idx int64 0 251k | question stringlengths 53 3.53k | target stringlengths 5 1.23k | len_question int64 20 893 | len_target int64 3 238 |
|---|---|---|---|---|
243,800 | def tar_open ( f ) : if isinstance ( f , six . string_types ) : return tarfile . open ( name = f ) else : return tarfile . open ( fileobj = f ) | Open either a filename or a file - like object as a TarFile . | 44 | 15 |
243,801 | def copy_from_server_to_local ( dataset_remote_dir , dataset_local_dir , remote_fname , local_fname ) : log . debug ( "Copying file `{}` to a local directory `{}`." . format ( remote_fname , dataset_local_dir ) ) head , tail = os . path . split ( local_fname ) head += os . path . sep if not os . path . exists ( head ) : os . makedirs ( os . path . dirname ( head ) ) shutil . copyfile ( remote_fname , local_fname ) # Copy the original group id and file permission st = os . stat ( remote_fname ) os . chmod ( local_fname , st . st_mode ) # If the user have read access to the data, but not a member # of the group, he can't set the group. So we must catch the # exception. But we still want to do this, for directory where # only member of the group can read that data. try : os . chown ( local_fname , - 1 , st . st_gid ) except OSError : pass # Need to give group write permission to the folders # For the locking mechanism # Try to set the original group as above dirs = os . path . dirname ( local_fname ) . replace ( dataset_local_dir , '' ) sep = dirs . split ( os . path . sep ) if sep [ 0 ] == "" : sep = sep [ 1 : ] for i in range ( len ( sep ) ) : orig_p = os . path . join ( dataset_remote_dir , * sep [ : i + 1 ] ) new_p = os . path . join ( dataset_local_dir , * sep [ : i + 1 ] ) orig_st = os . stat ( orig_p ) new_st = os . stat ( new_p ) if not new_st . st_mode & stat . S_IWGRP : os . chmod ( new_p , new_st . st_mode | stat . S_IWGRP ) if orig_st . st_gid != new_st . st_gid : try : os . chown ( new_p , - 1 , orig_st . st_gid ) except OSError : pass | Copies a remote file locally . | 514 | 7 |
243,802 | def convert_to_one_hot ( y ) : max_value = max ( y ) min_value = min ( y ) length = len ( y ) one_hot = numpy . zeros ( ( length , ( max_value - min_value + 1 ) ) ) one_hot [ numpy . arange ( length ) , y ] = 1 return one_hot | converts y into one hot reprsentation . | 81 | 10 |
243,803 | def convert_binarized_mnist ( directory , output_directory , output_filename = 'binarized_mnist.hdf5' ) : output_path = os . path . join ( output_directory , output_filename ) h5file = h5py . File ( output_path , mode = 'w' ) train_set = numpy . loadtxt ( os . path . join ( directory , TRAIN_FILE ) ) . reshape ( ( - 1 , 1 , 28 , 28 ) ) . astype ( 'uint8' ) valid_set = numpy . loadtxt ( os . path . join ( directory , VALID_FILE ) ) . reshape ( ( - 1 , 1 , 28 , 28 ) ) . astype ( 'uint8' ) test_set = numpy . loadtxt ( os . path . join ( directory , TEST_FILE ) ) . reshape ( ( - 1 , 1 , 28 , 28 ) ) . astype ( 'uint8' ) data = ( ( 'train' , 'features' , train_set ) , ( 'valid' , 'features' , valid_set ) , ( 'test' , 'features' , test_set ) ) fill_hdf5_file ( h5file , data ) for i , label in enumerate ( ( 'batch' , 'channel' , 'height' , 'width' ) ) : h5file [ 'features' ] . dims [ i ] . label = label h5file . flush ( ) h5file . close ( ) return ( output_path , ) | Converts the binarized MNIST dataset to HDF5 . | 340 | 14 |
243,804 | def fill_subparser ( subparser ) : url = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' filename = 'cifar-10-python.tar.gz' subparser . set_defaults ( urls = [ url ] , filenames = [ filename ] ) return default_downloader | Sets up a subparser to download the CIFAR - 10 dataset file . | 83 | 17 |
243,805 | def convert_celeba_aligned_cropped ( directory , output_directory , output_filename = OUTPUT_FILENAME ) : output_path = os . path . join ( output_directory , output_filename ) h5file = _initialize_conversion ( directory , output_path , ( 218 , 178 ) ) features_dataset = h5file [ 'features' ] image_file_path = os . path . join ( directory , IMAGE_FILE ) with zipfile . ZipFile ( image_file_path , 'r' ) as image_file : with progress_bar ( 'images' , NUM_EXAMPLES ) as bar : for i in range ( NUM_EXAMPLES ) : image_name = 'img_align_celeba/{:06d}.jpg' . format ( i + 1 ) features_dataset [ i ] = numpy . asarray ( Image . open ( image_file . open ( image_name , 'r' ) ) ) . transpose ( 2 , 0 , 1 ) bar . update ( i + 1 ) h5file . flush ( ) h5file . close ( ) return ( output_path , ) | Converts the aligned and cropped CelebA dataset to HDF5 . | 257 | 14 |
243,806 | def convert_celeba ( which_format , directory , output_directory , output_filename = None ) : if which_format not in ( 'aligned_cropped' , '64' ) : raise ValueError ( "CelebA format needs to be either " "'aligned_cropped' or '64'." ) if not output_filename : output_filename = 'celeba_{}.hdf5' . format ( which_format ) if which_format == 'aligned_cropped' : return convert_celeba_aligned_cropped ( directory , output_directory , output_filename ) else : return convert_celeba_64 ( directory , output_directory , output_filename ) | Converts the CelebA dataset to HDF5 . | 146 | 11 |
243,807 | def disk_usage ( path ) : st = os . statvfs ( path ) total = st . f_blocks * st . f_frsize used = ( st . f_blocks - st . f_bfree ) * st . f_frsize return total , used | Return free usage about the given path in bytes . | 61 | 10 |
243,808 | def safe_mkdir ( folder_name , force_perm = None ) : if os . path . exists ( folder_name ) : return intermediary_folders = folder_name . split ( os . path . sep ) # Remove invalid elements from intermediary_folders if intermediary_folders [ - 1 ] == "" : intermediary_folders = intermediary_folders [ : - 1 ] if force_perm : force_perm_path = folder_name . split ( os . path . sep ) if force_perm_path [ - 1 ] == "" : force_perm_path = force_perm_path [ : - 1 ] for i in range ( 1 , len ( intermediary_folders ) ) : folder_to_create = os . path . sep . join ( intermediary_folders [ : i + 1 ] ) if os . path . exists ( folder_to_create ) : continue os . mkdir ( folder_to_create ) if force_perm : os . chmod ( folder_to_create , force_perm ) | Create the specified folder . | 221 | 5 |
243,809 | def check_enough_space ( dataset_local_dir , remote_fname , local_fname , max_disk_usage = 0.9 ) : storage_need = os . path . getsize ( remote_fname ) storage_total , storage_used = disk_usage ( dataset_local_dir ) # Instead of only looking if there's enough space, we ensure we do not # go over max disk usage level to avoid filling the disk/partition return ( ( storage_used + storage_need ) < ( storage_total * max_disk_usage ) ) | Check if the given local folder has enough space . | 123 | 10 |
243,810 | def convert_cifar100 ( directory , output_directory , output_filename = 'cifar100.hdf5' ) : output_path = os . path . join ( output_directory , output_filename ) h5file = h5py . File ( output_path , mode = "w" ) input_file = os . path . join ( directory , 'cifar-100-python.tar.gz' ) tar_file = tarfile . open ( input_file , 'r:gz' ) file = tar_file . extractfile ( 'cifar-100-python/train' ) try : if six . PY3 : train = cPickle . load ( file , encoding = 'latin1' ) else : train = cPickle . load ( file ) finally : file . close ( ) train_features = train [ 'data' ] . reshape ( train [ 'data' ] . shape [ 0 ] , 3 , 32 , 32 ) train_coarse_labels = numpy . array ( train [ 'coarse_labels' ] , dtype = numpy . uint8 ) train_fine_labels = numpy . array ( train [ 'fine_labels' ] , dtype = numpy . uint8 ) file = tar_file . extractfile ( 'cifar-100-python/test' ) try : if six . PY3 : test = cPickle . load ( file , encoding = 'latin1' ) else : test = cPickle . load ( file ) finally : file . close ( ) test_features = test [ 'data' ] . reshape ( test [ 'data' ] . shape [ 0 ] , 3 , 32 , 32 ) test_coarse_labels = numpy . array ( test [ 'coarse_labels' ] , dtype = numpy . uint8 ) test_fine_labels = numpy . array ( test [ 'fine_labels' ] , dtype = numpy . uint8 ) data = ( ( 'train' , 'features' , train_features ) , ( 'train' , 'coarse_labels' , train_coarse_labels . reshape ( ( - 1 , 1 ) ) ) , ( 'train' , 'fine_labels' , train_fine_labels . reshape ( ( - 1 , 1 ) ) ) , ( 'test' , 'features' , test_features ) , ( 'test' , 'coarse_labels' , test_coarse_labels . reshape ( ( - 1 , 1 ) ) ) , ( 'test' , 'fine_labels' , test_fine_labels . reshape ( ( - 1 , 1 ) ) ) ) fill_hdf5_file ( h5file , data ) h5file [ 'features' ] . dims [ 0 ] . label = 'batch' h5file [ 'features' ] . dims [ 1 ] . label = 'channel' h5file [ 'features' ] . dims [ 2 ] . label = 'height' h5file [ 'features' ] . dims [ 3 ] . label = 'width' h5file [ 'coarse_labels' ] . dims [ 0 ] . label = 'batch' h5file [ 'coarse_labels' ] . dims [ 1 ] . label = 'index' h5file [ 'fine_labels' ] . dims [ 0 ] . label = 'batch' h5file [ 'fine_labels' ] . dims [ 1 ] . label = 'index' h5file . flush ( ) h5file . close ( ) return ( output_path , ) | Converts the CIFAR - 100 dataset to HDF5 . | 807 | 14 |
