def test_interengine_multiple_axes(init_pyDive):
for size in sizes:
for dtype in dtypes:
ref = (np.random.rand(*size) * 100.0).astype(dtype)
for distaxesA in [range(i+1) for i in range(len(size))]:
for distaxesB in [range(i,len(size)) for i in range(len(size))]:
test_arrayA = pyDive.empty(size, dtype, distaxesA)
test_arrayB = pyDive.empty(size, dtype, distaxesB)
test_arrayA[:] = ref
test_arrayB[:] = ref
for distaxis in range(len(size)):
if size[distaxis] < 5: continue
slicesA = [slice(None)] * len(size)
slicesB = list(slicesA)
slicesA[distaxis] = slice(0, 5)
slicesB[distaxis] = slice(-5, None)
ref_sum = ref[slicesA] + ref[slicesB]
test_array_sum = test_arrayA[slicesA] + test_arrayB[slicesB]
assert np.array_equal(ref_sum, test_array_sum.gather())
def test_interengine(init_pyDive):
for size in sizes:
for dtype in dtypes:
ref = (np.random.rand(*size) * 100.0).astype(dtype)
for distaxis in range(len(size)):
if size[distaxis] < 5: continue
test_array = pyDive.empty(size, dtype, distaxis)
test_array[:] = ref
slicesA = [slice(None)] * len(size)
slicesB = list(slicesA)
slicesA[distaxis] = slice(0, 5)
slicesB[distaxis] = slice(-5, None)
test_array[slicesA] = test_array[slicesB]
ref[slicesA] = ref[slicesB]
assert np.array_equal(test_array.gather(), ref)
slicesA = [s/2 for s in size]
slicesB = list(slicesA)
slicesA[distaxis] = slice(0, 5)
slicesB[distaxis] = slice(-5, None)
test_array[slicesA] = test_array[slicesB]
ref[slicesA] = ref[slicesB]
assert np.array_equal(test_array.gather(), ref)
def test_reduce(init_pyDive):
input_array = pyDive.h5.open(input_file, "fields").load()
energy_array = pyDive.empty(input_array.shape, dtype=input_array.dtype["fieldE"]["x"])
def energy(out, fields):
out[:] = fields["fieldE/x"]**2 + fields["fieldE/y"]**2 + fields["fieldB/z"]**2
pyDive.map(energy, energy_array, input_array)
test_total = pyDive.reduce(energy_array, np.add)
ref_total = np.add.reduce(energy_array.gather(), axis=None)
diff = abs(ref_total - test_total)
assert diff / ref_total < 1.0e-5
def test_map(init_pyDive):
input_array = pyDive.h5.open(input_file, "fields")
ref_array = input_array["fieldE/x"].load().gather()**2 \
+ input_array["fieldE/y"].load().gather()**2 \
+ input_array["fieldB/z"].load().gather()**2
test_array = pyDive.empty(input_array.shape, dtype=input_array.dtype["fieldE"]["x"])
def energy(out, h5fields):
fields = h5fields.load()
out[:] = fields["fieldE/x"]**2 + fields["fieldE/y"]**2 + fields["fieldB/z"]**2
pyDive.map(energy, test_array, input_array)
assert np.array_equal(ref_array, test_array.gather())
def test_reduce(init_pyDive):
input_array = pyDive.h5.open(input_file, "fields").load()
energy_array = pyDive.empty(input_array.shape,
dtype=input_array.dtype["fieldE"]["x"])
def energy(out, fields):
out[:] = fields["fieldE/x"]**2 + fields["fieldE/y"]**2 + fields[
"fieldB/z"]**2
pyDive.map(energy, energy_array, input_array)
test_total = pyDive.reduce(energy_array, np.add)
ref_total = np.add.reduce(energy_array.gather(), axis=None)
diff = abs(ref_total - test_total)
assert diff / ref_total < 1.0e-5
def test_map(init_pyDive):
input_array = pyDive.h5.open(input_file, "fields")
ref_array = input_array["fieldE/x"].load().gather()**2 \
+ input_array["fieldE/y"].load().gather()**2 \
+ input_array["fieldB/z"].load().gather()**2
test_array = pyDive.empty(input_array.shape,
dtype=input_array.dtype["fieldE"]["x"])
def energy(out, h5fields):
fields = h5fields.load()
out[:] = fields["fieldE/x"]**2 + fields["fieldE/y"]**2 + fields[
"fieldB/z"]**2
pyDive.map(energy, test_array, input_array)
assert np.array_equal(ref_array, test_array.gather())
def test_slicing(init_pyDive):
for size in sizes:
for dtype in dtypes:
ref = (np.random.rand(*size) * 100.0).astype(dtype)
for distaxis in range(len(size)):
test_array = pyDive.empty(size, dtype, distaxis)
test_array[:] = ref
slices = []
for i in range(len(size)):
start = size[i] / 3
stop = size[i] - size[i] / 5
step = 2
slices.append(slice(start, stop, step))
assert np.array_equal(ref[slices], test_array[slices].gather())
slices = []
for i in range(len(size)):
slices.append(slice(-5, None, None))
assert np.array_equal(ref[slices], test_array[slices].gather())
slices = []
for i in range(len(size)):
slices.append(slice(0, 5, None))
assert np.array_equal(ref[slices], test_array[slices].gather())
# bitmask indexing
bitmask = pyDive.array(np.random.rand(*size) > 0.5, distaxes=test_array.distaxes)
assert ref[bitmask.gather()].shape == test_array[bitmask].shape
# ordering can be distinct, thus merely check if sets are equal
assert set(ref[bitmask.gather()]) == set(test_array[bitmask].gather())
ref2 = ref.copy()
test_array2 = test_array.copy()
ref2[bitmask.gather()] = 1
test_array2[bitmask] = 1
assert np.array_equal(ref2, test_array2.gather())
def test_mesh2particles(init_pyDive):
particles = pyDive.h5.open(input_file, "/particles").load()
field = pyDive.h5.open(input_file, "/fields/fieldB/z").load().gather()
field_strengths = pyDive.empty(particles.shape)
@pyDive.map
def mesh2particles(field_strengths, particles, field):
total_pos = particles["cellidx"].astype(np.float32) + particles["pos"]
# convert total_pos to an (N, 2) shaped array
total_pos = np.hstack((total_pos["x"][:,np.newaxis],
total_pos["y"][:,np.newaxis]))
import pyDive.mappings
field_strengths[:] = pyDive.mappings.mesh2particles(field, total_pos, pyDive.mappings.CIC)
mesh2particles(field_strengths, particles, field=field)
ref_field_strengths = np.load(os.path.join(dirname, "m2p_CIC.npy"))
assert np.array_equal(ref_field_strengths, field_strengths.gather())
def test_mesh2particles(init_pyDive):
particles = pyDive.h5.open(input_file, "/particles").load()
field = pyDive.h5.open(input_file, "/fields/fieldB/z").load().gather()
field_strengths = pyDive.empty(particles.shape)
@pyDive.map
def mesh2particles(field_strengths, particles, field):
total_pos = particles["cellidx"].astype(np.float32) + particles["pos"]
# convert total_pos to an (N, 2) shaped array
total_pos = np.hstack(
(total_pos["x"][:, np.newaxis], total_pos["y"][:, np.newaxis]))
import pyDive.mappings
field_strengths[:] = pyDive.mappings.mesh2particles(
field, total_pos, pyDive.mappings.CIC)
mesh2particles(field_strengths, particles, field=field)
ref_field_strengths = np.load(os.path.join(dirname, "m2p_CIC.npy"))
assert np.array_equal(ref_field_strengths, field_strengths.gather())