Coregistration Tool
[+ show/hide code]# Pilot imports
from caps.toy_datasets import get_sample_data
from soma.study_config import StudyConfig
from soma.sorted_dictionary import SortedDictionary
from nsap.lib.base import ensure_is_dir
# Get toy dataset
toy_dataset = get_sample_data("localizer")
print toy_dataset.anatdcm
# Create
nifti_converter_pipeline = Converter_nifti()
# Print Input Spec
#print coreg_pipeline.get_input_spec()
#untar archive
converter_working_dir = os.path.join(working_dir, "converter")
ensure_is_dir(converter_working_dir)
tar_open = tarfile.open(toy_dataset.anatdcm)
tar_open.extractall(path=os.path.join(converter_working_dir,
"input_data"))
tar_open.close()
#get dicom dir
dicom_dir = os.listdir(os.path.join(converter_working_dir, "input_data"))
dicom_dir = dicom_dir[0]
# Initialize pipeline
nifti_converter_pipeline.dicom_dir = os.path.join(converter_working_dir,
"input_data", dicom_dir)
nifti_converter_pipeline.fill_header = False
# Execute the pipeline
default_config = SortedDictionary(
("output_directory", converter_working_dir),
("use_smart_caching", True),
("generate_logging", False)
)
study = StudyConfig(default_config)
study.run(nifti_converter_pipeline)
# Print all pipeline outputs
print "\nOUTPUTS\n"
for trait_name, trait_value in nifti_converter_pipeline.get_outputs().iteritems():
print "{0}: {1}".format(trait_name, trait_value)