243,811 | def verify_axis_labels ( self , expected , actual , source_name ) : if not getattr ( self , '_checked_axis_labels' , False ) : self . _checked_axis_labels = defaultdict ( bool ) if not self . _checked_axis_labels [ source_name ] : if actual is None : log . warning ( "%s instance could not verify (missing) axis " "expected %s, got None" , self . __class__ . __name__ , expected ) else : if expected != actual : raise AxisLabelsMismatchError ( "{} expected axis labels " "{}, got {} instead" . format ( self . __class__ . __name__ , expected , actual ) ) self . _checked_axis_labels [ source_name ] = True | Verify that axis labels for a given source are as expected . | 174 | 13 |
243,812 | def get_data ( self , request = None ) : if request is None : raise ValueError data = [ [ ] for _ in self . sources ] for i in range ( request ) : try : for source_data , example in zip ( data , next ( self . child_epoch_iterator ) ) : source_data . append ( example ) except StopIteration : # If some data has been extracted and `strict` is not set, # we should spit out this data before stopping iteration. if not self . strictness and data [ 0 ] : break elif self . strictness > 1 and data [ 0 ] : raise ValueError raise return tuple ( numpy . asarray ( source_data ) for source_data in data ) | Get data from the dataset . | 157 | 6 |
243,813 | def _producer_wrapper ( f , port , addr = 'tcp://127.0.0.1' ) : try : context = zmq . Context ( ) socket = context . socket ( zmq . PUSH ) socket . connect ( ':' . join ( [ addr , str ( port ) ] ) ) f ( socket ) finally : # Works around a Python 3.x bug. context . destroy ( ) | A shim that sets up a socket and starts the producer callable . | 91 | 15 |
243,814 | def _spawn_producer ( f , port , addr = 'tcp://127.0.0.1' ) : process = Process ( target = _producer_wrapper , args = ( f , port , addr ) ) process . start ( ) return process | Start a process that sends results on a PUSH socket . | 56 | 12 |
243,815 | def producer_consumer ( producer , consumer , addr = 'tcp://127.0.0.1' , port = None , context = None ) : context_created = False if context is None : context_created = True context = zmq . Context ( ) try : consumer_socket = context . socket ( zmq . PULL ) if port is None : port = consumer_socket . bind_to_random_port ( addr ) try : process = _spawn_producer ( producer , port ) result = consumer ( consumer_socket ) finally : process . terminate ( ) return result finally : # Works around a Python 3.x bug. if context_created : context . destroy ( ) | A producer - consumer pattern . | 148 | 6 |
243,816 | def main ( args = None ) : built_in_datasets = dict ( downloaders . all_downloaders ) if fuel . config . extra_downloaders : for name in fuel . config . extra_downloaders : extra_datasets = dict ( importlib . import_module ( name ) . all_downloaders ) if any ( key in built_in_datasets for key in extra_datasets . keys ( ) ) : raise ValueError ( 'extra downloaders conflict in name with ' 'built-in downloaders' ) built_in_datasets . update ( extra_datasets ) parser = argparse . ArgumentParser ( description = 'Download script for built-in datasets.' ) parent_parser = argparse . ArgumentParser ( add_help = False ) parent_parser . add_argument ( "-d" , "--directory" , help = "where to save the downloaded files" , type = str , default = os . getcwd ( ) ) parent_parser . add_argument ( "--clear" , help = "clear the downloaded files" , action = 'store_true' ) subparsers = parser . add_subparsers ( ) download_functions = { } for name , fill_subparser in built_in_datasets . items ( ) : subparser = subparsers . add_parser ( name , parents = [ parent_parser ] , help = 'Download the {} dataset' . format ( name ) ) # Allows the parser to know which subparser was called. subparser . set_defaults ( which_ = name ) download_functions [ name ] = fill_subparser ( subparser ) args = parser . parse_args ( ) args_dict = vars ( args ) download_function = download_functions [ args_dict . pop ( 'which_' ) ] try : download_function ( * * args_dict ) except NeedURLPrefix : parser . error ( url_prefix_message ) | Entry point for fuel - download script . | 427 | 8 |
243,817 | def fill_subparser ( subparser ) : filenames = [ 'train-images-idx3-ubyte.gz' , 'train-labels-idx1-ubyte.gz' , 't10k-images-idx3-ubyte.gz' , 't10k-labels-idx1-ubyte.gz' ] urls = [ 'http://yann.lecun.com/exdb/mnist/' + f for f in filenames ] subparser . set_defaults ( urls = urls , filenames = filenames ) return default_downloader | Sets up a subparser to download the MNIST dataset files . | 144 | 14 |
243,818 | def main ( args = None ) : parser = argparse . ArgumentParser ( description = 'Extracts metadata from a Fuel-converted HDF5 file.' ) parser . add_argument ( "filename" , help = "HDF5 file to analyze" ) args = parser . parse_args ( ) with h5py . File ( args . filename , 'r' ) as h5file : interface_version = h5file . attrs . get ( 'h5py_interface_version' , 'N/A' ) fuel_convert_version = h5file . attrs . get ( 'fuel_convert_version' , 'N/A' ) fuel_convert_command = h5file . attrs . get ( 'fuel_convert_command' , 'N/A' ) message_prefix = message_prefix_template . format ( os . path . basename ( args . filename ) ) message_body = message_body_template . format ( fuel_convert_command , interface_version , fuel_convert_version ) message = '' . join ( [ '\n' , message_prefix , '\n' , '=' * len ( message_prefix ) , message_body ] ) print ( message ) | Entry point for fuel - info script . | 270 | 8 |
243,819 | def convert_silhouettes ( size , directory , output_directory , output_filename = None ) : if size not in ( 16 , 28 ) : raise ValueError ( 'size must be 16 or 28' ) if output_filename is None : output_filename = 'caltech101_silhouettes{}.hdf5' . format ( size ) output_file = os . path . join ( output_directory , output_filename ) input_file = 'caltech101_silhouettes_{}_split1.mat' . format ( size ) input_file = os . path . join ( directory , input_file ) if not os . path . isfile ( input_file ) : raise MissingInputFiles ( 'Required files missing' , [ input_file ] ) with h5py . File ( output_file , mode = "w" ) as h5file : mat = loadmat ( input_file ) train_features = mat [ 'train_data' ] . reshape ( [ - 1 , 1 , size , size ] ) train_targets = mat [ 'train_labels' ] valid_features = mat [ 'val_data' ] . reshape ( [ - 1 , 1 , size , size ] ) valid_targets = mat [ 'val_labels' ] test_features = mat [ 'test_data' ] . reshape ( [ - 1 , 1 , size , size ] ) test_targets = mat [ 'test_labels' ] data = ( ( 'train' , 'features' , train_features ) , ( 'train' , 'targets' , train_targets ) , ( 'valid' , 'features' , valid_features ) , ( 'valid' , 'targets' , valid_targets ) , ( 'test' , 'features' , test_features ) , ( 'test' , 'targets' , test_targets ) , ) fill_hdf5_file ( h5file , data ) for i , label in enumerate ( ( 'batch' , 'channel' , 'height' , 'width' ) ) : h5file [ 'features' ] . dims [ i ] . label = label for i , label in enumerate ( ( 'batch' , 'index' ) ) : h5file [ 'targets' ] . dims [ i ] . label = label return ( output_file , ) | Convert the CalTech 101 Silhouettes Datasets . | 527 | 13 |
243,820 | def cross_validation ( scheme_class , num_examples , num_folds , strict = True , * * kwargs ) : if strict and num_examples % num_folds != 0 : raise ValueError ( ( "{} examples are not divisible in {} evenly-sized " + "folds. To allow this, have a look at the " + "`strict` argument." ) . format ( num_examples , num_folds ) ) for i in xrange ( num_folds ) : begin = num_examples * i // num_folds end = num_examples * ( i + 1 ) // num_folds train = scheme_class ( list ( chain ( xrange ( 0 , begin ) , xrange ( end , num_examples ) ) ) , * * kwargs ) valid = scheme_class ( xrange ( begin , end ) , * * kwargs ) if strict : yield ( train , valid ) else : yield ( train , valid , end - begin ) | Return pairs of schemes to be used for cross - validation . | 222 | 12 |
243,821 | def main ( args = None ) : built_in_datasets = dict ( converters . all_converters ) if fuel . config . extra_converters : for name in fuel . config . extra_converters : extra_datasets = dict ( importlib . import_module ( name ) . all_converters ) if any ( key in built_in_datasets for key in extra_datasets . keys ( ) ) : raise ValueError ( 'extra converters conflict in name with ' 'built-in converters' ) built_in_datasets . update ( extra_datasets ) parser = argparse . ArgumentParser ( description = 'Conversion script for built-in datasets.' ) subparsers = parser . add_subparsers ( ) parent_parser = argparse . ArgumentParser ( add_help = False ) parent_parser . add_argument ( "-d" , "--directory" , help = "directory in which input files reside" , type = str , default = os . getcwd ( ) ) convert_functions = { } for name , fill_subparser in built_in_datasets . items ( ) : subparser = subparsers . add_parser ( name , parents = [ parent_parser ] , help = 'Convert the {} dataset' . format ( name ) ) subparser . add_argument ( "-o" , "--output-directory" , help = "where to save the dataset" , type = str , default = os . getcwd ( ) , action = CheckDirectoryAction ) subparser . add_argument ( "-r" , "--output_filename" , help = "new name of the created dataset" , type = str , default = None ) # Allows the parser to know which subparser was called. subparser . set_defaults ( which_ = name ) convert_functions [ name ] = fill_subparser ( subparser ) args = parser . parse_args ( args ) args_dict = vars ( args ) if args_dict [ 'output_filename' ] is not None and os . path . splitext ( args_dict [ 'output_filename' ] ) [ 1 ] not in ( '.hdf5' , '.hdf' , '.h5' ) : args_dict [ 'output_filename' ] += '.hdf5' if args_dict [ 'output_filename' ] is None : args_dict . pop ( 'output_filename' ) convert_function = convert_functions [ args_dict . pop ( 'which_' ) ] try : output_paths = convert_function ( * * args_dict ) except MissingInputFiles as e : intro = "The following required files were not found:\n" message = "\n" . join ( [ intro ] + [ " * " + f for f in e . filenames ] ) message += "\n\nDid you forget to run fuel-download?" parser . error ( message ) # Tag the newly-created file(s) with H5PYDataset version and command-line # options for output_path in output_paths : h5file = h5py . File ( output_path , 'a' ) interface_version = H5PYDataset . interface_version . encode ( 'utf-8' ) h5file . attrs [ 'h5py_interface_version' ] = interface_version fuel_convert_version = converters . __version__ . encode ( 'utf-8' ) h5file . attrs [ 'fuel_convert_version' ] = fuel_convert_version command = [ os . path . basename ( sys . argv [ 0 ] ) ] + sys . argv [ 1 : ] h5file . attrs [ 'fuel_convert_command' ] = ( ' ' . join ( command ) . encode ( 'utf-8' ) ) h5file . flush ( ) h5file . close ( ) | Entry point for fuel - convert script . | 865 | 8 |
