def test_ants_apply_warp_func_mni_symm():
test_name = 'test_ants_apply_warps_func_mni_symm'
# get the config and strat for the mock
c, strat = configuration_strategy_mock()
num_strat = 0
node, out = strat['mean_functional']
mean_functional = node.inputs.file
# build the workflow
workflow = pe.Workflow(name=test_name)
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
workflow = ants_apply_warps_func_mni(
workflow,
'mean_functional_to_standard_symm',
'mean_functional',
'template_brain_for_func_preproc',
num_strat,
strat,
interpolation_method=c.funcRegANTSinterpolation,
distcor=False,
map_node=False,
inverse=False,
symmetry='symmetric',
input_image_type=0,
num_ants_cores=8)
workflow = ants_apply_warps_func_mni(
workflow,
'mean_functional_standard_to_original_symm',
'mean_functional_to_standard_symm',
'mean_functional',
num_strat,
strat,
interpolation_method=c.funcRegANTSinterpolation,
distcor=False,
map_node=False,
inverse=True,
symmetry='symmetric',
input_image_type=0,
num_ants_cores=1)
retval = workflow.run()
mean_functional_after_transform = os.path.join(
c.workingDirectory, test_name,
'apply_ants_warp_mean_functional_standard_to_original_symm_inverse_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_antswarp_antswarp.nii.gz'
)
assert (test_utils.pearson_correlation(
mean_functional, mean_functional_after_transform) > .93)
def test_smooth():
test_name = 'test_smooth_nodes'
c, strat = configuration_strategy_mock(method='FSL')
num_strat = 0
# build the workflow
workflow = pe.Workflow(name=test_name)
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
spatial_smooth(workflow, 'mean_functional', 'functional_brain_mask',
'mean_functional_smooth'.format(num_strat), strat,
num_strat, c)
func_node, func_output = strat['mean_functional']
mask_node, mask_output = strat['functional_brain_mask']
spatial_smooth(workflow, (func_node, func_output),
(mask_node, mask_output),
'mean_functional_smooth_nodes'.format(num_strat), strat,
num_strat, c)
print(workflow.list_node_names())
workflow.run()
correlations = []
for fwhm in c.fwhm:
out_name1 = os.path.join(
c.workingDirectory, test_name,
'_fwhm_{0}/mean_functional_smooth_0/'.format(fwhm),
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_maths.nii.gz'
)
out_name2 = os.path.join(
c.workingDirectory, test_name,
'_fwhm_{0}/mean_functional_smooth_nodes_0/'.format(fwhm),
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_maths.nii.gz'
)
correlations.append(
test_utils.pearson_correlation(out_name1, out_name2) > 0.99)
assert all(correlations)
def test_output_func_to_standard_FSL_linear():
test_name = 'test_output_func_to_standard_FSL_linear'
# get the config and strat for the mock
c, strat = configuration_strategy_mock(method='FSL')
strat.append_name('anat_mni_flirt_register_0')
num_strat = 0
# build the workflow
workflow = pe.Workflow(name='test_output_func_to_standard_FSL_linear')
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
output_func_to_standard(workflow,
'mean_functional',
'template_brain_for_func_preproc',
'mean_functional_to_standard',
strat,
num_strat,
c,
input_image_type='func_derivative')
out1_name = os.path.join(
c.workingDirectory, test_name,
'func_mni_fsl_warp_mean_functional_to_standard_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz'
)
func_node, func_out = strat['mean_functional']
output_func_to_standard(workflow, (func_node, func_out),
'template_brain_for_func_preproc',
'mean_functional_to_standard_node',
strat,
num_strat,
c,
input_image_type='func_derivative')
out2_name = os.path.join(
c.workingDirectory, test_name,
