def test_3d_reset_state(self):
shape = self.shape3D
known_data = np.random.normal(size=shape).astype(np.float32).view(
np.complex64)
idata = bf.ndarray(known_data, space='cuda')
odata = bf.empty_like(idata)
coeffs = self.coeffs * 1.0
coeffs.shape += (1, )
coeffs = np.repeat(coeffs, idata.shape[1] * idata.shape[2], axis=1)
coeffs.shape = (coeffs.shape[0], idata.shape[1], idata.shape[2])
coeffs2 = coeffs * 2.0
coeffs = bf.ndarray(coeffs, space='cuda')
coeffs2 = bf.ndarray(coeffs2, space='cuda')
fir = Fir()
fir.init(coeffs, 1)
fir.execute(idata, odata)
fir.reset_state()
fir.execute(idata, odata)
odata = odata.copy('system')
for i in range(known_data.shape[1]):
for j in range(known_data.shape[2]):
zf = lfiltic(self.coeffs, 1.0, 0.0)
known_result, zf = lfilter(self.coeffs,
1.0,
known_data[:, i, j],
zi=zf)
compare(odata[:, i, j], known_result)
def test_2d_decimate_active(self):
shape = self.shape2D
known_data = np.random.normal(size=shape).astype(np.float32).view(
np.complex64)
idata = bf.ndarray(known_data, space='cuda')
odata = bf.empty((idata.shape[0] // 2, idata.shape[1]),
dtype=idata.dtype,
space='cuda')
coeffs = self.coeffs * 1.0
coeffs.shape += (1, )
coeffs = np.repeat(coeffs, idata.shape[1], axis=1)
coeffs.shape = (coeffs.shape[0], idata.shape[1])
coeffs = bf.ndarray(coeffs, space='cuda')
fir = Fir()
fir.init(coeffs, 2)
fir.execute(idata, odata)
fir.execute(idata, odata)
odata = odata.copy('system')
for i in range(known_data.shape[1]):
zf = lfiltic(self.coeffs, 1.0, 0.0)
known_result, zf = lfilter(self.coeffs,
1.0,
known_data[:, i],
zi=zf)
known_result, zf = lfilter(self.coeffs,
1.0,
known_data[:, i],
zi=zf)
known_result = known_result[0::2]
compare(odata[:, i], known_result)
def test_2d_active(self):
shape = self.shape2D
known_data = np.random.normal(size=shape).astype(np.float32).view(
np.complex64)
idata = bf.ndarray(known_data, space='cuda_managed')
odata = bf.empty_like(idata)
coeffs = self.coeffs * 1.0
coeffs.shape += (1, )
coeffs = np.repeat(coeffs, idata.shape[1], axis=1)
coeffs.shape = (coeffs.shape[0], idata.shape[1])
coeffs = bf.ndarray(coeffs, space='cuda_managed')
fir = Fir()
fir.init(coeffs, 1)
fir.execute(idata, odata)
fir.execute(idata, odata)
stream_synchronize()
for i in range(known_data.shape[1]):
zf = lfiltic(self.coeffs, 1.0, 0.0)
known_result, zf = lfilter(self.coeffs,
1.0,
known_data[:, i],
zi=zf)
known_result, zf = lfilter(self.coeffs,
1.0,
known_data[:, i],
zi=zf)
compare(odata[:, i], known_result)