import os
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.utility as util
[docs]def compute_fisher_z_score(correlation_file, timeseries_one_d):
"""
Computes the fisher z transform of the input correlation map
If the correlation map contains data for multiple ROIs then
the function returns z score for each ROI as a seperate nifti
file
Parameters
----------
correlation_file : string
Input correlations file
Returns
-------
out_file : list (nifti files)
list of z_scores for mask or ROI
"""
import nibabel as nb
import numpy as np
import os
roi_numbers = []
if '#' in open(timeseries_one_d, 'r').readline().rstrip('\r\n'):
roi_numbers = open(timeseries_one_d, 'r').readline().rstrip('\r\n').replace('#', '').split('\t')
corr_img = nb.load(correlation_file)
corr_data = corr_img.get_fdata()
hdr = corr_img.header
corr_data = np.log((1 + corr_data) / (1 - corr_data)) / 2.0
dims = corr_data.shape
out_file = []
if len(dims) == 5 or len(roi_numbers) > 0:
if len(dims) == 5:
x, y, z, one, roi_number = dims
corr_data = np.reshape(corr_data, (x * y * z, roi_number), order='F')
for i in range(0, len(roi_numbers)):
sub_data = corr_data
if len(dims) == 5:
sub_data = np.reshape(corr_data[:, i], (x, y, z), order='F')
sub_img = nb.Nifti1Image(sub_data, header=corr_img.header, affine=corr_img.affine)
sub_z_score_file = os.path.join(os.getcwd(), 'z_score_ROI_number_%s.nii.gz' % (roi_numbers[i]))
sub_img.to_filename(sub_z_score_file)
out_file.append(sub_z_score_file)
else:
z_score_img = nb.Nifti1Image(corr_data, header=hdr, affine=corr_img.affine)
z_score_file = os.path.join(os.getcwd(), 'z_score.nii.gz')
z_score_img.to_filename(z_score_file)
out_file.append(z_score_file)
return out_file
def check_ts(in_file):
import os
import numpy as np
if in_file.endswith('.txt'):
try:
timepoints, rois = np.loadtxt(in_file).shape
except ValueError:
timepoints = np.loadtxt(in_file).shape[0]
rois = 1
out_file = in_file
elif in_file.endswith('.csv') or in_file.endswith('.1D'):
csv_array = np.genfromtxt(in_file, delimiter=',')
if np.isnan(csv_array[0][0]):
csv_array = csv_array[1:]
timepoints, rois = csv_array.shape
# multiple regression (fsl_glm) needs this format for -design input
out_file = os.path.join(os.getcwd(),
os.path.basename(in_file).replace('.csv', '.txt'))
np.savetxt(out_file, csv_array, delimiter='\t')
if rois > timepoints:
message = ('\n\n\n****The number of timepoints (' + str(timepoints)
+ ') is smaller than the number of ROIs to run ('
+ str(rois) + ') - therefore the GLM is'
+ ' underspecified and can\'t run.****\n\n\n')
print(message)
raise Exception(message)
else:
return out_file
[docs]def map_to_roi(timeseries, maps):
"""
Renames the outputs of the temporal multiple regression workflow for sca
according to the header information of the timeseries.txt file that was
passed
NOTE: This is only run if the temporal regression is run as part of sca
(which = 'RT') when calling the temporal regression workflow.
If you run the temporal regression workflow manually, don\'t set
(which = 'RT') unless you provide a timeseries.txt file with a header
containing the names of the timeseries
Parameters
----------
timeseries : string
Input timeseries.txt file
maps : List (nifti files)
List of output files generated by the temporal regression workflow if
(which == 'RT')
Returns
-------
labels : List (strings)
List of names that the output files should be renamed to
maps : List (nifti files)
List of output files generated by the temporal regression workflow if
(which == 'RT')
"""
import numpy as np
import pandas as pd
from nipype import logging
logger = logging.getLogger('nipype.workflow')
testMat = pd.read_csv(timeseries)
timepoints, rois = testMat.shape
if rois > timepoints:
logger.warning('The number of timepoints is smaller than the number '
'of ROIs to run - therefore the GLM is '
'underspecified and can\'t run.')
# pull in the ROI labels from the header of the extracted timeseries
# CSV file
with open(timeseries, "r") as f:
roi_file_lines = f.read().splitlines()
roi_err = "\n\n[!] The output of 3dROIstats, used in extracting " \
"the timeseries, was not in the expected format.\n\nROI " \
"output file: {0}\n\n".format(timeseries)
for line in roi_file_lines:
if "Mean_" in line:
try:
roi_list = line.split(",")
# clear out any blank strings/non ROI labels in the list
roi_list = [x for x in roi_list if "Mean" in x]
# rename labels
roi_list = \
[x.replace("Mean",
"sca_tempreg_z_maps_roi").replace(" ", "").replace("#", "")
for x in roi_list]
except:
raise Exception(roi_err)
break
else:
raise Exception(roi_err)
new_labels = []
for roi_label in roi_list:
new_labels.append(os.path.join(os.getcwd(), roi_label))
numMaps = len(maps)
maps.sort()
# if not numMaps / 2 == rois:
# raise Exception('You specified {0} timeseries but only {1} spatial '
# 'maps were generated'.format(str(rois),
# str(numMaps/2)))
maps = maps[:rois]
return roi_list, maps