def filter_subj_bids(df_files, location, bids_ids):
import clinica.iotools.bids_utils as bids
# Depending of the file that needs to be open, identify and
# do needed preprocessing on the column that contains the
# subjects ids
bids_ids = [x[8:] for x in bids_ids if "sub-ADNI" in x]
if location == "ADNIMERGE.csv":
df_files["RID"] = df_files["PTID"].apply(
lambda x: bids.remove_space_and_symbols(x)[4:])
else:
df_files["RID"] = df_files["RID"].apply(lambda x: pad_id(x))
return df_files.loc[df_files["RID"].isin([x[4:] for x in bids_ids])]
def create_adni_sessions_dict(bids_ids, clinic_specs_path, clinical_data_dir,
bids_subjs_paths):
"""
Extract all the data required for the sessions files and organize them in a
dictionary
Args: bids_ids: clinic_specs_path: path to the specifications for
converting the clinical data clinical_data_dir: path to the clinical data
folder bids_subjs_paths: a list with the path to all the BIDS subjects
Returns:
"""
import pandas as pd
from os import path
from datetime import datetime
import clinica.iotools.bids_utils as bids
from clinica.utils.stream import cprint
# Load data
sessions = pd.read_excel(clinic_specs_path, sheetname='sessions.tsv')
sessions_fields = sessions['ADNI']
field_location = sessions['ADNI location']
sessions_fields_bids = sessions['BIDS CLINICA']
fields_dataset = []
previous_location = ''
fields_bids = []
sessions_dict = {}
for i in range(0, len(sessions_fields)):
if not pd.isnull(sessions_fields[i]):
fields_bids.append(sessions_fields_bids[i])
fields_dataset.append(sessions_fields[i])
sessions_df = pd.DataFrame(columns=fields_bids)
for i in range(0, len(field_location)):
# If the i-th field is available
if (not pd.isnull(field_location[i])) and path.exists(
path.join(clinical_data_dir, field_location[i].split('/')[0])):
# Load the file
tmp = field_location[i].split('/')
location = tmp[0]
if location != previous_location:
previous_location = location
file_to_read_path = path.join(clinical_data_dir, location)
cprint('\tReading clinical data file : ' + location)
file_to_read = pd.read_csv(file_to_read_path, dtype=str)
for r in range(0, len(file_to_read.values)):
row = file_to_read.iloc[r]
# Depending of the file that needs to be open, identify and
# do needed preprocessing on the column that contains the
# subjects ids
if location == 'ADNIMERGE.csv':
id_ref = 'PTID'
# What was the meaning of this line ?
# subj_id = row[id_ref.decode('utf-8')]
subj_id = row[id_ref]
subj_id = bids.remove_space_and_symbols(subj_id)
else:
id_ref = 'RID'
rid = str(row[id_ref])
# Fill the rid with the needed number of zero
if 4 - len(rid) > 0:
zeros_to_add = 4 - len(rid)
subj_id = '0' * zeros_to_add + rid
else:
subj_id = rid
# Extract the BIDS subject id related with the original
# subject id
subj_bids = [s for s in bids_ids if subj_id in s]
if len(subj_bids) == 0:
pass
elif len(subj_bids) > 1:
raise (
'Error: multiple subjects found for the same RID')
else:
subj_bids = subj_bids[0]
for j in range(0, len(sessions_fields)):
# If the i-th field is available
if not pd.isnull(sessions_fields[j]):
# Extract only the fields related to the current file opened
if location in field_location[i]:
if location == 'ADAS_ADNIGO2.csv' or location == 'DXSUM_PDXCONV_ADNIALL.csv' \
or location == 'CDR.csv' or location == 'NEUROBAT.csv':
if type(row['VISCODE2']) == float:
continue
visit_id = row['VISCODE2']
# Convert sc to bl
if visit_id == 'sc':
visit_id = 'bl'
else:
visit_id = row['VISCODE']
try:
field_value = row[sessions_fields[j]]
bids_field_name = sessions_fields_bids[
j]
# Calculating age from ADNIMERGE
if (sessions_fields[j] == "AGE") and (
visit_id != "bl"):
examdate = datetime.strptime(
row["EXAMDATE"], "%Y-%m-%d")
examdate_bl = datetime.strptime(
row["EXAMDATE_bl"], "%Y-%m-%d")
delta = examdate - examdate_bl
# Adding time passed since bl to patient's age in current visit
field_value = round(