243,822 | def refresh_lock ( lock_file ) : unique_id = '%s_%s_%s' % ( os . getpid ( ) , '' . join ( [ str ( random . randint ( 0 , 9 ) ) for i in range ( 10 ) ] ) , hostname ) try : lock_write = open ( lock_file , 'w' ) lock_write . write ( unique_id + '\n' ) lock_write . close ( ) except Exception : # In some strange case, this happen. To prevent all tests # from failing, we release the lock, but as there is a # problem, we still keep the original exception. # This way, only 1 test would fail. while get_lock . n_lock > 0 : release_lock ( ) raise return unique_id | Refresh an existing lock . | 172 | 6 |
243,823 | def get_lock ( lock_dir , * * kw ) : if not hasattr ( get_lock , 'n_lock' ) : # Initialization. get_lock . n_lock = 0 if not hasattr ( get_lock , 'lock_is_enabled' ) : # Enable lock by default. get_lock . lock_is_enabled = True get_lock . lock_dir = lock_dir get_lock . unlocker = Unlocker ( get_lock . lock_dir ) else : if lock_dir != get_lock . lock_dir : # Compilation directory has changed. # First ensure all old locks were released. assert get_lock . n_lock == 0 # Update members for new compilation directory. get_lock . lock_dir = lock_dir get_lock . unlocker = Unlocker ( get_lock . lock_dir ) if get_lock . lock_is_enabled : # Only really try to acquire the lock if we do not have it already. if get_lock . n_lock == 0 : lock ( get_lock . lock_dir , * * kw ) atexit . register ( Unlocker . unlock , get_lock . unlocker ) # Store time at which the lock was set. get_lock . start_time = time . time ( ) else : # Check whether we need to 'refresh' the lock. We do this # every 'config.compile.timeout / 2' seconds to ensure # no one else tries to override our lock after their # 'config.compile.timeout' timeout period. if get_lock . start_time is None : # This should not happen. So if this happen, clean up # the lock state and raise an error. while get_lock . n_lock > 0 : release_lock ( ) raise Exception ( "For some unknow reason, the lock was already taken," " but no start time was registered." ) now = time . time ( ) if now - get_lock . start_time > TIMEOUT : lockpath = os . path . join ( get_lock . lock_dir , 'lock' ) logger . info ( 'Refreshing lock %s' , str ( lockpath ) ) refresh_lock ( lockpath ) get_lock . start_time = now get_lock . n_lock += 1 | Obtain lock on compilation directory . | 494 | 7 |
243,824 | def release_lock ( ) : get_lock . n_lock -= 1 assert get_lock . n_lock >= 0 # Only really release lock once all lock requests have ended. if get_lock . lock_is_enabled and get_lock . n_lock == 0 : get_lock . start_time = None get_lock . unlocker . unlock ( ) | Release lock on compilation directory . | 78 | 6 |
243,825 | def release_readlock ( lockdir_name ) : # Make sure the lock still exists before deleting it if os . path . exists ( lockdir_name ) and os . path . isdir ( lockdir_name ) : os . rmdir ( lockdir_name ) | Release a previously obtained readlock . | 59 | 7 |
243,826 | def get_readlock ( pid , path ) : timestamp = int ( time . time ( ) * 1e6 ) lockdir_name = "%s.readlock.%i.%i" % ( path , pid , timestamp ) os . mkdir ( lockdir_name ) # Register function to release the readlock at the end of the script atexit . register ( release_readlock , lockdir_name = lockdir_name ) | Obtain a readlock on a file . | 94 | 9 |
243,827 | def unlock ( self ) : # If any error occurs, we assume this is because someone else tried to # unlock this directory at the same time. # Note that it is important not to have both remove statements within # the same try/except block. The reason is that while the attempt to # remove the file may fail (e.g. because for some reason this file does # not exist), we still want to try and remove the directory. try : self . os . remove ( self . os . path . join ( self . tmp_dir , 'lock' ) ) except Exception : pass try : self . os . rmdir ( self . tmp_dir ) except Exception : pass | Remove current lock . | 142 | 4 |
243,828 | def filename_from_url ( url , path = None ) : r = requests . get ( url , stream = True ) if 'Content-Disposition' in r . headers : filename = re . findall ( r'filename=([^;]+)' , r . headers [ 'Content-Disposition' ] ) [ 0 ] . strip ( '"\"' ) else : filename = os . path . basename ( urllib . parse . urlparse ( url ) . path ) return filename | Parses a URL to determine a file name . | 106 | 11 |
243,829 | def download ( url , file_handle , chunk_size = 1024 ) : r = requests . get ( url , stream = True ) total_length = r . headers . get ( 'content-length' ) if total_length is None : maxval = UnknownLength else : maxval = int ( total_length ) name = file_handle . name with progress_bar ( name = name , maxval = maxval ) as bar : for i , chunk in enumerate ( r . iter_content ( chunk_size ) ) : if total_length : bar . update ( i * chunk_size ) file_handle . write ( chunk ) | Downloads a given URL to a specific file . | 135 | 10 |
243,830 | def default_downloader ( directory , urls , filenames , url_prefix = None , clear = False ) : # Parse file names from URL if not provided for i , url in enumerate ( urls ) : filename = filenames [ i ] if not filename : filename = filename_from_url ( url ) if not filename : raise ValueError ( "no filename available for URL '{}'" . format ( url ) ) filenames [ i ] = filename files = [ os . path . join ( directory , f ) for f in filenames ] if clear : for f in files : if os . path . isfile ( f ) : os . remove ( f ) else : print ( 'Downloading ' + ', ' . join ( filenames ) + '\n' ) ensure_directory_exists ( directory ) for url , f , n in zip ( urls , files , filenames ) : if not url : if url_prefix is None : raise NeedURLPrefix url = url_prefix + n with open ( f , 'wb' ) as file_handle : download ( url , file_handle ) | Downloads or clears files from URLs and filenames . | 245 | 12 |
243,831 | def find_in_data_path ( filename ) : for path in config . data_path : path = os . path . expanduser ( os . path . expandvars ( path ) ) file_path = os . path . join ( path , filename ) if os . path . isfile ( file_path ) : return file_path raise IOError ( "{} not found in Fuel's data path" . format ( filename ) ) | Searches for a file within Fuel s data path . | 92 | 12 |
243,832 | def lazy_property_factory ( lazy_property ) : def lazy_property_getter ( self ) : if not hasattr ( self , '_' + lazy_property ) : self . load ( ) if not hasattr ( self , '_' + lazy_property ) : raise ValueError ( "{} wasn't loaded" . format ( lazy_property ) ) return getattr ( self , '_' + lazy_property ) def lazy_property_setter ( self , value ) : setattr ( self , '_' + lazy_property , value ) return lazy_property_getter , lazy_property_setter | Create properties that perform lazy loading of attributes . | 135 | 9 |
243,833 | def do_not_pickle_attributes ( * lazy_properties ) : def wrap_class ( cls ) : if not hasattr ( cls , 'load' ) : raise ValueError ( "no load method implemented" ) # Attach the lazy loading properties to the class for lazy_property in lazy_properties : setattr ( cls , lazy_property , property ( * lazy_property_factory ( lazy_property ) ) ) # Delete the values of lazy properties when serializing if not hasattr ( cls , '__getstate__' ) : def __getstate__ ( self ) : serializable_state = self . __dict__ . copy ( ) for lazy_property in lazy_properties : attr = serializable_state . get ( '_' + lazy_property ) # Iterators would lose their state if isinstance ( attr , collections . Iterator ) : raise ValueError ( "Iterators can't be lazy loaded" ) serializable_state . pop ( '_' + lazy_property , None ) return serializable_state setattr ( cls , '__getstate__' , __getstate__ ) return cls return wrap_class | r Decorator to assign non - pickable properties . | 252 | 12 |
243,834 | def sorted_fancy_indexing ( indexable , request ) : if len ( request ) > 1 : indices = numpy . argsort ( request ) data = numpy . empty ( shape = ( len ( request ) , ) + indexable . shape [ 1 : ] , dtype = indexable . dtype ) data [ indices ] = indexable [ numpy . array ( request ) [ indices ] , ... ] else : data = indexable [ request ] return data | Safe fancy indexing . | 100 | 5 |
