
ctx.obj = Manager(connection=connection)
ctx.obj.bind()
|
def identifierify ( name ) : name = name . lower ( ) name = re . sub ( '[^a-z0-9]' , '_' , name ) return name
| 1 |
python function that cleans up name
|
Clean up name so it works for a Python identifier .
|
cosqa-train-13015
|
def identifierify(name):
""" Clean up name so it works for a Python identifier. """
name = name.lower()
name = re.sub('[^a-z0-9]', '_', name)
return name
|
def reduce_freqs ( freqlist ) : allfreqs = np . zeros_like ( freqlist [ 0 ] ) for f in freqlist : allfreqs += f return allfreqs
| 1 |
create a list of frequencies python
|
Add up a list of freq counts to get the total counts .
|
cosqa-train-13016
|
def reduce_freqs(freqlist):
"""
Add up a list of freq counts to get the total counts.
"""
allfreqs = np.zeros_like(freqlist[0])
for f in freqlist:
allfreqs += f
return allfreqs
|
def _is_leap_year ( year ) : isleap = ( ( np . mod ( year , 4 ) == 0 ) & ( ( np . mod ( year , 100 ) != 0 ) | ( np . mod ( year , 400 ) == 0 ) ) ) return isleap
| 1 |
python function to determine whether a leap year or not
|
Determine if a year is leap year .
|
cosqa-train-13017
|
def _is_leap_year(year):
"""Determine if a year is leap year.
Parameters
----------
year : numeric
Returns
-------
isleap : array of bools
"""
isleap = ((np.mod(year, 4) == 0) &
((np.mod(year, 100) != 0) | (np.mod(year, 400) == 0)))
return isleap
|
def sent2features ( sentence , template ) : return [ word2features ( sentence , i , template ) for i in range ( len ( sentence ) ) ]
| 1 |
create a list of words from a sentence python
|
extract features in a sentence
|
cosqa-train-13018
|
def sent2features(sentence, template):
""" extract features in a sentence
:type sentence: list of token, each token is a list of tag
"""
return [word2features(sentence, i, template) for i in range(len(sentence))]
|
def get_rounded ( self , digits ) : result = self . copy ( ) result . round ( digits ) return result
| 1 |
python function to round to variable number of digits
|
Return a vector with the elements rounded to the given number of digits .
|
cosqa-train-13019
|
def get_rounded(self, digits):
""" Return a vector with the elements rounded to the given number of digits. """
result = self.copy()
result.round(digits)
return result
|
def create ( self , ami , count , config = None ) : return self . Launcher ( config = config ) . launch ( ami , count )
| 1 |
create an ec2 instance from my ami python
|
Create an instance using the launcher .
|
cosqa-train-13020
|
def create(self, ami, count, config=None):
"""Create an instance using the launcher."""
return self.Launcher(config=config).launch(ami, count)
|
def def_linear ( fun ) : defjvp_argnum ( fun , lambda argnum , g , ans , args , kwargs : fun ( * subval ( args , argnum , g ) , * * kwargs ) )
| 1 |
python function undefinied inputs
|
Flags that a function is linear wrt all args
|
cosqa-train-13021
|
def def_linear(fun):
"""Flags that a function is linear wrt all args"""
defjvp_argnum(fun, lambda argnum, g, ans, args, kwargs:
fun(*subval(args, argnum, g), **kwargs))
|
def load ( cls , tree_path ) : with open ( tree_path ) as f : tree_dict = json . load ( f ) return cls . from_dict ( tree_dict )
| 1 |
create an object tree from a text file in python
|
Create a new instance from a file .
|
cosqa-train-13022
|
def load(cls, tree_path):
"""Create a new instance from a file."""
with open(tree_path) as f:
tree_dict = json.load(f)
return cls.from_dict(tree_dict)
|
def parsed_args ( ) : parser = argparse . ArgumentParser ( description = """python runtime functions""" , epilog = "" ) parser . add_argument ( 'command' , nargs = '*' , help = "Name of the function to run with arguments" ) args = parser . parse_args ( ) return ( args , parser )
| 1 |
python function's arguements from cmd
|
cosqa-train-13023
|
def parsed_args():
parser = argparse.ArgumentParser(description="""python runtime functions""", epilog="")
parser.add_argument('command',nargs='*',
help="Name of the function to run with arguments")
args = parser.parse_args()
return (args, parser)
|
|
def format_result ( input ) : items = list ( iteritems ( input ) ) return OrderedDict ( sorted ( items , key = lambda x : x [ 0 ] ) )
| 1 |
create an ordered dictionary from unordered dictionary python
|
From : http : // stackoverflow . com / questions / 13062300 / convert - a - dict - to - sorted - dict - in - python
|
cosqa-train-13024
|
def format_result(input):
"""From: http://stackoverflow.com/questions/13062300/convert-a-dict-to-sorted-dict-in-python
"""
items = list(iteritems(input))
return OrderedDict(sorted(items, key=lambda x: x[0]))
|
def interpolate_slice ( slice_rows , slice_cols , interpolator ) : fine_rows = np . arange ( slice_rows . start , slice_rows . stop , slice_rows . step ) fine_cols = np . arange ( slice_cols . start , slice_cols . stop , slice_cols . step ) return interpolator ( fine_cols , fine_rows )
| 1 |
python functional style passing array slices
|
Interpolate the given slice of the larger array .
|
cosqa-train-13025
|
def interpolate_slice(slice_rows, slice_cols, interpolator):
"""Interpolate the given slice of the larger array."""
fine_rows = np.arange(slice_rows.start, slice_rows.stop, slice_rows.step)
fine_cols = np.arange(slice_cols.start, slice_cols.stop, slice_cols.step)
return interpolator(fine_cols, fine_rows)
|
def vectorize ( values ) : if isinstance ( values , list ) : return ',' . join ( str ( v ) for v in values ) return values
| 1 |
create comma separated list from list python
|
Takes a value or list of values and returns a single result joined by if necessary .
|
cosqa-train-13026
|
def vectorize(values):
"""
Takes a value or list of values and returns a single result, joined by ","
if necessary.
