1710 lines
64 KiB
Python
1710 lines
64 KiB
Python
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from numpy.testing import (assert_allclose, assert_almost_equal,
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assert_array_equal, assert_array_almost_equal_nulp)
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import numpy as np
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import pytest
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import matplotlib.mlab as mlab
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from matplotlib.cbook.deprecation import MatplotlibDeprecationWarning
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def _stride_repeat(*args, **kwargs):
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with pytest.warns(MatplotlibDeprecationWarning):
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return mlab.stride_repeat(*args, **kwargs)
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class TestStride:
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def get_base(self, x):
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y = x
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while y.base is not None:
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y = y.base
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return y
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def calc_window_target(self, x, NFFT, noverlap=0, axis=0):
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"""
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This is an adaptation of the original window extraction algorithm.
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This is here to test to make sure the new implementation has the same
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result.
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"""
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step = NFFT - noverlap
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ind = np.arange(0, len(x) - NFFT + 1, step)
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n = len(ind)
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result = np.zeros((NFFT, n))
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# do the ffts of the slices
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for i in range(n):
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result[:, i] = x[ind[i]:ind[i]+NFFT]
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if axis == 1:
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result = result.T
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return result
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@pytest.mark.parametrize('shape', [(), (10, 1)], ids=['0D', '2D'])
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def test_stride_windows_invalid_input_shape(self, shape):
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x = np.arange(np.prod(shape)).reshape(shape)
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with pytest.raises(ValueError):
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mlab.stride_windows(x, 5)
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@pytest.mark.parametrize('n, noverlap',
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[(0, None), (11, None), (2, 2), (2, 3)],
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ids=['n less than 1', 'n greater than input',
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'noverlap greater than n',
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'noverlap equal to n'])
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def test_stride_windows_invalid_params(self, n, noverlap):
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x = np.arange(10)
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with pytest.raises(ValueError):
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mlab.stride_windows(x, n, noverlap)
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@pytest.mark.parametrize('shape', [(), (10, 1)], ids=['0D', '2D'])
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def test_stride_repeat_invalid_input_shape(self, shape):
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x = np.arange(np.prod(shape)).reshape(shape)
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with pytest.raises(ValueError):
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_stride_repeat(x, 5)
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@pytest.mark.parametrize('axis', [-1, 2],
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ids=['axis less than 0',
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'axis greater than input shape'])
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def test_stride_repeat_invalid_axis(self, axis):
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x = np.array(0)
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with pytest.raises(ValueError):
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_stride_repeat(x, 5, axis=axis)
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def test_stride_repeat_n_lt_1_ValueError(self):
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x = np.arange(10)
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with pytest.raises(ValueError):
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_stride_repeat(x, 0)
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@pytest.mark.parametrize('axis', [0, 1], ids=['axis0', 'axis1'])
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@pytest.mark.parametrize('n', [1, 5], ids=['n1', 'n5'])
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def test_stride_repeat(self, n, axis):
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x = np.arange(10)
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y = _stride_repeat(x, n, axis=axis)
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expected_shape = [10, 10]
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expected_shape[axis] = n
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yr = np.repeat(np.expand_dims(x, axis), n, axis=axis)
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assert yr.shape == y.shape
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assert_array_equal(yr, y)
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assert tuple(expected_shape) == y.shape
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assert self.get_base(y) is x
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@pytest.mark.parametrize('axis', [0, 1], ids=['axis0', 'axis1'])
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@pytest.mark.parametrize('n, noverlap',
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[(1, 0), (5, 0), (15, 2), (13, -3)],
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ids=['n1-noverlap0', 'n5-noverlap0',
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'n15-noverlap2', 'n13-noverlapn3'])
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def test_stride_windows(self, n, noverlap, axis):
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x = np.arange(100)
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y = mlab.stride_windows(x, n, noverlap=noverlap, axis=axis)
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expected_shape = [0, 0]
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expected_shape[axis] = n
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expected_shape[1 - axis] = 100 // (n - noverlap)
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yt = self.calc_window_target(x, n, noverlap=noverlap, axis=axis)
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assert yt.shape == y.shape
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assert_array_equal(yt, y)
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assert tuple(expected_shape) == y.shape
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assert self.get_base(y) is x
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@pytest.mark.parametrize('axis', [0, 1], ids=['axis0', 'axis1'])
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def test_stride_windows_n32_noverlap0_unflatten(self, axis):
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n = 32
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x = np.arange(n)[np.newaxis]
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x1 = np.tile(x, (21, 1))
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x2 = x1.flatten()
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y = mlab.stride_windows(x2, n, axis=axis)
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if axis == 0:
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x1 = x1.T
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assert y.shape == x1.shape
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assert_array_equal(y, x1)
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def test_stride_ensure_integer_type(self):
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N = 100
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x = np.full(N + 20, np.nan)
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y = x[10:-10]
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y[:] = 0.3
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# previous to #3845 lead to corrupt access
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y_strided = mlab.stride_windows(y, n=33, noverlap=0.6)
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assert_array_equal(y_strided, 0.3)
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# previous to #3845 lead to corrupt access
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y_strided = mlab.stride_windows(y, n=33.3, noverlap=0)
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assert_array_equal(y_strided, 0.3)
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# even previous to #3845 could not find any problematic
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# configuration however, let's be sure it's not accidentally
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# introduced
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y_strided = _stride_repeat(y, n=33.815)
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assert_array_equal(y_strided, 0.3)
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def _apply_window(*args, **kwargs):
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with pytest.warns(MatplotlibDeprecationWarning):
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return mlab.apply_window(*args, **kwargs)
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class TestWindow:
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def setup(self):
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np.random.seed(0)
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n = 1000
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self.sig_rand = np.random.standard_normal(n) + 100.
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self.sig_ones = np.ones(n)
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def check_window_apply_repeat(self, x, window, NFFT, noverlap):
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"""
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This is an adaptation of the original window application algorithm.
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This is here to test to make sure the new implementation has the same
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result.
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"""
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step = NFFT - noverlap
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ind = np.arange(0, len(x) - NFFT + 1, step)
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n = len(ind)
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result = np.zeros((NFFT, n))
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if np.iterable(window):
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windowVals = window
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else:
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windowVals = window(np.ones(NFFT, x.dtype))
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# do the ffts of the slices
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for i in range(n):
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result[:, i] = windowVals * x[ind[i]:ind[i]+NFFT]
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return result
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def test_window_none_rand(self):
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res = mlab.window_none(self.sig_ones)
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assert_array_equal(res, self.sig_ones)
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def test_window_none_ones(self):
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res = mlab.window_none(self.sig_rand)
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assert_array_equal(res, self.sig_rand)
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def test_window_hanning_rand(self):
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targ = np.hanning(len(self.sig_rand)) * self.sig_rand
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res = mlab.window_hanning(self.sig_rand)
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assert_allclose(targ, res, atol=1e-06)
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def test_window_hanning_ones(self):
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targ = np.hanning(len(self.sig_ones))
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res = mlab.window_hanning(self.sig_ones)
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assert_allclose(targ, res, atol=1e-06)
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def test_apply_window_1D_axis1_ValueError(self):
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x = self.sig_rand
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window = mlab.window_hanning
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with pytest.raises(ValueError):
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_apply_window(x, window, axis=1, return_window=False)
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def test_apply_window_1D_els_wrongsize_ValueError(self):
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x = self.sig_rand
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window = mlab.window_hanning(np.ones(x.shape[0]-1))
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with pytest.raises(ValueError):
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_apply_window(x, window)
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def test_apply_window_0D_ValueError(self):
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x = np.array(0)
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window = mlab.window_hanning
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with pytest.raises(ValueError):
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_apply_window(x, window, axis=1, return_window=False)
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def test_apply_window_3D_ValueError(self):
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x = self.sig_rand[np.newaxis][np.newaxis]
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window = mlab.window_hanning
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with pytest.raises(ValueError):
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_apply_window(x, window, axis=1, return_window=False)
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def test_apply_window_hanning_1D(self):
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x = self.sig_rand
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window = mlab.window_hanning
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window1 = mlab.window_hanning(np.ones(x.shape[0]))
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y, window2 = _apply_window(x, window, return_window=True)
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yt = window(x)
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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assert_array_equal(window1, window2)
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def test_apply_window_hanning_1D_axis0(self):
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x = self.sig_rand
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window = mlab.window_hanning
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y = _apply_window(x, window, axis=0, return_window=False)
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yt = window(x)
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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def test_apply_window_hanning_els_1D_axis0(self):
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x = self.sig_rand
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window = mlab.window_hanning(np.ones(x.shape[0]))
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window1 = mlab.window_hanning
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y = _apply_window(x, window, axis=0, return_window=False)
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yt = window1(x)
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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def test_apply_window_hanning_2D_axis0(self):
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x = np.random.standard_normal([1000, 10]) + 100.
