Voxelization (volume sampling) is controlled with 'grid', the integer dimensions of the target grid,
and 'window', which sets the extent of the grid in coordinate-space.
Change 'order' to 0, 1, or 2 to play with tetrahedral, trilinear, and triquadratic
finite elements. 'tol' controls the accuracy of the voxelization in units of the voxel size.
Try varying it between 0 and 1.
Fields to voxelize are requested via 'fields'. Setting 'm' to 'None' tells PSI to voxelize
the mass field to new numpy array.
'''
# initialize an element of a given order
order = 1
pos = psi.elementBase(order)
vel = np.zeros_like(pos)
mass = np.ones(1)
# perturb the control points, keeping them within the unit box
pos -= 0.5
pos *= 0.5 + 0.4 * np.random.sample(pos.shape)
pos += 0.5
# PyPSI voxelization options
fields = {'m': None}
grid = (64, 64, 64)
window = ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))
tol = 0.01
psi.elementMesh(fields, pos, vel, mass, grid=grid, window=window, tol=tol)
boxsize = 1.
box = ((0.0, 0.0, 0.0), (boxsize, boxsize, boxsize))
nx = ds.domain_dimensions[0]
## set voxelization options
order = 1
tol = 1000
subgrid = (subgridsize, subgridsize, subgridsize)
window = box
fields = {'m': None}
## chunk through Lagrangian patches
mtot = 0.0
tstart = time.time()
for pos, vel, mass, block, nblocks in psi.elementBlocksFromGrid(pos, vel, order=order, periodic=True):
## make periodic and voxelize it
psi.voxelize(fields, pos, vel, mass, tol=tol, window=window, \
grid=subgrid, periodic=True, box=box)
## bookkeeping
mtot += np.sum(mass)
tend = time.time()
sys.stdout.write("\rVoxelized block %d of %d. Time = %.1f s" % (block, nblocks, tend-tstart))
sys.stdout.flush()
print '\nGlobal error = %.10e' % np.abs(np.sum(fields['m'])/mtot-1.0)
# save density data to file
f = tables.open_file("density%04d_64.h5" % i, mode = 'w')
f.create_array(f.root, "density", fields['m'])
Voxelization (volume sampling) is controlled with 'grid', the integer dimensions of the target grid,
and 'window', which sets the extent of the grid in coordinate-space.
Change 'order' to 0, 1, or 2 to play with tetrahedral, trilinear, and triquadratic
finite elements. 'tol' controls the accuracy of the voxelization in units of the voxel size.
Try varying it between 0 and 1.
Fields to voxelize are requested via 'fields'. Setting 'm' to 'None' tells PSI to voxelize
the mass field to new numpy array.
'''
# initialize an element of a given order
order = 1
pos = psi.elementBase(order)
vel = np.zeros_like(pos)
mass = np.ones(1)
# perturb the control points, keeping them within the unit box
pos -= 0.5
pos *= 0.5 + 0.4*np.random.sample(pos.shape)
pos += 0.5
# PyPSI voxelization options
fields = {'m': None}
grid = (64, 64, 64)
window = ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))
tol = 0.01
psi.elementMesh(fields, pos, vel, mass, grid=grid, window=window, tol=tol)
import os
import sys
sys.path.insert(0, "/home/phsun/PyPSI")
import PyPSI as psi
import matplotlib.pyplot as plt
import numpy as np
import tables
fig, ax = plt.subplots()
f = tables.open_file("density0000.h5", mode = 'r')
density = np.array(f.get_node("/density"))
f.close()
## calculate power spectrum
power, k = psi.powerSpectrum(density, \
dims=(300., 300., 300.), bins = 256)
l = 2*np.pi/k
## plot spectrum
ax.loglog(l, power, label = "0000")
## save to file
np.savez_compressed("spectrum0000.npz", power=power, l=l)
ax.set_xlabel("l (Mpc/h)")
ax.set_ylabel("power")
legend = plt.legend(loc = 'upper right', shadow = True)
fig.savefig("spectrum_PSI_0000.png")
sys.path.insert(0, "/home/phsun/PyPSI")
import PyPSI as psi
import matplotlib.pyplot as plt
import numpy as np
import tables
fig, ax = plt.subplots()
i = 0
while os.path.isfile("density%04d_64.h5" % i):
f = tables.open_file("density%04d_64.h5" % i, mode="r")
density = np.array(f.get_node("/density"))
f.close()
## calculate power spectrum
power, k = psi.powerSpectrum(density, dims=(300.0 / 4, 300.0 / 4, 300.0 / 4), bins=100)
l = 2 * np.pi / k
## plot spectrum
ax.loglog(l, power, label="%04d" % i)
## save to file
np.savez_compressed("spectrum%04d_64.npz" % i, power=power, l=l)
i += 1
ax.set_xlabel("l (Mpc/h)")
ax.set_ylabel("power")
legend = plt.legend(loc="upper right", shadow=True)
fig.savefig("spectrum_PSI_64.png")
