def test_convolution_monte_carlo(input_1, input_2, mode):
y, Uy = convolve_unc(*input_1, *input_2, mode)
n_runs = 40000
XX1 = np.random.multivariate_normal(*input_1, size=n_runs)
XX2 = np.random.multivariate_normal(*input_2, size=n_runs)
if mode in numpy_modes:
convolve = np.convolve
else: # mode in scipy_modes:
convolve = sn.convolve1d
mc_results = [convolve(x1, x2, mode=mode) for x1, x2 in zip(XX1, XX2)]
y_mc = np.mean(mc_results, axis=0)
Uy_mc = np.cov(mc_results, rowvar=False)
# HACK: for visualization during debugging
# import matplotlib.pyplot as plt
# fig, ax = plt.subplots(nrows=1, ncols=4)
# _min = min(Uy.min(), Uy_mc.min())
# _max = max(Uy.max(), Uy_mc.max())
# ax[0].plot(y, label="fir")
# ax[0].plot(y_mc, label="mc")
# ax[0].set_title("mode: {0}, x1: {1}, x2: {2}".format(mode, len(x1), len(x2)))
# ax[0].legend()
# ax[1].imshow(Uy, vmin=_min, vmax=_max)
# ax[1].set_title("PyDynamic")
# ax[2].imshow(Uy_mc, vmin=_min, vmax=_max)
# ax[2].set_title("numpy MC")
# img = ax[3].imshow(np.log(np.abs(Uy-Uy_mc)))
# ax[3].set_title("log(abs(diff))")
# fig.colorbar(img, ax=ax[3])
# plt.show()
# /HACK
assert_allclose(y, y_mc, rtol=1e-1, atol=1e-1)
assert_allclose(Uy, Uy_mc, rtol=1e-1, atol=1e-1)
def test_convolution(input_1, input_2, mode):
# calculate the convolution of x1 and x2
y, Uy = convolve_unc(*input_1, *input_2, mode)
if mode in numpy_modes:
y_ref = np.convolve(input_1[0], input_2[0], mode=mode)
else: # mode in valid_modes("scipy"):
y_ref = sn.convolve1d(input_1[0], input_2[0], mode=mode)
# compare results
assert len(y) == len(Uy)
assert len(y) == len(y_ref)
assert_allclose(y + 1, y_ref + 1)
def test_convolution_common_call(input_1, input_2):
# check common execution of convolve_unc
assert convolve_unc(*input_1, *input_2)
def test_convolution_invalid_mode(input_1, input_2, mode):
assume(mode not in numpy_modes and mode not in scipy_modes)
with pytest.raises(ValueError):
convolve_unc(*input_1, *input_2, mode)
def test_convolution_invalid_mode(input_1, input_2, mode):
set_of_valid_modes = set(valid_modes())
assume(mode not in set_of_valid_modes)
with pytest.raises(ValueError):
convolve_unc(*input_1, *input_2, mode)