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window = mlab.window_hanning
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y = _apply_window(x, window, axis=0, return_window=False)
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yt = np.zeros_like(x)
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for i in range(x.shape[1]):
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yt[:, i] = window(x[:, i])
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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def test_apply_window_hanning_els1_2D_axis0(self):
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x = np.random.standard_normal([1000, 10]) + 100.
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window = mlab.window_hanning(np.ones(x.shape[0]))
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window1 = mlab.window_hanning
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y = _apply_window(x, window, axis=0, return_window=False)
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yt = np.zeros_like(x)
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for i in range(x.shape[1]):
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yt[:, i] = window1(x[:, i])
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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def test_apply_window_hanning_els2_2D_axis0(self):
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x = np.random.standard_normal([1000, 10]) + 100.
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window = mlab.window_hanning
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window1 = mlab.window_hanning(np.ones(x.shape[0]))
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y, window2 = _apply_window(x, window, axis=0, return_window=True)
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yt = np.zeros_like(x)
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for i in range(x.shape[1]):
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yt[:, i] = window1*x[:, i]
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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assert_array_equal(window1, window2)
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def test_apply_window_hanning_els3_2D_axis0(self):
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x = np.random.standard_normal([1000, 10]) + 100.
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window = mlab.window_hanning
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window1 = mlab.window_hanning(np.ones(x.shape[0]))
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y, window2 = _apply_window(x, window, axis=0, return_window=True)
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yt = _apply_window(x, window1, axis=0, return_window=False)
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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assert_array_equal(window1, window2)
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def test_apply_window_hanning_2D_axis1(self):
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x = np.random.standard_normal([10, 1000]) + 100.
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window = mlab.window_hanning
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y = _apply_window(x, window, axis=1, return_window=False)
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yt = np.zeros_like(x)
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for i in range(x.shape[0]):
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yt[i, :] = window(x[i, :])
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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def test_apply_window_hanning_2D_els1_axis1(self):
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x = np.random.standard_normal([10, 1000]) + 100.
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window = mlab.window_hanning(np.ones(x.shape[1]))
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window1 = mlab.window_hanning
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y = _apply_window(x, window, axis=1, return_window=False)
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yt = np.zeros_like(x)
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for i in range(x.shape[0]):
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yt[i, :] = window1(x[i, :])
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
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def test_apply_window_hanning_2D_els2_axis1(self):
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x = np.random.standard_normal([10, 1000]) + 100.
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window = mlab.window_hanning
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window1 = mlab.window_hanning(np.ones(x.shape[1]))
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y, window2 = _apply_window(x, window, axis=1, return_window=True)
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yt = np.zeros_like(x)
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for i in range(x.shape[0]):
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yt[i, :] = window1 * x[i, :]
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assert yt.shape == y.shape
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assert x.shape == y.shape
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assert_allclose(yt, y, atol=1e-06)
|
||
|
|
assert_array_equal(window1, window2)
|
||
|
|
|
||
|
|
def test_apply_window_hanning_2D_els3_axis1(self):
|
||
|
|
x = np.random.standard_normal([10, 1000]) + 100.
|
||
|
|
window = mlab.window_hanning
|
||
|
|
window1 = mlab.window_hanning(np.ones(x.shape[1]))
|
||
|
|
y = _apply_window(x, window, axis=1, return_window=False)
|
||
|
|
yt = _apply_window(x, window1, axis=1, return_window=False)
|
||
|
|
assert yt.shape == y.shape
|
||
|
|
assert x.shape == y.shape
|
||
|
|
assert_allclose(yt, y, atol=1e-06)
|
||
|
|
|
||
|
|
def test_apply_window_stride_windows_hanning_2D_n13_noverlapn3_axis0(self):
|
||
|
|
x = self.sig_rand
|
||
|
|
window = mlab.window_hanning
|
||
|
|
yi = mlab.stride_windows(x, n=13, noverlap=2, axis=0)
|
||
|
|
y = _apply_window(yi, window, axis=0, return_window=False)
|
||
|
|
yt = self.check_window_apply_repeat(x, window, 13, 2)
|
||
|
|
assert yt.shape == y.shape
|
||
|
|
assert x.shape != y.shape
|
||
|
|
assert_allclose(yt, y, atol=1e-06)
|
||
|
|
|
||
|
|
def test_apply_window_hanning_2D_stack_axis1(self):
|
||
|
|
ydata = np.arange(32)
|
||
|
|
ydata1 = ydata+5
|
||
|
|
ydata2 = ydata+3.3
|
||
|
|
ycontrol1 = _apply_window(ydata1, mlab.window_hanning)
|
||
|
|
ycontrol2 = mlab.window_hanning(ydata2)
|
||
|
|
ydata = np.vstack([ydata1, ydata2])
|
||
|
|
ycontrol = np.vstack([ycontrol1, ycontrol2])
|
||
|
|
ydata = np.tile(ydata, (20, 1))
|
||
|
|
ycontrol = np.tile(ycontrol, (20, 1))
|
||
|
|
result = _apply_window(ydata, mlab.window_hanning, axis=1,
|
||
|
|
return_window=False)
|
||
|
|
assert_allclose(ycontrol, result, atol=1e-08)
|
||
|
|
|
||
|
|
def test_apply_window_hanning_2D_stack_windows_axis1(self):
|
||
|
|
ydata = np.arange(32)
|
||
|
|
ydata1 = ydata+5
|
||
|
|
ydata2 = ydata+3.3
|
||
|
|
ycontrol1 = _apply_window(ydata1, mlab.window_hanning)
|
||
|
|
ycontrol2 = mlab.window_hanning(ydata2)
|
||
|
|
ydata = np.vstack([ydata1, ydata2])
|
||
|
|
ycontrol = np.vstack([ycontrol1, ycontrol2])
|
||
|
|
ydata = np.tile(ydata, (20, 1))
|
||
|
|
ycontrol = np.tile(ycontrol, (20, 1))
|
||
|
|
result = _apply_window(ydata, mlab.window_hanning, axis=1,
|
||
|
|
return_window=False)
|
||
|
|
assert_allclose(ycontrol, result, atol=1e-08)
|
||
|
|
|
||
|
|
def test_apply_window_hanning_2D_stack_windows_axis1_unflatten(self):
|
||
|
|
n = 32
|
||
|
|
ydata = np.arange(n)
|
||
|
|
ydata1 = ydata+5
|
||
|
|
ydata2 = ydata+3.3
|
||
|
|
ycontrol1 = _apply_window(ydata1, mlab.window_hanning)
|
||
|
|
ycontrol2 = mlab.window_hanning(ydata2)
|
||
|
|
ydata = np.vstack([ydata1, ydata2])
|
||
|
|
ycontrol = np.vstack([ycontrol1, ycontrol2])
|
||
|
|
ydata = np.tile(ydata, (20, 1))
|
||
|
|
ycontrol = np.tile(ycontrol, (20, 1))
|
||
|
|
ydata = ydata.flatten()
|
||
|
|
ydata1 = mlab.stride_windows(ydata, 32, noverlap=0, axis=0)
|
||
|
|
result = _apply_window(ydata1, mlab.window_hanning, axis=0,
|
||
|
|
return_window=False)
|
||
|
|
assert_allclose(ycontrol.T, result, atol=1e-08)
|
||
|
|
|
||
|
|
|
||
|
|
class TestDetrend:
|
||
|
|
def setup(self):
|
||
|
|
np.random.seed(0)
|
||
|
|
n = 1000
|
||
|
|
x = np.linspace(0., 100, n)
|
||
|
|
|
||
|
|
self.sig_zeros = np.zeros(n)
|
||
|
|
|
||
|
|
self.sig_off = self.sig_zeros + 100.
