def test_3d_decimate_active(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((idata.shape[0] // 2, idata.shape[1], idata.shape[2]),
dtype=idata.dtype,
space='cuda')
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])
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]):
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)
known_result, zf = lfilter(self.coeffs,
1.0,
known_data[:, i, j],
zi=zf)
known_result = known_result[0::2]
compare(odata[:, i, j], known_result)
def test_explicit_indexing(self):
shape = (55, 66, 77)
a = np.random.randint(65536, size=shape).astype(np.int32)
a = bf.asarray(a, space='cuda')
b = bf.empty((a.shape[2], a.shape[0], a.shape[1]), a.dtype, 'cuda')
bf.map("b(i,j,k) = a(j,k,i)", b.shape, 'i', 'j', 'k', a=a, b=b)
a = a.copy('system')
b = b.copy('system')
np.testing.assert_equal(b, a.transpose([2, 0, 1]))
def test_custom_shape(self):
shape = (55, 66, 77)
a = np.random.randint(65536, size=shape).astype(np.int32)
a = bf.asarray(a, space='cuda')
b = bf.empty((a.shape[0], a.shape[2]), a.dtype, 'cuda')
j = 11
bf.map("b(i,k) = a(i,j,k)", b.shape, 'i', 'k', a=a, b=b, j=j)
a = a.copy('system')
b = b.copy('system')
np.testing.assert_equal(b, a[:, j, :])
def test_explicit_indexing(self):
shape = (55,66,77)
a = np.random.randint(65536, size=shape).astype(np.int32)
a = bf.asarray(a, space='cuda')
b = bf.empty((a.shape[2],a.shape[0], a.shape[1]), a.dtype, 'cuda')
for _ in xrange(3):
bf.map("b(i,j,k) = a(j,k,i)", shape=b.shape, axis_names=('i','j','k'),
data={'a': a, 'b': b}, block_shape=(64,4), block_axes=('i','k'))
a = a.copy('system')
b = b.copy('system')
np.testing.assert_equal(b, a.transpose([2,0,1]))
def test_broadcast(self):
n = 89
a = np.arange(n).astype(np.float32)
a = bf.asarray(a, space='cuda')
b = a[:, None]
c = bf.empty((a.shape[0], b.shape[0]), a.dtype,
'cuda') # TODO: Need way to compute broadcast shape
bf.map("c = a*b", a=a, b=b, c=c)
a = a.copy('system')
b = b.copy('system')
c = c.copy('system')
np.testing.assert_equal(c, a * b)
def test_custom_shape(self):
shape = (55,66,77)
a = np.random.randint(65536, size=shape).astype(np.int32)
a = bf.asarray(a, space='cuda')
b = bf.empty((a.shape[0],a.shape[2]), a.dtype, 'cuda')
j = 11
for _ in xrange(3):
bf.map("b(i,k) = a(i,j,k)", shape=b.shape, axis_names=('i','k'),
data={'a': a, 'b': b, 'j': j})
a = a.copy('system')
b = b.copy('system')
np.testing.assert_equal(b, a[:,j,:])