"""
if isinstance(values, list):
return ','.join(str(v) for v in values)
return values
|
def write_str2file ( pathname , astr ) : fname = pathname fhandle = open ( fname , 'wb' ) fhandle . write ( astr ) fhandle . close ( )
| 1 |
python fwrite string to file
|
writes a string to file
|
cosqa-train-13027
|
def write_str2file(pathname, astr):
"""writes a string to file"""
fname = pathname
fhandle = open(fname, 'wb')
fhandle.write(astr)
fhandle.close()
|
def create_conda_env ( sandbox_dir , env_name , dependencies , options = ( ) ) : env_dir = os . path . join ( sandbox_dir , env_name ) cmdline = [ "conda" , "create" , "--yes" , "--copy" , "--quiet" , "-p" , env_dir ] + list ( options ) + dependencies log . info ( "Creating conda environment: " ) log . info ( " command line: %s" , cmdline ) subprocess . check_call ( cmdline , stderr = subprocess . PIPE , stdout = subprocess . PIPE ) log . debug ( "Environment created" ) return env_dir , env_name
| 1 |
create conda env in python
|
Create a conda environment inside the current sandbox for the given list of dependencies and options .
|
cosqa-train-13028
|
def create_conda_env(sandbox_dir, env_name, dependencies, options=()):
"""
Create a conda environment inside the current sandbox for the given list of dependencies and options.
Parameters
----------
sandbox_dir : str
env_name : str
dependencies : list
List of conda specs
options
List of additional options to pass to conda. Things like ["-c", "conda-forge"]
Returns
-------
(env_dir, env_name)
"""
env_dir = os.path.join(sandbox_dir, env_name)
cmdline = ["conda", "create", "--yes", "--copy", "--quiet", "-p", env_dir] + list(options) + dependencies
log.info("Creating conda environment: ")
log.info(" command line: %s", cmdline)
subprocess.check_call(cmdline, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
log.debug("Environment created")
return env_dir, env_name
|
def zeros ( self , name , * * kwargs ) : return self . _write_op ( self . _zeros_nosync , name , * * kwargs )
| 1 |
python generate numpy array of zeros
|
Create an array . Keyword arguments as per : func : zarr . creation . zeros .
|
cosqa-train-13029
|
def zeros(self, name, **kwargs):
"""Create an array. Keyword arguments as per
:func:`zarr.creation.zeros`."""
return self._write_op(self._zeros_nosync, name, **kwargs)
|
def create_conda_env ( sandbox_dir , env_name , dependencies , options = ( ) ) : env_dir = os . path . join ( sandbox_dir , env_name ) cmdline = [ "conda" , "create" , "--yes" , "--copy" , "--quiet" , "-p" , env_dir ] + list ( options ) + dependencies log . info ( "Creating conda environment: " ) log . info ( " command line: %s" , cmdline ) subprocess . check_call ( cmdline , stderr = subprocess . PIPE , stdout = subprocess . PIPE ) log . debug ( "Environment created" ) return env_dir , env_name
| 1 |
create conda environment for python 2
|
Create a conda environment inside the current sandbox for the given list of dependencies and options .
|
cosqa-train-13030
|
def create_conda_env(sandbox_dir, env_name, dependencies, options=()):
"""
Create a conda environment inside the current sandbox for the given list of dependencies and options.
Parameters
----------
sandbox_dir : str
env_name : str
dependencies : list
List of conda specs
options
List of additional options to pass to conda. Things like ["-c", "conda-forge"]
Returns
-------
(env_dir, env_name)
"""
env_dir = os.path.join(sandbox_dir, env_name)
cmdline = ["conda", "create", "--yes", "--copy", "--quiet", "-p", env_dir] + list(options) + dependencies
log.info("Creating conda environment: ")
log.info(" command line: %s", cmdline)
subprocess.check_call(cmdline, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
log.debug("Environment created")
return env_dir, env_name
|
def _rndPointDisposition ( dx , dy ) : x = int ( random . uniform ( - dx , dx ) ) y = int ( random . uniform ( - dy , dy ) ) return ( x , y )
| 1 |
python generate random x,y points
|
Return random disposition point .
|
cosqa-train-13031
|
def _rndPointDisposition(dx, dy):
"""Return random disposition point."""
x = int(random.uniform(-dx, dx))
y = int(random.uniform(-dy, dy))
return (x, y)
|
def dmap ( fn , record ) : values = ( fn ( v ) for k , v in record . items ( ) ) return dict ( itertools . izip ( record , values ) )
| 1 |
create dictionary using list comprehension in python sample
|
map for a directory
|
cosqa-train-13032
|
def dmap(fn, record):
"""map for a directory"""
values = (fn(v) for k, v in record.items())
return dict(itertools.izip(record, values))
|
def rlognormal ( mu , tau , size = None ) : return np . random . lognormal ( mu , np . sqrt ( 1. / tau ) , size )
| 1 |
python generating random variables from a mixture of normal distributions
|
Return random lognormal variates .
|
cosqa-train-13033
|
def rlognormal(mu, tau, size=None):
"""
Return random lognormal variates.
"""
return np.random.lognormal(mu, np.sqrt(1. / tau), size)
|
def create_dir_rec ( path : Path ) : if not path . exists ( ) : Path . mkdir ( path , parents = True , exist_ok = True )
| 1 |
create directory recursive in python
|
Create a folder recursive .
|
cosqa-train-13034
|
def create_dir_rec(path: Path):
"""
Create a folder recursive.