|
||
|
|
self.sig_slope = np.linspace(-10., 90., n)
|
||
|
|
|
||
|
|
self.sig_slope_mean = x - x.mean()
|
||
|
|
|
||
|
|
sig_rand = np.random.standard_normal(n)
|
||
|
|
sig_sin = np.sin(x*2*np.pi/(n/100))
|
||
|
|
|
||
|
|
sig_rand -= sig_rand.mean()
|
||
|
|
sig_sin -= sig_sin.mean()
|
||
|
|
|
||
|
|
self.sig_base = sig_rand + sig_sin
|
||
|
|
|
||
|
|
self.atol = 1e-08
|
||
|
|
|
||
|
|
def test_detrend_none_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = input
|
||
|
|
mlab.detrend_none(input)
|
||
|
|
assert input == targ
|
||
|
|
|
||
|
|
def test_detrend_none_0D_zeros_axis1(self):
|
||
|
|
input = 0.
|
||
|
|
targ = input
|
||
|
|
mlab.detrend_none(input, axis=1)
|
||
|
|
assert input == targ
|
||
|
|
|
||
|
|
def test_detrend_str_none_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = input
|
||
|
|
mlab.detrend(input, key='none')
|
||
|
|
assert input == targ
|
||
|
|
|
||
|
|
def test_detrend_detrend_none_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = input
|
||
|
|
mlab.detrend(input, key=mlab.detrend_none)
|
||
|
|
assert input == targ
|
||
|
|
|
||
|
|
def test_detrend_none_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = input
|
||
|
|
mlab.detrend_none(input)
|
||
|
|
assert input == targ
|
||
|
|
|
||
|
|
def test_detrend_none_1D_off(self):
|
||
|
|
input = self.sig_off
|
||
|
|
targ = input
|
||
|
|
res = mlab.detrend_none(input)
|
||
|
|
assert_array_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_none_1D_slope(self):
|
||
|
|
input = self.sig_slope
|
||
|
|
targ = input
|
||
|
|
res = mlab.detrend_none(input)
|
||
|
|
assert_array_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_none_1D_base(self):
|
||
|
|
input = self.sig_base
|
||
|
|
targ = input
|
||
|
|
res = mlab.detrend_none(input)
|
||
|
|
assert_array_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_none_1D_base_slope_off_list(self):
|
||
|
|
input = self.sig_base + self.sig_slope + self.sig_off
|
||
|
|
targ = input.tolist()
|
||
|
|
res = mlab.detrend_none(input.tolist())
|
||
|
|
assert res == targ
|
||
|
|
|
||
|
|
def test_detrend_none_2D(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = input
|
||
|
|
res = mlab.detrend_none(input)
|
||
|
|
assert_array_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_none_2D_T(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = input
|
||
|
|
res = mlab.detrend_none(input.T)
|
||
|
|
assert_array_equal(res.T, targ)
|
||
|
|
|
||
|
|
def test_detrend_mean_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_str_mean_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend(input, key='mean')
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_detrend_mean_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_mean)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_mean_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_str_mean_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend(input, key='mean')
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_detrend_mean_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_mean)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_zeros(self):
|
||
|
|
input = self.sig_zeros
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base(self):
|
||
|
|
input = self.sig_base
|
||
|
|
targ = self.sig_base
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base_off(self):
|
||
|
|
input = self.sig_base + self.sig_off
|
||
|
|
targ = self.sig_base
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base_slope(self):
|
||
|
|
input = self.sig_base + self.sig_slope
|
||
|
|
targ = self.sig_base + self.sig_slope_mean
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base_slope_off(self):
|
||
|
|
input = self.sig_base + self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_base + self.sig_slope_mean
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base_slope_off_axis0(self):
|
||
|
|
input = self.sig_base + self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_base + self.sig_slope_mean
|
||
|
|
res = mlab.detrend_mean(input, axis=0)
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base_slope_off_list(self):
|
||
|
|
input = self.sig_base + self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_base + self.sig_slope_mean
|
||
|
|
res = mlab.detrend_mean(input.tolist())
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_base_slope_off_list_axis0(self):
|
||
|
|
input = self.sig_base + self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_base + self.sig_slope_mean
|
||
|
|
res = mlab.detrend_mean(input.tolist(), axis=0)
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_default(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_base + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_base]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend_mean(input)
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_none(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_base + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_base]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend_mean(input, axis=None)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_none_T(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_base + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_base]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend_mean(input, axis=None)
|
||
|
|
assert_allclose(res.T, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_axis0(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
arrt = [self.sig_base,
|
||
|
|
self.sig_base,
|
||
|
|
self.sig_base + self.sig_slope_mean,
|
||
|
|
self.sig_base + self.sig_slope_mean]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt).T
|
||
|
|
res = mlab.detrend_mean(input, axis=0)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_axis1(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
arrt = [self.sig_base,
|
||
|
|
self.sig_base,
|
||
|
|
self.sig_base + self.sig_slope_mean,
|
||
|
|
self.sig_base + self.sig_slope_mean]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend_mean(input, axis=1)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_axism1(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
arrt = [self.sig_base,
|
||
|
|
self.sig_base,
|
||
|
|
self.sig_base + self.sig_slope_mean,
|
||
|
|
self.sig_base + self.sig_slope_mean]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend_mean(input, axis=-1)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_2D_default(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_base + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_base]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend(input)
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_2D_none(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_base + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_base]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend(input, axis=None)
|
||
|
|
assert_allclose(res, targ, atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_str_mean_2D_axis0(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
arrt = [self.sig_base,
|
||
|
|
self.sig_base,
|
||
|
|
self.sig_base + self.sig_slope_mean,
|
||
|
|
self.sig_base + self.sig_slope_mean]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt).T
|
||
|
|
res = mlab.detrend(input, key='mean', axis=0)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_str_constant_2D_none_T(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_base + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_base]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend(input, key='constant', axis=None)
|
||
|
|
assert_allclose(res.T, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_str_default_2D_axis1(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
arrt = [self.sig_base,
|
||
|
|
self.sig_base,
|
||
|
|
self.sig_base + self.sig_slope_mean,
|
||
|
|
self.sig_base + self.sig_slope_mean]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend(input, key='default', axis=1)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_detrend_mean_2D_axis0(self):
|
||
|
|
arri = [self.sig_base,
|
||
|
|
self.sig_base + self.sig_off,
|
||
|
|
self.sig_base + self.sig_slope,
|
||
|
|
self.sig_base + self.sig_off + self.sig_slope]
|
||
|
|
arrt = [self.sig_base,
|
||
|
|
self.sig_base,
|
||
|
|
self.sig_base + self.sig_slope_mean,
|
||
|
|
self.sig_base + self.sig_slope_mean]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt).T
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_mean, axis=0)
|
||
|
|
assert_allclose(res, targ,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
def test_detrend_bad_key_str_ValueError(self):
|
||
|
|
input = self.sig_slope[np.newaxis]
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend(input, key='spam')
|
||
|
|
|
||
|
|
def test_detrend_bad_key_var_ValueError(self):
|
||
|
|
input = self.sig_slope[np.newaxis]
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend(input, key=5)
|
||
|
|
|
||
|
|
def test_detrend_mean_0D_d0_ValueError(self):
|
||
|
|
input = 5.5
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend_mean(input, axis=0)
|
||
|
|
|
||
|
|
def test_detrend_0D_d0_ValueError(self):
|
||
|
|
input = 5.5
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend(input, axis=0)
|
||
|
|
|
||
|
|
def test_detrend_mean_1D_d1_ValueError(self):
|
||
|
|
input = self.sig_slope
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend_mean(input, axis=1)
|
||
|
|
|
||
|
|
def test_detrend_1D_d1_ValueError(self):
|
||
|
|
input = self.sig_slope
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend(input, axis=1)
|
||
|
|
|
||
|
|
def test_detrend_mean_2D_d2_ValueError(self):
|
||
|
|
input = self.sig_slope[np.newaxis]
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend_mean(input, axis=2)
|
||
|
|
|
||
|
|
def test_detrend_2D_d2_ValueError(self):
|
||
|
|
input = self.sig_slope[np.newaxis]
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend(input, axis=2)
|
||
|
|
|
||
|
|
def test_detrend_linear_0D_zeros(self):
|
||
|
|
input = 0.
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend_linear(input)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_linear_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend_linear(input)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_str_linear_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend(input, key='linear')
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_detrend_linear_0D_off(self):
|
||
|
|
input = 5.5
|
||
|
|
targ = 0.
