idx int64 0 251k | question stringlengths 53 3.53k | target stringlengths 5 1.23k | len_question int64 20 893 | len_target int64 3 238 |
|---|---|---|---|---|
244,100 | def handle_path ( backend_inst , path , * * kwargs ) : if callable ( getattr ( backend_inst , 'handle_path' , None ) ) : # Prefer handle_path() if present. LOGGER . debug ( "using handle_path" ) return backend_inst . handle_path ( path ) elif callable ( getattr ( backend_inst , 'handle_fobj' , None ) ) : # Fallback to ... | Handle a path . | 180 | 4 |
244,101 | def handle_fobj ( backend , f , * * kwargs ) : if not is_binary ( f ) : raise AssertionError ( 'File must be opened in binary mode.' ) if callable ( getattr ( backend , 'handle_fobj' , None ) ) : # Prefer handle_fobj() if present. LOGGER . debug ( "using handle_fobj" ) return backend . handle_fobj ( f ) elif callable (... | Handle a file - like object . | 257 | 7 |
244,102 | def backend_from_mime ( mime ) : try : mod_name = MIMETYPE_TO_BACKENDS [ mime ] except KeyError : msg = "No handler for %r, defaulting to %r" % ( mime , DEFAULT_MIME ) if 'FULLTEXT_TESTING' in os . environ : warn ( msg ) else : LOGGER . debug ( msg ) mod_name = MIMETYPE_TO_BACKENDS [ DEFAULT_MIME ] mod = import_mod ( m... | Determine backend module object from a mime string . | 126 | 12 |
244,103 | def backend_from_fname ( name ) : ext = splitext ( name ) [ 1 ] try : mime = EXTS_TO_MIMETYPES [ ext ] except KeyError : try : f = open ( name , 'rb' ) except IOError as e : # The file may not exist, we are being asked to determine it's type # from it's name. Other errors are unexpected. if e . errno != errno . ENOENT ... | Determine backend module object from a file name . | 239 | 11 |
244,104 | def backend_from_fobj ( f ) : if magic is None : warn ( "magic lib is not installed; assuming mime type %r" % ( DEFAULT_MIME ) ) return backend_from_mime ( DEFAULT_MIME ) else : offset = f . tell ( ) try : f . seek ( 0 ) chunk = f . read ( MAGIC_BUFFER_SIZE ) mime = magic . from_buffer ( chunk , mime = True ) return ba... | Determine backend module object from a file object . | 121 | 11 |
244,105 | def backend_inst_from_mod ( mod , encoding , encoding_errors , kwargs ) : kw = dict ( encoding = encoding , encoding_errors = encoding_errors , kwargs = kwargs ) try : klass = getattr ( mod , "Backend" ) except AttributeError : raise AttributeError ( "%r mod does not define any backend class" % mod ) inst = klass ( * *... | Given a mod and a set of opts return an instantiated Backend class . | 199 | 17 |
244,106 | def get ( path_or_file , default = SENTINAL , mime = None , name = None , backend = None , encoding = None , encoding_errors = None , kwargs = None , _wtitle = False ) : try : text , title = _get ( path_or_file , default = default , mime = mime , name = name , backend = backend , kwargs = kwargs , encoding = encoding ,... | Get document full text . | 150 | 5 |
244,107 | def hilite ( s , ok = True , bold = False ) : if not term_supports_colors ( ) : return s attr = [ ] if ok is None : # no color pass elif ok : # green attr . append ( '32' ) else : # red attr . append ( '31' ) if bold : attr . append ( '1' ) return '\x1b[%sm%s\x1b[0m' % ( ';' . join ( attr ) , s ) | Return an highlighted version of string . | 117 | 7 |
244,108 | def fobj_to_tempfile ( f , suffix = '' ) : with tempfile . NamedTemporaryFile ( dir = TEMPDIR , suffix = suffix , delete = False ) as t : shutil . copyfileobj ( f , t ) try : yield t . name finally : os . remove ( t . name ) | Context manager which copies a file object to disk and return its name . When done the file is deleted . | 70 | 21 |
244,109 | def rm ( pattern ) : paths = glob . glob ( pattern ) for path in paths : if path . startswith ( '.git/' ) : continue if os . path . isdir ( path ) : def onerror ( fun , path , excinfo ) : exc = excinfo [ 1 ] if exc . errno != errno . ENOENT : raise safe_print ( "rmdir -f %s" % path ) shutil . rmtree ( path , onerror = ... | Recursively remove a file or dir by pattern . | 128 | 11 |
244,110 | def help ( ) : safe_print ( 'Run "make [-p <PYTHON>] <target>" where <target> is one of:' ) for name in sorted ( _cmds ) : safe_print ( " %-20s %s" % ( name . replace ( '_' , '-' ) , _cmds [ name ] or '' ) ) sys . exit ( 1 ) | Print this help | 85 | 3 |
