def test_create_full_ants_subpipes_no_subpipes():
params = {
"general": {
"template_name": "NMT_v2.0_asym"
},
"short_preparation_pipe": {
"bet_crop": {}
},
"brain_extraction_pipe": {},
"brain_segment_pipe": {}
}
# params template
template_name = params["general"]["template_name"]
template_dir = load_test_data(template_name, data_path)
params_template = format_template(template_dir, template_name)
# running workflow
segment_pnh = create_full_ants_subpipes(
params=params,
params_template=params_template,
name="test_create_full_ants_subpipes_no_subpipes")
segment_pnh.base_dir = data_path
segment_pnh.write_graph(graph2use="colored")
assert op.exists(
op.join(data_path, "test_create_full_ants_subpipes_no_subpipes",
"graph.png"))
def test_create_full_ants_subpipes():
params = {
"general": {
"template_name": "NMT_v2.0_asym"
},
"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 = params["general"]["template_name"]
template_dir = load_test_data(template_name, data_path)
params_template = format_template(template_dir, template_name)
# running workflow
segment_pnh = create_full_ants_subpipes(
params=params,
params_template=params_template,
name="test_create_full_ants_subpipes")
segment_pnh.base_dir = data_path
segment_pnh.write_graph(graph2use="colored")
assert op.exists(
op.join(data_path, "test_create_full_ants_subpipes", "graph.png"))
def create_main_workflow(data_dir, process_dir, soft, species, subjects, sessions,
acquisitions, reconstructions, params_file,
indiv_params_file, mask_file, nprocs,
wf_name="macapype",
deriv=False, pad=False):
# macapype_pipeline
""" 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
"""
soft = soft.lower()
ssoft = soft.split("_")
new_ssoft = ssoft.copy()
if 'test' in ssoft:
new_ssoft.remove('test')
if 'prep' in ssoft:
new_ssoft.remove('prep')
soft = "_".join(new_ssoft)
# formating args
data_dir = op.abspath(data_dir)
try:
os.makedirs(process_dir)
except OSError:
print("process_dir {} already exists".format(process_dir))
# species
# params
params = {}
if params_file is None:
if species is not None:
species = species.lower()
rep_species = {"marmoset":"marmo", "chimpanzee":"chimp"}
if species in list(rep_species.keys()):
species = rep_species[species]
list_species = ["macaque", "marmo", "baboon", "chimp"]
assert species in list_species, \
"Error, species {} should in the following list {}".format(
species, list_species)
package_directory = os.path.dirname(os.path.abspath(__file__))
params_file = "{}/params_segment_{}_{}.json".format(
package_directory, species, soft)
else:
print("Error, no -params or no -species was found (one or the \
other is mandatory)")
exit(-1)
print("Params:", params_file)
assert os.path.exists(params_file), "Error with file {}".format(
params_file)
params = json.load(open(params_file))
# indiv_params
indiv_params = {}
if indiv_params_file is None:
print("No indiv params where found, modifing pipepline to default")
if "short_preparation_pipe" in params.keys():
if "crop_T1" in params["short_preparation_pipe"].keys():
print("Deleting crop_T1")
del params["short_preparation_pipe"]["crop_T1"]
print("Adding automated bet_crop")
params["short_preparation_pipe"]["bet_crop"] = {"m": True, "aT2": True, "c": 10, "n": 2}
print("Using default bet_crop parameters: {}".format(
params["short_preparation_pipe"]["bet_crop"]))
print("New params after modification")
pprint.pprint(params)
wf_name+="_bet_crop"
else:
assert "short_preparation_pipe" in params.keys(),\
"Error, short_preparation_pipe not found in params"
prep_pipe = "short_preparation_pipe"
count_all_sessions=0
count_T1_crops=0
count_long_crops=0
count_multi_long_crops=0
print("Indiv 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)
if subjects is None or sessions is None:
print("For whole BIDS dir, unable to assess if the indiv_params is correct")
