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#!/usr/bin/env python3
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
__doc__ = """
Example of a script that uses the BSA (Bayesian Structural Analysis) i.e.
nipy.labs.spatial_models.bayesian_structural_analysis module.
Author : Bertrand Thirion, 2008-2013
"""
print(__doc__)
#autoindent
from os import getcwd, mkdir, path
# Local import
from get_data_light import DATA_DIR, get_second_level_dataset
from numpy import array
from scipy import stats
from nipy.labs.spatial_models.bsa_io import make_bsa_image
# Get the data
nbsubj = 12
nbeta = 29
data_dir = path.join(DATA_DIR, 'group_t_images')
mask_images = [path.join(data_dir, 'mask_subj%02d.nii' % n)
for n in range(nbsubj)]
betas = [path.join(data_dir, 'spmT_%04d_subj_%02d.nii' % (nbeta, n))
for n in range(nbsubj)]
missing_file = array([not path.exists(m) for m in mask_images + betas]).any()
if missing_file:
get_second_level_dataset()
# set various parameters
subj_id = ['%04d' % i for i in range(12)]
threshold = float(stats.t.isf(0.01, 100))
sigma = 4.
prevalence_threshold = 2
prevalence_pval = 0.95
smin = 5
write_dir = path.join(getcwd(), 'results')
if not path.exists(write_dir):
mkdir(write_dir)
algorithm = 'density'
print('algorithm used:', algorithm)
# call the function
landmarks, individual_rois = make_bsa_image(
mask_images, betas, threshold, smin, sigma, prevalence_threshold,
prevalence_pval, write_dir, algorithm=algorithm,
contrast_id='%04d' % nbeta)
print(f"Wrote all the results in directory {write_dir}")
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