244,111 | def clean ( ) : rm ( "$testfn*" ) rm ( "*.bak" ) rm ( "*.core" ) rm ( "*.egg-info" ) rm ( "*.orig" ) rm ( "*.pyc" ) rm ( "*.pyd" ) rm ( "*.pyo" ) rm ( "*.rej" ) rm ( "*.so" ) rm ( "*.~" ) rm ( "*__pycache__" ) rm ( ".coverage" ) rm ( ".tox" ) rm ( ".coverage" ) rm ( "build" ) rm ( "dist" ) rm ( "docs/_build" ) rm ( "ht... | Deletes dev files | 162 | 4 |
244,112 | def lint ( ) : py_files = subprocess . check_output ( "git ls-files" ) if PY3 : py_files = py_files . decode ( ) py_files = [ x for x in py_files . split ( ) if x . endswith ( '.py' ) ] py_files = ' ' . join ( py_files ) sh ( "%s -m flake8 %s" % ( PYTHON , py_files ) , nolog = True ) | Run flake8 against all py files | 110 | 8 |
244,113 | def coverage ( ) : # Note: coverage options are controlled by .coveragerc file install ( ) test_setup ( ) sh ( "%s -m coverage run %s" % ( PYTHON , TEST_SCRIPT ) ) sh ( "%s -m coverage report" % PYTHON ) sh ( "%s -m coverage html" % PYTHON ) sh ( "%s -m webbrowser -t htmlcov/index.html" % PYTHON ) | Run coverage tests . | 104 | 4 |
244,114 | def venv ( ) : try : import virtualenv # NOQA except ImportError : sh ( "%s -m pip install virtualenv" % PYTHON ) if not os . path . isdir ( "venv" ) : sh ( "%s -m virtualenv venv" % PYTHON ) sh ( "venv\\Scripts\\pip install -r %s" % ( REQUIREMENTS_TXT ) ) | Install venv + deps . | 95 | 7 |
244,115 | def compute_header_hmac_hash ( context ) : return hmac . new ( hashlib . sha512 ( b'\xff' * 8 + hashlib . sha512 ( context . _ . header . value . dynamic_header . master_seed . data + context . transformed_key + b'\x01' ) . digest ( ) ) . digest ( ) , context . _ . header . data , hashlib . sha256 ) . digest ( ) | Compute HMAC - SHA256 hash of header . Used to prevent header tampering . | 101 | 17 |
244,116 | def compute_payload_block_hash ( this ) : return hmac . new ( hashlib . sha512 ( struct . pack ( '<Q' , this . _index ) + hashlib . sha512 ( this . _ . _ . header . value . dynamic_header . master_seed . data + this . _ . transformed_key + b'\x01' ) . digest ( ) ) . digest ( ) , struct . pack ( '<Q' , this . _index ) +... | Compute hash of each payload block . Used to prevent payload corruption and tampering . | 144 | 16 |
244,117 | def decrypt ( self , block ) : if len ( block ) % 16 : raise ValueError ( "block size must be a multiple of 16" ) plaintext = b'' while block : a , b , c , d = struct . unpack ( "<4L" , block [ : 16 ] ) temp = [ a , b , c , d ] decrypt ( self . context , temp ) plaintext += struct . pack ( "<4L" , * temp ) block = bloc... | Decrypt blocks . | 107 | 4 |
244,118 | def encrypt ( self , block ) : if len ( block ) % 16 : raise ValueError ( "block size must be a multiple of 16" ) ciphertext = b'' while block : a , b , c , d = struct . unpack ( "<4L" , block [ 0 : 16 ] ) temp = [ a , b , c , d ] encrypt ( self . context , temp ) ciphertext += struct . pack ( "<4L" , * temp ) block = ... | Encrypt blocks . | 108 | 4 |
244,119 | def aes_kdf ( key , rounds , password = None , keyfile = None ) : cipher = AES . new ( key , AES . MODE_ECB ) key_composite = compute_key_composite ( password = password , keyfile = keyfile ) # get the number of rounds from the header and transform the key_composite transformed_key = key_composite for _ in range ( 0 , ... | Set up a context for AES128 - ECB encryption to find transformed_key | 125 | 15 |
244,120 | def compute_key_composite ( password = None , keyfile = None ) : # hash the password if password : password_composite = hashlib . sha256 ( password . encode ( 'utf-8' ) ) . digest ( ) else : password_composite = b'' # hash the keyfile if keyfile : # try to read XML keyfile try : with open ( keyfile , 'r' ) as f : tree ... | Compute composite key . Used in header verification and payload decryption . | 375 | 14 |
244,121 | def compute_master ( context ) : # combine the transformed key with the header master seed to find the master_key master_key = hashlib . sha256 ( context . _ . header . value . dynamic_header . master_seed . data + context . transformed_key ) . digest ( ) return master_key | Computes master key from transformed key and master seed . Used in payload decryption . | 67 | 17 |
244,122 | def Unprotect ( protected_stream_id , protected_stream_key , subcon ) : return Switch ( protected_stream_id , { 'arcfourvariant' : ARCFourVariantStream ( protected_stream_key , subcon ) , 'salsa20' : Salsa20Stream ( protected_stream_key , subcon ) , 'chacha20' : ChaCha20Stream ( protected_stream_key , subcon ) , } , de... | Select stream cipher based on protected_stream_id | 103 | 10 |
