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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__ = """
Examples of design matrices specification and and computation (event-related
design, FIR design, etc)
Requires matplotlib
Author : Bertrand Thirion: 2009-2010
"""
print(__doc__)
import numpy as np
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
from nipy.modalities.fmri.design_matrix import make_dmtx
from nipy.modalities.fmri.experimental_paradigm import (
BlockParadigm,
EventRelatedParadigm,
)
# frame times
tr = 1.0
nscans = 128
frametimes = np.linspace(0, (nscans - 1) * tr, nscans)
# experimental paradigm
conditions = ['c0', 'c0', 'c0', 'c1', 'c1', 'c1', 'c3', 'c3', 'c3']
onsets = [30, 70, 100, 10, 30, 90, 30, 40, 60]
hrf_model = 'canonical'
motion = np.cumsum(np.random.randn(128, 6), 0)
add_reg_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
#event-related design matrix
paradigm = EventRelatedParadigm(conditions, onsets)
X1 = make_dmtx(
frametimes, paradigm, hrf_model=hrf_model, drift_model='polynomial',
drift_order=3, add_regs=motion, add_reg_names=add_reg_names)
# block design matrix
duration = 7 * np.ones(9)
paradigm = BlockParadigm(con_id=conditions, onset=onsets,
duration=duration)
X2 = make_dmtx(frametimes, paradigm, drift_model='polynomial',
drift_order=3)
# FIR model
paradigm = EventRelatedParadigm(conditions, onsets)
hrf_model = 'fir'
X3 = make_dmtx(frametimes, paradigm, hrf_model=hrf_model,
drift_model='polynomial', drift_order=3,
fir_delays=np.arange(1, 6))
# plot the results
fig = plt.figure(figsize=(10, 6))
ax = plt.subplot(1, 3, 1)
X1.show(ax=ax)
ax.set_title('Event-related design matrix', fontsize=12)
ax = plt.subplot(1, 3, 2)
X2.show(ax=ax)
ax.set_title('Block design matrix', fontsize=12)
ax = plt.subplot(1, 3, 3)
X3.show(ax=ax)
ax.set_title('FIR design matrix', fontsize=12)
plt.subplots_adjust(top=0.9, bottom=0.25)
plt.show()
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