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1709 lines (1493 loc) · 64.1 KB
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from numpy.testing import (assert_allclose, assert_almost_equal,
assert_array_equal, assert_array_almost_equal_nulp)
import numpy as np
import pytest
import matplotlib.mlab as mlab
from matplotlib.cbook.deprecation import MatplotlibDeprecationWarning
def _stride_repeat(*args, **kwargs):
with pytest.warns(MatplotlibDeprecationWarning):
return mlab.stride_repeat(*args, **kwargs)
class TestStride:
def get_base(self, x):
y = x
while y.base is not None:
y = y.base
return y
def calc_window_target(self, x, NFFT, noverlap=0, axis=0):
"""
This is an adaptation of the original window extraction algorithm.
This is here to test to make sure the new implementation has the same
result.
"""
step = NFFT - noverlap
ind = np.arange(0, len(x) - NFFT + 1, step)
n = len(ind)
result = np.zeros((NFFT, n))
# do the ffts of the slices
for i in range(n):
result[:, i] = x[ind[i]:ind[i]+NFFT]
if axis == 1:
result = result.T
return result
@pytest.mark.parametrize('shape', [(), (10, 1)], ids=['0D', '2D'])
def test_stride_windows_invalid_input_shape(self, shape):
x = np.arange(np.prod(shape)).reshape(shape)
with pytest.raises(ValueError):
mlab.stride_windows(x, 5)
@pytest.mark.parametrize('n, noverlap',
[(0, None), (11, None), (2, 2), (2, 3)],
ids=['n less than 1', 'n greater than input',
'noverlap greater than n',
'noverlap equal to n'])
def test_stride_windows_invalid_params(self, n, noverlap):
x = np.arange(10)
with pytest.raises(ValueError):
mlab.stride_windows(x, n, noverlap)
@pytest.mark.parametrize('shape', [(), (10, 1)], ids=['0D', '2D'])
def test_stride_repeat_invalid_input_shape(self, shape):
x = np.arange(np.prod(shape)).reshape(shape)
with pytest.raises(ValueError):
_stride_repeat(x, 5)
@pytest.mark.parametrize('axis', [-1, 2],
ids=['axis less than 0',
'axis greater than input shape'])
def test_stride_repeat_invalid_axis(self, axis):
x = np.array(0)
with pytest.raises(ValueError):
_stride_repeat(x, 5, axis=axis)
def test_stride_repeat_n_lt_1_ValueError(self):
x = np.arange(10)
with pytest.raises(ValueError):
_stride_repeat(x, 0)
@pytest.mark.parametrize('axis', [0, 1], ids=['axis0', 'axis1'])
@pytest.mark.parametrize('n', [1, 5], ids=['n1', 'n5'])
def test_stride_repeat(self, n, axis):
x = np.arange(10)
y = _stride_repeat(x, n, axis=axis)
expected_shape = [10, 10]
expected_shape[axis] = n
yr = np.repeat(np.expand_dims(x, axis), n, axis=axis)
assert yr.shape == y.shape
assert_array_equal(yr, y)
assert tuple(expected_shape) == y.shape
assert self.get_base(y) is x
@pytest.mark.parametrize('axis', [0, 1], ids=['axis0', 'axis1'])
@pytest.mark.parametrize('n, noverlap',
[(1, 0), (5, 0), (15, 2), (13, -3)],
ids=['n1-noverlap0', 'n5-noverlap0',
'n15-noverlap2', 'n13-noverlapn3'])
def test_stride_windows(self, n, noverlap, axis):
x = np.arange(100)
y = mlab.stride_windows(x, n, noverlap=noverlap, axis=axis)
expected_shape = [0, 0]
expected_shape[axis] = n
expected_shape[1 - axis] = 100 // (n - noverlap)
yt = self.calc_window_target(x, n, noverlap=noverlap, axis=axis)
assert yt.shape == y.shape
assert_array_equal(yt, y)
assert tuple(expected_shape) == y.shape
assert self.get_base(y) is x
@pytest.mark.parametrize('axis', [0, 1], ids=['axis0', 'axis1'])
def test_stride_windows_n32_noverlap0_unflatten(self, axis):
n = 32
x = np.arange(n)[np.newaxis]
x1 = np.tile(x, (21, 1))
x2 = x1.flatten()
y = mlab.stride_windows(x2, n, axis=axis)
if axis == 0:
x1 = x1.T
assert y.shape == x1.shape
assert_array_equal(y, x1)
def test_stride_ensure_integer_type(self):
N = 100
x = np.full(N + 20, np.nan)
y = x[10:-10]
y[:] = 0.3
# previous to #3845 lead to corrupt access
y_strided = mlab.stride_windows(y, n=33, noverlap=0.6)
assert_array_equal(y_strided, 0.3)
# previous to #3845 lead to corrupt access
y_strided = mlab.stride_windows(y, n=33.3, noverlap=0)
assert_array_equal(y_strided, 0.3)
# even previous to #3845 could not find any problematic
# configuration however, let's be sure it's not accidentally
# introduced
y_strided = _stride_repeat(y, n=33.815)
assert_array_equal(y_strided, 0.3)
def _apply_window(*args, **kwargs):
with pytest.warns(MatplotlibDeprecationWarning):
return mlab.apply_window(*args, **kwargs)
class TestWindow:
def setup(self):
np.random.seed(0)
n = 1000
self.sig_rand = np.random.standard_normal(n) + 100.
self.sig_ones = np.ones(n)
def check_window_apply_repeat(self, x, window, NFFT, noverlap):
"""
This is an adaptation of the original window application algorithm.
