Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/pywt/tests/test_matlab_compatibility.py

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