Uses pysurfer to perform interactive display of correlation matrices stored in hdf or matlab or numpy arrays.

instacorr.py

# -*- coding: utf-8 -*-
# <nbformat>3</nbformat>

# <codecell>

import argparse
import os

from surfer import Brain
import scipy.io as sio
import nibabel as nb
import numpy as np
from mayavi import mlab
from tables import openFile

overlay_added = False
brains = []
corrmats = []
combined = None
hemi = None

def do_overlay(idx):
    global overlay_added
    global brains
    global corrmats
    global combined
    global hemi
    if overlay_added:
        for brain in brains:
            brain.overlays['mean'].remove()
            brain.foci['foci'].remove()
        overlay_added = False
    for i, brain in enumerate(brains):
        mlab.figure(brain._f)
        if combined:
            totalverts = len(corrmats[i][0])
            nverts = len(brain._geo.x)
            if hemi == 'rh':
                startidx = totalverts - nverts
                rowidx = startidx + idx
                val = corrmats[i][rowidx][startidx:]
            else:
                val = corrmats[i][idx][:nverts]
        else:
            val = corrmats[i][idx]
        val[np.isnan(val)] = 0
        brain.add_overlay(val, min=0.3, max=1.0, sign='abs', name='mean')
        brain.add_foci(idx, coords_as_verts=True, name='foci', color=(.46,0.7,0.87))
    overlay_added = True

def picker_callback(picker_object):
    do_overlay(picker_object.point_id)

def display_matrices(filenames, target, hemi, surface):
    print '\n'.join(filenames)
    for name in filenames:
        try:
            corrmat = sio.loadmat(name)['corrmat']
        except:
            try:
                corrmat = np.load(name)['corrmat']
            except:
                try:
                    corrmat = np.load(name)['arr_0']
                except:
                    try:
                        corrmat = np.load(name)['avgcorr']
                    except:
                        h5file = openFile(name, 'r')
                        corrmat = h5file.root.corrmat
        corrmats.append(corrmat)
    for idx, name in enumerate(filenames):
        path, name = os.path.split(name)
        br = Brain(target, hemi, surface, title=name+'-%d' % idx)
        brains.append(br)
        for brain in brains[:-1]:
            mlab.sync_camera(br._f, brain._f)
            mlab.sync_camera(brain._f, br._f)
        br._f.on_mouse_pick(picker_callback)
    mlab.show()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="None")
    parser.add_argument('-f','--files',
                        dest='files',
                        nargs="+",
                        required=True,
                        help='correlation matrix files'
                        )
    parser.add_argument('--hemi',
                        dest = 'hemi',
                        required=True,
                        help='hemisphere')
    parser.add_argument('-s','--surface',
                        dest = 'surface',
                        required=True,
                        help='Type of surface')
    parser.add_argument('-t', '--target',
                        dest = 'target',
                        required=True,
                        help='Target fsaverage')
    parser.add_argument('-c', '--combined',
                        dest = 'combined',
                        default = False,
                        action = 'store_true',
                        help='file contains combined surfaces')
    
    args = parser.parse_args()
    combined = args.combined
    hemi = args.hemi
    display_matrices(args.files, args.target, args.hemi, args.surface)