def test_basic_t2smap4(self):
"""
A very simple test, to confirm that t2smap creates output
files when combmode is set to 'paid' and fitmode is set to 'ts'.
"""
data_dir = get_test_data_path()
data = [op.join(data_dir, 'echo1.nii.gz'),
op.join(data_dir, 'echo2.nii.gz'),
op.join(data_dir, 'echo3.nii.gz')]
out_dir = 'TED.echo1.t2smap'
workflows.t2smap_workflow(data, [14.5, 38.5, 62.5], combmode='paid',
fitmode='ts', out_dir=out_dir)
# Check outputs
assert op.isfile(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
img = nib.load(op.join(out_dir, 'T2starmap.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
assert len(img.shape) == 4
def test_basic_t2smap4(self):
"""
A very simple test, to confirm that t2smap creates output
files when combmode is set to 'paid' and fitmode is set to 'ts'.
Not sure why this fails.
"""
data_dir = get_test_data_path()
data = [op.join(data_dir, 'echo1.nii.gz'),
op.join(data_dir, 'echo2.nii.gz'),
op.join(data_dir, 'echo3.nii.gz')]
workflows.t2smap_workflow(data, [14.5, 38.5, 62.5], combmode='paid',
fitmode='ts', label='t2smap')
out_dir = 'TED.echo1.t2smap'
# Check outputs
assert op.isfile(op.join(out_dir, 'ts_OC.nii.gz'))
img = nib.load(op.join(out_dir, 't2sv.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 's0v.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 't2svG.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 's0vG.nii.gz'))
assert len(img.shape) == 4
img = nib.load(op.join(out_dir, 'ts_OC.nii.gz'))
assert len(img.shape) == 4
def test_t2smap_cli(self):
"""
Run test_basic_t2smap1, but use the CLI method.
"""
data_dir = get_test_data_path()
data = [op.join(data_dir, 'echo1.nii.gz'),
op.join(data_dir, 'echo2.nii.gz'),
op.join(data_dir, 'echo3.nii.gz')]
out_dir = 'TED.echo1.t2smap'
args = (['-d'] + data +
['-e', '14.5', '38.5', '62.5', '--combmode', 't2s',
'--fitmode', 'all', '--out-dir', out_dir])
workflows.t2smap._main(args)
# Check outputs
img = nib.load(op.join(out_dir, 'T2starmap.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 'S0map.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 'desc-full_T2starmap.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 'desc-full_S0map.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 'desc-optcom_bold.nii.gz'))
assert len(img.shape) == 4
def test_t2smap_cli(self):
"""
Run test_basic_t2smap1, but use the CLI method.
"""
data_dir = get_test_data_path()
data = [
op.join(data_dir, 'echo1.nii.gz'),
op.join(data_dir, 'echo2.nii.gz'),
op.join(data_dir, 'echo3.nii.gz')
]
args = (['-d'] + data + [
'-e', '14.5', '38.5', '62.5', '--combmode', 't2s', '--fitmode',
'all', '--label', 't2smap'
])
workflows.t2smap._main(args)
out_dir = 'TED.echo1.t2smap'
# Check outputs
img = nib.load(op.join(out_dir, 't2sv.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 's0v.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 't2svG.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 's0vG.nii.gz'))
assert len(img.shape) == 3
img = nib.load(op.join(out_dir, 'ts_OC.nii.gz'))
assert len(img.shape) == 4
def testdata1():
tes = np.array([14.5, 38.5, 62.5])
in_files = [op.join(get_test_data_path(), 'echo{0}.nii.gz'.format(i+1))
for i in range(3)]
data, _ = utils.load_data(in_files, n_echos=len(tes))
mask, mask_sum = utils.make_adaptive_mask(data, minimum=False, getsum=True)
data_dict = {'data': data,
'tes': tes,
'mask': mask,
'mask_sum': mask_sum,
}
return data_dict
def testdata1():
tes = np.array([14.5, 38.5, 62.5])
in_files = [
op.join(get_test_data_path(), 'echo{0}.nii.gz'.format(i + 1))
for i in range(3)
]
data, _ = io.load_data(in_files, n_echos=len(tes))
mask, adaptive_mask = utils.make_adaptive_mask(data, getsum=True)
fittype = 'loglin'
data_dict = {
'data': data,
'tes': tes,
'mask': mask,
'adaptive_mask': adaptive_mask,
'fittype': fittype,
}
return data_dict
def testdata1():
tes = np.array([14.5, 38.5, 62.5])
in_files = [
op.join(get_test_data_path(), "echo{0}.nii.gz".format(i + 1))
for i in range(3)
]
data, _ = io.load_data(in_files, n_echos=len(tes))
mask, adaptive_mask = utils.make_adaptive_mask(data, getsum=True)
fittype = "loglin"
data_dict = {
"data": data,
"tes": tes,
"mask": mask,
"adaptive_mask": adaptive_mask,
"fittype": fittype,
}
return data_dict
def testdata1():
"""Data used for tests of the metrics module."""
tes = np.array([14.5, 38.5, 62.5])
in_files = [
op.join(get_test_data_path(), "echo{0}.nii.gz".format(i + 1))
for i in range(3)
]
data_cat, ref_img = io.load_data(in_files, n_echos=len(tes))
_, adaptive_mask = utils.make_adaptive_mask(data_cat, getsum=True)
data_optcom = np.mean(data_cat, axis=1)
mixing = np.random.random((data_optcom.shape[1], 50))
io_generator = io.OutputGenerator(ref_img)
data_dict = {
"data_cat": data_cat,
"tes": tes,
"data_optcom": data_optcom,
"adaptive_mask": adaptive_mask,
"generator": io_generator,
"mixing": mixing,
}
return data_dict
"""
Tests for tedana.model.fit
"""
import os
import numpy as np
import pytest
import tedana.gscontrol as gsc
from tedana.io import OutputGenerator
from tedana.tests.utils import get_test_data_path
data_dir = get_test_data_path()
ref_img = os.path.join(data_dir, 'mask.nii.gz')
def test_break_gscontrol_raw():
"""
Ensure that gscontrol_raw fails when input data do not have the right
shapes.
"""
n_samples, n_echos, n_vols = 10000, 4, 100
catd = np.empty((n_samples, n_echos, n_vols))
optcom = np.empty((n_samples, n_vols))
io_generator = OutputGenerator(ref_img)
catd = np.empty((n_samples + 1, n_echos, n_vols))
with pytest.raises(ValueError) as e_info:
gsc.gscontrol_raw(catd=catd,
optcom=optcom,