print("Running with params as it is")
else:
print("Will modify params if necessary, given specified subjects and sessions;\n")
for sub in indiv_params.keys():
if sub.split('-')[1] not in subjects:
continue
for ses in indiv_params[sub].keys():
if ses.split('-')[1] not in sessions:
continue
count_all_sessions+=1
print (indiv_params[sub][ses].keys())
if "crop_T1" in indiv_params[sub][ses].keys():
count_T1_crops+=1
if "crop_T2" in indiv_params[sub][ses].keys() \
and 't1' not in ssoft:
count_long_crops+=1
if isinstance(
indiv_params[sub][ses]["crop_T1"]["args"],
list) and isinstance(
indiv_params[sub][ses]["crop_T2"]["args"],
list):
count_multi_long_crops+=1
print("count_all_sessions {}".format(count_all_sessions))
print("count_T1_crops {}".format(count_T1_crops))
print("count_long_crops {}".format(count_long_crops))
print("count_multi_long_crops {}".format(count_multi_long_crops))
if count_multi_long_crops==count_all_sessions:
print("**** Found list of crops for T1 and T2 for all sub/ses \
in indiv -> long_multi_preparation_pipe")
wf_name+="_multi_crop_T1_T2"
prep_pipe = "long_multi_preparation_pipe"
elif count_long_crops==count_all_sessions:
print("**** Found crop for T1 and crop for T2 for all sub/ses \
in indiv -> long_single_preparation_pipe")
wf_name+="_crop_T1_T2"
prep_pipe = "long_single_preparation_pipe"
elif count_T1_crops==count_all_sessions:
print("**** Found crop for T1 for all sub/ses in indiv \
-> keeping short_preparation_pipe")
wf_name+="_crop_T1"
else:
print("**** not all sub/ses have T1 and T2 crops ")
print("Error")
exit(0)
if prep_pipe != "short_preparation_pipe":
params[prep_pipe]={
"prep_T1": {"crop_T1": {"args": "should be defined in indiv"}},
"prep_T2": {"crop_T2": {"args": "should be defined in indiv"}},
"align_T2_on_T1": {"dof": 6, "cost": "normmi"}}
if "norm_intensity" in params["short_preparation_pipe"].keys():
norm_intensity= params["short_preparation_pipe"]["norm_intensity"]
params[prep_pipe]["prep_T1"]["norm_intensity"]=norm_intensity
params[prep_pipe]["prep_T2"]["norm_intensity"]=norm_intensity
if "denoise" in params["short_preparation_pipe"].keys():
denoise= params["short_preparation_pipe"]["denoise"]
params[prep_pipe]["prep_T1"]["denoise"]=denoise
params[prep_pipe]["prep_T2"]["denoise"]=denoise
del params["short_preparation_pipe"]
# prep for testing only preparation part
if "prep" in ssoft:
print("Found prep in soft")
if "brain_extraction_pipe" in params.keys():
del params["brain_extraction_pipe"]
print("Deleting brain_extraction_pipe")
if "brain_segment_pipe" in params.keys():
del params["brain_segment_pipe"]
print("Deleting brain_segment_pipe")
pprint.pprint(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
wf_name += "_{}".format(soft)
if mask_file is not None:
wf_name += "_mask"
assert "spm" in ssoft or "spm12" in ssoft or "ants" in ssoft, \
"error with {}, should be among [spm12, spm, ants]".format(ssoft)
# main_workflow
main_workflow = pe.Workflow(name= wf_name)
main_workflow.base_dir = process_dir
if "spm" in ssoft or "spm12" in ssoft:
if 'native' in ssoft:
space='native'
else:
space='template'
segment_pnh_pipe = create_full_spm_subpipes(
params_template=params_template, params=params, pad=pad,
space=space)
elif "ants" in ssoft:
if "template" in ssoft:
space="template"
else:
space="native"
if "t1" in ssoft:
segment_pnh_pipe = create_full_T1_ants_subpipes(
params_template=params_template, params=params, space=space,
pad=pad)
else:
segment_pnh_pipe = create_full_ants_subpipes(
params_template=params_template, params=params,
mask_file=mask_file, space=space, pad=pad)
# list of all required outputs
output_query = {}
# T1 (mandatory, always added)
output_query['T1'] = {
"datatype": "anat", "suffix": "T1w",
"extension": ["nii", ".nii.gz"]
}
# T2 is optional, if "_T1" is added in the -soft arg
if not 't1' in ssoft:
output_query['T2'] = {
"datatype": "anat", "suffix": "T2w",
"extension": ["nii", ".nii.gz"]}
# FLAIR is optional, if "_FLAIR" is added in the -soft arg
if 'flair' in ssoft:
output_query['FLAIR'] = {
"datatype": "anat", "suffix": "FLAIR",
"extension": ["nii", ".nii.gz"]}
# MD and b0mean are optional, if "_MD" is added in the -soft arg
if 'md' in ssoft:
output_query['MD'] = {
"datatype": "dwi", "acquisition": "MD", "suffix": "dwi",
"extension": ["nii", ".nii.gz"]}
output_query['b0mean'] = {
"datatype": "dwi", "acquisition": "b0mean", "suffix": "dwi",
"extension": ["nii", ".nii.gz"]}
# indiv_params
if indiv_params:
datasource = create_datasource_indiv_params(
output_query, data_dir, indiv_params, subjects, sessions,
acquisitions, reconstructions)
main_workflow.connect(datasource, "indiv_params",
segment_pnh_pipe,'inputnode.indiv_params')
else:
datasource = create_datasource(
output_query, data_dir, subjects, sessions, acquisitions,
reconstructions)
main_workflow.connect(datasource, 'T1',
segment_pnh_pipe, 'inputnode.list_T1')
if not "t1" in ssoft:
main_workflow.connect(datasource, 'T2',
segment_pnh_pipe, 'inputnode.list_T2')
elif "t1" in ssoft and "spm" in ssoft:
# cheating using T2 as T1
main_workflow.connect(datasource, 'T1',
segment_pnh_pipe, 'inputnode.list_T2')
if "flair" in ssoft:
transfo_FLAIR_pipe = create_transfo_FLAIR_pipe(params=params,
params_template=params_template)
if "t1" in ssoft:
main_workflow.connect(segment_pnh_pipe, "short_preparation_pipe.outputnode.preproc_T1",
transfo_FLAIR_pipe, 'inputnode.orig_T1')
else:
main_workflow.connect(segment_pnh_pipe, "debias.t1_debiased_file",
transfo_FLAIR_pipe, 'inputnode.orig_T1')
main_workflow.connect(segment_pnh_pipe, "reg.transfo_file",
transfo_FLAIR_pipe, 'inputnode.lin_transfo_file')
main_workflow.connect(datasource, ('FLAIR', get_first_elem),
transfo_FLAIR_pipe, 'inputnode.FLAIR')
if 'md' in ssoft:
transfo_MD_pipe = create_transfo_MD_pipe(params=params,
params_template=params_template)
main_workflow.connect(segment_pnh_pipe,
"old_segment_pipe.outputnode.threshold_wm",
transfo_MD_pipe, 'inputnode.threshold_wm')
main_workflow.connect(datasource, ('MD', get_first_elem),
transfo_MD_pipe, 'inputnode.MD')
main_workflow.connect(datasource, ('b0mean', get_first_elem),
transfo_MD_pipe, 'inputnode.b0mean')
main_workflow.connect(segment_pnh_pipe, "debias.t1_debiased_file",
transfo_MD_pipe, 'inputnode.orig_T1')
main_workflow.connect(segment_pnh_pipe, "debias.t2_debiased_brain_file",
transfo_MD_pipe, 'inputnode.SS_T2')
main_workflow.connect(segment_pnh_pipe, "reg.transfo_file",
transfo_MD_pipe, 'inputnode.lin_transfo_file')
main_workflow.connect(segment_pnh_pipe, "reg.inv_transfo_file",
transfo_MD_pipe, 'inputnode.inv_lin_transfo_file')
if deriv:
datasink_name = os.path.join("derivatives", "macapype_" + soft)
if "regex_subs" in params.keys():
params_regex_subs = params["regex_subs"]
else:
params_regex_subs={}
if "subs" in params.keys():
params_subs = params["rsubs"]
else:
params_subs={}
datasink = create_datasink(iterables=datasource.iterables,
name=datasink_name,
params_subs=params_subs,
params_regex_subs=params_regex_subs)
datasink.inputs.base_directory = process_dir
main_workflow.connect(
segment_pnh_pipe, 'outputnode.brain_mask',
datasink, '@brain_mask')
main_workflow.connect(
segment_pnh_pipe, 'outputnode.segmented_brain_mask',
datasink, '@segmented_brain_mask')
if 'flair' in ssoft :
main_workflow.connect(
transfo_FLAIR_pipe, 'outputnode.norm_FLAIR',
datasink, '@norm_flair')
main_workflow.write_graph(graph2use="colored")
main_workflow.config['execution'] = {'remove_unnecessary_outputs': 'false'}
if nprocs is None:
nprocs = 4
if not "test" in ssoft:
if "seq" in ssoft or nprocs==0:
main_workflow.run()
else:
main_workflow.run(plugin='MultiProc',
plugin_args={'n_procs' : nprocs})