:param path: path
:type path: ~pathlib.Path
"""
if not path.exists():
Path.mkdir(path, parents=True, exist_ok=True)
|
def _get_random_id ( ) : symbols = string . ascii_uppercase + string . ascii_lowercase + string . digits return '' . join ( random . choice ( symbols ) for _ in range ( 15 ) )
| 1 |
python genereate random string
|
Get a random ( i . e . unique ) string identifier
|
cosqa-train-13035
|
def _get_random_id():
""" Get a random (i.e., unique) string identifier"""
symbols = string.ascii_uppercase + string.ascii_lowercase + string.digits
return ''.join(random.choice(symbols) for _ in range(15))
|
def algo_exp ( x , m , t , b ) : return m * np . exp ( - t * x ) + b
| 1 |
create exponential function in python
|
mono - exponential curve .
|
cosqa-train-13036
|
def algo_exp(x, m, t, b):
"""mono-exponential curve."""
return m*np.exp(-t*x)+b
|
def list_files ( directory ) : return [ f for f in pathlib . Path ( directory ) . iterdir ( ) if f . is_file ( ) and not f . name . startswith ( '.' ) ]
| 1 |
python get a list of the files from a directory
|
Returns all files in a given directory
|
cosqa-train-13037
|
def list_files(directory):
"""Returns all files in a given directory
"""
return [f for f in pathlib.Path(directory).iterdir() if f.is_file() and not f.name.startswith('.')]
|
def ensure_index ( self , key , unique = False ) : return self . collection . ensure_index ( key , unique = unique )
| 1 |
create mongodb index using python
|
Wrapper for pymongo . Collection . ensure_index
|
cosqa-train-13038
|
def ensure_index(self, key, unique=False):
"""Wrapper for pymongo.Collection.ensure_index
"""
return self.collection.ensure_index(key, unique=unique)
|
def timespan ( start_time ) : timespan = datetime . datetime . now ( ) - start_time timespan_ms = timespan . total_seconds ( ) * 1000 return timespan_ms
| 1 |
python get a range of time
|
Return time in milliseconds from start_time
|
cosqa-train-13039
|
def timespan(start_time):
"""Return time in milliseconds from start_time"""
timespan = datetime.datetime.now() - start_time
timespan_ms = timespan.total_seconds() * 1000
return timespan_ms
|
def deskew ( S ) : x = np . zeros ( 3 ) x [ 0 ] = S [ 2 , 1 ] x [ 1 ] = S [ 0 , 2 ] x [ 2 ] = S [ 1 , 0 ] return x
| 1 |
create skew symmetric matrix from a vector python
|
Converts a skew - symmetric cross - product matrix to its corresponding vector . Only works for 3x3 matrices .
|
cosqa-train-13040
|
def deskew(S):
"""Converts a skew-symmetric cross-product matrix to its corresponding
vector. Only works for 3x3 matrices.
Parameters
----------
S : :obj:`numpy.ndarray` of float
A 3x3 skew-symmetric matrix.
Returns
-------
:obj:`numpy.ndarray` of float
A 3-entry vector that corresponds to the given cross product matrix.
"""
x = np.zeros(3)
x[0] = S[2,1]
x[1] = S[0,2]
x[2] = S[1,0]
return x
|
def get_the_node_dict ( G , name ) : for node in G . nodes ( data = True ) : if node [ 0 ] == name : return node [ 1 ]
| 0 |
python get a specific node
|
Helper function that returns the node data of the node with the name supplied
|
cosqa-train-13041
|
def get_the_node_dict(G, name):
"""
Helper function that returns the node data
of the node with the name supplied
"""
for node in G.nodes(data=True):
if node[0] == name:
return node[1]
|
def js_classnameify ( s ) : if not '_' in s : return s return '' . join ( w [ 0 ] . upper ( ) + w [ 1 : ] . lower ( ) for w in s . split ( '_' ) )
| 1 |
create valiable name by concatinate strings in python
|
Makes a classname .
|
cosqa-train-13042
|
def js_classnameify(s):
"""
Makes a classname.
"""
if not '_' in s:
return s
return ''.join(w[0].upper() + w[1:].lower() for w in s.split('_'))
|
def rel_path ( filename ) : return os . path . join ( os . getcwd ( ) , os . path . dirname ( __file__ ) , filename )
| 1 |
python get absolute path name
|
Function that gets relative path to the filename
|
cosqa-train-13043
|
def rel_path(filename):
"""
Function that gets relative path to the filename
"""
return os.path.join(os.getcwd(), os.path.dirname(__file__), filename)
|
def install_postgres ( user = None , dbname = None , password = None ) : execute ( pydiploy . django . install_postgres_server , user = user , dbname = dbname , password = password )
| 1 |
creating a database engine python posgres
|
Install Postgres on remote
|
cosqa-train-13044
|
def install_postgres(user=None, dbname=None, password=None):
"""Install Postgres on remote"""
execute(pydiploy.django.install_postgres_server,
user=user, dbname=dbname, password=password)
|
def url ( self ) : with switch_window ( self . _browser , self . name ) : return self . _browser . url
| 0 |
python get active window url and replace it
|
The url of this window
|
cosqa-train-13045
|
def url(self):
""" The url of this window """
with switch_window(self._browser, self.name):
return self._browser.url
|
def recarray ( self ) : return numpy . rec . fromrecords ( self . records , names = self . names )
| 1 |
creating a list in python without numpy
|
Returns data as : class : numpy . recarray .
|
cosqa-train-13046
|
def recarray(self):
"""Returns data as :class:`numpy.recarray`."""
return numpy.rec.fromrecords(self.records, names=self.names)
|
def uniq ( seq ) : seen = set ( ) return [ x for x in seq if str ( x ) not in seen and not seen . add ( str ( x ) ) ]
| 0 |
python get all unique strings of list
|
Return a copy of seq without duplicates .
|
cosqa-train-13047
|
def uniq(seq):
""" Return a copy of seq without duplicates. """
seen = set()
return [x for x in seq if str(x) not in seen and not seen.add(str(x))]
|
def from_bytes ( cls , b ) : im = cls ( ) im . chunks = list ( parse_chunks ( b ) ) im . init ( ) return im
| 1 |
creating an image in python from a byte array
|
Create : class : PNG from raw bytes . : arg bytes b : The raw bytes of the PNG file . : rtype : : class : PNG
|
cosqa-train-13048
|
def from_bytes(cls, b):
"""Create :class:`PNG` from raw bytes.