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_linear)
|
||
|
|
assert_almost_equal(res, targ)
|
||
|
|
|
||
|
|
def test_detrend_linear_1d_off(self):
|
||
|
|
input = self.sig_off
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend_linear(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_linear_1d_slope(self):
|
||
|
|
input = self.sig_slope
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend_linear(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_linear_1d_slope_off(self):
|
||
|
|
input = self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend_linear(input)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_str_linear_1d_slope_off(self):
|
||
|
|
input = self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend(input, key='linear')
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_detrend_linear_1d_slope_off(self):
|
||
|
|
input = self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_linear)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_linear_1d_slope_off_list(self):
|
||
|
|
input = self.sig_slope + self.sig_off
|
||
|
|
targ = self.sig_zeros
|
||
|
|
res = mlab.detrend_linear(input.tolist())
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_linear_2D_ValueError(self):
|
||
|
|
input = self.sig_slope[np.newaxis]
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.detrend_linear(input)
|
||
|
|
|
||
|
|
def test_detrend_str_linear_2d_slope_off_axis0(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_slope,
|
||
|
|
self.sig_slope + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_zeros,
|
||
|
|
self.sig_zeros]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt).T
|
||
|
|
res = mlab.detrend(input, key='linear', axis=0)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_detrend_linear_1d_slope_off_axis1(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_slope,
|
||
|
|
self.sig_slope + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_zeros,
|
||
|
|
self.sig_zeros]
|
||
|
|
input = np.vstack(arri).T
|
||
|
|
targ = np.vstack(arrt).T
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_linear, axis=0)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_str_linear_2d_slope_off_axis0_notranspose(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_slope,
|
||
|
|
self.sig_slope + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_zeros,
|
||
|
|
self.sig_zeros]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend(input, key='linear', axis=1)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
def test_detrend_detrend_linear_1d_slope_off_axis1_notranspose(self):
|
||
|
|
arri = [self.sig_off,
|
||
|
|
self.sig_slope,
|
||
|
|
self.sig_slope + self.sig_off]
|
||
|
|
arrt = [self.sig_zeros,
|
||
|
|
self.sig_zeros,
|
||
|
|
self.sig_zeros]
|
||
|
|
input = np.vstack(arri)
|
||
|
|
targ = np.vstack(arrt)
|
||
|
|
res = mlab.detrend(input, key=mlab.detrend_linear, axis=1)
|
||
|
|
assert_allclose(res, targ, atol=self.atol)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('iscomplex', [False, True],
|
||
|
|
ids=['real', 'complex'], scope='class')
|
||
|
|
@pytest.mark.parametrize('sides', ['onesided', 'twosided', 'default'],
|
||
|
|
scope='class')
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'fstims,len_x,NFFT_density,nover_density,pad_to_density,pad_to_spectrum',
|
||
|
|
[
|
||
|
|
([], None, -1, -1, -1, -1),
|
||
|
|
([4], None, -1, -1, -1, -1),
|
||
|
|
([4, 5, 10], None, -1, -1, -1, -1),
|
||
|
|
([], None, None, -1, -1, None),
|
||
|
|
([], None, -1, -1, None, None),
|
||
|
|
([], None, None, -1, None, None),
|
||
|
|
([], 1024, 512, -1, -1, 128),
|
||
|
|
([], 256, -1, -1, 33, 257),
|
||
|
|
([], 255, 33, -1, -1, None),
|
||
|
|
([], 256, 128, -1, 256, 256),
|
||
|
|
([], None, -1, 32, -1, -1),
|
||
|
|
],
|
||
|
|
ids=[
|
||
|
|
'nosig',
|
||
|
|
'Fs4',
|
||
|
|
'FsAll',
|
||
|
|
'nosig_noNFFT',
|
||
|
|
'nosig_nopad_to',
|
||
|
|
'nosig_noNFFT_no_pad_to',
|
||
|
|
'nosig_trim',
|
||
|
|
'nosig_odd',
|
||
|
|
'nosig_oddlen',
|
||
|
|
'nosig_stretch',
|
||
|
|
'nosig_overlap',
|
||
|
|
],
|
||
|
|
scope='class')
|
||
|
|
class TestSpectral:
|
||
|
|
@pytest.fixture(scope='class', autouse=True)
|
||
|
|
def stim(self, request, fstims, iscomplex, sides, len_x, NFFT_density,
|
||
|
|
nover_density, pad_to_density, pad_to_spectrum):
|
||
|
|
Fs = 100.
|
||
|
|
|
||
|
|
x = np.arange(0, 10, 1 / Fs)
|
||
|
|
if len_x is not None:
|
||
|
|
x = x[:len_x]
|
||
|
|
|
||
|
|
# get the stimulus frequencies, defaulting to None
|
||
|
|
fstims = [Fs / fstim for fstim in fstims]
|
||
|
|
|
||
|
|
# get the constants, default to calculated values
|
||
|
|
if NFFT_density is None:
|
||
|
|
NFFT_density_real = 256
|
||
|
|
elif NFFT_density < 0:
|
||
|
|
NFFT_density_real = NFFT_density = 100
|
||
|
|
else:
|
||
|
|
NFFT_density_real = NFFT_density
|
||
|
|
|
||
|
|
if nover_density is None:
|
||
|
|
nover_density_real = 0
|
||
|
|
elif nover_density < 0:
|
||
|
|
nover_density_real = nover_density = NFFT_density_real // 2
|
||
|
|
else:
|
||
|
|
nover_density_real = nover_density
|
||
|
|
|
||
|
|
if pad_to_density is None:
|
||
|
|
pad_to_density_real = NFFT_density_real
|
||
|
|
elif pad_to_density < 0:
|
||
|
|
pad_to_density = int(2**np.ceil(np.log2(NFFT_density_real)))
|
||
|
|
pad_to_density_real = pad_to_density
|
||
|
|
else:
|
||
|
|
pad_to_density_real = pad_to_density
|
||
|
|
|
||
|
|
if pad_to_spectrum is None:
|
||
|
|
pad_to_spectrum_real = len(x)
|
||
|
|
elif pad_to_spectrum < 0:
|
||
|
|
pad_to_spectrum_real = pad_to_spectrum = len(x)
|
||
|
|
else:
|
||
|
|
pad_to_spectrum_real = pad_to_spectrum
|
||
|
|
|
||
|
|
if pad_to_spectrum is None:
|
||
|
|
NFFT_spectrum_real = NFFT_spectrum = pad_to_spectrum_real
|
||
|
|
else:
|
||
|
|
NFFT_spectrum_real = NFFT_spectrum = len(x)
|
||
|
|
nover_spectrum = 0
|
||
|
|
|
||
|
|
NFFT_specgram = NFFT_density
|
||
|
|
nover_specgram = nover_density
|
||
|
|
pad_to_specgram = pad_to_density
|
||
|
|
NFFT_specgram_real = NFFT_density_real
|
||
|
|
nover_specgram_real = nover_density_real
|
||
|
|
|
||
|
|
if sides == 'onesided' or (sides == 'default' and not iscomplex):
|
||
|
|
# frequencies for specgram, psd, and csd
|
||
|
|
# need to handle even and odd differently
|
||
|
|
if pad_to_density_real % 2:
|
||
|
|
freqs_density = np.linspace(0, Fs / 2,
|
||
|
|
num=pad_to_density_real,
|
||
|
|
endpoint=False)[::2]
|
||
|
|
else:
|
||
|
|
freqs_density = np.linspace(0, Fs / 2,
|
||
|
|
num=pad_to_density_real // 2 + 1)
|
||
|
|
|
||
|
|
# frequencies for complex, magnitude, angle, and phase spectrums
|
||
|
|
# need to handle even and odd differently
|
||
|
|
if pad_to_spectrum_real % 2:
|
||
|
|
freqs_spectrum = np.linspace(0, Fs / 2,
|
||
|
|
num=pad_to_spectrum_real,
|
||
|
|
endpoint=False)[::2]
|
||
|
|
else:
|
||
|
|
freqs_spectrum = np.linspace(0, Fs / 2,
|
||
|
|
num=pad_to_spectrum_real // 2 + 1)
|
||
|
|
else:
|
||
|
|
# frequencies for specgram, psd, and csd
|
||
|
|
# need to handle even and odd differentl
|
||
|
|
if pad_to_density_real % 2:
|
||
|
|
freqs_density = np.linspace(-Fs / 2, Fs / 2,
|
||
|
|
num=2 * pad_to_density_real,
|
||
|
|
endpoint=False)[1::2]
|
||
|
|
else:
|
||
|
|
freqs_density = np.linspace(-Fs / 2, Fs / 2,
|
||
|
|
num=pad_to_density_real,
|
||
|
|
endpoint=False)
|
||
|
|
|
||
|
|
# frequencies for complex, magnitude, angle, and phase spectrums
|
||
|
|
# need to handle even and odd differently
|
||
|
|
if pad_to_spectrum_real % 2:
|
||
|
|
freqs_spectrum = np.linspace(-Fs / 2, Fs / 2,
|
||
|
|
num=2 * pad_to_spectrum_real,
|
||
|
|
endpoint=False)[1::2]
|
||
|
|
else:
|
||
|
|
freqs_spectrum = np.linspace(-Fs / 2, Fs / 2,
|
||
|
|
num=pad_to_spectrum_real,
|
||
|
|
endpoint=False)
|
||
|
|
|
||
|
|
freqs_specgram = freqs_density
|
||
|
|
# time points for specgram
|
||
|
|
t_start = NFFT_specgram_real // 2
|
||
|
|
t_stop = len(x) - NFFT_specgram_real // 2 + 1
|
||
|
|
t_step = NFFT_specgram_real - nover_specgram_real
|
||
|
|
t_specgram = x[t_start:t_stop:t_step]
|
||
|
|
if NFFT_specgram_real % 2:
|
||
|
|
t_specgram += 1 / Fs / 2
|
||
|
|
if len(t_specgram) == 0:
|
||
|
|
t_specgram = np.array([NFFT_specgram_real / (2 * Fs)])
|
||
|
|
t_spectrum = np.array([NFFT_spectrum_real / (2 * Fs)])
|
||
|
|
t_density = t_specgram
|
||
|
|
|
||
|
|
y = np.zeros_like(x)
|
||
|
|
for i, fstim in enumerate(fstims):
|
||
|
|
y += np.sin(fstim * x * np.pi * 2) * 10**i
|
||
|
|
|
||
|
|
if iscomplex:
|
||
|
|
y = y.astype('complex')