float(field_value) +
(delta.days / 365.25), 1)
sessions_dict = update_sessions_dict(
sessions_dict, subj_bids, visit_id,
field_value, bids_field_name)
except KeyError:
pass
# cprint('Field value ' + Fore.RED + sessions_fields[j] + Fore.RESET +
# ' could not be added to sessions.tsv')
else:
continue
# Write the sessions dictionary created in several tsv files
write_adni_sessions_tsv(sessions_dict, fields_bids, bids_subjs_paths)
def generate_subject_files(subj, images, dest_dir, mod_to_update):
import clinica.iotools.bids_utils as bids
import clinica.iotools.converters.adni_to_bids.adni_utils as adni_utils
from clinica.utils.stream import cprint
import subprocess
import os
import shutil
from os import path
from glob import glob
global counter
alpha_id = bids.remove_space_and_symbols(subj)
bids_id = 'sub-ADNI' + alpha_id
# Extract the list of sessions available from the dwi paths files, removing the duplicates
sess_list = images[(images['Subject_ID'] == subj)]['VISCODE'].unique()
if not os.path.exists(path.join(dest_dir, bids_id)):
os.mkdir(path.join(dest_dir, bids_id))
# For each session available, create the folder if doesn't exist and convert the files
for ses in sess_list:
with counter.get_lock():
counter.value += 1
cprint('[DWI] Processing subject ' + str(subj) + ' - session ' + ses +
', ' + str(counter.value) + ' / ' + str(len(images)))
ses_bids = adni_utils.viscode_to_session(ses)
bids_ses_id = 'ses-' + ses_bids
bids_file_name = bids_id + '_ses-' + ses_bids
ses_path = path.join(dest_dir, bids_id, bids_ses_id)
if mod_to_update:
if os.path.exists(path.join(ses_path, 'dwi')):
shutil.rmtree(path.join(ses_path, 'dwi'))
if not os.path.exists(ses_path):
os.mkdir(ses_path)
dwi_info = images[(images['Subject_ID'] == subj)
& (images['VISCODE'] == ses)]
# For the same subject, same session there could be multiple dwi with different acq label
for j in range(len(dwi_info)):
dwi_subj = dwi_info.iloc[j]
if type(dwi_subj['Path']) != float and dwi_subj['Path'] != '':
if not os.path.exists(path.join(ses_path, 'dwi')):
os.mkdir(path.join(ses_path, 'dwi'))
dwi_path = dwi_subj['Path']
bids_name = bids_file_name + '_acq-axial_dwi'
bids_dest_dir = path.join(ses_path, 'dwi')
if not os.path.exists(bids_dest_dir):
os.mkdir(dest_dir)
command = 'dcm2niix -b n -z y -o ' + bids_dest_dir + ' -f ' + bids_name + ' ' + dwi_path
subprocess.run(command,
shell=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
# Removing ADC images
adc_image = path.join(bids_dest_dir, bids_name + '_ADC.nii.gz')
if os.path.exists(adc_image):
os.remove(adc_image)
# If dcm2niix didn't work use dcm2nii
# print path.join(dest_dir, bids_name + '.nii.gz')
if not os.path.exists(
path.join(bids_dest_dir, bids_name + '.nii.gz')
) or not os.path.exists(
path.join(bids_dest_dir, bids_name + '.bvec')
or not os.path.exists(
path.join(bids_dest_dir, bids_name + '.bval'))):
cprint(
'\tConversion with dcm2niix failed, trying with dcm2nii'
)
# Find all the files eventually created by dcm2niix and remove them
dwi_dcm2niix = glob(
path.join(bids_dest_dir, bids_name + '*'))
for d in dwi_dcm2niix:
# print 'Removing the old', d
os.remove(d)
command = 'dcm2nii -a n -d n -e n -i y -g y -p n -m n -r n -x n -o %s %s' \
% (bids_dest_dir, dwi_path)
subprocess.run(command,
shell=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
nii_file = path.join(bids_dest_dir,
subj.replace('_', '') + '.nii.gz')
bvec_file = path.join(bids_dest_dir,
subj.replace('_', '') + '.bvec')
bval_file = path.join(bids_dest_dir,
subj.replace('_', '') + '.bval')
if os.path.exists(bvec_file) and os.path.exists(bval_file):
os.rename(
bvec_file,
path.join(bids_dest_dir, bids_name + '.bvec'))
os.rename(
bval_file,
path.join(bids_dest_dir, bids_name + '.bval'))
else:
cprint(
'WARNING: bvec and bval not generated by dcm2nii' +
' for subject ' + subj + ' and session ' + ses)
