def functional_connectivity(ts="~/ni_data/ofM.dr/preprocessing/as_composite/sub-5690/ses-ofM_aF/func/sub-5690_ses-ofM_aF_trial-EPI_CBV_chr_longSOA.nii.gz", labels_img='~/ni_data/templates/roi/DSURQEc_40micron_labels.nii', labels = '~/ni_data/templates/roi/DSURQE_mapping.csv', ): """ simple fc example """ figsize = (50,50) # incl. plotting correlation_matrix = fc.correlation_matrix(ts, labels_img, save_as = '~/correlation_matrix.csv') connectivity.plot_connectivity_matrix(correlation_matrix, figsize, labels, save_as = '~/correlation_matrix.png')
def functional_connectivity(ts="~/ni_data/ofM.dr/preprocessing/as_composite/sub-5690/ses-ofM_aF/func/sub-5690_ses-ofM_aF_task-EPI_CBV_chr_longSOA.nii.gz", labels_img='/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii', labels = '/usr/share/mouse-brain-atlases/dsurqec_mapping.csv', ): """ simple fc example """ from samri.plotting import connectivity figsize = (50,50) # incl. plotting correlation_matrix = fc.correlation_matrix(ts, labels_img, save_as = '~/correlation_matrix.csv') #TODO: to test with confounds #correlation_matrix = fc.correlation_matrix(ts, '~/confounds.csv', labels_img, save_as = '~/correlation_matrix.csv') connectivity.plot_connectivity_matrix(correlation_matrix, figsize, labels, save_as = '~/correlation_matrix.png')
def functional_connectivity(ts="~/ni_data/ofM.dr/preprocessing/as_composite/sub-5690/ses-ofM_aF/func/sub-5690_ses-ofM_aF_task-EPI_CBV_chr_longSOA.nii.gz", labels_img='/usr/share/mouse-brain-atlases/dsurqec_40micron_labels.nii', labels = '/usr/share/mouse-brain-atlases/dsurqec_mapping.csv', ): """ simple fc example """ from samri.plotting import connectivity figsize = (50,50) # incl. plotting correlation_matrix = fc.correlation_matrix(ts, labels_img, save_as = '~/correlation_matrix.csv') #TODO: to test with confounds #correlation_matrix = fc.correlation_matrix(ts, '~/confounds.csv', labels_img, save_as = '~/correlation_matrix.csv') connectivity.plot_connectivity_matrix(correlation_matrix, figsize, labels, save_as = '~/correlation_matrix.png')
# This example illustrates how to generate a functional connectivity matrix and its respective plot
import matplotlib
matplotlib.use('Agg')
import os
from os import path
from samri.analysis import fc
from samri.plotting import connectivity
from samri.fetch.templates import fetch_rat_waxholm
# fetch data templates and data
data_dir = path.join(path.dirname(path.realpath(__file__)),"../tests/data/")
results_dir = path.abspath(path.expanduser('~/.samri_files/results/fc/'))
# check if results dir exists, otherwise create
if not os.path.exists(path.abspath(path.expanduser(results_dir))):
os.makedirs(path.abspath(path.expanduser(results_dir)))
template = fetch_rat_waxholm()
trial = 'MhBu'
ts = path.abspath(path.expanduser('~/ni_data/data/preprocessing/composite/sub-22/ses-noFUSr0/func/sub-22_ses-noFUSr0_acq-seEPI_trial-'+trial+'.nii.gz'))
figsize=(50,50)
correlation_matrix = fc.correlation_matrix(ts, labels_img = template['atlas'], mask=template['mask'], save_as = results_dir + '/correlation_matrix_'+trial+'.csv')
connectivity.plot_connectivity_matrix(correlation_matrix, figsize = figsize, labels=template['labels'], save_as = results_dir + '/correlation_matrix_'+trial+'.png')
# also plot dendogram
fc.dendogram(correlation_matrix, figsize = figsize, save_as = results_dir + '/dendogram_'+trial+'.png')