:arg bytes b: The raw bytes of the PNG file.
:rtype: :class:`PNG`
"""
im = cls()
im.chunks = list(parse_chunks(b))
im.init()
return im
|
def items ( self ) : return [ ( value_descriptor . name , value_descriptor . number ) for value_descriptor in self . _enum_type . values ]
| 1 |
python get all values of enum
|
Return a list of the ( name value ) pairs of the enum .
|
cosqa-train-13049
|
def items(self):
"""Return a list of the (name, value) pairs of the enum.
These are returned in the order they were defined in the .proto file.
"""
return [(value_descriptor.name, value_descriptor.number)
for value_descriptor in self._enum_type.values]
|
def ensure_hbounds ( self ) : self . cursor . x = min ( max ( 0 , self . cursor . x ) , self . columns - 1 )
| 1 |
cursor positioning python windows
|
Ensure the cursor is within horizontal screen bounds .
|
cosqa-train-13050
|
def ensure_hbounds(self):
"""Ensure the cursor is within horizontal screen bounds."""
self.cursor.x = min(max(0, self.cursor.x), self.columns - 1)
|
def current_memory_usage ( ) : import psutil proc = psutil . Process ( os . getpid ( ) ) #meminfo = proc.get_memory_info() meminfo = proc . memory_info ( ) rss = meminfo [ 0 ] # Resident Set Size / Mem Usage vms = meminfo [ 1 ] # Virtual Memory Size / VM Size # NOQA return rss
| 0 |
python get amount of ram in computer
|
Returns this programs current memory usage in bytes
|
cosqa-train-13051
|
def current_memory_usage():
"""
Returns this programs current memory usage in bytes
"""
import psutil
proc = psutil.Process(os.getpid())
#meminfo = proc.get_memory_info()
meminfo = proc.memory_info()
rss = meminfo[0] # Resident Set Size / Mem Usage
vms = meminfo[1] # Virtual Memory Size / VM Size # NOQA
return rss
|
def double_sha256 ( data ) : return bytes_as_revhex ( hashlib . sha256 ( hashlib . sha256 ( data ) . digest ( ) ) . digest ( ) )
| 1 |
custom hash function python
|
A standard compound hash .
|
cosqa-train-13052
|
def double_sha256(data):
"""A standard compound hash."""
return bytes_as_revhex(hashlib.sha256(hashlib.sha256(data).digest()).digest())
|
def get_url_file_name ( url ) : assert isinstance ( url , ( str , _oldstr ) ) return urlparse . urlparse ( url ) . path . split ( '/' ) [ - 1 ]
| 1 |
python get basename url
|
Get the file name from an url Parameters ---------- url : str
|
cosqa-train-13053
|
def get_url_file_name(url):
"""Get the file name from an url
Parameters
----------
url : str
Returns
-------
str
The file name
"""
assert isinstance(url, (str, _oldstr))
return urlparse.urlparse(url).path.split('/')[-1]
|
def to_json ( value , * * kwargs ) : serial_list = [ val . serialize ( * * kwargs ) if isinstance ( val , HasProperties ) else val for val in value ] return serial_list
| 1 |
custom json serialize python tuple
|
Return a copy of the tuple as a list
|
cosqa-train-13054
|
def to_json(value, **kwargs):
"""Return a copy of the tuple as a list
If the tuple contains HasProperties instances, they are serialized.
"""
serial_list = [
val.serialize(**kwargs) if isinstance(val, HasProperties)
else val for val in value
]
return serial_list
|
def from_json ( value , * * kwargs ) : if isinstance ( value , string_types ) : value = value . upper ( ) if value in ( 'TRUE' , 'Y' , 'YES' , 'ON' ) : return True if value in ( 'FALSE' , 'N' , 'NO' , 'OFF' ) : return False if isinstance ( value , int ) : return value raise ValueError ( 'Could not load boolean from JSON: {}' . format ( value ) )
| 1 |
python get boolean from json object
|
Coerces JSON string to boolean
|
cosqa-train-13055
|
def from_json(value, **kwargs):
"""Coerces JSON string to boolean"""
if isinstance(value, string_types):
value = value.upper()
if value in ('TRUE', 'Y', 'YES', 'ON'):
return True
if value in ('FALSE', 'N', 'NO', 'OFF'):
return False
if isinstance(value, int):
return value
raise ValueError('Could not load boolean from JSON: {}'.format(value))
|
def validate ( self , value , model_instance , * * kwargs ) : self . get_choices_form_class ( ) . validate ( value , model_instance , * * kwargs )
| 1 |
custom validator python flaskform
|
This follows the validate rules for choices_form_class field used .
|
cosqa-train-13056
|
def validate(self, value, model_instance, **kwargs):
"""This follows the validate rules for choices_form_class field used.
"""
self.get_choices_form_class().validate(value, model_instance, **kwargs)
|
def to_camel_case ( text ) : split = text . split ( '_' ) return split [ 0 ] + "" . join ( x . title ( ) for x in split [ 1 : ] )
| 1 |
python get camel case for text
|
Convert to camel case .
|
cosqa-train-13057
|
def to_camel_case(text):
"""Convert to camel case.
