1034 lines
38 KiB
Python
1034 lines
38 KiB
Python
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#!/usr/bin/env python
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from __future__ import division, print_function, absolute_import
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import warnings
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from itertools import combinations
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import numpy as np
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import pytest
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from numpy.testing import (assert_almost_equal, assert_allclose, assert_,
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assert_equal, assert_raises, assert_raises_regex,
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assert_array_equal, assert_warns)
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import pywt
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# Check that float32, float64, complex64, complex128 are preserved.
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# Other real types get converted to float64.
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# complex256 gets converted to complex128
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dtypes_in = [np.int8, np.float16, np.float32, np.float64, np.complex64,
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np.complex128]
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dtypes_out = [np.float64, np.float32, np.float32, np.float64, np.complex64,
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np.complex128]
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# tolerances used in accuracy comparisons
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tol_single = 1e-6
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tol_double = 1e-13
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dtypes_and_tolerances = [(np.float16, tol_single), (np.float32, tol_single),
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(np.float64, tol_double), (np.int8, tol_double),
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(np.complex64, tol_single),
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(np.complex128, tol_double)]
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# test complex256 as well if it is available
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try:
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dtypes_in += [np.complex256, ]
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dtypes_out += [np.complex128, ]
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dtypes_and_tolerances += [(np.complex256, tol_double), ]
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except AttributeError:
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pass
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# determine which wavelets to test
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wavelist = pywt.wavelist()
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if 'dmey' in wavelist:
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# accuracy is very low for dmey, so omit it
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wavelist.remove('dmey')
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# removing wavelets with dwt_possible == False
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del_list = []
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for wavelet in wavelist:
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with warnings.catch_warnings():
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warnings.simplefilter('ignore', FutureWarning)
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if not isinstance(pywt.DiscreteContinuousWavelet(wavelet),
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pywt.Wavelet):
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del_list.append(wavelet)
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for del_ind in del_list:
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wavelist.remove(del_ind)
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####
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# 1d multilevel dwt tests
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####
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def test_wavedec():
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x = [3, 7, 1, 1, -2, 5, 4, 6]
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db1 = pywt.Wavelet('db1')
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cA3, cD3, cD2, cD1 = pywt.wavedec(x, db1)
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assert_almost_equal(cA3, [8.83883476])
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assert_almost_equal(cD3, [-0.35355339])
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assert_allclose(cD2, [4., -3.5])
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assert_allclose(cD1, [-2.82842712, 0, -4.94974747, -1.41421356])
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assert_(pywt.dwt_max_level(len(x), db1) == 3)
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def test_waverec_invalid_inputs():
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# input must be list or tuple
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assert_raises(ValueError, pywt.waverec, np.ones(8), 'haar')
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# input list cannot be empty
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assert_raises(ValueError, pywt.waverec, [], 'haar')
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# 'array_to_coeffs must specify 'output_format' to perform waverec
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x = [3, 7, 1, 1, -2, 5, 4, 6]
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coeffs = pywt.wavedec(x, 'db1')
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arr, coeff_slices = pywt.coeffs_to_array(coeffs)
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coeffs_from_arr = pywt.array_to_coeffs(arr, coeff_slices)
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message = "Unexpected detail coefficient type"
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assert_raises_regex(ValueError, message, pywt.waverec, coeffs_from_arr,
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'haar')
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def test_waverec_accuracies():
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rstate = np.random.RandomState(1234)
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x0 = rstate.randn(8)
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for dt, tol in dtypes_and_tolerances:
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x = x0.astype(dt)
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if np.iscomplexobj(x):
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x += 1j*rstate.randn(8).astype(x.real.dtype)
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coeffs = pywt.wavedec(x, 'db1')
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assert_allclose(pywt.waverec(coeffs, 'db1'), x, atol=tol, rtol=tol)
