def mk_participation_index_giftis(): pidata = numpy.loadtxt( os.path.join(rsfmridir, 'PIpos_weighted_louvain_bct.txt')) df = load_dataframe.load_dataframe(os.path.join( basedir, 'timeseries/out.dat.pindex_behav.txt'), thresh=0.05) associations = {} for v in df.iterkeys(): if not associations.has_key(v[1]): associations[v[1]] = numpy.zeros(634) vertexnum = int(v[0].replace('V', '')) - 1 associations[v[1]][vertexnum] = df[v][2] vars = associations.keys() vars.sort() data = numpy.zeros((634, len(vars))) for i in range(len(vars)): data[:, i] = associations[vars[i]] data = data[:620, :] meanpi = numpy.mean(pidata, 1) data = numpy.hstack((meanpi[:620, None], data)) vars = ['meanPI'] + vars labels_to_gii.labels_to_gii(data, vars, 'PI', basedir=basedir, outdir=rsfmridir)
def check_text_file(infile): local=numpy.loadtxt(os.path.join(basedir,infile)) remote=load_dataframe('%s/%s'%(dataurl,infile),thresh=1.01) if not len(local)==len(remote): print 'size mismatch for ',infile return localdata=numpy.zeros((len(local),4)) remotedata=numpy.zeros((len(remote),4)) keys=local.keys() for k in range(len(keys)): if not remote.has_key(keys[k]): print 'remote missing matching key',keys[k] localdata[k,:]=local[keys[k]] remotedata[k,:]=remote[keys[k]] if numpy.allclose(localdata,remotedata,rtol,atol): print 'PASS:'******'FAIL:',infile,'maxdiff =',maxdiff
def check_text_file(infile): local = numpy.loadtxt(os.path.join(basedir, infile)) remote = load_dataframe('%s/%s' % (dataurl, infile), thresh=1.01) if not len(local) == len(remote): print 'size mismatch for ', infile return localdata = numpy.zeros((len(local), 4)) remotedata = numpy.zeros((len(remote), 4)) keys = local.keys() for k in range(len(keys)): if not remote.has_key(keys[k]): print 'remote missing matching key', keys[k] localdata[k, :] = local[keys[k]] remotedata[k, :] = remote[keys[k]] if numpy.allclose(localdata, remotedata, rtol, atol): print 'PASS:'******'FAIL:', infile, 'maxdiff =', maxdiff
remote = numpy.genfromtxt('%s/rsfmri/%s' % (dataurl, f)) compare_matrices(local, remote, 'rsfmri/%s' % f) print '#### Timeseries analysis Results' tsresults = glob.glob(os.path.join(basedir, 'timeseries/out*.txt')) if len(tsresults) == 0: print 'no timeseries results found - somethign went wrong' else: for ts in tsresults: f = ts.replace(basedir + '/', '') if ts in downloads: print 'SKIPPING DOWNLOADED FILE:', f else: local = load_dataframe(os.path.join(basedir, f), thresh=1.01) remote = load_dataframe('%s/%s' % (dataurl, f), thresh=1.01) if not len(local) == len(remote): print 'size mismatch for ', f, len(local), len(remote) continue localdata = numpy.zeros((len(local), 4)) remotedata = numpy.zeros((len(remote), 4)) keys = local.keys() d = diff(keys, remote.keys()) if d: print 'Key mismatch for', f print d for k in range(len(keys)): if not remote.has_key(keys[k]): print 'remote missing matching key', keys[k] else:
""" make matrix showing bwcorr and wgcna relations """ from myconnectome.utils.load_dataframe import load_dataframe import numpy import os basedir=os.environ['MYCONNECTOME_DIR'] df=load_dataframe(os.path.join(basedir,'timeseries/out.dat.wgcna_bwcorr.txt'),0.1) genemodules=[] genenums=[] connections=[] for k in df.iterkeys(): genemodules.append(k[0]) genenums.append(int(k[0].split(':')[0].replace('ME',''))) c=k[1].replace('Cingulo-opercular','Cingulo_opercular').replace('Parieto-Occipital','Parieto_Occipital').replace('rontal-Parietal','rontal_Parietal') connections.append(c) modules=[] for c in connections: c_s=c.split('-') assert len(c_s)==2 modules+=c_s modules=list(set(modules)) modules.sort() modules=modules[4:]+modules[:4]
inputs to the R program used for the figures (mk_bwcorr_expression_figure.R) Created on Sun Jun 21 18:03:58 2015 @author: poldrack """ import os import numpy from myconnectome.utils.load_dataframe import load_dataframe import matplotlib.pyplot as plt import matplotlib basedir = os.environ['MYCONNECTOME_DIR'] wincorr_df = load_dataframe(os.path.join( basedir, 'timeseries/out.dat.wgcna_wincorr.txt'), thresh=1.0) netnames = [ 'DMN', 'V2', 'FP1', 'V1', 'DA', 'VA', 'Sal', 'CO', 'SM', 'FP2', 'MPar', 'ParOcc' ] netidx = { 1: 0, 2: 1, 3: 2, 4.5: 3, 5: 4, 7: 5, 8: 6, 9: 7, 10: 8,
compare_matrices(local,remote,'rsfmri/%s'%f) print '#### Timeseries analysis Results' tsresults=glob.glob(os.path.join(basedir,'timeseries/out*.txt')) if len(tsresults)==0: print 'no timeseries results found - somethign went wrong' else: for ts in tsresults: f=ts.replace(basedir+'/','') if ts in downloads: print 'SKIPPING DOWNLOADED FILE:',f else: local=load_dataframe(os.path.join(basedir,f),thresh=1.01) remote=load_dataframe('%s/%s'%(dataurl,f),thresh=1.01) if not len(local)==len(remote): print 'size mismatch for ',f,len(local),len(remote) continue localdata=numpy.zeros((len(local),4)) remotedata=numpy.zeros((len(remote),4)) keys=local.keys() d=diff(keys,remote.keys()) if d: print 'Key mismatch for',f print d for k in range(len(keys)): if not remote.has_key(keys[k]): print 'remote missing matching key',keys[k] else:
Created on Sun May 3 06:55:17 2015 @author: poldrack """ import os, sys import numpy import networkx from myconnectome.utils.load_dataframe import load_dataframe basedir = os.environ['MYCONNECTOME_DIR'] thresh = 0.1 winmod = load_dataframe(os.path.join(basedir, 'timeseries/out.dat.wincorr_netdat.txt'), thresh=1.0) bwmod = load_dataframe(os.path.join(basedir, 'timeseries/out.dat.bwcorr_netdat.txt'), thresh=1.0) nicenames = { '16_Parieto_Occipital': 'ParietoOccip', '4.5_Visual_1': 'V1', '9_Cingulo_opercular': 'CingOperc', '2_Visual_2': 'V2', '8_Salience': 'Salience', '1_Default': 'Default', '11.5_Fronto_Parietal_2': 'FPother', '7_Ventral_Attention': 'VentAttn', '5_Dorsal_Attention': 'DorsAttn',