def test_against_numpy_convolve(self, cpx, na, nv, mode):
a = self.create_vector(na, cpx)
v = self.create_vector(nv, cpx)
if mode is None:
y1 = np.convolve(v, a)
A = convolution_matrix(a, nv)
else:
y1 = np.convolve(v, a, mode)
A = convolution_matrix(a, nv, mode)
y2 = A @ v
assert_array_almost_equal(y1, y2)
def test_convolution_matrix():
'test convolution_matrix vs. numpy.convolve for various parameters'
def test_vector(n, cpx):
'make a complex or real test vector of length n'
if cpx:
return np.random.normal(size=(n, 2))\
.astype(np.float64).view(np.complex128).ravel()
else:
return np.random.normal(size=(n,))
# first arg must be a 1d array, otherwise ValueError
with assert_raises(ValueError):
convolution_matrix(1, 4)
# mode must be in ('full','valid','same')
with assert_raises(ValueError):
convolution_matrix((1, 1), 4, mode='invalid argument')
array_sizes = (2, 9, 100)
for cpx, na, nv, mode in itertools.product(
(False, True),
array_sizes,
array_sizes,
(None, 'full', 'valid', 'same')):
a = test_vector(na, cpx)
v = test_vector(nv, cpx)
if mode is None:
y1 = np.convolve(v, a)
A = convolution_matrix(a, nv)
else:
y1 = np.convolve(v, a, mode)
A = convolution_matrix(a, nv, mode)
y2 = A @ v
assert_array_almost_equal(y1, y2)
if mode == 'full':
for i in range(A.shape[0]):
for j in range(A.shape[1]):
assert_equal(A[i, j],
(a[i-j] if (0 <= (i-j) < len(a)) else 0))
def test_bad_mode(self):
# mode must be in ('full', 'valid', 'same')
with pytest.raises(ValueError, match='mode.*must be one of'):
convolution_matrix((1, 1), 4, mode='invalid argument')
def test_empty_first_arg(self):
# first arg must have at least one value
with pytest.raises(ValueError, match=r'len\(a\)'):
convolution_matrix([], 4)
def test_bad_first_arg(self):
# first arg must be a 1d array, otherwise ValueError
with pytest.raises(ValueError, match='one-dimensional'):
convolution_matrix(1, 4)
def test_bad_n(self):
# n must be a positive integer
with pytest.raises(ValueError, match='n must be a positive integer'):
convolution_matrix([1, 2, 3], 0)