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Create a blob generator . | def blob_generator ( self ) : # pylint: disable:F0401,W0612 import aa # pylint: disablF0401 # noqa from ROOT import EventFile # pylint: disable F0401 filename = self . filename log . info ( "Reading from file: {0}" . format ( filename ) ) if not os . path . exists ( filename ) : log . warning ( filename + " not available: continue without it" ) try : event_file = EventFile ( filename ) except Exception : raise SystemExit ( "Could not open file" ) log . info ( "Generating blobs through new aanet API..." ) self . print ( "Reading metadata using 'JPrintMeta'" ) meta_parser = MetaParser ( filename = filename ) meta = meta_parser . get_table ( ) if meta is None : self . log . warning ( "No metadata found, this means no data provenance!" ) if self . bare : log . info ( "Skipping data conversion, only passing bare aanet data" ) for event in event_file : yield Blob ( { 'evt' : event , 'event_file' : event_file } ) else : log . info ( "Unpacking aanet header into dictionary..." ) hdr = self . _parse_header ( event_file . header ) if not hdr : log . info ( "Empty header dict found, skipping..." ) self . raw_header = None else : log . info ( "Converting Header dict to Table..." ) self . raw_header = self . _convert_header_dict_to_table ( hdr ) log . info ( "Creating HDF5Header" ) self . header = HDF5Header . from_table ( self . raw_header ) for event in event_file : log . debug ( 'Reading event...' ) blob = self . _read_event ( event , filename ) log . debug ( 'Reading header...' ) blob [ "RawHeader" ] = self . raw_header blob [ "Header" ] = self . header if meta is not None : blob [ 'Meta' ] = meta self . group_id += 1 yield blob del event_file | 9,700 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/aanet.py#L264-L320 | [
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Parse ASCII output of JPrintMeta | def parse_string ( self , string ) : self . log . info ( "Parsing ASCII data" ) if not string : self . log . warning ( "Empty metadata" ) return lines = string . splitlines ( ) application_data = [ ] application = lines [ 0 ] . split ( ) [ 0 ] self . log . debug ( "Reading meta information for '%s'" % application ) for line in lines : if application is None : self . log . debug ( "Reading meta information for '%s'" % application ) application = line . split ( ) [ 0 ] application_data . append ( line ) if line . startswith ( application + b' Linux' ) : self . _record_app_data ( application_data ) application_data = [ ] application = None | 9,701 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/aanet.py#L692-L716 | [
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Parse raw metadata output for a single application | def _record_app_data ( self , data ) : name , revision = data [ 0 ] . split ( ) root_version = data [ 1 ] . split ( ) [ 1 ] command = b'\n' . join ( data [ 3 : ] ) . split ( b'\n' + name + b' Linux' ) [ 0 ] self . meta . append ( { 'application_name' : np . string_ ( name ) , 'revision' : np . string_ ( revision ) , 'root_version' : np . string_ ( root_version ) , 'command' : np . string_ ( command ) } ) | 9,702 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/aanet.py#L718-L741 | [
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Convert metadata to a KM3Pipe Table . | def get_table ( self , name = 'Meta' , h5loc = '/meta' ) : if not self . meta : return None data = defaultdict ( list ) for entry in self . meta : for key , value in entry . items ( ) : data [ key ] . append ( value ) dtypes = [ ] for key , values in data . items ( ) : max_len = max ( map ( len , values ) ) dtype = 'S{}' . format ( max_len ) dtypes . append ( ( key , dtype ) ) tab = Table ( data , dtype = dtypes , h5loc = h5loc , name = 'Meta' , h5singleton = True ) return tab | 9,703 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/aanet.py#L743-L767 | [
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Iterate over movies hash stored in the database . | def itermovieshash ( self ) : cur = self . _db . firstkey ( ) while cur is not None : yield cur cur = self . _db . nextkey ( cur ) | 9,704 | https://github.com/NaPs/Kolekto/blob/29c5469da8782780a06bf9a76c59414bb6fd8fe3/kolekto/db.py#L12-L18 | [
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Handles Datastore response errors according to their documentation . | def _max_retries_for_error ( self , error ) : status = error . get ( "status" ) if status == "ABORTED" and get_transactions ( ) > 0 : # Avoids retrying Conflicts when inside a transaction. return None return self . _MAX_RETRIES . get ( status ) | 9,705 | https://github.com/LeadPages/gcloud_requests/blob/8933363c4e9fa1e5ec0e90d683fca8ef8a949752/gcloud_requests/datastore.py#L56-L73 | [
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Initialize services without authenticating to Globus Auth . | def anonymous_login ( services ) : if isinstance ( services , str ) : services = [ services ] clients = { } # Initialize valid services for serv in services : try : clients [ serv ] = KNOWN_CLIENTS [ serv ] ( http_timeout = STD_TIMEOUT ) except KeyError : # No known client print ( "Error: No known client for '{}' service." . format ( serv ) ) except Exception : # Other issue, probably auth print ( "Error: Unable to create client for '{}' service.\n" "Anonymous access may not be allowed." . format ( serv ) ) return clients | 9,706 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L364-L390 | [
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Remove ALL tokens in the token directory . This will force re - authentication to all services . | def logout ( token_dir = DEFAULT_CRED_PATH ) : for f in os . listdir ( token_dir ) : if f . endswith ( "tokens.json" ) : try : os . remove ( os . path . join ( token_dir , f ) ) except OSError as e : # Eat ENOENT (no such file/dir, tokens already deleted) only, # raise any other issue (bad permissions, etc.) if e . errno != errno . ENOENT : raise | 9,707 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L393-L411 | [
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Format input into GMeta format suitable for ingesting into Globus Search . Formats a dictionary into a GMetaEntry . Formats a list of GMetaEntry into a GMetaList inside a GMetaIngest . | def format_gmeta ( data , acl = None , identifier = None ) : if isinstance ( data , dict ) : if acl is None or identifier is None : raise ValueError ( "acl and identifier are required when formatting a GMetaEntry." ) if isinstance ( acl , str ) : acl = [ acl ] # "Correctly" format ACL entries into URNs prefixed_acl = [ ] for uuid in acl : # If entry is not special value "public" and is not a URN, make URN # It is not known what the type of UUID is, so use both # This solution is known to be hacky if uuid != "public" and not uuid . lower ( ) . startswith ( "urn:" ) : prefixed_acl . append ( "urn:globus:auth:identity:" + uuid . lower ( ) ) prefixed_acl . append ( "urn:globus:groups:id:" + uuid . lower ( ) ) # Otherwise, no modification else : prefixed_acl . append ( uuid ) return { "@datatype" : "GMetaEntry" , "@version" : "2016-11-09" , "subject" : identifier , "visible_to" : prefixed_acl , "content" : data } elif isinstance ( data , list ) : return { "@datatype" : "GIngest" , "@version" : "2016-11-09" , "ingest_type" : "GMetaList" , "ingest_data" : { "@datatype" : "GMetaList" , "@version" : "2016-11-09" , "gmeta" : data } } else : raise TypeError ( "Cannot format '" + str ( type ( data ) ) + "' into GMeta." ) | 9,708 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L471-L539 | [
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Remove GMeta wrapping from a Globus Search result . This function can be called on the raw GlobusHTTPResponse that Search returns or a string or dictionary representation of it . | def gmeta_pop ( gmeta , info = False ) : if type ( gmeta ) is GlobusHTTPResponse : gmeta = json . loads ( gmeta . text ) elif type ( gmeta ) is str : gmeta = json . loads ( gmeta ) elif type ( gmeta ) is not dict : raise TypeError ( "gmeta must be dict, GlobusHTTPResponse, or JSON string" ) results = [ ] for res in gmeta [ "gmeta" ] : for con in res [ "content" ] : results . append ( con ) if info : fyi = { "total_query_matches" : gmeta . get ( "total" ) } return results , fyi else : return results | 9,709 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L542-L574 | [
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Translate a known Globus Search index into the index UUID . The UUID is the proper way to access indices and will eventually be the only way . This method will return names it cannot disambiguate . | def translate_index ( index_name ) : uuid = SEARCH_INDEX_UUIDS . get ( index_name . strip ( ) . lower ( ) ) if not uuid : try : index_info = globus_sdk . SearchClient ( ) . get_index ( index_name ) . data if not isinstance ( index_info , dict ) : raise ValueError ( "Multiple UUIDs possible" ) uuid = index_info . get ( "id" , index_name ) except Exception : uuid = index_name return uuid | 9,710 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L577-L598 | [
