def test_modelgen1():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, "test1.nii")
filename2 = os.path.join(tempdir, "test2.nii")
Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename2)
s = SpecifyModel()
s.inputs.input_units = "scans"
set_output_units = lambda: setattr(s.inputs, "output_units", "scans")
yield assert_raises, TraitError, set_output_units
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, 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), 2
yield assert_equal, len(res.outputs.session_info[0]["regress"]), 0
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([12, 300, 600, 1080])
info = [
Bunch(conditions=["cond1"], onsets=[[2]], durations=[[1]]),
Bunch(conditions=["cond1"], onsets=[[3]], durations=[[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([6.0])
yield assert_almost_equal, np.array(res.outputs.session_info[1]["cond"][0]["duration"]), np.array([6.0])
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]]),
]
s.inputs.subject_info = deepcopy(info)
s.inputs.input_units = "scans"
res = s.run()
yield assert_almost_equal, np.array(res.outputs.session_info[0]["cond"][0]["duration"]), np.array([6.0, 6.0])
yield assert_almost_equal, np.array(res.outputs.session_info[0]["cond"][1]["duration"]), np.array([6.0])
yield assert_almost_equal, np.array(res.outputs.session_info[1]["cond"][1]["duration"]), np.array([6.0, 6.0])
rmtree(tempdir)
def test_modelgen1(tmpdir):
tempdir = str(tmpdir)
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename2)
s = SpecifyModel()
s.inputs.input_units = 'scans'
set_output_units = lambda: setattr(s.inputs, 'output_units', 'scans')
with pytest.raises(TraitError): set_output_units()
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()
assert len(res.outputs.session_info) == 2
assert len(res.outputs.session_info[0]['regress']) == 0
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([12, 300, 600, 1080]))
info = [Bunch(conditions=['cond1'], onsets=[[2]], durations=[[1]]),
Bunch(conditions=['cond1'], onsets=[[3]], durations=[[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([6.]))
npt.assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][0]['duration']), np.array([6.]))
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]])]
s.inputs.subject_info = deepcopy(info)
s.inputs.input_units = 'scans'
res = s.run()
npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6., 6.]))
npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([6., ]))
npt.assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([6., 6.]))
def test_modelgen1():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 200),
np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 200),
np.eye(4)).to_filename(filename2)
s = SpecifyModel()
s.inputs.input_units = 'scans'
set_output_units = lambda: setattr(s.inputs, 'output_units', 'scans')
yield assert_raises, TraitError, set_output_units
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), 2
yield assert_equal, len(res.outputs.session_info[0]['regress']), 0
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(
[12, 300, 600, 1080])
rmtree(tempdir)
def test_modelgen1():
tempdir = mkdtemp()
filename1 = os.path.join(tempdir, 'test1.nii')
filename2 = os.path.join(tempdir, 'test2.nii')
Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename2)
s = SpecifyModel()
s.inputs.input_units = 'scans'
set_output_units = lambda: setattr(s.inputs, 'output_units', 'scans')
yield assert_raises, TraitError, set_output_units
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), 2
yield assert_equal, len(res.outputs.session_info[0]['regress']), 0
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([12, 300, 600, 1080])
rmtree(tempdir)
def runglmperun(self, subject, trtimeinsec):
s = SpecifyModel()
# loop on all runs and models within each run
modelfiles = subject._modelfiles
for model in modelfiles:
# Make directory results to store the results of the model
results_dir = os.path.join(subject._path, 'model', model[0],
'results', model[1])
dir_util.mkpath(results_dir)
os.chdir(results_dir)
s.inputs.event_files = model[2]
s.inputs.input_units = 'secs'
s.inputs.functional_runs = os.path.join(subject._path, 'BOLD',
model[1],
'bold_mcf_hp.nii.gz')
# use nibable to get the tr of from the .nii file
s.inputs.time_repetition = trtimeinsec
s.inputs.high_pass_filter_cutoff = 128.
# find par file that has motion
motionfiles = glob(
os.path.join(subject._path, 'BOLD', model[1], "*.par"))
s.inputs.realignment_parameters = motionfiles
#info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]]), Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
#s.inputs.subject_info = None
res = s.run()
res.runtime.cwd
print ">>>> preparing evs for model " + model[
1] + "and run " + model[0]
sessionInfo = res.outputs.session_info
level1design = Level1Design()
level1design.inputs.interscan_interval = trtimeinsec
level1design.inputs.bases = {'dgamma': {'derivs': False}}
level1design.inputs.session_info = sessionInfo
level1design.inputs.model_serial_correlations = True
#TODO: add contrasts to level 1 design so that I have just condition vs rest for each ev
#TODO: Look into changign this to FILM instead of FEAT - this also has the option of setting output directory
# http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide#Contrasts
#http://nipy.org/nipype/interfaces/generated/nipype.interfaces.fsl.model.html#filmgls
resLevel = level1design.run()
featModel = FEATModel()
featModel.inputs.fsf_file = resLevel.outputs.fsf_files
featModel.inputs.ev_files = resLevel.outputs.ev_files
resFeat = featModel.run()
print ">>>> creating fsf design files for " + model[
1] + "and run " + model[0]
# TODO: give mask here
glm = fsl.GLM(in_file=s.inputs.functional_runs[0],
design=resFeat.outputs.design_file,
output_type='NIFTI')
print ">>>> running glm for " + model[1] + "and run " + model[0]
resGlm = glm.run()
print ">>>> finished running glm for " + model[
1] + "and run " + model[0]
def test_modelgen1(tmpdir):
filename1 = tmpdir.join("test1.nii").strpath
filename2 = tmpdir.join("test2.nii").strpath
Nifti1Image(np.random.rand(10, 10, 10, 200),
np.eye(4)).to_filename(filename1)
Nifti1Image(np.random.rand(10, 10, 10, 200),
np.eye(4)).to_filename(filename2)
s = SpecifyModel()
s.inputs.input_units = "scans"
set_output_units = lambda: setattr(s.inputs, "output_units", "scans")
with pytest.raises(TraitError):
set_output_units()
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, 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()
assert len(res.outputs.session_info) == 2
assert len(res.outputs.session_info[0]["regress"]) == 0
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([12, 300, 600, 1080]),
)
info = [
Bunch(conditions=["cond1"], onsets=[[2]], durations=[[1]]),
Bunch(conditions=["cond1"], onsets=[[3]], durations=[[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([6.0]))
npt.assert_almost_equal(
np.array(res.outputs.session_info[1]["cond"][0]["duration"]),
np.array([6.0]))
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]],
),
]
s.inputs.subject_info = deepcopy(info)
s.inputs.input_units = "scans"
res = s.run()
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][0]["duration"]),
np.array([6.0, 6.0]),
)
npt.assert_almost_equal(
np.array(res.outputs.session_info[0]["cond"][1]["duration"]),
np.array([6.0]))
npt.assert_almost_equal(
np.array(res.outputs.session_info[1]["cond"][1]["duration"]),
np.array([6.0, 6.0]),
)
struct=[['subject_id']],
evs=[['subject_id', 'func_scans']])
datasource.inputs.sort_filelist = False
results = datasource.run()
print results.outputs
cont1 = ['Bundling-Control', 'T', ['Bundling', 'Control'], [1, -1]]
s = SpecifyModel()
s.inputs.input_units = 'secs'
s.inputs.functional_runs = results.outputs.func
s.inputs.time_repetition = 2
s.inputs.high_pass_filter_cutoff = 128.
s.inputs.event_files = results.outputs.evs
model = s.run()
level1design = Level1Design()
level1design.inputs.interscan_interval = 2.5
level1design.inputs.bases = {'dgamma': {'derivs': False}}
level1design.inputs.model_serial_correlations = False
level1design.inputs.session_info = model.outputs.session_info
level1design.inputs.contrasts = [cont1]
l1d = level1design.run()
print l1d.outputs.ev_files
modelgen = FEATModel()
modelgen.inputs.ev_files = l1d.outputs.ev_files
modelgen.inputs.fsf_file = l1d.outputs.fsf_files
model = modelgen.run()