In [460]:
from bids.grabbids import BIDSLayout
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
MCFLIRT, SliceTimer, Threshold,Info, ConvertXFM,MotionOutliers)
from nipype.interfaces.afni import Resample
from nipype.interfaces.io import DataSink
from nipype.pipeline import Node, MapNode, Workflow, JoinNode
from nipype.interfaces.utility import IdentityInterface, Function
import os
from os.path import join as opj
from nipype.interfaces import afni
import nibabel as nib
import json
import numpy as np
In [461]:
# Paths
path_cwd = os.getcwd()
path_split_list = path_cwd.split('/')
s = path_split_list[0:-2] # for getting to the parent dir of pwd
s = opj('/',*s) # *s converts list to path, # very important to add '/' in the begining so it is read as directory later
In [462]:
# json_path = opj(data_directory,'task-rest_bold.json')
json_path = '../scripts/json/paths.json'
with open(json_path, 'rt') as fp:
task_info = json.load(fp)
In [463]:
# base_directory = opj(s,'result')
# parent_wf_directory = 'preprocessPipeline_ABIDE2_GU1_withfloat'
# child_wf_directory = 'coregistrationPipeline'
# data_directory = opj(s,"data/ABIDE2-BIDS/GU1")
# datasink_name = 'datasink_preprocessed_ABIDE2_GU1_withfloat'
base_directory = opj(s,task_info["base_directory_for_results"])
motion_correction_bet_directory = task_info["motion_correction_bet_directory"]
parent_wf_directory = task_info["parent_wf_directory"]
# functional_connectivity_directory = task_info["functional_connectivity_directory"]
functional_connectivity_directory = 'temp_fc'
coreg_reg_directory = task_info["coreg_reg_directory"]
atlas_resize_reg_directory = task_info["atlas_resize_reg_directory"]
data_directory = opj(s,task_info["data_directory"])
datasink_name = task_info["datasink_name"]
fc_datasink_name = task_info["fc_datasink_name"]
fc_datasink_name = 'temp_dataSink'
atlasPath = opj(s,task_info["atlas_path"])
# mask_file = '/media/varun/LENOVO4/Projects/result/preprocessPipeline/coregistrationPipeline/_subject_id_0050952/skullStrip/sub-0050952_T1w_resample_brain_mask.nii.gz'
# os.chdir(path)
In [464]:
# opj(base_directory,parent_wf_directory,motion_correction_bet_directory,coreg_reg_directory,'resample_mni')
In [465]:
brain_path = opj(base_directory,datasink_name,'preprocessed_brain_paths/brain_file_list.npy')
mask_path = opj(base_directory,datasink_name,'preprocessed_mask_paths/mask_file_list.npy')
atlas_path = opj(base_directory,datasink_name,'atlas_paths/atlas_file_list.npy')
tr_path = opj(base_directory,datasink_name,'tr_paths/tr_list.npy')
motion_params_path = opj(base_directory,datasink_name,'motion_params_paths/motion_params_file_list.npy')
func2std_mat_path = opj(base_directory, datasink_name,'joint_xformation_matrix_paths/joint_xformation_matrix_file_list.npy')
MNI3mm_path = opj(base_directory,parent_wf_directory,motion_correction_bet_directory,coreg_reg_directory,'resample_mni/MNI152_T1_2mm_brain_resample.nii')
# brain_list = np.load('../results_again_again/ABIDE1_Preprocess_Datasink/preprocessed_brain_paths/brain_file_list.npy')
In [466]:
# brain_path,mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path
In [ ]:
In [467]:
brain_path = np.load(brain_path)
mask_path = np.load(mask_path)
atlas_path = np.load(atlas_path)
tr_path = np.load(tr_path)
motion_params_path = np.load(motion_params_path)
func2std_mat_path = np.load(func2std_mat_path)
In [468]:
# for a,b,c,d,e in zip(brain_path,mask_path,atlas_path,tr_path,motion_params_path):
# print (a,b,c,d,e,'\n')
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In [ ]:
In [469]:
layout = BIDSLayout(data_directory)
number_of_subjects = 2 # Number of subjects you wish to preprocess
# number_of_subjects = len(layout.get_subjects())
Checking the Data directory Structure
In [470]:
# len(layout.get_subjects()) # working!Gives us list of all the subjects
In [471]:
# layout.get_subjects();
To get the metadata associated with a subject. [Takes as argumment the filename of subject ]
Create a list of subjects
In [472]:
subject_list = (layout.get_subjects())#[0:number_of_subjects]
subject_list = list(map(int, subject_list))
In [473]:
import pandas as pd
demographics_file_path = '/home1/varunk/Autism-Connectome-Analysis-brain_connectivity/notebooks/demographics.csv'
