def test_particles2mesh(init_pyDive):
shape = [256, 256]
density = pyDive.cloned.zeros(shape)
particles = pyDive.h5.open(input_file, "/particles")
def particles2density(particles, density):
particles = particles.load()
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]))
par_weighting = np.ones(particles.shape)
import pyDive.mappings
pyDive.mappings.particles2mesh(density, par_weighting, total_pos,
pyDive.mappings.CIC)
pyDive.map(particles2density, particles, density)
test_density = density.sum() # add up all local copies
ref_density = np.load(os.path.join(dirname, "p2m_CIC.npy"))
np.testing.assert_array_almost_equal(ref_density, test_density)
def test_particles2mesh(init_pyDive):
shape = [256, 256]
density = pyDive.cloned.zeros(shape)
particles = pyDive.h5.open(input_file, "/particles")
def particles2density(particles, density):
particles = particles.load()
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]))
par_weighting = np.ones(particles.shape)
import pyDive.mappings
pyDive.mappings.particles2mesh(density, par_weighting, total_pos, pyDive.mappings.CIC)
pyDive.map(particles2density, particles, density)
test_density = density.sum() # add up all local copies
ref_density = np.load(os.path.join(dirname, "p2m_CIC.npy"))
np.testing.assert_array_almost_equal(ref_density, test_density)
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())
