#NEWLINE# def test_extensive( self ) : #NEWLINE# #INDENT# eigvals = [ 0.3984635 , 0.36405689 , 0.28804535 , 0.27479983 , 0.19165361 , 0.0 ] #NEWLINE# proportion_explained = [ 0.2626621381 , 0.2399817314 , 0.1898758748 , 0.1811445992 , 0.1263356565 , 0.0 ] #NEWLINE# sample_ids = [ str ( i ) for i in range ( 6 ) ] #NEWLINE# axis_labels = [ ( 'PC%d' % i ) for i in range ( 1 , 7 ) ] #NEWLINE# samples = [ [ ( - 0.028597 ) , 0.22903853 , 0.07055272 , 0.26163576 , 0.28398669 , 0.0 ] , [ 0.37494056 , 0.22334055 , ( - 0.20892914 ) , 0.05057395 , ( - 0.18710366 ) , 0.0 ] , [ ( - 0.33517593 ) , ( - 0.23855979 ) , ( - 0.3099887 ) , 0.11521787 , ( - 0.05021553 ) , 0.0 ] , [ 0.25412394 , ( - 0.4123464 ) , 0.23343642 , 0.06403168 , ( - 0.00482608 ) , 0.0 ] , [ ( - 0.28256844 ) , 0.18606911 , 0.28875631 , ( - 0.06455635 ) , ( - 0.21141632 ) , 0.0 ] , [ 0.01727687 , 0.012458 , ( - 0.07382761 ) , ( - 0.42690292 ) , 0.1695749 , 0.0 ] ] #NEWLINE# expected_results = OrdinationResults ( short_method_name = 'PCoA' , long_method_name = 'Principal Coordinate Analysis' , eigvals = pd . Series ( eigvals , index = axis_labels ) , samples = pd . DataFrame ( samples , index = sample_ids , columns = axis_labels ) , proportion_explained = pd . Series ( proportion_explained , index = axis_labels ) ) #NEWLINE# data = np . loadtxt ( get_data_path ( 'PCoA_sample_data_2' ) ) #NEWLINE# for dm in ( data , DistanceMatrix ( data ) ) : #NEWLINE# #INDENT# results = pcoa ( dm ) #NEWLINE# assert_ordination_results_equal ( results , expected_results , ignore_directionality = True )