phenotype_file_path = '/home1/varunk/data/ABIDE1/RawDataBIDs/composite_phenotypic_file.csv'
df_phenotype = pd.read_csv(phenotype_file_path)
df_phenotype = df_phenotype.sort_values(['SUB_ID'])
df_phenotype_sub_id = df_phenotype.as_matrix(['SITE_ID','SUB_ID']).squeeze()
df_demographics = pd.read_csv(demographics_file_path)
df_demographics_volumes = df_demographics.as_matrix(['SITE_NAME','VOLUMES']).squeeze()
In [474]:
# df_phenotype.sort_values(['SUB_ID'])
In [475]:
df_demographics_volumes
Out[475]:
In [476]:
# SUB_ID - Volumes Dictionary
site_vol_dict = dict(zip(df_demographics_volumes[:,0], df_demographics_volumes[:,1]))
# for site_subid in df_demographics_volumes:
# subid_site_dict = dict(zip(df_phenotype_sub_id[:,1], df_phenotype_sub_id[:,0]))
In [477]:
subid_vol_dict = dict(zip(df_phenotype_sub_id[:,1],[site_vol_dict[site] for site in df_phenotype_sub_id[:,0]] ))
In [478]:
(subid_vol_dict)
Out[478]:
In [479]:
vols = 120
del_idx = []
for idx,df in enumerate(df_demographics_volumes):
# print(idx,df[1])
if df[1] < vols:
del_idx.append(idx)
df_demographics_volumes = np.delete(df_demographics_volumes,del_idx, axis = 0)
In [ ]:
In [480]:
df_demographics_sites_refined = df_demographics_volumes[:,0]
In [481]:
df_demographics_sites_refined
Out[481]:
In [482]:
df_phenotype_sub_id
Out[482]:
In [ ]:
In [483]:
subjects_refined = []
for df in df_phenotype_sub_id:
if df[0] in df_demographics_sites_refined:
# print(df[1])
subjects_refined.append(df[1])
In [484]:
len(subjects_refined)
Out[484]:
In [485]:
subjects_refined = list(set(subjects_refined) - (set(df_phenotype_sub_id[:,1]) - set(subject_list) ) )
In [486]:
subject_list
Out[486]:
In [487]:
len(subjects_refined)
Out[487]:
In [488]:
subject_list = subjects_refined[0:number_of_subjects]
In [489]:
vols = vols - 4
In [490]:
def vol_correct(sub_id, subid_vol_dict, vols):
sub_vols = subid_vol_dict[sub_id] - 4
if sub_vols > vols:
t_min = sub_vols - vols
elif sub_vols == vols:
t_min = 0
else:
raise Exception('Volumes of Sub ',sub_id,' less than desired!')
return int(t_min)
In [491]:
volCorrect = Node(Function(function=vol_correct, input_names=['sub_id','subid_vol_dict','vols'],
output_names=['t_min']), name='volCorrect')
volCorrect.inputs.subid_vol_dict = subid_vol_dict
volCorrect.inputs.vols = vols
In [492]:
# os.chdir('/home1/varunk/results_again_again/temp/')
# volCorrect.inputs.sub_id = 51456
# res = volCorrect.run()
In [493]:
# res.outputs
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In [494]:
# layout.get();
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In [498]:
def get_subject_filenames(subject_id,brain_path,mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path,MNI3mm_path):
import re
for brain,mask,atlas,tr,motion_param,func2std_mat in zip(brain_path,mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path):
# sub_id_extracted = re.search('.+_subject_id_(\d+)', brain).group(1)
# if subject_id == sub_id_extracted:
if str(subject_id) in brain:
# print("Files for subject ",subject_id,brain,mask,atlas,tr,motion_param)
return brain,mask,atlas,tr,motion_param,func2std_mat,MNI3mm_path
print ('Unable to locate Subject: ',subject_id,'extracted: ',sub_id_extracted)
return 0
In [499]:
# Make a node
getSubjectFilenames = Node(Function(function=get_subject_filenames, input_names=['subject_id','brain_path','mask_path','atlas_path','tr_path','motion_params_path','func2std_mat_path','MNI3mm_path'],
output_names=['brain','mask','atlas','tr','motion_param','func2std_mat', 'MNI3mm_path']), name='getSubjectFilenames')
getSubjectFilenames.inputs.brain_path = brain_path
getSubjectFilenames.inputs.mask_path = mask_path
getSubjectFilenames.inputs.atlas_path = atlas_path
getSubjectFilenames.inputs.tr_path = tr_path
getSubjectFilenames.inputs.motion_params_path = motion_params_path
getSubjectFilenames.inputs.func2std_mat_path = func2std_mat_path
getSubjectFilenames.inputs.MNI3mm_path = MNI3mm_path
In [500]:
# import re
# text = '/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050004/111std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii'
# try:
# found = re.search('.+_subject_id_(\d+)', text).group(1)
# except AttributeError:
# # AAA, ZZZ not found in the original string
# found = '' # apply your error handling
# # found: 1234
In [501]:
