Post processing:

  • Global Signal Regression using orthogonalization
  • Band Pass filtering 0.1 - 0.01 Hz
  • Motion regression using GLM

In [60]:
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 [61]:
# Paths
os.chdir('/home1/varunk/Autism-Connectome-Analysis-brain_connectivity/notebooks/')
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 [62]:
# 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 [63]:
# 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 [64]:
# opj(base_directory,parent_wf_directory,motion_correction_bet_directory,coreg_reg_directory,'resample_mni')

In [65]:
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 [66]:
# brain_path,mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path

In [67]:
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 [68]:
# 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')

Read the smoothed files

  • The following files are filtered and smoothed motion parameter regressed I got the file name using the command:
    find . -name "sub-*_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz" > smoothed_brains.txt
  • Using the above extracted filenames to do the following:
    • Select the subjects having volumes atlest the required fixed number of volumes
    • perform correlation using just the fixed number of volumes
    • Register the file to the standard 3mm template

In [ ]:


In [97]:
smoothed_brains_in_file = '/home1/varunk/results_again_again/smoothed_brains.txt'
import re


smoothed_brains_paths = np.loadtxt(smoothed_brains_in_file, dtype = np.str)

smoothed_brains_subid = []
for path in smoothed_brains_paths:
    sub_id_extracted = re.search('.+_subject_id_(\d+)', path).group(1)
    print(sub_id_extracted)
    smoothed_brains_subid.append(int(sub_id_extracted))


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In [98]:
smoothed_brains_paths


Out[98]:
array([ './_subject_id_0050977/spatialSmooth/sub-0050977_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz',
       './_subject_id_0051151/spatialSmooth/sub-0051151_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz',
       './_subject_id_0051171/spatialSmooth/sub-0051171_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz',
       ...,
       './_subject_id_0050802/spatialSmooth/sub-0050802_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz',
       './_subject_id_0050791/spatialSmooth/sub-0050791_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz',
       './_subject_id_0051212/spatialSmooth/sub-0051212_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz'],
      dtype='<U107')

In [99]:
base_directory


Out[99]:
'/home1/varunk/results_again_again'

In [100]:
smoothed_brains_subid


Out[100]:
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In [ ]:


In [101]:
# Append the conplete brain path to smoothed brain paths
new_brain_paths = []
for path in smoothed_brains_paths:
    new_brain_paths.append(opj(base_directory, 'motionRegress1filt1global0smoothing1',path[2:]))

In [102]:
new_brain_paths


Out[102]:
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 ...]

Check which subjects have volumes > 'vol'


In [103]:
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 [104]:
# df_phenotype.sort_values(['SUB_ID'])

In [105]:
df_demographics_volumes


Out[105]:
array([['CALTECH', 150.0],
       ['CMU', 240.0],
       ['KKI', 156.0],
       ['LEUVEN_1', 250.0],
       ['LEUVEN_2', 250.0],
       ['MAX_MUN', 120.0],
       ['NYU', 180.0],
       ['OHSU', 82.0],
       ['OLIN', 210.0],
       ['PITT', 200.0],
       ['SBL', 200.0],
       ['SDSU', 180.0],
       ['STANFORD', 180.0],
       ['TRINITY', 150.0],
       ['UCLA_1', 120.0],
       ['UCLA_2', 120.0],
       ['UM_1', 300.0],
       ['UM_2', 300.0],
       ['USM', 240.0],
       ['YALE', 200.0]], dtype=object)

In [106]:
# 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 [107]:
subid_vol_dict = dict(zip(df_phenotype_sub_id[:,1],[site_vol_dict[site] for site in df_phenotype_sub_id[:,0]] ))

In [108]:
(subid_vol_dict)


