def test_nody():
""" Checking end to end nbody
"""
a0 = 0.1
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc], dtype='f4')
grid = pm.generate_uniform_particle_grid(shift=0).astype(np.float32)
solver = Solver(pm, Planck15, B=1)
stages = np.linspace(0.1, 1.0, 10, endpoint=True)
# Generate initial state with fastpm
whitec = pm.generate_whitenoise(100, mode='complex', unitary=False)
lineark = whitec.apply(lambda k, v: Planck15.get_pklin(
sum(ki**2 for ki in k)**0.5, 0)**0.5 * v / v.BoxSize.prod()**0.5)
statelpt = solver.lpt(lineark, grid, a0, order=1)
finalstate = solver.nbody(statelpt, leapfrog(stages))
final_cube = pm.paint(finalstate.X)
# Same thing with flowpm
tlinear = tf.expand_dims(np.array(lineark.c2r()), 0)
state = tfpm.lpt_init(tlinear, a0, order=1)
state = tfpm.nbody(state, stages, nc)
tfread = pmutils.cic_paint(tf.zeros_like(tlinear), state[0]).numpy()
assert_allclose(final_cube, tfread[0], atol=1.2)
def test_lpt2():
""" Checking lpt2_source, this also checks the laplace and gradient kernels
"""
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc], dtype='f4')
grid = pm.generate_uniform_particle_grid(shift=0).astype(np.float32)
whitec = pm.generate_whitenoise(100, mode='complex', unitary=False)
lineark = whitec.apply(lambda k, v: Planck15.get_pklin(
sum(ki**2 for ki in k)**0.5, 0)**0.5 * v / v.BoxSize.prod()**0.5)
# Compute lpt1 from fastpm with matching kernel order
source = fpmops.lpt2source(lineark).c2r()
# Same thing from tensorflow
tfsource = tfpm.lpt2_source(
pmutils.r2c3d(tf.expand_dims(np.array(lineark.c2r()), axis=0)))
tfread = pmutils.c2r3d(tfsource).numpy()
assert_allclose(source, tfread[0], atol=1e-5)
def test_lpt1_64():
""" Checking lpt1, this also checks the laplace and gradient kernels
This variant of the test checks that it works for cubes of size 64
"""
nc = 64
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc], dtype='f4')
grid = pm.generate_uniform_particle_grid(shift=0).astype(np.float32)
whitec = pm.generate_whitenoise(100, mode='complex', unitary=False)
lineark = whitec.apply(lambda k, v: Planck15.get_pklin(
sum(ki**2 for ki in k)**0.5, 0)**0.5 * v / v.BoxSize.prod()**0.5)
# Compute lpt1 from fastpm with matching kernel order
lpt = fpmops.lpt1(lineark, grid)
# Same thing from tensorflow
tfread = tfpm.lpt1(
pmutils.r2c3d(tf.expand_dims(np.array(lineark.c2r()), axis=0)),
grid.reshape((1, -1, 3)) * nc / bs).numpy()
assert_allclose(lpt, tfread[0] * bs / nc, atol=5e-5)
def test_lpt_init():
"""
Checking lpt init
"""
a0 = 0.1
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc], dtype='f4')
grid = pm.generate_uniform_particle_grid(shift=0).astype(np.float32)
solver = Solver(pm, Planck15, B=1)
# Generate initial state with fastpm
whitec = pm.generate_whitenoise(100, mode='complex', unitary=False)
lineark = whitec.apply(lambda k, v: Planck15.get_pklin(
sum(ki**2 for ki in k)**0.5, 0)**0.5 * v / v.BoxSize.prod()**0.5)
statelpt = solver.lpt(lineark, grid, a0, order=1)
# Same thing with flowpm
tlinear = tf.expand_dims(np.array(lineark.c2r()), 0)
tfread = tfpm.lpt_init(tlinear, a0, order=1).numpy()
assert_allclose(statelpt.X, tfread[0, 0] * bs / nc, rtol=1e-2)
def test_lpt1():
""" Checking lpt1, this also checks the laplace and gradient kernels
"""
pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc], dtype='f4')
grid = pm.generate_uniform_particle_grid(shift=0).astype(np.float32)
whitec = pm.generate_whitenoise(100, mode='complex', unitary=False)
lineark = whitec.apply(lambda k, v: Planck15.get_pklin(
sum(ki**2 for ki in k)**0.5, 0)**0.5 * v / v.BoxSize.prod()**0.5)
# Compute lpt1 from fastpm with matching kernel order
lpt = fpmops.lpt1(lineark, grid)
# Same thing from tensorflow
with tf.Session() as sess:
state = tfpm.lpt1(
pmutils.r2c3d(tf.expand_dims(tf.constant(lineark.c2r()), axis=0)),
grid.reshape((1, -1, 3)) * nc / bs)
tfread = sess.run(state)
assert_allclose(lpt, tfread[0] * bs / nc, atol=1e-5)