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def test_waverec_none():
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x = [3, 7, 1, 1, -2, 5, 4, 6]
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coeffs = pywt.wavedec(x, 'db1')
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# set some coefficients to None
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coeffs[2] = None
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coeffs[0] = None
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assert_(pywt.waverec(coeffs, 'db1').size, len(x))
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def test_waverec_odd_length():
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x = [3, 7, 1, 1, -2, 5]
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coeffs = pywt.wavedec(x, 'db1')
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assert_allclose(pywt.waverec(coeffs, 'db1'), x, rtol=1e-12)
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def test_waverec_complex():
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x = np.array([3, 7, 1, 1, -2, 5, 4, 6])
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x = x + 1j
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coeffs = pywt.wavedec(x, 'db1')
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assert_allclose(pywt.waverec(coeffs, 'db1'), x, rtol=1e-12)
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def test_multilevel_dtypes_1d():
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# only checks that the result is of the expected type
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wavelet = pywt.Wavelet('haar')
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for dt_in, dt_out in zip(dtypes_in, dtypes_out):
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# wavedec, waverec
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x = np.ones(8, dtype=dt_in)
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errmsg = "wrong dtype returned for {0} input".format(dt_in)
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coeffs = pywt.wavedec(x, wavelet, level=2)
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for c in coeffs:
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assert_(c.dtype == dt_out, "wavedec: " + errmsg)
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x_roundtrip = pywt.waverec(coeffs, wavelet)
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assert_(x_roundtrip.dtype == dt_out, "waverec: " + errmsg)
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def test_waverec_all_wavelets_modes():
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# test 2D case using all wavelets and modes
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rstate = np.random.RandomState(1234)
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r = rstate.randn(80)
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for wavelet in wavelist:
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for mode in pywt.Modes.modes:
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coeffs = pywt.wavedec(r, wavelet, mode=mode)
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assert_allclose(pywt.waverec(coeffs, wavelet, mode=mode),
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r, rtol=tol_single, atol=tol_single)
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####
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# 2d multilevel dwt function tests
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####
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def test_waverec2_accuracies():
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rstate = np.random.RandomState(1234)
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x0 = rstate.randn(4, 4)
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for dt, tol in dtypes_and_tolerances:
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x = x0.astype(dt)
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if np.iscomplexobj(x):
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x += 1j*rstate.randn(4, 4).astype(x.real.dtype)
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coeffs = pywt.wavedec2(x, 'db1')
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assert_(len(coeffs) == 3)
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assert_allclose(pywt.waverec2(coeffs, 'db1'), x, atol=tol, rtol=tol)
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def test_multilevel_dtypes_2d():
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wavelet = pywt.Wavelet('haar')
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for dt_in, dt_out in zip(dtypes_in, dtypes_out):
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# wavedec2, waverec2
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x = np.ones((8, 8), dtype=dt_in)
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errmsg = "wrong dtype returned for {0} input".format(dt_in)
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cA, coeffsD2, coeffsD1 = pywt.wavedec2(x, wavelet, level=2)
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assert_(cA.dtype == dt_out, "wavedec2: " + errmsg)
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for c in coeffsD1:
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assert_(c.dtype == dt_out, "wavedec2: " + errmsg)
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for c in coeffsD2:
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assert_(c.dtype == dt_out, "wavedec2: " + errmsg)
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x_roundtrip = pywt.waverec2([cA, coeffsD2, coeffsD1], wavelet)
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assert_(x_roundtrip.dtype == dt_out, "waverec2: " + errmsg)
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@pytest.mark.slow
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def test_waverec2_all_wavelets_modes():
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# test 2D case using all wavelets and modes
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rstate = np.random.RandomState(1234)
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r = rstate.randn(80, 96)
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for wavelet in wavelist:
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for mode in pywt.Modes.modes:
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coeffs = pywt.wavedec2(r, wavelet, mode=mode)
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assert_allclose(pywt.waverec2(coeffs, wavelet, mode=mode),
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r, rtol=tol_single, atol=tol_single)
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def test_wavedec2_complex():
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data = np.ones((4, 4)) + 1j
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coeffs = pywt.wavedec2(data, 'db1')
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assert_(len(coeffs) == 3)
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assert_allclose(pywt.waverec2(coeffs, 'db1'), data, rtol=1e-12)
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def test_wavedec2_invalid_inputs():