'func_mni_fsl_warp_mean_functional_to_standard_node_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz'
)
workflow.run()
assert (test_utils.pearson_correlation(out1_name, out2_name) > 0.99)
def pearson_correlation(file_out, file_ref=None, name=None, **kwargs):
corr = test_utils.pearson_correlation(file_out[0], file_ref[0])
print(f'\nCorrelation = {round(corr,3)}\n')
out = {}
out["corr"] = f"{corr:.2f}"
try:
assert corr > .99
out["regr"] = "PASSED"
except (AssertionError):
out["regr"] = "FAILED"
report_filename = Path(f"report_{name}.json")
with report_filename.open("w") as f:
json.dump(out, f)
def test_ants_apply_warps_func_mni_mapnode():
test_name = 'test_ants_apply_warps_func_mni_mapnode'
# get the config and strat for the mock
c, strat = configuration_strategy_mock()
num_strat = 0
node, out = strat['dr_tempreg_maps_files']
dr_spatmaps = node.inputs.file
# build the workflow
workflow = pe.Workflow(name='test_ants_apply_warps_func_mni_mapnode')
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
workflow = ants_apply_warps_func_mni(
workflow,
'dr_tempreg_maps_to_standard',
'dr_tempreg_maps_files',
'template_brain_for_func_preproc',
num_strat,
strat,
interpolation_method=c.funcRegANTSinterpolation,
distcor=False,
map_node=True,
inverse=False,
input_image_type=0,
num_ants_cores=1)
workflow = ants_apply_warps_func_mni(
workflow,
'dr_tempreg_maps_standard_to_original',
'dr_tempreg_maps_to_standard',
'mean_functional',
num_strat,
strat,
interpolation_method=c.funcRegANTSinterpolation,
distcor=False,
map_node=True,
inverse=True,
input_image_type=0,
num_ants_cores=8)
workflow.run()
dr_spatmaps_after_transform = [
os.path.join(
c.workingDirectory, test_name,
'apply_ants_warp_dr_tempreg_maps_standard_to_original_mapnode_inverse_0',
'mapflow',
'_apply_ants_warp_dr_tempreg_maps_standard_to_original_mapnode_inverse_0{0}'
.format(n),
'temp_reg_map_000{0}_antswarp_antswarp.nii.gz'.format(n))
for n in range(0, 10)
]
test_results = [ test_utils.pearson_correlation(orig_file, xformed_file) > 0.99 \
for orig_file, xformed_file in zip(dr_spatmaps, dr_spatmaps_after_transform)]
assert all(test_results)
def test_smooth_mapnode():
test_name = 'test_smooth_mapnode'
c, strat = configuration_strategy_mock(method='FSL')
num_strat = 0
# build the workflow
workflow = pe.Workflow(name=test_name)
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
spatial_smooth(workflow,
'dr_tempreg_maps_files',
'functional_brain_mask',
'dr_tempreg_maps_smooth'.format(num_strat),
strat,
num_strat,
c,
input_image_type='func_derivative_multi')
func_node, func_output = strat['dr_tempreg_maps_files']
mask_node, mask_output = strat['functional_brain_mask']
spatial_smooth(workflow, (func_node, func_output),
(mask_node, mask_output),
'dr_tempreg_maps_smooth_nodes'.format(num_strat),
strat,
num_strat,
c,
input_image_type='func_derivative_multi')
print(workflow.list_node_names())
workflow.run()
correlations = []
for fwhm in c.fwhm:
dr_spatmaps_after_smooth1 = [
os.path.join(
c.workingDirectory, test_name,
'_fwhm_{0}/dr_tempreg_maps_smooth_multi_0/mapflow'.format(
fwhm),
'_dr_tempreg_maps_smooth_multi_0{0}/temp_reg_map_000{0}_maths.nii.gz'
.format(n)) for n in range(0, 10)
]
dr_spatmaps_after_smooth2 = [
os.path.join(
c.workingDirectory, test_name,
'_fwhm_{0}/dr_tempreg_maps_smooth_nodes_multi_0/mapflow'.
format(fwhm),
'_dr_tempreg_maps_smooth_nodes_multi_0{0}/temp_reg_map_000{0}_maths.nii.gz'
.format(n)) for n in range(0, 10)
]
correlations += [test_utils.pearson_correlation(file1, file2) > 0.99 \
for file1, file2 in zip(dr_spatmaps_after_smooth1, dr_spatmaps_after_smooth2)]
assert all(correlations)