This is here to test to make sure the new implementation has the same
result.
"""
step = NFFT - noverlap
ind = np.arange(0, len(x) - NFFT + 1, step)
n = len(ind)
result = np.zeros((NFFT, n))
if np.iterable(window):
windowVals = window
else:
windowVals = window(np.ones(NFFT, x.dtype))
# do the ffts of the slices
for i in range(n):
result[:, i] = windowVals * x[ind[i]:ind[i]+NFFT]
return result
def test_window_none_rand(self):
res = mlab.window_none(self.sig_ones)
assert_array_equal(res, self.sig_ones)
def test_window_none_ones(self):
res = mlab.window_none(self.sig_rand)
assert_array_equal(res, self.sig_rand)
def test_window_hanning_rand(self):
targ = np.hanning(len(self.sig_rand)) * self.sig_rand
res = mlab.window_hanning(self.sig_rand)
assert_allclose(targ, res, atol=1e-06)
def test_window_hanning_ones(self):
targ = np.hanning(len(self.sig_ones))
res = mlab.window_hanning(self.sig_ones)
assert_allclose(targ, res, atol=1e-06)
def test_apply_window_1D_axis1_ValueError(self):
x = self.sig_rand
window = mlab.window_hanning
with pytest.raises(ValueError):
_apply_window(x, window, axis=1, return_window=False)
def test_apply_window_1D_els_wrongsize_ValueError(self):
x = self.sig_rand
window = mlab.window_hanning(np.ones(x.shape[0]-1))
with pytest.raises(ValueError):
_apply_window(x, window)
def test_apply_window_0D_ValueError(self):
x = np.array(0)
window = mlab.window_hanning
with pytest.raises(ValueError):
_apply_window(x, window, axis=1, return_window=False)
def test_apply_window_3D_ValueError(self):
x = self.sig_rand[np.newaxis][np.newaxis]
window = mlab.window_hanning
with pytest.raises(ValueError):
_apply_window(x, window, axis=1, return_window=False)
def test_apply_window_hanning_1D(self):
x = self.sig_rand
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[0]))
y, window2 = _apply_window(x, window, return_window=True)
yt = window(x)
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_1D_axis0(self):
x = self.sig_rand
window = mlab.window_hanning
y = _apply_window(x, window, axis=0, return_window=False)
yt = window(x)
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_els_1D_axis0(self):
x = self.sig_rand
window = mlab.window_hanning(np.ones(x.shape[0]))
window1 = mlab.window_hanning
y = _apply_window(x, window, axis=0, return_window=False)
yt = window1(x)
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning
y = _apply_window(x, window, axis=0, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[1]):
yt[:, i] = window(x[:, i])
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_els1_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning(np.ones(x.shape[0]))
window1 = mlab.window_hanning
y = _apply_window(x, window, axis=0, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[1]):
yt[:, i] = window1(x[:, i])
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_els2_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[0]))
y, window2 = _apply_window(x, window, axis=0, return_window=True)
yt = np.zeros_like(x)
for i in range(x.shape[1]):
yt[:, i] = window1*x[:, i]
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_els3_2D_axis0(self):
x = np.random.standard_normal([1000, 10]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[0]))
y, window2 = _apply_window(x, window, axis=0, return_window=True)
yt = _apply_window(x, window1, axis=0, return_window=False)
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
assert_array_equal(window1, window2)
def test_apply_window_hanning_2D_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning
y = _apply_window(x, window, axis=1, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[0]):
yt[i, :] = window(x[i, :])
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D__els1_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning(np.ones(x.shape[1]))
window1 = mlab.window_hanning
y = _apply_window(x, window, axis=1, return_window=False)
yt = np.zeros_like(x)
for i in range(x.shape[0]):
yt[i, :] = window1(x[i, :])
assert yt.shape == y.shape
assert x.shape == y.shape
assert_allclose(yt, y, atol=1e-06)
def test_apply_window_hanning_2D_els2_axis1(self):
x = np.random.standard_normal([10, 1000]) + 100.
window = mlab.window_hanning
window1 = mlab.window_hanning(np.ones(x.shape[1]))
y, window2 = _apply_window(x, window, axis=1, return_window=True)
yt = np.zeros_like(x)
for i in range(x.shape[0]):
yt[i, :] = window1 * x[i, :]
assert yt.shape == y.shape
assert x.shape == y.shape
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: