def test_read_paradigm():
""" test that a paradigm is correctly read
"""
import tempfile
tmpdir = tempfile.mkdtemp()
for paradigm in (block_paradigm(), modulated_event_paradigm(),
modulated_block_paradigm(), basic_paradigm()):
csvfile = write_paradigm(paradigm, tmpdir)
read_paradigm = paradigm_from_csv(csvfile)
assert_true((read_paradigm['onset'] == paradigm['onset']).all())
def test_read_paradigm():
""" test that a paradigm is correctly read
"""
import tempfile
tmpdir = tempfile.mkdtemp()
for paradigm in (block_paradigm(), modulated_event_paradigm(), modulated_block_paradigm(), basic_paradigm()):
csvfile = write_paradigm(paradigm, tmpdir)
read_paradigm = paradigm_from_csv(csvfile)
assert_true((read_paradigm["onset"] == paradigm["onset"]).all())
# This is just a flag to be able to use the same script for the plotting
if False:
for study in studies:
voxel_fn = op.join(folder, study + '.npy')
# Paradigm file
paradigm_fn = op.join(folder0, 'onsets.csv')
########################################################################
# Load data and parameters
n_scans = 144
t_r = 3.
ys = np.load(voxel_fn)
# Create design matrix
frametimes = np.arange(0, n_scans * t_r, t_r)
paradigm = experimental_paradigm.paradigm_from_csv(paradigm_fn)
dm = design_matrix.make_design_matrix(frametimes, paradigm=paradigm)
modulation = np.array(paradigm)[:, 4]
# GP parameters
time_offset = 10
gamma = 10.
fmin_max_iter = 50
n_restarts_optimizer = 10
n_iter = 3
normalize_y = False
optimize = True
zeros_extremes = True
# Estimation
gp = SuperDuperGP(hrf_length=hrf_length, t_r=t_r, oversampling=1./dt,
'func BOLD': 'nipype_mem/*Smooth/*/swrvismot1*.nii'
})
func_file = preprocessed_heroes['func BOLD'][0]
anat_file = preprocessed_heroes['anat'][0]
# Give the path to the paradigm
heroes = datasets.load_heroes_dataset(
subjects=subjects,
subjects_parent_directory=os.path.join(
os.path.expanduser('~/procasl_data'), 'heroes'),
paths_patterns={'paradigm': 'paradigms/acquisition1/*BOLD*1b.csv'})
paradigm_file = heroes['paradigm'][0]
# Read the paradigm
from nistats import experimental_paradigm
paradigm = experimental_paradigm.paradigm_from_csv(paradigm_file)
# Create the design matrix
import numpy as np
import matplotlib.pyplot as plt
import nibabel
from nistats.design_matrix import make_design_matrix, plot_design_matrix
tr = 2.5
n_scans = nibabel.load(func_file).get_data().shape[-1]
frametimes = np.arange(0, n_scans * tr, tr)
design_matrix = make_design_matrix(frametimes, paradigm)
plot_design_matrix(design_matrix)
plt.tight_layout()
# Fit GLM
print('Fitting a GLM')
