def test_convolve_1d_axis_3d():
f = np.random.random((128,32,6))
w = np.array([.1, .2, .4])
fw = mh.convolve1d(f, w, 0)
for i in range(f.shape[1]):
for j in range(f.shape[2]):
assert np.allclose(np.correlate(f[:,i,j], w,'same')[1:-1:] , fw[:,i,j][1:-1])
def test_convolve1d():
ws = [
np.array([-.1, .5,.7,.7,.5]),
np.array([.1,.7,.5]),
]
for i in range(8):
for w in ws:
f = np.random.random((128,96))
ww = np.atleast_2d(w)
fw = mh.convolve(f, ww)
fww = mh.convolve(f, ww.T)
f0w = mh.convolve1d(f, w, 0)
f1w = mh.convolve1d(f, w, 1)
assert np.all(fw == f1w)
assert np.all(fww == f0w)
def test_convolve_1d_axis_3d():
f = np.random.random((128, 32, 6))
w = np.array([.1, .2, .4])
fw = mh.convolve1d(f, w, 0)
for i in range(f.shape[1]):
for j in range(f.shape[2]):
assert np.allclose(
np.correlate(f[:, i, j], w, 'same')[1:-1:], fw[:, i, j][1:-1])
def test_convolve1d():
ws = [
np.array([-.1, .5, .7, .7, .5]),
np.array([.1, .7, .5]),
]
for i in range(8):
for w in ws:
f = np.random.random((128, 96))
ww = np.atleast_2d(w)
fw = mh.convolve(f, ww)
fww = mh.convolve(f, ww.T)
f0w = mh.convolve1d(f, w, 0)
f1w = mh.convolve1d(f, w, 1)
assert np.all(fw == f1w)
assert np.all(fww == f0w)
def test_convolve1d_axis():
f = np.random.random((128,32))
w = np.array([.1, .2, .4])
fw = mh.convolve1d(f, w, 0)
for i in range(32):
assert np.allclose(np.correlate(f.T[i], w, 'same')[1:-1], fw[1:-1,i])
def test_convolve1d_axis():
f = np.random.random((128, 32))
w = np.array([.1, .2, .4])
fw = mh.convolve1d(f, w, 0)
for i in range(32):
assert np.allclose(np.correlate(f.T[i], w, 'same')[1:-1], fw[1:-1, i])