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Perform a Globus Transfer and monitor for success . | def quick_transfer ( transfer_client , source_ep , dest_ep , path_list , interval = None , retries = 10 , notify = True ) : if retries is None : retries = 0 iterations = 0 transfer = custom_transfer ( transfer_client , source_ep , dest_ep , path_list , notify = notify ) res = next ( transfer ) try : # Loop ends on StopIteration from generator exhaustion while True : if iterations < retries or retries == - 1 : res = transfer . send ( True ) iterations += 1 else : res = transfer . send ( False ) except StopIteration : pass if res [ "success" ] : error = "No error" else : error = "{}: {}" . format ( res . get ( "fatal_error" , { } ) . get ( "code" , "Error" ) , res . get ( "fatal_error" , { } ) . get ( "description" , "Unknown" ) ) return { "success" : res [ "success" ] , "task_id" : res [ "task_id" ] , "error" : error } | 9,711 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L805-L863 | [
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Compare two items without regard to order . | def insensitive_comparison ( item1 , item2 , type_insensitive = False , string_insensitive = False ) : # If type-sensitive, check types if not type_insensitive and type ( item1 ) != type ( item2 ) : return False # Handle Mapping objects (dict) if isinstance ( item1 , Mapping ) : # Second item must be Mapping if not isinstance ( item2 , Mapping ) : return False # Items must have the same number of elements if not len ( item1 ) == len ( item2 ) : return False # Keys must be the same if not insensitive_comparison ( list ( item1 . keys ( ) ) , list ( item2 . keys ( ) ) , type_insensitive = True ) : return False # Each key's value must be the same # We can just check item1.items because the keys are the same for key , val in item1 . items ( ) : if not insensitive_comparison ( item1 [ key ] , item2 [ key ] , type_insensitive = type_insensitive , string_insensitive = string_insensitive ) : return False # Keys and values are the same return True # Handle strings elif isinstance ( item1 , str ) : # Second item must be string if not isinstance ( item2 , str ) : return False # Items must have the same number of elements (except string_insensitive) if not len ( item1 ) == len ( item2 ) and not string_insensitive : return False # If we're insensitive to case, spaces, and order, compare characters if string_insensitive : # If the string is one character long, skip additional comparison if len ( item1 ) <= 1 : return item1 . lower ( ) == item2 . lower ( ) # Make strings into containers (lists) and discard whitespace item1_list = [ c for c in item1 . lower ( ) if not c . isspace ( ) ] item2_list = [ c for c in item2 . lower ( ) if not c . isspace ( ) ] # The insensitive args shouldn't matter, but they're here just in case return insensitive_comparison ( item1_list , item2_list , type_insensitive = type_insensitive , string_insensitive = string_insensitive ) # Otherwise, case and order matter else : return item1 == item2 # Handle other Iterable Containers elif isinstance ( item1 , Container ) and isinstance ( item1 , Iterable ) : # Second item must be an Iterable Container if not isinstance ( item2 , Container ) or not isinstance ( item2 , Iterable ) : return False # Items must have the same number of elements if not len ( item1 ) == len ( item2 ) : return False # Every element in item1 must be in item2, and vice-versa # Painfully slow, but unavoidable for deep comparison # Each match in item1 removes the corresponding element from item2_copy # If they're the same, item2_copy should be empty at the end, # unless a .remove() failed, in which case we have to re-match using item2 item2_copy = list ( deepcopy ( item2 ) ) remove_failed = False for elem in item1 : matched = False # Try every element for candidate in item2 : # If comparison succeeds, flag a match, remove match from copy, and dump out if insensitive_comparison ( elem , candidate , type_insensitive = type_insensitive , string_insensitive = string_insensitive ) : matched = True try : item2_copy . remove ( candidate ) except ValueError : # list.remove(x): x not in list remove_failed = True break # One failure indicates unequivalence if not matched : return False # If all removes succeeded, we can shortcut checking all item2 elements in item1 if not remove_failed : # If the Containers are equivalent, all elements in item2_copy should be removed # Otherwise return len ( item2_copy ) == 0 # If something failed, we have to verify all of item2 # We can't assume item2 != item1, because removal is comparative else : for elem in item2 : matched = False # Try every element for candidate in item1 : # If comparison succeeds, flag a match, remove match from copy, and dump out if insensitive_comparison ( elem , candidate , type_insensitive = type_insensitive , string_insensitive = string_insensitive ) : matched = True break # One failure indicates unequivalence if not matched : return False # All elements have a match return True # Handle otherwise unhandled type (catchall) else : return item1 == item2 | 9,712 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/toolbox.py#L933-L1071 | [
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Yield screen_name text tuples from a json file . | def parse_json ( json_file , include_date = False ) : if json_file [ - 2 : ] == 'gz' : fh = gzip . open ( json_file , 'rt' ) else : fh = io . open ( json_file , mode = 'rt' , encoding = 'utf8' ) for line in fh : try : jj = json . loads ( line ) if type ( jj ) is not list : jj = [ jj ] for j in jj : if include_date : yield ( j [ 'user' ] [ 'screen_name' ] . lower ( ) , j [ 'text' ] , j [ 'created_at' ] ) else : if 'full_text' in j : # get untruncated text if available. yield ( j [ 'user' ] [ 'screen_name' ] . lower ( ) , j [ 'full_text' ] ) else : yield ( j [ 'user' ] [ 'screen_name' ] . lower ( ) , j [ 'text' ] ) except Exception as e : sys . stderr . write ( 'skipping json error: %s\n' % e ) | 9,713 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/analyze.py#L50-L71 | [
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Yield screen_name string tuples where the string is the concatenation of all tweets of this user . | def extract_tweets ( json_file ) : for screen_name , tweet_iter in groupby ( parse_json ( json_file ) , lambda x : x [ 0 ] ) : tweets = [ t [ 1 ] for t in tweet_iter ] yield screen_name , ' ' . join ( tweets ) | 9,714 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/analyze.py#L74-L79 | [
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Return a matrix where each row corresponds to a Twitter account and each column corresponds to the number of times a term is used by that account . | def vectorize ( json_file , vec , dofit = True ) : ## CountVectorizer, efficiently. screen_names = [ x [ 0 ] for x in extract_tweets ( json_file ) ] if dofit : X = vec . fit_transform ( x [ 1 ] for x in extract_tweets ( json_file ) ) else : X = vec . transform ( x [ 1 ] for x in extract_tweets ( json_file ) ) return screen_names , X | 9,715 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/analyze.py#L99-L109 | [
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Read a file of follower information and return a dictionary mapping screen_name to a set of follower ids . | def read_follower_file ( fname , min_followers = 0 , max_followers = 1e10 , blacklist = set ( ) ) : result = { } with open ( fname , 'rt' ) as f : for line in f : parts = line . split ( ) if len ( parts ) > 3 : if parts [ 1 ] . lower ( ) not in blacklist : followers = set ( int ( x ) for x in parts [ 2 : ] ) if len ( followers ) > min_followers and len ( followers ) <= max_followers : result [ parts [ 1 ] . lower ( ) ] = followers else : print ( 'skipping exemplar' , parts [ 1 ] . lower ( ) ) return result | 9,716 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/analyze.py#L171-L184 | [
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Return the average Jaccard similarity between a brand s followers and the followers of each exemplar . We merge all exemplar followers into one big pseudo - account . | def jaccard_merge ( brands , exemplars ) : scores = { } exemplar_followers = set ( ) for followers in exemplars . values ( ) : exemplar_followers |= followers for brand , followers in brands : scores [ brand ] = _jaccard ( followers , exemplar_followers ) return scores | 9,717 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/analyze.py#L235-L246 | [
