def test_create_full_segment_pnh_subpipes_no_args():
params = {
"short_preparation_pipe": {
"bet_crop": {}
},
"brain_extraction_pipe": {
"correct_bias_pipe": {},
"extract_pipe": {}
},
"brain_segment_pipe": {
"masked_correct_bias_pipe": {},
"register_NMT_pipe": {},
"segment_atropos_pipe": {}
}
}
# params template
template_name = 'NMT_v1.2'
template_dir = load_test_data(template_name, data_path)
params_template = format_template(template_dir, template_name)
# running workflow
segment_pnh = create_full_segment_pnh_subpipes(
params=params,
params_template=params_template,
name="test_create_full_segment_pnh_subpipes_no_args")
segment_pnh.base_dir = data_path
segment_pnh.write_graph(graph2use="colored")
assert op.exists(
op.join(data_path, "test_create_full_segment_pnh_subpipes_no_args",
"graph.png"))
def test_create_full_segment_pnh_subpipes():
params = {
"short_preparation_pipe": {
"bet_crop": {
"m": True,
"aT2": True,
"c": 10,
"n": 2
}
},
"brain_extraction_pipe": {
"correct_bias_pipe": {
"smooth": {
"args": "-bin -s 2"
},
"norm_smooth": {
"op_string": "-s 2 -div %s"
},
"smooth_bias": {
"sigma": 2
}
},
"extract_pipe": {
"atlas_brex": {
"f": 0.5,
"reg": 1,
"wrp": "10,10,10",
"msk": "a,0,0",
"dil": 2,
"nrm": 1
}
}
},
"brain_segment_pipe": {
"masked_correct_bias_pipe": {
"smooth": {
"args": "-bin -s 2"
},
"norm_smooth": {
"op_string": "-s 2 -div %s"
},
"smooth_bias": {
"sigma": 2
}
},
"register_NMT_pipe": {
"norm_intensity": {
"dimension": 3,
"bspline_fitting_distance": 200,
"n_iterations": [50, 50, 40, 30],
"convergence_threshold": 0.00000001,
"shrink_factor": 2,
"args": "-r 0 --verbose 1"
}
},
"segment_atropos_pipe": {
"Atropos": {
"dimension": 3,
"numberOfClasses": 3
},
"threshold_gm": {
"thresh": 0.5
},
"threshold_wm": {
"thresh": 0.5
},
"threshold_csf": {
"thresh": 0.5
}
}
}
}
# params template
template_name = 'NMT_v1.2'
template_dir = load_test_data(template_name, data_path)
params_template = format_template(template_dir, template_name)
# running workflow
segment_pnh = create_full_segment_pnh_subpipes(
params=params,
params_template=params_template,
name="test_create_full_segment_pnh_subpipes")
segment_pnh.base_dir = data_path
segment_pnh.write_graph(graph2use="colored")
assert op.exists(
op.join(data_path, "test_create_full_segment_pnh_subpipes",
"graph.png"))
template_dir = load_test_data(template_name)
params_template = format_template(template_dir, template_name)
print (params_template)
# en local
data_path = "/home/INT/meunier.d/Data/Baboon/data/sub-Babar/ses-test/anat"
main_path = "/home/INT/meunier.d/data_macapype/"
# sur frioul
#data_path = "/hpc/meca/users/loh.k/baboon_proc/data/sub-Odor/ses-T1/anat"
#main_path = "/hpc/crise/meunier.d/Data"
## data file
T1_file = op.join(data_path, "sub-Odor_ses-T1_T1w.nii.gz")
T2_file = op.join(data_path, "sub-Odor_ses-T1_T2w.nii.gz")
# running workflow
segment_pnh = create_full_segment_pnh_subpipes(params=params,
params_template=params_template,
name = "example_segment_baboon_ants_based_Odor")
segment_pnh.base_dir = main_path
segment_pnh.inputs.inputnode.list_T1 = [T1_file]
segment_pnh.inputs.inputnode.list_T2 = [T2_file]
segment_pnh.inputs.inputnode.indiv_params = indiv_params["sub-Odor"]["ses-T1"]
segment_pnh.write_graph(graph2use="colored")
segment_pnh.config['execution'] = {'remove_unnecessary_outputs': 'false'}
segment_pnh.run()
template_name = 'NMT_v1.2'
if "general" in params.keys() and "my_path" in params["general"].keys():
my_path = params["general"]["my_path"]
else:
my_path = ''
template_dir = load_test_data(template_name)
params_template = format_template(template_dir, template_name)
print(params_template)
data_path = load_test_data("data_test_macaque", my_path)
# data file
T1_file = op.join(data_path, "non_cropped", "sub-Apache_ses-01_T1w.nii")
T2_file = op.join(data_path, "non_cropped", "sub-Apache_ses-01_T2w.nii")
# running workflow
segment_pnh = create_full_segment_pnh_subpipes(
params=params,
params_template=params_template,
name="example_segment_macaque_ants_based")
segment_pnh.base_dir = data_path
segment_pnh.inputs.inputnode.list_T1 = [T1_file]
segment_pnh.inputs.inputnode.list_T2 = [T2_file]
segment_pnh.write_graph(graph2use="colored")
segment_pnh.config['execution'] = {'remove_unnecessary_outputs': 'false'}
segment_pnh.run()
def create_main_workflow(data_dir,
process_dir,
soft,
subjects,
sessions,
acquisitions,
params_file,
indiv_params_file,
wf_name="test_pipeline_single"):
""" Set up the segmentatiopn pipeline based on ANTS
Arguments
---------
data_path: pathlike str
Path to the BIDS directory that contains anatomical images
out_path: pathlike str
Path to the ouput directory (will be created if not alredy existing).