:param str text:
:rtype: str
:return:
"""
split = text.split('_')
return split[0] + "".join(x.title() for x in split[1:])
|
def snap_to_beginning_of_week ( day , weekday_start = "Sunday" ) : delta_days = ( ( day . weekday ( ) + 1 ) % 7 ) if weekday_start is "Sunday" else day . weekday ( ) return day - timedelta ( days = delta_days )
| 0 |
custom week start day for python weekday
|
Get the first day of the current week .
|
cosqa-train-13058
|
def snap_to_beginning_of_week(day, weekday_start="Sunday"):
""" Get the first day of the current week.
:param day: The input date to snap.
:param weekday_start: Either "Monday" or "Sunday", indicating the first day of the week.
:returns: A date representing the first day of the current week.
"""
delta_days = ((day.weekday() + 1) % 7) if weekday_start is "Sunday" else day.weekday()
return day - timedelta(days=delta_days)
|
def osx_clipboard_get ( ) : p = subprocess . Popen ( [ 'pbpaste' , '-Prefer' , 'ascii' ] , stdout = subprocess . PIPE ) text , stderr = p . communicate ( ) # Text comes in with old Mac \r line endings. Change them to \n. text = text . replace ( '\r' , '\n' ) return text
| 1 |
python get clipboard data
|
Get the clipboard s text on OS X .
|
cosqa-train-13059
|
def osx_clipboard_get():
""" Get the clipboard's text on OS X.
"""
p = subprocess.Popen(['pbpaste', '-Prefer', 'ascii'],
stdout=subprocess.PIPE)
text, stderr = p.communicate()
# Text comes in with old Mac \r line endings. Change them to \n.
text = text.replace('\r', '\n')
return text
|
def cric__decision_tree ( ) : model = sklearn . tree . DecisionTreeClassifier ( random_state = 0 , max_depth = 4 ) # we want to explain the raw probability outputs of the trees model . predict = lambda X : model . predict_proba ( X ) [ : , 1 ] return model
| 1 |
customize the output of a decision tree in python
|
Decision Tree
|
cosqa-train-13060
|
def cric__decision_tree():
""" Decision Tree
"""
model = sklearn.tree.DecisionTreeClassifier(random_state=0, max_depth=4)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model
|
def get_column ( self , X , column ) : if isinstance ( X , pd . DataFrame ) : return X [ column ] . values return X [ : , column ]
| 1 |
python get column from a matrix
|
Return a column of the given matrix .
|
cosqa-train-13061
|
def get_column(self, X, column):
"""Return a column of the given matrix.
Args:
X: `numpy.ndarray` or `pandas.DataFrame`.
column: `int` or `str`.
Returns:
np.ndarray: Selected column.
"""
if isinstance(X, pd.DataFrame):
return X[column].values
return X[:, column]
|
def data_directory ( ) : package_directory = os . path . abspath ( os . path . dirname ( __file__ ) ) return os . path . join ( package_directory , "data" )
| 1 |
data folder path python
|
Return the absolute path to the directory containing the package data .
|
cosqa-train-13062
|
def data_directory():
"""Return the absolute path to the directory containing the package data."""
package_directory = os.path.abspath(os.path.dirname(__file__))
return os.path.join(package_directory, "data")
|
def get_free_memory_win ( ) : stat = MEMORYSTATUSEX ( ) ctypes . windll . kernel32 . GlobalMemoryStatusEx ( ctypes . byref ( stat ) ) return int ( stat . ullAvailPhys / 1024 / 1024 )
| 1 |
python get computer ram usage
|
Return current free memory on the machine for windows .
|
cosqa-train-13063
|
def get_free_memory_win():
"""Return current free memory on the machine for windows.
Warning : this script is really not robust
Return in MB unit
"""
stat = MEMORYSTATUSEX()
ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(stat))
return int(stat.ullAvailPhys / 1024 / 1024)
|
def start_of_month ( val ) : if type ( val ) == date : val = datetime . fromordinal ( val . toordinal ( ) ) return start_of_day ( val ) . replace ( day = 1 )
| 1 |
datetime add a month to a date python
|
Return a new datetime . datetime object with values that represent a start of a month . : param val : Date to ... : type val : datetime . datetime | datetime . date : rtype : datetime . datetime
|
cosqa-train-13064
|
def start_of_month(val):
"""
Return a new datetime.datetime object with values that represent
a start of a month.
:param val: Date to ...
:type val: datetime.datetime | datetime.date
:rtype: datetime.datetime
"""
if type(val) == date:
val = datetime.fromordinal(val.toordinal())
return start_of_day(val).replace(day=1)
|
def parse_cookies ( self , req , name , field ) : return core . get_value ( req . COOKIES , name , field )
| 1 |
python get cookie for request
|
Pull the value from the cookiejar .
|
cosqa-train-13065
|
def parse_cookies(self, req, name, field):
"""Pull the value from the cookiejar."""
return core.get_value(req.COOKIES, name, field)
|
def _DateToEpoch ( date ) : tz_zero = datetime . datetime . utcfromtimestamp ( 0 ) diff_sec = int ( ( date - tz_zero ) . total_seconds ( ) ) return diff_sec * 1000000
| 1 |
datetime python get seconds from epoch
|
Converts python datetime to epoch microseconds .
|
cosqa-train-13066
|
def _DateToEpoch(date):
"""Converts python datetime to epoch microseconds."""
tz_zero = datetime.datetime.utcfromtimestamp(0)
diff_sec = int((date - tz_zero).total_seconds())
return diff_sec * 1000000
|
def get_last_commit ( git_path = None ) : if git_path is None : git_path = GIT_PATH line = get_last_commit_line ( git_path ) revision_id = line . split ( ) [ 1 ] return revision_id
| 1 |
python get current git branch
|
Get the HEAD commit SHA1 of repository in current dir .
|
cosqa-train-13067
|
def get_last_commit(git_path=None):
"""
Get the HEAD commit SHA1 of repository in current dir.