|
||
|
|
|
||
|
|
# Interestingly, the instance on which this fixture is called is not
|
||
|
|
# the same as the one on which a test is run. So we need to modify the
|
||
|
|
# class itself when using a class-scoped fixture.
|
||
|
|
cls = request.cls
|
||
|
|
|
||
|
|
cls.Fs = Fs
|
||
|
|
cls.sides = sides
|
||
|
|
cls.fstims = fstims
|
||
|
|
|
||
|
|
cls.NFFT_density = NFFT_density
|
||
|
|
cls.nover_density = nover_density
|
||
|
|
cls.pad_to_density = pad_to_density
|
||
|
|
|
||
|
|
cls.NFFT_spectrum = NFFT_spectrum
|
||
|
|
cls.nover_spectrum = nover_spectrum
|
||
|
|
cls.pad_to_spectrum = pad_to_spectrum
|
||
|
|
|
||
|
|
cls.NFFT_specgram = NFFT_specgram
|
||
|
|
cls.nover_specgram = nover_specgram
|
||
|
|
cls.pad_to_specgram = pad_to_specgram
|
||
|
|
|
||
|
|
cls.t_specgram = t_specgram
|
||
|
|
cls.t_density = t_density
|
||
|
|
cls.t_spectrum = t_spectrum
|
||
|
|
cls.y = y
|
||
|
|
|
||
|
|
cls.freqs_density = freqs_density
|
||
|
|
cls.freqs_spectrum = freqs_spectrum
|
||
|
|
cls.freqs_specgram = freqs_specgram
|
||
|
|
|
||
|
|
cls.NFFT_density_real = NFFT_density_real
|
||
|
|
|
||
|
|
def check_freqs(self, vals, targfreqs, resfreqs, fstims):
|
||
|
|
assert resfreqs.argmin() == 0
|
||
|
|
assert resfreqs.argmax() == len(resfreqs)-1
|
||
|
|
assert_allclose(resfreqs, targfreqs, atol=1e-06)
|
||
|
|
for fstim in fstims:
|
||
|
|
i = np.abs(resfreqs - fstim).argmin()
|
||
|
|
assert vals[i] > vals[i+2]
|
||
|
|
assert vals[i] > vals[i-2]
|
||
|
|
|
||
|
|
def check_maxfreq(self, spec, fsp, fstims):
|
||
|
|
# skip the test if there are no frequencies
|
||
|
|
if len(fstims) == 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
# if twosided, do the test for each side
|
||
|
|
if fsp.min() < 0:
|
||
|
|
fspa = np.abs(fsp)
|
||
|
|
zeroind = fspa.argmin()
|
||
|
|
self.check_maxfreq(spec[:zeroind], fspa[:zeroind], fstims)
|
||
|
|
self.check_maxfreq(spec[zeroind:], fspa[zeroind:], fstims)
|
||
|
|
return
|
||
|
|
|
||
|
|
fstimst = fstims[:]
|
||
|
|
spect = spec.copy()
|
||
|
|
|
||
|
|
# go through each peak and make sure it is correctly the maximum peak
|
||
|
|
while fstimst:
|
||
|
|
maxind = spect.argmax()
|
||
|
|
maxfreq = fsp[maxind]
|
||
|
|
assert_almost_equal(maxfreq, fstimst[-1])
|
||
|
|
del fstimst[-1]
|
||
|
|
spect[maxind-5:maxind+5] = 0
|
||
|
|
|
||
|
|
def test_spectral_helper_raises(self):
|
||
|
|
# We don't use parametrize here to handle ``y = self.y``.
|
||
|
|
for kwargs in [ # Various error conditions:
|
||
|
|
{"y": self.y+1, "mode": "complex"}, # Modes requiring ``x is y``.
|
||
|
|
{"y": self.y+1, "mode": "magnitude"},
|
||
|
|
{"y": self.y+1, "mode": "angle"},
|
||
|
|
{"y": self.y+1, "mode": "phase"},
|
||
|
|
{"mode": "spam"}, # Bad mode.
|
||
|
|
{"y": self.y, "sides": "eggs"}, # Bad sides.
|
||
|
|
{"y": self.y, "NFFT": 10, "noverlap": 20}, # noverlap > NFFT.
|
||
|
|
{"NFFT": 10, "noverlap": 10}, # noverlap == NFFT.
|
||
|
|
{"y": self.y, "NFFT": 10,
|
||
|
|
"window": np.ones(9)}, # len(win) != NFFT.
|
||
|
|
]:
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab._spectral_helper(x=self.y, **kwargs)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('mode', ['default', 'psd'])
|
||
|
|
def test_single_spectrum_helper_unsupported_modes(self, mode):
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab._single_spectrum_helper(x=self.y, mode=mode)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("mode, case", [
|
||
|
|
("psd", "density"),
|
||
|
|
("magnitude", "specgram"),
|
||
|
|
("magnitude", "spectrum"),
|
||
|
|
])
|
||
|
|
def test_spectral_helper_psd(self, mode, case):
|
||
|
|
freqs = getattr(self, f"freqs_{case}")
|
||
|
|
spec, fsp, t = mlab._spectral_helper(
|
||
|
|
x=self.y, y=self.y,
|
||
|
|
NFFT=getattr(self, f"NFFT_{case}"),
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=getattr(self, f"nover_{case}"),
|
||
|
|
pad_to=getattr(self, f"pad_to_{case}"),
|
||
|
|
sides=self.sides,
|
||
|
|
mode=mode)
|
||
|
|
|
||
|
|
assert_allclose(fsp, freqs, atol=1e-06)
|
||
|
|
assert_allclose(t, getattr(self, f"t_{case}"), atol=1e-06)
|
||
|
|
assert spec.shape[0] == freqs.shape[0]
|
||
|
|
assert spec.shape[1] == getattr(self, f"t_{case}").shape[0]
|
||
|
|
|
||
|
|
def test_csd(self):
|
||
|
|
freqs = self.freqs_density
|
||
|
|
spec, fsp = mlab.csd(x=self.y, y=self.y+1,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides)
|
||
|
|
assert_allclose(fsp, freqs, atol=1e-06)
|
||
|
|
assert spec.shape == freqs.shape
|
||
|
|
|
||
|
|
def test_csd_padding(self):
|
||
|
|
"""Test zero padding of csd()."""
