def upfirdn_naive(x, h, up=1, down=1):
"""Naive upfirdn processing in Python
Note: arg order (x, h) differs to facilitate apply_along_axis use.
"""
h = np.asarray(h)
out = np.zeros(len(x) * up, x.dtype)
out[::up] = x
out = np.convolve(h, out)[::down][:_output_len(len(h), len(x), up, down)]
return out
def upfirdn_naive(x, h, up=1, down=1):
"""Naive upfirdn processing in Python
Note: arg order (x, h) differs to facilitate apply_along_axis use.
"""
h = np.asarray(h)
out = np.zeros(len(x) * up, x.dtype)
out[::up] = x
out = np.convolve(h, out)[::down][:_output_len(len(h), len(x), up, down)]
return out
def scrub(self, x, axis=-1):
yr = np.apply_along_axis(upfirdn_naive, axis, x, self.h, self.up,
self.down)
want_len = _output_len(len(self.h), x.shape[axis], self.up, self.down)
assert yr.shape[axis] == want_len
y = upfirdn(self.h, x, self.up, self.down, axis=axis)
assert y.shape[axis] == want_len
assert y.shape == yr.shape
dtypes = (self.h.dtype, x.dtype)
if all(d == np.complex64 for d in dtypes):
assert_equal(y.dtype, np.complex64)
elif np.complex64 in dtypes and np.float32 in dtypes:
assert_equal(y.dtype, np.complex64)
elif all(d == np.float32 for d in dtypes):
assert_equal(y.dtype, np.float32)
elif np.complex128 in dtypes or np.complex64 in dtypes:
assert_equal(y.dtype, np.complex128)
else:
assert_equal(y.dtype, np.float64)
assert_allclose(yr, y)