160 lines
5.7 KiB
Python
160 lines
5.7 KiB
Python
"""
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Test used to verify PyWavelets Discrete Wavelet Transform computation
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accuracy against MathWorks Wavelet Toolbox.
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"""
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from __future__ import division, print_function, absolute_import
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import numpy as np
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import pytest
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from numpy.testing import assert_
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import pywt
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from pywt._pytest import (uses_pymatbridge, uses_precomputed, size_set)
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from pywt._pytest import matlab_result_dict_dwt as matlab_result_dict
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# list of mode names in pywt and matlab
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modes = [('zero', 'zpd'),
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('constant', 'sp0'),
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('symmetric', 'sym'),
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('reflect', 'symw'),
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('periodic', 'ppd'),
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('smooth', 'sp1'),
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('periodization', 'per'),
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# TODO: Now have implemented asymmetric modes too.
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# Would be nice to update the Matlab data to test these as well.
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('antisymmetric', 'asym'),
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('antireflect', 'asymw'),
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]
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families = ('db', 'sym', 'coif', 'bior', 'rbio')
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wavelets = sum([pywt.wavelist(name) for name in families], [])
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def _get_data_sizes(w):
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""" Return the sizes to test for wavelet w. """
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if size_set == 'full':
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data_sizes = list(range(w.dec_len, 40)) + \
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[100, 200, 500, 1000, 50000]
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else:
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data_sizes = (w.dec_len, w.dec_len + 1)
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return data_sizes
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@uses_pymatbridge
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@pytest.mark.slow
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def test_accuracy_pymatbridge():
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Matlab = pytest.importorskip("pymatbridge.Matlab")
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mlab = Matlab()
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rstate = np.random.RandomState(1234)
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# max RMSE (was 1.0e-10, is reduced to 5.0e-5 due to different coefficents)
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epsilon = 5.0e-5
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epsilon_pywt_coeffs = 1.0e-10
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mlab.start()
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try:
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for wavelet in wavelets:
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w = pywt.Wavelet(wavelet)
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mlab.set_variable('wavelet', wavelet)
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for N in _get_data_sizes(w):
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data = rstate.randn(N)
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mlab.set_variable('data', data)
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for pmode, mmode in modes:
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ma, md = _compute_matlab_result(data, wavelet, mmode, mlab)
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_check_accuracy(data, w, pmode, ma, md, wavelet, epsilon)
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ma, md = _load_matlab_result_pywt_coeffs(data, wavelet, mmode)
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_check_accuracy(data, w, pmode, ma, md, wavelet, epsilon_pywt_coeffs)
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finally:
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mlab.stop()
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@uses_precomputed
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@pytest.mark.slow
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def test_accuracy_precomputed():
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# Keep this specific random seed to match the precomputed Matlab result.
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rstate = np.random.RandomState(1234)
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# max RMSE (was 1.0e-10, is reduced to 5.0e-5 due to different coefficents)
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epsilon = 5.0e-5
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epsilon_pywt_coeffs = 1.0e-10
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for wavelet in wavelets:
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w = pywt.Wavelet(wavelet)
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for N in _get_data_sizes(w):
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data = rstate.randn(N)
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for pmode, mmode in modes:
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ma, md = _load_matlab_result(data, wavelet, mmode)
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_check_accuracy(data, w, pmode, ma, md, wavelet, epsilon)
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ma, md = _load_matlab_result_pywt_coeffs(data, wavelet, mmode)
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_check_accuracy(data, w, pmode, ma, md, wavelet, epsilon_pywt_coeffs)
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def _compute_matlab_result(data, wavelet, mmode, mlab):
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""" Compute the result using MATLAB.
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This function assumes that the Matlab variables `wavelet` and `data` have
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already been set externally.
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"""
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if np.any((wavelet == np.array(['coif6', 'coif7', 'coif8', 'coif9', 'coif10', 'coif11', 'coif12', 'coif13', 'coif14', 'coif15', 'coif16', 'coif17'])),axis=0):
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w = pywt.Wavelet(wavelet)
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mlab.set_variable('Lo_D', w.dec_lo)
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mlab.set_variable('Hi_D', w.dec_hi)
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mlab_code = ("[ma, md] = dwt(data, Lo_D, Hi_D, 'mode', '%s');" % mmode)
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else:
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mlab_code = "[ma, md] = dwt(data, wavelet, 'mode', '%s');" % mmode
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res = mlab.run_code(mlab_code)
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if not res['success']:
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raise RuntimeError("Matlab failed to execute the provided code. "
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"Check that the wavelet toolbox is installed.")
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# need np.asarray because sometimes the output is a single float64
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ma = np.asarray(mlab.get_variable('ma'))
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md = np.asarray(mlab.get_variable('md'))
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return ma, md
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def _load_matlab_result(data, wavelet, mmode):
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""" Load the precomputed result.
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"""
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N = len(data)
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ma_key = '_'.join([mmode, wavelet, str(N), 'ma'])
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md_key = '_'.join([mmode, wavelet, str(N), 'md'])
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if (ma_key not in matlab_result_dict) or \
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(md_key not in matlab_result_dict):
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raise KeyError(
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"Precompted Matlab result not found for wavelet: "
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"{0}, mode: {1}, size: {2}".format(wavelet, mmode, N))
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ma = matlab_result_dict[ma_key]
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md = matlab_result_dict[md_key]
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return ma, md
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def _load_matlab_result_pywt_coeffs(data, wavelet, mmode):
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""" Load the precomputed result.
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"""
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N = len(data)
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ma_key = '_'.join([mmode, wavelet, str(N), 'ma_pywtCoeffs'])
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md_key = '_'.join([mmode, wavelet, str(N), 'md_pywtCoeffs'])
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if (ma_key not in matlab_result_dict) or \
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(md_key not in matlab_result_dict):
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raise KeyError(
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"Precompted Matlab result not found for wavelet: "
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"{0}, mode: {1}, size: {2}".format(wavelet, mmode, N))
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ma = matlab_result_dict[ma_key]
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md = matlab_result_dict[md_key]
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return ma, md
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def _check_accuracy(data, w, pmode, ma, md, wavelet, epsilon):
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# PyWavelets result
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pa, pd = pywt.dwt(data, w, pmode)
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# calculate error measures
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rms_a = np.sqrt(np.mean((pa - ma) ** 2))
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rms_d = np.sqrt(np.mean((pd - md) ** 2))
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msg = ('[RMS_A > EPSILON] for Mode: %s, Wavelet: %s, '
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'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_a))
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assert_(rms_a < epsilon, msg=msg)
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msg = ('[RMS_D > EPSILON] for Mode: %s, Wavelet: %s, '
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'Length: %d, rms=%.3g' % (pmode, wavelet, len(data), rms_d))
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assert_(rms_d < epsilon, msg=msg)
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