def test_modelgen_spm_concat():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename2)
s = SpecifySPMModel()
s.inputs.input_units = 'secs'
s.inputs.concatenate_runs = True
setattr(s.inputs, 'output_units', 'secs')
yield assert_equal, s.inputs.output_units, 'secs'
s.inputs.functional_runs = [filename1, filename2]
s.inputs.time_repetition = 6
s.inputs.high_pass_filter_cutoff = 128.
info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 170]], durations=[[1]]),
Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_equal, len(res.outputs.session_info), 1
yield assert_equal, len(res.outputs.session_info[0]['regress']), 1
yield assert_equal, np.sum(res.outputs.session_info[0]['regress'][0]['val']), 30
yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])
setattr(s.inputs, 'output_units', 'scans')
yield assert_equal, s.inputs.output_units, 'scans'
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])/6
s.inputs.concatenate_runs = False
s.inputs.subject_info = deepcopy(info)
s.inputs.output_units = 'secs'
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0])
rmtree(tempdir)
def test_modelgen_spm_concat():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename2)
# Test case when only one duration is passed, as being the same for all onsets.
s = SpecifySPMModel()
s.inputs.input_units = 'secs'
s.inputs.concatenate_runs = True
setattr(s.inputs, 'output_units', 'secs')
yield assert_equal, s.inputs.output_units, 'secs'
s.inputs.functional_runs = [filename1, filename2]
s.inputs.time_repetition = 6
s.inputs.high_pass_filter_cutoff = 128.
info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 170]], durations=[[1]]),
Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_equal, len(res.outputs.session_info), 1
yield assert_equal, len(res.outputs.session_info[0]['regress']), 1
yield assert_equal, np.sum(res.outputs.session_info[0]['regress'][0]['val']), 30
yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1., 1., 1.])
# Test case of scans as output units instead of seconds
setattr(s.inputs, 'output_units', 'scans')
yield assert_equal, s.inputs.output_units, 'scans'
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]) / 6
# Test case for no concatenation with seconds as output units
s.inputs.concatenate_runs = False
s.inputs.subject_info = deepcopy(info)
s.inputs.output_units = 'secs'
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0])
# Test case for variable number of events in separate runs, sometimes unique.
filename3 = os.path.join(tempdir, 'test3.nii')
Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename3)
s.inputs.functional_runs = [filename1, filename2, filename3]
info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2, 4]], durations=[[1, 1], [1, 1]]),
Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]])]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1.])
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1.,])
yield assert_almost_equal, np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([1., 1.])
yield assert_almost_equal, np.array(res.outputs.session_info[2]['cond'][1]['duration']), np.array([1.,])
# Test case for variable number of events in concatenated runs, sometimes unique.
s.inputs.concatenate_runs = True
info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2, 4]], durations=[[1, 1], [1, 1]]),
Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]])]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1.])
yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1., 1., 1., 1.])
rmtree(tempdir)
def test_modelgen_spm_concat():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename2)
s = SpecifySPMModel()
s.inputs.input_units = 'secs'
s.inputs.concatenate_runs = True
setattr(s.inputs, 'output_units', 'secs')
yield assert_equal, s.inputs.output_units, 'secs'
s.inputs.functional_runs = [filename1, filename2]
s.inputs.time_repetition = 6
s.inputs.high_pass_filter_cutoff = 128.
info = [
Bunch(conditions=['cond1'],
onsets=[[2, 50, 100, 170]],
durations=[[1]]),
Bunch(conditions=['cond1'],
onsets=[[30, 40, 100, 150]],
durations=[[1]])
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_equal, len(res.outputs.session_info), 1
yield assert_equal, len(res.outputs.session_info[0]['regress']), 1
yield assert_equal, np.sum(
res.outputs.session_info[0]['regress'][0]['val']), 30
yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['onset']), np.array(
[2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])
setattr(s.inputs, 'output_units', 'scans')
yield assert_equal, s.inputs.output_units, 'scans'
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['onset']), np.array(
[2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]) / 6
s.inputs.concatenate_runs = False
s.inputs.subject_info = deepcopy(info)
s.inputs.output_units = 'secs'
res = s.run()
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['onset']), np.array(
[2.0, 50.0, 100.0, 170.0])
rmtree(tempdir)
def test_modelgen_spm_concat():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir,'test1.nii')
filename2 = os.path.join(tempdir,'test2.nii')
Nifti1Image(np.random.rand(10,10,10,50), np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10,10,10,50), np.eye(4)).to_filename(filename2)
s = SpecifySPMModel()
s.inputs.input_units = 'secs'
s.inputs.output_units = 'scans'
s.inputs.concatenate_runs = True
setattr(s.inputs, 'output_units', 'scans')
yield assert_equal, s.inputs.output_units, 'scans'
s.inputs.functional_runs = [filename1, filename2]
s.inputs.time_repetition = 6
s.inputs.high_pass_filter_cutoff = 128.