"""
if git_path is None: git_path = GIT_PATH
line = get_last_commit_line(git_path)
revision_id = line.split()[1]
return revision_id
|
def datetime_local_to_utc ( local ) : timestamp = time . mktime ( local . timetuple ( ) ) return datetime . datetime . utcfromtimestamp ( timestamp )
| 0 |
datetime python time to utc
|
Simple function to convert naive : std : datetime . datetime object containing local time to a naive : std : datetime . datetime object with UTC time .
|
cosqa-train-13068
|
def datetime_local_to_utc(local):
"""
Simple function to convert naive :std:`datetime.datetime` object containing
local time to a naive :std:`datetime.datetime` object with UTC time.
"""
timestamp = time.mktime(local.timetuple())
return datetime.datetime.utcfromtimestamp(timestamp)
|
def get_current_branch ( ) : cmd = [ "git" , "rev-parse" , "--abbrev-ref" , "HEAD" ] output = subprocess . check_output ( cmd , stderr = subprocess . STDOUT ) return output . strip ( ) . decode ( "utf-8" )
| 1 |
python get current git commit
|
Return the current branch
|
cosqa-train-13069
|
def get_current_branch():
"""
Return the current branch
"""
cmd = ["git", "rev-parse", "--abbrev-ref", "HEAD"]
output = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
return output.strip().decode("utf-8")
|
def ToDatetime ( self ) : return datetime . utcfromtimestamp ( self . seconds + self . nanos / float ( _NANOS_PER_SECOND ) )
| 1 |
datetime python3 removing the microseconds
|
Converts Timestamp to datetime .
|
cosqa-train-13070
|
def ToDatetime(self):
"""Converts Timestamp to datetime."""
return datetime.utcfromtimestamp(
self.seconds + self.nanos / float(_NANOS_PER_SECOND))
|
def get_current_desktop ( self ) : desktop = ctypes . c_long ( 0 ) _libxdo . xdo_get_current_desktop ( self . _xdo , ctypes . byref ( desktop ) ) return desktop . value
| 1 |
python get current users desktop
|
Get the current desktop . Uses _NET_CURRENT_DESKTOP of the EWMH spec .
|
cosqa-train-13071
|
def get_current_desktop(self):
"""
Get the current desktop.
Uses ``_NET_CURRENT_DESKTOP`` of the EWMH spec.
"""
desktop = ctypes.c_long(0)
_libxdo.xdo_get_current_desktop(self._xdo, ctypes.byref(desktop))
return desktop.value
|
def datetime_to_year_quarter ( dt ) : year = dt . year quarter = int ( math . ceil ( float ( dt . month ) / 3 ) ) return ( year , quarter )
| 1 |
datetime to quarter and year python
|
Args : dt : a datetime Returns : tuple of the datetime s year and quarter
|
cosqa-train-13072
|
def datetime_to_year_quarter(dt):
"""
Args:
dt: a datetime
Returns:
tuple of the datetime's year and quarter
"""
year = dt.year
quarter = int(math.ceil(float(dt.month)/3))
return (year, quarter)
|
def date_to_timestamp ( date ) : date_tuple = date . timetuple ( ) timestamp = calendar . timegm ( date_tuple ) * 1000 return timestamp
| 1 |
python get datetime from timestamp
|
date to unix timestamp in milliseconds
|
cosqa-train-13073
|
def date_to_timestamp(date):
"""
date to unix timestamp in milliseconds
"""
date_tuple = date.timetuple()
timestamp = calendar.timegm(date_tuple) * 1000
return timestamp
|
def empty ( self , name , * * kwargs ) : return self . _write_op ( self . _empty_nosync , name , * * kwargs )
| 1 |
declaring empty numpy array in python
|
Create an array . Keyword arguments as per : func : zarr . creation . empty .
|
cosqa-train-13074
|
def empty(self, name, **kwargs):
"""Create an array. Keyword arguments as per
:func:`zarr.creation.empty`."""
return self._write_op(self._empty_nosync, name, **kwargs)
|
def get_best_encoding ( stream ) : rv = getattr ( stream , 'encoding' , None ) or sys . getdefaultencoding ( ) if is_ascii_encoding ( rv ) : return 'utf-8' return rv
| 1 |
python get default encoding
|
Returns the default stream encoding if not found .
|
cosqa-train-13075
|
def get_best_encoding(stream):
"""Returns the default stream encoding if not found."""
rv = getattr(stream, 'encoding', None) or sys.getdefaultencoding()
if is_ascii_encoding(rv):
return 'utf-8'
return rv
|
def _defaultdict ( dct , fallback = _illegal_character ) : out = defaultdict ( lambda : fallback ) for k , v in six . iteritems ( dct ) : out [ k ] = v return out
| 1 |
default value for all keys in a dict python
|
Wraps the given dictionary such that the given fallback function will be called when a nonexistent key is accessed .
|
cosqa-train-13076
|
def _defaultdict(dct, fallback=_illegal_character):
"""Wraps the given dictionary such that the given fallback function will be called when a nonexistent key is
accessed.
