def test_find_peaks(self):
# amp as 1d array - single peak
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([[1, 1, 2, 1, 1]])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertNestedListEqual([[2]],
hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0])
# amp as 2d array - single peak
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([[1, 1, 2, 1, 1], [1, 2, 1, 1, 1], [1, 1, 2, 1, 1]])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertNestedListEqual([[2], [1], [2]],
hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0])
# amp as 1d array - multiple peak
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([[1, 2, 1, 2, 1]])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertNestedListEqual([[1, 3]],
hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0])
# amp as 2d array - multiple peak
frq = np.array([1, 2, 3, 4, 5, 6, 7])
amp = np.array([[1, 2, 1, 2, 1, 6, 1], [1, 3, 7, 6, 1, 1, 1],
[1, 1, 8, 7, 9, 1, 1]])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertNestedListEqual([[1, 3, 5], [2], [2, 4]],
hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0])
def test_find_peaks(self):
# amp as 1d array - single peak
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([0, 0, 1, 0, 0])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertListEqual(
[2], hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0].tolist())
# amp as 2d array - single peak
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([[0, 0, 1, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertListEqual(
[[2], [1], [2]], hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0])
# amp as 1d array - multiple peak
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([0, 1, 0, 1, 0])
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertListEqual(
[1, 3], hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0].tolist())
# amp as 2d array - multiple peak
frq = np.array([1, 2, 3, 4, 5, 6, 7])
amp = np.array([[0, 1, 0, 1, 0, 5, 0],
[0, 2, 6, 5, 0, 0, 0],
[0, 0, 7, 6, 8, 0, 0]])
myhvsr = hvsrpy.Hvsr(amp, frq)
for known, test in zip([[1, 3, 5], [2], [2, 4]],
hvsrpy.Hvsr.find_peaks(myhvsr.amp)[0]):
self.assertListEqual(known, test.tolist())
def test_mean_std_curve(self):
frq = np.array([0, 1])
amp = np.array([[1, 1], [3, 4], [5, 7]])
myhv = hvsrpy.Hvsr(amp, frq, find_peaks=False)
# Lognormal
mean_curve = myhv.mean_curve(distribution='lognormal')
std_curve = myhv.std_curve(distribution='lognormal')
for col in range(amp.shape[1]):
self.assertEqual(np.exp(np.mean(np.log(amp[:, col]))),
mean_curve[col])
self.assertEqual(np.std(np.log(amp[:, col]), ddof=1),
std_curve[col])
# Normal
mean_curve = myhv.mean_curve(distribution='normal')
std_curve = myhv.std_curve(distribution='normal')
for col in range(amp.shape[1]):
self.assertEqual(np.mean(amp[:, col]), mean_curve[col])
self.assertEqual(np.std(amp[:, col], ddof=1), std_curve[col])
# Single-Window
frq = [1, 2]
amp = [1, 2]
_hv = hvsrpy.Hvsr(amp, frq, find_peaks=False)
self.assertRaises(ValueError, _hv.std_curve)
self.assertListEqual(amp, _hv.mean_curve().tolist())
def test_init(self):
# amp as 1d array
frq = np.linspace(1, 10, 20)
amp = np.sin(2*np.pi*5*np.linspace(0, 10, 20))+10
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertArrayEqual(frq, myhvsr.frq)
self.assertArrayEqual(amp, myhvsr.amp)
# amp as 2d array
frq = np.linspace(1, 10, 20)
amp = (np.sin(2*np.pi*5*np.linspace(0, 10, 20))+10)*np.ones((20, 20))
myhvsr = hvsrpy.Hvsr(amp, frq)
self.assertArrayEqual(frq, myhvsr.frq)
self.assertArrayEqual(amp, myhvsr.amp)
# amp as string
frq = np.ndarray([1, 2, 3])
amp = "abc"
self.assertRaises(TypeError, hvsrpy.Hvsr, amp, frq)
# negative amplitude
frq = np.array([1, 2, 3])
amp = np.array([1, -1, 3])
self.assertRaises(ValueError, hvsrpy.Hvsr, amp, frq)
# nan value
frq = np.array([1, 2, 3])
amp = np.array([1, 2, np.nan])
self.assertRaises(ValueError, hvsrpy.Hvsr, amp, frq)
def setUpClass(cls):
cls.frq = np.array([1, 2, 3, 4, 5])
cls.hv1 = np.array([[1, 1, 2, 1, 1],
[1, 4, 1, 5, 1],
[1, 1, 3, 1, 1],
[1, 2, 4, 5, 1]])
hv1 = hvsrpy.Hvsr(cls.hv1, cls.frq)
cls.hv2 = np.array([[1, 1, 2, 1, 1],
[1, 1, 1, 3, 1],
[4, 5, 6, 7, 5]])
hv2 = hvsrpy.Hvsr(cls.hv2, cls.frq)
cls.hv3 = np.array([[1, 1, 2, 1, 1],
[1, 1, 2, 1, 1],
[1, 1, 2, 1, 1],
[1, 3, 1, 1, 1]])
hv3 = hvsrpy.Hvsr(cls.hv3, cls.frq)
cls.hv4 = np.array([[1, 1, 1, 2, 1],
[1, 2, 1, 3, 1],
[1, 2, 1, 1, 1]])
hv4 = hvsrpy.Hvsr(cls.hv4, cls.frq)
cls.azi = [0, 45, 90, 135]
cls.hvrot = hvsrpy.HvsrRotated.from_iter([hv1, hv2, hv3, hv4],
cls.azi)
cls.hvrot_for_rej = hvsrpy.HvsrRotated.from_iter([hv1, hv2, hv3, hv4],
cls.azi)
cls.full_path = get_full_path(__file__)
def test_mc_peak_frq(self):
frq = np.arange(0, 10, 1)
amp = np.ones((10, 10))
col = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 7])
amp[np.arange(10), col] = 2
myhv = hvsrpy.Hvsr(amp, frq)
self.assertEqual(1., myhv.mc_peak_frq())
def test_update_peaks(self):
frq = np.arange(1, 2, 0.1)
amp = np.ones((10, 10))
peak_ids = np.array([1, 2, 4, 6, 8, 1, 3, 5, 7, 6])
amp[np.arange(10), peak_ids] = 2
myhv = hvsrpy.Hvsr(amp, frq)
self.assertArrayEqual(myhv.peak_frq, frq[peak_ids])
def test_reject_windows(self):
# Reject single window, end due to zero std
frq = np.arange(0, 10, 1)
amp = np.ones((10, 10))
col = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 7])
amp[np.arange(10), col] = 2
myhv = hvsrpy.Hvsr(amp, frq)
myhv.reject_windows(n=2)
self.assertEqual(myhv.mean_f0_frq(), 1.0)
# Reject single window, end due to convergence criteria
frq = np.arange(0, 10, 1, dtype=np.double)
amp = np.ones((10, 10))
col = np.array([1, 2, 2, 2, 2, 1, 2, 2, 1, 9])
amp[np.arange(10), col] = 2
myhv = hvsrpy.Hvsr(amp, frq, find_peaks=False, meta={})
myhv.reject_windows(n=2)
self.assertArrayEqual(myhv.peak_frq, frq[col[:-1]])
def test_init(self):
# Simple case
frq = np.array([1, 2, 3])
hv = hvsrpy.Hvsr(np.array([1, 2, 1]), frq)
az = 20
hvrot = hvsrpy.HvsrRotated(hv, az)
self.assertEqual(hv, hvrot.hvsrs[0])
self.assertEqual(az, hvrot.azimuths[0])
self.assertEqual(1, hvrot.azimuth_count)
# Bad hv
hv = np.array([1, 2, 1])
self.assertRaises(TypeError, hvsrpy.HvsrRotated, hv, az)
# Bad azimuths
hv = hvsrpy.Hvsr(np.array([1, 2, 1]), frq)
bad_azs = [-5, 181, 190]
for az in bad_azs:
self.assertRaises(ValueError, hvsrpy.HvsrRotated, hv, az)
def test_mean_std_f0_frq(self):
frq = np.arange(0, 10, 1)
amp = np.zeros((10, 10))
col = np.array([1, 2, 4, 6, 8, 1, 3, 5, 7, 6])
amp[np.arange(10), col] = 1
myhv = hvsrpy.Hvsr(amp, frq)
self.assertEqual(myhv.mean_f0_frq(distribution='log-normal'),
np.exp(np.mean(np.log(col))))
self.assertEqual(myhv.mean_f0_frq(distribution='normal'), np.mean(col))
self.assertEqual(myhv.std_f0_frq(distribution='log-normal'),
np.std(np.log(col), ddof=1))
self.assertEqual(myhv.std_f0_frq(distribution='normal'),
np.std(col, ddof=1))
def test_mc_peak(self):
frq = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
amp = np.array([[1, 1, 2, 3, 2, 1, 1, 1, 1],
[1, 1.5, 3, 5, 3, 1.5, 1, 1, 1],
[1, 1, 5, 1, 1, 1, 1, 1, 1]])
hv = hvsrpy.Hvsr(amp, frq)