info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]], amplitudes=None,
pmod=None, regressors = None, regressor_names = None, tmod=None),
Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]], amplitudes=None,
pmod=None, regressors = None, regressor_names = None, tmod=None)]
s.inputs.subject_info = info
res = s.run()
yield assert_equal, len(res.outputs.session_info), 1
yield assert_equal, len(res.outputs.session_info[0]['regress']), 1
yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
yield assert_almost_equal, res.outputs.session_info[0]['cond'][0]['onset'], [2.0, 50.0, 100.0, 180.0, 330.0, 340.0, 400.0, 450.0]
rmtree(tempdir)
def spm_model_specification(behavioral_data, fmri_sessions, onset_name,
condition_name, duration_name, time_repetition,
realignment_parameters, delimiter, start,
concatenate_runs, high_pass_filter_cutoff,
output_directory):
""" Specify the SPM model used in the GLM and estimate the design matrix.
.. note::
* `fmri_sessions` and `behavioral_data` must have the same number
of elements.
* `onsets` and `durations` values must have the same units as the
TR used in the processings (ie. seconds).
<unit>
<input name="behavioral_data" type="List" content="File" desc="list of
.csv session behavioral data." />
<input name="fmri_sessions" type="List" content="File" desc="list of
path to fMRI sessions." />
<input name="onset_name" type="String" desc="the name of the column
in the `behavioral_data` file containing the onsets."/>
<input name="condition_name" type="String" desc="the name of the
column in the `behavioral_data` file containing the conditions."/>
<input name="duration_name" type="String" desc="the name of the column
in the `behavioral_data` file containing the condition durations.
"/>
<input name="time_repetition" type="Float" desc="the repetition time
in seconds (in seconds)."/>
<input name="realignment_parameters" type="File" desc="path to the SPM
realign output parameters."/>
<input name="delimiter" type="String" desc="separator used to split
the `behavioral_data` file."/>
<input name="start" type="Int" desc="line from which we start reading
the `behavioral_data` file."/>
<input name="concatenate_runs" type="Bool" desc="concatenate all runs
to look like a single session."/>
<input name="high_pass_filter_cutoff" type="Float" desc="high-pass
filter cutoff in secs."/>
<input name="output_directory" type="Directory" desc="Where to store
the output file"/>
<output name="session_info" type="Any" desc="session info to leverage
the first level design."/>
<output name="model_specifications" type="File" desc="file containing
all model specifications" />
</unit>
"""
# Local imports
from nipype.interfaces.base import Bunch
from nipype.algorithms.modelgen import SpecifySPMModel
# Assert that we have one behavioral data per session
if len(behavioral_data) != len(fmri_sessions):
raise ValueError("One behavioral data per session is required, "
"got {0} behaviral data and {1} session.".format(
len(behavioral_data), len(fmri_sessions)))
# Get each session acquisition conditions
info = []
for csvfile in behavioral_data:
# Parse the behavioural file
all_onsets = get_onsets(csvfile,
condition_name, onset_name, duration_name,
delimiter, start)
# Create a nipype Bunch (dictionary-like) structure
conditions = []
onsets = []
durations = []
for condition_name, item in all_onsets.items():
conditions.append(condition_name)
onsets.append([float(x) for x in item["onsets"]])
durations.append([float(x) for x in item["durations"]])
info.append(
Bunch(conditions=conditions, onsets=onsets, durations=durations))
# Make a model specification compatible with spm designer
spec_interface = SpecifySPMModel(
concatenate_runs=concatenate_runs,
input_units="secs",
output_units="secs",
time_repetition=time_repetition,
high_pass_filter_cutoff=high_pass_filter_cutoff,
functional_runs=fmri_sessions,
subject_info=info,
realignment_parameters=realignment_parameters)
spec_interface.run()
# The previous interface use numpy in double precision. In order to be
# python-json compliant need to cast expicitely all float items
def cast_to_float(obj):
""" Recursive method that cast numpy.double items.