# found
In [502]:
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id',subject_list)]
# ,'brain_path','mask_path','atlas_path','tr_path','motion_params_path'
# infosource.brain_path = brain_path
# infosource.mask_path = mask_path
# infosource.atlas_path = atlas_path
# infosource.tr_path = tr_path
# infosource.motion_params_path = motion_params_path
Let's do a band pass filtering on the data using the code from https://neurostars.org/t/bandpass-filtering-different-outputs-from-fsl-and-nipype-custom-function/824/2
In [503]:
### AFNI
bandpass = Node(afni.Bandpass(highpass=0.01, lowpass=0.1,
despike=False, no_detrend=True, notrans=True,
outputtype='NIFTI_GZ'),name='bandpass')
# bandpass = Node(afni.Bandpass(highpass=0.001, lowpass=0.01,
# despike=False, no_detrend=True, notrans=True,
# tr=2.0,outputtype='NIFTI_GZ'),name='bandpass')
# bandpass.inputs.mask = MNI152_2mm.outputs.mask_file
# highpass=0.008, lowpass=0.08,
In [504]:
# Testing bandpass on the func data in subject's space
# First comment out the bandpass.inputs.mask as it is in standard space.
# subject_id = layout.get_subjects()[0] # gives the first subject's ID
# func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', extensions=['nii', 'nii.gz'])]
# bandpass.inputs.in_file = func_file_path[0]
# res = bandpass.run();
In [505]:
# res.outputs.out_file
In [506]:
# https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dBandpass.html
# os.chdir('/home1/varunk/Autism-Connectome-Analysis-bids-related/')
highpass = Node(afni.Bandpass(highpass=0.01, lowpass=99999,
despike=False, no_detrend=True, notrans=True,
outputtype='NIFTI_GZ'),name='highpass')
In [507]:
# # Test
# subject_id = layout.get_subjects()[0] # gives the first subject's ID
# func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', extensions=['nii', 'nii.gz'])]
# highpass.inputs.in_file = func_file_path[0]
# res = highpass.run();
In [508]:
spatialSmooth = Node(interface=ImageMaths(op_string='-s 2.5479',
suffix='_smoothed'),
name='spatialSmooth')
In [509]:
def orthogonalize(in_file, mask_file):
import numpy as np
import nibabel as nib
import os
from os.path import join as opj
def gram_schmidt(voxel_time_series, mean_vector):
numerator = np.dot(voxel_time_series,mean_vector)
dinominator = np.dot(mean_vector,mean_vector)
voxel_time_series_orthogonalized = voxel_time_series - (numerator/dinominator)*mean_vector
# TO CONFIRM IF THE VECTORS ARE ORTHOGONAL
# sum_dot_prod = np.sum(np.dot(voxel_time_series_orthogonalized,mean_vector))
# print('Sum of entries of orthogonalized vector = ',sum_dot_prod)
return voxel_time_series_orthogonalized
mask_data = nib.load(mask_file)
mask = mask_data.get_data()
brain_data = nib.load(in_file)
brain = brain_data.get_data()
x_dim, y_dim, z_dim, t_dim = brain_data.shape
# Find mean brain
mean_vector = np.zeros(t_dim)
num_brain_voxels = 0
# Count the number of brain voxels
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
mean_vector = mean_vector + brain[i,j,k,:]
num_brain_voxels = num_brain_voxels + 1
mean_vector = mean_vector / num_brain_voxels
# Orthogonalize
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
brain[i,j,k,:] = gram_schmidt(brain[i,j,k,:], mean_vector)
sub_id = in_file.split('/')[-1].split('.')[0].split('_')[0].split('-')[1]
gsr_file_name = 'sub-' + sub_id + '_task-rest_run-1_bold.nii.gz'
# gsr_file_name_nii = gsr_file_name + '.nii.gz'
out_file = opj(os.getcwd(),gsr_file_name) # path
brain_with_header = nib.Nifti1Image(brain, affine=brain_data.affine,header = brain_data.header)
nib.save(brain_with_header,gsr_file_name)
return out_file
In [510]:
globalSignalRemoval = Node(Function(function=orthogonalize, input_names=['in_file','mask_file'],
output_names=['out_file']), name='globalSignalRemoval' )
# globalSignalRemoval.inputs.mask_file = mask_file
# globalSignalRemoval.iterables = [('in_file',file_paths)]
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In [ ]:
In [ ]:
In [511]:
def calc_residuals(subject,
motion_file):
"""
Calculates residuals of nuisance regressors -motion parameters for every voxel for a subject using GLM.