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Return the cosine similarity betwee a brand s followers and the exemplars . | def cosine ( brands , exemplars , weighted_avg = False , sqrt = False ) : scores = { } for brand , followers in brands : if weighted_avg : scores [ brand ] = np . average ( [ _cosine ( followers , others ) for others in exemplars . values ( ) ] , weights = [ 1. / len ( others ) for others in exemplars . values ( ) ] ) else : scores [ brand ] = 1. * sum ( _cosine ( followers , others ) for others in exemplars . values ( ) ) / len ( exemplars ) if sqrt : scores = dict ( [ ( b , math . sqrt ( s ) ) for b , s in scores . items ( ) ] ) return scores | 9,718 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/analyze.py#L319-L332 | [
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Generate a filename like Google for a song based on metadata . | def suggest_filename ( metadata ) : if 'title' in metadata and 'track_number' in metadata : # Music Manager. suggested_filename = f"{metadata['track_number']:0>2} {metadata['title']}" elif 'title' in metadata and 'trackNumber' in metadata : # Mobile. suggested_filename = f"{metadata['trackNumber']:0>2} {metadata['title']}" elif 'title' in metadata and 'tracknumber' in metadata : # audio-metadata/mutagen. track_number = _split_number_field ( list_to_single_value ( metadata [ 'tracknumber' ] ) ) title = list_to_single_value ( metadata [ 'title' ] ) suggested_filename = f"{track_number:0>2} {title}" else : suggested_filename = f"00 {list_to_single_value(metadata.get('title', ['']))}" return _replace_invalid_characters ( suggested_filename ) | 9,719 | https://github.com/thebigmunch/google-music-utils/blob/2e8873defe7d5aab7321b9d5ec8a80d72687578e/src/google_music_utils/misc.py#L25-L51 | [
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Create directory structure and file name based on metadata template . | def template_to_filepath ( template , metadata , template_patterns = None ) : path = Path ( template ) if template_patterns is None : template_patterns = TEMPLATE_PATTERNS suggested_filename = suggest_filename ( metadata ) if ( path == Path . cwd ( ) or path == Path ( '%suggested%' ) ) : filepath = Path ( suggested_filename ) elif any ( template_pattern in path . parts for template_pattern in template_patterns ) : if template . endswith ( ( '/' , '\\' ) ) : template += suggested_filename path = Path ( template . replace ( '%suggested%' , suggested_filename ) ) parts = [ ] for part in path . parts : if part == path . anchor : parts . append ( part ) else : for key in template_patterns : if ( # pragma: no branch key in part and any ( field in metadata for field in template_patterns [ key ] ) ) : field = more_itertools . first_true ( template_patterns [ key ] , pred = lambda k : k in metadata ) if key . startswith ( ( '%disc' , '%track' ) ) : number = _split_number_field ( str ( list_to_single_value ( metadata [ field ] ) ) ) if key . endswith ( '2%' ) : metadata [ field ] = number . zfill ( 2 ) else : metadata [ field ] = number part = part . replace ( key , list_to_single_value ( metadata [ field ] ) ) parts . append ( _replace_invalid_characters ( part ) ) filepath = Path ( * parts ) elif '%suggested%' in template : filepath = Path ( template . replace ( '%suggested%' , suggested_filename ) ) elif template . endswith ( ( '/' , '\\' ) ) : filepath = path / suggested_filename else : filepath = path return filepath | 9,720 | https://github.com/thebigmunch/google-music-utils/blob/2e8873defe7d5aab7321b9d5ec8a80d72687578e/src/google_music_utils/misc.py#L54-L138 | [
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Match an item metadata field value by pattern . | def _match_field ( field_value , pattern , ignore_case = False , normalize_values = False ) : if normalize_values : ignore_case = True normalize = normalize_value if normalize_values else lambda x : str ( x ) search = functools . partial ( re . search , flags = re . I ) if ignore_case else re . search # audio_metadata fields contain a list of values. if isinstance ( field_value , list ) : return any ( search ( pattern , normalize ( value ) ) for value in field_value ) else : return search ( pattern , normalize ( field_value ) ) | 9,721 | https://github.com/thebigmunch/google-music-utils/blob/2e8873defe7d5aab7321b9d5ec8a80d72687578e/src/google_music_utils/filter.py#L14-L43 | [
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Compute the qth percentile of the data along the specified axis . Simpler version than the numpy version that always flattens input arrays . | def percentile ( a , q ) : if not a : return None if isinstance ( q , ( float , int ) ) : qq = [ q ] elif isinstance ( q , ( tuple , list ) ) : qq = q else : raise ValueError ( "Quantile type {} not understood" . format ( type ( q ) ) ) if isinstance ( a , ( float , int ) ) : a = [ a ] for i in range ( len ( qq ) ) : if qq [ i ] < 0. or qq [ i ] > 100. : raise ValueError ( "Percentiles must be in the range [0,100]" ) qq [ i ] /= 100. a = sorted ( flatten ( a ) ) r = [ ] for q in qq : k = ( len ( a ) - 1 ) * q f = math . floor ( k ) c = math . ceil ( k ) if f == c : r . append ( float ( a [ int ( k ) ] ) ) continue d0 = a [ int ( f ) ] * ( c - k ) d1 = a [ int ( c ) ] * ( k - f ) r . append ( float ( d0 + d1 ) ) if len ( r ) == 1 : return r [ 0 ] return r | 9,725 | https://github.com/IRC-SPHERE/HyperStream/blob/98478f4d31ed938f4aa7c958ed0d4c3ffcb2e780/hyperstream/utils/statistics/percentile.py#L33-L90 | [
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Helper to filter the closest station to a given location . | def _filter_closest ( self , lat , lon , stations ) : current_location = ( lat , lon ) closest = None closest_distance = None for station in stations : station_loc = ( station . latitude , station . longitude ) station_distance = distance . distance ( current_location , station_loc ) . km if not closest or station_distance < closest_distance : closest = station closest_distance = station_distance return closest | 9,726 | https://github.com/dgomes/pyipma/blob/cd808abeb70dca0e336afdf55bef3f73973eaa71/pyipma/station.py#L20-L34 | [
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Retrieve the nearest station . | async def get ( cls , websession , lat , lon ) : self = Station ( websession ) stations = await self . api . stations ( ) self . station = self . _filter_closest ( lat , lon , stations ) logger . info ( "Using %s as weather station" , self . station . local ) return self | 9,727 | https://github.com/dgomes/pyipma/blob/cd808abeb70dca0e336afdf55bef3f73973eaa71/pyipma/station.py#L37-L48 | [
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Loads the channels and tools given the plugin path specified | def load_channels ( self ) : channels = [ ] # Try to get channels for channel_name in self . channel_names : channel_path = os . path . join ( self . path , "channels" ) sys . path . append ( self . path ) mod = imp . load_module ( channel_name , * imp . find_module ( channel_name , [ channel_path ] ) ) cls = getattr ( mod , channel_name . title ( ) . replace ( "_" , "" ) ) channel_id = channel_name . split ( "_" ) [ 0 ] # TODO: what about up_to_timestamp? try : channels . append ( cls ( channel_id , up_to_timestamp = None ) ) except TypeError : channels . append ( cls ( channel_id ) ) # Try to get tools if self . has_tools : tool_path = os . path . join ( self . path , "tools" ) # Create a tool channel using this path channel_id = self . channel_id_prefix + "_" + "tools" channel = ToolChannel ( channel_id , tool_path , up_to_timestamp = utcnow ( ) ) channels . append ( channel ) if self . has_assets : assset_path = os . path . join ( os . path . abspath ( self . path ) , "assets" ) channel_id = self . channel_id_prefix + "_" + "assets" channel = AssetsFileChannel ( channel_id , assset_path , up_to_timestamp = utcnow ( ) ) channels . append ( channel ) # # from . import TimeInterval # channel.streams.values()[0].window(TimeInterval.up_to_now()).items() return channels | 9,728 | https://github.com/IRC-SPHERE/HyperStream/blob/98478f4d31ed938f4aa7c958ed0d4c3ffcb2e780/hyperstream/plugin_manager.py#L38-L76 | [
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return the number of byte - chunks in a swatch object | def chunk_count ( swatch ) : if type ( swatch ) is dict : if 'data' in swatch : return 1 if 'swatches' in swatch : return 2 + len ( swatch [ 'swatches' ] ) else : return sum ( map ( chunk_count , swatch ) ) | 9,729 | https://github.com/nsfmc/swatch/blob/8654edf4f1aeef37d42211ff3fe6a3e9e4325859/swatch/writer.py#L18-L30 | [
