idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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48,700 | def is_in_data_type_range ( self , raise_exception = True ) : return self . _header . data_type . is_in_range ( self . _values , self . _header . unit , raise_exception ) | Check if collection values are in physically possible ranges for the data_type . |
48,701 | def get_highest_values ( self , count ) : count = int ( count ) assert count <= len ( self . _values ) , 'count must be smaller than or equal to values length. {} > {}.' . format ( count , len ( self . _values ) ) assert count > 0 , 'count must be greater than 0. Got {}.' . format ( count ) highest_values = sorted ( se... | Get a list of the the x highest values of the Data Collection and their indices . |
48,702 | def get_lowest_values ( self , count ) : count = int ( count ) assert count <= len ( self . _values ) , 'count must be <= to Data Collection len. {} > {}.' . format ( count , len ( self . _values ) ) assert count > 0 , 'count must be greater than 0. Got {}.' . format ( count ) lowest_values = sorted ( self . _values ) ... | Get a list of the the x lowest values of the Data Collection and their indices . |
48,703 | def get_percentile ( self , percentile ) : assert 0 <= percentile <= 100 , 'percentile must be between 0 and 100. Got {}' . format ( percentile ) return self . _percentile ( self . _values , percentile ) | Get a value representing a the input percentile of the Data Collection . |
48,704 | def get_aligned_collection ( self , value = 0 , data_type = None , unit = None , mutable = None ) : header = self . _check_aligned_header ( data_type , unit ) values = self . _check_aligned_value ( value ) if mutable is None : collection = self . __class__ ( header , values , self . datetimes ) else : if self . _enumer... | Return a Collection aligned with this one composed of one repeated value . |
48,705 | def duplicate ( self ) : collection = self . __class__ ( self . header . duplicate ( ) , self . values , self . datetimes ) collection . _validated_a_period = self . _validated_a_period return collection | Return a copy of the current Data Collection . |
48,706 | def to_json ( self ) : return { 'header' : self . header . to_json ( ) , 'values' : self . _values , 'datetimes' : self . datetimes , 'validated_a_period' : self . _validated_a_period } | Convert Data Collection to a dictionary . |
48,707 | def filter_collections_by_statement ( data_collections , statement ) : pattern = BaseCollection . pattern_from_collections_and_statement ( data_collections , statement ) collections = [ coll . filter_by_pattern ( pattern ) for coll in data_collections ] return collections | Generate a filtered data collections according to a conditional statement . |
48,708 | def pattern_from_collections_and_statement ( data_collections , statement ) : BaseCollection . are_collections_aligned ( data_collections ) correct_var = BaseCollection . _check_conditional_statement ( statement , len ( data_collections ) ) num_statement_clean = BaseCollection . _replace_operators ( statement ) pattern... | Generate a list of booleans from data collections and a conditional statement . |
48,709 | def are_collections_aligned ( data_collections , raise_exception = True ) : if len ( data_collections ) > 1 : first_coll = data_collections [ 0 ] for coll in data_collections [ 1 : ] : if not first_coll . is_collection_aligned ( coll ) : if raise_exception is True : error_msg = '{} Data Collection is not aligned with '... | Test if a series of Data Collections are aligned with one another . |
48,710 | def compute_function_aligned ( funct , data_collections , data_type , unit ) : data_colls = [ ] for i , func_input in enumerate ( data_collections ) : if isinstance ( func_input , BaseCollection ) : data_colls . append ( func_input ) else : try : data_collections [ i ] = float ( func_input ) except ValueError : raise T... | Compute a function with a list of aligned data collections or individual values . |
48,711 | def _check_conditional_statement ( statement , num_collections ) : correct_var = list ( ascii_lowercase ) [ : num_collections ] st_statement = BaseCollection . _remove_operators ( statement ) parsed_st = [ s for s in st_statement if s . isalpha ( ) ] for var in parsed_st : if var not in correct_var : raise ValueError (... | Method to check conditional statements to be sure that they are valid . |