Out[108]:
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 51053: 180.0,
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 51055: 180.0,
 51056: 180.0,
 51057: 180.0,
 51058: 180.0,
 51059: 180.0,
 51060: 180.0,
 51061: 180.0,
 51062: 180.0,
 51063: 180.0,
 51064: 180.0,
 51065: 180.0,
 51066: 180.0,
 51067: 180.0,
 51068: 180.0,
 51069: 180.0,
 51070: 180.0,
 51071: 180.0,
 51072: 180.0,
 51073: 180.0,
 51074: 180.0,
 51075: 180.0,
 51076: 180.0,
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 51078: 180.0,
 51079: 180.0,
 51080: 180.0,
 51081: 180.0,
 51082: 180.0,
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 51085: 180.0,
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 51097: 180.0,
 51098: 180.0,
 51099: 180.0,
 51100: 180.0,
 51101: 180.0,
 51102: 180.0,
 51103: 180.0,
 51104: 180.0,
 51105: 180.0,
 51106: 180.0,
 51107: 180.0,
 51108: 180.0,
 51109: 180.0,
 51110: 180.0,
 51111: 180.0,
 51112: 180.0,
 51113: 180.0,
 51114: 180.0,
 51115: 180.0,
 51116: 180.0,
 51117: 180.0,
 51118: 180.0,
 51119: 180.0,
 51120: 180.0,
 51121: 180.0,
 51122: 180.0,
 51123: 180.0,
 51124: 180.0,
 51125: 180.0,
 51126: 180.0,
 51127: 180.0,
 51128: 180.0,
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 51130: 180.0,
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 51132: 150.0,
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 51137: 150.0,
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 51142: 150.0,
 51146: 180.0,
 51147: 180.0,
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 51149: 180.0,
 51150: 180.0,
 51151: 180.0,
 51152: 180.0,
 51153: 180.0,
 51154: 180.0,
 51155: 180.0,
 51156: 180.0,
 51159: 180.0,
 51160: 180.0,
 51161: 180.0,
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 51165: 180.0,
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 51330: 120.0,
 ...}

In [109]:
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 [110]:
df_demographics_sites_refined = df_demographics_volumes[:,0]

In [111]:
df_demographics_sites_refined


Out[111]:
array(['CALTECH', 'CMU', 'KKI', 'LEUVEN_1', 'LEUVEN_2', 'MAX_MUN', 'NYU',
       'OLIN', 'PITT', 'SBL', 'SDSU', 'STANFORD', 'TRINITY', 'UCLA_1',
       'UCLA_2', 'UM_1', 'UM_2', 'USM', 'YALE'], dtype=object)

In [112]:
df_phenotype_sub_id


Out[112]:
array([['PITT', 50002],
       ['PITT', 50003],
       ['PITT', 50004],
       ..., 
       ['SBL', 51585],
       ['MAX_MUN', 51606],
       ['MAX_MUN', 51607]], dtype=object)

In [ ]:

Subject IDs to be considered -

subjects_refined


In [117]:
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 [118]:
subjects_refined;

In [130]:
subjects_refined = list(set(subjects_refined) - (set(df_phenotype_sub_id[:,1]) - set(smoothed_brains_subid) ) )

In [131]:
len(subjects_refined)


Out[131]:
1074

Below are subject IDs to be ignored


In [26]:
#sanity check
set(df_phenotype_sub_id[:,1]) - set(subjects_refined)


Out[26]:
{50142,
 50143,
 50144,
 50145,
 50146,
 50147,
 50148,
 50149,
 50150,
 50152,
 50153,
 50155,
 50156,
 50157,
 50158,
 50159,
 50160,
 50161,
 50162,
 50163,
 50164,
 50165,
 50166,
 50167,
 50168,
 50169,
 50170,
 50171}

Volume correction

  • I have already extracted 4 volumes.
  • Now extract 120 - 4 = 116 volumes from each subject
  • So define vols = 114

In [27]:
vols = vols - 4

In [28]:
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 [29]:
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 [30]:
os.chdir('/home1/varunk/results_again_again/temp/')
volCorrect.inputs.sub_id = 51456
res = volCorrect.run()


171229-15:24:16,511 workflow INFO:
	 Executing node volCorrect in dir: /tmp/tmpf64ev894/volCorrect

In [31]:
res.outputs


Out[31]:
t_min = 30

In [32]:
# 146 - 116

# 0...145

# 145 - 30 + 1

In [33]:
# 146 - 116

In [34]:
# fslroi /home1/varunk/results_again_again/motionRegress1filt1global0smoothing1/_subject_id_0051456/spatialSmooth/sub-0051456_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed.nii.gz /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/extract/sub-0051456_task-rest_run-1_bold_roi_st_mcf_residual_bp_smoothed_roi.nii.gz 35 -1

In [35]:
# # ExtractROI - skip dummy scans
# extract = Node(ExtractROI(t_size=-1),
#                output_type='NIFTI',
#                name="extract")

# # t_min=4,

In [36]:
# layout = BIDSLayout(data_directory)

number_of_subjects = 2 # Number of subjects you wish to preprocess
# number_of_subjects = len(layout.get_subjects())

Define a function to fetch the filenames of a particular subject ID


In [37]:
def get_subject_filenames_old(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)
        
