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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|---|---|---|---|---|---|
def _is_root ( ) : import os import ctypes try : return os . geteuid ( ) == 0 except AttributeError : return ctypes . windll . shell32 . IsUserAnAdmin ( ) != 0 return False
| 1 |
python modles have execute access to root and not user in linux
|
Checks if the user is rooted .
|
cosqa-train-13600
|
def _is_root():
"""Checks if the user is rooted."""
import os
import ctypes
try:
return os.geteuid() == 0
except AttributeError:
return ctypes.windll.shell32.IsUserAnAdmin() != 0
return False
|
def generic_add ( a , b ) : logger . debug ( 'Called generic_add({}, {})' . format ( a , b ) ) return a + b
| 1 |
how to correctly add two functions in python
|
Simple function to add two numbers
|
cosqa-train-13601
|
def generic_add(a, b):
"""Simple function to add two numbers"""
logger.debug('Called generic_add({}, {})'.format(a, b))
return a + b
|
def MatrixInverse ( a , adj ) : return np . linalg . inv ( a if not adj else _adjoint ( a ) ) ,
| 0 |
python modular inverse matrix
|
Matrix inversion op .
|
cosqa-train-13602
|
def MatrixInverse(a, adj):
"""
Matrix inversion op.
"""
return np.linalg.inv(a if not adj else _adjoint(a)),
|
def entropy ( string ) : p , lns = Counter ( string ) , float ( len ( string ) ) return - sum ( count / lns * math . log ( count / lns , 2 ) for count in p . values ( ) )
| 1 |
how to count frequency of every element present in a string in python
|
Compute entropy on the string
|
cosqa-train-13603
|
def entropy(string):
"""Compute entropy on the string"""
p, lns = Counter(string), float(len(string))
return -sum(count/lns * math.log(count/lns, 2) for count in p.values())
|
def write ( self , value ) : self . get_collection ( ) . update_one ( { '_id' : self . _document_id } , { '$set' : { self . _path : value } } , upsert = True )
| 0 |
python mongodb update a nested field
|
Write value to the target
|
cosqa-train-13604
|
def write(self, value):
"""
Write value to the target
"""
self.get_collection().update_one(
{'_id': self._document_id},
{'$set': {self._path: value}},
upsert=True
)
|
def line_count ( fn ) : with open ( fn ) as f : for i , l in enumerate ( f ) : pass return i + 1
| 1 |
how to count the number of lines in a file in python
|
Get line count of file
|
cosqa-train-13605
|
def line_count(fn):
""" Get line count of file
Args:
fn (str): Path to file
Return:
Number of lines in file (int)
"""
with open(fn) as f:
for i, l in enumerate(f):
pass
return i + 1
|
def _calc_dir_size ( path ) : dir_size = 0 for ( root , dirs , files ) in os . walk ( path ) : for fn in files : full_fn = os . path . join ( root , fn ) dir_size += os . path . getsize ( full_fn ) return dir_size
| 1 |
python most efficient way to get size of all files in a directory
|
Calculate size of all files in path .
|
cosqa-train-13606
|
def _calc_dir_size(path):
"""
Calculate size of all files in `path`.
Args:
path (str): Path to the directory.
Returns:
int: Size of the directory in bytes.
"""
dir_size = 0
for (root, dirs, files) in os.walk(path):
for fn in files:
full_fn = os.path.join(root, fn)
dir_size += os.path.getsize(full_fn)
return dir_size
|
def _crop_list_to_size ( l , size ) : for x in range ( size - len ( l ) ) : l . append ( False ) for x in range ( len ( l ) - size ) : l . pop ( ) return l
| 1 |
how to crate a list of a certain size in python
|
Make a list a certain size
|
cosqa-train-13607
|
def _crop_list_to_size(l, size):
"""Make a list a certain size"""
for x in range(size - len(l)):
l.append(False)
for x in range(len(l) - size):
l.pop()
return l
|
def go_to_line ( self , line ) : cursor = self . textCursor ( ) cursor . setPosition ( self . document ( ) . findBlockByNumber ( line - 1 ) . position ( ) ) self . setTextCursor ( cursor ) return True
| 1 |
python move a line
|
Moves the text cursor to given line .
|
cosqa-train-13608
|
def go_to_line(self, line):
"""
Moves the text cursor to given line.
:param line: Line to go to.
:type line: int
:return: Method success.
:rtype: bool
"""
cursor = self.textCursor()
cursor.setPosition(self.document().findBlockByNumber(line - 1).position())
self.setTextCursor(cursor)
return True
|
def new_from_list ( cls , items , * * kwargs ) : obj = cls ( * * kwargs ) for item in items : obj . append ( ListItem ( item ) ) return obj
| 1 |
how to creat objects from a list python
|
Populates the ListView with a string list .
|
cosqa-train-13609
|
def new_from_list(cls, items, **kwargs):
"""Populates the ListView with a string list.
Args:
items (list): list of strings to fill the widget with.
"""
obj = cls(**kwargs)
for item in items:
obj.append(ListItem(item))
return obj
|
def mouse_move_event ( self , event ) : self . example . mouse_position_event ( event . x ( ) , event . y ( ) )
| 1 |
python move on once mouse clicked
|
Forward mouse cursor position events to the example
|
cosqa-train-13610
|
def mouse_move_event(self, event):
"""
Forward mouse cursor position events to the example
"""
self.example.mouse_position_event(event.x(), event.y())
|
def to_dataframe ( products ) : try : import pandas as pd except ImportError : raise ImportError ( "to_dataframe requires the optional dependency Pandas." ) return pd . DataFrame . from_dict ( products , orient = 'index' )
| 1 |
how to create a data frame from a dictionary python
|
Return the products from a query response as a Pandas DataFrame with the values in their appropriate Python types .
|
cosqa-train-13611
|
def to_dataframe(products):
"""Return the products from a query response as a Pandas DataFrame
with the values in their appropriate Python types.