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# input array has too few dimensions
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data = np.ones(4)
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assert_raises(ValueError, pywt.wavedec2, data, 'haar')
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def test_waverec2_invalid_inputs():
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# input must be list or tuple
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assert_raises(ValueError, pywt.waverec2, np.ones((8, 8)), 'haar')
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# input list cannot be empty
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assert_raises(ValueError, pywt.waverec2, [], 'haar')
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# coefficients from a difference decomposition used as input
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for dec_func in [pywt.wavedec, pywt.wavedecn]:
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coeffs = dec_func(np.ones((8, 8)), 'haar')
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message = "Unexpected detail coefficient type"
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assert_raises_regex(ValueError, message, pywt.waverec2, coeffs,
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'haar')
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def test_waverec2_coeff_shape_mismatch():
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x = np.ones((8, 8))
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coeffs = pywt.wavedec2(x, 'db1')
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# introduce a shape mismatch in the coefficients
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coeffs = list(coeffs)
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coeffs[1] = list(coeffs[1])
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coeffs[1][1] = np.zeros((16, 1))
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assert_raises(ValueError, pywt.waverec2, coeffs, 'db1')
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def test_waverec2_odd_length():
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x = np.ones((10, 6))
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coeffs = pywt.wavedec2(x, 'db1')
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assert_allclose(pywt.waverec2(coeffs, 'db1'), x, rtol=1e-12)
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def test_waverec2_none_coeffs():
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x = np.arange(24).reshape(6, 4)
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coeffs = pywt.wavedec2(x, 'db1')
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coeffs[1] = (None, None, None)
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assert_(x.shape == pywt.waverec2(coeffs, 'db1').shape)
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####
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# nd multilevel dwt function tests
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####
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def test_waverecn():
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rstate = np.random.RandomState(1234)
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# test 1D through 4D cases
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for nd in range(1, 5):
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x = rstate.randn(*(4, )*nd)
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coeffs = pywt.wavedecn(x, 'db1')
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assert_(len(coeffs) == 3)
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assert_allclose(pywt.waverecn(coeffs, 'db1'), x, rtol=tol_double)
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def test_waverecn_empty_coeff():
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coeffs = [np.ones((2, 2, 2)), {}, {}]
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assert_equal(pywt.waverecn(coeffs, 'db1').shape, (8, 8, 8))
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assert_equal(pywt.waverecn(coeffs, 'db1').shape, (8, 8, 8))
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coeffs = [np.ones((2, 2, 2)), {}, {'daa': np.ones((4, 4, 4))}]
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coeffs = [np.ones((2, 2, 2)), {}, {}, {'daa': np.ones((8, 8, 8))}]
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assert_equal(pywt.waverecn(coeffs, 'db1').shape, (16, 16, 16))
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def test_waverecn_invalid_coeffs():
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# approximation coeffs as None and no valid detail oeffs
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coeffs = [None, {}]
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assert_raises(ValueError, pywt.waverecn, coeffs, 'db1')
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# use of None for a coefficient value
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coeffs = [np.ones((2, 2, 2)), {}, {'daa': None}, ]
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assert_raises(ValueError, pywt.waverecn, coeffs, 'db1')
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# invalid key names in coefficient list
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coeffs = [np.ones((4, 4, 4)), {'daa': np.ones((4, 4, 4)),
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'foo': np.ones((4, 4, 4))}]
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assert_raises(ValueError, pywt.waverecn, coeffs, 'db1')
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# mismatched key name lengths
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coeffs = [np.ones((4, 4, 4)), {'daa': np.ones((4, 4, 4)),
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'da': np.ones((4, 4, 4))}]
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assert_raises(ValueError, pywt.waverecn, coeffs, 'db1')
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# key name lengths don't match the array dimensions
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coeffs = [[[[1.0]]], {'ad': [[[0.0]]], 'da': [[[0.0]]], 'dd': [[[0.0]]]}]
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assert_raises(ValueError, pywt.waverecn, coeffs, 'db1')
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# input list cannot be empty
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assert_raises(ValueError, pywt.waverecn, [], 'haar')
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def test_waverecn_invalid_inputs():
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# coefficients from a difference decomposition used as input
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for dec_func in [pywt.wavedec, pywt.wavedec2]:
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coeffs = dec_func(np.ones((8, 8)), 'haar')
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message = "Unexpected detail coefficient type"
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assert_raises_regex(ValueError, message, pywt.waverecn, coeffs,