243,835 | def slice_to_numerical_args ( slice_ , num_examples ) : start = slice_ . start if slice_ . start is not None else 0 stop = slice_ . stop if slice_ . stop is not None else num_examples step = slice_ . step if slice_ . step is not None else 1 return start , stop , step | Translate a slice s attributes into numerical attributes . | 77 | 10 |
243,836 | def get_list_representation ( self ) : if self . is_list : return self . list_or_slice else : return self [ list ( range ( self . num_examples ) ) ] | Returns this subset s representation as a list of indices . | 44 | 11 |
243,837 | def index_within_subset ( self , indexable , subset_request , sort_indices = False ) : # Translate the request within the context of this subset to a # request to the indexable object if isinstance ( subset_request , numbers . Integral ) : request , = self [ [ subset_request ] ] else : request = self [ subset_request ] # Integer or slice requests can be processed directly. if isinstance ( request , numbers . Integral ) or hasattr ( request , 'step' ) : return indexable [ request ] # If requested, we do fancy indexing in sorted order and reshuffle the # result back in the original order. if sort_indices : return self . sorted_fancy_indexing ( indexable , request ) # If the indexable supports fancy indexing (numpy array, HDF5 dataset), # the request can be processed directly. if isinstance ( indexable , ( numpy . ndarray , h5py . Dataset ) ) : return indexable [ request ] # Anything else (e.g. lists) isn't considered to support fancy # indexing, so Subset does it manually. return iterable_fancy_indexing ( indexable , request ) | Index an indexable object within the context of this subset . | 264 | 12 |
243,838 | def num_examples ( self ) : if self . is_list : return len ( self . list_or_slice ) else : start , stop , step = self . slice_to_numerical_args ( self . list_or_slice , self . original_num_examples ) return stop - start | The number of examples this subset spans . | 68 | 8 |
243,839 | def get_epoch_iterator ( self , * * kwargs ) : if not self . _fresh_state : self . next_epoch ( ) else : self . _fresh_state = False return super ( DataStream , self ) . get_epoch_iterator ( * * kwargs ) | Get an epoch iterator for the data stream . | 66 | 9 |
243,840 | def fill_subparser ( subparser ) : sets = [ 'train' , 'valid' , 'test' ] urls = [ 'http://www.cs.toronto.edu/~larocheh/public/datasets/' + 'binarized_mnist/binarized_mnist_{}.amat' . format ( s ) for s in sets ] filenames = [ 'binarized_mnist_{}.amat' . format ( s ) for s in sets ] subparser . set_defaults ( urls = urls , filenames = filenames ) return default_downloader | Sets up a subparser to download the binarized MNIST dataset files . | 137 | 17 |
243,841 | def download ( directory , youtube_id , clear = False ) : filepath = os . path . join ( directory , '{}.m4a' . format ( youtube_id ) ) if clear : os . remove ( filepath ) return if not PAFY_AVAILABLE : raise ImportError ( "pafy is required to download YouTube videos" ) url = 'https://www.youtube.com/watch?v={}' . format ( youtube_id ) video = pafy . new ( url ) audio = video . getbestaudio ( ) audio . download ( quiet = False , filepath = filepath ) | Download the audio of a YouTube video . | 134 | 8 |
243,842 | def fill_subparser ( subparser ) : subparser . add_argument ( '--youtube-id' , type = str , required = True , help = ( "The YouTube ID of the video from which to extract audio, " "usually an 11-character string." ) ) return download | Sets up a subparser to download audio of YouTube videos . | 61 | 13 |
243,843 | def convert_youtube_audio ( directory , output_directory , youtube_id , channels , sample , output_filename = None ) : input_file = os . path . join ( directory , '{}.m4a' . format ( youtube_id ) ) wav_filename = '{}.wav' . format ( youtube_id ) wav_file = os . path . join ( directory , wav_filename ) ffmpeg_not_available = subprocess . call ( [ 'ffmpeg' , '-version' ] ) if ffmpeg_not_available : raise RuntimeError ( 'conversion requires ffmpeg' ) subprocess . check_call ( [ 'ffmpeg' , '-y' , '-i' , input_file , '-ac' , str ( channels ) , '-ar' , str ( sample ) , wav_file ] , stdout = sys . stdout ) # Load WAV into array _ , data = scipy . io . wavfile . read ( wav_file ) if data . ndim == 1 : data = data [ : , None ] data = data [ None , : ] # Store in HDF5 if output_filename is None : output_filename = '{}.hdf5' . format ( youtube_id ) output_file = os . path . join ( output_directory , output_filename ) with h5py . File ( output_file , 'w' ) as h5file : fill_hdf5_file ( h5file , ( ( 'train' , 'features' , data ) , ) ) h5file [ 'features' ] . dims [ 0 ] . label = 'batch' h5file [ 'features' ] . dims [ 1 ] . label = 'time' h5file [ 'features' ] . dims [ 2 ] . label = 'feature' return ( output_file , ) | Converts downloaded YouTube audio to HDF5 format . | 409 | 11 |
243,844 | def fill_subparser ( subparser ) : subparser . add_argument ( '--youtube-id' , type = str , required = True , help = ( "The YouTube ID of the video from which to extract audio, " "usually an 11-character string." ) ) subparser . add_argument ( '--channels' , type = int , default = 1 , help = ( "The number of audio channels to convert to. The default of 1" "means audio is converted to mono." ) ) subparser . add_argument ( '--sample' , type = int , default = 16000 , help = ( "The sampling rate in Hz. The default of 16000 is " "significantly downsampled compared to normal WAVE files; " "pass 44100 for the usual sampling rate." ) ) return convert_youtube_audio | Sets up a subparser to convert YouTube audio files . | 179 | 12 |
243,845 | def convert_ilsvrc2012 ( directory , output_directory , output_filename = 'ilsvrc2012.hdf5' , shuffle_seed = config . default_seed ) : devkit_path = os . path . join ( directory , DEVKIT_ARCHIVE ) train , valid , test = [ os . path . join ( directory , fn ) for fn in IMAGE_TARS ] n_train , valid_groundtruth , n_test , wnid_map = prepare_metadata ( devkit_path ) n_valid = len ( valid_groundtruth ) output_path = os . path . join ( output_directory , output_filename ) with h5py . File ( output_path , 'w' ) as f , create_temp_tar ( ) as patch : log . info ( 'Creating HDF5 datasets...' ) prepare_hdf5_file ( f , n_train , n_valid , n_test ) log . info ( 'Processing training set...' ) process_train_set ( f , train , patch , n_train , wnid_map , shuffle_seed ) log . info ( 'Processing validation set...' ) process_other_set ( f , 'valid' , valid , patch , valid_groundtruth , n_train ) log . info ( 'Processing test set...' ) process_other_set ( f , 'test' , test , patch , ( None , ) * n_test , n_train + n_valid ) log . info ( 'Done.' ) return ( output_path , ) | Converter for data from the ILSVRC 2012 competition . | 341 | 14 |
243,846 | def fill_subparser ( subparser ) : subparser . add_argument ( "--shuffle-seed" , help = "Seed to use for randomizing order of the " "training set on disk." , default = config . default_seed , type = int , required = False ) return convert_ilsvrc2012 | Sets up a subparser to convert the ILSVRC2012 dataset files . | 69 | 17 |
243,847 | def read_metadata_mat_file ( meta_mat ) : mat = loadmat ( meta_mat , squeeze_me = True ) synsets = mat [ 'synsets' ] new_dtype = numpy . dtype ( [ ( 'ILSVRC2012_ID' , numpy . int16 ) , ( 'WNID' , ( 'S' , max ( map ( len , synsets [ 'WNID' ] ) ) ) ) , ( 'wordnet_height' , numpy . int8 ) , ( 'gloss' , ( 'S' , max ( map ( len , synsets [ 'gloss' ] ) ) ) ) , ( 'num_children' , numpy . int8 ) , ( 'words' , ( 'S' , max ( map ( len , synsets [ 'words' ] ) ) ) ) , ( 'children' , ( numpy . int8 , max ( synsets [ 'num_children' ] ) ) ) , ( 'num_train_images' , numpy . uint16 ) ] ) new_synsets = numpy . empty ( synsets . shape , dtype = new_dtype ) for attr in [ 'ILSVRC2012_ID' , 'WNID' , 'wordnet_height' , 'gloss' , 'num_children' , 'words' , 'num_train_images' ] : new_synsets [ attr ] = synsets [ attr ] children = [ numpy . atleast_1d ( ch ) for ch in synsets [ 'children' ] ] padded_children = [ numpy . concatenate ( ( c , - numpy . ones ( new_dtype [ 'children' ] . shape [ 0 ] - len ( c ) , dtype = numpy . int16 ) ) ) for c in children ] new_synsets [ 'children' ] = padded_children return new_synsets | Read ILSVRC2012 metadata from the distributed MAT file . | 431 | 13 |
243,848 | def multiple_paths_parser ( value ) : if isinstance ( value , six . string_types ) : value = value . split ( os . path . pathsep ) return value | Parses data_path argument . | 40 | 8 |
243,849 | def add_config ( self , key , type_ , default = NOT_SET , env_var = None ) : self . config [ key ] = { 'type' : type_ } if env_var is not None : self . config [ key ] [ 'env_var' ] = env_var if default is not NOT_SET : self . config [ key ] [ 'default' ] = default | Add a configuration setting . | 86 | 5 |
243,850 | def send_arrays ( socket , arrays , stop = False ) : if arrays : # The buffer protocol only works on contiguous arrays arrays = [ numpy . ascontiguousarray ( array ) for array in arrays ] if stop : headers = { 'stop' : True } socket . send_json ( headers ) else : headers = [ header_data_from_array_1_0 ( array ) for array in arrays ] socket . send_json ( headers , zmq . SNDMORE ) for array in arrays [ : - 1 ] : socket . send ( array , zmq . SNDMORE ) socket . send ( arrays [ - 1 ] ) | Send NumPy arrays using the buffer interface and some metadata . | 139 | 12 |
