def check_errors_first_order(thr, mshape, batch, add_points=None, dtype=numpy.complex64):
test_func = TestFunction(mshape, dtype, batch=batch, order=1)
if add_points is None:
add_points = [0] * len(mshape)
xs = [get_spatial_grid(n, 1, add_points=ap) for n, ap in zip(mshape, add_points)]
mdata_dev = thr.array((batch,) + mshape, dtype)
axes = list(range(1, len(mshape)+1))
dht_fw = DHT(mdata_dev, inverse=False, axes=axes, add_points=[0] + add_points)
dht_inv = DHT(mdata_dev, inverse=True, axes=axes, add_points=[0] + add_points)
dht_fw_c = dht_fw.compile(thr)
dht_inv_c = dht_inv.compile(thr)
xdata = test_func(*xs)
xdata_dev = thr.to_device(xdata)
# forward transform
dht_fw_c(mdata_dev, xdata_dev)
assert diff_is_negligible(mdata_dev.get(), test_func.mdata)
# inverse transform
dht_inv_c(xdata_dev, mdata_dev)
assert diff_is_negligible(xdata_dev.get(), xdata)
def test_high_order_forward(thr, ho_order, ho_shape):
"""
Checks that if we change the mode space while keeping mode population the same,
the result of forward transformation for orders higher than 1 do not change.
"""
dtype = numpy.float32
modes = TestFunction.generate_modes((ho_shape,), dtype)
f1 = TestFunction((ho_shape,), dtype, order=ho_order, modes=modes)
f2 = TestFunction((ho_shape + 1,), dtype, order=ho_order, modes=modes)
xs1 = get_spatial_grid(ho_shape, ho_order)
xs2 = get_spatial_grid(ho_shape + 1, ho_order)
xdata1 = f1(xs1)
xdata2 = f2(xs2)
xdata1_dev = thr.to_device(xdata1)
xdata2_dev = thr.to_device(xdata2)
mdata1_dev = thr.array(ho_shape, dtype)
mdata2_dev = thr.array(ho_shape + 1, dtype)
dht_fw1 = DHT(mdata1_dev, inverse=False, order=ho_order)
dht_fw2 = DHT(mdata2_dev, inverse=False, order=ho_order)
dht_fw1_c = dht_fw1.compile(thr)
dht_fw2_c = dht_fw2.compile(thr)
dht_fw1_c(mdata1_dev, xdata1_dev)
dht_fw2_c(mdata2_dev, xdata2_dev)
mdata1 = mdata1_dev.get()
mdata2 = mdata2_dev.get()
assert diff_is_negligible(mdata1, mdata2[:-1])
def check_errors_first_order(thr,
mshape,
batch,
add_points=None,
dtype=numpy.complex64):
test_func = TestFunction(mshape, dtype, batch=batch, order=1)
if add_points is None:
add_points = [0] * len(mshape)
xs = [
get_spatial_grid(n, 1, add_points=ap)
for n, ap in zip(mshape, add_points)
]
mdata_dev = thr.array((batch, ) + mshape, dtype)
axes = list(range(1, len(mshape) + 1))
dht_fw = DHT(mdata_dev,
inverse=False,
axes=axes,
add_points=[0] + add_points)
dht_inv = DHT(mdata_dev,
inverse=True,
axes=axes,
add_points=[0] + add_points)
dht_fw_c = dht_fw.compile(thr)
dht_inv_c = dht_inv.compile(thr)
xdata = test_func(*xs)
xdata_dev = thr.to_device(xdata)
# forward transform
dht_fw_c(mdata_dev, xdata_dev)
assert diff_is_negligible(mdata_dev.get(), test_func.mdata)
# inverse transform
dht_inv_c(xdata_dev, mdata_dev)
assert diff_is_negligible(xdata_dev.get(), xdata)
def test_high_order_forward(thr, ho_order, ho_shape):
"""
Checks that if we change the mode space while keeping mode population the same,
the result of forward transformation for orders higher than 1 do not change.
"""
dtype = numpy.float32
modes = TestFunction.generate_modes((ho_shape, ), dtype)
f1 = TestFunction((ho_shape, ), dtype, order=ho_order, modes=modes)
f2 = TestFunction((ho_shape + 1, ), dtype, order=ho_order, modes=modes)
xs1 = get_spatial_grid(ho_shape, ho_order)
xs2 = get_spatial_grid(ho_shape + 1, ho_order)
xdata1 = f1(xs1)
xdata2 = f2(xs2)
xdata1_dev = thr.to_device(xdata1)
xdata2_dev = thr.to_device(xdata2)
mdata1_dev = thr.array(ho_shape, dtype)
mdata2_dev = thr.array(ho_shape + 1, dtype)
dht_fw1 = DHT(mdata1_dev, inverse=False, order=ho_order)
dht_fw2 = DHT(mdata2_dev, inverse=False, order=ho_order)
dht_fw1_c = dht_fw1.compile(thr)
dht_fw2_c = dht_fw2.compile(thr)
dht_fw1_c(mdata1_dev, xdata1_dev)
dht_fw2_c(mdata2_dev, xdata2_dev)
mdata1 = mdata1_dev.get()
mdata2 = mdata2_dev.get()
assert diff_is_negligible(mdata1, mdata2[:-1])