if os.path.exists(nii_file):
os.rename(
nii_file,
path.join(bids_dest_dir, bids_name + '.nii.gz'))
else:
cprint('WARNING: CONVERSION FAILED...' +
' for subject ' + subj + ' and session ' + ses)
def dwi_paths_to_bids(images, dest_dir, mod_to_update=False):
"""
Convert DWI images
Args:
images: dataframe returned by the method compute_dwi_paths
dest_dir: path to the destination directory
mod_to_update: if is true and an image is already existing it will overwrite the old version
"""
import clinica.iotools.bids_utils as bids
import clinica.iotools.converters.adni_to_bids.adni_utils as adni_utils
from os import path
import os
from glob import glob
import shutil
from clinica.utils.stream import cprint
subjs_list = images['Subject_ID'].unique()
for i in range(0, len(subjs_list)):
# print '--Converting dwi for subject ', subjs_list[i], '--'
alpha_id = bids.remove_space_and_symbols(subjs_list[i])
bids_id = 'sub-ADNI' + alpha_id
# Extract the list of sessions available from the dwi paths files, removing the duplicates
sess_list = images[(
images['Subject_ID'] == subjs_list[i])]['VISCODE'].unique()
if not os.path.exists(path.join(dest_dir, bids_id)):
os.mkdir(path.join(dest_dir, bids_id))
# For each session available, create the folder if doesn't exist and convert the files
for ses in sess_list:
ses_bids = adni_utils.viscode_to_session(ses)
bids_ses_id = 'ses-' + ses_bids
bids_file_name = bids_id + '_ses-' + ses_bids
ses_path = path.join(dest_dir, bids_id, bids_ses_id)
if mod_to_update:
if os.path.exists(path.join(ses_path, 'dwi')):
shutil.rmtree(path.join(ses_path, 'dwi'))
if not os.path.exists(ses_path):
os.mkdir(ses_path)
dwi_info = images[(images['Subject_ID'] == subjs_list[i])
& (images['VISCODE'] == ses)]
# For the same subject, same session there could be multiple dwi with different acq label
for j in range(0, len(dwi_info)):
dwi_subj = dwi_info.iloc[j]
# TODO For now in CLINICA we ignore Enhanced DWI.
if dwi_subj['Enhanced']:
continue
if type(dwi_subj['Path']) != float and dwi_subj['Path'] != '':
if not os.path.exists(path.join(ses_path, 'dwi')):
os.mkdir(path.join(ses_path, 'dwi'))
dwi_path = dwi_subj['Path']
bids_name = bids_file_name + '_acq-' + (
'axialEnhanced'
if dwi_subj['Enhanced'] else 'axial') + '_dwi'
# bids.dcm_to_nii(dwi_path, path.join(ses_path, 'dwi'), bids_name)
bids_dest_dir = path.join(ses_path, 'dwi')
if not os.path.exists(bids_dest_dir):
os.mkdir(dest_dir)
os.system('dcm2niix -b n -z y -o ' + bids_dest_dir +
' -f ' + bids_name + ' ' + dwi_path)
# If dcm2niix didn't work use dcm2nii
# print path.join(dest_dir, bids_name + '.nii.gz')
if not os.path.exists(
path.join(bids_dest_dir, bids_name + '.nii.gz')
) or not os.path.exists(
path.join(bids_dest_dir, bids_name + '.bvec')
or not os.path.exists(
path.join(bids_dest_dir,
bids_name + '.bval'))):
cprint(
'\nConversion with dcm2niix failed, trying with dcm2nii'
)
# Find all the files eventually created by dcm2niix and remove them
dwi_dcm2niix = glob(
path.join(bids_dest_dir, bids_name + '*'))
for d in dwi_dcm2niix:
# print 'Removing the old', d
os.remove(d)
os.system(
'dcm2nii -a n -d n -e n -i y -g y -p n -m n -r n -x n -o '
+ bids_dest_dir + ' ' + dwi_path)
nii_file = path.join(
bids_dest_dir,
subjs_list[i].replace('_', '') + '.nii.gz')
bvec_file = path.join(
bids_dest_dir,
subjs_list[i].replace('_', '') + '.bvec')
bval_file = path.join(
bids_dest_dir,
subjs_list[i].replace('_', '') + '.bval')
if os.path.exists(bvec_file) and os.path.exists(
bval_file):
os.rename(
bvec_file,
path.join(bids_dest_dir, bids_name + '.bvec'))
os.rename(
bval_file,
path.join(bids_dest_dir, bids_name + '.bval'))
else:
cprint(
'WARNING: bvec and bval not generated by dcm2nii'
)
if os.path.exists(nii_file):
os.rename(
nii_file,
path.join(bids_dest_dir,
bids_name + '.nii.gz'))
else:
cprint('WARNING: CONVERSION FAILED...')