244,123 | def encrypt ( self , plaintext , n = '' ) : #self.ed = 'e' if chain is encrypting, 'd' if decrypting, # None if nothing happened with the chain yet #assert self.ed in ('e',None) # makes sure you don't encrypt with a cipher that has started decrypting self . ed = 'e' if self . mode == MODE_XTS : # data sequence number (... | Encrypt some plaintext | 138 | 5 |
244,124 | def decrypt ( self , ciphertext , n = '' ) : #self.ed = 'e' if chain is encrypting, 'd' if decrypting, # None if nothing happened with the chain yet #assert self.ed in ('d',None) # makes sure you don't decrypt with a cipher that has started encrypting self . ed = 'd' if self . mode == MODE_XTS : # data sequence number ... | Decrypt some ciphertext | 138 | 5 |
244,125 | def final ( self , style = 'pkcs7' ) : # TODO: after calling final, reset the IV? so the cipher is as good as new? assert self . mode not in ( MODE_XTS , MODE_CMAC ) # finalizing (=padding) doesn't make sense when in XTS or CMAC mode if self . ed == b'e' : # when the chain is in encryption mode, finalizing will pad the... | Finalizes the encryption by padding the cache | 282 | 8 |
244,126 | def _datetime_to_utc ( self , dt ) : if not dt . tzinfo : dt = dt . replace ( tzinfo = tz . gettz ( ) ) return dt . astimezone ( tz . gettz ( 'UTC' ) ) | Convert naive datetimes to UTC | 64 | 7 |
244,127 | def _encode_time ( self , value ) : if self . _kp . version >= ( 4 , 0 ) : diff_seconds = int ( ( self . _datetime_to_utc ( value ) - datetime ( year = 1 , month = 1 , day = 1 , tzinfo = tz . gettz ( 'UTC' ) ) ) . total_seconds ( ) ) return base64 . b64encode ( struct . pack ( '<Q' , diff_seconds ) ) . decode ( 'utf-8'... | Convert datetime to base64 or plaintext string | 139 | 11 |
244,128 | def _decode_time ( self , text ) : if self . _kp . version >= ( 4 , 0 ) : # decode KDBX4 date from b64 format try : return ( datetime ( year = 1 , month = 1 , day = 1 , tzinfo = tz . gettz ( 'UTC' ) ) + timedelta ( seconds = struct . unpack ( '<Q' , base64 . b64decode ( text ) ) [ 0 ] ) ) except BinasciiError : return ... | Convert base64 time or plaintext time to datetime | 171 | 12 |
244,129 | def fromrdd ( rdd , dims = None , nrecords = None , dtype = None , labels = None , ordered = False ) : from . images import Images from bolt . spark . array import BoltArraySpark if dims is None or dtype is None : item = rdd . values ( ) . first ( ) dtype = item . dtype dims = item . shape if nrecords is None : nrecord... | Load images from a Spark RDD . | 189 | 8 |
244,130 | def fromarray ( values , labels = None , npartitions = None , engine = None ) : from . images import Images import bolt if isinstance ( values , bolt . spark . array . BoltArraySpark ) : return Images ( values ) values = asarray ( values ) if values . ndim < 2 : raise ValueError ( 'Array for images must have at least 2... | Load images from an array . | 308 | 6 |
244,131 | def fromlist ( items , accessor = None , keys = None , dims = None , dtype = None , labels = None , npartitions = None , engine = None ) : if spark and isinstance ( engine , spark ) : nrecords = len ( items ) if keys : items = zip ( keys , items ) else : keys = [ ( i , ) for i in range ( nrecords ) ] items = zip ( keys... | Load images from a list of items using the given accessor . | 212 | 13 |
244,132 | def frompath ( path , accessor = None , ext = None , start = None , stop = None , recursive = False , npartitions = None , dims = None , dtype = None , labels = None , recount = False , engine = None , credentials = None ) : from thunder . readers import get_parallel_reader reader = get_parallel_reader ( path ) ( engin... | Load images from a path using the given accessor . | 296 | 11 |
244,133 | def fromtif ( path , ext = 'tif' , start = None , stop = None , recursive = False , nplanes = None , npartitions = None , labels = None , engine = None , credentials = None , discard_extra = False ) : from tifffile import TiffFile if nplanes is not None and nplanes <= 0 : raise ValueError ( 'nplanes must be positive if... | Loads images from single or multi - page TIF files . | 463 | 13 |
244,134 | def frompng ( path , ext = 'png' , start = None , stop = None , recursive = False , npartitions = None , labels = None , engine = None , credentials = None ) : from scipy . misc import imread def getarray ( idx_buffer_filename ) : idx , buf , _ = idx_buffer_filename fbuf = BytesIO ( buf ) yield ( idx , ) , imread ( fbu... | Load images from PNG files . | 147 | 6 |