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builds up a byte - chunk for a color | def chunk_for_color ( obj ) : title = obj [ 'name' ] + '\0' title_length = len ( title ) chunk = struct . pack ( '>H' , title_length ) chunk += title . encode ( 'utf-16be' ) mode = obj [ 'data' ] [ 'mode' ] . encode ( ) values = obj [ 'data' ] [ 'values' ] color_type = obj [ 'type' ] fmt = { b'RGB' : '!fff' , b'Gray' : '!f' , b'CMYK' : '!ffff' , b'LAB' : '!fff' } if mode in fmt : padded_mode = mode . decode ( ) . ljust ( 4 ) . encode ( ) chunk += struct . pack ( '!4s' , padded_mode ) # the color mode chunk += struct . pack ( fmt [ mode ] , * values ) # the color values color_types = [ 'Global' , 'Spot' , 'Process' ] if color_type in color_types : color_int = color_types . index ( color_type ) chunk += struct . pack ( '>h' , color_int ) # append swatch mode chunk = struct . pack ( '>I' , len ( chunk ) ) + chunk # prepend the chunk size return b'\x00\x01' + chunk | 9,730 | https://github.com/nsfmc/swatch/blob/8654edf4f1aeef37d42211ff3fe6a3e9e4325859/swatch/writer.py#L39-L83 | [
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produce a byte - chunk for a folder of colors | def chunk_for_folder ( obj ) : title = obj [ 'name' ] + '\0' title_length = len ( title ) chunk_body = struct . pack ( '>H' , title_length ) # title length chunk_body += title . encode ( 'utf-16be' ) # title chunk_head = b'\xC0\x01' # folder header chunk_head += struct . pack ( '>I' , len ( chunk_body ) ) # precede entire chunk by folder header and size of folder chunk = chunk_head + chunk_body chunk += b'' . join ( [ chunk_for_color ( c ) for c in obj [ 'swatches' ] ] ) chunk += b'\xC0\x02' # folder terminator chunk chunk += b'\x00\x00\x00\x00' # folder terminator return chunk | 9,731 | https://github.com/nsfmc/swatch/blob/8654edf4f1aeef37d42211ff3fe6a3e9e4325859/swatch/writer.py#L85-L121 | [
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Fetch up to limit tweets for each account in account_file and write to outfile . | def fetch_tweets ( account_file , outfile , limit ) : print ( 'fetching tweets for accounts in' , account_file ) outf = io . open ( outfile , 'wt' ) for screen_name in iter_lines ( account_file ) : print ( '\nFetching tweets for %s' % screen_name ) for tweet in twutil . collect . tweets_for_user ( screen_name , limit ) : tweet [ 'user' ] [ 'screen_name' ] = screen_name outf . write ( '%s\n' % json . dumps ( tweet , ensure_ascii = False ) ) outf . flush ( ) | 9,733 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/collect.py#L85-L95 | [
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Fetch top lists matching this keyword then return Twitter screen names along with the number of different lists on which each appers .. | def fetch_exemplars ( keyword , outfile , n = 50 ) : list_urls = fetch_lists ( keyword , n ) print ( 'found %d lists for %s' % ( len ( list_urls ) , keyword ) ) counts = Counter ( ) for list_url in list_urls : counts . update ( fetch_list_members ( list_url ) ) # Write to file. outf = io . open ( outfile , 'wt' ) for handle in sorted ( counts ) : outf . write ( '%s\t%d\n' % ( handle , counts [ handle ] ) ) outf . close ( ) print ( 'saved exemplars to' , outfile ) | 9,734 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/collect.py#L171-L184 | [
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Wait for the next packet and put it in the blob | def process ( self , blob ) : # self._add_process_dt() try : log . debug ( "Waiting for queue items." ) prefix , data = self . queue . get ( timeout = self . timeout ) log . debug ( "Got {0} bytes from queue." . format ( len ( data ) ) ) except Empty : log . warning ( "ControlHost timeout ({0}s) reached" . format ( self . timeout ) ) raise StopIteration ( "ControlHost timeout reached." ) blob [ self . key_for_prefix ] = prefix blob [ self . key_for_data ] = data return blob | 9,736 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/ch.py#L140-L154 | [
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Clean up the JLigier controlhost connection | def finish ( self ) : log . debug ( "Disconnecting from JLigier." ) self . client . socket . shutdown ( socket . SHUT_RDWR ) self . client . _disconnect ( ) | 9,737 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/ch.py#L176-L180 | [
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List available variables and applies any filters . | def list_variables ( self ) : station_codes = self . _get_station_codes ( ) station_codes = self . _apply_features_filter ( station_codes ) variables = self . _list_variables ( station_codes ) if hasattr ( self , "_variables" ) and self . variables is not None : variables . intersection_update ( set ( self . variables ) ) return list ( variables ) | 9,738 | https://github.com/ioos/pyoos/blob/908660385029ecd8eccda8ab3a6b20b47b915c77/pyoos/collectors/hads/hads.py#L50-L61 | [
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Internal helper to list the variables for the given station codes . | def _list_variables ( self , station_codes ) : # sample output from obs retrieval: # # DD9452D0 # HP(SRBM5) # 2013-07-22 19:30 45.97 # HT(SRBM5) # 2013-07-22 19:30 44.29 # PC(SRBM5) # 2013-07-22 19:30 36.19 # rvar = re . compile ( r"\n\s([A-Z]{2}[A-Z0-9]{0,1})\(\w+\)" ) variables = set ( ) resp = requests . post ( self . obs_retrieval_url , data = { "state" : "nil" , "hsa" : "nil" , "of" : "3" , "extraids" : " " . join ( station_codes ) , "sinceday" : - 1 , } , ) resp . raise_for_status ( ) list ( map ( variables . add , rvar . findall ( resp . text ) ) ) return variables | 9,739 | https://github.com/ioos/pyoos/blob/908660385029ecd8eccda8ab3a6b20b47b915c77/pyoos/collectors/hads/hads.py#L63-L93 | [
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If the features filter is set this will return the intersection of those filter items and the given station codes . | def _apply_features_filter ( self , station_codes ) : # apply features filter if hasattr ( self , "features" ) and self . features is not None : station_codes = set ( station_codes ) station_codes = list ( station_codes . intersection ( set ( self . features ) ) ) return station_codes | 9,740 | https://github.com/ioos/pyoos/blob/908660385029ecd8eccda8ab3a6b20b47b915c77/pyoos/collectors/hads/hads.py#L124-L136 | [
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Gets and caches a list of station codes optionally within a bbox . | def _get_station_codes ( self , force = False ) : if not force and self . station_codes is not None : return self . station_codes state_urls = self . _get_state_urls ( ) # filter by bounding box against a shapefile state_matches = None if self . bbox : with collection ( os . path . join ( "resources" , "ne_50m_admin_1_states_provinces_lakes_shp.shp" , ) , "r" , ) as c : geom_matches = [ x [ "properties" ] for x in c . filter ( bbox = self . bbox ) ] state_matches = [ x [ "postal" ] if x [ "admin" ] != "Canada" else "CN" for x in geom_matches ] self . station_codes = [ ] for state_url in state_urls : if state_matches is not None : state_abbr = state_url . split ( "/" ) [ - 1 ] . split ( "." ) [ 0 ] if state_abbr not in state_matches : continue self . station_codes . extend ( self . _get_stations_for_state ( state_url ) ) if self . bbox : # retrieve metadata for all stations to properly filter them metadata = self . _get_metadata ( self . station_codes ) parsed_metadata = self . parser . _parse_metadata ( metadata ) def in_bbox ( code ) : lat = parsed_metadata [ code ] [ "latitude" ] lon = parsed_metadata [ code ] [ "longitude" ] return ( lon >= self . bbox [ 0 ] and lon <= self . bbox [ 2 ] and lat >= self . bbox [ 1 ] and lat <= self . bbox [ 3 ] ) self . station_codes = list ( filter ( in_bbox , self . station_codes ) ) return self . station_codes | 9,741 | https://github.com/ioos/pyoos/blob/908660385029ecd8eccda8ab3a6b20b47b915c77/pyoos/collectors/hads/hads.py#L158-L216 | [
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Wrapper that handles the actual asynchronous monitoring of the task state . | def _monitor_task ( self ) : if self . task . state in states . UNREADY_STATES : reactor . callLater ( self . POLL_PERIOD , self . _monitor_task ) return if self . task . state == 'SUCCESS' : self . callback ( self . task . result ) elif self . task . state == 'FAILURE' : self . errback ( Failure ( self . task . result ) ) elif self . task . state == 'REVOKED' : self . errback ( Failure ( defer . CancelledError ( 'Task {0}' . format ( self . task . id ) ) ) ) else : self . errback ( ValueError ( 'Cannot respond to `{}` state' . format ( self . task . state ) ) ) | 9,742 | https://github.com/SentimensRG/txCelery/blob/15b9705198009f5ce6db1bfd0a8af9b8949d6277/txcelery/defer.py#L52-L71 | [