|
||
|
|
if self.NFFT_density is None: # for derived classes
|
||
|
|
return
|
||
|
|
sargs = dict(x=self.y, y=self.y+1, Fs=self.Fs, window=mlab.window_none,
|
||
|
|
sides=self.sides)
|
||
|
|
|
||
|
|
spec0, _ = mlab.csd(NFFT=self.NFFT_density, **sargs)
|
||
|
|
spec1, _ = mlab.csd(NFFT=self.NFFT_density*2, **sargs)
|
||
|
|
assert_almost_equal(np.sum(np.conjugate(spec0)*spec0).real,
|
||
|
|
np.sum(np.conjugate(spec1/2)*spec1/2).real)
|
||
|
|
|
||
|
|
def test_psd(self):
|
||
|
|
freqs = self.freqs_density
|
||
|
|
spec, fsp = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides)
|
||
|
|
assert spec.shape == freqs.shape
|
||
|
|
self.check_freqs(spec, freqs, fsp, self.fstims)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'make_data, detrend',
|
||
|
|
[(np.zeros, mlab.detrend_mean), (np.zeros, 'mean'),
|
||
|
|
(np.arange, mlab.detrend_linear), (np.arange, 'linear')])
|
||
|
|
def test_psd_detrend(self, make_data, detrend):
|
||
|
|
if self.NFFT_density is None:
|
||
|
|
return
|
||
|
|
ydata = make_data(self.NFFT_density)
|
||
|
|
ydata1 = ydata+5
|
||
|
|
ydata2 = ydata+3.3
|
||
|
|
ydata = np.vstack([ydata1, ydata2])
|
||
|
|
ydata = np.tile(ydata, (20, 1))
|
||
|
|
ydatab = ydata.T.flatten()
|
||
|
|
ydata = ydata.flatten()
|
||
|
|
ycontrol = np.zeros_like(ydata)
|
||
|
|
spec_g, fsp_g = mlab.psd(x=ydata,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
detrend=detrend)
|
||
|
|
spec_b, fsp_b = mlab.psd(x=ydatab,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
detrend=detrend)
|
||
|
|
spec_c, fsp_c = mlab.psd(x=ycontrol,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides)
|
||
|
|
assert_array_equal(fsp_g, fsp_c)
|
||
|
|
assert_array_equal(fsp_b, fsp_c)
|
||
|
|
assert_allclose(spec_g, spec_c, atol=1e-08)
|
||
|
|
# these should not be almost equal
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
assert_allclose(spec_b, spec_c, atol=1e-08)
|
||
|
|
|
||
|
|
def test_psd_window_hanning(self):
|
||
|
|
if self.NFFT_density is None:
|
||
|
|
return
|
||
|
|
ydata = np.arange(self.NFFT_density)
|
||
|
|
ydata1 = ydata+5
|
||
|
|
ydata2 = ydata+3.3
|
||
|
|
ycontrol1, windowVals = _apply_window(ydata1,
|
||
|
|
mlab.window_hanning,
|
||
|
|
return_window=True)
|
||
|
|
ycontrol2 = mlab.window_hanning(ydata2)
|
||
|
|
ydata = np.vstack([ydata1, ydata2])
|
||
|
|
ycontrol = np.vstack([ycontrol1, ycontrol2])
|
||
|
|
ydata = np.tile(ydata, (20, 1))
|
||
|
|
ycontrol = np.tile(ycontrol, (20, 1))
|
||
|
|
ydatab = ydata.T.flatten()
|
||
|
|
ydataf = ydata.flatten()
|
||
|
|
ycontrol = ycontrol.flatten()
|
||
|
|
spec_g, fsp_g = mlab.psd(x=ydataf,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_hanning)
|
||
|
|
spec_b, fsp_b = mlab.psd(x=ydatab,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_hanning)
|
||
|
|
spec_c, fsp_c = mlab.psd(x=ycontrol,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_none)
|
||
|
|
spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
|
||
|
|
assert_array_equal(fsp_g, fsp_c)
|
||
|
|
assert_array_equal(fsp_b, fsp_c)
|
||
|
|
assert_allclose(spec_g, spec_c, atol=1e-08)
|
||
|
|
# these should not be almost equal
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
assert_allclose(spec_b, spec_c, atol=1e-08)
|
||
|
|
|
||
|
|
def test_psd_window_hanning_detrend_linear(self):
|
||
|
|
if self.NFFT_density is None:
|
||
|
|
return
|
||
|
|
ydata = np.arange(self.NFFT_density)
|
||
|
|
ycontrol = np.zeros(self.NFFT_density)
|
||
|
|
ydata1 = ydata+5
|
||
|
|
ydata2 = ydata+3.3
|
||
|
|
ycontrol1 = ycontrol
|
||
|
|
ycontrol2 = ycontrol
|
||
|
|
ycontrol1, windowVals = _apply_window(ycontrol1,
|
||
|
|
mlab.window_hanning,
|
||
|
|
return_window=True)
|
||
|
|
ycontrol2 = mlab.window_hanning(ycontrol2)
|
||
|
|
ydata = np.vstack([ydata1, ydata2])
|
||
|
|
ycontrol = np.vstack([ycontrol1, ycontrol2])
|
||
|
|
ydata = np.tile(ydata, (20, 1))
|
||
|
|
ycontrol = np.tile(ycontrol, (20, 1))
|
||
|
|
ydatab = ydata.T.flatten()
|
||
|
|
ydataf = ydata.flatten()
|
||
|
|
ycontrol = ycontrol.flatten()
|
||
|
|
spec_g, fsp_g = mlab.psd(x=ydataf,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
detrend=mlab.detrend_linear,
|
||
|
|
window=mlab.window_hanning)
|
||
|
|
spec_b, fsp_b = mlab.psd(x=ydatab,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
detrend=mlab.detrend_linear,
|
||
|
|
window=mlab.window_hanning)
|
||
|
|
spec_c, fsp_c = mlab.psd(x=ycontrol,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=0,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_none)
|
||
|
|
spec_c *= len(ycontrol1)/(np.abs(windowVals)**2).sum()
|
||
|
|
assert_array_equal(fsp_g, fsp_c)
|
||
|
|
assert_array_equal(fsp_b, fsp_c)
|
||
|
|
assert_allclose(spec_g, spec_c, atol=1e-08)
|
||
|
|
# these should not be almost equal
|
||
|
|
with pytest.raises(AssertionError):
|
||
|
|
assert_allclose(spec_b, spec_c, atol=1e-08)
|
||
|
|
|
||
|
|
def test_psd_windowarray(self):
|
||
|
|
freqs = self.freqs_density
|
||
|
|
spec, fsp = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides,
|
||
|
|
window=np.ones(self.NFFT_density_real))
|
||
|
|
assert_allclose(fsp, freqs, atol=1e-06)
|
||
|
|
assert spec.shape == freqs.shape
|
||
|
|
|
||
|
|
def test_psd_windowarray_scale_by_freq(self):
|
||
|
|
win = mlab.window_hanning(np.ones(self.NFFT_density_real))
|
||
|
|
|
||
|
|
spec, fsp = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_hanning)
|
||
|
|
spec_s, fsp_s = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_hanning,
|
||
|
|
scale_by_freq=True)
|
||
|
|
spec_n, fsp_n = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides,
|
||
|
|
window=mlab.window_hanning,
|
||
|
|
scale_by_freq=False)
|
||
|
|
assert_array_equal(fsp, fsp_s)
|
||
|
|
assert_array_equal(fsp, fsp_n)
|
||
|
|
assert_array_equal(spec, spec_s)
|
||
|
|
assert_allclose(spec_s*(win**2).sum(),
|
||
|
|
spec_n/self.Fs*win.sum()**2,
|
||
|
|
atol=1e-08)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"kind", ["complex", "magnitude", "angle", "phase"])
|
||
|
|
def test_spectrum(self, kind):
|
||
|
|
freqs = self.freqs_spectrum
|
||
|
|
spec, fsp = getattr(mlab, f"{kind}_spectrum")(
|
||
|
|
x=self.y,
|
||
|
|
Fs=self.Fs, sides=self.sides, pad_to=self.pad_to_spectrum)
|
||
|
|
assert_allclose(fsp, freqs, atol=1e-06)
|
||
|
|
assert spec.shape == freqs.shape
|
||
|
|
if kind == "magnitude":
|
||
|
|
self.check_maxfreq(spec, fsp, self.fstims)
|
||
|
|
self.check_freqs(spec, freqs, fsp, self.fstims)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
'kwargs',
|
||
|
|
[{}, {'mode': 'default'}, {'mode': 'psd'}, {'mode': 'magnitude'},
|
||
|
|
{'mode': 'complex'}, {'mode': 'angle'}, {'mode': 'phase'}])
|
||
|
|
def test_specgram(self, kwargs):
|
||
|
|
freqs = self.freqs_specgram
|
||
|
|
spec, fsp, t = mlab.specgram(x=self.y,
|
||
|
|
NFFT=self.NFFT_specgram,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_specgram,
|
||
|
|
pad_to=self.pad_to_specgram,
|
||
|
|
sides=self.sides,
|
||
|
|
**kwargs)
|
||
|
|
if kwargs.get('mode') == 'complex':
|
||
|
|
spec = np.abs(spec)
|
||
|
|
specm = np.mean(spec, axis=1)
|
||
|
|
|
||
|
|
assert_allclose(fsp, freqs, atol=1e-06)
|
||
|
|
assert_allclose(t, self.t_specgram, atol=1e-06)
|
||
|
|
|
||
|
|
assert spec.shape[0] == freqs.shape[0]
|
||
|
|
assert spec.shape[1] == self.t_specgram.shape[0]
|
||
|
|
|
||
|
|
if kwargs.get('mode') not in ['complex', 'angle', 'phase']:
|
||
|
|
# using a single freq, so all time slices should be about the same
|
||
|
|
if np.abs(spec.max()) != 0:
|
||
|
|
assert_allclose(
|
||
|
|
np.diff(spec, axis=1).max() / np.abs(spec.max()), 0,
|
||
|
|
atol=1e-02)
|
||
|
|
if kwargs.get('mode') not in ['angle', 'phase']:
|
||
|
|
self.check_freqs(specm, freqs, fsp, self.fstims)
|
||
|
|
|
||
|
|
def test_specgram_warn_only1seg(self):
|
||
|
|
"""Warning should be raised if len(x) <= NFFT."""