Parameters
----------
obj: object
a generic python object.
Returns
-------
out: object
the float-casted input object.
"""
# Deal with dictionary
if isinstance(obj, dict):
out = {}
for key, val in obj.items():
out[key] = cast_to_float(val)
# Deal with tuple and list
elif isinstance(obj, (list, tuple)):
out = []
for val in obj:
out.append(cast_to_float(val))
if isinstance(obj, tuple):
out = tuple(out)
# Otherwise cast if it is a numpy.double
else:
out = obj
if isinstance(obj, float):
out = float(obj)
return out
session_info = cast_to_float(spec_interface.aggregate_outputs().get()[
"session_info"])
model_specifications = os.path.join(output_directory,
"model_specifications.json")
# save the design parameters
with open(model_specifications, "w") as _file:
json.dump(session_info, _file, indent=4)
return session_info, model_specifications
def test_modelgen_spm_concat(tmpdir):
filename1 = tmpdir.join("test1.nii").strpath
filename2 = tmpdir.join("test2.nii").strpath
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename2)
# Test case when only one duration is passed, as being the same for all onsets.
s = SpecifySPMModel()
s.inputs.input_units = "secs"
s.inputs.concatenate_runs = True
setattr(s.inputs, "output_units", "secs")
assert s.inputs.output_units == "secs"
s.inputs.functional_runs = [filename1, filename2]
s.inputs.time_repetition = 6
s.inputs.high_pass_filter_cutoff = 128.0
info = [
Bunch(conditions=["cond1"],
onsets=[[2, 50, 100, 170]],
durations=[[1]]),
Bunch(conditions=["cond1"],
onsets=[[30, 40, 100, 150]],
durations=[[1]]),
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
assert len(res.outputs.session_info) == 1
assert len(res.outputs.session_info[0]["regress"]) == 1
assert np.sum(res.outputs.session_info[0]["regress"][0]["val"]) == 30
assert len(res.outputs.session_info[0]["cond"]) == 1
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["onset"]),
np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]),
)
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["duration"]),
np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
)
# Test case of scans as output units instead of seconds
setattr(s.inputs, "output_units", "scans")
assert s.inputs.output_units == "scans"
s.inputs.subject_info = deepcopy(info)
res = s.run()
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["onset"]),
np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]) / 6,
)
# Test case for no concatenation with seconds as output units
s.inputs.concatenate_runs = False
s.inputs.subject_info = deepcopy(info)
s.inputs.output_units = "secs"
res = s.run()
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["onset"]),
np.array([2.0, 50.0, 100.0, 170.0]),
)
# Test case for variable number of events in separate runs, sometimes unique.
filename3 = tmpdir.join("test3.nii").strpath
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename3)
s.inputs.functional_runs = [filename1, filename2, filename3]
info = [
Bunch(conditions=["cond1", "cond2"],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]]),
Bunch(
conditions=["cond1", "cond2"],
onsets=[[2, 3], [2, 4]],
durations=[[1, 1], [1, 1]],
),
Bunch(conditions=["cond1", "cond2"],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]]),
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["duration"]),
np.array([1.0, 1.0]),
)
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][1]["duration"]),
np.array([1.0]))
npt.assert_almost_equal(
np.array(res.outputs.session_info[1]["cond"][1]["duration"]),
np.array([1.0, 1.0]),
)
npt.assert_almost_equal(
np.array(res.outputs.session_info[2]["cond"][1]["duration"]),
np.array([1.0]))
# Test case for variable number of events in concatenated runs, sometimes unique.
s.inputs.concatenate_runs = True
info = [
Bunch(conditions=["cond1", "cond2"],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]]),
Bunch(
conditions=["cond1", "cond2"],
onsets=[[2, 3], [2, 4]],
durations=[[1, 1], [1, 1]],
),
Bunch(conditions=["cond1", "cond2"],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]]),
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["duration"]),
np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
)
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][1]["duration"]),
np.array([1.0, 1.0, 1.0, 1.0]),
)
def test_modelgen_spm_concat():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename2)
# Test case when only one duration is passed, as being the same for all onsets.
s = SpecifySPMModel()
s.inputs.input_units = 'secs'
s.inputs.concatenate_runs = True
setattr(s.inputs, 'output_units', 'secs')
yield assert_equal, s.inputs.output_units, 'secs'
s.inputs.functional_runs = [filename1, filename2]
s.inputs.time_repetition = 6
s.inputs.high_pass_filter_cutoff = 128.
