def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not len(args.tractograms) == len(args.t1):
parser.error("Not the same number of images in input.")
all_images = np.concatenate([args.tractograms, args.t1])
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 = "Tracking"
columns = ["Nb streamlines"]
warning_dict = {}
summary, stats = stats_tractogram(columns, args.tractograms)
warning_dict[name] = analyse_qa(summary, stats, ["Nb streamlines"])
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_tractogram("Tracking", columns, summary)
graphs.append(graph)
summary_dict = {}
stats_html = dataframe_to_html(stats)
summary_dict[name] = stats_html
metrics_dict = {}
subjects_dict = {}
for subj_metric, t1 in zip(args.tractograms, args.t1):
screenshot_path = screenshot_tracking(subj_metric, t1, "data")
summary_html = dataframe_to_html(summary.loc[subj_metric])
subjects_dict[subj_metric] = {}
subjects_dict[subj_metric]['screenshot'] = screenshot_path
subjects_dict[subj_metric]['stats'] = summary_html
metrics_dict[name] = subjects_dict
nb_subjects = len(args.tractograms)
report = Report(args.output_report)
report.generate(title="Quality Assurance tractograms",
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()
assert_inputs_exist(parser, args.frf)
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 = "FRF"
metrics_names = ["Mean Eigen value 1", "Mean Eigen value 2", "Mean B0"]
warning_dict = {}
summary, stats = stats_frf(metrics_names, args.frf)
warning_dict[name] = analyse_qa(summary, stats, metrics_names)
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_frf("FRF", metrics_names, summary)
graphs.append(graph)
summary_dict = {}
stats_html = dataframe_to_html(stats)
summary_dict[name] = stats_html
metrics_dict = {}
subjects_dict = {}
for subj_metric in args.frf:
summary_html = dataframe_to_html(summary.loc[subj_metric])
subjects_dict[subj_metric] = {}
subjects_dict[subj_metric]['stats'] = summary_html
metrics_dict[name] = subjects_dict
nb_subjects = len(args.frf)
report = Report(args.output_report)
report.generate(title="Quality Assurance FRF",
nb_subjects=nb_subjects, summary_dict=summary_dict,
graph_array=graphs, metrics_dict=metrics_dict,
warning_dict=warning_dict)
def _subj_parralel(subj_metric, summary, name, skip, nb_columns):
subjects_dict = {}
screenshot_path = screenshot_mosaic_wrapper(subj_metric,
output_prefix=name,
directory="data",
skip=skip,
nb_columns=nb_columns)
summary_html = dataframe_to_html(summary.loc[subj_metric])
subjects_dict[subj_metric] = {}
subjects_dict[subj_metric]['screenshot'] = screenshot_path
subjects_dict[subj_metric]['stats'] = summary_html
return subjects_dict
def _subj_parralel(images_no_bet, images_bet_mask, name, skip, summary,
nb_columns):
subjects_dict = {}
for subj_metric, mask in zip(images_no_bet, images_bet_mask):
screenshot_path = screenshot_mosaic_blend(subj_metric,
mask,
output_prefix=name,
directory="data",
blend_val=0.3,
skip=skip,
nb_columns=nb_columns,
is_mask=True)
summary_html = dataframe_to_html(summary.loc[subj_metric])
subjects_dict[subj_metric] = {}
subjects_dict[subj_metric]['screenshot'] = screenshot_path
subjects_dict[subj_metric]['stats'] = summary_html
return subjects_dict
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not len(args.images_no_bet) == len(args.images_bet_mask):
parser.error("Not the same number of images in input.")
all_images = np.concatenate([args.images_no_bet, args.images_bet_mask])
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 = args.images_no_bet
name = args.image_type
curr_metrics = ['Mean {}'.format(name), 'Median {}'.format(name)]
summary, stats = stats_mean_median(curr_metrics, metrics)
warning_dict = {}
warning_dict[name] = analyse_qa(summary, stats, curr_metrics)
warning_images = [filenames for filenames in warning_dict[name].values()]
warning_list = np.concatenate([warning_images])
warning_dict[name]['nb_warnings'] = len(np.unique(warning_list))
graphs = []
graph = graph_mean_median('Mean {}'.format(name), curr_metrics, 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(np.array_split(np.array(args.images_no_bet), args.nb_threads),
np.array_split(np.array(args.images_bet_mask), args.nb_threads),
itertools.repeat(name), itertools.repeat(args.skip),
itertools.repeat(summary), 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.images_no_bet)
report = Report(args.output_report)
report.generate(title="Quality Assurance BET " + args.image_type,
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 args.tracking_type == "local":
if not len(args.seeding_mask) == len(args.tracking_mask):
parser.error("Not the same number of images in input.")
