forked from mrahim/post_learning_analysis
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compute_connectivity.py
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compute_connectivity.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 30 13:30:42 2015
@author: mr243268
"""
import os
import numpy as np
from loader import load_dynacomp, load_dynacomp_roi_timeseries, \
load_roi_names_and_coords, set_figure_base_dir,\
set_data_base_dir, load_dynacomp_msdl_timeseries, \
load_msdl_names_and_coords
import matplotlib.pyplot as plt
from matplotlib import cm
from nilearn.plotting import plot_connectome
from sklearn import covariance
from nilearn.group_sparse_covariance import GroupSparseCovarianceCV
def plot_connectivity_matrix(subject_id, group, pc, roi_names,
suffix, session='func1',
preprocessing_folder='pipeline_1',
save=True, msdl=False):
"""Plots connectivity matrix of pc
"""
title = '-'.join([suffix, group, subject_id, session])
# plot matrix
output_folder = os.path.join(set_figure_base_dir('connectivity'), suffix)
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
output_file = os.path.join(output_folder,
'_'.join([suffix, 'matrix', group,
session,
preprocessing_folder, subject_id]))
if msdl:
title += '_msdl'
output_file += '_msdl'
if msdl:
plt.figure(figsize=(12, 12))
else:
plt.figure(figsize=(8, 8))
plt.imshow(pc, cmap=cm.bwr, interpolation='nearest',
vmin=-1, vmax=1)
plt.colorbar()
plt.xticks(range(len(roi_names)), roi_names,
rotation='vertical', fontsize=16)
plt.yticks(range(len(roi_names)), roi_names, fontsize=16)
plt.title(title, fontsize=20)
plt.tight_layout()
if save:
plt.savefig(output_file)
def plot_connectivity_glassbrain(subject_id, group, pc, roi_coords,
suffix, session='func1',
preprocessing_folder='pipeline_1',
save=True, msdl=False):
"""Plots connectome of pc
"""
title = '-'.join([suffix, group, subject_id, session])
output_folder = os.path.join(set_figure_base_dir('connectivity'), suffix)
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
output_file = os.path.join(output_folder,
'_'.join([suffix, 'connectome', group,
session,
preprocessing_folder, subject_id]))
if msdl:
title += '_msdl'
output_file += '_msdl'
plt.figure(figsize=(10, 20), dpi=90)
if save:
plot_connectome(pc, roi_coords, edge_threshold='90%', title=title,
output_file=output_file)
else:
plot_connectome(pc, roi_coords, edge_threshold='90%', title=title)
def compute_pearson_connectivity(subject_id, group, session='func1',
preprocessing_folder='pipeline_1',
plot=True, save=True, save_file=True,
msdl=False):
"""Returns Pearson correlation coefficient for a subject_id
"""
# load timeseries
if msdl:
ts = load_dynacomp_msdl_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
roi_names, roi_coords = load_msdl_names_and_coords()
else:
ts = load_dynacomp_roi_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
# pearson correlation
pc = np.corrcoef(ts.T)
if plot:
print session
plot_connectivity_matrix(subject_id, group, pc,
roi_names, 'pc',
session,
preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(subject_id, group, pc,
roi_coords, 'pc', session,
preprocessing_folder, save, msdl)
if save_file:
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(CONN_DIR, subject_id,
'pc_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file, correlation=pc,
roi_names=roi_names, roi_coords=roi_coords)
return pc, roi_names, roi_coords
def compute_graph_lasso_covariance(subject_id, group, session='func1',
preprocessing_folder='pipeline_1',
plot=True, save=True, save_file=True,
msdl=False):
"""Returns graph lasso covariance for a subject_id
"""
# load timeseries
if msdl:
ts = load_dynacomp_msdl_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
roi_names, roi_coords = load_msdl_names_and_coords()
else:
ts = load_dynacomp_roi_timeseries(subject_id, session=session,
preprocessing_folder=preprocessing_folder)
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
# compute covariance
gl = covariance.GraphLassoCV(verbose=2)
gl.fit(ts)
if plot:
plot_connectivity_matrix(subject_id, group, gl.covariance_,
roi_names, 'gl_covariance', session,
preprocessing_folder, save, msdl)
plot_connectivity_matrix(subject_id, group, gl.precision_,
roi_names, 'gl_precision', session,
preprocessing_folder, save, msdl)
sparsity = (gl.precision_ == 0)
plot_connectivity_matrix(subject_id, group, sparsity,
roi_names, 'gl_sparsity', session,
preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(subject_id, group, gl.covariance_,
roi_coords, 'gl_covariance', session,
preprocessing_folder, save, msdl)
if save_file:
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
sparsity = (gl.precision_ == 0)
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(CONN_DIR, subject_id,
'gl_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file, covariance=gl.covariance_,
precision=gl.precision_, sparsity=sparsity,
roi_names=roi_names, roi_coords=roi_coords)
return gl, roi_names, roi_coords
def compute_group_sparse_covariance(dataset, session='func1',
preprocessing_folder='pipeline_1',
plot=True, save=True, save_file=True,
msdl=False):
"""Returns Group sparse covariance for all subjects
"""
ts = []
# load timeseries
if msdl:
for subject_id in dataset.subjects:
ts.append(load_dynacomp_msdl_timeseries(subject_id,\
session=session, preprocessing_folder=preprocessing_folder))
roi_names, roi_coords = load_msdl_names_and_coords()
else:
for subject_id in dataset.subjects:
ts.append(load_dynacomp_roi_timeseries(subject_id, session=session,\
preprocessing_folder=preprocessing_folder))
# load rois
roi_names, roi_coords = load_roi_names_and_coords(subject_id)
gsc = GroupSparseCovarianceCV(verbose=2)
gsc.fit(ts)
if plot:
for i in range(len(dataset.subjects)):
if not msdl:
# load rois
roi_names,\
roi_coords = load_roi_names_and_coords(dataset.subjects[i])
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
gsc.covariances_[..., i],
roi_names, 'gsc_covariance', session,
preprocessing_folder, save, msdl)
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
gsc.precisions_[..., i],
roi_names, 'gsc_precision', session,
preprocessing_folder, save, msdl)
sparsity = (gsc.precisions_[..., i] == 0)
plot_connectivity_matrix(dataset.subjects[i], dataset.group[i],
sparsity,
roi_names, 'gsc_sparsity', session,
preprocessing_folder, save, msdl)
plot_connectivity_glassbrain(dataset.subjects[i], dataset.group[i],
gsc.covariances_[..., i],
roi_coords, 'gsc_covariance', session,
preprocessing_folder, save, msdl)
for i in range(len(dataset.subjects)):
if not msdl:
# load rois
roi_names,\
roi_coords = load_roi_names_and_coords(dataset.subjects[i])
sparsity = (gsc.precisions_[..., i] == 0)
CONN_DIR = set_data_base_dir('Dynacomp/connectivity')
subject_id = dataset.subjects[i]
if not os.path.isdir(os.path.join(CONN_DIR, subject_id)):
os.mkdir(os.path.join(CONN_DIR, subject_id))
output_file = os.path.join(CONN_DIR, subject_id,
'gsc_' + session + '_' + preprocessing_folder)
if msdl:
output_file += '_msdl'
np.savez(output_file, covariance=gsc.covariances_[..., i],
precision=gsc.precisions_[..., i], sparsity=sparsity,
roi_names=roi_names, roi_coords=roi_coords)
return gsc, roi_names, roi_coords
##############################################################################
preprocessing_folder = 'pipeline_1'
prefix = 'swr'
#preprocessing_folder = 'pipeline_2'
#prefix = 'resampled_wr'
msdl = False
dataset = load_dynacomp(preprocessing_folder=preprocessing_folder,
prefix=prefix)
for session_i in ['func1', 'func2']:
for i in range(len(dataset.subjects)):
print dataset.subjects[i], session_i
compute_pearson_connectivity(dataset.subjects[i],
dataset.group[i],
session=session_i,
preprocessing_folder=preprocessing_folder,
plot=True,
save=True,
save_file=True,
msdl=msdl)
compute_graph_lasso_covariance(dataset.subjects[i],
dataset.group[i],
session=session_i,
preprocessing_folder=preprocessing_folder,
plot=True,
save=True,
save_file=True,
msdl=msdl)
compute_group_sparse_covariance(dataset,
session=session_i,
preprocessing_folder=preprocessing_folder,
plot=True,
save=True,
save_file=True,
msdl=msdl)