243,851 | def recv_arrays ( socket ) : headers = socket . recv_json ( ) if 'stop' in headers : raise StopIteration arrays = [ ] for header in headers : data = socket . recv ( copy = False ) buf = buffer_ ( data ) array = numpy . frombuffer ( buf , dtype = numpy . dtype ( header [ 'descr' ] ) ) array . shape = header [ 'shape' ] if header [ 'fortran_order' ] : array . shape = header [ 'shape' ] [ : : - 1 ] array = array . transpose ( ) arrays . append ( array ) return arrays | Receive a list of NumPy arrays . | 139 | 9 |
243,852 | def start_server ( data_stream , port = 5557 , hwm = 10 ) : logging . basicConfig ( level = 'INFO' ) context = zmq . Context ( ) socket = context . socket ( zmq . PUSH ) socket . set_hwm ( hwm ) socket . bind ( 'tcp://*:{}' . format ( port ) ) it = data_stream . get_epoch_iterator ( ) logger . info ( 'server started' ) while True : try : data = next ( it ) stop = False logger . debug ( "sending {} arrays" . format ( len ( data ) ) ) except StopIteration : it = data_stream . get_epoch_iterator ( ) data = None stop = True logger . debug ( "sending StopIteration" ) send_arrays ( socket , data , stop = stop ) | Start a data processing server . | 187 | 6 |
243,853 | def create_images ( raw_data_directory : str , destination_directory : str , stroke_thicknesses : List [ int ] , canvas_width : int = None , canvas_height : int = None , staff_line_spacing : int = 14 , staff_line_vertical_offsets : List [ int ] = None , random_position_on_canvas : bool = False ) -> dict : all_symbol_files = [ y for x in os . walk ( raw_data_directory ) for y in glob ( os . path . join ( x [ 0 ] , '*.txt' ) ) ] staff_line_multiplier = 1 if staff_line_vertical_offsets is not None and staff_line_vertical_offsets : staff_line_multiplier = len ( staff_line_vertical_offsets ) total_number_of_symbols = len ( all_symbol_files ) * len ( stroke_thicknesses ) * staff_line_multiplier output = "Generating {0} images with {1} symbols in {2} different stroke thicknesses ({3})" . format ( total_number_of_symbols , len ( all_symbol_files ) , len ( stroke_thicknesses ) , stroke_thicknesses ) if staff_line_vertical_offsets is not None : output += " and with staff-lines with {0} different offsets from the top ({1})" . format ( staff_line_multiplier , staff_line_vertical_offsets ) if canvas_width is not None and canvas_height is not None : if random_position_on_canvas is False : output += "\nRandomly drawn on a fixed canvas of size {0}x{1} (Width x Height)" . format ( canvas_width , canvas_height ) else : output += "\nCentrally drawn on a fixed canvas of size {0}x{1} (Width x Height)" . format ( canvas_width , canvas_height ) print ( output ) print ( "In directory {0}" . format ( os . path . abspath ( destination_directory ) ) , flush = True ) bounding_boxes = dict ( ) progress_bar = tqdm ( total = total_number_of_symbols , mininterval = 0.25 ) for symbol_file in all_symbol_files : with open ( symbol_file ) as file : content = file . read ( ) symbol = HomusSymbol . initialize_from_string ( content ) target_directory = os . path . join ( destination_directory , symbol . symbol_class ) os . makedirs ( target_directory , exist_ok = True ) raw_file_name_without_extension = os . path . splitext ( os . path . basename ( symbol_file ) ) [ 0 ] for stroke_thickness in stroke_thicknesses : export_path = ExportPath ( destination_directory , symbol . symbol_class , raw_file_name_without_extension , 'png' , stroke_thickness ) if canvas_width is None and canvas_height is None : symbol . draw_into_bitmap ( export_path , stroke_thickness , margin = 2 ) else : symbol . draw_onto_canvas ( export_path , stroke_thickness , 0 , canvas_width , canvas_height , staff_line_spacing , staff_line_vertical_offsets , bounding_boxes , random_position_on_canvas ) progress_bar . update ( 1 * staff_line_multiplier ) progress_bar . close ( ) return bounding_boxes | Creates a visual representation of the Homus Dataset by parsing all text - files and the symbols as specified by the parameters by drawing lines that connect the points from each stroke of each symbol . | 800 | 40 |
243,854 | def extract_and_render_all_symbol_masks ( self , raw_data_directory : str , destination_directory : str ) : print ( "Extracting Symbols from Muscima++ Dataset..." ) xml_files = self . get_all_xml_file_paths ( raw_data_directory ) crop_objects = self . load_crop_objects_from_xml_files ( xml_files ) self . render_masks_of_crop_objects_into_image ( crop_objects , destination_directory ) | Extracts all symbols from the raw XML documents and generates individual symbols from the masks | 120 | 17 |
243,855 | def invert_images ( self , image_directory : str , image_file_ending : str = "*.bmp" ) : image_paths = [ y for x in os . walk ( image_directory ) for y in glob ( os . path . join ( x [ 0 ] , image_file_ending ) ) ] for image_path in tqdm ( image_paths , desc = "Inverting all images in directory {0}" . format ( image_directory ) ) : white_on_black_image = Image . open ( image_path ) . convert ( "L" ) black_on_white_image = ImageOps . invert ( white_on_black_image ) black_on_white_image . save ( os . path . splitext ( image_path ) [ 0 ] + ".png" ) | In - situ converts the white on black images of a directory to black on white images | 181 | 17 |
243,856 | def create_capitan_images ( self , raw_data_directory : str , destination_directory : str , stroke_thicknesses : List [ int ] ) -> None : symbols = self . load_capitan_symbols ( raw_data_directory ) self . draw_capitan_stroke_images ( symbols , destination_directory , stroke_thicknesses ) self . draw_capitan_score_images ( symbols , destination_directory ) | Creates a visual representation of the Capitan strokes by parsing all text - files and the symbols as specified by the parameters by drawing lines that connect the points from each stroke of each symbol . | 97 | 38 |
243,857 | def draw_capitan_stroke_images ( self , symbols : List [ CapitanSymbol ] , destination_directory : str , stroke_thicknesses : List [ int ] ) -> None : total_number_of_symbols = len ( symbols ) * len ( stroke_thicknesses ) output = "Generating {0} images with {1} symbols in {2} different stroke thicknesses ({3})" . format ( total_number_of_symbols , len ( symbols ) , len ( stroke_thicknesses ) , stroke_thicknesses ) print ( output ) print ( "In directory {0}" . format ( os . path . abspath ( destination_directory ) ) , flush = True ) progress_bar = tqdm ( total = total_number_of_symbols , mininterval = 0.25 , desc = "Rendering strokes" ) capitan_file_name_counter = 0 for symbol in symbols : capitan_file_name_counter += 1 target_directory = os . path . join ( destination_directory , symbol . symbol_class ) os . makedirs ( target_directory , exist_ok = True ) raw_file_name_without_extension = "capitan-{0}-{1}-stroke" . format ( symbol . symbol_class , capitan_file_name_counter ) for stroke_thickness in stroke_thicknesses : export_path = ExportPath ( destination_directory , symbol . symbol_class , raw_file_name_without_extension , 'png' , stroke_thickness ) symbol . draw_capitan_stroke_onto_canvas ( export_path , stroke_thickness , 0 ) progress_bar . update ( 1 ) progress_bar . close ( ) | Creates a visual representation of the Capitan strokes by drawing lines that connect the points from each stroke of each symbol . | 389 | 24 |
243,858 | def overlap ( r1 : 'Rectangle' , r2 : 'Rectangle' ) : h_overlaps = ( r1 . left <= r2 . right ) and ( r1 . right >= r2 . left ) v_overlaps = ( r1 . bottom >= r2 . top ) and ( r1 . top <= r2 . bottom ) return h_overlaps and v_overlaps | Overlapping rectangles overlap both horizontally & vertically | 90 | 10 |
243,859 | def extract_symbols ( self , raw_data_directory : str , destination_directory : str ) : print ( "Extracting Symbols from Audiveris OMR Dataset..." ) all_xml_files = [ y for x in os . walk ( raw_data_directory ) for y in glob ( os . path . join ( x [ 0 ] , '*.xml' ) ) ] all_image_files = [ y for x in os . walk ( raw_data_directory ) for y in glob ( os . path . join ( x [ 0 ] , '*.png' ) ) ] data_pairs = [ ] for i in range ( len ( all_xml_files ) ) : data_pairs . append ( ( all_xml_files [ i ] , all_image_files [ i ] ) ) for data_pair in data_pairs : self . __extract_symbols ( data_pair [ 0 ] , data_pair [ 1 ] , destination_directory ) | Extracts the symbols from the raw XML documents and matching images of the Audiveris OMR dataset into individual symbols | 218 | 24 |
243,860 | def initialize_from_string ( content : str ) -> 'HomusSymbol' : if content is None or content is "" : return None lines = content . splitlines ( ) min_x = sys . maxsize max_x = 0 min_y = sys . maxsize max_y = 0 symbol_name = lines [ 0 ] strokes = [ ] for stroke_string in lines [ 1 : ] : stroke = [ ] for point_string in stroke_string . split ( ";" ) : if point_string is "" : continue # Skip the last element, that is due to a trailing ; in each line point_x , point_y = point_string . split ( "," ) x = int ( point_x ) y = int ( point_y ) stroke . append ( Point2D ( x , y ) ) max_x = max ( max_x , x ) min_x = min ( min_x , x ) max_y = max ( max_y , y ) min_y = min ( min_y , y ) strokes . append ( stroke ) dimensions = Rectangle ( Point2D ( min_x , min_y ) , max_x - min_x + 1 , max_y - min_y + 1 ) return HomusSymbol ( content , strokes , symbol_name , dimensions ) | Create and initializes a new symbol from a string | 284 | 10 |