        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 [53]:
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 in brain_path:
        sub_id_extracted = re.search('.+_subject_id_(\d+)', brain)
        print(sub_id_extracted)
        
        if str(subject_id) in brain:
            for mask,atlas,tr,motion_param,func2std_mat in zip(mask_path,atlas_path,tr_path,motion_params_path,func2std_mat_path):
                if str(subject_id) in mask:
        #             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)
    raise Exception('Unable to locate Subject: ',subject_id,'extracted: ',sub_id_extracted)
    return 0

In [58]:
# 555/60 # mask_path,new_brain_paths


Out[58]:
9.25

In [55]:
len(new_brain_paths),len(mask_path), len(subjects_refined)


Out[55]:
(1102, 1102, 1084)

In [56]:
# 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.brain_path = new_brain_paths
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 [57]:
getSubjectFilenames.inputs.subject_id = 51270
res = getSubjectFilenames.run()


171229-15:39:39,400 workflow INFO:
	 Executing node getSubjectFilenames in dir: /tmp/tmp0tw34ts6/getSubjectFilenames
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
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<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
<_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
Unable to locate Subject:  51270 extracted:  <_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>
---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-57-400b9917b314> in <module>()
      1 getSubjectFilenames.inputs.subject_id = 51270
----> 2 res = getSubjectFilenames.run()

~/anaconda3/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py in run(self, updatehash)
    370                     self.inputs.get_traitsfree())
    371             try:
--> 372                 self._run_interface()
    373             except:
    374                 os.remove(hashfile_unfinished)

~/anaconda3/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py in _run_interface(self, execute, updatehash)
    480         old_cwd = os.getcwd()
    481         os.chdir(self.output_dir())
--> 482         self._result = self._run_command(execute)
    483         os.chdir(old_cwd)
    484 

~/anaconda3/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py in _run_command(self, execute, copyfiles)
    611                 logger.info('Running: %s' % cmd)
    612             try:
--> 613                 result = self._interface.run()
    614             except Exception as msg:
    615                 self._save_results(result, cwd)

~/anaconda3/lib/python3.6/site-packages/nipype/interfaces/base.py in run(self, **inputs)
   1079                         version=self.version)
   1080         try:
-> 1081             runtime = self._run_wrapper(runtime)
   1082             outputs = self.aggregate_outputs(runtime)
   1083             runtime.endTime = dt.isoformat(dt.utcnow())

~/anaconda3/lib/python3.6/site-packages/nipype/interfaces/base.py in _run_wrapper(self, runtime)
   1027             runtime.environ['DISPLAY'] = ':%d' % vdisp_num
   1028 
-> 1029         runtime = self._run_interface(runtime)
   1030 
   1031         if self._redirect_x:

~/anaconda3/lib/python3.6/site-packages/nipype/interfaces/utility/wrappers.py in _run_interface(self, runtime)
    192             setattr(runtime, 'runtime_threads', num_threads)
    193         else:
--> 194             out = function_handle(**args)
    195 
    196         if len(self._output_names) == 1:

<string> in get_subject_filenames(subject_id, brain_path, mask_path, atlas_path, tr_path, motion_params_path, func2std_mat_path, MNI3mm_path)

Exception: ('Unable to locate Subject: ', 51270, 'extracted: ', <_sre.SRE_Match object; span=(0, 90), match='/home1/varunk/results_again_again/motionRegress1f>, '\nInterface Function failed to run.')

In [1544]:
# 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 [1545]:
# found

In [1546]:
subject_list = subjects_refined[0:number_of_subjects]

In [1547]:
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

Extract volumes


In [1548]:
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_size=-1),
               output_type='NIFTI',
               name="extract")

# t_min=4,

Datasink

I needed to define the structure of what files are saved and where.


In [1549]:
# 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 [1550]:
# 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 [1551]:
base_directory


Out[1551]:
'/home1/varunk/results_again_again'

Following is a Join Node that collects the preprocessed file paths and saves them in a file


In [1552]:
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 [1553]:
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")

Create a FC node

This node:

  1. Exracts the average time series of the brain ROI's using the atlas and stores it as a matrix of size [ROIs x Volumes].
  2. Extracts the Voxel time series and stores it in matrix of size [Voxels x Volumes]

Saving the Brains instead of FC matrices


In [1554]:
# 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) # I get nan coz of this! At places where num voxels = 0

    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))
    
    
    print('Saving X ')
    np.save('X',X)
    
    print('saved')
        
    # 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 [1555]:
# 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

IMPORTANT:

  • The ROI 255 has been removed due to resampling. Therefore the FC maps will have nan at that row. So don't use that ROI :)
  • I came to know coz I keep getting this error: RuntimeWarning: invalid value encountered in true_divide
  • To debug it, I read the coff matrix and checked its diagnol to discover the nan value.