244,135 | def fromrandom ( shape = ( 10 , 50 , 50 ) , npartitions = 1 , seed = 42 , engine = None ) : seed = hash ( seed ) def generate ( v ) : random . seed ( seed + v ) return random . randn ( * shape [ 1 : ] ) return fromlist ( range ( shape [ 0 ] ) , accessor = generate , npartitions = npartitions , engine = engine ) | Generate random image data . | 91 | 6 |
244,136 | def fromexample ( name = None , engine = None ) : datasets = [ 'mouse' , 'fish' ] if name is None : print ( 'Availiable example image datasets' ) for d in datasets : print ( '- ' + d ) return check_options ( name , datasets ) path = 's3n://thunder-sample-data/images/' + name if name == 'mouse' : data = frombinary ( pat... | Load example image data . | 162 | 5 |
244,137 | def unchunk ( self ) : if self . padding != len ( self . shape ) * ( 0 , ) : shape = self . values . shape arr = empty ( shape , dtype = object ) for inds in product ( * [ arange ( s ) for s in shape ] ) : slices = [ ] for i , p , n in zip ( inds , self . padding , shape ) : start = None if ( i == 0 or p == 0 ) else p ... | Reconstitute the chunked array back into a full ndarray . | 168 | 16 |
244,138 | def chunk ( arr , chunk_size = "150" , padding = None ) : plan , _ = LocalChunks . getplan ( chunk_size , arr . shape [ 1 : ] , arr . dtype ) plan = r_ [ arr . shape [ 0 ] , plan ] if padding is None : pad = arr . ndim * ( 0 , ) elif isinstance ( padding , int ) : pad = ( 0 , ) + ( arr . ndim - 1 ) * ( padding , ) else... | Created a chunked array from a full array and a chunk size . | 466 | 14 |
244,139 | def filter ( self , func ) : if self . mode == 'local' : reshaped = self . _align ( self . baseaxes ) filtered = asarray ( list ( filter ( func , reshaped ) ) ) if self . labels is not None : mask = asarray ( list ( map ( func , reshaped ) ) ) if self . mode == 'spark' : sort = False if self . labels is None else True ... | Filter array along an axis . | 282 | 6 |
244,140 | def map ( self , func , value_shape = None , dtype = None , with_keys = False ) : axis = self . baseaxes if self . mode == 'local' : axes = sorted ( tupleize ( axis ) ) key_shape = [ self . shape [ axis ] for axis in axes ] reshaped = self . _align ( axes , key_shape = key_shape ) if with_keys : keys = zip ( * unravel_... | Apply an array - > array function across an axis . | 363 | 11 |
244,141 | def _reduce ( self , func , axis = 0 ) : if self . mode == 'local' : axes = sorted ( tupleize ( axis ) ) # if the function is a ufunc, it can automatically handle reducing over multiple axes if isinstance ( func , ufunc ) : inshape ( self . shape , axes ) reduced = func . reduce ( self , axis = tuple ( axes ) ) else : ... | Reduce an array along an axis . | 243 | 8 |
244,142 | def element_wise ( self , other , op ) : if not isscalar ( other ) and not self . shape == other . shape : raise ValueError ( "shapes %s and %s must be equal" % ( self . shape , other . shape ) ) if not isscalar ( other ) and isinstance ( other , Data ) and not self . mode == other . mode : raise NotImplementedError if... | Apply an elementwise operation to data . | 324 | 8 |
244,143 | def clip ( self , min = None , max = None ) : return self . _constructor ( self . values . clip ( min = min , max = max ) ) . __finalize__ ( self ) | Clip values above and below . | 44 | 7 |
244,144 | def fromrdd ( rdd , nrecords = None , shape = None , index = None , labels = None , dtype = None , ordered = False ) : from . series import Series from bolt . spark . array import BoltArraySpark if index is None or dtype is None : item = rdd . values ( ) . first ( ) if index is None : index = range ( len ( item ) ) if ... | Load series data from a Spark RDD . | 251 | 9 |
244,145 | def fromarray ( values , index = None , labels = None , npartitions = None , engine = None ) : from . series import Series import bolt if isinstance ( values , bolt . spark . array . BoltArraySpark ) : return Series ( values ) values = asarray ( values ) if values . ndim < 2 : values = expand_dims ( values , 0 ) if ind... | Load series data from an array . | 241 | 7 |
244,146 | def fromlist ( items , accessor = None , index = None , labels = None , dtype = None , npartitions = None , engine = None ) : if spark and isinstance ( engine , spark ) : if dtype is None : dtype = accessor ( items [ 0 ] ) . dtype if accessor else items [ 0 ] . dtype nrecords = len ( items ) keys = map ( lambda k : ( k... | Load series data from a list with an optional accessor function . | 226 | 13 |