243,861 | def draw_into_bitmap ( self , export_path : ExportPath , stroke_thickness : int , margin : int = 0 ) -> None : self . draw_onto_canvas ( export_path , stroke_thickness , margin , self . dimensions . width + 2 * margin , self . dimensions . height + 2 * margin ) | Draws the symbol in the original size that it has plus an optional margin | 75 | 15 |
243,862 | def update_locals ( locals_instance , instance_iterator , * args , * * kwargs ) : # http://stackoverflow.com/a/4526709/564709 # http://stackoverflow.com/a/511059/564709 for instance in instance_iterator ( ) : locals_instance . update ( { type ( instance ) . __name__ : instance . __class__ } ) | import all of the detector classes into the local namespace to make it easy to do things like import scrubadub . detectors . NameDetector without having to add each new Detector or Filth | 92 | 39 |
243,863 | def iter_filth_clss ( ) : return iter_subclasses ( os . path . dirname ( os . path . abspath ( __file__ ) ) , Filth , _is_abstract_filth , ) | Iterate over all of the filths that are included in this sub - package . This is a convenience method for capturing all new Filth that are added over time . | 50 | 34 |
243,864 | def iter_filths ( ) : for filth_cls in iter_filth_clss ( ) : if issubclass ( filth_cls , RegexFilth ) : m = next ( re . finditer ( r"\s+" , "fake pattern string" ) ) yield filth_cls ( m ) else : yield filth_cls ( ) | Iterate over all instances of filth | 84 | 8 |
243,865 | def _update_content ( self , other_filth ) : if self . end < other_filth . beg or other_filth . end < self . beg : raise exceptions . FilthMergeError ( "a_filth goes from [%s, %s) and b_filth goes from [%s, %s)" % ( self . beg , self . end , other_filth . beg , other_filth . end ) ) # get the text over lap correct if self . beg < other_filth . beg : first = self second = other_filth else : second = self first = other_filth end_offset = second . end - first . end if end_offset > 0 : self . text = first . text + second . text [ - end_offset : ] # update the beg/end strings self . beg = min ( self . beg , other_filth . beg ) self . end = max ( self . end , other_filth . end ) if self . end - self . beg != len ( self . text ) : raise exceptions . FilthMergeError ( "text length isn't consistent" ) # update the placeholder self . filths . append ( other_filth ) self . _placeholder = '+' . join ( [ filth . type for filth in self . filths ] ) | this updates the bounds text and placeholder for the merged filth | 291 | 12 |
243,866 | def add_detector ( self , detector_cls ) : if not issubclass ( detector_cls , detectors . base . Detector ) : raise TypeError ( ( '"%(detector_cls)s" is not a subclass of Detector' ) % locals ( ) ) # TODO: should add tests to make sure filth_cls is actually a proper # filth_cls name = detector_cls . filth_cls . type if name in self . _detectors : raise KeyError ( ( 'can not add Detector "%(name)s"---it already exists. ' 'Try removing it first.' ) % locals ( ) ) self . _detectors [ name ] = detector_cls ( ) | Add a Detector to scrubadub | 164 | 8 |
243,867 | def clean ( self , text , * * kwargs ) : if sys . version_info < ( 3 , 0 ) : # Only in Python 2. In 3 every string is a Python 2 unicode if not isinstance ( text , unicode ) : raise exceptions . UnicodeRequired clean_chunks = [ ] filth = Filth ( ) for next_filth in self . iter_filth ( text ) : clean_chunks . append ( text [ filth . end : next_filth . beg ] ) clean_chunks . append ( next_filth . replace_with ( * * kwargs ) ) filth = next_filth clean_chunks . append ( text [ filth . end : ] ) return u'' . join ( clean_chunks ) | This is the master method that cleans all of the filth out of the dirty dirty text . All keyword arguments to this function are passed through to the Filth . replace_with method to fine - tune how the Filth is cleaned . | 168 | 48 |
243,868 | def iter_filth ( self , text ) : # currently doing this by aggregating all_filths and then sorting # inline instead of with a Filth.__cmp__ method, which is apparently # much slower http://stackoverflow.com/a/988728/564709 # # NOTE: we could probably do this in a more efficient way by iterating # over all detectors simultaneously. just trying to get something # working right now and we can worry about efficiency later all_filths = [ ] for detector in self . _detectors . values ( ) : for filth in detector . iter_filth ( text ) : if not isinstance ( filth , Filth ) : raise TypeError ( 'iter_filth must always yield Filth' ) all_filths . append ( filth ) # Sort by start position. If two filths start in the same place then # return the longer one first all_filths . sort ( key = lambda f : ( f . beg , - f . end ) ) # this is where the Scrubber does its hard work and merges any # overlapping filths. if not all_filths : raise StopIteration filth = all_filths [ 0 ] for next_filth in all_filths [ 1 : ] : if filth . end < next_filth . beg : yield filth filth = next_filth else : filth = filth . merge ( next_filth ) yield filth | Iterate over the different types of filth that can exist . | 317 | 13 |
243,869 | async def download_file ( self , Bucket , Key , Filename , ExtraArgs = None , Callback = None , Config = None ) : with open ( Filename , 'wb' ) as open_file : await download_fileobj ( self , Bucket , Key , open_file , ExtraArgs = ExtraArgs , Callback = Callback , Config = Config ) | Download an S3 object to a file . | 78 | 9 |
243,870 | async def download_fileobj ( self , Bucket , Key , Fileobj , ExtraArgs = None , Callback = None , Config = None ) : try : resp = await self . get_object ( Bucket = Bucket , Key = Key ) except ClientError as err : if err . response [ 'Error' ] [ 'Code' ] == 'NoSuchKey' : # Convert to 404 so it looks the same when boto3.download_file fails raise ClientError ( { 'Error' : { 'Code' : '404' , 'Message' : 'Not Found' } } , 'HeadObject' ) raise body = resp [ 'Body' ] while True : data = await body . read ( 4096 ) if data == b'' : break if Callback : try : Callback ( len ( data ) ) except : # noqa: E722 pass Fileobj . write ( data ) await asyncio . sleep ( 0.0 ) | Download an object from S3 to a file - like object . | 199 | 13 |
243,871 | async def upload_fileobj ( self , Fileobj : BinaryIO , Bucket : str , Key : str , ExtraArgs : Optional [ Dict [ str , Any ] ] = None , Callback : Optional [ Callable [ [ int ] , None ] ] = None , Config : Optional [ S3TransferConfig ] = None ) : if not ExtraArgs : ExtraArgs = { } # I was debating setting up a queue etc... # If its too slow I'll then be bothered multipart_chunksize = 8388608 if Config is None else Config . multipart_chunksize io_chunksize = 262144 if Config is None else Config . io_chunksize # max_concurrency = 10 if Config is None else Config.max_concurrency # max_io_queue = 100 if config is None else Config.max_io_queue # Start multipart upload resp = await self . create_multipart_upload ( Bucket = Bucket , Key = Key , * * ExtraArgs ) upload_id = resp [ 'UploadId' ] part = 0 parts = [ ] running = True sent_bytes = 0 try : while running : part += 1 multipart_payload = b'' while len ( multipart_payload ) < multipart_chunksize : if asyncio . iscoroutinefunction ( Fileobj . read ) : # handles if we pass in aiofiles obj data = await Fileobj . read ( io_chunksize ) else : data = Fileobj . read ( io_chunksize ) if data == b'' : # End of file running = False break multipart_payload += data # Submit part to S3 resp = await self . upload_part ( Body = multipart_payload , Bucket = Bucket , Key = Key , PartNumber = part , UploadId = upload_id ) parts . append ( { 'ETag' : resp [ 'ETag' ] , 'PartNumber' : part } ) sent_bytes += len ( multipart_payload ) try : Callback ( sent_bytes ) # Attempt to call the callback, if it fails, ignore, if no callback, ignore except : # noqa: E722 pass # By now the uploads must have been done await self . complete_multipart_upload ( Bucket = Bucket , Key = Key , UploadId = upload_id , MultipartUpload = { 'Parts' : parts } ) except : # noqa: E722 # Cancel multipart upload await self . abort_multipart_upload ( Bucket = Bucket , Key = Key , UploadId = upload_id ) raise | Upload a file - like object to S3 . | 555 | 10 |
243,872 | async def upload_file ( self , Filename , Bucket , Key , ExtraArgs = None , Callback = None , Config = None ) : with open ( Filename , 'rb' ) as open_file : await upload_fileobj ( self , open_file , Bucket , Key , ExtraArgs = ExtraArgs , Callback = Callback , Config = Config ) | Upload a file to an S3 object . | 78 | 9 |