Node for applying xformation matrix to functional data


In [1556]:
func2std_xform = Node(FLIRT(output_type='NIFTI_GZ',
                         apply_xfm=True), name="func2std_xform")

In [1557]:
# %%time
# pearcoff.run()

In [1558]:
# 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)

Workflow to do just the pearcoff and fun2std transformation


In [1559]:
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([(infosource, volCorrect, [('subject_id','sub_id')])])
    wf.connect([( getSubjectFilenames, extract, [('brain','in_file')])])
    wf.connect([( volCorrect, extract, [('t_min','t_min')])])
    
    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)


Combination:  motionRegress1filt1global0smoothing1
171228-18:15:53,897 workflow INFO:
	 Generated workflow graph: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/graph.dot.png (graph2use=exec, simple_form=True).
171228-18:15:53,977 workflow INFO:
	 Workflow motionRegress1filt1global0smoothing1 settings: ['check', 'execution', 'logging']
171228-18:15:54,18 workflow INFO:
	 Running in parallel.
171228-18:15:54,20 workflow INFO:
	 Executing: volCorrect.a1 ID: 0
171228-18:15:54,27 workflow INFO:
	 [Job finished] jobname: volCorrect.a1 jobid: 0
171228-18:15:54,29 workflow INFO:
	 Executing: getSubjectFilenames.a1 ID: 1
171228-18:15:54,54 workflow INFO:
	 [Job finished] jobname: getSubjectFilenames.a1 jobid: 1
171228-18:15:54,55 workflow INFO:
	 Executing: volCorrect.a0 ID: 5
171228-18:15:54,62 workflow INFO:
	 [Job finished] jobname: volCorrect.a0 jobid: 5
171228-18:15:54,64 workflow INFO:
	 Executing: getSubjectFilenames.a0 ID: 6
171228-18:15:54,86 workflow INFO:
	 [Job finished] jobname: getSubjectFilenames.a0 jobid: 6
171228-18:15:54,88 workflow INFO:
	 Executing: extract.a1 ID: 2
171228-18:15:54,97 workflow INFO:
	 [Job finished] jobname: extract.a1 jobid: 2
171228-18:15:54,99 workflow INFO:
	 Executing: extract.a0 ID: 7
171228-18:15:54,106 workflow INFO:
	 [Job finished] jobname: extract.a0 jobid: 7
171228-18:15:54,108 workflow INFO:
	 Executing: pearcoff.a1 ID: 3
171228-18:15:54,122 workflow INFO:
	 Executing: pearcoff.a0 ID: 8
171228-18:15:54,125 workflow INFO:
	 Executing node pearcoff.a1 in dir: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51457/pearcoff
171228-18:15:54,139 workflow INFO:
	 Executing node pearcoff.a0 in dir: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/pearcoff
<string>:71: RuntimeWarning: invalid value encountered in true_divide
<string>:71: RuntimeWarning: invalid value encountered in true_divide
Saving X 
saved
Some ROIs are not present. Replacing NAN in coff matrix by 0
Saving X 
saved
Pear Matrix calculated for subject:  0051456
Creating brain for Subject- 0051456
Some ROIs are not present. Replacing NAN in coff matrix by 0
Pear Matrix calculated for subject:  0051457
Creating brain for Subject- 0051457
Created brain for Subject- 0051456
Created brain for Subject- 0051457
171228-18:16:49,366 workflow INFO:
	 [Job finished] jobname: pearcoff.a0 jobid: 8
171228-18:16:49,370 workflow INFO:
	 Executing: func2std_xform.a0 ID: 9
171228-18:16:49,391 workflow INFO:
	 Executing node func2std_xform.a0 in dir: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/func2std_xform
171228-18:16:49,408 workflow INFO:
	 Running: flirt -in /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/pearcoff/0051456_fc_map.nii.gz -ref /home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/resample_mni/MNI152_T1_2mm_brain_resample.nii -out 0051456_fc_map_flirt.nii.gz -omat 0051456_fc_map_flirt.mat -applyxfm -init /home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0051456/concat_xform/sub-0051456_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0051456_T1w_brain_resample_flirt.mat
171228-18:16:49,484 workflow INFO:
	 [Job finished] jobname: pearcoff.a1 jobid: 3
171228-18:16:49,487 workflow INFO:
	 Executing: func2std_xform.a1 ID: 4
171228-18:16:49,503 workflow INFO:
	 Executing node func2std_xform.a1 in dir: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51457/func2std_xform
171228-18:16:49,521 workflow INFO:
	 Running: flirt -in /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51457/pearcoff/0051457_fc_map.nii.gz -ref /home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/resample_mni/MNI152_T1_2mm_brain_resample.nii -out 0051457_fc_map_flirt.nii.gz -omat 0051457_fc_map_flirt.mat -applyxfm -init /home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0051457/concat_xform/sub-0051457_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0051457_T1w_brain_resample_flirt.mat
171228-18:16:58,441 workflow INFO:
	 [Job finished] jobname: func2std_xform.a0 jobid: 9
171228-18:16:58,452 workflow INFO:
	 [Job finished] jobname: func2std_xform.a1 jobid: 4
171228-18:16:58,454 workflow INFO:
	 Executing: save_file_list ID: 10
171228-18:16:58,457 workflow INFO:
	 Executing node save_file_list in dir: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/save_file_list
######################## File List ######################: 
 [ '/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/func2std_xform/0051456_fc_map_flirt.nii.gz'
 '/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51457/func2std_xform/0051457_fc_map_flirt.nii.gz']
171228-18:16:58,468 workflow INFO:
	 [Job finished] jobname: save_file_list jobid: 10
171228-18:16:58,470 workflow INFO:
	 Executing: datasink ID: 11
171228-18:16:58,475 workflow INFO:
	 Executing node datasink in dir: /home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/datasink
171228-18:16:58,488 workflow INFO:
	 [Job finished] jobname: datasink jobid: 11

