#NEWLINE# def test_featurize_time_series_uneven_multichannel( ) : #NEWLINE# #INDENT# 'Test featurize wrapper function for uneven-length multichannel data' #NEWLINE# n_channels = 3 #NEWLINE# ( t , m , e ) = sample_time_series ( channels = n_channels ) #NEWLINE# t = [ [ t , t [ 0 : ( - 5 ) ] , t [ 0 : ( - 10 ) ] ] ] #NEWLINE# m = [ [ m [ 0 ] , m [ 1 ] [ 0 : ( - 5 ) ] , m [ 2 ] [ 0 : ( - 10 ) ] ] ] #NEWLINE# e = [ [ e [ 0 ] , e [ 1 ] [ 0 : ( - 5 ) ] , e [ 2 ] [ 0 : ( - 10 ) ] ] ] #NEWLINE# features_to_use = [ 'amplitude' , 'std_err' ] #NEWLINE# target = 'class1' #NEWLINE# meta_features = { #NEWLINE# #INDENT# 'meta1' : 0.5 , #NEWLINE# #UNINDENT# } #NEWLINE# fset = featurize . featurize_time_series ( t , m , e , features_to_use , target , meta_features , use_celery = False ) #NEWLINE# npt . assert_array_equal ( sorted ( fset . data_vars ) , [ 'amplitude' , 'meta1' , 'std_err' ] ) #NEWLINE# npt . assert_array_equal ( fset . channel , np . arange ( e ) ) #NEWLINE# npt . assert_array_equal ( sorted ( fset . amplitude . coords ) , [ 'channel' , 'name' , 'target' ] ) #NEWLINE# npt . assert_array_equal ( fset . target . values , [ 'class1' ] )