"""
out = defaultdict(lambda: fallback)
for k, v in six.iteritems(dct):
out[k] = v
return out
|
def parse_domain ( url ) : domain_match = lib . DOMAIN_REGEX . match ( url ) if domain_match : return domain_match . group ( )
| 0 |
python get domain from url
|
parse the domain from the url
|
cosqa-train-13077
|
def parse_domain(url):
""" parse the domain from the url """
domain_match = lib.DOMAIN_REGEX.match(url)
if domain_match:
return domain_match.group()
|
def fromDict ( cls , _dict ) : obj = cls ( ) obj . __dict__ . update ( _dict ) return obj
| 1 |
defining constructor of dict in python
|
Builds instance from dictionary of properties .
|
cosqa-train-13078
|
def fromDict(cls, _dict):
""" Builds instance from dictionary of properties. """
obj = cls()
obj.__dict__.update(_dict)
return obj
|
def get_filetype_icon ( fname ) : ext = osp . splitext ( fname ) [ 1 ] if ext . startswith ( '.' ) : ext = ext [ 1 : ] return get_icon ( "%s.png" % ext , ima . icon ( 'FileIcon' ) )
| 1 |
python get file icon from extension
|
Return file type icon
|
cosqa-train-13079
|
def get_filetype_icon(fname):
"""Return file type icon"""
ext = osp.splitext(fname)[1]
if ext.startswith('.'):
ext = ext[1:]
return get_icon( "%s.png" % ext, ima.icon('FileIcon') )
|
def remove_examples_all ( ) : d = examples_all_dir ( ) if d . exists ( ) : log . debug ( 'remove %s' , d ) d . rmtree ( ) else : log . debug ( 'nothing to remove: %s' , d )
| 1 |
delete any file in folder in python
|
remove arduino / examples / all directory .
|
cosqa-train-13080
|
def remove_examples_all():
"""remove arduino/examples/all directory.
:rtype: None
"""
d = examples_all_dir()
if d.exists():
log.debug('remove %s', d)
d.rmtree()
else:
log.debug('nothing to remove: %s', d)
|
def get_time ( filename ) : ts = os . stat ( filename ) . st_mtime return datetime . datetime . utcfromtimestamp ( ts )
| 1 |
python get file last modified time datetime
|
Get the modified time for a file as a datetime instance
|
cosqa-train-13081
|
def get_time(filename):
"""
Get the modified time for a file as a datetime instance
"""
ts = os.stat(filename).st_mtime
return datetime.datetime.utcfromtimestamp(ts)
|
def remove_columns ( self , data , columns ) : for column in columns : if column in data . columns : data = data . drop ( column , axis = 1 ) return data
| 1 |
delete columns from data frame in python
|
This method removes columns in data
|
cosqa-train-13082
|
def remove_columns(self, data, columns):
""" This method removes columns in data
:param data: original Pandas dataframe
:param columns: list of columns to remove
:type data: pandas.DataFrame
:type columns: list of strings
:returns: Pandas dataframe with removed columns
:rtype: pandas.DataFrame
"""
for column in columns:
if column in data.columns:
data = data.drop(column, axis=1)
return data
|
def guess_media_type ( filepath ) : o = subprocess . check_output ( [ 'file' , '--mime-type' , '-Lb' , filepath ] ) o = o . strip ( ) return o
| 1 |
python get file mime type
|
Returns the media - type of the file at the given filepath
|
cosqa-train-13083
|
def guess_media_type(filepath):
"""Returns the media-type of the file at the given ``filepath``"""
o = subprocess.check_output(['file', '--mime-type', '-Lb', filepath])
o = o.strip()
return o
|
def unique ( seq ) : cleaned = [ ] for each in seq : if each not in cleaned : cleaned . append ( each ) return cleaned
| 1 |
delete empty elements in list python3
|
Return the unique elements of a collection even if those elements are unhashable and unsortable like dicts and sets
|
cosqa-train-13084
|
def unique(seq):
"""Return the unique elements of a collection even if those elements are
unhashable and unsortable, like dicts and sets"""
cleaned = []
for each in seq:
if each not in cleaned:
cleaned.append(each)
return cleaned
|
def get_file_name ( url ) : return os . path . basename ( urllib . parse . urlparse ( url ) . path ) or 'unknown_name'
| 1 |
python get filename according url
|
Returns file name of file at given url .
|
cosqa-train-13085
|
def get_file_name(url):
"""Returns file name of file at given url."""
return os.path.basename(urllib.parse.urlparse(url).path) or 'unknown_name'
|
def clear_global ( self ) : vname = self . varname logger . debug ( f'global clearning {vname}' ) if vname in globals ( ) : logger . debug ( 'removing global instance var: {}' . format ( vname ) ) del globals ( ) [ vname ]
| 1 |
delete variables from globals python
|
Clear only any cached global data .
|
cosqa-train-13086
|
def clear_global(self):
"""Clear only any cached global data.
"""
vname = self.varname
logger.debug(f'global clearning {vname}')
if vname in globals():
logger.debug('removing global instance var: {}'.format(vname))
del globals()[vname]
|
def get_hash ( self , handle ) : fpath = self . _fpath_from_handle ( handle ) return DiskStorageBroker . hasher ( fpath )
| 1 |
python get hash of file filestorage
|
Return the hash .
|
cosqa-train-13087
|
def get_hash(self, handle):
"""Return the hash."""
fpath = self._fpath_from_handle(handle)
return DiskStorageBroker.hasher(fpath)
|
def rm_keys_from_dict ( d , keys ) : # Loop for each key given for key in keys : # Is the key in the dictionary? if key in d : try : d . pop ( key , None ) except KeyError : # Not concerned with an error. Keep going. pass return d
| 1 |
deleting keys in python dictionaries
|
Given a dictionary and a key list remove any data in the dictionary with the given keys .
|
cosqa-train-13088
|
def rm_keys_from_dict(d, keys):
"""
Given a dictionary and a key list, remove any data in the dictionary with the given keys.
