def load(fname, verbose=0):
data = utils.parse_hvsrpy_output(fname)
std_curve = np.log(data["upper"]) - np.log(data["curve"])
clarity = utils.sesame_clarity(data["frequency"],
data["curve"],
std_curve,
data["std_f0"],
verbose=verbose)
reliability = utils.sesame_reliability(data["windowlength"],
data["accepted_windows"],
data["frequency"],
data["curve"],
std_curve,
verbose=verbose)
return (reliability, clarity)
def test_sesame_with_limits(self):
frequency = np.array([0.3, 0.4, 0.5, 0.6, 0.9, 1, 1.1, 1.2, 1.3])
mean_curve = np.array([1, 3, 1, 1, 1, 1, 1, 3, 1])
std_curve = np.ones(9) * 0.2
f0_std = 0.07
# reliability
expecteds = [3, 3]
settings = [(0.3, 0.5), (0.9, 1.3)]
for expected, limits in zip(expecteds, settings):
with open(os.devnull, "w") as sys.stdout:
returned = np.sum(
utils.sesame_reliability(60,
10,
frequency,
mean_curve,
std_curve,
search_limits=limits,
verbose=2))
sys.stdout = sys.__stdout__
self.assertEqual(expected, returned)
# clarity
expecteds = [6, 6]
settings = [(0.3, 0.5), (0.9, 1.3)]
for expected, limits in zip(expecteds, settings):
with open(os.devnull, "w") as sys.stdout:
returned = np.sum(
utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
search_limits=limits,
verbose=2))
sys.stdout = sys.__stdout__
self.assertEqual(expected, returned)