all_images = np.concatenate([args.seeding_mask, args.tracking_mask])
else:
if not len(args.seeding_mask) == len(args.map_include) ==\
len(args.map_exclude):
parser.error("Not the same number of images in input.")
all_images = np.concatenate(
[args.seeding_mask, args.map_include, args.map_exclude])
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")
if args.tracking_type == "local":
metrics_names = [[args.seeding_mask, 'Seeding mask'],
[args.tracking_mask, 'Tracking mask']]
else:
metrics_names = [[args.seeding_mask, 'Seeding mask'],
[args.map_include, 'Map include'],
[args.map_exclude, 'Maps exclude']]
metrics_dict = {}
summary_dict = {}
graphs = []
warning_dict = {}
for metrics, name in metrics_names:
columns = ["{} volume".format(name)]
summary, stats = stats_mask_volume(columns, metrics)
warning_dict[name] = analyse_qa(summary, stats, columns)
warning_list = np.concatenate(
[filenames for filenames in warning_dict[name].values()])
warning_dict[name]['nb_warnings'] = len(np.unique(warning_list))
graph = graph_mask_volume('{} mean volume'.format(name), columns,
summary)
graphs.append(graph)
stats_html = dataframe_to_html(stats)
summary_dict[name] = stats_html
subjects_dict = {}
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
nb_subjects = len(args.seeding_mask)
report = Report(args.output_report)
report.generate(title="Quality Assurance tracking maps",
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.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)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if not len(args.bval) == len(args.bvec):
parser.error("Not the same number of images in input.")
all_data = np.concatenate([args.bval, args.bvec])
assert_inputs_exist(parser, all_data)
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 = "DWI Protocol"
summary, stats_for_graph, stats_all, shells = dwi_protocol(args.bval)
warning_dict = {}
warning_dict[name] = analyse_qa(stats_for_graph, stats_all,
["Nbr shells", "Nbr directions"])
warning_images = [filenames for filenames in warning_dict[name].values()]
warning_list = np.concatenate(warning_images)
warning_dict[name]['nb_warnings'] = len(np.unique(warning_list))
stats_html = dataframe_to_html(stats_all)
summary_dict = {}
summary_dict[name] = stats_html
graphs = []
graphs.append(
graph_directions_per_shells("Nbr directions per shell", shells))
graphs.append(graph_subjects_per_shells("Nbr subjects per shell", shells))
for c in ["Nbr shells", "Nbr directions"]:
graph = graph_dwi_protocol(c, c, stats_for_graph)
graphs.append(graph)
subjects_dict = {}
for bval, bvec in zip(args.bval, args.bvec):
filename = os.path.basename(bval)
subjects_dict[bval] = {}
points = np.genfromtxt(bvec)
if points.shape[0] == 3:
points = points.T
bvals = np.genfromtxt(bval)
centroids, shell_idx = identify_shells(bvals)
ms = build_ms_from_shell_idx(points, shell_idx)
plot_proj_shell(ms,
centroids,
use_sym=True,
use_sphere=True,
same_color=False,
rad=0.025,
opacity=0.2,
ofile=os.path.join("data", name + filename),
ores=(800, 800))
subjects_dict[bval]['screenshot'] = os.path.join(
"data", name + filename + '.png')
metrics_dict = {}
for subj in args.bval:
summary_html = dataframe_to_html(summary[subj])
subjects_dict[subj]['stats'] = summary_html
metrics_dict[name] = subjects_dict
nb_subjects = len(args.bval)
report = Report(args.output_report)
report.generate(title="Quality Assurance DWI protocol",
nb_subjects=nb_subjects,
metrics_dict=metrics_dict,
summary_dict=summary_dict,
graph_array=graphs,
warning_dict=warning_dict)