243,873 | def _create_action ( factory_self , action_model , resource_name , service_context , is_load = False ) : # Create the action in in this closure but before the ``do_action`` # method below is invoked, which allows instances of the resource # to share the ServiceAction instance. action = AIOServiceAction ( action_model , factory = factory_self , service_context = service_context ) # A resource's ``load`` method is special because it sets # values on the resource instead of returning the response. if is_load : # We need a new method here because we want access to the # instance via ``self``. async def do_action ( self , * args , * * kwargs ) : # response = action(self, *args, **kwargs) response = await action . async_call ( self , * args , * * kwargs ) self . meta . data = response # Create the docstring for the load/reload mehtods. lazy_docstring = docstring . LoadReloadDocstring ( action_name = action_model . name , resource_name = resource_name , event_emitter = factory_self . _emitter , load_model = action_model , service_model = service_context . service_model , include_signature = False ) else : # We need a new method here because we want access to the # instance via ``self``. async def do_action ( self , * args , * * kwargs ) : response = await action . async_call ( self , * args , * * kwargs ) if hasattr ( self , 'load' ) : # Clear cached data. It will be reloaded the next # time that an attribute is accessed. # TODO: Make this configurable in the future? self . meta . data = None return response lazy_docstring = docstring . ActionDocstring ( resource_name = resource_name , event_emitter = factory_self . _emitter , action_model = action_model , service_model = service_context . service_model , include_signature = False ) do_action . __name__ = str ( action_model . name ) do_action . __doc__ = lazy_docstring return do_action | Creates a new method which makes a request to the underlying AWS service . | 490 | 15 |
243,874 | def from_der_private_key ( data : bytes , password : Optional [ str ] = None ) -> _RSAPrivateKey : return serialization . load_der_private_key ( data , password , default_backend ( ) ) | Convert private key in DER encoding to a Private key object | 53 | 13 |
243,875 | async def get_object ( self , Bucket : str , Key : str , * * kwargs ) -> dict : if self . _s3_client is None : await self . setup ( ) # Ok so if we are doing a range get. We need to align the range start/end with AES block boundaries # 9223372036854775806 is 8EiB so I have no issue with hardcoding it. # We pass the actual start, desired start and desired end to the decrypt function so that it can # generate the correct IV's for starting decryption at that block and then chop off the start and end of the # AES block so it matches what the user is expecting. _range = kwargs . get ( 'Range' ) actual_range_start = None desired_range_start = None desired_range_end = None if _range : range_match = RANGE_REGEX . match ( _range ) if not range_match : raise ValueError ( 'Dont understand this range value {0}' . format ( _range ) ) desired_range_start = int ( range_match . group ( 1 ) ) desired_range_end = range_match . group ( 2 ) if desired_range_end is None : desired_range_end = 9223372036854775806 else : desired_range_end = int ( desired_range_end ) actual_range_start , actual_range_end = _get_adjusted_crypto_range ( desired_range_start , desired_range_end ) # Update range with actual start_end kwargs [ 'Range' ] = 'bytes={0}-{1}' . format ( actual_range_start , actual_range_end ) s3_response = await self . _s3_client . get_object ( Bucket = Bucket , Key = Key , * * kwargs ) file_data = await s3_response [ 'Body' ] . read ( ) metadata = s3_response [ 'Metadata' ] whole_file_length = int ( s3_response [ 'ResponseMetadata' ] [ 'HTTPHeaders' ] [ 'content-length' ] ) if 'x-amz-key' not in metadata and 'x-amz-key-v2' not in metadata : # No crypto return s3_response if 'x-amz-key' in metadata : # Crypto V1 body = await self . _decrypt_v1 ( file_data , metadata , actual_range_start ) else : # Crypto V2 body = await self . _decrypt_v2 ( file_data , metadata , whole_file_length , actual_range_start , desired_range_start , desired_range_end ) s3_response [ 'Body' ] = DummyAIOFile ( body ) return s3_response | S3 GetObject . Takes same args as Boto3 documentation | 619 | 13 |
243,876 | async def put_object ( self , Body : Union [ bytes , IO ] , Bucket : str , Key : str , Metadata : Dict = None , * * kwargs ) : if self . _s3_client is None : await self . setup ( ) if hasattr ( Body , 'read' ) : if inspect . iscoroutinefunction ( Body . read ) : Body = await Body . read ( ) else : Body = Body . read ( ) # We do some different V2 stuff if using kms is_kms = isinstance ( self . _crypto_context , KMSCryptoContext ) # noinspection PyUnresolvedReferences authenticated_crypto = is_kms and self . _crypto_context . authenticated_encryption Metadata = Metadata if Metadata is not None else { } aes_key , matdesc_metadata , key_metadata = await self . _crypto_context . get_encryption_aes_key ( ) if is_kms and authenticated_crypto : Metadata [ 'x-amz-cek-alg' ] = 'AES/GCM/NoPadding' Metadata [ 'x-amz-tag-len' ] = str ( AES_BLOCK_SIZE ) iv = os . urandom ( 12 ) # 16byte 128bit authentication tag forced aesgcm = AESGCM ( aes_key ) result = await self . _loop . run_in_executor ( None , lambda : aesgcm . encrypt ( iv , Body , None ) ) else : if is_kms : # V1 is always AES/CBC/PKCS5Padding Metadata [ 'x-amz-cek-alg' ] = 'AES/CBC/PKCS5Padding' iv = os . urandom ( 16 ) padder = PKCS7 ( AES . block_size ) . padder ( ) padded_result = await self . _loop . run_in_executor ( None , lambda : ( padder . update ( Body ) + padder . finalize ( ) ) ) aescbc = Cipher ( AES ( aes_key ) , CBC ( iv ) , backend = self . _backend ) . encryptor ( ) result = await self . _loop . run_in_executor ( None , lambda : ( aescbc . update ( padded_result ) + aescbc . finalize ( ) ) ) # For all V1 and V2 Metadata [ 'x-amz-unencrypted-content-length' ] = str ( len ( Body ) ) Metadata [ 'x-amz-iv' ] = base64 . b64encode ( iv ) . decode ( ) Metadata [ 'x-amz-matdesc' ] = json . dumps ( matdesc_metadata ) if is_kms : Metadata [ 'x-amz-wrap-alg' ] = 'kms' Metadata [ 'x-amz-key-v2' ] = key_metadata else : Metadata [ 'x-amz-key' ] = key_metadata await self . _s3_client . put_object ( Bucket = Bucket , Key = Key , Body = result , Metadata = Metadata , * * kwargs ) | PutObject . Takes same args as Boto3 documentation | 709 | 11 |
243,877 | def histogram1d ( x , bins , range , weights = None ) : nx = bins if not np . isscalar ( bins ) : raise TypeError ( 'bins should be an integer' ) xmin , xmax = range if not np . isfinite ( xmin ) : raise ValueError ( "xmin should be finite" ) if not np . isfinite ( xmax ) : raise ValueError ( "xmax should be finite" ) if xmax <= xmin : raise ValueError ( "xmax should be greater than xmin" ) if nx <= 0 : raise ValueError ( "nx should be strictly positive" ) if weights is None : return _histogram1d ( x , nx , xmin , xmax ) else : return _histogram1d_weighted ( x , weights , nx , xmin , xmax ) | Compute a 1D histogram assuming equally spaced bins . | 189 | 12 |
243,878 | def histogram2d ( x , y , bins , range , weights = None ) : if isinstance ( bins , numbers . Integral ) : nx = ny = bins else : nx , ny = bins if not np . isscalar ( nx ) or not np . isscalar ( ny ) : raise TypeError ( 'bins should be an iterable of two integers' ) ( xmin , xmax ) , ( ymin , ymax ) = range if not np . isfinite ( xmin ) : raise ValueError ( "xmin should be finite" ) if not np . isfinite ( xmax ) : raise ValueError ( "xmax should be finite" ) if not np . isfinite ( ymin ) : raise ValueError ( "ymin should be finite" ) if not np . isfinite ( ymax ) : raise ValueError ( "ymax should be finite" ) if xmax <= xmin : raise ValueError ( "xmax should be greater than xmin" ) if ymax <= ymin : raise ValueError ( "xmax should be greater than xmin" ) if nx <= 0 : raise ValueError ( "nx should be strictly positive" ) if ny <= 0 : raise ValueError ( "ny should be strictly positive" ) if weights is None : return _histogram2d ( x , y , nx , xmin , xmax , ny , ymin , ymax ) else : return _histogram2d_weighted ( x , y , weights , nx , xmin , xmax , ny , ymin , ymax ) | Compute a 2D histogram assuming equally spaced bins . | 351 | 12 |
243,879 | def to_networkx ( self ) : return nx_util . to_networkx ( self . session . get ( self . __url ) . json ( ) ) | Return this network in NetworkX graph object . | 36 | 9 |
243,880 | def to_dataframe ( self , extra_edges_columns = [ ] ) : return df_util . to_dataframe ( self . session . get ( self . __url ) . json ( ) , edges_attr_cols = extra_edges_columns ) | Return this network in pandas DataFrame . | 61 | 9 |
243,881 | def add_node ( self , node_name , dataframe = False ) : if node_name is None : return None return self . add_nodes ( [ node_name ] , dataframe = dataframe ) | Add a single node to the network . | 46 | 8 |
243,882 | def add_nodes ( self , node_name_list , dataframe = False ) : res = self . session . post ( self . __url + 'nodes' , data = json . dumps ( node_name_list ) , headers = HEADERS ) check_response ( res ) nodes = res . json ( ) if dataframe : return pd . DataFrame ( nodes ) . set_index ( [ 'SUID' ] ) else : return { node [ 'name' ] : node [ 'SUID' ] for node in nodes } | Add new nodes to the network | 117 | 6 |
243,883 | def add_edge ( self , source , target , interaction = '-' , directed = True , dataframe = True ) : new_edge = { 'source' : source , 'target' : target , 'interaction' : interaction , 'directed' : directed } return self . add_edges ( [ new_edge ] , dataframe = dataframe ) | Add a single edge from source to target . | 76 | 9 |
243,884 | def get_views ( self ) : url = self . __url + 'views' return self . session . get ( url ) . json ( ) | Get views as a list of SUIDs | 31 | 8 |
