How to use compare_matrices method in avocado

Best Python code snippet using avocado_python

check_results.py

Source: check_results.py Github

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...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)...

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parabolic_operator_system_test.py

Source: parabolic_operator_system_test.py Github

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...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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