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Clean up a query string for searching . | def _clean_query_string ( q ) : q = q . replace ( "()" , "" ) . strip ( ) if q . endswith ( "(" ) : q = q [ : - 1 ] . strip ( ) # Remove misplaced AND/OR/NOT at end if q [ - 3 : ] == "AND" or q [ - 3 : ] == "NOT" : q = q [ : - 3 ] elif q [ - 2 : ] == "OR" : q = q [ : - 2 ] # Balance parentheses while q . count ( "(" ) > q . count ( ")" ) : q += ")" while q . count ( ")" ) > q . count ( "(" ) : q = "(" + q return q . strip ( ) | 9,743 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L40-L66 | [
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Validate and clean up a query to be sent to Search . Cleans the query string removes unneeded parameters and validates for correctness . Does not modify the original argument . Raises an Exception on invalid input . | def _validate_query ( query ) : query = deepcopy ( query ) # q is always required if query [ "q" ] == BLANK_QUERY [ "q" ] : raise ValueError ( "No query specified." ) query [ "q" ] = _clean_query_string ( query [ "q" ] ) # limit should be set to appropriate default if not specified if query [ "limit" ] is None : query [ "limit" ] = SEARCH_LIMIT if query [ "advanced" ] else NONADVANCED_LIMIT # If specified, the limit should not be greater than the Search maximum elif query [ "limit" ] > SEARCH_LIMIT : warnings . warn ( 'Reduced result limit from {} to the Search maximum: {}' . format ( query [ "limit" ] , SEARCH_LIMIT ) , RuntimeWarning ) query [ "limit" ] = SEARCH_LIMIT # Remove all blank/default values for key , val in BLANK_QUERY . items ( ) : # Default for get is NaN so comparison is always False if query . get ( key , float ( 'nan' ) ) == val : query . pop ( key ) # Remove unsupported fields to_remove = [ field for field in query . keys ( ) if field not in BLANK_QUERY . keys ( ) ] [ query . pop ( field ) for field in to_remove ] return query | 9,744 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L69-L107 | [
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Add an operator between terms . There must be a term added before using this method . All operators have helpers so this method is usually not necessary to directly invoke . | def _operator ( self , op , close_group = False ) : op = op . upper ( ) . strip ( ) if op not in OP_LIST : raise ValueError ( "Error: '{}' is not a valid operator." . format ( op ) ) else : if close_group : op = ") " + op + " (" else : op = " " + op + " " self . __query [ "q" ] += op return self | 9,746 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L244-L271 | [
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Combine terms with AND . There must be a term added before using this method . | def _and_join ( self , close_group = False ) : if not self . initialized : raise ValueError ( "You must add a search term before adding an operator." ) else : self . _operator ( "AND" , close_group = close_group ) return self | 9,747 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L273-L294 | [
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Combine terms with OR . There must be a term added before using this method . | def _or_join ( self , close_group = False ) : if not self . initialized : raise ValueError ( "You must add a search term before adding an operator." ) else : self . _operator ( "OR" , close_group = close_group ) return self | 9,748 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L296-L317 | [
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Fetch the entire mapping for the specified index . | def _mapping ( self ) : return ( self . __search_client . get ( "/unstable/index/{}/mapping" . format ( mdf_toolbox . translate_index ( self . index ) ) ) [ "mappings" ] ) | 9,749 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L418-L426 | [
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Add a fulltext search term to the query . | def match_term ( self , value , required = True , new_group = False ) : # If not the start of the query string, add an AND or OR if self . initialized : if required : self . _and_join ( new_group ) else : self . _or_join ( new_group ) self . _term ( value ) return self | 9,750 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L435-L463 | [
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Require a field to exist in the results . Matches will have some value in field . | def match_exists ( self , field , required = True , new_group = False ) : return self . match_field ( field , "*" , required = required , new_group = new_group ) | 9,751 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L522-L541 | [
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Retrieve and return the mapping for the given metadata block . | def show_fields ( self , block = None ) : mapping = self . _mapping ( ) if block is None : return mapping elif block == "top" : blocks = set ( ) for key in mapping . keys ( ) : blocks . add ( key . split ( "." ) [ 0 ] ) block_map = { } for b in blocks : block_map [ b ] = "object" else : block_map = { } for key , value in mapping . items ( ) : if key . startswith ( block ) : block_map [ key ] = value return block_map | 9,753 | https://github.com/materials-data-facility/toolbox/blob/2a4ac2b6a892238263008efa6a5f3923d9a83505/mdf_toolbox/search_helper.py#L764-L792 | [
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Create structured dtype from a 2d ndarray with unstructured dtype . | def inflate_dtype ( arr , names ) : arr = np . asanyarray ( arr ) if has_structured_dt ( arr ) : return arr . dtype s_dt = arr . dtype dt = [ ( n , s_dt ) for n in names ] dt = np . dtype ( dt ) return dt | 9,754 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L48-L56 | [
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Generate a table from a dictionary of arrays . | def from_dict ( cls , arr_dict , dtype = None , fillna = False , * * kwargs ) : # i hope order of keys == order or values if dtype is None : names = sorted ( list ( arr_dict . keys ( ) ) ) else : dtype = np . dtype ( dtype ) dt_names = [ f for f in dtype . names ] dict_names = [ k for k in arr_dict . keys ( ) ] missing_names = set ( dt_names ) - set ( dict_names ) if missing_names : if fillna : dict_names = dt_names for missing_name in missing_names : arr_dict [ missing_name ] = np . nan else : raise KeyError ( 'Dictionary keys and dtype fields do not match!' ) names = list ( dtype . names ) arr_dict = cls . _expand_scalars ( arr_dict ) data = [ arr_dict [ key ] for key in names ] return cls ( np . rec . fromarrays ( data , names = names , dtype = dtype ) , * * kwargs ) | 9,755 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L246-L270 | [
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Create a table from a predefined datatype . | def from_template ( cls , data , template ) : name = DEFAULT_NAME if isinstance ( template , str ) : name = template table_info = TEMPLATES [ name ] else : table_info = template if 'name' in table_info : name = table_info [ 'name' ] dt = table_info [ 'dtype' ] loc = table_info [ 'h5loc' ] split = table_info [ 'split_h5' ] h5singleton = table_info [ 'h5singleton' ] return cls ( data , h5loc = loc , dtype = dt , split_h5 = split , name = name , h5singleton = h5singleton ) | 9,756 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L314-L348 | [
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Append new columns to the table . | def append_columns ( self , colnames , values , * * kwargs ) : n = len ( self ) if np . isscalar ( values ) : values = np . full ( n , values ) values = np . atleast_1d ( values ) if not isinstance ( colnames , str ) and len ( colnames ) > 1 : values = np . atleast_2d ( values ) self . _check_column_length ( values , n ) if values . ndim == 1 : if len ( values ) > n : raise ValueError ( "New Column is longer than existing table!" ) elif len ( values ) > 1 and len ( values ) < n : raise ValueError ( "New Column is shorter than existing table, " "but not just one element!" ) elif len ( values ) == 1 : values = np . full ( n , values [ 0 ] ) new_arr = rfn . append_fields ( self , colnames , values , usemask = False , asrecarray = True , * * kwargs ) return self . __class__ ( new_arr , h5loc = self . h5loc , split_h5 = self . split_h5 , name = self . name , h5singleton = self . h5singleton ) | 9,757 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L362-L402 | [
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Drop columns from the table . | def drop_columns ( self , colnames , * * kwargs ) : new_arr = rfn . drop_fields ( self , colnames , usemask = False , asrecarray = True , * * kwargs ) return self . __class__ ( new_arr , h5loc = self . h5loc , split_h5 = self . split_h5 , name = self . name , h5singleton = self . h5singleton ) | 9,758 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L404-L419 | [
