def test_create_with_float_metric(self):
write_smt_txt(1.239, "tests", metric="xy")
with open(self.outcome_path, "r") as outcome_file:
outcome = json.load(outcome_file)
self.assertEqual(outcome["text_outcome"],
"| xy: 1.24")
def test_new_outcome_keys(self):
write_smt_txt("Awesome", "tests")
with open(self.outcome_path, "r") as outcome_file:
outcome = json.load(outcome_file)
self.assertEqual(len(outcome), 2)
self.assertIn("text_outcome", outcome)
self.assertIn("numeric_outcome", outcome)
def test_append_with_metric_inline(self):
write_smt_txt("Awesome", "tests")
write_smt_txt(22, "tests", metric="xy", inline=True)
with open(self.outcome_path, "r") as outcome_file:
outcome = json.load(outcome_file)
self.assertEqual(outcome["text_outcome"],
"| Awesome xy: 22")
def test_appending_content_inline(self):
write_smt_txt("Awesome", "tests")
write_smt_txt("Great", "tests", inline=True)
with open(self.outcome_path, "r") as outcome_file:
outcome = json.load(outcome_file)
self.assertEqual(outcome["text_outcome"],
"| Awesome Great")
def score(self, trk_file=None, y=None, args=None):
if isinstance(trk_file, list):
trk_file = trk_file[0]
if trk_file is None:
TM_DATA = [
"/local/entrack/data/tractometer/125mm/FODl4.nii.gz",
"/local/entrack/data/tractometer/125mm/wm_aligned.nii.gz"
]
args.file_name = "tm_fibers.trk"
self.predict(TM_DATA, args)
trk_file = os.path.join(self.save_path, args.file_name)
TM_PATH = ("./.tractometer/ismrm_2015_tractography_challenge_scoring/"
"score_tractogram.py")
SCORING_DATA = ("./.tractometer/ismrm_2015_tractography_challenge_"
"scoring/scoring_data/")
scoring_cmd = "python {command} {tracts} {base} {out}".format(
command=TM_PATH,
tracts=trk_file,
base=SCORING_DATA,
out=self.save_path)
subprocess.run([
"bash", "-c",
"source activate entrack_tm && {}".format(scoring_cmd)
])
eval_path = os.path.join(self.save_path, "scores", "tm_fibers.json")
eval_data = json.load(open(eval_path))
for metric in [
"mean_OL", "mean_OR", "VC", "NC", "IC", "VB", "IB", "mean_F1"
]:
write_smt_txt(eval_data[metric],
self.save_path,
metric=metric,
inline=True)
if "score" in args:
return eval_data[args.score]
def save_scalars(self,
trk_file,
nii_file,
min_pts_per_fiber=2,
every_n_fibers=1,
file_name="scalars.trk"):
scalars, tracks, trk_hdr = self.fvm_scalars(trk_file, nii_file,
min_pts_per_fiber,
every_n_fibers)
for metric in scalars.keys():
for q in [25, 50, 75]:
write_smt_txt(flat_percentile(scalars[metric], q),
self.save_path,
metric=metric + "_" + str(q),
inline=q > 25)
save_fibers(tracks,
trk_hdr,
os.path.join(self.save_path, file_name),
scalars=scalars)
def test_new_outcome_content(self):
write_smt_txt("Awesome", "tests")
with open(self.outcome_path, "r") as outcome_file:
outcome = json.load(outcome_file)
self.assertEqual(outcome["text_outcome"], "| Awesome")
def test_new_outcome_is_dict(self):
write_smt_txt("Awesome", "tests")
with open(self.outcome_path, "r") as outcome_file:
outcome = json.load(outcome_file)
self.assertIsInstance(outcome, dict)
def test_create_new_outcome(self):
write_smt_txt("Awesome", "tests")
self.assertTrue(os.path.exists(self.outcome_path))