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How to use compare_matrices method in avocado

Best Python code snippet using avocado_python

check_results.py

Source:check_results.py

...24        ls=l.strip().split()25        genes.append(ls[0])26        data.append([float(x) for x in ls[1:]])27    return numpy.array(data),genes28def compare_matrices(localdata,remotedata,infile,atol=1e-04,rtol=1e-04):29    if numpy.allclose(localdata,remotedata,rtol,atol):30        print 'PASS:',infile31    else:32        maxdiff=numpy.max(localdata - remotedata)33        print 'FAIL:',infile,'maxdiff =',maxdiff34    35def check_text_file(infile):36    local=numpy.loadtxt(os.path.join(basedir,infile))37    remote=load_dataframe('%s/%s'%(dataurl,infile),thresh=1.01)38    if not len(local)==len(remote):39        print 'size mismatch for ',infile40        return41    localdata=numpy.zeros((len(local),4))42    remotedata=numpy.zeros((len(remote),4))43    keys=local.keys()44    for k in range(len(keys)):45        if not remote.has_key(keys[k]):46            print 'remote missing matching key',keys[k]47        localdata[k,:]=local[keys[k]]48        remotedata[k,:]=remote[keys[k]]49    if numpy.allclose(localdata,remotedata,rtol,atol):50        print 'PASS:',infile51    else:52        maxdiff=numpy.max(localdata - remotedata,0)53        print 'FAIL:',infile,'maxdiff =',maxdiff54def usage():55    """Print the docstring and exit with error."""56    sys.stdout.write(__doc__)57    sys.exit(2)58def parse_arguments():59    # parse command line arguments60    # setting testing flag to true will turn off required flags61    # to allow manually running without command line flags62    parser = argparse.ArgumentParser(description='check_results')63    parser.add_argument('-b', dest='basedir',64        default=os.environ['MYCONNECTOME_DIR'],help='local base dir')65    parser.add_argument('-r',dest='remotebase',66        default='myconnectome-vm',help='remote base')67    return parser.parse_args()68    69basedir=os.environ['MYCONNECTOME_DIR']70dataurl='https://s3.amazonaws.com/openfmri/ds031/myconnectome-vm'71args=parse_arguments()72basedir=args.basedir73dataurl='https://s3.amazonaws.com/openfmri/ds031/'+args.remotebase74print 'BASEDIR:',basedir75print 'REMOTEBASE:',dataurl76# first load download log and get list of downloaded files77try:78    downloads=[i.strip().split('\t')[0] for i in open(os.path.join(basedir,'logs/data_downloads.log')).readlines()]79except:80    downloads=[]81    82print 'checking local results against benchmark data (computed on Ubuntu VM)'83print '#### Variance stabilized expression data'84if not 'rna-seq/varstab_data_prefiltered_rin_3PC_regressed.txt' in downloads:85    local,local_genes=load_varstab_data(os.path.join(basedir,'rna-seq/varstab_data_prefiltered_rin_3PC_regressed.txt'))86    remote,remote_genes=load_varstab_data('%s/%s'%(dataurl,'rna-seq/varstab_data_prefiltered_rin_3PC_regressed.txt'))87    compare_matrices(local,remote,'rna-seq/varstab_data_prefiltered_rin_3PC_regressed.txt')88else:89    print 'SKIPPING DOWNLOADED FILE: rna-seq/varstab_data_prefiltered_rin_3PC_regressed.txt'90    91print '### WGCNA'92if not'rna-seq/WGCNA/MEs-thr8-prefilt-rinPCreg-48sess.txt' in downloads:93    local=numpy.genfromtxt(os.path.join(basedir,'rna-seq/WGCNA/MEs-thr8-prefilt-rinPCreg-48sess.txt'),skip_header=1)94    remote=numpy.genfromtxt('%s/rna-seq/WGCNA/MEs-thr8-prefilt-rinPCreg-48sess.txt'%dataurl,skip_header=1)95    compare_matrices(local,remote,'rna-seq/WGCNA/MEs-thr8-prefilt-rinPCreg-48sess.txt')96else:97    print 'SKIPPING DOWNLOADED FILE:   rna-seq/WGCNA/MEs-thr8-prefilt-rinPCreg-48sess.txt'  98    99if not 'rna-seq/WGCNA/module_assignments_thr8_prefilt_rinPCreg.txt' in downloads:100    local,local_genes=load_wgcna_module_assignments(os.path.join(basedir,'rna-seq/WGCNA/module_assignments_thr8_prefilt_rinPCreg.txt'))101    remote,remote_genes=load_wgcna_module_assignments('%s/rna-seq/WGCNA/module_assignments_thr8_prefilt_rinPCreg.txt'%dataurl)102    compare_matrices(local,remote,'rna-seq/WGCNA/module_assignments_thr8_prefilt_rinPCreg.txt')103else:104    print 'SKIPPING DOWNLOADED FILE:   rna-seq/WGCNA/module_assignments_thr8_prefilt_rinPCreg.txt'  105print '#### Metabolomics data'106if not 'metabolomics/apclust_eigenconcentrations.txt' in downloads:107    local,local_subs=load_varstab_data(os.path.join(basedir,'metabolomics/apclust_eigenconcentrations.txt'))108    remote,remote_subs=load_varstab_data('%s/metabolomics/apclust_eigenconcentrations.txt'%dataurl)109    compare_matrices(local,remote,'metabolomics/apclust_eigenconcentrations.txt')110else:111    print 'SKIPPING DOWNLOADED FILE:  metabolomics/apclust_eigenconcentrations.txt'112print '#### BCT analyses'113for f in ['PIpos_weighted_louvain_bct.txt','modularity_weighted_louvain_bct.txt','geff_pos.txt']:114    local=numpy.genfromtxt(os.path.join(basedir,'rsfmri',f))115    remote=numpy.genfromtxt('%s/rsfmri/%s'%(dataurl,f))116    compare_matrices(local,remote,'rsfmri/%s'%f)117    118print '#### Timeseries analysis Results'119tsresults=glob.glob(os.path.join(basedir,'timeseries/out*.txt'))120if len(tsresults)==0:121    print 'no timeseries results found - somethign went wrong'122else:123    for ts in tsresults:124        f=ts.replace(basedir+'/','')125        126        if ts in downloads:127            print 'SKIPPING DOWNLOADED FILE:',f128        else:129            local=load_dataframe(os.path.join(basedir,f),thresh=1.01)130            remote=load_dataframe('%s/%s'%(dataurl,f),thresh=1.01)...