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Sort array by a column . | def sorted ( self , by , * * kwargs ) : sort_idc = np . argsort ( self [ by ] , * * kwargs ) return self . __class__ ( self [ sort_idc ] , h5loc = self . h5loc , split_h5 = self . split_h5 , name = self . name ) | 9,759 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L421-L435 | [
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Merge a list of tables | def merge ( cls , tables , fillna = False ) : cols = set ( itertools . chain ( * [ table . dtype . descr for table in tables ] ) ) tables_to_merge = [ ] for table in tables : missing_cols = cols - set ( table . dtype . descr ) if missing_cols : if fillna : n = len ( table ) n_cols = len ( missing_cols ) col_names = [ ] for col_name , col_dtype in missing_cols : if 'f' not in col_dtype : raise ValueError ( "Cannot create NaNs for non-float" " type column '{}'" . format ( col_name ) ) col_names . append ( col_name ) table = table . append_columns ( col_names , np . full ( ( n_cols , n ) , np . nan ) ) else : raise ValueError ( "Table columns do not match. Use fill_na=True" " if you want to append missing values with NaNs" ) tables_to_merge . append ( table ) first_table = tables_to_merge [ 0 ] merged_table = sum ( tables_to_merge [ 1 : ] , first_table ) merged_table . h5loc = first_table . h5loc merged_table . h5singleton = first_table . h5singleton merged_table . split_h5 = first_table . split_h5 merged_table . name = first_table . name return merged_table | 9,760 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/dataclasses.py#L447-L487 | [
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An helper function to create index tuples for fast lookup in HDF5Pump | def create_index_tuple ( group_ids ) : max_group_id = np . max ( group_ids ) start_idx_arr = np . full ( max_group_id + 1 , 0 ) n_items_arr = np . full ( max_group_id + 1 , 0 ) current_group_id = group_ids [ 0 ] current_idx = 0 item_count = 0 for group_id in group_ids : if group_id != current_group_id : start_idx_arr [ current_group_id ] = current_idx n_items_arr [ current_group_id ] = item_count current_idx += item_count item_count = 0 current_group_id = group_id item_count += 1 else : start_idx_arr [ current_group_id ] = current_idx n_items_arr [ current_group_id ] = item_count return ( start_idx_arr , n_items_arr ) | 9,761 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/hdf5.py#L892-L915 | [
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Traverse the internal dictionary and set the getters | def _set_attributes ( self ) : for parameter , data in self . _data . items ( ) : if isinstance ( data , dict ) or isinstance ( data , OrderedDict ) : field_names , field_values = zip ( * data . items ( ) ) sorted_indices = np . argsort ( field_names ) attr = namedtuple ( parameter , [ field_names [ i ] for i in sorted_indices ] ) setattr ( self , parameter , attr ( * [ field_values [ i ] for i in sorted_indices ] ) ) else : setattr ( self , parameter , data ) | 9,762 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/hdf5.py#L74-L88 | [
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Writes all the cached NDArrays to disk and empties the cache | def _write_ndarrays_cache_to_disk ( self ) : for h5loc , arrs in self . _ndarrays_cache . items ( ) : title = arrs [ 0 ] . title chunkshape = ( self . chunksize , ) + arrs [ 0 ] . shape [ 1 : ] if self . chunksize is not None else None arr = NDArray ( np . concatenate ( arrs ) , h5loc = h5loc , title = title ) if h5loc not in self . _ndarrays : loc , tabname = os . path . split ( h5loc ) ndarr = self . h5file . create_earray ( loc , tabname , tb . Atom . from_dtype ( arr . dtype ) , ( 0 , ) + arr . shape [ 1 : ] , chunkshape = chunkshape , title = title , filters = self . filters , createparents = True , ) self . _ndarrays [ h5loc ] = ndarr else : ndarr = self . _ndarrays [ h5loc ] idx_table_h5loc = h5loc + '_indices' if idx_table_h5loc not in self . indices : self . indices [ idx_table_h5loc ] = HDF5IndexTable ( idx_table_h5loc ) idx_tab = self . indices [ idx_table_h5loc ] for arr_length in ( len ( a ) for a in arrs ) : idx_tab . append ( arr_length ) ndarr . append ( arr ) self . _ndarrays_cache = defaultdict ( list ) | 9,763 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/hdf5.py#L254-L289 | [
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Sample program to test GLWindow . | def main ( ) : print ( 'GLWindow:' , GLWindow . __version__ ) print ( 'Python:' , sys . version ) print ( 'Platform:' , sys . platform ) wnd = GLWindow . create_window ( ( 480 , 480 ) , title = 'GLWindow Sample' ) wnd . vsync = False ctx = ModernGL . create_context ( ) prog = ctx . program ( [ ctx . vertex_shader ( '''
#version 330
in vec2 vert;
in vec4 vert_color;
out vec4 frag_color;
uniform vec2 scale;
uniform float rotation;
void main() {
frag_color = vert_color;
float r = rotation * (0.5 + gl_InstanceID * 0.05);
mat2 rot = mat2(cos(r), sin(r), -sin(r), cos(r));
gl_Position = vec4((rot * vert) * scale, 0.0, 1.0);
}
''' ) , ctx . fragment_shader ( '''
#version 330
in vec4 frag_color;
out vec4 color;
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''' ) , ] ) scale = prog . uniforms [ 'scale' ] rotation = prog . uniforms [ 'rotation' ] vbo = ctx . buffer ( struct . pack ( '18f' , 1.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.5 , - 0.5 , 0.86 , 0.0 , 1.0 , 0.0 , 0.5 , - 0.5 , - 0.86 , 0.0 , 0.0 , 1.0 , 0.5 , ) ) vao = ctx . simple_vertex_array ( prog , vbo , [ 'vert' , 'vert_color' ] ) while wnd . update ( ) : wnd . clear ( 0.95 , 0.95 , 0.95 ) width , height = wnd . size scale . value = ( height / width * 0.75 , 0.75 ) ctx . viewport = wnd . viewport ctx . enable ( ModernGL . BLEND ) rotation . value = wnd . time vao . render ( instances = 10 ) | 9,765 | https://github.com/cprogrammer1994/GLWindow/blob/521e18fcbc15e88d3c1f3547aa313c3a07386ee5/GLWindow/__main__.py#L12-L71 | [
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Add the header to the CSV file | def write_header ( fobj ) : fobj . write ( "# K40 calibration results\n" ) fobj . write ( "det_id\trun_id\tdom_id" ) for param in [ 't0' , 'qe' ] : for i in range ( 31 ) : fobj . write ( "\t{}_ch{}" . format ( param , i ) ) | 9,766 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/examples/offline_analysis/k40summary.py#L30-L36 | [
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Return the azimuth angle in radians . | def azimuth ( v ) : v = np . atleast_2d ( v ) azi = phi ( v ) - np . pi azi [ azi < 0 ] += 2 * np . pi if len ( azi ) == 1 : return azi [ 0 ] return azi | 9,767 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L119-L133 | [
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Returns the unit vector of the vector . | def unit_vector ( vector , * * kwargs ) : # This also works for a dataframe with columns ['x', 'y', 'z'] # However, the division operation is picky about the shapes # So, remember input vector shape, cast all up to 2d, # do the (ugly) conversion, then return unit in same shape as input # of course, the numpy-ized version of the input... vector = np . array ( vector ) out_shape = vector . shape vector = np . atleast_2d ( vector ) unit = vector / np . linalg . norm ( vector , axis = 1 , * * kwargs ) [ : , None ] return unit . reshape ( out_shape ) | 9,768 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L175-L186 | [
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Calculate the point - line - distance for given point and line . | def pld3 ( pos , line_vertex , line_dir ) : pos = np . atleast_2d ( pos ) line_vertex = np . atleast_1d ( line_vertex ) line_dir = np . atleast_1d ( line_dir ) c = np . cross ( line_dir , line_vertex - pos ) n1 = np . linalg . norm ( c , axis = 1 ) n2 = np . linalg . norm ( line_dir ) out = n1 / n2 if out . ndim == 1 and len ( out ) == 1 : return out [ 0 ] return out | 9,769 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L189-L200 | [
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Return the distance between two points . | def dist ( x1 , x2 , axis = 0 ) : return np . linalg . norm ( x2 - x1 , axis = axis ) | 9,770 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L207-L212 | [
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Calculate center of mass for given points . If masses is not set assume equal masses . | def com ( points , masses = None ) : if masses is None : return np . average ( points , axis = 0 ) else : return np . average ( points , axis = 0 , weights = masses ) | 9,771 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L215-L221 | [
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Calculate the circular permutation for a given list of items . | def circ_permutation ( items ) : permutations = [ ] for i in range ( len ( items ) ) : permutations . append ( items [ i : ] + items [ : i ] ) return permutations | 9,772 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L224-L229 | [
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Inertia tensor stolen of thomas | def inertia ( x , y , z , weight = None ) : if weight is None : weight = 1 tensor_of_inertia = np . zeros ( ( 3 , 3 ) , dtype = float ) tensor_of_inertia [ 0 ] [ 0 ] = ( y * y + z * z ) * weight tensor_of_inertia [ 0 ] [ 1 ] = ( - 1 ) * x * y * weight tensor_of_inertia [ 0 ] [ 2 ] = ( - 1 ) * x * z * weight tensor_of_inertia [ 1 ] [ 0 ] = ( - 1 ) * x * y * weight tensor_of_inertia [ 1 ] [ 1 ] = ( x * x + z * z ) * weight tensor_of_inertia [ 1 ] [ 2 ] = ( - 1 ) * y * z * weight tensor_of_inertia [ 2 ] [ 0 ] = ( - 1 ) * x * z * weight tensor_of_inertia [ 2 ] [ 1 ] = ( - 1 ) * z * y * weight tensor_of_inertia [ 2 ] [ 2 ] = ( x * x + y * y ) * weight eigen_values = np . linalg . eigvals ( tensor_of_inertia ) small_inertia = eigen_values [ 2 ] [ 2 ] middle_inertia = eigen_values [ 1 ] [ 1 ] big_inertia = eigen_values [ 0 ] [ 0 ] return small_inertia , middle_inertia , big_inertia | 9,773 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L381-L400 | [
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Rotate a 3D vector using quaternion algebra . | def qrot ( vector , quaternion ) : t = 2 * np . cross ( quaternion [ 1 : ] , vector ) v_rot = vector + quaternion [ 0 ] * t + np . cross ( quaternion [ 1 : ] , t ) return v_rot | 9,774 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L425-L442 | [
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Convert Euler angle to quaternion . | def qeuler ( yaw , pitch , roll ) : yaw = np . radians ( yaw ) pitch = np . radians ( pitch ) roll = np . radians ( roll ) cy = np . cos ( yaw * 0.5 ) sy = np . sin ( yaw * 0.5 ) cr = np . cos ( roll * 0.5 ) sr = np . sin ( roll * 0.5 ) cp = np . cos ( pitch * 0.5 ) sp = np . sin ( pitch * 0.5 ) q = np . array ( ( cy * cr * cp + sy * sr * sp , cy * sr * cp - sy * cr * sp , cy * cr * sp + sy * sr * cp , sy * cr * cp - cy * sr * sp ) ) return q | 9,775 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L445-L474 | [
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Find the closes point for a given set of lines in 3D . | def intersect_3d ( p1 , p2 ) : v = p2 - p1 normed_v = unit_vector ( v ) nx = normed_v [ : , 0 ] ny = normed_v [ : , 1 ] nz = normed_v [ : , 2 ] xx = np . sum ( nx ** 2 - 1 ) yy = np . sum ( ny ** 2 - 1 ) zz = np . sum ( nz ** 2 - 1 ) xy = np . sum ( nx * ny ) xz = np . sum ( nx * nz ) yz = np . sum ( ny * nz ) M = np . array ( [ ( xx , xy , xz ) , ( xy , yy , yz ) , ( xz , yz , zz ) ] ) x = np . sum ( p1 [ : , 0 ] * ( nx ** 2 - 1 ) + p1 [ : , 1 ] * ( nx * ny ) + p1 [ : , 2 ] * ( nx * nz ) ) y = np . sum ( p1 [ : , 0 ] * ( nx * ny ) + p1 [ : , 1 ] * ( ny * ny - 1 ) + p1 [ : , 2 ] * ( ny * nz ) ) z = np . sum ( p1 [ : , 0 ] * ( nx * nz ) + p1 [ : , 1 ] * ( ny * nz ) + p1 [ : , 2 ] * ( nz ** 2 - 1 ) ) return np . linalg . lstsq ( M , np . array ( ( x , y , z ) ) , rcond = None ) [ 0 ] | 9,776 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/math.py#L494-L536 | [
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For Python 2 3 compatibility . | def compat_py2_py3 ( ) : if ( sys . version_info > ( 3 , 0 ) ) : def iteritems ( dictionary ) : return dictionary . items ( ) def itervalues ( dictionary ) : return dictionary . values ( ) else : def iteritems ( dictionary ) : return dictionary . iteritems ( ) def itervalues ( dictionary ) : return dictionary . itervalues ( ) return iteritems , itervalues | 9,777 | https://github.com/astooke/gtimer/blob/2146dab459e5d959feb291821733d3d3ba7c523c/gtimer/util.py#L20-L36 | [
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Uses slice ID as iterator | def timeslice_generator ( self ) : slice_id = 0 while slice_id < self . n_timeslices : blob = self . get_blob ( slice_id ) yield blob slice_id += 1 | 9,778 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/jpp.py#L185-L191 | [
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Index is slice ID | def get_blob ( self , index ) : blob = self . _current_blob self . r . retrieve_timeslice ( index ) timeslice_info = Table . from_template ( { 'frame_index' : self . r . frame_index , 'slice_id' : index , 'timestamp' : self . r . utc_seconds , 'nanoseconds' : self . r . utc_nanoseconds , 'n_frames' : self . r . n_frames , } , 'TimesliceInfo' ) hits = self . _extract_hits ( ) hits . group_id = index blob [ 'TimesliceInfo' ] = timeslice_info blob [ self . _hits_blob_key ] = hits return blob | 9,779 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/jpp.py#L193-L208 | [
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A simple slice generator for iterations | def _slice_generator ( self , index ) : start , stop , step = index . indices ( len ( self ) ) for i in range ( start , stop , step ) : yield self . get_blob ( i ) | 9,780 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/io/jpp.py#L270-L274 | [
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Report the overall correlation with the validation scores using each exemplar in isolation . | def correlation_by_exemplar ( brands , exemplars , validation_scores , analyze_fn_str , outf ) : analyze_fn = getattr ( analyze , analyze_fn_str ) keys = sorted ( k for k in validation_scores . keys ( ) if k in set ( x [ 0 ] for x in brands ) ) truth = [ validation_scores [ k ] for k in keys ] result = { } outf . write ( 'exemplar\tcorr\tn_followers\n' ) outf . flush ( ) for exemplar in exemplars : single_exemplar = { exemplar : exemplars [ exemplar ] } social_scores = analyze_fn ( brands , single_exemplar ) predicted = [ social_scores [ k ] for k in keys ] outf . write ( '%s\t%g\t%d\n' % ( exemplar , scistat . pearsonr ( predicted , truth ) [ 0 ] , len ( exemplars [ exemplar ] ) ) ) outf . flush ( ) result [ exemplar ] = scistat . pearsonr ( predicted , truth ) [ 0 ] outf . close ( ) return result | 9,781 | https://github.com/tapilab/brandelion/blob/40a5a5333cf704182c8666d1fbbbdadc7ff88546/brandelion/cli/diagnose.py#L50-L66 | [
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Summarise the differences between this node and the other node . | def difference ( self , other ) : diff = ( tuple ( set ( self . plates ) - set ( other . plates ) ) , tuple ( set ( other . plates ) - set ( self . plates ) ) ) counts = map ( len , diff ) # is_sub_plate = counts == [1, 1] and diff[1][0].is_sub_plate(diff[0][0]) is_sub_plate = counts == [ 1 , 1 ] and diff [ 0 ] [ 0 ] . is_sub_plate ( diff [ 1 ] [ 0 ] ) # MK fixed if len ( other . plates ) == 1 and counts == [ 1 , 0 ] and diff [ 0 ] [ 0 ] . parent == other . plates [ 0 ] . parent : is_sub_plate = True return diff , counts , is_sub_plate | 9,782 | https://github.com/IRC-SPHERE/HyperStream/blob/98478f4d31ed938f4aa7c958ed0d4c3ffcb2e780/hyperstream/node/node.py#L113-L127 | [
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Produce a formatted report of the current timing data . | def report ( times = None , include_itrs = True , include_stats = True , delim_mode = False , format_options = None ) : if times is None : if f . root . stopped : return report_loc . report ( f . root . times , include_itrs , include_stats , delim_mode , format_options ) else : t = timer ( ) rep = report_loc . report ( collapse . collapse_times ( ) , include_itrs , include_stats , delim_mode , format_options , timer_state = 'running' ) f . root . self_cut += timer ( ) - t return rep else : if not isinstance ( times , Times ) : raise TypeError ( "Expected Times instance for param 'times' (default is root)." ) return report_loc . report ( times , include_itrs , include_stats , delim_mode , format_options ) | 9,783 | https://github.com/astooke/gtimer/blob/2146dab459e5d959feb291821733d3d3ba7c523c/gtimer/public/report.py#L22-L86 | [
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Produce a formatted comparison of timing datas . | def compare ( times_list = None , name = None , include_list = True , include_stats = True , delim_mode = False , format_options = None ) : if times_list is None : rep = '' for par_dict in itervalues ( f . root . times . par_subdvsn ) : for par_name , par_list in iteritems ( par_dict ) : rep += report_loc . compare ( par_list , par_name , include_list , include_stats , delim_mode , format_options ) else : if not isinstance ( times_list , ( list , tuple ) ) : raise TypeError ( "Expected a list/tuple of times instances for param 'times_list'." ) if not all ( [ isinstance ( times , Times ) for times in times_list ] ) : raise TypeError ( "At least one member of param 'times_list' is not a Times object." ) rep = report_loc . compare ( times_list , name , include_list , include_stats , delim_mode , format_options ) return rep | 9,784 | https://github.com/astooke/gtimer/blob/2146dab459e5d959feb291821733d3d3ba7c523c/gtimer/public/report.py#L89-L155 | [