"""
try:
import pandas as pd
except ImportError:
raise ImportError("to_dataframe requires the optional dependency Pandas.")
return pd.DataFrame.from_dict(products, orient='index')
|
def store_many ( self , sql , values ) : cursor = self . get_cursor ( ) cursor . executemany ( sql , values ) self . conn . commit ( )
| 0 |
python multi query execute
|
Abstraction over executemany method
|
cosqa-train-13612
|
def store_many(self, sql, values):
"""Abstraction over executemany method"""
cursor = self.get_cursor()
cursor.executemany(sql, values)
self.conn.commit()
|
def read_dict_from_file ( file_path ) : with open ( file_path ) as file : lines = file . read ( ) . splitlines ( ) obj = { } for line in lines : key , value = line . split ( ':' , maxsplit = 1 ) obj [ key ] = eval ( value ) return obj
| 1 |
how to create a dictionary from a file in python
|
Read a dictionary of strings from a file
|
cosqa-train-13613
|
def read_dict_from_file(file_path):
"""
Read a dictionary of strings from a file
"""
with open(file_path) as file:
lines = file.read().splitlines()
obj = {}
for line in lines:
key, value = line.split(':', maxsplit=1)
obj[key] = eval(value)
return obj
|
def __enter__ ( self ) : self . fd = open ( self . filename , 'a' ) fcntl . lockf ( self . fd , fcntl . LOCK_EX ) return self . fd
| 1 |
python multiprocess file lock
|
Acquire a lock on the output file prevents collisions between multiple runs .
|
cosqa-train-13614
|
def __enter__(self):
"""Acquire a lock on the output file, prevents collisions between multiple runs."""
self.fd = open(self.filename, 'a')
fcntl.lockf(self.fd, fcntl.LOCK_EX)
return self.fd
|
def read_dict_from_file ( file_path ) : with open ( file_path ) as file : lines = file . read ( ) . splitlines ( ) obj = { } for line in lines : key , value = line . split ( ':' , maxsplit = 1 ) obj [ key ] = eval ( value ) return obj
| 1 |
how to create a dictionary from a file python
|
Read a dictionary of strings from a file
|
cosqa-train-13615
|
def read_dict_from_file(file_path):
"""
Read a dictionary of strings from a file
"""
with open(file_path) as file:
lines = file.read().splitlines()
obj = {}
for line in lines:
key, value = line.split(':', maxsplit=1)
obj[key] = eval(value)
return obj
|
def machine_info ( ) : import psutil BYTES_IN_GIG = 1073741824.0 free_bytes = psutil . virtual_memory ( ) . total return [ { "memory" : float ( "%.1f" % ( free_bytes / BYTES_IN_GIG ) ) , "cores" : multiprocessing . cpu_count ( ) , "name" : socket . gethostname ( ) } ]
| 1 |
python multiprocessing get cpu usage
|
Retrieve core and memory information for the current machine .
|
cosqa-train-13616
|
def machine_info():
"""Retrieve core and memory information for the current machine.
"""
import psutil
BYTES_IN_GIG = 1073741824.0
free_bytes = psutil.virtual_memory().total
return [{"memory": float("%.1f" % (free_bytes / BYTES_IN_GIG)), "cores": multiprocessing.cpu_count(),
"name": socket.gethostname()}]
|
def write_file ( filename , content ) : print 'Generating {0}' . format ( filename ) with open ( filename , 'wb' ) as out_f : out_f . write ( content )
| 1 |
how to create a file in python
|
Create the file with the given content
|
cosqa-train-13617
|
def write_file(filename, content):
"""Create the file with the given content"""
print 'Generating {0}'.format(filename)
with open(filename, 'wb') as out_f:
out_f.write(content)
|
def compute ( args ) : x , y , params = args return x , y , mandelbrot ( x , y , params )
| 1 |
python multiprocessing pool apply arg
|
Callable function for the multiprocessing pool .
|
cosqa-train-13618
|
def compute(args):
x, y, params = args
"""Callable function for the multiprocessing pool."""
return x, y, mandelbrot(x, y, params)
|
def unique ( _list ) : ret = [ ] for item in _list : if item not in ret : ret . append ( item ) return ret
| 1 |
how to create a list in python with no duplicate
|
Makes the list have unique items only and maintains the order
|
cosqa-train-13619
|
def unique(_list):
"""
Makes the list have unique items only and maintains the order
list(set()) won't provide that
:type _list list
:rtype: list
"""
ret = []
for item in _list:
if item not in ret:
ret.append(item)
return ret
|
def parallel ( processes , threads ) : pool = multithread ( threads ) pool . map ( run_process , processes ) pool . close ( ) pool . join ( )
| 1 |
python multiprocessing start a pool of processes
|
execute jobs in processes using N threads
|
cosqa-train-13620
|
def parallel(processes, threads):
"""
execute jobs in processes using N threads
"""
pool = multithread(threads)
pool.map(run_process, processes)
pool.close()
pool.join()
|
def percent_d ( data , period ) : p_k = percent_k ( data , period ) percent_d = sma ( p_k , 3 ) return percent_d
| 1 |
how to create a percent in python formatrix result
|
%D .
|
cosqa-train-13621
|
def percent_d(data, period):
"""
%D.
Formula:
%D = SMA(%K, 3)
"""
p_k = percent_k(data, period)
percent_d = sma(p_k, 3)
return percent_d
|
def handle_m2m ( self , sender , instance , * * kwargs ) : self . handle_save ( instance . __class__ , instance )
| 1 |
python mutiple many to one relationship
|
Handle many to many relationships
|
cosqa-train-13622
|
def handle_m2m(self, sender, instance, **kwargs):
""" Handle many to many relationships """
self.handle_save(instance.__class__, instance)
|
def token ( name ) : def wrap ( f ) : tokenizers . append ( ( name , f ) ) return f return wrap
| 1 |
how to create a tokenization code in python
|
Marker for a token
|
cosqa-train-13623
|
def token(name):
"""Marker for a token
:param str name: Name of tokenizer
"""
def wrap(f):
tokenizers.append((name, f))
return f
return wrap
|
def title ( self ) : with switch_window ( self . _browser , self . name ) : return self . _browser . title
| 1 |
python mygui set title of window
|
The title of this window
|
cosqa-train-13624
|
def title(self):
""" The title of this window """
with switch_window(self._browser, self.name):
return self._browser.title
|
def input_yn ( conf_mess ) : ui_erase_ln ( ) ui_print ( conf_mess ) with term . cbreak ( ) : input_flush ( ) val = input_by_key ( ) return bool ( val . lower ( ) == 'y' )
| 1 |
how to create a yes or no response in python
|
Print Confirmation Message and Get Y / N response from user .
|
cosqa-train-13625
|
def input_yn(conf_mess):
"""Print Confirmation Message and Get Y/N response from user."""
ui_erase_ln()
ui_print(conf_mess)
with term.cbreak():
input_flush()
val = input_by_key()
return bool(val.lower() == 'y')
|
def get_last_id ( self , cur , table = 'reaction' ) : cur . execute ( "SELECT seq FROM sqlite_sequence WHERE name='{0}'" . format ( table ) ) result = cur . fetchone ( ) if result is not None : id = result [ 0 ] else : id = 0 return id
| 0 |
python mysql get last id
|
Get the id of the last written row in table
|
cosqa-train-13626
|
def get_last_id(self, cur, table='reaction'):
"""
Get the id of the last written row in table
Parameters
----------
cur: database connection().cursor() object
table: str
'reaction', 'publication', 'publication_system', 'reaction_system'
Returns: id
"""
cur.execute("SELECT seq FROM sqlite_sequence WHERE name='{0}'"
.format(table))
result = cur.fetchone()
if result is not None:
id = result[0]
else:
id = 0
return id
|
def from_dict ( cls , d ) : return cls ( * * { k : v for k , v in d . items ( ) if k in cls . ENTRIES } )
| 1 |
how to create an object from a dictionary key in python
|
Create an instance from a dictionary .
|
cosqa-train-13627
|
def from_dict(cls, d):
"""Create an instance from a dictionary."""
return cls(**{k: v for k, v in d.items() if k in cls.ENTRIES})
|
def _dictfetchall ( self , cursor ) : columns = [ col [ 0 ] for col in cursor . description ] return [ dict ( zip ( columns , row ) ) for row in cursor . fetchall ( ) ]
| 1 |
python mysql result as dict
|
Return all rows from a cursor as a dict .
|
cosqa-train-13628
|
def _dictfetchall(self, cursor):
""" Return all rows from a cursor as a dict. """
columns = [col[0] for col in cursor.description]
return [
dict(zip(columns, row))
for row in cursor.fetchall()
]
|
def create_node ( self , network , participant ) : return self . models . MCMCPAgent ( network = network , participant = participant )
| 0 |
how to create empty node python
|
Create a node for a participant .
|
cosqa-train-13629
|
def create_node(self, network, participant):
"""Create a node for a participant."""
return self.models.MCMCPAgent(network=network, participant=participant)
|
def index_nearest ( array , value ) : idx = ( np . abs ( array - value ) ) . argmin ( ) return idx
| 1 |
python nearest value in a list
|
Finds index of nearest value in array . Args : array : numpy array value : Returns : int http : // stackoverflow . com / questions / 2566412 / find - nearest - value - in - numpy - array
|
cosqa-train-13630
|
def index_nearest(array, value):
"""
Finds index of nearest value in array.
Args:
array: numpy array
value:
Returns:
int
http://stackoverflow.com/questions/2566412/find-nearest-value-in-numpy-array
"""
idx = (np.abs(array-value)).argmin()
return idx
|
def a2s ( a ) : s = np . zeros ( ( 6 , ) , 'f' ) # make the a matrix for i in range ( 3 ) : s [ i ] = a [ i ] [ i ] s [ 3 ] = a [ 0 ] [ 1 ] s [ 4 ] = a [ 1 ] [ 2 ] s [ 5 ] = a [ 0 ] [ 2 ] return s
| 1 |
how to create matrix in python 10?10 all ones
|
convert 3 3 a matrix to 6 element s list ( see Tauxe 1998 )
|
cosqa-train-13631
|
def a2s(a):
"""
convert 3,3 a matrix to 6 element "s" list (see Tauxe 1998)
"""
s = np.zeros((6,), 'f') # make the a matrix
for i in range(3):
s[i] = a[i][i]
s[3] = a[0][1]
s[4] = a[1][2]
s[5] = a[0][2]
return s
|
def flatten ( lis ) : new_lis = [ ] for item in lis : if isinstance ( item , collections . Sequence ) and not isinstance ( item , basestring ) : new_lis . extend ( flatten ( item ) ) else : new_lis . append ( item ) return new_lis
| 1 |
python nested list flatten
|
Given a list possibly nested to any level return it flattened .
|
cosqa-train-13632
|
def flatten(lis):
"""Given a list, possibly nested to any level, return it flattened."""
new_lis = []
for item in lis:
if isinstance(item, collections.Sequence) and not isinstance(item, basestring):
new_lis.extend(flatten(item))
else:
new_lis.append(item)
return new_lis
|
def doc_parser ( ) : parser = argparse . ArgumentParser ( prog = 'ambry' , description = 'Ambry {}. Management interface for ambry, libraries ' 'and repositories. ' . format ( ambry . _meta . __version__ ) ) return parser
| 1 |
how to create nested argparse in python
|
Utility function to allow getting the arguments for a single command for Sphinx documentation
|
cosqa-train-13633
|
def doc_parser():
"""Utility function to allow getting the arguments for a single command, for Sphinx documentation"""
parser = argparse.ArgumentParser(
prog='ambry',
description='Ambry {}. Management interface for ambry, libraries '
'and repositories. '.format(ambry._meta.__version__))
return parser
|
def has_edge ( self , edge ) : u , v = edge return ( u , v ) in self . edge_properties
| 0 |
python networkx check edge attribute exist
|
Return whether an edge exists .
|
cosqa-train-13634
|
def has_edge(self, edge):
"""
Return whether an edge exists.
@type edge: tuple
@param edge: Edge.
@rtype: boolean
@return: Truth-value for edge existence.
"""
u, v = edge
return (u, v) in self.edge_properties
|
def safe_int ( val , default = None ) : try : val = int ( val ) except ( ValueError , TypeError ) : val = default return val
| 1 |
how to default value in python
|
Returns int () of val if val is not convertable to int use default instead
|
cosqa-train-13635
|
def safe_int(val, default=None):
"""
Returns int() of val if val is not convertable to int use default
instead
:param val:
:param default:
"""
try:
val = int(val)
except (ValueError, TypeError):
val = default
return val
|
def next ( self ) : item = six . next ( self . _item_iter ) result = self . _item_to_value ( self . _parent , item ) # Since we've successfully got the next value from the # iterator, we update the number of remaining. self . _remaining -= 1 return result
| 1 |
python next item in loop
|
Get the next value in the page .
|
cosqa-train-13636
|
def next(self):
"""Get the next value in the page."""
item = six.next(self._item_iter)
result = self._item_to_value(self._parent, item)
# Since we've successfully got the next value from the
# iterator, we update the number of remaining.
self._remaining -= 1
return result
|
def _Enum ( docstring , * names ) : enums = dict ( zip ( names , range ( len ( names ) ) ) ) reverse = dict ( ( value , key ) for key , value in enums . iteritems ( ) ) enums [ 'reverse_mapping' ] = reverse enums [ '__doc__' ] = docstring return type ( 'Enum' , ( object , ) , enums )
| 1 |
how to define an enum in python
|
Utility to generate enum classes used by annotations .
|
cosqa-train-13637
|
def _Enum(docstring, *names):
"""Utility to generate enum classes used by annotations.