info = [
Bunch(conditions=['cond1'],
onsets=[[2, 50, 100, 170]],
durations=[[1]]),
Bunch(conditions=['cond1'],
onsets=[[30, 40, 100, 150]],
durations=[[1]])
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_equal, len(res.outputs.session_info), 1
yield assert_equal, len(res.outputs.session_info[0]['regress']), 1
yield assert_equal, np.sum(
res.outputs.session_info[0]['regress'][0]['val']), 30
yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['onset']), np.array(
[2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['duration']), np.array(
[1., 1., 1., 1., 1., 1., 1., 1.])
# Test case of scans as output units instead of seconds
setattr(s.inputs, 'output_units', 'scans')
yield assert_equal, s.inputs.output_units, 'scans'
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['onset']), np.array(
[2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]) / 6
# Test case for no concatenation with seconds as output units
s.inputs.concatenate_runs = False
s.inputs.subject_info = deepcopy(info)
s.inputs.output_units = 'secs'
res = s.run()
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['onset']), np.array(
[2.0, 50.0, 100.0, 170.0])
# Test case for variable number of events in separate runs, sometimes unique.
filename3 = os.path.join(tempdir, 'test3.nii')
Nifti1Image(np.random.rand(10, 10, 10, 30),
np.eye(4)).to_filename(filename3)
s.inputs.functional_runs = [filename1, filename2, filename3]
info = [
Bunch(conditions=['cond1', 'cond2'],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]]),
Bunch(conditions=['cond1', 'cond2'],
onsets=[[2, 3], [2, 4]],
durations=[[1, 1], [1, 1]]),
Bunch(conditions=['cond1', 'cond2'],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]])
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1.])
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][1]['duration']), np.array([
1.,
])
yield assert_almost_equal, np.array(
res.outputs.session_info[1]['cond'][1]['duration']), np.array([1., 1.])
yield assert_almost_equal, np.array(
res.outputs.session_info[2]['cond'][1]['duration']), np.array([
1.,
])
# Test case for variable number of events in concatenated runs, sometimes unique.
s.inputs.concatenate_runs = True
info = [
Bunch(conditions=['cond1', 'cond2'],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]]),
Bunch(conditions=['cond1', 'cond2'],
onsets=[[2, 3], [2, 4]],
durations=[[1, 1], [1, 1]]),
Bunch(conditions=['cond1', 'cond2'],
onsets=[[2, 3], [2]],
durations=[[1, 1], [1]])
]
s.inputs.subject_info = deepcopy(info)
res = s.run()
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][0]['duration']), np.array(
[1., 1., 1., 1., 1., 1.])
yield assert_almost_equal, np.array(
res.outputs.session_info[0]['cond'][1]['duration']), np.array(
[1., 1., 1., 1.])
rmtree(tempdir)
def spm_model_specification(behavioral_data, fmri_sessions, onset_name,
condition_name, duration_name, time_repetition,
realignment_parameters, delimiter, start,
concatenate_runs, high_pass_filter_cutoff):
""" Specify the SPM model used in the GLM and estimate the design matrix.
.. note::
* `fmri_sessions` and `behavioral_data` must have the same number
of elements.
* `onsets` and `durations` values must have the same units as the
TR used in the processings (ie. seconds).