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Produce a formatted record of a times data structure . | def write_structure ( times = None ) : if times is None : return report_loc . write_structure ( f . root . times ) else : if not isinstance ( times , Times ) : raise TypeError ( "Expected Times instance for param 'times' (default is root)." ) return report_loc . write_structure ( times ) | 9,785 | https://github.com/astooke/gtimer/blob/2146dab459e5d959feb291821733d3d3ba7c523c/gtimer/public/report.py#L158-L176 | [
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Parse the configuration file and return dictionary of configuration options . | def parse_conf_files ( conf_paths ) : conf_file = ConfigParser . RawConfigParser ( ) conf_read = conf_file . read ( conf_paths ) conf = { } try : if conf_read : conf [ 'client_id' ] = conf_file . get ( 'runkeeper' , 'client_id' ) conf [ 'client_secret' ] = conf_file . get ( 'runkeeper' , 'client_secret' ) if conf_file . has_option ( 'runkeeper' , 'bindport' ) : conf [ 'bindport' ] = conf_file . getint ( 'runkeeper' , 'bindport' ) if conf_file . has_option ( 'runkeeper' , 'bindaddr' ) : conf [ 'bindaddr' ] = conf_file . get ( 'runkeeper' , 'bindaddr' ) if conf_file . has_option ( 'runkeeper' , 'baseurl' ) : conf [ 'baseurl' ] = conf_file . get ( 'runkeeper' , 'baseurl' ) return conf except ConfigParser . Error : raise ConfigurationError ( "Error parsing configuration file(s): %s\n" % sys . exc_info ( ) [ 1 ] ) else : raise ConfigurationError ( "No valid configuration file (%s) found." % defaultConfFilename ) | 9,787 | https://github.com/aouyar/healthgraph-api/blob/fc5135ab353ca1f05e8a70ec784ff921e686c072/samples/bottle/runkeeper_demo.py#L148-L175 | [
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Main Block - Configure and run the Bottle Web Server . | def main ( argv = None ) : cmd_opts = parse_cmdline ( argv ) [ 0 ] if cmd_opts . confpath is not None : if os . path . exists ( cmd_opts . confpath ) : conf_paths = [ cmd_opts . confpath , ] else : return "Configuration file not found: %s" % cmd_opts . confpath else : conf_paths = [ os . path . join ( path , defaultConfFilename ) for path in ( '/etc' , '.' , ) ] try : conf . update ( parse_conf_files ( conf_paths ) ) except ConfigurationError : return ( sys . exc_info ( ) [ 1 ] ) if cmd_opts . bindport is not None : conf [ 'bindport' ] = cmd_opts . bindport if cmd_opts . bindaddr is not None : conf [ 'bindaddr' ] = cmd_opts . bindaddr if cmd_opts . baseurl is not None : conf [ 'baseurl' ] = cmd_opts . baseurl if cmd_opts . devel : from bottle import debug debug ( True ) app = SessionMiddleware ( bottle . app ( ) , sessionOpts ) bottle . run ( app = app , host = conf [ 'bindaddr' ] , port = conf [ 'bindport' ] , reloader = cmd_opts . devel ) | 9,788 | https://github.com/aouyar/healthgraph-api/blob/fc5135ab353ca1f05e8a70ec784ff921e686c072/samples/bottle/runkeeper_demo.py#L178-L204 | [
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Return the hash of the movie depending on the input string . | def get_hash ( input_string ) : # Check if the input looks like a link to a movie: if os . path . islink ( input_string ) : directory , movie_hash = os . path . split ( os . readlink ( input_string ) ) input_string = movie_hash return input_string . lower ( ) | 9,789 | https://github.com/NaPs/Kolekto/blob/29c5469da8782780a06bf9a76c59414bb6fd8fe3/kolekto/helpers.py#L8-L20 | [
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Save data associated with key . | def save ( self , key , data ) : self . _db [ key ] = json . dumps ( data ) self . _db . sync ( ) | 9,791 | https://github.com/NaPs/Kolekto/blob/29c5469da8782780a06bf9a76c59414bb6fd8fe3/kolekto/helpers.py#L45-L49 | [
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Get the global meta data which will be stored in a tree structure | def global_meta_data ( self ) : with switch_db ( MetaDataModel , 'hyperstream' ) : return sorted ( map ( lambda x : x . to_dict ( ) , MetaDataModel . objects ) , key = lambda x : len ( x [ 'identifier' ] . split ( '.' ) ) , reverse = True ) | 9,792 | https://github.com/IRC-SPHERE/HyperStream/blob/98478f4d31ed938f4aa7c958ed0d4c3ffcb2e780/hyperstream/meta_data/meta_data_manager.py#L56-L65 | [
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Insert the given meta data into the database | def insert ( self , tag , identifier , parent , data ) : # First try to add it into the tree if self . global_plate_definitions . contains ( identifier ) : raise KeyError ( "Identifier {} already exists in tree" . format ( identifier ) ) self . global_plate_definitions . create_node ( tag = tag , identifier = identifier , parent = parent , data = data ) # Now try to add it into the database with switch_db ( MetaDataModel , 'hyperstream' ) : meta_data = MetaDataModel ( tag = tag , parent = parent , data = data ) meta_data . save ( ) logging . info ( "Meta data {} inserted" . format ( identifier ) ) | 9,793 | https://github.com/IRC-SPHERE/HyperStream/blob/98478f4d31ed938f4aa7c958ed0d4c3ffcb2e780/hyperstream/meta_data/meta_data_manager.py#L76-L97 | [
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Delete the meta data with the given identifier from the database | def delete ( self , identifier ) : try : node = self . global_plate_definitions [ identifier ] except NodeIDAbsentError : logging . info ( "Meta data {} not present during deletion" . format ( identifier ) ) return # First delete any children of the node: REMOVED as this seemed to be unreliable # It's now better to call delete_plate with delete_meta_data=True # for child in node.fpointer: # self.delete(child) self . global_plate_definitions . remove_node ( identifier ) with switch_db ( MetaDataModel , 'hyperstream' ) : meta_data = MetaDataModel . objects ( tag = node . tag , data = node . data , parent = node . bpointer ) . first ( ) if meta_data is not None : meta_data . delete ( ) logging . info ( "Meta data {} deleted" . format ( identifier ) ) | 9,794 | https://github.com/IRC-SPHERE/HyperStream/blob/98478f4d31ed938f4aa7c958ed0d4c3ffcb2e780/hyperstream/meta_data/meta_data_manager.py#L99-L125 | [
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Loads this stream by calling River View for data . | def load ( self ) : print "Loading data for %s..." % self . getName ( ) self . _dataHandle = self . _stream . data ( since = self . _since , until = self . _until , limit = self . _limit , aggregate = self . _aggregate ) self . _data = self . _dataHandle . data ( ) self . _headers = self . _dataHandle . headers ( ) print "Loaded %i rows." % len ( self ) | 9,795 | https://github.com/htm-community/menorah/blob/1991b01eda3f6361b22ed165b4a688ae3fb2deaf/menorah/riverstream.py#L80-L91 | [
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Seaborn - compatible hexbin plot . | def hexbin ( x , y , color = "purple" , * * kwargs ) : if HAS_SEABORN : cmap = sns . light_palette ( color , as_cmap = True ) else : cmap = "Purples" plt . hexbin ( x , y , cmap = cmap , * * kwargs ) | 9,796 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/plot.py#L33-L42 | [
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Plot the diagonal . | def diag ( ax = None , linecolor = '0.0' , linestyle = '--' , * * kwargs ) : ax = get_ax ( ax ) xy_min = np . min ( ( ax . get_xlim ( ) , ax . get_ylim ( ) ) ) xy_max = np . max ( ( ax . get_ylim ( ) , ax . get_xlim ( ) ) ) return ax . plot ( [ xy_min , xy_max ] , [ xy_min , xy_max ] , ls = linestyle , c = linecolor , * * kwargs ) | 9,797 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/plot.py#L52-L60 | [
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Make a meshgrid inferred from data . | def automeshgrid ( x , y , step = 0.02 , xstep = None , ystep = None , pad = 0.5 , xpad = None , ypad = None ) : if xpad is None : xpad = pad if xstep is None : xstep = step if ypad is None : ypad = pad if ystep is None : ystep = step xmin = x . min ( ) - xpad xmax = x . max ( ) + xpad ymin = y . min ( ) - ypad ymax = y . max ( ) + ypad return meshgrid ( xmin , xmax , step , ymin , ymax , ystep ) | 9,798 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/plot.py#L63-L79 | [
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Plot a histogram with counts binlims already given . | def prebinned_hist ( counts , binlims , ax = None , * args , * * kwargs ) : ax = get_ax ( ax ) x = bincenters ( binlims ) weights = counts return ax . hist ( x , bins = binlims , weights = weights , * args , * * kwargs ) | 9,799 | https://github.com/tamasgal/km3pipe/blob/7a9b59ac899a28775b5bdc5d391d9a5340d08040/km3pipe/plot.py#L96-L108 | [
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