def test_white_noise_surrogates():
ts = Surrogates.SmallTestData().original_data
surrogates = Surrogates.SmallTestData().white_noise_surrogates(ts)
assert (np.allclose(
np.histogram(ts[0, :])[0],
np.histogram(surrogates[0, :])[0]))
def testTwinSurrogates():
tdata = create_test_data()
n_index, n_times = tdata.shape
s = Surrogates(tdata)
tsurro = s.twin_surrogates(tdata, 1, 0, 0.2)
corrcoef = np.corrcoef(tdata, tsurro)[n_index:, :n_index]
for i in range(n_index):
corrcoef[i, i] = 0.0
assert (corrcoef >= -1.0).all() and (corrcoef <= 1.0).all()
def testTwinSurrogates():
tdata = create_test_data()
n_index, n_times = tdata.shape
s = Surrogates(tdata)
tsurro = s.twin_surrogates(tdata, 1, 0, 0.2)
corrcoef = np.corrcoef(tdata, tsurro)[n_index:,:n_index]
for i in xrange(n_index):
corrcoef[i,i]=0.0
assert (corrcoef>=-1.0).all() and (corrcoef<=1.0).all()
def testNormalizeTimeSeriesArray():
ts = Surrogates.SmallTestData().original_data
Surrogates.SmallTestData().normalize_time_series_array(ts)
res = ts.mean(axis=1)
exp = np.array([0., 0., 0., 0., 0., 0.])
assert np.allclose(res, exp, atol=1e-04)
res = ts.std(axis=1)
exp = np.array([1., 1., 1., 1., 1., 1.])
assert np.allclose(res, exp, atol=1e-04)
def testEmbedTimeSeriesArray():
ts = Surrogates.SmallTestData().original_data
res = Surrogates.SmallTestData().embed_time_series_array(
time_series_array=ts, dimension=3, delay=2)[0, :6, :]
exp = np.array([[0., 0.61464833, 1.14988147],
[0.31244015, 0.89680225, 1.3660254],
[0.61464833, 1.14988147, 1.53884177],
[0.89680225, 1.3660254, 1.6636525],
[1.14988147, 1.53884177, 1.73766672],
[1.3660254, 1.6636525, 1.76007351]])
assert np.allclose(res, exp, atol=1e-04)
def test_TestMutualInformation():
tdata = create_test_data()
n_bins = 32
test_mi = Surrogates.test_mutual_information(tdata[:1],
tdata[:1],
n_bins=n_bins)
assert (test_mi >= -1.0).all() and (test_mi <= 1.0).all()
def test_TestPearsonCorrelation():
tdata = create_test_data()
n_index, n_times = tdata.shape
norm = 1.0 / float(n_times)
c = Surrogates.test_pearson_correlation(tdata, tdata)
corrcoef = np.corrcoef(tdata, tdata)[n_index:, :n_index] * norm
for i in range(n_index):
corrcoef[i, i] = 0.0
assert c.shape == (n_index, n_index)
assert_array_almost_equal(c, corrcoef, decimal=5)
def test_TestPearsonCorrelation():
tdata = create_test_data()
n_index, n_times = tdata.shape
norm = 1.0 / float(n_times)
c = Surrogates.test_pearson_correlation(tdata, tdata)
corrcoef = np.corrcoef(tdata, tdata)[n_index:,:n_index]*norm
for i in xrange(n_index):
corrcoef[i,i]=0.0
assert c.shape == (n_index, n_index)
assert_array_almost_equal(c, corrcoef, decimal=5)
def test_TestMutualInformation():
tdata = create_test_data()
n_bins=32
test_mi = Surrogates.test_mutual_information(tdata[:1], tdata[:1],
n_bins=n_bins)
assert (test_mi>=-1.0).all() and (test_mi<=1.0).all()
def testCorrelatedNoiseSurrogates():
ts = Surrogates.SmallTestData().original_data
surrogates = Surrogates.SmallTestData().correlated_noise_surrogates(ts)
assert np.allclose(np.abs(np.fft.fft(ts, axis=1))[0, 1:10],
np.abs(np.fft.fft(surrogates, axis=1))[0, 1:10])