distribution = "normal"
expected = np.mean(amp, axis=0)
returned = hv.mean_curve(distribution=distribution)
self.assertArrayEqual(expected, returned)
self.assertEqual(10 / 3, hv.mc_peak_amp(distribution=distribution))
self.assertEqual(3, hv.mc_peak_frq(distribution=distribution))
def test_peaks_with_limits(self):
# amp as 1d array - two peak
frq = np.array([1, 2, 3, 4, 5, 6, 7, 8])
amp = np.array([[1, 1, 2, 1, 1, 5, 1, 1]])
hvsr = hvsrpy.Hvsr(amp, frq, f_low=None, f_high=4, find_peaks=True)
self.assertArrayEqual(np.array([3.0]), hvsr.peak_frq)
# amp as 1d array - two peak
frq = np.array([1, 2, 3, 4, 5, 6, 7, 8])
amp = np.array([[1, 1, 2, 1, 1, 5, 1, 1]])
hvsr = hvsrpy.Hvsr(amp, frq, f_low=4, f_high=None, find_peaks=True)
self.assertArrayEqual(np.array([6.0]), hvsr.peak_frq)
# amp as 1d array - two peak
frq = np.array([1, 2, 3, 4, 5, 6, 7, 8])
amp = np.array([[1, 1, 2, 1, 1, 5, 1, 1]])
hvsr = hvsrpy.Hvsr(amp, frq, f_low=1, f_high=8, find_peaks=True)
self.assertArrayEqual(np.array([6.0]), hvsr.peak_frq)
# amp as 1d array - two peak
frq = np.array([1, 2, 3, 4, 5, 6, 7, 8])
amp = np.array([[1, 1, 2, 1, 1, 5, 1, 1]])
hvsr = hvsrpy.Hvsr(amp, frq, f_low=None, f_high=None, find_peaks=True)
self.assertArrayEqual(np.array([6.0]), hvsr.peak_frq)
def test_nstd_f0(self):
frq = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
amp = np.array([[1, 1, 2, 3, 5, 1, 1, 1, 1],
[1, 2, 3, 7, 3, 1, 1, 1, 1],
[1, 1, 12, 1, 1, 1, 1, 1, 1]])
hv = hvsrpy.Hvsr(amp, frq)
distribution = "normal"
n = 1.5
# nstd_f0_frq
f0s = [3, 4, 5]
expected = np.mean(f0s) + n * np.std(f0s, ddof=1)
returned = hv.nstd_f0_frq(n=n, distribution=distribution)
self.assertEqual(expected, returned)
# nstd_f0_amp
amps = [5, 7, 12]
expected = np.mean(amps) + n * np.std(amps, ddof=1)
returned = hv.nstd_f0_amp(n=n, distribution=distribution)
self.assertEqual(expected, returned)
def test_nstd_curve(self):
frq = np.array([1, 2, 3, 4, 5])
amp = np.array([[1, 6, 1, 1, 1], [6, 1, 1, 1, 1], [1, 1, 6, 1, 1],
[1, 1, 1, 1, 6], [1, 1, 1, 6, 1]])
hv = hvsrpy.Hvsr(amp, frq)
# Curve - normal
distribution = "normal"
mean_curve = hv.mean_curve(distribution=distribution)
std_curve = hv.std_curve(distribution=distribution)
expected = mean_curve + 2 * std_curve
returned = hv.nstd_curve(2, distribution=distribution)
self.assertArrayEqual(expected, returned)
# Curve - lognormal
distribution = "lognormal"
mean_curve = hv.mean_curve(distribution=distribution)
std_curve = hv.std_curve(distribution=distribution)
expected = np.exp(np.log(mean_curve) + 2 * std_curve)
returned = hv.nstd_curve(2, distribution=distribution)
self.assertArrayEqual(expected, returned)
def test_mean_std_f0_frq(self):
frq = np.arange(0, 10, 1)
amp = np.ones((10, 10))
col = np.array([1, 2, 4, 6, 8, 1, 3, 5, 7, 6])
amp[np.arange(10), col] = 2
myhv = hvsrpy.Hvsr(amp, frq)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for distribution in ["log-normal", "lognormal"]:
self.assertEqual(myhv.mean_f0_frq(distribution=distribution),
np.exp(np.mean(np.log(col))))
self.assertEqual(myhv.mean_f0_frq(distribution='normal'), np.mean(col))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for distribution in ["log-normal", "lognormal"]:
self.assertEqual(myhv.std_f0_frq(distribution=distribution),
np.std(np.log(col), ddof=1))
self.assertEqual(myhv.std_f0_frq(distribution='normal'),
np.std(col, ddof=1))
def setUpClass(cls):
frq = [1, 2, 3, 4]
amp = [[1, 2, 1, 1], [1, 2, 4, 1], [1, 1, 5, 1]]
cls.hv = hvsrpy.Hvsr(amp, frq)
cls.full_path = get_full_path(__file__)