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'haar')
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def test_waverecn_lists():
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# support coefficient arrays specified as lists instead of arrays
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coeffs = [[[1.0]], {'ad': [[0.0]], 'da': [[0.0]], 'dd': [[0.0]]}]
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assert_equal(pywt.waverecn(coeffs, 'db1').shape, (2, 2))
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def test_waverecn_invalid_coeffs2():
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# shape mismatch should raise an error
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coeffs = [np.ones((4, 4, 4)), {'ada': np.ones((4, 4))}]
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assert_raises(ValueError, pywt.waverecn, coeffs, 'db1')
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def test_wavedecn_invalid_inputs():
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# input array has too few dimensions
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data = np.array(0)
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assert_raises(ValueError, pywt.wavedecn, data, 'haar')
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# invalid number of levels
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data = np.ones(16)
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assert_raises(ValueError, pywt.wavedecn, data, 'haar', level=-1)
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def test_wavedecn_many_levels():
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# perfect reconstruction even when level > pywt.dwt_max_level
|
||
|
|
data = np.arange(64).reshape(8, 8)
|
||
|
|
tol = 1e-12
|
||
|
|
dec_funcs = [pywt.wavedec, pywt.wavedec2, pywt.wavedecn]
|
||
|
|
rec_funcs = [pywt.waverec, pywt.waverec2, pywt.waverecn]
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter('ignore', UserWarning)
|
||
|
|
for dec_func, rec_func in zip(dec_funcs, rec_funcs):
|
||
|
|
for mode in ['periodization', 'symmetric']:
|
||
|
|
coeffs = dec_func(data, 'haar', mode=mode, level=20)
|
||
|
|
r = rec_func(coeffs, 'haar', mode=mode)
|
||
|
|
assert_allclose(data, r, atol=tol, rtol=tol)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_accuracies():
|
||
|
|
# testing 3D only here
|
||
|
|
rstate = np.random.RandomState(1234)
|
||
|
|
x0 = rstate.randn(4, 4, 4)
|
||
|
|
for dt, tol in dtypes_and_tolerances:
|
||
|
|
x = x0.astype(dt)
|
||
|
|
if np.iscomplexobj(x):
|
||
|
|
x += 1j*rstate.randn(4, 4, 4).astype(x.real.dtype)
|
||
|
|
coeffs = pywt.wavedecn(x.astype(dt), 'db1')
|
||
|
|
assert_allclose(pywt.waverecn(coeffs, 'db1'), x, atol=tol, rtol=tol)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multilevel_dtypes_nd():
|
||
|
|
wavelet = pywt.Wavelet('haar')
|
||
|
|
for dt_in, dt_out in zip(dtypes_in, dtypes_out):
|
||
|
|
# wavedecn, waverecn
|
||
|
|
x = np.ones((8, 8), dtype=dt_in)
|
||
|
|
errmsg = "wrong dtype returned for {0} input".format(dt_in)
|
||
|
|
cA, coeffsD2, coeffsD1 = pywt.wavedecn(x, wavelet, level=2)
|
||
|
|
assert_(cA.dtype == dt_out, "wavedecn: " + errmsg)
|
||
|
|
for key, c in coeffsD1.items():
|
||
|
|
assert_(c.dtype == dt_out, "wavedecn: " + errmsg)
|
||
|
|
for key, c in coeffsD2.items():
|
||
|
|
assert_(c.dtype == dt_out, "wavedecn: " + errmsg)
|
||
|
|
x_roundtrip = pywt.waverecn([cA, coeffsD2, coeffsD1], wavelet)
|
||
|
|
assert_(x_roundtrip.dtype == dt_out, "waverecn: " + errmsg)
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_complex():
|
||
|
|
data = np.ones((4, 4, 4)) + 1j
|
||
|
|
coeffs = pywt.wavedecn(data, 'db1')
|
||
|
|
assert_allclose(pywt.waverecn(coeffs, 'db1'), data, rtol=1e-12)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_dtypes():
|
||
|
|
x = np.ones((4, 4, 4))
|
||
|
|
for dt, tol in dtypes_and_tolerances:
|
||
|
|
coeffs = pywt.wavedecn(x.astype(dt), 'db1')
|
||
|
|
assert_allclose(pywt.waverecn(coeffs, 'db1'), x, atol=tol, rtol=tol)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.slow
|
||
|
|
def test_waverecn_all_wavelets_modes():
|
||
|
|
# test 2D case using all wavelets and modes
|
||
|
|
rstate = np.random.RandomState(1234)
|
||
|
|
r = rstate.randn(80, 96)
|
||
|
|
for wavelet in wavelist:
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
coeffs = pywt.wavedecn(r, wavelet, mode=mode)
|
||
|
|
assert_allclose(pywt.waverecn(coeffs, wavelet, mode=mode),
|
||
|
|
r, rtol=tol_single, atol=tol_single)
|
||
|
|
|
||
|
|
|
||
|
|
def test_coeffs_to_array():
|
||
|
|
# single element list returns the first element
|
||
|
|
a_coeffs = [np.arange(8).reshape(2, 4), ]
|
||
|
|
arr, arr_slices = pywt.coeffs_to_array(a_coeffs)
|
||
|
|
assert_allclose(arr, a_coeffs[0])
|
||
|
|
assert_allclose(arr, arr[arr_slices[0]])
|
||
|
|
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, [])
|
||
|
|
# invalid second element: array as in wavedec, but not 1D
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, [a_coeffs[0], ] * 2)
|
||
|
|
# invalid second element: tuple as in wavedec2, but not a 3-tuple
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, [a_coeffs[0],
|
||
|
|
(a_coeffs[0], )])
|
||
|
|
# coefficients as None is not supported
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, [None, ])
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, [a_coeffs,
|
||
|
|
(None, None, None)])
|
||
|
|
|
||
|
|
# invalid type for second coefficient list element
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, [a_coeffs, None])
|
||
|
|
|
||
|
|
# use an invalid key name in the coef dictionary
|
||
|
|
coeffs = [np.array([0]), dict(d=np.array([0]), c=np.array([0]))]
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, coeffs)
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_coeff_reshape_even():
|
||
|
|
# verify round trip is correct:
|
||
|
|
# wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
|
||
|
|
# This is done for wavedec{1, 2, n}
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
params = {'wavedec': {'d': 1, 'dec': pywt.wavedec, 'rec': pywt.waverec},
|
||
|
|
'wavedec2': {'d': 2, 'dec': pywt.wavedec2, 'rec': pywt.waverec2},
|
||
|
|
'wavedecn': {'d': 3, 'dec': pywt.wavedecn, 'rec': pywt.waverecn}}
|
||
|
|
N = 28
|
||
|
|
for f in params:
|
||
|
|
x1 = rng.randn(*([N] * params[f]['d']))
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
for wave in wavelist:
|
||
|
|
w = pywt.Wavelet(wave)
|
||
|
|
maxlevel = pywt.dwt_max_level(np.min(x1.shape), w.dec_len)
|
||
|
|
if maxlevel == 0:
|
||
|
|
continue
|
||
|
|
|
||
|
|
coeffs = params[f]['dec'](x1, w, mode=mode)
|
||
|
|
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs)
|
||
|
|
coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices,
|
||
|
|
output_format=f)
|
||
|
|
x1r = params[f]['rec'](coeffs2, w, mode=mode)
|
||
|
|
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_coeff_reshape_axes_subset():
|
||
|
|
# verify round trip is correct when only a subset of axes are transformed:
|
||
|
|
# wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
|
||
|
|
# This is done for wavedec{1, 2, n}
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
mode = 'symmetric'
|
||
|
|
w = pywt.Wavelet('db2')
|
||
|
|
N = 16
|
||
|
|
ndim = 3
|
||
|
|
for axes in [(-1, ), (0, ), (1, ), (0, 1), (1, 2), (0, 2), None]:
|
||
|
|
x1 = rng.randn(*([N] * ndim))
|
||
|
|
coeffs = pywt.wavedecn(x1, w, mode=mode, axes=axes)
|
||
|
|
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, axes=axes)
|
||
|
|
if axes is not None:
|
||
|
|
# if axes is not None, it must be provided to coeffs_to_array
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, coeffs)
|
||
|
|
|
||
|
|
# mismatched axes size
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, coeffs,
|
||
|
|
axes=(0, 1, 2, 3))
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, coeffs,
|
||
|
|
axes=())
|
||
|
|
|
||
|
|
coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices)
|
||
|
|
x1r = pywt.waverecn(coeffs2, w, mode=mode, axes=axes)
|
||
|
|
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_coeffs_to_array_padding():
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
x1 = rng.randn(32, 32)
|
||
|
|
mode = 'symmetric'
|
||
|
|
coeffs = pywt.wavedecn(x1, 'db2', mode=mode)
|
||
|
|
|
||
|
|
# padding=None raises a ValueError when tight packing is not possible
|
||
|
|
assert_raises(ValueError, pywt.coeffs_to_array, coeffs, padding=None)
|
||
|
|
|
||
|
|
# set padded values to nan
|
||
|
|
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, padding=np.nan)
|
||
|
|
npad = np.sum(np.isnan(coeff_arr))
|
||
|
|
assert_(npad > 0)
|
||
|
|
|
||
|
|
# pad with zeros
|
||
|
|
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs, padding=0)
|
||
|
|
assert_(np.sum(np.isnan(coeff_arr)) == 0)
|
||
|
|
assert_(np.sum(coeff_arr == 0) == npad)
|
||
|
|
|
||
|
|
# Haar case with N as a power of 2 can be tightly packed
|
||
|
|
coeffs_haar = pywt.wavedecn(x1, 'haar', mode=mode)
|
||
|
|
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs_haar, padding=None)
|
||
|
|
# shape of coeff_arr will match in this case, but not in general
|
||
|
|
assert_equal(coeff_arr.shape, x1.shape)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_coeff_reshape_odd():
|
||
|
|
# verify round trip is correct:
|
||
|
|
# wavedecn - >coeffs_to_array-> array_to_coeffs -> waverecn
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
x1 = rng.randn(35, 33)
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
for wave in ['haar', ]:
|
||
|
|
w = pywt.Wavelet(wave)
|
||
|
|
maxlevel = pywt.dwt_max_level(np.min(x1.shape), w.dec_len)
|
||
|
|
if maxlevel == 0:
|
||
|
|
continue
|
||
|
|
coeffs = pywt.wavedecn(x1, w, mode=mode)
|
||
|
|
coeff_arr, coeff_slices = pywt.coeffs_to_array(coeffs)
|
||
|
|
coeffs2 = pywt.array_to_coeffs(coeff_arr, coeff_slices)
|
||
|
|
x1r = pywt.waverecn(coeffs2, w, mode=mode)
|
||
|
|
# truncate reconstructed values to original shape
|
||
|
|
x1r = x1r[tuple([slice(s) for s in x1.shape])]
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_array_to_coeffs_invalid_inputs():
|
||
|
|
coeffs = pywt.wavedecn(np.ones(2), 'haar')
|
||
|
|
arr, arr_slices = pywt.coeffs_to_array(coeffs)
|
||
|
|
|
||
|
|
# empty list of array slices
|
||
|
|
assert_raises(ValueError, pywt.array_to_coeffs, arr, [])
|
||
|
|
|
||
|
|
# invalid format name
|
||
|
|
assert_raises(ValueError, pywt.array_to_coeffs, arr, arr_slices, 'foo')
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_coeff_ravel():
|
||
|
|
# verify round trip is correct:
|
||
|
|
# wavedecn - >ravel_coeffs-> unravel_coeffs -> waverecn
|
||
|
|
# This is done for wavedec{1, 2, n}
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
params = {'wavedec': {'d': 1, 'dec': pywt.wavedec, 'rec': pywt.waverec},
|
||
|
|
'wavedec2': {'d': 2, 'dec': pywt.wavedec2, 'rec': pywt.waverec2},
|
||
|
|
'wavedecn': {'d': 3, 'dec': pywt.wavedecn, 'rec': pywt.waverecn}}
|
||
|
|
N = 12
|
||
|
|
for f in params:
|
||
|
|
x1 = rng.randn(*([N] * params[f]['d']))
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
for wave in wavelist:
|
||
|
|
w = pywt.Wavelet(wave)
|
||
|
|
maxlevel = pywt.dwt_max_level(np.min(x1.shape), w.dec_len)
|
||
|
|
if maxlevel == 0:
|
||
|
|
continue
|
||
|
|
|
||
|
|
coeffs = params[f]['dec'](x1, w, mode=mode)
|
||
|
|
coeff_arr, slices, shapes = pywt.ravel_coeffs(coeffs)
|
||
|
|
coeffs2 = pywt.unravel_coeffs(coeff_arr, slices, shapes,
|
||
|
|
output_format=f)
|
||
|
|
x1r = params[f]['rec'](coeffs2, w, mode=mode)
|
||
|
|
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_coeff_ravel_zero_level():
|
||
|
|
# verify round trip is correct:
|
||
|
|
# wavedecn - >ravel_coeffs-> unravel_coeffs -> waverecn
|
||
|
|
# This is done for wavedec{1, 2, n}
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
params = {'wavedec': {'d': 1, 'dec': pywt.wavedec, 'rec': pywt.waverec},
|
||
|
|
'wavedec2': {'d': 2, 'dec': pywt.wavedec2, 'rec': pywt.waverec2},
|
||
|
|
'wavedecn': {'d': 3, 'dec': pywt.wavedecn, 'rec': pywt.waverecn}}
|
||
|
|
N = 16
|
||
|
|
for f in params:
|
||
|
|
x1 = rng.randn(*([N] * params[f]['d']))
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
w = pywt.Wavelet('db2')
|
||
|
|
|
||
|
|
coeffs = params[f]['dec'](x1, w, mode=mode, level=0)
|
||
|
|
coeff_arr, slices, shapes = pywt.ravel_coeffs(coeffs)
|
||
|
|
coeffs2 = pywt.unravel_coeffs(coeff_arr, slices, shapes,
|
||
|
|
output_format=f)
|
||
|
|
x1r = params[f]['rec'](coeffs2, w, mode=mode)
|
||
|
|
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_coeff_ravel_odd():
|
||
|
|
# verify round trip is correct:
|
||
|
|
# wavedecn - >ravel_coeffs-> unravel_coeffs -> waverecn
|
||
|
|
rng = np.random.RandomState(1234)
|
||
|
|
x1 = rng.randn(35, 33)
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
for wave in ['haar', ]:
|
||
|
|
w = pywt.Wavelet(wave)
|
||
|
|
maxlevel = pywt.dwt_max_level(np.min(x1.shape), w.dec_len)
|
||
|
|
if maxlevel == 0:
|
||
|
|
continue
|
||
|
|
coeffs = pywt.wavedecn(x1, w, mode=mode)
|
||
|
|
coeff_arr, slices, shapes = pywt.ravel_coeffs(coeffs)
|
||
|
|
coeffs2 = pywt.unravel_coeffs(coeff_arr, slices, shapes)
|
||
|
|
x1r = pywt.waverecn(coeffs2, w, mode=mode)
|
||
|
|
# truncate reconstructed values to original shape
|
||
|
|
x1r = x1r[tuple([slice(s) for s in x1.shape])]
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ravel_wavedec2_with_lists():
|
||
|
|
x1 = np.ones((8, 8))
|
||
|
|
wav = pywt.Wavelet('haar')
|
||
|
|
coeffs = pywt.wavedec2(x1, wav)
|
||
|
|
|
||
|
|
# list [cHn, cVn, cDn] instead of tuple is okay
|
||
|
|
coeffs[1:] = [list(c) for c in coeffs[1:]]
|
||
|
|
coeff_arr, slices, shapes = pywt.ravel_coeffs(coeffs)
|
||
|
|
coeffs2 = pywt.unravel_coeffs(coeff_arr, slices, shapes,
|
||
|
|
output_format='wavedec2')
|
||
|
|
x1r = pywt.waverec2(coeffs2, wav)
|
||
|
|
assert_allclose(x1, x1r, rtol=1e-4, atol=1e-4)
|
||
|
|
|
||
|
|
# wrong length list will cause a ValueError
|
||
|
|
coeffs[1:] = [list(c[:-1]) for c in coeffs[1:]] # truncate diag coeffs
|
||
|
|
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ravel_invalid_input():
|
||
|
|
# wavedec ravel does not support any coefficient arrays being set to None
|
||
|
|
coeffs = pywt.wavedec(np.ones(8), 'haar')
|
||
|
|
coeffs[1] = None
|
||
|
|
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
|
||
|
|
|
||
|
|
# wavedec2 ravel cannot have None or a tuple/list of None
|
||
|
|
coeffs = pywt.wavedec2(np.ones((8, 8)), 'haar')
|
||
|
|
coeffs[1] = (None, None, None)
|
||
|
|
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
|
||
|
|
coeffs[1] = [None, None, None]
|
||
|
|
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
|
||
|
|
coeffs[1] = None
|
||
|
|
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
|
||
|
|
|
||
|
|
# wavedecn ravel cannot have any dictionary elements as None
|
||
|
|
coeffs = pywt.wavedecn(np.ones((8, 8, 8)), 'haar')
|
||
|
|
coeffs[1]['ddd'] = None
|
||
|
|
assert_raises(ValueError, pywt.ravel_coeffs, coeffs)
|
||
|
|
|
||
|
|
|
||
|
|
def test_unravel_invalid_inputs():
|
||
|
|
coeffs = pywt.wavedecn(np.ones(2), 'haar')
|
||
|
|
arr, slices, shapes = pywt.ravel_coeffs(coeffs)
|
||
|
|
|
||
|
|
# empty list for slices or shapes
|
||
|
|
assert_raises(ValueError, pywt.unravel_coeffs, arr, slices, [])
|
||
|
|
assert_raises(ValueError, pywt.unravel_coeffs, arr, [], shapes)
|
||
|
|
|
||
|
|
# unequal length for slices/shapes
|
||
|
|
assert_raises(ValueError, pywt.unravel_coeffs, arr, slices[:-1], shapes)
|
||
|
|
|
||
|
|
# invalid format name
|
||
|
|
assert_raises(ValueError, pywt.unravel_coeffs, arr, slices, shapes, 'foo')
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_shapes_and_size():
|
||
|
|
wav = pywt.Wavelet('db2')
|
||
|
|
for data_shape in [(33, ), (64, 32), (1, 15, 30)]:
|
||
|
|
for axes in [None, 0, -1]:
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
coeffs = pywt.wavedecn(np.ones(data_shape), wav,
|
||
|
|
mode=mode, axes=axes)
|
||
|
|
|
||
|
|
# verify that the shapes match the coefficient shapes
|
||
|
|
shapes = pywt.wavedecn_shapes(data_shape, wav,
|
||
|
|
mode=mode, axes=axes)
|
||
|
|
|
||
|
|
assert_equal(coeffs[0].shape, shapes[0])
|
||
|
|
expected_size = coeffs[0].size
|
||
|
|
for level in range(1, len(coeffs)):
|
||
|
|
for k, v in coeffs[level].items():
|
||
|
|
expected_size += v.size
|
||
|
|
assert_equal(shapes[level][k], v.shape)
|
||
|
|
|
||
|
|
# size can be determined from either the shapes or coeffs
|
||
|
|
size = pywt.wavedecn_size(shapes)
|
||
|
|
assert_equal(size, expected_size)
|
||
|
|
|
||
|
|
size = pywt.wavedecn_size(coeffs)
|
||
|
|
assert_equal(size, expected_size)
|
||
|
|
|
||
|
|
|
||
|
|
def test_dwtn_max_level():
|
||
|
|
# predicted and empirical dwtn_max_level match
|
||
|
|
for wav in [pywt.Wavelet('db2'), 'sym8']:
|
||
|
|
for data_shape in [(33, ), (64, 32), (1, 15, 30)]:
|
||
|
|
for axes in [None, 0, -1]:
|
||
|
|
for mode in pywt.Modes.modes:
|
||
|
|
coeffs = pywt.wavedecn(np.ones(data_shape), wav,
|
||
|
|
mode=mode, axes=axes)
|
||
|
|
max_lev = pywt.dwtn_max_level(data_shape, wav, axes)
|
||
|
|
assert_equal(len(coeffs[1:]), max_lev)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec_axes_subsets():
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
data = rstate.standard_normal((8, 8, 8))
|
||
|
|
# test all combinations of 1 out of 3 axes transformed
|
||
|
|
for axis in [0, 1, 2]:
|
||
|
|
coefs = pywt.wavedec(data, 'haar', axis=axis)
|
||
|
|
rec = pywt.waverec(coefs, 'haar', axis=axis)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec_axis_db2():
|
||
|
|
# test for fix to issue gh-293
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
data = rstate.standard_normal((16, 16))
|
||
|
|
for axis in [0, 1]:
|
||
|
|
coefs = pywt.wavedec(data, 'db2', axis=axis)
|
||
|
|
rec = pywt.waverec(coefs, 'db2', axis=axis)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec2_axes_subsets():
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
data = rstate.standard_normal((8, 8, 8))
|
||
|
|
# test all combinations of 2 out of 3 axes transformed
|
||
|
|
for axes in combinations((0, 1, 2), 2):
|
||
|
|
coefs = pywt.wavedec2(data, 'haar', axes=axes)
|
||
|
|
rec = pywt.waverec2(coefs, 'haar', axes=axes)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_axes_subsets():
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
data = rstate.standard_normal((8, 8, 8, 8))
|
||
|
|
# test all combinations of 3 out of 4 axes transformed
|
||
|
|
for axes in combinations((0, 1, 2, 3), 3):
|
||
|
|
coefs = pywt.wavedecn(data, 'haar', axes=axes)
|
||
|
|
rec = pywt.waverecn(coefs, 'haar', axes=axes)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_int_axis():
|
||
|
|
# waverecn should also work for axes as an integer
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
data = rstate.standard_normal((8, 8))
|
||
|
|
for axis in [0, 1]:
|
||
|
|
coefs = pywt.wavedecn(data, 'haar', axes=axis)
|
||
|
|
rec = pywt.waverecn(coefs, 'haar', axes=axis)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedec_axis_error():
|
||
|
|
data = np.ones(4)
|
||
|
|
# out of range axis not allowed
|
||
|
|
assert_raises(ValueError, pywt.wavedec, data, 'haar', axis=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec_axis_error():
|
||
|
|
c = pywt.wavedec(np.ones(4), 'haar')
|
||
|
|
# out of range axis not allowed
|
||
|
|
assert_raises(ValueError, pywt.waverec, c, 'haar', axis=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec_shape_mismatch_error():
|
||
|
|
c = pywt.wavedec(np.ones(16), 'haar')
|
||
|
|
# truncate a detail coefficient to an incorrect shape
|
||
|
|
c[3] = c[3][:-1]
|
||
|
|
assert_raises(ValueError, pywt.waverec, c, 'haar', axis=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedec2_axes_errors():
|
||
|
|
data = np.ones((4, 4))
|
||
|
|
# integer axes not allowed
|
||
|
|
assert_raises(TypeError, pywt.wavedec2, data, 'haar', axes=1)
|
||
|
|
# non-unique axes not allowed
|
||
|
|
assert_raises(ValueError, pywt.wavedec2, data, 'haar', axes=(0, 0))
|
||
|
|
# out of range axis not allowed
|
||
|
|
assert_raises(ValueError, pywt.wavedec2, data, 'haar', axes=(0, 2))
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec2_axes_errors():
|
||
|
|
data = np.ones((4, 4))
|
||
|
|
c = pywt.wavedec2(data, 'haar')
|
||
|
|
# integer axes not allowed
|
||
|
|
assert_raises(TypeError, pywt.waverec2, c, 'haar', axes=1)
|
||
|
|
# non-unique axes not allowed
|
||
|
|
assert_raises(ValueError, pywt.waverec2, c, 'haar', axes=(0, 0))
|
||
|
|
# out of range axis not allowed
|
||
|
|
assert_raises(ValueError, pywt.waverec2, c, 'haar', axes=(0, 2))
|
||
|
|
|
||
|
|
|
||
|
|
def test_wavedecn_axes_errors():
|
||
|
|
data = np.ones((8, 8, 8))
|
||
|
|
# repeated axes not allowed
|
||
|
|
assert_raises(ValueError, pywt.wavedecn, data, 'haar', axes=(1, 1))
|
||
|
|
# out of range axis not allowed
|
||
|
|
assert_raises(ValueError, pywt.wavedecn, data, 'haar', axes=(0, 1, 3))
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverecn_axes_errors():
|
||
|
|
data = np.ones((8, 8, 8))
|
||
|
|
c = pywt.wavedecn(data, 'haar')
|
||
|
|
# repeated axes not allowed
|
||
|
|
assert_raises(ValueError, pywt.waverecn, c, 'haar', axes=(1, 1))
|
||
|
|
# out of range axis not allowed
|
||
|
|
assert_raises(ValueError, pywt.waverecn, c, 'haar', axes=(0, 1, 3))
|
||
|
|
|
||
|
|
|
||
|
|
def test_per_axis_wavelets_and_modes():