Parameters
----------
subject : string
Path of a subject's motion corrected nifti file.
motion_par_file : string
path of a subject's motion parameters
Returns
-------
residual_file : string
Path of residual file in nifti format
"""
import nibabel as nb
import numpy as np
import os
from os.path import join as opj
nii = nb.load(subject)
data = nii.get_data().astype(np.float32)
global_mask = (data != 0).sum(-1) != 0
# Check and define regressors which are provided from files
if motion_file is not None:
motion = np.genfromtxt(motion_file)
if motion.shape[0] != data.shape[3]:
raise ValueError('Motion parameters {0} do not match data '
'timepoints {1}'.format(motion.shape[0],
data.shape[3]))
if motion.size == 0:
raise ValueError('Motion signal file {0} is '
'empty'.format(motion_file))
# Calculate regressors
regressor_map = {'constant' : np.ones((data.shape[3],1))}
regressor_map['motion'] = motion
X = np.zeros((data.shape[3], 1))
for rname, rval in regressor_map.items():
X = np.hstack((X, rval.reshape(rval.shape[0],-1)))
X = X[:,1:]
if np.isnan(X).any() or np.isnan(X).any():
raise ValueError('Regressor file contains NaN')
Y = data[global_mask].T
try:
B = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)
except np.linalg.LinAlgError as e:
if "Singular matrix" in e:
raise Exception("Error details: {0}\n\nSingular matrix error: "
"The nuisance regression configuration you "
"selected may have been too stringent, and the "
"regression could not be completed. Ensure your "
"parameters are not too "
"extreme.\n\n".format(e))
else:
raise Exception("Error details: {0}\n\nSomething went wrong with "
"nuisance regression.\n\n".format(e))
Y_res = Y - X.dot(B)
data[global_mask] = Y_res.T
img = nb.Nifti1Image(data, header=nii.get_header(),
affine=nii.get_affine())
subject_name = subject.split('/')[-1].split('.')[0]
filename = subject_name + '_residual.nii.gz'
residual_file = os.path.join(os.getcwd(),filename )
img.to_filename(residual_file) # alt to nib.save
return residual_file
In [512]:
# Create a Node for above
calc_residuals = Node(Function(function=calc_residuals, input_names=['subject','motion_file'],
output_names=['residual_file']), name='calc_residuals')
In [ ]:
In [513]:
# Create DataSink object
dataSink = Node(DataSink(), name='datasink')
# Name of the output folder
dataSink.inputs.base_directory = opj(base_directory,fc_datasink_name)
To create the substitutions I looked the datasink folder where I was redirecting the output. I manually selected the part of file/folder name that I wanted to change and copied below to be substituted.
