def test_smoothingMatrix(self):
"""
Tests some aspects of the matrix.
"""
# Disable div by zero errors.
temp = np.geterr()
np.seterr(all="ignore")
frequencies = np.array([0.0, 1.0, 2.0, 10.0, 25.0, 50.0, 100.0], dtype=np.float32)
matrix = calculate_smoothing_matrix(frequencies, 20.0)
self.assertEqual(matrix.dtype, np.float32)
for _i, freq in enumerate(frequencies):
np.testing.assert_array_equal(matrix[_i], konno_ohmachi_smoothing_window(frequencies, freq, 20.0))
# Should not be normalized. Test only for larger frequencies
# because smaller ones have a smaller window.
if freq >= 10.0:
self.assertGreater(matrix[_i].sum(), 1.0)
# Input should be output dtype.
frequencies = np.array([0.0, 1.0, 2.0, 10.0, 25.0, 50.0, 100.0], dtype=np.float64)
matrix = calculate_smoothing_matrix(frequencies, 20.0)
self.assertEqual(matrix.dtype, np.float64)
# Check normalization.
frequencies = np.array([0.0, 1.0, 2.0, 10.0, 25.0, 50.0, 100.0], dtype=np.float32)
matrix = calculate_smoothing_matrix(frequencies, 20.0, normalize=True)
self.assertEqual(matrix.dtype, np.float32)
for _i, freq in enumerate(frequencies):
np.testing.assert_array_equal(
matrix[_i], konno_ohmachi_smoothing_window(frequencies, freq, 20.0, normalize=True)
)
# Should not be normalized. Test only for larger frequencies
# because smaller ones have a smaller window.
self.assertAlmostEqual(matrix[_i].sum(), 1.0, 5)
np.seterr(**temp)
def test_smoothingWindow(self):
"""
Tests the creation of the smoothing window.
"""
# Disable div by zero errors.
temp = np.geterr()
np.seterr(all="ignore")
# Frequency of zero results in a delta peak at zero (there usually
# should be just one zero in the frequency array.
window = konno_ohmachi_smoothing_window(np.array([0, 1, 0, 3], dtype=np.float32), 0)
np.testing.assert_array_equal(window, np.array([1, 0, 1, 0], dtype=np.float32))
# Wrong dtypes raises.
self.assertRaises(ValueError, konno_ohmachi_smoothing_window, np.arange(10, dtype=np.int32), 10)
# If frequency=center frequency, log results in infinity. Limit of
# whole formulae is 1.
window = konno_ohmachi_smoothing_window(np.array([5.0, 1.0, 5.0, 2.0], dtype=np.float32), 5)
np.testing.assert_array_equal(window[[0, 2]], np.array([1.0, 1.0], dtype=np.float32))
# Output dtype should be the dtype of frequencies.
self.assertEqual(konno_ohmachi_smoothing_window(np.array([1, 6, 12], dtype=np.float32), 5).dtype, np.float32)
self.assertEqual(konno_ohmachi_smoothing_window(np.array([1, 6, 12], dtype=np.float64), 5).dtype, np.float64)
# Check if normalizing works.
window = konno_ohmachi_smoothing_window(self.frequencies, 20)
self.assertGreater(window.sum(), 1.0)
window = konno_ohmachi_smoothing_window(self.frequencies, 20, normalize=True)
self.assertAlmostEqual(window.sum(), 1.0, 5)
# Just one more to test if there are no invalid values and the range if
# ok.
window = konno_ohmachi_smoothing_window(self.frequencies, 20)
self.assertEqual(np.any(np.isnan(window)), False)
self.assertEqual(np.any(np.isinf(window)), False)
self.assertTrue(np.all(window <= 1.0))
self.assertTrue(np.all(window >= 0.0))
np.seterr(**temp)
def test_smoothing_matrix(self):
"""
Tests some aspects of the matrix.
"""
# Disable div by zero errors.
with np.errstate(all='ignore'):
frequencies = np.array([0.0, 1.0, 2.0, 10.0, 25.0, 50.0, 100.0],
dtype=np.float32)
matrix = calculate_smoothing_matrix(frequencies, 20.0)
self.assertEqual(matrix.dtype, np.float32)
for _i, freq in enumerate(frequencies):
np.testing.assert_array_equal(
matrix[_i],
konno_ohmachi_smoothing_window(frequencies, freq, 20.0))
# Should not be normalized. Test only for larger frequencies
# because smaller ones have a smaller window.
if freq >= 10.0:
self.assertGreater(matrix[_i].sum(), 1.0)
# Input should be output dtype.
frequencies = np.array([0.0, 1.0, 2.0, 10.0, 25.0, 50.0, 100.0],
dtype=np.float64)
matrix = calculate_smoothing_matrix(frequencies, 20.0)
self.assertEqual(matrix.dtype, np.float64)
# Check normalization.
frequencies = np.array([0.0, 1.0, 2.0, 10.0, 25.0, 50.0, 100.0],
dtype=np.float32)
matrix = calculate_smoothing_matrix(frequencies,
20.0,
normalize=True)
self.assertEqual(matrix.dtype, np.float32)
for _i, freq in enumerate(frequencies):
np.testing.assert_array_equal(
matrix[_i],
konno_ohmachi_smoothing_window(frequencies,
freq,
20.0,
normalize=True))
# Should not be normalized. Test only for larger frequencies
# because smaller ones have a smaller window.
self.assertAlmostEqual(matrix[_i].sum(), 1.0, 5)
def test_smoothing_window(self):
"""
Tests the creation of the smoothing window.
"""
# Disable div by zero errors.
with np.errstate(all='ignore'):
# Frequency of zero results in a delta peak at zero (there usually
# should be just one zero in the frequency array.
window = konno_ohmachi_smoothing_window(
np.array([0, 1, 0, 3], dtype=np.float32), 0)
np.testing.assert_array_equal(
window, np.array([1, 0, 1, 0], dtype=np.float32))
# Wrong dtypes raises.
self.assertRaises(ValueError, konno_ohmachi_smoothing_window,
np.arange(10, dtype=np.int32), 10)
# If frequency=center frequency, log results in infinity. Limit of
# whole formulae is 1.
window = konno_ohmachi_smoothing_window(
np.array([5.0, 1.0, 5.0, 2.0], dtype=np.float32), 5)
np.testing.assert_array_equal(
window[[0, 2]], np.array([1.0, 1.0], dtype=np.float32))
# Output dtype should be the dtype of frequencies.
self.assertEqual(
konno_ohmachi_smoothing_window(
np.array([1, 6, 12], dtype=np.float32), 5).dtype,
np.float32)
self.assertEqual(
konno_ohmachi_smoothing_window(
np.array([1, 6, 12], dtype=np.float64), 5).dtype,
np.float64)
# Check if normalizing works.
window = konno_ohmachi_smoothing_window(self.frequencies, 20)
self.assertGreater(window.sum(), 1.0)
window = konno_ohmachi_smoothing_window(self.frequencies,
20,
normalize=True)
self.assertAlmostEqual(window.sum(), 1.0, 5)
# Just one more to test if there are no invalid values and the
# range if ok.
window = konno_ohmachi_smoothing_window(self.frequencies, 20)
self.assertEqual(np.any(np.isnan(window)), False)
self.assertEqual(np.any(np.isinf(window)), False)
self.assertTrue(np.all(window <= 1.0))
self.assertTrue(np.all(window >= 0.0))