48,712 | def _filter_by_statement ( self , statement ) : self . __class__ . _check_conditional_statement ( statement , 1 ) _filt_values , _filt_datetimes = [ ] , [ ] for i , a in enumerate ( self . _values ) : if eval ( statement , { 'a' : a } ) : _filt_values . append ( a ) _filt_datetimes . append ( self . datetimes [ i ] ) r... | Filter the data collection based on a conditional statement . |
48,713 | def _filter_by_pattern ( self , pattern ) : try : _len = len ( pattern ) except TypeError : raise TypeError ( "pattern is not a list of Booleans. Got {}" . format ( type ( pattern ) ) ) _filt_values = [ d for i , d in enumerate ( self . _values ) if pattern [ i % _len ] ] _filt_datetimes = [ d for i , d in enumerate ( ... | Filter the Filter the Data Collection based on a list of booleans . |
48,714 | def _check_aligned_header ( self , data_type , unit ) : if data_type is not None : assert isinstance ( data_type , DataTypeBase ) , 'data_type must be a Ladybug DataType. Got {}' . format ( type ( data_type ) ) if unit is None : unit = data_type . units [ 0 ] else : data_type = self . header . data_type unit = unit or ... | Check the header inputs whenever get_aligned_collection is called . |
48,715 | def _check_aligned_value ( self , value ) : if isinstance ( value , Iterable ) and not isinstance ( value , ( str , dict , bytes , bytearray ) ) : assert len ( value ) == len ( self . _values ) , "Length of value ({}) must match " "the length of this collection's values ({})" . format ( len ( value ) , len ( self . _va... | Check the value input whenever get_aligned_collection is called . |
48,716 | def from_json ( cls , data ) : if 'month' not in data : data [ 'month' ] = 1 if 'day' not in data : data [ 'day' ] = 1 if 'hour' not in data : data [ 'hour' ] = 0 if 'minute' not in data : data [ 'minute' ] = 0 if 'year' not in data : data [ 'year' ] = 2017 leap_year = True if int ( data [ 'year' ] ) == 2016 else False... | Creat datetime from a dictionary . |
48,717 | def from_hoy ( cls , hoy , leap_year = False ) : return cls . from_moy ( round ( hoy * 60 ) , leap_year ) | Create Ladybug Datetime from an hour of the year . |
48,718 | def from_moy ( cls , moy , leap_year = False ) : if not leap_year : num_of_minutes_until_month = ( 0 , 44640 , 84960 , 129600 , 172800 , 217440 , 260640 , 305280 , 349920 , 393120 , 437760 , 480960 , 525600 ) else : num_of_minutes_until_month = ( 0 , 44640 , 84960 + 1440 , 129600 + 1440 , 172800 + 1440 , 217440 + 1440 ... | Create Ladybug Datetime from a minute of the year . |
48,719 | def from_date_time_string ( cls , datetime_string , leap_year = False ) : dt = datetime . strptime ( datetime_string , '%d %b %H:%M' ) return cls ( dt . month , dt . day , dt . hour , dt . minute , leap_year ) | Create Ladybug DateTime from a DateTime string . |
48,720 | def _calculate_hour_and_minute ( float_hour ) : hour , minute = int ( float_hour ) , int ( round ( ( float_hour - int ( float_hour ) ) * 60 ) ) if minute == 60 : return hour + 1 , 0 else : return hour , minute | Calculate hour and minutes as integers from a float hour . |
48,721 | def add_minute ( self , minute ) : _moy = self . moy + int ( minute ) return self . __class__ . from_moy ( _moy ) | Create a new DateTime after the minutes are added . |
48,722 | def to_json ( self ) : return { 'year' : self . year , 'month' : self . month , 'day' : self . day , 'hour' : self . hour , 'minute' : self . minute } | Get date time as a dictionary . |
48,723 | def fullConn ( self , preCellsTags , postCellsTags , connParam ) : from . . import sim if sim . cfg . verbose : print ( 'Generating set of all-to-all connections (rule: %s) ...' % ( connParam [ 'label' ] ) ) paramsStrFunc = [ param for param in [ p + 'Func' for p in self . connStringFuncParams ] if param in connParam ]... | Generates connections between all pre and post - syn cells |