|
||
|
|
# tests seperate wavelet and edge mode for each axis.
|
||
|
|
rstate = np.random.RandomState(1234)
|
||
|
|
data = rstate.randn(24, 24, 16)
|
||
|
|
|
||
|
|
# wavelet can be a string or wavelet object
|
||
|
|
wavelets = (pywt.Wavelet('haar'), 'sym2', 'db2')
|
||
|
|
|
||
|
|
# The default number of levels should be the minimum over this list
|
||
|
|
max_levels = [pywt._dwt.dwt_max_level(nd, nf) for nd, nf in
|
||
|
|
zip(data.shape, wavelets)]
|
||
|
|
|
||
|
|
# mode can be a string or a Modes enum
|
||
|
|
modes = ('symmetric', 'periodization',
|
||
|
|
pywt._extensions._pywt.Modes.reflect)
|
||
|
|
|
||
|
|
coefs = pywt.wavedecn(data, wavelets, modes)
|
||
|
|
assert_allclose(pywt.waverecn(coefs, wavelets, modes), data, atol=1e-14)
|
||
|
|
assert_equal(min(max_levels), len(coefs[1:]))
|
||
|
|
|
||
|
|
coefs = pywt.wavedecn(data, wavelets[:1], modes)
|
||
|
|
assert_allclose(pywt.waverecn(coefs, wavelets[:1], modes), data,
|
||
|
|
atol=1e-14)
|
||
|
|
|
||
|
|
coefs = pywt.wavedecn(data, wavelets, modes[:1])
|
||
|
|
assert_allclose(pywt.waverecn(coefs, wavelets, modes[:1]), data,
|
||
|
|
atol=1e-14)
|
||
|
|
|
||
|
|
# length of wavelets or modes doesn't match the length of axes
|
||
|
|
assert_raises(ValueError, pywt.wavedecn, data, wavelets[:2])
|
||
|
|
assert_raises(ValueError, pywt.wavedecn, data, wavelets, mode=modes[:2])
|
||
|
|
assert_raises(ValueError, pywt.waverecn, coefs, wavelets[:2])
|
||
|
|
assert_raises(ValueError, pywt.waverecn, coefs, wavelets, mode=modes[:2])
|
||
|
|
|
||
|
|
# dwt2/idwt2 also support per-axis wavelets/modes
|
||
|
|
data2 = data[..., 0]
|
||
|
|
coefs2 = pywt.wavedec2(data2, wavelets[:2], modes[:2])
|
||
|
|
assert_allclose(pywt.waverec2(coefs2, wavelets[:2], modes[:2]), data2,
|
||
|
|
atol=1e-14)
|
||
|
|
assert_equal(min(max_levels[:2]), len(coefs2[1:]))
|
||
|
|
|
||
|
|
# Tests for fully separable multi-level transforms
|
||
|
|
|
||
|
|
|
||
|
|
def test_fswavedecn_fswaverecn_roundtrip():
|
||
|
|
# verify proper round trip result for 1D through 4D data
|
||
|
|
# same DWT as wavedecn/waverecn so don't need to test all modes/wavelets
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
for ndim in range(1, 5):
|
||
|
|
for dt_in, dt_out in zip(dtypes_in, dtypes_out):
|
||
|
|
for levels in (1, None):
|
||
|
|
data = rstate.standard_normal((8, )*ndim)
|
||
|
|
data = data.astype(dt_in)
|
||
|
|
T = pywt.fswavedecn(data, 'haar', levels=levels)
|
||
|
|
rec = pywt.fswaverecn(T)
|
||
|
|
if data.real.dtype in [np.float32, np.float16]:
|
||
|
|
assert_allclose(rec, data, rtol=1e-6, atol=1e-6)
|
||
|
|
else:
|
||
|
|
assert_allclose(rec, data, rtol=1e-14, atol=1e-14)
|
||
|
|
assert_(T.coeffs.dtype == dt_out)
|
||
|
|
assert_(rec.dtype == dt_out)
|
||
|
|
|
||
|
|
|
||
|
|
def test_fswavedecn_fswaverecn_zero_levels():
|
||
|
|
# zero level transform gives coefs matching the original data
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
ndim = 2
|
||
|
|
data = rstate.standard_normal((8, )*ndim)
|
||
|
|
T = pywt.fswavedecn(data, 'haar', levels=0)
|
||
|
|
assert_array_equal(T.coeffs, data)
|
||
|
|
rec = pywt.fswaverecn(T)
|
||
|
|
assert_array_equal(T.coeffs, rec)
|
||
|
|
|
||
|
|
|
||
|
|
def test_fswavedecn_fswaverecn_variable_levels():
|
||
|
|
# test with differing number of transform levels per axis
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
ndim = 3
|
||
|
|
data = rstate.standard_normal((16, )*ndim)
|
||
|
|
T = pywt.fswavedecn(data, 'haar', levels=(1, 2, 3))
|
||
|
|
rec = pywt.fswaverecn(T)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
# levels doesn't match number of axes
|
||
|
|
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', levels=(1, 1))
|
||
|
|
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', levels=(1, 1, 1, 1))
|
||
|
|
|
||
|
|
# levels too large for array size
|
||
|
|
assert_warns(UserWarning, pywt.fswavedecn, data, 'haar',
|
||
|
|
levels=int(np.log2(np.min(data.shape)))+1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_fswavedecn_fswaverecn_variable_wavelets_and_modes():
|
||
|
|
# test with differing number of transform levels per axis
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
ndim = 3
|
||
|
|
data = rstate.standard_normal((16, )*ndim)
|
||
|
|
wavelets = ('haar', 'db2', 'sym3')
|
||
|
|
modes = ('periodic', 'symmetric', 'periodization')
|
||
|
|
T = pywt.fswavedecn(data, wavelet=wavelets, mode=modes)
|
||
|
|
for ax in range(ndim):
|
||
|
|
# expect approx + dwt_max_level detail coeffs along each axis
|
||
|
|
assert_equal(len(T.coeff_slices[ax]),
|
||
|
|
pywt.dwt_max_level(data.shape[ax], wavelets[ax])+1)
|
||
|
|
|
||
|
|
rec = pywt.fswaverecn(T)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
# number of wavelets doesn't match number of axes
|
||
|
|
assert_raises(ValueError, pywt.fswavedecn, data, wavelets[:2])
|
||
|
|
|
||
|
|
# number of modes doesn't match number of axes
|
||
|
