def make_histograms():
os.makedirs('Histograms', exist_ok=True)
task_dofs = load_dofs()
for task in contrasts:
subjs = load_covars_df(task, return_perf=False).index
plt.hist(task_dofs[task].loc[subjs], bins=50)
plt.title(task + ' DOF Histogram')
plt.ylabel('Counts')
plt.xlabel('Value')
plt.savefig('Histograms/' + task + '_dof_histogram.png', dpi=750)
plt.show()
plt.clf()
plt.close()
mean_motions = load_motion()
for task in contrasts:
subjs = load_covars_df(task, return_perf=False).index
plt.hist(mean_motions[task].loc[subjs], bins=50)
plt.title(task + ' FD Histogram')
plt.ylabel('Counts')
plt.xlabel('Value')
if task == 'nBack':
plt.xlim(0, 3)
plt.savefig('Histograms/' + task + '_fd_histogram.png', dpi=750)
plt.show()
plt.clf()
plt.close()
def load_raw_data(task, resid=False, overlap_with=None):
all_data = {}
# Subjects will be same for all
# overwrite w/ latest
subjects = None
# Get covars df + de-mean
covars = load_covars_df(task, return_perf=False)
covars = proc_covars_func(covars)
# Overlap with one or more series
if overlap_with is not None:
if not isinstance(overlap_with, list):
overlap_with = [overlap_with]
for ow in overlap_with:
to_keep = np.intersect1d(covars.index, ow.index)
covars = covars.loc[to_keep]
# For each contrast
for contrast in contrasts[task]:
for is_cortical in [True, False]:
# Get mask
mask = get_mask(task, contrast, is_cortical=is_cortical)
# Generate template path
tp = get_template_path(task, is_cortical=is_cortical)
# Load raw data
subjects, data =\
load_resid_data(covars, contrast, tp, mask=mask,
resid=resid, n_jobs=16, verbose=1)
# Add to all_data dict
app_name = '.subcortical'
if is_cortical:
app_name = '.cortical'
all_data[contrast + app_name] = data
return all_data, covars.loc[subjects]
import sys
from Rely import load_resid_data
import numpy as np
import os
from info import (load_covars_df, proc_covars_func, get_mask,
get_template_path, contrasts)
from helpers import get_cohens, fast_corr
# Process arguments
task = str(sys.argv[1])
contrast = int(str(sys.argv[2]))
is_cortical = bool(int(str(sys.argv[3])))
# Load the covars df
covars_df, perf_df = load_covars_df(task, return_perf=True)
# De-MEAN!
covars_df = proc_covars_func(covars_df)
# Generate the proper mask based off contrast + cortical vs. subcortical
mask = get_mask(task, contrast, is_cortical)
# Generate template path
template_path = get_template_path(task, is_cortical)
# Load resid data
subjects, resid_data =\
load_resid_data(covars_df, contrast, template_path, mask=mask,
n_jobs=8, verbose=1)
print('Final Shape:', resid_data.shape, flush=True)
import matplotlib.pyplot as plt
import numpy as np
from info import (load_covars_df, proc_covars_func, get_mask,
get_template_path, get_strat_series)
# Process arguments
task = str(sys.argv[1])
contrast = int(str(sys.argv[2]))
is_cortical = bool(int(str(sys.argv[3])))
run = str(sys.argv[4])
print('sys.argv=', sys.argv, flush=True)
# Load the covars df
covars_df = load_covars_df(task, return_perf=False)
# Get the strat series
strat_series = get_strat_series(covars_df)
# Generate the proper mask based off contrast + cortical vs. subcortical
mask = get_mask(task, contrast, is_cortical)
# Generate template path
template_path = get_template_path(task, is_cortical)
print('is_cortical=', is_cortical)
print('mask.shape=', mask.shape)
print('contrast=', contrast)
print('template_path=', template_path)