Args:
docstring: Docstring for the generated enum class.
*names: Enum names.
Returns:
A class that contains enum names as attributes.
"""
enums = dict(zip(names, range(len(names))))
reverse = dict((value, key) for key, value in enums.iteritems())
enums['reverse_mapping'] = reverse
enums['__doc__'] = docstring
return type('Enum', (object,), enums)
|
def purge_duplicates ( list_in ) : _list = [ ] for item in list_in : if item not in _list : _list . append ( item ) return _list
| 1 |
python no duplicate in list
|
Remove duplicates from list while preserving order .
|
cosqa-train-13638
|
def purge_duplicates(list_in):
"""Remove duplicates from list while preserving order.
Parameters
----------
list_in: Iterable
Returns
-------
list
List of first occurences in order
"""
_list = []
for item in list_in:
if item not in _list:
_list.append(item)
return _list
|
def __eq__ ( self , other ) : return isinstance ( other , self . __class__ ) and self . _freeze ( ) == other . _freeze ( )
| 1 |
how to define object equality python
|
Determine if two objects are equal .
|
cosqa-train-13639
|
def __eq__(self, other):
"""Determine if two objects are equal."""
return isinstance(other, self.__class__) \
and self._freeze() == other._freeze()
|
def is_a_sequence ( var , allow_none = False ) : return isinstance ( var , ( list , tuple ) ) or ( var is None and allow_none )
| 0 |
python nonetype in a if
|
Returns True if var is a list or a tuple ( but not a string! )
|
cosqa-train-13640
|
def is_a_sequence(var, allow_none=False):
""" Returns True if var is a list or a tuple (but not a string!)
"""
return isinstance(var, (list, tuple)) or (var is None and allow_none)
|
def earth_orientation ( date ) : x_p , y_p , s_prime = np . deg2rad ( _earth_orientation ( date ) ) return rot3 ( - s_prime ) @ rot2 ( x_p ) @ rot1 ( y_p )
| 1 |
how to deifne a rotation in python
|
Earth orientation as a rotating matrix
|
cosqa-train-13641
|
def earth_orientation(date):
"""Earth orientation as a rotating matrix
"""
x_p, y_p, s_prime = np.deg2rad(_earth_orientation(date))
return rot3(-s_prime) @ rot2(x_p) @ rot1(y_p)
|
def listlike ( obj ) : return hasattr ( obj , "__iter__" ) and not issubclass ( type ( obj ) , str ) and not issubclass ( type ( obj ) , unicode )
| 0 |
python nonetype' object is not iterable
|
Is an object iterable like a list ( and not a string ) ?
|
cosqa-train-13642
|
def listlike(obj):
"""Is an object iterable like a list (and not a string)?"""
return hasattr(obj, "__iter__") \
and not issubclass(type(obj), str)\
and not issubclass(type(obj), unicode)
|
def delete_all_eggs ( self ) : path_to_delete = os . path . join ( self . egg_directory , "lib" , "python" ) if os . path . exists ( path_to_delete ) : shutil . rmtree ( path_to_delete )
| 0 |
how to delete all python files on my computer windows 10
|
delete all the eggs in the directory specified
|
cosqa-train-13643
|
def delete_all_eggs(self):
""" delete all the eggs in the directory specified """
path_to_delete = os.path.join(self.egg_directory, "lib", "python")
if os.path.exists(path_to_delete):
shutil.rmtree(path_to_delete)
|
def Gaussian ( x , mu , sig ) : return sympy . exp ( - ( x - mu ) ** 2 / ( 2 * sig ** 2 ) ) / sympy . sqrt ( 2 * sympy . pi * sig ** 2 )
| 0 |
python normal distribution scipy
|
Gaussian pdf . : param x : free variable . : param mu : mean of the distribution . : param sig : standard deviation of the distribution . : return : sympy . Expr for a Gaussian pdf .
|
cosqa-train-13644
|
def Gaussian(x, mu, sig):
"""
Gaussian pdf.
:param x: free variable.
:param mu: mean of the distribution.
:param sig: standard deviation of the distribution.
:return: sympy.Expr for a Gaussian pdf.
"""
return sympy.exp(-(x - mu)**2/(2*sig**2))/sympy.sqrt(2*sympy.pi*sig**2)
|
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 |
how to delete directory if exists in python
|
remove arduino / examples / all directory .
|
cosqa-train-13645
|
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 denorm ( self , arr ) : if type ( arr ) is not np . ndarray : arr = to_np ( arr ) if len ( arr . shape ) == 3 : arr = arr [ None ] return self . transform . denorm ( np . rollaxis ( arr , 1 , 4 ) )
| 1 |
python normalise image array
|
Reverse the normalization done to a batch of images .
|
cosqa-train-13646
|
def denorm(self,arr):
"""Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz)
"""
if type(arr) is not np.ndarray: arr = to_np(arr)
if len(arr.shape)==3: arr = arr[None]
return self.transform.denorm(np.rollaxis(arr,1,4))
|
def delete_index ( index ) : logger . info ( "Deleting search index: '%s'" , index ) client = get_client ( ) return client . indices . delete ( index = index )
| 1 |
how to delete item at indice python
|
Delete index entirely ( removes all documents and mapping ) .
|
cosqa-train-13647
|
def delete_index(index):
"""Delete index entirely (removes all documents and mapping)."""