48,724 | def fromListConn ( self , preCellsTags , postCellsTags , connParam ) : from . . import sim if sim . cfg . verbose : print ( 'Generating set of connections from list (rule: %s) ...' % ( connParam [ 'label' ] ) ) orderedPreGids = sorted ( preCellsTags ) orderedPostGids = sorted ( postCellsTags ) paramsStrFunc = [ param f... | Generates connections between all pre and post - syn cells based list of relative cell ids |
48,725 | def setImembPtr ( self ) : jseg = 0 for sec in list ( self . secs . values ( ) ) : hSec = sec [ 'hObj' ] for iseg , seg in enumerate ( hSec ) : self . imembPtr . pset ( jseg , seg . _ref_i_membrane_ ) jseg += 1 | Set PtrVector to point to the i_membrane_ |
48,726 | def saveWeights ( sim ) : with open ( sim . weightsfilename , 'w' ) as fid : for weightdata in sim . allWeights : fid . write ( '%0.0f' % weightdata [ 0 ] ) for i in range ( 1 , len ( weightdata ) ) : fid . write ( '\t%0.8f' % weightdata [ i ] ) fid . write ( '\n' ) print ( ( 'Saved weights as %s' % sim . weightsfilena... | Save the weights for each plastic synapse |
48,727 | def validateFunction ( strFunc , netParamsVars ) : from math import exp , log , sqrt , sin , cos , tan , asin , acos , atan , sinh , cosh , tanh , pi , e rand = h . Random ( ) stringFuncRandMethods = [ 'binomial' , 'discunif' , 'erlang' , 'geometric' , 'hypergeo' , 'lognormal' , 'negexp' , 'normal' , 'poisson' , 'unifo... | returns True if strFunc can be evaluated |
48,728 | def bandpass ( data , freqmin , freqmax , df , corners = 4 , zerophase = True ) : fe = 0.5 * df low = freqmin / fe high = freqmax / fe if high - 1.0 > - 1e-6 : msg = ( "Selected high corner frequency ({}) of bandpass is at or " "above Nyquist ({}). Applying a high-pass instead." ) . format ( freqmax , fe ) warnings . w... | Butterworth - Bandpass Filter . |
48,729 | def bandstop ( data , freqmin , freqmax , df , corners = 4 , zerophase = False ) : fe = 0.5 * df low = freqmin / fe high = freqmax / fe if high > 1 : high = 1.0 msg = "Selected high corner frequency is above Nyquist. " + "Setting Nyquist as high corner." warnings . warn ( msg ) if low > 1 : msg = "Selected low corner f... | Butterworth - Bandstop Filter . |
48,730 | def lowpass ( data , freq , df , corners = 4 , zerophase = False ) : fe = 0.5 * df f = freq / fe if f > 1 : f = 1.0 msg = "Selected corner frequency is above Nyquist. " + "Setting Nyquist as high corner." warnings . warn ( msg ) z , p , k = iirfilter ( corners , f , btype = 'lowpass' , ftype = 'butter' , output = 'zpk'... | Butterworth - Lowpass Filter . |
48,731 | def integer_decimation ( data , decimation_factor ) : if not isinstance ( decimation_factor , int ) : msg = "Decimation_factor must be an integer!" raise TypeError ( msg ) data = np . array ( data [ : : decimation_factor ] ) return data | Downsampling by applying a simple integer decimation . |
48,732 | def _distributeCells ( numCellsPop ) : from . . import sim hostCells = { } for i in range ( sim . nhosts ) : hostCells [ i ] = [ ] for i in range ( numCellsPop ) : hostCells [ sim . nextHost ] . append ( i ) sim . nextHost += 1 if sim . nextHost >= sim . nhosts : sim . nextHost = 0 if sim . cfg . verbose : print ( ( "D... | distribute cells across compute nodes using round - robin |
48,733 | def getCSD ( lfps , sampr , minf = 0.05 , maxf = 300 , norm = True , vaknin = False , spacing = 1.0 ) : datband = getbandpass ( lfps , sampr , minf , maxf ) if datband . shape [ 0 ] > datband . shape [ 1 ] : ax = 1 else : ax = 0 if vaknin : datband = Vaknin ( datband ) if norm : removemean ( datband , ax = ax ) CSD = -... | get current source density approximation using set of local field potentials with equidistant spacing first performs a lowpass filter lfps is a list or numpy array of LFPs arranged spatially by column spacing is in microns |
48,734 | def createSynapses ( self ) : synsoma = h . ExpSyn ( self . soma ( 0.5 ) ) synsoma . tau = 2 synsoma . e = 0 syndend = h . ExpSyn ( self . dend ( 0.5 ) ) syndend . tau = 2 syndend . e = 0 self . synlist . append ( synsoma ) self . synlist . append ( syndend ) | Add an exponentially decaying synapse |
48,735 | def createNetcon ( self , thresh = 10 ) : nc = h . NetCon ( self . soma ( 0.5 ) . _ref_v , None , sec = self . soma ) nc . threshold = thresh return nc | created netcon to record spikes |