def create_adni_sessions_dict(bids_ids, clinic_specs_path, clinical_data_dir,
bids_subjs_paths):
"""Extract all the data required for the sessions files and organize them in a dictionary.
Args:
bids_ids:
clinic_specs_path: path to the specifications for converting the clinical data
clinical_data_dir: path to the clinical data folder
bids_subjs_paths: a list with the path to all the BIDS subjects
"""
from datetime import datetime
from os import path
import pandas as pd
import clinica.iotools.bids_utils as bids
from clinica.utils.stream import cprint
# Load data
sessions = pd.read_excel(clinic_specs_path, sheet_name="sessions.tsv")
sessions_fields = sessions["ADNI"]
field_location = sessions["ADNI location"]
sessions_fields_bids = sessions["BIDS CLINICA"]
fields_dataset = []
previous_location = ""
fields_bids = []
sessions_dict = {}
for i in range(0, len(sessions_fields)):
if not pd.isnull(sessions_fields[i]):
fields_bids.append(sessions_fields_bids[i])
fields_dataset.append(sessions_fields[i])
sessions_df = pd.DataFrame(columns=fields_bids)
for i in range(0, len(field_location)):
# If the i-th field is available
if (not pd.isnull(field_location[i])) and path.exists(
path.join(clinical_data_dir, field_location[i].split("/")[0])):
# Load the file
tmp = field_location[i].split("/")
location = tmp[0]
if location != previous_location:
previous_location = location
file_to_read_path = path.join(clinical_data_dir, location)
cprint(f"\tReading clinical data file: {location}")
file_to_read = pd.read_csv(file_to_read_path, dtype=str)
for r in range(0, len(file_to_read.values)):
row = file_to_read.iloc[r]
# Depending of the file that needs to be open, identify and
# do needed preprocessing on the column that contains the
# subjects ids
if location == "ADNIMERGE.csv":
id_ref = "PTID"
# What was the meaning of this line ?
# subj_id = row[id_ref.decode('utf-8')]
subj_id = row[id_ref]
subj_id = bids.remove_space_and_symbols(subj_id)
else:
id_ref = "RID"
rid = str(row[id_ref])
# Fill the rid with the needed number of zero
if 4 - len(rid) > 0:
zeros_to_add = 4 - len(rid)
subj_id = "0" * zeros_to_add + rid
else:
subj_id = rid
# Extract the BIDS subject id related with the original
# subject id
subj_bids = [s for s in bids_ids if subj_id in s]
if len(subj_bids) == 0:
pass
elif len(subj_bids) > 1:
raise (
"Error: multiple subjects found for the same RID")
else:
subj_bids = subj_bids[0]
for j in range(0, len(sessions_fields)):
# If the i-th field is available
if not pd.isnull(sessions_fields[j]):
# Extract only the fields related to the current file opened
if location in field_location[i]:
if location in [
"ADAS_ADNIGO2.csv",
"DXSUM_PDXCONV_ADNIALL.csv",
"CDR.csv",
"NEUROBAT.csv",
"GDSCALE.csv",
"MODHACH.csv",
"MOCA.csv",
"NPIQ.csv",
"MEDHIST.csv",
"VITALS.csv",
"UWNPSYCHSUM_03_07_19.csv",
"ECOGPT.csv",
"ECOGSP.csv",
"FCI.csv",
"CCI.csv",
"NPIQ.csv",
"NPI.csv",
]:
if (pd.isnull(row["VISCODE2"])
or row["VISCODE2"] == "f"):
continue
visit_id = row["VISCODE2"]
# Convert sc to bl
if visit_id == "sc":
visit_id = "bl"
elif location in [
"BHR_EVERYDAY_COGNITION.csv",
"BHR_BASELINE_QUESTIONNAIRE.csv",
"BHR_LONGITUDINAL_QUESTIONNAIRE.csv",
]:
visit_id = row["Timepoint"]
else:
visit_id = row["VISCODE"]
try:
field_value = row[sessions_fields[j]]
bids_field_name = sessions_fields_bids[
j]