|
assert_raises(ValueError, pywt.fswavedecn, data, wavelets[0], mode=modes[:2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_fswavedecn_fswaverecn_axes_subsets():
|
||
|
|
"""Fully separable DWT over only a subset of axes"""
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
# use anisotropic data to result in unique number of levels per axis
|
||
|
|
data = rstate.standard_normal((4, 8, 16, 32))
|
||
|
|
# test all combinations of 3 out of 4 axes transformed
|
||
|
|
for axes in combinations((0, 1, 2, 3), 3):
|
||
|
|
T = pywt.fswavedecn(data, 'haar', axes=axes)
|
||
|
|
rec = pywt.fswaverecn(T)
|
||
|
|
assert_allclose(rec, data, atol=1e-14)
|
||
|
|
|
||
|
|
# some axes exceed data dimensions
|
||
|
|
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', axes=(1, 5))
|
||
|
|
|
||
|
|
|
||
|
|
def test_fswavedecnresult():
|
||
|
|
data = np.ones((32, 32))
|
||
|
|
levels = (1, 2)
|
||
|
|
result = pywt.fswavedecn(data, 'sym2', levels=levels)
|
||
|
|
|
||
|
|
# can access the lowpass band via .approx or via __getitem__
|
||
|
|
approx_key = (0, ) * data.ndim
|
||
|
|
assert_array_equal(result[approx_key], result.approx)
|
||
|
|
|
||
|
|
dkeys = result.detail_keys()
|
||
|
|
# the approximation key shouldn't be present in the detail_keys
|
||
|
|
assert_(approx_key not in dkeys)
|
||
|
|
|
||
|
|
# can access all detail coefficients and they have matching ndim
|
||
|
|
for k in dkeys:
|
||
|
|
d = result[k]
|
||
|
|
assert_equal(d.ndim, data.ndim)
|
||
|
|
|
||
|
|
# can assign modified coefficients
|
||
|
|
result[k] = np.zeros_like(d)
|
||
|
|
|
||
|
|
# assigning a differently sized array raises a ValueError
|
||
|
|
assert_raises(ValueError, result.__setitem__,
|
||
|
|
k, np.zeros(tuple([s + 1 for s in d.shape])))
|
||
|
|
|
||
|
|
# warns on assigning with a non-matching dtype
|
||
|
|
assert_warns(UserWarning, result.__setitem__,
|
||
|
|
k, np.zeros_like(d).astype(np.float32))
|
||
|
|
|
||
|
|
# all coefficients are stacked into result.coeffs (same ndim)
|
||
|
|
assert_equal(result.coeffs.ndim, data.ndim)
|
||
|
|
|
||
|
|
|
||
|
|
def test_error_on_continuous_wavelet():
|
||
|
|
# A ValueError is raised if a Continuous wavelet is selected
|
||
|
|
data = np.ones((16, 16))
|
||
|
|
for dec_fun, rec_fun in zip([pywt.wavedec, pywt.wavedec2, pywt.wavedecn],
|
||
|
|
[pywt.waverec, pywt.waverec2, pywt.waverecn]):
|
||
|
|
for cwave in ['morl', pywt.DiscreteContinuousWavelet('morl')]:
|
||
|
|
assert_raises(ValueError, dec_fun, data, wavelet=cwave)
|
||
|
|
|
||
|
|
c = dec_fun(data, 'db1')
|
||
|
|
assert_raises(ValueError, rec_fun, c, wavelet=cwave)
|
||
|
|
|
||
|
|
|
||
|
|
def test_default_level():
|
||
|
|
# default level is the maximum permissible for the transformed axes
|
||
|
|
data = np.ones((128, 32, 4))
|
||
|
|
wavelet = ('db8', 'db1')
|
||
|
|
for dec_func in [pywt.wavedec2, pywt.wavedecn]:
|
||
|
|
for axes in [(0, 1), (2, 1), (0, 2)]:
|
||
|
|
c = dec_func(data, wavelet, axes=axes)
|
||
|
|
max_lev = np.min([pywt.dwt_max_level(data.shape[ax], wav)
|
||
|
|
for ax, wav in zip(axes, wavelet)])
|
||
|
|
assert_equal(len(c[1:]), max_lev)
|
||
|
|
|
||
|
|
for ax in [0, 1]:
|
||
|
|
c = pywt.wavedecn(data, wavelet[ax], axes=(ax, ))
|
||
|
|
assert_equal(len(c[1:]),
|
||
|
|
pywt.dwt_max_level(data.shape[ax], wavelet[ax]))
|
||
|
|
|
||
|
|
|
||
|
|
def test_waverec_mixed_precision():
|
||
|
|
rstate = np.random.RandomState(0)
|
||
|
|
for func, ifunc, shape in [(pywt.wavedec, pywt.waverec, (8, )),
|
||
|
|
(pywt.wavedec2, pywt.waverec2, (8, 8)),
|
||
|
|
(pywt.wavedecn, pywt.waverecn, (8, 8, 8))]:
|
||
|
|
x = rstate.randn(*shape)
|
||
|
|
coeffs_real = func(x, 'db1')
|
||
|
|
|
||
|
|
# real: single precision approx, double precision details
|
||
|
|
coeffs_real[0] = coeffs_real[0].astype(np.float32)
|
||
|
|
r = ifunc(coeffs_real, 'db1')
|
||
|
|
assert_allclose(r, x, rtol=1e-7, atol=1e-7)
|
||
|
|
assert_equal(r.dtype, np.float64)
|
||
|
|
|
||
|
|
x = x + 1j*x
|
||
|
|
coeffs = func(x, 'db1')
|
||
|
|
|
||
|
|
# complex: single precision approx, double precision details
|
||
|
|
coeffs[0] = coeffs[0].astype(np.complex64)
|
||
|
|
r = ifunc(coeffs, 'db1')
|
||
|
|
assert_allclose(r, x, rtol=1e-7, atol=1e-7)
|
||
|
|
assert_equal(r.dtype, np.complex128)
|
||
|
|
|
||
|
|
# complex: double precision approx, single precision details
|
||
|
|
if x.ndim == 1:
|
||
|
|
coeffs[0] = coeffs[0].astype(np.complex128)
|
||
|
|
coeffs[1] = coeffs[1].astype(np.complex64)
|
||
|
|
if x.ndim == 2:
|
||
|
|
coeffs[0] = coeffs[0].astype(np.complex128)
|
||
|
|
coeffs[1] = tuple([v.astype(np.complex64) for v in coeffs[1]])
|
||
|
|
if x.ndim == 3:
|
||
|
|
coeffs[0] = coeffs[0].astype(np.complex128)
|
||
|
|
coeffs[1] = {k: v.astype(np.complex64)
|
||
|
|
for k, v in coeffs[1].items()}
|
||
|
|
r = ifunc(coeffs, 'db1')
|
||
|
|
assert_allclose(r, x, rtol=1e-7, atol=1e-7)
|
||
|
|
assert_equal(r.dtype, np.complex128)
|