243,885 | def diffuse_advanced ( self , heatColumnName = None , time = None , verbose = False ) : PARAMS = set_param ( [ "heatColumnName" , "time" ] , [ heatColumnName , time ] ) response = api ( url = self . __url + "/diffuse_advanced" , PARAMS = PARAMS , method = "POST" , verbose = verbose ) return response | Diffusion will send the selected network view and its selected nodes to a web - based REST service to calculate network propagation . Results are returned and represented by columns in the node table . Columns are created for each execution of Diffusion and their names are returned in the response . | 90 | 55 |
243,886 | def to_networkx ( cyjs , directed = True ) : if directed : g = nx . MultiDiGraph ( ) else : g = nx . MultiGraph ( ) network_data = cyjs [ DATA ] if network_data is not None : for key in network_data . keys ( ) : g . graph [ key ] = network_data [ key ] nodes = cyjs [ ELEMENTS ] [ NODES ] edges = cyjs [ ELEMENTS ] [ EDGES ] for node in nodes : data = node [ DATA ] g . add_node ( data [ ID ] , attr_dict = data ) for edge in edges : data = edge [ DATA ] source = data [ SOURCE ] target = data [ TARGET ] g . add_edge ( source , target , attr_dict = data ) return g | Convert Cytoscape . js - style JSON object into NetworkX object . | 177 | 17 |
243,887 | def dialog ( self = None , wid = None , text = None , title = None , url = None , debug = False , verbose = False ) : PARAMS = set_param ( [ "id" , "text" , "title" , "url" , "debug" ] , [ wid , text , title , url , debug ] ) response = api ( url = self . __url + "/dialog?" , PARAMS = PARAMS , method = "GET" , verbose = verbose ) return response | Launch and HTML browser in a separate window . | 110 | 9 |
243,888 | def hide ( self , wid , verbose = False ) : PARAMS = { "id" : wid } response = api ( url = self . __url + "/hide?" , PARAMS = PARAMS , method = "GET" , verbose = verbose ) return response | Hide and HTML browser in the Results Panel . | 58 | 9 |
243,889 | def show ( self , wid = None , text = None , title = None , url = None , verbose = False ) : PARAMS = { } for p , v in zip ( [ "id" , "text" , "title" , "url" ] , [ wid , text , title , url ] ) : if v : PARAMS [ p ] = v response = api ( url = self . __url + "/show?" , PARAMS = PARAMS , method = "GET" , verbose = verbose ) return response | Launch an HTML browser in the Results Panel . | 113 | 9 |
243,890 | def check_response ( res ) : try : res . raise_for_status ( ) # Alternative is res.ok except Exception as exc : # Bad response code, e.g. if adding an edge with nodes that doesn't exist try : err_info = res . json ( ) err_msg = err_info [ 'message' ] # or 'localizeMessage' except ValueError : err_msg = res . text [ : 40 ] # Take the first 40 chars of the response except KeyError : err_msg = res . text [ : 40 ] + ( "(No 'message' in err_info dict: %s" % list ( err_info . keys ( ) ) ) exc . args += ( err_msg , ) raise exc | Check HTTP response and raise exception if response is not OK . | 158 | 12 |
243,891 | def from_dataframe ( df , source_col = 'source' , target_col = 'target' , interaction_col = 'interaction' , name = 'From DataFrame' , edge_attr_cols = [ ] ) : network = cyjs . get_empty_network ( name = name ) nodes = set ( ) if edge_attr_cols is None : edge_attr_cols = [ ] for index , row in df . iterrows ( ) : s = row [ source_col ] t = row [ target_col ] if s not in nodes : nodes . add ( s ) source = get_node ( s ) network [ 'elements' ] [ 'nodes' ] . append ( source ) if t not in nodes : nodes . add ( t ) target = get_node ( t ) network [ 'elements' ] [ 'nodes' ] . append ( target ) extra_values = { column : row [ column ] for column in edge_attr_cols if column in df . columns } network [ 'elements' ] [ 'edges' ] . append ( get_edge ( s , t , interaction = row [ interaction_col ] , * * extra_values ) ) return network | Utility to convert Pandas DataFrame object into Cytoscape . js JSON | 264 | 17 |
243,892 | def to_dataframe ( network , interaction = 'interaction' , default_interaction = '-' , edges_attr_cols = [ ] ) : edges = network [ 'elements' ] [ 'edges' ] if edges_attr_cols is None : edges_attr_cols = [ ] edges_attr_cols = sorted ( edges_attr_cols ) network_array = [ ] # the set avoids duplicates valid_extra_cols = set ( ) for edge in edges : edge_data = edge [ 'data' ] source = edge_data [ 'source' ] target = edge_data [ 'target' ] if interaction in edge_data : itr = edge_data [ interaction ] else : itr = default_interaction extra_values = [ ] for extra_column in edges_attr_cols : if extra_column in edge_data : extra_values . append ( edge_data [ extra_column ] ) valid_extra_cols . add ( extra_column ) row = tuple ( [ source , itr , target ] + extra_values ) network_array . append ( row ) return pd . DataFrame ( network_array , columns = [ 'source' , 'interaction' , 'target' ] + sorted ( valid_extra_cols ) ) | Utility to convert a Cytoscape dictionary into a Pandas Dataframe . | 284 | 17 |
243,893 | def render ( network , style = DEF_STYLE , layout_algorithm = DEF_LAYOUT , background = DEF_BACKGROUND_COLOR , height = DEF_HEIGHT , width = DEF_WIDTH , style_file = STYLE_FILE , def_nodes = DEF_NODES , def_edges = DEF_EDGES ) : from jinja2 import Template from IPython . core . display import display , HTML STYLES = set_styles ( style_file ) # Load style file if none available if isinstance ( style , str ) : # Specified by name style = STYLES [ style ] if network is None : nodes = def_nodes edges = def_edges else : nodes = network [ 'elements' ] [ 'nodes' ] edges = network [ 'elements' ] [ 'edges' ] path = os . path . abspath ( os . path . dirname ( __file__ ) ) + '/' + HTML_TEMPLATE_FILE template = Template ( open ( path ) . read ( ) ) cyjs_widget = template . render ( nodes = json . dumps ( nodes ) , edges = json . dumps ( edges ) , background = background , uuid = "cy" + str ( uuid . uuid4 ( ) ) , widget_width = str ( width ) , widget_height = str ( height ) , layout = layout_algorithm , style_json = json . dumps ( style ) ) display ( HTML ( cyjs_widget ) ) | Render network data with embedded Cytoscape . js widget . | 330 | 13 |
243,894 | def create_attribute ( self , column = None , listType = None , namespace = None , network = None , atype = None , verbose = False ) : network = check_network ( self , network , verbose = verbose ) PARAMS = set_param ( [ "column" , "listType" , "namespace" , "network" , "type" ] , [ column , listType , namespace , network , atype ] ) response = api ( url = self . __url + "/create attribute" , PARAMS = PARAMS , method = "POST" , verbose = verbose ) return response | Creates a new edge column . | 132 | 7 |
243,895 | def get ( self , edge = None , network = None , sourceNode = None , targetNode = None , atype = None , verbose = False ) : network = check_network ( self , network , verbose = verbose ) PARAMS = set_param ( [ "edge" , "network" , "sourceNode" , "targetNode" , "type" ] , [ edge , network , sourceNode , targetNode , atype ] ) response = api ( url = self . __url + "/get" , PARAMS = PARAMS , method = "POST" , verbose = verbose ) return response | Returns the SUID of an edge that matches the passed parameters . If multiple edges are found only one will be returned and a warning will be reported in the Cytoscape Task History dialog . | 131 | 39 |
243,896 | def add_edge ( self , isDirected = None , name = None , network = None , sourceName = None , targetName = None , verbose = False ) : network = check_network ( self , network , verbose = verbose ) PARAMS = set_param ( [ "isDirected" , "name" , "network" , "sourceName" , "targetName" ] , [ isDirected , name , network , sourceName , targetName ] ) response = api ( url = self . __url + "/add edge" , PARAMS = PARAMS , method = "POST" , verbose = verbose ) return response | Add a new edge between two existing nodes in a network . The names of the nodes must be specified and much match the value in the name column for each node . | 138 | 33 |
243,897 | def create ( self , edgeList = None , excludeEdges = None , networkName = None , nodeList = None , source = None , verbose = False ) : network = check_network ( self , source , verbose = verbose ) PARAMS = set_param ( [ "edgeList" , "excludeEdges" , "networkName" , "nodeList" , "source" ] , [ edgeList , excludeEdges , networkName , nodeList , network ] ) response = api ( url = self . __url + "/create" , PARAMS = PARAMS , method = "POST" , verbose = verbose ) return response | Create a new network from a list of nodes and edges in an existing source network . The SUID of the network and view are returned . | 139 | 28 |
243,898 | def create_empty ( self , name = None , renderers = None , RootNetworkList = None , verbose = False ) : PARAMS = set_param ( [ "name" , "renderers" , "RootNetworkList" ] , [ name , renderers , RootNetworkList ] ) response = api ( url = self . __url + "/create empty" , PARAMS = PARAMS , method = "POST" , verbose = verbose ) return response | Create a new empty network . The new network may be created as part of an existing network collection or a new network collection . | 99 | 25 |
243,899 | def list ( self , verbose = False ) : response = api ( url = self . __url + "/list" , method = "POST" , verbose = verbose ) return response | List all of the networks in the current session . | 40 | 10 |
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