48,736 | def createSections ( self ) : self . soma = h . Section ( name = 'soma' , cell = self ) self . dend = h . Section ( name = 'dend' , cell = self ) | Create the sections of the cell . |
48,737 | def defineGeometry ( self ) : self . soma . L = 18.8 self . soma . diam = 18.8 self . soma . Ra = 123.0 self . dend . L = 200.0 self . dend . diam = 1.0 self . dend . Ra = 100.0 | Set the 3D geometry of the cell . |
48,738 | def defineBiophysics ( self ) : self . soma . insert ( 'hh' ) self . soma . gnabar_hh = 0.12 self . soma . gkbar_hh = 0.036 self . soma . gl_hh = 0.003 self . soma . el_hh = - 70 self . dend . insert ( 'pas' ) self . dend . g_pas = 0.001 self . dend . e_pas = - 65 self . dend . nseg = 1000 | Assign the membrane properties across the cell . |
48,739 | def shapeplot ( h , ax , sections = None , order = 'pre' , cvals = None , clim = None , cmap = cm . YlOrBr_r , legend = True , ** kwargs ) : if sections is None : if order == 'pre' : sections = allsec_preorder ( h ) else : sections = list ( h . allsec ( ) ) if cvals is not None and clim is None : clim = [ np . nanmin (... | Plots a 3D shapeplot |
48,740 | def shapeplot_animate ( v , lines , nframes = None , tscale = 'linear' , clim = [ - 80 , 50 ] , cmap = cm . YlOrBr_r ) : if nframes is None : nframes = v . shape [ 0 ] if tscale == 'linear' : def animate ( i ) : i_t = int ( ( i / nframes ) * v . shape [ 0 ] ) for i_seg in range ( v . shape [ 1 ] ) : lines [ i_seg ] . s... | Returns animate function which updates color of shapeplot |
48,741 | def mark_locations ( h , section , locs , markspec = 'or' , ** kwargs ) : xyz = get_section_path ( h , section ) ( r , theta , phi ) = sequential_spherical ( xyz ) rcum = np . append ( 0 , np . cumsum ( r ) ) if type ( locs ) is float or type ( locs ) is np . float64 : locs = np . array ( [ locs ] ) if type ( locs ) is... | Marks one or more locations on along a section . Could be used to mark the location of a recording or electrical stimulation . |
48,742 | def root_sections ( h ) : roots = [ ] for section in h . allsec ( ) : sref = h . SectionRef ( sec = section ) if sref . has_parent ( ) < 0.9 : roots . append ( section ) return roots | Returns a list of all sections that have no parent . |
48,743 | def leaf_sections ( h ) : leaves = [ ] for section in h . allsec ( ) : sref = h . SectionRef ( sec = section ) if sref . nchild ( ) < 0.9 : leaves . append ( section ) return leaves | Returns a list of all sections that have no children . |
48,744 | def root_indices ( sec_list ) : roots = [ ] for i , section in enumerate ( sec_list ) : sref = h . SectionRef ( sec = section ) if sref . has_parent ( ) < 0.9 : roots . append ( i ) return roots | Returns the index of all sections without a parent . |
48,745 | def branch_order ( h , section , path = [ ] ) : path . append ( section ) sref = h . SectionRef ( sec = section ) if sref . has_parent ( ) < 0.9 : return 0 else : nchild = len ( list ( h . SectionRef ( sec = sref . parent ) . child ) ) if nchild <= 1.1 : return branch_order ( h , sref . parent , path ) else : return 1 ... | Returns the branch order of a section |
48,746 | def createCells ( self ) : if 'cellsList' in self . tags : cells = self . createCellsList ( ) elif 'numCells' in self . tags : cells = self . createCellsFixedNum ( ) elif 'density' in self . tags : cells = self . createCellsDensity ( ) elif 'gridSpacing' in self . tags : cells = self . createCellsGrid ( ) else : self .... | Function to instantiate Cell objects based on the characteristics of this population |
48,747 | def createCellsList ( self ) : from . . import sim cells = [ ] self . tags [ 'numCells' ] = len ( self . tags [ 'cellsList' ] ) for i in self . _distributeCells ( len ( self . tags [ 'cellsList' ] ) ) [ sim . rank ] : gid = sim . net . lastGid + i self . cellGids . append ( gid ) cellTags = { k : v for ( k , v ) in sel... | Create population cells based on list of individual cells |
48,748 | def create ( netParams = None , simConfig = None , output = False ) : from . . import sim import __main__ as top if not netParams : netParams = top . netParams if not simConfig : simConfig = top . simConfig sim . initialize ( netParams , simConfig ) pops = sim . net . createPops ( ) cells = sim . net . createCells ( ) ... | Sequence of commands to create network |