244,147 | def fromtext ( path , ext = 'txt' , dtype = 'float64' , skip = 0 , shape = None , index = None , labels = None , npartitions = None , engine = None , credentials = None ) : from thunder . readers import normalize_scheme , get_parallel_reader path = normalize_scheme ( path , ext ) if spark and isinstance ( engine , spar... | Loads series data from text files . | 365 | 8 |
244,148 | def frombinary ( path , ext = 'bin' , conf = 'conf.json' , dtype = None , shape = None , skip = 0 , index = None , labels = None , engine = None , credentials = None ) : shape , dtype = _binaryconfig ( path , conf , dtype , shape , credentials ) from thunder . readers import normalize_scheme , get_parallel_reader path ... | Load series data from flat binary files . | 528 | 8 |
244,149 | def _binaryconfig ( path , conf , dtype = None , shape = None , credentials = None ) : import json from thunder . readers import get_file_reader , FileNotFoundError reader = get_file_reader ( path ) ( credentials = credentials ) try : buf = reader . read ( path , filename = conf ) params = json . loads ( str ( buf . de... | Collects parameters to use for binary series loading . | 201 | 10 |
244,150 | def fromexample ( name = None , engine = None ) : import os import tempfile import shutil from boto . s3 . connection import S3Connection datasets = [ 'iris' , 'mouse' , 'fish' ] if name is None : print ( 'Availiable example series datasets' ) for d in datasets : print ( '- ' + d ) return check_options ( name , dataset... | Load example series data . | 285 | 5 |
244,151 | def tobinary ( series , path , prefix = 'series' , overwrite = False , credentials = None ) : from six import BytesIO from thunder . utils import check_path from thunder . writers import get_parallel_writer if not overwrite : check_path ( path , credentials = credentials ) overwrite = True def tobuffer ( kv ) : firstke... | Writes out data to binary format . | 353 | 8 |
244,152 | def write_config ( path , shape = None , dtype = None , name = "conf.json" , overwrite = True , credentials = None ) : import json from thunder . writers import get_file_writer writer = get_file_writer ( path ) conf = { 'shape' : shape , 'dtype' : str ( dtype ) } confwriter = writer ( path , name , overwrite = overwrit... | Write a conf . json file with required information to load Series binary data . | 136 | 15 |
244,153 | def toblocks ( self , chunk_size = 'auto' , padding = None ) : from thunder . blocks . blocks import Blocks from thunder . blocks . local import LocalChunks if self . mode == 'spark' : if chunk_size is 'auto' : chunk_size = str ( max ( [ int ( 1e5 / self . shape [ 0 ] ) , 1 ] ) ) chunks = self . values . chunk ( chunk_... | Convert to blocks which represent subdivisions of the images data . | 165 | 13 |
244,154 | def toseries ( self , chunk_size = 'auto' ) : from thunder . series . series import Series if chunk_size is 'auto' : chunk_size = str ( max ( [ int ( 1e5 / self . shape [ 0 ] ) , 1 ] ) ) n = len ( self . shape ) - 1 index = arange ( self . shape [ 0 ] ) if self . mode == 'spark' : return Series ( self . values . swap (... | Converts to series data . | 165 | 6 |
244,155 | def tospark ( self , engine = None ) : from thunder . images . readers import fromarray if self . mode == 'spark' : logging . getLogger ( 'thunder' ) . warn ( 'images already in spark mode' ) pass if engine is None : raise ValueError ( 'Must provide a SparkContext' ) return fromarray ( self . toarray ( ) , engine = eng... | Convert to distributed spark mode . | 86 | 7 |
244,156 | def foreach ( self , func ) : if self . mode == 'spark' : self . values . tordd ( ) . map ( lambda kv : ( kv [ 0 ] [ 0 ] , kv [ 1 ] ) ) . foreach ( func ) else : [ func ( kv ) for kv in enumerate ( self . values ) ] | Execute a function on each image . | 78 | 8 |
244,157 | def sample ( self , nsamples = 100 , seed = None ) : if nsamples < 1 : raise ValueError ( "Number of samples must be larger than 0, got '%g'" % nsamples ) if seed is None : seed = random . randint ( 0 , 2 ** 32 ) if self . mode == 'spark' : result = asarray ( self . values . tordd ( ) . values ( ) . takeSample ( False ... | Extract a random sample of images . | 158 | 8 |
244,158 | def var ( self ) : return self . _constructor ( self . values . var ( axis = 0 , keepdims = True ) ) | Compute the variance across images . | 30 | 7 |
