def f(n, p):
N = 2 * n
gd = GridDescriptor((N, N, N), (L, L, L))
a = gd.zeros()
print(a.shape)
#p = PoissonSolver(nn=1, relax=relax)
p.set_grid_descriptor(gd)
cut = N / 2.0 * 0.9
s = Spline(l=0, rmax=cut, f_g=np.array([1, 0.5, 0.0]))
c = LFC(gd, [[s], [s]])
c.set_positions([(0, 0, 0), (0.5, 0.5, 0.5)])
c.add(a)
I0 = gd.integrate(a)
a -= I0 / L**3
b = gd.zeros()
p.solve(b, a, charge=0, eps=1e-20)
return gd.collect(b, broadcast=1)
def f(n, p):
N = 2 * n
gd = GridDescriptor((N, N, N), (L, L, L))
a = gd.zeros()
print(a.shape)
#p = PoissonSolver(nn=1, relax=relax)
p.set_grid_descriptor(gd)
p.initialize()
cut = N / 2.0 * 0.9
s = Spline(l=0, rmax=cut, f_g=np.array([1, 0.5, 0.0]))
c = LFC(gd, [[s], [s]])
c.set_positions([(0, 0, 0), (0.5, 0.5, 0.5)])
c.add(a)
I0 = gd.integrate(a)
a -= gd.integrate(a) / L**3
I = gd.integrate(a)
b = gd.zeros()
p.solve(b, a, charge=0)#, eps=1e-20)
return gd.collect(b, broadcast=1)
cell_c = N_c / float(N)
gd = GridDescriptor(N_c, cell_c, False)
# Construct model density
coord_vg = gd.get_grid_point_coordinates()
x_g = coord_vg[0, :]
y_g = coord_vg[1, :]
z_g = coord_vg[2, :]
rho_g = gd.zeros()
for z0 in [1, 2]:
rho_g += 10 * (z_g - z0) * \
np.exp(-20 * np.sum((coord_vg.T - np.array([.5, .5, z0])).T**2,
axis=0))
if do_plot:
big_rho_g = gd.collect(rho_g)
if gd.comm.rank == 0:
import matplotlib.pyplot as plt
fig, ax_ij = plt.subplots(3, 4, figsize=(20, 10))
ax_i = ax_ij.ravel()
ploti = 0
Ng_c = gd.get_size_of_global_array()
plt.sca(ax_i[ploti])
ploti += 1
plt.pcolormesh(big_rho_g[Ng_c[0] / 2])
plt.sca(ax_i[ploti])
ploti += 1
plt.plot(big_rho_g[Ng_c[0] / 2, Ng_c[1] / 2])
def plot_phi(phi_g):
def playground():
np.set_printoptions(linewidth=176)
#N_c = [4, 7, 9]
N_c = [4, 4, 2]
pbc_c = (1, 1, 1)
# 210
distribute_dir = 1
reduce_dir = 0
parsize_c = (2, 2, 2)
parsize2_c = list(parsize_c)
parsize2_c[reduce_dir] = 1
parsize2_c[distribute_dir] *= parsize_c[reduce_dir]
assert np.prod(parsize2_c) == np.prod(parsize_c)
gd = GridDescriptor(N_c=N_c,
pbc_c=pbc_c,
cell_cv=0.2 * np.array(N_c),
parsize_c=parsize_c)
gd2 = get_compatible_grid_descriptor(gd, distribute_dir, reduce_dir)
src = gd.zeros(dtype=complex)
src[:] = gd.comm.rank
src_global = gd.collect(src)
if gd.comm.rank == 0:
ind = np.indices(src_global.shape)
src_global += 1j * (ind[0] / 10. + ind[1] / 100. + ind[2] / 1000.)
#src_global[1] += 0.5j
print('GLOBAL ARRAY', src_global.shape)
print(src_global.squeeze())
gd.distribute(src_global, src)
goal = gd2.zeros(dtype=float)
goal[:] = gd.comm.rank # get_members()[gd2.comm.rank]
goal_global = gd2.collect(goal)
if gd.comm.rank == 0:
print('GOAL GLOBAL')
print(goal_global.squeeze())
gd.comm.barrier()
#return
recvbuf = redistribute(gd,
gd2,
src,
distribute_dir,
reduce_dir,
operation='forth')
recvbuf_master = gd2.collect(recvbuf)
if gd2.comm.rank == 0:
print('RECV')
print(recvbuf_master)
err = src_global - recvbuf_master
print('MAXERR', np.abs(err).max())
hopefully_orig = redistribute(gd,
gd2,
recvbuf,
distribute_dir,
reduce_dir,
operation='back')
tmp = gd.collect(hopefully_orig)
if gd.comm.rank == 0:
print('FINALLY')
print(tmp)
err2 = src_global - tmp
print('MAXERR', np.abs(err2).max())
gd = GridDescriptor((8, 1, 1), (8.0, 1.0, 1.0), comm=domain_comm) a = gd.zeros() dadx = gd.zeros() a[:, 0, 0] = np.arange(gd.beg_c[0], gd.end_c[0]) gradx = Gradient(gd, v=0) print a.itemsize, a.dtype, a.shape print dadx.itemsize, dadx.dtype, dadx.shape gradx.apply(a, dadx) # a = [ 0. 1. 2. 3. 4. 5. 6. 7.] # # da # -- = [-2.5 1. 1. 1. 1. 1. 1. -2.5] # dx dadx = gd.collect(dadx, broadcast=True) assert dadx[3, 0, 0] == 1.0 and np.sum(dadx[:, 0, 0]) == 0.0 gd = GridDescriptor((1, 8, 1), (1.0, 8.0, 1.0), (1, 0, 1), comm=domain_comm) dady = gd.zeros() a = gd.zeros() grady = Gradient(gd, v=1) a[0, :, 0] = np.arange(gd.beg_c[1], gd.end_c[1]) - 1 grady.apply(a, dady) # da # -- = [0.5 1. 1. 1. 1. 1. -2.5] # dy dady = gd.collect(dady, broadcast=True) assert dady[0, 0, 0] == 0.5 and np.sum(dady[0, :, 0]) == 3.0
gd = GridDescriptor((8, 1, 1), (8.0, 1.0, 1.0), comm=domain_comm) a = gd.zeros() dadx = gd.zeros() a[:, 0, 0] = np.arange(gd.beg_c[0], gd.end_c[0]) gradx = Gradient(gd, v=0) print a.itemsize, a.dtype, a.shape print dadx.itemsize, dadx.dtype, dadx.shape gradx.apply(a, dadx) # a = [ 0. 1. 2. 3. 4. 5. 6. 7.] # # da # -- = [-2.5 1. 1. 1. 1. 1. 1. -2.5] # dx dadx = gd.collect(dadx, broadcast=True) assert dadx[3, 0, 0] == 1.0 and np.sum(dadx[:, 0, 0]) == 0.0 gd = GridDescriptor((1, 8, 1), (1.0, 8.0, 1.0), (1, 0, 1), comm=domain_comm) dady = gd.zeros() a = gd.zeros() grady = Gradient(gd, v=1) a[0, :, 0] = np.arange(gd.beg_c[1], gd.end_c[1]) - 1 grady.apply(a, dady) # da # -- = [0.5 1. 1. 1. 1. 1. -2.5] # dy dady = gd.collect(dady, broadcast=True) assert dady[0, 0, 0] == 0.5 and np.sum(dady[0, :, 0]) == 3.0
from __future__ import print_function
import numpy as np
from gpaw.grid_descriptor import GridDescriptor
from gpaw.xc.libvdwxc import vdw_df, vdw_df2, vdw_df_cx, \
vdw_optPBE, vdw_optB88, vdw_C09, vdw_beef, \
libvdwxc_has_mpi, libvdwxc_has_pfft
# This test verifies that the results returned by the van der Waals
# functionals implemented in libvdwxc do not change.
N_c = np.array([23, 10, 6])
gd = GridDescriptor(N_c, N_c * 0.2, pbc_c=(1, 0, 1))
n_sg = gd.zeros(1)
nG_sg = gd.collect(n_sg)
if gd.comm.rank == 0:
gen = np.random.RandomState(0)
nG_sg[:] = gen.rand(*nG_sg.shape)
gd.distribute(nG_sg, n_sg)
for mode in ['serial', 'mpi', 'pfft']:
if mode == 'serial' and gd.comm.size > 1:
continue
if mode == 'mpi' and not libvdwxc_has_mpi():
continue
if mode == 'pfft' and not libvdwxc_has_pfft():
continue
def test(vdwxcclass, Eref=np.nan, nvref=np.nan):
xc = vdwxcclass(mode=mode)
xc._initialize(gd)