# Calculating age from ADNIMERGE
if (sessions_fields[j] == "AGE") and (
visit_id != "bl"):
examdate = datetime.strptime(
row["EXAMDATE"], "%Y-%m-%d")
examdate_bl = datetime.strptime(
row["EXAMDATE_bl"], "%Y-%m-%d")
delta = examdate - examdate_bl
# Adding time passed since bl to patient's age in current visit
field_value = round(
float(field_value) +
(delta.days / 365.25),
1,
)
sessions_dict = update_sessions_dict(
sessions_dict,
subj_bids,
visit_id,
field_value,
bids_field_name,
)
except KeyError:
pass
# cprint('Field value ' + Fore.RED + sessions_fields[j] + Fore.RESET +
# ' could not be added to sessions.tsv')
else:
continue
# Write the sessions dictionary created in several tsv files
write_adni_sessions_tsv(sessions_dict, fields_bids, bids_subjs_paths)
def create_adni_sessions_dict(bids_ids, clinic_specs_path, clinical_data_dir,
bids_subjs_paths):
"""
Extract all the data required for the sessions files and organize them in a dictionary
Args:
bids_ids:
clinic_specs_path: path to the specifications for converting the clinical data
clinical_data_dir: path to the clinical data folder
bids_subjs_paths: a list with the path to all the BIDS subjects
Returns:
"""
import pandas as pd
from os import path
import clinica.iotools.bids_utils as bids
# Load data
sessions = pd.read_excel(clinic_specs_path, sheetname='sessions.tsv')
sessions_fields = sessions['ADNI']
field_location = sessions['ADNI location']
sessions_fields_bids = sessions['BIDS CLINICA']
fields_dataset = []
previous_location = ''
fields_bids = []
sessions_dict = {}
for i in range(0, len(sessions_fields)):
if not pd.isnull(sessions_fields[i]):
fields_bids.append(sessions_fields_bids[i])
fields_dataset.append(sessions_fields[i])
sessions_df = pd.DataFrame(columns=fields_bids)
for i in range(0, len(field_location)):
# If the i-th field is available
if not pd.isnull(field_location[i]):
# Load the file
tmp = field_location[i].split('/')
location = tmp[0]
if location != previous_location:
previous_location = location
file_to_read_path = path.join(clinical_data_dir, location)
file_to_read = pd.read_csv(file_to_read_path, dtype=str)
for r in range(0, len(file_to_read.values)):
row = file_to_read.iloc[r]
# Depending of the file that needs to be open, identify and do needed preprocessing on the column
# that contains the subjects ids
if location == 'ADNIMERGE.csv':
id_ref = 'PTID'
subj_id = row[id_ref.decode('utf-8')]
subj_id = bids.remove_space_and_symbols(subj_id)
else:
id_ref = 'RID'
rid = str(row[id_ref.decode('utf-8')])
# Fill the rid with the needed number of zero
if 4 - len(rid) > 0:
zeros_to_add = 4 - len(rid)
subj_id = '0' * zeros_to_add + rid
else:
subj_id = rid
# Extract the BIDS subject id related with the original subject id
subj_bids = [s for s in bids_ids if subj_id in s]
if len(subj_bids) == 0:
pass
elif len(subj_bids) > 1:
raise 'Error: multiple subjects found for the same RID'
else:
subj_bids = subj_bids[0]
for i in range(0, len(sessions_fields)):
# If the i-th field is available
if not pd.isnull(sessions_fields[i]):
# Extract only the fields related to the current file opened
if location in field_location[i]:
if location == 'ADAS_ADNIGO2.csv' or location == 'DXSUM_PDXCONV_ADNIALL.csv' or location == 'CDR.csv' or location == 'NEUROBAT.csv':
if type(row['VISCODE2']) == float:
continue
visit_id = row['VISCODE2']
# Convert sc to bl
if visit_id == 'sc':