244,159 | def std ( self ) : return self . _constructor ( self . values . std ( axis = 0 , keepdims = True ) ) | Compute the standard deviation across images . | 30 | 8 |
244,160 | def squeeze ( self ) : axis = tuple ( range ( 1 , len ( self . shape ) - 1 ) ) if prod ( self . shape [ 1 : ] ) == 1 else None return self . map ( lambda x : x . squeeze ( axis = axis ) ) | Remove single - dimensional axes from images . | 56 | 8 |
244,161 | def max_projection ( self , axis = 2 ) : if axis >= size ( self . value_shape ) : raise Exception ( 'Axis for projection (%s) exceeds ' 'image dimensions (%s-%s)' % ( axis , 0 , size ( self . value_shape ) - 1 ) ) new_value_shape = list ( self . value_shape ) del new_value_shape [ axis ] return self . map ( lambda x : ... | Compute maximum projections of images along a dimension . | 114 | 10 |
244,162 | def max_min_projection ( self , axis = 2 ) : if axis >= size ( self . value_shape ) : raise Exception ( 'Axis for projection (%s) exceeds ' 'image dimensions (%s-%s)' % ( axis , 0 , size ( self . value_shape ) - 1 ) ) new_value_shape = list ( self . value_shape ) del new_value_shape [ axis ] return self . map ( lambda ... | Compute maximum - minimum projection along a dimension . | 124 | 10 |
244,163 | def subsample ( self , factor ) : value_shape = self . value_shape ndims = len ( value_shape ) if not hasattr ( factor , '__len__' ) : factor = [ factor ] * ndims factor = [ int ( sf ) for sf in factor ] if any ( ( sf <= 0 for sf in factor ) ) : raise ValueError ( 'All sampling factors must be positive; got ' + str ( f... | Downsample images by an integer factor . | 205 | 8 |
244,164 | def gaussian_filter ( self , sigma = 2 , order = 0 ) : from scipy . ndimage . filters import gaussian_filter return self . map ( lambda v : gaussian_filter ( v , sigma , order ) , value_shape = self . value_shape ) | Spatially smooth images with a gaussian filter . | 64 | 11 |
244,165 | def _image_filter ( self , filter = None , size = 2 ) : from numpy import isscalar from scipy . ndimage . filters import median_filter , uniform_filter FILTERS = { 'median' : median_filter , 'uniform' : uniform_filter } func = FILTERS [ filter ] mode = self . mode value_shape = self . value_shape ndims = len ( value_sh... | Generic function for maping a filtering operation over images . | 252 | 11 |
244,166 | def localcorr ( self , size = 2 ) : from thunder . images . readers import fromarray , fromrdd from numpy import corrcoef , concatenate nimages = self . shape [ 0 ] # spatially average the original image set over the specified neighborhood blurred = self . uniform_filter ( size ) # union the averaged images with the or... | Correlate every pixel in an image sequence to the average of its local neighborhood . | 251 | 17 |
244,167 | def subtract ( self , val ) : if isinstance ( val , ndarray ) : if val . shape != self . value_shape : raise Exception ( 'Cannot subtract image with dimensions %s ' 'from images with dimension %s' % ( str ( val . shape ) , str ( self . value_shape ) ) ) return self . map ( lambda x : x - val , value_shape = self . valu... | Subtract a constant value or an image from all images . | 92 | 13 |
244,168 | def topng ( self , path , prefix = 'image' , overwrite = False ) : from thunder . images . writers import topng # TODO add back colormap and vmin/vmax topng ( self , path , prefix = prefix , overwrite = overwrite ) | Write 2d images as PNG files . | 57 | 8 |
244,169 | def map_as_series ( self , func , value_size = None , dtype = None , chunk_size = 'auto' ) : blocks = self . toblocks ( chunk_size = chunk_size ) if value_size is not None : dims = list ( blocks . blockshape ) dims [ 0 ] = value_size else : dims = None def f ( block ) : return apply_along_axis ( func , 0 , block ) retu... | Efficiently apply a function to images as series data . | 124 | 12 |
244,170 | def count ( self ) : if self . mode == 'spark' : return self . tordd ( ) . count ( ) if self . mode == 'local' : return prod ( self . values . values . shape ) | Explicit count of the number of items . | 48 | 9 |
244,171 | def collect_blocks ( self ) : if self . mode == 'spark' : return self . values . tordd ( ) . sortByKey ( ) . values ( ) . collect ( ) if self . mode == 'local' : return self . values . values . flatten ( ) . tolist ( ) | Collect the blocks in a list | 67 | 6 |