|
||
|
|
with pytest.warns(UserWarning, match="Only one segment is calculated"):
|
||
|
|
mlab.specgram(x=self.y, NFFT=len(self.y), Fs=self.Fs)
|
||
|
|
|
||
|
|
def test_psd_csd_equal(self):
|
||
|
|
Pxx, freqsxx = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides)
|
||
|
|
Pxy, freqsxy = mlab.csd(x=self.y, y=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides)
|
||
|
|
assert_array_almost_equal_nulp(Pxx, Pxy)
|
||
|
|
assert_array_equal(freqsxx, freqsxy)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("mode", ["default", "psd"])
|
||
|
|
def test_specgram_auto_default_psd_equal(self, mode):
|
||
|
|
"""
|
||
|
|
Test that mlab.specgram without mode and with mode 'default' and 'psd'
|
||
|
|
are all the same.
|
||
|
|
"""
|
||
|
|
speca, freqspeca, ta = mlab.specgram(x=self.y,
|
||
|
|
NFFT=self.NFFT_specgram,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_specgram,
|
||
|
|
pad_to=self.pad_to_specgram,
|
||
|
|
sides=self.sides)
|
||
|
|
specb, freqspecb, tb = mlab.specgram(x=self.y,
|
||
|
|
NFFT=self.NFFT_specgram,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_specgram,
|
||
|
|
pad_to=self.pad_to_specgram,
|
||
|
|
sides=self.sides,
|
||
|
|
mode=mode)
|
||
|
|
assert_array_equal(speca, specb)
|
||
|
|
assert_array_equal(freqspeca, freqspecb)
|
||
|
|
assert_array_equal(ta, tb)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"mode, conv", [
|
||
|
|
("magnitude", np.abs),
|
||
|
|
("angle", np.angle),
|
||
|
|
("phase", lambda x: np.unwrap(np.angle(x), axis=0))
|
||
|
|
])
|
||
|
|
def test_specgram_complex_equivalent(self, mode, conv):
|
||
|
|
specc, freqspecc, tc = mlab.specgram(x=self.y,
|
||
|
|
NFFT=self.NFFT_specgram,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_specgram,
|
||
|
|
pad_to=self.pad_to_specgram,
|
||
|
|
sides=self.sides,
|
||
|
|
mode='complex')
|
||
|
|
specm, freqspecm, tm = mlab.specgram(x=self.y,
|
||
|
|
NFFT=self.NFFT_specgram,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_specgram,
|
||
|
|
pad_to=self.pad_to_specgram,
|
||
|
|
sides=self.sides,
|
||
|
|
mode=mode)
|
||
|
|
|
||
|
|
assert_array_equal(freqspecc, freqspecm)
|
||
|
|
assert_array_equal(tc, tm)
|
||
|
|
assert_allclose(conv(specc), specm, atol=1e-06)
|
||
|
|
|
||
|
|
def test_psd_windowarray_equal(self):
|
||
|
|
win = mlab.window_hanning(np.ones(self.NFFT_density_real))
|
||
|
|
speca, fspa = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides,
|
||
|
|
window=win)
|
||
|
|
specb, fspb = mlab.psd(x=self.y,
|
||
|
|
NFFT=self.NFFT_density,
|
||
|
|
Fs=self.Fs,
|
||
|
|
noverlap=self.nover_density,
|
||
|
|
pad_to=self.pad_to_density,
|
||
|
|
sides=self.sides)
|
||
|
|
assert_array_equal(fspa, fspb)
|
||
|
|
assert_allclose(speca, specb, atol=1e-08)
|
||
|
|
|
||
|
|
|
||
|
|
# extra test for cohere...
|
||
|
|
def test_cohere():
|
||
|
|
N = 1024
|
||
|
|
np.random.seed(19680801)
|
||
|
|
x = np.random.randn(N)
|
||
|
|
# phase offset
|
||
|
|
y = np.roll(x, 20)
|
||
|
|
# high-freq roll-off
|
||
|
|
y = np.convolve(y, np.ones(20) / 20., mode='same')
|
||
|
|
cohsq, f = mlab.cohere(x, y, NFFT=256, Fs=2, noverlap=128)
|
||
|
|
assert_allclose(np.mean(cohsq), 0.837, atol=1.e-3)
|
||
|
|
assert np.isreal(np.mean(cohsq))
|
||
|
|
|
||
|
|
|
||
|
|
#*****************************************************************
|
||
|
|
# These Tests where taken from SCIPY with some minor modifications
|
||
|
|
# this can be retrieved from:
|
||
|
|
# https://github.com/scipy/scipy/blob/master/scipy/stats/tests/test_kdeoth.py
|
||
|
|
#*****************************************************************
|
||
|
|
|
||
|
|
class TestGaussianKDE:
|
||
|
|
|
||
|
|
def test_kde_integer_input(self):
|
||
|
|
"""Regression test for #1181."""
|
||
|
|
x1 = np.arange(5)
|
||
|
|
kde = mlab.GaussianKDE(x1)
|
||
|
|
y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869,
|
||
|
|
0.13480721]
|
||
|
|
np.testing.assert_array_almost_equal(kde(x1), y_expected, decimal=6)
|
||
|
|
|
||
|
|
def test_gaussian_kde_covariance_caching(self):
|
||
|
|
x1 = np.array([-7, -5, 1, 4, 5], dtype=float)
|
||
|
|
xs = np.linspace(-10, 10, num=5)
|
||
|
|
# These expected values are from scipy 0.10, before some changes to
|
||
|
|
# gaussian_kde. They were not compared with any external reference.
|
||
|
|
y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754,
|
||
|
|
0.01664475]
|
||
|
|
|
||
|
|
# set it to the default bandwidth.
|
||
|
|
kde2 = mlab.GaussianKDE(x1, 'scott')
|
||
|
|
y2 = kde2(xs)
|
||
|
|
|
||
|
|
np.testing.assert_array_almost_equal(y_expected, y2, decimal=7)
|
||
|
|
|
||
|
|
def test_kde_bandwidth_method(self):
|
||
|
|
|
||
|
|
np.random.seed(8765678)
|
||
|
|
n_basesample = 50
|
||
|
|
xn = np.random.randn(n_basesample)
|
||
|
|
|
||
|
|
# Default
|
||
|
|
gkde = mlab.GaussianKDE(xn)
|
||
|
|
# Supply a callable
|
||
|
|
gkde2 = mlab.GaussianKDE(xn, 'scott')
|
||
|
|
# Supply a scalar
|
||
|
|
gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)
|
||
|
|
|
||
|
|
xs = np.linspace(-7, 7, 51)
|
||
|
|
kdepdf = gkde.evaluate(xs)
|
||
|
|
kdepdf2 = gkde2.evaluate(xs)
|
||
|
|
assert kdepdf.all() == kdepdf2.all()
|
||
|
|
kdepdf3 = gkde3.evaluate(xs)
|
||
|
|
assert kdepdf.all() == kdepdf3.all()
|
||
|
|
|
||
|
|
|
||
|
|
class TestGaussianKDECustom:
|
||
|
|
def test_no_data(self):
|
||
|
|
"""Pass no data into the GaussianKDE class."""
