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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__ = """
This script generates a noisy multi-subject activation image dataset
and applies the Bayesian structural analysis on it
Requires matplotlib
Author : Bertrand Thirion, 2009-2013
"""
print(__doc__)
import numpy as np
import scipy.stats as st
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
import nipy.labs.utils.simul_multisubject_fmri_dataset as simul
from nipy.labs.spatial_models.bayesian_structural_analysis import compute_landmarks
from nipy.labs.spatial_models.discrete_domain import grid_domain_from_shape
def display_landmarks_2d(landmarks, hrois, stats):
""" Plots the landmarks and associated rois as images"""
shape = stats[0].shape
n_subjects = len(stats)
lmax = 0
grp_map, density = np.zeros(shape), np.zeros(shape)
if landmarks is not None:
domain = landmarks.domain
grp_map = landmarks.map_label(domain.coord, .8, sigma).reshape(shape)
density = landmarks.kernel_density(k=None, coord=domain.coord,
sigma=sigma).reshape(shape)
lmax = landmarks.k + 2
# Figure 1: input data
fig_input = plt.figure(figsize=(8, 3.5))
fig_input.text(.5,.9, "Input activation maps", ha='center')
vmin, vmax = stats.min(), stats.max()
for subject in range(n_subjects):
plt.subplot(n_subjects // 5, 5, subject + 1)
plt.imshow(stats[subject], interpolation='nearest',
vmin=vmin, vmax=vmax)
plt.axis('off')
# Figure 2: individual hrois
fig_output = plt.figure(figsize=(8, 3.5))
fig_output.text(.5, .9, "Individual landmark regions", ha="center")
for subject in range(n_subjects):
plt.subplot(n_subjects // 5, 5, subject + 1)
lw = - np.ones(shape)
if hrois[subject].k > 0:
nls = hrois[subject].get_roi_feature('label')
nls[nls == - 1] = np.size(landmarks) + 2
for k in range(hrois[subject].k):
np.ravel(lw)[hrois[subject].label == k] = nls[k]
plt.imshow(lw, interpolation='nearest', vmin=-1, vmax=lmax)
plt.axis('off')
# Figure 3: Group-level results
plt.figure(figsize=(6, 3))
plt.subplot(1, 2, 1)
plt.imshow(grp_map, interpolation='nearest', vmin=-1, vmax=lmax)
plt.title('group-level position 80% \n confidence regions', fontsize=10)
plt.axis('off')
plt.colorbar(shrink=.8)
plt.subplot(1, 2, 2)
plt.imshow(density, interpolation='nearest')
plt.title('Spatial density under h1', fontsize=10)
plt.axis('off')
plt.colorbar(shrink=.8)
###############################################################################
# Main script
###############################################################################
# generate the data
n_subjects = 10
shape = (60, 60)
pos = np.array([[12, 14],
[20, 20],
[30, 20]])
ampli = np.array([5, 7, 6])
sjitter = 1.0
stats = simul.surrogate_2d_dataset(n_subj=n_subjects, shape=shape, pos=pos,
ampli=ampli, width=5.0)
# set various parameters
threshold = float(st.t.isf(0.01, 100))
sigma = 4. / 1.5
prevalence_threshold = n_subjects * .25
prevalence_pval = 0.9
smin = 5
algorithm = 'co-occurrence' # 'density'
domain = grid_domain_from_shape(shape)
# get the functional information
stats_ = np.array([np.ravel(stats[k]) for k in range(n_subjects)]).T
# run the algo
landmarks, hrois = compute_landmarks(
domain, stats_, sigma, prevalence_pval, prevalence_threshold,
threshold, smin, method='prior', algorithm=algorithm)
display_landmarks_2d(landmarks, hrois, stats)
if landmarks is not None:
landmarks.show()
plt.show()
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