logger.info("Deleting search index: '%s'", index)
client = get_client()
return client.indices.delete(index=index)
|
def _normalize ( mat : np . ndarray ) : return ( ( mat - mat . min ( ) ) * ( 255 / mat . max ( ) ) ) . astype ( np . uint8 )
| 0 |
python normalize grayscale image
|
rescales a numpy array so that min is 0 and max is 255
|
cosqa-train-13648
|
def _normalize(mat: np.ndarray):
"""rescales a numpy array, so that min is 0 and max is 255"""
return ((mat - mat.min()) * (255 / mat.max())).astype(np.uint8)
|
def _removeTags ( tags , objects ) : for t in tags : for o in objects : o . tags . remove ( t ) return True
| 1 |
how to delete objects in python
|
Removes tags from objects
|
cosqa-train-13649
|
def _removeTags(tags, objects):
""" Removes tags from objects """
for t in tags:
for o in objects:
o.tags.remove(t)
return True
|
def normalize ( X ) : X = coo_matrix ( X ) X . data = X . data / sqrt ( bincount ( X . row , X . data ** 2 ) ) [ X . row ] return X
| 0 |
python normalize matrix column
|
equivalent to scipy . preprocessing . normalize on sparse matrices but lets avoid another depedency just for a small utility function
|
cosqa-train-13650
|
def normalize(X):
""" equivalent to scipy.preprocessing.normalize on sparse matrices
, but lets avoid another depedency just for a small utility function """
X = coo_matrix(X)
X.data = X.data / sqrt(bincount(X.row, X.data ** 2))[X.row]
return X
|
def rm ( venv_name ) : inenv = InenvManager ( ) venv = inenv . get_venv ( venv_name ) click . confirm ( "Delete dir {}" . format ( venv . path ) ) shutil . rmtree ( venv . path )
| 1 |
how to delete one environment in python
|
Removes the venv by name
|
cosqa-train-13651
|
def rm(venv_name):
""" Removes the venv by name """
inenv = InenvManager()
venv = inenv.get_venv(venv_name)
click.confirm("Delete dir {}".format(venv.path))
shutil.rmtree(venv.path)
|
def test ( nose_argsuments ) : from nose import run params = [ '__main__' , '-c' , 'nose.ini' ] params . extend ( nose_argsuments ) run ( argv = params )
| 1 |
python nose start context
|
Run application tests
|
cosqa-train-13652
|
def test(nose_argsuments):
""" Run application tests """
from nose import run
params = ['__main__', '-c', 'nose.ini']
params.extend(nose_argsuments)
run(argv=params)
|
def __del__ ( self ) : if self . _delete_file : try : os . remove ( self . name ) except ( OSError , IOError ) : pass
| 1 |
how to delete self file in python
|
Deletes the database file .
|
cosqa-train-13653
|
def __del__(self):
"""Deletes the database file."""
if self._delete_file:
try:
os.remove(self.name)
except (OSError, IOError):
pass
|
def request ( self , method , url , body = None , headers = { } ) : self . _send_request ( method , url , body , headers )
| 1 |
python not sending requests
|
Send a complete request to the server .
|
cosqa-train-13654
|
def request(self, method, url, body=None, headers={}):
"""Send a complete request to the server."""
self._send_request(method, url, body, headers)
|
def sometimesish ( fn ) : def wrapped ( * args , * * kwargs ) : if random . randint ( 1 , 2 ) == 1 : return fn ( * args , * * kwargs ) return wrapped
| 0 |
how to detect the output of random function in python
|
Has a 50 / 50 chance of calling a function
|
cosqa-train-13655
|
def sometimesish(fn):
"""
Has a 50/50 chance of calling a function
"""
def wrapped(*args, **kwargs):
if random.randint(1, 2) == 1:
return fn(*args, **kwargs)
return wrapped
|
def _not ( condition = None , * * kwargs ) : result = True if condition is not None : result = not run ( condition , * * kwargs ) return result
| 0 |
python not with multiple conditions
|
Return the opposite of input condition .
|
cosqa-train-13656
|
def _not(condition=None, **kwargs):
"""
Return the opposite of input condition.
:param condition: condition to process.
:result: not condition.
:rtype: bool
"""
result = True
if condition is not None:
result = not run(condition, **kwargs)
return result
|
def is_numeric_dtype ( dtype ) : dtype = np . dtype ( dtype ) return np . issubsctype ( getattr ( dtype , 'base' , None ) , np . number )
| 1 |
how to determine data types in python
|
Return True if dtype is a numeric type .
|
cosqa-train-13657
|
def is_numeric_dtype(dtype):
"""Return ``True`` if ``dtype`` is a numeric type."""
dtype = np.dtype(dtype)
return np.issubsctype(getattr(dtype, 'base', None), np.number)
|
def fn_min ( self , a , axis = None ) : return numpy . nanmin ( self . _to_ndarray ( a ) , axis = axis )
| 1 |
python np array get min values
|
Return the minimum of an array ignoring any NaNs .
|
cosqa-train-13658
|
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 load_library ( version ) : check_version ( version ) module_name = SUPPORTED_LIBRARIES [ version ] lib = sys . modules . get ( module_name ) if lib is None : lib = importlib . import_module ( module_name ) return lib
| 1 |
how to determine language for python libraries
|
Load the correct module according to the version
|
cosqa-train-13659
|
def load_library(version):
"""
Load the correct module according to the version
:type version: ``str``
:param version: the version of the library to be loaded (e.g. '2.6')
:rtype: module object
"""
check_version(version)
module_name = SUPPORTED_LIBRARIES[version]
lib = sys.modules.get(module_name)
if lib is None:
lib = importlib.import_module(module_name)
return lib
|
def _scale_shape ( dshape , scale = ( 1 , 1 , 1 ) ) : nshape = np . round ( np . array ( dshape ) * np . array ( scale ) ) return tuple ( nshape . astype ( np . int ) )
| 1 |
python np image scale
|
returns the shape after scaling ( should be the same as ndimage . zoom
|
cosqa-train-13660
|
def _scale_shape(dshape, scale = (1,1,1)):
"""returns the shape after scaling (should be the same as ndimage.zoom"""
nshape = np.round(np.array(dshape) * np.array(scale))
return tuple(nshape.astype(np.int))
|
def find_geom ( geom , geoms ) : for i , g in enumerate ( geoms ) : if g is geom : return i
| 1 |
how to determine the index of an object on a list python
|
Returns the index of a geometry in a list of geometries avoiding expensive equality checks of in operator .
|
cosqa-train-13661
|
def find_geom(geom, geoms):
"""
Returns the index of a geometry in a list of geometries avoiding
expensive equality checks of `in` operator.
"""
for i, g in enumerate(geoms):
if g is geom:
return i
|
def torecarray ( * args , * * kwargs ) : import numpy as np return toarray ( * args , * * kwargs ) . view ( np . recarray )
| 1 |
python numpy arrary with same space
|
Convenient shorthand for toarray ( * args ** kwargs ) . view ( np . recarray ) .
|
cosqa-train-13662
|
def torecarray(*args, **kwargs):
"""
Convenient shorthand for ``toarray(*args, **kwargs).view(np.recarray)``.