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.GaussianKDE([])
|
||
|
|
|
||
|
|
def test_single_dataset_element(self):
|
||
|
|
"""Pass a single dataset element into the GaussianKDE class."""
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.GaussianKDE([42])
|
||
|
|
|
||
|
|
def test_silverman_multidim_dataset(self):
|
||
|
|
"""Test silverman's for a multi-dimensional array."""
|
||
|
|
x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||
|
|
with pytest.raises(np.linalg.LinAlgError):
|
||
|
|
mlab.GaussianKDE(x1, "silverman")
|
||
|
|
|
||
|
|
def test_silverman_singledim_dataset(self):
|
||
|
|
"""Test silverman's output for a single dimension list."""
|
||
|
|
x1 = np.array([-7, -5, 1, 4, 5])
|
||
|
|
mygauss = mlab.GaussianKDE(x1, "silverman")
|
||
|
|
y_expected = 0.76770389927475502
|
||
|
|
assert_almost_equal(mygauss.covariance_factor(), y_expected, 7)
|
||
|
|
|
||
|
|
def test_scott_multidim_dataset(self):
|
||
|
|
"""Test scott's output for a multi-dimensional array."""
|
||
|
|
x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||
|
|
with pytest.raises(np.linalg.LinAlgError):
|
||
|
|
mlab.GaussianKDE(x1, "scott")
|
||
|
|
|
||
|
|
def test_scott_singledim_dataset(self):
|
||
|
|
"""Test scott's output a single-dimensional array."""
|
||
|
|
x1 = np.array([-7, -5, 1, 4, 5])
|
||
|
|
mygauss = mlab.GaussianKDE(x1, "scott")
|
||
|
|
y_expected = 0.72477966367769553
|
||
|
|
assert_almost_equal(mygauss.covariance_factor(), y_expected, 7)
|
||
|
|
|
||
|
|
def test_scalar_empty_dataset(self):
|
||
|
|
"""Test the scalar's cov factor for an empty array."""
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.GaussianKDE([], bw_method=5)
|
||
|
|
|
||
|
|
def test_scalar_covariance_dataset(self):
|
||
|
|
"""Test a scalar's cov factor."""
|
||
|
|
np.random.seed(8765678)
|
||
|
|
n_basesample = 50
|
||
|
|
multidim_data = [np.random.randn(n_basesample) for i in range(5)]
|
||
|
|
|
||
|
|
kde = mlab.GaussianKDE(multidim_data, bw_method=0.5)
|
||
|
|
assert kde.covariance_factor() == 0.5
|
||
|
|
|
||
|
|
def test_callable_covariance_dataset(self):
|
||
|
|
"""Test the callable's cov factor for a multi-dimensional array."""
|
||
|
|
np.random.seed(8765678)
|
||
|
|
n_basesample = 50
|
||
|
|
multidim_data = [np.random.randn(n_basesample) for i in range(5)]
|
||
|
|
|
||
|
|
def callable_fun(x):
|
||
|
|
return 0.55
|
||
|
|
kde = mlab.GaussianKDE(multidim_data, bw_method=callable_fun)
|
||
|
|
assert kde.covariance_factor() == 0.55
|
||
|
|
|
||
|
|
def test_callable_singledim_dataset(self):
|
||
|
|
"""Test the callable's cov factor for a single-dimensional array."""
|
||
|
|
np.random.seed(8765678)
|
||
|
|
n_basesample = 50
|
||
|
|
multidim_data = np.random.randn(n_basesample)
|
||
|
|
|
||
|
|
kde = mlab.GaussianKDE(multidim_data, bw_method='silverman')
|
||
|
|
y_expected = 0.48438841363348911
|
||
|
|
assert_almost_equal(kde.covariance_factor(), y_expected, 7)
|
||
|
|
|
||
|
|
def test_wrong_bw_method(self):
|
||
|
|
"""Test the error message that should be called when bw is invalid."""
|
||
|
|
np.random.seed(8765678)
|
||
|
|
n_basesample = 50
|
||
|
|
data = np.random.randn(n_basesample)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
mlab.GaussianKDE(data, bw_method="invalid")
|
||
|
|
|
||
|
|
|
||
|
|
class TestGaussianKDEEvaluate:
|
||
|
|
|
||
|
|
def test_evaluate_diff_dim(self):
|
||
|
|
"""
|
||
|
|
Test the evaluate method when the dim's of dataset and points have
|
||
|
|
different dimensions.
|
||
|
|
"""
|
||
|
|
x1 = np.arange(3, 10, 2)
|
||
|
|
kde = mlab.GaussianKDE(x1)
|
||
|
|
x2 = np.arange(3, 12, 2)
|
||
|
|
y_expected = [
|
||
|
|
0.08797252, 0.11774109, 0.11774109, 0.08797252, 0.0370153
|
||
|
|
]
|
||
|
|
y = kde.evaluate(x2)
|
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np.testing.assert_array_almost_equal(y, y_expected, 7)
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def test_evaluate_inv_dim(self):
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"""
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Invert the dimensions; i.e., for a dataset of dimension 1 [3, 2, 4],
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the points should have a dimension of 3 [[3], [2], [4]].
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"""
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np.random.seed(8765678)
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n_basesample = 50
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multidim_data = np.random.randn(n_basesample)
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kde = mlab.GaussianKDE(multidim_data)
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x2 = [[1], [2], [3]]
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with pytest.raises(ValueError):
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kde.evaluate(x2)
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|
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def test_evaluate_dim_and_num(self):
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"""Tests if evaluated against a one by one array"""
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x1 = np.arange(3, 10, 2)
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x2 = np.array([3])
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kde = mlab.GaussianKDE(x1)
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|
y_expected = [0.08797252]
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||
|
|
y = kde.evaluate(x2)
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||
|
|
np.testing.assert_array_almost_equal(y, y_expected, 7)
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|
|
|
||
|
|
def test_evaluate_point_dim_not_one(self):
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|
x1 = np.arange(3, 10, 2)
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|
x2 = [np.arange(3, 10, 2), np.arange(3, 10, 2)]
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||
|
|
kde = mlab.GaussianKDE(x1)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
kde.evaluate(x2)
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||
|
|
|
||
|
|
def test_evaluate_equal_dim_and_num_lt(self):
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|
|
x1 = np.arange(3, 10, 2)
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||
|
|
x2 = np.arange(3, 8, 2)
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||
|
|
kde = mlab.GaussianKDE(x1)
|
||
|
|
y_expected = [0.08797252, 0.11774109, 0.11774109]
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||
|
|
y = kde.evaluate(x2)
|
||
|
|
np.testing.assert_array_almost_equal(y, y_expected, 7)
|
||
|
|
|
||
|
|
|
||
|
|
def test_psd_onesided_norm():
|
||
|
|
u = np.array([0, 1, 2, 3, 1, 2, 1])
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||
|
|
dt = 1.0
|
||
|
|
Su = np.abs(np.fft.fft(u) * dt)**2 / (dt * u.size)
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||
|
|
P, f = mlab.psd(u, NFFT=u.size, Fs=1/dt, window=mlab.window_none,
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||
|
|
detrend=mlab.detrend_none, noverlap=0, pad_to=None,
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||
|
|
scale_by_freq=None,
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||
|
|
sides='onesided')
|
||
|
|
Su_1side = np.append([Su[0]], Su[1:4] + Su[4:][::-1])
|
||
|
|
assert_allclose(P, Su_1side, atol=1e-06)
|
||
|
|
|
||
|
|
|
||
|
|
def test_psd_oversampling():
|
||
|
|
"""Test the case len(x) < NFFT for psd()."""
|
||
|
|
u = np.array([0, 1, 2, 3, 1, 2, 1])
|
||
|
|
dt = 1.0
|
||
|
|
Su = np.abs(np.fft.fft(u) * dt)**2 / (dt * u.size)
|
||
|
|
P, f = mlab.psd(u, NFFT=u.size*2, Fs=1/dt, window=mlab.window_none,
|
||
|
|
detrend=mlab.detrend_none, noverlap=0, pad_to=None,
|
||
|
|
scale_by_freq=None,
|
||
|
|
sides='onesided')
|
||
|
|
Su_1side = np.append([Su[0]], Su[1:4] + Su[4:][::-1])
|
||
|
|
assert_almost_equal(np.sum(P), np.sum(Su_1side)) # same energy
|