Previous outputs maybe overwritten.
soft: str
Indicate which analysis should be launched; so for, only spm and ants
are accepted; can be extended
subjects: list of str (optional)
Subject's IDs to match to BIDS specification (sub-[SUB1], sub-[SUB2]...)
sessions: list of str (optional)
Session's IDs to match to BIDS specification (ses-[SES1], ses-[SES2]...)
acquisitions: list of str (optional)
Acquisition name to match to BIDS specification (acq-[ACQ1]...)
indiv_params_file: path to a JSON file
JSON file that specify some parameters of the pipeline,
unique for the subjects/sessions.
params_file: path to a JSON file
JSON file that specify some parameters of the pipeline.
Returns
-------
workflow: nipype.pipeline.engine.Workflow
"""
# formating args
data_dir = op.abspath(data_dir)
if not op.isdir(process_dir):
os.makedirs(process_dir)
# params
params = {}
if params_file is not None:
print("Params:", params_file)
assert os.path.exists(params_file), "Error with file {}".format(
params_file)
params = json.load(open(params_file))
pprint.pprint(params)
# indiv_params
indiv_params = {}
if indiv_params_file is not None:
print("Multi Params:", indiv_params_file)
assert os.path.exists(indiv_params_file), "Error with file {}".format(
indiv_params_file)
indiv_params = json.load(open(indiv_params_file))
wf_name += "_indiv_params"
pprint.pprint(indiv_params)
# params_template
assert ("general" in params.keys() and \
"template_name" in params["general"].keys()), \
"Error, the params.json should contains a general/template_name"
template_name = params["general"]["template_name"]
if "general" in params.keys() and "my_path" in params["general"].keys():
my_path = params["general"]["my_path"]
else:
my_path = ""
nmt_dir = load_test_data(template_name, path_to=my_path)
params_template = format_template(nmt_dir, template_name)
print(params_template)
# soft
soft = soft.lower()
assert soft in ["spm12", "spm", "ants", "spm_t1"], \
"error with {}, should be among [spm12, spm, ants]".format(soft)
wf_name += "_{}".format(soft)
# main_workflow
main_workflow = pe.Workflow(name=wf_name)
main_workflow.base_dir = process_dir
if soft in ["spm", "spm12"]:
segment_pnh = create_full_T1xT2_segment_pnh_subpipes(
params_template=params_template, params=params)
elif soft == "ants":
segment_pnh = create_full_segment_pnh_subpipes(
params_template=params_template, params=params)
elif soft == "spm_t1":
segment_pnh = create_full_spm_subpipes(params_template=params_template,
params=params)
if indiv_params:
datasource = create_datasource_indiv_params(data_dir, indiv_params,
subjects, sessions)
main_workflow.connect(datasource, "indiv_params", segment_pnh,
'inputnode.indiv_params')
else:
datasource = create_datasource(data_dir, subjects, sessions,
acquisitions)
main_workflow.connect(datasource, 'T1', segment_pnh, 'inputnode.list_T1')
if soft != "spm_t1":
main_workflow.connect(datasource, 'T2', segment_pnh,
'inputnode.list_T2')
return main_workflow