In [1560]:
file_name = opj(base_dir,combination,'graph_detailed.dot.png')
Image(filename=file_name)


Out[1560]:

In [1561]:
X = np.load("../../results_again_again/temp_dataSink/pearcoff_motionRegress1filt1global0smoothing1/fc_map_brain_file_list.npy")
X


Out[1561]:
array([ '/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/func2std_xform/0051456_fc_map_flirt.nii.gz',
       '/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51457/func2std_xform/0051457_fc_map_flirt.nii.gz'],
      dtype='<U139')

In [1562]:
!nipypecli show crash-20171228-144509-root-getSubjectFilenames.a1-6a701e42-c600-4b38-b4ba-71c175cbf567.pklz


Usage: nipypecli show [OPTIONS] PKLZ_FILE

Error: Invalid value for "pklz_file": Path "crash-20171228-144509-root-getSubjectFilenames.a1-6a701e42-c600-4b38-b4ba-71c175cbf567.pklz" does not exist.

In [1563]:
import numpy as np
X = np.load('../results_again_again/fc_datasink/pearcoff_motionRegress0filt0global1/fc_map_brain_file_list.npy')
X.shape


---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-1563-737099d92b13> in <module>()
      1 import numpy as np
----> 2 X = np.load('../results_again_again/fc_datasink/pearcoff_motionRegress0filt0global1/fc_map_brain_file_list.npy')
      3 X.shape

~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)
    368     own_fid = False
    369     if isinstance(file, basestring):
--> 370         fid = open(file, "rb")
    371         own_fid = True
    372     elif is_pathlib_path(file):

FileNotFoundError: [Errno 2] No such file or directory: '../results_again_again/fc_datasink/pearcoff_motionRegress0filt0global1/fc_map_brain_file_list.npy'

In [ ]:
X_temp4 = np.load('/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/temp4_again.npy')
np.diag(X_temp4)

In [ ]:
X_temp3 = np.load('/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/temp3.npy')
(X_temp3).shape

In [ ]:
X_temp2 = np.load('/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/temp2.npy')
(X_temp2).shape

In [ ]:
np.isnan(X_temp3).sum()

In [ ]:
np.isnan(X_temp2).sum()

In [ ]:
X_temp2[254]

In [1566]:
X = np.load('/home1/varunk/results_again_again/temp_fc/motionRegress1filt1global0smoothing1/_subject_id_51456/X.npy')
X[254,:]


Out[1566]:
array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan])

In [132]:
X = np.load('/home1/varunk/results_again_again/fc_datasink_volumes_corrected/pearcoff_motionRegress1filt1global0smoothing1/fc_map_brain_file_list.npy')
X.shape


Out[132]:
(1074,)

In [ ]: