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',