In [514]:
# Define substitution strings so that the data is similar to BIDS
substitutions = [('_subject_id_', 'sub-')]
# Feed the substitution strings to the DataSink node
dataSink.inputs.substitutions = substitutions
# ('_resample_brain_flirt.nii_brain', ''),
# ('_roi_st_mcf_flirt.nii_brain_flirt', ''),
In [515]:
base_directory
Out[515]:
In [516]:
def save_file_list_function(in_fc_map_brain_file):
# Imports
import numpy as np
import os
from os.path import join as opj
file_list = np.asarray(in_fc_map_brain_file)
print('######################## File List ######################: \n',file_list)
np.save('fc_map_brain_file_list',file_list)
file_name = 'fc_map_brain_file_list.npy'
out_fc_map_brain_file = opj(os.getcwd(),file_name) # path
return out_fc_map_brain_file
In [517]:
save_file_list = JoinNode(Function(function=save_file_list_function, input_names=['in_fc_map_brain_file'],
output_names=['out_fc_map_brain_file']),
joinsource="infosource",
joinfield=['in_fc_map_brain_file'],
name="save_file_list")
In [520]:
# Saves the brains instead of FC matrix files
def pear_coff(in_file, atlas_file, mask_file):
# code to find how many voxels are in the brain region using the mask
# imports
import numpy as np
import nibabel as nib
import os
from os.path import join as opj
mask_data = nib.load(mask_file)
mask = mask_data.get_data()
x_dim, y_dim, z_dim = mask_data.shape
atlasPath = atlas_file
# Read the atlas
atlasObject = nib.load(atlasPath)
atlas = atlasObject.get_data()
num_ROIs = int((np.max(atlas) - np.min(atlas) ))
# Read the brain in_file
brain_data = nib.load(in_file)
brain = brain_data.get_data()
x_dim, y_dim, z_dim, num_volumes = brain.shape
num_brain_voxels = 0
x_dim, y_dim, z_dim = mask_data.shape
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
num_brain_voxels = num_brain_voxels + 1
# Initialize a matrix of ROI time series and voxel time series
ROI_matrix = np.zeros((num_ROIs, num_volumes))
voxel_matrix = np.zeros((num_brain_voxels, num_volumes))
# Fill up the voxel_matrix
voxel_counter = 0
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
voxel_matrix[voxel_counter,:] = brain[i,j,k,:]
voxel_counter = voxel_counter + 1
# Fill up the ROI_matrix
# Keep track of number of voxels per ROI as well by using an array - num_voxels_in_ROI[]
num_voxels_in_ROI = np.zeros((num_ROIs,1)) # A column arrray containing number of voxels in each ROI
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
label = int(atlas[i,j,k]) - 1
if label != -1:
ROI_matrix[label,:] = np.add(ROI_matrix[label,:], brain[i,j,k,:])
num_voxels_in_ROI[label,0] = num_voxels_in_ROI[label,0] + 1
ROI_matrix = np.divide(ROI_matrix,num_voxels_in_ROI) # Check if divide is working correctly
X, Y = ROI_matrix, voxel_matrix
# Subtract mean from X and Y
X = np.subtract(X, np.mean(X, axis=1, keepdims=True))
Y = np.subtract(Y, np.mean(Y, axis=1, keepdims=True))
temp1 = np.dot(X,Y.T)
temp2 = np.sqrt(np.sum(np.multiply(X,X), axis=1, keepdims=True))
temp3 = np.sqrt(np.sum(np.multiply(Y,Y), axis=1, keepdims=True))
temp4 = np.dot(temp2,temp3.T)
coff_matrix = np.divide(temp1, (temp4 + 1e-7))
# Check if any ROI is missing and replace the NAN values in coff_matrix by 0
if np.argwhere(np.isnan(coff_matrix)).shape[0] != 0:
print("Some ROIs are not present. Replacing NAN in coff matrix by 0")
np.nan_to_num(coff_matrix, copy=False)
# TODO: when I have added 1e-7 in the dinominator, then why did I feel the need to replace NAN by zeros
sub_id = in_file.split('/')[-1].split('.')[0].split('_')[0].split('-')[1]
fc_file_name = sub_id + '_fc_map'
print ("Pear Matrix calculated for subject: ",sub_id)
roi_brain_matrix = coff_matrix
brain_file = in_file
x_dim, y_dim, z_dim, t_dim = brain.shape
(brain_data.header).set_data_shape([x_dim,y_dim,z_dim,num_ROIs])
brain_roi_tensor = np.zeros((brain_data.header.get_data_shape()))
print("Creating brain for Subject-",sub_id)
for roi in range(num_ROIs):
brain_voxel_counter = 0
for i in range(x_dim):
for j in range(y_dim):
for k in range(z_dim):
if mask[i,j,k] == 1:
brain_roi_tensor[i,j,k,roi] = roi_brain_matrix[roi,brain_voxel_counter]
brain_voxel_counter = brain_voxel_counter + 1
assert (brain_voxel_counter == len(roi_brain_matrix[roi,:]))
print("Created brain for Subject-",sub_id)
path = os.getcwd()
fc_file_name = fc_file_name + '.nii.gz'
out_file = opj(path,fc_file_name)
brain_with_header = nib.Nifti1Image(brain_roi_tensor, affine=brain_data.affine,header = brain_data.header)
nib.save(brain_with_header,out_file)
fc_map_brain_file = out_file
return fc_map_brain_file
In [521]:
# Again Create the Node and set default values to paths
pearcoff = Node(Function(function=pear_coff, input_names=['in_file','atlas_file','mask_file'],
output_names=['fc_map_brain_file']), name='pearcoff')
# output_names=['fc_map_brain_file']
# pearcoff.inputs.atlas_file = atlasPath
# pearcoff.inputs.num_brain_voxels = num_brain_voxels
# pearcoff.inputs.mask_file = mask_file
In [522]:
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_size=-1),
output_type='NIFTI',