<process>
<return name="session_info" type="List" desc="session info to leverage
the first level design."/>
<input name="behavioral_data" type="List_File" desc="list of .csv
session behavioral data." />
<input name="fmri_sessions" type="List_File" desc="list of path to
fMRI sessions." />
<input name="onset_name" type="String" desc="the name of the column
in the `behavioral_data` file containing the onsets."/>
<input name="condition_name" type="String" desc="the name of the
column in the `behavioral_data` file containing the conditions."/>
<input name="duration_name" type="String" desc="the name of the column
in the `behavioral_data` file containing the condition durations."/>
<input name="time_repetition" type="Float" desc="the repetition time
in seconds (in seconds)."/>
<input name="realignment_parameters" type="File" desc="path to the SPM
realign output parameters."/>
<input name="delimiter" type="String" desc="separator used to split
the `behavioral_data` file."/>
<input name="start" type="Int" desc="line from which we start reading
the `behavioral_data` file."/>
<input name="concatenate_runs" type="Bool" desc="concatenate all runs
to look like a single session."/>
<input name="high_pass_filter_cutoff" type="Float" desc="high-pass
filter cutoff in secs."/>
</process>
"""
# Local imports
from nipype.interfaces.base import Bunch
from nipype.algorithms.modelgen import SpecifySPMModel
# Assert that we have one behavioral data per session
if len(behavioral_data) != len(fmri_sessions):
raise ValueError("One behavioral data per session is required, "
"got {0} behaviral data and {1} session.".format(
len(behavioral_data), len(fmri_sessions)))
# Get each session acquisition conditions
info = []
for csvfile in behavioral_data:
# Parse the behavioural file
all_onsets = get_onsets(csvfile, condition_name, onset_name,
duration_name, delimiter, start)
# Create a nipype Bunch (dictionary-like) structure
conditions = []
onsets = []
durations = []
for condition_name, item in all_onsets.items():
conditions.append(condition_name)
onsets.append([float(x) for x in item["onsets"]])
durations.append([float(x) for x in item["durations"]])
info.append(
Bunch(conditions=conditions, onsets=onsets, durations=durations))
# Make a model specification compatible with spm designer
spec_interface = SpecifySPMModel(
concatenate_runs=concatenate_runs,
input_units="secs",
output_units="secs",
time_repetition=time_repetition,
high_pass_filter_cutoff=high_pass_filter_cutoff,
functional_runs=fmri_sessions,
subject_info=info,
realignment_parameters=realignment_parameters)
spec_interface.run()
# The previous interface use numpy in double precision. In order to be
# python-json compliant need to cast expicitely all float items
def cast_to_float(obj):
""" Recursive method that cast numpy.double items.
Parameters
----------
obj: object
a generic python object.
Returns
-------
out: object
the float-casted input object.
"""
# Deal with dictionary
if isinstance(obj, dict):
out = {}
for key, val in obj.items():
out[key] = cast_to_float(val)
# Deal with tuple and list
elif isinstance(obj, (list, tuple)):
out = []
for val in obj:
out.append(cast_to_float(val))
if isinstance(obj, tuple):
out = tuple(out)
# Otherwise cast if it is a numpy.double
else:
out = obj
if isinstance(obj, float):
out = float(obj)
return out
session_info = cast_to_float(
spec_interface.aggregate_outputs().get()["session_info"])
return session_info
time_repetition=tr,
high_pass_filter_cutoff=128)
modelspec.inputs.realignment_parameters = realignment_parameters
modelspec.inputs.functional_runs = func_file
# Read the conditions
import numpy as np
from nipype.interfaces.base import Bunch
paradigm = np.recfromcsv(paradigm_file)
conditions = np.unique(paradigm['name'])
onsets = [paradigm['onset'][paradigm['name'] == condition].tolist()
for condition in conditions]
durations = [paradigm['duration'][paradigm['name'] == condition].tolist()
for condition in conditions]
modelspec.inputs.subject_info = Bunch(conditions=conditions, onsets=onsets,
durations=durations)
out_modelspec = modelspec.run()
# Generate an SPM design matrix
from procasl.first_level import Level1PerfusionDesign
from procasl.preprocessing import compute_brain_mask
spm_mat = os.path.join(os.getcwd(), 'SPM.mat')
if os.path.isfile(spm_mat):
os.remove(spm_mat) # design crashes if existant SPM.mat
level1design = Level1PerfusionDesign(bases={'hrf': {'derivs': [0, 0]}},
perfusion_bases='bases',
timing_units='secs',
interscan_interval=tr,
model_serial_correlations='AR(1)')
level1design.inputs.mask_image = compute_brain_mask(
mean_func_file, frac=.2) # Compute cut neck mask
