def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not len(args.t1_warped) == len(args.rgb) == len(args.wm) ==\
len(args.gm) == len(args.csf):
parser.error("Not the same number of images in input.")
all_images = np.concatenate(
[args.t1_warped, args.rgb, args.wm, args.gm, args.csf])
assert_inputs_exist(parser, all_images)
assert_outputs_exist(parser, args, [args.output_report, "data", "libs"])
if os.path.exists("data"):
shutil.rmtree("data")
os.makedirs("data")
if os.path.exists("libs"):
shutil.rmtree("libs")
name = "Register T1"
curr_metrics = [
'Mean {} in WM'.format(name), 'Mean {} in GM'.format(name),
'Mean {} in CSF'.format(name), 'Max {} in WM'.format(name)
]
warning_dict = {}
summary, stats = stats_mean_in_tissues(curr_metrics, args.t1_warped,
args.wm, args.gm, args.csf)
warning_dict[name] = analyse_qa(summary, stats, curr_metrics[:3])
warning_list = np.concatenate(
[filenames for filenames in warning_dict[name].values()])
warning_dict[name]['nb_warnings'] = len(np.unique(warning_list))
graphs = []
graph = graph_mean_in_tissues('Mean {}'.format(name), curr_metrics[:3],
summary)
graphs.append(graph)
stats_html = dataframe_to_html(stats)
summary_dict = {}
summary_dict[name] = stats_html
pool = Pool(args.nb_threads)
subjects_dict_pool = pool.starmap(
_subj_parralel,
zip(args.t1_warped, args.rgb, itertools.repeat(summary),
itertools.repeat(name), itertools.repeat(args.skip),
itertools.repeat(args.nb_columns)))
pool.close()
pool.join()
metrics_dict = {}
subjects_dict = {}
for dict_sub in subjects_dict_pool:
for key in dict_sub:
subjects_dict[key] = dict_sub[key]
metrics_dict[name] = subjects_dict
nb_subjects = len(args.t1_warped)
report = Report(args.output_report)
report.generate(title="Quality Assurance registration",
nb_subjects=nb_subjects,
summary_dict=summary_dict,
graph_array=graphs,
metrics_dict=metrics_dict,
warning_dict=warning_dict)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not len(args.fa) == len(args.md) == len(args.rd) == len(args.ad) ==\
len(args.residual) == len(args.evecs_v1) == len(args.wm) ==\
len(args.gm) == len(args.csf):
parser.error("Not the same number of images in input.")
all_images = np.concatenate([
args.fa, args.md, args.rd, args.ad, args.residual, args.evecs_v1,
args.wm, args.gm, args.csf
])
assert_inputs_exist(parser, all_images)
assert_outputs_exist(parser, args, [args.output_report, "data", "libs"])
if os.path.exists("data"):
shutil.rmtree("data")
os.makedirs("data")
if os.path.exists("libs"):
shutil.rmtree("libs")
metrics_names = [[args.fa, 'FA'], [args.md, 'MD'], [args.rd, 'RD'],
[args.ad, 'AD'], [args.residual, "Residual"]]
metrics_dict = {}
summary_dict = {}
graphs = []
warning_dict = {}
for metrics, name in metrics_names:
subjects_dict = {}
curr_metrics = [
'Mean {} in WM'.format(name), 'Mean {} in GM'.format(name),
'Mean {} in CSF'.format(name), 'Max {} in WM'.format(name)
]
summary, stats = stats_mean_in_tissues(curr_metrics, metrics, args.wm,
args.gm, args.csf)
warning_dict[name] = analyse_qa(summary, stats, curr_metrics[:3])
warning_list = np.concatenate(
[filenames for filenames in warning_dict[name].values()])
warning_dict[name]['nb_warnings'] = len(np.unique(warning_list))
graph = graph_mean_in_tissues('Mean {}'.format(name), curr_metrics[:3],
summary)
graphs.append(graph)
stats_html = dataframe_to_html(stats)
summary_dict[name] = stats_html
pool = Pool(args.nb_threads)
subjects_dict_pool = pool.starmap(
_subj_parralel,
zip(metrics, itertools.repeat(summary), itertools.repeat(name),
itertools.repeat(args.skip),
itertools.repeat(args.nb_columns)))
pool.close()
pool.join()
for dict_sub in subjects_dict_pool:
for key in dict_sub:
subjects_dict[key] = dict_sub[key]
metrics_dict[name] = subjects_dict
subjects_dict = {}
name = "Peaks"
for fa, evecs in zip(args.fa, args.evecs_v1):
screenshot_path = screenshot_fa_peaks(fa, evecs, "data")
subjects_dict[evecs] = {}
subjects_dict[evecs]['screenshot'] = screenshot_path
metrics_dict[name] = subjects_dict
nb_subjects = len(args.fa)
report = Report(args.output_report)
report.generate(title="Quality Assurance DTI metrics",
nb_subjects=nb_subjects,
summary_dict=summary_dict,
graph_array=graphs,
metrics_dict=metrics_dict,
warning_dict=warning_dict)