"""
import numpy as np
return toarray(*args, **kwargs).view(np.recarray)
|
def isTestCaseDisabled ( test_case_class , method_name ) : test_method = getattr ( test_case_class , method_name ) return getattr ( test_method , "__test__" , 'not nose' ) is False
| 0 |
how to disable a test in python
|
I check to see if a method on a TestCase has been disabled via nose s convention for disabling a TestCase . This makes it so that users can mix nose s parameterized tests with green as a runner .
|
cosqa-train-13663
|
def isTestCaseDisabled(test_case_class, method_name):
"""
I check to see if a method on a TestCase has been disabled via nose's
convention for disabling a TestCase. This makes it so that users can
mix nose's parameterized tests with green as a runner.
"""
test_method = getattr(test_case_class, method_name)
return getattr(test_method, "__test__", 'not nose') is False
|
def ma ( self ) : a = self . array return numpy . ma . MaskedArray ( a , mask = numpy . logical_not ( numpy . isfinite ( a ) ) )
| 1 |
python numpy array how to return rows not include nan
|
Represent data as a masked array .
|
cosqa-train-13664
|
def ma(self):
"""Represent data as a masked array.
The array is returned with column-first indexing, i.e. for a data file with
columns X Y1 Y2 Y3 ... the array a will be a[0] = X, a[1] = Y1, ... .
inf and nan are filtered via :func:`numpy.isfinite`.
"""
a = self.array
return numpy.ma.MaskedArray(a, mask=numpy.logical_not(numpy.isfinite(a)))
|
def gtype ( n ) : t = type ( n ) . __name__ return str ( t ) if t != 'Literal' else 'Literal, {}' . format ( n . language )
| 1 |
how to display the data type python
|
Return the a string with the data type of a value for Graph data
|
cosqa-train-13665
|
def gtype(n):
"""
Return the a string with the data type of a value, for Graph data
"""
t = type(n).__name__
return str(t) if t != 'Literal' else 'Literal, {}'.format(n.language)
|
def get_column ( self , X , column ) : if isinstance ( X , pd . DataFrame ) : return X [ column ] . values return X [ : , column ]
| 1 |
python numpy array how to select column
|
Return a column of the given matrix .
|
cosqa-train-13666
|
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 filter_symlog ( y , base = 10.0 ) : log_base = np . log ( base ) sign = np . sign ( y ) logs = np . log ( np . abs ( y ) / log_base ) return sign * logs
| 1 |
how to do a logriithmic scale graph in python
|
Symmetrical logarithmic scale .
|
cosqa-train-13667
|
def filter_symlog(y, base=10.0):
"""Symmetrical logarithmic scale.
Optional arguments:
*base*:
The base of the logarithm.
"""
log_base = np.log(base)
sign = np.sign(y)
logs = np.log(np.abs(y) / log_base)
return sign * logs
|
def length ( self ) : return np . sqrt ( np . sum ( self ** 2 , axis = 1 ) ) . view ( np . ndarray )
| 0 |
python numpy array two dim list
|
Array of vector lengths
|
cosqa-train-13668
|
def length(self):
"""Array of vector lengths"""
return np.sqrt(np.sum(self**2, axis=1)).view(np.ndarray)
|
def assert_is_not ( expected , actual , message = None , extra = None ) : assert expected is not actual , _assert_fail_message ( message , expected , actual , "is" , extra )
| 0 |
how to do an assert to check for none in python
|
Raises an AssertionError if expected is actual .
|
cosqa-train-13669
|
def assert_is_not(expected, actual, message=None, extra=None):
"""Raises an AssertionError if expected is actual."""
assert expected is not actual, _assert_fail_message(
message, expected, actual, "is", extra
)
|
def flatten_array ( grid ) : grid = [ grid [ i ] [ j ] for i in range ( len ( grid ) ) for j in range ( len ( grid [ i ] ) ) ] while type ( grid [ 0 ] ) is list : grid = flatten_array ( grid ) return grid
| 1 |
python numpy flatten reshape
|
Takes a multi - dimensional array and returns a 1 dimensional array with the same contents .
|
cosqa-train-13670
|
def flatten_array(grid):
"""
Takes a multi-dimensional array and returns a 1 dimensional array with the
same contents.
"""
grid = [grid[i][j] for i in range(len(grid)) for j in range(len(grid[i]))]
while type(grid[0]) is list:
grid = flatten_array(grid)
return grid
|
def exp_fit_fun ( x , a , tau , c ) : # pylint: disable=invalid-name return a * np . exp ( - x / tau ) + c
| 1 |
how to do an exponential fit in python
|
Function used to fit the exponential decay .
|
cosqa-train-13671
|
def exp_fit_fun(x, a, tau, c):
"""Function used to fit the exponential decay."""
# pylint: disable=invalid-name
return a * np.exp(-x / tau) + c
|
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 )
| 0 |
python numpy log of float array
|
Returns numpy array of natural logarithms of values .
|
cosqa-train-13672
|
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 downcaseTokens ( s , l , t ) : return [ tt . lower ( ) for tt in map ( _ustr , t ) ]
| 1 |
how to do lowercase in python
|
Helper parse action to convert tokens to lower case .
|
cosqa-train-13673
|
def downcaseTokens(s,l,t):
"""Helper parse action to convert tokens to lower case."""
return [ tt.lower() for tt in map(_ustr,t) ]
|
def fn_min ( self , a , axis = None ) : return numpy . nanmin ( self . _to_ndarray ( a ) , axis = axis )
| 1 |
python numpy minimum value of array
|
Return the minimum of an array ignoring any NaNs .
|
cosqa-train-13674
|
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 downcaseTokens ( s , l , t ) : return [ tt . lower ( ) for tt in map ( _ustr , t ) ]
| 1 |
how to do lowercase on python
|
Helper parse action to convert tokens to lower case .
|
cosqa-train-13675
|
def downcaseTokens(s,l,t):
"""Helper parse action to convert tokens to lower case."""
return [ tt.lower() for tt in map(_ustr,t) ]
|
def get_tweepy_auth ( twitter_api_key , twitter_api_secret , twitter_access_token , twitter_access_token_secret ) : auth = tweepy . OAuthHandler ( twitter_api_key , twitter_api_secret ) auth . set_access_token ( twitter_access_token , twitter_access_token_secret ) return auth
| 1 |
python oauth2 authentication for twitter
|
Make a tweepy auth object
|
cosqa-train-13676
|
def get_tweepy_auth(twitter_api_key,
twitter_api_secret,
twitter_access_token,
twitter_access_token_secret):
"""Make a tweepy auth object"""
auth = tweepy.OAuthHandler(twitter_api_key, twitter_api_secret)
auth.set_access_token(twitter_access_token, twitter_access_token_secret)
return auth
|
def replace_tab_indent ( s , replace = " " ) : prefix = get_indent_prefix ( s ) return prefix . replace ( "\t" , replace ) + s [ len ( prefix ) : ]
| 0 |
how to do tabs in a python string
|
: param str s : string with tabs : param str replace : e . g . 4 spaces : rtype : str
|
cosqa-train-13677
|
def replace_tab_indent(s, replace=" "):
"""
:param str s: string with tabs
:param str replace: e.g. 4 spaces
:rtype: str
"""
prefix = get_indent_prefix(s)
return prefix.replace("\t", replace) + s[len(prefix):]
|
def to_json ( obj ) : i = StringIO . StringIO ( ) w = Writer ( i , encoding = 'UTF-8' ) w . write_value ( obj ) return i . getvalue ( )
| 0 |
python object into json
|
Return a json string representing the python object obj .
|
cosqa-train-13678
|
def to_json(obj):
"""Return a json string representing the python object obj."""
i = StringIO.StringIO()
w = Writer(i, encoding='UTF-8')
w.write_value(obj)
return i.getvalue()
|
def seconds ( num ) : now = pytime . time ( ) end = now + num until ( end )
| 1 |
how to do things after a set amount of time in python
|
Pause for this many seconds
|
cosqa-train-13679
|
def seconds(num):
"""
Pause for this many seconds
"""
now = pytime.time()
end = now + num
until(end)
|
def dict_merge ( set1 , set2 ) : return dict ( list ( set1 . items ( ) ) + list ( set2 . items ( ) ) )
| 1 |
python one liner union of two dicts
|
Joins two dictionaries .
|
cosqa-train-13680
|
def dict_merge(set1, set2):
"""Joins two dictionaries."""
return dict(list(set1.items()) + list(set2.items()))
|
def _draw_lines_internal ( self , coords , colour , bg ) : for i , ( x , y ) in enumerate ( coords ) : if i == 0 : self . _screen . move ( x , y ) else : self . _screen . draw ( x , y , colour = colour , bg = bg , thin = True )
| 0 |
how to draw a line in python screen
|
Helper to draw lines connecting a set of nodes that are scaled for the Screen .
|
cosqa-train-13681
|
def _draw_lines_internal(self, coords, colour, bg):
"""Helper to draw lines connecting a set of nodes that are scaled for the Screen."""
for i, (x, y) in enumerate(coords):
if i == 0:
self._screen.move(x, y)
else:
self._screen.draw(x, y, colour=colour, bg=bg, thin=True)
|
def _replace_file ( path , content ) : if os . path . exists ( path ) : with open ( path , 'r' ) as f : if content == f . read ( ) : print ( "Not overwriting {} because it is unchanged" . format ( path ) , file = sys . stderr ) return with open ( path , 'w' ) as f : f . write ( content )
| 1 |
python only write file if doesn't already have contents
|
Writes a file if it doesn t already exist with the same content .
|
cosqa-train-13682
|
def _replace_file(path, content):
"""Writes a file if it doesn't already exist with the same content.
This is useful because cargo uses timestamps to decide whether to compile things."""
if os.path.exists(path):
with open(path, 'r') as f:
if content == f.read():
print("Not overwriting {} because it is unchanged".format(path), file=sys.stderr)
return
with open(path, 'w') as f:
f.write(content)
|
def vline ( self , x , y , height , color ) : self . rect ( x , y , 1 , height , color , fill = True )
| 0 |
how to draw the straight line in python
|
Draw a vertical line up to a given length .
|
cosqa-train-13683
|
def vline(self, x, y, height, color):
"""Draw a vertical line up to a given length."""
self.rect(x, y, 1, height, color, fill=True)
|
def do_serial ( self , p ) : try : self . serial . port = p self . serial . open ( ) print 'Opening serial port: %s' % p except Exception , e : print 'Unable to open serial port: %s' % p
| 1 |
python open serial port on windows
|
Set the serial port e . g . : / dev / tty . usbserial - A4001ib8
|
cosqa-train-13684
|
def do_serial(self, p):
"""Set the serial port, e.g.: /dev/tty.usbserial-A4001ib8"""
try:
self.serial.port = p
self.serial.open()
print 'Opening serial port: %s' % p
except Exception, e:
print 'Unable to open serial port: %s' % p
|
def chmod_add_excute ( filename ) : st = os . stat ( filename ) os . chmod ( filename , st . st_mode | stat . S_IEXEC )
| 0 |
how to edit a fie in python without permission
|
Adds execute permission to file . : param filename : : return :
|
cosqa-train-13685
|
def chmod_add_excute(filename):
"""
Adds execute permission to file.
:param filename:
:return:
"""
st = os.stat(filename)
os.chmod(filename, st.st_mode | stat.S_IEXEC)
|
def delete ( filething ) : f = FLAC ( filething ) filething . fileobj . seek ( 0 ) f . delete ( filething )
| 1 |
python open wipe out a file
|
Remove tags from a file .
|
cosqa-train-13686
|
def delete(filething):
"""Remove tags from a file.
Args:
filething (filething)
Raises:
mutagen.MutagenError
"""
f = FLAC(filething)
filething.fileobj.seek(0)
f.delete(filething)
|
def _float_feature ( value ) : if not isinstance ( value , list ) : value = [ value ] return tf . train . Feature ( float_list = tf . train . FloatList ( value = value ) )
| 1 |
how to enable float values in python
|
Wrapper for inserting float features into Example proto .
|
cosqa-train-13687
|
def _float_feature(value):
"""Wrapper for inserting float features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
|
def paste ( xsel = False ) : selection = "primary" if xsel else "clipboard" try : return subprocess . Popen ( [ "xclip" , "-selection" , selection , "-o" ] , stdout = subprocess . PIPE ) . communicate ( ) [ 0 ] . decode ( "utf-8" ) except OSError as why : raise XclipNotFound
| 1 |
python openclipboard access is denied win32clipboard
|
Returns system clipboard contents .
|
cosqa-train-13688
|
def paste(xsel=False):
"""Returns system clipboard contents."""
selection = "primary" if xsel else "clipboard"
try:
return subprocess.Popen(["xclip", "-selection", selection, "-o"], stdout=subprocess.PIPE).communicate()[0].decode("utf-8")
except OSError as why:
raise XclipNotFound
|
def read_credentials ( fname ) : with open ( fname , 'r' ) as f : username = f . readline ( ) . strip ( '\n' ) password = f . readline ( ) . strip ( '\n' ) return username , password
| 1 |
how to extract username and password from password file in python
|
read a simple text file from a private location to get username and password
|
cosqa-train-13689
|
def read_credentials(fname):
"""
read a simple text file from a private location to get
username and password
"""
with open(fname, 'r') as f:
username = f.readline().strip('\n')
password = f.readline().strip('\n')
return username, password
|
def _rgbtomask ( self , obj ) : dat = obj . get_image ( ) . get_data ( ) # RGB arrays return dat . sum ( axis = 2 ) . astype ( np . bool )
| 0 |
python opencv apply 2d mask to 3 band image
|
Convert RGB arrays from mask canvas object back to boolean mask .
|
cosqa-train-13690
|
def _rgbtomask(self, obj):
"""Convert RGB arrays from mask canvas object back to boolean mask."""
dat = obj.get_image().get_data() # RGB arrays
return dat.sum(axis=2).astype(np.bool)
|
def get_bound ( pts ) : ( x0 , y0 , x1 , y1 ) = ( INF , INF , - INF , - INF ) for ( x , y ) in pts : x0 = min ( x0 , x ) y0 = min ( y0 , y ) x1 = max ( x1 , x ) y1 = max ( y1 , y ) return ( x0 , y0 , x1 , y1 )
| 1 |
how to figure out bounds in python
|
Compute a minimal rectangle that covers all the points .
|
cosqa-train-13691
|
def get_bound(pts):
"""Compute a minimal rectangle that covers all the points."""
(x0, y0, x1, y1) = (INF, INF, -INF, -INF)
for (x, y) in pts:
x0 = min(x0, x)
y0 = min(y0, y)
x1 = max(x1, x)
y1 = max(y1, y)
return (x0, y0, x1, y1)
|
def screen_cv2 ( self ) : pil_image = self . screen . convert ( 'RGB' ) cv2_image = np . array ( pil_image ) pil_image . close ( ) # Convert RGB to BGR
cv2_image = cv2_image [ : , : , : : - 1 ] return cv2_image
| 1 |
python opencv black screen
|
cv2 Image of current window screen
|
cosqa-train-13692
|
def screen_cv2(self):
"""cv2 Image of current window screen"""
pil_image = self.screen.convert('RGB')
cv2_image = np.array(pil_image)
pil_image.close()
# Convert RGB to BGR
cv2_image = cv2_image[:, :, ::-1]
return cv2_image
|
def match_aspect_to_viewport ( self ) : viewport = self . viewport self . aspect = float ( viewport . width ) / viewport . height
| 0 |
python opencv camera default resolution
|
Updates Camera . aspect to match the viewport s aspect ratio .
|
cosqa-train-13693
|
def match_aspect_to_viewport(self):
"""Updates Camera.aspect to match the viewport's aspect ratio."""
viewport = self.viewport
self.aspect = float(viewport.width) / viewport.height
|
def filter_list_by_indices ( lst , indices ) : return [ x for i , x in enumerate ( lst ) if i in indices ]
| 1 |
how to filter numbers based on list of indices in python
|
Return a modified list containing only the indices indicated .
|
cosqa-train-13694
|
def filter_list_by_indices(lst, indices):
"""Return a modified list containing only the indices indicated.
Args:
lst: Original list of values
indices: List of indices to keep from the original list
Returns:
list: Filtered list of values
"""
return [x for i, x in enumerate(lst) if i in indices]
|
def read_img ( path ) : img = cv2 . resize ( cv2 . imread ( path , 0 ) , ( 80 , 30 ) ) . astype ( np . float32 ) / 255 img = np . expand_dims ( img . transpose ( 1 , 0 ) , 0 ) return img
| 1 |
python opencv load image to numpy
|
Reads image specified by path into numpy . ndarray
|
cosqa-train-13695
|
def read_img(path):
""" Reads image specified by path into numpy.ndarray"""
img = cv2.resize(cv2.imread(path, 0), (80, 30)).astype(np.float32) / 255
img = np.expand_dims(img.transpose(1, 0), 0)
return img
|
def _remove_keywords ( d ) : return { k : v for k , v in iteritems ( d ) if k not in RESERVED }
| 1 |
how to filter through dict python
|
copy the dict filter_keywords
|
cosqa-train-13696
|
def _remove_keywords(d):
"""
copy the dict, filter_keywords
Parameters
----------
d : dict
"""
return { k:v for k, v in iteritems(d) if k not in RESERVED }
|
def read_img ( path ) : img = cv2 . resize ( cv2 . imread ( path , 0 ) , ( 80 , 30 ) ) . astype ( np . float32 ) / 255 img = np . expand_dims ( img . transpose ( 1 , 0 ) , 0 ) return img
| 1 |
python opencv not reading image from full path
|
Reads image specified by path into numpy . ndarray
|
cosqa-train-13697
|
def read_img(path):
""" Reads image specified by path into numpy.ndarray"""
img = cv2.resize(cv2.imread(path, 0), (80, 30)).astype(np.float32) / 255
img = np.expand_dims(img.transpose(1, 0), 0)
return img
|
def filter_bolts ( table , header ) : bolts_info = [ ] for row in table : if row [ 0 ] == 'bolt' : bolts_info . append ( row ) return bolts_info , header
| 1 |
how to filter value from a table in python
|
filter to keep bolts
|
cosqa-train-13698
|
def filter_bolts(table, header):
""" filter to keep bolts """
bolts_info = []
for row in table:
if row[0] == 'bolt':
bolts_info.append(row)
return bolts_info, header
|
def _openpyxl_read_xl ( xl_path : str ) : try : wb = load_workbook ( filename = xl_path , read_only = True ) except : raise else : return wb
| 1 |
python openpyxl remain style
|
Use openpyxl to read an Excel file .
|
cosqa-train-13699
|
def _openpyxl_read_xl(xl_path: str):
""" Use openpyxl to read an Excel file. """
try:
wb = load_workbook(filename=xl_path, read_only=True)
except:
raise
else:
return wb
|
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