:param dict d: Metadata
:param list keys: Keys to be removed
:return dict d: Metadata
"""
# Loop for each key given
for key in keys:
# Is the key in the dictionary?
if key in d:
try:
d.pop(key, None)
except KeyError:
# Not concerned with an error. Keep going.
pass
return d
|
def get_system_uid ( ) : try : if os . name == 'nt' : return get_nt_system_uid ( ) if sys . platform == 'darwin' : return get_osx_system_uid ( ) except Exception : return get_mac_uid ( ) else : return get_mac_uid ( )
| 0 |
python get id of windows
|
Get a ( probably ) unique ID to identify a system . Used to differentiate votes .
|
cosqa-train-13089
|
def get_system_uid():
"""Get a (probably) unique ID to identify a system.
Used to differentiate votes.
"""
try:
if os.name == 'nt':
return get_nt_system_uid()
if sys.platform == 'darwin':
return get_osx_system_uid()
except Exception:
return get_mac_uid()
else:
return get_mac_uid()
|
def find_le ( a , x ) : i = bs . bisect_right ( a , x ) if i : return i - 1 raise ValueError
| 1 |
python get index of lowest value in list
|
Find rightmost value less than or equal to x .
|
cosqa-train-13090
|
def find_le(a, x):
"""Find rightmost value less than or equal to x."""
i = bs.bisect_right(a, x)
if i: return i - 1
raise ValueError
|
def drop_empty ( rows ) : return zip ( * [ col for col in zip ( * rows ) if bool ( filter ( bool , col [ 1 : ] ) ) ] )
| 0 |
detect all empty column python
|
Transpose the columns into rows remove all of the rows that are empty after the first cell then transpose back . The result is that columns that have a header but no data in the body are removed assuming the header is the first row .
|
cosqa-train-13091
|
def drop_empty(rows):
"""Transpose the columns into rows, remove all of the rows that are empty after the first cell, then
transpose back. The result is that columns that have a header but no data in the body are removed, assuming
the header is the first row. """
return zip(*[col for col in zip(*rows) if bool(filter(bool, col[1:]))])
|
def get_property_by_name ( pif , name ) : return next ( ( x for x in pif . properties if x . name == name ) , None )
| 1 |
python get instance property by name
|
Get a property by name
|
cosqa-train-13092
|
def get_property_by_name(pif, name):
"""Get a property by name"""
return next((x for x in pif.properties if x.name == name), None)
|
def on_press_key ( key , callback , suppress = False ) : return hook_key ( key , lambda e : e . event_type == KEY_UP or callback ( e ) , suppress = suppress )
| 1 |
detect any key press python
|
Invokes callback for KEY_DOWN event related to the given key . For details see hook .
|
cosqa-train-13093
|
def on_press_key(key, callback, suppress=False):
"""
Invokes `callback` for KEY_DOWN event related to the given key. For details see `hook`.
"""
return hook_key(key, lambda e: e.event_type == KEY_UP or callback(e), suppress=suppress)
|
def __getitem__ ( self , index ) : row , col = index return self . rows [ row ] [ col ]
| 1 |
python get item at an index
|
Get the item at the given index .
|
cosqa-train-13094
|
def __getitem__(self, index):
"""Get the item at the given index.
Index is a tuple of (row, col)
"""
row, col = index
return self.rows[row][col]
|
def pdf ( x , mu , std ) : return ( 1.0 / ( std * sqrt ( 2 * pi ) ) ) * np . exp ( - ( x - mu ) ** 2 / ( 2 * std ** 2 ) )
| 1 |
determine probability distribution of data python
|
Probability density function ( normal distribution )
|
cosqa-train-13095
|
def pdf(x, mu, std):
"""Probability density function (normal distribution)"""
return (1.0 / (std * sqrt(2 * pi))) * np.exp(-(x - mu) ** 2 / (2 * std ** 2))
|
def get_mtime ( fname ) : try : mtime = os . stat ( fname ) . st_mtime_ns except OSError : # The file might be right in the middle of being written # so sleep time . sleep ( 1 ) mtime = os . stat ( fname ) . st_mtime_ns return mtime
| 1 |
python get last file modified
|
Find the time this file was last modified .
|
cosqa-train-13096
|
def get_mtime(fname):
"""
Find the time this file was last modified.
:param fname: File name
:return: The last time the file was modified.
"""
try:
mtime = os.stat(fname).st_mtime_ns
except OSError:
# The file might be right in the middle of being written
# so sleep
time.sleep(1)
mtime = os.stat(fname).st_mtime_ns
return mtime
|
def linregress ( x , y , return_stats = False ) : a1 , a0 , r_value , p_value , stderr = scipy . stats . linregress ( x , y ) retval = a1 , a0 if return_stats : retval += r_value , p_value , stderr return retval
| 1 |
determine variable value multi value regression python
|
linear regression calculation
|
cosqa-train-13097
|
def linregress(x, y, return_stats=False):
"""linear regression calculation
Parameters
----
x : independent variable (series)
y : dependent variable (series)
return_stats : returns statistical values as well if required (bool)
Returns
----
list of parameters (and statistics)
"""
a1, a0, r_value, p_value, stderr = scipy.stats.linregress(x, y)
retval = a1, a0
if return_stats:
retval += r_value, p_value, stderr
return retval
|
def _get_current_label ( self ) : if len ( self . _last ) == 0 : raise StopIteration return self . _last [ : self . _last . find ( ":" ) ]
| 1 |
python get last line of varable
|
Get the label from the last line read
|
cosqa-train-13098
|
def _get_current_label(self):
"""Get the label from the last line read"""
if len(self._last) == 0:
raise StopIteration
return self._last[:self._last.find(":")]
|
def is_function ( self ) : if self . is_instance ( ) or self . is_class ( ) : return False return isinstance ( self . callback , ( Callable , classmethod ) )
| 1 |
determining if something is a callable python
|
return True if callback is a vanilla plain jane function
|
cosqa-train-13099
|
def is_function(self):
"""return True if callback is a vanilla plain jane function"""
if self.is_instance() or self.is_class(): return False
return isinstance(self.callback, (Callable, classmethod))
|
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