def test_vmhc_ants():
test_name = 'test_vmhc_ants'
# get the config and strat for the mock
pipeline_config, strat = configuration_strategy_mock(method='ANTS')
num_strat = 0
workflow = pe.Workflow(name=test_name)
workflow.base_dir = pipeline_config.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(pipeline_config.crashLogDirectory)
}
nodes = strat.get_nodes_names()
print('nodes {0}'.format(nodes))
workflow, strat = create_vmhc(workflow,
num_strat,
strat,
pipeline_config,
output_name='vmhc_{0}'.format(num_strat))
workflow.run()
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_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)