visit_id = 'bl'
else:
visit_id = row['VISCODE']
field_value = row[sessions_fields[i]]
bids_field_name = sessions_fields_bids[i]
sessions_dict = update_sessions_dict(
sessions_dict, subj_bids, visit_id,
field_value, bids_field_name)
else:
continue
# Write the sessions dictionary created in several tsv files
write_adni_sessions_tsv(sessions_dict, fields_bids, bids_subjs_paths)
def generate_subject_files(subj, images, dest_dir, mod_to_update):
import clinica.iotools.bids_utils as bids
import clinica.iotools.converters.adni_to_bids.adni_utils as adni_utils
from clinica.utils.stream import cprint
import subprocess
import os
import shutil
from os import path
from glob import glob
alpha_id = bids.remove_space_and_symbols(subj)
bids_id = 'sub-ADNI' + alpha_id
# Extract the list of sessions available from the flair paths files, removing the duplicates
sess_list = images[(images['Subject_ID'] == subj)]['VISCODE'].unique()
if not os.path.exists(path.join(dest_dir, bids_id)):
os.mkdir(path.join(dest_dir, bids_id))
# For each session available, create the folder if doesn't exist and convert the files
for ses in sess_list:
with counter.get_lock():
counter.value += 1
cprint('[FLAIR] Processing subject ' + str(subj) + ' - session ' +
ses + ', ' + str(counter.value) + ' / ' + str(len(images)))
ses_bids = adni_utils.viscode_to_session(ses)
bids_ses_id = 'ses-' + ses_bids
bids_file_name = bids_id + '_ses-' + ses_bids
ses_path = path.join(dest_dir, bids_id, bids_ses_id)
if mod_to_update:
if os.path.exists(path.join(ses_path, 'FLAIR')):
shutil.rmtree(path.join(ses_path, 'FLAIR'))
if not os.path.exists(ses_path):
os.mkdir(ses_path)
flair_info = images[(images['Subject_ID'] == subj)
& (images['VISCODE'] == ses)]
# For the same subject, same session there could be multiple flar with different acq label
for j in range(len(flair_info)):
flair_subj = flair_info.iloc[j]
# TODO For now in CLINICA we ignore Enhanced FLAIR.
if flair_subj['Enhanced']:
continue
if type(flair_subj['Path']) != float and flair_subj['Path'] != '':
if not os.path.exists(path.join(ses_path, 'FLAIR')):
os.mkdir(path.join(ses_path, 'FLAIR'))
flair_path = flair_subj['Path']
bids_name = bids_file_name + '_FLAIR'
# bids.dcm_to_nii(dwi_path, path.join(ses_path, 'dwi'), bids_name)
bids_dest_dir = path.join(ses_path, 'FLAIR')
if not os.path.exists(bids_dest_dir):
os.mkdir(dest_dir)
command = 'dcm2niix -b y -z y -o ' + bids_dest_dir + ' -f ' + bids_name + ' ' + flair_path
subprocess.run(command,
shell=True,
stderr=subprocess.DEVNULL,
stdout=subprocess.DEVNULL)
# If dcm2niix didn't work use dcm2nii
# print path.join(dest_dir, bids_name + '.nii.gz')
if not os.path.exists(
path.join(bids_dest_dir, bids_name + '.nii.gz')):
cprint(
'\tConversion with dcm2niix failed, trying with dcm2nii'
)
# Find all the files eventually created by dcm2niix and remove them
flair_dcm2niix = glob(
path.join(bids_dest_dir, bids_name + '*'))
for d in flair_dcm2niix:
os.remove(d)
command = 'dcm2nii -a n -d n -e n -i y -g y -p n -m n -r n -x n -o ' + bids_dest_dir + ' ' + flair_path
subprocess.run(command,
shell=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
nii_file = path.join(bids_dest_dir,
subj.replace('_', '') + '.nii.gz')
if os.path.exists(nii_file):
os.rename(
nii_file,
path.join(bids_dest_dir, bids_name + '.nii.gz'))
else:
cprint('WARNING: CONVERSION FAILED...')