244,172 | def map ( self , func , value_shape = None , dtype = None ) : mapped = self . values . map ( func , value_shape = value_shape , dtype = dtype ) return self . _constructor ( mapped ) . __finalize__ ( self , noprop = ( 'dtype' , ) ) | Apply an array - > array function to each block | 72 | 10 |
244,173 | def toimages ( self ) : from thunder . images . images import Images if self . mode == 'spark' : values = self . values . values_to_keys ( ( 0 , ) ) . unchunk ( ) if self . mode == 'local' : values = self . values . unchunk ( ) return Images ( values ) | Convert blocks to images . | 71 | 6 |
244,174 | def toseries ( self ) : from thunder . series . series import Series if self . mode == 'spark' : values = self . values . values_to_keys ( tuple ( range ( 1 , len ( self . shape ) ) ) ) . unchunk ( ) if self . mode == 'local' : values = self . values . unchunk ( ) values = rollaxis ( values , 0 , values . ndim ) return... | Converts blocks to series . | 95 | 6 |
244,175 | def toarray ( self ) : if self . mode == 'spark' : return self . values . unchunk ( ) . toarray ( ) if self . mode == 'local' : return self . values . unchunk ( ) | Convert blocks to local ndarray | 49 | 8 |
244,176 | def flatten ( self ) : size = prod ( self . shape [ : - 1 ] ) return self . reshape ( size , self . shape [ - 1 ] ) | Reshape all dimensions but the last into a single dimension | 36 | 12 |
244,177 | def tospark ( self , engine = None ) : from thunder . series . readers import fromarray if self . mode == 'spark' : logging . getLogger ( 'thunder' ) . warn ( 'images already in local mode' ) pass if engine is None : raise ValueError ( 'Must provide SparkContext' ) return fromarray ( self . toarray ( ) , index = self .... | Convert to spark mode . | 97 | 6 |
244,178 | def sample ( self , n = 100 , seed = None ) : if n < 1 : raise ValueError ( "Number of samples must be larger than 0, got '%g'" % n ) if seed is None : seed = random . randint ( 0 , 2 ** 32 ) if self . mode == 'spark' : result = asarray ( self . values . tordd ( ) . values ( ) . takeSample ( False , n , seed ) ) else :... | Extract random sample of records . | 199 | 7 |
244,179 | def map ( self , func , index = None , value_shape = None , dtype = None , with_keys = False ) : # if new index is given, can infer missing value_shape if value_shape is None and index is not None : value_shape = len ( index ) if isinstance ( value_shape , int ) : values_shape = ( value_shape , ) new = super ( Series ,... | Map an array - > array function over each record . | 171 | 11 |
244,180 | def mean ( self ) : return self . _constructor ( self . values . mean ( axis = self . baseaxes , keepdims = True ) ) | Compute the mean across records | 34 | 6 |
244,181 | def sum ( self ) : return self . _constructor ( self . values . sum ( axis = self . baseaxes , keepdims = True ) ) | Compute the sum across records . | 34 | 7 |
244,182 | def max ( self ) : return self . _constructor ( self . values . max ( axis = self . baseaxes , keepdims = True ) ) | Compute the max across records . | 34 | 7 |
244,183 | def min ( self ) : return self . _constructor ( self . values . min ( axis = self . baseaxes , keepdims = True ) ) | Compute the min across records . | 34 | 7 |
244,184 | def reshape ( self , * shape ) : if prod ( self . shape ) != prod ( shape ) : raise ValueError ( "Reshaping must leave the number of elements unchanged" ) if self . shape [ - 1 ] != shape [ - 1 ] : raise ValueError ( "Reshaping cannot change the size of the constituent series (last dimension)" ) if self . labels is not... | Reshape the Series object | 153 | 6 |
244,185 | def between ( self , left , right ) : crit = lambda x : left <= x < right return self . select ( crit ) | Select subset of values within the given index range . | 27 | 10 |
244,186 | def select ( self , crit ) : import types # handle lists, strings, and ints if not isinstance ( crit , types . FunctionType ) : # set("foo") -> {"f", "o"}; wrap in list to prevent: if isinstance ( crit , string_types ) : critlist = set ( [ crit ] ) else : try : critlist = set ( crit ) except TypeError : # typically mea... | Select subset of values that match a given index criterion . | 386 | 11 |
244,187 | def center ( self , axis = 1 ) : if axis == 1 : return self . map ( lambda x : x - mean ( x ) ) elif axis == 0 : meanval = self . mean ( ) . toarray ( ) return self . map ( lambda x : x - meanval ) else : raise Exception ( 'Axis must be 0 or 1' ) | Subtract the mean either within or across records . | 77 | 11 |
244,188 | def standardize ( self , axis = 1 ) : if axis == 1 : return self . map ( lambda x : x / std ( x ) ) elif axis == 0 : stdval = self . std ( ) . toarray ( ) return self . map ( lambda x : x / stdval ) else : raise Exception ( 'Axis must be 0 or 1' ) | Divide by standard deviation either within or across records . | 78 | 11 |
244,189 | def zscore ( self , axis = 1 ) : if axis == 1 : return self . map ( lambda x : ( x - mean ( x ) ) / std ( x ) ) elif axis == 0 : meanval = self . mean ( ) . toarray ( ) stdval = self . std ( ) . toarray ( ) return self . map ( lambda x : ( x - meanval ) / stdval ) else : raise Exception ( 'Axis must be 0 or 1' ) | Subtract the mean and divide by standard deviation within or across records . | 103 | 15 |
244,190 | def squelch ( self , threshold ) : func = lambda x : zeros ( x . shape ) if max ( x ) < threshold else x return self . map ( func ) | Set all records that do not exceed the given threhsold to 0 . | 38 | 16 |
244,191 | def correlate ( self , signal ) : s = asarray ( signal ) if s . ndim == 1 : if size ( s ) != self . shape [ - 1 ] : raise ValueError ( "Length of signal '%g' does not match record length '%g'" % ( size ( s ) , self . shape [ - 1 ] ) ) return self . map ( lambda x : corrcoef ( x , s ) [ 0 , 1 ] , index = [ 1 ] ) elif s ... | Correlate records against one or many one - dimensional arrays . | 238 | 13 |
244,192 | def _check_panel ( self , length ) : n = len ( self . index ) if divmod ( n , length ) [ 1 ] != 0 : raise ValueError ( "Panel length '%g' must evenly divide length of series '%g'" % ( length , n ) ) if n == length : raise ValueError ( "Panel length '%g' cannot be length of series '%g'" % ( length , n ) ) | Check that given fixed panel length evenly divides index . | 93 | 10 |
244,193 | def mean_by_panel ( self , length ) : self . _check_panel ( length ) func = lambda v : v . reshape ( - 1 , length ) . mean ( axis = 0 ) newindex = arange ( length ) return self . map ( func , index = newindex ) | Compute the mean across fixed sized panels of each record . | 63 | 12 |
244,194 | def _makemasks ( self , index = None , level = 0 ) : if index is None : index = self . index try : dims = len ( array ( index ) . shape ) if dims == 1 : index = array ( index , ndmin = 2 ) . T except : raise TypeError ( 'A multi-index must be convertible to a numpy ndarray' ) try : index = index [ : , level ] except : ... | Internal function for generating masks for selecting values based on multi - index values . | 247 | 15 |
244,195 | def _map_by_index ( self , function , level = 0 ) : if type ( level ) is int : level = [ level ] masks , ind = self . _makemasks ( index = self . index , level = level ) nMasks = len ( masks ) newindex = array ( ind ) if len ( newindex [ 0 ] ) == 1 : newindex = ravel ( newindex ) return self . map ( lambda v : asarray ... | An internal function for maping a function to groups of values based on a multi - index | 129 | 18 |
244,196 | def aggregate_by_index ( self , function , level = 0 ) : result = self . _map_by_index ( function , level = level ) return result . map ( lambda v : array ( v ) , index = result . index ) | Aggregrate data in each record grouping by index values . | 52 | 13 |
244,197 | def gramian ( self ) : if self . mode == 'spark' : rdd = self . values . tordd ( ) from pyspark . accumulators import AccumulatorParam class MatrixAccumulator ( AccumulatorParam ) : def zero ( self , value ) : return zeros ( shape ( value ) ) def addInPlace ( self , val1 , val2 ) : val1 += val2 return val1 global mat i... | Compute gramian of a distributed matrix . | 218 | 9 |
244,198 | def times ( self , other ) : if isinstance ( other , ScalarType ) : other = asarray ( other ) index = self . index else : if isinstance ( other , list ) : other = asarray ( other ) if isinstance ( other , ndarray ) and other . ndim < 2 : other = expand_dims ( other , 1 ) if not self . shape [ 1 ] == other . shape [ 0 ]... | Multiply a matrix by another one . | 366 | 9 |
244,199 | def _makewindows ( self , indices , window ) : div = divmod ( window , 2 ) before = div [ 0 ] after = div [ 0 ] + div [ 1 ] index = asarray ( self . index ) indices = asarray ( indices ) if where ( index == max ( indices ) ) [ 0 ] [ 0 ] + after > len ( index ) : raise ValueError ( "Maximum requested index %g, with wind... | Make masks used by windowing functions | 216 | 7 |
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