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)
def test_sesame_by_condition(self):
# Defaults
frequency = np.array([0.7, 0.8, 0.9, 1, 1.1])
mean_curve = np.array([1, 1, 2.1, 1, 1])
std_curve = np.array([0.2, 0.2, 0.2, 0.2, 0.2])
f0_std = 0.1
# Condition 1: there exists an f in [f0/4, f0] where A(f) < A(f0)/2
frequency = np.array([0.2, 0.4, 0.7, 1, 1.5])
potential_curves = [(1, np.array([1.0, 1.0, 1.0, 3.0, 1.0])),
(0, np.array([2.9, 2.9, 2.9, 3.0, 1.0])),
(0, np.array([1.6, 2.0, 2.9, 3.0, 1.0])),
(1, np.array([1.4, 1.4, 2.0, 3.0, 1.0])),
(0, np.array([1.5, 1.5, 2.9, 3.0, 1.0]))]
for expected, mean_curve in potential_curves:
returned = utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
verbose=0)[0]
self.assertEqual(expected, returned)
# Condition 2: there exists an f in [f0, 4f0] where A(f) < A(f0)/2
frequency = np.array([0.7, 1, 2.5, 3.5, 5])
potential_curves = [(1, np.array([1.0, 3.0, 1.0, 1.0, 1.0])),
(0, np.array([1.0, 3.0, 2.9, 2.9, 2.9])),
(0, np.array([1.0, 3.0, 2.9, 2.0, 1.6])),
(1, np.array([1.0, 3.0, 2.0, 1.4, 1.4])),
(0, np.array([1.0, 3.0, 2.9, 1.5, 1.5]))]
for expected, mean_curve in potential_curves:
returned = utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
verbose=0)[1]
self.assertEqual(expected, returned)
# Condition 3: A0>2
potential_curves = [(0, np.array([1.0, 1.0, 1.1, 1.0, 1.0])),
(0, np.array([1.0, 2.0, 1.0, 1.0, 1.0])),
(0, np.array([1.0, 1.0, 1.0, 1.2, 1.0])),
(1, np.array([1.0, 3.0, 2.0, 1.4, 1.4])),
(1, np.array([1.0, 5.0, 2.9, 1.5, 1.5])),
(1, np.array([1.0, 3.0, 2.9, 7.5, 1.5]))]
for expected, mean_curve in potential_curves:
returned = utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
verbose=0)[2]
self.assertEqual(expected, returned)
# Condition 4: fpeak[Ahv(f) +/- sigma_a(f)] = f0 +/- 5%
frequency = np.array([0.90, 0.94, 0.96, 1, 1.04, 1.06, 1.1])
mean_curves = [
np.array([1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0]),
np.array([1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0]),
np.array([1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0]),
np.array([1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0]),
np.array([1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0]),
np.array([1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0]),
np.array([1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0])
]
uppers = [
np.array([1.0, 1.1, 1.0, 1.0, 1.0, 1.0, 1.0]) + 0.1,
np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.1, 1.0]) + 0.1,
np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.1, 1.0]) + 0.1,
np.array([1.0, 1.1, 1.0, 1.0, 1.0, 1.0, 1.0]) + 0.1,
np.array([1.0, 1.0, 1.0, 2.1, 1.0, 1.0, 1.0]) + 0.1,
np.array([1.0, 1.0, 1.1, 1.0, 1.0, 1.0, 1.0]) + 0.1,
np.array([1.0, 1.0, 1.0, 1.0, 1.1, 1.0, 1.0]) + 0.1
]
rating = [0, 0, 0, 0, 1, 1, 1]
for expected, upper, mean_curve in zip(rating, uppers, mean_curves):
std_curve = np.log(upper) - np.log(mean_curve)
curve = np.exp(np.log(mean_curve) + std_curve)
self.assertArrayEqual(upper, curve)
returned = utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
verbose=0)[3]
self.assertEqual(expected, returned)
# Condition 5: f0_std < epsilon(f0)
frequency = np.array([0.05, 0.1, 0.35, 0.75, 1.5, 3, 5])
factors = [0.25, 0.2, 0.15, 0.1, 0.05]
for peak_frequency, factor in zip(frequency[1:-1], factors):
mean_curve = np.ones_like(frequency)
mean_curve[peak_frequency == frequency] = 2
std_curve = 0.1 * mean_curve
for adjust, expected in zip([0.9, 1.1], [1, 0]):
f0_std = factor * peak_frequency * adjust
returned = utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
verbose=0)[4]
self.assertEqual(expected, returned)
# Condition 6: sigma_a(f0) < theta(f0)
frequency = np.array([0.05, 0.1, 0.35, 0.75, 1.5, 3, 5])
factors = [3.0, 2.5, 2.0, 1.78, 1.58]
for peak_frequency, factor in zip(frequency[1:-1], factors):
mean_curve = np.ones_like(frequency)
mean_curve[peak_frequency == frequency] = 2
for adjust, expected in zip([0.9, 1.1], [1, 0]):
sigma_a = np.ones_like(frequency) * adjust * factor
upper = mean_curve * sigma_a
std_curve = np.log(upper) - np.log(mean_curve)
curve = np.exp(np.log(mean_curve) + std_curve)
self.assertArrayAlmostEqual(upper, curve, places=6)
f0_std = factor * peak_frequency * adjust
returned = utils.sesame_clarity(frequency,
mean_curve,
std_curve,
f0_std,
verbose=0)[5]
self.assertEqual(expected, returned)