48,749 | def intervalSimulate ( interval ) : from . . import sim sim . runSimWithIntervalFunc ( interval , sim . intervalSave ) sim . fileGather ( ) | Sequence of commands to simulate network |
48,750 | def load ( filename , simConfig = None , output = False , instantiate = True , createNEURONObj = True ) : from . . import sim sim . initialize ( ) sim . cfg . createNEURONObj = createNEURONObj sim . loadAll ( filename , instantiate = instantiate , createNEURONObj = createNEURONObj ) if simConfig : sim . setSimCfg ( sim... | Sequence of commands load simulate and analyse network |
48,751 | def createExportNeuroML2 ( netParams = None , simConfig = None , reference = None , connections = True , stimulations = True , output = False , format = 'xml' ) : from . . import sim import __main__ as top if not netParams : netParams = top . netParams if not simConfig : simConfig = top . simConfig sim . initialize ( n... | Sequence of commands to create and export network to NeuroML2 |
48,752 | def exception ( function ) : @ functools . wraps ( function ) def wrapper ( * args , ** kwargs ) : try : return function ( * args , ** kwargs ) except Exception as e : err = "There was an exception in %s():" % ( function . __name__ ) print ( ( "%s \n %s \n%s" % ( err , e , sys . exc_info ( ) ) ) ) return - 1 return wra... | A decorator that wraps the passed in function and prints exception should one occur |
48,753 | def getSpktSpkid ( cellGids = [ ] , timeRange = None , allCells = False ) : from . . import sim import pandas as pd try : from pandas import _lib as pandaslib except : from pandas import lib as pandaslib df = pd . DataFrame ( pandaslib . to_object_array ( [ sim . allSimData [ 'spkt' ] , sim . allSimData [ 'spkid' ] ] )... | return spike ids and times ; with allCells = True just need to identify slice of time so can omit cellGids |
48,754 | def calcTransferResistance ( self , gid , seg_coords ) : sigma = 0.3 r05 = ( seg_coords [ 'p0' ] + seg_coords [ 'p1' ] ) / 2 dl = seg_coords [ 'p1' ] - seg_coords [ 'p0' ] nseg = r05 . shape [ 1 ] tr = np . zeros ( ( self . nsites , nseg ) ) for j in range ( self . nsites ) : rel = np . expand_dims ( self . pos [ : , j... | Precompute mapping from segment to electrode locations |
48,755 | def importConnFromExcel ( fileName , sheetName ) : import openpyxl as xl colPreTags = 0 colPostTags = 1 colConnFunc = 2 colSyn = 3 colProb = 5 colWeight = 6 colAnnot = 8 outFileName = fileName [ : - 5 ] + '_' + sheetName + '.py' connText = wb = xl . load_workbook ( fileName ) sheet = wb . get_sheet_by_name ( sheetName ... | Import connectivity rules from Excel sheet |
48,756 | def safe_dump ( data , stream = None , ** kwds ) : return yaml . dump ( data , stream = stream , Dumper = ODYD , ** kwds ) | implementation of safe dumper using Ordered Dict Yaml Dumper |
48,757 | def dump ( data , ** kwds ) : if _usedefaultyamlloader : return yaml . safe_dump ( data , ** kwds ) else : return odyldo . safe_dump ( data , ** kwds ) | dump the data as YAML |
48,758 | def bibtex ( self ) : warnings . warn ( "bibtex should be queried with ads.ExportQuery(); You will " "hit API ratelimits very quickly otherwise." , UserWarning ) return ExportQuery ( bibcodes = self . bibcode , format = "bibtex" ) . execute ( ) | Return a BiBTeX entry for the current article . |
48,759 | def get_pdf ( article , debug = False ) : print ( 'Retrieving {0}' . format ( article ) ) identifier = [ _ for _ in article . identifier if 'arXiv' in _ ] if identifier : url = 'http://arXiv.org/pdf/{0}.{1}' . format ( identifier [ 0 ] [ 9 : 13 ] , '' . join ( _ for _ in identifier [ 0 ] [ 14 : ] if _ . isdigit ( ) ) )... | Download an article PDF from arXiv . |
48,760 | def summarise_pdfs ( pdfs ) : print ( 'Summarising {0} articles ({1} had errors)' . format ( len ( pdfs ) , pdfs . count ( None ) ) ) pdfs = [ _ for _ in pdfs if _ is not None ] summary = PdfFileWriter ( ) for pdf in pdfs : summary . addPage ( PdfFileReader ( StringIO ( pdf ) ) . getPage ( 0 ) ) return summary | Collate the first page from each of the PDFs provided into a single PDF . |
48,761 | def execute ( self ) : self . response = MetricsResponse . load_http_response ( self . session . post ( self . HTTP_ENDPOINT , data = self . json_payload ) ) return self . response . metrics | Execute the http request to the metrics service |
48,762 | def get_info ( cls ) : return '\n' . join ( [ str ( cls . _instances [ key ] ) for key in cls . _instances ] ) | Print all of the instantiated Singletons |
48,763 | def load_http_response ( cls , http_response ) : if not http_response . ok : raise APIResponseError ( http_response . text ) c = cls ( http_response ) c . response = http_response RateLimits . getRateLimits ( cls . __name__ ) . set ( c . response . headers ) return c | This method should return an instantiated class and set its response to the requests . Response object . |
48,764 | def token ( self ) : if self . _token is None : for v in map ( os . environ . get , TOKEN_ENVIRON_VARS ) : if v is not None : self . _token = v return self . _token for f in TOKEN_FILES : try : with open ( f ) as fp : self . _token = fp . read ( ) . strip ( ) return self . _token except IOError : pass if ads . config .... | set the instance attribute token following the following logic stopping whenever a token is found . Raises NoTokenFound is no token is found - environment variables TOKEN_ENVIRON_VARS - file containing plaintext as the contents in TOKEN_FILES - ads . config . token |
48,765 | def session ( self ) : if self . _session is None : self . _session = requests . session ( ) self . _session . headers . update ( { "Authorization" : "Bearer {}" . format ( self . token ) , "User-Agent" : "ads-api-client/{}" . format ( __version__ ) , "Content-Type" : "application/json" , } ) return self . _session | http session interface transparent proxy to requests . session |
48,766 | def from_csv ( input_csv_pattern , headers = None , schema_file = None ) : if headers is not None : names = headers elif schema_file is not None : with _util . open_local_or_gcs ( schema_file , mode = 'r' ) as f : schema = json . load ( f ) names = [ x [ 'name' ] for x in schema ] else : raise ValueError ( 'Either head... | Create a Metrics instance from csv file pattern . |
48,767 | def from_bigquery ( sql ) : if isinstance ( sql , bq . Query ) : sql = sql . _expanded_sql ( ) parts = sql . split ( '.' ) if len ( parts ) == 1 or len ( parts ) > 3 or any ( ' ' in x for x in parts ) : sql = '(' + sql + ')' else : sql = '`' + sql + '`' metrics = Metrics ( bigquery = sql ) return metrics | Create a Metrics instance from a bigquery query or table . |
48,768 | def _get_data_from_csv_files ( self ) : all_df = [ ] for file_name in self . _input_csv_files : with _util . open_local_or_gcs ( file_name , mode = 'r' ) as f : all_df . append ( pd . read_csv ( f , names = self . _headers ) ) df = pd . concat ( all_df , ignore_index = True ) return df | Get data from input csv files . |
48,769 | def _get_data_from_bigquery ( self , queries ) : all_df = [ ] for query in queries : all_df . append ( query . execute ( ) . result ( ) . to_dataframe ( ) ) df = pd . concat ( all_df , ignore_index = True ) return df | Get data from bigquery table or query . |
48,770 | def _expanded_sql ( self ) : if not self . _sql : self . _sql = UDF . _build_udf ( self . _name , self . _code , self . _return_type , self . _params , self . _language , self . _imports ) return self . _sql | Get the expanded BigQuery SQL string of this UDF |
48,771 | def _build_udf ( name , code , return_type , params , language , imports ) : params = ',' . join ( [ '%s %s' % named_param for named_param in params ] ) imports = ',' . join ( [ 'library="%s"' % i for i in imports ] ) if language . lower ( ) == 'sql' : udf = 'CREATE TEMPORARY FUNCTION {name} ({params})\n' + 'RETURNS {r... | Creates the UDF part of a BigQuery query using its pieces |
48,772 | def created_on ( self ) : s = self . _info . get ( 'timeCreated' , None ) return dateutil . parser . parse ( s ) if s else None | The created timestamp of the bucket as a datetime . datetime . |
48,773 | def metadata ( self ) : if self . _info is None : try : self . _info = self . _api . buckets_get ( self . _name ) except Exception as e : raise e return BucketMetadata ( self . _info ) if self . _info else None | Retrieves metadata about the bucket . |
48,774 | def object ( self , key ) : return _object . Object ( self . _name , key , context = self . _context ) | Retrieves a Storage Object for the specified key in this bucket . |
48,775 | def objects ( self , prefix = None , delimiter = None ) : return _object . Objects ( self . _name , prefix , delimiter , context = self . _context ) | Get an iterator for the objects within this bucket . |
48,776 | def delete ( self ) : if self . exists ( ) : try : self . _api . buckets_delete ( self . _name ) except Exception as e : raise e | Deletes the bucket . |
48,777 | def contains ( self , name ) : try : self . _api . buckets_get ( name ) except google . datalab . utils . RequestException as e : if e . status == 404 : return False raise e except Exception as e : raise e return True | Checks if the specified bucket exists . |
48,778 | def item ( self , key ) : return _item . Item ( self . _name , key , context = self . _context ) | Retrieves an Item object for the specified key in this bucket . |
48,779 | def items ( self , prefix = None , delimiter = None ) : return _item . Items ( self . _name , prefix , delimiter , context = self . _context ) | Get an iterator for the items within this bucket . |
48,780 | def create ( self , project_id = None ) : if not self . exists ( ) : if project_id is None : project_id = self . _api . project_id try : self . _info = self . _api . buckets_insert ( self . _name , project_id = project_id ) except Exception as e : raise e return self | Creates the bucket . |
48,781 | def create ( self , name ) : return Bucket ( name , context = self . _context ) . create ( self . _project_id ) | Creates a new bucket . |
48,782 | def train ( train_dataset , eval_dataset , analysis_dir , output_dir , features , layer_sizes , max_steps = 5000 , num_epochs = None , train_batch_size = 100 , eval_batch_size = 16 , min_eval_frequency = 100 , learning_rate = 0.01 , epsilon = 0.0005 , job_name = None , cloud = None , ) : job = train_async ( train_datas... | Blocking version of train_async . See documentation for train_async . |
48,783 | def list ( self , pattern = '*' ) : if self . _descriptors is None : self . _descriptors = self . _client . list_resource_descriptors ( filter_string = self . _filter_string ) return [ resource for resource in self . _descriptors if fnmatch . fnmatch ( resource . type , pattern ) ] | Returns a list of resource descriptors that match the filters . |
48,784 | def _gcs_list_buckets ( project , pattern ) : data = [ { 'Bucket' : 'gs://' + bucket . name , 'Created' : bucket . metadata . created_on } for bucket in google . datalab . storage . Buckets ( _make_context ( project ) ) if fnmatch . fnmatch ( bucket . name , pattern ) ] return google . datalab . utils . commands . rend... | List all Google Cloud Storage buckets that match a pattern . |
48,785 | def _gcs_list_keys ( bucket , pattern ) : data = [ { 'Name' : obj . metadata . name , 'Type' : obj . metadata . content_type , 'Size' : obj . metadata . size , 'Updated' : obj . metadata . updated_on } for obj in _gcs_get_keys ( bucket , pattern ) ] return google . datalab . utils . commands . render_dictionary ( data ... | List all Google Cloud Storage keys in a specified bucket that match a pattern . |
48,786 | def prepare_image_transforms ( element , image_columns ) : import base64 import cStringIO from PIL import Image from tensorflow . python . lib . io import file_io as tf_file_io from apache_beam . metrics import Metrics img_error_count = Metrics . counter ( 'main' , 'ImgErrorCount' ) img_missing_count = Metrics . counte... | Replace an images url with its jpeg bytes . |
48,787 | def decode_csv ( csv_string , column_names ) : import csv r = next ( csv . reader ( [ csv_string ] ) ) if len ( r ) != len ( column_names ) : raise ValueError ( 'csv line %s does not have %d columns' % ( csv_string , len ( column_names ) ) ) return { k : v for k , v in zip ( column_names , r ) } | Parse a csv line into a dict . |
48,788 | def encode_csv ( data_dict , column_names ) : import csv import six values = [ str ( data_dict [ x ] ) for x in column_names ] str_buff = six . StringIO ( ) writer = csv . writer ( str_buff , lineterminator = '' ) writer . writerow ( values ) return str_buff . getvalue ( ) | Builds a csv string . |
48,789 | def serialize_example ( transformed_json_data , info_dict ) : import six import tensorflow as tf def _make_int64_list ( x ) : return tf . train . Feature ( int64_list = tf . train . Int64List ( value = x ) ) def _make_bytes_list ( x ) : return tf . train . Feature ( bytes_list = tf . train . BytesList ( value = x ) ) d... | Makes a serialized tf . example . |
48,790 | def preprocess ( pipeline , args ) : from tensorflow . python . lib . io import file_io from trainer import feature_transforms schema = json . loads ( file_io . read_file_to_string ( os . path . join ( args . analysis , feature_transforms . SCHEMA_FILE ) ) . decode ( ) ) features = json . loads ( file_io . read_file_to... | Transfrom csv data into transfromed tf . example files . |
48,791 | def main ( argv = None ) : args = parse_arguments ( sys . argv if argv is None else argv ) temp_dir = os . path . join ( args . output , 'tmp' ) if args . cloud : pipeline_name = 'DataflowRunner' else : pipeline_name = 'DirectRunner' os . environ [ 'TF_CPP_MIN_LOG_LEVEL' ] = '3' options = { 'job_name' : args . job_name... | Run Preprocessing as a Dataflow . |
48,792 | def start_bundle ( self , element = None ) : import tensorflow as tf from trainer import feature_transforms g = tf . Graph ( ) session = tf . Session ( graph = g ) with g . as_default ( ) : transformed_features , _ , placeholders = ( feature_transforms . build_csv_serving_tensors_for_transform_step ( analysis_path = se... | Build the transfromation graph once . |
48,793 | def process ( self , element ) : import apache_beam as beam import six import tensorflow as tf tf . logging . set_verbosity ( tf . logging . ERROR ) try : clean_element = [ ] for line in element : clean_element . append ( line . rstrip ( ) ) batch_result = self . _session . run ( fetches = self . _transformed_features ... | Run the transformation graph on batched input data |
48,794 | def parse_row ( schema , data ) : def parse_value ( data_type , value ) : if value is not None : if value == 'null' : value = None elif data_type == 'INTEGER' : value = int ( value ) elif data_type == 'FLOAT' : value = float ( value ) elif data_type == 'TIMESTAMP' : value = datetime . datetime . utcfromtimestamp ( floa... | Parses a row from query results into an equivalent object . |
48,795 | def _tf_predict ( model_dir , input_csvlines ) : with tf . Graph ( ) . as_default ( ) , tf . Session ( ) as sess : input_alias_map , output_alias_map = _tf_load_model ( sess , model_dir ) csv_tensor_name = list ( input_alias_map . values ( ) ) [ 0 ] results = sess . run ( fetches = output_alias_map , feed_dict = { csv_... | Prediction with a tf savedmodel . |
48,796 | def _download_images ( data , img_cols ) : images = collections . defaultdict ( list ) for d in data : for img_col in img_cols : if d . get ( img_col , None ) : if isinstance ( d [ img_col ] , Image . Image ) : images [ img_col ] . append ( d [ img_col ] ) else : with file_io . FileIO ( d [ img_col ] , 'rb' ) as fi : i... | Download images given image columns . |
48,797 | def _get_predicton_csv_lines ( data , headers , images ) : if images : data = copy . deepcopy ( data ) for img_col in images : for d , im in zip ( data , images [ img_col ] ) : if im == '' : continue im = im . copy ( ) im . thumbnail ( ( 299 , 299 ) , Image . ANTIALIAS ) buf = BytesIO ( ) im . save ( buf , "JPEG" ) con... | Create CSV lines from list - of - dict data . |
48,798 | def _get_display_data_with_images ( data , images ) : if not images : return data display_data = copy . deepcopy ( data ) for img_col in images : for d , im in zip ( display_data , images [ img_col ] ) : if im == '' : d [ img_col + '_image' ] = '' else : im = im . copy ( ) im . thumbnail ( ( 128 , 128 ) , Image . ANTIA... | Create display data by converting image urls to base64 strings . |
48,799 | def get_model_schema_and_features ( model_dir ) : schema_file = os . path . join ( model_dir , 'assets.extra' , 'schema.json' ) schema = json . loads ( file_io . read_file_to_string ( schema_file ) ) features_file = os . path . join ( model_dir , 'assets.extra' , 'features.json' ) features_config = json . loads ( file_... | Get a local model s schema and features config . |
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