name="extract")
# t_min=4,
In [523]:
func2std_xform = Node(FLIRT(output_type='NIFTI_GZ',
apply_xfm=True), name="func2std_xform")
In [524]:
# %%time
# pearcoff.run()
In [525]:
# motion_param_reg = [True, False]
# global_signal_reg = [True, False]
# band_pass_filt= [True, False]
# for motion_param_regression, global_signal_regression, band_pass_filtering in zip(motion_param_reg, global_signal_reg, band_pass_filt):
# print (motion_param_regression, global_signal_regression, band_pass_filtering)
In [526]:
# import itertools
# itr = (list(itertools.product([0, 1], repeat=3)))
# for motion_param_regression, global_signal_regression, band_pass_filtering in itr:
# print(motion_param_regression, global_signal_regression, band_pass_filtering)
In [527]:
# import itertools
# itr = (list(itertools.product([0, 1], repeat=3)))
# for motion_param_regression, global_signal_regression, band_pass_filtering in itr:
In [528]:
# base_directory
In [529]:
motion_param_regression = 1
band_pass_filtering = 1
global_signal_regression = 0
smoothing = 1
volcorrect = 1
num_proc = 7
combination = 'motionRegress' + str(int(motion_param_regression)) + 'filt' + \
str(int(band_pass_filtering)) + 'global' + str(int(global_signal_regression)) + \
'smoothing' + str(int(smoothing))
print("Combination: ",combination)
base_dir = opj(base_directory,functional_connectivity_directory)
# wf = Workflow(name=functional_connectivity_directory)
wf = Workflow(name=combination)
wf.base_dir = base_dir # Dir where all the outputs will be stored.
wf.connect([(infosource , getSubjectFilenames, [('subject_id','subject_id')])])
if motion_param_regression == 1 and global_signal_regression == 0 and band_pass_filtering == 1 and smoothing == 1 and volcorrect == 1: # 101
wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
wf.connect([( calc_residuals, extract, [('residual_file','in_file')])])
wf.connect([(infosource, volCorrect, [('subject_id','sub_id')])])
wf.connect([( volCorrect, extract, [('t_min','t_min')])])
wf.connect([(extract, bandpass, [('roi_file','in_file')])])
wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
wf.connect([( bandpass, spatialSmooth, [('out_file','in_file')])])
wf.connect([( spatialSmooth, pearcoff, [('out_file','in_file')])])
# wf.connect([( extract, pearcoff, [('roi_file','in_file')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# ---------------------------------------------------------------------------------------
wf.connect([(pearcoff, func2std_xform, [('fc_map_brain_file','in_file')])])
wf.connect([(getSubjectFilenames, func2std_xform, [('func2std_mat','in_matrix_file')])])
wf.connect([(getSubjectFilenames, func2std_xform, [('MNI3mm_path','reference')])])
# -- send out file to save file list and then save the outputs
folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
wf.connect([(func2std_xform, save_file_list, [('out_file','in_fc_map_brain_file')])])
# --------------------------------------------------------------------------------------------
wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
wf.write_graph(graph2use='flat', format='png')
# from IPython.display import Image
# wf.write_graph(graph2use='exec', format='png', simple_form=True)
wf.run('MultiProc', plugin_args={'n_procs': num_proc})
# file_name = opj(base_dir,combination,'graph_detailed.dot.png')
# Image(filename=file_name)
elif motion_param_regression == 1 and global_signal_regression == 1 and band_pass_filtering == 1 and smoothing == 1: #111
wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
wf.connect([( calc_residuals, extract, [('residual_file','in_file')])])
wf.connect([(infosource, volCorrect, [('subject_id','sub_id')])])
wf.connect([( volCorrect, extract, [('t_min','t_min')])])
wf.connect([(extract, globalSignalRemoval, [('roi_file','in_file')])])
# wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
wf.connect([( bandpass, spatialSmooth, [('out_file','in_file')])])
wf.connect([( spatialSmooth, pearcoff, [('out_file','in_file')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# ---------------------------------------------------------------------------------------
wf.connect([(pearcoff, func2std_xform, [('fc_map_brain_file','in_file')])])
wf.connect([(getSubjectFilenames, func2std_xform, [('func2std_mat','in_matrix_file')])])
wf.connect([(getSubjectFilenames, func2std_xform, [('MNI3mm_path','reference')])])
# -- send out file to save file list and then save the outputs
folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
wf.connect([(func2std_xform, save_file_list, [('out_file','in_fc_map_brain_file')])])
# --------------------------------------------------------------------------------------------
wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# wf.connect([(bandpass, dataSink, [('out_file','motionRegress_filt_global.@out_file')])])
# if motion_param_regression == 1 and global_signal_regression == 1:
wf.write_graph(graph2use='flat', format='png')
wf.run('MultiProc', plugin_args={'n_procs': num_proc})
In [530]:
# motion_param_regression = 1
# band_pass_filtering = 1
# global_signal_regression = 1
# smoothing = 1
# combination = 'motionRegress' + str(int(motion_param_regression)) + 'filt' + \
# str(int(band_pass_filtering)) + 'global' + str(int(global_signal_regression)) + \
# 'smoothing' + str(int(smoothing))
# print("Combination: ",combination)
# wf = Workflow(name=functional_connectivity_directory)
# wf.base_dir = base_directory # Dir where all the outputs will be stored(inside BETFlow folder).
# wf.connect([(infosource , getSubjectFilenames, [('subject_id','subject_id')])])
# if motion_param_regression == 1 and global_signal_regression == 1 and band_pass_filtering == 1 and smoothing = 1:
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( bandpass, spatialSmooth, [('out_file','in_file')])])
# wf.connect([( spatialSmooth, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# wf.connect([(pearcoff, func2std_xform, [('fc_map_brain_file','in_file')])])
# wf.connect([(getSubjectFilenames, func2std_xform, [('func2std_mat','in_matrix_file')])])
# wf.connect([(getSubjectFilenames, func2std_xform, [('MNI3mm_path','reference')])])
# # -- send out file to save file list and then save the outputs
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(func2std_xform, save_file_list, [('out_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# # wf.connect([(bandpass, dataSink, [('out_file','motionRegress_filt_global.@out_file')])])
# # if motion_param_regression == 1 and global_signal_regression == 1:
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# from IPython.display import Image
# wf.write_graph(graph2use='exec', format='png', simple_form=True)
# file_name = opj(base_directory,functional_connectivity_directory,'graph_detailed.dot.png')
# Image(filename=file_name)
# elif motion_param_regression == 1 and global_signal_regression == 1 and band_pass_filtering == 0 and smoothing = 1: # 110
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( globalSignalRemoval, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == 1 and global_signal_regression == 0 and band_pass_filtering == 1 and smoothing = 1: # 101
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# # wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# wf.connect([(calc_residuals, bandpass, [('residual_file','in_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == 1 and global_signal_regression == 0 and band_pass_filtering == 0 and smoothing = 1: # 100
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# # wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(calc_residuals, bandpass, [('residual_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( calc_residuals, pearcoff, [('residual_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == 0 and global_signal_regression == 1 and band_pass_filtering == 1 and smoothing = 1: # 011
# # wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# # wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('brain','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == 0 and global_signal_regression == 1 and band_pass_filtering == 0 and smoothing = 1: # 010
# # wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# # wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('brain','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( globalSignalRemoval, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == 0 and global_signal_regression == 0 and band_pass_filtering == 1 and smoothing = 1: # 001
# # wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# # wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('brain','in_file')] )])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('out_file','in_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# else:
# wf.connect([( getSubjectFilenames, pearcoff, [('brain','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
In [531]:
X = np.load("../results_again_again/temp_dataSink/pearcoff_motionRegress1filt1global1/fc_map_brain_file_list.npy")
X
In [ ]:
!nipypecli show crash-20171125-133018-varunk-pearcoff.a1-7b869482-76ad-4f55-af87-8b01e34e975c.pklz
In [ ]:
# X = np.load('../results_again_again/fc_datasink/pearcoff_motionRegress1filt1global1/fc_map_brain_file_list.npy')
# X.shape
In [ ]:
# elif motion_param_regression == True and global_signal_regression == True and band_pass_filtering == False: # 110
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( globalSignalRemoval, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == True and global_signal_regression == False and band_pass_filtering == True: # 101
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# # wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# wf.connect([(calc_residuals, bandpass, [('residual_file','in_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == True and global_signal_regression == False and band_pass_filtering == False: # 100
# wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# # wf.connect([(calc_residuals, globalSignalRemoval, [('residual_file','in_file')] )])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(calc_residuals, bandpass, [('residual_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( calc_residuals, pearcoff, [('residual_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == False and global_signal_regression == True and band_pass_filtering == True: # 011
# # wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# # wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('brain','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( bandpass, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == False and global_signal_regression == True and band_pass_filtering == False: # 010
# # wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# # wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('brain','in_file')] )])
# wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(globalSignalRemoval, bandpass, [('out_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( globalSignalRemoval, pearcoff, [('out_file','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
# elif motion_param_regression == False and global_signal_regression == False and band_pass_filtering == True: # 001
# # wf.connect([(getSubjectFilenames, calc_residuals, [('brain','subject')])])
# # wf.connect([(getSubjectFilenames, calc_residuals, [('motion_param', 'motion_file')])])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('brain','in_file')] )])
# # wf.connect([(getSubjectFilenames, globalSignalRemoval, [('mask','mask_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('out_file','in_file')])])
# # wf.connect([(getSubjectFilenames, bandpass, [('tr','tr')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('brain','in_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('atlas','atlas_file')])])
# wf.connect([( getSubjectFilenames, pearcoff, [('mask','mask_file')])])
# folder_name = 'pearcoff_' + combination + '.@fc_map_brain_file'
# wf.connect([(pearcoff, save_file_list, [('fc_map_brain_file','in_fc_map_brain_file')])])
# wf.connect([(save_file_list, dataSink, [('out_fc_map_brain_file',folder_name)])])
# %time wf.run('MultiProc', plugin_args={'n_procs': 7})
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# 111 -
# 110 -
# 101 -
# 100
# 011
# 010
# 001
# 000
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# Visualize the detailed graph
# from IPython.display import Image
# wf.write_graph(graph2use='exec', format='png', simple_form=True)
# file_name = opj(base_directory,functional_connectivity_directory,'graph_detailed.dot.png')
# Image(filename=file_name)
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# def calc_residuals(subject,
# motion_file):
# """
# Calculates residuals of nuisance regressors -motion parameters for every voxel for a subject using GLM.
# Parameters
# ----------
# subject : string
# Path of a subject's motion corrected nifti file.
# motion_par_file : string
# path of a subject's motion parameters
# Returns
# -------
# residual_file : string
# Path of residual file in nifti format
# """
# import nibabel as nb
# nii = nb.load(subject)
# data = nii.get_data().astype(np.float32)
# global_mask = (data != 0).sum(-1) != 0
# # Check and define regressors which are provided from files
# if motion_file is not None:
# motion = np.genfromtxt(motion_file)
# if motion.shape[0] != data.shape[3]:
# raise ValueError('Motion parameters {0} do not match data '
# 'timepoints {1}'.format(motion.shape[0],
# data.shape[3]))
# if motion.size == 0:
# raise ValueError('Motion signal file {0} is '
# 'empty'.format(motion_file))
# # Calculate regressors
# regressor_map = {'constant' : np.ones((data.shape[3],1))}
# regressor_map['motion'] = motion
# X = np.zeros((data.shape[3], 1))
# for rname, rval in regressor_map.items():
# X = np.hstack((X, rval.reshape(rval.shape[0],-1)))
# X = X[:,1:]
# if np.isnan(X).any() or np.isnan(X).any():
# raise ValueError('Regressor file contains NaN')
# Y = data[global_mask].T
# try:
# B = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)
# except np.linalg.LinAlgError as e:
# if "Singular matrix" in e:
# raise Exception("Error details: {0}\n\nSingular matrix error: "
# "The nuisance regression configuration you "
# "selected may have been too stringent, and the "
# "regression could not be completed. Ensure your "
# "parameters are not too "
# "extreme.\n\n".format(e))
# else:
# raise Exception("Error details: {0}\n\nSomething went wrong with "
# "nuisance regression.\n\n".format(e))
# Y_res = Y - X.dot(B)
# data[global_mask] = Y_res.T
# img = nb.Nifti1Image(data, header=nii.get_header(),
# affine=nii.get_affine())
# subject_name = subject.split('/')[-1].split('.')[0]
# filename = subject_name + '_residual.nii.gz'
# residual_file = os.path.join(os.getcwd(),filename )
# img.to_filename(residual_file) # alt to nib.save
# return residual_file
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# # Test
# brain = '/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050005/applyMask/sub-0050005_task-rest_run-1_bold_roi_st_mcf.nii_brain.nii.gz'
# mask = '/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050005/meanfuncmask/sub-0050005_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz'
# atlas = '/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050005/std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii '
# # tr = 1.5
# par_file = '/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050005/mcflirt/sub-0050005_task-rest_run-1_bold_roi_st_mcf.nii.par'
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# calc_residuals(brain,par_file)
In [ ]:
# motion = np.genfromtxt(par_file)
In [ ]:
import numpy as np
X = np.load('../results_again_again/fc_datasink/pearcoff_motionRegress0filt0global1/fc_map_brain_file_list.npy')
X.shape
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