parabolic_operator_system_test.py

Source:parabolic_operator_system_test.py

...32                                       [[x], [y], [dt, theta]]33                                       )34        base_compare = compile_sympy([A, neumann], [A_bar, neumann_bar], k, [[x], [y]])35        test_pts = np.linspace(0,1,51)[:, None]36        def compare_matrices(base_key, parabolic_key):37            base_mat = base_op_system[base_key](test_pts, test_pts, fun_args)38            test_mat = parabolic_op_system[parabolic_key](test_pts, test_pts, np.array([]))39            np.testing.assert_almost_equal(base_mat, test_mat, err_msg='Failed for {} vs. {}'.format(base_key, parabolic_key))40        for theta_val in [0., 0.5, 1.]:41            dt_val = 0.142            parabolic_op_system = ParabolicOperatorSystem(base_compare, A, A_bar, theta_val, dt_val)43            association = {44                (): (),45                L_explicit: parabolic_op_system.explicit_op,46                L_bar_explicit: parabolic_op_system.explicit_op_bar,47                L_implicit: parabolic_op_system.implicit_op,48                L_bar_implicit: parabolic_op_system.implicit_op_bar,49                neumann: neumann,50                neumann_bar: neumann_bar51            }52            fun_args = np.array([dt_val, theta_val])53            for k in [(), L_explicit, L_bar_explicit, L_implicit, L_bar_implicit, neumann, neumann_bar]:54                left_key = (k,)55                right_key = (association[k], )56                compare_matrices(left_key, right_key)57            for k1, k2 in itertools.product([(), L_explicit, L_implicit, neumann], [(), L_bar_explicit, L_bar_implicit, neumann_bar]):58                left_key = (k1, k2)59                right_key = (association[k1], association[k2])...

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